# Cookbook

## Docs

- [构建一个基于 Gemma、Elasticsearch 和 Hugging Face 模型的 RAG 系统](https://huggingface.co/learn/cookbook/zh-CN/rag_with_hugging_face_gemma_elasticsearch.md)
- [利用知识图谱增强 RAG 推理能力](https://huggingface.co/learn/cookbook/zh-CN/rag_with_knowledge_graphs_neo4j.md)
- [使用结构化生成进行带源高亮的 RAG](https://huggingface.co/learn/cookbook/zh-CN/structured_generation.md)
- [使用 distilabel 生成偏好数据集](https://huggingface.co/learn/cookbook/zh-CN/generate_preference_dataset_distilabel.md)
- [分析艺术风格与多模态嵌入](https://huggingface.co/learn/cookbook/zh-CN/analyzing_art_with_hf_and_fiftyone.md)
- [使用 TGI 的消息 API 从 OpenAI 迁移到 Open LLMs](https://huggingface.co/learn/cookbook/zh-CN/tgi_messages_api_demo.md)
- [使用 LangChain 在 HuggingFace 文档上构建高级 RAG](https://huggingface.co/learn/cookbook/zh-CN/advanced_rag.md)
- [基于文档检索（ColPali）和视觉语言模型（VLMs）的多模态检索增强生成（RAG）](https://huggingface.co/learn/cookbook/zh-CN/multimodal_rag_using_document_retrieval_and_vlms.md)
- [使用 Cleanlab 检测文本数据集中的问题](https://huggingface.co/learn/cookbook/zh-CN/issues_in_text_dataset.md)
- [使用 Elasticsearch 和 Hugging Face 进行语义重排序](https://huggingface.co/learn/cookbook/zh-CN/semantic_reranking_elasticsearch.md)
- [数据分析智能体：瞬间获取数据洞察 ✨](https://huggingface.co/learn/cookbook/zh-CN/agent_data_analyst.md)
- [用自定义数据集微调目标检测模型🖼，部署至 Spaces，并进行 Gradio API 集成](https://huggingface.co/learn/cookbook/zh-CN/fine_tuning_detr_custom_dataset.md)
- [怎么使用推理端点去嵌入文档](https://huggingface.co/learn/cookbook/zh-CN/automatic_embedding_tei_inference_endpoints.md)
- [RAG 评估](https://huggingface.co/learn/cookbook/zh-CN/rag_evaluation.md)
- [使用 PEFT 进行提示微调](https://huggingface.co/learn/cookbook/zh-CN/prompt_tuning_peft.md)
- [企业级 Hub 操作指南](https://huggingface.co/learn/cookbook/zh-CN/enterprise_cookbook_overview.md)
- [使用 LLM 作为评判者🧑‍⚖️进行自动化和多方面的评估](https://huggingface.co/learn/cookbook/zh-CN/llm_judge.md)
- [使用 Hugging Face 和 Milvus 构建 RAG 系统](https://huggingface.co/learn/cookbook/zh-CN/rag_with_hf_and_milvus.md)
- [微调大型语言模型以生成波斯语产品目录的 JSON 格式](https://huggingface.co/learn/cookbook/zh-CN/fine_tuning_llm_to_generate_persian_product_catalogs_in_json_format.md)
- [使用 SetFit 进行零样本文本分类的数据标注建议](https://huggingface.co/learn/cookbook/zh-CN/labelling_feedback_setfit.md)
- [用自定义生物医学数据集微调视觉 Transformer 模型](https://huggingface.co/learn/cookbook/zh-CN/fine_tuning_vit_custom_dataset.md)
- [通过引入语义缓存到 FAISS 中以增强 RAG 系统的性能](https://huggingface.co/learn/cookbook/zh-CN/semantic_cache_chroma_vector_database.md)
- [智能体 RAG：通过查询重构和自查询来增强你的 RAG ！🚀](https://huggingface.co/learn/cookbook/zh-CN/agent_rag.md)
- [用 🤗 transformers, 🤗 datasets 和 FAISS 嵌入多模态数据进行相似度搜索](https://huggingface.co/learn/cookbook/zh-CN/faiss_with_hf_datasets_and_clip.md)
- [在自定义数据集上微调语义分割模型并通过推理 API 使用](https://huggingface.co/learn/cookbook/zh-CN/semantic_segmentation_fine_tuning_inference.md)
- [用于文本到 SQL 的智能体，带有自动错误修正功能](https://huggingface.co/learn/cookbook/zh-CN/agent_text_to_sql.md)
- [数据标注与 Argilla Spaces](https://huggingface.co/learn/cookbook/zh-CN/enterprise_cookbook_argilla.md)
- [个人身份信息（PII）检测的 LLM 网关](https://huggingface.co/learn/cookbook/zh-CN/llm_gateway_pii_detection.md)
- [使用 Transformers Agents 构建具有工具调用超能力的智能体 🦸](https://huggingface.co/learn/cookbook/zh-CN/agents.md)
- [使用自定义非结构化数据构建 RAG](https://huggingface.co/learn/cookbook/zh-CN/rag_with_unstructured_data.md)
- [推理端点（专用）](https://huggingface.co/learn/cookbook/zh-CN/enterprise_dedicated_endpoints.md)
- [使用向量嵌入和 Qdrant 进行代码搜索](https://huggingface.co/learn/cookbook/zh-CN/code_search.md)
- [让多个智能体在多智能体层级中协作 🤖🤝🤖](https://huggingface.co/learn/cookbook/zh-CN/multiagent_web_assistant.md)
- [介绍](https://huggingface.co/learn/cookbook/zh-CN/benchmarking_tgi.md)
- [使用 Haystack 和 NuExtract 进行信息提取](https://huggingface.co/learn/cookbook/zh-CN/information_extraction_haystack_nuextract.md)
- [在 Hugging Face Spaces 上设置 Phoenix 观察性仪表板以进行 LLM 应用程序追踪](https://huggingface.co/learn/cookbook/zh-CN/phoenix_observability_on_hf_spaces.md)
- [无服务器推理 API](https://huggingface.co/learn/cookbook/zh-CN/enterprise_hub_serverless_inference_api.md)
- [用 Gemma, MongoDB 和开源模型构建 RAG 系统](https://huggingface.co/learn/cookbook/zh-CN/rag_with_hugging_face_gemma_mongodb.md)
- [在单个 GPU 上针对自定义代码微调代码 LLM](https://huggingface.co/learn/cookbook/zh-CN/fine_tuning_code_llm_on_single_gpu.md)
- [使用 Spaces 和 Gradio 创建演示](https://huggingface.co/learn/cookbook/zh-CN/enterprise_cookbook_gradio.md)
- [基于 SQL 和 Jina Reranker v2 的 RAG](https://huggingface.co/learn/cookbook/zh-CN/rag_with_sql_reranker.md)
- [用 LlamaIndex 构建一个 RAG 电子书库智能助手](https://huggingface.co/learn/cookbook/zh-CN/rag_llamaindex_librarian.md)
- [多智能体 RAG 系统 🤖🤝🤖](https://huggingface.co/learn/cookbook/zh-CN/multiagent_rag_system.md)
- [使用大型语言模型作为评审者清理现有的偏好数据集](https://huggingface.co/learn/cookbook/zh-CN/clean_dataset_judges_distilabel.md)
- [用 Hugging Face Zephyr 和 LangChain 针对 Github issues 构建简单的 RAG](https://huggingface.co/learn/cookbook/zh-CN/rag_zephyr_langchain.md)
- [使用 Stable Diffusion 进行图像插值](https://huggingface.co/learn/cookbook/zh-CN/stable_diffusion_interpolation.md)
- [开源 AI 指南 (Cookbook)](https://huggingface.co/learn/cookbook/zh-CN/index.md)
- [使用 Hugging Face 生态系统（TRL）对视觉语言模型 (Qwen2-VL-7B) 进行微调](https://huggingface.co/learn/cookbook/zh-CN/fine_tuning_vlm_trl.md)
- [在 HF 空间中的互动开发](https://huggingface.co/learn/cookbook/zh-CN/enterprise_cookbook_dev_spaces.md)
- [使用 Cleanlab 进行主动学习标注文本数据](https://huggingface.co/learn/cookbook/zh-CN/annotate_text_data_transformers_via_active_learning.md)

### 构建一个基于 Gemma、Elasticsearch 和 Hugging Face 模型的 RAG 系统
https://huggingface.co/learn/cookbook/zh-CN/rag_with_hugging_face_gemma_elasticsearch.md

# 构建一个基于 Gemma、Elasticsearch 和 Hugging Face 模型的 RAG 系统

作者: [lloydmeta](https://huggingface.co/lloydmeta)


这个 notebook 将引导你构建一个由 Elasticsearch（ES）和 Hugging Face 模型支持的检索增强生成（RAG）系统，允许你在 ES 向量化（你的 ES 集群在摄取和查询时为你向量化）与自向量化（你在将数据发送到 ES 之前对所有数据进行向量化）之间切换。

你的用例应该使用哪种方式？*这取决于* 🤷‍♂️。ES 向量化意味着你的客户端不需要实现这一点，所以这里默认使用；但是，如果你没有机器学习节点，或者你自己的嵌入设置更好/更快，可以在下面的“选择数据和查询向量化选项”部分将 `USE_ELASTICSEARCH_VECTORISATION` 设置为 `False`！

> [!提示]
> 这个 notebook 已经使用 ES 8.13.x 和 8.14.x 进行了测试。


## 步骤 1：安装相关库


```python
!pip install elasticsearch sentence_transformers transformers eland==8.12.1 # accelerate # uncomment if using GPU
!pip install datasets==2.19.2 # Remove version lock if https://github.com/huggingface/datasets/pull/6978 has been released
```

## 步骤 2：设置

### Hugging Face

这允许你通过 Hugging Face 进行身份验证，以便下载模型和数据集。

```python
from huggingface_hub import notebook_login

notebook_login()
```

#### Elasticsearch 部署

让我们确保你可以访问你的 Elasticsearch 部署。如果你还没有，可以在 [Elastic Cloud](https://www.elastic.co/search-labs/tutorials/install-elasticsearch/elastic-cloud#creating-a-cloud-deployment)上创建一个。

确保你已经将 `CLOUD_ID` 和 `ELASTIC_DEPL_API_KEY` 保存为 Colab secrets。

![Image of how to set up secrets using Google Colab](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/colab-secrets.jpeg)

```python
from google.colab import userdata

# https://www.elastic.co/search-labs/tutorials/install-elasticsearch/elastic-cloud#finding-your-cloud-id
CLOUD_ID = userdata.get("CLOUD_ID") # or "<YOUR CLOUD_ID>"

# https://www.elastic.co/search-labs/tutorials/install-elasticsearch/elastic-cloud#creating-an-api-key
ELASTIC_API_KEY = userdata.get("ELASTIC_DEPL_API_KEY")  # or "<YOUR API KEY>"
```

设置客户端并确保凭据有效。

```python
from elasticsearch import Elasticsearch, helpers

# Create the client instance
client = Elasticsearch(cloud_id=CLOUD_ID, api_key=ELASTIC_API_KEY)

# Successful response!
client.info()
```

## 步骤 3：数据获取和准备

本教程中使用的数据来源于 Hugging Face 数据集，特别是来自 [MongoDB/embedded_movies 数据集](https://huggingface.co/datasets/MongoDB/embedded_movies)。



```python
# Load Dataset
from datasets import load_dataset

# https://huggingface.co/datasets/MongoDB/embedded_movies
dataset = load_dataset("MongoDB/embedded_movies")

dataset
```

以下代码片段中的操作专注于强制数据完整性和质量。
1. 第一个过程确保每个数据点的 `fullplot` 属性不为空，因为这是我们在嵌入过程中主要使用的数据。
2. 第二步还确保我们移除所有数据点的 `plot_embedding` 属性，因为这将被一个使用不同的嵌入模型 `gte-large` 创建的新嵌入所替换。


```python
# Data Preparation

# Remove data point where plot coloumn is missing
dataset = dataset.filter(lambda x: x["fullplot"] is not None)

if "plot_embedding" in sum(dataset.column_names.values(), []):
    # Remove the plot_embedding from each data point in the dataset as we are going to create new embeddings with an open source embedding model from Hugging Face
    dataset = dataset.remove_columns("plot_embedding")

dataset["train"]
```

## 步骤 4：使用向量化数据加载 Elasticsearch

### 选择数据和查询向量化选项

在这里，你需要做出一个决定：你希望 Elasticsearch 对你的数据和查询进行向量化，还是希望你自己来做？

将 `USE_ELASTICSEARCH_VECTORISATION` 设置为 `True` 将使本 notebook 的其余部分为你数据和查询设置并使用 ES 托管的向量化，但**请注意**，这要求你的 ES 部署至少有 1 个 ML 节点（我建议在你的云部署上设置自动缩放为 true，以防你选择的模型过大）。

如果 `USE_ELASTICSEARCH_VECTORISATION` 是 `False`，这个 notebook 将为你数据和查询向量化设置并使用提供的模型“本地”。

在这里，我选择了 [thenlper/gte-small](https://huggingface.co/thenlper/gte-small) 模型，没有其他原因，只是因为它在另一个指南中使用过，而且对我来说效果足够好。如果你愿意，请随时尝试其他模型——唯一重要的是你根据模型更新 `EMBEDDING_DIMENSIONS`。

**注意**：如果你更改这些值，你可能需要从这一步重新运行 notebook。



```python
USE_ELASTICSEARCH_VECTORISATION = True

EMBEDDING_MODEL_ID = "thenlper/gte-small"
# https://huggingface.co/thenlper/gte-small's page shows the dimensions of the model
# If you use the `gte-base` or `gte-large` embedding models, the numDimension
# value in the vector search index must be set to 768 and 1024, respectively.
EMBEDDING_DIMENSIONS = 384
```

### 如有必要，将 Hugging Face 模型加载到 Elasticsearch 中

如果 `USE_ELASTICSEARCH_VECTORISATION` 设置为 `True`，这一步将使用 [Eland](https://eland.readthedocs.io/en/v8.12.1/)将 Hugging Face 模型加载并部署到 Elasticsearch 中。这允许 Elasticsearch 在后续步骤中对你的查询和数据进行向量化。


```python
import locale
locale.getpreferredencoding = lambda: "UTF-8"
!(if [ "True" == $USE_ELASTICSEARCH_VECTORISATION ]; then \
  eland_import_hub_model --cloud-id $CLOUD_ID --hub-model-id $EMBEDDING_MODEL_ID --task-type text_embedding --es-api-key $ELASTIC_API_KEY --start --clear-previous; \
fi)
```

这一步添加了在本地为文本创建嵌入的函数，并通过嵌入丰富了数据集，这样数据就可以作为向量被摄取到 Elasticsearch 中。如果 `USE_ELASTICSEARCH_VECTORISATION` 为 True，则不会运行。


```python
from sentence_transformers import SentenceTransformer

if not USE_ELASTICSEARCH_VECTORISATION:
    embedding_model = SentenceTransformer(EMBEDDING_MODEL_ID)


def get_embedding(text: str) -> list[float]:
    if USE_ELASTICSEARCH_VECTORISATION:
        raise Exception(
            f"Disabled when USE_ELASTICSEARCH_VECTORISATION is [{USE_ELASTICSEARCH_VECTORISATION}]"
        )
    else:
        if not text.strip():
            print("Attempted to get embedding for empty text.")
            return []

        embedding = embedding_model.encode(text)
        return embedding.tolist()


def add_fullplot_embedding(x):
    if USE_ELASTICSEARCH_VECTORISATION:
        raise Exception(
            f"Disabled when USE_ELASTICSEARCH_VECTORISATION is [{USE_ELASTICSEARCH_VECTORISATION}]"
        )
    else:
        full_plots = x["fullplot"]
        return {"embedding": [get_embedding(full_plot) for full_plot in full_plots]}


if not USE_ELASTICSEARCH_VECTORISATION:
    dataset = dataset.map(add_fullplot_embedding, batched=True)
    dataset["train"]
```

## 步骤 5：创建一个带有向量搜索映射的搜索索引。

在这一点上，我们将在 Elasticsearch 中创建一个索引，并设置正确的索引映射来处理向量搜索。

请点击这里了解更多关于 [Elasticsearch 向量搜索能力](https://www.elastic.co/what-is/vector-search)的信息。


```python
>>> # Needs to match the id returned from Eland
>>> # in general for Hugging Face models, you just replace the forward slash with
>>> # double underscore
>>> model_id = EMBEDDING_MODEL_ID.replace("/", "__")

>>> index_name = "movies"

>>> index_mapping = {
...     "properties": {
...         "fullplot": {"type": "text"},
...         "plot": {"type": "text"},
...         "title": {"type": "text"},
...     }
... }
>>> # define index mapping
>>> if USE_ELASTICSEARCH_VECTORISATION:
...     index_mapping["properties"]["embedding"] = {
...         "properties": {
...             "is_truncated": {"type": "boolean"},
...             "model_id": {
...                 "type": "text",
...                 "fields": {"keyword": {"type": "keyword", "ignore_above": 256}},
...             },
...             "predicted_value": {
...                 "type": "dense_vector",
...                 "dims": EMBEDDING_DIMENSIONS,
...                 "index": True,
...                 "similarity": "cosine",
...             },
...         }
...     }
>>> else:
...     index_mapping["properties"]["embedding"] = {
...         "type": "dense_vector",
...         "dims": EMBEDDING_DIMENSIONS,
...         "index": "true",
...         "similarity": "cosine",
...     }

>>> # flag to check if index has to be deleted before creating
>>> should_delete_index = True

>>> # check if we want to delete index before creating the index
>>> if should_delete_index:
...     if client.indices.exists(index=index_name):
...         print("Deleting existing %s" % index_name)
...         client.indices.delete(index=index_name, ignore=[400, 404])

>>> print("Creating index %s" % index_name)


>>> # ingest pipeline definition
>>> if USE_ELASTICSEARCH_VECTORISATION:
...     pipeline_id = "vectorize_fullplots"

...     client.ingest.put_pipeline(
...         id=pipeline_id,
...         processors=[
...             {
...                 "inference": {
...                     "model_id": model_id,
...                     "target_field": "embedding",
...                     "field_map": {"fullplot": "text_field"},
...                 }
...             }
...         ],
...     )

...     index_settings = {
...         "index": {
...             "default_pipeline": pipeline_id,
...         }
...     }
>>> else:
...     index_settings = {}

>>> client.options(ignore_status=[400, 404]).indices.create(
...     index=index_name, mappings=index_mapping, settings=index_settings
... )
```

<pre>
Creating index movies
</pre>

将数据摄取到 Elasticsearch 中最好是批量进行。幸运的是，`helpers` 提供了一个简单的方法来执行这个操作。

```python
>>> from elasticsearch.helpers import BulkIndexError

>>> def batch_to_bulk_actions(batch):
...     for record in batch:
...         action = {
...             "_index": "movies",
...             "_source": {
...                 "title": record["title"],
...                 "fullplot": record["fullplot"],
...                 "plot": record["plot"],
...             },
...         }
...         if not USE_ELASTICSEARCH_VECTORISATION:
...             action["_source"]["embedding"] = record["embedding"]
...         yield action


>>> def bulk_index(ds):
...     start = 0
...     end = len(ds)
...     batch_size = 100
...     if USE_ELASTICSEARCH_VECTORISATION:
...         # If using auto-embedding, bulk requests can take a lot longer,
...         # so pass a longer request_timeout here (defaults to 10s), otherwise
...         # we could get Connection timeouts
...         batch_client = client.options(request_timeout=600)
...     else:
...         batch_client = client
...     for batch_start in range(start, end, batch_size):
...         batch_end = min(batch_start + batch_size, end)
...         print(f"batch: start [{batch_start}], end [{batch_end}]")
...         batch = ds.select(range(batch_start, batch_end))
...         actions = batch_to_bulk_actions(batch)
...         helpers.bulk(batch_client, actions)


>>> try:
...     bulk_index(dataset["train"])
>>> except BulkIndexError as e:
...     print(f"{e.errors}")

>>> print("Data ingestion into Elasticsearch complete!")
```

<pre>
batch: start [0], end [100]
batch: start [100], end [200]
batch: start [200], end [300]
batch: start [300], end [400]
batch: start [400], end [500]
batch: start [500], end [600]
batch: start [600], end [700]
batch: start [700], end [800]
batch: start [800], end [900]
batch: start [900], end [1000]
batch: start [1000], end [1100]
batch: start [1100], end [1200]
batch: start [1200], end [1300]
batch: start [1300], end [1400]
batch: start [1400], end [1452]
Data ingestion into Elasticsearch complete!
</pre>

## 步骤 6：对用户查询执行向量搜索

以下步骤实现了一个函数，该函数返回一个向量搜索结果。

如果 `USE_ELASTICSEARCH_VECTORISATION` 为 true，文本查询将直接发送到 ES，在那里将首先使用上传的模型对其进行向量化，然后执行向量搜索。如果 `USE_ELASTICSEARCH_VECTORISATION` 为 false，那么我们在发送查询之前先在本地进行向量化，然后发送查询的向量化形式。


```python
def vector_search(plot_query):
    if USE_ELASTICSEARCH_VECTORISATION:
        knn = {
            "field": "embedding.predicted_value",
            "k": 10,
            "query_vector_builder": {
                "text_embedding": {
                    "model_id": model_id,
                    "model_text": plot_query,
                }
            },
            "num_candidates": 150,
        }
    else:
        question_embedding = get_embedding(plot_query)
        knn = {
            "field": "embedding",
            "query_vector": question_embedding,
            "k": 10,
            "num_candidates": 150,
        }

    response = client.search(index="movies", knn=knn, size=5)
    results = []
    for hit in response["hits"]["hits"]:
        id = hit["_id"]
        score = hit["_score"]
        title = hit["_source"]["title"]
        plot = hit["_source"]["plot"]
        fullplot = hit["_source"]["fullplot"]
        result = {
            "id": id,
            "_score": score,
            "title": title,
            "plot": plot,
            "fullplot": fullplot,
        }
        results.append(result)
    return results

def pretty_search(query):

    get_knowledge = vector_search(query)

    search_result = ""
    for result in get_knowledge:
        search_result += f"Title: {result.get('title', 'N/A')}, Plot: {result.get('fullplot', 'N/A')}\n"

    return search_result
```

## 步骤 7：处理用户查询并加载 Gemma


```python
>>> # Conduct query with retrival of sources, combining results into something that
>>> # we can feed to Gemma
>>> def combined_query(query):
...     source_information = pretty_search(query)
...     return f"Query: {query}\nContinue to answer the query by using these Search Results:\n{source_information}."


>>> query = "What is the best romantic movie to watch and why?"
>>> combined_results = combined_query(query)

>>> print(combined_results)
```

<pre>
Query: What is the best romantic movie to watch and why?
Continue to answer the query by using these Search Results:
Title: Shut Up and Kiss Me!, Plot: Ryan and Pete are 27-year old best friends in Miami, born on the same day and each searching for the perfect woman. Ryan is a rookie stockbroker living with his psychic Mom. Pete is a slick surfer dude yet to find commitment. Each meets the women of their dreams on the same day. Ryan knocks heads in an elevator with the gorgeous Jessica, passing out before getting her number. Pete falls for the insatiable Tiara, but Tiara's uncle is mob boss Vincent Bublione, charged with her protection. This high-energy romantic comedy asks to what extent will you go for true love?
Title: Titanic, Plot: The plot focuses on the romances of two couples upon the doomed ship's maiden voyage. Isabella Paradine (Catherine Zeta-Jones) is a wealthy woman mourning the loss of her aunt, who reignites a romance with former flame Wynn Park (Peter Gallagher). Meanwhile, a charming ne'er-do-well named Jamie Perse (Mike Doyle) steals a ticket for the ship, and falls for a sweet innocent Irish girl on board. But their romance is threatened by the villainous Simon Doonan (Tim Curry), who has discovered about the ticket and makes Jamie his unwilling accomplice, as well as having sinister plans for the girl.
Title: Dark Blue World, Plot: March 15, 1939: Germany invades Czechoslovakia. Czech and Slovak pilots flee to England, joining the RAF. After the war, back home, they are put in labor camps, suspected of anti-Communist ideas. This film cuts between a post-war camp where Franta is a prisoner and England during the war, where Franta is like a big brother to Karel, a very young pilot. On maneuvers, Karel crash lands by the rural home of Susan, an English woman whose husband is MIA. She spends one night with Karel, and he thinks he's found the love of his life. It's complicated by Susan's attraction to Franta. How will the three handle innocence, Eros, friendship, and the heat of battle? When war ends, what then?
Title: Dark Blue World, Plot: March 15, 1939: Germany invades Czechoslovakia. Czech and Slovak pilots flee to England, joining the RAF. After the war, back home, they are put in labor camps, suspected of anti-Communist ideas. This film cuts between a post-war camp where Franta is a prisoner and England during the war, where Franta is like a big brother to Karel, a very young pilot. On maneuvers, Karel crash lands by the rural home of Susan, an English woman whose husband is MIA. She spends one night with Karel, and he thinks he's found the love of his life. It's complicated by Susan's attraction to Franta. How will the three handle innocence, Eros, friendship, and the heat of battle? When war ends, what then?
Title: No Good Deed, Plot: About a police detective, Jack, who, while doing a friend a favor and searching for a runaway teenager on Turk Street, stumbles upon a bizarre band of criminals about to pull off a bank robbery. Jack finds himself being held hostage while the criminals decide what to do with him, and the leader's beautiful girlfriend, Erin, is left alone to watch Jack. Erin, who we discover is a master manipulator of the men in the gang, reveals another side to Jack - a melancholy romantic who could have been a classical cellist. She finds Jack's captivity an irresistible turn-on and he can't figure out if she's for real, or manipulating him, too. Before the gang returns, Jack and Erin's connection intensifies and who ends up with the money is anyone's guess.
.
</pre>

加载我们的 LLM (这里我们用 [google/gemma-2b-lt](https://huggingface.co/google/gemma-2b-it))

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
# CPU Enabled uncomment below 👇🏽
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it")
# GPU Enabled use below 👇🏽
# model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", device_map="auto")
```

定义一个方法，从 ES 中的向量搜索获取格式化的结果，然后将这些结果传递给 LLM（大型语言模型）以获取我们的结果。

```python
>>> def rag_query(query):
...     combined_information = combined_query(query)

...     # Moving tensors to GPU
...     input_ids = tokenizer(combined_information, return_tensors="pt") # .to("cuda") # Add if using GPU
...     response = model.generate(**input_ids, max_new_tokens=700)

...     return tokenizer.decode(response[0], skip_special_tokens=True)


>>> print(rag_query("What's a romantic movie that I can watch with my wife?"))
```

<pre>
Query: What's a romantic movie that I can watch with my wife?
Continue to answer the query by using these Search Results:
Title: King Solomon's Mines, Plot: Guide Allan Quatermain helps a young lady (Beth) find her lost husband somewhere in Africa. It's a spectacular adventure story with romance, because while they fight with wild animals and cannibals, they fall in love. Will they find the lost husband and finish the nice connection?
Title: Shut Up and Kiss Me!, Plot: Ryan and Pete are 27-year old best friends in Miami, born on the same day and each searching for the perfect woman. Ryan is a rookie stockbroker living with his psychic Mom. Pete is a slick surfer dude yet to find commitment. Each meets the women of their dreams on the same day. Ryan knocks heads in an elevator with the gorgeous Jessica, passing out before getting her number. Pete falls for the insatiable Tiara, but Tiara's uncle is mob boss Vincent Bublione, charged with her protection. This high-energy romantic comedy asks to what extent will you go for true love?
Title: Titanic, Plot: The plot focuses on the romances of two couples upon the doomed ship's maiden voyage. Isabella Paradine (Catherine Zeta-Jones) is a wealthy woman mourning the loss of her aunt, who reignites a romance with former flame Wynn Park (Peter Gallagher). Meanwhile, a charming ne'er-do-well named Jamie Perse (Mike Doyle) steals a ticket for the ship, and falls for a sweet innocent Irish girl on board. But their romance is threatened by the villainous Simon Doonan (Tim Curry), who has discovered about the ticket and makes Jamie his unwilling accomplice, as well as having sinister plans for the girl.
Title: Fortress, Plot: A futuristic prison movie. Protagonist and wife are nabbed at a future US emigration point with an illegal baby during population control. The resulting prison experience is the subject of the movie. The prison is a futuristic one run by a private corporation bent on mind control in various ways.
Title: Varalaaru, Plot: Relationships become entangled in an emotional web.
.

Which movie would you recommend for a romantic evening with your wife?

From the provided titles, the movie that would be recommended for a romantic evening with your wife is **King Solomon's Mines**. It's a romantic adventure story with romance, and it's a great choice for a date night.
</pre>

## 致谢

这个 notebook 改编自
* [MongoDB 的 RAG 指南](https://huggingface.co/learn/cookbook/rag_with_hugging_face_gemma_mongodb)
* OpenAI 的 [ES RAG 指南](https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/elasticsearch/elasticsearch-retrieval-augmented-generation.ipynb)
* Elasticsearch-labs 的 [从-Hugging-Face-加载模型-cookbook](https://github.com/elastic/elasticsearch-labs/blob/main/notebooks/integrations/hugging-face/loading-model-from-hugging-face.ipynb)

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/rag_with_hugging_face_gemma_elasticsearch.md" />

### 利用知识图谱增强 RAG 推理能力
https://huggingface.co/learn/cookbook/zh-CN/rag_with_knowledge_graphs_neo4j.md

# 利用知识图谱增强 RAG 推理能力

_作者: [Diego Carpintero](https://github.com/dcarpintero)_

知识图谱提供了一种以既能为人类又能为机器理解的格式建模和存储互联信息的方法。这些图谱由*节点*和*边*组成，分别表示实体及其关系。与传统数据库不同，图谱固有的表达能力允许更丰富的语义理解，同时提供了灵活性，可以在不受固定模式限制的情况下，适应新的实体类型和关系。

通过将知识图谱与嵌入（向量搜索）结合，我们可以利用*多跳连接性*和*信息的上下文理解*，来增强大语言模型（LLMs）的推理能力和可解释性。

本文档探讨了这一方法的实际应用，展示了如何：
- 使用合成数据集在 [Neo4j](https://neo4j.com/docs/) 中构建与研究出版物相关的知识图谱，
- 使用[嵌入模型](https://python.langchain.com/v0.2/docs/integrations/text_embedding/)将我们的部分数据字段投影到高维向量空间，
- 在这些嵌入上构建向量索引以启用相似性搜索，
- 使用自然语言从我们的图谱中提取洞见，通过 [LangChain](https://python.langchain.com/v0.2/docs/introduction/) 轻松将用户查询转换为 [Cypher](https://neo4j.com/docs/cypher-manual/current/introduction/) 语句：

<p align="center">
  <img src="https://raw.githubusercontent.com/dcarpintero/generative-ai-101/main/static/knowledge-graphs.png">
</p>

## 初始化

```python
%pip install neo4j langchain langchain_openai langchain-community python-dotenv --quiet
```

### 设置 Neo4j 实例

我们将使用 [Neo4j](https://neo4j.com/docs/) 来创建我们的知识图谱，它是一个开源的数据库管理系统，专门用于图数据库技术。

为了快速且简便地设置，您可以在 [Neo4j Aura](https://neo4j.com/product/auradb/)上 启动一个免费的实例。

接着，你可以使用 `.env` 文件将 `NEO4J_URI`、`NEO4J_USERNAME` 和 `NEO4J_PASSWORD` 设置为环境变量：

```python
import dotenv
dotenv.load_dotenv('.env', override=True)
```

LangChain 提供了 `Neo4jGraph` 类来与 Neo4j 进行交互：

```python
import os
from langchain_community.graphs import Neo4jGraph

graph = Neo4jGraph(
    url=os.environ['NEO4J_URI'], 
    username=os.environ['NEO4J_USERNAME'],
    password=os.environ['NEO4J_PASSWORD'],
)
```

### 将数据集加载到图谱中

以下示例演示了如何与我们的 `Neo4j` 数据库建立连接，并使用[合成数据](https://github.com/dcarpintero/generative-ai-101/blob/main/dataset/synthetic_articles.csv)填充它，这些数据包括研究文章及其作者。

实体包括：
- *研究人员*（Researcher）
- *文章*（Article）
- *主题*（Topic）

关系包括：
- *研究人员* --[PUBLISHED]--> *文章*
- *文章* --[IN_TOPIC]--> *主题*

```python
from langchain_community.graphs import Neo4jGraph

graph = Neo4jGraph()

q_load_articles = """
LOAD CSV WITH HEADERS
FROM 'https://raw.githubusercontent.com/dcarpintero/generative-ai-101/main/dataset/synthetic_articles.csv' 
AS row 
FIELDTERMINATOR ';'
MERGE (a:Article {title:row.Title})
SET a.abstract = row.Abstract,
    a.publication_date = date(row.Publication_Date)
FOREACH (researcher in split(row.Authors, ',') | 
    MERGE (p:Researcher {name:trim(researcher)})
    MERGE (p)-[:PUBLISHED]->(a))
FOREACH (topic in [row.Topic] | 
    MERGE (t:Topic {name:trim(topic)})
    MERGE (a)-[:IN_TOPIC]->(t))
"""

graph.query(q_load_articles)
```

让我们检查节点和关系是否已正确初始化：

```python
>>> graph.refresh_schema()
>>> print(graph.get_schema)
```

<pre>
Node properties:
Article {title: STRING, abstract: STRING, publication_date: DATE, embedding: LIST}
Researcher {name: STRING}
Topic {name: STRING}
Relationship properties:

The relationships:
(:Article)-[:IN_TOPIC]->(:Topic)
(:Researcher)-[:PUBLISHED]->(:Article)
</pre>

我们可以在 Neo4j 工作区中检查我们的知识图谱：

<p>
  <img src="https://raw.githubusercontent.com/dcarpintero/generative-ai-101/main/static/kg_sample_00.png">
</p>

### 构建向量索引

现在，我们构建一个向量索引，以便根据*主题、标题和摘要*高效地搜索相关的*文章*。这一过程包括使用这些字段计算每篇文章的嵌入。在查询时，系统通过使用相似性度量（例如余弦距离）来找到与用户输入最相似的文章。

```python
from langchain_community.vectorstores import Neo4jVector
from langchain_openai import OpenAIEmbeddings

vector_index = Neo4jVector.from_existing_graph(
    OpenAIEmbeddings(),
    url=os.environ['NEO4J_URI'],
    username=os.environ['NEO4J_USERNAME'],
    password=os.environ['NEO4J_PASSWORD'],
    index_name='articles',
    node_label="Article",
    text_node_properties=['topic', 'title', 'abstract'],
    embedding_node_property='embedding',
)
```

**注意：** 要访问 OpenAI 嵌入模型，你需要创建一个 OpenAI 账户，获取 API 密钥，并将 `OPENAI_API_KEY` 设置为环境变量。你还可以尝试使用其他的[嵌入模型](https://python.langchain.com/v0.2/docs/integrations/text_embedding/)集成，进行实验和对比。

## 基于相似性的问答

`Langchain RetrievalQA` 创建了一个问答（QA）链，使用上述的向量索引作为检索器。

```python
from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI

vector_qa = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(),
    chain_type="stuff",
    retriever=vector_index.as_retriever()
)
```

我们来问一下：“*哪些文章讨论了人工智能如何影响我们的日常生活？*”

```python
>>> r = vector_qa.invoke(
...     {"query": "which articles discuss how AI might affect our daily life? include the article titles and abstracts."}
... )
>>> print(r['result'])
```

<pre>
The articles that discuss how AI might affect our daily life are:

1. **The Impact of AI on Employment: A Comprehensive Study**
   *Abstract:* This study analyzes the potential effects of AI on various job sectors and suggests policy recommendations to mitigate negative impacts.

2. **The Societal Implications of Advanced AI: A Multidisciplinary Analysis**
   *Abstract:* Our study brings together experts from various fields to analyze the potential long-term impacts of advanced AI on society, economy, and culture.

These two articles would provide insights into how AI could potentially impact our daily lives from different perspectives.
</pre>

## 通过知识图谱进行推理

知识图谱非常适合于在实体之间建立连接，能够提取模式并发现新的洞察。

本节将演示如何实现这一过程，并通过自然语言查询将结果集成到大语言模型（LLM）管道中。

### Graph-Cypher-Chain 与 LangChain

为了构建富有表现力且高效的查询，`Neo4j` 使用 `Cypher`，一种受 SQL 启发的声明式查询语言。`LangChain` 提供了封装器 `GraphCypherQAChain`，它是一个抽象层，允许通过自然语言查询图数据库，从而更容易将基于图的数据检索集成到大语言模型（LLM）管道中。

在实际应用中，`GraphCypherQAChain`：
- 从用户输入（自然语言）生成 Cypher 语句（图数据库的查询，如 Neo4j），并应用上下文学习（提示工程），
- 将这些语句执行到图数据库中，
- 将结果作为上下文提供，帮助 LLM 基于准确、最新的信息生成回答。

**注意：** 该实现涉及执行模型生成的图查询，这可能带来潜在风险，例如意外访问或修改数据库中的敏感数据。为减少这些风险，请确保数据库连接权限尽可能受限，以满足链/代理的特定需求。虽然这种方法能够降低风险，但并不能完全消除风险。

```python
from langchain.chains import GraphCypherQAChain
from langchain_openai import ChatOpenAI

graph.refresh_schema()

cypher_chain = GraphCypherQAChain.from_llm(
    cypher_llm = ChatOpenAI(temperature=0, model_name='gpt-4o'),
    qa_llm = ChatOpenAI(temperature=0, model_name='gpt-4o'), 
    graph=graph,
    verbose=True,
)
```

### 使用自然语言的查询示例

请注意，在以下示例中，Cypher 查询执行的结果是如何作为上下文提供给大语言模型（LLM）的：

#### **"*Emily Chen 发布了多少篇文章？*"**

在这个示例中，我们的问题“*Emily Chen 发布了多少篇文章？*”将被转换为以下 Cypher 查询：

```
MATCH (r:Researcher {name: "Emily Chen"})-[:PUBLISHED]->(a:Article)
RETURN COUNT(a) AS numberOfArticles
```

该查询通过匹配名称为“Emily Chen”的 `Researcher` 节点，并遍历与之相关的 `PUBLISHED` 关系，连接到 `Article` 节点。然后，它计算与“Emily Chen”连接的 `Article` 节点的数量。

执行查询后，结果将作为上下文提供给 LLM，LLM 基于这个上下文来生成回答。

<p>
  <img src="https://raw.githubusercontent.com/dcarpintero/generative-ai-101/main/static/kg_sample_01.png" width="40%">
</p>

```python
>>> # the answer should be '7'
>>> cypher_chain.invoke(
...     {"query": "How many articles has published Emily Chen?"}
... )
```

<pre>
[1m> Entering new GraphCypherQAChain chain...[0m
Generated Cypher:
[32;1m[1;3mcypher
MATCH (r:Researcher {name: "Emily Chen"})-[:PUBLISHED]->(a:Article)
RETURN COUNT(a) AS numberOfArticles
[0m
Full Context:
[32;1m[1;3m[{'numberOfArticles': 7}][0m

[1m> Finished chain.[0m
</pre>

#### **"*是否有任何一对研究人员共同发布了超过三篇文章？*"**

在这个示例中，查询“*是否有任何一对研究人员共同发布了超过三篇文章？*”将结果转换为以下 Cypher 查询：

```
MATCH (r1:Researcher)-[:PUBLISHED]->(a:Article)<-[:PUBLISHED]-(r2:Researcher)
WHERE r1 <> r2
WITH r1, r2, COUNT(a) AS sharedArticles
WHERE sharedArticles > 3
RETURN r1.name, r2.name, sharedArticles
```

该查询首先从 `Researcher` 节点出发，遍历 `PUBLISHED` 关系，找到与之连接的 `Article` 节点，然后再次遍历，查找与另一位 `Researcher` 节点的连接。通过这种方式，查询找出那些共同发表了超过三篇文章的研究人员对。最终，结果将作为上下文提供给 LLM，LLM 会基于这个上下文生成回答。

<p>
  <img src="https://raw.githubusercontent.com/dcarpintero/generative-ai-101/main/static/kg_sample_02.png">
</p>

```python
>>> # the answer should be David Johnson & Emily Chen, Robert Taylor & Emily Chen
>>> cypher_chain.invoke(
...     {"query": "are there any pair of researchers who have published more than three articles together?"}
... )
```

<pre>
[1m> Entering new GraphCypherQAChain chain...[0m
Generated Cypher:
[32;1m[1;3mcypher
MATCH (r1:Researcher)-[:PUBLISHED]->(a:Article)<-[:PUBLISHED]-(r2:Researcher)
WHERE r1 <> r2
WITH r1, r2, COUNT(a) AS sharedArticles
WHERE sharedArticles > 3
RETURN r1.name, r2.name, sharedArticles
[0m
Full Context:
[32;1m[1;3m[{'r1.name': 'David Johnson', 'r2.name': 'Emily Chen', 'sharedArticles': 4}, {'r1.name': 'Robert Taylor', 'r2.name': 'Emily Chen', 'sharedArticles': 4}, {'r1.name': 'Emily Chen', 'r2.name': 'David Johnson', 'sharedArticles': 4}, {'r1.name': 'Emily Chen', 'r2.name': 'Robert Taylor', 'sharedArticles': 4}][0m

[1m> Finished chain.[0m
</pre>

#### **"*哪位研究人员与最多的同行合作过？*"**

让我们找出哪位研究人员与最多的同行合作过。  
我们的查询“*哪位研究人员与最多的同行合作过？*”现在转换为以下 Cypher 查询：

```
MATCH (r:Researcher)-[:PUBLISHED]->(:Article)<-[:PUBLISHED]-(peer:Researcher)
WITH r, COUNT(DISTINCT peer) AS peerCount
RETURN r.name AS researcher, peerCount
ORDER BY peerCount DESC
LIMIT 1
```

在这个查询中，我们从所有 `Researcher` 节点出发，遍历它们的 `PUBLISHED` 关系，找到与之连接的 `Article` 节点。对于每个 `Article` 节点，Neo4j 会继续回溯，查找那些也发表了同一篇文章的其他 `Researcher` 节点（同行）。通过这种方式，查询能够计算出每位研究人员与多少位同行合作，并按合作次数降序排列，最终返回合作最多的研究人员及其同行数量。

<p>
  <img src="https://raw.githubusercontent.com/dcarpintero/generative-ai-101/main/static/kg_sample_03.png">
</p>

```python
>>> # the answer should be 'David Johnson'
>>> cypher_chain.invoke(
...     {"query": "Which researcher has collaborated with the most peers?"}
... )
```

<pre>
[1m> Entering new GraphCypherQAChain chain...[0m
Generated Cypher:
[32;1m[1;3mcypher
MATCH (r1:Researcher)-[:PUBLISHED]->(:Article)<-[:PUBLISHED]-(r2:Researcher)
WHERE r1 <> r2
WITH r1, COUNT(DISTINCT r2) AS collaborators
RETURN r1.name AS researcher, collaborators
ORDER BY collaborators DESC
LIMIT 1
[0m
Full Context:
[32;1m[1;3m[{'researcher': 'David Johnson', 'collaborators': 6}][0m

[1m> Finished chain.[0m
</pre>

----

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/rag_with_knowledge_graphs_neo4j.md" />

### 使用结构化生成进行带源高亮的 RAG
https://huggingface.co/learn/cookbook/zh-CN/structured_generation.md

# 使用结构化生成进行带源高亮的 RAG 
_作者: [Aymeric Roucher](https://huggingface.co/m-ric)_

**结构化生成是一种方法**，它强制 LLN 的输出遵循某些约束，例如遵循特定的模式。

这有许多用例：

- ✅ 输出一个具有特定键的字典
- 📏 确保输出长度超过 N 个字符
- ⚙️ 更一般地说，强制输出遵循特定的正则表达式模式以进行下游处理。
- 💡 在检索增强生成（RAG）中突出显示支持答案的源

在这个 notebook 中，我们特别演示了最后一个用例：

**➡️ 我们构建了一个 RAG 系统，它不仅提供答案，还突出显示这个答案所基于的支持片段。**

_如果你需要 RAG 的入门介绍，可以查看[这个其他的教程](advanced_rag)。_

这个 notebook 首先展示了通过提示进行结构化生成的简单方法，并突出了其局限性，然后演示了受限解码以实现更高效的结构化生成。

它利用了 HuggingFace 推理端点（示例展示了一个[无服务器](https://huggingface.co/docs/api-inference/quicktour)端点，但你可以直接将端点更改为[专用](https://huggingface.co/docs/inference-endpoints/en/guides/access)端点），然后还展示了一个使用[outlines](https://github.com/outlines-dev/outlines)，一个结构化文本生成库的本地流水线。

```python
!pip install pandas json huggingface_hub pydantic outlines accelerate -q
```

```python
import pandas as pd
import json
from huggingface_hub import InferenceClient

pd.set_option("display.max_colwidth", None)
```

```python
repo_id = "meta-llama/Meta-Llama-3-8B-Instruct"

llm_client = InferenceClient(model=repo_id, timeout=120)

# Test your LLM client
llm_client.text_generation(prompt="How are you today?", max_new_tokens=20)
```

## 提示模型

为了从模型中获得结构化输出，你可以简单地用适当的指导原则提示一个足够强大的模型，并且大多数时候它应该能够直接工作。

在这种情况下，我们希望 RAG 模型不仅生成答案，还生成一个置信度分数和一些源代码片段。
我们希望将这些生成为一个 JSON 字典，然后可以轻松地解析它以进行下游处理（在这里，我们将只突出显示源代码片段）。

```python
RELEVANT_CONTEXT = """
Document:

The weather is really nice in Paris today.
To define a stop sequence in Transformers, you should pass the stop_sequence argument in your pipeline or model.

"""
```

```python
RAG_PROMPT_TEMPLATE_JSON = """
Answer the user query based on the source documents.

Here are the source documents: {context}


You should provide your answer as a JSON blob, and also provide all relevant short source snippets from the documents on which you directly based your answer, and a confidence score as a float between 0 and 1.
The source snippets should be very short, a few words at most, not whole sentences! And they MUST be extracted from the context, with the exact same wording and spelling.

Your answer should be built as follows, it must contain the "Answer:" and "End of answer." sequences.

Answer:
{{
  "answer": your_answer,
  "confidence_score": your_confidence_score,
  "source_snippets": ["snippet_1", "snippet_2", ...]
}}
End of answer.

Now begin!
Here is the user question: {user_query}.
Answer:
"""
```

```python
USER_QUERY = "How can I define a stop sequence in Transformers?"
```

```python
>>> prompt = RAG_PROMPT_TEMPLATE_JSON.format(
...     context=RELEVANT_CONTEXT, user_query=USER_QUERY
... )
>>> print(prompt)
```

<pre>
Answer the user query based on the source documents.

Here are the source documents: 
Document:

The weather is really nice in Paris today.
To define a stop sequence in Transformers, you should pass the stop_sequence argument in your pipeline or model.




You should provide your answer as a JSON blob, and also provide all relevant short source snippets from the documents on which you directly based your answer, and a confidence score as a float between 0 and 1.
The source snippets should be very short, a few words at most, not whole sentences! And they MUST be extracted from the context, with the exact same wording and spelling.

Your answer should be built as follows, it must contain the "Answer:" and "End of answer." sequences.

Answer:
{
  "answer": your_answer,
  "confidence_score": your_confidence_score,
  "source_snippets": ["snippet_1", "snippet_2", ...]
}
End of answer.

Now begin!
Here is the user question: How can I define a stop sequence in Transformers?.
Answer:
</pre>

```python
>>> answer = llm_client.text_generation(
...     prompt,
...     max_new_tokens=1000,
... )

>>> answer = answer.split("End of answer.")[0]
>>> print(answer)
```

<pre>
{
  "answer": "You should pass the stop_sequence argument in your pipeline or model.",
  "confidence_score": 0.9,
  "source_snippets": ["stop_sequence", "pipeline or model"]
}
</pre>

LLM 的输出是一个字典的字符串表示：所以我们只需使用 `literal_eval` 将其作为字典加载。

```python
from ast import literal_eval

parsed_answer = literal_eval(answer)
```

```python
>>> def highlight(s):
...     return "\x1b[1;32m" + s + "\x1b[0m"


>>> def print_results(answer, source_text, highlight_snippets):
...     print("Answer:", highlight(answer))
...     print("\n\n", "=" * 10 + " Source documents " + "=" * 10)
...     for snippet in highlight_snippets:
...         source_text = source_text.replace(snippet.strip(), highlight(snippet.strip()))
...     print(source_text)


>>> print_results(
...     parsed_answer["answer"], RELEVANT_CONTEXT, parsed_answer["source_snippets"]
... )
```

<pre>
Answer: [1;32mYou should pass the stop_sequence argument in your pipeline or model.[0m


 ========== Source documents ==========

Document:

The weather is really nice in Paris today.
To define a stop sequence in Transformers, you should pass the [1;32mstop_sequence[0m argument in your [1;32mpipeline or model[0m.
</pre>

成功了！🥳

但是使用一个不那么强大的模型会怎么样呢？

为了模拟一个不那么强大的模型可能产生的连贯性较差的输出，我们增加了温度（temperature）。

```python
>>> answer = llm_client.text_generation(
...     prompt,
...     max_new_tokens=250,
...     temperature=1.6,
...     return_full_text=False,
... )
>>> print(answer)
```

<pre>
{
  "answer": Canter_pass_each_losses_periodsFINITE summariesiculardimension suites TRANTR年のeachাঃshaft_PAR getattrANGE atualvíce région bu理解 Rubru_mass SH一直Batch Sets Soviet тощо B.q Iv.ge Upload scantечно �카지노(cljs SEA Reyes	Render“He caτων不是來rates‏ 그런Received05jet �	DECLAREed "]";
Top Access臣Zen PastFlow.TabBand                                                
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</pre>

现在，输出甚至不是正确的 JSON 格式。

## 👉 受限解码

为了强制输出 JSON，我们将使用**受限解码**，在这种解码方式中，我们强制 LLM 只输出符合称为**语法**的一组规则的令牌。

这个语法可以使用 Pydantic 模型、JSON 模式或正则表达式来定义。然后 AI 将生成符合指定语法的响应。

例如，这里我们遵循[Pydantic 类型](https://docs.pydantic.dev/latest/api/types/)。


```python
from pydantic import BaseModel, confloat, StringConstraints
from typing import List, Annotated


class AnswerWithSnippets(BaseModel):
    answer: Annotated[str, StringConstraints(min_length=10, max_length=100)]
    confidence: Annotated[float, confloat(ge=0.0, le=1.0)]
    source_snippets: List[Annotated[str, StringConstraints(max_length=30)]]
```

我建议检查生成的模式，以确保它正确地表示了你的需求：

```python
AnswerWithSnippets.schema()
```

你可以使用客户端的 `text_generation` 方法，或者使用其 `post` 方法。

```python
>>> # Using text_generation
>>> answer = llm_client.text_generation(
...     prompt,
...     grammar={"type": "json", "value": AnswerWithSnippets.schema()},
...     max_new_tokens=250,
...     temperature=1.6,
...     return_full_text=False,
... )
>>> print(answer)

>>> # Using post
>>> data = {
...     "inputs": prompt,
...     "parameters": {
...         "temperature": 1.6,
...         "return_full_text": False,
...         "grammar": {"type": "json", "value": AnswerWithSnippets.schema()},
...         "max_new_tokens": 250,
...     },
... }
>>> answer = json.loads(llm_client.post(json=data))[0]["generated_text"]
>>> print(answer)
```

<pre>
{
  "answer": "You should pass the stop_sequence argument in your modemÏallerbate hassceneable measles updatedAt原因",
            "confidence": 0.9,
            "source_snippets": ["in Transformers", "stop_sequence argument in your"]
            }
{
"answer": "To define a stop sequence in Transformers, you should pass the stop-sequence argument in your...giÃ",  "confidence": 1,  "source_snippets": ["seq이야","stration nhiên thị ji是什么hpeldo"]
}
</pre>

✅ 尽管由于温度较高，答案仍然没有意义，但现在生成的输出是正确的 JSON 格式，具有我们在语法中定义的确切键和类型！

然后它可以被解析以进行进一步处理。

### 使用 Outlines 在本地流水线上应用语法

[Outlines](https://github.com/outlines-dev/outlines/) 是在我们的推理 API 底层运行的库，用于约束输出生成。你也可以在本地使用它。

它通过 [在 logits 上施加 bias](https://github.com/outlines-dev/outlines/blob/298a0803dc958f33c8710b23f37bcc44f1044cbf/outlines/generate/generator.py#L143) 来强制选择仅符合你约束的选项。


```python
import outlines

repo_id = "mustafaaljadery/gemma-2B-10M"
# Load model locally
model = outlines.models.transformers(repo_id)

schema_as_str = json.dumps(AnswerWithSnippets.schema())

generator = outlines.generate.json(model, schema_as_str)

# Use the `generator` to sample an output from the model
result = generator(prompt)
print(result)
```

你还可以使用 [文本生成推理](https://huggingface.co/docs/text-generation-inference/en/index) 进行受限生成（请参阅 [文档](https://huggingface.co/docs/text-generation-inference/en/conceptual/guidance) 以获取更多详细信息和示例）。

现在我们已经展示了一个特定的 RAG 用例，但受限生成对于更多的事情都非常有帮助。

例如，在你的 [LLM 判断](llm_judge) 工作流程中，你也可以使用受限生成来输出一个 JSON，如下所示：

```
{
    "score": 1,
    "rationale": "The answer does not match the true answer at all.",
    "confidence_level": 0.85
}
```

今天的内容就到这里，恭喜你跟到最后！👏

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/structured_generation.md" />

### 使用 distilabel 生成偏好数据集
https://huggingface.co/learn/cookbook/zh-CN/generate_preference_dataset_distilabel.md

# 使用 distilabel 生成偏好数据集

_作者：[David Berenstein](https://huggingface.co/davidberenstein1957) 和 [Sara Han Díaz](https://huggingface.co/sdiazlor)_

- **库**: [argilla](https://github.com/argilla-io/argilla), [hf-inference-endpoints](https://github.com/huggingface/huggingface_hub)
- **组件**: [LoadDataFromHub](https://distilabel.argilla.io/latest/components-gallery/steps/loaddatafromhub/), [TextGeneration](https://distilabel.argilla.io/latest/components-gallery/tasks/textgeneration/), [UltraFeedback](https://distilabel.argilla.io/latest/components-gallery/tasks/ultrafeedback/), [GroupColumns](https://distilabel.argilla.io/latest/components-gallery/steps/groupcolumns/), [FormatTextGenerationDPO](https://distilabel.argilla.io/latest/components-gallery/steps/formattextgenerationdpo/), [PreferenceToArgilla](https://distilabel.argilla.io/latest/components-gallery/steps/textgenerationtoargilla/), [InferenceEndpointsLLM](https://distilabel.argilla.io/latest/components-gallery/llms/inferenceendpointsllm/)

在本教程中，我们将使用 **distilabel** 生成一个用于 DPO、ORPO 或 RLHF 的合成偏好数据集。**distilabel** 是一个为工程师设计的合成数据和 AI 反馈框架，旨在提供基于经过验证的研究论文的快速、可靠和可扩展的管道。请查看[文档](https://distilabel.argilla.io/latest/)了解更多信息。

为了生成响应并对其进行评估，我们将使用与 **distilabel** 集成的 [无服务器 HF 推理 API](https://huggingface.co/docs/api-inference/index)。该服务是免费的，但有使用限制，允许您通过简单的 HTTP 请求测试和评估超过 150,000 个公开模型，或您自己的私有模型，推理速度快，托管在 Hugging Face 的共享基础设施上。如果您需要更多计算能力，可以使用 [Hugging Face 推理端点](https://huggingface.co/docs/inference-endpoints/guides/create_endpoint)部署您自己的推理端点。

最后，为了进一步整理数据，我们将使用 [Argilla](https://github.com/argilla-io/argilla)，它允许我们对数据质量提供人工反馈。Argilla 是一个为 AI 工程师和领域专家设计的协作工具，帮助他们为项目构建高质量的数据集。请查看 [文档](https://docs.argilla.io/latest/) 了解更多信息。

## 开始

### 安装依赖

为了完成本教程，您需要通过 pip 安装 distilabel SDK 和一些第三方库。由于本教程中将使用 **免费的但有使用限制的 Hugging Face 无服务器推理 API**，因此我们需要额外安装该依赖。您可以通过运行以下命令来安装所有必要的依赖：

```python
!pip install "distilabel[hf-inference-endpoints]"
```

```python
!pip install "transformers~=4.0" "torch~=2.0"
```

让我们进行所需的导入：

```python
from distilabel.llms import InferenceEndpointsLLM
from distilabel.pipeline import Pipeline
from distilabel.steps import (
    LoadDataFromHub,
    GroupColumns,
    FormatTextGenerationDPO,
    PreferenceToArgilla,
)
from distilabel.steps.tasks import TextGeneration, UltraFeedback
```

你需要一个 `HF_TOKEN` 来使用 HF 推理端点。请登录以便在此 Notebook 中直接使用它。

```python
import os
from huggingface_hub import login

login(token=os.getenv("HF_TOKEN"), add_to_git_credential=True)
```

### （可选）部署 Argilla

你可以跳过此步骤或将其替换为任何其他数据评估工具，但如果缺乏数据质量，你的模型质量将受到影响，因此我们建议查看您的数据。如果你已经部署了 Argilla，可以跳过此步骤。否则，你可以按照 [此指南](https://docs.argilla.io/latest/getting_started/quickstart/) 快速部署 Argilla。

同时，你需要将 Argilla 安装为 distilabel 的附加依赖。

```python
!pip install "distilabel[argilla, hf-inference-endpoints]"
```

## 定义管道

为了生成我们的偏好数据集，我们需要定义一个包含所有必要步骤的 `Pipeline`。接下来，我们将详细介绍每个步骤。

### 加载数据集

我们将使用来自 Hugging Face Hub 的 [`argilla/10Kprompts-mini`](https://huggingface.co/datasets/argilla/10Kprompts-mini) 数据集作为源数据。

<iframe
  src="https://huggingface.co/datasets/argilla/10Kprompts-mini/embed/viewer/default/train"
  frameborder="0"
  width="100%"
  height="560px"
></iframe>

- 组件: `LoadDataFromHub`
- 输入列: `instruction` 和 `topic`，与加载的数据集中的列相同
- 输出列: `instruction` 和 `topic`

```python
load_dataset = LoadDataFromHub(
        repo_id= "argilla/10Kprompts-mini",
        num_examples=1,
        pipeline=Pipeline(name="showcase-pipeline"),
    )
load_dataset.load()
next(load_dataset.process())
```

### 生成响应

我们需要为给定的指令生成响应。我们将使用通过无服务器推理 API 在 Hugging Face Hub 上提供的两个不同模型：[`meta-llama/Meta-Llama-3-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) 和 [`mistralai/Mixtral-8x7B-Instruct-v0.1`](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)。我们还将为每个模型指定生成参数。

- 组件: `TextGeneration` 任务，使用 `InferenceEndpointsLLM` 调用 LLM
- 输入列: `instruction`
- 输出列: `generation`、`distilabel_metadata`、`model_name`，针对每个模型

根据你的使用案例并为提高结果，你可以选择使用任何 [其他 LLM](https://distilabel.argilla.io/latest/components-gallery/llms/)。

```python
>>> generate_responses = [
...     TextGeneration(
...         llm=InferenceEndpointsLLM(
...             model_id="meta-llama/Meta-Llama-3-8B-Instruct",
...             tokenizer_id="meta-llama/Meta-Llama-3-8B-Instruct",
...             generation_kwargs={"max_new_tokens": 512, "temperature": 0.7},
...         ),
...         pipeline=Pipeline(name="showcase-pipeline"),
...     ),
...     TextGeneration(
...         llm=InferenceEndpointsLLM(
...             model_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
...             tokenizer_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
...             generation_kwargs={"max_new_tokens": 512, "temperature": 0.7},
...         ),
...         pipeline=Pipeline(name="showcase-pipeline"),
...     ),
... ]
>>> for task in generate_responses:
...     task.load()
...     print(next(task.process([{"instruction": "Which are the top cities in Spain?"}])))
```

<pre>
[{'instruction': 'Which are the top cities in Spain?', 'generation': 'Spain is a country with a rich culture, history, and architecture, and it has many great cities to visit. Here are some of the top cities in Spain:\n\n1. **Madrid**: The capital city of Spain, known for its vibrant nightlife, museums, and historic landmarks like the Royal Palace and Prado Museum.\n2. **Barcelona**: The second-largest city in Spain, famous for its modernist architecture, beaches, and iconic landmarks like La Sagrada Família and Park Güell, designed by Antoni Gaudí.\n3. **Valencia**: Located on the Mediterranean coast, Valencia is known for its beautiful beaches, City of Arts and Sciences, and delicious local cuisine, such as paella.\n4. **Seville**: The capital of Andalusia, Seville is famous for its stunning cathedral, Royal Alcázar Palace, and lively flamenco music scene.\n5. **Málaga**: A coastal city in southern Spain, Málaga is known for its rich history, beautiful beaches, and being the birthplace of Pablo Picasso.\n6. **Zaragoza**: Located in the northeastern region of Aragon, Zaragoza is a city with a rich history, known for its Roman ruins, Gothic cathedral, and beautiful parks.\n7. **Granada**: A city in the Andalusian region, Granada is famous for its stunning Alhambra palace and generalife gardens, a UNESCO World Heritage Site.\n8. **Bilbao**: A city in the Basque Country, Bilbao is known for its modern architecture, including the Guggenheim Museum, and its rich cultural heritage.\n9. **Alicante**: A coastal city in the Valencia region, Alicante is famous for its beautiful beaches, historic castle, and lively nightlife.\n10. **San Sebastián**: A city in the Basque Country, San Sebastián is known for its stunning beaches, gastronomic scene, and cultural events like the San Sebastián International Film Festival.\n\nThese are just a few of the many great cities in Spain, each with its own unique character and attractions.', 'distilabel_metadata': {'raw_output_text_generation_0': 'Spain is a country with a rich culture, history, and architecture, and it has many great cities to visit. Here are some of the top cities in Spain:\n\n1. **Madrid**: The capital city of Spain, known for its vibrant nightlife, museums, and historic landmarks like the Royal Palace and Prado Museum.\n2. **Barcelona**: The second-largest city in Spain, famous for its modernist architecture, beaches, and iconic landmarks like La Sagrada Família and Park Güell, designed by Antoni Gaudí.\n3. **Valencia**: Located on the Mediterranean coast, Valencia is known for its beautiful beaches, City of Arts and Sciences, and delicious local cuisine, such as paella.\n4. **Seville**: The capital of Andalusia, Seville is famous for its stunning cathedral, Royal Alcázar Palace, and lively flamenco music scene.\n5. **Málaga**: A coastal city in southern Spain, Málaga is known for its rich history, beautiful beaches, and being the birthplace of Pablo Picasso.\n6. **Zaragoza**: Located in the northeastern region of Aragon, Zaragoza is a city with a rich history, known for its Roman ruins, Gothic cathedral, and beautiful parks.\n7. **Granada**: A city in the Andalusian region, Granada is famous for its stunning Alhambra palace and generalife gardens, a UNESCO World Heritage Site.\n8. **Bilbao**: A city in the Basque Country, Bilbao is known for its modern architecture, including the Guggenheim Museum, and its rich cultural heritage.\n9. **Alicante**: A coastal city in the Valencia region, Alicante is famous for its beautiful beaches, historic castle, and lively nightlife.\n10. **San Sebastián**: A city in the Basque Country, San Sebastián is known for its stunning beaches, gastronomic scene, and cultural events like the San Sebastián International Film Festival.\n\nThese are just a few of the many great cities in Spain, each with its own unique character and attractions.'}, 'model_name': 'meta-llama/Meta-Llama-3-8B-Instruct'}]
[{'instruction': 'Which are the top cities in Spain?', 'generation': ' Here are some of the top cities in Spain based on various factors such as tourism, culture, history, and quality of life:\n\n1. Madrid: The capital and largest city in Spain, Madrid is known for its vibrant nightlife, world-class museums (such as the Prado Museum and Reina Sofia Museum), stunning parks (such as the Retiro Park), and delicious food.\n\n2. Barcelona: Famous for its unique architecture, Barcelona is home to several UNESCO World Heritage sites designed by Antoni Gaudí, including the Sagrada Familia and Park Güell. The city also boasts beautiful beaches, a lively arts scene, and delicious Catalan cuisine.\n\n3. Valencia: A coastal city located in the east of Spain, Valencia is known for its City of Arts and Sciences, a modern architectural complex that includes a planetarium, opera house, and museum of interactive science. The city is also famous for its paella, a traditional Spanish dish made with rice, vegetables, and seafood.\n\n4. Seville: The capital of Andalusia, Seville is famous for its flamenco dancing, stunning cathedral (the largest Gothic cathedral in the world), and the Alcázar, a beautiful palace made up of a series of rooms and courtyards.\n\n5. Granada: Located in the foothills of the Sierra Nevada mountains, Granada is known for its stunning Alhambra palace, a Moorish fortress that dates back to the 9th century. The city is also famous for its tapas, a traditional Spanish dish that is often served for free with drinks.\n\n6. Bilbao: A city in the Basque Country, Bilbao is famous for its modern architecture, including the Guggenheim Museum, a contemporary art museum designed by Frank Gehry. The city is also known for its pintxos, a type of Basque tapas that are served in bars and restaurants.\n\n7. Málaga: A coastal city in Andalusia, Málaga is known for its beautiful beaches, historic sites (including the Alcazaba and Gibralfaro castles), and the Picasso Museum, which is dedicated to the famous Spanish artist who was born in the city.\n\nThese are just a few of the many wonderful cities in Spain.', 'distilabel_metadata': {'raw_output_text_generation_0': ' Here are some of the top cities in Spain based on various factors such as tourism, culture, history, and quality of life:\n\n1. Madrid: The capital and largest city in Spain, Madrid is known for its vibrant nightlife, world-class museums (such as the Prado Museum and Reina Sofia Museum), stunning parks (such as the Retiro Park), and delicious food.\n\n2. Barcelona: Famous for its unique architecture, Barcelona is home to several UNESCO World Heritage sites designed by Antoni Gaudí, including the Sagrada Familia and Park Güell. The city also boasts beautiful beaches, a lively arts scene, and delicious Catalan cuisine.\n\n3. Valencia: A coastal city located in the east of Spain, Valencia is known for its City of Arts and Sciences, a modern architectural complex that includes a planetarium, opera house, and museum of interactive science. The city is also famous for its paella, a traditional Spanish dish made with rice, vegetables, and seafood.\n\n4. Seville: The capital of Andalusia, Seville is famous for its flamenco dancing, stunning cathedral (the largest Gothic cathedral in the world), and the Alcázar, a beautiful palace made up of a series of rooms and courtyards.\n\n5. Granada: Located in the foothills of the Sierra Nevada mountains, Granada is known for its stunning Alhambra palace, a Moorish fortress that dates back to the 9th century. The city is also famous for its tapas, a traditional Spanish dish that is often served for free with drinks.\n\n6. Bilbao: A city in the Basque Country, Bilbao is famous for its modern architecture, including the Guggenheim Museum, a contemporary art museum designed by Frank Gehry. The city is also known for its pintxos, a type of Basque tapas that are served in bars and restaurants.\n\n7. Málaga: A coastal city in Andalusia, Málaga is known for its beautiful beaches, historic sites (including the Alcazaba and Gibralfaro castles), and the Picasso Museum, which is dedicated to the famous Spanish artist who was born in the city.\n\nThese are just a few of the many wonderful cities in Spain.'}, 'model_name': 'mistralai/Mixtral-8x7B-Instruct-v0.1'}]
</pre>

### 组响应

该任务需要评估响应，并且输入需要一个生成列表。然而，每个模型的响应都保存在子集 `text_generation_0` 和 `text_generation_1` 的 `generation` 列中。我们将把这两列合并成一个单一的列，并应用于 `default` 子集。

- 组件：`GroupColumns`
- 输入列：来自 `text_generation_0` 和 `text_generation_1` 的 `generation` 和 `model_name`
- 输出列：`generations` 和 `model_names`

```python
group_responses = GroupColumns(
    columns=["generation", "model_name"],
    output_columns=["generations", "model_names"],
    pipeline=Pipeline(name="showcase-pipeline"),
)
next(
    group_responses.process(
        [
            {
                "generation": "Madrid",
                "model_name": "meta-llama/Meta-Llama-3-8B-Instruct",
            },
        ],
        [
            {
                "generation": "Barcelona",
                "model_name": "mistralai/Mixtral-8x7B-Instruct-v0.1",
            }
        ],
    )
)
```

### 评估响应

为了构建我们的偏好数据集，我们需要评估模型生成的响应。我们将使用 [`meta-llama/Meta-Llama-3-70B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) 来进行此操作，应用 `UltraFeedback` 任务，根据不同维度（有用性、诚实性、遵循指令程度、真实性）对响应进行评分。

- 组件：使用 `InferenceEndpointsLLM` 执行的 `UltraFeedback` 任务
- 输入列：`instruction`，`generations`
- 输出列：`ratings`，`rationales`，`distilabel_metadata`，`model_name`

根据你的使用场景，你还可以使用任何你选择的 [其他 LLM](https://distilabel.argilla.io/latest/components-gallery/llms/) 来改进结果。

```python
evaluate_responses = UltraFeedback(
    aspect="overall-rating",
    llm=InferenceEndpointsLLM(
        model_id="meta-llama/Meta-Llama-3-70B-Instruct",
        tokenizer_id="meta-llama/Meta-Llama-3-70B-Instruct",
        generation_kwargs={"max_new_tokens": 512, "temperature": 0.7},
    ),
    pipeline=Pipeline(name="showcase-pipeline"),
)
evaluate_responses.load()
next(
    evaluate_responses.process(
        [
            {
                "instruction": "What's the capital of Spain?",
                "generations": ["Madrid", "Barcelona"],
            }
        ]
    )
)
```

### 转换为偏好数据集

- 你可以使用 `chosen` 和 `rejected` 列自动将其转换为偏好数据集。
    - 组件：`FormatTextGenerationDPO` 步骤
    - 输入列：`instruction`，`generations`，`generation_models`，`ratings`
    - 输出列：`prompt`，`prompt_id`，`chosen`，`chosen_model`，`chosen_rating`，`rejected`，`rejected_model`，`rejected_rating`

```python
format_dpo = FormatTextGenerationDPO(pipeline=Pipeline(name="showcase-pipeline"))
format_dpo.load()
next(
    format_dpo.process(
        [
            {
                "instruction": "What's the capital of Spain?",
                "generations": ["Madrid", "Barcelona"],
                "generation_models": [
                    "Meta-Llama-3-8B-Instruct",
                    "Mixtral-8x7B-Instruct-v0.1",
                ],
                "ratings": [5, 1],
            }
        ]
    )
)
```

- 或者，你可以使用 Argilla 手动标注数据并将其转换为偏好数据集。
    - 组件：`PreferenceToArgilla` 步骤
    - 输入列：`instruction`，`generations`，`generation_models`，`ratings`
    - 输出列：`instruction`，`generations`，`generation_models`，`ratings`

```python
to_argilla = PreferenceToArgilla(
    dataset_name="preference-dataset",
    dataset_workspace="argilla",
    api_url="https://[your-owner-name]-[your-space-name].hf.space",
    api_key="[your-api-key]",
    num_generations=2
)
```

## 运行管道

以下是完整的管道定义：

```python
with Pipeline(name="generate-dataset") as pipeline:

    load_dataset = LoadDataFromHub(repo_id="argilla/10Kprompts-mini")

    generate_responses = [
        TextGeneration(
            llm=InferenceEndpointsLLM(
                model_id="meta-llama/Meta-Llama-3-8B-Instruct",
                tokenizer_id="meta-llama/Meta-Llama-3-8B-Instruct",
                generation_kwargs={"max_new_tokens": 512, "temperature": 0.7},
            )
        ),
        TextGeneration(
            llm=InferenceEndpointsLLM(
                model_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
                tokenizer_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
                generation_kwargs={"max_new_tokens": 512, "temperature": 0.7},
            )
        ),
    ]

    group_responses = GroupColumns(
        columns=["generation", "model_name"],
        output_columns=["generations", "model_names"],
    )

    evaluate_responses = UltraFeedback(
        aspect="overall-rating",
        llm=InferenceEndpointsLLM(
            model_id="meta-llama/Meta-Llama-3-70B-Instruct",
            tokenizer_id="meta-llama/Meta-Llama-3-70B-Instruct",
            generation_kwargs={"max_new_tokens": 512, "temperature": 0.7},
        )
    )

    format_dpo = FormatTextGenerationDPO()

    to_argilla = PreferenceToArgilla(
        dataset_name="preference-dataset",
        dataset_workspace="argilla",
        api_url="https://[your-owner-name]-[your-space-name].hf.space",
        api_key="[your-api-key]",
        num_generations=2
    )

    for task in generate_responses:
        load_dataset.connect(task)
        task.connect(group_responses)
    group_responses.connect(evaluate_responses)
    evaluate_responses.connect(format_dpo, to_argilla)
```

运行管道并生成偏好数据集

```python
distiset = pipeline.run()
```

让我们检查偏好数据集！如果你已经将数据加载到 Argilla，你可以通过 [在 Argilla UI 中开始标注](https://docs.argilla.io/latest/how_to_guides/annotate/)。

你可以将数据集推送到 Hub 与社区共享，并 [嵌入它以探索数据](https://huggingface.co/docs/hub/datasets-viewer-embed)。

```python
distiset.push_to_hub("[your-owner-name]/example-preference-dataset")
```

<iframe
  src="https://huggingface.co/datasets/distilabel-internal-testing/example-generate-preference-dataset/embed/viewer/format_text_generation_d_p_o_0/train"
  frameborder="0"
  width="100%"
  height="560px"
></iframe>

## 总结

在本教程中，我们展示了使用 Distilabel 构建生成偏好数据集的流水线的详细步骤。您可以根据自己的使用案例定制此流水线，并通过 Hugging Face Hub 与社区共享您的数据集，或使用它们训练 DPO 或 ORPO 模型。

我们使用了一个包含提示的数据集，通过无服务器的 Hugging Face 推理 API 使用两个不同的模型生成响应。接下来，我们使用第三个模型根据 UltraFeedback 标准对响应进行了评估。最后，我们将数据转换为偏好数据集，并使用 Argilla 进行进一步的整理。

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/generate_preference_dataset_distilabel.md" />

### 分析艺术风格与多模态嵌入
https://huggingface.co/learn/cookbook/zh-CN/analyzing_art_with_hf_and_fiftyone.md

# 分析艺术风格与多模态嵌入

*作者: [Jacob Marks](https://huggingface.co/jamarks)*

![Art Analysis Cover Image](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/art_analysis_cover_image.jpg)

**视觉数据如图像非常富含信息，但其非结构化的性质使得分析变得困难。**

在这个 Notebook 中，我们将探索如何使用多模态嵌入和计算属性来分析图像中的艺术风格。我们将使用来自 🤗 Hub 的 [WikiArt 数据集](https://huggingface.co/datasets/huggan/wikiart)，并将其加载到 **FiftyOne** 中进行数据分析和可视化。我们将以多种方式深入分析数据：

- **图像相似性搜索与语义搜索**：我们将使用预训练的 [CLIP](https://huggingface.co/openai/clip-vit-base-patch32) 模型，从 🤗 Transformers 生成数据集中的图像的多模态嵌入，并索引数据以进行无结构搜索。

- **聚类与可视化**：我们将基于艺术风格使用嵌入对图像进行聚类，并使用 UMAP 降维算法可视化结果。

- **独特性分析**：我们将使用嵌入为每张图像分配一个独特性评分，依据它与数据集中其他图像的相似度。

- **图像质量分析**：我们将计算每张图像的亮度、对比度和饱和度等质量指标，并查看这些指标与图像的艺术风格如何相关。



## 让我们开始吧! 🚀

为了运行这个 Notebook ，你需要安装下面的库：

```python
!pip install -U transformers huggingface_hub fiftyone umap-learn
```

为了下载的更加轻量快捷，安装 [HF Transfer](https://pypi.org/project/hf-transfer/):

```bash
pip install hf-transfer
```

并且通过设置环境变量 `HF_HUB_ENABLE_HF_TRANSFER` 来启用它：

```bash
import os
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
```

<div class="alert alert-block alert-info">
<b>注意：</b> 本 Notebook 在以下版本下进行了测试：<code>transformers==4.40.0</code>，<code>huggingface_hub==0.22.2</code>，和 <code>fiftyone==0.23.8</code>。
</div>

现在我们来导入本 Notebook 中需要的模块：

```python
import fiftyone as fo # base library and app
import fiftyone.zoo as foz # zoo datasets and models
import fiftyone.brain as fob # ML routines
from fiftyone import ViewField as F # for defining custom views
import fiftyone.utils.huggingface as fouh # for loading datasets from Hugging Face
```

我们首先通过 🤗 Hub 将 WikiArt 数据集加载到 **FiftyOne** 中。这个数据集也可以通过 Hugging Face 的 `datasets` 库加载，但我们将使用 [FiftyOne 的 🤗 Hub 集成](https://docs.voxel51.com/integrations/huggingface.html#huggingface-hub) 直接从 Datasets 服务器获取数据。为了加速计算，我们只下载前 1,000 个样本。

```python
dataset = fouh.load_from_hub(
    "huggan/wikiart", ## repo_id
    format="parquet", ## for Parquet format
    classification_fields=["artist", "style", "genre"], # columns to store as classification fields
    max_samples=1000, # number of samples to load
    name="wikiart", # name of the dataset in FiftyOne
)
```

打印数据集的摘要，以查看其包含的内容：

```python
>>> print(dataset)
```

<pre>
Name:        wikiart
Media type:  image
Num samples: 1000
Persistent:  False
Tags:        []
Sample fields:
    id:       fiftyone.core.fields.ObjectIdField
    filepath: fiftyone.core.fields.StringField
    tags:     fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)
    metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata)
    artist:   fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)
    style:    fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)
    genre:    fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)
    row_idx:  fiftyone.core.fields.IntField
</pre>

在 [FiftyOne 应用](https://docs.voxel51.com/user_guide/app.html) 中可视化数据集：

```python
session = fo.launch_app(dataset)
```

![WikiArt Dataset](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/art_analysis_wikiart_dataset.jpg)

让我们列出我们将要分析其风格的艺术家名字：

```python
>>> artists = dataset.distinct("artist.label")
>>> print(artists)
```

<pre>
['Unknown Artist', 'albrecht-durer', 'boris-kustodiev', 'camille-pissarro', 'childe-hassam', 'claude-monet', 'edgar-degas', 'eugene-boudin', 'gustave-dore', 'ilya-repin', 'ivan-aivazovsky', 'ivan-shishkin', 'john-singer-sargent', 'marc-chagall', 'martiros-saryan', 'nicholas-roerich', 'pablo-picasso', 'paul-cezanne', 'pierre-auguste-renoir', 'pyotr-konchalovsky', 'raphael-kirchner', 'rembrandt', 'salvador-dali', 'vincent-van-gogh']
</pre>

## 查找相似的艺术作品

当你发现一件你喜欢的艺术作品时，想要找到相似的作品是很自然的。我们可以通过向量嵌入来实现这一点！更重要的是，通过使用多模态嵌入，我们将能够根据给定的文本查询（例如描述一幅画或甚至是一首诗）找到与之相似的画作。

让我们使用来自 🤗 Transformers 的预训练 **CLIP Vision Transformer (ViT)** 模型为图像生成多模态嵌入。运行 [FiftyOne Brain](https://docs.voxel51.com/user_guide/brain.html) 中的 `compute_similarity()` 函数，将计算这些嵌入并基于这些嵌入生成数据集的相似性索引。

```python
>>> fob.compute_similarity(
...     dataset, 
...     model="zero-shot-classification-transformer-torch", ## type of model to load from model zoo
...     name_or_path="openai/clip-vit-base-patch32", ## repo_id of checkpoint
...     embeddings="clip_embeddings", ## name of the field to store embeddings
...     brain_key="clip_sim", ## key to store similarity index info
...     batch_size=32, ## batch size for inference
...     )
```

<pre>
Computing embeddings...
 100% |███████████████| 1000/1000 [5.0m elapsed, 0s remaining, 3.3 samples/s]
</pre>

<div style="padding: 10px; border-left: 5px solid #0078d4; font-family: Arial, sans-serif; margin: 10px 0;">

或者，您可以直接从 🤗 Transformers 库加载模型并将模型传递进去：

```python
from transformers import CLIPModel
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
fob.compute_similarity(
    dataset, 
    model=model,
    embeddings="clip_embeddings",  ## 存储嵌入的字段名称
    brain_key="clip_sim"  ## 存储相似性索引信息的键
)
```

有关详细指南，请查看 <a href="https://docs.voxel51.com/integrations/huggingface.html#transformers-library">FiftyOne 的 🤗 Transformers 集成</a>。
</div>

刷新 **FiftyOne 应用**，在样本网格中选择一个图像的复选框，然后点击照片图标，即可查看数据集中与该图像最相似的图像。在后台，点击此按钮会触发对相似性索引的查询，基于预计算的嵌入，找到与所选图像最相似的图像，并在应用中显示它们。

![Image Similarity Search](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/art_analysis_image_search.gif)

我们可以用这个方法查看与给定艺术作品最相似的艺术作品。这对于寻找相似的艺术作品（推荐给用户或添加到收藏）或为新作品获取灵感非常有用。

但这还不止于此！由于 CLIP 是多模态的，我们还可以利用它进行语义搜索。这意味着我们可以根据文本查询来搜索图像。例如，我们可以搜索“粉彩树木”，并查看数据集中与该查询相似的所有图像。要做到这一点，请点击 **FiftyOne 应用** 中的搜索图标，并输入文本查询：

![Semantic Search](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/art_analysis_semantic_search.gif)

在后台，文本会被标记化，并通过 CLIP 的文本编码器进行嵌入，然后用来查询相似性索引，从而找到数据集中与之最相似的图像。这是一种基于文本查询搜索图像的强大方法，非常适合用于寻找符合特定主题或风格的图像。而且，这不仅仅局限于 CLIP；你可以使用任何可以为图像和文本生成嵌入的 CLIP 类模型，从 🤗 Transformers 中获取！

<div class="alert alert-block alert-info">
💡 为了在大型数据集上进行高效的向量搜索和索引，FiftyOne 提供了与开源向量数据库的原生 <a href="https://voxel51.com/vector-search">集成</a>。
</div>

## 通过聚类和可视化揭示艺术主题

通过执行相似性和语义搜索，我们可以更有效地与数据进行交互。但我们还可以更进一步，加入一些无监督学习的元素。这将帮助我们在 WikiArt 数据集中识别艺术模式，包括风格、主题，甚至是那些难以用语言描述的艺术主题。

我们将通过两种方式进行：

1. **降维**：我们将使用 UMAP 将嵌入的维度减少到 2D，并在散点图中可视化数据。这将帮助我们观察图像如何基于风格、流派和艺术家进行聚类。
   
2. **聚类**：我们将使用 K-Means 聚类算法，基于图像的嵌入进行聚类，并观察哪些组别会自然形成。

对于降维，我们将运行 **FiftyOne Brain** 中的 `compute_visualization()`，并传入之前计算的嵌入。我们指定 `method="umap"` 来使用 UMAP 进行降维，但也可以选择使用 PCA 或 t-SNE：

```python
>>> fob.compute_visualization(dataset, embeddings="clip_embeddings", method="umap", brain_key="clip_vis")
```

<pre>
Generating visualization...
</pre>

现在我们可以在 **FiftyOne 应用** 中打开一个面板，在那里我们将看到数据集中每张图像对应的一个 2D 点。我们可以通过数据集中的任何字段（例如艺术家或流派）为这些点着色，以查看这些属性在我们的图像特征中是如何被捕捉的：

![UMAP Visualization](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/art_analysis_visualize_embeddings.gif)

我们还可以在嵌入上运行聚类，将相似的图像分为一组——也许这些艺术作品的主导特征并未通过现有标签捕捉到，或者我们可能想要识别出不同的子流派。为了对我们的数据进行聚类，我们需要下载 [FiftyOne Clustering 插件](https://github.com/jacobmarks/clustering-plugin)：

```python
!fiftyone plugins download https://github.com/jacobmarks/clustering-plugin
```

再次刷新应用后，我们可以通过应用中的操作符访问聚类功能。按下反引号键（`）打开操作符列表，输入“cluster”并从下拉菜单中选择该操作符。这将打开一个交互式面板，我们可以在其中指定聚类算法、超参数以及要聚类的字段。为了简化操作，我们将使用 K-Means 聚类，并设置为 10 个簇。

然后，我们可以在应用中可视化这些聚类，查看图像如何根据它们的嵌入进行分组：

![K-means Clustering](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/art_analysis_clustering.gif)

我们可以看到，部分聚类是按艺术家进行分组的；其他聚类则是按流派或风格进行分组的。还有一些聚类更为抽象，可能代表了子流派或其他不容易从数据中直接看出的分组。

## 识别最独特的艺术作品

我们可以问一个关于数据集的有趣问题，那就是每张图像的**独特性**如何。这对于许多应用场景都非常重要，比如推荐相似图像、检测重复图像或识别异常值。在艺术的语境中，一幅画的独特性可能是决定其价值的重要因素。

尽管有很多方法可以表征独特性，我们的图像嵌入允许我们基于每张图像与数据集中其他图像的相似度，定量地为每个样本分配一个独特性分数。具体而言，**FiftyOne Brain** 的 `compute_uniqueness()` 函数会查看每个样本的嵌入与其最近邻之间的距离，并基于这个距离计算一个介于 0 和 1 之间的分数。分数为 0 意味着样本没有特色或与其他样本非常相似，而分数为 1 意味着样本非常独特。

```python
>>> fob.compute_uniqueness(dataset, embeddings="clip_embeddings") # compute uniqueness using CLIP embeddings
```

<pre>
Computing uniqueness...
Uniqueness computation complete
</pre>

然后，我们可以在嵌入面板中根据独特性分数为图像着色，或者通过独特性分数进行筛选，甚至按分数排序，以查看数据集中最独特的图像。

```python
most_unique_view = dataset.sort_by("uniqueness", reverse=True)
session.view = most_unique_view.view() # Most unique images
```

![Most Unique Images](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/art_analysis_most_unique.jpg)

```python
least_unique_view = dataset.sort_by("uniqueness", reverse=False)
session.view = least_unique_view.view() # Least unique images
```

![Least Unique Images](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/art_analysis_least_unique.jpg)

更进一步，我们还可以回答哪个艺术家通常创作出最独特的作品这个问题。我们可以计算每个艺术家在其所有作品中的平均独特性分数：

```python
>>> artist_unique_scores = {
...     artist: dataset.match(F("artist.label") == artist).mean("uniqueness")
...     for artist in artists
... }

>>> sorted_artists = sorted(
...     artist_unique_scores, key=artist_unique_scores.get, reverse=True
... )

>>> for artist in sorted_artists:
...     print(f"{artist}: {artist_unique_scores[artist]}")
```

<pre>
Unknown Artist: 0.7932221632002723
boris-kustodiev: 0.7480731948424676
salvador-dali: 0.7368807620414014
raphael-kirchner: 0.7315448102204755
ilya-repin: 0.7204744626806383
marc-chagall: 0.7169373812321908
rembrandt: 0.715205220292227
martiros-saryan: 0.708560775790436
childe-hassam: 0.7018343391132756
edgar-degas: 0.699912746806587
albrecht-durer: 0.6969358680800216
john-singer-sargent: 0.6839955708720844
pablo-picasso: 0.6835137858302969
pyotr-konchalovsky: 0.6780653000855895
nicholas-roerich: 0.6676504687452387
ivan-aivazovsky: 0.6484361530090199
vincent-van-gogh: 0.6472004520699081
gustave-dore: 0.6307283287457358
pierre-auguste-renoir: 0.6271467146993583
paul-cezanne: 0.6251076007168186
eugene-boudin: 0.6103397516167454
camille-pissarro: 0.6046182609119615
claude-monet: 0.5998234558947573
ivan-shishkin: 0.589796389836674
</pre>

看起来，在我们的数据集中，创作出最独特作品的艺术家是 **Boris Kustodiev**！让我们来看看他的部分作品：

```python
kustodiev_view = dataset.match(F("artist.label") == "boris-kustodiev")
session.view = kustodiev_view.view()
```

![Boris Kustodiev Artwork](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/art_analysis_kustodiev_view.jpg)

## 通过视觉特征描述艺术作品

为了全面分析，我们将回归基础，分析数据集中图像的一些核心特征。我们将计算每张图像的标准指标，如亮度、对比度和饱和度，并查看这些指标与艺术作品的艺术风格和流派之间的相关性。

为了进行这些分析，我们需要下载 [FiftyOne 图像质量插件](https://github.com/jacobmarks/image-quality-issues)：

```python
!fiftyone plugins download https://github.com/jacobmarks/image-quality-issues/
```

再次刷新应用并打开操作符列表。这次输入 `compute` 并选择其中一个图像质量操作符。我们将从 **亮度** 开始：

![Compute Brightness](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/art_analysis_compute_brightness.gif)

当操作符运行完成后，我们的数据集中将新增一个字段，其中包含每张图像的亮度分数。然后，我们可以在应用中可视化这些数据：

![Brightness](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/art_analysis_brightness.gif)

我们还可以根据亮度为图像着色，甚至查看亮度与数据集中的其他字段（如风格）之间的相关性：

![Style by Brightness](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/art_analysis_style_by_brightness.gif)

现在对比度和饱和度也做相同的操作。以下是饱和度的结果：

![Filter by Saturation](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/art_analysis_filter_by_saturation.jpg)

希望这能说明，并非所有的分析都依赖于将深度神经网络应用于数据。有时候，简单的指标同样可以提供有价值的信息，并能为你的数据提供不同的视角 🤓！

<div class="alert alert-block alert-info">
📚 对于更大的数据集，你可能希望 <a href="https://docs.voxel51.com/plugins/using_plugins.html#delegated-operations">委托操作</a> 以便稍后执行。
</div>

## 下一步是什么？

在本 Notebook 中，我们探讨了如何使用多模态嵌入、无监督学习和传统图像处理技术来分析图像中的艺术风格。我们展示了如何执行图像相似性和语义搜索，基于风格对图像进行聚类，分析图像的独特性，并计算图像质量指标。这些技术可以应用于广泛的视觉数据集，从艺术收藏到医学图像再到卫星图像。试着 [加载来自 Hugging Face Hub 的不同数据集](https://docs.voxel51.com/integrations/huggingface.html#loading-datasets-from-the-hub)，看看你能发现哪些有趣的见解！

如果你想进一步探索，以下是一些可以尝试的附加分析：

- **零样本分类**：使用 🤗 Transformers 提供的预训练视觉语言模型，通过主题或对象对数据集中的图像进行分类，无需任何训练数据。更多信息请参见这个 [零样本分类教程](https://docs.voxel51.com/tutorials/zero_shot_classification.html)。
- **图像标注**：使用 🤗 Transformers 提供的预训练视觉语言模型为数据集中的图像生成标注。然后，可以利用这些标注进行主题建模，或根据标注的嵌入对艺术作品进行聚类。更多信息请查看 FiftyOne 的 [图像标注插件](https://github.com/jacobmarks/fiftyone-image-captioning-plugin)。

### 📚 资源

- [FiftyOne 🤝 🤗 Hub 集成](https://docs.voxel51.com/integrations/huggingface.html#huggingface-hub)
- [FiftyOne 🤝 🤗 Transformers 集成](https://docs.voxel51.com/integrations/huggingface.html#transformers-library)
- [FiftyOne 向量搜索集成](https://voxel51.com/vector-search/)
- [使用降维技术可视化数据](https://docs.voxel51.com/tutorials/dimension_reduction.html)
- [使用嵌入对图像进行聚类](https://docs.voxel51.com/tutorials/clustering.html)
- [使用 FiftyOne 探索图像独特性](https://docs.voxel51.com/tutorials/uniqueness.html)

## FiftyOne 开源项目

[FiftyOne](https://github.com/voxel51/fiftyone/) 是构建高质量数据集和计算机视觉模型的领先开源工具包。FiftyOne 已经拥有超过 200 万次下载，全球的开发者和研究人员都信任并使用它。

💪 FiftyOne 团队欢迎开源社区的贡献！如果你有兴趣为 FiftyOne 做贡献，可以查看 [贡献指南](https://github.com/voxel51/fiftyone/blob/develop/CONTRIBUTING.md)。

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/analyzing_art_with_hf_and_fiftyone.md" />

### 使用 TGI 的消息 API 从 OpenAI 迁移到 Open LLMs
https://huggingface.co/learn/cookbook/zh-CN/tgi_messages_api_demo.md

# 使用 TGI 的消息 API 从 OpenAI 迁移到 Open LLMs

_作者: [Andrew Reed](https://huggingface.co/andrewrreed)_

这个 notebook 展示了如何轻松地从 OpenAI 模型过渡到 Open LLMs，而无需重构任何现有代码。

[文本生成推理（TGI）](https://github.com/huggingface/text-generation-inference)现在提供了一个[消息 API](https://huggingface.co/blog/tgi-messages-api)，使其与 OpenAI 的聊天完成 API 的直接兼容。这意味着任何使用 OpenAI 的模型（通过 OpenAI 客户端库或像 LangChain 或 LlamaIndex 这样的第三方工具）的现有脚本都可以直接替换为使用运行在 TGI 端点上的任何开源 LLM！

这允许你快速测试并受益于开源模型提供的众多优势。例如：

- 对模型和数据的完全控制和透明度

- 不再担心速率限制

- 能够根据你的具体需求完全定制系统

在这个 notebook 中，我们将向你展示具体流程：

1. [使用 TGI 创建推理端点来部署模型](#section_1)
2. [使用 OpenAI 客户端库查询推理端点](#section_2)
3. [将端点与 LangChain 和 LlamaIndex 工作流程集成](#section_3)

**让我们开始吧！**

## 初始化设置

首先，我们需要安装依赖项和设置一个 HF API 密钥。

```python
!pip install --upgrade -q huggingface_hub langchain langchain-community langchainhub langchain-openai llama-index chromadb bs4 sentence_transformers torch
```

```python
import os
import getpass

# enter API key
os.environ["HUGGINGFACEHUB_API_TOKEN"] = HF_API_KEY = getpass.getpass()
```

<a id="section_1"></a>

## 1. 创建一个推理端点

一开始，让我们使用 TGI 将[Nous-Hermes-2-Mixtral-8x7B-DPO](https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO)，一个微调的 Mixtral 模型，部署到推理端点。

我们只需通过 UI 的[几次点击](https://ui.endpoints.huggingface.co/new?vendor=aws&repository=NousResearch%2FNous-Hermes-2-Mixtral-8x7B-DPO&tgi_max_total_tokens=32000&tgi=true&tgi_max_input_length=1024&task=text-generation&instance_size=2xlarge&tgi_max_batch_prefill_tokens=2048&tgi_max_batch_total_tokens=1024000&no_suggested_compute=true&accelerator=gpu&region=us-east-1)，就可以部署模型，或者利用 `huggingface_hub` Python 库以编程方式创建和管理推理端点。

在这里，我们将使用 Hub 库，通过指定端点名称和模型仓库，以及 `text-generation` 任务。在这个例子中，我们使用 `protected` 类型，因此访问部署的模型将需要一个有效的 Hugging Face token。我们还需要配置硬件要求，如供应商、地区、加速器、实例类型和大小。你可以使用[this API call](https://api.endpoints.huggingface.cloud/#get-/v2/provider)查看可用的资源选项列表，并在目录中[这里](https://ui.endpoints.huggingface.co/catalog)查看为选定模型推荐配置。


```python
>>> from huggingface_hub import create_inference_endpoint

>>> endpoint = create_inference_endpoint(
...     "nous-hermes-2-mixtral-8x7b-demo",
...     repository="NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
...     framework="pytorch",
...     task="text-generation",
...     accelerator="gpu",
...     vendor="aws",
...     region="us-east-1",
...     type="protected",
...     instance_type="p4de",
...     instance_size="2xlarge",
...     custom_image={
...         "health_route": "/health",
...         "env": {
...             "MAX_INPUT_LENGTH": "4096",
...             "MAX_BATCH_PREFILL_TOKENS": "4096",
...             "MAX_TOTAL_TOKENS": "32000",
...             "MAX_BATCH_TOTAL_TOKENS": "1024000",
...             "MODEL_ID": "/repository",
...         },
...         "url": "ghcr.io/huggingface/text-generation-inference:sha-1734540",  # must be >= 1.4.0
...     },
... )

>>> endpoint.wait()
>>> print(endpoint.status)
```

<pre>
running
</pre>

部署启动需要几分钟时间。我们可以使用 `.wait()` 工具来阻塞运行线程，直到端点达到最终的“运行”状态。一旦运行，我们可以在 UI 播放器中确认其状态并试用：

![IE UI Overview](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/messages-api/endpoint-overview.png)

太好了，现在我们有一个可用的端点！

_注意：使用 `huggingface_hub` 部署时，默认情况下，在15分钟空闲时间后，你的端点会自动缩放到零，以在非活动期间优化成本。查看[ Hub Python 库文档](https://huggingface.co/docs/huggingface_hub/guides/inference_endpoints)以了解可用于管理端点生命周期的所有功能。_

<a id="section_2"></a>

## 2. 使用 OpenAI 客户端库查询推理端点

如上所述，由于我们的模型托管在 TGI 上，现在支持消息 API，这意味着我们可以直接使用熟悉的 OpenAI 客户端库来查询它。



### 使用 Python 客户端

下面的例子展示了如何使用[ OpenAI Python 库](https://github.com/openai/openai-python)进行这种转换。只需将 `<ENDPOINT_URL>` 替换为你的端点 URL（确保包含 `v1/` 后缀），并将 `<HF_API_KEY>` 字段填充为有效的 Hugging Face 用户 token。`<ENDPOINT_URL>` 可以从推理端点的 UI 中获取，或者从我们上面使用 `endpoint.url` 创建的端点对象中获取。

然后我们可以像往常一样使用客户端，传递一个消息列表以从我们的推理端点流式传输响应。



```python
>>> from openai import OpenAI

>>> BASE_URL = endpoint.url

>>> # init the client but point it to TGI
>>> client = OpenAI(
...     base_url=os.path.join(BASE_URL, "v1/"),
...     api_key=HF_API_KEY,
... )
>>> chat_completion = client.chat.completions.create(
...     model="tgi",
...     messages=[
...         {"role": "system", "content": "You are a helpful assistant."},
...         {"role": "user", "content": "Why is open-source software important?"},
...     ],
...     stream=True,
...     max_tokens=500,
... )

>>> # iterate and print stream
>>> for message in chat_completion:
...     print(message.choices[0].delta.content, end="")
```

<pre>
Open-source software is important due to a number of reasons, including:

1. Collaboration: The collaborative nature of open-source software allows developers from around the world to work together, share their ideas and improve the code. This often results in faster progress and better software.

2. Transparency: With open-source software, the code is publicly available, making it easy to see exactly how the software functions, and allowing users to determine if there are any security vulnerabilities.

3. Customization: Being able to access the code also allows users to customize the software to better suit their needs. This makes open-source software incredibly versatile, as users can tweak it to suit their specific use case.

4. Quality: Open-source software is often developed by large communities of dedicated developers, who work together to improve the software. This results in a higher level of quality than might be found in proprietary software.

5. Cost: Open-source software is often provided free of charge, which makes it accessible to a wider range of users. This can be especially important for organizations with limited budgets for software.

6. Shared Benefit: By sharing the code of open-source software, everyone can benefit from the hard work of the developers. This contributes to the overall advancement of technology, as users and developers work together to improve and build upon the software.

In summary, open-source software provides a collaborative platform that leads to high-quality, customizable, and transparent software, all available at little or no cost, benefiting both individuals and the technology community as a whole.<|im_end|>
</pre>

在幕后，TGI 的消息 API 自动使用其[聊天模板](https://huggingface.co/docs/transformers/chat_templating)将消息列表转换为模型所需的指令格式。

_注意：某些 OpenAI 功能，如函数调用，与 TGI 不兼容。目前，消息 API 支持以下 chat completion 参数：`stream`、`max_new_tokens`、`frequency_penalty`、`logprobs`、`seed`、`temperature` 和 `top_p`._




### 使用 JavaScript 客户端

这里是与上面相同的流式示例，但是使用了[ OpenAI Javascript/Typescript 库](https://github.com/openai/openai-node)。


```js
import OpenAI from "openai";

const openai = new OpenAI({
  baseURL: "<ENDPOINT_URL>" + "/v1/", // replace with your endpoint url
  apiKey: "<HF_API_TOKEN>", // replace with your token
});

async function main() {
  const stream = await openai.chat.completions.create({
    model: "tgi",
    messages: [
      { role: "system", content: "You are a helpful assistant." },
      { role: "user", content: "Why is open-source software important?" },
    ],
    stream: true,
    max_tokens: 500,
  });
  for await (const chunk of stream) {
    process.stdout.write(chunk.choices[0]?.delta?.content || "");
  }
}

main();
```


<a id="section_3"></a>

## 3. 与 LangChain 和 LlamaIndex 集成

现在，让我们看看如何将这个新创建的端点与像 LangChain 和 LlamaIndex 这样的流行 RAG 框架一起使用。

### 如何与 LangChain 一起使用

要在 [LangChain](https://python.langchain.com/docs/get_started/introduction) 中使用，只需创建一个 `ChatOpenAI` 的实例，并按如下方式传递你的 `<ENDPOINT_URL>` 和 `<HF_API_TOKEN>`：


```python
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model_name="tgi",
    openai_api_key=HF_API_KEY,
    openai_api_base=os.path.join(BASE_URL, "v1/"),
)
llm.invoke("Why is open-source software important?")
```

我们能够直接利用与 OpenAI 模型相同的 `ChatOpenAI` 类。这使得所有之前的代码只需更改一行代码，就能与我们的端点一起工作。

现在，让我们在简单的 RAG 流水线中使用我们的 Mixtral 模型，来回答一个关于 HF 博客内容的问题。


```python
from langchain import hub
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import Chroma
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_core.runnables import RunnableParallel
from langchain_community.embeddings import HuggingFaceEmbeddings

# Load, chunk and index the contents of the blog
loader = WebBaseLoader(
    web_paths=("https://huggingface.co/blog/open-source-llms-as-agents",),
)
docs = loader.load()

# declare an HF embedding model
hf_embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-large-en-v1.5")

text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=200)
splits = text_splitter.split_documents(docs)
vectorstore = Chroma.from_documents(documents=splits, embedding=hf_embeddings)

# Retrieve and generate using the relevant snippets of the blog
retriever = vectorstore.as_retriever()
prompt = hub.pull("rlm/rag-prompt")


def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)


rag_chain_from_docs = (
    RunnablePassthrough.assign(context=(lambda x: format_docs(x["context"])))
    | prompt
    | llm
    | StrOutputParser()
)

rag_chain_with_source = RunnableParallel(
    {"context": retriever, "question": RunnablePassthrough()}
).assign(answer=rag_chain_from_docs)

rag_chain_with_source.invoke(
    "According to this article which open-source model is the best for an agent behaviour?"
)
```

### 如何与 LlamaIndex 一起使用

类似地，你也可以在 [LlamaIndex](https://www.llamaindex.ai/) 中使用 TGI 端点。我们将使用 `OpenAILike` 类，并通过配置一些额外的参数（即 `is_local`、`is_function_calling_model`、`is_chat_model`、`context_window`）来实例化它。

_注意：上下文窗口参数应与之前为端点的 `MAX_TOTAL_TOKENS` 设置的值相匹配。_



```python
from llama_index.llms import OpenAILike

llm = OpenAILike(
    model="tgi",
    api_key=HF_API_KEY,
    api_base=BASE_URL + "/v1/",
    is_chat_model=True,
    is_local=False,
    is_function_calling_model=False,
    context_window=4096,
)

llm.complete("Why is open-source software important?")
```

现在我们可以使用它在类似的 RAG 流水线中。请记住，之前在推理端点选择的 `MAX_INPUT_LENGTH` 将直接影响模型可以处理的检索到的数据块（`similarity_top_k`）的数量。


```python
from llama_index import (
    ServiceContext,
    VectorStoreIndex,
)
from llama_index import download_loader
from llama_index.embeddings import HuggingFaceEmbedding
from llama_index.query_engine import CitationQueryEngine


SimpleWebPageReader = download_loader("SimpleWebPageReader")

documents = SimpleWebPageReader(html_to_text=True).load_data(
    ["https://huggingface.co/blog/open-source-llms-as-agents"]
)

# Load embedding model
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-large-en-v1.5")

# Pass LLM to pipeline
service_context = ServiceContext.from_defaults(embed_model=embed_model, llm=llm)
index = VectorStoreIndex.from_documents(
    documents, service_context=service_context, show_progress=True
)

# Query the index
query_engine = CitationQueryEngine.from_args(
    index,
    similarity_top_k=2,
)
response = query_engine.query(
    "According to this article which open-source model is the best for an agent behaviour?"
)

response.response
```

## 总结

完成端点使用后，你可以暂停或删除它。这一步可以通过 UI 完成，或者像下面这样以编程方式完成。


```python
# pause our running endpoint
endpoint.pause()

# optionally delete
# endpoint.delete()
```

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/tgi_messages_api_demo.md" />

### 使用 LangChain 在 HuggingFace 文档上构建高级 RAG
https://huggingface.co/learn/cookbook/zh-CN/advanced_rag.md

# 使用 LangChain 在 HuggingFace 文档上构建高级 RAG
_作者: [Aymeric Roucher](https://huggingface.co/m-ric)_

这个 notebook 主要讲述了你怎么构建一个高级的 RAG，用于回答一个关于特定知识库的问题（这里，是 HuggingFace 文档），使用 LangChain。

对于 RAG 的介绍，你可以查看[这个教程](rag_zephyr_langch)

RAG 系统是复杂的，它有许多组块:这里画一个简单的 RAG 图表，其中用蓝色标注了所有系统增强的可能性。

<img src="https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/RAG_workflow.png" height="700">

> 💡 可以看到，这个架构中有许多步骤可以调整：正确调整系统将带来显著的性能提升。

在这个 notebook 中，我们将研究许多这些蓝色标注的部分，看看如何调整你的 RAG 系统以获得最佳性能。

__让我们深入研究模型架构吧！__ 首先，安装所需的模型依赖项。

```python
!pip install -q torch transformers transformers accelerate bitsandbytes langchain sentence-transformers faiss-gpu openpyxl pacmap datasets langchain-community ragatouille
```

```python
%reload_ext dotenv
%dotenv
```

```python
from tqdm.notebook import tqdm
import pandas as pd
from typing import Optional, List, Tuple
from datasets import Dataset
import matplotlib.pyplot as plt

pd.set_option(
    "display.max_colwidth", None
)  # this will be helpful when visualizing retriever outputs
```

### 加载你的知识基础

```python
import datasets

ds = datasets.load_dataset("m-ric/huggingface_doc", split="train")
```

```python
from langchain.docstore.document import Document as LangchainDocument

RAW_KNOWLEDGE_BASE = [
    LangchainDocument(page_content=doc["text"], metadata={"source": doc["source"]})
    for doc in tqdm(ds)
]
```

# 1. 检索器- 嵌入 🗂️
__检索器的作用类似于内部搜索引擎__：给定用户查询，它从你的知识库中返回几个相关的片段。

这些片段随后将被输入到阅读器模型中，以帮助其生成答案。

所以 __我们的目标在这里是，给定一个用户问题，从我们的知识库中找到最多的片段来回答这个问题。__

这是一个宽泛的目标，它留下了一些问题。我们应该检索多少片段？这个参数将被命名为`top_k`。

这些片段应该有多长？这被称为 `chunk size` （片段大小）。没有一刀切的答案，但这里有一些要点：
- 🔀 你的 `chunk size` 允许从一段片段到另一段片段有所不同。
- 由于你的检索中总会存在一些噪音，增加 `top_k` 可以提高你检索到的片段中包含相关元素的概率。🎯 射更多的箭增加了你命中目标的概率。
- 同时，你检索到的文档的总长度不应过高：例如，对于大多数当前模型来说，16k 个 token 可能会因为[中间丢失现象](https://huggingface.co/papers/2307.03172)而在信息中淹没你的阅读器模型。🎯 只给你的阅读器模型提供最相关的见解，而不是一堆书！


> 在这个 notebook 中，我们使用 Langchain 库，因为 __它为向量数据库提供了大量的选项，并允许我们在整个处理过程中保留文档的元数据__。


### 1.1 将文档拆分为片段(chuncks)

- 在这一部分，__我们将知识库中的文档拆分成更小的片段__，这些片段将是喂给阅读器 LLM 生成答案的片段。
- 目标是准备一组**语义上相关的片段**。因此，它们的大小应该适配确切的想法：太小会截断想法，太大则会稀释它们。

💡 _对于文本拆分存在许多选项：按单词拆分，按句子边界拆分，递归拆分以树状方式处理文档以保留结构信息... 要了解更多关于拆分的信息，我建议你阅读[这个很棒的 notebook](https://github.com/FullStackRetrieval-com/RetrievalTutorials/blob/main/5_Levels_Of_Text_Splitting.ipynb)，这是由 Greg Kamradt 编写的。_


- **递归拆分**使用给定的一组分隔符逐步将文本分解为更小的部分，这些分隔符按从最重要到最不重要的顺序排序。如果第一次拆分没有给出正确大小或形状的片段，该方法会使用不同的分隔符在新的片段上重复自身。例如，使用分隔符列表`["\n\n", "\n", ".", ""]`：
    - 该方法首先在出现双行中断`"\n\n"`的任何地方拆分文档。
    - 结果文档将在简单的行中断`"\n"`处再次拆分，然后在句子结尾`"."`处拆分。
    - 最后，如果有些片段仍然太大，它们将在超过最大大小时拆分。

- 使用这种方法，整体结构得到了很好的保留，代价是片段大小会有轻微的变化。

> [这个空间](https://huggingface.co/spaces/A-Roucher/chunk_visualizer)让你可视化不同的拆分选项如何影响你得到的片段。

🔬 让我们用片段大小做一些实验，从任意大小开始，看看拆分是如何工作的。我们使用 Langchain 的 `RecursiveCharacterTextSplitter` 实现递归拆分。
- 参数 `chunk_size` 控制单个片段的长度：这个长度默认计算为片段中的字符数。
- 参数 `chunk_overlap` 允许相邻片段彼此有一些重叠。这减少了想法被两个相邻片段之间的拆分切割成两半的概率。我们武断地将这个设置为片段大小的1/10，你可以尝试不同的值！

```python
from langchain.text_splitter import RecursiveCharacterTextSplitter

# We use a hierarchical list of separators specifically tailored for splitting Markdown documents
# This list is taken from LangChain's MarkdownTextSplitter class.
MARKDOWN_SEPARATORS = [
    "\n#{1,6} ",
    "```\n",
    "\n\\*\\*\\*+\n",
    "\n---+\n",
    "\n___+\n",
    "\n\n",
    "\n",
    " ",
    "",
]

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,  # the maximum number of characters in a chunk: we selected this value arbitrarily
    chunk_overlap=100,  # the number of characters to overlap between chunks
    add_start_index=True,  # If `True`, includes chunk's start index in metadata
    strip_whitespace=True,  # If `True`, strips whitespace from the start and end of every document
    separators=MARKDOWN_SEPARATORS,
)

docs_processed = []
for doc in RAW_KNOWLEDGE_BASE:
    docs_processed += text_splitter.split_documents([doc])
```

我们还必须记住，当我们嵌入文档时，我们将使用一个接受特定最大序列长度 `max_seq_length` 的嵌入模型。

因此，我们应该确保我们的片段大小低于这个限制，因为任何更长的片段在处理之前都会被截断，从而失去相关性。


```python
>>> from sentence_transformers import SentenceTransformer

>>> # To get the value of the max sequence_length, we will query the underlying `SentenceTransformer` object used in the RecursiveCharacterTextSplitter.
>>> print(
...     f"Model's maximum sequence length: {SentenceTransformer('thenlper/gte-small').max_seq_length}"
... )

>>> from transformers import AutoTokenizer

>>> tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-small")
>>> lengths = [len(tokenizer.encode(doc.page_content)) for doc in tqdm(docs_processed)]

>>> # Plot the distribution of document lengths, counted as the number of tokens
>>> fig = pd.Series(lengths).hist()
>>> plt.title("Distribution of document lengths in the knowledge base (in count of tokens)")
>>> plt.show()
```

<pre>
Model's maximum sequence length: 512
</pre>

👀 可以看到，__片段长度与我们的 512 个 token 的限制不匹配__，并且有些文档超出了限制，因此它们的一部分将在截断中丢失！
 - 因此，我们应该更改 `RecursiveCharacterTextSplitter` 类，以计算 token 数量而不是字符数量。
 - 然后，我们可以选择一个特定的片段大小，这里我们会选择低于 512 的阈值：
    - 较小的文档可能允许拆分更专注于特定想法的内容。
    - 但太小的片段会拆分句子，从而再次失去意义：适当的调整是一个平衡的问题。

```python
>>> from langchain.text_splitter import RecursiveCharacterTextSplitter
>>> from transformers import AutoTokenizer

>>> EMBEDDING_MODEL_NAME = "thenlper/gte-small"


>>> def split_documents(
...     chunk_size: int,
...     knowledge_base: List[LangchainDocument],
...     tokenizer_name: Optional[str] = EMBEDDING_MODEL_NAME,
... ) -> List[LangchainDocument]:
...     """
...     Split documents into chunks of maximum size `chunk_size` tokens and return a list of documents.
...     """
...     text_splitter = RecursiveCharacterTextSplitter.from_huggingface_tokenizer(
...         AutoTokenizer.from_pretrained(tokenizer_name),
...         chunk_size=chunk_size,
...         chunk_overlap=int(chunk_size / 10),
...         add_start_index=True,
...         strip_whitespace=True,
...         separators=MARKDOWN_SEPARATORS,
...     )

...     docs_processed = []
...     for doc in knowledge_base:
...         docs_processed += text_splitter.split_documents([doc])

...     # Remove duplicates
...     unique_texts = {}
...     docs_processed_unique = []
...     for doc in docs_processed:
...         if doc.page_content not in unique_texts:
...             unique_texts[doc.page_content] = True
...             docs_processed_unique.append(doc)

...     return docs_processed_unique


>>> docs_processed = split_documents(
...     512,  # We choose a chunk size adapted to our model
...     RAW_KNOWLEDGE_BASE,
...     tokenizer_name=EMBEDDING_MODEL_NAME,
... )

>>> # Let's visualize the chunk sizes we would have in tokens from a common model
>>> from transformers import AutoTokenizer

>>> tokenizer = AutoTokenizer.from_pretrained(EMBEDDING_MODEL_NAME)
>>> lengths = [len(tokenizer.encode(doc.page_content)) for doc in tqdm(docs_processed)]
>>> fig = pd.Series(lengths).hist()
>>> plt.title("Distribution of document lengths in the knowledge base (in count of tokens)")
>>> plt.show()
```

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➡️ 现在分块长度分布看起来好多了!

### 1.2 构建向量数据库

我们希望为我们知识库的所有片段计算嵌入向量：要了解更多关于句子嵌入的信息，我们建议阅读[这个指南](https://osanseviero.github.io/hackerllama/blog/posts/sentence_embeddings/)。

#### 检索的工作原理

一旦所有片段都被嵌入，我们就将它们存储到一个向量数据库中。当用户输入一个查询时，它会被之前使用的同一模型嵌入，并且相似性搜索会返回向量数据库中最接近的文档。

因此，技术挑战在于，给定一个查询向量，快速找到向量数据库中这个向量的最近邻。为此，我们需要选择两件事：一个距离度量，以及一个搜索算法，以便在成千上万的记录数据库中快速找到最近邻。

##### 最近邻搜索算法

最近邻搜索算法有很多选择：我们选择 Facebook 的 [FAISS](https://github.com/facebookresearch/faiss)，因为 FAISS 对于大多数用例来说性能足够好，而且它广为人知，因此被广泛实现。

##### 距离度量

关于距离度量，你可以在[这里](https://osanseviero.github.io/hackerllama/blog/posts/sentence_embeddings/#distance-between-embeddings)找到一个很好的指南。简而言之：
- **余弦相似度**计算两个向量之间的相似性，作为它们相对角度的余弦值：它允许我们比较向量的方向，而不考虑它们的幅度。使用它需要对所有向量进行归一化，将它们重新缩放到单位范数。
- **点积**考虑幅度，有时会有不希望的效果，即增加向量的长度会使它与所有其他向量更相似。
- **欧氏距离**是向量末端之间的距离。

你可以尝试[这个小测](https://developers.google.com/machine-learning/clustering/similarity/check-your-understanding)来检查你对这些概念的理解。但是一旦向量被归一化，[选择特定的距离度量并不重要](https://platform.openai.com/docs/guides/embeddings/which-distance-function-should-i-use)。

我们的特定模型与余弦相似度配合得很好，所以我们选择这个距离度量，并在嵌入模型中以及 FAISS 索引的 `distance_strategy` 参数中设置它。使用余弦相似度，我们需要归一化我们的嵌入向量。

🚨👇 下面的单元格需要在 A10G 上运行几分钟！



```python
from langchain.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores.utils import DistanceStrategy

embedding_model = HuggingFaceEmbeddings(
    model_name=EMBEDDING_MODEL_NAME,
    multi_process=True,
    model_kwargs={"device": "cuda"},
    encode_kwargs={"normalize_embeddings": True},  # set True for cosine similarity
)

KNOWLEDGE_VECTOR_DATABASE = FAISS.from_documents(
    docs_processed, embedding_model, distance_strategy=DistanceStrategy.COSINE
)
```

👀 为了可视化搜索最接近的文档，我们使用 PaCMAP 将我们的嵌入向量从 384 维降至 2 维。

💡 _我们选择 PaCMAP 而不是其他技术，如 t-SNE 或 UMAP，因为[它效率高（保留局部和全局结构），对初始化参数鲁棒且速度快](https://www.nature.com/articles/s42003-022-03628-x#Abs1)。_


```python
# embed a user query in the same space
user_query = "How to create a pipeline object?"
query_vector = embedding_model.embed_query(user_query)
```

```python
import pacmap
import numpy as np
import plotly.express as px

embedding_projector = pacmap.PaCMAP(
    n_components=2, n_neighbors=None, MN_ratio=0.5, FP_ratio=2.0, random_state=1
)

embeddings_2d = [
    list(KNOWLEDGE_VECTOR_DATABASE.index.reconstruct_n(idx, 1)[0])
    for idx in range(len(docs_processed))
] + [query_vector]

# fit the data (The index of transformed data corresponds to the index of the original data)
documents_projected = embedding_projector.fit_transform(np.array(embeddings_2d), init="pca")
```

```python
df = pd.DataFrame.from_dict(
    [
        {
            "x": documents_projected[i, 0],
            "y": documents_projected[i, 1],
            "source": docs_processed[i].metadata["source"].split("/")[1],
            "extract": docs_processed[i].page_content[:100] + "...",
            "symbol": "circle",
            "size_col": 4,
        }
        for i in range(len(docs_processed))
    ]
    + [
        {
            "x": documents_projected[-1, 0],
            "y": documents_projected[-1, 1],
            "source": "User query",
            "extract": user_query,
            "size_col": 100,
            "symbol": "star",
        }
    ]
)

# visualize the embedding
fig = px.scatter(
    df,
    x="x",
    y="y",
    color="source",
    hover_data="extract",
    size="size_col",
    symbol="symbol",
    color_discrete_map={"User query": "black"},
    width=1000,
    height=700,
)
fig.update_traces(
    marker=dict(opacity=1, line=dict(width=0, color="DarkSlateGrey")), selector=dict(mode="markers")
)
fig.update_layout(
    legend_title_text="<b>Chunk source</b>",
    title="<b>2D Projection of Chunk Embeddings via PaCMAP</b>",
)
fig.show()
```

<img src="https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/PaCMAP_embeddings.png" height="700">

➡️ 在上面的图表中，你可以看到知识库文档的空间表示。由于向量嵌入代表了文档的含义，它们在意义上的接近应该在它们的嵌入的接近程度上反映出来。

用户查询的嵌入也被显示出来：我们想要找到意义最接近的 `k` 个文档，因此我们选择最接近的 `k` 个向量。

在 LangChain 向量数据库实现中，这个搜索操作是由方法 `vector_database.similarity_search(query)` 执行的。

这里是结果：


```python
>>> print(f"\nStarting retrieval for {user_query=}...")
>>> retrieved_docs = KNOWLEDGE_VECTOR_DATABASE.similarity_search(query=user_query, k=5)
>>> print("\n==================================Top document==================================")
>>> print(retrieved_docs[0].page_content)
>>> print("==================================Metadata==================================")
>>> print(retrieved_docs[0].metadata)
```

<pre>
Starting retrieval for user_query='How to create a pipeline object?'...

==================================Top document==================================
```

## Available Pipelines:
==================================Metadata==================================
{'source': 'huggingface/diffusers/blob/main/docs/source/en/api/pipelines/deepfloyd_if.md', 'start_index': 16887}
</pre>

# 2. 阅读器- LLM 💬

在这一部分，__LLM 阅读器读取检索到的上下文以形成其答案。__

实际上有多个可以调整的子步骤：
1. 检索到的文档内容被聚合并放入“上下文”中，这其中有许多处理选项，如_提示压缩_。
2. 上下文和用户查询被聚合并形成一个提示(prompt)，然后交给 LLM 生成其答案。


### 2.1. 阅读器模型

在选择阅读器模型时，有几个方面很重要：
- 阅读器模型的 `max_seq_length` 必须适应我们的提示(prompt)，其中包括检索器调用输出的上下文：上下文包括 5 个每份 512 个 token 的文档，所以我们至少需要 4k 个 token 的上下文长度。
- 阅读器模型

在这个例子中，我们选择了 [`HuggingFaceH4/zephyr-7b-beta`](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)，这是一个小而强大的模型。

由于每周都会发布许多模型，你可能想要用最新最好的模型替换这个模型。跟踪开源 LLM 的最佳方式是查看 [Open-source LLM leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)。

为了加速推理，我们将加载模型的量化版本：


```python
from transformers import pipeline
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

READER_MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)
model = AutoModelForCausalLM.from_pretrained(READER_MODEL_NAME, quantization_config=bnb_config)
tokenizer = AutoTokenizer.from_pretrained(READER_MODEL_NAME)

READER_LLM = pipeline(
    model=model,
    tokenizer=tokenizer,
    task="text-generation",
    do_sample=True,
    temperature=0.2,
    repetition_penalty=1.1,
    return_full_text=False,
    max_new_tokens=500,
)
```

```python
READER_LLM("What is 4+4? Answer:")
```

### 2.2. 提示(Prompt)

下面的 RAG 提示模板是我们将要提供给阅读器 LLM 的内容：需要将其格式化为阅读器 LLM 的聊天模板,这点非常重要。

我们向其提供我们的上下文和用户的问题。

```python
>>> prompt_in_chat_format = [
...     {
...         "role": "system",
...         "content": """Using the information contained in the context,
... give a comprehensive answer to the question.
... Respond only to the question asked, response should be concise and relevant to the question.
... Provide the number of the source document when relevant.
... If the answer cannot be deduced from the context, do not give an answer.""",
...     },
...     {
...         "role": "user",
...         "content": """Context:
... {context}
... ---
... Now here is the question you need to answer.

... Question: {question}""",
...     },
... ]
>>> RAG_PROMPT_TEMPLATE = tokenizer.apply_chat_template(
...     prompt_in_chat_format, tokenize=False, add_generation_prompt=True
... )
>>> print(RAG_PROMPT_TEMPLATE)
```

<pre>
<|system|>
Using the information contained in the context, 
give a comprehensive answer to the question.
Respond only to the question asked, response should be concise and relevant to the question.
Provide the number of the source document when relevant.
If the answer cannot be deduced from the context, do not give an answer.</s>
<|user|>
Context:
{context}
---
Now here is the question you need to answer.

Question: {question}</s>
<|assistant|>
</pre>

让我们在之前检索的文档上测试我们的阅读器!

```python
>>> retrieved_docs_text = [
...     doc.page_content for doc in retrieved_docs
... ]  # we only need the text of the documents
>>> context = "\nExtracted documents:\n"
>>> context += "".join([f"Document {str(i)}:::\n" + doc for i, doc in enumerate(retrieved_docs_text)])

>>> final_prompt = RAG_PROMPT_TEMPLATE.format(
...     question="How to create a pipeline object?", context=context
... )

>>> # Redact an answer
>>> answer = READER_LLM(final_prompt)[0]["generated_text"]
>>> print(answer)
```

<pre>
To create a pipeline object, follow these steps:

1. Define the inputs and outputs of your pipeline. These could be strings, dictionaries, or any other format that best suits your use case.

2. Inherit the `Pipeline` class from the `transformers` module and implement the following methods:

   - `preprocess`: This method takes the raw inputs and returns a preprocessed dictionary that can be passed to the model.

   - `_forward`: This method performs the actual inference using the model and returns the output tensor.

   - `postprocess`: This method takes the output tensor and returns the final output in the desired format.

   - `_sanitize_parameters`: This method is used to sanitize the input parameters before passing them to the model.

3. Load the necessary components, such as the model and scheduler, into the pipeline object.

4. Instantiate the pipeline object and return it.

Here's an example implementation based on the given context:

```python
from transformers import Pipeline
import torch
from diffusers import StableDiffusionPipeline

class MyPipeline(Pipeline):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.pipe = StableDiffusionPipeline.from_pretrained("my_model")

    def preprocess(self, inputs):
        # Preprocess the inputs as needed
        return {"input_ids":...}

    def _forward(self, inputs):
        # Run the forward pass of the model
        return self.pipe(**inputs).images[0]

    def postprocess(self, outputs):
        # Postprocess the outputs as needed
        return outputs["sample"]

    def _sanitize_parameters(self, params):
        # Sanitize the input parameters
        return params

my_pipeline = MyPipeline()
result = my_pipeline("My input string")
print(result)
```

Note that this implementation assumes that the model and scheduler are already loaded into memory. If they need to be loaded dynamically, you can modify the `__init__` method accordingly.
</pre>

### 2.3. 重排序(rerank)

对于 RAG 来说，通常更好的选择会最终检索出比你想要的更多的文档，然后在保留 `top_k` 之前，使用更强大的检索模型对结果进行重新排序。

为此，[Colbertv2](https://arxiv.org/abs/2112.01488)是一个很好的选择：它不是像我们传统的嵌入模型那样的双向编码器，而是一个交叉编码器，它计算查询 token 与每个文档 token 之间更细致的交互。

由于有了 [RAGatouille 库](https://github.com/bclavie/RAGatouille)，它的使用变得非常简单。


```python
from ragatouille import RAGPretrainedModel

RERANKER = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
```

# 3. 集成所有组件

```python
from transformers import Pipeline


def answer_with_rag(
    question: str,
    llm: Pipeline,
    knowledge_index: FAISS,
    reranker: Optional[RAGPretrainedModel] = None,
    num_retrieved_docs: int = 30,
    num_docs_final: int = 5,
) -> Tuple[str, List[LangchainDocument]]:
    # Gather documents with retriever
    print("=> Retrieving documents...")
    relevant_docs = knowledge_index.similarity_search(query=question, k=num_retrieved_docs)
    relevant_docs = [doc.page_content for doc in relevant_docs]  # keep only the text

    # Optionally rerank results
    if reranker:
        print("=> Reranking documents...")
        relevant_docs = reranker.rerank(question, relevant_docs, k=num_docs_final)
        relevant_docs = [doc["content"] for doc in relevant_docs]

    relevant_docs = relevant_docs[:num_docs_final]

    # Build the final prompt
    context = "\nExtracted documents:\n"
    context += "".join([f"Document {str(i)}:::\n" + doc for i, doc in enumerate(relevant_docs)])

    final_prompt = RAG_PROMPT_TEMPLATE.format(question=question, context=context)

    # Redact an answer
    print("=> Generating answer...")
    answer = llm(final_prompt)[0]["generated_text"]

    return answer, relevant_docs
```

让我们看看我们的 RAG 流水线是怎么回答用户的询问的。

```python
>>> question = "how to create a pipeline object?"

>>> answer, relevant_docs = answer_with_rag(
...     question, READER_LLM, KNOWLEDGE_VECTOR_DATABASE, reranker=RERANKER
... )
```

<pre>
=> Retrieving documents...
</pre>

```python
>>> print("==================================Answer==================================")
>>> print(f"{answer}")
>>> print("==================================Source docs==================================")
>>> for i, doc in enumerate(relevant_docs):
...     print(f"Document {i}------------------------------------------------------------")
...     print(doc)
```

<pre>
==================================Answer==================================
To create a pipeline object, follow these steps:

1. Import the `pipeline` function from the `transformers` module:

   ```python
   from transformers import pipeline
   ```

2. Choose the task you want to perform, such as object detection, sentiment analysis, or image generation, and pass it as an argument to the `pipeline` function:

   - For object detection:

     ```python
     >>> object_detector = pipeline('object-detection')
     >>> object_detector(image)
     [{'score': 0.9982201457023621,
       'label':'remote',
       'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
     ...]
     ```

   - For sentiment analysis:

     ```python
     >>> classifier = pipeline("sentiment-analysis")
     >>> classifier("This is a great product!")
     {'labels': ['POSITIVE'],'scores': tensor([0.9999], device='cpu', dtype=torch.float32)}
     ```

   - For image generation:

     ```python
     >>> image = pipeline(
    ... "stained glass of darth vader, backlight, centered composition, masterpiece, photorealistic, 8k"
    ... ).images[0]
     >>> image
     PILImage mode RGB size 7680x4320 at 0 DPI
     ```

Note that the exact syntax may vary depending on the specific pipeline being used. Refer to the documentation for more details on how to use each pipeline.

In general, the process involves importing the necessary modules, selecting the desired pipeline task, and passing it to the `pipeline` function along with any required arguments. The resulting pipeline object can then be used to perform the selected task on input data.
==================================Source docs==================================
Document 0------------------------------------------------------------
# Allocate a pipeline for object detection
>>> object_detector = pipeline('object-detection')
>>> object_detector(image)
[{'score': 0.9982201457023621,
  'label': 'remote',
  'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
 {'score': 0.9960021376609802,
  'label': 'remote',
  'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
 {'score': 0.9954745173454285,
  'label': 'couch',
  'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
 {'score': 0.9988006353378296,
  'label': 'cat',
  'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
 {'score': 0.9986783862113953,
  'label': 'cat',
  'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}]
Document 1------------------------------------------------------------
# Allocate a pipeline for object detection
>>> object_detector = pipeline('object_detection')
>>> object_detector(image)
[{'score': 0.9982201457023621,
  'label': 'remote',
  'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
 {'score': 0.9960021376609802,
  'label': 'remote',
  'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
 {'score': 0.9954745173454285,
  'label': 'couch',
  'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
 {'score': 0.9988006353378296,
  'label': 'cat',
  'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
 {'score': 0.9986783862113953,
  'label': 'cat',
  'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}]
Document 2------------------------------------------------------------
Start by creating an instance of [`pipeline`] and specifying a task you want to use it for. In this guide, you'll use the [`pipeline`] for sentiment analysis as an example:

```py
>>> from transformers import pipeline

>>> classifier = pipeline("sentiment-analysis")
Document 3------------------------------------------------------------
```

## Add the pipeline to 🤗 Transformers

If you want to contribute your pipeline to 🤗 Transformers, you will need to add a new module in the `pipelines` submodule
with the code of your pipeline, then add it to the list of tasks defined in `pipelines/__init__.py`.

Then you will need to add tests. Create a new file `tests/test_pipelines_MY_PIPELINE.py` with examples of the other tests.

The `run_pipeline_test` function will be very generic and run on small random models on every possible
architecture as defined by `model_mapping` and `tf_model_mapping`.

This is very important to test future compatibility, meaning if someone adds a new model for
`XXXForQuestionAnswering` then the pipeline test will attempt to run on it. Because the models are random it's
impossible to check for actual values, that's why there is a helper `ANY` that will simply attempt to match the
output of the pipeline TYPE.

You also *need* to implement 2 (ideally 4) tests.

- `test_small_model_pt` : Define 1 small model for this pipeline (doesn't matter if the results don't make sense)
  and test the pipeline outputs. The results should be the same as `test_small_model_tf`.
- `test_small_model_tf` : Define 1 small model for this pipeline (doesn't matter if the results don't make sense)
  and test the pipeline outputs. The results should be the same as `test_small_model_pt`.
- `test_large_model_pt` (`optional`): Tests the pipeline on a real pipeline where the results are supposed to
  make sense. These tests are slow and should be marked as such. Here the goal is to showcase the pipeline and to make
  sure there is no drift in future releases.
- `test_large_model_tf` (`optional`): Tests the pipeline on a real pipeline where the results are supposed to
  make sense. These tests are slow and should be marked as such. Here the goal is to showcase the pipeline and to make
  sure there is no drift in future releases.
Document 4------------------------------------------------------------
```

2. Pass a prompt to the pipeline to generate an image:

```py
image = pipeline(
	"stained glass of darth vader, backlight, centered composition, masterpiece, photorealistic, 8k"
).images[0]
image
</pre>

✅ 现在我们已经拥有了一个完整且性能出色的 RAG 系统。今天的教程就到这里！恭喜你坚持到了最后 🥳

# 进一步探索 🗺️

这并不是旅程的终点！你可以尝试许多步骤来改进你的 RAG 系统。我们建议以迭代的方式进行：对系统进行小的更改，看看哪些可以提升性能。

### 设置评估流水线

- 💬 “你不能改进你没有衡量的模型性能”，甘地说过... 或者至少 Llama2 告诉我他这么说过。无论如何，你绝对应该从衡量性能开始：这意味着构建一个小的评估数据集，然后在评估数据集上监控你的 RAG 系统的性能。

### 改进检索器

🛠️ __你可以使用这些选项来调整结果：__

- 调整分块方法：
    - 片段的大小
    - 方法：使用不同的分隔符进行拆分，使用[语义分块](https://python.langchain.com/docs/modules/data_connection/document_transformers/semantic-chunker)...

- 更改嵌入模型

👷‍♀️ __还可以考虑以下事项：__

- 尝试另一种分块方法，如语义分块
- 更改使用的索引（这里使用的是 FAISS）
- 查询扩展：以略微不同的方式重新构建用户查询以检索更多文档。

### 改进阅读器
🛠️ __这里你可以尝试以下选项来改善结果：__

- 调整提示
- 开启/关闭重排序
- 选择一个更强大的阅读器模型

💡 __这里有许多选项可以考虑以进一步改善结果：__
- 压缩检索到的上下文，只保留与回答查询最相关的部分。
- 扩展 RAG 系统，使其更加用户友好：
    - 引用来源
    - 使其能够进行对话

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/advanced_rag.md" />

### 基于文档检索（ColPali）和视觉语言模型（VLMs）的多模态检索增强生成（RAG）
https://huggingface.co/learn/cookbook/zh-CN/multimodal_rag_using_document_retrieval_and_vlms.md

# 基于文档检索（ColPali）和视觉语言模型（VLMs）的多模态检索增强生成（RAG）

_作者：[Sergio Paniego](https://github.com/sergiopaniego)_

**🚨 警告**: 这个 Notebook 资源消耗较大，需要强大的计算能力。如果你在 Colab 上运行，它将使用 A100 GPU。

在这个 Notebook 中，我们展示了如何构建一个 **多模态检索增强生成（RAG）** 系统，通过将 [**ColPali**](https://huggingface.co/blog/manu/colpali) 文档检索器与 [**Qwen2-VL**](https://qwenlm.github.io/blog/qwen2-vl/) 视觉语言模型（VLM）结合。通过这两个模型，我们可以构建一个强大的 RAG 系统，能够通过文本文档和视觉数据共同增强查询响应。

我们将不依赖于复杂的文档处理流水线（如通过 OCR 提取数据），而是利用文档检索模型高效地根据特定用户查询检索相关文档。

我还推荐查看并为 [smol-vision](https://github.com/merveenoyan/smol-vision) 仓库加星，它启发了这个 Notebook ——特别是 [这个 Notebook](https://github.com/merveenoyan/smol-vision/blob/main/ColPali_%2B_Qwen2_VL.ipynb)。如果你想了解 RAG 的更多内容，可以查看 [这本其他的指南](rag_zephyr_langchain)!

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)

该图表灵感来源于 [Aymeric Roucher](https://huggingface.co/m-ric) 在 [高级 RAG](https://huggingface.co/learn/cookbook/advanced_rag) 或 [RAG 评估](https://huggingface.co/learn/cookbook/rag_evaluation) 指南中的工作。

## 1. 安装依赖

让我们首先安装项目所需的基本库！🚀

```python
!pip install -U -q byaldi pdf2image qwen-vl-utils transformers
# Tested with byaldi==0.0.4, pdf2image==1.17.0, qwen-vl-utils==0.0.8, transformers==4.45.0
```

我们还将安装 **poppler-utils** 来方便 PDF 操作。这个工具集提供了处理 PDF 文件的基本工具，确保我们能够高效地处理项目中的任何与文档相关的任务。

```python
!sudo apt-get install -y poppler-utils
```

## 2. 加载数据集 📁

在这一部分，我们将使用 IKEA 组装说明作为数据集。这些 PDF 文件包含了逐步的家具组装指南。试想一下，在组装新家具时，我们的助手能够为你提供帮助！🛋

要下载组装说明，你可以按照 [这些步骤](https://www.ikea.com/us/en/customer-service/assembly-instructions-puba2cdc880) 进行。

对于这个 Notebook，我选择了一些示例，但在现实场景中，我们可以使用大量的 PDF 文件来增强模型的能力。

```python
import requests
import os

pdfs = {
    "MALM": "https://www.ikea.com/us/en/assembly_instructions/malm-4-drawer-chest-white__AA-2398381-2-100.pdf",
    "BILLY": "https://www.ikea.com/us/en/assembly_instructions/billy-bookcase-white__AA-1844854-6-2.pdf",
    "BOAXEL": "https://www.ikea.com/us/en/assembly_instructions/boaxel-wall-upright-white__AA-2341341-2-100.pdf",
    "ADILS": "https://www.ikea.com/us/en/assembly_instructions/adils-leg-white__AA-844478-6-2.pdf",
    "MICKE": "https://www.ikea.com/us/en/assembly_instructions/micke-desk-white__AA-476626-10-100.pdf"
}

output_dir = "data"
os.makedirs(output_dir, exist_ok=True)

for name, url in pdfs.items():
    response = requests.get(url)
    pdf_path = os.path.join(output_dir, f"{name}.pdf")

    with open(pdf_path, "wb") as f:
        f.write(response.content)

    print(f"Downloaded {name} to {pdf_path}")

print("Downloaded files:", os.listdir(output_dir))
```

下载组装说明后，我们将把 PDF 转换成图像。这一步至关重要，因为它允许文档检索模型（ColPali）有效地处理和操作视觉内容。

```python
import os
from pdf2image import convert_from_path


def convert_pdfs_to_images(pdf_folder):
    pdf_files = [f for f in os.listdir(pdf_folder) if f.endswith('.pdf')]
    all_images = {}

    for doc_id, pdf_file in enumerate(pdf_files):
        pdf_path = os.path.join(pdf_folder, pdf_file)
        images = convert_from_path(pdf_path)
        all_images[doc_id] = images

    return all_images

all_images = convert_pdfs_to_images("/content/data/")
```

让我们先来可视化一个示例的组装指南，看看这些说明是如何呈现的！这将帮助我们了解我们将要处理的内容格式和布局。👀

```python
>>> import matplotlib.pyplot as plt

>>> fig, axes = plt.subplots(1, 8, figsize=(15, 10))

>>> for i, ax in enumerate(axes.flat):
...     img = all_images[0][i]
...     ax.imshow(img)
...     ax.axis('off')

>>> plt.tight_layout()
>>> plt.show()
```

<img 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## 3. 初始化 ColPali 多模态文档检索模型 🤖


现在我们的数据集已经准备好，我们将初始化文档检索模型，该模型将负责从原始图像中提取相关信息，并根据我们的查询提供合适的文档。

通过利用这个模型，我们可以显著增强我们的对话能力。

为此任务，我们将使用 **[Byaldi](https://github.com/AnswerDotAI/byaldi)**。开发者对这个库的描述如下：_“Byaldi 是 RAGatouille 的小型姊妹项目。它是 ColPali 仓库的一个简单封装，使得使用像 ColPALI 这样的延迟交互多模态模型变得更加简便，且具有熟悉的 API。”_

在这个项目中，我们将特别关注 **ColPali**。

![ColPali 架构](https://github.com/illuin-tech/colpali/blob/main/assets/colpali_architecture.webp?raw=true)

此外，你还可以探索 **[ViDore（视觉文档检索基准）](https://huggingface.co/spaces/vidore/vidore-leaderboard)**，查看表现最佳的检索模型。

首先，我们将从检查点加载模型。

```python
from byaldi import RAGMultiModalModel

docs_retrieval_model = RAGMultiModalModel.from_pretrained("vidore/colpali-v1.2")
```

接下来，我们可以通过指定存储 PDF 的文件夹，直接使用文档检索模型对文档进行索引。这将允许模型处理和组织文档，以便根据我们的查询高效地进行检索。

```python
docs_retrieval_model.index(
    input_path="data/",
    index_name="image_index",
    store_collection_with_index=False,
    overwrite=True
)
```

## 4. 使用文档检索模型检索文档 🤔

在初始化文档检索模型之后，我们现在可以通过提交用户查询并检查其检索到的相关文档来测试其功能。

模型将返回结果，并按与查询的相关性进行排名。

让我们来试试看！

```python
text_query = "How many people are needed to assemble the Malm?"

results = docs_retrieval_model.search(text_query, k=3)
results
```

现在，让我们查看模型检索到的具体文档（图像）。这将帮助我们看到与查询相关的视觉内容，并理解模型是如何选择相关信息的。

```python
def get_grouped_images(results, all_images):
    grouped_images = []

    for result in results:
        doc_id = result['doc_id']
        page_num = result['page_num']
        grouped_images.append(all_images[doc_id][page_num - 1]) # page_num are 1-indexed, while doc_ids are 0-indexed. Source https://github.com/AnswerDotAI/byaldi?tab=readme-ov-file#searching

    return grouped_images

grouped_images = get_grouped_images(results, all_images)
```

让我们仔细查看检索到的文档，了解它们包含的信息。这一检查将帮助我们评估检索内容与查询的相关性和质量。

```python
>>> import matplotlib.pyplot as plt

>>> fig, axes = plt.subplots(1, 3, figsize=(15, 10))

>>> for i, ax in enumerate(axes.flat):
...     img = grouped_images[i]
...     ax.imshow(img)
...     ax.axis('off')

>>> plt.tight_layout()
>>> plt.show()
```

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## 5. 初始化用于问答的视觉语言模型 (VLM) 🙋

接下来，我们将初始化用于问答的视觉语言模型（VLM）。在这个案例中，我们将使用 **[Qwen2_VL](https://huggingface.co/docs/transformers/main/en/model_doc/qwen2_vl)**。

![Qwen2_VL 架构](https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/Qwen2-VL/qwen2_vl.jpg)

你可以通过查看 [Open VLM 的排行榜](https://huggingface.co/spaces/opencompass/open_vlm_leaderboard)，了解最新的进展。

首先，我们将从预训练检查点加载模型，并将其移动到 GPU 上，以实现最佳性能。你可以查看该模型 [这里](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct)。

```python
from transformers import Qwen2VLForConditionalGeneration, Qwen2VLProcessor
from qwen_vl_utils import process_vision_info
import torch

vl_model = Qwen2VLForConditionalGeneration.from_pretrained(
    "Qwen/Qwen2-VL-7B-Instruct",
    torch_dtype=torch.bfloat16,
)
vl_model.cuda().eval()
```

接下来，我们将初始化 VLM 处理器。在这一步中，我们将指定最小和最大像素大小，以优化将更多图像适配到 GPU 内存中。

有关优化图像分辨率以提高性能的更多细节，您可以参考文档 [这里](https://huggingface.co/docs/transformers/main/en/model_doc/qwen2_vl#image-resolution-for-performance-boost)。

```python
min_pixels = 224*224
max_pixels = 1024*1024
vl_model_processor = Qwen2VLProcessor.from_pretrained(
    "Qwen/Qwen2-VL-7B-Instruct",
    min_pixels=min_pixels,
    max_pixels=max_pixels
)
```

## 6. 组装 VLM 模型并测试系统 🔧

所有组件加载完成后，我们现在可以组装系统进行测试。首先，我们将通过提供三张检索到的图像和用户查询来创建聊天结构。这一步可以根据你的具体需求进行定制，从而提供更大的灵活性，让你能够以不同的方式与模型进行交互！

```python
chat_template = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": grouped_images[0],
            },
            {
                "type": "image",
                "image": grouped_images[1],
            },
            {
                "type": "image",
                "image": grouped_images[2],
            },
            {
                "type": "text",
                "text": text_query
            },
        ],
    }
]
```

现在，让我们应用这个聊天结构。

```python
text = vl_model_processor.apply_chat_template(
    chat_template, tokenize=False, add_generation_prompt=True
)
```

接下来，我们将处理输入，以确保它们正确格式化并准备好作为视觉语言模型（VLM）的输入。这一步对使模型能够根据提供的数据有效生成响应至关重要。

```python
image_inputs, _ = process_vision_info(chat_template)
inputs = vl_model_processor(
    text=[text],
    images=image_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to("cuda")
```

现在我们准备好生成答案了！让我们看看系统如何利用处理后的输入，根据用户查询和检索到的图像提供响应。

```python
generated_ids = vl_model.generate(**inputs, max_new_tokens=500)
```

一旦模型生成了输出，我们将对其进行后处理，以生成最终的答案。

```python
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = vl_model_processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
```

```python
>>> print(output_text[0])
```

<pre>
The Malm requires two people to assemble it.
</pre>

## 7. 整合所有组件！🧑‍🏭️

现在，让我们创建一个方法，涵盖整个流程，使我们能够在未来的应用中轻松复用它。

```python
def answer_with_multimodal_rag(vl_model, docs_retrieval_model, vl_model_processor, grouped_images, text_query, top_k, max_new_tokens):
    results = docs_retrieval_model.search(text_query, k=top_k)
    grouped_images = get_grouped_images(results, all_images)

    chat_template = [
    {
      "role": "user",
      "content": [
          {"type": "image", "image": image} for image in grouped_images
            ] + [
          {"type": "text", "text": text_query}
        ],
      }
    ]

    # Prepare the inputs
    text = vl_model_processor.apply_chat_template(chat_template, tokenize=False, add_generation_prompt=True)
    image_inputs, video_inputs = process_vision_info(chat_template)
    inputs = vl_model_processor(
        text=[text],
        images=image_inputs,
        padding=True,
        return_tensors="pt",
    )
    inputs = inputs.to("cuda")

    # Generate text from the vl_model
    generated_ids = vl_model.generate(**inputs, max_new_tokens=max_new_tokens)
    generated_ids_trimmed = [
        out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
    ]

    # Decode the generated text
    output_text = vl_model_processor.batch_decode(
        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
    )

    return output_text
```

让我们看看完整的 RAG 系统是如何运作的！

```python
>>> output_text = answer_with_multimodal_rag(
...     vl_model=vl_model,
...     docs_retrieval_model=docs_retrieval_model,
...     vl_model_processor=vl_model_processor,
...     grouped_images=grouped_images,
...     text_query="How do I assemble the Micke desk?",
...     top_k=3,
...     max_new_tokens=500
... )
>>> print(output_text[0])
```

<pre>
To assemble the Micke desk, follow these steps:

1. **Prepare the Components**: Lay out all the components of the desk on a flat surface.

2. **Attach the Legs**: Place the legs on the bottom of the desk frame. Ensure they are securely attached.

3. **Attach the Top**: Place the top of the desk on the frame, making sure it is level and stable.

4. **Secure with Screws**: Use the provided screws to secure the top to the frame. Ensure all screws are tightened securely.

5. **Final Check**: Double-check that all parts are properly attached and the desk is stable.

Refer to the detailed instructions provided in the image for specific steps and any additional information needed for assembly.
</pre>

🏆 现在，我们拥有一个完全可操作的 RAG 流水线，它同时利用了文档检索模型和视觉语言模型！这个强大的组合使我们能够根据用户查询和相关文档生成有深度的回答。

## 8. 继续探索之旅 🧑‍🎓️

这个指南只是探索多模态 RAG 系统潜力的起点。如果你渴望深入了解，以下是一些可以引导你下一步的思路和资源：

### 🔍 进一步探索 ColPali：
- [使用 ColPali 进行文档相似度搜索](https://huggingface.co/blog/fsommers/document-similarity-colpali)
- [ColPali 指南 👀](https://github.com/tonywu71/colpali-cookbooks/tree/main)
- [ColPali 微调查询生成器](https://huggingface.co/spaces/davanstrien/ColPali-Query-Generator)
- [为 UFO 数据集生成查询数据集并微调 ColPali](https://danielvanstrien.xyz/posts/post-with-code/colpali/2024-09-23-generate_colpali_dataset.html)

### 📖 额外阅读：
- [超越文本：视觉驱动文档检索在 RAG 中的崛起](https://blog.vespa.ai/the-rise-of-vision-driven-document-retrieval-for-rag/)
- [使用 Vespa 扩展 ColPali 至数十亿个 PDF](https://blog.vespa.ai/scaling-colpali-to-billions/)

### 💡 有用的集合：
- [多模态 RAG 集合](https://huggingface.co/collections/merve/multimodal-rag-66d97602e781122aae0a5139)

### 📝 论文与原始代码：
- [ColPali：利用视觉语言模型进行高效文档检索（论文）](https://arxiv.org/pdf/2407.01449)
- [ColPali：利用视觉语言模型进行高效文档检索（代码库）](https://github.com/illuin-tech/colpali)

在继续你对多模态检索和生成系统的探索旅程时，随时可以参考这些资源！

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/multimodal_rag_using_document_retrieval_and_vlms.md" />

### 使用 Cleanlab 检测文本数据集中的问题
https://huggingface.co/learn/cookbook/zh-CN/issues_in_text_dataset.md

# 使用 Cleanlab 检测文本数据集中的问题



作者: [Aravind Putrevu](https://huggingface.co/aravindputrevu)

在这个 5 分钟的快速入门教程中，我们将使用 Cleanlab 检测一个由在线银行（文本）客户服务请求组成的意图分类数据集中的各种问题。我们考虑的是 [Banking77-OOS数据集](https://arxiv.org/abs/2106.04564) 的一个子集，包含 1,000 个客户服务请求，根据它们的意图被分类为 10 个类别（你可以在任何文本分类数据集上运行相同的代码）。[Cleanlab](https://github.com/cleanlab/cleanlab)自动识别我们数据集中的坏例子，包括错误标记的数据、范围外的示例（离群值）或其他模糊不清的示例。在深入建模你的数据之前，请考虑过滤或更正这样的坏例子！

**本教程我们将要做的事情概述：**

- 使用预训练的 transformer 模型从客户服务请求中提取文本嵌入

- 在文本嵌入上训练一个简单的逻辑回归模型，以计算样本外的预测概率

- 使用这些预测和嵌入运行 Cleanlab 的 `Datalab` 审核，以识别数据集中的问题，如：标签问题、离群值和近重复项。

## 快速入门

已经有一个模型在现有标签集上训练得到的（样本外）`pred_probs` 了吗？也许你还有一些数值`特征`？运行下面的代码来查找数据集中的任何潜在标签错误。

**注意：** 如果在 Colab 上运行，可能需要使用 GPU（选择：Runtime > Change runtime type > Hardware accelerator > GPU）



```python
from cleanlab import Datalab

lab = Datalab(data=your_dataset, label_name="column_name_of_labels")
lab.find_issues(pred_probs=your_pred_probs, features=your_features)

lab.report()
lab.get_issues()
```

## 安装需要的依赖


你可以使用 `pip` 按照以下方式安装本教程所需的所有包：


```python
!pip install -U scikit-learn sentence-transformers datasets
!pip install -U "cleanlab[datalab]"
```

```python
import re
import string
import pandas as pd
from sklearn.metrics import accuracy_score, log_loss
from sklearn.model_selection import cross_val_predict
from sklearn.linear_model import LogisticRegression
from sentence_transformers import SentenceTransformer

from cleanlab import Datalab
```

```python
import random
import numpy as np

pd.set_option("display.max_colwidth", None)

SEED = 123456  # for reproducibility
np.random.seed(SEED)
random.seed(SEED)
```

## 加载和格式化文本数据集

```python
from datasets import load_dataset

dataset = load_dataset("PolyAI/banking77", split="train")
data = pd.DataFrame(dataset[:1000])
data.head()
```

```python
>>> raw_texts, labels = data["text"].values, data["label"].values
>>> num_classes = len(set(labels))

>>> print(f"This dataset has {num_classes} classes.")
>>> print(f"Classes: {set(labels)}")
```

<pre>
This dataset has 7 classes.
Classes: {32, 34, 36, 11, 13, 46, 17}
</pre>

让我们查看数据集中的第 i 个示例：

```python
>>> i = 1  # change this to view other examples from the dataset
>>> print(f"Example Label: {labels[i]}")
>>> print(f"Example Text: {raw_texts[i]}")
```

<pre>
Example Label: 11
Example Text: What can I do if my card still hasn't arrived after 2 weeks?
</pre>

数据以两个 numpy 数组的形式存储：
1. `raw_texts` 以文本格式存储客户服务请求的话语
2. `labels` 存储每个示例的意图类别（标签）

<div class="alert alert-info">

自有数据？

你可以轻松地将上述内容替换为你自己的文本数据集，并继续进行教程的其余部分。


</div>

接下来，我们将文本字符串转换为更适合作为机器学习模型输入的向量。

我们将使用预训练的 Transformer 模型提供的数值表示作为我们文本的嵌入。[Sentence Transformers](https://huggingface.co/docs/hub/sentence-transformers) 库提供了计算文本数据嵌入的简单方法。在这里，我们加载了预训练的 `electra-small-discriminator` 模型，然后通过网络运行我们的数据，以提取每个示例的向量嵌入。


```python
transformer = SentenceTransformer('google/electra-small-discriminator')
text_embeddings = transformer.encode(raw_texts)
```

我们后续的机器学习模型将直接在 `text_embeddings` 的元素上操作，以便对客户服务请求进行分类。

## 定义一个分类模型并计算样本外的预测概率

   为了利用预训练网络进行特定的分类任务，一种典型的方法是添加一个线性输出层，并在新数据上微调网络参数。然而，这可能需要大量的计算资源。另一种方法是冻结网络的预训练权重，只训练输出层，而不依赖于 GPU。在这里，我们通过在提取的嵌入顶部拟合一个 scikit-learn 线性模型来方便地实现这一点。

   为了识别标签问题，cleanlab 需要你的模型为每个数据点提供概率预测。然而，对于模型之前训练过的数据点，这些预测将是过拟合的（因此不可靠）。cleanlab 旨在仅与**样本外**的预测类概率一起使用，即在模型训练期间保持不变的数据点。

   在这里，我们使用带有交叉验证的逻辑回归模型来获得数据集中每个示例的样本外预测类概率。
   确保你的 `pred_probs` 列根据类的排序正确排序，对于 Datalab 来说，是：按类名字典顺序排序。

```python
model = LogisticRegression(max_iter=400)

pred_probs = cross_val_predict(model, text_embeddings, labels, method="predict_proba")
```

## 使用 Cleanlab 查找数据集中的问题

在给定来自你拥有的任何模型的特征嵌入和（样本外）预测类概率的情况下，cleanlab 可以帮助你快速识别数据中的低质量示例。

在这里，我们使用 Cleanlab 的 `Datalab` 来查找数据中的问题。Datalab 提供了几种加载数据的方式；我们将简单地在字典中包装训练特征和噪声标签。

```python
data_dict = {"texts": raw_texts, "labels": labels}
```

审核你的数据所需的全部操作就是调用 `find_issues()`。我们传入上面获得的预测概率和特征嵌入，但你不一定需要提供所有这些信息，具体取决于你对哪些类型的问题感兴趣。你提供的输入越多，`Datalab` 就能在你的数据中检测到更多类型的问题。使用更好的模型来生成这些输入将确保 cleanlab 更准确地估计问题。


```python
lab = Datalab(data_dict, label_name="labels")
lab.find_issues(pred_probs=pred_probs, features=text_embeddings)
```

输出看起来如下：

```bash
Finding null issues ...
Finding label issues ...
Finding outlier issues ...
Fitting OOD estimator based on provided features ...
Finding near_duplicate issues ...
Finding non_iid issues ...
Finding class_imbalance issues ...
Finding underperforming_group issues ...

Audit complete. 62 issues found in the dataset.
```

审计完成后，使用 `report` 方法来查看审计结果。

```python
>>> lab.report()
```

<pre>
Here is a summary of the different kinds of issues found in the data:

    issue_type  num_issues
       outlier          37
near_duplicate          14
         label          10
       non_iid           1

Dataset Information: num_examples: 1000, num_classes: 7


---------------------- outlier issues ----------------------

About this issue:
	Examples that are very different from the rest of the dataset 
    (i.e. potentially out-of-distribution or rare/anomalous instances).
    

Number of examples with this issue: 37
Overall dataset quality in terms of this issue: 0.3671

Examples representing most severe instances of this issue:
     is_outlier_issue  outlier_score
791              True       0.024866
601              True       0.031162
863              True       0.060738
355              True       0.064199
157              True       0.065075


------------------ near_duplicate issues -------------------

About this issue:
	A (near) duplicate issue refers to two or more examples in
    a dataset that are extremely similar to each other, relative
    to the rest of the dataset.  The examples flagged with this issue
    may be exactly duplicated, or lie atypically close together when
    represented as vectors (i.e. feature embeddings).
    

Number of examples with this issue: 14
Overall dataset quality in terms of this issue: 0.5961

Examples representing most severe instances of this issue:
     is_near_duplicate_issue  near_duplicate_score near_duplicate_sets  distance_to_nearest_neighbor
459                     True              0.009544               [429]                      0.000566
429                     True              0.009544               [459]                      0.000566
501                     True              0.046044          [412, 517]                      0.002781
412                     True              0.046044               [501]                      0.002781
698                     True              0.054626               [607]                      0.003314


----------------------- label issues -----------------------

About this issue:
	Examples whose given label is estimated to be potentially incorrect
    (e.g. due to annotation error) are flagged as having label issues.
    

Number of examples with this issue: 10
Overall dataset quality in terms of this issue: 0.9930

Examples representing most severe instances of this issue:
     is_label_issue  label_score  given_label  predicted_label
379           False     0.025486           32               11
100           False     0.032102           11               36
300           False     0.037742           32               46
485            True     0.057666           17               34
159            True     0.059408           13               11


---------------------- non_iid issues ----------------------

About this issue:
	Whether the dataset exhibits statistically significant
    violations of the IID assumption like:
    changepoints or shift, drift, autocorrelation, etc.
    The specific violation considered is whether the
    examples are ordered such that almost adjacent examples
    tend to have more similar feature values.
    

Number of examples with this issue: 1
Overall dataset quality in terms of this issue: 0.0000

Examples representing most severe instances of this issue:
     is_non_iid_issue  non_iid_score
988              True       0.563774
975             False       0.570179
997             False       0.571891
967             False       0.572357
956             False       0.577413

Additional Information: 
p-value: 0.0
</pre>

### 标签问题

报告显示 cleanlab 在我们的数据集中识别出了许多标签问题。我们可以使用 `get_issues` 方法来查看哪些示例被标记为可能标签错误，以及每个示例的标签质量分数，通过指定 `label` 作为参数来关注数据中的标签问题。

```python
label_issues = lab.get_issues("label")
label_issues.head()
```

| | is_label_issue | label_score | given_label | predicted_label |
|----------------|-------------|-------------|-----------------|-----------------|
| 0              | False       | 0.903926    | 11              | 11 |
| 1              | False       | 0.860544    | 11              | 11 |
| 2              | False       | 0.658309    | 11              | 11 |
| 3              | False       | 0.697085    | 11              | 11 |
| 4              | False       | 0.434934    | 11              | 11 |


此方法返回一个包含每个示例的标签质量分数的数据框。这些数值分数介于 0 和 1 之间，其中较低的分数表示更可能是错误标记的示例。数据框还包含一个布尔列，指定是否将每个示例识别为具有标签问题（表明它可能是错误标记的）。

我们可以获取标记有标签问题的示例的子集，并且还可以按标签质量分数排序，以找到数据集中最可能错误标记的 5 个示例的索引。

```python
>>> identified_label_issues = label_issues[label_issues["is_label_issue"] == True]
>>> lowest_quality_labels = label_issues["label_score"].argsort()[:5].to_numpy()

>>> print(
...     f"cleanlab found {len(identified_label_issues)} potential label errors in the dataset.\n"
...     f"Here are indices of the top 5 most likely errors: \n {lowest_quality_labels}"
... )
```

<pre>
cleanlab found 10 potential label errors in the dataset.
Here are indices of the top 5 most likely errors: 
 [379 100 300 485 159]
</pre>

让我们查看一些最可能的标签错误。

这里我们展示了数据集中被识别为最可能的标签错误的前 5 个示例，以及它们的给定（原始）标签和 cleanlab 提供的建议替代标签。




```python
data_with_suggested_labels = pd.DataFrame(
    {"text": raw_texts, "given_label": labels, "suggested_label": label_issues["predicted_label"]}
)
data_with_suggested_labels.iloc[lowest_quality_labels]
```

上面命令的输出如下所示：
  
|      | text                                                                                                      | given_label    | suggested_label |
|------|-----------------------------------------------------------------------------------------------------------|----------------|-----------------|
| 379  | Is there a specific source that the exchange rate for the transfer I'm planning on making is pulled from? | 32             | 11              |
| 100  | can you share card tracking number?                                                                       | 11             | 36              |
| 300  | If I need to cash foreign transfers, how does that work?                                                  | 32             | 46              |
| 485  | Was I charged more than I should of been for a currency exchange?                                         | 17             | 34              |
| 159  | Is there any way to see my card in the app?                                                               | 13             | 11              |


这些是 cleanlab 在此数据中识别的非常清晰的标签错误！请注意，`given_label` 并没有正确反映这些请求的意图，无论谁制作了这个数据集，在建模数据之前都需要解决许多错误。


### 离群值问题

根据报告，我们的数据集中包含一些离群值。

我们可以通过 `get_issues` 查看哪些示例是离群值（以及一个数值质量分数，量化每个示例看起来有多么典型）。我们将结果数据框按照 cleanlab 的离群值质量分数排序，以查看数据集中最严重的离群值。


```python
outlier_issues = lab.get_issues("outlier")
outlier_issues.sort_values("outlier_score").head()
```

输出如下所示：

|   | is_outlier_issue | outlier_score |
|---| ----------------|---------------|
| 791 | True             | 0.024866      |
| 601 | True             | 0.031162      |
| 863 | True             | 0.060738      |
| 355 | True             | 0.064199      |
| 157 | True             | 0.065075      |

```python
lowest_quality_outliers = outlier_issues["outlier_score"].argsort()[:5]

data.iloc[lowest_quality_outliers]
```

对于质量最低的离群值，样本输出将如下所示：

|index|text|label|
|---|---|---|
|791|withdrawal pending meaning?|46|
|601|$1 charge in transaction\.|34|
|863|My atm withdraw is stillpending|46|
|355|explain the interbank exchange rate|32|
|157|lost card found, want to put it back in app|13|


我们看到 cleanlab 已经识别出这个数据集中的条目，这些条目看起来并不是正确的客户请求。此数据集中的离群值似乎是不在范围内的客户请求和其他对意图分类没有意义的非语义文本。仔细考虑这些离群值是否可能对你的数据建模产生不利影响，如果有可能的话，考虑从数据集中移除它们。


### 近重复问题

根据报告，我们的数据集中包含一些几乎重复的示例集。
我们可以通过 `get_issues` 查看哪些示例是（几乎）重复的（以及一个数值质量分数，量化每个示例与数据集中最近邻的相似程度）。我们将结果数据框按照 cleanlab 的近重复质量分数排序，以查看数据集中最接近重复的文本示例。

```python
duplicate_issues = lab.get_issues("near_duplicate")
duplicate_issues.sort_values("near_duplicate_score").head()
```

上面的结果显示了 cleanlab 认为哪些示例是近重复的（`is_near_duplicate_issue == True` 的行）。在这里，我们看到示例 459 和 429 是近重复的，示例 501 和 412 也是近重复的。

让我们查看这些示例，看看它们有多么相似。


```python
data.iloc[[459, 429]]
```

样本输出：

|index|text|label|
|---|---|---|
|459|I purchased something abroad and the incorrect exchange rate was applied\.|17|
|429|I purchased something overseas and the incorrect exchange rate was applied\.|17|

```python
data.iloc[[501, 412]]
```

样本输出：

|index|text|label|
|---|---|---|
|501|The exchange rate you are using is really bad\.This can't be the official interbank exchange rate\.|17|
|412|The exchange rate you are using is bad\.This can't be the official interbank exchange rate\.|17|

我们看到这两组请求确实非常相似！在数据集中包含近重复项可能会对模型产生意想不到的影响，并且要小心不要将它们分割到训练/测试集中。从[常见问题解答](../faq.html#How-to-handle-near-duplicate-data-identified-by-cleanlab?)中了解更多关于处理数据集中的近重复数据的信息。

### 非独立同分布问题（数据漂移）
根据报告，我们的数据集似乎不是独立同分布的（IID）。数据集的整体非 IID 分数（如下所示）对应于一个统计测试的 `p 值`，该测试用于判断数据集中样本的排序是否与它们特征值之间的相似性有关。一个低的 `p 值`强烈表明数据集违反了 IID 假设，这是从数据集产生的结论（模型）推广到更大总体所需的关键假设。

```python
p_value = lab.get_info('non_iid')['p-value']
p_value
```

在这里，我们的数据集被标记为非 IID，因为原始数据中的行恰好是按类别标签排序的。如果我们记得在模型训练和数据拆分之前打乱行，这可能是不重要的。但是，如果你不知道为什么你的数据被标记为非IID，那么你应该担心可能的数据漂移或数据点之间的意外交互（它们的价值可能不是统计独立的）。仔细考虑未来的测试数据可能看起来如何（以及你的数据是否代表你关心的人群）。在非 IID 测试运行之前，你不应该打乱数据（这将使结论无效）。


    如上所示，cleanlab 可以自动筛选出数据集中最可能的问题，帮助你更好地为后续建模整理数据集。有了这个短名单，你可以选择修复这些标签问题，或者从数据集中移除非语义或重复的示例，以获得更高质量的数据集来训练你的下一个机器学习模型。cleanlab 的问题检测可以与你最初训练的*任何*类型的模型的输出一起运行。



### Cleanlab 开源项目

[Cleanlab](https://github.com/cleanlab/cleanlab) 是一个标准的以数据为中心的人工智能包，旨在解决混乱的现实世界数据的质量问题。

请考虑给 Cleanlab Github 仓库一个星标，如果你有兴趣，也可以参与到这个[项目](https://github.com/cleanlab/cleanlab/issues?q=is:issue+is:open+label:%22good+first+issue%22)中来，比如帮助解决一些简单的问题。。


<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/issues_in_text_dataset.md" />

### 使用 Elasticsearch 和 Hugging Face 进行语义重排序
https://huggingface.co/learn/cookbook/zh-CN/semantic_reranking_elasticsearch.md

# 使用 Elasticsearch 和 Hugging Face 进行语义重排序

_作者：[Liam Thompson](https://github.com/leemthompo)_

在本 Notebook 中，我们将学习如何通过将 Hugging Face 中的模型上传到 Elasticsearch 集群来实现语义重排序。我们将使用 `retriever` 抽象，它是一个简化的 Elasticsearch 查询语法，便于构建查询并结合不同的搜索操作。

你将会：

- 从 Hugging Face 选择一个跨编码器模型来执行语义重排序
- 使用 [Eland](https://www.elastic.co/guide/en/elasticsearch/client/eland/current/machine-learning.html) —— 一个用于与 Elasticsearch 进行机器学习的 Python 客户端，将模型上传到你的 Elasticsearch 部署
- 创建一个推理端点来管理你的 `rerank` 任务
- 使用 `text_similarity_rerank` 检索器查询你的数据

## 🧰 必备条件

为了运行此示例，你需要：

- 一个版本为 8.15.0 或更高版本的 Elastic 部署（对于非无服务器部署）

    - 我们将在本示例中使用 Elastic Cloud（可通过 [免费试用](https://cloud.elastic.co/registration) 进行访问）。
    - 查看其他 [部署选项](https://www.elastic.co/guide/en/elasticsearch/reference/current/elasticsearch-intro.html#elasticsearch-intro-deploy)
- 你需要找到你的部署的 Cloud ID 并创建一个 API 密钥。 [了解更多](https://www.elastic.co/search-labs/tutorials/install-elasticsearch/elastic-cloud#finding-your-cloud-id)。


## 安装并导入包

ℹ️ `eland` 的安装可能需要几分钟时间。

```python
!pip install -qU elasticsearch
!pip install eland[pytorch]
from elasticsearch import Elasticsearch, helpers
```

## 初始化 Elasticsearch Python 客户端

首先，你需要连接到你的 Elasticsearch 实例。

```python
>>> from getpass import getpass

>>> # https://www.elastic.co/search-labs/tutorials/install-elasticsearch/elastic-cloud#finding-your-cloud-id
>>> ELASTIC_CLOUD_ID = getpass("Elastic Cloud ID: ")

>>> # https://www.elastic.co/search-labs/tutorials/install-elasticsearch/elastic-cloud#creating-an-api-key
>>> ELASTIC_API_KEY = getpass("Elastic Api Key: ")

>>> # Create the client instance
>>> client = Elasticsearch(
...     # For local development
...     # hosts=["http://localhost:9200"]
...     cloud_id=ELASTIC_CLOUD_ID,
...     api_key=ELASTIC_API_KEY,
... )
```

<pre>
Elastic Cloud ID: ··········
Elastic Api Key: ··········
</pre>

## 测试连接

通过以下测试确认 Python 客户端是否已成功连接到你的 Elasticsearch 实例。

```python
print(client.info())
```

这个示例使用了一个小型的电影数据集。

```python
>>> from urllib.request import urlopen
>>> import json
>>> import time

>>> url = "https://huggingface.co/datasets/leemthompo/small-movies/raw/main/small-movies.json"
>>> response = urlopen(url)

>>> # Load the response data into a JSON object
>>> data_json = json.loads(response.read())

>>> # Prepare the documents to be indexed
>>> documents = []
>>> for doc in data_json:
...     documents.append(
...         {
...             "_index": "movies",
...             "_source": doc,
...         }
...     )

>>> # Use helpers.bulk to index
>>> helpers.bulk(client, documents)

>>> print("Done indexing documents into `movies` index!")
>>> time.sleep(3)
```

<pre>
Done indexing documents into `movies` index!
</pre>

## 使用 Eland 上传 Hugging Face 模型

现在，我们将使用 Eland 的 `eland_import_hub_model` 命令将模型上传到 Elasticsearch。在这个示例中，我们选择了 `cross-encoder/ms-marco-MiniLM-L-6-v2` 文本相似度模型。

```python
>>> !eland_import_hub_model \
...   --cloud-id $ELASTIC_CLOUD_ID \
...   --es-api-key $ELASTIC_API_KEY \
...   --hub-model-id cross-encoder/ms-marco-MiniLM-L-6-v2 \
...   --task-type text_similarity \
...   --clear-previous \
...   --start
```

<pre>
2024-08-13 17:04:12,386 INFO : Establishing connection to Elasticsearch
2024-08-13 17:04:12,567 INFO : Connected to serverless cluster 'bd8c004c050e4654ad32fb86ab159889'
2024-08-13 17:04:12,568 INFO : Loading HuggingFace transformer tokenizer and model 'cross-encoder/ms-marco-MiniLM-L-6-v2'
/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
  warnings.warn(
tokenizer_config.json: 100% 316/316 [00:00<00:00, 1.81MB/s]
config.json: 100% 794/794 [00:00<00:00, 4.09MB/s]
vocab.txt: 100% 232k/232k [00:00<00:00, 2.37MB/s]
special_tokens_map.json: 100% 112/112 [00:00<00:00, 549kB/s]
pytorch_model.bin: 100% 90.9M/90.9M [00:00<00:00, 135MB/s]
STAGE:2024-08-13 17:04:15 1454:1454 ActivityProfilerController.cpp:312] Completed Stage: Warm Up
STAGE:2024-08-13 17:04:15 1454:1454 ActivityProfilerController.cpp:318] Completed Stage: Collection
STAGE:2024-08-13 17:04:15 1454:1454 ActivityProfilerController.cpp:322] Completed Stage: Post Processing
2024-08-13 17:04:18,789 INFO : Creating model with id 'cross-encoder__ms-marco-minilm-l-6-v2'
2024-08-13 17:04:21,123 INFO : Uploading model definition
100% 87/87 [00:55<00:00,  1.57 parts/s]
2024-08-13 17:05:16,416 INFO : Uploading model vocabulary
2024-08-13 17:05:16,987 INFO : Starting model deployment
2024-08-13 17:05:18,238 INFO : Model successfully imported with id 'cross-encoder__ms-marco-minilm-l-6-v2'
</pre>

## 创建推理端点

接下来，我们将为 `rerank` 任务创建一个推理端点，以部署和管理我们的模型，并在需要时启动必要的机器学习资源。

```python
client.inference.put(
    task_type="rerank",
    inference_id="my-msmarco-minilm-model",
    inference_config={
        "service": "elasticsearch",
        "service_settings": {
            "model_id": "cross-encoder__ms-marco-minilm-l-6-v2",
            "num_allocations": 1,
            "num_threads": 1,
        },
    },
)
```

运行以下命令以确认你的推理端点已成功部署。

```python
client.inference.get()
```

⚠️ 当你部署模型时，可能需要在 Kibana（或无服务器）UI 中同步你的机器学习保存对象。  
请前往 **训练模型** 并选择 **同步保存对象**。

## 词汇查询

首先，让我们使用一个 `standard` 检索器来测试一些词汇（或全文）搜索，然后我们将比较在加入语义重排序后所带来的改进。

### 使用 `query_string` 查询进行词汇匹配

假设我们模糊记得有一部关于吃人肉的杀手的著名电影。为了论证，假设我们暂时忘记了“食人者”这个词。

让我们执行一个 [`query_string` 查询](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-query-string-query.html)，在 Elasticsearch 文档的 `plot` 字段中查找短语 “flesh-eating bad guy”。

```python
>>> resp = client.search(
...     index="movies",
...     retriever={
...         "standard": {
...             "query": {
...                 "query_string": {
...                     "query": "flesh-eating bad guy",
...                     "default_field": "plot",
...                 }
...             }
...         }
...     },
... )

>>> if resp["hits"]["hits"]:
...     for hit in resp["hits"]["hits"]:
...         title = hit["_source"]["title"]
...         plot = hit["_source"]["plot"]
...         print(f"Title: {title}\nPlot: {plot}\n")
>>> else:
...     print("No search results found")
```

<pre>
No search results found
</pre>

没有结果！不幸的是，我们没有找到与 “flesh-eating bad guy” 精确匹配的结果。由于我们没有关于 Elasticsearch 数据中确切措辞的更多信息，我们需要扩大搜索范围。

### 简单的 `multi_match` 查询

这个词汇查询在我们的 Elasticsearch 文档的 `plot` 和 `genre` 字段中执行了一个标准的关键词搜索，查找术语 “crime”。

```python
>>> resp = client.search(
...     index="movies",
...     retriever={
...         "standard": {
...             "query": {"multi_match": {"query": "crime", "fields": ["plot", "genre"]}}
...         }
...     },
... )

>>> for hit in resp["hits"]["hits"]:
...     title = hit["_source"]["title"]
...     plot = hit["_source"]["plot"]
...     print(f"Title: {title}\nPlot: {plot}\n")
```

<pre>
Title: The Godfather
Plot: An organized crime dynasty's aging patriarch transfers control of his clandestine empire to his reluctant son.

Title: Goodfellas
Plot: The story of Henry Hill and his life in the mob, covering his relationship with his wife Karen Hill and his mob partners Jimmy Conway and Tommy DeVito in the Italian-American crime syndicate.

Title: The Silence of the Lambs
Plot: A young F.B.I. cadet must receive the help of an incarcerated and manipulative cannibal killer to help catch another serial killer, a madman who skins his victims.

Title: Pulp Fiction
Plot: The lives of two mob hitmen, a boxer, a gangster and his wife, and a pair of diner bandits intertwine in four tales of violence and redemption.

Title: Se7en
Plot: Two detectives, a rookie and a veteran, hunt a serial killer who uses the seven deadly sins as his motives.

Title: The Departed
Plot: An undercover cop and a mole in the police attempt to identify each other while infiltrating an Irish gang in South Boston.

Title: The Usual Suspects
Plot: A sole survivor tells of the twisty events leading up to a horrific gun battle on a boat, which began when five criminals met at a seemingly random police lineup.

Title: The Dark Knight
Plot: When the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.
</pre>

好多了！至少现在我们有了一些结果。我们扩大了搜索标准，以提高找到相关结果的机会。

不过，这些结果在我们原始查询 "flesh-eating bad guy" 的语境下并不是很精确。我们可以看到，“沉默的羔羊”出现在结果列表的中间，这是一个使用了通用 `match` 查询的结果。让我们看看是否可以使用我们的语义重排序模型，来更接近搜索者的原始意图。

## 语义重排序

在以下的 `retriever` 语法中，我们将标准查询检索器包装在一个 `text_similarity_reranker` 中。这样，我们就可以利用我们部署到 Elasticsearch 的 NLP 模型，根据短语 "flesh-eating bad guy" 对结果进行重排序。

```python
>>> resp = client.search(
...     index="movies",
...     retriever={
...         "text_similarity_reranker": {
...             "retriever": {
...                 "standard": {
...                     "query": {
...                         "multi_match": {"query": "crime", "fields": ["plot", "genre"]}
...                     }
...                 }
...             },
...             "field": "plot",
...             "inference_id": "my-msmarco-minilm-model",
...             "inference_text": "flesh-eating bad guy",
...         }
...     },
... )

>>> for hit in resp["hits"]["hits"]:
...     title = hit["_source"]["title"]
...     plot = hit["_source"]["plot"]
...     print(f"Title: {title}\nPlot: {plot}\n")
```

<pre>
Title: The Silence of the Lambs
Plot: A young F.B.I. cadet must receive the help of an incarcerated and manipulative cannibal killer to help catch another serial killer, a madman who skins his victims.

Title: Pulp Fiction
Plot: The lives of two mob hitmen, a boxer, a gangster and his wife, and a pair of diner bandits intertwine in four tales of violence and redemption.

Title: Se7en
Plot: Two detectives, a rookie and a veteran, hunt a serial killer who uses the seven deadly sins as his motives.

Title: Goodfellas
Plot: The story of Henry Hill and his life in the mob, covering his relationship with his wife Karen Hill and his mob partners Jimmy Conway and Tommy DeVito in the Italian-American crime syndicate.

Title: The Dark Knight
Plot: When the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.

Title: The Godfather
Plot: An organized crime dynasty's aging patriarch transfers control of his clandestine empire to his reluctant son.

Title: The Departed
Plot: An undercover cop and a mole in the police attempt to identify each other while infiltrating an Irish gang in South Boston.

Title: The Usual Suspects
Plot: A sole survivor tells of the twisty events leading up to a horrific gun battle on a boat, which began when five criminals met at a seemingly random police lineup.
</pre>

成功了！“沉默的羔羊”是我们的首个结果。语义重排序通过解析自然语言查询，帮助我们找到最相关的结果，克服了依赖精确匹配的词汇搜索的局限性。

语义重排序通过几个步骤实现了语义搜索，而无需生成和存储嵌入。能够在 Elasticsearch 集群中本地使用托管在 Hugging Face 上的开源模型，非常适合原型开发、测试和构建搜索体验。

## 了解更多

- 在本示例中，我们选择了 [`cross-encoder/ms-marco-MiniLM-L-6-v2`](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2) 文本相似度模型。请参考 [Elastic NLP 模型参考](https://www.elastic.co/guide/en/machine-learning/8.15/ml-nlp-model-ref.html#ml-nlp-model-ref-text-similarity) 获取 Elasticsearch 支持的第三方文本相似度模型列表。
- 了解更多关于 [将 Hugging Face 与 Elasticsearch 集成](https://www.elastic.co/search-labs/integrations/hugging-face) 的信息。
- 查看 Elastic 的 Python 笔记本目录，访问 [`elasticsearch-labs` 仓库](https://github.com/elastic/elasticsearch-labs/tree/main/notebooks)。
- 了解更多关于 [Elasticsearch 中的检索器和重排序](https://www.elastic.co/guide/en/elasticsearch/reference/current/retrievers-reranking-overview.html)。

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/semantic_reranking_elasticsearch.md" />

### 数据分析智能体：瞬间获取数据洞察 ✨
https://huggingface.co/learn/cookbook/zh-CN/agent_data_analyst.md

# 数据分析智能体：瞬间获取数据洞察 ✨  
_作者：[Aymeric Roucher](https://huggingface.co/m-ric)_  

> 本教程为高级教程。建议先了解[另一本手册](agents)的内容！

在本 Notebook 中，我们将创建一个**数据分析智能体：一个配备数据分析库的代码智能体，能够加载和转换数据框，从中提取洞察，甚至绘制结果图表！**

假设我想分析 [Kaggle Titanic 挑战](https://www.kaggle.com/competitions/titanic)的数据，以预测每个乘客的生还情况。但在我深入挖掘之前，我希望一个自动化智能体为我准备分析，提取趋势并绘制一些图形来寻找洞察。

让我们开始设置这个系统。

运行下面的代码以安装所需的依赖：



```python
!pip install seaborn "transformers[agents]"
```

我们首先创建智能体。我们使用了 `ReactCodeAgent`（请阅读[文档](https://huggingface.co/docs/transformers/en/agents)了解更多关于智能体类型的信息），因此我们甚至不需要为其提供任何工具：它可以直接运行代码。

我们只需要确保它能够使用与数据科学相关的库，方法是将这些库传递给 `additional_authorized_imports` 参数：`["numpy", "pandas", "matplotlib.pyplot", "seaborn"]`。

一般来说，当在 `additional_authorized_imports` 中传递库时，确保这些库已在本地环境中安装，因为 Python 解释器只能使用已安装的库。

⚙ 我们的智能体将由 [meta-llama/Meta-Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct) 提供支持，使用 `HfEngine` 类，这个类通过 HF 的推理 API 实现：推理 API 使得运行任何操作系统模型变得快速而简单。

```python
from transformers.agents import HfEngine, ReactCodeAgent
from huggingface_hub import login
import os

login(os.getenv("HUGGINGFACEHUB_API_TOKEN"))

llm_engine = HfEngine("meta-llama/Meta-Llama-3.1-70B-Instruct")

agent = ReactCodeAgent(
    tools=[],
    llm_engine=llm_engine,
    additional_authorized_imports=["numpy", "pandas", "matplotlib.pyplot", "seaborn"],
    max_iterations=10,
)
```

## 数据分析 📊🤔

在运行智能体时，我们提供了来自竞赛的额外说明，并将其作为关键字参数（kwarg）传递给 `run` 方法：

```python
import os

os.mkdir("./figures")
```

```python
additional_notes = """
### Variable Notes
pclass: A proxy for socio-economic status (SES)
1st = Upper
2nd = Middle
3rd = Lower
age: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5
sibsp: The dataset defines family relations in this way...
Sibling = brother, sister, stepbrother, stepsister
Spouse = husband, wife (mistresses and fiancés were ignored)
parch: The dataset defines family relations in this way...
Parent = mother, father
Child = daughter, son, stepdaughter, stepson
Some children travelled only with a nanny, therefore parch=0 for them.
"""

analysis = agent.run(
    """You are an expert data analyst.
Please load the source file and analyze its content.
According to the variables you have, begin by listing 3 interesting questions that could be asked on this data, for instance about specific correlations with survival rate.
Then answer these questions one by one, by finding the relevant numbers.
Meanwhile, plot some figures using matplotlib/seaborn and save them to the (already existing) folder './figures/': take care to clear each figure with plt.clf() before doing another plot.

In your final answer: summarize these correlations and trends
After each number derive real worlds insights, for instance: "Correlation between is_december and boredness is 1.3453, which suggest people are more bored in winter".
Your final answer should have at least 3 numbered and detailed parts.
""",
    additional_notes=additional_notes,
    source_file="titanic/train.csv",
)
```

```python
>>> print(analysis)
```

<pre>
Here are the correlations and trends found in the data:

1. **Correlation between age and survival rate**: The correlation is -0.0772, which suggests that as age increases, the survival rate decreases. This implies that older passengers were less likely to survive the Titanic disaster.

2. **Relationship between Pclass and survival rate**: The survival rates for each Pclass are:
   - Pclass 1: 62.96%
   - Pclass 2: 47.28%
   - Pclass 3: 24.24%
   This shows that passengers in higher socio-economic classes (Pclass 1 and 2) had a significantly higher survival rate compared to those in the lower class (Pclass 3).

3. **Relationship between fare and survival rate**: The correlation is 0.2573, which suggests a moderate positive relationship between fare and survival rate. This implies that passengers who paid higher fares were more likely to survive the disaster.
</pre>

令人印象深刻，不是吗？你还可以为你的智能体提供一个可视化工具，让它能够反思自己绘制的图表！

## 数据科学智能体：进行预测 🛠️

👉 现在让我们深入一步：**我们将让我们的模型在数据上执行预测。**

为此，我们还需要让它使用 `sklearn`，并将其添加到 `additional_authorized_imports` 中。

```python
agent = ReactCodeAgent(
    tools=[],
    llm_engine=llm_engine,
    additional_authorized_imports=[
        "numpy",
        "pandas",
        "matplotlib.pyplot",
        "seaborn",
        "sklearn",
    ],
    max_iterations=12,
)

output = agent.run(
    """You are an expert machine learning engineer.
Please train a ML model on "titanic/train.csv" to predict the survival for rows of "titanic/test.csv".
Output the results under './output.csv'.
Take care to import functions and modules before using them!
""",
    additional_notes=additional_notes + "\n" + analysis,
)
```

智能体输出的测试预测，一旦提交到 Kaggle，得分为 **0.78229**，在 17,360 名参赛者中排名 **#2824**，而且比我几年前第一次尝试这个挑战时艰难取得的成绩还要好。

你的结果可能会有所不同，但无论如何，我认为能够在几秒钟内通过智能体实现这一点，实在是非常令人印象深刻。

🚀 以上只是一个数据分析智能体的简单尝试：它肯定可以在很多方面进行改进，以更好地适应你的具体使用场景！

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/agent_data_analyst.md" />

### 用自定义数据集微调目标检测模型🖼，部署至 Spaces，并进行 Gradio API 集成
https://huggingface.co/learn/cookbook/zh-CN/fine_tuning_detr_custom_dataset.md

# 用自定义数据集微调目标检测模型🖼，部署至 Spaces，并进行 Gradio API 集成

_作者：[Sergio Paniego](https://github.com/sergiopaniego)_

在本 Notebook 中，我们将微调一个 [目标检测](https://huggingface.co/docs/transformers/tasks/object_detection) 模型——具体来说是 [DETR](https://huggingface.co/docs/transformers/model_doc/detr)——使用一个自定义数据集。我们将利用 [Hugging Face 生态系统](https://huggingface.co/docs) 来完成此任务。

我们的做法是从一个预训练的 DETR 模型开始，并在一个标注的时尚图像自定义数据集上对其进行微调，即 [Fashionpedia](https://huggingface.co/datasets/detection-datasets/fashionpedia)。通过这种方式，我们将调整模型，使其更好地识别和检测时尚领域中的物体。

在成功微调模型后，我们将把它部署为 Hugging Face 上的 Gradio Space。此外，我们还将探索如何通过 Gradio API 与部署的模型进行交互，实现与托管的 Space 之间的无缝通信，并为现实世界应用解锁新的可能性。

![DETR 架构](https://github.com/facebookresearch/detr/raw/main/.github/DETR.png)

## 1. 安装依赖项

首先，我们需要安装用于微调目标检测模型的必要库。

```python
!pip install -U -q datasets transformers[torch] timm wandb torchmetrics matplotlib albumentations
# Tested with datasets==2.21.0, transformers==4.44.2 timm==1.0.9, wandb==0.17.9 torchmetrics==1.4.1
```

## 2. 加载数据集 📁

<img src="https://fashionpedia.github.io/home/img/dataset/teaser.png" alt="数据集示例" width="80%">

📁 我们将使用的数据集是 [Fashionpedia](https://huggingface.co/datasets/detection-datasets/fashionpedia)，该数据集来源于论文 [Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset](https://arxiv.org/abs/2004.12276)。作者对其描述如下：

```
Fashionpedia 是一个包含两部分的数据集：（1）由时尚专家构建的本体，包含27个主要服装类别、19个服装部件、294个细粒度属性及其关系；（2）一个包含48,000张日常生活和名人活动时尚图像的数据集，这些图像使用分割掩膜进行了标注，并附有每个掩膜的细粒度属性，基于 Fashionpedia 本体构建。
```

该数据集包括：

* **46,781 张图像** 🖼
* **342,182 个边界框** 📦

它可以在 Hugging Face 上获取：[Fashionpedia 数据集](https://huggingface.co/datasets/detection-datasets/fashionpedia)

```python
from datasets import load_dataset

dataset = load_dataset('detection-datasets/fashionpedia')
```

```python
dataset
```

审查一个示例的内部结构

```python
dataset["train"][0]
```

## 3. 获取数据集的训练集和测试集拆分 ➗

该数据集包含两个拆分：**训练集**和**测试集**。我们将使用训练集来微调模型，使用测试集进行验证。

```python
train_dataset = dataset['train']
test_dataset = dataset['val']
```

**可选**

在接下来的注释单元格中，我们从原始数据集中随机抽取了 1% 的样本用于训练集和测试集。这种方法用于加速训练过程，因为数据集包含大量示例。

为了获得最佳结果，建议跳过这两个单元格并使用完整数据集。 但如果需要，你可以取消注释这些单元格。

```python
'''
def create_sample(dataset, sample_fraction=0.01, seed=42):
    sample_size = int(sample_fraction * len(dataset))
    sampled_dataset = dataset.shuffle(seed=seed).select(range(sample_size))
    print(f"Original size: {len(dataset)}")
    print(f"Sample size: {len(sampled_dataset)}")
    return sampled_dataset

# Apply function to both splits
train_dataset = create_sample(train_dataset)
test_dataset = create_sample(test_dataset)
'''
```

## 4. 可视化数据集中的一个示例及其物体 👀

现在我们已经加载了数据集，让我们可视化一个示例图像及其标注的物体。

### 生成 `id2label` 和 `label2id`

这两个变量包含物体 ID 与其对应标签之间的映射关系。`id2label` 将 ID 映射到标签，而 `label2id` 则将标签映射到 ID。

```python
import numpy as np
from PIL import Image, ImageDraw


id2label = {
    0: 'shirt, blouse', 1: 'top, t-shirt, sweatshirt', 2: 'sweater', 3: 'cardigan',
    4: 'jacket', 5: 'vest', 6: 'pants', 7: 'shorts', 8: 'skirt', 9: 'coat',
    10: 'dress', 11: 'jumpsuit', 12: 'cape', 13: 'glasses', 14: 'hat',
    15: 'headband, head covering, hair accessory', 16: 'tie', 17: 'glove',
    18: 'watch', 19: 'belt', 20: 'leg warmer', 21: 'tights, stockings',
    22: 'sock', 23: 'shoe', 24: 'bag, wallet', 25: 'scarf', 26: 'umbrella',
    27: 'hood', 28: 'collar', 29: 'lapel', 30: 'epaulette', 31: 'sleeve',
    32: 'pocket', 33: 'neckline', 34: 'buckle', 35: 'zipper', 36: 'applique',
    37: 'bead', 38: 'bow', 39: 'flower', 40: 'fringe', 41: 'ribbon',
    42: 'rivet', 43: 'ruffle', 44: 'sequin', 45: 'tassel'
}


label2id = {v: k for k, v in id2label.items()}
```

### 我们来绘制一张图像！ 🎨

现在，让我们从数据集中可视化一张图像，以便更好地了解它的样子。

```python
>>> def draw_image_from_idx(dataset, idx):
...     sample = dataset[idx]
...     image = sample["image"]
...     annotations = sample["objects"]
...     draw = ImageDraw.Draw(image)
...     width, height = sample["width"], sample["height"]

...     print(annotations)

...     for i in range(len(annotations["bbox_id"])):
...         box = annotations["bbox"][i]
...         x1, y1, x2, y2 = tuple(box)
...         draw.rectangle((x1, y1, x2, y2), outline="red", width=3)
...         draw.text((x1, y1), id2label[annotations["category"][i]], fill="green")

...     return image

>>> draw_image_from_idx(dataset=train_dataset, idx=10) # You can test changing this id
```

<pre>
{'bbox_id': [158977, 158978, 158979, 158980, 158981, 158982, 158983], 'category': [1, 23, 23, 6, 31, 31, 33], 'bbox': [[210.0, 225.0, 536.0, 784.0], [290.0, 897.0, 350.0, 1015.0], [464.0, 950.0, 534.0, 1021.0], [313.0, 407.0, 524.0, 954.0], [268.0, 229.0, 333.0, 563.0], [489.0, 247.0, 528.0, 591.0], [387.0, 225.0, 450.0, 253.0]], 'area': [69960, 2449, 1788, 75418, 15149, 5998, 479]}
</pre>

### 让我们可视化更多图像 📸

现在，让我们看几张来自数据集的图像，以便更全面地了解数据。

```python
>>> import matplotlib.pyplot as plt

>>> def plot_images(dataset, indices):
...     """
...     Plot images and their annotations.
...     """
...     num_cols = 3
...     num_rows = int(np.ceil(len(indices) / num_cols))
...     fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, 10))

...     for i, idx in enumerate(indices):
...         row = i // num_cols
...         col = i % num_cols

...         image = draw_image_from_idx(dataset, idx)

...         axes[row, col].imshow(image)
...         axes[row, col].axis("off")

...     for j in range(i + 1, num_rows * num_cols):
...         fig.delaxes(axes.flatten()[j])

...     plt.tight_layout()
...     plt.show()

>>> plot_images(train_dataset, range(9))
```

<pre>
{'bbox_id': [150311, 150312, 150313, 150314], 'category': [23, 23, 33, 10], 'bbox': [[445.0, 910.0, 505.0, 983.0], [239.0, 940.0, 284.0, 994.0], [298.0, 282.0, 386.0, 352.0], [210.0, 282.0, 448.0, 665.0]], 'area': [1422, 843, 373, 56375]}
{'bbox_id': [158953, 158954, 158955, 158956, 158957, 158958, 158959, 158960, 158961, 158962], 'category': [2, 33, 31, 31, 13, 7, 22, 22, 23, 23], 'bbox': [[182.0, 220.0, 472.0, 647.0], [294.0, 221.0, 407.0, 257.0], [405.0, 297.0, 472.0, 647.0], [182.0, 264.0, 266.0, 621.0], [284.0, 135.0, 372.0, 169.0], [238.0, 537.0, 414.0, 606.0], [351.0, 732.0, 417.0, 922.0], [202.0, 749.0, 270.0, 930.0], [200.0, 921.0, 256.0, 979.0], [373.0, 903.0, 455.0, 966.0]], 'area': [87267, 1220, 16895, 18541, 1468, 9360, 8629, 8270, 2717, 3121]}
{'bbox_id': [169196, 169197, 169198, 169199, 169200, 169201, 169202, 169203, 169204, 169205, 169206, 169207, 169208, 169209, 169210], 'category': [13, 29, 28, 32, 32, 31, 31, 0, 31, 31, 18, 4, 6, 23, 23], 'bbox': [[441.0, 132.0, 499.0, 150.0], [412.0, 164.0, 494.0, 295.0], [427.0, 164.0, 476.0, 207.0], [406.0, 326.0, 448.0, 335.0], [484.0, 327.0, 508.0, 334.0], [366.0, 323.0, 395.0, 372.0], [496.0, 271.0, 523.0, 302.0], [366.0, 164.0, 523.0, 372.0], [360.0, 186.0, 406.0, 332.0], [502.0, 201.0, 534.0, 321.0], [496.0, 259.0, 515.0, 278.0], [360.0, 164.0, 534.0, 411.0], [403.0, 384.0, 510.0, 638.0], [393.0, 584.0, 430.0, 663.0], [449.0, 638.0, 518.0, 681.0]], 'area': [587, 2922, 931, 262, 111, 1171, 540, 3981, 4457, 1724, 188, 26621, 16954, 2167, 1773]}
{'bbox_id': [167967, 167968, 167969, 167970, 167971, 167972, 167973, 167974, 167975, 167976, 167977, 167978, 167979, 167980, 167981, 167982, 167983, 167984, 167985, 167986, 167987, 167988, 167989, 167990, 167991, 167992, 167993, 167994, 167995, 167996, 167997, 167998, 167999, 168000, 168001, 168002, 168003, 168004, 168005, 168006, 168007, 168008, 168009, 168010, 168011, 168012, 168013, 168014, 168015, 168016, 168017, 168018, 168019, 168020, 168021, 168022, 168023, 168024, 168025, 168026, 168027, 168028, 168029, 168030, 168031, 168032, 168033, 168034, 168035, 168036, 168037, 168038, 168039, 168040], 'category': [6, 23, 23, 31, 31, 4, 1, 35, 32, 35, 35, 35, 35, 28, 35, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 33], 'bbox': [[300.0, 421.0, 460.0, 846.0], [383.0, 841.0, 432.0, 899.0], [304.0, 740.0, 347.0, 831.0], [246.0, 222.0, 295.0, 505.0], [456.0, 229.0, 492.0, 517.0], [246.0, 169.0, 492.0, 517.0], [355.0, 213.0, 450.0, 433.0], [289.0, 353.0, 303.0, 427.0], [442.0, 288.0, 460.0, 340.0], [451.0, 290.0, 458.0, 304.0], [407.0, 238.0, 473.0, 486.0], [487.0, 501.0, 491.0, 517.0], [246.0, 455.0, 252.0, 505.0], [340.0, 169.0, 442.0, 238.0], [348.0, 230.0, 372.0, 476.0], [411.0, 179.0, 414.0, 182.0], [414.0, 183.0, 418.0, 186.0], [418.0, 187.0, 421.0, 190.0], [421.0, 192.0, 425.0, 195.0], [424.0, 196.0, 428.0, 199.0], [426.0, 200.0, 430.0, 204.0], [429.0, 204.0, 433.0, 208.0], [431.0, 209.0, 435.0, 213.0], [433.0, 214.0, 437.0, 218.0], [434.0, 218.0, 438.0, 222.0], [436.0, 223.0, 440.0, 226.0], [437.0, 227.0, 441.0, 231.0], [438.0, 232.0, 442.0, 235.0], [433.0, 232.0, 437.0, 236.0], [429.0, 233.0, 432.0, 237.0], [423.0, 233.0, 426.0, 237.0], [417.0, 233.0, 421.0, 237.0], [353.0, 172.0, 355.0, 174.0], [353.0, 175.0, 354.0, 177.0], [351.0, 178.0, 353.0, 181.0], [350.0, 182.0, 351.0, 184.0], [347.0, 187.0, 350.0, 189.0], [346.0, 190.0, 349.0, 193.0], [345.0, 194.0, 348.0, 197.0], [344.0, 199.0, 347.0, 202.0], [342.0, 204.0, 346.0, 207.0], [342.0, 208.0, 345.0, 211.0], [342.0, 212.0, 344.0, 215.0], [342.0, 217.0, 345.0, 220.0], [344.0, 221.0, 346.0, 224.0], [348.0, 222.0, 350.0, 225.0], [353.0, 223.0, 356.0, 226.0], [359.0, 223.0, 361.0, 226.0], [364.0, 223.0, 366.0, 226.0], [247.0, 448.0, 253.0, 454.0], [251.0, 454.0, 254.0, 456.0], [252.0, 460.0, 255.0, 463.0], [252.0, 466.0, 255.0, 469.0], [253.0, 471.0, 255.0, 475.0], [253.0, 478.0, 255.0, 481.0], [253.0, 483.0, 256.0, 486.0], [254.0, 489.0, 256.0, 492.0], [254.0, 495.0, 256.0, 497.0], [247.0, 457.0, 249.0, 460.0], [247.0, 463.0, 249.0, 466.0], [248.0, 469.0, 249.0, 471.0], [248.0, 476.0, 250.0, 478.0], [248.0, 481.0, 250.0, 483.0], [249.0, 486.0, 250.0, 488.0], [487.0, 459.0, 490.0, 461.0], [487.0, 465.0, 490.0, 467.0], [487.0, 471.0, 490.0, 472.0], [487.0, 476.0, 489.0, 478.0], [486.0, 482.0, 489.0, 484.0], [486.0, 488.0, 489.0, 490.0], [486.0, 494.0, 488.0, 496.0], [486.0, 500.0, 488.0, 501.0], [485.0, 505.0, 487.0, 507.0], [365.0, 213.0, 409.0, 226.0]], 'area': [44062, 2140, 2633, 9206, 5905, 44791, 12948, 211, 335, 43, 691, 62, 104, 2169, 439, 9, 10, 9, 8, 9, 14, 10, 13, 13, 11, 11, 10, 10, 12, 10, 10, 14, 4, 2, 4, 2, 5, 6, 7, 7, 8, 7, 6, 7, 5, 5, 7, 6, 5, 12, 5, 7, 8, 6, 6, 6, 4, 4, 6, 5, 2, 4, 4, 2, 6, 6, 3, 4, 6, 6, 4, 2, 4, 94]}
{'bbox_id': [168041, 168042, 168043, 168044, 168045, 168046, 168047], 'category': [10, 32, 35, 31, 4, 29, 33], 'bbox': [[238.0, 309.0, 471.0, 1022.0], [234.0, 572.0, 331.0, 602.0], [235.0, 580.0, 324.0, 599.0], [119.0, 318.0, 343.0, 856.0], [111.0, 262.0, 518.0, 1022.0], [166.0, 262.0, 393.0, 492.0], [238.0, 309.0, 278.0, 324.0]], 'area': [12132, 1548, 755, 43926, 178328, 9316, 136]}
{'bbox_id': [160050, 160051, 160052, 160053, 160054, 160055], 'category': [10, 31, 31, 23, 23, 33], 'bbox': [[290.0, 364.0, 429.0, 665.0], [304.0, 369.0, 397.0, 508.0], [290.0, 468.0, 310.0, 522.0], [213.0, 842.0, 294.0, 905.0], [446.0, 840.0, 536.0, 896.0], [311.0, 364.0, 354.0, 379.0]], 'area': [26873, 5301, 747, 1438, 1677, 71]}
{'bbox_id': [160056, 160057, 160058, 160059, 160060, 160061, 160062, 160063, 160064, 160065, 160066], 'category': [10, 36, 42, 42, 42, 42, 42, 42, 42, 23, 33], 'bbox': [[127.0, 198.0, 451.0, 949.0], [277.0, 336.0, 319.0, 402.0], [340.0, 343.0, 344.0, 347.0], [321.0, 338.0, 327.0, 343.0], [336.0, 361.0, 342.0, 365.0], [329.0, 321.0, 333.0, 326.0], [313.0, 294.0, 319.0, 300.0], [330.0, 299.0, 334.0, 304.0], [295.0, 330.0, 300.0, 334.0], [332.0, 926.0, 376.0, 946.0], [284.0, 198.0, 412.0, 270.0]], 'area': [137575, 1915, 14, 24, 18, 15, 25, 16, 16, 740, 586]}
{'bbox_id': [158963, 158964, 158965, 158966, 158967, 158968, 158969, 158970, 158971], 'category': [1, 31, 31, 7, 22, 22, 23, 23, 33], 'bbox': [[262.0, 449.0, 435.0, 686.0], [399.0, 471.0, 435.0, 686.0], [262.0, 451.0, 294.0, 662.0], [276.0, 603.0, 423.0, 726.0], [291.0, 759.0, 343.0, 934.0], [341.0, 749.0, 401.0, 947.0], [302.0, 919.0, 337.0, 994.0], [323.0, 925.0, 374.0, 1005.0], [343.0, 456.0, 366.0, 467.0]], 'area': [22330, 4422, 4846, 14000, 6190, 6997, 1547, 2107, 49]}
{'bbox_id': [158972, 158973, 158974, 158975, 158976], 'category': [23, 23, 28, 10, 5], 'bbox': [[412.0, 588.0, 451.0, 631.0], [333.0, 585.0, 357.0, 627.0], [361.0, 243.0, 396.0, 257.0], [303.0, 243.0, 447.0, 517.0], [330.0, 259.0, 425.0, 324.0]], 'area': [949, 737, 133, 17839, 2916]}
</pre>

## 5. 过滤无效的边界框 ❌

作为数据预处理的第一步，我们将过滤掉一些无效的边界框。在审查数据集后，我们发现某些边界框没有有效的结构。因此，我们将丢弃这些无效的条目。

```python
>>> from datasets import Dataset

>>> def filter_invalid_bboxes(example):
...     valid_bboxes = []
...     valid_bbox_ids = []
...     valid_categories = []
...     valid_areas = []

...     for i, bbox in enumerate(example['objects']['bbox']):
...         x_min, y_min, x_max, y_max = bbox[:4]
...         if x_min < x_max and y_min < y_max:
...             valid_bboxes.append(bbox)
...             valid_bbox_ids.append(example['objects']['bbox_id'][i])
...             valid_categories.append(example['objects']['category'][i])
...             valid_areas.append(example['objects']['area'][i])
...         else:
...             print(f"Image with invalid bbox: {example['image_id']} Invalid bbox detected and discarded: {bbox} - bbox_id: {example['objects']['bbox_id'][i]} - category: {example['objects']['category'][i]}")

...     example['objects']['bbox'] = valid_bboxes
...     example['objects']['bbox_id'] = valid_bbox_ids
...     example['objects']['category'] = valid_categories
...     example['objects']['area'] = valid_areas

...     return example

>>> train_dataset = train_dataset.map(filter_invalid_bboxes)
>>> test_dataset = test_dataset.map(filter_invalid_bboxes)
```

<pre>
Image with invalid bbox: 8396 Invalid bbox detected and discarded: [0.0, 0.0, 0.0, 0.0] - bbox_id: 139952 - category: 42
Image with invalid bbox: 19725 Invalid bbox detected and discarded: [0.0, 0.0, 0.0, 0.0] - bbox_id: 23298 - category: 42
Image with invalid bbox: 19725 Invalid bbox detected and discarded: [0.0, 0.0, 0.0, 0.0] - bbox_id: 23299 - category: 42
Image with invalid bbox: 21696 Invalid bbox detected and discarded: [0.0, 0.0, 0.0, 0.0] - bbox_id: 277148 - category: 42
Image with invalid bbox: 23055 Invalid bbox detected and discarded: [0.0, 0.0, 0.0, 0.0] - bbox_id: 287029 - category: 33
Image with invalid bbox: 23671 Invalid bbox detected and discarded: [0.0, 0.0, 0.0, 0.0] - bbox_id: 290142 - category: 42
Image with invalid bbox: 26549 Invalid bbox detected and discarded: [0.0, 0.0, 0.0, 0.0] - bbox_id: 311943 - category: 37
Image with invalid bbox: 26834 Invalid bbox detected and discarded: [0.0, 0.0, 0.0, 0.0] - bbox_id: 309141 - category: 37
Image with invalid bbox: 31748 Invalid bbox detected and discarded: [0.0, 0.0, 0.0, 0.0] - bbox_id: 262063 - category: 42
Image with invalid bbox: 34253 Invalid bbox detected and discarded: [0.0, 0.0, 0.0, 0.0] - bbox_id: 315750 - category: 19
</pre>

```python
>>> print(train_dataset)
>>> print(test_dataset)
```

<pre>
Dataset({
    features: ['image_id', 'image', 'width', 'height', 'objects'],
    num_rows: 45623
})
Dataset({
    features: ['image_id', 'image', 'width', 'height', 'objects'],
    num_rows: 1158
})
</pre>

## 6. 可视化类别分布 👀

让我们通过绘制每个类别的出现次数来进一步探索数据集。这将帮助我们了解类别的分布情况，并识别任何潜在的偏差。

```python
id_list = []
category_examples = {}
for example in train_dataset:
  id_list += example['objects']['bbox_id']
  for category in example['objects']['category']:
    if id2label[category] not in category_examples:
      category_examples[id2label[category]] = 1
    else:
      category_examples[id2label[category]] += 1

id_list.sort()
```

```python
>>> import matplotlib.pyplot as plt

>>> categories = list(category_examples.keys())
>>> values = list(category_examples.values())

>>> fig, ax = plt.subplots(figsize=(12, 8))

>>> bars = ax.bar(categories, values, color='skyblue')

>>> ax.set_xlabel('Categories', fontsize=14)
>>> ax.set_ylabel('Number of Occurrences', fontsize=14)
>>> ax.set_title('Number of Occurrences by Category', fontsize=16)

>>> ax.set_xticklabels(categories, rotation=90, ha='right')
>>> ax.grid(axis='y', linestyle='--', alpha=0.7)

>>> for bar in bars:
...     height = bar.get_height()
...     ax.text(
...         bar.get_x() + bar.get_width() / 2.0, height,
...         f'{height}', ha='center', va='bottom', fontsize=10
...     )

>>> plt.tight_layout()
>>> plt.show()
```

<img 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我们可以观察到，某些类别，如“鞋子”或“袖子”，在数据集中出现的频率较高。这表明数据集可能存在类别不平衡的情况，某些类别的出现频率远高于其他类别。识别这些不平衡对于解决模型训练中的潜在偏差至关重要。

## 7. 为数据集添加数据增强

数据增强 🪄 在目标检测任务中对于提高性能至关重要。在本节中，我们将利用 [Albumentations](https://albumentations.ai/) 的功能来有效地增强我们的数据集。

Albumentations 提供了一系列强大的增强技术，专为目标检测量身定制。它允许进行各种转换，同时确保边界框得到准确调整。这些功能有助于生成更具多样性的数据集，提高模型的鲁棒性和泛化能力。

<img src="https://albumentations.ai/docs/images/introduction/dedicated_library/pixel_and_spatial_level_augmentations_for_object_detection.jpg" alt="Albumentations 图像" width="90%">

```python
import albumentations as A

train_transform = A.Compose(
    [
        A.LongestMaxSize(500),
        A.PadIfNeeded(500, 500, border_mode=0, value=(0, 0, 0)),
        A.HorizontalFlip(p=0.5),
        A.RandomBrightnessContrast(p=0.5),
        A.HueSaturationValue(p=0.5),
        A.Rotate(limit=10, p=0.5),
        A.RandomScale(scale_limit=0.2, p=0.5),
        A.GaussianBlur(p=0.5),
        A.GaussNoise(p=0.5),
    ],
    bbox_params=A.BboxParams(
        format="pascal_voc",
        label_fields=["category"]
    ),
)

val_transform = A.Compose(
    [
        A.LongestMaxSize(500),
        A.PadIfNeeded(500, 500, border_mode=0, value=(0, 0, 0)),
    ],
    bbox_params=A.BboxParams(
        format="pascal_voc",
        label_fields=["category"]
    ),
)
```

## 8. 从模型检查点初始化图像处理器 🎆

我们将使用预训练的模型检查点来实例化图像处理器。在这种情况下，我们使用的是 [facebook/detr-resnet-50-dc5](https://huggingface.co/facebook/detr-resnet-50-dc5) 模型。

```python
from transformers import AutoImageProcessor

checkpoint = "facebook/detr-resnet-50-dc5"
image_processor = AutoImageProcessor.from_pretrained(checkpoint)
```

### 添加处理数据集的方法

接下来，我们将添加方法来处理数据集。这些方法将处理图像和标注的转换任务，以确保它们与模型兼容。

```python
def formatted_anns(image_id, category, area, bbox):
    annotations = []
    for i in range(0, len(category)):
        new_ann = {
            "image_id": image_id,
            "category_id": category[i],
            "isCrowd": 0,
            "area": area[i],
            "bbox": list(bbox[i]),
        }
        annotations.append(new_ann)

    return annotations

def convert_voc_to_coco(bbox):
    xmin, ymin, xmax, ymax = bbox
    width = xmax - xmin
    height = ymax - ymin
    return [xmin, ymin, width, height]

def transform_aug_ann(examples, transform):
    image_ids = examples["image_id"]
    images, bboxes, area, categories = [], [], [], []
    for image, objects in zip(examples["image"], examples["objects"]):
        image = np.array(image.convert("RGB"))[:, :, ::-1]
        out = transform(image=image, bboxes=objects["bbox"], category=objects["category"])

        area.append(objects["area"])
        images.append(out["image"])

        # Convert to COCO format
        converted_bboxes = [convert_voc_to_coco(bbox) for bbox in out["bboxes"]]
        bboxes.append(converted_bboxes)

        categories.append(out["category"])

    targets = [
        {"image_id": id_, "annotations": formatted_anns(id_, cat_, ar_, box_)}
        for id_, cat_, ar_, box_ in zip(image_ids, categories, area, bboxes)
    ]

    return image_processor(images=images, annotations=targets, return_tensors="pt")

def transform_train(examples):
    return transform_aug_ann(examples, transform=train_transform)

def transform_val(examples):
    return transform_aug_ann(examples, transform=val_transform)


train_dataset_transformed = train_dataset.with_transform(transform_train)
test_dataset_transformed = test_dataset.with_transform(transform_val)
```

## 9. 绘制增强后的示例 🎆

我们即将进入模型训练阶段！在继续之前，让我们可视化一些增强后的样本。这将帮助我们再次确认这些增强方法是否适合且有效，为训练过程做好准备。

```python
>>> # Updated draw function to accept an optional transform
>>> def draw_augmented_image_from_idx(dataset, idx, transform=None):
...     sample = dataset[idx]
...     image = sample["image"]
...     annotations = sample["objects"]

...     # Convert image to RGB and NumPy array
...     image = np.array(image.convert("RGB"))[:, :, ::-1]

...     if transform:
...         augmented = transform(image=image, bboxes=annotations["bbox"], category=annotations["category"])
...         image = augmented["image"]
...         annotations["bbox"] = augmented["bboxes"]
...         annotations["category"] = augmented["category"]

...     image = Image.fromarray(image[:, :, ::-1])  # Convert back to PIL Image
...     draw = ImageDraw.Draw(image)
...     width, height = sample["width"], sample["height"]

...     for i in range(len(annotations["bbox_id"])):
...         box = annotations["bbox"][i]
...         x1, y1, x2, y2 = tuple(box)

...         # Normalize coordinates if necessary
...         if max(box) <= 1.0:
...             x1, y1 = int(x1 * width), int(y1 * height)
...             x2, y2 = int(x2 * width), int(y2 * height)
...         else:
...             x1, y1 = int(x1), int(y1)
...             x2, y2 = int(x2), int(y2)

...         draw.rectangle((x1, y1, x2, y2), outline="red", width=3)
...         draw.text((x1, y1), id2label[annotations["category"][i]], fill="green")

...     return image

>>> # Updated plot function to include augmentation
>>> def plot_augmented_images(dataset, indices, transform=None):
...     """
...     Plot images and their annotations with optional augmentation.
...     """
...     num_rows = len(indices) // 3
...     num_cols = 3
...     fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, 10))

...     for i, idx in enumerate(indices):
...         row = i // num_cols
...         col = i % num_cols

...         # Draw augmented image
...         image = draw_augmented_image_from_idx(dataset, idx, transform=transform)

...         # Display image on the corresponding subplot
...         axes[row, col].imshow(image)
...         axes[row, col].axis("off")

...     plt.tight_layout()
...     plt.show()

>>> # Now use the function to plot augmented images
>>> plot_augmented_images(train_dataset, range(9), transform=train_transform)
```

<img 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">


## 10. 从检查点初始化模型

我们将使用与图像处理器相同的检查点来初始化模型。这包括加载一个预训练模型，我们将对其进行微调，以适应我们的特定数据集。

```python
from transformers import AutoModelForObjectDetection

model = AutoModelForObjectDetection.from_pretrained(
    checkpoint,
    id2label=id2label,
    label2id=label2id,
    ignore_mismatched_sizes=True,
)
```

```python
output_dir = "detr-resnet-50-dc5-fashionpedia-finetuned" # change this
```

## 11. 连接到 HF Hub 上传微调后的模型 🔌

我们将连接到 Hugging Face Hub 以上传我们的微调模型。这使我们能够共享和部署模型，供他人使用或进行进一步评估。

```python
from huggingface_hub import notebook_login

notebook_login()
```

## 12. 设置训练参数，连接到 W&B，并开始训练！

接下来，我们将设置训练参数，连接到 [Weights & Biases (W&B)](https://wandb.ai/)，并启动训练过程。W&B 将帮助我们跟踪实验、可视化指标，并管理我们的模型训练工作流。

```python
from transformers import TrainingArguments
from transformers import Trainer

import torch

# Define the training arguments

training_args = TrainingArguments(
    output_dir=output_dir,
    per_device_train_batch_size=4,
    per_device_eval_batch_size=4,
    max_steps=10000,
    fp16=True,
    save_steps=10,
    logging_steps=1,
    learning_rate=1e-5,
    weight_decay=1e-4,
    save_total_limit=2,
    remove_unused_columns=False,
    evaluation_strategy="steps",
    eval_steps=50,
    eval_strategy = "steps",
    report_to="wandb",
    push_to_hub=True,
    batch_eval_metrics=True
)
```

### 连接到 W&B 跟踪训练

```python
import wandb

wandb.init(
    project="detr-resnet-50-dc5-fashionpedia-finetuned", # change this
    name="detr-resnet-50-dc5-fashionpedia-finetuned", # change this
    config=training_args
)
```

### 让我们开始训练模型！ 🚀

现在是时候开始训练模型了。让我们运行训练过程，看看我们的微调模型如何从数据中学习！

首先，我们声明 `compute_metrics` 方法，用于在评估时计算指标。

```python
from torchmetrics.detection.mean_ap import MeanAveragePrecision
from torch.nn.functional import softmax

def denormalize_boxes(boxes, width, height):
    boxes = boxes.clone()
    boxes[:, 0] *= width  # xmin
    boxes[:, 1] *= height  # ymin
    boxes[:, 2] *= width  # xmax
    boxes[:, 3] *= height  # ymax
    return boxes

batch_metrics = []
def compute_metrics(eval_pred, compute_result):
    global batch_metrics

    (loss_dict, scores, pred_boxes, last_hidden_state, encoder_last_hidden_state), labels = eval_pred

    image_sizes = []
    target = []
    for label in labels:

        image_sizes.append(label['orig_size'])
        width, height = label['orig_size']
        denormalized_boxes = denormalize_boxes(label["boxes"], width, height)
        target.append(
            {
                "boxes": denormalized_boxes,
                "labels": label["class_labels"],
            }
        )
    predictions = []
    for score, box, target_sizes in zip(scores, pred_boxes, image_sizes):
        # Extract the bounding boxes, labels, and scores from the model's output
        pred_scores = score[:, :-1]  # Exclude the no-object class
        pred_scores = softmax(pred_scores, dim=-1)
        width, height = target_sizes
        pred_boxes = denormalize_boxes(box, width, height)
        pred_labels = torch.argmax(pred_scores, dim=-1)

        # Get the scores corresponding to the predicted labels
        pred_scores_for_labels = torch.gather(pred_scores, 1, pred_labels.unsqueeze(-1)).squeeze(-1)
        predictions.append(
            {
                "boxes": pred_boxes,
                "scores": pred_scores_for_labels,
                "labels": pred_labels,
            }
        )

    metric = MeanAveragePrecision(box_format='xywh', class_metrics=True)

    if not compute_result:
        # Accumulate batch-level metrics
        batch_metrics.append({"preds": predictions, "target": target})
        return {}
    else:
        # Compute final aggregated metrics
        # Aggregate batch-level metrics (this should be done based on your metric library's needs)
        all_preds = []
        all_targets = []
        for batch in batch_metrics:
            all_preds.extend(batch["preds"])
            all_targets.extend(batch["target"])

        # Update metric with all accumulated predictions and targets
        metric.update(preds=all_preds, target=all_targets)
        metrics = metric.compute()

        # Convert and format metrics as needed
        classes = metrics.pop("classes")
        map_per_class = metrics.pop("map_per_class")
        mar_100_per_class = metrics.pop("mar_100_per_class")

        for class_id, class_map, class_mar in zip(classes, map_per_class, mar_100_per_class):
            class_name = id2label[class_id.item()] if id2label is not None else class_id.item()
            metrics[f"map_{class_name}"] = class_map
            metrics[f"mar_100_{class_name}"] = class_mar

        # Round metrics for cleaner output
        metrics = {k: round(v.item(), 4) for k, v in metrics.items()}

        # Clear batch metrics for next evaluation
        batch_metrics = []

        return metrics
```

```python
def collate_fn(batch):
    pixel_values = [item["pixel_values"] for item in batch]
    encoding = image_processor.pad(pixel_values, return_tensors="pt")
    labels = [item["labels"] for item in batch]

    batch = {}
    batch["pixel_values"] = encoding["pixel_values"]
    batch["pixel_mask"] = encoding["pixel_mask"]
    batch["labels"] = labels

    return batch
```

```python
trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=collate_fn,
    train_dataset=train_dataset_transformed,
    eval_dataset=test_dataset_transformed,
    tokenizer=image_processor,
    compute_metrics=compute_metrics
)
```

```python
trainer.train()
```

```python
trainer.push_to_hub()
```

## 13. 测试模型在测试图像上的表现 📝

模型训练完成后，我们可以评估其在测试图像上的表现。由于该模型已作为 Hugging Face 模型提供，因此进行预测非常简单。在接下来的单元格中，我们将展示如何在新图像上运行推理并评估模型的能力。

```python
import requests
from transformers import pipeline
import numpy as np
from PIL import Image, ImageDraw

url = "https://images.unsplash.com/photo-1536243298747-ea8874136d64?q=80&w=640"

image = Image.open(requests.get(url, stream=True).raw)

obj_detector = pipeline(
    "object-detection", model="sergiopaniego/detr-resnet-50-dc5-fashionpedia-finetuned" # Change with your model name
)


results = obj_detector(image)
print(results)
```

### 现在，让我们展示结果

我们将展示模型在测试图像上的预测结果。这将让我们了解模型的表现情况，并突出其优势和需要改进的地方。

```python
from PIL import Image, ImageDraw
import numpy as np

def plot_results(image, results, threshold=0.6):
    image = Image.fromarray(np.uint8(image))
    draw = ImageDraw.Draw(image)
    width, height = image.size

    for result in results:
        score = result['score']
        label = result['label']
        box = list(result['box'].values())

        if score > threshold:
            x1, y1, x2, y2 = tuple(box)
            draw.rectangle((x1, y1, x2, y2), outline="red", width=3)
            draw.text((x1 + 5, y1 - 10), label, fill="white")
            draw.text((x1 + 5, y1 + 10), f'{score:.2f}', fill='green' if score > 0.7 else 'red')

    return image
```

```python
>>> plot_results(image, results)
```

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">


## 14. 在测试集上评估模型 📝

在训练并可视化测试图像的结果后，我们将对整个测试数据集进行模型评估。这一步包括生成评估指标，以评估模型在所有测试样本上的整体表现和有效性。

```python
metrics = trainer.evaluate(test_dataset_transformed)
print(metrics)
```

## 15. 将模型部署到 HF Space

<img src="https://huggingface.co/front/thumbnails/spaces.png" alt="HF Spaces logo" width="20%">

现在我们的模型已经在 Hugging Face 上可用，我们可以将其部署到 HF Space。Hugging Face 为小型应用提供免费的 Spaces，使我们能够创建一个交互式网页应用，用户可以上传测试图像并评估模型的能力。

我已经在这里创建了一个示例应用：[DETR 目标检测 Fashionpedia - 微调版](https://huggingface.co/spaces/sergiopaniego/DETR_object_detection_fashionpedia-finetuned)

```python
from IPython.display import IFrame
IFrame(src='https://sergiopaniego-detr-object-detection-fashionpedia-fa0081f.hf.space', width=1000, height=800)
```

### 使用以下代码创建应用

你可以通过将以下代码复制并粘贴到一个名为 `app.py` 的文件中来创建一个新应用。

```python
# app.py

import gradio as gr
import spaces
import torch

from PIL import Image
from transformers import pipeline
import matplotlib.pyplot as plt
import io

model_pipeline = pipeline("object-detection", model="sergiopaniego/detr-resnet-50-dc5-fashionpedia-finetuned")


COLORS = [[0.000, 0.447, 0.741], [0.850, 0.325, 0.098], [0.929, 0.694, 0.125],
          [0.494, 0.184, 0.556], [0.466, 0.674, 0.188], [0.301, 0.745, 0.933]]


def get_output_figure(pil_img, results, threshold):
    plt.figure(figsize=(16, 10))
    plt.imshow(pil_img)
    ax = plt.gca()
    colors = COLORS * 100

    for result in results:
        score = result['score']
        label = result['label']
        box = list(result['box'].values())
        if score > threshold:
            c = COLORS[hash(label) % len(COLORS)]
            ax.add_patch(plt.Rectangle((box[0], box[1]), box[2] - box[0], box[3] - box[1], fill=False, color=c, linewidth=3))
            text = f'{label}: {score:0.2f}'
            ax.text(box[0], box[1], text, fontsize=15,
                    bbox=dict(facecolor='yellow', alpha=0.5))
    plt.axis('off')

    return plt.gcf()

@spaces.GPU
def detect(image):
    results = model_pipeline(image)
    print(results)

    output_figure = get_output_figure(image, results, threshold=0.7)

    buf = io.BytesIO()
    output_figure.savefig(buf, bbox_inches='tight')
    buf.seek(0)
    output_pil_img = Image.open(buf)

    return output_pil_img

with gr.Blocks() as demo:
    gr.Markdown("# Object detection with DETR fine tuned on detection-datasets/fashionpedia")
    gr.Markdown(
        """
        This application uses a fine tuned DETR (DEtection TRansformers) to detect objects on images.
        This version was trained using detection-datasets/fashionpedia dataset.
        You can load an image and see the predictions for the objects detected.
        """
    )

    gr.Interface(
        fn=detect,
        inputs=gr.Image(label="Input image", type="pil"),
        outputs=[
            gr.Image(label="Output prediction", type="pil")
        ]
    )

demo.launch(show_error=True)
```

### 别忘了设置 `requirements.txt`

不要忘记创建一个 `requirements.txt` 文件，以指定应用程序的依赖项。

```python
!touch requirements.txt
!echo -e "transformers\ntimm\ntorch\ngradio\nmatplotlib" > requirements.txt
```

## 16. 将 Space 作为 API 访问 🧑‍💻️

Hugging Face Spaces 的一个优点是，它们提供了一个可以从外部应用程序访问的 API。这使得将模型集成到各种应用程序中变得非常容易，无论是使用 JavaScript、Python 还是其他语言开发的应用程序。想象一下扩展和利用模型功能的各种可能性！

你可以在这里找到更多关于如何使用 API 的信息：[Hugging Face 企业指南：Gradio](https://huggingface.co/learn/cookbook/enterprise_cookbook_gradio)

```python
!pip install gradio_client
```

```python
from gradio_client import Client, handle_file

client = Client("sergiopaniego/DETR_object_detection_fashionpedia-finetuned") # change this with your Space
result = client.predict(
		image=handle_file("https://images.unsplash.com/photo-1536243298747-ea8874136d64?q=80&w=640"),
		api_name="/predict"
)
```

```python
from PIL import Image

img = Image.open(result).convert('RGB')
```

```python
>>> from IPython.display import display
>>> display(img)
```

<img 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">


## 结论

在本教程中，我们成功地在自定义数据集上微调了一个目标检测模型，并将其部署为 Gradio Space。我们还演示了如何使用 Gradio API 调用该 Space，展示了将其集成到各种应用程序中的简便性。

希望本指南能帮助您自信地微调和部署自己的模型！ 🚀

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/fine_tuning_detr_custom_dataset.md" />

### 怎么使用推理端点去嵌入文档
https://huggingface.co/learn/cookbook/zh-CN/automatic_embedding_tei_inference_endpoints.md

# 怎么使用推理端点去嵌入文档

_作者: [Derek Thomas](https://huggingface.co/derek-thomas)_

## 目标

我有一个数据集，我想为其嵌入语义搜索（或问答，或 RAG），我希望以最简单的方式嵌入这个数据集并将其放入一个新的数据集中。

## 方法

我将使用我最喜欢的 subreddit [r/bestofredditorupdates](https://www.reddit.com/r/bestofredditorupdates/) 中的数据集。因为它有很长的条目，同时使用新的 [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) 嵌入模型，因为它有 8k 的上下文长度。还将使用 [推理端点](https://huggingface.co/inference-endpoints) 部署这个，以节省时间和金钱。要跟随这个教程，你需要**已经添加了支付方式**。如果你还没有添加，可以在 [账单](https://huggingface.co/docs/hub/billing#billing) 中添加。为了使操作更加简单，我将完全基于 API 进行操作。

为了使这个过程更快，我将使用 [Text Embeddings Inference](https://github.com/huggingface/text-embeddings-inference) 镜像。这有许多好处，比如：
- 无需模型图编译步骤
- Docker 镜像小，启动时间快。真正的无服务器！
- 基于 token 的动态批处理
- 使用 Flash 注意力机制、Candle 和 cuBLASLt 优化的 transformers 代码进行推理
- Safetensors 权重加载
- 生产就绪（使用 Open Telemetry 进行分布式跟踪，Prometheus 指标）


![img](https://media.githubusercontent.com/media/huggingface/text-embeddings-inference/main/assets/bs1-tp.png)

## 环境(Requirements)

```python
!pip install -q aiohttp==3.8.3 datasets==2.14.6 pandas==1.5.3 requests==2.31.0 tqdm==4.66.1 huggingface-hub>=0.20
```

## 导入包

```python
import asyncio
from getpass import getpass
import json
from pathlib import Path
import time
from typing import Optional

from aiohttp import ClientSession, ClientTimeout
from datasets import load_dataset, Dataset, DatasetDict
from huggingface_hub import notebook_login, create_inference_endpoint, list_inference_endpoints, whoami
import numpy as np
import pandas as pd
import requests
from tqdm.auto import tqdm
```

## 设置(Config)
`DATASET_IN` 你文本数据的位置
`DATASET_OUT` 你的嵌入储存的位置

注意：我将 `MAX_WORKERS` 设置为 5，因为 `jina-embeddings-v2` 对内存的需求较大。

```python
DATASET_IN = 'derek-thomas/dataset-creator-reddit-bestofredditorupdates'
DATASET_OUT = "processed-subset-bestofredditorupdates"
ENDPOINT_NAME = "boru-jina-embeddings-demo-ie"

MAX_WORKERS = 5  # This is for how many async workers you want. Choose based on the model and hardware 
ROW_COUNT = 100  # Choose None to use all rows, Im using 100 just for a demo
```

Hugging Face 在推理端点中提供了多种 GPU 供选择。下面以表格形式呈现：

| GPU                 | 实例类型   | 实例大小 | vRAM  |
|---------------------|----------------|--------------|-------|
| 1x Nvidia Tesla T4 | g4dn.xlarge | small | 16GB |
| 4x Nvidia Tesla T4 | g4dn.12xlarge | large | 64GB |
| 1x Nvidia A10G | g5.2xlarge | medium | 24GB |
| 4x Nvidia A10G | g5.12xlarge | xxlarge | 96GB |
| 1x Nvidia A100* | p4de | xlarge | 80GB |
| 2x Nvidia A100* | p4de | 2xlarge | 160GB |

\*注意，对于 A100 的机型你需要发邮件给我们来获取权限。

```python
# GPU Choice
VENDOR="aws"
REGION="us-east-1"
INSTANCE_SIZE="medium"
INSTANCE_TYPE="g5.2xlarge"
```

```python
notebook_login()
```

有些用户可能会在组织中注册支付信息。这肯能会使你的支付方式链接组织。

如果你想使用你自己的用户名，请将其留空。

```python
>>> who = whoami()
>>> organization = getpass(prompt="What is your Hugging Face 🤗 username or organization? (with an added payment method)")

>>> namespace = organization or who['name']
```

<pre>
What is your Hugging Face 🤗 username or organization? (with an added payment method) ········
</pre>

## 获取数据

```python
dataset = load_dataset(DATASET_IN)
dataset['train']
```

```python
documents = dataset['train'].to_pandas().to_dict('records')[:ROW_COUNT]
len(documents), documents[0]
```

# 推理端点
## 创建推理端点

我们将使用 [API](https://huggingface.co/docs/inference-endpoints/api_reference) 来创建一个 [推理端点](https://huggingface.co/inference-endpoints)。主要有以下几个好处：
- 方便（无需点击）
- 可重复（我们有代码可以轻松运行它）
- 更便宜（无需花费时间等待加载，并且可以自动关闭）



```python
try:
    endpoint = create_inference_endpoint(
        ENDPOINT_NAME,
        repository="jinaai/jina-embeddings-v2-base-en",
        revision="7302ac470bed880590f9344bfeee32ff8722d0e5",
        task="sentence-embeddings",
        framework="pytorch",
        accelerator="gpu",
        instance_size=INSTANCE_SIZE,
        instance_type=INSTANCE_TYPE,
        region=REGION,
        vendor=VENDOR,
        namespace=namespace,
        custom_image={
            "health_route": "/health",
            "env": {
                "MAX_BATCH_TOKENS": str(MAX_WORKERS * 2048),
                "MAX_CONCURRENT_REQUESTS": "512",
                "MODEL_ID": "/repository"
            },
            "url": "ghcr.io/huggingface/text-embeddings-inference:0.5.0",
        },
        type="protected",
    )
except:
    endpoint = [ie for ie in list_inference_endpoints(namespace=namespace) if ie.name == ENDPOINT_NAME][0]
    print('Loaded endpoint')
```

这里有几个设计选择：
- 像之前所说，我们使用 `jinaai/jina-embeddings-v2-base-en` 作为我们的模型。
    - 为了可复现性，我们将它固定到一个特定的修订版本。
- 如果你对更多模型感兴趣，可以查看支持[列表](https://huggingface.co/docs/text-embeddings-inference/supported_models)。
    - 请注意，大多数嵌入模型都是基于 BERT 架构的。
- `MAX_BATCH_TOKENS` 是根据我们的工作数量和嵌入模型的上下文窗口来选择的。
- `type="protected"` 利用的是推理端点详细说明的安全功能。
- 我使用 **1x Nvidia A10**，因为 `jina-embeddings-v2` 对内存的需求很大（记住 8k 的上下文长度）。
- 如果你有高工作负载的需求，你应该考虑进一步调整 `MAX_BATCH_TOKENS` 和 `MAX_CONCURRENT_REQUESTS`。


## 等待直到它运行起来

```python
>>> %%time
>>> endpoint.wait()
```

<pre>
CPU times: user 48.1 ms, sys: 15.7 ms, total: 63.8 ms
Wall time: 52.6 s
</pre>

当我们使用 `endpoint.client.post` 时，我们得到一个字节字符串。这有点繁琐，因为我们需要将这个字节字符串转换为一个 `np.array`，但这只是 Python 中的几行快速代码。

```python
response = endpoint.client.post(json={"inputs": 'This sound track was beautiful! It paints the senery in your mind so well I would recomend it even to people who hate vid. game music!', 'truncate': True}, task="feature-extraction")
response = np.array(json.loads(response.decode()))
response[0][:20]
```

你可能遇到超过上下文长度的输入。在这种情况下，需要你来处理它们。在我的情况下，我更愿意截断而不是出现错误。让我们测试一下这是否有效。


```python
>>> embedding_input = 'This input will get multiplied' * 10000
>>> print(f'The length of the embedding_input is: {len(embedding_input)}')
>>> response = endpoint.client.post(json={"inputs": embedding_input, 'truncate': True}, task="feature-extraction")
>>> response = np.array(json.loads(response.decode()))
>>> response[0][:20]
```

<pre>
The length of the embedding_input is: 300000
</pre>

# 获取嵌入

在这里，我发送一个文档，用嵌入更新它，然后返回它。这是与 `MAX_WORKERS` 并行的发生的。

```python
async def request(document, semaphore):
    # Semaphore guard
    async with semaphore:
        result = await endpoint.async_client.post(json={"inputs": document['content'], 'truncate': True}, task="feature-extraction")
        result = np.array(json.loads(result.decode()))
        document['embedding'] = result[0]  # Assuming the API's output can be directly assigned
        return document

async def main(documents):
    # Semaphore to limit concurrent requests. Adjust the number as needed.
    semaphore = asyncio.BoundedSemaphore(MAX_WORKERS)

    # Creating a list of tasks
    tasks = [request(document, semaphore) for document in documents]
    
    # Using tqdm to show progress. It's been integrated into the async loop.
    for f in tqdm(asyncio.as_completed(tasks), total=len(documents)):
        await f
```

```python
>>> start = time.perf_counter()

>>> # Get embeddings
>>> await main(documents)

>>> # Make sure we got it all
>>> count = 0
>>> for document in documents:
...     if 'embedding' in document.keys() and len(document['embedding']) == 768:
...         count += 1
>>> print(f'Embeddings = {count} documents = {len(documents)}')

            
>>> # Print elapsed time
>>> elapsed_time = time.perf_counter() - start
>>> minutes, seconds = divmod(elapsed_time, 60)
>>> print(f"{int(minutes)} min {seconds:.2f} sec")
```

<pre>
Embeddings = 100 documents = 100
0 min 21.33 sec
</pre>

## 暂停推理端点

现在我们已经完成了嵌入，让我们暂停端点，以免产生任何额外费用，同时这也允许我们分析成本。

```python
>>> endpoint = endpoint.pause()

>>> print(f"Endpoint Status: {endpoint.status}")
```

<pre>
Endpoint Status: paused
</pre>

# 将更新后的数据集推送到 Hub
现在我们的文档已经更新了我们想要的嵌入。首先我们需要将其转换回 `Dataset` 格式。我发现从字典列表 -> `pd.DataFrame` -> `Dataset` 这条路径最为简单。


```python
df = pd.DataFrame(documents)
dd = DatasetDict({'train': Dataset.from_pandas(df)})
```

我默认将其上传到用户的账户（而不是上传到组织），但你可以通过在 `repo_id` 中设置用户或在配置中通过设置 `DATASET_OUT` 来自由推送到任何你想要的地方。


```python
dd.push_to_hub(repo_id=DATASET_OUT)
```

```python
>>> print(f'Dataset is at https://huggingface.co/datasets/{who["name"]}/{DATASET_OUT}')
```

<pre>
Dataset is at https://huggingface.co/datasets/derek-thomas/processed-subset-bestofredditorupdates
</pre>

# 分析使用情况
1. 前往下面打印的 `dashboard_url`
2. 点击使用与成本 (Usage & Cost) 标签
3. 查看你已经花费了多少

```python
>>> dashboard_url = f'https://ui.endpoints.huggingface.co/{namespace}/endpoints/{ENDPOINT_NAME}'
>>> print(dashboard_url)
```

<pre>
https://ui.endpoints.huggingface.co/HF-test-lab/endpoints/boru-jina-embeddings-demo-ie
</pre>

```python
>>> input("Hit enter to continue with the notebook")
```

<pre>
Hit enter to continue with the notebook
</pre>

我们可以看到只花了 `$0.04` !



# 删除端点
现在我们已经完成了，不再需要我们的端点了。我们可以以编程方式删除端点。

![Cost](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/automatic_embedding_tei_inference_endpoints.png)

```python
>>> endpoint = endpoint.delete()

>>> if not endpoint:
...     print('Endpoint deleted successfully')
>>> else:
...     print('Delete Endpoint in manually')
```

<pre>
Endpoint deleted successfully
</pre>

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/automatic_embedding_tei_inference_endpoints.md" />

### RAG 评估
https://huggingface.co/learn/cookbook/zh-CN/rag_evaluation.md

# RAG 评估
_作者 [Aymeric Roucher](https://huggingface.co/m-ric)_

本 notebook 演示了如何评估你的 RAG（Retrieval Augmented Generation），通过构建一个合成评估数据集并使用 LLM-as-a-judge 来计算你系统的准确性。

对于 RAG 系统的介绍，你可以查看[这个技术指南](rag_zephyr_langchain)!

RAG 系统很复杂: 这里有一个 RAG 流程图，我们用蓝色标注了系统增强的所有可能性：

<img src="https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/RAG_workflow.png" height="700">

实施上述任何改进都可能会带来巨大的性能提升；但如果无法监控对系统性能的影响，那么进行任何更改都是无用的！让我们看看如何评估我们的 RAG 系统。

### 评估RAG性能

由于有如此多的部分需要调整，这些部分对性能有很大影响，因此对 RAG 系统进行基准测试是至关重要的。

对于我们的评估流水线，我们将需要：
1. 一个带有问题-答案对的评估数据集（QA 对）
2. 一个评估器，用于计算我们的系统在上面的评估数据集上的准确性。

➡️ 结果发现，我们可以在整个过程中使用 LLMs 来帮助！
1. 评估数据集将由 LLM 🤖 合成生成，并且问题将由其他 LLM 🤖 过滤掉
2. 然后，[LLM-as-a-judge](https://huggingface.co/papers/2306.05685) 智能体 🤖 将在这个合成数据集上执行评估。


__让我们深入挖掘并开始构建我们的评估流水线！__ 首先，安装所需的模型依赖项。

```python
!pip install -q torch transformers transformers langchain sentence-transformers tqdm openpyxl openai pandas datasets langchain-community ragatouille
```

```python
%reload_ext autoreload
%autoreload 2
```

```python
from tqdm.auto import tqdm
import pandas as pd
from typing import Optional, List, Tuple
import json
import datasets

pd.set_option("display.max_colwidth", None)
```

```python
from huggingface_hub import notebook_login

notebook_login()
```

### 加载你的知识基础

```python
ds = datasets.load_dataset("m-ric/huggingface_doc", split="train")
```

# 1. 为评估构建合成数据集

我们首先构建一个问题和相关上下文的综合数据集。方法是先从我们的知识库中获取元素，并让 LLM 根据这些文档生成问题。

然后，我们设置其他 LLM 智能体作为生成问答对的质置过滤器：每个智能体将作为一个特定缺陷的过滤器。


### 1.1. 准备源数据文档

```python
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.docstore.document import Document as LangchainDocument

langchain_docs = [
    LangchainDocument(page_content=doc["text"], metadata={"source": doc["source"]})
    for doc in tqdm(ds)
]


text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=2000,
    chunk_overlap=200,
    add_start_index=True,
    separators=["\n\n", "\n", ".", " ", ""],
)

docs_processed = []
for doc in langchain_docs:
    docs_processed += text_splitter.split_documents([doc])
```

### 1.2. 为问题生成设置智能体

我们采用 [Mixtral](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) 作为问答对的生成，因为他在各个排行榜上表现极佳，比如 [Chatbot Arena](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard)。

```python
from huggingface_hub import InferenceClient


repo_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"

llm_client = InferenceClient(
    model=repo_id,
    timeout=120,
)


def call_llm(inference_client: InferenceClient, prompt: str):
    response = inference_client.post(
        json={
            "inputs": prompt,
            "parameters": {"max_new_tokens": 1000},
            "task": "text-generation",
        },
    )
    return json.loads(response.decode())[0]["generated_text"]


call_llm(llm_client, "This is a test context")
```

```python
QA_generation_prompt = """
Your task is to write a factoid question and an answer given a context.
Your factoid question should be answerable with a specific, concise piece of factual information from the context.
Your factoid question should be formulated in the same style as questions users could ask in a search engine.
This means that your factoid question MUST NOT mention something like "according to the passage" or "context".

Provide your answer as follows:

Output:::
Factoid question: (your factoid question)
Answer: (your answer to the factoid question)

Now here is the context.

Context: {context}\n
Output:::"""
```

现在让我们生成我们的问答对。

对于这个例子，我们只生成 10 个问答对，并从 Hub 加载其余的。

但是对于你的特定知识库，考虑到你想要获得至少约 100 个测试样本，并且考虑到我们稍后会用我们的批判智能体过滤掉大约一半的样本，你应该生成更多的样本，超过 200 个。


```python
import random

N_GENERATIONS = 10  # We intentionally generate only 10 QA couples here for cost and time considerations

print(f"Generating {N_GENERATIONS} QA couples...")

outputs = []
for sampled_context in tqdm(random.sample(docs_processed, N_GENERATIONS)):
    # Generate QA couple
    output_QA_couple = call_llm(
        llm_client, QA_generation_prompt.format(context=sampled_context.page_content)
    )
    try:
        question = output_QA_couple.split("Factoid question: ")[-1].split("Answer: ")[0]
        answer = output_QA_couple.split("Answer: ")[-1]
        assert len(answer) < 300, "Answer is too long"
        outputs.append(
            {
                "context": sampled_context.page_content,
                "question": question,
                "answer": answer,
                "source_doc": sampled_context.metadata["source"],
            }
        )
    except:
        continue
```

```python
display(pd.DataFrame(outputs).head(1))
```

### 1.3. 设置批判智能体

之前的智能体生成的问题可能存在许多缺陷：在验证这些问题之前，我们应该进行质量检查。

因此，我们构建了批判智能体，它们将根据以下几个标准对每个问题进行评分，这些标准在[这篇论文](https://huggingface.co/papers/2312.10003)中给出：
- **具体性（Groundedness）**：问题是否可以从给定的上下文中得到回答？
- **相关性（Relevance）**：问题对用户是否相关？例如，`"transformers 4.29.1 发布的日期是什么？"`对于 ML 用户来说并不相关。

我们注意到的一个最后的失败案例是，当一个函数是为生成问题的特定环境量身定做的，但本身难以理解，比如`"这个指南中使用的函数的名称是什么？"`。 
我们也为这个标准构建了一个批判智能体：
- **独立（Stand-alone）**：对于一个具有领域知识/互联网访问权限的人来说，问题在没有任何上下文的情况下是否可以理解？与此相反的是，对于从特定博客文章生成的问题比如"这篇文章中使用的函数是什么？"

我们系统地用所有这些智能体对函数进行评分，每当任何一个智能体的分数太低时，我们就从我们的评估数据集中删除这个问题。

💡 ___当要求智能体输出分数时，我们首先要求它们产生其理由。这将帮助我们验证分数，但最重要的是，要求它首先输出理由给了模型更多的 token 来思考和详细阐述答案，然后再将其总结成一个单一的分数 token。___

我们现在构建并运行这些批判智能体。

```python
question_groundedness_critique_prompt = """
You will be given a context and a question.
Your task is to provide a 'total rating' scoring how well one can answer the given question unambiguously with the given context.
Give your answer on a scale of 1 to 5, where 1 means that the question is not answerable at all given the context, and 5 means that the question is clearly and unambiguously answerable with the context.

Provide your answer as follows:

Answer:::
Evaluation: (your rationale for the rating, as a text)
Total rating: (your rating, as a number between 1 and 5)

You MUST provide values for 'Evaluation:' and 'Total rating:' in your answer.

Now here are the question and context.

Question: {question}\n
Context: {context}\n
Answer::: """

question_relevance_critique_prompt = """
You will be given a question.
Your task is to provide a 'total rating' representing how useful this question can be to machine learning developers building NLP applications with the Hugging Face ecosystem.
Give your answer on a scale of 1 to 5, where 1 means that the question is not useful at all, and 5 means that the question is extremely useful.

Provide your answer as follows:

Answer:::
Evaluation: (your rationale for the rating, as a text)
Total rating: (your rating, as a number between 1 and 5)

You MUST provide values for 'Evaluation:' and 'Total rating:' in your answer.

Now here is the question.

Question: {question}\n
Answer::: """

question_standalone_critique_prompt = """
You will be given a question.
Your task is to provide a 'total rating' representing how context-independant this question is.
Give your answer on a scale of 1 to 5, where 1 means that the question depends on additional information to be understood, and 5 means that the question makes sense by itself.
For instance, if the question refers to a particular setting, like 'in the context' or 'in the document', the rating must be 1.
The questions can contain obscure technical nouns or acronyms like Gradio, Hub, Hugging Face or Space and still be a 5: it must simply be clear to an operator with access to documentation what the question is about.

For instance, "What is the name of the checkpoint from which the ViT model is imported?" should receive a 1, since there is an implicit mention of a context, thus the question is not independant from the context.

Provide your answer as follows:

Answer:::
Evaluation: (your rationale for the rating, as a text)
Total rating: (your rating, as a number between 1 and 5)

You MUST provide values for 'Evaluation:' and 'Total rating:' in your answer.

Now here is the question.

Question: {question}\n
Answer::: """
```

```python
print("Generating critique for each QA couple...")
for output in tqdm(outputs):
    evaluations = {
        "groundedness": call_llm(
            llm_client,
            question_groundedness_critique_prompt.format(
                context=output["context"], question=output["question"]
            ),
        ),
        "relevance": call_llm(
            llm_client,
            question_relevance_critique_prompt.format(question=output["question"]),
        ),
        "standalone": call_llm(
            llm_client,
            question_standalone_critique_prompt.format(question=output["question"]),
        ),
    }
    try:
        for criterion, evaluation in evaluations.items():
            score, eval = (
                int(evaluation.split("Total rating: ")[-1].strip()),
                evaluation.split("Total rating: ")[-2].split("Evaluation: ")[1],
            )
            output.update(
                {
                    f"{criterion}_score": score,
                    f"{criterion}_eval": eval,
                }
            )
    except Exception as e:
        continue
```

现在让我们基于我们批判智能体的分数过滤掉不好的问题：

```python
>>> import pandas as pd

>>> pd.set_option("display.max_colwidth", None)

>>> generated_questions = pd.DataFrame.from_dict(outputs)

>>> print("Evaluation dataset before filtering:")
>>> display(
...     generated_questions[
...         [
...             "question",
...             "answer",
...             "groundedness_score",
...             "relevance_score",
...             "standalone_score",
...         ]
...     ]
... )
>>> generated_questions = generated_questions.loc[
...     (generated_questions["groundedness_score"] >= 4)
...     & (generated_questions["relevance_score"] >= 4)
...     & (generated_questions["standalone_score"] >= 4)
... ]
>>> print("============================================")
>>> print("Final evaluation dataset:")
>>> display(
...     generated_questions[
...         [
...             "question",
...             "answer",
...             "groundedness_score",
...             "relevance_score",
...             "standalone_score",
...         ]
...     ]
... )

>>> eval_dataset = datasets.Dataset.from_pandas(
...     generated_questions, split="train", preserve_index=False
... )
```

<pre>
Evaluation dataset before filtering:
</pre>

现在我们合成评估数据集已完成！我们可以在这个评估数据集上评估不同的 RAG 系统。

我们在这里只生成了少数几个问答对，以减少时间和成本。下面，让我们通过加载一个预先生成的数据集来进行下一部分：

```python
eval_dataset = datasets.load_dataset("m-ric/huggingface_doc_qa_eval", split="train")
```

# 2. 构建我们的 RAG 系统

### 2.1. 预处理文档来构建我们的向量数据库

- 在这一部分，__我们将知识库中的文档分割成更小的片段__：这些将是被检索器选取的片段，然后被阅读器 LLM 作为支持其答案的元素。
- 目标是构建语义上相关的片段：不要太小，以免不足以支持答案，也不要太大，以免稀释单个内容。

文本分割有许多选项：
- 每隔 `n` 个单词/字符分割，但这有可能割裂段落甚至句子
- 在 `n` 个单词/字符后分割，但只在句子边界处
- **递归分割** 尝试通过树状处理文档来保留更多文档结构，首先在最大单元（章节）上分割，然后递归地在更小单元（段落，句子）上分割。
要了解更多关于分块的信息，我建议你阅读由 Greg Kamradt 编写的[不错的教程](https://github.com/FullStackRetrieval-com/RetrievalTutorials/blob/main/5_Levels_Of_Text_Splitting.ipynb) 。

[这个 space](https://huggingface.co/spaces/m-ric/chunk_visualizer) 让你可视化不同的分割选项是如何影响你得到的片段的流程。

> 在以下内容中，我们使用 Langchain 的 `RecursiveCharacterTextSplitter`。
💡 _为了在我们的文本分割器中测量片段长度，我们的长度函数将不是字符的数量，而是 token 化文本中的 token 数量：实际上，对于后续处理 token 的嵌入器来说，以 token 为单位测量长度更为相关，并且在经验上表现更好._


```python
from langchain.docstore.document import Document as LangchainDocument

RAW_KNOWLEDGE_BASE = [
    LangchainDocument(page_content=doc["text"], metadata={"source": doc["source"]})
    for doc in tqdm(ds)
]
```

```python
from langchain.text_splitter import RecursiveCharacterTextSplitter
from transformers import AutoTokenizer


def split_documents(
    chunk_size: int,
    knowledge_base: List[LangchainDocument],
    tokenizer_name: str,
) -> List[LangchainDocument]:
    """
    Split documents into chunks of size `chunk_size` characters and return a list of documents.
    """
    text_splitter = RecursiveCharacterTextSplitter.from_huggingface_tokenizer(
        AutoTokenizer.from_pretrained(tokenizer_name),
        chunk_size=chunk_size,
        chunk_overlap=int(chunk_size / 10),
        add_start_index=True,
        strip_whitespace=True,
        separators=["\n\n", "\n", ".", " ", ""],
    )

    docs_processed = []
    for doc in knowledge_base:
        docs_processed += text_splitter.split_documents([doc])

    # Remove duplicates
    unique_texts = {}
    docs_processed_unique = []
    for doc in docs_processed:
        if doc.page_content not in unique_texts:
            unique_texts[doc.page_content] = True
            docs_processed_unique.append(doc)

    return docs_processed_unique
```

### 2.2.  检索器 - 嵌入 🗂️

__检索器的作用类似于内部搜索引擎__：给定用户查询，它从你的知识库中返回最相关的文档。

> 对于知识库，我们使用 Langchain 向量数据库，因为它提供了一个方便的 [FAISS](https://github.com/facebookresearch/faiss) 索引，并允许我们在整个处理过程中保留文档元数据。

🛠️ __包含可选项：__

- 调整分块方法：
    - 片段(chunks)的大小
    - 方法：在不同的分隔符上分割，使用[语义分块](https://python.langchain.com/docs/modules/data_connection/document_transformers/semantic-chunker)...
- 更改嵌入模型

```python
from langchain.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores.utils import DistanceStrategy
import os


def load_embeddings(
    langchain_docs: List[LangchainDocument],
    chunk_size: int,
    embedding_model_name: Optional[str] = "thenlper/gte-small",
) -> FAISS:
    """
    Creates a FAISS index from the given embedding model and documents. Loads the index directly if it already exists.

    Args:
        langchain_docs: list of documents
        chunk_size: size of the chunks to split the documents into
        embedding_model_name: name of the embedding model to use

    Returns:
        FAISS index
    """
    # load embedding_model
    embedding_model = HuggingFaceEmbeddings(
        model_name=embedding_model_name,
        multi_process=True,
        model_kwargs={"device": "cuda"},
        encode_kwargs={
            "normalize_embeddings": True
        },  # set True to compute cosine similarity
    )

    # Check if embeddings already exist on disk
    index_name = (
        f"index_chunk:{chunk_size}_embeddings:{embedding_model_name.replace('/', '~')}"
    )
    index_folder_path = f"./data/indexes/{index_name}/"
    if os.path.isdir(index_folder_path):
        return FAISS.load_local(
            index_folder_path,
            embedding_model,
            distance_strategy=DistanceStrategy.COSINE,
        )

    else:
        print("Index not found, generating it...")
        docs_processed = split_documents(
            chunk_size,
            langchain_docs,
            embedding_model_name,
        )
        knowledge_index = FAISS.from_documents(
            docs_processed, embedding_model, distance_strategy=DistanceStrategy.COSINE
        )
        knowledge_index.save_local(index_folder_path)
        return knowledge_index
```

### 2.3. 阅读器 - LLM 💬

在这一部分，__LLM 阅读器读取检索到的文档以形成其答案。__

🛠️ 为了改善结果，我们尝试了以下选项：
- 切换重排序开启或关闭的状态
- 更改阅读器模型

```python
RAG_PROMPT_TEMPLATE = """
<|system|>
Using the information contained in the context,
give a comprehensive answer to the question.
Respond only to the question asked, response should be concise and relevant to the question.
Provide the number of the source document when relevant.
If the answer cannot be deduced from the context, do not give an answer.</s>
<|user|>
Context:
{context}
---
Now here is the question you need to answer.

Question: {question}
</s>
<|assistant|>
"""
```

```python
from langchain_community.llms import HuggingFaceHub

repo_id = "HuggingFaceH4/zephyr-7b-beta"
READER_MODEL_NAME = "zephyr-7b-beta"
HF_API_TOKEN = ""

READER_LLM = HuggingFaceHub(
    repo_id=repo_id,
    task="text-generation",
    huggingfacehub_api_token=HF_API_TOKEN,
    model_kwargs={
        "max_new_tokens": 512,
        "top_k": 30,
        "temperature": 0.1,
        "repetition_penalty": 1.03,
    },
)
```

```python
from ragatouille import RAGPretrainedModel
from langchain_core.vectorstores import VectorStore
from langchain_core.language_models.llms import LLM


def answer_with_rag(
    question: str,
    llm: LLM,
    knowledge_index: VectorStore,
    reranker: Optional[RAGPretrainedModel] = None,
    num_retrieved_docs: int = 30,
    num_docs_final: int = 7,
) -> Tuple[str, List[LangchainDocument]]:
    """Answer a question using RAG with the given knowledge index."""
    # Gather documents with retriever
    relevant_docs = knowledge_index.similarity_search(
        query=question, k=num_retrieved_docs
    )
    relevant_docs = [doc.page_content for doc in relevant_docs]  # keep only the text

    # Optionally rerank results
    if reranker:
        relevant_docs = reranker.rerank(question, relevant_docs, k=num_docs_final)
        relevant_docs = [doc["content"] for doc in relevant_docs]

    relevant_docs = relevant_docs[:num_docs_final]

    # Build the final prompt
    context = "\nExtracted documents:\n"
    context += "".join(
        [f"Document {str(i)}:::\n" + doc for i, doc in enumerate(relevant_docs)]
    )

    final_prompt = RAG_PROMPT_TEMPLATE.format(question=question, context=context)

    # Redact an answer
    answer = llm(final_prompt)

    return answer, relevant_docs
```

# 3. 对 RAG 系统进行基准测试

RAG 系统和评估数据集现在准备好了。最后一步是在这个评估数据集上判断 RAG 系统的输出。
为此，__我们设置了一个裁判智能体__。 ⚖️🤖

在[不同的 RAG 评估指标](https://docs.ragas.io/en/latest/concepts/metrics/index.html)中，我们选择只关注忠实度，因为这是衡量我们系统性能的最佳的端到端指标。

> 我们使用 GPT4 作为评判者，因为它在实际应用中表现良好，但你也可以尝试其他模型，例如 [kaist-ai/prometheus-13b-v1.0](https://huggingface.co/kaist-ai/prometheus-13b-v1.0) 或 [BAAI/JudgeLM-33B-v1.0](https://huggingface.co/BAAI/JudgeLM-33B-v1.0)。

💡 _在评估提示中，我们给出了每个指标的详细描述，采用 1-5 分的评分刻度，正如 [Prometheus 的提示模板](https://huggingface.co/kaist-ai/prometheus-13b-v1.0) 所做的那样：这有助于模型精确地确定其指标。如果你给评判 LLM 一个模糊的评分刻度，那么不同示例之间的输出将不够一致。_

💡 _再次提示 LLM 在给出最终评分之前先输出其理由，这样它就有更多的 token 来帮助它正式化和详细阐述评判。_

```python
from langchain_core.language_models import BaseChatModel

def run_rag_tests(
    eval_dataset: datasets.Dataset,
    llm,
    knowledge_index: VectorStore,
    output_file: str,
    reranker: Optional[RAGPretrainedModel] = None,
    verbose: Optional[bool] = True,
    test_settings: Optional[str] = None,  # To document the test settings used
):
    """Runs RAG tests on the given dataset and saves the results to the given output file."""
    try:  # load previous generations if they exist
        with open(output_file, "r") as f:
            outputs = json.load(f)
    except:
        outputs = []

    for example in tqdm(eval_dataset):
        question = example["question"]
        if question in [output["question"] for output in outputs]:
            continue

        answer, relevant_docs = answer_with_rag(
            question, llm, knowledge_index, reranker=reranker
        )
        if verbose:
            print("=======================================================")
            print(f"Question: {question}")
            print(f"Answer: {answer}")
            print(f'True answer: {example["answer"]}')
        result = {
            "question": question,
            "true_answer": example["answer"],
            "source_doc": example["source_doc"],
            "generated_answer": answer,
            "retrieved_docs": [doc for doc in relevant_docs],
        }
        if test_settings:
            result["test_settings"] = test_settings
        outputs.append(result)

        with open(output_file, "w") as f:
            json.dump(outputs, f)
```

```python
EVALUATION_PROMPT = """###Task Description:
An instruction (might include an Input inside it), a response to evaluate, a reference answer that gets a score of 5, and a score rubric representing a evaluation criteria are given.
1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general.
2. After writing a feedback, write a score that is an integer between 1 and 5. You should refer to the score rubric.
3. The output format should look as follows: \"Feedback: {{write a feedback for criteria}} [RESULT] {{an integer number between 1 and 5}}\"
4. Please do not generate any other opening, closing, and explanations. Be sure to include [RESULT] in your output.

###The instruction to evaluate:
{instruction}

###Response to evaluate:
{response}

###Reference Answer (Score 5):
{reference_answer}

###Score Rubrics:
[Is the response correct, accurate, and factual based on the reference answer?]
Score 1: The response is completely incorrect, inaccurate, and/or not factual.
Score 2: The response is mostly incorrect, inaccurate, and/or not factual.
Score 3: The response is somewhat correct, accurate, and/or factual.
Score 4: The response is mostly correct, accurate, and factual.
Score 5: The response is completely correct, accurate, and factual.

###Feedback:"""

from langchain.prompts.chat import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
)
from langchain.schema import SystemMessage


evaluation_prompt_template = ChatPromptTemplate.from_messages(
    [
        SystemMessage(content="You are a fair evaluator language model."),
        HumanMessagePromptTemplate.from_template(EVALUATION_PROMPT),
    ]
)
```

```python
from langchain.chat_models import ChatOpenAI

OPENAI_API_KEY = ""

eval_chat_model = ChatOpenAI(model="gpt-4-1106-preview", temperature=0, openai_api_key=OPENAI_API_KEY)
evaluator_name = "GPT4"


def evaluate_answers(
    answer_path: str,
    eval_chat_model,
    evaluator_name: str,
    evaluation_prompt_template: ChatPromptTemplate,
) -> None:
    """Evaluates generated answers. Modifies the given answer file in place for better checkpointing."""
    answers = []
    if os.path.isfile(answer_path):  # load previous generations if they exist
        answers = json.load(open(answer_path, "r"))

    for experiment in tqdm(answers):
        if f"eval_score_{evaluator_name}" in experiment:
            continue

        eval_prompt = evaluation_prompt_template.format_messages(
            instruction=experiment["question"],
            response=experiment["generated_answer"],
            reference_answer=experiment["true_answer"],
        )
        eval_result = eval_chat_model.invoke(eval_prompt)
        feedback, score = [
            item.strip() for item in eval_result.content.split("[RESULT]")
        ]
        experiment[f"eval_score_{evaluator_name}"] = score
        experiment[f"eval_feedback_{evaluator_name}"] = feedback

        with open(answer_path, "w") as f:
            json.dump(answers, f)
```

🚀 让我们运行一下测试和评估一下答案!👇

```python
if not os.path.exists("./output"):
    os.mkdir("./output")

for chunk_size in [200]:  # Add other chunk sizes (in tokens) as needed
    for embeddings in ["thenlper/gte-small"]:  # Add other embeddings as needed
        for rerank in [True, False]:
            settings_name = f"chunk:{chunk_size}_embeddings:{embeddings.replace('/', '~')}_rerank:{rerank}_reader-model:{READER_MODEL_NAME}"
            output_file_name = f"./output/rag_{settings_name}.json"

            print(f"Running evaluation for {settings_name}:")

            print("Loading knowledge base embeddings...")
            knowledge_index = load_embeddings(
                RAW_KNOWLEDGE_BASE,
                chunk_size=chunk_size,
                embedding_model_name=embeddings,
            )

            print("Running RAG...")
            reranker = (
                RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
                if rerank
                else None
            )
            run_rag_tests(
                eval_dataset=eval_dataset,
                llm=READER_LLM,
                knowledge_index=knowledge_index,
                output_file=output_file_name,
                reranker=reranker,
                verbose=False,
                test_settings=settings_name,
            )

            print("Running evaluation...")
            evaluate_answers(
                output_file_name,
                eval_chat_model,
                evaluator_name,
                evaluation_prompt_template,
            )
```

### 检查结果

```python
import glob

outputs = []
for file in glob.glob("./output/*.json"):
    output = pd.DataFrame(json.load(open(file, "r")))
    output["settings"] = file
    outputs.append(output)
result = pd.concat(outputs)
```

```python
result["eval_score_GPT4"] = result["eval_score_GPT4"].apply(
    lambda x: int(x) if isinstance(x, str) else 1
)
result["eval_score_GPT4"] = (result["eval_score_GPT4"] - 1) / 4
```

```python
average_scores = result.groupby("settings")["eval_score_GPT4"].mean()
average_scores.sort_values()
```

## 结果示例

让我们加载通过调整这个 notebook 中可用的不同选项所获得的结果。关于这些选项为何有效或无效的更多细节，请参阅 [高级 RAG](advanced_rag) 的 notebook。

正如在下面的图表中所看到的，一些调整并没有带来任何改善，而有些则带来了巨大的性能提升。

➡️ ___所以没有单一的好方法：在调整你的 RAG 系统时，应该尝试几种不同的方向。___


```python
import plotly.express as px

scores = datasets.load_dataset("m-ric/rag_scores_cookbook", split="train")
scores = pd.Series(scores["score"], index=scores["settings"])
```

```python
fig = px.bar(
    scores,
    color=scores,
    labels={
        "value": "Accuracy",
        "settings": "Configuration",
    },
    color_continuous_scale="bluered",
)
fig.update_layout(
    width=1000,
    height=600,
    barmode="group",
    yaxis_range=[0, 100],
    title="<b>Accuracy of different RAG configurations</b>",
    xaxis_title="RAG settings",
    font=dict(size=15),
)
fig.layout.yaxis.ticksuffix = "%"
fig.update_coloraxes(showscale=False)
fig.update_traces(texttemplate="%{y:.1f}", textposition="outside")
fig.show()
```

<img src="https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/RAG_settings_accuracy.png" height="500" width="800">

如上图所示，这些调整对性能的影响各不相同。尤其是调整片段大小，既简单又非常有影响力。

但这只是针对我们的情况：你的结果可能大不相同：现在你已经有了一个可靠的评估流水线，可以开始探索其他选项了！🗺️

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/rag_evaluation.md" />

### 使用 PEFT 进行提示微调
https://huggingface.co/learn/cookbook/zh-CN/prompt_tuning_peft.md

# 使用 PEFT 进行提示微调

_作者: [Pere Martra](https://github.com/peremartra)_


在这个 notebook 中，我们将介绍如何使用 PEFT 库对预训练模型进行提示微调。

要查看与 PEFT 兼容的完整模型列表，请参考他们的[文档](https://huggingface.co/docs/peft/main/en/index#supported-methods)。

可以使用 PEFT 进行训练的模型示例包括 Bloom、Llama、GPT-J、GPT-2、BERT 等等。Hugging Face 正在努力将更多模型添加到库中。

## 提示微调简要介绍

这是一种用于模型的附加微调技术。这意味着我们不会修改原始模型的任何权重。你可能会想，那么我们将如何进行微调呢？好吧，我们将训练添加到模型中的额外层。这就是为什么它被称为附加技术。

考虑到它是一种附加技术，并且它的名字是提示调整，似乎很明显我们将要添加和训练的层与提示有关。

![Prompt_Tuning_Diagram](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/Martra_Figure_5_Prompt_Tuning.jpg)

我们通过使模型能够用其获取的知识增强提示的一部分来创建一种超提示。然而，提示的这部分不能翻译成自然语言。**这就好像我们已经掌握了用嵌入表达自己并生成高效提示的能力。**

在每次训练周期中，唯一可以修改以最小化损失函数的权重是集成到提示中的权重。

这种技术的主要结果是，要训练的参数数量确实很少。然而，我们遇到了第二个，也许更重要的结果，即**由于我们不修改预训练模型的权重，它不会改变其行为或忘记它以前学到的任何信息。**

训练更快，更具成本效益。此外，我们可以训练各种模型，在推理时，我们只需要加载一个基础模型以及新的较小的训练模型，因为原始模型的权重没有被修改。

## 我们将在 notebook 中做什么？

我们将使用两个数据集训练两个不同的模型，每个数据集只使用 Bloom 家族的一个预训练模型。一个模型将使用提示数据集进行训练，而另一个模型将使用激励句子数据集进行训练。我们将比较两个模型在训练前后对同一问题的结果。

此外，我们还将探讨如何只加载基础模型的一个副本到内存中，同时加载两个模型。


## 加载 PEFT 库
这个库包含了各种微调技术的 Hugging Face 实现，包括提示调整。

```python
!pip install -q peft==0.8.2
```

```python
!pip install -q datasets==2.14.5
```

从 transformers 库中，我们导入必要的类来实例化模型和分词器。

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
```

### 加载模型和分词器。

Bloom 是使用 PEFT 库进行提示调整训练的可用模型中最小最智能的模型之一。你可以从 Bloom 家族中选择任何模型，我鼓励你至少尝试其中两个以观察它们之间的差异。

我选择最小的模型以最小化训练时间并避免在 Colab 中出现内存问题。


```python
model_name = "bigscience/bloomz-560m"
#model_name="bigscience/bloom-1b1"
NUM_VIRTUAL_TOKENS = 4
NUM_EPOCHS = 6
```

```python
tokenizer = AutoTokenizer.from_pretrained(model_name)
foundational_model = AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True
)
```

## 使用预训练的 bloom 模型进行推理
如果你想要实现更多样化和原创的生成，取消注释下面的 *model.generate* 中的参数：temperature、top_p 和 do_sample。

在默认配置下，模型的响应在多次调用中保持一致。

```python
#this function returns the outputs from the model received, and inputs.
def get_outputs(model, inputs, max_new_tokens=100):
    outputs = model.generate(
        input_ids=inputs["input_ids"],
        attention_mask=inputs["attention_mask"],
        max_new_tokens=max_new_tokens,
        #temperature=0.2,
        #top_p=0.95,
        #do_sample=True,
        repetition_penalty=1.5, #Avoid repetition.
        early_stopping=True, #The model can stop before reach the max_length
        eos_token_id=tokenizer.eos_token_id
    )
    return outputs
```

由于我们希望有两个不同的训练模型，我将创建两个不同的提示。

第一个模型将使用包含提示的数据集进行训练，第二个模型将使用激励句子的数据集进行训练。

第一个模型将收到提示 "我希望你扮演一个励志教练。"，第二个模型将收到提示 "有两件对你来说很重要的事情："

但首先，我要收集一些未经微调的模型的结果。

```python
>>> input_prompt = tokenizer("I want you to act as a motivational coach. ", return_tensors="pt")
>>> foundational_outputs_prompt = get_outputs(foundational_model, input_prompt, max_new_tokens=50)

>>> print(tokenizer.batch_decode(foundational_outputs_prompt, skip_special_tokens=True))
```

<pre>
["I want you to act as a motivational coach.  Don't be afraid of being challenged."]
</pre>

```python
>>> input_sentences = tokenizer("There are two nice things that should matter to you:", return_tensors="pt")
>>> foundational_outputs_sentence = get_outputs(foundational_model, input_sentences, max_new_tokens=50)

>>> print(tokenizer.batch_decode(foundational_outputs_sentence, skip_special_tokens=True))
```

<pre>
['There are two nice things that should matter to you: the price and quality of your product.']
</pre>

两个答案或多或少都是正确的。任何 Bloom 模型都是预先训练的，能够准确和合理地生成句子。让我们看看，在训练之后，响应是否相等或者生成得更加准确。

## 准备数据集
使用的数据集包括：
* https://huggingface.co/datasets/fka/awesome-chatgpt-prompts
* https://huggingface.co/datasets/Abirate/english_quotes



```python
import os
#os.environ["TOKENIZERS_PARALLELISM"] = "false"
```

```python
from datasets import load_dataset

dataset_prompt = "fka/awesome-chatgpt-prompts"

#Create the Dataset to create prompts.
data_prompt = load_dataset(dataset_prompt)
data_prompt = data_prompt.map(lambda samples: tokenizer(samples["prompt"]), batched=True)
train_sample_prompt = data_prompt["train"].select(range(50))
```

```python
display(train_sample_prompt)
```

```python
>>> print(train_sample_prompt[:1])
```

<pre>
{'act': ['Linux Terminal'], 'prompt': ['I want you to act as a linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. do not write explanations. do not type commands unless I instruct you to do so. when i need to tell you something in english, i will do so by putting text inside curly brackets {like this}. my first command is pwd'], 'input_ids': [[44, 4026, 1152, 427, 1769, 661, 267, 104105, 28434, 17, 473, 2152, 4105, 49123, 530, 1152, 2152, 57502, 1002, 3595, 368, 28434, 3403, 6460, 17, 473, 4026, 1152, 427, 3804, 57502, 1002, 368, 28434, 10014, 14652, 2592, 19826, 4400, 10973, 15, 530, 16915, 4384, 17, 727, 1130, 11602, 184637, 17, 727, 1130, 4105, 49123, 35262, 473, 32247, 1152, 427, 727, 1427, 17, 3262, 707, 3423, 427, 13485, 1152, 7747, 361, 170205, 15, 707, 2152, 727, 1427, 1331, 55385, 5484, 14652, 6291, 999, 117805, 731, 29726, 1119, 96, 17, 2670, 3968, 9361, 632, 269, 42512]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
</pre>

```python
dataset_sentences = load_dataset("Abirate/english_quotes")

data_sentences = dataset_sentences.map(lambda samples: tokenizer(samples["quote"]), batched=True)
train_sample_sentences = data_sentences["train"].select(range(25))
train_sample_sentences = train_sample_sentences.remove_columns(['author', 'tags'])
```

```python
display(train_sample_sentences)
```

## 微调

### PEFT 配置

API 文档：
https://huggingface.co/docs/peft/main/en/package_reference/tuners#peft.PromptTuningConfig

我们可以对两个要训练的模型使用相同的配置。


```python
from peft import  get_peft_model, PromptTuningConfig, TaskType, PromptTuningInit

generation_config = PromptTuningConfig(
    task_type=TaskType.CAUSAL_LM, #This type indicates the model will generate text.
    prompt_tuning_init=PromptTuningInit.RANDOM,  #The added virtual tokens are initializad with random numbers
    num_virtual_tokens=NUM_VIRTUAL_TOKENS, #Number of virtual tokens to be added and trained.
    tokenizer_name_or_path=model_name #The pre-trained model.
)
```

### 创建两个提示调整模型。
我们将使用相同的预训练模型和相同的配置来创建两个相同的提示调整模型。

```python
>>> peft_model_prompt = get_peft_model(foundational_model, generation_config)
>>> print(peft_model_prompt.print_trainable_parameters())
```

<pre>
trainable params: 4,096 || all params: 559,218,688 || trainable%: 0.0007324504863471229
None
</pre>

```python
>>> peft_model_sentences = get_peft_model(foundational_model, generation_config)
>>> print(peft_model_sentences.print_trainable_parameters())
```

<pre>
trainable params: 4,096 || all params: 559,218,688 || trainable%: 0.0007324504863471229
None
</pre>

**太神奇了：你看到可训练参数的减少了吗？我们将要训练可用参数的 0.001%。**

现在我们要创建训练参数，并且在这两次训练中我们将使用相同的配置。

```python
from transformers import TrainingArguments
def create_training_arguments(path, learning_rate=0.0035, epochs=6):
    training_args = TrainingArguments(
        output_dir=path, # Where the model predictions and checkpoints will be written
        use_cpu=True, # This is necessary for CPU clusters.
        auto_find_batch_size=True, # Find a suitable batch size that will fit into memory automatically
        learning_rate= learning_rate, # Higher learning rate than full Fine-Tuning
        num_train_epochs=epochs
    )
    return training_args
```

```python
import os

working_dir = "./"

#Is best to store the models in separate folders.
#Create the name of the directories where to store the models.
output_directory_prompt =  os.path.join(working_dir, "peft_outputs_prompt")
output_directory_sentences = os.path.join(working_dir, "peft_outputs_sentences")

#Just creating the directoris if not exist.
if not os.path.exists(working_dir):
    os.mkdir(working_dir)
if not os.path.exists(output_directory_prompt):
    os.mkdir(output_directory_prompt)
if not os.path.exists(output_directory_sentences):
    os.mkdir(output_directory_sentences)
```

在创建 TrainingArguments 时，我们需要指明包含模型的目录。

```python
training_args_prompt = create_training_arguments(output_directory_prompt, 0.003, NUM_EPOCHS)
training_args_sentences = create_training_arguments(output_directory_sentences, 0.003, NUM_EPOCHS)
```

## 训练

我们将为每个要训练的模型创建一个 trainer 对象。

```python
from transformers import Trainer, DataCollatorForLanguageModeling
def create_trainer(model, training_args, train_dataset):
    trainer = Trainer(
        model=model, # We pass in the PEFT version of the foundation model, bloomz-560M
        args=training_args, #The args for the training.
        train_dataset=train_dataset, #The dataset used to tyrain the model.
        data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False) # mlm=False indicates not to use masked language modeling
    )
    return trainer
```

```python
#Training first model.
trainer_prompt = create_trainer(peft_model_prompt, training_args_prompt, train_sample_prompt)
trainer_prompt.train()
```

```python
#Training second model.
trainer_sentences = create_trainer(peft_model_sentences, training_args_sentences, train_sample_sentences)
trainer_sentences.train()
```

在不到 10 分钟的时间内（在 M1 Pro上的 CPU 时间），我们使用同一个基础模型训练了两个不同任务的模型。

## 保存模型
我们将要保存模型。只要我们有创建它们的预训练模型在内存中，这些模型就可以使用了。

```python
trainer_prompt.model.save_pretrained(output_directory_prompt)
trainer_sentences.model.save_pretrained(output_directory_sentences)
```

## 推理
你可以从之前保存的路径加载模型，并根据我们的输入要求模型生成文本！

```python
from peft import PeftModel

loaded_model_prompt = PeftModel.from_pretrained(foundational_model,
                                         output_directory_prompt,
                                         #device_map='auto',
                                         is_trainable=False)
```

```python
>>> loaded_model_prompt_outputs = get_outputs(loaded_model_prompt, input_prompt)
>>> print(tokenizer.batch_decode(loaded_model_prompt_outputs, skip_special_tokens=True))
```

<pre>
['I want you to act as a motivational coach.  You will be helping students learn how they can improve their performance in the classroom and at school.']
</pre>

如果我们比较两个答案，有些东西改变了。
* ***预训练模型：*** *我希望你扮演一个激励教练。不要害怕被挑战。*
* ***微调模型：*** *我希望你扮演一个激励教练。如果你感到焦虑，你可以使用这个方法。*

我们必须记住，我们只训练了模型几分钟，但它们已经足够让我们得到更接近我们想要的结果的响应。


```python
loaded_model_prompt.load_adapter(output_directory_sentences, adapter_name="quotes")
loaded_model_prompt.set_adapter("quotes")
```

```python
>>> loaded_model_sentences_outputs = get_outputs(loaded_model_prompt, input_sentences)
>>> print(tokenizer.batch_decode(loaded_model_sentences_outputs, skip_special_tokens=True))
```

<pre>
['There are two nice things that should matter to you: the weather and your health.']
</pre>

对于第二个模型，我们得到了类似的结果。
* **预训练模型：** *有两件对你来说很重要的事情：你的产品的价格和质量。*
* **微调模型：** *有两件对你来说很重要的事情：天气和你的健康。*


# 结论
提示微调是一种惊人的技术，可以节省我们数小时的训练时间和大量的金钱。在这个 notebook 中，我们只用了几分钟就训练了两个模型，并且我们可以将两个模型都保存在内存中，为不同的客户提供服务。

如果你想要尝试不同的组合和模型，这个 notebook 已经准备好使用 Bloom 家族中的另一个模型。

你可以更改训练的轮数、虚拟 token 的数量和第三个单元格中的模型。然而，有许多配置需要更改。如果你正在寻找一个很好的练习，你可以用固定值替换虚拟 token 的随机初始化。

*微调模型的响应可能在每次我们训练它们时都会有所不同。我粘贴了我的一次训练的结果，但实际结果可能会有所不同。*


<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/prompt_tuning_peft.md" />

### 企业级 Hub 操作指南
https://huggingface.co/learn/cookbook/zh-CN/enterprise_cookbook_overview.md

# 企业级 Hub 操作指南

企业级 Hub 操作指南专为高级用户和企业设计，旨在帮助他们超越 Hugging Face Hub 的标准免费功能，将机器学习更深入地集成到生产工作流程中。本指南通过一系列可复制粘贴代码的 Jupyter Notebook 来帮助你开始使用 Hub 的高级功能。

<Youtube id="CPQGBn-yXJQ"/>


## 在 HF Spaces 中进行交互式开发
使用 JupyterLab Spaces，你可以像在 Google Colab 中一样启动个人 Jupyter Notebook，也可以选择更多可靠的 CPU 和 GPU（例如 H100 或 4xA10G），并可以随时切换。此外，通过激活 Spaces 开发模式，你还可以从本地 IDE（如 VSCode）使用这些云端硬件。阅读此指南以了解如何启动 GPU 并通过本地 IDE 连接到它。

更多详情，请参阅 [JupyterLab Spaces](https://huggingface.co/docs/hub/spaces-sdks-docker-jupyter) 和 [开发模式](https://huggingface.co/dev-mode-explorers) 文档。


## 推理 API（无服务器）
使用我们的无服务器推理 API，你可以通过简单的 API 调用测试各种开源模型（例如生成式 LLM、高效嵌入模型或图像生成器）。无服务器推理 API 有速率限制，主要用于初始测试或低容量使用。阅读此指南以了解如何查询无服务器推理 API。

更多详情，请参阅 [无服务器 API](https://huggingface.co/docs/api-inference/index) 文档。


## 推理端点（专用）

使用我们的专用推理端点，你可以轻松地在各种硬件上部署任何模型，本质上是通过几次点击就创建了你的个人生产就绪 API。阅读此指南以了解如何创建和配置你自己的专用端点。

更多详情，请参阅 [专用端点](https://huggingface.co/docs/inference-endpoints/index) 文档。 


## 使用 Argilla Spaces 进行数据标注

无论你是在进行 LLM 的零样本测试还是训练自己的模型，在机器学习之旅开始时，创建优质的测试或训练数据可能是最有价值的投资。Argilla 是一个免费、开源的数据标注工具，使你能够为文本、图像或音频任务创建高质量数据。阅读此指南以了解如何在浏览器中创建数据标注工作流程（单独或在更大的团队中）。

更多详情，请参阅 [Argilla](https://docs.argilla.io/en/latest/) 文档和 [HF Argilla Spaces](https://huggingface.co/docs/hub/spaces-sdks-docker-argilla) 集成。


## AutoTrain Spaces（即将推出）
使用 AutoTrain Spaces，你可以在简单的界面中训练自己的机器学习模型，无需任何代码。阅读此指南以了解如何在 Hub 上的 AutoTrain Space 中使用各种 GPU 微调你自己的 LLM。 

更多信息，请参阅 [AutoTrain](https://huggingface.co/docs/autotrain/index) 文档。


## 使用 Spaces 和 Gradio 创建私有演示

视觉演示比言语更有说服力。如果你想说服利益相关者认可机器学习最小可行产品（MVP），演示尤其重要。阅读此指南以了解如何使用 Gradio 在 Spaces 上创建私有机器学习演示。

更多信息，请参阅 [Spaces](https://huggingface.co/docs/hub/spaces-overview) 和 [Gradio Spaces](https://huggingface.co/docs/hub/spaces-sdks-gradio) 文档。


## Hub 上的高级协作（即将推出）

随着你的团队和用例的增长，管理数据集、模型和团队成员变得更加复杂。阅读此指南以了解高级协作功能，如特定资源组的私有数据集、基于 git 的版本控制以及模型卡片中的 YAML 标签。 

更多信息，请查看 [Hub](https://huggingface.co/docs/hub/index) 和 [Hub Python 库](https://huggingface.co/docs/huggingface_hub/index) 文档。

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/enterprise_cookbook_overview.md" />

### 使用 LLM 作为评判者🧑‍⚖️进行自动化和多方面的评估
https://huggingface.co/learn/cookbook/zh-CN/llm_judge.md

# 使用 LLM 作为评判者🧑‍⚖️进行自动化和多方面的评估
_作者: [Aymeric Roucher](https://huggingface.co/m-ric)_

评估大型语言模型（LLMs）通常是一项困难的任务：由于他们能力广泛，给它们分配的任务时通常应该根据非常泛且松散的要求来判断。例如，AI 对问题的回答可能是：
- 不基于上下文
- 重复、重复、重复
- 语法错误
- 过于冗长，用词过多，导致话语或书面内容过于详细和拖沓
- 不连贯
- ...

这些标准的列表还有很多。即使我们有一个有限的列表，每一个标准的衡量都是困难的："制定一个基于规则的程序来评估输出是非常具有挑战性的。传统的评估指标，基于输出和参考答案之间的相似性（例如，ROUGE、BLEU），对于这些问题也无效。"

✅ 一种强大的解决方案，可以在不需要昂贵人力的前提下，以人类的方式评估输出，就是使用 LLM 作为评判者。
这种方法在 [《Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena》](https://huggingface.co/papers/2306.05685) 中被介绍 - 推荐阅读这篇文章。

💡 这个想法很简单：让 LLM 为你评分。 🤖✓ 
但我们将会看到，它不能直接很好地适配：你需要仔细设置才能得到好的结果。



```python
!pip install huggingface_hub datasets pandas tqdm -q
```

```python
import re
import pandas as pd
from tqdm.auto import tqdm
from datasets import load_dataset
from huggingface_hub import InferenceClient, notebook_login

tqdm.pandas()  # load tqdm's pandas support
pd.set_option("display.max_colwidth", None)

notebook_login()
```

```python
repo_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"

llm_client = InferenceClient(
    model=repo_id,
    timeout=120,
)

# Test your LLM client
llm_client.text_generation(prompt="How are you today?", max_new_tokens=20)
```

## 1. 准备创建和评估我们的 LLM 评判者

假设你想给 LLM 一个特定任务，比如回答开放式问题。

困难在于，正如我们上面讨论的，衡量答案质量是困难的，例如，精确的字符串匹配会错误地将许多正确但措辞不同的答案标记为错误。

你可以让人类标签员评判输出，但这会花费他们很多时间，如果你想更新模型或问题，你必须重新做一遍。

✅ 在这种情况下，你可以设置一个 LLM 作为评判者。

**但是要使用 LLM 作为评判者，你首先需要评估它对模型输出的评分有多可靠。**

➡️ 所以第一步将是... 创建一个人工评估数据集。但你只需要为少数示例获取人工标注 - 大约 30 个应该足以对性能有一个好的了解。

每次你想测试你的 LLM 作为评判者时，你都可以重新使用这个数据集。

在我们的案例中，我们将使用 [`feedbackQA`](https://huggingface.co/datasets/McGill-NLP/feedbackQA)，它包含每个问题/答案对的 2 个人类评估和评分：使用 30 个示例的样本将代表你的小型评估数据集可能的样子。


```python
ratings = load_dataset("McGill-NLP/feedbackQA")["train"]
ratings = pd.DataFrame(ratings)

ratings["review_1"] = ratings["feedback"].apply(lambda x: x["rating"][0])
ratings["explanation_1"] = ratings["feedback"].apply(lambda x: x["explanation"][0])
ratings["review_2"] = ratings["feedback"].apply(lambda x: x["rating"][1])
ratings["explanation_2"] = ratings["feedback"].apply(lambda x: x["explanation"][1])
ratings = ratings.drop(columns=["feedback"])

# Map scores to numeric values
conversion_dict = {"Excellent": 4, "Acceptable": 3, "Could be Improved": 2, "Bad": 1}
ratings["score_1"] = ratings["review_1"].map(conversion_dict)
ratings["score_2"] = ratings["review_2"].map(conversion_dict)
```

计算性能基准线是一个好主意：例如，这里可以是两个人类评分者之间的评分一致性，通过他们给出的分数的[皮尔逊相关系数](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient)来衡量。

```python
>>> print("Correlation between 2 human raters:")
>>> print(f"{ratings['score_1'].corr(ratings['score_2'], method='pearson'):.3f}")
```

<pre>
Correlation between 2 human raters:
0.563
</pre>

两个真人评委之间的相关性并不是那么好。如果你们的真人评分真的很差，这可能意味着评分标准不够清晰。

这意味着我们的“真实情况”包含了一些噪音：因此我们不能期望任何算法评估能够非常接近它。

然而，我们可以减少这种噪音：
- 通过取平均分作为我们的真实情况，而不是任何一个单独的分数，我们应该能够平衡一些不规则性。
- 只选择人类评审员达成一致意见的样本。

在这里，我们将选择最后一个选项，并且**只保留两个人类评审员达成一致意见的示例**。

```python
# Sample examples
ratings_where_raters_agree = ratings.loc[ratings["score_1"] == ratings["score_2"]]
examples = ratings_where_raters_agree.groupby("score_1").sample(7, random_state=1214)
examples["human_score"] = examples["score_1"]

# Visualize 1 sample for each score
display(examples.groupby("human_score").first())
```

## 2. 创建我们的 LLM 评判者

我们使用一个基本提示来构建我们的 LLM 评判者，包含以下元素：
- 任务描述
- 标度描述：`最小值`，`最大值`，值类型（这里为`浮点数`）
- 输出格式的解释
- 一个答案的开头，尽可能引导 LLM


```python
JUDGE_PROMPT = """
You will be given a user_question and system_answer couple.
Your task is to provide a 'total rating' scoring how well the system_answer answers the user concerns expressed in the user_question.
Give your answer as a float on a scale of 0 to 10, where 0 means that the system_answer is not helpful at all, and 10 means that the answer completely and helpfully addresses the question.

Provide your feedback as follows:

Feedback:::
Total rating: (your rating, as a float between 0 and 10)

Now here are the question and answer.

Question: {question}
Answer: {answer}

Feedback:::
Total rating: """
```

```python
examples["llm_judge"] = examples.progress_apply(
    lambda x: llm_client.text_generation(
        prompt=JUDGE_PROMPT.format(question=x["question"], answer=x["answer"]),
        max_new_tokens=1000,
    ),
    axis=1,
)
```

```python
def extract_judge_score(answer: str, split_str: str = "Total rating:") -> int:
    try:
        if split_str in answer:
            rating = answer.split(split_str)[1]
        else:
            rating = answer
        digit_groups = [el.strip() for el in re.findall(r"\d+(?:\.\d+)?", rating)]
        return float(digit_groups[0])
    except Exception as e:
        print(e)
        return None


examples["llm_judge_score"] = examples["llm_judge"].apply(extract_judge_score)
# Rescale the score given by the LLM on the same scale as the human score
examples["llm_judge_score"] = (examples["llm_judge_score"] / 10) + 1
```

```python
>>> print("Correlation between LLM-as-a-judge and the human raters:")
>>> print(
...     f"{examples['llm_judge_score'].corr(examples['human_score'], method='pearson'):.3f}"
... )
```

<pre>
Correlation between LLM-as-a-judge and the human raters:
0.567
</pre>

这已经不错了，考虑到两个随机、独立变量之间的皮尔逊相关系数会是 0！

但我们很容易做得更好。🔝

## 3. 改进 LLM 评判者

正如 [Aparna Dhinakaran](https://twitter.com/aparnadhinak/status/1748368364395721128) 所说的，LLM 在评估连续范围的输出方面表现不佳。
[这篇文章](https://www.databricks.com/blog/LLM-auto-eval-best-practices-RAG)为我们提供了一些构建更好提示的最佳实践：
- ⏳ **增加思考时间**，在最终答案前添加一个`评估`字段。
- 🔢 **使用较小的整数刻度**，比如 1-4 或 1-5，而不是我们之前使用的大范围浮点刻度。
- 👩‍🏫 **提供一个指导性的刻度**。
- 我们甚至添加了一个激励 LLM 的“胡萝卜”(这里指给它一点额外的激励，就像给人一个奖励一样。)！

```python
IMPROVED_JUDGE_PROMPT = """
You will be given a user_question and system_answer couple.
Your task is to provide a 'total rating' scoring how well the system_answer answers the user concerns expressed in the user_question.
Give your answer on a scale of 1 to 4, where 1 means that the system_answer is not helpful at all, and 4 means that the system_answer completely and helpfully addresses the user_question.

Here is the scale you should use to build your answer:
1: The system_answer is terrible: completely irrelevant to the question asked, or very partial
2: The system_answer is mostly not helpful: misses some key aspects of the question
3: The system_answer is mostly helpful: provides support, but still could be improved
4: The system_answer is excellent: relevant, direct, detailed, and addresses all the concerns raised in the question

Provide your feedback as follows:

Feedback:::
Evaluation: (your rationale for the rating, as a text)
Total rating: (your rating, as a number between 1 and 4)

You MUST provide values for 'Evaluation:' and 'Total rating:' in your answer.

Now here are the question and answer.

Question: {question}
Answer: {answer}

Provide your feedback. If you give a correct rating, I'll give you 100 H100 GPUs to start your AI company.
Feedback:::
Evaluation: """
```

```python
examples["llm_judge_improved"] = examples.progress_apply(
    lambda x: llm_client.text_generation(
        prompt=IMPROVED_JUDGE_PROMPT.format(question=x["question"], answer=x["answer"]),
        max_new_tokens=500,
    ),
    axis=1,
)
examples["llm_judge_improved_score"] = examples["llm_judge_improved"].apply(
    extract_judge_score
)
```

```python
>>> print("Correlation between LLM-as-a-judge and the human raters:")
>>> print(
...     f"{examples['llm_judge_improved_score'].corr(examples['human_score'], method='pearson'):.3f}"
... )
```

<pre>
Correlation between LLM-as-a-judge and the human raters:
0.843
</pre>

通过对提示的少量调整，相关性**提高了近 30%**（其中几个百分点是因为我无耻地给了 LLM 一个小提示，我在此声明该提示不具有法律约束力）。

相当令人印象深刻！👏

让我们展示一些 LLM 评判者的错误来分析它们：

```python
errors = pd.concat(
    [
        examples.loc[
            examples["llm_judge_improved_score"] > examples["human_score"]
        ].head(1),
        examples.loc[
            examples["llm_judge_improved_score"] < examples["human_score"]
        ].head(2),
    ]
)

display(
    errors[
        [
            "question",
            "answer",
            "human_score",
            "explanation_1",
            "llm_judge_improved_score",
            "llm_judge_improved",
        ]
    ]
)
```

我们的 LLM 评判者的不一致之处很微小：总体来看，我们的系统似乎已经达到了不错的性能水平！

## 4. 我们如何进一步提高 LLM 评判者的水平？

🎯 **你永远达不到 100%：** 首先，我们的人类基准肯定包含一些噪音，所以即使有完美的 LLM 评判者，一致性和相关性也不可能达到 100%。

🧭 **提供参考信息：** 如果你每个问题都有一个参考答案，你绝对应该在 LLM 评判者的提示中提供这些信息，以获得更好的结果！

▶️ **提供少量示例：** 在提示中添加一些问题和基准评估的少量示例可以改善结果。 _(我这里尝试了，但在这个案例中并没有改善结果，所以我省略了，但这可能对你的数据集有效！)_

➕ **累加刻度：** 当评判可以分解为原子性标准时，使用累加刻度可以进一步改善结果：如下所示 👇

```python
ADDITIVE_PROMPT = """
(...)
- Award 1 point if the answer is related to the question.
- Give 1 additional point if the answer is clear and precise.
- Provide 1 further point if the answer is true.
- One final point should be awarded if the answer provides additional resources to support the user.
...
"""
```

## 总结
今天的内容就到这里，恭喜你跟到这里！🥳

我必须得走了，一群奇奇怪怪的人正在敲我的门，声称他们是代表 Mixtral 来收取 H100s 的。🤔

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/llm_judge.md" />

### 使用 Hugging Face 和 Milvus 构建 RAG 系统
https://huggingface.co/learn/cookbook/zh-CN/rag_with_hf_and_milvus.md

# 使用 Hugging Face 和 Milvus 构建 RAG 系统

_作者: [张晨](https://github.com/zc277584121)_


[Milvus](https://milvus.io/) 是一个广受欢迎的开源向量数据库，为人工智能应用提供高性能和可扩展的向量相似性搜索。在本教程中，我们将向您展示如何使用 Hugging Face 和 Milvus 构建 RAG（检索增强生成）流程。

RAG 系统将检索系统与 LLM 相结合。该系统首先使用向量数据库 Milvus 从语料库中检索相关文档，然后使用 Hugging Face 的 LLM 根据检索到的文档生成回答。

## 准备
### 依赖关系和环境

```python
! pip install --upgrade pymilvus sentence-transformers huggingface-hub langchain_community langchain-text-splitters pypdf tqdm
```

> 如果您使用 Google Colab，要启用刚刚安装的依赖项，您可能需要 **重新启动运行时**（单击屏幕顶部的“运行时”菜单，然后从下拉菜单中选择“重新启动会话”） 。

另外，我们建议您配置您的 [Hugging Face User Access Token](https://huggingface.co/docs/hub/security-tokens)，并将其设置在您的环境变量中，因为我们将使用 Hugging Face Hub 的 LLM 。如果不设置 token 环境变量，您可能会受到较低的请求速率限制。

```python
import os

os.environ["HF_TOKEN"] = "hf_..."
```

### 准备数据

我们使用 [AI Act PDF](https://artificialintelligenceact.eu/wp-content/uploads/2021/08/The-AI-Act.pdf) 作为我们 RAG 中的私有知识。这是一个针对人工智能的监管框架，具有不同风险级别，对应或多或少的监管。

```python
%%bash

if [ ! -f "The-AI-Act.pdf" ]; then
    wget -q https://artificialintelligenceact.eu/wp-content/uploads/2021/08/The-AI-Act.pdf
fi
```

我们使用 LangChain 的 [`PyPDFLoader`](https://python.langchain.com/v0.1/docs/modules/data_connection/document_loaders/pdf/) 从 PDF 中提取文本，然后将文本拆分为更小的块。默认情况下，我们将块大小设置为 1000，重叠设置为 200，这意味着每个块将有近 1000 个字符，两个块之间的重叠将是 200 个字符。

```python
>>> from langchain_community.document_loaders import PyPDFLoader

>>> loader = PyPDFLoader("The-AI-Act.pdf")
>>> docs = loader.load()
>>> print(len(docs))
```

<pre>
108
</pre>

```python
from langchain_text_splitters import RecursiveCharacterTextSplitter

text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = text_splitter.split_documents(docs)
```

```python
text_lines = [chunk.page_content for chunk in chunks]
```

### 准备 embedding 模型
定义一个函数来生成文本 embedding。我们使用 [BGE embedding模型](https://huggingface.co/BAAI/bge-small-en-v1.5) 作为示例，但您也可以将其更改为任何其他 embedding 模型，例如在 [MTEB排行榜](https://huggingface.co/spaces/mteb/leaderboard) 上选择。

```python
from sentence_transformers import SentenceTransformer

embedding_model = SentenceTransformer("BAAI/bge-small-en-v1.5")

def emb_text(text):
    return embedding_model.encode([text], normalize_embeddings=True).tolist()[0]
```

生成测试 embedding 并打印其大小和前几个元素。

```python
>>> test_embedding = emb_text("This is a test")
>>> embedding_dim = len(test_embedding)
>>> print(embedding_dim)
>>> print(test_embedding[:10])
```

<pre>
384
[-0.07660683244466782, 0.025316666811704636, 0.012505513615906239, 0.004595153499394655, 0.025780051946640015, 0.03816710412502289, 0.08050819486379623, 0.003035430097952485, 0.02439221926033497, 0.0048803347162902355]
</pre>

## 将数据加载到 Milvus 中

### 创建 collection

```python
from pymilvus import MilvusClient

milvus_client = MilvusClient(uri="./hf_milvus_demo.db")

collection_name = "rag_collection"
```

> 对于 `MilvusClient` 的参数：
> - 将 `uri` 设置为本地文件，例如 `./hf_milvus_demo.db` ，是最方便的方法，因为它会自动使用 [Milvus Lite](https://milvus.io/docs/milvus_lite.md) 将所有数据存储在此文件中。
> - 如果您有大量数据，例如超过一百万个向量，您可以在 [Docker 或 Kubernetes](https://milvus.io/docs/quickstart.md) 上设置性能更高的 Milvus 服务器。在此设置中，请使用服务器 uri，例如 `http://localhost:19530` 作为您的 `uri` 。
> - 如果您想使用 Milvus 的全托管云服务 [Zilliz Cloud](https://zilliz.com/cloud) ，请调整 `uri` 和 `token`，分别对应 Zilliz Cloud 中 [Public Endpoint 和 Api key](https://docs.zilliz.com/docs/on-zilliz-cloud-console#cluster-details) 。


检查 collection 是否已存在，如果存在则将其删除。

```python
if milvus_client.has_collection(collection_name):
    milvus_client.drop_collection(collection_name)
```

使用指定参数创建一个新 collection。

如果我们不指定任何字段信息，Milvus 会自动创建一个默认的 `id` 字段作为主键，并创建一个 `vector` 字段来存储向量数据。保留的 JSON 字段用于存储未定义 schema 的字段及其值。

```python
milvus_client.create_collection(
    collection_name=collection_name,
    dimension=embedding_dim,
    metric_type="IP",  # Inner product distance
    consistency_level="Strong",  # Strong consistency level
)
```

### 插入数据
迭代文本行，创建 embedding，然后将数据插入到 Milvus 中。

这是一个新字段 `text`，它是 collection schema 中的未定义字段。它将自动添加到保留的 JSON 动态字段中，从更高的层次看来，它可被视为普通字段。

```python
from tqdm import tqdm

data = []

for i, line in enumerate(tqdm(text_lines, desc="Creating embeddings")):
    data.append({"id": i, "vector": emb_text(line), "text": line})

insert_res = milvus_client.insert(collection_name=collection_name, data=data)
insert_res["insert_count"]
```

## 构建 RAG

### 检索查询数据

让我们具体指定一个有关该语料的问题。

```python
question = "What is the legal basis for the proposal?"
```

在 collection 中搜索问题并检索语义前 3 个匹配项。

```python
search_res = milvus_client.search(
    collection_name=collection_name,
    data=[
        emb_text(question)
    ],  # Use the `emb_text` function to convert the question to an embedding vector
    limit=3,  # Return top 3 results
    search_params={"metric_type": "IP", "params": {}},  # Inner product distance
    output_fields=["text"],  # Return the text field
)
```

我们来看看查询的搜索结果


```python
>>> import json

>>> retrieved_lines_with_distances = [
...     (res["entity"]["text"], res["distance"]) for res in search_res[0]
... ]
>>> print(json.dumps(retrieved_lines_with_distances, indent=4))
```

<pre>
[
    [
        "EN 6  EN 2. LEGAL  BASIS,  SUBSIDIARITY  AND  PROPORTIONALITY  \n2.1. Legal  basis  \nThe legal basis for the proposal is in the first place Article 114 of the Treaty on the \nFunctioning of the European Union (TFEU), which provides for the adoption of measures to \nensure the establishment and f unctioning of the internal market.  \nThis proposal constitutes a core part of the EU digital single market strategy. The primary \nobjective of this proposal is to ensure the proper functioning of the internal market by setting \nharmonised rules in particular on the development, placing on the Union market and the use \nof products and services making use of AI technologies or provided as stand -alone AI \nsystems. Some Member States are already considering national rules to ensure that AI is safe \nand is developed a nd used in compliance with fundamental rights obligations. This will likely \nlead to two main problems: i) a fragmentation of the internal market on essential elements",
        0.7412998080253601
    ],
    [
        "applications and prevent market fragmentation.  \nTo achieve those objectives, this proposal presents a balanced and proportionate horizontal \nregulatory approach to AI that is limited to the minimum necessary requirements to address \nthe risks and problems linked to AI, withou t unduly constraining or hindering technological \ndevelopment or otherwise disproportionately increasing the cost of placing AI solutions on \nthe market.  The proposal sets a robust and flexible legal framework. On the one hand, it is \ncomprehensive and future -proof in its fundamental regulatory choices, including the \nprinciple -based requirements that AI systems should comply with. On the other hand, it puts \nin place a proportionate regulatory system centred on a well -defined risk -based regulatory \napproach that  does not create unnecessary restrictions to trade, whereby legal intervention is \ntailored to those concrete situations where there is a justified cause for concern or where such",
        0.696428656578064
    ],
    [
        "approach that  does not create unnecessary restrictions to trade, whereby legal intervention is \ntailored to those concrete situations where there is a justified cause for concern or where such \nconcern can reasonably be anticipated in the near future. At the same time, t he legal \nframework includes flexible mechanisms that enable it to be dynamically adapted as the \ntechnology evolves and new concerning situations emerge.  \nThe proposal sets harmonised rules for the development, placement on the market and use of \nAI systems i n the Union following a proportionate risk -based approach. It proposes a single \nfuture -proof definition of AI. Certain particularly harmful AI practices are prohibited as \ncontravening Union values, while specific restrictions and safeguards are proposed in  relation \nto certain uses of remote biometric identification systems for the purpose of law enforcement. \nThe proposal lays down a solid risk methodology to define \u201chigh -risk\u201d AI systems that pose",
        0.6891457438468933
    ]
]
</pre>

### 使用 LLM 获取 RAG 回答

在合并到 LLM 提示词之前，我们首先将检索到的文档列表展平为纯字符串。

```python
context = "\n".join(
    [line_with_distance[0] for line_with_distance in retrieved_lines_with_distances]
)
```

定义语言模型的提示词。该提示与从 Milvus 检索到的文档组合在一起。

```python
PROMPT = """
Use the following pieces of information enclosed in <context> tags to provide an answer to the question enclosed in <question> tags.
<context>
{context}
</context>
<question>
{question}
</question>
"""
```

我们使用部署在 Hugging Face 推理服务上的 [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) 根据提示词生成响应。

```python
from huggingface_hub import InferenceClient

repo_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"

llm_client = InferenceClient(model=repo_id, timeout=120)
```

我们设置提示词格式并生成最后的回答。

```python
prompt = PROMPT.format(context=context, question=question)
```

```python
>>> answer = llm_client.text_generation(
...     prompt,
...     max_new_tokens=1000,
... ).strip()
>>> print(answer)
```

<pre>
The legal basis for the proposal is Article 114 of the Treaty on the Functioning of the European Union (TFEU), which provides for the adoption of measures to ensure the establishment and functioning of the internal market. The proposal aims to establish harmonized rules for the development, placing on the market, and use of AI systems in the Union following a proportionate risk-based approach.
</pre>

恭喜！您已经使用 Hugging Face 和 Milvus 构建了 RAG 流程。

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/rag_with_hf_and_milvus.md" />

### 微调大型语言模型以生成波斯语产品目录的 JSON 格式
https://huggingface.co/learn/cookbook/zh-CN/fine_tuning_llm_to_generate_persian_product_catalogs_in_json_format.md

# 微调大型语言模型以生成波斯语产品目录的 JSON 格式

_作者：[Mohammadreza Esmaeiliyan](https://github.com/MrzEsma)_

在这个 Notebook 中，我们尝试对大型语言模型进行微调，且没有添加额外复杂性。该模型已针对客户级别的 GPU 进行优化，用于生成波斯语产品目录并以 JSON 格式生成结构化输出。它特别适用于从伊朗平台（如 [Basalam](https://basalam.com)、[Divar](https://divar.ir/)、[Digikala](https://www.digikala.com/) 等）上的用户生成内容的非结构化标题和描述中创建结构化输出。

你可以在 [我们的 Hugging Face 账户](https://huggingface.co/BaSalam/Llama2-7b-entity-attr-v1) 查看微调后的 LLM。此外，我们还使用了最快的开源推理引擎之一 —— [Vllm](https://github.com/vllm-project/vllm) 来进行推理。

让我们开始吧！

```python
import torch
from datasets import load_dataset
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    TrainingArguments,
)
from peft import LoraConfig, PeftModel
from trl import SFTTrainer, DataCollatorForCompletionOnlyLM
```

`peft` 库，或称为参数高效微调（Parameter Efficient Fine-Tuning），旨在更高效地对大型语言模型（LLMs）进行微调。如果像传统神经网络那样打开并微调网络的上层，它将需要大量的计算和显著的 VRAM（显存）。随着近期论文中提出的方法，`peft` 库已被实现，用于高效地对 LLM 进行微调。你可以在这里了解更多关于 `peft` 的信息：[Hugging Face PEFT](https://huggingface.co/blog/peft)。

## 设置超参数

```python
# General parameters
model_name = "NousResearch/Llama-2-7b-chat-hf"  # The model that you want to train from the Hugging Face hub
dataset_name = "BaSalam/entity-attribute-dataset-GPT-3.5-generated-v1"  # The instruction dataset to use
new_model = "llama-persian-catalog-generator"  # The name for fine-tuned LoRA Adaptor
```

```python
# LoRA parameters
lora_r = 64
lora_alpha = lora_r * 2
lora_dropout = 0.1
target_modules = ["q_proj", "v_proj", 'k_proj']
```

LoRA（低秩适配，Low-Rank Adaptation）通过构建并将低秩矩阵添加到每个模型层中来存储权重变化。这种方法仅打开这些层进行微调，而不改变原始模型权重，也不需要长时间训练。生成的权重非常轻量，可以多次产生，从而允许在将 LLM 加载到 RAM 中后微调多个任务。

你可以在 [Lightning AI](https://lightning.ai/pages/community/tutorial/lora-llm/) 了解更多关于 LoRA 的信息。对于其他高效的训练方法，请参阅 [Hugging Face 性能训练文档](https://huggingface.co/docs/transformers/perf_train_gpu_one) 和 [SFT Trainer 增强](https://huggingface.co/docs/trl/main/en/sft_trainer#enhance-models-performances-using-neftune)。

```python
# QLoRA parameters
load_in_4bit = True
bnb_4bit_compute_dtype = "float16"
bnb_4bit_quant_type = "nf4"
bnb_4bit_use_double_quant = False
```

QLoRA（量化低秩适配，Quantized Low-Rank Adaptation）是一种高效的微调方法，通过使用 4 位量化，使大型语言模型能够在更小的 GPU 上运行。该方法在减少内存使用的同时，保持了 16 位微调的全部性能，使得在单个 48GB GPU 上也可以微调最多 650 亿参数的模型。QLoRA 结合了 4 位 NormalFloat 数据类型、双重量化和分页优化器，有效管理内存。它允许对具有低秩适配器的模型进行微调，显著提高了 AI 模型开发的可访问性。

你可以在 [Hugging Face](https://huggingface.co/blog/4bit-transformers-bitsandbytes) 上了解更多关于 QLoRA 的信息。

```python
# TrainingArguments parameters
num_train_epochs = 1
fp16 = False
bf16 = False
per_device_train_batch_size = 4
gradient_accumulation_steps = 1
gradient_checkpointing = True
learning_rate = 0.00015
weight_decay = 0.01
optim = "paged_adamw_32bit"
lr_scheduler_type = "cosine"
max_steps = -1
warmup_ratio = 0.03
group_by_length = True
save_steps = 0
logging_steps = 25

# SFT parameters
max_seq_length = None
packing = False
device_map = {"": 0}

# Dataset parameters
use_special_template = True
response_template = ' ### Answer:'
instruction_prompt_template = '"### Human:"'
use_llama_like_model = True
```

## 模型训练

```python
# Load dataset (you can process it here)
dataset = load_dataset(dataset_name, split="train")
percent_of_train_dataset = 0.95
other_columns = [i for i in dataset.column_names if i not in ['instruction', 'output']]
dataset = dataset.remove_columns(other_columns)
split_dataset = dataset.train_test_split(train_size=int(dataset.num_rows * percent_of_train_dataset), seed=19, shuffle=False)
train_dataset = split_dataset["train"]
eval_dataset = split_dataset["test"]
print(f"Size of the train set: {len(train_dataset)}. Size of the validation set: {len(eval_dataset)}")
```

```python
# Load LoRA configuration
peft_config = LoraConfig(
    r=lora_r,
    lora_alpha=lora_alpha,
    lora_dropout=lora_dropout,
    bias="none",
    task_type="CAUSAL_LM",
    target_modules=target_modules
)
```

`LoraConfig` 对象用于在使用 Peft 库时配置 LoRA（低秩适配）设置。它可以帮助减少需要微调的参数数量，从而加速训练并减少内存使用。以下是各个参数的详细说明：

- **`r`**：LoRA 中低秩矩阵的秩。该参数控制低秩适配的维度，直接影响模型适应能力和计算成本。
- **`lora_alpha`**：该参数控制低秩适配矩阵的缩放因子。较高的 alpha 值可以提高模型学习新任务的能力。
- **`lora_dropout`**：LoRA 的 dropout 比率。该参数有助于在微调过程中防止过拟合。在此案例中，设为 0.1。
- **`bias`**：指定是否向低秩矩阵添加偏置项。在此案例中，设为 `"none"`，表示不添加偏置项。
- **`task_type`**：定义模型微调的任务类型。在这里，`"CAUSAL_LM"` 表示任务是因果语言建模任务，即预测序列中的下一个单词。
- **`target_modules`**：指定模型中将应用 LoRA 的模块。在此案例中，设为 `["q_proj", "v_proj", 'k_proj']`，即模型注意力机制中的查询（query）、值（value）和键（key）投影层。

通过调整这些参数，`LoraConfig` 有助于在微调过程中高效地应用 LoRA 方法，从而优化计算资源和训练效率。

```python
# Load QLoRA configuration
compute_dtype = getattr(torch, bnb_4bit_compute_dtype)

bnb_config = BitsAndBytesConfig(
    load_in_4bit=load_in_4bit,
    bnb_4bit_quant_type=bnb_4bit_quant_type,
    bnb_4bit_compute_dtype=compute_dtype,
    bnb_4bit_use_double_quant=bnb_4bit_use_double_quant,
)
```

这个代码块配置了使用 **BitsAndBytes (bnb)** 库的设置，该库提供了高效的内存管理和压缩技术，专为 PyTorch 模型设计。具体来说，它定义了如何加载和量化模型权重为 4 位精度，这对于减少内存使用并可能加速推理非常有用。

- **`load_in_4bit`**：一个布尔值，决定是否以 4 位精度加载模型。
- **`bnb_4bit_quant_type`**：指定使用哪种类型的 4 位量化。在这里，设置为 4 位 NormalFloat (NF4) 量化类型，这是 QLoRA 中引入的一种新数据类型。该类型对于正态分布权重在信息理论上最优，提供了一种高效的方式来对模型进行量化以进行微调。
- **`bnb_4bit_compute_dtype`**：设置用于涉及量化模型计算的数据类型。在 QLoRA 中，设置为 `"float16"`，这是混合精度训练中常用的类型，旨在平衡性能和精度。
- **`bnb_4bit_use_double_quant`**：一个布尔值，指示是否使用双重量化。设置为 `False` 表示只使用单次量化，通常速度更快，但可能精度稍低。

### 为什么我们有两种数据类型（quant_type 和 compute_type）？

QLoRA 使用了两种不同的数据类型：一种用于存储基础模型权重（在这里是 4 位 NormalFloat），另一种用于计算操作（16 位）。在前向和反向传播过程中，QLoRA 会将权重从存储格式解量化为计算格式。然而，它仅为 LoRA 参数计算梯度，这些参数使用的是 16 位 bfloat 数据类型。这样的做法确保了权重只有在必要时才会解压，从而在训练和推理过程中保持较低的内存使用量。

```python
# Load base model
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    device_map=device_map
)
model.config.use_cache = False
```

```python
# Set training parameters
training_arguments = TrainingArguments(
    output_dir=new_model,
    num_train_epochs=num_train_epochs,
    per_device_train_batch_size=per_device_train_batch_size,
    gradient_accumulation_steps=gradient_accumulation_steps,
    optim=optim,
    save_steps=save_steps,
    logging_steps=logging_steps,
    learning_rate=learning_rate,
    weight_decay=weight_decay,
    fp16=fp16,
    bf16=bf16,
    max_steps=max_steps,
    warmup_ratio=warmup_ratio,
    gradient_checkpointing=gradient_checkpointing,
    group_by_length=group_by_length,
    lr_scheduler_type=lr_scheduler_type
)
```

```python
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"  # Fix weird overflow issue with fp16 training
if not tokenizer.chat_template:
    tokenizer.chat_template = "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}"
```

关于聊天模板，我们简要说明一下，为了理解用户和模型在模型训练过程中对话的结构，创建了一系列预留短语来区分用户的消息和模型的回复。这确保了模型能够准确理解每条消息的来源，并保持对话结构的连贯性。通常，遵循聊天模板有助于提高任务的准确性。然而，当微调数据集与模型之间存在分布偏移时，使用特定的聊天模板可能更加有助于提升效果。

欲了解更多信息，请访问 [Hugging Face 博客关于聊天模板](https://huggingface.co/blog/chat-templates)。

```python
def special_formatting_prompts(example):
    output_texts = []
    for i in range(len(example['instruction'])):
        text = f"{instruction_prompt_template}{example['instruction'][i]}\n{response_template} {example['output'][i]}"
        output_texts.append(text)
    return output_texts


def normal_formatting_prompts(example):
    output_texts = []
    for i in range(len(example['instruction'])):
        chat_temp = [{"role": "system", "content": example['instruction'][i]},
                     {"role": "assistant", "content": example['output'][i]}]
        text = tokenizer.apply_chat_template(chat_temp, tokenize=False)
        output_texts.append(text)
    return output_texts
```

```python
if use_special_template:
    formatting_func = special_formatting_prompts
    if use_llama_like_model:
        response_template_ids = tokenizer.encode(response_template, add_special_tokens=False)[2:]
        collator = DataCollatorForCompletionOnlyLM(response_template=response_template_ids, tokenizer=tokenizer)
    else:
        collator = DataCollatorForCompletionOnlyLM(response_template=response_template, tokenizer=tokenizer)
else:
    formatting_func = normal_formatting_prompts
```

```python
trainer = SFTTrainer(
    model=model,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    peft_config=peft_config,
    formatting_func=formatting_func,
    data_collator=collator,
    max_seq_length=max_seq_length,
    processing_class=tokenizer,
    args=training_arguments,
    packing=packing
)
```

`SFTTrainer` 随后被实例化，用于处理模型的监督微调（SFT）。这个训练器专门针对 SFT 进行设计，并包括了一些额外的参数，例如 `formatting_func` 和 `packing`，这些参数通常在标准的训练器类中是找不到的。

**`formatting_func`**：一个自定义函数，用于格式化训练样本，将指令和回复模板组合在一起。
**`packing`**：禁用将多个样本打包成一个序列，这是标准 `Trainer` 类中没有的参数。

```python
# Train model
trainer.train()

# Save fine tuned Lora Adaptor 
trainer.model.save_pretrained(new_model)
```

## 推理

```python
import torch
import gc


def clear_hardwares():
    torch.clear_autocast_cache()
    torch.cuda.ipc_collect()
    torch.cuda.empty_cache()
    gc.collect()


clear_hardwares()
clear_hardwares()
```

```python
def generate(model, prompt: str, kwargs):
    tokenized_prompt = tokenizer(prompt, return_tensors='pt').to(model.device)

    prompt_length = len(tokenized_prompt.get('input_ids')[0])

    with torch.cuda.amp.autocast():
        output_tokens = model.generate(**tokenized_prompt, **kwargs) if kwargs else model.generate(**tokenized_prompt)
        output = tokenizer.decode(output_tokens[0][prompt_length:], skip_special_tokens=True)

    return output
```

```python
base_model = AutoModelForCausalLM.from_pretrained(new_model, return_dict=True, device_map='auto', token='')
tokenizer = AutoTokenizer.from_pretrained(new_model, max_length=max_seq_length)
model = PeftModel.from_pretrained(base_model, new_model)
del base_model
```

```python
sample = eval_dataset[0]
if use_special_template:
    prompt = f"{instruction_prompt_template}{sample['instruction']}\n{response_template}"
else:
    chat_temp = [{"role": "system", "content": sample['instruction']}]
    prompt = tokenizer.apply_chat_template(chat_temp, tokenize=False, add_generation_prompt=True)
```

```python
gen_kwargs = {"max_new_tokens": 1024}
generated_texts = generate(model=model, prompt=prompt, kwargs=gen_kwargs)
print(generated_texts)
```

## 合并基础模型

```python
clear_hardwares()
merged_model = model.merge_and_unload()
clear_hardwares()
del model
adapter_model_name = 'your_hf_account/your_desired_name'
merged_model.push_to_hub(adapter_model_name)
```

在这里，我们将适配器与基础模型合并，并将合并后的模型推送到模型库中。你也可以只推送适配器到模型库，而避免推送沉重的基础模型文件，方法如下：

```python
model.push_to_hub(adapter_model_name)
```

然后，你可以按照以下方式加载模型：

```python
config = PeftConfig.from_pretrained(adapter_model_name)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)

# 加载 Lora 模型
model = PeftModel.from_pretrained(model, adapter_model_name)
```

通过这种方法，你能够高效地加载适配器模型，而不需要加载完整的基础模型，从而减少内存消耗并提高推理速度。

## 用 [Vllm](https://github.com/vllm-project/vllm) 快速推理


`vllm` 库是目前用于大型语言模型（LLM）推理的最快引擎之一。有关可用选项的比较概览，你可以参考这篇博客：[7 Frameworks for Serving LLMs](https://medium.com/@gsuresh957/7-frameworks-for-serving-llms-5044b533ee88)。

在这个示例中，我们对该任务使用了我们微调模型的第一个版本进行推理。

```python
from vllm import LLM, SamplingParams

prompt = """### Question: here is a product title from a Iranian marketplace.  \n         give me the Product Entity and Attributes of this product in Persian language.\n         give the output in this json format: {'attributes': {'attribute_name' : <attribute value>, ...}, 'product_entity': '<product entity>'}.\n         Don't make assumptions about what values to plug into json. Just give Json not a single word more.\n         \nproduct title:"""
user_prompt_template = '### Question: '
response_template = ' ### Answer:'

llm = LLM(model='BaSalam/Llama2-7b-entity-attr-v1', gpu_memory_utilization=0.9, trust_remote_code=True)

product = 'مانتو اسپرت پانیذ قد جلوی کار حدودا 85 سانتی متر قد پشت کار حدودا 88 سانتی متر'
sampling_params = SamplingParams(temperature=0.0, max_tokens=75)
prompt = f'{user_prompt_template} {prompt}{product}\n {response_template}'
outputs = llm.generate(prompt, sampling_params)

print(outputs[0].outputs[0].text)
```

### 样例输出

```
{
    "attributes": {
        "قد جلوی کار": "85 سانتی متر",
        "قد پشت کار": "88 سانتی متر"
    },
    "product_entity": "مانتو اسپرت"
}
```


在这篇博客中，你可以阅读关于微调大型语言模型（LLMs）的最佳实践：[Sebastian Raschka 的杂志](https://magazine.sebastianraschka.com/p/practical-tips-for-finetuning-llms?r=1h0eu9&utm_campaign=post&utm_medium=web)。

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/fine_tuning_llm_to_generate_persian_product_catalogs_in_json_format.md" />

### 使用 SetFit 进行零样本文本分类的数据标注建议
https://huggingface.co/learn/cookbook/zh-CN/labelling_feedback_setfit.md

# 使用 SetFit 进行零样本文本分类的数据标注建议


_作者: [David Berenstein](https://huggingface.co/davidberenstein1957) 和 [Sara Han Díaz](https://huggingface.co/sdiazlor)_

建议是使标注团队工作更加轻松快捷的绝佳方式。这些预设选项将使标注过程更加高效，因为标注者只需纠正建议即可。在这个例子中，我们将展示如何使用 SetFit 实现零样本方法，以获取 Argilla 中一个数据集的初步建议，该数据集结合了两个文本分类任务，包括一个 `LabelQuestion` 和一个 `MultiLabelQuestion`。

[Argilla](https://github.com/argilla-io/argilla) 是一个开源的数据策展平台，旨在提升小型和大型语言模型（LLMs）的开发。使用 Argilla，每个人都可以通过使用人类和机器的反馈来更快地进行数据策展，从而构建健壮的语言模型。因此，它为 MLOps 周期的每一步提供支持，从数据标注到模型监控。

反馈是数据策展过程的一个关键部分，Argilla 也提供了一种管理和可视化反馈的方式，以便策展的数据可以后来用于改进语言模型。在本教程中，我们将展示一个实际的例子，说明如何通过提供建议来使我们的标注者工作更加轻松。为此，你将学习如何使用 SetFit 训练零样本情感和主题分类器，然后使用它们为数据集提供建议。

在本教程中，我们将遵循以下步骤：
- 在 Argilla 中创建一个数据集。
- 使用 SetFit 训练零样本分类器。
- 使用训练好的分类器为数据集提供建议。
- 在 Argilla 中可视化这些建议。

让我们开始吧！

## 初始化设置

对于本教程，你需要运行一个 Argilla 服务器。如果你还没有，请查看我们的[快速入门](https://docs.argilla.io/en/latest/getting_started/quickstart.html)或[安装](https://docs.argilla.io/en/latest/getting_started/quickstart_installation.html)页面。完成后，请完成以下步骤：

1. 使用`pip`安装Argilla客户端和所需的第三方库：

```python
!pip install argilla setfit
```

2. 导入必要的库和包

```python
import argilla as rg
from datasets import load_dataset
from setfit import get_templated_dataset
from setfit import SetFitModel, SetFitTrainer
```

3. 如果你使用 Docker 快速启动镜像或 Hugging Face Spaces 运行 Argilla，你需要使用 `URL` 和 `API_KEY` 初始化 Argilla 客户端：

```python
# Replace api_url with the url to your HF Spaces URL if using Spaces
# Replace api_key if you configured a custom API key
rg.init(
    api_url="http://localhost:6900", 
    api_key="admin.apikey",
    workspace="admin"
    )
```

如果你正在运行一个私有的 Hugging Face Space，你还需要按照以下方式设置 [HF_TOKEN](https://huggingface.co/settings/tokens)：

```python
# # Set the HF_TOKEN environment variable
# import os
# os.environ['HF_TOKEN'] = "your-hf-token"

# # Replace api_url with the url to your HF Spaces URL
# # Replace api_key if you configured a custom API key
# rg.init(
#     api_url="https://[your-owner-name]-[your_space_name].hf.space", 
#     api_key="admin.apikey",
#     workspace="admin",
#     extra_headers={"Authorization": f"Bearer {os.environ['HF_TOKEN']}"},
# )
```

## 配置数据集

在这个例子中，我们将加载 [banking77](https://huggingface.co/datasets/banking77) 数据集，这是一个流行的开源数据集，包含了银行领域的客户请求。

```python
data = load_dataset("PolyAI/banking77", split="test")
```

Argilla 使用 `FeedbackDataset`，它可以轻松地让你创建数据集并管理数据和反馈。`FeedbackDataset` 首先需要通过指明两个主要组件（尽管可以添加更多）来进行配置：要添加标注数据 的 *字段* 和标注者的 *问题*。关于 `FeedbackDataset` 和可选组件的更多信息，请查看 [Argilla 文档](https://docs.argilla.io/en/latest/practical_guides/create_update_dataset/create_dataset.html) 和我们的 [端到端教程](https://docs.argilla.io/en/latest/tutorials_and_integrations/tutorials/tutorials.html)。

>你也可以直接使用 [默认模板](https://docs.argilla.io/en/latest/practical_guides/create_update_dataset/create_dataset.html#task-templates) 来创建。

在这种情况下，我们将配置一个自定义数据集，其中包含两个不同的问题，以便我们能够同时处理两个文本分类任务。我们将加载该数据集的原始标签，以对请求中提到的主题进行多标签分类，并且我们还将设置一个问题，以将请求的情感分类为“积极”、“中性”或“消极”。

```python
dataset = rg.FeedbackDataset(
    fields = [rg.TextField(name="text")],
    questions = [
        rg.MultiLabelQuestion(
            name="topics",
            title="Select the topic(s) of the request",
            labels=data.info.features['label'].names, #these are the original labels present in the dataset
            visible_labels=10
        ),
        rg.LabelQuestion(
            name="sentiment",
            title="What is the sentiment of the message?",
            labels=["positive", "neutral", "negative"]
        )
    ]
)
```

## 训练模型

现在我们将使用我们加载的数据以及为数据集配置的标签和问题来训练数据集中的每个问题的零样本文本分类模型。如前面所述，我们将使用 [SetFit](https://github.com/huggingface/setfit) 框架对两个分类器中的 Sentence Transformers 进行少样本微调。此外，我们将使用的模型是 [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)，这是一个在 10 亿句子对数据集上使用对比目标进行微调的句子嵌入模型。

```python
def train_model(question_name, template, multi_label=False):
    # build a training dataset that uses the labels of a specific question in our Argilla dataset
    train_dataset = get_templated_dataset(
        candidate_labels=dataset.question_by_name(question_name).labels,
        sample_size=8,
        template=template,
        multi_label=multi_label
    )

    # train a model using the training dataset we just built
    if multi_label:
        model = SetFitModel.from_pretrained(
            "all-MiniLM-L6-v2",
            multi_target_strategy="one-vs-rest"
        )
    else:
        model = SetFitModel.from_pretrained(
            "all-MiniLM-L6-v2"
        )

    trainer = SetFitTrainer(
        model=model,
        train_dataset=train_dataset
    )
    trainer.train()
    return model
```

```python
topic_model = train_model(
    question_name="topics", 
    template="The customer request is about {}", 
    multi_label=True
)
```

```python
sentiment_model = train_model(
    question_name="sentiment", 
    template="This message is {}", 
    multi_label=False
)
```

## 预测

一旦训练步骤结束，我们就可以通过我们的数据进行预测了。

```python
def get_predictions(texts, model, question_name):
    probas = model.predict_proba(texts, as_numpy=True)
    labels = dataset.question_by_name(question_name).labels
    for pred in probas:
        yield [{"label": label, "score": score} for label, score in zip(labels, pred)]
```

```python
data = data.map(
    lambda batch: {
        "topics": list(get_predictions(batch["text"], topic_model, "topics")),
        "sentiment": list(get_predictions(batch["text"], sentiment_model, "sentiment")),
    },
    batched=True,
)
```

```python
data.to_pandas().head()
```

## 构建记录并推送

有了我们生成的数据和预测，现在我们可以构建记录（将由标注团队标注的每个数据项），其中包括我们模型的建议。对于 `LabelQuestion`，我们将使用概率得分最高的标签，而对于 `MultiLabelQuestion`，我们将包含所有得分高于一定阈值的标签。在这种情况下，我们决定使用 `2/len(labels)` 作为阈值，但你可以根据你的数据实验，并决定采用更严格或更宽松的阈值。

> 注意，更宽松的阈值（接近或等于 `1/len(labels)`）将建议更多的标签，而严格的阈值（在 2 到 3 之间）将选择更少的标签（或没有标签）。

```python
def add_suggestions(record):
    suggestions = []
    
    # get label with max score for sentiment question
    sentiment = max(record['sentiment'], key=lambda x: x['score'])['label']
    suggestions.append({"question_name": "sentiment", "value": sentiment})

    # get all labels above a threshold for topics questions
    threshold = 2 / len(dataset.question_by_name("topics").labels)
    topics = [label['label'] for label in record['topics'] if label['score'] >= threshold]
    # apply the suggestion only if at least one label was over the threshold
    if topics:
        suggestions.append({"question_name": "topics", "value": topics})
    return suggestions
```

```python
records = [
    rg.FeedbackRecord(fields={"text": record['text']}, suggestions=add_suggestions(record))
    for record in data
]
```

一旦我们对结果满意，我们可以将记录添加到我们上面配置的数据集中。最后，为了可视化并开始标注，你需要将其推送到 Argilla。这意味着将你的数据集添加到运行的 Argilla 服务器上，并使其对标注者可用。

```python
dataset.add_records(records)
```

```python
dataset.push_to_argilla("setfit_tutorial", workspace="admin")
```

这是从我们的模型看建议的 UI 样式

![Feedback Task dataset with suggestions made using SetFit](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/snapshot_setfit_suggestions.png)

这部分可选，你还可以将你的 `FeedbackDataset` 保存并加载到 Hugging Face Hub。请参阅[文档](https://docs.argilla.io/en/latest/practical_guides/export_dataset.html)以获取更多关于如何执行此操作的信息。


```python
# Push to HuggingFace Hub
dataset.push_to_huggingface("argilla/my-dataset")

# Load a public dataset
dataset = rg.FeedbackDataset.from_huggingface("argilla/my-dataset")
```

## 总结

在本教程中，我们介绍了如何使用 SetFit 库的零样本方法向 Feedback Task 数据集添加建议。这将通过减少标注团队必须做出的决定和编辑数量来提高标注过程的效率。

要了解更多关于 SetFit 的信息，请查看以下链接：

- [更多 Argilla 教程](https://docs.argilla.io/en/latest/tutorials_and_integrations/tutorials/tutorials.html)
- [SetFit 在 GitHub 的仓库](https://github.com/huggingface/setfit)
- [SetFit 文档](https://huggingface.co/docs/setfit/index)


<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/labelling_feedback_setfit.md" />

### 用自定义生物医学数据集微调视觉 Transformer 模型
https://huggingface.co/learn/cookbook/zh-CN/fine_tuning_vit_custom_dataset.md

# 用自定义生物医学数据集微调视觉 Transformer 模型
_作者: [Emre Albayrak](https://github.com/emre570)_

本指南概述了在自定义生物医学数据集上微调视觉 transformer（ViT）模型的过程。它包括加载数据集和准备数据集的步骤，为不同的数据拆分设置图像转换，配置和初始化 ViT 模型，以及定义具有评估和可视化工具的训练过程。

## 数据集信息
自定义数据集是手工制作的，包含 780 张图片，分为 3 类（良性，恶性，正常）。

![attachment:datasetinfo.png](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/102d6c23e6cc24db857fbc60186461ded6cdfb75/datasetinfo.png)

## 模型信息
我们所要微调的模型是 Google 的 [`"vit-large-patch16-224"`](https://huggingface.co/google/vit-large-patch16-224) 模型，该模型可以在 Hugging Face 的模型库中找到。这个模型是在 ImageNet-21k 数据集上进行预训练的，该数据集包含 1400 万张图片和 21,843 个类别。之后，它在 ImageNet 2012 数据集上进行了微调，该数据集有 100 万张图片和 1000 个类别，图像分辨率统一为 224x224。Google 还提供了其他几种 ViT（Vision Transformer）模型，它们具有不同的图像尺寸和分割块大小。

现在，让我们开始吧。

## 开始
首先，让我们安装库。

```python
!pip install datasets transformers accelerate torch scikit-learn matplotlib wandb
```

(可选) 我们会把我们的模型推送到 hugging face hub 上，所以我们必须登录。

```python
#from huggingface_hub import notebook_login
#notebook_login()
```

## 数据集准备
数据集库会自动从数据集中拉取图片和类别。若需更详细的信息，你可以访问这个[`链接`](https://huggingface.co/docs/datasets/image_load)。

```python
from datasets import load_dataset

dataset = load_dataset("emre570/breastcancer-ultrasound-images")
dataset
```

我们已获得数据集。但我们没有验证集。为了创建验证集，我们将根据测试集的大小计算验证集作为训练集的一部分的大小。然后我们将训练数据集拆分为新的训练和验证子集。

```python
# Get the numbers of each set
test_num = len(dataset["test"])
train_num = len(dataset["train"])

val_size = test_num / train_num

train_val_split = dataset["train"].train_test_split(test_size=val_size)
train_val_split
```

我们已获得分离的训练集。现在让我们将其与测试集合并。

```python
from datasets import DatasetDict

dataset = DatasetDict({
    "train": train_val_split["train"],
    "validation": train_val_split["test"],
    "test": dataset["test"]
})
dataset
```

完美！我们的数据集已经准备好了。现在我们将子集分配给不同的变量。我们将在后面使用它们以便于引用。

```python
train_ds = dataset['train']
val_ds = dataset['validation']
test_ds = dataset['test']
```

我们可以看到这张图片是一个与标签关联的PIL.Image对象。

```python
train_ds[0]
```

我们还可以查看训练集的特征。

```python
train_ds.features
```

让我们从数据集中显示每个类的一个图像。

```python
>>> import matplotlib.pyplot as plt

>>> # Initialize a set to keep track of shown labels
>>> shown_labels = set()

>>> # Initialize the figure for plotting
>>> plt.figure(figsize=(10, 10))

>>> # Loop through the dataset and plot the first image of each label
>>> for i, sample in enumerate(train_ds):
...     label = train_ds.features['label'].names[sample['label']]
...     if label not in shown_labels:
...         plt.subplot(1, len(train_ds.features['label'].names), len(shown_labels) + 1)
...         plt.imshow(sample['image'])
...         plt.title(label)
...         plt.axis('off')
...         shown_labels.add(label)
...         if len(shown_labels) == len(train_ds.features['label'].names):
...             break

>>> plt.show()
```

<img 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## 数据处理

数据集已经准备好了。但我们还没有为微调做好准备。我们将按照以下步骤进行：

- **标签映射：** 我们将标签 ID 与其对应的名字之间进行转换，这对于模型训练和评估非常有用。

- **图像处理：** 然后，我们使用 ViTImageProcessor 来标准化输入图像的大小，并应用特定于预训练模型的归一化。同时，我们还将为训练、验证和测试定义不同的转换，以使用 torchvision 提高模型的泛化能力。

- **转换函数：** 实现函数以将转换应用于数据集，将图像转换为 ViT 模型所需格式和尺寸。

- **数据加载：** 设置一个自定义的整理函数以正确地批量处理图像和标签，并创建一个 DataLoader 以在模型训练期间高效地加载数据和批量处理。

- **批量准备：** 检索并显示样本批量中数据的形状，以验证处理是否正确并为模型输入做好准备。


### 标签映射

```python
id2label = {id:label for id, label in enumerate(train_ds.features['label'].names)}
label2id = {label:id for id,label in id2label.items()}
id2label, id2label[train_ds[0]['label']]
```

### 图像处理

```python
from transformers import ViTImageProcessor

model_name = "google/vit-large-patch16-224"
processor = ViTImageProcessor.from_pretrained(model_name)
```

```python
from torchvision.transforms import CenterCrop, Compose, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor, Resize

image_mean, image_std = processor.image_mean, processor.image_std
size = processor.size["height"]

normalize = Normalize(mean=image_mean, std=image_std)

train_transforms = Compose([        
    RandomResizedCrop(size),
    RandomHorizontalFlip(),
    ToTensor(),
    normalize,
])
val_transforms = Compose([
    Resize(size),
    CenterCrop(size),
    ToTensor(),
    normalize,
])
test_transforms = Compose([
    Resize(size),
    CenterCrop(size),
    ToTensor(),
    normalize,
])
```

### 创建转换函数

```python
def apply_train_transforms(examples):
    examples['pixel_values'] = [train_transforms(image.convert("RGB")) for image in examples['image']]
    return examples

def apply_val_transforms(examples):
    examples['pixel_values'] = [val_transforms(image.convert("RGB")) for image in examples['image']]
    return examples

def apply_test_transforms(examples):
    examples['pixel_values'] = [val_transforms(image.convert("RGB")) for image in examples['image']]
    return examples
```

### 将转换函数应用于每个集合

```python
train_ds.set_transform(apply_train_transforms)
val_ds.set_transform(apply_val_transforms)
test_ds.set_transform(apply_test_transforms)
```

```python
train_ds.features
```

```python
train_ds[0]
```

看起来我们将像素值转换成了张量。

### 数据加载

```python
import torch
from torch.utils.data import DataLoader

def collate_fn(examples):
    pixel_values = torch.stack([example["pixel_values"] for example in examples])
    labels = torch.tensor([example["label"] for example in examples])
    return {"pixel_values": pixel_values, "labels": labels}

train_dl = DataLoader(train_ds, collate_fn=collate_fn, batch_size=4)
```

### 批量准备

```python
>>> batch = next(iter(train_dl))
>>> for k,v in batch.items():
...   if isinstance(v, torch.Tensor):
...     print(k, v.shape)
```

<pre>
pixel_values torch.Size([4, 3, 224, 224])
labels torch.Size([4])
</pre>

完美！现在我们为微调过程做好了准备。

## 微调模型
现在我们将配置和微调模型。我们首先使用特定的标签映射和预训练设置初始化模型，调整大小不匹配的问题。训练参数被设置用来定义模型的学习过程，包括保存策略、批量大小和训练轮次，结果将通过 Weights & Biases 进行记录。Hugging Face Trainer 然后将实例化以管理训练和评估，利用自定义数据整理器和模型的内置处理器。最后，在训练之后，模型的性能将在测试数据集上进行评估，并打印指标以评估其准确性。

首先，我们调用我们的模型。

```python
from transformers import ViTForImageClassification

model = ViTForImageClassification.from_pretrained(model_name, id2label=id2label, label2id=label2id, ignore_mismatched_sizes=True)
```

这里有一个微妙的细节。`ignore_mismatched_sizes` 参数。

当你在一个新数据集上微调预训练模型时，有时你的图像的输入大小或模型架构的特定细节（比如分类层中的标签数量）可能与模型最初训练时的大小不完全匹配。这可能会由于各种原因而发生，例如当你在完全不同类型的图像数据（如医学图像或专业相机图像）上使用在一种类型的图像数据（如ImageNet中的自然图像）上训练的模型时。
将 `ignore_mismatched_sizes` 设置为 `True` 允许模型调整其层以适应大小差异，而不会抛出错误。

例如，这个模型训练的类数是1000，即 `torch.Size([1000])`，它期望一个具有 `torch.Size([1000])` 类的输入。我们的数据集有3类，即 `torch.Size([3])` 类。如果我们直接给它，它会抛出错误，因为类别数量不匹配。

然后，为这个模型定义来自谷歌的训练参数。

(可选) 注意，由于我们将 `report_to` 参数设置为 `wandb`，指标将被保存在 Weights & Biases 中。W&B 将要求你提供一个 API 密钥，因此你应该创建一个账户和一个API密钥。如果你不希望这样做，你可以删除 `report_to` 参数。


```python
from transformers import TrainingArguments, Trainer
import numpy as np

train_args = TrainingArguments(
    output_dir = "output-models",
    save_total_limit=2,
    report_to="wandb",
    save_strategy="epoch",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=10,
    per_device_eval_batch_size=4,
    num_train_epochs=40,
    weight_decay=0.01,
    load_best_model_at_end=True,
    logging_dir='logs',
    remove_unused_columns=False,
)
```

我们现在可以使用 `Trainer` 开始微调过程。

```python
trainer = Trainer(
    model,
    train_args,
    train_dataset=train_ds,
    eval_dataset=val_ds,
    data_collator=collate_fn,
    tokenizer=processor,
)
trainer.train()
```

| Epoch | 训练损失 | 验证损失 | 准确率 |
|-------|-----------|-------------|---------|
| 40    | 0.174700  | 0.596288    | 0.903846 |

微调过程已完成。接下来，我们继续使用测试集评估模型。


```python
>>> outputs = trainer.predict(test_ds)
>>> print(outputs.metrics)
```

<pre>
{'test_loss': 0.40843912959098816, 'test_runtime': 4.9934, 'test_samples_per_second': 31.242, 'test_steps_per_second': 7.81}
</pre>

`{'test_loss': 0.3219967782497406, 'test_accuracy': 0.9102564102564102, 'test_runtime': 4.0543, 'test_samples_per_second': 38.478, 'test_steps_per_second': 9.619}`

### (可选) 将模型推送到 Hub

我们可以使用 `push_to_hub` 将我们的模型推送到 Hugging Face Hub。

```python
model.push_to_hub("your_model_name")
```

太棒了！让我们可视化结果。

## 结果
我们已经完成了微调。让我们看看我们的模型是如何预测类别的，使用 scikit-learn 的混淆矩阵显示，并展示召回率。

### 什么是混淆矩阵？

混淆矩阵是一种特定的表格布局，它允许可视化算法的性能，通常是监督学习模型，在一组已知真实值的测试数据上。它特别有用，因为可以检查分类模型的性能，因为它显示了真实标签与预测标签的频率。

让我们绘制我们模型的混淆矩阵。


```python
>>> from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay

>>> y_true = outputs.label_ids
>>> y_pred = outputs.predictions.argmax(1)

>>> labels = train_ds.features['label'].names
>>> cm = confusion_matrix(y_true, y_pred)
>>> disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)
>>> disp.plot(xticks_rotation=45)
```

<img 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### 什么是召回率？

召回率是分类任务中使用的性能指标，用于衡量模型正确识别数据集中所有相关实例的能力。具体来说，召回率评估了模型正确预测为阳性的实际阳性比例。

让我们使用 scikit-learn 打印召回率。


```python
>>> from sklearn.metrics import recall_score

>>> # Calculate the recall scores
>>> # 'None' calculates recall for each class separately
>>> recall = recall_score(y_true, y_pred, average=None)

>>> # Print the recall for each class
>>> for label, score in zip(labels, recall):
...     print(f'Recall for {label}: {score:.2f}')
```

<pre>
Recall for benign: 0.90
Recall for malignant: 0.86
Recall for normal: 0.78
</pre>

`良性召回率为0.90，
恶性召回率为0.86，
正常召回率为0.78`


## 结论
在这个指南中，我们介绍了如何使用医学数据集训练一个 ViT 模型。它涵盖了关键的步骤，如数据集准备、图像预处理、模型配置、训练、评估和结果可视化。通过利用 Hugging Face 的 Transformers 库、scikit-learn 和 PyTorch Torchvision，它促进了高效的模型训练和评估，提供了关于模型性能及其准确分类生物医学图像能力的宝贵见解。

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/fine_tuning_vit_custom_dataset.md" />

### 通过引入语义缓存到 FAISS 中以增强 RAG 系统的性能
https://huggingface.co/learn/cookbook/zh-CN/semantic_cache_chroma_vector_database.md

# 通过引入语义缓存到 FAISS 中以增强 RAG 系统的性能

_作者:[Pere Martra](https://github.com/peremartra)_

在这个 notebook 中，我们将使用一个现成的模型和 Chroma 数据库来搭建一个常见的 RAG 系统。**但我们会加入一个新功能，就是一个语义缓存系统，它会保存用户的各种问题，并决定是直接用数据库的信息来回答问题，还是用之前保存的问题答案。**

这个语义缓存系统的目的是找出用户提出的问题中哪些是相似的或者是一样的。如果找到了一个之前问过的问题，系统就会直接用缓存里的答案来回答，这样就不用再去数据库里找了。

因为这个系统会考虑问题的实际意思，所以即使问题表达的方式不同，或者有些小错误，比如拼写或句子结构不对，系统也能识别出用户其实是在问同一个问题。

比如，像 **法国的首都是什么？**、**告诉我法国的首都叫什么？** 和 **法国的首都是什么？** 这样的问题，虽然问法不一样，但都是在问同一个事情。

虽然根据问题的不同，模型的回答可能会有点不一样，但基本上从数据库里拿到的信息应该是相同的。这就是为什么我们把缓存系统放在用户和数据库之间，而不是用户和语言模型之间。



<img src="https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/semantic_cache.jpg">


大多数教程指导你创建一个 RAG 系统，这些教程都是为单个用户设计的，用于在测试环境中运行。换句话说，就是在笔记本中与本地向量数据库交互，以及进行 API 调用或使用本地存储的模型。

当尝试将其中一种模型过渡到生产环境时，这种架构很快就显得不够用了，在生产环境中，它们可能会遇到从几十到成千上万次的重复请求。

提高性能的一种方法是通过一个或多个语义缓存。这个缓存保留了以前请求的结果，并且在解决新请求之前，它会检查是否之前收到过类似的请求。如果是这样，它就不会重新执行过程，而是从缓存中检索信息。

在 RAG 系统中，有两个耗时的点：

* 检索用于构建丰富提示的信息：
* 调用大型语言模型以获得响应。

在这两点上，都可以实现语义缓存系统，我们甚至可以有两个缓存，每个点一个。

将缓存系统放在模型的响应点可能会导致对获得响应的影响减少。我们的缓存系统可能会将"用 10 个词解释法国大革命"和"用 100 个词解释法国大革命"视为相同的查询。如果我们的缓存系统存储模型响应，用户可能会认为他们的指令没有被准确地遵循。

但是，两个请求都需要相同的信息来丰富提示。这就是我选择将语义缓存系统放置在用户请求和从向量数据库检索信息之间的主要原因。

然而，这是一个设计决策。根据响应类型和系统请求的不同，它可以被放置在一个点或另一个点。很明显，缓存模型响应会节省最多的时间，但正如我已经解释过的，这样做会牺牲用户对响应的影响。





# 导入并加载库。
首先，我们需要安装必要的 Python 包。
* **[sentence transformers](http:/www.sbert.net/)**。这个库用于将句子转换为固定长度的向量，也称为嵌入。
* **[xformers](https://github.com/facebookresearch/xformers)**。这是一个提供库和工具的包，以便与 transformers 模型一起使用。我们需要安装它，以避免在处理模型和嵌入时出现错误。
* **[chromadb](https://www.trychroma.com/)**。这是我们的向量数据库。ChromaDB 易于使用且开源，可能是用于存储嵌入的最常用的向量数据库。
* **[accelerate](https://github.com/huggingface/accelerate)**。在 GPU 上运行模型的必要条件。


```python
!pip install -q transformers==4.38.1
!pip install -q accelerate==0.27.2
!pip install -q sentence-transformers==2.5.1
!pip install -q xformers==0.0.24
!pip install -q chromadb==0.4.24
!pip install -q datasets==2.17.1
```

```python
import numpy as np
import pandas as pd
```

# 加载数据集

由于我们在一个免费且有限的空间中工作，并且只能使用几 GB 的内存，我通过变量 `MAX_ROWS` 限制了从数据集中使用的行数。

```python
#Login to Hugging Face. It is mandatory to use the Gemma Model,
#and recommended to acces public models and Datasets.
from getpass import getpass
if 'hf_key' not in locals():
  hf_key = getpass("Your Hugging Face API Key: ")
!huggingface-cli login --token $hf_key
```

```python
from datasets import load_dataset

data = load_dataset("keivalya/MedQuad-MedicalQnADataset", split='train')
```

ChromaDB 要求数据具有唯一的标识符。我们可以使用这个语句来创建一个名为**Id**的新列。


```python
data = data.to_pandas()
data["id"]=data.index
data.head(10)
```

```python
MAX_ROWS = 15000
DOCUMENT="Answer"
TOPIC="qtype"
```

```python
#Because it is just a sample we select a small portion of News.
subset_data = data.head(MAX_ROWS)
```

# 导入并配置向量数据库

为了存储信息，我选择使用 ChromaDB，这是最知名且广泛使用的开源向量数据库之一。

首先我们需要导入 ChromaDB。


```python
import chromadb
```

现在我们只需要指定存储向量数据库的路径。

```python
chroma_client = chromadb.PersistentClient(path="/path/to/persist/directory")
```

# 填充和查询 ChromaDB 数据库

ChromaDB 中的数据存储在集合中。如果集合已存在，我们需要删除它。
在接下来的行中，我们通过调用上面创建的 `chroma_client` 中的 `create_collection` 函数来创建集合。


```python
collection_name = "news_collection"
if len(chroma_client.list_collections()) > 0 and collection_name in [chroma_client.list_collections()[0].name]:
    chroma_client.delete_collection(name=collection_name)

collection = chroma_client.create_collection(name=collection_name)
```

现在我们准备好使用 `add` 函数将数据添加到集合中。这个函数需要三个关键信息：

* 在 **文档** 中，我们存储数据集中 `Answer` 列的内容。
* 在 **元数据** 中，我们可以提供一个主题列表。我使用了 `qtype` 列中的值。
* 在 **id** 中，我们需要为每一行提供一个唯一的标识符。我使用 `MAX_ROWS` 的范围来创建ID。

```python
collection.add(
    documents=subset_data[DOCUMENT].tolist(),
    metadatas=[{TOPIC: topic} for topic in subset_data[TOPIC].tolist()],
    ids=[f"id{x}" for x in range(MAX_ROWS)],
)
```

一旦我们在数据库中有了信息，我们就可以查询它，并请求符合我们需求的数据。搜索是在文档内容内部进行的，它不会查找确切的单词或短语。结果将基于搜索词与文档内容之间的相似性。

元数据在初始搜索过程中并不直接参与，它可以在检索后用于过滤或细化结果，从而实现进一步的定制和精确性。

让我们定义一个函数来查询 ChromaDB 数据库。


```python
def query_database(query_text, n_results=10):
    results = collection.query(query_texts=query_text, n_results=n_results )
    return results
```

## 创建语义缓存系统
为了实现缓存系统，我们将使用 Faiss 库，该库允许在内存中存储嵌入。这和 Chroma 做的事情很相似，但没有其持久性。

为此，我们将创建一个名为 `semantic_cache` 的类，它将使用自己的编码器，并为用户提供执行查询所需的函数。

在这个类中，我们首先查询使用 Faiss 实现的缓存，其中包含以前的请求，如果返回的结果超过了一个指定的阈值，它将返回缓存的内容。否则，它将从 Chroma 数据库获取结果。
缓存存储在一个 .json 文件中。


```python
!pip install -q faiss-cpu==1.8.0
```

```python
import faiss
from sentence_transformers import SentenceTransformer
import time
import json
```

下面的 `init_cache()` 函数初始化了语义缓存。

它使用了 FlatLS 索引，这可能不是最快的，但对于小数据集来说是理想的。如果我们需要根据数据的具体内容和大小来选择缓存（临时存储）数据的方式，我们还可以考虑使用其他的索引方法，比如 HNSW 或 IVF。

我选择这个索引是因为它与示例非常契合。它可以用于高维向量，消耗的内存最少，并且在小数据集上表现良好。

下面概述了 Faiss 可用的各种索引的关键特性。

* FlatL2 或 FlatIP。非常适合小数据集，可能不是最快的，但其内存消耗并不过分。
* LSH。它在小数据集上工作效果很好，并且推荐用于最多 128 维的向量。
* HNSW。非常快，但需要大量的 RAM。
* IVF。在大数据集上工作良好，而且不会消耗太多内存或影响性能。

关于 Faiss 可用的不同索引的更多信息可以在以下链接中找到：https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index



```python
def init_cache():
  index = faiss.IndexFlatL2(768)
  if index.is_trained:
    print('Index trained')

  # Initialize Sentence Transformer model
  encoder = SentenceTransformer('all-mpnet-base-v2')

  return index, encoder
```

在 `retrieve_cache` 函数中，.json 文件从磁盘中被检索出来，以便在需要跨会话重用缓存时使用。

```python
def retrieve_cache(json_file):
  try:
    with open(json_file, 'r') as file:
      cache = json.load(file)
  except FileNotFoundError:
      cache = {'questions': [], 'embeddings': [], 'answers': [], 'response_text': []}

  return cache
```

`store_cache` 函数将包含缓存数据的文件保存到磁盘上。

```python
def store_cache(json_file, cache):
  with open(json_file, 'w') as file:
    json.dump(cache, file)
```

这些函数将在 `SemanticCache` 类中使用，该类包括搜索函数及其初始化函数。

尽管 `ask` 函数的代码量相当大，但它的目的非常直接。它在缓存中查找与用户刚刚提出的问题最接近的问题。

然后，检查它是否在指定的阈值内。如果是肯定的，它直接从缓存中返回响应；否则，它调用 `query_database` 函数从 ChromaDB 检索数据。

我使用了欧几里得距离而不是广泛应用于向量比较的余弦距离。这个选择是基于欧几里得距离是 Faiss 默认使用的度量标准。尽管也可以计算余弦距离，但这样做会增加复杂性，可能不会显著有助于最终结果。


```python
class semantic_cache:
  def __init__(self, json_file="cache_file.json", thresold=0.35):
      # Initialize Faiss index with Euclidean distance
      self.index, self.encoder = init_cache()

      # Set Euclidean distance threshold
      # a distance of 0 means identicals sentences
      # We only return from cache sentences under this thresold
      self.euclidean_threshold = thresold

      self.json_file = json_file
      self.cache = retrieve_cache(self.json_file)

  def ask(self, question: str) -> str:
      # Method to retrieve an answer from the cache or generate a new one
      start_time = time.time()
      try:
          #First we obtain the embeddings corresponding to the user question
          embedding = self.encoder.encode([question])

          # Search for the nearest neighbor in the index
          self.index.nprobe = 8
          D, I = self.index.search(embedding, 1)

          if D[0] >= 0:
              if I[0][0] >= 0 and D[0][0] <= self.euclidean_threshold:
                  row_id = int(I[0][0])

                  print('Answer recovered from Cache. ')
                  print(f'{D[0][0]:.3f} smaller than {self.euclidean_threshold}')
                  print(f'Found cache in row: {row_id} with score {D[0][0]:.3f}')
                  print(f'response_text: ' + self.cache['response_text'][row_id])

                  end_time = time.time()
                  elapsed_time = end_time - start_time
                  print(f"Time taken: {elapsed_time:.3f} seconds")
                  return self.cache['response_text'][row_id]

          # Handle the case when there are not enough results
          # or Euclidean distance is not met, asking to chromaDB.
          answer  = query_database([question], 1)
          response_text = answer['documents'][0][0]

          self.cache['questions'].append(question)
          self.cache['embeddings'].append(embedding[0].tolist())
          self.cache['answers'].append(answer)
          self.cache['response_text'].append(response_text)

          print('Answer recovered from ChromaDB. ')
          print(f'response_text: {response_text}')

          self.index.add(embedding)
          store_cache(self.json_file, self.cache)
          end_time = time.time()
          elapsed_time = end_time - start_time
          print(f"Time taken: {elapsed_time:.3f} seconds")

          return response_text
      except Exception as e:
          raise RuntimeError(f"Error during 'ask' method: {e}")
```

### 测试 semantic_cache 类。


```python
>>> # Initialize the cache.
>>> cache = semantic_cache('4cache.json')
```

<pre>
Index trained
</pre>

```python
>>> results = cache.ask("How do vaccines work?")
```

<pre>
Answer recovered from ChromaDB. 
response_text: Summary : Shots may hurt a little, but the diseases they can prevent are a lot worse. Some are even life-threatening. Immunization shots, or vaccinations, are essential. They protect against things like measles, mumps, rubella, hepatitis B, polio, tetanus, diphtheria, and pertussis (whooping cough). Immunizations are important for adults as well as children.    Your immune system helps your body fight germs by producing substances to combat them. Once it does, the immune system "remembers" the germ and can fight it again. Vaccines contain germs that have been killed or weakened. When given to a healthy person, the vaccine triggers the immune system to respond and thus build immunity.     Before vaccines, people became immune only by actually getting a disease and surviving it. Immunizations are an easier and less risky way to become immune.     NIH: National Institute of Allergy and Infectious Diseases
Time taken: 0.057 seconds
</pre>

正如预期的那样，这个响应是从 ChromaDB 获取的。然后，该类将其存储在缓存中。

现在，如果我们发送一个完全不同的问题，响应也应该从 ChromaDB 中检索。这是因为先前存储的问题与当前问题如此不同，以至于它在欧几里得距离方面会超过指定的阈值。


```python
>>> results = cache.ask("Explain briefly what is a Sydenham chorea")
```

<pre>
Answer recovered from ChromaDB. 
response_text: Sydenham chorea (SD) is a neurological disorder of childhood resulting from infection via Group A beta-hemolytic streptococcus (GABHS), the bacterium that causes rheumatic fever. SD is characterized by rapid, irregular, and aimless involuntary movements of the arms and legs, trunk, and facial muscles. It affects girls more often than boys and typically occurs between 5 and 15 years of age. Some children will have a sore throat several weeks before the symptoms begin, but the disorder can also strike up to 6 months after the fever or infection has cleared. Symptoms can appear gradually or all at once, and also may include uncoordinated movements, muscular weakness, stumbling and falling, slurred speech, difficulty concentrating and writing, and emotional instability. The symptoms of SD can vary from a halting gait and slight grimacing to involuntary movements that are frequent and severe enough to be incapacitating. The random, writhing movements of chorea are caused by an auto-immune reaction to the bacterium that interferes with the normal function of a part of the brain (the basal ganglia) that controls motor movements. Due to better sanitary conditions and the use of antibiotics to treat streptococcal infections, rheumatic fever, and consequently SD, are rare in North America and Europe. The disease can still be found in developing nations.
Time taken: 0.082 seconds
</pre>

完美，语义缓存系统正如预期那样运行。

让我们继续用一个非常类似于我们刚才问的问题来测试它。

在这种情况下，响应应该直接来自缓存，而不需要访问 ChromaDB 数据库。


```python
>>> results = cache.ask("Briefly explain me what is a Sydenham chorea.")
```

<pre>
Answer recovered from Cache. 
0.028 smaller than 0.35
Found cache in row: 1 with score 0.028
response_text: Sydenham chorea (SD) is a neurological disorder of childhood resulting from infection via Group A beta-hemolytic streptococcus (GABHS), the bacterium that causes rheumatic fever. SD is characterized by rapid, irregular, and aimless involuntary movements of the arms and legs, trunk, and facial muscles. It affects girls more often than boys and typically occurs between 5 and 15 years of age. Some children will have a sore throat several weeks before the symptoms begin, but the disorder can also strike up to 6 months after the fever or infection has cleared. Symptoms can appear gradually or all at once, and also may include uncoordinated movements, muscular weakness, stumbling and falling, slurred speech, difficulty concentrating and writing, and emotional instability. The symptoms of SD can vary from a halting gait and slight grimacing to involuntary movements that are frequent and severe enough to be incapacitating. The random, writhing movements of chorea are caused by an auto-immune reaction to the bacterium that interferes with the normal function of a part of the brain (the basal ganglia) that controls motor movements. Due to better sanitary conditions and the use of antibiotics to treat streptococcal infections, rheumatic fever, and consequently SD, are rare in North America and Europe. The disease can still be found in developing nations.
Time taken: 0.019 seconds
</pre>

这两个问题非常相似，它们的欧几里得距离非常小，几乎就像它们是相同的。

现在，让我们尝试另一个问题，这次稍微有些不同，观察系统的表现。

```python
>>> question_def = "Write in 20 words what is a Sydenham chorea."
>>> results = cache.ask(question_def)
```

<pre>
Answer recovered from Cache. 
0.228 smaller than 0.35
Found cache in row: 1 with score 0.228
response_text: Sydenham chorea (SD) is a neurological disorder of childhood resulting from infection via Group A beta-hemolytic streptococcus (GABHS), the bacterium that causes rheumatic fever. SD is characterized by rapid, irregular, and aimless involuntary movements of the arms and legs, trunk, and facial muscles. It affects girls more often than boys and typically occurs between 5 and 15 years of age. Some children will have a sore throat several weeks before the symptoms begin, but the disorder can also strike up to 6 months after the fever or infection has cleared. Symptoms can appear gradually or all at once, and also may include uncoordinated movements, muscular weakness, stumbling and falling, slurred speech, difficulty concentrating and writing, and emotional instability. The symptoms of SD can vary from a halting gait and slight grimacing to involuntary movements that are frequent and severe enough to be incapacitating. The random, writhing movements of chorea are caused by an auto-immune reaction to the bacterium that interferes with the normal function of a part of the brain (the basal ganglia) that controls motor movements. Due to better sanitary conditions and the use of antibiotics to treat streptococcal infections, rheumatic fever, and consequently SD, are rare in North America and Europe. The disease can still be found in developing nations.
Time taken: 0.016 seconds
</pre>

我们观察到欧几里得距离已经增加，但它仍然在指定的阈值范围内。因此，它继续直接从缓存中返回响应。

# 加载模型并创建提示

是时候使用 **transformers** 库了，这是[ hugging face ](https://huggingface.co/)最著名的库，用于处理语言模型。

我们将导入：
* **Autotokenizer**：这是一个实用程序类，用于标记化与各种预训练语言模型兼容的文本输入。
* **AutoModelForCausalLM**：它提供了一个接口，用于预训练的语言模型，特别适用于使用因果语言建模（例如，GPT 模型）的语言生成任务，或者是这个 Notebook 中使用的模型 [Gemma-2b-it](https://huggingface.co/google/gemma-2b-it)。
请随意测试 [不同的模型](https://huggingface.co/models?pipeline_tag=text-generation&sort=trending)，你需要搜索训练用于文本生成的 NLP 模型。


```python
!pip install torch
```

```python
from torch import cuda, torch
#In a MAC Silicon the device must be 'mps'
# device = torch.device('mps') #to use with MAC Silicon
device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'
```

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "google/gemma-2b-it"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id,
                                             device_map="cuda",
                                            torch_dtype=torch.bfloat16)
```

## 创建扩展提示

为了创建提示，我们使用从查询 'semantic_cache' 类得到的结果以及用户提出的问题。

提示有两部分，**相关上下文**是从数据库中恢复的信息，以及**用户的问题**。

我们只需要将这两部分放在一起来创建提示，然后将其发送给模型。

```python
prompt_template = f"Relevant context: {results}\n\n The user's question: {question_def}"
prompt_template
```

```python
input_ids = tokenizer(prompt_template, return_tensors="pt").to("cuda")
```

现在剩下的就是将提示发送给模型，等待它的响应！


```python
>>> outputs = model.generate(**input_ids,
...                          max_new_tokens=256)
>>> print(tokenizer.decode(outputs[0]))
```

<pre>
<bos>Relevant context: Sydenham chorea (SD) is a neurological disorder of childhood resulting from infection via Group A beta-hemolytic streptococcus (GABHS), the bacterium that causes rheumatic fever. SD is characterized by rapid, irregular, and aimless involuntary movements of the arms and legs, trunk, and facial muscles. It affects girls more often than boys and typically occurs between 5 and 15 years of age. Some children will have a sore throat several weeks before the symptoms begin, but the disorder can also strike up to 6 months after the fever or infection has cleared. Symptoms can appear gradually or all at once, and also may include uncoordinated movements, muscular weakness, stumbling and falling, slurred speech, difficulty concentrating and writing, and emotional instability. The symptoms of SD can vary from a halting gait and slight grimacing to involuntary movements that are frequent and severe enough to be incapacitating. The random, writhing movements of chorea are caused by an auto-immune reaction to the bacterium that interferes with the normal function of a part of the brain (the basal ganglia) that controls motor movements. Due to better sanitary conditions and the use of antibiotics to treat streptococcal infections, rheumatic fever, and consequently SD, are rare in North America and Europe. The disease can still be found in developing nations.

 The user's question: Write in 20 words what is a Sydenham chorea.

Sure, here is a 20-word answer:

Sydenham chorea is a neurological disorder of childhood resulting from infection via Group A beta-hemolytic streptococcus (GABHS).<eos>
</pre>

# 结论

在访问 ChromaDB 和直接访问缓存之间，数据检索时间减少了 50%。然而，在更大的项目中，这种差异会增加，导致性能提升达到 90-95%。

我们在 Chroma 中的数据非常少，只有一个缓存类的实例。通常，缓存系统背后的数据要大得多，可能不仅仅是对向量数据库的查询，而是来自各种来源。

通常会有多个缓存类的实例，通常基于用户类型，因为共享共同特征的用户之间的问题往往更容易重复。

总之，我们创建了一个非常简单的 RAG 系统，并通过在用户的问题和获取创建丰富提示所需信息之间增加一个语义缓存层来增强它。


<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/semantic_cache_chroma_vector_database.md" />

### 智能体 RAG：通过查询重构和自查询来增强你的 RAG ！🚀
https://huggingface.co/learn/cookbook/zh-CN/agent_rag.md

# 智能体 RAG：通过查询重构和自查询来增强你的 RAG ！🚀

_作者: [Aymeric Roucher](https://huggingface.co/m-ric)_

>这个教程比较高级，建议你先看看另一个[更基础的教程](advanced_rag)。

>检索增强生成（RAG）是一种用大型语言模型（LLM）来回答问题的方法，但它会先从知识库中查找相关信息。这种方法比只用大型语言模型有很多好处，比如可以基于真实的事实来回答问题，减少虚构内容，还可以让模型获取特定领域的知识，并且可以精确控制模型从知识库中获取信息。

不过，普通的RAG方法有两个主要问题：

- 它只进行**一次信息检索**，如果检索的结果不好，那么回答也会差。
- 它计算**语义相似性时是以用户的提问为参照**，这可能不太理想。比如，用户提出的问题通常是用疑问句，而包含答案的文档通常是陈述句，这样就会导致真正含有答案的文档和用户提问的相似性得分不高，可能会错过重要的信息。

为了解决这些问题，我们可以创建**一个带有检索功能的 RAG 智能体。**

这个智能体可以 ✅ 自己构建查询，并且 ✅ 在需要的时候重新检索信息。

所以，我们得用点高级的 RAG 技术！

- 不直接使用用户的提问去搜索，而是智能体自行制定一个更接近目标文档的参考句子，就像 [HyDE](https://huggingface.co/papers/2212.10496) 那样
- 智能体能生成片段并在需要时重新检索，就像 [Self-Query](https://docs.llamaindex.ai/en/stable/examples/evaluation/RetryQuery/) 那样

让我们开始做这个系统吧。🛠️

运行下面的命令来安装所需的软件包：


```python
!pip install pandas langchain langchain-community sentence-transformers faiss-cpu smolagents
```

我们首先加载一个知识库，以便在其上执行 RAG：这个数据集是许多 `huggingface` 软件包的文档页面的汇总，以 markdown 格式存储。

```python
import datasets

knowledge_base = datasets.load_dataset("m-ric/huggingface_doc", split="train")
```

现在我们通过处理数据集并将其存储到向量数据库中，为检索器准备知识库。我们使用 [LangChain](https://python.langchain.com/)，因为它具有出色的向量数据库工具。对于嵌入模型，我们使用 [thenlper/gte-small](https://huggingface.co/thenlper/gte-small)，因为它在我们的 `RAG_evaluation` 指南中表现良好。

```python
>>> from transformers import AutoTokenizer
>>> from langchain.docstore.document import Document
>>> from langchain.text_splitter import RecursiveCharacterTextSplitter
>>> from langchain.vectorstores import FAISS
>>> from langchain_community.embeddings import HuggingFaceEmbeddings
>>> from langchain_community.vectorstores.utils import DistanceStrategy
>>> from tqdm import tqdm

>>> source_docs = [
...     Document(page_content=doc["text"], metadata={"source": doc["source"].split("/")[1]})
...     for doc in knowledge_base
... ]

>>> text_splitter = RecursiveCharacterTextSplitter.from_huggingface_tokenizer(
...     AutoTokenizer.from_pretrained("thenlper/gte-small"),
...     chunk_size=200,
...     chunk_overlap=20,
...     add_start_index=True,
...     strip_whitespace=True,
...     separators=["\n\n", "\n", ".", " ", ""],
... )

>>> # Split docs and keep only unique ones
>>> print("Splitting documents...")
>>> docs_processed = []
>>> unique_texts = {}
>>> for doc in tqdm(source_docs):
...     new_docs = text_splitter.split_documents([doc])
...     for new_doc in new_docs:
...         if new_doc.page_content not in unique_texts:
...             unique_texts[doc.page_content] = True
...             docs_processed.append(new_doc)

>>> print(
...     "Embedding documents... This should take a few minutes (5 minutes on MacBook with M1 Pro)"
... )
>>> embedding_model = HuggingFaceEmbeddings(model_name="thenlper/gte-small")
>>> vectordb = FAISS.from_documents(
...     documents=docs_processed,
...     embedding=embedding_model,
...     distance_strategy=DistanceStrategy.COSINE,
... )
```

<pre>
Splitting documents...
</pre>

现在数据库已经准备好了：让我们构建我们的智能体 RAG 系统吧！

👉 我们只需要一个 `RetrieverTool`，我们的智能体可以利用它从知识库中检索信息。


```python
from smolagents import Tool
from langchain_core.vectorstores import VectorStore


class RetrieverTool(Tool):
    name = "retriever"
    description = "Using semantic similarity, retrieves some documents from the knowledge base that have the closest embeddings to the input query."
    inputs = {
        "query": {
            "type": "text",
            "description": "The query to perform. This should be semantically close to your target documents. Use the affirmative form rather than a question.",
        }
    }
    output_type = "text"

    def __init__(self, vectordb: VectorStore, **kwargs):
        super().__init__(**kwargs)
        self.vectordb = vectordb

    def forward(self, query: str) -> str:
        assert isinstance(query, str), "Your search query must be a string"

        docs = self.vectordb.similarity_search(
            query,
            k=7,
        )

        return "\nRetrieved documents:\n" + "".join(
            [
                f"===== Document {str(i)} =====\n" + doc.page_content
                for i, doc in enumerate(docs)
            ]
        )
```

现在创建一个利用这个工具的智能体就简单了！

智能体在初始化时需要以下参数：
- *`tools`*：智能体能够调用的工具列表。
- *`llm_engine`*：为智能体提供动力的LLM。

我们的 `llm_engine` 必须是一个可调用的对象，它接受一个 [messages](https://huggingface.co/docs/transformers/main/chat_templating) 列表作为输入并返回文本。它还需要接受一个 `stop_sequences` 参数，该参数指示何时停止生成。为了方便起见，我们直接使用包中提供的 `HfModel` 类来获取一个调用我们的 [Inference API](https://huggingface.co/docs/api-inference/en/index) 的 LLM 引擎。
我们使用 [CohereForAI/c4ai-command-r-plus](https://huggingface.co/CohereForAI/c4ai-command-r-plus) 作为 llm 引擎，因为：
- 它有一个长达 128k 的上下文，这对于处理长源文档很有帮助
- 它在 HF 的 Inference API 上始终免费提供！


```python
from smolagents import HfModel, ToolCallingAgent

model = HfModel("CohereForAI/c4ai-command-r-plus")

retriever_tool = RetrieverTool(vectordb)
agent = ToolCallingAgent(
    tools=[retriever_tool], model=model, max_iterations=4, verbose=2
)
```

既然我们已经将智能体初始化为 `ToolCallingAgent`，它就已经自动赋予了一个默认的系统提示，告诉 LLM 引擎要逐步处理并生成工具调用作为 JSON 块（你可以根据需要用你自己的提示模板替换这个）。

然后，当它的 `.run()` 方法被启动时，智能体负责调用 LLM 引擎，解析工具调用的 JSON 块并执行这些工具调用，所有这些都在一个循环中进行，只有当提供最终答案时才会结束。

```python
>>> agent_output = agent.run("How can I push a model to the Hub?")

>>> print("Final output:")
>>> print(agent_output)
```

<pre>
Final output:
There are multiple ways to push a model to the Hub. Here are a few examples using different libraries and functions:

Using the `api`:
python
api.upload_folder(
    repo_id=repo_id,
    folder_path=repo_local_path,
    path_in_repo='.',
)

print('Your model is pushed to the Hub. You can view your model here:', repo_url)


With Transformers:
python
from transformers import PushToHubCallback

# Initialize the callback with the output directory,
tokenizer, and your Hub username and model name
push_to_hub_callback = PushToHubCallback(
    output_dir='./your_model_save_path',
    tokenizer=tokenizer,
    hub_model_id='your-username/my-awesome-model'
)

# Assuming `trainer` is your Trainer object
trainer.add_callback(push_to_hub_callback)


Using `timm`:
python
from timm.models.hub import push_to_hf_hub

# Assuming `model` is your fine-tuned model
model_cfg = {'labels': ['a', 'b', 'c', 'd']}
push_to_hf_hub(model, 'resnet18-random', model_config=model_cfg)


For computer vision models, you can also use `push_to_hub`:
python
processor.push_to_hub(hub_model_id)
trainer.push_to_hub(**kwargs)


You can also manually push a model with `model.push_to_hub()`:
python
model.push_to_hub()


Additionally, you can opt to push your model to the Hub at the end of training by specifying `push_to_hub=True` in the training configuration. Don't forget to have git-lfs installed and be logged into your Hugging Face account.
</pre>

## 智能体RAG与标准RAG的比较

智能体 RAG 和标准 RAG，哪个更好？我们用 LLM Judge 来比一比。

我们会用一个非常强的模型 [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) 来做这个评估。


```python
eval_dataset = datasets.load_dataset("m-ric/huggingface_doc_qa_eval", split="train")
```

在运行测试之前，让我们让智能体输出更简洁一些。

```python
import logging

agent.logger.setLevel(logging.WARNING)
```

```python
outputs_agentic_rag = []

for example in tqdm(eval_dataset):
    question = example["question"]

    enhanced_question = f"""Using the information contained in your knowledge base, which you can access with the 'retriever' tool,
give a comprehensive answer to the question below.
Respond only to the question asked, response should be concise and relevant to the question.
If you cannot find information, do not give up and try calling your retriever again with different arguments!
Make sure to have covered the question completely by calling the retriever tool several times with semantically different queries.
Your queries should not be questions but affirmative form sentences: e.g. rather than "How do I load a model from the Hub in bf16?", query should be "load a model from the Hub bf16 weights".

Question:
{question}"""
    answer = agent.run(enhanced_question)
    print("=======================================================")
    print(f"Question: {question}")
    print(f"Answer: {answer}")
    print(f'True answer: {example["answer"]}')

    results_agentic = {
        "question": question,
        "true_answer": example["answer"],
        "source_doc": example["source_doc"],
        "generated_answer": answer,
    }
    outputs_agentic_rag.append(results_agentic)
```

```python
from huggingface_hub import InferenceClient

reader_llm = InferenceClient("CohereForAI/c4ai-command-r-plus")

outputs_standard_rag = []

for example in tqdm(eval_dataset):
    question = example["question"]
    context = retriever_tool(question)

    prompt = f"""Given the question and supporting documents below, give a comprehensive answer to the question.
Respond only to the question asked, response should be concise and relevant to the question.
Provide the number of the source document when relevant.
If you cannot find information, do not give up and try calling your retriever again with different arguments!

Question:
{question}

{context}
"""
    messages = [{"role": "user", "content": prompt}]
    answer = reader_llm.chat_completion(messages).choices[0].message.content

    print("=======================================================")
    print(f"Question: {question}")
    print(f"Answer: {answer}")
    print(f'True answer: {example["answer"]}')

    results_agentic = {
        "question": question,
        "true_answer": example["answer"],
        "source_doc": example["source_doc"],
        "generated_answer": answer,
    }
    outputs_standard_rag.append(results_agentic)
```

评估提示遵循了[我们的 llm_judge cookbook](llm_judge) 中展示的一些最佳原则：它遵循一个小的整数李克特量表，有明确的评分标准和每个分数的描述。

```python
EVALUATION_PROMPT = """You are a fair evaluator language model.

You will be given an instruction, a response to evaluate, a reference answer that gets a score of 3, and a score rubric representing a evaluation criteria are given.
1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general.
2. After writing a feedback, write a score that is an integer between 1 and 3. You should refer to the score rubric.
3. The output format should look as follows: \"Feedback: {{write a feedback for criteria}} [RESULT] {{an integer number between 1 and 3}}\"
4. Please do not generate any other opening, closing, and explanations. Be sure to include [RESULT] in your output.
5. Do not score conciseness: a correct answer that covers the question should receive max score, even if it contains additional useless information.

The instruction to evaluate:
{instruction}

Response to evaluate:
{response}

Reference Answer (Score 3):
{reference_answer}

Score Rubrics:
[Is the response complete, accurate, and factual based on the reference answer?]
Score 1: The response is completely incomplete, inaccurate, and/or not factual.
Score 2: The response is somewhat complete, accurate, and/or factual.
Score 3: The response is completely complete, accurate, and/or factual.

Feedback:"""
```

```python
from huggingface_hub import InferenceClient

evaluation_client = InferenceClient("meta-llama/Meta-Llama-3-70B-Instruct")
```

```python
>>> import pandas as pd

>>> for type, outputs in [
...     ("agentic", outputs_agentic_rag),
...     ("standard", outputs_standard_rag),
... ]:
...     for experiment in tqdm(outputs):
...         eval_prompt = EVALUATION_PROMPT.format(
...             instruction=experiment["question"],
...             response=experiment["generated_answer"],
...             reference_answer=experiment["true_answer"],
...         )
...         messages = [
...             {"role": "system", "content": "You are a fair evaluator language model."},
...             {"role": "user", "content": eval_prompt},
...         ]

...         eval_result = evaluation_client.text_generation(
...             eval_prompt, max_new_tokens=1000
...         )
...         try:
...             feedback, score = [item.strip() for item in eval_result.split("[RESULT]")]
...             experiment["eval_score_LLM_judge"] = score
...             experiment["eval_feedback_LLM_judge"] = feedback
...         except:
...             print(f"Parsing failed - output was: {eval_result}")

...     results = pd.DataFrame.from_dict(outputs)
...     results = results.loc[~results["generated_answer"].str.contains("Error")]
...     results["eval_score_LLM_judge_int"] = (
...         results["eval_score_LLM_judge"].fillna(1).apply(lambda x: int(x))
...     )
...     results["eval_score_LLM_judge_int"] = (results["eval_score_LLM_judge_int"] - 1) / 2

...     print(
...         f"Average score for {type} RAG: {results['eval_score_LLM_judge_int'].mean()*100:.1f}%"
...     )
```

<pre>
Average score for agentic RAG: 78.5%
</pre>

**让我们回顾一下：与标准的 RAG 相比，智能体设置提高了 8.5% 的得分！**（从 70.0% 提高到 78.5%）

这是一个巨大的改进，而且设置非常简单🚀

（作为基准，不使用知识库的 Llama-3-70B 得分为 36%）


<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/agent_rag.md" />

### 用 🤗 transformers, 🤗 datasets 和 FAISS 嵌入多模态数据进行相似度搜索
https://huggingface.co/learn/cookbook/zh-CN/faiss_with_hf_datasets_and_clip.md

# 用 🤗 transformers, 🤗 datasets 和 FAISS 嵌入多模态数据进行相似度搜索

嵌入对于语义压缩信息很有用。它们可用于执行相似性搜索、零样本分类或简单地训练新模型。相似性搜索用例包括在电子商务中搜索类似的产品、在社交媒体中搜索内容等等。  
本 notebook 指导你使用 🤗transformers、🤗datasets 和 FAISS 从特征提取模型创建和索引嵌入，以便稍后使用它们进行相似性搜索。  
让我们安装必要的库。  

```python
!pip install -q datasets faiss-gpu transformers sentencepiece
```

对于本教程，我们将会使用 [CLIP 模型](https://huggingface.co/openai/clip-vit-base-patch16)来提取特征。 CLIP 是一个革命性的模型，其成功的将文本数据和图片数据两个模态联合起来训练。

```python
import torch
from PIL import Image
from transformers import AutoImageProcessor, AutoModel, AutoTokenizer
import faiss
import numpy as np

device = torch.device('cuda' if torch.cuda.is_available() else "cpu")

model = AutoModel.from_pretrained("openai/clip-vit-base-patch16").to(device)
processor = AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch16")
tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch16")
```

载入数据集，为了保持 notebook 的轻量性，我们这里会使用一个小型字幕数据集 [jmhessel/newyorker_caption_contest](https://huggingface.co/datasets/jmhessel/newyorker_caption_contest).

```python
from datasets import load_dataset

ds = load_dataset("jmhessel/newyorker_caption_contest", "explanation")
```

让我们来看一个例子

```python
>>> ds["train"][0]["image"]
```

<img src="data:image/jpeg;base64,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">


```python
ds["train"][0]["image_description"]
```

我们没必要去写任何函数去嵌入例子或创建索引。 🤗 datasets 库的 FAISS 组件抽象这些过程。我们可以仅仅简单使用 dataset 的 `map` 方法就可以创建一个新的带有每个例子嵌入的列，就像下面所示。让我们针对提示列中的文本特征创建一个嵌入。

```python
dataset = ds["train"]
ds_with_embeddings = dataset.map(lambda example:
                                {'embeddings': model.get_text_features(
                                    **tokenizer([example["image_description"]],
                                                truncation=True, return_tensors="pt")
                                    .to("cuda"))[0].detach().cpu().numpy()})
```

```python
ds_with_embeddings.add_faiss_index(column='embeddings')
```

我们可以同样处理图像嵌入

```python
ds_with_embeddings = ds_with_embeddings.map(lambda example:
                                          {'image_embeddings': model.get_image_features(
                                              **processor([example["image"]], return_tensors="pt")
                                              .to("cuda"))[0].detach().cpu().numpy()})
```

```python
ds_with_embeddings.add_faiss_index(column='image_embeddings')
```

## 用文本提示( prompts )查询相关数据

我们现在可以用文本或者图片查询数据集来获取相似的项目

```python
prmt = "a snowy day"
prmt_embedding = model.get_text_features(**tokenizer([prmt], return_tensors="pt", truncation=True).to("cuda"))[0].detach().cpu().numpy()
scores, retrieved_examples = ds_with_embeddings.get_nearest_examples('embeddings', prmt_embedding, k=1)
```

```python
>>> def downscale_images(image):
...   width = 200
...   ratio = (width / float(image.size[0]))
...   height = int((float(image.size[1]) * float(ratio)))
...   img = image.resize((width, height), Image.Resampling.LANCZOS)
...   return img

>>> images = [downscale_images(image) for image in retrieved_examples["image"]]
>>> # see the closest text and image
>>> print(retrieved_examples["image_description"])
>>> display(images[0])
```

<pre>
['A man is in the snow. A boy with a huge snow shovel is there too. They are outside a house.']
</pre>

## 用图片提示( prompts )来查询数据

图片相似度推理也类似，你只需要调用 `get_image_features` 函数即可。

```python
>>> import requests
>>> # image of a beaver
>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/beaver.png"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> display(downscale_images(image))
```

<img 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">


搜索相似的图片

```python
img_embedding = model.get_image_features(**processor([image], return_tensors="pt", truncation=True).to("cuda"))[0].detach().cpu().numpy()
scores, retrieved_examples = ds_with_embeddings.get_nearest_examples('image_embeddings', img_embedding, k=1)
```

显示与海狸图像最相似的图像。

```python
>>> images = [downscale_images(image) for image in retrieved_examples["image"]]
>>> # see the closest text and image
>>> print(retrieved_examples["image_description"])
>>> display(images[0])
```

<pre>
['Salmon swim upstream but they see a grizzly bear and are in shock. The bear has a smug look on his face when he sees the salmon.']
</pre>

## 保存，推送，加载嵌入( embeddings )
我们可以用 `save_faiss_index` 函数储存数据集的嵌入。


```python
ds_with_embeddings.save_faiss_index('embeddings', 'embeddings/embeddings.faiss')
```

```python
ds_with_embeddings.save_faiss_index('image_embeddings', 'embeddings/image_embeddings.faiss')
```

去储存一个数据集仓库的嵌入是一个很好的练习，所以我们将在那里创建一个，并将我们的嵌入稍后推送到那里。  
我们会登录 Hugging Face Hub， 创建一个数据集仓库，推送我们的所以，然后使用 `snapshot_download` 加载。

```python
from huggingface_hub import HfApi, notebook_login, snapshot_download
notebook_login()
```

```python
from huggingface_hub import HfApi
api = HfApi()
api.create_repo("merve/faiss_embeddings", repo_type="dataset")
api.upload_folder(
    folder_path="./embeddings",
    repo_id="merve/faiss_embeddings",
    repo_type="dataset",
)
```

```python
snapshot_download(repo_id="merve/faiss_embeddings", repo_type="dataset",
                  local_dir="downloaded_embeddings")
```

我们可以使用 `load_faiss_index` 将嵌入加载到没有嵌入的数据集中。

```python
ds = ds["train"]
ds.load_faiss_index('embeddings', './downloaded_embeddings/embeddings.faiss')
# infer again
prmt = "people under the rain"
```

```python
prmt_embedding = model.get_text_features(
                        **tokenizer([prmt], return_tensors="pt", truncation=True)
                        .to("cuda"))[0].detach().cpu().numpy()

scores, retrieved_examples = ds.get_nearest_examples('embeddings', prmt_embedding, k=1)
```

```python
>>> display(retrieved_examples["image"][0])
```

<img 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">


<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/faiss_with_hf_datasets_and_clip.md" />

### 在自定义数据集上微调语义分割模型并通过推理 API 使用
https://huggingface.co/learn/cookbook/zh-CN/semantic_segmentation_fine_tuning_inference.md

# 在自定义数据集上微调语义分割模型并通过推理 API 使用


_作者：[Sergio Paniego](https://github.com/sergiopaniego)_

在本 Notebook 中，我们将介绍如何在自定义数据集上微调一个 [语义分割](https://huggingface.co/tasks/image-segmentation) 模型。我们将使用的模型是预训练的 [Segformer](https://huggingface.co/docs/transformers/model_doc/segformer)，这是一个强大且灵活的基于 Transformer 的分割架构，适用于各种分割任务。

![Segformer 架构](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/segformer_architecture.png)

对于我们的数据集，我们将使用 [segments/sidewalk-semantic](https://huggingface.co/datasets/segments/sidewalk-semantic)，它包含了标注好的人行道图像，十分适合用于城市环境中的应用。

### 示例应用场景： 
该模型可以部署在送餐机器人中，使其能够自动在城市人行道上导航，将披萨直接送到您家门口 🍕。

一旦我们微调完成该模型，接下来会演示如何使用 [Serverless 推理 API](https://huggingface.co/docs/api-inference/index) 将模型部署，使其可以通过一个简单的 API 端点进行访问。

## 1. 安装依赖
首先，我们需要安装微调语义分割模型所需的必要库。以下是安装依赖的步骤：


```python
!pip install -q datasets transformers evaluate wandb
# Tested with datasets==3.0.0, transformers==4.44.2, evaluate==0.4.3, wandb==0.18.1
```

## 2. 加载数据集 📁

我们将使用 [sidewalk-semantic](https://huggingface.co/datasets/segments/sidewalk-semantic) 数据集，该数据集包含了 2021 年夏季在比利时收集的关于人行道的图像。

该数据集包括：

* **1,000 张图像及其对应的语义分割掩膜** 🖼
* **34 个不同的类别** 📦

由于该数据集是受限访问的，你需要登录并接受许可才能访问。同时，我们还需要身份验证来上传微调后的模型到 Hugging Face Hub。

```python
from huggingface_hub import notebook_login

notebook_login()
```

```python
sidewalk_dataset_identifier = "segments/sidewalk-semantic"
```

```python
from datasets import load_dataset

dataset = load_dataset(sidewalk_dataset_identifier)
```

审查数据集的内部结构以熟悉它！

```python
dataset
```

由于数据集仅包含训练集，我们将手动将其分为 `训练集` 和 `测试集`。我们将 80% 的数据分配用于训练，剩余的 20% 用于评估和测试。 ➗

```python
dataset = dataset.shuffle(seed=42)
dataset = dataset["train"].train_test_split(test_size=0.2)
train_ds = dataset["train"]
test_ds = dataset["test"]
```

让我们检查一个示例中存在的对象类型。我们可以看到，`pixels_values` 包含了 RGB 图像，而 `label` 包含了 ground truth mask。ground truth mask 是一个单通道图像，其中每个像素表示对应 RGB 图像中像素的类别。

```python
image = train_ds[0]
image
```

## 3. 可视化示例！ 👀

现在我们已经加载了数据集，让我们可视化一些示例以及它们的掩膜，以便更好地理解数据集的结构。

数据集包含一个 JSON [文件](https://huggingface.co/datasets/segments/sidewalk-semantic/blob/main/id2label.json)，该文件包含了 `id2label` 映射。我们将打开这个文件，读取与每个 ID 关联的类别标签。

```python
>>> import json
>>> from huggingface_hub import hf_hub_download

>>> filename = "id2label.json"
>>> id2label = json.load(open(hf_hub_download(repo_id=sidewalk_dataset_identifier, filename=filename, repo_type="dataset"), "r"))
>>> id2label = {int(k): v for k, v in id2label.items()}
>>> label2id = {v: k for k, v in id2label.items()}

>>> num_labels = len(id2label)
>>> print("Id2label:", id2label)
```

<pre>
Id2label: {0: 'unlabeled', 1: 'flat-road', 2: 'flat-sidewalk', 3: 'flat-crosswalk', 4: 'flat-cyclinglane', 5: 'flat-parkingdriveway', 6: 'flat-railtrack', 7: 'flat-curb', 8: 'human-person', 9: 'human-rider', 10: 'vehicle-car', 11: 'vehicle-truck', 12: 'vehicle-bus', 13: 'vehicle-tramtrain', 14: 'vehicle-motorcycle', 15: 'vehicle-bicycle', 16: 'vehicle-caravan', 17: 'vehicle-cartrailer', 18: 'construction-building', 19: 'construction-door', 20: 'construction-wall', 21: 'construction-fenceguardrail', 22: 'construction-bridge', 23: 'construction-tunnel', 24: 'construction-stairs', 25: 'object-pole', 26: 'object-trafficsign', 27: 'object-trafficlight', 28: 'nature-vegetation', 29: 'nature-terrain', 30: 'sky', 31: 'void-ground', 32: 'void-dynamic', 33: 'void-static', 34: 'void-unclear'}
</pre>

让我们为每个类别分配颜色 🎨。这将帮助我们更有效地可视化分割结果，并使得在图像中解释不同类别变得更加容易。

```python
sidewalk_palette = [
  [0, 0, 0], # unlabeled
  [216, 82, 24], # flat-road
  [255, 255, 0], # flat-sidewalk
  [125, 46, 141], # flat-crosswalk
  [118, 171, 47], # flat-cyclinglane
  [161, 19, 46], # flat-parkingdriveway
  [255, 0, 0], # flat-railtrack
  [0, 128, 128], # flat-curb
  [190, 190, 0], # human-person
  [0, 255, 0], # human-rider
  [0, 0, 255], # vehicle-car
  [170, 0, 255], # vehicle-truck
  [84, 84, 0], # vehicle-bus
  [84, 170, 0], # vehicle-tramtrain
  [84, 255, 0], # vehicle-motorcycle
  [170, 84, 0], # vehicle-bicycle
  [170, 170, 0], # vehicle-caravan
  [170, 255, 0], # vehicle-cartrailer
  [255, 84, 0], # construction-building
  [255, 170, 0], # construction-door
  [255, 255, 0], # construction-wall
  [33, 138, 200], # construction-fenceguardrail
  [0, 170, 127], # construction-bridge
  [0, 255, 127], # construction-tunnel
  [84, 0, 127], # construction-stairs
  [84, 84, 127], # object-pole
  [84, 170, 127], # object-trafficsign
  [84, 255, 127], # object-trafficlight
  [170, 0, 127], # nature-vegetation
  [170, 84, 127], # nature-terrain
  [170, 170, 127], # sky
  [170, 255, 127], # void-ground
  [255, 0, 127], # void-dynamic
  [255, 84, 127], # void-static
  [255, 170, 127], # void-unclear
]
```

我们可以可视化数据集中的一些示例，包括 RGB 图像、对应的掩膜以及掩膜覆盖在图像上的效果。这将帮助我们更好地理解数据集以及掩膜如何与图像对应。📸

```python
>>> from matplotlib import pyplot as plt
>>> import numpy as np
>>> from PIL import Image
>>> import matplotlib.patches as patches

>>> # Create and show the legend separately
>>> fig, ax = plt.subplots(figsize=(18, 2))

>>> legend_patches = [patches.Patch(color=np.array(color)/255, label=label) for label, color in zip(id2label.values(), sidewalk_palette)]

>>> ax.legend(handles=legend_patches, loc='center', bbox_to_anchor=(0.5, 0.5), ncol=5, fontsize=8)
>>> ax.axis('off')

>>> plt.show()

>>> for i in range(5):
...     image = train_ds[i]

...     fig, ax = plt.subplots(1, 3, figsize=(18, 6))

...     # Show the original image
...     ax[0].imshow(image['pixel_values'])
...     ax[0].set_title('Original Image')
...     ax[0].axis('off')

...     mask_np = np.array(image['label'])

...     # Create a new empty RGB image
...     colored_mask = np.zeros((mask_np.shape[0], mask_np.shape[1], 3), dtype=np.uint8)

...     # Assign colors to each value in the mask
...     for label_id, color in enumerate(sidewalk_palette):
...         colored_mask[mask_np == label_id] = color

...     colored_mask_img = Image.fromarray(colored_mask, 'RGB')

...     # Show the segmentation mask
...     ax[1].imshow(colored_mask_img)
...     ax[1].set_title('Segmentation Mask')
...     ax[1].axis('off')

...     # Convert the original image to RGBA to support transparency
...     image_rgba = image['pixel_values'].convert("RGBA")
...     colored_mask_rgba = colored_mask_img.convert("RGBA")

...     # Adjust transparency of the mask
...     alpha = 128  # Transparency level (0 fully transparent, 255 fully opaque)
...     image_2_with_alpha = Image.new("RGBA", colored_mask_rgba.size)
...     for x in range(colored_mask_rgba.width):
...         for y in range(colored_mask_rgba.height):
...             r, g, b, a = colored_mask_rgba.getpixel((x, y))
...             image_2_with_alpha.putpixel((x, y), (r, g, b, alpha))

...     superposed = Image.alpha_composite(image_rgba, image_2_with_alpha)

...     # Show the mask overlay
...     ax[2].imshow(superposed)
...     ax[2].set_title('Mask Overlay')
...     ax[2].axis('off')

...     plt.show()
```

<img 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## 4. 可视化类别分布 📊

为了更深入地了解数据集，让我们绘制每个类别的出现次数。这将帮助我们理解各类别的分布情况，并识别数据集中的潜在偏差或不平衡问题。

```python
import matplotlib.pyplot as plt
import numpy as np

class_counts = np.zeros(len(id2label))

for example in train_ds:
    mask_np = np.array(example['label'])
    unique, counts = np.unique(mask_np, return_counts=True)
    for u, c in zip(unique, counts):
        class_counts[u] += c
```

```python
>>> from matplotlib import pyplot as plt
>>> import numpy as np
>>> from matplotlib import patches

>>> labels = list(id2label.values())

>>> # Normalize colors to be in the range [0, 1]
>>> normalized_palette = [tuple(c / 255 for c in color) for color in sidewalk_palette]

>>> # Visualization
>>> fig, ax = plt.subplots(figsize=(12, 8))

>>> bars = ax.bar(range(len(labels)), class_counts, color=[normalized_palette[i] for i in range(len(labels))])

>>> ax.set_xticks(range(len(labels)))
>>> ax.set_xticklabels(labels, rotation=90, ha="right")

>>> ax.set_xlabel("Categories", fontsize=14)
>>> ax.set_ylabel("Number of Occurrences", fontsize=14)
>>> ax.set_title("Number of Occurrences by Category", fontsize=16)

>>> ax.grid(axis="y", linestyle="--", alpha=0.7)

>>> # Adjust the y-axis limit
>>> y_max = max(class_counts)
>>> ax.set_ylim(0, y_max * 1.25)

>>> for bar in bars:
...     height = int(bar.get_height())
...     offset = 10  # Adjust the text location
...     ax.text(bar.get_x() + bar.get_width() / 2.0, height + offset, f"{height}",
...             ha="center", va="bottom", rotation=90, fontsize=10, color='black')

>>> fig.legend(handles=legend_patches, loc='center left', bbox_to_anchor=(1, 0.5), ncol=1, fontsize=8)  # Adjust ncol as needed

>>> plt.tight_layout()
>>> plt.show()
```

<img 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## 5. 初始化图像处理器并使用 Albumentations 添加数据增强 📸

我们将首先初始化图像处理器，然后使用 [Albumentations](https://albumentations.ai/) 应用数据增强 🪄。这将有助于增强我们的数据集，并提高语义分割模型的性能。

```python
import albumentations as A
from transformers import SegformerImageProcessor

image_processor = SegformerImageProcessor()

albumentations_transform = A.Compose([
    A.HorizontalFlip(p=0.5),
    A.ShiftScaleRotate(shift_limit=0.1, scale_limit=0.1, rotate_limit=30, p=0.7),
    A.RandomResizedCrop(height=512, width=512, scale=(0.8, 1.0), ratio=(0.75, 1.33), p=0.5),
    A.RandomBrightnessContrast(brightness_limit=0.25, contrast_limit=0.25, p=0.5),
    A.HueSaturationValue(hue_shift_limit=10, sat_shift_limit=25, val_shift_limit=20, p=0.5),
    A.GaussianBlur(blur_limit=(3, 5), p=0.3),
    A.GaussNoise(var_limit=(10, 50), p=0.4),
])

def train_transforms(example_batch):
    augmented_images = [albumentations_transform(image=np.array(x))['image'] for x in example_batch['pixel_values']]
    labels = [x for x in example_batch['label']]
    inputs = image_processor(augmented_images, labels)
    return inputs

def val_transforms(example_batch):
    images = [x for x in example_batch['pixel_values']]
    labels = [x for x in example_batch['label']]
    inputs = image_processor(images, labels)
    return inputs


# Set transforms
train_ds.set_transform(train_transforms)
test_ds.set_transform(val_transforms)
```

## 6. 从检查点初始化模型

我们将使用来自检查点的预训练 Segformer 模型：[nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0)。该架构在论文 [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) 中有详细描述，并且已在 ImageNet-1k 数据集上进行训练。

```python
from transformers import SegformerForSemanticSegmentation

pretrained_model_name = "nvidia/mit-b0"
model = SegformerForSemanticSegmentation.from_pretrained(
    pretrained_model_name,
    id2label=id2label,
    label2id=label2id
)
```

## 7. 设置训练参数并连接到 Weights & Biases (W&B) 📉

接下来，我们将配置训练参数，并连接到 [Weights & Biases (W&B)](https://wandb.ai/)。W&B 将帮助我们跟踪实验、可视化指标，并管理模型训练工作流，在整个过程中提供有价值的洞察。

```python
from transformers import TrainingArguments

output_dir = "segformer-b0-segments-sidewalk-finetuned"

training_args = TrainingArguments(
    output_dir=output_dir,
    learning_rate=6e-5,
    num_train_epochs=20,
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    save_total_limit=2,
    evaluation_strategy="steps",
    save_strategy="steps",
    save_steps=20,
    eval_steps=20,
    logging_steps=1,
    eval_accumulation_steps=5,
    load_best_model_at_end=True,
    push_to_hub=True,
    report_to="wandb"
)
```

```python
import wandb

wandb.init(
    project="segformer-b0-segments-sidewalk-finetuned",  # change this
    name="segformer-b0-segments-sidewalk-finetuned",  # change this
    config=training_args,
)
```

## 8. 设置自定义 `compute_metrics` 方法以增强使用 `evaluate` 的日志记录

我们将使用 [平均交并比 (mean IoU)](https://huggingface.co/spaces/evaluate-metric/mean_iou) 作为评估模型性能的主要指标。这将使我们能够详细跟踪每个类别的性能。

此外，我们将调整评估模块的日志记录级别，以减少输出中的警告。如果图像中没有检测到某个类别，你可能会看到类似以下的警告：

```
RuntimeWarning: invalid value encountered in divide iou = total_area_intersect / total_area_union
```

如果你希望跳过这些警告并继续执行后续步骤，可以跳过此单元。

```python
import evaluate
evaluate.logging.set_verbosity_error()
```

```python
import torch
from torch import nn
import multiprocessing

metric = evaluate.load("mean_iou")

def compute_metrics(eval_pred):
  with torch.no_grad():
    logits, labels = eval_pred
    logits_tensor = torch.from_numpy(logits)
    # scale the logits to the size of the label
    logits_tensor = nn.functional.interpolate(
        logits_tensor,
        size=labels.shape[-2:],
        mode="bilinear",
        align_corners=False,
    ).argmax(dim=1)

    # currently using _compute instead of compute: https://github.com/huggingface/evaluate/pull/328#issuecomment-1286866576
    pred_labels = logits_tensor.detach().cpu().numpy()
    import warnings
    with warnings.catch_warnings():
        warnings.simplefilter("ignore", RuntimeWarning)
        metrics = metric._compute(
                predictions=pred_labels,
                references=labels,
                num_labels=len(id2label),
                ignore_index=0,
                reduce_labels=image_processor.do_reduce_labels,
            )

    # add per category metrics as individual key-value pairs
    per_category_accuracy = metrics.pop("per_category_accuracy").tolist()
    per_category_iou = metrics.pop("per_category_iou").tolist()

    metrics.update({f"accuracy_{id2label[i]}": v for i, v in enumerate(per_category_accuracy)})
    metrics.update({f"iou_{id2label[i]}": v for i, v in enumerate(per_category_iou)})

    return metrics
```

## 9. 在我们的数据集上训练模型 🏋

现在是时候在我们的自定义数据集上训练模型了。我们将使用已经准备好的训练参数，并通过连接的 Weights & Biases 集成来监控训练过程，并根据需要进行调整。让我们开始训练，并观察模型如何提高其性能！

```python
from transformers import Trainer

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_ds,
    eval_dataset=test_ds,
    tokenizer=image_processor,
    compute_metrics=compute_metrics,
)
```

```python
trainer.train()
```

## 10. 在新图像上评估模型性能 📸

训练完成后，我们将评估模型在新图像上的表现。我们将使用一张测试图像，并利用 [pipeline](https://huggingface.co/docs/transformers/main_classes/pipelines) 来评估模型在未见过的数据上的表现。

```python
import requests
from transformers import pipeline
import numpy as np
from PIL import Image, ImageDraw

url = "https://images.unsplash.com/photo-1594098742644-314fedf61fb6?q=80&w=2672&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D"

image = Image.open(requests.get(url, stream=True).raw)

image_segmentator = pipeline(
    "image-segmentation", model="sergiopaniego/segformer-b0-segments-sidewalk-finetuned" # Change with your model name
)

results = image_segmentator(image)
```

```python
>>> plt.imshow(image)
>>> plt.axis('off')
>>> plt.show()
```

<img src="data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAGFAQoDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iivmX4q+KPEenfFHVrKz1zVLWzXyfLigu5ERcwxk4AOBySfxoA+mqK+OZvGHivbkeJ9bDDk41CXGP8AvqqzeMvFyrk+Ktb9f+QhL0/76oFc+z6K+JZPG/i9G/5GrXMf9hGX/wCKrp/h/wCOfEUnimG3v/EWqzxToyBZr2RwGxkcE9eMfjVQjzSSFKXKrn1nRXl/9q6j/wBBC6/7/N/jR/auo/8AQQuv+/zf411fU5dzn+tLseoUV5f/AGrqP/P/AHX/AH+b/Gk/tXUf+ghdf9/m/wAaPqcu4fWl2PUaK8u/tXUv+ghdf9/m/wAaP7V1L/oIXX/f5v8AGj6nLuH1pdj1GivLv7V1H/oIXX/f5v8AGj+1dR/6CF1/3+b/ABp/U5dw+tLseo0V5d/aupf9BC6/7/N/jR/auo/9BC6/7/N/jR9Sl3D60ux6jRXl39q6l/0ELv8A7/N/jR/auo/9BC6/7/N/jR9Sl3D60ux6jRXl39q6l/0ELr/v83+NH9q6j/0ELr/v83+NH1KXcPrS7HqNFeX/ANq6j/z/AN1/3+b/ABo/tXUf+ghdf9/m/wAaPqUu4fWl2PUKK8v/ALU1H/n/ALr/AL/N/jS/2pqP/P8A3X/f5v8AGl9Tl3D60ux6fRXmH9qaj/z/AN1/3+b/ABpf7U1H/n/uv+/zf40fU5dw+tLsenUV5g2paiw/5CF2PpO3+NUtT13VLTTLqRr+8+WJiHSVsg4OOhpPCSXUaxKfQ9corxnwnrGsT+GbOa51S8lkkXO97hmY89zmrmo6lqBhES6lfrLKdiFLmQEE9+D2GT+FRDDuUOe5c6yhPkPWqK8elh1CQkRa9rMJA6i+kbn/AIETUM2oeMLMEwam18gH3ZJWjf8AMHFV9Vl1J+sR6Hs9FUNElmn0DTprlSs8lrE0is24hioJBPfnvV+uZ6M6EFfLfxijaT4j60YlBkVoOv8A1wjr6kr5Y+LRlj+LOsspOwtBkev7iOkhM4h8tFEwXJY7SPY9RVSWPbGUDN8vqORV+8SaNw8QzGcPgDn3pt3te1EwGeMZ9RTEZDIzOEb0zmo4ZZbWdZI2ZJEOVZTgg+orYuLYCxglXrtHIrIlzncRQM928A6lNqvhWGa4meWZJGjd3OSecjP4EV0+K8j+GXieDTnfSbkPi6mXyWUZAY8c/pXr+K9WjNSgjza0eWbGYpMVJikxWxkMxRin4oxQAzFGKfijFADMUYp+KMUAMxRin4oxTAZijFPxRikMZilxTsUuKAGYpcU7FBQN1FADcUuKPKT0/Kk8ohCEcgnuef50XHZA6b1K5Iz3FZ2rCSLR73lpA0Lge2VNaJMkYJZd4/2ev5VR11x/wj+oYIDfZpCAev3T2qJvRlwWqKvhIr/wimmoBhki2sD65P8AjV2FvtOpSv8A8s7ceWh9WOC35cD86xPD0rW3ha1vHZnxEN5Y9eOMVv2EH2exjRyGkPzsw/iY8k/mTWGHlenFG+IjapJljKoCSQMmqckz3btDbhti/fcHGfYU6SWW4A+zsFQtt3Y5Y98e3vUsWnx28QEBKOABu65+vr1re9zCyW56fo67NEsFxjFtGMZzj5RV2qmlZ/siyz18iPP/AHyKt15EviZ6UdkFfLvxokjj+ImpAsCzmEbfbyU5r6ir5c+LdujfFLWWf+MQAfhClJAzgxdfZ0CsSUByCOcGnO6OjBH/AHcvK8fdbrio0bZc7GU4J+X6+lJFGZTJDIpB2eYn9P50CLAlLaaYX+8eF981n3Vm6QqQd3uetaVtEHtmhOTjIz/n3qOc74lKsCxJBH0PNAGXplybLVLW5Gcwyq/5HNfTFvPDdQJNBIskbDIZTkGvmCTiQkAgZ717F8IrwTaLfWxI3xzh8ezDH/stdeFnZ8pz4mF1zdj0LFJipMUYrvOIjxRin4oxQIZijFPxRigBmKMU/FGKBjMUYp+KMUwGYoxT8UYoAbijFPxRigBmKXFOxRikA3FLihmCDLEAepNR/aUK5QNJkkAKM5/HpRcdmSYrL8RMkWgX0hKq4gcox4IbBxj3q8BdSnnbAm3/AHmz/IfrWL4ht4f+Ef1GVU891tnzNIc447f/AFqib91l017yMW2khu/A0Fv9tEdyV3JiUbjJnIyO/PrUlnqNxYQxaZfjypZFEQmDmSKT+8Q3Y4zx61qeE7WFNGsJWUNJLFt3N2xzgVtXGk2NzHsltYyMEcDGM/SuWjBumnE6sRNKrJMWCNfMwgXZCoRQo6cAn+lWNyhgueSM1zyQ6zosshhVb6wDEiIcTKOMYP8AF/Ormla3YaoZhGx+0q214JFKuvoMH/PWuiM+j0ZzOHVao9b03/kF2f8A1wT/ANBFWqq6bn+yrPPXyE/9BFWq8uW7PRjsgr5g+L6ufidqbIc7RDuX1/dJ0r6fr5k+K5Q/FLVRuwdsIP18lKSBnn1wEaVRImcnIPQj2p5Rnl8qPIOCAexXtz9c1YhEd1LPG6hkUhenQ1G+bKVI2fJA4PqO1AivbystyZtwCH7w9Kty+UYyoVQ6twwHfNVinlyrI0BEbj5xj9aEQXQlCs48tg+4Hkj3pgZ9yqyFmXhieV9DVzwtqtxpmvWbxzvEjToJNrEBl3DIPqKRrczF1xhl6HFZTbo5cnqD+tEXZ3Bq6sfVGKMVyfw21WfV/Colup3mmjmaNmc5OOCP512GK9aMuZXPLlHldiPFGKk20mKoQzFJipMUYoAjxRipMUYouBHijFSYpMUAMxRipMUYouAzFGKfijFADMUxiTxGAT6noKmxS4oGVhABLl/nJHVu309KlO1FJJAAp5B7CmiEbtzHcw6Z7UAReW03L5VP7nr9f8KzPFKhPCmphQAPs78D6Vt4rF8X8eEdTP8A0wNRP4WXT+JEfhmIN4YsDj5lQFT6GtxeVB9RmsvwsuPDVj/1zFa6ptGB0rPD/wAKPobYr+NL1ITKvIUM5BxhRWHq/h19WkF3HILG9i5hnjGX6dG9R7V0YUKMAAAdhQVyMGtWk1ZmKbTujtfDyTx+GtKS6YPcrZwiVgcgvsGT+ea0qracMaZaD/pin/oIqzXkS3Z6S2CvmP4vwJJ8QtYY5DAQsGB5GIUr6cr5d+LMk4+KmtxlQ8LJFt56HyI6EDOPikS1t4p2B2OMOcdD71JdL9pdbiD/AF0aq23qWB7VNBtms9kkeOMEcH8ayorj7PcJcLuKxfJg9xTYkX7uVZIEQHcxUtx121Bbj7O8TErhl5b1U8Y/CrRtA90XgjUb4y456H0/HNU5LkfZTtUb933W4KtQIeYJLVVmkw6qmN3Q1kXybpWZBkMQwx9K2mSW4RYGDbGO5WJGUbutV3gRopFQ/NEoVlI/I0DNb4Yao9n4utreS4aO3mDhlL4UnacZHTtXvoGRkdK+UnVops9CecV9KeENWtdZ8NWc1tJvMUSRSggja4UZFdmGndcpyYmGvMbOKMU/FGK6zmI8UYqTFGKAI8UYqTFGKAI8UYp+KMUAMxRin4oxQAzFGKfilxQBHilxT9tG2gBmKMVJik20DGYrB8aceD9TP/TL+orottc945+XwZqR/wCmaj/x4VFT4GXTXvr1LHhlceHLH/rmK18VneHFx4esh/0zFamKih/Cj6GuJ/jS9WMxS4p+KMVrcwO4sP8AkHWv/XFP5CrFV7D/AJB1t/1yT+QqxXkS3Z6cdkFfLvxXJX4s6v8AI3lkQhmxwD5EdfUVfMXxSuZIPinrgcHyj5BDYzj9zHQgZxLQb3UpOYnTJQ9jUmnxZuGin2ncNwHb3pt5JGYvOjADKcNs6EGq/mFpoYlkIJIYkdQBR1F0L5t5bO6m+ykKWXCBunPpVKV08tJTGCImG4nv6/rirzXjNslkG1UYAt654p0dijXYAIeMgkqw6HNUIdPtQRTICQzBjg+1Q3MyvC0sZVkdgo5wT61NIAY3jXJKHCqD29KhyEiRYYuBnAI6Enp+dJgjBvYcsTzlRycda9D+D+vyxX76CUUxT7pg5J3Bgo4/IVx0luMtlcxrkA55HtWfZ3MulatBcxSMmxwwKnBxnmqpz5JJinHmjY+qMUYqvpeo2+saZBqFqWME67k3DB646fhVvFenc86wzFGKfijFFxWI9tIRgEgZPpUmKMU7gV/MYEB4mGe45xT0ZZFypBFSHgVA8UkhLAbD2I+9SuytGS4oxVdWvE3F40kUdNpwx/PipopkmB2nkHBUjBH1FCkHKOxQMHkU/FV23LOEBwrAsc+2P8aGwSuS4oxmonkeR/KQEDu3pxUyRhFwAfx5ouFrBijFPxRii4DMVznj7jwVqP8AuoP/AB9a6bFcl8RXP/CI3ca9yhP03is6j9xmlJXmvU09CkK6FZqi7iIxk9hWqkZB3MxYn34/KqWhxkaHZsvXyh+NaYUAcDFTR/hx9EXiP4svVjcUYp+KXFamJ2Nj/wAg+2/65L/IVYqCy/48Lf8A65L/ACFT15Ut2elHZBXzF8V5k/4WhrUZGGAhx7/uI6+na+aPinaRXHxL1kuGDboQGU8/6iOhAzjFsluUDBtpYAMR0Ix3qkluLe9e2un5U5jk6ZqzbedawuJgZFRtp2DnHrio7+YXLhhAWjwP3gbBBpkkljO7yXEYQtGGwGyKm01ViuGZnkIwUJk4CkHoP1qH7WyyII4sPsBYvwAPX3qWQrcJJbS/KGbduB4HOTQBbulCLK2wH93nPvzUDs00jHcPMVSdo/jweD+VJBIES5t5pC4jHVu6Yq0IyJoBGFIPB9fr+n6imBnA5txIQoBYnB4yc1nXVt9ogURDLxKSR7f/AKq3fI8iF48AqhYjI7g5BqzokIvdaghKIBJuUjHB46fnUvQpHYfB/XGudOm0medmaHBgQjgLzu5+p716hivCdPhm+H/xLSGZTHaTH92c53RMeP5Yr3lcMoYdCMiu3Dz5oHHXhyyuMxSYqTbRtrcwI8UYqTbSbaYDMUmKk20jJuGD0oCxAzknbHgnux6LVeS2WRhIHbzVBxKDjHt6Y9queSo7Zx0FQk/vMzAlQOgGQPr70mUivDeKHW3kwshHDfwv64P9KSWWOSeF0dXVc7ivPbP9KsTxwX0Qh4eNjk4PQCs27ums7iC1uh8vmApMBwy8jB9Dzj0P6VEpWWpUVdl+GPM3zDDquTg8c/8A6qtAetURciN5mZGBDhV3EZ6DgfnT5tRhtIPtF5LHBD2LHr/9f2pqSBxYsmo2cLqklwiu0nlhSeS3pire2uNnjtPEHjLSdQsLuO4gtw3nRhgu0j7pwcE8n36V2rYRtrcH3pRndtBKFkiCd/KjyBlidqj1Ncj47t3g8F3zSuHkkkiyQMADeOBXTwMbu9a4DHyY8xxrngnu39B+PrXP/EiRP+EQmQNljNFwP94VNR3g2XSVpxRu6IuNEs/+uQrQxVHSnWLQrWRzhFhBJ9OK5HV/ijYWYVbO2kkZicNKNq8Hkev/AOulCcY0437FVouVWVu7O5mkEMEkrAkIpYgdeBWboWv2XiGCaayEoWJ9jeYuDms/w74mtPGNhdwhFiZlZfKJLEIeMt0xznjPSuU8Eahd+Hdbn0XUrOQTXVwI42HHQH5senvTdTVW2Zmo6PufQFl/x42//XJf5VPUNn/x5W//AFzX+VTVwS3Z3R2Cvmv4pTG3+JermZlETmEK2PunyU4NfSlfMXxfmkPxD1qPaNitAOvbyYzQgZyKXi3V1KI0KhSMN/e96lkMEMrwqM/LuZT71Dbz7rfiIB41+ZV6nHWoLmKaWSOcNs81cDIzjFUSCxvIjK4VDtZRn3p8Mk8sDQvAc7tmQO2OKdB5iQDzANwGAT0JqVLuVoyYEUTRt86E8ED3pAPVVS2iZIljI+Vyw4IGQRTifs9tbzuxJikKkDupzgH9KbJIr6fIboBVaQMBGeQD1plzMRFbk7giuGLHnPHf86BltbkNGZmQgHqCegzir3hcRx60rqGVhnGBnJ7AfXt71nQXCzQPkMgG7B/rWz4Ush/bEiSrI0ZRv9WSCOPb65FKWwR3Oj+KuivqWjWesRqqzWyZJYcvHwePxPf1rq/AGvJr3hmCQsvnw/JIu7J+vP8AnitO6lNzEkdzbG4s1gG5gB8zHjkdR0x+NeV+BLmTwr8QbjRZnZbW4JVUY4weq5Hr1FGGnyysFeHNE9qxRipMUYr0rnn2I8UmKkxRii4rEZHFV2aSJmYjcmMkZ6f41cxTWjDDGOvWhsaKi3SyLkRSEfh/jQLiBn2FipBI2sMZP9aW7iOQ8TBXA6dNwHamA21yAFAdP7oGcev41PMyrIJLVXkMgJSQ9HXqP8ayr2ZJYXzF506OgdQflYbgOfY5PHXmrvmM1y9pIx8peY2PViR90/Tr7/gaNXtgNOBiIiaEqRt9MjI/T9BSk7plR0Zz1tLcaXe+Xcp/olxMUtm3l/Lbd9zp0ODj6EelW/FmhPrunwxQXQiaN93lyDAkOOmeoNWLq0t7rQjBN5jAynHUY+fjHv0pdOvWt5v7Mv4v+JiQTE+PlnT+8PTHcdvxFZrX3XsW9NUebf2DrWh6rBGsTQ3c4YQeVIG345P+TXW2UfijUSkGoiGIxx5Kgb2HPcg/LkdMZPWrHiRmTxV4bFviSYNNjPdsKOfbmuvtLJLaLBO+Rjukc/xN3NZxormdmXKq+VXOOuvEFtoV5DaXcRhjC43lX2+w6c0mvXOm+J/DslrbahbRqrrKzJ8xAU5+71ruJbeKaMxyxJIh6qygg/hXm/xG8L6TY6It7Y2q2tw9wkZMJKgg5/h6U5wlGL10CnNSktNTskTb4fWBJjG6wACRQT268c15mbN21NbS3iivYXPlSRSTIPKOCSVyFbrk9McDv077SPD9tHoFmv2y6dmhUBw43EkfSuG1Dw00nia4sdfmnkBjIsJ3YfODheeBkjdnjHI5qLyUVfYqXK5PuTeFL228M+N5dDitC320qPMd+YuCSCcc9h25q14hk1O18XaTaMIi6zJMtwIlQzDdlwMdAO/POTmubvdNl8H6hZ3N3cSXkTuyQXG37kYZWyDkhtwLcZ4rvdfs4fGXhW21SOF0e3/f+UoHmkbfug9uCD78VcXdNGbWtz12xdZNPtnRgyNEpVh0IwKsVl+GpHm8LaRLLjzHsoWbAwMlBnitSud7nStgr5i+LrLH8RtZaRsJmAH3/cx19O18ufFeJU+LWsvc8wuICmegIhjH+frQgZy1pcx/bmBAG5AAwHDVBqd0YrmJ1DeWFAyBxnNLqSpAtrKi8crxz9KLSEkujdDtbnnOafkSu5ZedJraOSI5B6qMZ7VBI/kuJIEY785xznFSw20UUsgQck5+nFMbet1GXQKjPgY5yaLDuWLbPkSSSZCs/C9xnAI/OoHXzWlRsjB4JHAq1L5skcfkkj5mBz074z7VUlEjwSiUgZUtkDofSgRfjVYY9vlkREMrHrk+tdb4HKtrKSF+IkyxzwRxya40SPbx8uZIWPpzkj/P5113w5eOTXWViuQhJDHgjAz+BGetTLYqO56tNAVnuI4GdMxh23ncOGGF/L+dcH8Q/DsMFj/wkViCLu0kUqV4wquwbgduh/CvTJvKWRAhAihhICdgFI/oCPypgsI7vSJbSVVKPvJU8jY27j+dYbGpwB1jWNXbTNSsp5xYzLGriE8KwPzbgOn49q7nT5pJJpYnbcqgEE9a878CyXHh/wAR6r4WuWOyPdJbZb7ynpj8D+leg2U0MV26uwUvhVB7mumnUk6iuzKpCPsmkjSxRipMUYruuefYjxRipMUmKY7ERjB6is0q1tOFjO1JiQHYcB+pH4j9R71rMMKcde1VLy0S4gMLttAGQ3909j+fNTIcTBvpL2OKA+bFtuLlQrAEFPQ/TAqnJrkmpNqVisTo8Fszu56A7cnHetHUJpprawEaotyl2IwrZVQ4RvboeDn0rior69stV8QR3SLLNLH5YkjOQpYAKM/LwRxnsetc1R22Oqmu52VrBLcwQIJwCZpMlewDN2/L86r694aS8WK7fU72Oe3O+MpIBsI7jisTw0dSuFee3/eIjEJ5khHXk9Mn861tcvNTtNIuXubK3ELoFOLg7hnAPGOetJSTjsNxtLc52+1W7i8YaBb6nFGJLTfm5icbZ1O3DAdm45HvXpC38TSLEUZHb7obAB/HOK8g1PVRPqek3tzZRrI6yP8AI/3lHAXnp9w/nXQ3HxMSWxeOTQXO9CDhgOo9amNWS2HKlFno4ORkYck4AU5/z0P5VxPxRa4h0KzPlOB9tjIIwRnnAPfP6V0fw/vpdahm1GG3S1Ty0VVYbi3LAkn6j61i/GO4ma00fT5NrNJdiQrH3A45H40TrScWh06MVJFLwp4ss2h0/TXkLzKnku5U539AuPy5qt8Rglx9lk8oCZAwRZXjBI7mPkndnHbB+uKh17R1gsrHWfD8Eh2wjdLa23mQKOjA7f4gQScg/wAhU+qeI7CbSLq3nso2M8G+EIihwEGGOG4+8p+uayVaSjysbpR5rnOeLLjWtd8OafGtg0rJGGYQR7to2rgn+6cg8ccGtH4d69b6xox0GdEgkt42dbguCXY5/gPB445rM8PagNUtljvNGmvYGxD5EKbVJ3KVyegyTjjGAO/FdV4V8E6HBql4t7YT2KTxK1oPtBfIH3zwOOSOCT1pxnJ6icYo9e0i1FjotjaAowgt44gUXap2qBwMnA46Zq5VewgjtdOtreJi0UUSIjHqQAADVimUFfOHxYRf+E81Z3AIXyj0/wCmSV9H182fFeS6/wCFh6skKKygw5BHUeVHnmnEmRwTXiTzRwomFVwWYjr2H86vXTrBNCzA5ZimR3H/AOuqLQTXAW4WBI1UFSpPJ96stYAx+fcy+bIpBUZ+Uc9KoRWe8SO5lZwwQMF8zHyninzGJ0MzEvGhBKjsfX9anmkQSyxSRjbJFu+jDtj8RSxyYmWSYKkDEKSRgscccdqAIoGEmWjumEW0OE9h1x39Kn27AH2O0Kjc27qOf8mmbYXhEMZKyKSACCCBnt+FSw3qR7o5hgLwWI4IyVAP5UgFubaOThJtm5scfQf4V0PgG2X+0BOgkYhtrIvU5GCP1rBiMf2SAQohKgqcc4xxXUeBrdmvJFgMkZVwQwwcNnjjuMj8qmexUdz2XVgDZeYieZH5R3/MAF+U/wA84q1p2DpUaEdsDd1/x6561n3NlD5QmZAjNEq43YXI2nbgnG3I6exq7pakabZM8wklaFTvA4PfmsTQ83+JemzaZPpvii0XE1rKEm294zjr9D/6FXXaNbQ65CmpRzEIgSZQBndnHHt0rV1/S4tT0drOT54pl2EdODgdvrXI/Cm4lsrTWPDl6/8ApunyBQP70eeCPbn9aqn8SFJ+6zusUmKk20ba9I4LEeKhlaVZFKxlox97B5/AVa201gQpweaTBEEcscq5U8jkg8EfWmJ85diytg8AdBWfczhbhmuB5a8BZl42/wC8O4zTfNa4bMUscDgZBA4kx1x/nI/nPOVylDX8W11YXbybYftIWQBOSArYOe2OaxdOtYdS8Y68AQ0bxBD9CuD16GtjXpoEjsXjeGO4juTuaZxwfLf7x9OlcZpEOpjUdct7c/8AEwEJdBCSNw7r1XBAOM9uKwm9WjeC0R03hm38jTtNtYc8hxM3f5ScZ9zz+VWfGXlSaRcRKhcxKrucZwdwABPb1/Kub8N3Oq2OmJdxp5ixQsZEmb+JeD685zxjnNa+vJr9n4bvZbq3s/sxHmShJSZGJYH+7gc49elOMvdtYTj717nO+KtOQp4el8pAq3k8eE4GCcgfoa3fHlnbRrZG3t4kUq2fLUDPT0rir/xG2pTaXG9oYRFM8xLSbixK/oMmtHUvGqarbxtd6G+6NOPLlCj+VYxdmay1R6T4d1qw0VrixFrcPKzM0cVrAXyPMcY46fjXI/E69v7rU9GvXszZIZRCiSuGkPIOSAMD8zXafDLU11fSbu7+zG3fztjKWyTxu6+mWNYHxjWNrzw4AFMv2o5x128fpnNKT0KglzK52mhaZp1h4Yi0xZi8TqXbzTzuY7j0x3NeReMvBTaDcwX9vLaMsxYNFAGVFbjGMkkZA6ZI/PFe7ae0M+m2zxskiGNcMpBB4rzD4x6kLFLW0jt93mRtICq9DnHP4ZostLifWxzOj67aaFrKac0aW8BuF80BywUgg7gozztVc+nOK9Jub1pPCMGqbo3hhgFxDPENzKMencYOCK8mbWNP1UNaWmk3ct7dmNSkoDjcMAYYnI575xzWncTSwaVFpNtd6fptxFbxRy/apGVpMjBAGCjdBgk8Z7UkuVCerPc9Nma50u0naNY2khRyinIUlQcCrVZnh2FrfwxpMLsGeOzhRmDbgSEAznv9a06pAFfMvxXvVt/ibq5ySVa3DAdswpX01XzR8Vyg+I+tqVXLLDk46/uo+tVEUjmJWEkBA5GB0FUryaRIIwY2KhxyAOnXNOAuEIeErJDnle+PQetWIpQ8jxzKVbGRnoeO1USUYtSSa7DpG4GCpYjp74qb7ZHd3E0So0qoB90ZGakYRwWBaAx54yM471BBLdJeozQIscgPBbnP1oAmFwWQwRRt5indmReF/GiKWEw75plMki7G+uf/ANdSyLOk8E6YEMpCurDlcU1ZYPnYnk5jyRxwfXFAEo2wpiPYIydzMrcZOK7X4dgw30uI2l2NkBjyx46GuDiEf2ZoQoZ+SVLdR2Nd78PBuvNu8hQ/JAJbkj/A1E9io7nrnm+ZbyozDEeWYgdQRwefr+tN0PTpbKK3jF27QIn+rf7wPrn0p0qptnheTykeJlTLckYOTj8zWlGQQAH3hEGG7HgdxWBoVNQujbvZQBMpPNs3A8ggbv6GvP8AXkbwx4/g8SRjbZXUgtL3aMAbvusfxH8q7nV4HuX0to8kRXm5sddpVh/WodZ0i38QaRqOmyfckjCZxkh8ZB57jiqTs7iavoawIYAg5BoxXKeE9ee78FGWbH23T1a3uFb/AJ6Jxz9eD+NUbXxDdXQlF7dpC0mfKeIk+Tt5wwA6kD9a7faK1zmUG2dfJqNlFc/Z3u4Fm/55mQBvyqcsuCc5GM/hXjuoaJNpnjWCeW/+2NdAy+aEIB4Hfofw6V6fcXCWtjC7/KuFTcOccdDn6frSVVN2B07K5zvieaymN628vIlsxC+YwGcHBAzircek6QsSq8EbROABKg3Mjn3569j+FcRrci2+pz27YnJgmKspzhQDz/L8a7mx1S1vbEtbSQzxoBG6I4Yj2x+dDaBJ2K1/DLDd6XbS26IVuTsukiA3jy34K9j7d8cY6DEto5j4j1u8j/19ribbHk7gAdy9O4z+OKj1PUt/iSLRp7tWto/38TzE7slSANxPbOQfpWn4SjeTxRrKXMhmZlUO+MbsjrWTd7mqVkiHwKBfLIqLmNZzLIexwxKLn6ksfoPWu+u7KK+s5La4XdHIMMPauW+GkSJ4am2KAPtkoGPQHArsJZI4IXmldUjRSzMxwAB1JrWGkdTKfxHlnirRLMeOvDmmwI3lndJIpbPyllH9DXcv4S0d02Na5XGCNxxXnml3sniX4uG9iLy2sTbYW5AWNVz09N3869gxUwUW3oVJtWOf0h9D0m5v7NLWb7QJF2wRRszsNo547deTxXB/EZLxfE+jXMmn/ZIbjESJM4kbCtklgDgZ3dM9q9P055INW1EpJCpkkQAODk4jXPP41wvxPupJfEGg286H5XLgx/dbp69xWFR2vY3pK9rnd+FtKg0/RYtOt7y5cW/8ZG0NuO7ge2cfhXnXxU102et29nFaC/McZEhmjY4J52gjGfwr1GG4ltVJS1jy+GbEmMnAGf0ryr4q6wP7Zsg8fluIsHD5xz6/jUSlZXCxzVn4plCQo2jokYYOwUMuCDkHG7+tdVq9prGteG4refwvPcQvArwXMUy+chwCCCMkj1B/OuFW/JYJsQSEgkMvtXrOircnTLQ2fjCBZDAn+jyFW2HA4wT26ValGULojVSO48Nwvb+F9IhlSRJI7KFGWQ5ZSEAIJ9a06gsfN+wW3nSLLL5S75FGAzYGSPqanqhhXy/8XPOT4oaxJ5TNGRAq47/ukJxX1BXzN8W7nb8SdViUfMPJIOP+mKU0JnFH93pbtDIW3DAH4/pUm57mBF2bYzwcn5qpwlxcuIpwBgF0Izk/SrQuIgfswUmRW3c9KZJFqMapKkqLlAu1gOuBUkN21xCJIVHyHHzn73H6VIGNwn+rZMEnnB3DpUaJ5BVNwjiIJYle/vQBrLLBd2Hlll3Lt3KGwVIqkn2cRNatIrEnBUsATmmWttb3F85LjcAG+XHI470SQLdXCOMwsuVfjqoNNghipI0aKAA/3Se4UjAz+lekfCdZLu5vB5qo8TmJZEXngjsfwrgEtpRJL5TAKgEg3c7iD0rufhJDHFqM86sFmnZnVD2GRkD1zj+VRPYqO56xfEQwPOmPKETuHIz26ex46VoWzSpbpI0O2MIDtx8wA6cfSqGuyzrplw21APmRUJPORjt3J71et5VmeSLerBV2khs8EZ/L/CsDQLkKEhwSP3w47kj1p8WFMjDgs2cEemB/Sq1++9rXuROvHUEc/wCNTwKQ85kJb958pzz9KYHD3MEei+LLy1EoitNbhYMx5VZ1Hy/Tcu78qxYBA0zTQ28qiOIrK/lsRvbcAeCMZ4H4V2HiXS49XeWzZghETSRyD+B1wQf5V5zY6rBY/aS0cd2jt+6+UjcvJBY9z83X0Aq4yeiJa6mv4ghK+JtNlt2MqCAjzTzk+hPX8wPxrY8R6m9vZyW8smx5F4ALKCwXIxkkA4rKk8f2MOmJFJpzmUDacIu0D23AnpiuP8ZeIINZjSe2t0hIXYQFAK85HQdf8atRsyXK6KP9oTtct9smMhiimjiwwGNy5z05zXpmnQ2q6ul1LIP9TuOAGLEnABPcYyfr9K8H+1fOrNhiMg5HXIwPyrpV8TD7OLfysONreaOo5J49M5rS5DR7Je6Xaf2xpce1lS4kdWOBypQsuCemGHGKreExInjHXVmlEsiPtL4xnBxWbpV/c6zo2kS2MRa8g8xIzOSckAsD3/2fypvgCY3ep6zq0zfNKTOy5IUZJY9s/pWXNuacux0HwwdpPCRZuP8ASZP5iuwngiubeSCZFkikUo6MMhgRgg14x4T1O9tNKjhindFDs+zdjk13fiHxLJong2PUY5FedYVJDHliTjPvjr+FaRqJKxDhqefeBJ7Hwt4x1eHULryre1nlhiZ/Xdj+Qr2ixvrXUrOO7s5lmgk+669+xr5Xm1OXUE1WaZ/MeaXzjIw5J3DNdt8HvFM9rro0WSZBaXWWxIed+ONv19KUJW0Ca2PYvsrXep3DEhFhuV+Yclh5aEqQRjB4561yHxKbbrnh1BtUPJKzOVHYDAz17121qwa61PDgFbleSf8AplHXnHxV1NHvdGjt5oS+2YOQA5j+7+POMcdiaKvwMdP4keeyX9xHLEkepXEQdiGKzOMkfXHWs1pJp7h5Lmd5eMr5j7j+tb9z4dvNdmVtLs2ZQFfYzKh6H1Iz1HIFRXXgXxDFZ4eww2CcCRSfU965405yXNZ2NHUgna5l3l2EkBB24CkHPQV3VlrfhecabHd6HcFlt9skqKcOQB83DCvOdT069slgW9tXhLrhSw4b3B6GtLQr7Wf7QtILN4y6hoY1cfwnk/8A66qMbKwm7n1Zo7W7aJYNaKy2xtozCrdQm0bQfwxV2qOi+f8A2Dp32lFS4+yx+aqfdVtoyB7Zq9Wggr5k+LpEfxE1mUfeHkYOM4/cx19N18wfFixjuPidrbGdsnyMx7sDPkx00JnD4mMvmRRgs6gE5wAferYX93/pEqEgZXnGDVDEVlLNHHuyVX7x756Vq29nbmP/AFcZL85K89PWmSN+dEttpXJVl56Zpv7yF1M4VyWJ46AD/wCvUU6XDpGExEI2OXbkEGknt5riJpBdqwUfLsXHP+RTAu3MKLLbyIwjY/KWUYypFMzH56pKd3yjk9Bk1DZxzE+XcnJQ7lJPrUplWdsZDDkMwGACKQFtPNhikjeTf77eortPADKjSSpHK6xybmw20r9M49K4NwY5FMc+VkUEs3zEV3ngC1uZ5GuIZFJHSOZMiQE+gI6D+dTPYqO56pqs8raZKx/dl4twYnDKeCCB6jj8as6NaRwWe+NSS3LSuSHY46t6n+VVtQjkk0Z0lgZZTKkG9tpyGcLuX8xjitVZCZCS43rlSic4OM/yrE0Kl2yxLZgj5muAORjkqen6Vct38wSMuTzgg8HpWVrriJNNdvkVr6Hkk8dsflmtSBeW5OCSfagDAvmkGuAK3yfZ5cj8VrwNZPJV7Z5EWMt8gI5xnJGfavedfu7Ow1OK5u7iOKEWsq7nOAGJX9T0rwC7VZCwcbhurakZzY66IIYLLGwyTwvXNUp7fbCZXyysMK6nv7irMAeCUfO0kXdGOD+Bq5f6bc3VnFdWlrdm1llKBtoPOO+3oauQotHLxqv2WeWTBAwqH0bI/pn8q3obRbRYi9osnnx7l3sARjvyR1rDicrY3kTJklg3P8OGA/rW5r9ykrwKnzmNNvIxg+lWuVK7Jab0PSPB19Z2ej2c10EiijmuAVkK4BCrjnkdxUPgkRL4f1yaRVCrYsCTgcbD61xoN0fAkEbRBIN87iRgcHmMcEd88fiK6vw+dngjxOQelkw/8dNc73ZqtkZHhu/t7OxSOaXY2wPlyccj1zio/iD4hs9R06xhs7qJ2jjUOEY56HIPbHP61mTxXA8PrO6xpG2xI3Y8N13flgVyd1H5V2F86KQBhzGSR+tWoaJkN9C1pkFxI9xbYKNJHgK4I5PStaw8OXun3cNyLtYpkcMjxqSUIOc84rIs7yeee4keTMxhO1unTpUtlruoR2sqbyzGNxvYZPI7fShcquDu7Holxqk97cySytIzzbXYqMLnGD368AVRS2e71ezVkG6MMzRk8sCD25HT39K52DUHvfDsdqrOs6lmZ+AWRRuIz1P3c/gK1vCTTXmrMkKPM827BdySFKjv7elKdeTpuAQilPmOt0jxDb2+pytHHGqoG224xgsMZA6YPX2NbF14rSZFRbS7WUqZEA24K4PJwfr07iuCLNdyrNArGbcyiUZwOMYHJ+uKjnubxWhMt1JOsSbVAXByeijjnn60RrVIxdmKVGDkmztb/UtKvvCiafq2nXMUZQhHZOUZeNwPXPf/ABrziXw5aaNNYXk+qb7W5/5aQ5DLwc5GPX/PFQf29rkixQ30yeTGxUK6jkk4OCOp/wAKsu9xLbRQSLFCpuDsdgRnBBxjJx19qXNKb1GoqOx9P+GjGfC2kGKRpIjZQ7Hf7zDYME+9alZfhrd/wiuj723P9ih3N6nYOa1KZQV8wfFvThN8T9WmWVomYwAsD1/cpX0/XzF8W2mX4l6q5YeSj2/G3r+5j7/nTQmcXLEluFixnLgZY8n3qzYkLbhtxIx8v4VFcwF7yJ5VzgEhfSo1LLuRRiLfuA9PaqJC4eRoSlzGmwYIK+v0/GlWDZbZWRm2jISNx19DipnfcgLx/JkKNw4bmqmILS/81gyrtK/KvAJ+lAIupcLl2XcGQYYNwRT4bKRch3XyicqQDnJ9aphxcTsDGD5i53Ac45HNXEne2kSZQ7weWMqeob+lIBVhHkNGqBQozkdOT/hXd+AJpLdLdXAffMIjs4IDBlzknoMiuEecSB3kTCnCsoOT1/8Ar16D8PoFkls3SMOyhnPrkDPJ7dOlTPYqO56X4hkvjZwGEIYTdQLIzDayfvU5HqK2TH5TuOCCNzZ7dTn/AD2rF14SXNjZRBXJa+hSTBxtUP8AqflH51rvGVMajkkhmyeg5/OsTQxPFG7ytK5Uu2pwLjA5+Y1tJM4UFSGDOwPbAGfz6YrI8S+WZNFEjDA1KIghTjgMa0YjhLfarkG4lDEj/foA8++J1ws9xa6dKgaLCyStnGMsSP8A0A155rtlbwbZbckZxvQnOMjgj24ruvijOba/iSS2by7lEjSUYxlXJOe/fH4msyfwo+t28M6TFAECsPU/n71cJW3Jkrnnt1deRbNIBkqOBXV+BviLDpGjfZrrSprp45pLgMhwpCxE/nkfgDVmf4dytEyb1YEEEnNVo/BF5Z25ghaPBBziQgnK7T+Y4rXnj0Znyu2qPNmnM13JIMqsjliufU5xXe32htue4E8clxMfN8qOMlEzyVJ7EH0FYOp+EL7SmeV1baBkHaSD34PTNUo9Uuw21pWx0Klu/wDkVEptbGkYp7nVpAo0nWI0SVN0UKr5v8TeahO3/Paul0ZPK8D+K13A7bVhkd+K4nTvEIgni82NGXq+5uuOTz29PxrZtPF1sVaAI0UUq5kBGRJ7ED+tcsq8k37pVo9DPht7gwKv2Xzrd9jlSjsGIXHIUe9Z+o+Fr+BEv2sVgtHViOTjIBz15GPT6V2keo2k8RYRooRcgsw2n0wRUc1/pd+qxtGkoX5mX7348D6dKSxqVvdYJbnn+nRw/wBqxpGhKeWMhTkt6/SoxZLJfoLchRwGK54OPfucH8jW8bdI9Z+12qJBEYhlFjYBfQ8+o5rLSCzS73x3jEZJIeMjBwR/h+dbKtCWuxNiMaVei1dLiEiRSPLCbR65zjqOa7nwBGNO1y2W4jFviH/lo478Z9snFcxP9ouIYY7W9HmjOSyY7jj6966LwtZmXWZIrqWPDRszSqeuemD2PNVKcGvdYK9yMFYGnH2tRG+WAK4UEMMAZ78E5HXNU9SkuDZzWcCq8AO5ZkOT1zkZxx8v6mty3h0w2Lx3qpncTEzZ+UnsM9K0LbV7NTLsRmZmOW68Z7H0/wAaVOMG43mrMJOSvaJw8lqmqT2w8wo6fNuYZJIHI/PP5itK70+8junkvUzFbuJWZ8kMHIUED1yP0rtrnUdFv7WzivNMaKSA7hKkio3rgkckGq/iqdNW8OzWVrOY55mQqx5wAd3b6Vv+6ppyc1ZdCP3kmlynsfhs7vC2kMM4NlCeRj+AVqVleGPM/wCES0bzmDS/YYN7KMAt5a5IrVqb31KCvmn4n4b4n60WIOww4X/thGf619LV8vfFWYwfFTXgVJ3eQUx6+RH+VOO4pbHKXNzI11EsKFwM7j2HoM1JaWcqzEyMrIPmGOpPpS2z+XZxvOyowHzFqRLtrkH7M21R1cjP5VZBZuVEsEisecHHsapCSKIywEMdu0nI6+9EmyOJi5HmMeCepqNnEYYMd7FOBjJNJgiW2kMgfKBWxhT3waFN2kzmaMBJAF2g5II70IgkVZCpU8EDdg+2fxqzJG4jT5gTu4BHc0DGRTJKrqcgH5cnrxg16F8NY0Mzt9pZXa3wIsnkbwCcDvg15xAvkgF4/MkYjp3xk8fpXqHw8szJc2kjwIFZyGYHnAUnH8+fas57Fx3PSr8M0Fqjs4C3ibSoIIw38XrxWmcbnOxsgYDdc5rLmvoTfjT7Ri8qTRNM7fMFXBPU9Tha02ARWOTuyMbTgH/69Ymhj+IVacaMCmQmox7jjt8wz+v61bedLVbYAkJ579Tz0c/0pmryoI7dWOHaZWUA4xjnJ/KsaeeSUGN4mYCR2XawxgsSD+tJvoFjmfio0154ad4c5S4iK8/MPmx8vHXn8q2LC3a10+3iKjcsahu+Wxzzmlu7aC4gMU1o5RiCRuHJBz61J5yjAKycf7BP8qaegBIrOjqQRkYyP/11xGoWWlid1lvZhIDzlSf6V2zSRujI4JUjoyHH6ivPdZsJLe9kfYFiY5XawIrWG5EincaXpcylf7WZQexjb/CseXwrpnmb49chX3aIjH61adcN90keuarTRAr0P0rSUL9SVKxz2p20djd/Z47mO4QAbZI+hzUG9txUEAKOxq/eWCsQQc45ArPeN4VO4jHcnrXNKFityWa5dogVc7cAU6PV7uN/MEjl8BSwJO70BFUwy+SdzH8quWnkNbTNIUL7TtWTP5jn8PxqLJbiNK4vLgzxxPEYfmy5R8JnaMdOBwD09agEqG2kmmVkZnRM7Sd4Iz36Y2/rUNyTborWErtaXIxs3ZIOeA2eh71YmuLm2dTJD50c6YkSQfLnHXC8A/rUpJDIrd5HUGGULGGwVZBlWxnt9K6rTrsLJJMs7HjBZDtYmuEtJFaaRgCSW+QDtnj+X8q3rOVV3M7MrYBAI53ZxSqwurGlN2Oiubu33B2ZZUJwQT908Ac1DLeL9oBDE7/7/T8qx2dGBeXa5zx32jsajIXyvMSVSM4xjnA9q5lTOnmNx7syFcXQDAjgtgc9eRxUUc7NeLFLPJtByjg8HFZ6ybo02whi3v0NTHzVUEsTECACBkD8PzqeRDvc+o/DnPhjSTx/x5Q9P9wVp1leGCD4T0Yqcg2MGDjGfkFatetH4UcL3CvmX4s5b4l6uvGP3Pb/AKYp1r6ar5k+LMbv8S9X3kiI+SAF4z+5TqatEs4JNOFzveWZmRWO1c1JE7WkJQDIBOPeo0nliaaOOLlc8nmnJAAvnNnzM5K+pNMkk5m05WcBn2Dn1x0p25orhGYApwSe47f1qG2utsLQyRnzAPu461dt1FzYlJFwWzux+lADXEPkOWx50fKkDkY9M0ivlcSStlmITJx70qqyzoJem3Oc8H/OKkvPKAWQYLpyvPagBUaQSJvbAfgEDgV7H8MreRNPVxOnmF3kJYcbRgH/APXXj2zzLZCwUoRzz9a9V+F+pCK2ubckYjjDpvH8LHt685rOpsXDc72S1ig1iG7lO2S4n2EM3ACpJjFXbu7js7Zp9wAIIQf3j2P6Vm6xe/Z72zaQxmSN3kKKwyRsZQQD9azp5pbpg9xggfcQDpWDfY1RDe3pu5VluAy4zsAbHWkj8gqSrTFvTe3+NWI1JwduM9yf6U8wgg/KpP0oWgFCSQK+D54HqMGmedtb/WzAf7UYP8qumzQj5oIj9aj+x2iEhtoP5f1qkxEKzsScXKn2aEj+tZeuWF/eQ4gngjHtlSf51q/YrKR8LuU56qxFMm0yPa2LmbGPu5Bz+lUnYVjiX8L6pIuSsUn0cVUl8M6sMgWS/UOtda1jaxOVa6aMn+Jjz/hUhhhRcRahHu92xn9av2jJ5Uefz6DqkZ+axlJHom7+VZk+h3vV9NnP/bNq9Oc3MDbm1K1VeuGPWqst9JLujaSwkBHP70UnJsdjyK80l4gwlilt8gkb42wTWW0LQFJFIZgfQ8V6tqVsohDpZWEgHpMf6MK5JnW1uC7aXAwznaxfH/oVS4voHqVbbVmUJGyIuQMgDjH+c1qxxxtCm+TZuywJPGPTHr1rmNRAa9kuLOA20ZHEKtnA+tZ817LJgMxOOM5rndG+2g72O5tPs5leOOFJAQN+1QR/+vBNY87xWuomJ43Cr/A3VcE1iWk0wk8yKUofY/zrTDkzvcTFZWkJdgn69RU+zcb3ZalcJY5GlJiB8hhgmTjFWYoZBuJnXym4YdOn4ULM00IDrhR0A+XFNknD/KiheMFtv4c1F29GWmi9BD5kewAjavJ68dyfTHJo+0Hy2YSMMZABPy9MdPp3rN81oTsU7vlLAgH8jVlJxHHneqhlztJOTxxn1HtRylcx9VeEP+RK0HP/AEDrf/0WtbNY3hD/AJErQec/8S635/7ZrWzXorY5XuFfMfxbuSvxK1eOJDLL+5O3OAP3KV9OV8y/FZVX4naxIR0EOT/2xSqRLOGgu4JFAaQbwcFMYOfSp/LWSRWYjCkYx61Whhjkt/MxhmYkexzUgWYoVQbWXv64qiSWVk+1OUCgrFhjT47Q2dvCgk/eOMMSck/SozYgxO7FlZhk88E1deNbhI3ycAjHPb/69AFXa0yeVyApLA+vNUXbzpNq/wCqB+YgYqXzxCkqZbd9wccgCqkOHyittViWODyMVLGi+NoV0MuWABRW7CvUPAzxWWjZkUSzGRTFvfaobHY9uV9K8mKqkYG/fKM/MetdNomuT2FmIQo2Y/i64/wqJptaFRaW56a8cF0RLqNmZ5FwoaTY549DmpI00uQ/NZyBxztKFcfj0rhIfEHmyjdCD6fLgVsReINqAKWjx3XH+FZ8jRfMjrFeyzhTPER/00cf1oMib8x6jcKf7u9T+hFcw3iiVCB5oYHruQf0IqSHX4nbzJLaGT0IAB/WnysLo6Jrm4bKJfoT6Sxg/wAiKck1+i9bR+c4G5c/zrHHiKBlAFs4X0wD/I05dR01gC0jxMevyFf5UWYXRrNqF6v/AC4ROf8ApnMP6gVWuNXuo1GNPmQ55yFbP5E1QEtsX3299PnOf9b/AI5qZ7+5CsF5IHV0HP45H8qdguUL3XFLbZrZvQq0JB/UVROo2TcKEjJHTO3FS3epPIWVlJYjGY3z/SqEt9MseJvlz2cAj+VUkTcZcRRXCtIsy59FkyTUK6O8satFNErehbpU6fZ2CrKbVyw/hA4/Cn/ZbGQKAAG5OUYjI+hp3sBnzaZMoIaSKVicYjfP9P61Tl0e9UEeW4wehNa8mmw9mlUH7uD/AImo2szAQUubuIDv5YYfpRcVjlb3TnGRIFB+tYl1abhjYdwPUCu0vmkc4edXAPO+EqaypLXzDiMq2em3NDSe4Xsce8ckPTp609buVEKKc5rdudOmZCojcn0ANY0llKjnCke2KhruP0GpcmVgjM317irsVwxVUeRVAHGP4qz5FeI5Zcbh1HGaj85gwyc8VDgnsF2jY80q+6PI+TIC4GM//rp8dxwpZUeQHHrnp1rLS9B2hl4HvVtL+FuTHtYNlju5Izxj361EotdC1M+w/Bxz4H0A4xnTbfj/ALZrW3WL4OKnwRoBThTptvj6eWtbVdK2M2FfL/xbtj/wsvXZN8hDrCNoPA/cJX1BXzP8WGJ+I+sKOn7nd9PJSqQmefQzRwjymbDYyM1bgl8zdIGB44XPNNaaGBS5IJA6dzTYyTFJOY13gYC1RI+6lnK/NMsUYGNuM5981aWbdAsasAcDkelY8kdxcQiR9vlBvmAPPpU5UR4jhAXIIz6DikMbfsIy4GSGGSwPGaqwMFRSnOQQfaldXng8hMb0JzzjpUcFvMjYwQM8kHNS3qMv28KqMnbV+F1J5OB/OqaRk8dfWr1tGvV8+1WSaEL7jtHNWlzkEjpVSLYnIyM1Osg5G4+tIoe0shGfLB/CmebJn7gH4UhlbceTj3FIZNuSWx9aaQrjwZGBbzCpJ6elTfaJF6P+tUXnxjawJJqM3L5PIx9KpIm5fNy7A4Kg561Il9PCco5H0Y/41itK5ycjqat+acZ4p2QXNH+05VXL5dR23E/zqP8AtcSMU8sKDyKoluMbQarujBMjqORScUHMzeGpRtEFaKPj65NWotUhUEpJKhIwFUiuTWbaeRkH3qZJdx4HP1qXBFcx1UOq+am2aRhjsyKaS4Wzmjz8+R1GTg/rxXMbiOcH86NzDPXH1pcg+Y25J4URo0dl7HIUkfnzWe8Ee1t03z/3SjKapGQt/ez9ajd5QflJ596OUOYe4gI2uHDd2BH+FZtxCp+7Iw9jVhvOPUnP1qFiwbDg/wCFFguZk8DBeCrgf571nywlRkkY61vyRlh8p4qnLbnuucVDh2HcxTkdacjnqasTwEscYFVWRo+xx61IWPtrwOc/D/w2f+oXa/8Aopa3qwPAv/JPvDX/AGCrX/0Utb9WAV8xfFZtvxP1oEEg+T+H7iOvp2vmD4tsf+Fm60obHyw8Y/6YpTQmcOsPlTC4VAxlOGDdveiGVIxI0swChzgUlpNutk3AgqMU1bZFaRvlYuTk/WmIdJGZLeUIxAZt4HTmo0kEahQxdu/Oeacr7YSoPKAjFEUIwDtCgDp6mjcQ6GEDLYIyc49ae7gDaOvfFOGTwM+59KkVcAVdrCCEnA6Y9KuR44qBQM1KFDYGPrSGWg2ASTwKalwwJwBkmmhlHBxS5iI425pWGWC7e35UyQs64yBz6VH5if3x+dRPNmQgNx9aoRMqENnIOBTt5JIwOOKhjcncS3sKcD+tUiSQn2FIxzxgdaZk46mmAsV6nNMRK0iqORTfNTOKifJGCxpvlnIO6kMeuwkrtHB44p4Cjtg1XYlGDAj0NPWQlgDjmkBMWwM84qNi3VS2Kkwcf/WpqqQOooGRfP8A7WaN79Tn8qczshxtH59aA+76+lACF0/vfnUbKrDrSyLuOeAaRVZeMgipsMi2AZAOKjcDu1Tucc44qNiGHSgZRliVhnIqnKh3Ek5rUb6VXkjB7c0mkwTPr7wRx4A8Of8AYLtv/RS1vVh+CxjwJ4eH/UMtv/RS1uVBQV8x/FravxL1liT0hz/35Svpyvl74vyL/wALI1pAwB/cbv8AvylNCZwaqcAFz64pd+1zt5NRqSzBj93p9asIgwGK4H86pIm4yOEZ3t09PWpHcDkDntQ7Ac9faoslzkg1WwiZZgABg1KsoyAAarCNv7tTRjZ8xHNAFlWI7GpVc4ztP0qAOO9SK4oAAHPJBpyqyr0Oe9OEqjjPNK7jbgHrxQMiAYjO05PPSp4vlQZHJ5ORR5i9iKXeMcEfnTEKSue1GV9qTePUUE5HGKYhqkYzkdT/ADp3A6Yqr3P1P86lixsPyg80xDzg5/xph+71I/Gn7QewpCFPBApDIMsykEn0NSxKHUE5z3571DMoEnHANRBmRsKTz796QF/Oc/MeKOSfvVVikIk2sSQferBwOMGmAONw75FMCjqCfpTs8dT+dM2jJ5J/GkMcfY8Uxy2Pl60MOMq3HtTMt/eOKQDPObPOB+FGO4IIoZNxySc0wqyD5T+BoGKR71GUJ70hkbrSF2I4oA+vPBv/ACI/h/8A7Btv/wCi1rbrE8G8+BvD/wD2Dbb/ANFLW3WRYV8rfF6FpPiprYYfuz5BGD1/cR19U18vfFzn4n6yD0/cf+iY6qKuxNnDpuSPYMBO/Ayfx60rNgZJGKUnAz0poXJ3E1psQMBJOTjNSxjv+VLjmlFIB3XvTgeaj49qePpTAkGKeDz1qFcFz6Dj8ak2g+tIBy4PzH+Lp9KlziolVeOuB704qPfP1pgSZ96ZKcR49TUBY72AJwDinR5kkVWJIAJ/z+dACE4Bo2jA4qf7OjDuPxo8hR3OKYiBQNnTuf51LHgg4Hel8gAAZNKIwucNyaAFPB7/AJ03bn/9dD5UqMjk4owc9RTAGRSRk5x71HJANhK5yORUmDnoPzo+b/Z/OkBXC9wT65p6yueppoUhmTjjkc9qNj+g/OkBIrMw+8c0vT+I5qNdynOPrzT8nPQfnQMXJA4xTB0IBx7UpyeMY/Ko2k2kgqaAFbcB14+lNOc9f0o85eymm+YAehxQAjJk9aaY8HrTvMAPelzx0NID648G/wDIj6B/2Dbf/wBFrW3WJ4O/5EfQP+wbb/8Aota26yNAr5d+Lh/4ujrHOP8AUf8AomOvqKvlf4v8/FPWRnj9x/6Ijqo7ky2ONLZOccDpTkYk9KjXJ4HNTAALgVZIvXpijnpSA/8A16UHqDQAoGeaGJRScfSgHnFRuwZz6CgCRJAoxg1MpJGQDg1VUFiAOpq4oA6dOlMBRn0NKWP90/lS0o44oArAP12tk8nipbdgrsWBHHHFS9vrTdx4B/GmIk81P7wpPMTOdwz9ahf/AFkY9yaCeT6UwJS6/wB4Uu8HuKhJppx1wKAJJMbFPow/wp2O/pVfAPYCl49BSAm3YpSRmqgHLdeDSOSMYLfnQBPISpV+g6H6UobnmoGyyFSTg+9OibdGCScjgj3oAkPB5PFKDxim7euf50FR1BP50hknaopU3rx1FLjjIJ/OmJkghiSR79aAISOOMUAggjvU3kqT3/Ok8hc9/wA6AITx9PWhXxwRxUjRqeMn3o8lfWkB9c+Dv+RH0D/sG2//AKLWtusTwcMeB9AHpptv/wCi1rbrI0Cvlv4vLn4o6x/2x/8ARMdfUlfLvxc/5KfrP/bD/wBEx1UdyZbHDoNh68/SnbieOPypM0A8VZI7P0oV9wOBxTGOQFHfrThwBxTAeCdpx1+tR+Ww9Kf3zTloASJShycVOJPUGogR6U4HmgCRXwehxQZlBxyKbziq5O6Rj74oAt+ep70ocMARnB6GqTHCnHWrS8IB0IGKBCk/vFzngHnFIWzS5yRmjPNMBm7HegkE9RmpM8VGzYKkjvQAmenSjOOc5+lOYZPTFIQP8igCMsN59xQfmXAIp5VT2FIEHcD64oAYGyPTikjYpKQTw3T61IUAbGBg0yWMNGSMZHIpDJgwzjNLuH1BqooG3Kk468VJFgttOeemTQBMT+lMLEEMOopxQZzzj60hRcZGfzoAergrnPB6UMcDI5NMCYHf8DSFeOGIP1oAcRkZxScEYquXYMcsRQHwfmY4PfNAH1/4P/5EjQP+wdb/APota2qxfB3/ACJGgf8AYNt//Ra1tViaBXy18XmA+KOsgn/nh/6Jjr6lrgfEfwj0DxPr9zrF7ealHcXGzesMkYQbVCjAKE9FHemnYTVz5i3j1o3Dnpivon/hQvhb/n/1j/v9F/8AG6P+FCeFv+f/AFn/AL/Rf/G6vmRPKz51DgDqMn3pwYHuK+iP+FC+Fv8An/1j/v8ARf8Axumn4BeFWOft+s/9/ov/AI3RzIOVnz3uGeDTwfWvoH/hQXhX/n/1n/v9F/8AG6UfAPwqP+X/AFj/AL/Rf/G6OZBys+fQfWlDYOO9fQP/AAoPwr/z/wCsf9/ov/jdA+AnhYf8xDWf+/0X/wAbo5kHKzwDfhSx7VXGcCvoj/hQvhbGP7Q1n/v/AB//ABuj/hQvhX/n+1j/AL/x/wDxujmQcrPnlRuZV9SKs9P8K99HwF8LAgi/1nI/6bx//G6d/wAKJ8M/9BHWf+/0X/xujmQcrPAfWjsK9/8A+FFeGf8AoI6x/wB/Yv8A43R/worwzjH9o6x/3+i/+N0+dBys+f8AOOSaRvug8feH86+gv+FF+Gf+f/WP+/sX/wAbob4F+GGXBv8AV/8Av9F/8bo50HKz59Y88U3Jr6E/4UV4Yxj7fq//AH+j/wDjdIPgT4YH/L/rH/f6L/43RzoOVnz5kECnDAOK+gf+FE+GP+f/AFj/AL/Rf/G6UfAvwwB/x/av/wB/o/8A43S50HKz59JycUZ5Ir6C/wCFFeGM5+3av/3+j/8AjdJ/wonwxnP2/WM/9do//jdHMg5WfOeArMp7HI+lPz0wenSvoZ/gN4Wdgxv9YyBjiaL/AON0v/ChvC//AD/6x/3+i/8AjdHMg5WeAq+5QR3oB+te/r8CPC6dL/WP+/0X/wAbp3/Ci/DH/P8A6v8A9/ov/jdHMg5WfP30pp5r6DPwK8MH/l+1f/v9F/8AG6T/AIUT4X/5/tX/AO/0X/xujmQcrPnmVdx3AcjtUHGOlfRp+BPhg/8AL/q//f6L/wCN0w/ATwsT/wAf+sf9/ov/AI3RzIOVnbeDP+RF8P8A/YNtv/RS1uVV0ywi0rSbPToGdobSBII2kILFUUKCcADOB6VarMsKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD//2Q==">


模型已经生成了一些掩码，我们可以可视化这些掩码以评估和理解其性能。这将帮助我们查看模型在图像分割上的表现如何，并识别需要改进的地方。

```python
>>> image_array = np.array(image)

>>> segmentation_map = np.zeros_like(image_array)

>>> for result in results:
...     mask = np.array(result['mask'])
...     label = result['label']

...     label_index = list(id2label.values()).index(label)

...     color = sidewalk_palette[label_index]

...     for c in range(3):
...         segmentation_map[:, :, c] = np.where(mask, color[c], segmentation_map[:, :, c])

>>> plt.figure(figsize=(10, 10))
>>> plt.imshow(image_array)
>>> plt.imshow(segmentation_map, alpha=0.5)
>>> plt.axis('off')
>>> plt.show()
```

<img 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">


## 11. 在测试集上评估性能 📊


```python
metrics = trainer.evaluate(test_ds)
print(metrics)
```

## 12. 使用推理 API 访问模型并可视化结果 🔌


Hugging Face 🤗 提供了一个 [无服务器推理 API](https://huggingface.co/docs/api-inference/index)，允许你通过 API 端点直接测试模型，且可以免费使用。有关如何使用该 API 的详细指导，请参阅这个 [指南](https://huggingface.co/learn/cookbook/enterprise_hub_serverless_inference_api)。

我们将利用这个 API 来探索其功能，并看看它如何帮助我们测试模型。

**重要提示**

在使用无服务器推理 API 之前，你需要通过创建模型卡来设置模型任务。在为你的微调模型创建模型卡时，确保正确指定任务。

![image.png](data:image/png;base64,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)


一旦模型任务设置完成，我们可以下载一张图片并使用 [InferenceClient](https://huggingface.co/docs/huggingface_hub/v0.25.0/en/package_reference/inference_client) 来测试模型。这个客户端将允许我们通过 API 将图片发送到模型，并获取结果以进行评估。

```python
>>> url = "https://images.unsplash.com/photo-1594098742644-314fedf61fb6?q=80&w=2672&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D"
>>> image = Image.open(requests.get(url, stream=True).raw)

>>> plt.imshow(image)
>>> plt.axis('off')
>>> plt.show()
```

<img 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">


我们将使用 InferenceClient 的 [image_segmentation](https://huggingface.co/docs/huggingface_hub/v0.25.0/en/package_reference/inference_client#huggingface_hub.InferenceClient.image_segmentation) 方法。该方法接受模型和图片作为输入，并返回预测的掩码。这将帮助我们测试模型在新图片上的表现。

```python
from huggingface_hub import InferenceClient

client = InferenceClient()

response = client.image_segmentation(
    model="sergiopaniego/segformer-b0-segments-sidewalk-finetuned", # Change with your model name
    image='https://images.unsplash.com/photo-1594098742644-314fedf61fb6?q=80&w=2672&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D'
)

print(response)
```

通过预测的掩码，我们可以展示结果。

```python
>>> image_array = np.array(image)
>>> segmentation_map = np.zeros_like(image_array)

>>> for result in response:
...     mask = np.array(result['mask'])
...     label = result['label']

...     label_index = list(id2label.values()).index(label)

...     color = sidewalk_palette[label_index]

...     for c in range(3):
...         segmentation_map[:, :, c] = np.where(mask, color[c], segmentation_map[:, :, c])

>>> plt.figure(figsize=(10, 10))
>>> plt.imshow(image_array)
>>> plt.imshow(segmentation_map, alpha=0.5)
>>> plt.axis('off')
>>> plt.show()
```

<img 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">


也可以使用 [JavaScript 版 Inference API](https://huggingface.co/tasks/image-segmentation)。下面是如何使用 JavaScript 调用该 API 的示例：

```javascript
import { HfInference } from "@huggingface/inference";

const inference = new HfInference(HF_TOKEN);
await inference.imageSegmentation({
    data: await (await fetch("https://picsum.photos/300/300")).blob(),
    model: "sergiopaniego/segformer-b0-segments-sidewalk-finetuned",
});
```

这个示例展示了如何用 JavaScript 获取图片并将其传递给指定的模型进行图像分割任务。

**额外加分**

你还可以通过 Hugging Face Space 部署微调后的模型。例如，我创建了一个自定义 Space 来展示这个过程：[Semantic Segmentation with SegFormer Fine-Tuned on Segments/Sidewalk](https://huggingface.co/spaces/sergiopaniego/segformer-b0-segments-sidewalk-finetuned)。

<img src="https://huggingface.co/front/thumbnails/spaces.png" alt="HF Spaces logo" width="20%">

```python
from IPython.display import IFrame
IFrame(src='https://sergiopaniego-segformer-b0-segments-sidewalk-finetuned.hf.space', width=1000, height=800)
```

## 总结

在本指南中，我们成功地在自定义数据集上微调了一个语义分割模型，并利用无服务器推理 API 进行了测试。这展示了如何轻松地将模型集成到各种应用中，并利用 Hugging Face 工具进行部署。

希望本指南能够为你提供所需的工具和知识，使你能够自信地微调和部署自己的模型！🚀

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/semantic_segmentation_fine_tuning_inference.md" />

### 用于文本到 SQL 的智能体，带有自动错误修正功能
https://huggingface.co/learn/cookbook/zh-CN/agent_text_to_sql.md

# 用于文本到 SQL 的智能体，带有自动错误修正功能  
_作者：[Aymeric Roucher](https://huggingface.co/m-ric)_

在本教程中，我们将学习如何实现一个利用 SQL 的智能体，使用 `transformers.agents`。

与标准文本到 SQL pipeline 相比，**它有什么优势？**

标准的文本到 SQL pipeline 是脆弱的，因为生成的 SQL 查询可能是错误的。更糟糕的是，查询可能是错误的，但并不会引发错误，而是返回一些错误的/无用的输出，且不会发出警报。

👉 相比之下，**智能体系统能够批判性地检查输出，并决定是否需要更改查询**，从而大大提高其性能。

让我们开始构建这个智能体吧！💪

## 设置 SQL 表

```python
from sqlalchemy import (
    create_engine,
    MetaData,
    Table,
    Column,
    String,
    Integer,
    Float,
    insert,
    inspect,
    text,
)

engine = create_engine("sqlite:///:memory:")
metadata_obj = MetaData()

# create city SQL table
table_name = "receipts"
receipts = Table(
    table_name,
    metadata_obj,
    Column("receipt_id", Integer, primary_key=True),
    Column("customer_name", String(16), primary_key=True),
    Column("price", Float),
    Column("tip", Float),
)
metadata_obj.create_all(engine)
```

```python
rows = [
    {"receipt_id": 1, "customer_name": "Alan Payne", "price": 12.06, "tip": 1.20},
    {"receipt_id": 2, "customer_name": "Alex Mason", "price": 23.86, "tip": 0.24},
    {"receipt_id": 3, "customer_name": "Woodrow Wilson", "price": 53.43, "tip": 5.43},
    {"receipt_id": 4, "customer_name": "Margaret James", "price": 21.11, "tip": 1.00},
]
for row in rows:
    stmt = insert(receipts).values(**row)
    with engine.begin() as connection:
        cursor = connection.execute(stmt)
```

让我们检查系统是否能通过一个基本查询正常工作：

```python
>>> with engine.connect() as con:
...     rows = con.execute(text("""SELECT * from receipts"""))
...     for row in rows:
...         print(row)
```

<pre>
(1, 'Alan Payne', 12.06, 1.2)
(2, 'Alex Mason', 23.86, 0.24)
(3, 'Woodrow Wilson', 53.43, 5.43)
(4, 'Margaret James', 21.11, 1.0)
</pre>

## 构建我们的智能体

现在，让我们将 SQL 表格使其可由智能体（工具）检索。

智能体的 `description` 属性将被嵌入到大语言模型（LLM）的提示中，这样它就能了解如何使用这个工具。在这个步骤中，我们需要描述 SQL 表格的结构，以便让智能体能够正确地执行查询并与数据库交互。

```python
>>> inspector = inspect(engine)
>>> columns_info = [(col["name"], col["type"]) for col in inspector.get_columns("receipts")]

>>> table_description = "Columns:\n" + "\n".join(
...     [f"  - {name}: {col_type}" for name, col_type in columns_info]
... )
>>> print(table_description)
```

<pre>
Columns:
  - receipt_id: INTEGER
  - customer_name: VARCHAR(16)
  - price: FLOAT
  - tip: FLOAT
</pre>

现在，让我们构建我们的工具。它需要以下内容：（详细信息请参阅[文档](https://huggingface.co/docs/transformers/en/agents#create-a-new-tool)）

- 带有 `Args:` 部分的文档字符串
- 类型提示

```python
from transformers.agents import tool


@tool
def sql_engine(query: str) -> str:
    """
    Allows you to perform SQL queries on the table. Returns a string representation of the result.
    The table is named 'receipts'. Its description is as follows:
        Columns:
        - receipt_id: INTEGER
        - customer_name: VARCHAR(16)
        - price: FLOAT
        - tip: FLOAT

    Args:
        query: The query to perform. This should be correct SQL.
    """
    output = ""
    with engine.connect() as con:
        rows = con.execute(text(query))
        for row in rows:
            output += "\n" + str(row)
    return output
```

现在让我们创建一个利用这个工具的智能体。

我们将使用 `ReactCodeAgent`，它是 `transformers.agents` 的主要智能体类：一个根据 ReAct 框架编写代码并能迭代先前输出的智能体。

`llm_engine` 是驱动智能体系统的 LLM。`HfEngine` 允许你通过 HF 的推理 API 调用 LLM，无论是通过无服务器或专用端点，但你也可以使用任何专有的 API：查看[这个指南](agent_change_llm)以了解如何进行适配。

```python
from transformers.agents import ReactCodeAgent, HfApiEngine

agent = ReactCodeAgent(
    tools=[sql_engine],
    llm_engine=HfApiEngine("meta-llama/Meta-Llama-3-8B-Instruct"),
)
```

```python
agent.run("Can you give me the name of the client who got the most expensive receipt?")
```

## 提高难度：表格连接

现在让我们增加一点难度！我们希望智能体能够处理跨多个表的连接查询。

因此，让我们创建一个第二个表，用于记录每个 `receipt_id` 对应的服务员姓名！

```python
table_name = "waiters"
receipts = Table(
    table_name,
    metadata_obj,
    Column("receipt_id", Integer, primary_key=True),
    Column("waiter_name", String(16), primary_key=True),
)
metadata_obj.create_all(engine)

rows = [
    {"receipt_id": 1, "waiter_name": "Corey Johnson"},
    {"receipt_id": 2, "waiter_name": "Michael Watts"},
    {"receipt_id": 3, "waiter_name": "Michael Watts"},
    {"receipt_id": 4, "waiter_name": "Margaret James"},
]
for row in rows:
    stmt = insert(receipts).values(**row)
    with engine.begin() as connection:
        cursor = connection.execute(stmt)
```

我们需要更新 `SQLExecutorTool`，将这个表的描述添加进去，以便让 LLM 能够正确地利用这个表中的信息。

```python
>>> updated_description = """Allows you to perform SQL queries on the table. Beware that this tool's output is a string representation of the execution output.
... It can use the following tables:"""

>>> inspector = inspect(engine)
>>> for table in ["receipts", "waiters"]:
...     columns_info = [(col["name"], col["type"]) for col in inspector.get_columns(table)]

...     table_description = f"Table '{table}':\n"

...     table_description += "Columns:\n" + "\n".join(
...         [f"  - {name}: {col_type}" for name, col_type in columns_info]
...     )
...     updated_description += "\n\n" + table_description

>>> print(updated_description)
```

<pre>
Allows you to perform SQL queries on the table. Beware that this tool's output is a string representation of the execution output.
It can use the following tables:

Table 'receipts':
Columns:
  - receipt_id: INTEGER
  - customer_name: VARCHAR(16)
  - price: FLOAT
  - tip: FLOAT

Table 'waiters':
Columns:
  - receipt_id: INTEGER
  - waiter_name: VARCHAR(16)
</pre>

由于这个请求比之前的更具挑战性，我们将切换 LLM 引擎，使用更强大的 [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct)！

```python
sql_engine.description = updated_description

agent = ReactCodeAgent(
    tools=[sql_engine],
    llm_engine=HfApiEngine("Qwen/Qwen2.5-72B-Instruct"),
)

agent.run("Which waiter got more total money from tips?")
```

它直接就能工作！设置过程出乎意料地简单，不是吗？

✅ 现在你可以去构建你一直梦想的文本到 SQL 系统了！✨

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/agent_text_to_sql.md" />

### 数据标注与 Argilla Spaces
https://huggingface.co/learn/cookbook/zh-CN/enterprise_cookbook_argilla.md

# 数据标注与 Argilla Spaces  
_作者: [Moritz Laurer](https://huggingface.co/MoritzLaurer)_  

本 Notebook 展示了系统地评估大型语言模型（LLM）输出并创建 LLM 训练数据的工作流程。你可以通过使用本 Notebook 评估你最喜爱的 LLM 在没有任何微调的情况下在特定任务上的零样本性能。如果你希望提高性能，你可以轻松地重用此工作流程来创建训练数据。

**示例用例：代码生成。** 在本教程中，我们展示了如何为代码生成任务创建高质量的测试和训练数据。然而，相同的工作流程也可以适应其他与你特定用例相关的任务。

**在本 Notebook 中，我们将进行以下步骤：**
1. 下载示例任务的数据。
2. 提示两个 LLM 对这些任务作出响应。这将生成“合成数据”以加速手动数据创建。
3. 在 HF Spaces 上创建一个 Argilla 标注界面，以比较和评估两个 LLM 的输出。
4. 将示例数据和零样本 LLM 的响应上传到 Argilla 标注界面。
5. 下载已标注的数据。

你可以根据自己的需求调整本 Notebook，例如在步骤 (2) 使用不同的 LLM 和 API 提供商，或在步骤 (3) 调整标注任务。

## 安装需要的包并连接到 HF Hub

```python
!pip install argilla~=2.0.0
!pip install transformers~=4.40.0
!pip install datasets~=2.19.0
!pip install huggingface_hub~=0.23.2
```

```python
# Login to the HF Hub. We recommend using this login method 
# to avoid the need to explicitly store your HF token in variables 
import huggingface_hub
!git config --global credential.helper store
huggingface_hub.login(add_to_git_credential=True)
```

## 下载示例任务数据

首先，我们下载一个包含 LLM 代码生成任务的示例数据集。我们希望评估两个不同的 LLM 在这些代码生成任务上的表现。我们使用来自 [bigcode/self-oss-instruct-sc2-exec-filter-50k](https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k) 数据集的指令，该数据集用于训练 [StarCoder2-Instruct](https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1) 模型。

```python
>>> from datasets import load_dataset

>>> # Small sample for faster testing
>>> dataset_codetask = load_dataset("bigcode/self-oss-instruct-sc2-exec-filter-50k", split="train[:3]")
>>> print("Dataset structure:\n", dataset_codetask, "\n")

>>> # We are only interested in the instructions/prompts provided in the dataset
>>> instructions_lst = dataset_codetask["instruction"]
>>> print("Example instructions:\n", instructions_lst[:2])
```

<pre>
Dataset structure:
 Dataset({
    features: ['fingerprint', 'sha1', 'seed', 'response', 'concepts', 'prompt', 'instruction', 'id'],
    num_rows: 3
}) 

Example instructions:
 ['Write a Python function named `get_value` that takes a matrix (represented by a list of lists) and a tuple of indices, and returns the value at that index in the matrix. The function should handle index out of range errors by returning None.', 'Write a Python function `check_collision` that takes a list of `rectangles` as input and checks if there are any collisions between any two rectangles. A rectangle is represented as a tuple (x, y, w, h) where (x, y) is the top-left corner of the rectangle, `w` is the width, and `h` is the height.\n\nThe function should return True if any pair of rectangles collide, and False otherwise. Use an iterative approach and check for collisions based on the bounding box collision detection algorithm. If a collision is found, return True immediately without checking for more collisions.']
</pre>

## 对示例任务提示两个 LLM 以获取它们的响应。    

#### 使用 `chat_template` 格式化指令

在将指令发送到 LLM API 之前，我们需要使用正确的 `chat_template` 格式化指令，以便对每个需要评估的模型进行适配。这实际上是指在指令周围加上特殊的标记。有关 `chat_template` 的详细信息，请参阅 [文档](https://huggingface.co/docs/transformers/main/en/chat_templating)。

```python
>>> # Apply correct chat formatting to instructions from the dataset 
>>> from transformers import AutoTokenizer

>>> models_to_compare = ["mistralai/Mixtral-8x7B-Instruct-v0.1", "meta-llama/Meta-Llama-3-70B-Instruct"]

>>> def format_prompt(prompt, tokenizer):
...     messages = [{"role": "user", "content": prompt}]
...     messages_tokenized = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, return_tensors="pt")
...     return messages_tokenized


>>> prompts_formatted_dic = {}
>>> for model in models_to_compare:
...     tokenizer = AutoTokenizer.from_pretrained(model)

...     prompt_formatted = []
...     for instruction in instructions_lst: 
...         prompt_formatted.append(format_prompt(instruction, tokenizer))
        
...     prompts_formatted_dic.update({model: prompt_formatted})


>>> print(f"\nFirst prompt formatted for {models_to_compare[0]}:\n\n", prompts_formatted_dic[models_to_compare[0]][0], "\n\n")
>>> print(f"First prompt formatted for {models_to_compare[1]}:\n\n", prompts_formatted_dic[models_to_compare[1]][0], "\n\n")
```

<pre>
First prompt formatted for mistralai/Mixtral-8x7B-Instruct-v0.1:

 <s>[INST] Write a Python function named `get_value` that takes a matrix (represented by a list of lists) and a tuple of indices, and returns the value at that index in the matrix. The function should handle index out of range errors by returning None. [/INST] 


First prompt formatted for meta-llama/Meta-Llama-3-70B-Instruct:

 <|begin_of_text|><|start_header_id|>user<|end_header_id|>

Write a Python function named `get_value` that takes a matrix (represented by a list of lists) and a tuple of indices, and returns the value at that index in the matrix. The function should handle index out of range errors by returning None.<|eot_id|><|start_header_id|>assistant<|end_header_id|>
</pre>

#### 将指令发送到 HF 推理 API

现在，我们可以将指令发送到两个 LLM 的 API，以获取可以评估的输出。首先，我们需要定义一些参数，以确保正确生成响应。Hugging Face 的 LLM API 由 [文本生成推理 (TGI)](https://huggingface.co/docs/text-generation-inference/index) 容器提供支持。有关 TGI OpenAPI 规范，请参见 [此处](https://huggingface.github.io/text-generation-inference/#/Text%20Generation%20Inference/generate)，并可参考 Transformers 生成参数的详细说明 [文档](https://huggingface.co/docs/transformers/v4.30.0/main_classes/text_generation#transformers.GenerationConfig)。

```python
generation_params = dict(
    # we use low temperature and top_p to reduce creativity and increase likelihood of highly probable tokens
    temperature=0.2,
    top_p=0.60,
    top_k=None,
    repetition_penalty=1.0,
    do_sample=True,
    max_new_tokens=512*2,
    return_full_text=False,
    seed=42,
    #details=True,
    #stop=["<|END_OF_TURN_TOKEN|>"],
    #grammar={"type": "json"}
    max_time=None, 
    stream=False,
    use_cache=False,
    wait_for_model=False,
)
```

现在，我们可以向 Serverless 推理 API 发出标准的 API 请求 ([文档](https://huggingface.co/docs/api-inference/index))。需要注意的是，Serverless 推理 API 主要用于测试，并且有速率限制。如果需要在没有速率限制的情况下进行测试，可以通过 Hugging Face 专用端点创建自己的 API ([文档](https://huggingface.co/docs/inference-endpoints/index))。另外，你还可以参考我们的相关教程，了解更多关于推理 API 的使用，详见 [开源 AI 指南](https://huggingface.co/learn/cookbook/index)。

> [!TIP]  
> 一旦推理 API 示例完成，下面的代码将会更新。

```python
>>> import requests
>>> from tqdm.auto import tqdm

>>> # Hint: use asynchronous API calls (and dedicated endpoints) to increase speed
>>> def query(payload=None, api_url=None):
...     response = requests.post(api_url, headers=headers, json=payload)
...     return response.json()

>>> headers = {"Authorization": f"Bearer {huggingface_hub.get_token()}"}

>>> output_dic = {}
>>> for model in models_to_compare:
...     # Create API urls for each model
...     # When using dedicated endpoints, you can reuse the same code and simply replace this URL
...     api_url = "https://api-inference.huggingface.co/models/" + model
    
...     # send requests to API 
...     output_lst = []
...     for prompt in tqdm(prompt_formatted):
...         output = query(
...             payload={
...                 "inputs": prompt,
...                 "parameters": {**generation_params}
...             },
...             api_url=api_url 
...         )
...         output_lst.append(output[0]["generated_text"])
    
...     output_dic.update({model: output_lst})

>>> print(f"---First generation of {models_to_compare[0]}:\n{output_dic[models_to_compare[0]][0]}\n\n")
>>> print(f"---First generation of {models_to_compare[1]}:\n{output_dic[models_to_compare[1]][0]}")
```

<pre>
---First generation of mistralai/Mixtral-8x7B-Instruct-v0.1:
Here's a Python function that meets your requirements:

```python
def get_value(matrix, indices):
    try:
        return matrix[indices[0]][indices[1]]
    except IndexError:
        return None
```

This function takes a matrix (represented by a list of lists) and a tuple of indices as input. It first tries to access the value at the given indices in the matrix. If the indices are out of range, it catches the `IndexError` exception and returns `None`.


---First generation of meta-llama/Meta-Llama-3-70B-Instruct:
Here is a Python function that does what you described:
```
def get_value(matrix, indices):
    try:
        row, col = indices
        return matrix[row][col]
    except IndexError:
        return None
```
Here's an explanation of how the function works:

1. The function takes two arguments: `matrix` (a list of lists) and `indices` (a tuple of two integers, representing the row and column indices).
2. The function tries to access the value at the specified indices using `matrix[row][col]`.
3. If the indices are out of range (i.e., `row` or `col` is greater than the length of the corresponding dimension of the matrix), an `IndexError` exception is raised.
4. The `except` block catches the `IndexError` exception and returns `None` instead of raising an error.

Here's an example usage of the function:
```
matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

print(get_value(matrix, (0, 0)))  # prints 1
print(get_value(matrix, (1, 1)))  # prints 5
print(get_value(matrix, (3, 0)))  # prints None (out of range)
print(get_value(matrix, (0, 3)))  # prints None (out of range)
```
I hope this helps! Let me know if you have any questions.
</pre>

#### 将 LLM 输出存储在数据集中

现在，我们可以将 LLM 的输出与原始指令一起存储在一个数据集中。

```python
# create a HF dataset with the instructions and model outputs
from datasets import Dataset

dataset = Dataset.from_dict({
    "instructions": instructions_lst,
    "response_model_1": output_dic[models_to_compare[0]],
    "response_model_2": output_dic[models_to_compare[1]]
})

dataset
```

## 创建并配置你的 Argilla 数据集

我们使用 [Argilla](https://argilla.io/)，这是一个为 AI 工程师和领域专家设计的协作工具，用于构建高质量的数据集以支持他们的项目。

我们通过 HF Space 运行 Argilla，你可以通过几次点击便能设置好，无需进行本地配置。你可以按照 [这些指引](https://docs.argilla.io/latest/getting_started/quickstart/) 创建 HF Argilla Space。有关 HF Argilla Spaces 的更多配置，请参阅详细的 [文档](https://docs.argilla.io/latest/getting_started/how-to-configure-argilla-on-huggingface/)。如果你希望，也可以通过 Argilla 的 Docker 容器在本地运行 Argilla（参见 [Argilla 文档](https://docs.argilla.io/latest/getting_started/how-to-deploy-argilla-with-docker/))。

![Argilla 登录界面](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/argilla-login-screen.png)

#### 程序化地与 Argilla 交互

在我们定制数据集以适应特定任务并上传将在 UI 中显示的数据之前，我们需要先完成一些设置。

**将此 notebook 连接到 Argilla：** 现在，我们可以将此 notebook 连接到 Argilla，以便程序化地配置你的数据集并上传/下载数据。

```python
# After starting the Argilla Space (or local docker container) you can connect to the Space with the code below.
import argilla as rg

client = rg.Argilla(
    api_url="https://username-spacename.hf.space",  # Locally: "http://localhost:6900"
    api_key="your-apikey",  # You'll find it in the UI "My Settings > API key"
    # To use a private HF Argilla Space, also pass your HF token
    headers={"Authorization": f"Bearer {huggingface_hub.get_token()}"},
)
```

```python
user = client.me
user
```

#### 编写良好的标注指南

为你的人工标注员编写良好的指南与编写高质量的训练代码一样重要（也是一项具有挑战性的任务）。良好的指南应满足以下标准：

- **简洁明了**：指南应简洁明了，以便那些对任务一无所知的人也能理解。在发布之前，最好请至少一位同事重新阅读指南，确保没有歧义。
- **可重复和明确**：完成标注任务所需的所有信息应包含在指南中。一个常见的错误是，在与选定的标注员沟通时，未能正式解释指南的内容。未来的标注员将无法获取这些信息，如果没有在指南中明确说明，他们可能会以不同于预期的方式完成任务。
- **简短而全面**：指南应尽可能简短，但包含所有必要的信息。标注员通常不会认真阅读冗长的指南，因此请尽量保持简洁，同时确保内容全面。

请注意，编写标注指南是一个迭代过程。建议你自己先做几十个标注，并根据数据中学到的经验来完善指南，然后再将任务分配给他人。随着任务的演变，对指南进行版本管理也很有帮助。有关更多提示，请参见这篇 [博客文章](https://argilla.io/blog/annotation-guidelines-practices/)。

```python
annotator_guidelines = """\
Your task is to evaluate the responses of two LLMs to code generation tasks. 

First, you need to score each response on a scale from 0 to 7. You add points to your final score based on the following criteria:
- Add up to +2 points, if the code is properly commented, with inline comments and doc strings for functions.
- Add up to +2 points, if the code contains a good example for testing. 
- Add up to +3 points, if the code runs and works correctly. Copy the code into an IDE and test it with at least two different inputs. Attribute one point if the code is overall correct, but has some issues. Attribute three points if the code is fully correct and robust against different scenarios. 
Your resulting final score can be any value between 0 to 7. 

If both responses have a final score of <= 4, select one response and correct it manually in the text field. 
The corrected response must fulfill all criteria from above. 
"""

rating_tooltip = """\
- Add up to +2 points, if the code is properly commented, with inline comments and doc strings for functions.
- Add up to +2 points, if the code contains a good example for testing. 
- Add up to +3 points, if the code runs and works correctly. Copy the code into an IDE and test it with at least two different inputs. Attribute one point if the code works mostly correctly, but has some issues. Attribute three points if the code is fully correct and robust against different scenarios. 
"""
```

**累积评分 vs. Likert 量表：** 注意，上述指南要求标注员通过为明确的标准加分来进行累积评分。另一种方法是使用 "Likert 量表"，在这种方法中，标注员被要求在一个连续的尺度上对响应进行评分，例如从 1（非常差）到 3（一般）再到 5（非常好）。我们通常推荐使用累积评分，因为它迫使你和标注员将质量标准明确化，而仅仅将响应评分为 "4"（好）是模糊的，不同的标注员可能会对其有不同的解释。

#### 将你的 Argilla 数据集定制为特定任务

现在，我们可以为特定任务创建自己的 `code-llm` 任务，定义标注所需的字段、问题和元数据。有关如何配置 Argilla 数据集的更多信息，请参阅 [Argilla 文档](https://docs.argilla.io/latest/how_to_guides/dataset/#create-a-dataset)。

```python
dataset_argilla_name = "code-llm"
workspace_name = "argilla"
reuse_existing_dataset = False  # for easier iterative testing

# Configure your dataset settings
settings = rg.Settings(
    # The overall annotation guidelines, which human annotators can refer back to inside of the interface
    guidelines="my guidelines",
    fields=[
        rg.TextField(
            name="instruction", title="Instruction:", use_markdown=True, required=True
        ),
        rg.TextField(
            name="generation_1",
            title="Response model 1:",
            use_markdown=True,
            required=True,
        ),
        rg.TextField(
            name="generation_2",
            title="Response model 2:",
            use_markdown=True,
            required=True,
        ),
    ],
    # These are the questions we ask annotators about the fields in the dataset
    questions=[
        rg.RatingQuestion(
            name="score_response_1",
            title="Your score for the response of model 1:",
            description="0=very bad, 7=very good",
            values=[0, 1, 2, 3, 4, 5, 6, 7],
            required=True,
        ),
        rg.RatingQuestion(
            name="score_response_2",
            title="Your score for the response of model 2:",
            description="0=very bad, 7=very good",
            values=[0, 1, 2, 3, 4, 5, 6, 7],
            required=True,
        ),
        rg.LabelQuestion(
            name="which_response_corrected",
            title="If both responses score below 4, select a response to correct:",
            description="Select the response you will correct in the text field below.",
            labels=["Response 1", "Response 2", "Combination of both", "Neither"],
            required=False,
        ),
        rg.TextQuestion(
            name="correction",
            title="Paste the selected response below and correct it manually:",
            description="Your corrected response must fulfill all criteria from the annotation guidelines.",
            use_markdown=True,
            required=False,
        ),
        rg.TextQuestion(
            name="comments",
            title="Annotator Comments",
            description="Add any additional comments here. E.g.: edge cases, issues with the interface etc.",
            use_markdown=True,
            required=False,
        ),
    ],
    metadata=[
        rg.TermsMetadataProperty(
            name="source-dataset",
            title="Original dataset source",
        ),
    ],
    allow_extra_metadata=False,
)

if reuse_existing_dataset:
    dataset_argilla = client.datasets(dataset_argilla_name, workspace=workspace_name)
else:
    dataset_argilla = rg.Dataset(
        name=dataset_argilla_name,
        settings=settings,
        workspace=workspace_name,
    )
    if client.datasets(dataset_argilla_name, workspace=workspace_name) is not None:
        client.datasets(dataset_argilla_name, workspace=workspace_name).delete()
    dataset_argilla = dataset_argilla.create()

dataset_argilla
```

运行上述代码后，你将在 Argilla 中看到新的自定义 `code-llm` 数据集（以及之前可能创建的任何其他数据集）。

#### 将数据加载到 Argilla

此时，数据集仍然为空。让我们使用以下代码加载一些数据。

```python
# Iterate over the samples in the dataset
records = [
    rg.Record(
        fields={
            "instruction": example["instructions"],
            "generation_1": example["response_model_1"],
            "generation_2": example["response_model_2"],
        },
        metadata={
            "source-dataset": "bigcode/self-oss-instruct-sc2-exec-filter-50k",
        },
        # Optional: add suggestions from an LLM-as-a-judge system
        # They will be indicated with a sparkle icon and shown as pre-filled responses
        # It will speed up manual annotation
        # suggestions=[
        #     rg.Suggestion(
        #         question_name="score_response_1",
        #         value=example["llm_judge_rating"],
        #         agent="llama-3-70b-instruct",
        #     ),
        # ],
    )
    for example in dataset
]

try:
    dataset_argilla.records.log(records)
except Exception as e:
    print("Exception:", e)
```

**Argilla 的标注 UI** 将类似于下图所示：

![Argilla UI](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/argilla-code-llm.png)

## 标注

就是这样，我们已经创建了 Argilla 数据集，现在可以开始在 UI 中进行标注了！默认情况下，记录会在完成 1 次标注后标记为已完成。查看以下指南，了解如何 [自动分配标注任务](https://docs.argilla.io/latest/how_to_guides/distribution/) 和 [在 Argilla 中进行标注](https://docs.argilla.io/latest/how_to_guides/annotate/)。

**重要提示**：如果你在 HF Space 中使用 Argilla，请确保启用持久存储，以便你的数据安全存储，并且不会在一段时间后被自动删除。对于生产环境，确保在进行任何标注之前就启用持久存储，以避免数据丢失。

## 下载标注数据

标注完成后，你可以从 Argilla 中提取数据，并以任何表格格式在本地存储和处理（详见 [文档](https://docs.argilla.io/latest/how_to_guides/import_export/)）。你还可以下载数据集的过滤版本（详见 [文档](https://docs.argilla.io/latest/how_to_guides/query/)）。

```python
annotated_dataset = client.datasets(dataset_argilla_name, workspace=workspace_name)

hf_dataset = annotated_dataset.records.to_datasets()

# This HF dataset can then be formatted, stored and processed into any tabular data format
hf_dataset.to_pandas()
```

```python
# Store the dataset locally
hf_dataset.to_csv("argilla-dataset-local.csv")  # Save as CSV
#hf_dataset.to_json("argilla-dataset-local.json")  # Save as JSON
#hf_dataset.save_to_disk("argilla-dataset-local")  # Save as a `datasets.Dataset` in the local filesystem
#hf_dataset.to_parquet()  # Save as Parquet
```

## 下一步

就这样！你已经使用 HF 推理 API 创建了合成 LLM 数据，在 Argilla 中创建了数据集，将 LLM 数据上传到 Argilla，评估/修正了数据，并在标注后将数据以简单的表格格式下载，用于后续使用。

我们特别设计了这个流程和界面来支持 **两个主要用例**：

1. **评估**：你现在可以简单地使用 `score_response_1` 和 `score_response_2` 列中的数值分数来计算哪一个模型整体表现更好。你还可以检查评分非常低或非常高的响应，以进行详细的错误分析。在测试或训练不同模型时，你可以重复使用这个流程，并跟踪不同模型随时间的改进。
  
2. **训练**：在标注足够数据后，你可以从数据中创建训练-测试集，并微调你自己的模型。你可以使用高度评价的响应文本进行监督式微调，利用 [TRL SFTTrainer](https://huggingface.co/docs/trl/en/sft_trainer)，或者直接使用评分来进行偏好微调技术，如 DPO，使用 [TRL DPOTrainer](https://huggingface.co/docs/trl/en/dpo_trainer)。有关不同 LLM 微调技术的优缺点，请参见 [TRL 文档](https://huggingface.co/docs/trl/en/index)。

**调整和改进**：许多方面可以进行改进，以便将此流程定制为适应你的特定用例。例如，你可以提示一个 LLM 来评估两个 LLM 输出的结果，指令与人类标注员指南非常相似（"LLM 作为评审" 方法）。这可以帮助进一步加速你的评估流程。请参见我们的 [LLM 作为评审的示例实现](https://huggingface.co/learn/cookbook/llm_judge) 和我们的整体 [开源 AI 指南](https://huggingface.co/learn/cookbook/index)，其中包含许多其他创意。

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/enterprise_cookbook_argilla.md" />

### 个人身份信息（PII）检测的 LLM 网关
https://huggingface.co/learn/cookbook/zh-CN/llm_gateway_pii_detection.md

# 个人身份信息（PII）检测的 LLM 网关
*作者: [Anthony Susevski](https://github.com/asusevski)*

采用大语言模型（LLM）进行企业级应用时，常见的投诉之一就是数据隐私问题，尤其是对于处理敏感数据的团队来说。尽管开源模型通常是一个不错的选择，*如果可能的话应该尝试使用*，但有时我们只想快速演示一下，或者有充分的理由使用 LLM API。在这种情况下，最好采用某种网关来处理个人身份信息（PII）数据的清洗，从而降低 PII 泄露的风险。

总部位于加拿大多伦多的金融科技公司 **Wealthsimple** 已经为了这个目的 [开源了一个代码库](https://github.com/wealthsimple/llm-gateway)。在本 Notebook 中，我们将探索如何利用这个代码库，在向 LLM 提供商发出 API 调用之前，对数据进行清洗。为此，我们将使用来自[AI4Privacy](https://huggingface.co/datasets/ai4privacy/pii-masking-200k)的 PII 数据集，并使用 Cohere 的 [Command R+](https://huggingface.co/CohereForAI/c4ai-command-r-plus)模型的[免费试用 API](https://cohere.com/blog/free-developer-tier-announcement)，演示 Wealthsimple 的 PII 清洗功能。

首先，请按照 [README](https://github.com/wealthsimple/llm-gateway) 中的说明进行安装：
1. 安装 Poetry 和 Pyenv
2. 安装 pyenv 版本 3.11.3
3. 安装项目所需的依赖：
```
brew install gitleaks
poetry install
poetry run pre-commit install
```
4. 运行 `cp .envrc.example .envrc` 并用 API 密钥更新配置。

```python
import os
from llm_gateway.providers.cohere import CohereWrapper
from datasets import load_dataset
import cohere
import types
import re
```

```python
COHERE_API_KEY = os.environ['COHERE_API_KEY']
DATABASE_URL = os.environ['DATABASE_URL'] # default database url: "postgresql://postgres:postgres@postgres:5432/llm_gateway"
```

## LLM 包装器

包装器对象是一个简单的封装器，它在发起 API 调用之前，将“清洗器”应用到输入的提示语上。使用包装器发起请求时，我们将获得一个响应和一个 `db_record` 对象。在深入了解更多细节之前，让我们先来看一下它的实际应用。

```python
wrapper = CohereWrapper()
```

```python
example = "Michael Smith (msmith@gmail.com, (+1) 111-111-1111) committed a mistake when he used PyTorch Trainer instead of HF Trainer."
```

```python
>>> response, db_record = wrapper.send_cohere_request(
...     endpoint="generate",
...     model="command-r-plus",
...     max_tokens=25,
...     prompt=f"{example}\n\nSummarize the above text in 1-2 sentences.",
...     temperature=0.3,
... )

>>> print(response)
```

<pre>
{'data': ['Michael Smith made a mistake by using PyTorch Trainer instead of HF Trainer.'], 'return_likelihoods': None, 'meta': {'api_version': {'version': '1'}, 'billed_units': {'input_tokens': 48, 'output_tokens': 14}}}
</pre>

响应返回的是 LLM 的输出；在这个例子中，由于我们要求模型对一个已经很简短的句子进行总结，它返回了以下消息：

`['Michael Smith made a mistake by using PyTorch Trainer instead of HF Trainer.']`

```python
>>> print(db_record)
```

<pre>
{'user_input': 'Michael Smith ([REDACTED EMAIL ADDRESS], (+1) [REDACTED PHONE NUMBER]) committed a mistake when he used PyTorch Trainer instead of HF Trainer.\n\nSummarize the above text in 1-2 sentences.', 'user_email': None, 'cohere_response': {'data': ['Michael Smith made a mistake by using PyTorch Trainer instead of HF Trainer.'], 'return_likelihoods': None, 'meta': {'api_version': {'version': '1'}, 'billed_units': {'input_tokens': 48, 'output_tokens': 14}}}, 'cohere_model': 'command-r-plus', 'temperature': 0.3, 'extras': '{}', 'created_at': datetime.datetime(2024, 6, 10, 2, 16, 7, 666438), 'cohere_endpoint': 'generate'}
</pre>

第二个返回项是数据库记录。该代码库是为使用 Postgres 后端而设计的；实际上，代码库自带一个使用 Docker 构建的完整前端。Postgres 数据库用于存储网关的聊天历史记录。然而，它也非常有用，因为它展示了每个请求中实际发送的数据。如我们所见，提示语经过了清洗，实际发送的内容如下：

`Michael Smith ([REDACTED EMAIL ADDRESS], (+1) [REDACTED PHONE NUMBER]) committed a mistake when he used PyTorch Trainer instead of HF Trainer.\n\nSummarize the above text in 1-2 sentences.`

但等等，我听到你在想。Michael Smith 不就是 PII 吗？确实是。但是这个代码库实际上并没有实现姓名清洗功能。接下来，我们将探讨在提示语中应用了哪些清洗器：

> [!提示]  
> Cohere 的 `generate` 端点实际上已经被弃用，因此，如果有人能为 Cohere API 的新的 Chat 端点创建并提交集成，这将是一个非常棒的开源贡献。

## 清洗器！

根据他们的代码库，以下是实现的清洗器：

```python
ALL_SCRUBBERS = [
    scrub_phone_numbers,
    scrub_credit_card_numbers,
    scrub_email_addresses,
    scrub_postal_codes,
    scrub_sin_numbers,
]
```

网关会依次应用每个清洗器。

这虽然有些 “hacky”（不够优雅），但如果你确实需要实现另一个清洗器，可以通过修改包装器方法来实现。以下是一个演示：

> [!提示]  
> 作者提到，SIN（社会保险号）清洗器特别容易误清洗数据，因此它会被放在最后，以确保其他与数字相关的 PII 先被清洗。

```python
def my_custom_scrubber(text: str) -> str:
    """
    Scrub Michael Smith in text

    :param text: Input text to scrub
    :type text: str
    :return: Input text with any mentions of Michael Smith scrubbed
    :rtype: str
    """
    return re.sub(
        r"Michael Smith",

        
        "[REDACTED PERSON]",
        text,
        re.IGNORECASE
    )
```

```python
original_method = wrapper.send_cohere_request

def modified_method(self, **kwargs):
    self._validate_cohere_endpoint(kwargs.get('endpoint', None)) # Unfortunate double validate cohere endpoint call
    prompt = kwargs.get('prompt', None)
    text = my_custom_scrubber(prompt)
    kwargs['prompt'] = text
    return original_method(**kwargs)

# Assign the new method to the instance
wrapper.send_cohere_request = types.MethodType(modified_method, wrapper)
```

```python
>>> response, db_record = wrapper.send_cohere_request(
...     endpoint="generate",
...     model="command-r-plus",
...     max_tokens=25,
...     prompt=f"{example}\n\nSummarize the above text in 1-2 sentences.",
...     temperature=0.3,
... )

>>> print(response)
```

<pre>
{'data': ['[REDACTED PERSON] made an error by using PyTorch Trainer instead of HF Trainer. They can be contacted at [RED'], 'return_likelihoods': None, 'meta': {'api_version': {'version': '1'}, 'billed_units': {'input_tokens': 52, 'output_tokens': 25}}}
</pre>

```python
>>> print(db_record)
```

<pre>
{'user_input': '[REDACTED PERSON] ([REDACTED EMAIL ADDRESS], (+1) [REDACTED PHONE NUMBER]) committed a mistake when he used PyTorch Trainer instead of HF Trainer.\n\nSummarize the above text in 1-2 sentences.', 'user_email': None, 'cohere_response': {'data': ['[REDACTED PERSON] made an error by using PyTorch Trainer instead of HF Trainer. They can be contacted at [RED'], 'return_likelihoods': None, 'meta': {'api_version': {'version': '1'}, 'billed_units': {'input_tokens': 52, 'output_tokens': 25}}}, 'cohere_model': 'command-r-plus', 'temperature': 0.3, 'extras': '{}', 'created_at': datetime.datetime(2024, 6, 10, 2, 59, 58, 733195), 'cohere_endpoint': 'generate'}
</pre>

如果你确实需要这样做，请务必记住，清洗器是按顺序应用的，因此，如果你的自定义清洗器与任何默认清洗器发生冲突，可能会导致一些意外行为。

例如，针对姓名的清洗，实际上有[其他清洗库](https://github.com/kylemclaren/scrub)可以探索，这些库采用更复杂的算法来清洗 PII。这个代码库涵盖了更多的PII类型，例如 [IP 地址、主机名等](https://github.com/kylemclaren/scrub/blob/master/scrubadubdub/scrub.py)。然而，如果你仅仅需要删除特定的匹配项，你仍然可以使用上述代码进行处理。

## 数据集

让我们在一个完整的数据集上演示这个包装器的实际应用。

```python
pii_ds = load_dataset("ai4privacy/pii-masking-200k")
```

```python
pii_ds['train'][36]['source_text']
```

```python
>>> example = pii_ds['train'][36]['source_text']

>>> response, db_record = wrapper.send_cohere_request(
...     endpoint="generate",
...     model="command-r-plus",
...     max_tokens=50,
...     prompt=f"{example}\n\nSummarize the above text in 1-2 sentences.",
...     temperature=0.3,
... )

>>> print(response)
```

<pre>
{'data': ["The person is requesting an update on assessment results and is offering Kip 100,000 in exchange for the information and the recipient's account details."], 'return_likelihoods': None, 'meta': {'api_version': {'version': '1'}, 'billed_units': {'input_tokens': 64, 'output_tokens': 33}}}
</pre>

```python
>>> print(db_record)
```

<pre>
{'user_input': "I need the latest update on assessment results. Please send the files to V[REDACTED EMAIL ADDRESS]. For your extra time, we'll offer you Kip 100,000 but please provide your лв account details.\n\nSummarize the above text in 1-2 sentences.", 'user_email': None, 'cohere_response': {'data': ["The person is requesting an update on assessment results and is offering Kip 100,000 in exchange for the information and the recipient's account details."], 'return_likelihoods': None, 'meta': {'api_version': {'version': '1'}, 'billed_units': {'input_tokens': 64, 'output_tokens': 33}}}, 'cohere_model': 'command-r-plus', 'temperature': 0.3, 'extras': '{}', 'created_at': datetime.datetime(2024, 6, 10, 3, 10, 51, 416091), 'cohere_endpoint': 'generate'}
</pre>

## 常规输出

如果我们直接将文本发送到端点而不进行任何清洗，摘要结果如下所示：

```python
 co = cohere.Client(
    api_key=os.environ['COHERE_API_KEY']


 esponse_vanilla = co.generate(
    prompt=f"{example}\n\nSummarize the above text in 1-2 sentences.",
    model="command-r-plus",
    max_tokens=50,
    temperature=0.3
```

```python
response_vanilla
```

总结一下，在 Notebook 中，我们演示了如何使用 Wealthsimple 开源的 PII 检测示例网关，并在此基础上添加了自定义清洗器。如果你真的需要可靠的 PII 检测，确保运行自己的测试，验证你所采用的清洗算法是否真正覆盖了你的应用场景。最重要的是，尽可能在你自己托管的基础设施上部署开源模型，这将始终是构建 LLM 应用时最安全、最可靠的选择 :)

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/llm_gateway_pii_detection.md" />

### 使用 Transformers Agents 构建具有工具调用超能力的智能体 🦸
https://huggingface.co/learn/cookbook/zh-CN/agents.md

# 使用 Transformers Agents 构建具有工具调用超能力的智能体 🦸

_作者: [Aymeric Roucher](https://huggingface.co/m-ric)_


这个 notebook 展示了如何使用 [**Transformers Agents**](https://huggingface.co/docs/transformers/en/agents) 来构建出色的**智能体**！

什么是**智能体**？智能体是由大型语言模型（LLM）驱动的系统，它们使得 LLM（通过精心设计的提示和输出解析）能够使用特定的*工具*来解决问题。

这些*工具*基本上是 LLM 自身无法很好执行的功能：例如，对于像 [Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) 这样的文本生成 LLM，这可能是一个图像生成工具、网络搜索工具、计算器...

什么是 **Transformers Agents** ？它是我们 `transformers` 库的一个扩展，提供了构建自己的智能体的构建块！在[文档](https://huggingface.co/docs/transformers/en/agents)中了解更多信息。

让我们看看如何使用它，以及它能解决哪些用例。

我们从源代码安装 transformers agents ，你可以使用 `pip install transformers[agents]` 轻松安装。


```python
>>> !pip install smolagents
```

<pre>
Requirement already satisfied: smolagents in /Users/aymeric/.pyenv/versions/3.12.0/lib/python3.12/site-packages (1.0.0)
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Requirement already satisfied: torchaudio in /Users/aymeric/.pyenv/versions/3.12.0/lib/python3.12/site-packages (from smolagents) (2.3.0)
Requirement already satisfied: torchvision in /Users/aymeric/.pyenv/versions/3.12.0/lib/python3.12/site-packages (from smolagents) (0.18.0)
Requirement already satisfied: transformers>=4.0.0 in /Users/aymeric/.pyenv/versions/3.12.0/lib/python3.12/site-packages (from smolagents) (4.47.1)
Requirement already satisfied: requests>=2.32.3 in /Users/aymeric/.pyenv/versions/3.12.0/lib/python3.12/site-packages (from smolagents) (2.32.3)
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</pre>

```python
!pip install datasets huggingface_hub langchain sentence-transformers faiss-cpu serpapi google-search-results openai -q
```

## 1. 🏞️ 多模态 + 🌐 网络浏览助手

对于这个用例，我们想要展示一个能够浏览网络并能够生成图像的智能体。

为了构建它，我们只需要准备两个工具：图像生成和网络搜索。
- 对于图像生成，我们从 Hub 加载一个工具，该工具使用 HF 推理 API（无服务器）使用 Stable Diffusion 生成图像。
- 对于网络搜索，我们加载一个 LangChain 工具。

```python
from transformers import Tool, load_tool, CodeAgent, InferenceClientModel

# Import tool from Hub
image_generation_tool = load_tool("m-ric/text-to-image", trust_remote_code=True)

# Import tool from LangChain
from langchain.agents import load_tools

search_tool = Tool.from_langchain(load_tools(["serpapi"])[0])


model = InferenceClientModel("meta-llama/Llama-3.1-70B-Instruct")
# Initialize the agent with both tools
agent = CodeAgent(
    tools=[image_generation_tool, search_tool], model=model
)

# Run it!
result = agent.run(
    "Generate me a photo of the car that James bond drove in the latest movie.",
)
result
```

![Image of an Aston Martin DB5](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/agents_db5.png)

## 2. 📚💬 带有迭代查询优化和来源选择的 RAG
快速定义：检索增强生成（RAG）是 ___“使用大型语言模型（LLM）来回答用户查询，但基于从知识库检索到的信息来构建答案”___。

这种方法相比使用普通或微调的 LLM 有许多优势：列举一些，它允许将答案建立在真实事实的基础上并减少虚构，它允许为 LLM 提供特定领域的知识，并且它允许对知识库中的信息访问进行细粒度控制。

- 现在假设我们想要执行 RAG，但增加了动态生成某些参数的约束。例如，根据用户查询，我们可能想要将搜索限制在知识库的特定子集，或者我们可能想要调整检索到的文档数量。难点在于：**如何根据用户查询动态调整这些参数？**

- RAG 的一个常见失败案例是基于用户查询的检索没有返回任何相关的支持文档。**有没有一种方法，在之前的结果不相关时，通过修改查询重新调用检索器来进行迭代？**

🔧 好吧，我们可以以简单的方式解决上述问题：我们将**让我们的智能体控制检索器的参数！**

➡️ 让我们展示如何做到这一点。我们首先加载一个我们想要执行 RAG 的知识库：这个数据集是许多 `huggingface` 包的文档页面汇总，以 markdown 格式存储。



```python
import datasets

knowledge_base = datasets.load_dataset("m-ric/huggingface_doc", split="train")
```

现在我们通过处理数据集并将其存储到向量数据库中来准备知识库，以便检索器使用。我们将使用 LangChain，因为它具有用于向量数据库的优秀工具：


```python
from langchain.docstore.document import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings

source_docs = [
    Document(page_content=doc["text"], metadata={"source": doc["source"].split("/")[1]})
    for doc in knowledge_base
]

docs_processed = RecursiveCharacterTextSplitter(chunk_size=500).split_documents(
    source_docs
)[:1000]

embedding_model = HuggingFaceEmbeddings(model_name="thenlper/gte-small")
vectordb = FAISS.from_documents(documents=docs_processed, embedding=embedding_model)
```

现在我们已经准备好了数据库，让我们构建一个基于它回答用户查询的 RAG 系统！

我们希望我们的系统根据查询只从最相关的信息来源中选择。

我们的文档页面来自以下来源：

```python
>>> all_sources = list(set([doc.metadata["source"] for doc in docs_processed]))
>>> print(all_sources)
```

<pre>
['evaluate', 'course', 'deep-rl-class', 'peft', 'hf-endpoints-documentation', 'blog', 'gradio', 'datasets', 'datasets-server', 'transformers', 'optimum', 'hub-docs', 'pytorch-image-models', 'diffusers']
</pre>

```python
import json
from smolagents import Tool
from langchain_core.vectorstores import VectorStore


class RetrieverTool(Tool):
    name = "retriever"
    description = "Retrieves some documents from the knowledge base that have the closest embeddings to the input query."
    inputs = {
        "query": {
            "type": "text",
            "description": "The query to perform. This should be semantically close to your target documents. Use the affirmative form rather than a question.",
        },
        "source": {"type": "text", "description": ""},
        "number_of_documents": {
            "type": "text",
            "description": "the number of documents to retrieve. Stay under 10 to avoid drowning in docs",
        },
    }
    output_type = "text"

    def __init__(self, vectordb: VectorStore, all_sources: str, **kwargs):
        super().__init__(**kwargs)
        self.vectordb = vectordb
        self.inputs["source"][
            "description"
        ] = f"The source of the documents to search, as a str representation of a list. Possible values in the list are: {all_sources}. If this argument is not provided, all sources will be searched."

    def forward(self, query: str, source: str = None, number_of_documents=7) -> str:
        assert isinstance(query, str), "Your search query must be a string"
        number_of_documents = int(number_of_documents)

        if source:
            if isinstance(source, str) and "[" not in str(
                source
            ):  # if the source is not representing a list
                source = [source]
            source = json.loads(str(source).replace("'", '"'))

        docs = self.vectordb.similarity_search(
            query,
            filter=({"source": source} if source else None),
            k=number_of_documents,
        )

        if len(docs) == 0:
            return "No documents found with this filtering. Try removing the source filter."
        return "Retrieved documents:\n\n" + "\n===Document===\n".join(
            [doc.page_content for doc in docs]
        )
```

### 可选：将你的检索器工具分享到 Hub

要将你的工具分享到 Hub，首先将检索器工具定义单元格中的代码复制粘贴到一个名为例如 `retriever.py` 的新文件中。

当工具从单独的文件加载后，你可以使用以下代码将其推送到 Hub（确保使用具有`写入`访问权限的 token 登录）

```python
share_to_hub = False

if share_to_hub:
    from huggingface_hub import login
    from retriever import RetrieverTool

    login("your_token")

    tool = RetrieverTool(vectordb, all_sources)

    tool.push_to_hub(repo_id="m-ric/retriever-tool")
```

### 运行智能体!

```python
>>> from smolagents import HfModel, ToolCallingAgent, load_tool

>>> model = HfModel("meta-llama/Meta-Llama-3-70B-Instruct")

>>> retriever_tool = load_tool(
...     "m-ric/retriever-tool", vectordb=vectordb, all_sources=all_sources
... )
>>> agent = ToolCallingAgent(tools=[retriever_tool], model=model, verbose=0)

>>> agent_output = agent.run("Please show me a LORA finetuning script")

>>> print("Final output:")
>>> print(agent_output)
```

<pre>
Final output:
https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py
</pre>

发生了什么？首先，智能体启动了检索器，并考虑了特定的来源（`['transformers', 'blog']`）。

但是这次检索没有产生足够的结果 ⇒ 没关系！智能体可以迭代之前的结果，因此它只是用不那么严格的搜索参数重新运行了它的检索。

因此，研究成功了！

请注意，**使用调用检索器作为工具并可以动态修改查询和其他检索参数的 LLM 智能体**是 RAG 的**更一般的表述**，这也涵盖了像迭代查询优化这样的许多 RAG 改进技术。

## 3. 💻 调试 Python 代码

```python
from smolagents import CodeAgent

agent = CodeAgent(tools=[])

code = """
list=[0, 1, 2]

for i in range(4):
    print(list(i))
"""

final_answer = agent.run(
    "I have some code that creates a bug: please debug it and return the final code",
    code=code,
)
```

正如你所看到的，智能体尝试了给定的代码，遇到错误，分析错误，纠正代码，并在验证代码可以正常工作后返回它！

最终的代码是纠正后的代码：


```python
>>> print(final_answer)
```

<pre>
list=[0, 1, 2]

for i in range(4):
    print(list(i))
</pre>

## 4. 创建你自己的 LLM 引擎（OpenAI）

设置你自己的 LLM 引擎真的非常简单：
它只需要一个具有以下标准的`__call__`方法：
1. 接受[ChatML 格式](https://huggingface.co/docs/transformers/main/en/chat_templating#introduction)的消息列表作为输入并输出答案。
2. 接受一个 `stop_sequences` 参数，以传递生成停止的序列。
3. 根据你的 LLM 接受哪种类型的消息角色，你可能还需要转换一些消息角色。


```python
import os
from openai import OpenAI
from smolagents.model import MessageRole, get_clean_message_list

openai_role_conversions = {
    MessageRole.TOOL_RESPONSE: "user",
}


class OpenAIModel:
    def __init__(self, model_name="gpt-4o-2024-05-13"):
        self.model_name = model_name
        self.client = OpenAI(
            api_key=os.getenv("OPENAI_API_KEY"),
        )

    def __call__(self, messages, stop_sequences=[]):
        # Get clean message list
        messages = get_clean_message_list(
            messages, role_conversions=openai_role_conversions
        )

        # Get LLM output
        response = self.client.chat.completions.create(
            model=self.model_name,
            messages=messages,
            stop=stop_sequences,
        )
        return response.choices[0].message.content


openai_engine = OpenAIModel()
agent = CodeAgent(model=openai_engine, tools=[])

code = """
list=[0, 1, 2]

for i in range(4):
    print(list(i))
"""

final_answer = agent.run(
    "I have some code that creates a bug: please debug it and return the final code",
    code=code,
)
```

```python
>>> print(final_answer)
```

<pre>
my_list = [0, 1, 2]  # Renamed the list to avoid using the built-in name

for i in range(len(my_list)):  # Changed the range to be within the length of the list
    print(my_list[i])  # Corrected the list access syntax
</pre>

## ➡️ 结论

上述用例应该让你对我们智能体框架的可能性有了初步了解！

想要了解更多高级用法，请阅读[文档](https://huggingface.co/docs/transformers/en/transformers_agents)， 以及[此实验](https://github.com/aymeric-roucher/agent_reasoning_benchmark/blob/main/benchmark_gaia.ipynb)，它让我们能够基于 Llama-3-70B 构建自己的智能体，并在非常困难的[GAIA 排行榜](https://huggingface.co/spaces/gaia-benchmark/leaderboard)上击败许多 GPT-4 智能体！

欢迎所有反馈，这将帮助我们改进框架！ 🚀

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/agents.md" />

### 使用自定义非结构化数据构建 RAG
https://huggingface.co/learn/cookbook/zh-CN/rag_with_unstructured_data.md

# 使用自定义非结构化数据构建 RAG

_作者: [Maria Khalusova](https://github.com/MKhalusova)_

如果你是 RAG 的新手，请先在[这个其他笔记](https://huggingface.co/learn/cookbook/rag_zephyr_langchain)中探索 RAG 的基础知识，然后回到这里学习如何使用自定义数据构建 RAG。

无论你是正在构建基于 RAG 的个人助理、宠物项目还是企业级 RAG 系统，你很快就会发现，许多重要的知识存储在各种格式中，如 PDF 文件、电子邮件、Markdown 文件、PowerPoint 演示文稿、HTML 页面、Word 文档等。

你如何预处理所有这些数据，以便你能将其用于 RAG？

在这个快速教程中，你将学习如何构建一个将包含多种数据类型的 RAG 系统。你将使用 [Unstructured](https://github.com/Unstructured-IO/unstructured) 进行数据预处理，Hugging Face Hub 上的开源模型进行嵌入和文本生成，ChromaDB 作为向量存储，以及 LangChain 将所有内容整合在一起。

让我们开始吧！我们首先安装所需的依赖项：


```python
!pip install -q torch transformers accelerate bitsandbytes sentence-transformers unstructured[all-docs] langchain chromadb langchain_community
```

接下来，让我们获取一些文档的混合体。假设我想构建一个 RAG 系统，帮助我管理花园中的害虫。为此，我将使用涵盖 IPM（综合害虫管理）主题的多样化文档：
* PDF: `https://www.gov.nl.ca/ecc/files/env-protection-pesticides-business-manuals-applic-chapter7.pdf`
* PowerPoint: `https://ipm.ifas.ufl.edu/pdfs/Citrus_IPM_090913.pptx`
* EPUB: `https://www.gutenberg.org/ebooks/45957`
* HTML: `https://blog.fifthroom.com/what-to-do-about-harmful-garden-and-plant-insects-and-pests.html`

请随意使用你自己选择的主题文档，这些文档类型由 Unstructured 支持：`.eml`, `.html`, `.md`, `.msg`, `.rst`, `.rtf`, `.txt`, `.xml`, `.png`, `.jpg`, `.jpeg`, `.tiff`, `.bmp`, `.heic`, `.csv`, `.doc`, `.docx`, `.epub`, `.odt`, `.pdf`, `.ppt`, `.pptx`, `.tsv`, `.xlsx`。

```python
!mkdir -p "./documents"
!wget https://www.gov.nl.ca/ecc/files/env-protection-pesticides-business-manuals-applic-chapter7.pdf -O "./documents/env-protection-pesticides-business-manuals-applic-chapter7.pdf"
!wget https://ipm.ifas.ufl.edu/pdfs/Citrus_IPM_090913.pptx -O "./documents/Citrus_IPM_090913.pptx"
!wget https://www.gutenberg.org/ebooks/45957.epub3.images -O "./documents/45957.epub"
!wget https://blog.fifthroom.com/what-to-do-about-harmful-garden-and-plant-insects-and-pests.html -O "./documents/what-to-do-about-harmful-garden-and-plant-insects-and-pests.html"
```

## 非结构化数据预处理

你可以使用 Unstructured 库逐个预处理文档，并编写自己的脚本来遍历一个目录，但使用本地源连接器（Local source connector）来摄取给定目录中的所有文档会更加简单。Unstructured 可以从本地目录、S3 存储桶、Blob 存储、SFTP 以及许多其他可能存储文档的地方摄取文档。从这些来源摄取文档的过程非常相似，主要区别在于认证选项。

在这里，你将使用本地源连接器，但也可以自由探索[Unstructured 文档](https://docs.unstructured.io/open-source/ingest/source-connectors/overview)中的其他选项。
可选地，你还可以为处理后的文档选择一个[目的地](https://docs.unstructured.io/open-source/ingest/destination-connectors/overview) - 这可以是 MongoDB、Pinecone、Weaviate 等。在这个 notebook 中，我们将保持所有内容为本地。


```python
# Optional cell to reduce the amount of logs

import logging

logger = logging.getLogger("unstructured.ingest")
logger.root.removeHandler(logger.root.handlers[0])
```

```python
>>> import os

>>> from unstructured.ingest.connector.local import SimpleLocalConfig
>>> from unstructured.ingest.interfaces import PartitionConfig, ProcessorConfig, ReadConfig
>>> from unstructured.ingest.runner import LocalRunner

>>> output_path = "./local-ingest-output"

>>> runner = LocalRunner(
...     processor_config=ProcessorConfig(
...         # logs verbosity
...         verbose=True,
...         # the local directory to store outputs
...         output_dir=output_path,
...         num_processes=2,
...         ),
...     read_config=ReadConfig(),
...     partition_config=PartitionConfig(
...         partition_by_api=True,
...         api_key="YOUR_UNSTRUCTURED_API_KEY",
...         ),
...     connector_config=SimpleLocalConfig(
...         input_path="./documents",
...         # whether to get the documents recursively from given directory
...         recursive=False,
...         ),
...     )
>>> runner.run()
```

<pre>
INFO: NumExpr defaulting to 2 threads.
</pre>

让我们更详细地看看这里的配置。

`ProcessorConfig` 控制处理管道的各个方面，包括输出位置、工作线程数量、错误处理行为、日志详细程度等。这里的唯一必填参数是 `output_dir` - 你希望存储输出的本地目录。

`ReadConfig` 可以用来为不同场景自定义数据读取过程，例如重新下载数据、保留已下载的文件或限制处理的文档数量。在大多数情况下，默认的 `ReadConfig` 将适用。

在 `PartitionConfig` 中，你可以选择是在本地还是通过 API 对文档进行分区。这个例子使用 API，因此需要 Unstructured API 密钥。你可以在这里[获取](https://unstructured.io/api-key-free)。免费的 Unstructured API 限制为 1000 页，并且为基于图像的文档提供了比本地安装的 Unstructured 更好的 OCR 模型。

如果你删除这两个参数，文档将本地处理，但如果文档需要 OCR 和/或文档理解模型，你可能需要安装额外的依赖项。具体来说，在这种情况下，你可能需要安装 poppler 和 tesseract，你可以使用 brew 来获取：

```
!brew install poppler
!brew install tesseract
```

如果你使用的是 Windows 系统，你可以在[Unstructured 文档](https://docs.unstructured.io/open-source/installation/full-installation)中找到替代的安装说明。

最后，在 `SimpleLocalConfig` 中，你需要指定原始文档所在的位置，以及你是否想要递归地遍历目录。


一旦文档被处理，你将在 `local-ingest-output` 目录中找到 4 个 json 文件，每个被处理的文档对应一个。

Unstructured 以统一的方式对所有类型的文档进行分区，并返回带有文档元素的 json。

[文档元素](https://docs.unstructured.io/api-reference/api-services/document-elements) 有一个类型，例如 `NarrativeText`，`Title` 或 `Table`，它们包含提取的文本，以及 Unstructured 能够获取的元数据。一些元数据对所有元素都是通用的，比如元素所在的文档的文件名。其他元数据取决于文件类型或元素类型。例如，`Table` 元素将在元数据中包含表格的 html 表示，而电子邮件的元数据将包含关于发件人和收件人的信息。

让我们从这些 json 文件中导入元素对象。

```python
from unstructured.staging.base import elements_from_json

elements = []

for filename in os.listdir(output_path):
    filepath = os.path.join(output_path, filename)
    elements.extend(elements_from_json(filepath))
```

现在你已经从文档中提取了元素，你可以将它们分块以适应嵌入模型的上下文窗口。

## 分块

如果你熟悉将长文本文档分割成较小块的分块方法，你会注意到 Unstructured 的分块方法略有不同，因为分区步骤已经将整个文档分割成其结构元素：标题、列表项、表格、文本等。通过这种方式对文档进行分区，你可以避免不相关的文本片段最终出现在同一个元素，甚至是同一个块中的情况。

现在，当你使用 Unstructured 对文档元素进行分块时，单个元素已经是小的，因此只有当它们超过所需的最大块大小时才会被分割。否则，它们将保持原样。你还可以选择性地将连续的文本元素（例如列表项）组合在一起，使它们共同符合块大小限制。


```python
from unstructured.chunking.title import chunk_by_title

chunked_elements = chunk_by_title(elements,
                                  # maximum for chunk size
                                  max_characters=512,
                                  # You can choose to combine consecutive elements that are too small
                                  # e.g. individual list items
                                  combine_text_under_n_chars=200,
                                  )
```

这些块已经准备好用于 RAG 了。为了将它们与 LangChain 一起使用，你可以轻松地将 Unstructured 元素转换为 LangChain 文档。

```python
from langchain_core.documents import Document

documents = []
for chunked_element in chunked_elements:
    metadata = chunked_element.metadata.to_dict()
    metadata["source"] = metadata["filename"]
    del metadata["languages"]
    documents.append(Document(page_content=chunked_element.text, metadata=metadata))
```

## 设置检索器

这个例子使用 ChromaDB 作为向量存储，以及 [`BAAI/bge-base-en-v1.5`](https://huggingface.co/BAAI/bge-base-en-v1.5) 嵌入模型，你可以自由使用任何其他向量存储。


```python
from langchain_community.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings

from langchain.vectorstores import utils as chromautils

# ChromaDB doesn't support complex metadata, e.g. lists, so we drop it here.
# If you're using a different vector store, you may not need to do this
docs = chromautils.filter_complex_metadata(documents)

embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-base-en-v1.5")
vectorstore = Chroma.from_documents(documents, embeddings)
retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 3})
```

如果你打算使用 Hugging Face Hub 上的门控模型，无论是嵌入模型还是文本生成模型，你都需要使用你的 Hugging Face token 进行身份验证，你可以在你的 Hugging Face 个人资料设置中获取这个 token 。

```python
from huggingface_hub import notebook_login

notebook_login()
```

## 使用 LangChain 构建 RAG

让我们将所有内容整合在一起，使用 LangChain 构建 RAG。

在这个例子中，我们将使用来自 Meta 的[`Llama-3-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)。为了确保它可以在 Google Colab 的免费 T4 运行时中顺利运行，你需要对其进行量化。

```python
from langchain.prompts import PromptTemplate
from langchain.llms import HuggingFacePipeline
from transformers import pipeline
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from langchain.chains import RetrievalQA
```

```python
model_name = "meta-llama/Meta-Llama-3-8B-Instruct"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16
)

model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=bnb_config)
tokenizer = AutoTokenizer.from_pretrained(model_name)

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

text_generation_pipeline = pipeline(
    model=model,
    tokenizer=tokenizer,
    task="text-generation",
    temperature=0.2,
    do_sample=True,
    repetition_penalty=1.1,
    return_full_text=False,
    max_new_tokens=200,
    eos_token_id=terminators,
)

llm = HuggingFacePipeline(pipeline=text_generation_pipeline)

prompt_template = """
<|start_header_id|>user<|end_header_id|>
You are an assistant for answering questions using provided context.
You are given the extracted parts of a long document and a question. Provide a conversational answer.
If you don't know the answer, just say "I do not know." Don't make up an answer.
Question: {question}
Context: {context}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
"""

prompt = PromptTemplate(
    input_variables=["context", "question"],
    template=prompt_template,
)


qa_chain = RetrievalQA.from_chain_type(
    llm,
    retriever=retriever,
    chain_type_kwargs={"prompt": prompt}
)
```

## 结果和下一步

现在你已经有了 RAG 链，让我们问问它关于蚜虫的问题。在我的花园里，它们是害虫吗？


```python
question = "Are aphids a pest?"

qa_chain.invoke(question)['result']
```

输出:

```bash
Yes, aphids are considered pests because they feed on the nutrient-rich liquids within plants, causing damage and potentially spreading disease. In fact, they're known to multiply quickly, which is why it's essential to control them promptly. As mentioned in the text, aphids can also attract ants, which are attracted to the sweet, sticky substance they produce called honeydew. So, yes, aphids are indeed a pest that requires attention to prevent further harm to your plants!
```

这看起来是一个很有希望的开始！现在你已经了解了预处理复杂非结构化数据以供 RAG 使用的基础知识，你可以继续改进这个例子。以下是一些建议：

* 你可以连接到不同的源来摄取文档，例如，从一个 S3 存储桶。
* 你可以在 `qa_chain` 参数中添加 `return_source_documents=True`，使链在返回答案时同时返回作为上下文传递给提示的文档。这有助于理解生成答案时使用了哪些源。
* 如果你想要在检索阶段利用元素元数据，可以考虑使用 Hugging Face Agent 并创建一个自定义检索器工具，如[这个其他 notebook](https://huggingface.co/learn/cookbook/agents#2--rag-with-iterative-query-refinement--source-selection) 中所述。
* 有许多方法可以改善搜索结果。例如，你可以使用混合搜索代替单一的相似性搜索检索器。混合搜索结合了多种搜索算法，以提高搜索结果的准确性和相关性。通常，它是基于关键词的搜索算法与向量搜索方法的结合。

在使用非结构化数据构建 RAG 应用程序时玩得开心！


<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/rag_with_unstructured_data.md" />

### 推理端点（专用）
https://huggingface.co/learn/cookbook/zh-CN/enterprise_dedicated_endpoints.md

# 推理端点（专用）  
_作者：[Moritz Laurer](https://huggingface.co/MoritzLaurer)_

你是否曾想过创建自己的机器学习 API？在这篇教程中，我们将使用 [HF 专用推理端点](https://huggingface.co/docs/inference-endpoints/index) 来实现这一目标。推理端点允许你选择 HF Hub 上成千上万的模型，创建你自己的 API，并将其部署在你控制的平台和你选择的硬件上。

[无服务器推理 API](link-to-recipe) 非常适合初步测试，但它们仅限于一组预配置的流行模型，并且有速率限制，因为无服务器 API 的硬件同时被多个用户共享。使用专用推理端点，你可以自定义模型的部署，并且硬件仅为你独享。

在本教程中，我们将：
- 通过一个简单的 UI 创建推理端点，并向该端点发送标准的 HTTP 请求
- 使用 `huggingface_hub` 库编程方式创建和管理不同的推理端点
- 涵盖三个用例：使用大型语言模型（LLM）进行文本生成、使用 Stable Diffusion 进行图像生成、以及使用 Idefics2 进行图像推理

## 安装与登录  
如果你还没有 Hugging Face 账号，可以 [在这里](https://huggingface.co/join) 创建一个账号。如果你在一个较大的团队中工作，也可以创建一个 [HF 组织](https://huggingface.co/organizations)，通过该组织管理所有模型、数据集和推理端点。专用推理端点是付费服务，因此你需要在个人 HF 账户或 HF 组织的 [计费设置](https://huggingface.co/settings/billing) 中添加信用卡信息。  

接下来，你可以在 [这里](https://huggingface.co/docs/hub/security-tokens) 创建一个用户访问令牌。对于本教程，带有 `read` 或 `write` 权限的令牌即可使用，但我们建议使用更细粒度的令牌以提高安全性。对于本笔记本，你需要一个细粒度令牌，具备以下权限：
- `用户权限 > 推理 > 调用推理端点与管理推理端点`
- `仓库权限 > google/gemma-1.1-2b-it 和 HuggingFaceM4/idefics2-8b-chatty`

```python
!pip install huggingface_hub~=0.23.3
!pip install transformers~=4.41.2
```

```python
# Login to the HF Hub. We recommend using this login method 
# to avoid the need for explicitly storing your HF token in variables 
import huggingface_hub
huggingface_hub.interpreter_login()
```

## 创建你的第一个推理端点

完成初步设置后，我们可以开始创建第一个推理端点。请访问 [https://ui.endpoints.huggingface.co/](https://ui.endpoints.huggingface.co/) 并点击 `+ New`，位于 `Dedicated Endpoints` 旁边。然后，你将看到一个用于创建新端点的界面，提供以下选项（见下图）：

- **模型仓库**：在这里，你可以输入 HF Hub 上任何模型的标识符。为了本次演示，我们使用 [google/gemma-1.1-2b-it](https://huggingface.co/google/gemma-1.1-2b-it)，这是一个小型生成式大型语言模型（2.5B 参数）。
- **端点名称**：端点名称会根据模型标识符自动生成，但你可以修改它。有效的端点名称只能包含小写字母、数字或连字符（"-"），并且长度必须在 4 到 32 个字符之间。
- **实例配置**：在这里，你可以选择来自主要云平台的一系列 CPU 或 GPU 配置。你还可以调整区域设置，例如，如果你需要将端点托管在欧盟地区。
- **自动缩放到零**：你可以将端点配置为在一段时间后缩放为零 GPU/CPU。缩放到零的端点将不再计费。请注意，重新启动端点时需要将模型重新加载到内存中（并可能重新下载），这对于大型模型可能需要几分钟时间。
- **端点安全级别**：默认的安全级别是 `Protected`，需要授权的 HF 令牌才能访问该端点。`Public` 端点任何人都可以访问，无需令牌认证。`Private` 端点仅通过跨区域的安全 AWS 或 Azure PrivateLink 连接可用。
- **高级配置**：在这里，你可以选择一些高级选项，例如 Docker 容器类型。由于 Gemma 与 [文本生成推理（TGI）](https://huggingface.co/docs/text-generation-inference/index) 容器兼容，系统会自动选择 TGI 作为容器类型，并提供其他默认设置。

按照下图中的选项进行选择，并点击 `Create Endpoint`。

<div style="display: flex; justify-content: center !important;">
    <img src="https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/enterprise-endpoints-creation.png">  
</div>

大约一分钟后，你的推理端点将被创建，你将看到类似下图的页面。

在端点的 `概览` 页面上，你可以找到用于查询该端点的 URL，一个用于测试模型的 Playground，以及其他标签页，包括 `分析`、`使用情况与费用`、`日志` 和 `设置`。

<div style="flex justify-center">
    <img src="https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/enterprise-endpoint-overviewpage.png">  
</div>


### 编程方式创建和管理端点

在进入生产环境时，你不一定希望手动启动、停止和修改推理端点。`huggingface_hub` 库提供了很好的功能，用于编程方式管理推理端点。可以在 [这里](https://huggingface.co/docs/huggingface_hub/guides/inference_endpoints) 查看文档，以及在 [这里](https://huggingface.co/docs/huggingface_hub/en/package_reference/inference_endpoints) 获取所有函数的详细信息。以下是一些关键函数：

```python
# list all your inference endpoints
huggingface_hub.list_inference_endpoints()

# get an existing endpoint and check it's status
endpoint = huggingface_hub.get_inference_endpoint(
    name="gemma-1-1-2b-it-yci",  # the name of the endpoint 
    namespace="MoritzLaurer"  # your user name or organization name
)
print(endpoint)

# Pause endpoint to stop billing
endpoint.pause()

# Resume and wait until the endpoint is ready
#endpoint.resume()
#endpoint.wait()

# Update the endpoint to a different GPU
# You can find the correct arguments for different hardware types in this table: https://huggingface.co/docs/inference-endpoints/pricing#gpu-instances
#endpoint.update(
#    instance_size="x1",
#    instance_type="nvidia-a100",  # nvidia-a10g
#)
```

你也可以通过编程方式创建推理端点。让我们重新创建与 UI 创建的相同的 `gemma` LLM 推理端点。

```python
from huggingface_hub import create_inference_endpoint


model_id = "google/gemma-1.1-2b-it"
endpoint_name = "gemma-1-1-2b-it-001"  # Valid Endpoint names must only contain lower-case characters, numbers or hyphens ("-") and are between 4 to 32 characters long.
namespace = "MoritzLaurer"  # your user or organization name


# check if endpoint with this name already exists from previous tests
available_endpoints_names = [endpoint.name for endpoint in huggingface_hub.list_inference_endpoints()]
if endpoint_name in available_endpoints_names:
    endpoint_exists = True
else: 
    endpoint_exists = False
print("Does the endpoint already exist?", endpoint_exists)
    

# create new endpoint
if not endpoint_exists:
    endpoint = create_inference_endpoint(
        endpoint_name,
        repository=model_id,
        namespace=namespace,
        framework="pytorch",
        task="text-generation",
        # see the available hardware options here: https://huggingface.co/docs/inference-endpoints/pricing#pricing
        accelerator="gpu",
        vendor="aws",
        region="us-east-1",
        instance_size="x1",
        instance_type="nvidia-a10g",
        min_replica=0,
        max_replica=1,
        type="protected",
        # since the LLM is compatible with TGI, we specify that we want to use the latest TGI image
        custom_image={
            "health_route": "/health",
            "env": {
                "MODEL_ID": "/repository"
            },
            "url": "ghcr.io/huggingface/text-generation-inference:latest",
        },
    )
    print("Waiting for endpoint to be created")
    endpoint.wait()
    print("Endpoint ready")

# if endpoint with this name already exists, get and resume existing endpoint
else:
    endpoint = huggingface_hub.get_inference_endpoint(name=endpoint_name, namespace=namespace)
    if endpoint.status in ["paused", "scaledToZero"]:
        print("Resuming endpoint")
        endpoint.resume()
    print("Waiting for endpoint to start")
    endpoint.wait()
    print("Endpoint ready")
```

```python
# access the endpoint url for API calls
print(endpoint.url)
```

## 查询你的推理端点

现在，让我们像查询其他 LLM API 一样查询这个端点。首先，从界面复制端点 URL（或者使用 `endpoint.url`）并将其赋值给下面的 `API_URL`。接着，我们使用标准化的消息格式来传递文本输入，即一个包含用户和助手消息的字典，这种格式你可能在其他 LLM API 服务中见过。接下来，我们需要将聊天模板应用到这些消息上，这是像 Gemma、Llama-3 等 LLM 模型已经训练过的格式（详细信息请参见 [文档](https://huggingface.co/docs/transformers/main/en/chat_templating)）。对于大多数最新的生成式 LLM，应用这个聊天模板是非常重要的，否则模型的表现会退化，且不会抛出错误。

```python
>>> import requests
>>> from transformers import AutoTokenizer

>>> # paste your endpoint URL here or reuse endpoint.url if you created the endpoint programmatically
>>> API_URL = endpoint.url  # or paste link like "https://dz07884a53qjqb98.us-east-1.aws.endpoints.huggingface.cloud" 
>>> HEADERS = {"Authorization": f"Bearer {huggingface_hub.get_token()}"}

>>> # function for standard http requests
>>> def query(payload=None, api_url=None):
...     response = requests.post(api_url, headers=HEADERS, json=payload)
...     return response.json()


>>> # define conversation input in messages format
>>> # you can also provide multiple turns between user and assistant
>>> messages = [
...     {"role": "user", "content": "Please write a short poem about open source for me."},
...     #{"role": "assistant", "content": "I am not in the mood."},
...     #{"role": "user", "content": "Can you please do this for me?"},
... ]

>>> # apply the chat template for the respective model
>>> model_id = "google/gemma-1.1-2b-it"
>>> tokenizer = AutoTokenizer.from_pretrained(model_id) 
>>> messages_with_template = tokenizer.apply_chat_template(messages, tokenize=False)
>>> print("Your text input looks like this, after the chat template has been applied:\n")
>>> print(messages_with_template)
```

<pre>
Your text input looks like this, after the chat template has been applied:

<bos><start_of_turn>user
Please write a short poem about open source for me.<end_of_turn>
</pre>

```python
>>> # send standard http request to endpoint
>>> output = query(
...     payload = {
...         "inputs": messages_with_template,
...         "parameters": {"temperature": 0.2, "max_new_tokens": 100, "seed": 42, "return_full_text": False},
...     },
...     api_url = API_URL
... )

>>> print("The output from your API/Endpoint call:\n")
>>> print(output)
```

<pre>
The output from your API/Endpoint call:

[{'generated_text': "Free to use, free to share,\nA collaborative code, a community's care.\n\nCode transparent, bugs readily found,\nContributions welcome, stories unbound.\nOpen source, a gift to all,\nBuilding the future, one line at a call.\n\nSo join the movement, embrace the light,\nOpen source, shining ever so bright."}]
</pre>

就这样，你已经向你的推理端点发出了第一次请求——你自己的 API！

如果你希望端点自动处理聊天模板，并且你的 LLM 运行在 TGI 容器上，你也可以通过在 URL 后追加 `/v1/chat/completions` 路径来使用 [messages API](https://huggingface.co/docs/text-generation-inference/en/messages_api)。使用 `/v1/chat/completions` 路径时，运行在端点上的 [TGI](https://huggingface.co/docs/text-generation-inference/index) 容器会自动应用聊天模板，并且与 OpenAI 的 API 结构完全兼容，便于互操作性。你可以查看 [TGI Swagger UI](https://huggingface.github.io/text-generation-inference/#/Text%20Generation%20Inference/chat_completions) 获取所有可用参数。请注意，默认 `/` 路径和 `/v1/chat/completions` 路径接受的参数略有不同。以下是使用 messages API 的稍微修改过的代码：

```python
>>> API_URL_CHAT = API_URL + "/v1/chat/completions"

>>> output = query(
...     payload = {
...         "messages": messages,
...         "model": "tgi",
...         "parameters": {"temperature": 0.2, "max_tokens": 100, "seed": 42},
...     },
...     api_url = API_URL_CHAT
... )

>>> print("The output from your API/Endpoint call with the OpenAI-compatible messages API route:\n")
>>> print(output)
```

<pre>
The output from your API/Endpoint call with the OpenAI-compatible messages API route:

{'id': '', 'object': 'text_completion', 'created': 1718283608, 'model': '/repository', 'system_fingerprint': '2.0.5-dev0-sha-90184df', 'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': '**Open Source**\n\nA license for the mind,\nTo share, distribute, and bind,\nIdeas freely given birth,\nFor the good of all to sort.\n\nCode transparent, eyes open wide,\nA permission for the wise,\nTo learn, to build, to use at will,\nA future bright, we help fill.\n\nFrom servers vast to candles low,\nOpen source, a guiding key,\nFor progress made, knowledge shared,\nA future brimming with'}, 'logprobs': None, 'finish_reason': 'length'}], 'usage': {'prompt_tokens': 20, 'completion_tokens': 100, 'total_tokens': 120}}
</pre>

### 使用 InferenceClient 简化端点使用

你还可以使用 [`InferenceClient`](https://huggingface.co/docs/huggingface_hub/en/package_reference/inference_client#huggingface_hub.InferenceClient) 来轻松地向你的端点发送请求。该客户端是 `huggingface_hub` Python 库中提供的一个便捷工具，允许你轻松调用 [专用推理端点](https://huggingface.co/docs/inference-endpoints/index) 和 [无服务器推理 API](https://huggingface.co/docs/api-inference/index)。详细信息请参阅 [文档](https://huggingface.co/docs/huggingface_hub/en/package_reference/inference_client#inference)。

这是向你的端点发送请求的最简洁方式：

```python
from huggingface_hub import InferenceClient

client = InferenceClient()

output = client.chat_completion(
    messages,  # the chat template is applied automatically, if your endpoint uses a TGI container
    model=API_URL, 
    temperature=0.2, max_tokens=100, seed=42,
)

print("The output from your API/Endpoint call with the InferenceClient:\n")
print(output)
```

```python
# pause the endpoint to stop billing
#endpoint.pause()
```

## 为各种模型创建推理端点

按照相同的过程，你可以为 HF Hub 上的任何模型创建推理端点。接下来，我们将展示一些其他的使用场景。

### 使用 Stable Diffusion 进行图像生成

我们可以使用与 LLM 几乎相同的代码来创建一个图像生成推理端点。唯一的区别是，在这种情况下，我们不使用 TGI 容器，因为 TGI 仅为 LLM（以及视觉 LLM）设计。

```python
>>> !pip install Pillow  # for image processing
```

<pre>
Collecting Pillow
  Downloading pillow-10.3.0-cp39-cp39-manylinux_2_28_x86_64.whl (4.5 MB)
[K     |████████████████████████████████| 4.5 MB 24.7 MB/s eta 0:00:01
[?25hInstalling collected packages: Pillow
Successfully installed Pillow-10.3.0
</pre>

```python
>>> from huggingface_hub import create_inference_endpoint

>>> model_id = "stabilityai/stable-diffusion-xl-base-1.0"
>>> endpoint_name = "stable-diffusion-xl-base-1-0-001"  # Valid Endpoint names must only contain lower-case characters, numbers or hyphens ("-") and are between 4 to 32 characters long.
>>> namespace = "MoritzLaurer"  # your user or organization name
>>> task = "text-to-image"

>>> # check if endpoint with this name already exists from previous tests
>>> available_endpoints_names = [endpoint.name for endpoint in huggingface_hub.list_inference_endpoints()]
>>> if endpoint_name in available_endpoints_names:
...     endpoint_exists = True
>>> else: 
...     endpoint_exists = False
>>> print("Does the endpoint already exist?", endpoint_exists)
    

>>> # create new endpoint
>>> if not endpoint_exists:
...     endpoint = create_inference_endpoint(
...         endpoint_name,
...         repository=model_id,
...         namespace=namespace,
...         framework="pytorch",
...         task=task,
...         # see the available hardware options here: https://huggingface.co/docs/inference-endpoints/pricing#pricing
...         accelerator="gpu",
...         vendor="aws",
...         region="us-east-1",
...         instance_size="x1",
...         instance_type="nvidia-a100",
...         min_replica=0,
...         max_replica=1,
...         type="protected",
...     )
...     print("Waiting for endpoint to be created")
...     endpoint.wait()
...     print("Endpoint ready")

>>> # if endpoint with this name already exists, get existing endpoint
>>> else:
...     endpoint = huggingface_hub.get_inference_endpoint(name=endpoint_name, namespace=namespace)
...     if endpoint.status in ["paused", "scaledToZero"]:
...         print("Resuming endpoint")
...         endpoint.resume()
...     print("Waiting for endpoint to start")
...     endpoint.wait()
...     print("Endpoint ready")
```

<pre>
Does the endpoint already exist? True
Waiting for endpoint to start
Endpoint ready
</pre>

```python
>>> prompt = "A whimsical illustration of a fashionably dressed llama proudly holding a worn, vintage cookbook, with a warm cup of tea and a few freshly baked treats scattered around, set against a cozy background of rustic wood and blooming flowers."

>>> image = client.text_to_image(
...     prompt=prompt,
...     model=endpoint.url,  #"stabilityai/stable-diffusion-xl-base-1.0",
...     guidance_scale=8,
... )

>>> print("PROMPT: ", prompt)
>>> display(image.resize((image.width // 2, image.height // 2)))
```

<pre>
PROMPT:  A whimsical illustration of a fashionably dressed llama proudly holding a worn, vintage cookbook, with a warm cup of tea and a few freshly baked treats scattered around, set against a cozy background of rustic wood and blooming flowers.
</pre>

我们再次暂停端点以停止计费。

```python
endpoint.pause()
```

### 视觉语言模型：对文本和图像的推理

现在，让我们为视觉语言模型（VLM）创建一个推理端点。VLM 与 LLM 非常相似，不同之处在于它们可以同时接受文本和图像作为输入。它们的输出是自回归生成的文本，就像标准的 LLM 一样。VLM 可以处理许多任务，从视觉问答到文档理解。在这个例子中，我们使用 [Idefics2](https://huggingface.co/blog/idefics2)，一个强大的 8B 参数 VLM。

首先，我们需要将使用 Stable Diffusion 生成的 PIL 图像转换为 `base64` 编码的字符串，以便通过网络将其发送给模型。

```python
import base64
from io import BytesIO


def pil_image_to_base64(image):
    buffered = BytesIO()
    image.save(buffered, format="JPEG")
    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
    return img_str


image_b64 = pil_image_to_base64(image)
```

由于 VLM 和 LLM 非常相似，我们可以再次使用几乎相同的消息格式和聊天模板，只需添加一些代码以将图像包含在提示中。有关提示格式的具体细节，请参阅 [Idefics2 模型卡](https://huggingface.co/HuggingFaceM4/idefics2-8b)。

```python
from transformers import AutoProcessor

# load the processor
model_id_vlm = "HuggingFaceM4/idefics2-8b-chatty"
processor = AutoProcessor.from_pretrained(model_id_vlm)

# define the user messages
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image"},  # the image is placed here in the prompt. You can add multiple images throughout the conversation.
            {"type": "text", "text": "Write a short limerick about this image."},
        ],
    },
]

# apply the chat template to the messages
prompt = processor.apply_chat_template(messages, add_generation_prompt=True)

# the chat template places a special "<image>" token at the position where the image should go
# here we replace the "<image>" token with the base64 encoded image string in the prompt
# to be able to send the image via an API request
image_input = f"data:image/jpeg;base64,{image_b64}"
image_input = f"![]({image_input})"
prompt = prompt.replace("<image>", image_input)
```

> [!TIP]  
> 对于 VLM，图像代表一定数量的 tokens。例如，对于 Idefics2，一张低分辨率的图像代表 64 个 tokens，而高分辨率图像则代表 5*64=320 个 tokens。高分辨率是 TGI 中的默认设置（详情请参见 [模型卡](https://huggingface.co/HuggingFaceM4/idefics2-8b-chatty) 中的 `do_image_splitting`）。这意味着一张图像消耗 320 个 tokens。

像 Idefics2 这样的多个 VLM 也受到 TGI 支持（请参见 [支持的模型列表](https://huggingface.co/docs/text-generation-inference/supported_models)），因此在创建端点时，我们再次使用 TGI 容器。

```python
>>> from huggingface_hub import create_inference_endpoint

>>> endpoint_name = "idefics2-8b-chatty-001"
>>> namespace = "MoritzLaurer"
>>> task = "text-generation"

>>> # check if endpoint with this name already exists from previous tests
>>> available_endpoints_names = [endpoint.name for endpoint in huggingface_hub.list_inference_endpoints()]
>>> if endpoint_name in available_endpoints_names:
...     endpoint_exists = True
>>> else: 
...     endpoint_exists = False
>>> print("Does the endpoint already exist?", endpoint_exists)
    

>>> if endpoint_exists:
...     endpoint = huggingface_hub.get_inference_endpoint(name=endpoint_name, namespace=namespace)
...     if endpoint.status in ["paused", "scaledToZero"]:
...         print("Resuming endpoint")
...         endpoint.resume()
...     print("Waiting for endpoint to start")
...     endpoint.wait()
...     print("Endpoint ready")

>>> else:
...     endpoint = create_inference_endpoint(
...         endpoint_name,
...         repository=model_id_vlm,
...         namespace=namespace,
...         framework="pytorch",
...         task=task,
...         accelerator="gpu",
...         vendor="aws",
...         region="us-east-1",
...         type="protected",
...         instance_size="x1",
...         instance_type="nvidia-a100",
...         min_replica=0,
...         max_replica=1,
...         custom_image={
...             "health_route": "/health",
...             "env": {
...                 "MAX_BATCH_PREFILL_TOKENS": "2048",
...                 "MAX_INPUT_LENGTH": "1024",
...                 "MAX_TOTAL_TOKENS": "1536",
...                 "MODEL_ID": "/repository"
...             },
...             "url": "ghcr.io/huggingface/text-generation-inference:latest",
...         },
...     )

...     print("Waiting for endpoint to be created")
...     endpoint.wait()
...     print("Endpoint ready")
```

<pre>
Does the endpoint already exist? False
Waiting for endpoint to be created
Endpoint ready
</pre>

```python
>>> output = client.text_generation(
...     prompt, model=model_id_vlm, max_new_tokens=200, seed=42
... )

>>> print(output)
```

<pre>
In a quaint little café, there lived a llama,
With glasses on his face, he was quite a charm.
He'd sit at the table,
With a book and a mable,
And sip from a cup of warm tea.
</pre>

```python
endpoint.pause()
```

## 额外信息  
- 在创建多个端点时，你可能会收到一个错误消息，提示你的 GPU 配额已满。不要犹豫，直接发送邮件到错误消息中的邮箱地址，我们很可能会增加你的 GPU 配额。
- `paused` 和 `scaled-to-zero` 端点有什么区别？`scaled-to-zero` 端点可以通过用户请求灵活唤醒并扩展，而 `paused` 端点需要由端点创建者手动解除暂停。此外，`scaled-to-zero` 端点会占用你的 GPU 配额（按其可能扩展到的最大副本数计算），而 `paused` 端点则不会。因此，一个简单的释放 GPU 配额的方法是暂停一些端点。

## 结论与下一步

就是这样，你已经为文本到文本、文本到图像、图像到文本的生成创建了三个不同的端点（你自己的 API！），同样的过程也适用于许多其他模型和任务。

我们鼓励你阅读专用推理端点的 [文档](https://huggingface.co/docs/inference-endpoints/index)，以了解更多信息。如果你正在使用生成式 LLM 和 VLM，我们还建议阅读 TGI 的 [文档](https://huggingface.co/docs/text-generation-inference/index)，因为最流行的 LLM 和 VLM 也都支持 TGI，这将使你的端点更加高效。

例如，你可以通过 [TGI Guidance](https://huggingface.co/docs/text-generation-inference/basic_tutorials/using_guidance) 使用 **JSON 模式或函数调用** 来与开源模型交互（还可以参考这个 [食谱](https://huggingface.co/learn/cookbook/structured_generation) 中的结构化生成示例，适用于 RAG）。

当将端点投入生产时，你可能需要进行一些额外的改进，以提高设置的效率。使用 TGI 时，你应通过异步函数调用向端点发送请求批次，以充分利用端点的硬件，并且你可以调整多个容器参数，以优化延迟和吞吐量，针对你的具体用例进行优化。我们将在另一篇指南中介绍这些优化。



<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/enterprise_dedicated_endpoints.md" />

### 使用向量嵌入和 Qdrant 进行代码搜索
https://huggingface.co/learn/cookbook/zh-CN/code_search.md

## 使用向量嵌入和 Qdrant 进行代码搜索

*作者：[Qdrant 团队](https://qdrant.tech/)*

在这个 Notebook 中，我们演示了如何使用向量嵌入来导航代码库并找到相关的代码片段。我们将使用自然语义查询来搜索代码库，并根据相似的逻辑查找代码。

你可以查看这个方法的[实时部署](https://code-search.qdrant.tech/)，该部署通过一个网页界面提供 Qdrant 代码库的搜索功能。

### 方法

为了实现我们的目标，我们需要两个模型：

- 用于自然语言处理（NLP）的通用神经编码器， 在我们的案例中使用的是 [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) 模型。我们将称之为 **NLP 模型**。

- 用于代码间相似度搜索的专门嵌入模型。我们将使用 [jinaai/jina-embeddings-v2-base-code](https://huggingface.co/jinaai/jina-embeddings-v2-base-code) 模型来完成此任务。它支持英语和 30 种广泛使用的编程语言，且具有 8192 的序列长度。我们将称之为 **代码模型**。

为了准备 NLP 模型所需的代码，我们需要将代码预处理成一种更接近自然语言的格式。由于代码模型已经支持多种标准编程语言，因此无需对代码片段进行预处理。我们可以直接使用原始代码。

## 安装依赖项

让我们安装将要使用的包。

- [inflection](https://pypi.org/project/inflection/) - 一个字符串转换库。它可以将英文单词的复数转为单数，单数转为复数，并将驼峰命名法（CamelCase）转换为下划线分隔的字符串。
- [fastembed](https://pypi.org/project/fastembed/) - 一个轻量级的库，用于生成向量嵌入，优先支持 CPU。[支持 GPU](https://github.com/qdrant/fastembed#%EF%B8%8F-fastembed-on-a-gpu)。
- [qdrant-client](https://pypi.org/project/qdrant-client/) - 官方的 Python 库，用于与 Qdrant 服务器进行交互。

```python
%pip install inflection qdrant-client fastembed
```

### 数据准备

将应用程序源代码分割成更小的部分是一个复杂的任务。通常，函数、类方法、结构体、枚举以及所有其他语言特定的构造体都是很好的分块候选。它们足够大，能包含一些有意义的信息，但又足够小，适合被嵌入模型处理，因为这些模型有一个有限的上下文窗口。你还可以使用文档字符串（docstrings）、注释和其他元数据来丰富这些块，增加额外的信息。

<div style="text-align:center"><img src="https://huggingface.co/datasets/Anush008/cookbook-images/resolve/main/data-chunking.png" /></div>

基于文本的搜索通常是基于函数签名的，但代码搜索可能会返回更小的部分，比如循环。因此，如果我们从 NLP 模型收到一个特定的函数签名，并且从代码模型收到该函数部分实现的代码，我们将合并这些结果。

### 解析代码库

我们将使用 [Qdrant 代码库](https://github.com/qdrant/qdrant) 来进行演示。虽然该代码库使用的是 Rust，但你可以使用此方法处理任何其他编程语言。你可以使用 [语言服务器协议（LSP）](https://microsoft.github.io/language-server-protocol/) 工具来构建代码库的图谱，然后提取代码块。我们使用了 [rust-analyzer](https://rust-analyzer.github.io/) 完成这项工作。我们将解析后的代码库导出为 [LSIF](https://microsoft.github.io/language-server-protocol/specifications/lsif/0.4.0/specification/) 格式，这是一个用于代码智能的数据标准。接下来，我们利用 LSIF 数据来导航代码库并提取代码块。

你可以对其他编程语言使用相同的方法。市面上有[大量的实现](https://microsoft.github.io/language-server-protocol/implementors/servers/)可供选择。

接下来，我们将把代码块导出为 JSON 文档，文档中不仅包含代码本身，还会包括代码在项目中的位置信息（上下文）。

你可以在我们的 Google Cloud Storage 存储桶中查看解析为 JSON 格式的 Qdrant 结构，文件名为 [structures.jsonl 文件](https://storage.googleapis.com/tutorial-attachments/code-search/structures.jsonl)。下载该文件，并将其用作我们的代码搜索数据源。

```python
!wget https://storage.googleapis.com/tutorial-attachments/code-search/structures.jsonl
```

接下来，加载文件并将每一行解析为字典列表：

```python
import json

structures = []
with open("structures.jsonl", "r") as fp:
    for i, row in enumerate(fp):
        entry = json.loads(row)
        structures.append(entry)
```

我们来看一下一个条目的结构是怎样的

```python
structures[0]
```

```python
{'name': 'InvertedIndexRam',
 'signature': '# [doc = " Inverted flatten index from dimension id to posting list"] # [derive (Debug , Clone , PartialEq)] pub struct InvertedIndexRam { # [doc = " Posting lists for each dimension flattened (dimension id -> posting list)"] # [doc = " Gaps are filled with empty posting lists"] pub postings : Vec < PostingList > , # [doc = " Number of unique indexed vectors"] # [doc = " pre-computed on build and upsert to avoid having to traverse the posting lists."] pub vector_count : usize , }',
 'code_type': 'Struct',
 'docstring': '= " Inverted flatten index from dimension id to posting list"',
 'line': 15,
 'line_from': 13,
 'line_to': 22,
 'context': {'module': 'inverted_index',
  'file_path': 'lib/sparse/src/index/inverted_index/inverted_index_ram.rs',
  'file_name': 'inverted_index_ram.rs',
  'struct_name': None,
  'snippet': '/// Inverted flatten index from dimension id to posting list\n#[derive(Debug, Clone, PartialEq)]\npub struct InvertedIndexRam {\n    /// Posting lists for each dimension flattened (dimension id -> posting list)\n    /// Gaps are filled with empty posting lists\n    pub postings: Vec<PostingList>,\n    /// Number of unique indexed vectors\n    /// pre-computed on build and upsert to avoid having to traverse the posting lists.\n    pub vector_count: usize,\n}\n'}}
  ```

### 代码到自然语言的转换

每种编程语言都有其特定的语法，而这些语法并不是自然语言的一部分。因此，一个通用模型可能无法直接理解代码。然而，我们可以通过去除代码特有的部分并加入额外的上下文（如模块、类、函数和文件名）来规范化数据。我们采取以下步骤：

1. 提取函数、方法或其他代码结构的签名。
2. 将驼峰命名法（CamelCase）和下划线命名法（snake_case）中的名称拆分为单独的单词。
3. 获取文档字符串（docstring）、注释和其他重要的元数据。
4. 使用预定义的模板根据提取的数据构建句子。
5. 移除特殊字符并用空格替代。

我们现在可以定义 `textify` 函数，利用 `inflection` 库来执行我们的转换操作：

```python
import inflection
import re

from typing import Dict, Any


def textify(chunk: Dict[str, Any]) -> str:
<CopyLLMTxtMenu containerStyle="float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"></CopyLLMTxtMenu>

    # Get rid of all the camel case / snake case
    # - inflection.underscore changes the camel case to snake case
    # - inflection.humanize converts the snake case to human readable form
    name = inflection.humanize(inflection.underscore(chunk["name"]))
    signature = inflection.humanize(inflection.underscore(chunk["signature"]))

    # Check if docstring is provided
    docstring = ""
    if chunk["docstring"]:
        docstring = f"that does {chunk['docstring']} "

    # Extract the location of that snippet of code
    context = (
        f"module {chunk['context']['module']} " f"file {chunk['context']['file_name']}"
    )
    if chunk["context"]["struct_name"]:
        struct_name = inflection.humanize(
            inflection.underscore(chunk["context"]["struct_name"])
        )
        context = f"defined in struct {struct_name} {context}"

    # Combine all the bits and pieces together
    text_representation = (
        f"{chunk['code_type']} {name} "
        f"{docstring}"
        f"defined as {signature} "
        f"{context}"
    )

    # Remove any special characters and concatenate the tokens
    tokens = re.split(r"\W", text_representation)
    tokens = filter(lambda x: x, tokens)
    return " ".join(tokens)
```

现在我们可以使用 `textify` 函数将所有的代码块转换为文本表示

```python
text_representations = list(map(textify, structures))
```

让我们看看其中一个转换后的自然语言表示是什么样子的：

```python
text_representations[1000]
```

```python
'Function Hnsw discover precision that does Checks discovery search precision when using hnsw index this is different from the tests in defined as Fn hnsw discover precision module integration file hnsw_discover_test rs'
```

### 自然语言嵌入



```python
from fastembed import TextEmbedding

batch_size = 5

nlp_model = TextEmbedding("sentence-transformers/all-MiniLM-L6-v2", threads=0)
nlp_embeddings = nlp_model.embed(text_representations, batch_size=batch_size)
```

### 代码嵌入

```python
code_snippets = [structure["context"]["snippet"] for structure in structures]

code_model = TextEmbedding("jinaai/jina-embeddings-v2-base-code")

code_embeddings = code_model.embed(code_snippets, batch_size=batch_size)
```

### 构建 Qdrant 集合

Qdrant 支持多种部署模式，包括内存模式（用于原型开发）、Docker 和 Qdrant Cloud。你可以参考 [安装指南](https://qdrant.tech/documentation/guides/installation/) 了解更多信息。

我们将在此教程中使用内存实例继续操作。

> **提示**  
> 内存模式只能用于快速原型开发和测试。它是 Qdrant 服务器方法的 Python 实现。

现在，让我们创建一个集合来存储我们的向量。

```python
from qdrant_client import QdrantClient, models

COLLECTION_NAME = "qdrant-sources"

client = QdrantClient(":memory:")  # Use in-memory storage
# client = QdrantClient("http://locahost:6333")  # For Qdrant server

client.create_collection(
    COLLECTION_NAME,
    vectors_config={
        "text": models.VectorParams(
            size=384,
            distance=models.Distance.COSINE,
        ),
        "code": models.VectorParams(
            size=768,
            distance=models.Distance.COSINE,
        ),
    },
)
```

我们新创建的集合已经准备好接受数据。接下来，让我们上传嵌入向量：

```python
from tqdm import tqdm

points = []
total = len(structures)
print("Number of points to upload: ", total)

for id, (text_embedding, code_embedding, structure) in tqdm(enumerate(zip(nlp_embeddings, code_embeddings, structures)), total=total):
    # FastEmbed returns generators. Embeddings are computed as consumed.
    points.append(
        models.PointStruct(
            id=id,
            vector={
                "text": text_embedding,
                "code": code_embedding,
            },
            payload=structure,
        )
    )

    # Upload points in batches
    if len(points) >= batch_size:
        client.upload_points(COLLECTION_NAME, points=points, wait=True)
        points = []

# Ensure any remaining points are uploaded
if points:
    client.upload_points(COLLECTION_NAME, points=points)

print(f"Total points in collection: {client.count(COLLECTION_NAME).count}")
```

上传的点立即可以用于搜索。接下来，查询集合以找到相关的代码片段。

### 查询代码库

我们使用其中一个模型通过 Qdrant 的新 [查询 API](https://qdrant.tech/blog/qdrant-1.10.x/) 搜索集合。首先使用文本嵌入。运行以下查询：“如何计算集合中的点数？”。然后查看查询结果。

```python
query = "How do I count points in a collection?"

hits = client.query_points(
    COLLECTION_NAME,
    query=next(nlp_model.query_embed(query)).tolist(),
    using="text",
    limit=3,
).points
```

现在，查看查询结果。以下表格列出了模块、文件名和得分。每一行都包含一个指向签名的链接。

| 模块             | 文件名            | 得分      | 签名                                                                                                                                                                                                                                                                               |
|------------------|-------------------|-----------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| operations       | types.rs          | 0.5493385  | [`pub struct CountRequestInternal`](https://github.com/qdrant/qdrant/blob/4aac02315bb3ca461a29484094cf6d19025fce99/lib/collection/src/operations/types.rs#L794)                             |
| map_index        | types.rs          | 0.49973965 | [`fn get_points_with_value_count`](https://github.com/qdrant/qdrant/blob/4aac02315bb3ca461a29484094cf6d19025fce99/lib/segment/src/index/field_index/map_index/mod.rs#L89)                        |
| map_index        | mutable_map_index.rs | 0.49941066 | [`pub fn get_points_with_value_count`](https://github.com/qdrant/qdrant/blob/4aac02315bb3ca461a29484094cf6d19025fce99/lib/segment/src/index/field_index/map_index/mutable_map_index.rs#L143) |

看起来我们已经能够找到一些相关的代码结构。接下来，让我们使用代码嵌入（code embeddings）再试一次。

```python
hits = client.query_points(
    COLLECTION_NAME,
    query=next(code_model.query_embed(query)).tolist(),
    using="code",
    limit=3,
).points
```

输出结果：

| 模块           | 文件名                     | 得分       | 签名                                                                                                                                                                                                                                                                   |
|----------------|----------------------------|------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| field_index    | geo_index.rs               | 0.7217579  | [`fn count_indexed_points`](https://github.com/qdrant/qdrant/blob/4aac02315bb3ca461a29484094cf6d19025fce99/lib/segment/src/index/field_index/geo_index/mod.rs#L319)         |
| numeric_index  | mod.rs                     | 0.7113214  | [`fn count_indexed_points`](https://github.com/qdrant/qdrant/blob/4aac02315bb3ca461a29484094cf6d19025fce99/lib/segment/src/index/field_index/numeric_index/mod.rs#L317) |
| full_text_index| text_index.rs              | 0.6993165  | [`fn count_indexed_points`](https://github.com/qdrant/qdrant/blob/4aac02315bb3ca461a29484094cf6d19025fce99/lib/segment/src/index/field_index/full_text_index/text_index.rs#L179)     |

虽然不同模型得到的得分不可直接比较，但我们可以看到，结果是不同的。代码嵌入和文本嵌入能够捕捉到代码库的不同方面。我们可以使用这两种模型查询集合，然后将结果结合起来，以获取最相关的代码片段。

```python
from qdrant_client import models

hits = client.query_points(
    collection_name=COLLECTION_NAME,
    prefetch=[
        models.Prefetch(
            query=next(nlp_model.query_embed(query)).tolist(),
            using="text",
            limit=5,
        ),
        models.Prefetch(
            query=next(code_model.query_embed(query)).tolist(),
            using="code",
            limit=5,
        ),
    ],
    query=models.FusionQuery(fusion=models.Fusion.RRF)
).points
```

```python
>>> for hit in hits:
...     print(
...         "| ",
...         hit.payload["context"]["module"], " | ",
...         hit.payload["context"]["file_path"], " | ",
...         hit.score, " | `",
...         hit.payload["signature"], "` |"
...     )
```

<pre>
|  operations  |  lib/collection/src/operations/types.rs  |  0.5  | ` # [doc = " Count Request"] # [doc = " Counts the number of points which satisfy the given filter."] # [doc = " If filter is not provided, the count of all points in the collection will be returned."] # [derive (Debug , Deserialize , Serialize , JsonSchema , Validate)] # [serde (rename_all = "snake_case")] pub struct CountRequestInternal { # [doc = " Look only for points which satisfies this conditions"] # [validate] pub filter : Option < Filter > , # [doc = " If true, count exact number of points. If false, count approximate number of points faster."] # [doc = " Approximate count might be unreliable during the indexing process. Default: true"] # [serde (default = "default_exact_count")] pub exact : bool , } ` |
|  field_index  |  lib/segment/src/index/field_index/geo_index.rs  |  0.5  | ` fn count_indexed_points (& self) -> usize ` |
|  map_index  |  lib/segment/src/index/field_index/map_index/mod.rs  |  0.33333334  | ` fn get_points_with_value_count < Q > (& self , value : & Q) -> Option < usize > where Q : ? Sized , N : std :: borrow :: Borrow < Q > , Q : Hash + Eq , ` |
|  numeric_index  |  lib/segment/src/index/field_index/numeric_index/mod.rs  |  0.33333334  | ` fn count_indexed_points (& self) -> usize ` |
|  fixtures  |  lib/segment/src/fixtures/payload_context_fixture.rs  |  0.25  | ` fn total_point_count (& self) -> usize ` |
|  map_index  |  lib/segment/src/index/field_index/map_index/mutable_map_index.rs  |  0.25  | ` fn get_points_with_value_count < Q > (& self , value : & Q) -> Option < usize > where Q : ? Sized , N : std :: borrow :: Borrow < Q > , Q : Hash + Eq , ` |
|  id_tracker  |  lib/segment/src/id_tracker/simple_id_tracker.rs  |  0.2  | ` fn total_point_count (& self) -> usize ` |
|  map_index  |  lib/segment/src/index/field_index/map_index/mod.rs  |  0.2  | ` fn count_indexed_points (& self) -> usize ` |
|  map_index  |  lib/segment/src/index/field_index/map_index/mod.rs  |  0.16666667  | ` fn count_indexed_points (& self) -> usize ` |
|  field_index  |  lib/segment/src/index/field_index/stat_tools.rs  |  0.16666667  | ` fn number_of_selected_points (points : usize , values : usize) -> usize ` |
</pre>

这是一个如何融合不同模型结果的示例。在实际场景中，你可能会进行一些重新排序（reranking）和去重（deduplication），以及对结果进行额外的处理。

### 对结果进行分组

你可以通过按负载属性对搜索结果进行分组，从而改进搜索结果。在我们的例子中，我们可以按模块对结果进行分组。如果我们使用代码嵌入，可能会看到来自 `map_index` 模块的多个结果。让我们对结果进行分组，并假设每个模块只显示一个结果：

```python
results = client.query_points_groups(
    COLLECTION_NAME,
    query=next(code_model.query_embed(query)).tolist(),
    using="code",
    group_by="context.module",
    limit=5,
    group_size=1,
)
```

```python
>>> for group in results.groups:
...     for hit in group.hits:
...         print(
...             "| ",
...             hit.payload["context"]["module"], " | ",
...             hit.payload["context"]["file_name"], " | ",
...             hit.score, " | `",
...             hit.payload["signature"], "` |"
...         )
```

<pre>
|  field_index  |  geo_index.rs  |  0.7217579  | ` fn count_indexed_points (& self) -> usize ` |
|  numeric_index  |  mod.rs  |  0.7113214  | ` fn count_indexed_points (& self) -> usize ` |
|  fixtures  |  payload_context_fixture.rs  |  0.6993165  | ` fn total_point_count (& self) -> usize ` |
|  map_index  |  mod.rs  |  0.68385994  | ` fn count_indexed_points (& self) -> usize ` |
|  full_text_index  |  text_index.rs  |  0.6660142  | ` fn count_indexed_points (& self) -> usize ` |
</pre>

这就结束了我们的教程。感谢你花时间跟随我们完成这个过程。我们刚刚开始探索使用向量嵌入的可能性以及如何改进它。请随意进行实验，或许你能构建出非常酷的东西！如果你有任何创意，欢迎与我们分享 🙏 我们的联系方式在 [这里](https://qdrant.tech/contact-us/)。

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/code_search.md" />

### 让多个智能体在多智能体层级中协作 🤖🤝🤖
https://huggingface.co/learn/cookbook/zh-CN/multiagent_web_assistant.md

# 让多个智能体在多智能体层级中协作 🤖🤝🤖  
_作者：[Aymeric Roucher](https://huggingface.co/m-ric)_

> 本教程属于进阶内容，建议先了解[另一本指南](agents)中的概念！

在本笔记本中，我们将构建一个**多智能体网页浏览器：一个多个智能体协作，通过互联网解决问题的智能体系统！**

它将是一个简单的层级结构，使用 `ManagedAgent` 对象来封装受管理的网页搜索智能体：

```
              +----------------+
              | 管理员智能体    |
              +----------------+
                       |
        _______________|______________
       |                              |
  代码解释器       +--------------------------------+
       工具         |         受管理的智能体        |
                     |      +------------------+      |
                     |      | 网页搜索智能体   |      |
                     |      +------------------+      |
                     |         |            |         |
                     |  网页搜索工具       |         |
                     |             访问网页工具   |
                     +--------------------------------+
```

让我们开始搭建这个系统。

⚡️ 我们的智能体将由 [meta-llama/Meta-Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct) 提供支持，使用 `HfApiEngine` 类，它利用了 HF 的推理 API：推理 API 可以快速、轻松地运行任何操作系统模型。

运行以下命令来安装所需的依赖项：

```python
!pip install markdownify duckduckgo-search "transformers[agents]" --upgrade -q
```

我们将选择使用由 [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) 提供支持的模型，因为它非常强大，并且在 HF API 中可以免费使用。

```python
model = "Qwen/Qwen2.5-72B-Instruct"
```

### 🔍 创建一个网页搜索工具

对于网页浏览，我们可以直接使用我们现有的 [`DuckDuckGoSearchTool`](https://github.com/huggingface/transformers/blob/main/src/transformers/agents/search.py) 工具来提供一个类似于 Google 搜索的功能。

但是，我们还需要能够查看 `DuckDuckGoSearchTool` 找到的网页内容。  
为此，我们可以导入库中内建的 `VisitWebpageTool`，但我们将重新构建这个工具，以便了解它是如何实现的。

因此，让我们从零开始，使用 `markdownify` 创建我们的 `VisitWebpageTool` 工具。

```python
import re
import requests
from markdownify import markdownify as md
from requests.exceptions import RequestException
from transformers.agents import tool


@tool
def visit_webpage(url: str) -> str:
    """Visits a webpage at the given URL and returns its content as a markdown string.

    Args:
        url: The URL of the webpage to visit.

    Returns:
        The content of the webpage converted to Markdown, or an error message if the request fails.
    """
    try:
        # Send a GET request to the URL
        response = requests.get(url)
        response.raise_for_status()  # Raise an exception for bad status codes

        # Convert the HTML content to Markdown
        markdown_content = md(response.text).strip()

        # Remove multiple line breaks
        markdown_content = re.sub(r"\n{3,}", "\n\n", markdown_content)

        return markdown_content

    except RequestException as e:
        return f"Error fetching the webpage: {str(e)}"
    except Exception as e:
        return f"An unexpected error occurred: {str(e)}"
```

好了，现在让我们初始化并测试我们的工具！

```python
>>> print(visit_webpage("https://en.wikipedia.org/wiki/Hugging_Face")[:500])
```

<pre>
Hugging Face \- Wikipedia

[Jump to content](#bodyContent)

Main menu

Main menu
move to sidebar
hide

 Navigation
 

* [Main page](/wiki/Main_Page "Visit the main page [z]")
* [Contents](/wiki/Wikipedia:Contents "Guides to browsing Wikipedia")
* [Current events](/wiki/Portal:Current_events "Articles related to current events")
* [Random article](/wiki/Special:Random "Visit a randomly selected article [x]")
* [About Wikipedia](/wiki/Wikipedia:About "Learn about Wikipedia and how it works")
* [Co
</pre>

## 构建我们的多智能体系统 🤖🤝🤖

现在我们已经拥有了所有的工具 `search` 和 `visit_webpage`，可以使用它们来创建网页智能体。

该选择什么配置呢？  
- 网页浏览是一个单线程任务，不需要并行调用工具，因此使用 JSON 调用工具非常合适。因此，我们选择 `ReactJsonAgent`。  
- 此外，由于有时网页搜索需要浏览多个页面才能找到正确答案，我们更倾向于将 `max_iterations` 增加到 10。

```python
from transformers.agents import (
    ReactCodeAgent,
    ReactJsonAgent,
    HfApiEngine,
    ManagedAgent,
)
from transformers.agents.search import DuckDuckGoSearchTool

llm_engine = HfApiEngine(model)

web_agent = ReactJsonAgent(
    tools=[DuckDuckGoSearchTool(), visit_webpage],
    llm_engine=llm_engine,
    max_iterations=10,
)
```

然后，我们将这个智能体封装到一个 `ManagedAgent` 中，这样它就可以通过管理员智能体进行调用了。


```python
managed_web_agent = ManagedAgent(
    agent=web_agent,
    name="search",
    description="Runs web searches for you. Give it your query as an argument.",
)
```


最后，我们创建一个管理员智能体，并在初始化时将我们的受管理智能体通过 `managed_agents` 参数传递给它。

由于这个智能体负责规划和思考，因此高级推理将非常有帮助，所以选择 `ReactCodeAgent` 是最合适的。

另外，我们想提一个涉及当前年份的问题：因此，我们需要添加 `additional_authorized_imports=["time", "datetime"]`。

```python
manager_agent = ReactCodeAgent(
    tools=[],
    llm_engine=llm_engine,
    managed_agents=[managed_web_agent],
    additional_authorized_imports=["time", "datetime"],
)
```

就这样！现在让我们运行我们的系统！我们选择一个需要进行一些计算的问题

```python
manager_agent.run("How many years ago was Stripe founded?")
```

我们的智能体成功地高效协作，解决了任务！ ✅

💡 你可以轻松地扩展到更多智能体：一个负责代码执行，一个负责网页搜索，一个负责文件加载……

🤔💭 甚至可以考虑构建更复杂的树状层级结构，一个 CEO 智能体负责管理多个中层经理，每个经理下有多个报告。

我们甚至可以添加更多的中间管理层，每层都有多个日常会议，进行大量敏捷工作和 Scrum 主管，每个新增的组件都会增加足够的摩擦力，确保任务永远无法完成……嗯，等等，不，我们还是坚持我们的简单结构吧。


<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/multiagent_web_assistant.md" />

### 介绍
https://huggingface.co/learn/cookbook/zh-CN/benchmarking_tgi.md

# 介绍
本指南的目的是向你展示如何正确地对 TGI 进行基准测试。如需更多背景信息和解释，请先查看这篇[热门博客](https://huggingface.co/blog/tgi-benchmarking)。

## 设置
确保你有一个已安装 TGI 的环境；Docker 是一个很好的选择。这里的命令可以很容易地复制/粘贴到终端中，这可能更为便捷。无需强制使用 Jupyter。如果你只是想测试一下，可以复制并使用[derek-thomas/tgi-benchmark-space](https://huggingface.co/spaces/derek-thomas/tgi-benchmark-space)。

# TGI 启动器

```python
>>> !text-generation-launcher --version
```

<pre>
text-generation-launcher 2.2.1-dev0
</pre>

以下是 TGI 的不同设置选项，请确保通读并决定哪些设置对你的使用场景最为重要。

以下是一些最重要的设置：
- `--model-id` 
- `--quantize` 量化可以节省内存，但并不总是能提高速度
- `--max-input-tokens` 这可以让 TGI 优化预填充操作
- `--max-total-tokens` 与上述设置结合，TGI 现在知道最大输入和输出 token 的限制
- `--max-batch-size` 这个设置让 TGI 知道它每次可以处理多少个请求。

最后这三个设置共同提供了必要的限制，以便为你的使用场景进行优化。通过合理设置这些选项，你可以显著提高性能。

```python
>>> !text-generation-launcher -h
```

<pre>
Text Generation Launcher

[1m[4mUsage:[0m [1mtext-generation-launcher[0m [OPTIONS]

[1m[4mOptions:[0m
      [1m--model-id[0m <MODEL_ID>
          The name of the model to load. Can be a MODEL_ID as listed on <https://hf.co/models> like `gpt2` or `OpenAssistant/oasst-sft-1-pythia-12b`. Or it can be a local directory containing the necessary files as saved by `save_pretrained(...)` methods of transformers [env: MODEL_ID=] [default: bigscience/bloom-560m]
      [1m--revision[0m <REVISION>
          The actual revision of the model if you're referring to a model on the hub. You can use a specific commit id or a branch like `refs/pr/2` [env: REVISION=]
      [1m--validation-workers[0m <VALIDATION_WORKERS>
          The number of tokenizer workers used for payload validation and truncation inside the router [env: VALIDATION_WORKERS=] [default: 2]
      [1m--sharded[0m <SHARDED>
          Whether to shard the model across multiple GPUs By default text-generation-inference will use all available GPUs to run the model. Setting it to `false` deactivates `num_shard` [env: SHARDED=] [possible values: true, false]
      [1m--num-shard[0m <NUM_SHARD>
          The number of shards to use if you don't want to use all GPUs on a given machine. You can use `CUDA_VISIBLE_DEVICES=0,1 text-generation-launcher... --num_shard 2` and `CUDA_VISIBLE_DEVICES=2,3 text-generation-launcher... --num_shard 2` to launch 2 copies with 2 shard each on a given machine with 4 GPUs for instance [env: NUM_SHARD=]
      [1m--quantize[0m <QUANTIZE>
          Whether you want the model to be quantized [env: QUANTIZE=] [possible values: awq, eetq, exl2, gptq, marlin, bitsandbytes, bitsandbytes-nf4, bitsandbytes-fp4, fp8]
      [1m--speculate[0m <SPECULATE>
          The number of input_ids to speculate on If using a medusa model, the heads will be picked up automatically Other wise, it will use n-gram speculation which is relatively free in terms of compute, but the speedup heavily depends on the task [env: SPECULATE=]
      [1m--dtype[0m <DTYPE>
          The dtype to be forced upon the model. This option cannot be used with `--quantize` [env: DTYPE=] [possible values: float16, bfloat16]
      [1m--trust-remote-code[0m
          Whether you want to execute hub modelling code. Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure no malicious code has been contributed in a newer revision [env: TRUST_REMOTE_CODE=]
      [1m--max-concurrent-requests[0m <MAX_CONCURRENT_REQUESTS>
          The maximum amount of concurrent requests for this particular deployment. Having a low limit will refuse clients requests instead of having them wait for too long and is usually good to handle backpressure correctly [env: MAX_CONCURRENT_REQUESTS=] [default: 128]
      [1m--max-best-of[0m <MAX_BEST_OF>
          This is the maximum allowed value for clients to set `best_of`. Best of makes `n` generations at the same time, and return the best in terms of overall log probability over the entire generated sequence [env: MAX_BEST_OF=] [default: 2]
      [1m--max-stop-sequences[0m <MAX_STOP_SEQUENCES>
          This is the maximum allowed value for clients to set `stop_sequences`. Stop sequences are used to allow the model to stop on more than just the EOS token, and enable more complex "prompting" where users can preprompt the model in a specific way and define their "own" stop token aligned with their prompt [env: MAX_STOP_SEQUENCES=] [default: 4]
      [1m--max-top-n-tokens[0m <MAX_TOP_N_TOKENS>
          This is the maximum allowed value for clients to set `top_n_tokens`. `top_n_tokens` is used to return information about the the `n` most likely tokens at each generation step, instead of just the sampled token. This information can be used for downstream tasks like for classification or ranking [env: MAX_TOP_N_TOKENS=] [default: 5]
      [1m--max-input-tokens[0m <MAX_INPUT_TOKENS>
          This is the maximum allowed input length (expressed in number of tokens) for users. The larger this value, the longer prompt users can send which can impact the overall memory required to handle the load. Please note that some models have a finite range of sequence they can handle. Default to min(max_position_embeddings - 1, 4095) [env: MAX_INPUT_TOKENS=]
      [1m--max-input-length[0m <MAX_INPUT_LENGTH>
          Legacy version of [`Args::max_input_tokens`] [env: MAX_INPUT_LENGTH=]
      [1m--max-total-tokens[0m <MAX_TOTAL_TOKENS>
          This is the most important value to set as it defines the "memory budget" of running clients requests. Clients will send input sequences and ask to generate `max_new_tokens` on top. with a value of `1512` users can send either a prompt of `1000` and ask for `512` new tokens, or send a prompt of `1` and ask for `1511` max_new_tokens. The larger this value, the larger amount each request will be in your RAM and the less effective batching can be. Default to min(max_position_embeddings, 4096) [env: MAX_TOTAL_TOKENS=]
      [1m--waiting-served-ratio[0m <WAITING_SERVED_RATIO>
          This represents the ratio of waiting queries vs running queries where you want to start considering pausing the running queries to include the waiting ones into the same batch. `waiting_served_ratio=1.2` Means when 12 queries are waiting and there's only 10 queries left in the current batch we check if we can fit those 12 waiting queries into the batching strategy, and if yes, then batching happens delaying the 10 running queries by a `prefill` run [env: WAITING_SERVED_RATIO=] [default: 0.3]
      [1m--max-batch-prefill-tokens[0m <MAX_BATCH_PREFILL_TOKENS>
          Limits the number of tokens for the prefill operation. Since this operation take the most memory and is compute bound, it is interesting to limit the number of requests that can be sent. Default to `max_input_tokens + 50` to give a bit of room [env: MAX_BATCH_PREFILL_TOKENS=]
      [1m--max-batch-total-tokens[0m <MAX_BATCH_TOTAL_TOKENS>
          **IMPORTANT** This is one critical control to allow maximum usage of the available hardware [env: MAX_BATCH_TOTAL_TOKENS=]
      [1m--max-waiting-tokens[0m <MAX_WAITING_TOKENS>
          This setting defines how many tokens can be passed before forcing the waiting queries to be put on the batch (if the size of the batch allows for it). New queries require 1 `prefill` forward, which is different from `decode` and therefore you need to pause the running batch in order to run `prefill` to create the correct values for the waiting queries to be able to join the batch [env: MAX_WAITING_TOKENS=] [default: 20]
      [1m--max-batch-size[0m <MAX_BATCH_SIZE>
          Enforce a maximum number of requests per batch Specific flag for hardware targets that do not support unpadded inference [env: MAX_BATCH_SIZE=]
      [1m--cuda-graphs[0m <CUDA_GRAPHS>
          Specify the batch sizes to compute cuda graphs for. Use "0" to disable. Default = "1,2,4,8,16,32" [env: CUDA_GRAPHS=]
      [1m--hostname[0m <HOSTNAME>
          The IP address to listen on [env: HOSTNAME=r-derek-thomas-tgi-benchmark-space-geij6846-b385a-lont4] [default: 0.0.0.0]
  [1m-p[0m, [1m--port[0m <PORT>
          The port to listen on [env: PORT=80] [default: 3000]
      [1m--shard-uds-path[0m <SHARD_UDS_PATH>
          The name of the socket for gRPC communication between the webserver and the shards [env: SHARD_UDS_PATH=] [default: /tmp/text-generation-server]
      [1m--master-addr[0m <MASTER_ADDR>
          The address the master shard will listen on. (setting used by torch distributed) [env: MASTER_ADDR=] [default: localhost]
      [1m--master-port[0m <MASTER_PORT>
          The address the master port will listen on. (setting used by torch distributed) [env: MASTER_PORT=] [default: 29500]
      [1m--huggingface-hub-cache[0m <HUGGINGFACE_HUB_CACHE>
          The location of the huggingface hub cache. Used to override the location if you want to provide a mounted disk for instance [env: HUGGINGFACE_HUB_CACHE=]
      [1m--weights-cache-override[0m <WEIGHTS_CACHE_OVERRIDE>
          The location of the huggingface hub cache. Used to override the location if you want to provide a mounted disk for instance [env: WEIGHTS_CACHE_OVERRIDE=]
      [1m--disable-custom-kernels[0m
          For some models (like bloom), text-generation-inference implemented custom cuda kernels to speed up inference. Those kernels were only tested on A100. Use this flag to disable them if you're running on different hardware and encounter issues [env: DISABLE_CUSTOM_KERNELS=]
      [1m--cuda-memory-fraction[0m <CUDA_MEMORY_FRACTION>
          Limit the CUDA available memory. The allowed value equals the total visible memory multiplied by cuda-memory-fraction [env: CUDA_MEMORY_FRACTION=] [default: 1.0]
      [1m--rope-scaling[0m <ROPE_SCALING>
          Rope scaling will only be used for RoPE models and allow rescaling the position rotary to accomodate for larger prompts [env: ROPE_SCALING=] [possible values: linear, dynamic]
      [1m--rope-factor[0m <ROPE_FACTOR>
          Rope scaling will only be used for RoPE models See `rope_scaling` [env: ROPE_FACTOR=]
      [1m--json-output[0m
          Outputs the logs in JSON format (useful for telemetry) [env: JSON_OUTPUT=]
      [1m--otlp-endpoint[0m <OTLP_ENDPOINT>
          [env: OTLP_ENDPOINT=]
      [1m--otlp-service-name[0m <OTLP_SERVICE_NAME>
          [env: OTLP_SERVICE_NAME=] [default: text-generation-inference.router]
      [1m--cors-allow-origin[0m <CORS_ALLOW_ORIGIN>
          [env: CORS_ALLOW_ORIGIN=]
      [1m--api-key[0m <API_KEY>
          [env: API_KEY=]
      [1m--watermark-gamma[0m <WATERMARK_GAMMA>
          [env: WATERMARK_GAMMA=]
      [1m--watermark-delta[0m <WATERMARK_DELTA>
          [env: WATERMARK_DELTA=]
      [1m--ngrok[0m
          Enable ngrok tunneling [env: NGROK=]
      [1m--ngrok-authtoken[0m <NGROK_AUTHTOKEN>
          ngrok authentication token [env: NGROK_AUTHTOKEN=]
      [1m--ngrok-edge[0m <NGROK_EDGE>
          ngrok edge [env: NGROK_EDGE=]
      [1m--tokenizer-config-path[0m <TOKENIZER_CONFIG_PATH>
          The path to the tokenizer config file. This path is used to load the tokenizer configuration which may include a `chat_template`. If not provided, the default config will be used from the model hub [env: TOKENIZER_CONFIG_PATH=]
      [1m--disable-grammar-support[0m
          Disable outlines grammar constrained generation. This is a feature that allows you to generate text that follows a specific grammar [env: DISABLE_GRAMMAR_SUPPORT=]
  [1m-e[0m, [1m--env[0m
          Display a lot of information about your runtime environment
      [1m--max-client-batch-size[0m <MAX_CLIENT_BATCH_SIZE>
          Control the maximum number of inputs that a client can send in a single request [env: MAX_CLIENT_BATCH_SIZE=] [default: 4]
      [1m--lora-adapters[0m <LORA_ADAPTERS>
          Lora Adapters a list of adapter ids i.e. `repo/adapter1,repo/adapter2` to load during startup that will be available to callers via the `adapter_id` field in a request [env: LORA_ADAPTERS=]
      [1m--usage-stats[0m <USAGE_STATS>
          Control if anonymous usage stats are collected. Options are "on", "off" and "no-stack" Defaul is on [env: USAGE_STATS=] [default: on] [possible values: on, off, no-stack]
  [1m-h[0m, [1m--help[0m
          Print help (see more with '--help')
  [1m-V[0m, [1m--version[0m
          Print version
</pre>

我们可以直接从本指南中启动，因为我们不需要命令是交互式的。

在本指南中，我们将使用默认设置，因为目的是理解基准测试工具。

如果你在 Space 上运行，以下参数已被更改，因为我们不希望与 Spaces 服务器发生冲突：
- `--hostname`
- `--port`

根据你的需求，你可以随意更改或删除这些参数。

```python
>>> !RUST_BACKTRACE=1 \
>>> text-generation-launcher \
>>> --model-id astronomer/Llama-3-8B-Instruct-GPTQ-8-Bit \
>>> --quantize gptq \
>>> --hostname 0.0.0.0 \
>>> --port 1337
```

<pre>
[2m2024-08-16T12:07:56.411768Z[0m [32m INFO[0m [2mtext_generation_launcher[0m[2m:[0m Args {
    model_id: "astronomer/Llama-3-8B-Instruct-GPTQ-8-Bit",
    revision: None,
    validation_workers: 2,
    sharded: None,
    num_shard: None,
    quantize: Some(
        Gptq,
    ),
    speculate: None,
    dtype: None,
    trust_remote_code: false,
    max_concurrent_requests: 128,
    max_best_of: 2,
    max_stop_sequences: 4,
    max_top_n_tokens: 5,
    max_input_tokens: None,
    max_input_length: None,
    max_total_tokens: None,
    waiting_served_ratio: 0.3,
    max_batch_prefill_tokens: None,
    max_batch_total_tokens: None,
    max_waiting_tokens: 20,
    max_batch_size: None,
    cuda_graphs: None,
    hostname: "0.0.0.0",
    port: 1337,
    shard_uds_path: "/tmp/text-generation-server",
    master_addr: "localhost",
    master_port: 29500,
    huggingface_hub_cache: None,
    weights_cache_override: None,
    disable_custom_kernels: false,
    cuda_memory_fraction: 1.0,
    rope_scaling: None,
    rope_factor: None,
    json_output: false,
    otlp_endpoint: None,
    otlp_service_name: "text-generation-inference.router",
    cors_allow_origin: [],
    api_key: None,
    watermark_gamma: None,
    watermark_delta: None,
    ngrok: false,
    ngrok_authtoken: None,
    ngrok_edge: None,
    tokenizer_config_path: None,
    disable_grammar_support: false,
    env: false,
    max_client_batch_size: 4,
    lora_adapters: None,
    usage_stats: On,
}
[2m2024-08-16T12:07:56.411941Z[0m [32m INFO[0m [2mhf_hub[0m[2m:[0m Token file not found "/data/token"    
[2Kconfig.json [00:00:00] [████████████████████████] 1021 B/1021 B 50.70 KiB/s (0s)[2m2024-08-16T12:07:56.458451Z[0m [32m INFO[0m [2mtext_generation_launcher[0m[2m:[0m Model supports up to 8192 but tgi will now set its default to 4096 instead. This is to save VRAM by refusing large prompts in order to allow more users on the same hardware. You can increase that size using `--max-batch-prefill-tokens=8242 --max-total-tokens=8192 --max-input-tokens=8191`.
[2m2024-08-16T12:07:56.458473Z[0m [32m INFO[0m [2mtext_generation_launcher[0m[2m:[0m Default `max_input_tokens` to 4095
[2m2024-08-16T12:07:56.458480Z[0m [32m INFO[0m [2mtext_generation_launcher[0m[2m:[0m Default `max_total_tokens` to 4096
[2m2024-08-16T12:07:56.458487Z[0m [32m INFO[0m [2mtext_generation_launcher[0m[2m:[0m Default `max_batch_prefill_tokens` to 4145
[2m2024-08-16T12:07:56.458494Z[0m [32m INFO[0m [2mtext_generation_launcher[0m[2m:[0m Using default cuda graphs [1, 2, 4, 8, 16, 32]
[2m2024-08-16T12:07:56.458606Z[0m [32m INFO[0m [1mdownload[0m: [2mtext_generation_launcher[0m[2m:[0m Starting check and download process for astronomer/Llama-3-8B-Instruct-GPTQ-8-Bit
[2m2024-08-16T12:07:59.750101Z[0m [32m INFO[0m [2mtext_generation_launcher[0m[2m:[0m Download file: model.safetensors
^C
[2m2024-08-16T12:08:09.101893Z[0m [32m INFO[0m [1mdownload[0m: [2mtext_generation_launcher[0m[2m:[0m Terminating download
[2m2024-08-16T12:08:09.102368Z[0m [32m INFO[0m [1mdownload[0m: [2mtext_generation_launcher[0m[2m:[0m Waiting for download to gracefully shutdown
</pre>

# TGI 基准测试
现在让我们学习如何启动基准测试工具！

在这里，我们可以看到 TGI 基准测试的不同设置选项。

以下是一些更重要的 TGI 基准测试设置：

- `--tokenizer-name` 这是必需的，以便工具知道使用哪个分词器
- `--batch-size` 这对于负载测试非常重要。我们应该使用足够的值来查看吞吐量和延迟的变化。请注意，在基准测试工具中，batch-size 是虚拟用户的数量。
- `--sequence-length` 也就是输入 tokens，这对于匹配你的使用场景需求非常重要
- `--decode-length` 也就是输出 tokens，这对于匹配你的使用场景需求非常重要
- `--runs` 默认值是 10

<blockquote style="border-left: 5px solid #80CBC4; background: #263238; color: #CFD8DC; padding: 0.5em 1em; margin: 1em 0;">
  <strong>💡 提示：</strong> 当你在探索时，使用较小的 <code style="background: #37474F; color: #FFFFFF; padding: 2px 4px; border-radius: 4px;">--runs</code> 值，但在最终确定时使用较高的值，以获得更精确的统计数据。
</blockquote>

```python
>>> !text-generation-benchmark -h
```

<pre>
Text Generation Benchmarking tool

[1m[4mUsage:[0m [1mtext-generation-benchmark[0m [OPTIONS] [1m--tokenizer-name[0m <TOKENIZER_NAME>

[1m[4mOptions:[0m
  [1m-t[0m, [1m--tokenizer-name[0m <TOKENIZER_NAME>
          The name of the tokenizer (as in model_id on the huggingface hub, or local path) [env: TOKENIZER_NAME=]
      [1m--revision[0m <REVISION>
          The revision to use for the tokenizer if on the hub [env: REVISION=] [default: main]
  [1m-b[0m, [1m--batch-size[0m <BATCH_SIZE>
          The various batch sizes to benchmark for, the idea is to get enough batching to start seeing increased latency, this usually means you're moving from memory bound (usual as BS=1) to compute bound, and this is a sweet spot for the maximum batch size for the model under test
  [1m-s[0m, [1m--sequence-length[0m <SEQUENCE_LENGTH>
          This is the initial prompt sent to the text-generation-server length in token. Longer prompt will slow down the benchmark. Usually the latency grows somewhat linearly with this for the prefill step [env: SEQUENCE_LENGTH=] [default: 10]
  [1m-d[0m, [1m--decode-length[0m <DECODE_LENGTH>
          This is how many tokens will be generated by the server and averaged out to give the `decode` latency. This is the *critical* number you want to optimize for LLM spend most of their time doing decoding [env: DECODE_LENGTH=] [default: 8]
  [1m-r[0m, [1m--runs[0m <RUNS>
          How many runs should we average from [env: RUNS=] [default: 10]
  [1m-w[0m, [1m--warmups[0m <WARMUPS>
          Number of warmup cycles [env: WARMUPS=] [default: 1]
  [1m-m[0m, [1m--master-shard-uds-path[0m <MASTER_SHARD_UDS_PATH>
          The location of the grpc socket. This benchmark tool bypasses the router completely and directly talks to the gRPC processes [env: MASTER_SHARD_UDS_PATH=] [default: /tmp/text-generation-server-0]
      [1m--temperature[0m <TEMPERATURE>
          Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: TEMPERATURE=]
      [1m--top-k[0m <TOP_K>
          Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: TOP_K=]
      [1m--top-p[0m <TOP_P>
          Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: TOP_P=]
      [1m--typical-p[0m <TYPICAL_P>
          Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: TYPICAL_P=]
      [1m--repetition-penalty[0m <REPETITION_PENALTY>
          Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: REPETITION_PENALTY=]
      [1m--frequency-penalty[0m <FREQUENCY_PENALTY>
          Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: FREQUENCY_PENALTY=]
      [1m--watermark[0m
          Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: WATERMARK=]
      [1m--do-sample[0m
          Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: DO_SAMPLE=]
      [1m--top-n-tokens[0m <TOP_N_TOKENS>
          Generation parameter in case you want to specifically test/debug particular decoding strategies, for full doc refer to the `text-generation-server` [env: TOP_N_TOKENS=]
  [1m-h[0m, [1m--help[0m
          Print help (see more with '--help')
  [1m-V[0m, [1m--version[0m
          Print version
</pre>

这是一个示例命令。请注意，我多次添加了感兴趣的 batch sizes，以确保所有的值都能被基准测试工具使用。我还根据预计的用户活动，考虑了哪些 batch sizes 是重要的。

<blockquote style="border-left: 5px solid #FFAB91; background: #37474F; color: #FFCCBC; padding: 0.5em 1em; margin: 1em 0;">
  <strong>⚠️ 警告：</strong> 请注意，TGI 基准测试工具是设计用来在终端中运行的，而不是在 Jupyter 笔记本中。这意味着你需要将命令复制/粘贴到 Jupyter 终端标签中。我在这里放置它是为了方便。
</blockquote>

```python
!text-generation-benchmark \
--tokenizer-name astronomer/Llama-3-8B-Instruct-GPTQ-8-Bit \
--sequence-length 70 \
--decode-length 50 \
--batch-size 1 \
--batch-size 2 \
--batch-size 4 \
--batch-size 8 \
--batch-size 16 \
--batch-size 32 \
--batch-size 64 \
--batch-size 128
```

```python

```

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/benchmarking_tgi.md" />

### 使用 Haystack 和 NuExtract 进行信息提取
https://huggingface.co/learn/cookbook/zh-CN/information_extraction_haystack_nuextract.md

# 使用 Haystack 和 NuExtract 进行信息提取

*作者：[Stefano Fiorucci](https://github.com/anakin87)*

在本 Notebook 中，我们将展示如何使用语言模型自动化从文本数据中提取信息。

🎯 **目标**：创建一个应用程序，从给定的文本或 URL 中提取特定信息，遵循用户定义的结构。

🧰 **技术栈**
- [Haystack 🏗️](https://haystack.deepset.ai?utm_campaign=developer-relations&utm_source=hf-cookbook)：一个可定制的编排框架，用于构建 LLM 应用程序。我们将使用 Haystack 来构建信息提取管道。

- [NuExtract](https://huggingface.co/numind/NuExtract)：一个小型语言模型，专门针对结构化数据提取进行了微调。

## 安装依赖

```python
! pip install haystack-ai trafilatura transformers pyvis
```

## 组件

Haystack 有两个主要概念：[组件和管道](https://docs.haystack.deepset.ai/docs/components_overview?utm_campaign=developer-relations&utm_source=hf-cookbook)。

🧩 **组件** 是执行单一任务的构建块：文件转换、文本生成、嵌入创建等。

➿ **管道** 允许你通过将组件组合成有向（或循环）图，来定义数据在 LLM 应用程序中的流动。

*接下来，我们将介绍我们信息提取应用程序的各个组件。之后，我们将把它们集成到一个管道中。*

### `LinkContentFetcher` 和 `HTMLToDocument`：从网页提取文本

在我们的实验中，我们将从网络上的初创公司融资公告中提取数据。

为了下载网页并提取文本，我们使用两个组件：
- [`LinkContentFetcher`](https://docs.haystack.deepset.ai/docs/linkcontentfetcher?utm_campaign=developer-relations&utm_source=hf-cookbook)：获取某些URL的内容，并返回内容流的列表（作为[`ByteStream`对象](https://docs.haystack.deepset.ai/docs/data-classes#bytestream?utm_campaign=developer-relations&utm_source=hf-cookbook)）。
- [`HTMLToDocument`](https://docs.haystack.deepset.ai/docs/htmltodocument?utm_campaign=developer-relations&utm_source=hf-cookbook)：将HTML源代码转换为文本[`Documents`](https://docs.haystack.deepset.ai/docs/data-classes#document?utm_campaign=developer-relations&utm_source=hf-cookbook)。

```python
>>> from haystack.components.fetchers import LinkContentFetcher
>>> from haystack.components.converters import HTMLToDocument


>>> fetcher = LinkContentFetcher()

>>> streams = fetcher.run(urls=["https://example.com/"])["streams"]

>>> converter = HTMLToDocument()
>>> docs = converter.run(sources=streams)

>>> print(docs)
```

<pre>
{'documents': [Document(id=65bb1ce4b6db2f154d3acfa145fa03363ef93f751fb8599dcec3aaf75aa325b9, content: 'This domain is for use in illustrative examples in documents. You may use this domain in literature ...', meta: {'content_type': 'text/html', 'url': 'https://example.com/'})]}
</pre>

### `HuggingFaceLocalGenerator`：加载并尝试模型

我们使用 [`HuggingFaceLocalGenerator`](https://docs.haystack.deepset.ai/docs/huggingfacelocalgenerator?utm_campaign=developer-relations&utm_source=hf-cookbook)，这是一个文本生成组件，允许使用 Transformers 库加载托管在 Hugging Face 上的模型。

Haystack 还支持许多其他的 [生成器](https://docs.haystack.deepset.ai/docs/generators?utm_campaign=developer-relations&utm_source=hf-cookbook)，包括 [`HuggingFaceAPIGenerator`](https://docs.haystack.deepset.ai/docs/huggingfaceapigenerator?utm_campaign=developer-relations&utm_source=hf-cookbook)（与 Hugging Face APIs 和 TGI 兼容）。

我们加载 [NuExtract](https://huggingface.co/numind/NuExtract)，这是一个基于 `microsoft/Phi-3-mini-4k-instruct` 进行微调的模型，用于从文本中执行结构化数据提取。该模型的大小为 3.8B 参数。还有其他变体可用：`NuExtract-tiny`（0.5B）和 `NuExtract-large`（7B）。

该模型以 `bfloat16` 精度加载，以适应 Colab 环境，并且与 FP32 相比，性能几乎没有损失，正如模型卡片中所建议的那样。

#### 关于 Flash Attention 的说明

在推理时，你可能会看到一个警告：“You are not running the flash-attention implementation”。

像 Colab 或 Kaggle 这样的免费环境中提供的 GPU 不支持 Flash Attention，因此我们决定在本笔记本中不使用它。

如果你的 GPU 架构支持 Flash Attention（[详情](https://github.com/Dao-AILab/flash-attention)），你可以安装它并获得加速，方法如下：
```bash
pip install flash-attn --no-build-isolation
```

然后，将 `"attn_implementation": "flash_attention_2"` 添加到 `model_kwargs` 中。

```python
from haystack.components.generators import HuggingFaceLocalGenerator
import torch

generator = HuggingFaceLocalGenerator(model="numind/NuExtract",
                                      huggingface_pipeline_kwargs={"model_kwargs": {"torch_dtype":torch.bfloat16}})

# effectively load the model (warm_up is automatically invoked when the generator is part of a Pipeline)
generator.warm_up()
```

该模型支持特定的提示结构，可以从模型卡片中推断出来。

我们将手动创建一个提示来尝试该模型。稍后，我们将看到如何根据不同的输入动态生成提示。

```python
>>> prompt="""<|input|>\n### Template:
... {
...     "Car": {
...         "Name": "",
...         "Manufacturer": "",
...         "Designers": [],
...         "Number of units produced": "",
...     }
... }
... ### Text:
... The Fiat Panda is a city car manufactured and marketed by Fiat since 1980, currently in its third generation. The first generation Panda, introduced in 1980, was a two-box, three-door hatchback designed by Giorgetto Giugiaro and Aldo Mantovani of Italdesign and was manufactured through 2003 — receiving an all-wheel drive variant in 1983. SEAT of Spain marketed a variation of the first generation Panda under license to Fiat, initially as the Panda and subsequently as the Marbella (1986–1998).

... The second-generation Panda, launched in 2003 as a 5-door hatchback, was designed by Giuliano Biasio of Bertone, and won the European Car of the Year in 2004. The third-generation Panda debuted at the Frankfurt Motor Show in September 2011, was designed at Fiat Centro Stilo under the direction of Roberto Giolito and remains in production in Italy at Pomigliano d'Arco.[1] The fourth-generation Panda is marketed as Grande Panda, to differentiate it with the third-generation that is sold alongside it. Developed under Stellantis, the Grande Panda is produced in Serbia.

... In 40 years, Panda production has reached over 7.8 million,[2] of those, approximately 4.5 million were the first generation.[3] In early 2020, its 23-year production was counted as the twenty-ninth most long-lived single generation car in history by Autocar.[4] During its initial design phase, Italdesign referred to the car as il Zero. Fiat later proposed the name Rustica. Ultimately, the Panda was named after Empanda, the Roman goddess and patroness of travelers.
... <|output|>
... """

>>> result = generator.run(prompt=prompt)
>>> print(result)
```

<pre>
{'replies': ['{\n    "Car": {\n        "Name": "Fiat Panda",\n        "Manufacturer": "Fiat",\n        "Designers": [\n            "Giorgetto Giugiaro",\n            "Aldo Mantovani",\n            "Giuliano Biasio",\n            "Roberto Giolito"\n        ],\n        "Number of units produced": "over 7.8 million"\n    }\n}\n']}
</pre>

漂亮 ✅

### `PromptBuilder`：动态生成提示

[`PromptBuilder`](https://docs.haystack.deepset.ai/docs/promptbuilder?utm_campaign=developer-relations&utm_source=hf-cookbook) 是通过 Jinja2 提示模板初始化的，并通过填充传递的关键字参数来渲染该模板。

我们的提示模板复现了 [模型卡片](https://huggingface.co/numind/NuExtract) 中显示的结构。

在实验过程中，我们发现缩进模式对于确保良好的结果非常重要。这可能与模型的训练方式有关。

```python
from haystack.components.builders import PromptBuilder
from haystack import Document

prompt_template = '''<|input|>
### Template:
{{ schema | tojson(indent=4) }}
{% for example in examples %}
### Example:
{{ example | tojson(indent=4) }}\n
{% endfor %}
### Text
{{documents[0].content}}
<|output|>
'''

prompt_builder = PromptBuilder(template=prompt_template)
```

```python
>>> example_document = Document(content="The Fiat Panda is a city car...")

>>> example_schema = {
...     "Car": {
...         "Name": "",
...         "Manufacturer": "",
...         "Designers": [],
...         "Number of units produced": "",
...     }
... }

>>> prompt=prompt_builder.run(documents=[example_document], schema=example_schema)["prompt"]

>>> print(prompt)
```

<pre>
<|input|>
### Template:
{
    "Car": {
        "Designers": [],
        "Manufacturer": "",
        "Name": "",
        "Number of units produced": ""
    }
}

### Text
The Fiat Panda is a city car...
<|output|>
</pre>

运行良好 ✅

### `OutputAdapter`

你可能已经注意到，提取结果是 `replies` 列表中的第一个元素，并且是一个 JSON 字符串。

我们希望能够为每个源文档获得一个字典。

为了在管道中执行这个转换，我们可以使用 [`OutputAdapter`](https://docs.haystack.deepset.ai/docs/outputadapter?utm_campaign=developer-relations&utm_source=hf-cookbook)。

```python
>>> import json
>>> from haystack.components.converters import OutputAdapter


>>> adapter = OutputAdapter(template="""{{ replies[0]| replace("'",'"') | json_loads}}""",
...                                          output_type=dict,
...                                          custom_filters={"json_loads": json.loads})

... print(adapter.run(**result))
```

<pre>
{'output': {'Car': {'Name': 'Fiat Panda', 'Manufacturer': 'Fiat', 'Designers': ['Giorgetto Giugiaro', 'Aldo Mantovani', 'Giuliano Biasio', 'Roberto Giolito'], 'Number of units produced': 'over 7.8 million'}}}
</pre>

## 信息提取管道

### 构建管道

现在我们可以通过添加和连接各个组件来[创建我们的管道](https://docs.haystack.deepset.ai/docs/creating-pipelines?utm_campaign=developer-relations&utm_source=hf-cookbook)。

```python
from haystack import Pipeline

ie_pipe = Pipeline()
ie_pipe.add_component("fetcher", fetcher)
ie_pipe.add_component("converter", converter)
ie_pipe.add_component("prompt_builder", prompt_builder)
ie_pipe.add_component("generator", generator)
ie_pipe.add_component("adapter", adapter)

ie_pipe.connect("fetcher", "converter")
ie_pipe.connect("converter", "prompt_builder")
ie_pipe.connect("prompt_builder", "generator")
ie_pipe.connect("generator", "adapter")
```

```python
# IN CASE YOU NEED TO RECREATE THE PIPELINE FROM SCRATCH, YOU CAN UNCOMMENT THIS CELL

# ie_pipe = Pipeline()
# ie_pipe.add_component("fetcher", LinkContentFetcher())
# ie_pipe.add_component("converter", HTMLToDocument())
# ie_pipe.add_component("prompt_builder", PromptBuilder(template=prompt_template))
# ie_pipe.add_component("generator", HuggingFaceLocalGenerator(model="numind/NuExtract",
#                                       huggingface_pipeline_kwargs={"model_kwargs": {"torch_dtype":torch.bfloat16}})
# )
# ie_pipe.add_component("adapter", OutputAdapter(template="""{{ replies[0]| replace("'",'"') | json_loads}}""",
#                                          output_type=dict,
#                                          custom_filters={"json_loads": json.loads}))

# ie_pipe.connect("fetcher", "converter")
# ie_pipe.connect("converter", "prompt_builder")
# ie_pipe.connect("prompt_builder", "generator")
# ie_pipe.connect("generator", "adapter")
```

让我们回顾一下我们的管道设置：

```python
>>> ie_pipe.show()
```

<img 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### 定义源和提取模式

我们选择了一些与最近初创公司融资公告相关的URL列表。

此外，我们定义了一个用于提取结构化信息的模式。

```python
urls = ["https://techcrunch.com/2023/04/27/pinecone-drops-100m-investment-on-750m-valuation-as-vector-database-demand-grows/",
        "https://techcrunch.com/2023/04/27/replit-funding-100m-generative-ai/",
        "https://www.cnbc.com/2024/06/12/mistral-ai-raises-645-million-at-a-6-billion-valuation.html",
        "https://techcrunch.com/2024/01/23/qdrant-open-source-vector-database/",
        "https://www.intelcapital.com/anyscale-secures-100m-series-c-at-1b-valuation-to-radically-simplify-scaling-and-productionizing-ai-applications/",
        "https://techcrunch.com/2023/04/28/openai-funding-valuation-chatgpt/",
        "https://techcrunch.com/2024/03/27/amazon-doubles-down-on-anthropic-completing-its-planned-4b-investment/",
        "https://techcrunch.com/2024/01/22/voice-cloning-startup-elevenlabs-lands-80m-achieves-unicorn-status/",
        "https://techcrunch.com/2023/08/24/hugging-face-raises-235m-from-investors-including-salesforce-and-nvidia",
        "https://www.prnewswire.com/news-releases/ai21-completes-208-million-oversubscribed-series-c-round-301994393.html",
        "https://techcrunch.com/2023/03/15/adept-a-startup-training-ai-to-use-existing-software-and-apis-raises-350m/",
        "https://www.cnbc.com/2023/03/23/characterai-valued-at-1-billion-after-150-million-round-from-a16z.html"]


schema={
    "Funding": {
        "New funding": "",
        "Investors": [],
    },
     "Company": {
        "Name": "",
        "Activity": "",
        "Country": "",
        "Total valuation": "",
        "Total funding": ""
    }
}
```

### 运行管道！

我们将所需的数据传递给每个组件。

请注意，大多数组件从先前执行的组件中接收数据。

```python
from tqdm import tqdm

extracted_data=[]

for url in tqdm(urls):
    result = ie_pipe.run({"fetcher":{"urls":[url]},
                          "prompt_builder": {"schema":schema}})

    extracted_data.append(result["adapter"]["output"])
```

让我们检查一些提取的数据。

```python
extracted_data[:2]
```

## 数据探索与可视化

让我们探索提取的数据，以评估其正确性并获得一些洞察。

### 数据框（Dataframe）

我们首先创建一个 Pandas DataFrame。为了简化，我们将提取的数据展平。

```python
def flatten_dict(d, parent_key=''):
    items = []
    for k, v in d.items():
        new_key = f"{parent_key} - {k}" if parent_key else k
        if isinstance(v, dict):
            items.extend(flatten_dict(v, new_key).items())
        elif isinstance(v, list):
            items.append((new_key, ', '.join(v)))
        else:
            items.append((new_key, v))
    return dict(items)
```

```python
import pandas as pd

df = pd.DataFrame([flatten_dict(el) for el in extracted_data])
df = df.sort_values(by='Company - Name')

df
```

![dataframe](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/haystack_dataframe.png)

除了“公司 - 国家”部分的一些错误外，提取的数据看起来是正确的。

### 构建简单图表

为了理解公司与投资者之间的关系，我们构建一个图表并进行可视化。

首先，我们使用 NetworkX 构建图表。

[NetworkX](https://networkx.org/) 是一个 Python 包，允许我们以简单的方式创建和操作网络/图表。

我们的简单图表将以公司和投资者作为节点。如果投资者和公司出现在同一文档中，我们将连接这些节点。

```python
import networkx as nx

# Create a new graph
G = nx.Graph()

# Add nodes and edges
for el in extracted_data:
    company_name = el["Company"]["Name"]
    G.add_node(company_name, label=company_name, title="Company")

    investors = el["Funding"]["Investors"]
    for investor in investors:
        if not G.has_node(investor):
            G.add_node(investor, label=investor, title="Investor", color="red")
        G.add_edge(company_name, investor)
```

接下来，我们使用 Pyvis 来可视化图表。

[Pyvis](https://pyvis.readthedocs.io/en/latest/) 是一个用于网络/图表交互式可视化的 Python 包。它与 NetworkX 无缝集成，非常适合用于图形的可视化。

```python
from pyvis.network import Network
from IPython.display import display, HTML


net = Network(notebook=True, cdn_resources='in_line')
net.from_nx(G)

net.show('simple_graph.html')
display(HTML('simple_graph.html'))
```

![graph visualization](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/haystack_graph.png)

看起来 Andreessen Horowitz 在选定的融资公告中出现得非常频繁 😊

## 结论与思考

在本 Notebook 中，我们演示了如何使用小型语言模型（NuExtract）和 Haystack（一个可定制的 LLM 应用编排框架）来设置信息提取系统。

我们可以如何利用提取的数据？

一些思路：
- 提取的数据可以添加到存储在 [Document Store](https://docs.haystack.deepset.ai/docs/document-store?utm_campaign=developer-relations&utm_source=hf-cookbook) 中的原始文档中。这允许通过 [元数据过滤](https://docs.haystack.deepset.ai/docs/metadata-filtering?utm_campaign=developer-relations&utm_source=hf-cookbook) 实现更强大的搜索功能。
- 在前一个思路的基础上，你可以进行 RAG（检索增强提取），通过从查询中提取元数据，如 [这篇博客文章](https://haystack.deepset.ai/blog/extracting-metadata-filter?utm_campaign=developer-relations&utm_source=hf-cookbook) 中所述。
- 将文档和提取的数据存储在知识图谱中，并执行图形 RAG（[Neo4j-Haystack 集成](https://prosto.github.io/neo4j-haystack)）。

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/information_extraction_haystack_nuextract.md" />

### 在 Hugging Face Spaces 上设置 Phoenix 观察性仪表板以进行 LLM 应用程序追踪
https://huggingface.co/learn/cookbook/zh-CN/phoenix_observability_on_hf_spaces.md

# 在 Hugging Face Spaces 上设置 Phoenix 观察性仪表板以进行 LLM 应用程序追踪

_作者：[Andrew Reed](https://huggingface.co/andrewrreed)_

[Phoenix](https://docs.arize.com/phoenix) 是由 [Arize AI](https://arize.com/) 开发的开源观察性库，旨在进行实验、评估和故障排除。它允许 AI 工程师和数据科学家快速可视化他们的数据、评估性能、追踪问题，并导出数据以进行改进。

<div style="display: flex; justify-content: space-between; gap: 20px; margin: 20px 0;">
    <div style="width: 32%; text-align: center; display: flex; flex-direction: column; align-items: center; justify-content: flex-start;">
        <img src="https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/phoenix-tracing.png" style="width: 100%; height: 200px; object-fit: contain;"/>
        <p style="margin-top: 10px; margin-bottom: 0;">追踪</p>
    </div>
    <div style="width: 32%; text-align: center; display: flex; flex-direction: column; align-items: center; justify-content: flex-start;">
        <img src="https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/phoenix-datasets.png" style="width: 100%; height: 200px; object-fit: contain;"/>
        <p style="margin-top: 10px; margin-bottom: 0;">数据集</p>
    </div>
    <div style="width: 32%; text-align: center; display: flex; flex-direction: column; align-items: center; justify-content: flex-start;">
        <img src="https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/phoenix-experiments.png" style="width: 100%; height: 200px; object-fit: contain;"/>
        <p style="margin-top: 10px; margin-bottom: 0;">实验</p>
    </div>
</div>

在本 Notebook 中，我们将展示如何在 [Hugging Face Spaces](https://huggingface.co/spaces) 上部署 Phoenix 观察性仪表板，并配置它以自动追踪 LLM 调用，从而全面了解 LLM 应用程序的内部工作原理。

## 第一步：在 Hugging Face Spaces 上部署 Phoenix

虽然 Phoenix 提供了一个用于本地开发的 [Notebook 高级选项](https://docs.arize.com/phoenix/deployment/environments#notebooks)，它也可以通过 [Docker 作为独立仪表板部署](https://docs.arize.com/phoenix/deployment/environments#container)。长时间运行的托管仪表板是提供 LLM 应用行为集中视图的好方法，并且有助于团队协作。Hugging Face Spaces 提供了一种简单的方式来托管 ML 应用，并支持可选的持久存储，且它对自定义 Docker 镜像的支持使其成为托管 Phoenix 的理想平台——我们来看看它是如何工作的！

首先，我们将 [复制示例空间](https://huggingface.co/spaces/andrewrreed/phoenix-arize-observability-demo?duplicate=true)

![](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/duplicate.png)

我们可以将空间配置为私有或公开，并且它可以位于我们的用户命名空间或组织命名空间中。我们可以使用默认的免费 CPU 配置，并且重要的是，我们可以指定要为该空间附加一个持久磁盘。

> [!TIP] 为了使追踪数据在 Space 重启后保持持久，我们 _必须_ 配置持久磁盘，否则所有数据将在 Space 重启时丢失。配置持久磁盘是付费功能，并会产生 Space 存续期间的费用。在本例中，我们将使用小型 - 20GB 磁盘选项，每小时 $0.01。

点击“复制空间”后，Docker 镜像将开始构建。这将需要几分钟时间完成，之后我们将看到一个空的 Phoenix 仪表板。

![](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/empty-dashboard.png)

## 第二步：配置应用追踪

现在我们已经有了一个运行中的 Phoenix 仪表板，可以使用 [OpenTelemetry TracerProvider](https://docs.arize.com/phoenix/quickstart#connect-your-app-to-phoenix) 配置应用程序，以自动追踪 LLM 调用。在这个例子中，我们将使用 OpenAI 客户端库来给应用程序加上监控，并追踪从 `openai` Python 包发出的 LLM 调用，这些调用会被发送到 [Hugging Face 的 Serverless 推理 API](https://huggingface.co/docs/api-inference/en/index) 上运行的 LLM。

> [!TIP] Phoenix 支持对 [多种 LLM 框架](https://docs.arize.com/phoenix/tracing/integrations-tracing) 进行追踪，包括 LangChain、LlamaIndex、AWS Bedrock 等。

首先，我们需要安装必要的库：

```python
!pip install -q arize-phoenix arize-phoenix-otel openinference-instrumentation-openai openai huggingface-hub
```

接下来，我们将使用 `huggingface_hub` 库登录到 Hugging Face。这将允许我们生成所需的身份验证，以便为我们的 Space 和 Serverless 推理 API 提供认证。请确保用于身份验证的 Hugging Face 令牌具有正确的权限，以便访问存放你 Space 的组织。

```python
from huggingface_hub import interpreter_login

interpreter_login()
```

现在，我们可以将 [Phoenix 客户端配置](https://docs.arize.com/phoenix/deployment/configuration#client-configuration) 为连接到我们运行中的 Phoenix 仪表板：

1. 注册 Phoenix 追踪提供者：
    - 指定我们选择的 `project_name`
    - 将 `endpoint` 设置为我们 Space 的主机名（可以通过仪表板 UI 中的“设置”标签找到，如下图所示）
    - 将 `headers` 设置为访问 Space 所需的 Hugging Face 头信息
2. 在我们的应用代码中，使用 OpenAI 追踪提供者进行监控

![](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/settings.png)

```python
>>> from phoenix.otel import register
>>> from huggingface_hub.utils import build_hf_headers
>>> from openinference.instrumentation.openai import OpenAIInstrumentor

>>> # 1. Register the Phoenix tracer provider
>>> tracer_provider = register(
...     project_name="test",
...     endpoint="https://andrewrreed-phoenix-arize-observability-demo.hf.space"
...     + "/v1/traces",
...     headers=build_hf_headers(),
... )

>>> # 2. Instrument our application code to use the OpenAI tracer provider
>>> OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)
```

<pre>
🔭 OpenTelemetry Tracing Details 🔭
|  Phoenix Project: test
|  Span Processor: SimpleSpanProcessor
|  Collector Endpoint: https://andrewrreed-phoenix-arize-observability-demo.hf.space/v1/traces
|  Transport: HTTP
|  Transport Headers: {'user-agent': '****', 'authorization': '****'}
|  
|  Using a default SpanProcessor. `add_span_processor` will overwrite this default.
|  
|  `register` has set this TracerProvider as the global OpenTelemetry default.
|  To disable this behavior, call `register` with `set_global_tracer_provider=False`.
</pre>

## 第三步：发起调用并在 Phoenix 仪表板中查看追踪

现在，我们可以调用 LLM 并在 Phoenix 仪表板中查看追踪。我们使用 OpenAI 客户端向 Hugging Face 的 Serverless 推理 API 发起调用，该 API 已经配置为与 Phoenix 配合工作。在这个例子中，我们使用的是 `meta-llama/Llama-3.1-8B-Instruct` 模型。

```python
>>> from openai import OpenAI
>>> from huggingface_hub import get_token

>>> client = OpenAI(
...     base_url="https://api-inference.huggingface.co/v1/",
...     api_key=get_token(),
... )

>>> messages = [{"role": "user", "content": "What does a llama do for fun?"}]

>>> response = client.chat.completions.create(
...     model="meta-llama/Llama-3.1-8B-Instruct",
...     messages=messages,
...     max_tokens=500,
... )

>>> print(response.choices[0].message.content)
```

<pre>
Llamas are intelligent and social animals, and they do have ways to entertain themselves and have fun. While we can't directly ask a llama about its personal preferences, we can observe their natural behaviors and make some educated guesses. Here are some things that might bring a llama joy and excitement:

1. **Socializing**: Llamas are herd animals and they love to interact with each other. They'll often engage in play-fighting, neck-wrestling, and other activities to establish dominance and strengthen social bonds. When llamas have a strong social network, it can make them feel happy and content.
2. **Exploring new environments**: Llamas are naturally curious creatures, and they love to explore new surroundings. They'll often investigate their surroundings, sniffing and investigating new sights, sounds, and smells.
3. **Playing with toys**: While llamas don't need expensive toys, they do enjoy playing with objects that stimulate their natural behaviors. For example, a ball or a toy that mimics a target can be an entertaining way to engage them.
4. **Climbing and jumping**: Llamas are agile and athletic animals, and they enjoy using their limbs to climb and jump over obstacles. Providing a safe and stable area for them to exercise their physical abilities can be a fun and engaging experience.
5. **Browsing and foraging**: Llamas have a natural instinct to graze and browse, and they enjoy searching for tasty plants and shrubs. Providing a variety of plants to munch on can keep them engaged and entertained.
6. **Mentally stimulating activities**: Llamas are intelligent animals, and they can benefit from mentally stimulating activities like problem-solving puzzles or learning new behaviors (like agility training or obedience training).

Some fun activities you can try with a llama include:

* Setting up an obstacle course or agility challenge
* Creating a "scavenger hunt" with treats and toys
* Introducing new toys or objects to stimulate their curiosity
* Providing a variety of plants and shrubs to browse and graze on
* Engaging in interactive games like "follow the leader" or "find the treat"

Remember to always prioritize the llama's safety and well-being, and to consult with a veterinarian or a trained llama handler before attempting any new activities or introducing new toys.
</pre>

如果我们返回到 Phoenix 仪表板，我们可以看到从 LLM 调用捕获并显示的追踪信息！如果你在配置 Space 时使用了持久磁盘，那么每次重启 Space 时，所有的追踪信息都会被保存。

![](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/test-trace.png)

## 奖励部分：使用 CrewAI 追踪多代理应用程序

观察性的真正优势在于能够追踪和检查复杂的 LLM 工作流。在这个例子中，我们将安装并使用 [CrewAI](https://www.crewai.com/) 来追踪一个多智能体应用程序。

> [!TIP] `openinference-instrumentation-crewai` 包目前要求 Python 3.10 或更高版本。安装 `crewai` 库后，你可能需要重启 Notebook 内核以避免出现错误。

```python
!pip install -q openinference-instrumentation-crewai crewai crewai-tools
```

和之前一样，我们将注册 Phoenix 追踪提供者并给应用代码加上监控，但这次我们还需要取消现有 OpenAI 追踪提供者的监控，以避免冲突。

```python
>>> from opentelemetry import trace
>>> from openinference.instrumentation.crewai import CrewAIInstrumentor

>>> # 0. Uninstrument existing tracer provider and clear the global tracer provider
>>> OpenAIInstrumentor().uninstrument()
>>> if trace.get_tracer_provider():
...     trace.get_tracer_provider().shutdown()
...     trace._TRACER_PROVIDER = None  # Reset the global tracer provider

>>> # 1. Register the Phoenix tracer provider
>>> tracer_provider = register(
...     project_name="crewai",
...     endpoint="https://andrewrreed-phoenix-arize-observability-demo.hf.space"
...     + "/v1/traces",
...     headers=build_hf_headers(),
... )

>>> # 2. Instrument our application code to use the OpenAI tracer provider
>>> CrewAIInstrumentor().instrument(tracer_provider=tracer_provider)
```

<pre>
🔭 OpenTelemetry Tracing Details 🔭
|  Phoenix Project: crewai
|  Span Processor: SimpleSpanProcessor
|  Collector Endpoint: https://andrewrreed-phoenix-arize-observability-demo.hf.space/v1/traces
|  Transport: HTTP
|  Transport Headers: {'user-agent': '****', 'authorization': '****'}
|  
|  Using a default SpanProcessor. `add_span_processor` will overwrite this default.
|  
|  `register` has set this TracerProvider as the global OpenTelemetry default.
|  To disable this behavior, call `register` with `set_global_tracer_provider=False`.
</pre>

现在，我们将使用 CrewAI 定义一个多智能体应用程序，研究并撰写一篇关于观察性和追踪在 LLM 应用中的重要性的博客。

> [!TIP] 这个例子借鉴并修改自 [此处](https://docs.arize.com/phoenix/tracing/integrations-tracing/crewai)。

```python
>>> import os
>>> from huggingface_hub import get_token
>>> from crewai_tools import SerperDevTool
>>> from crewai import LLM, Agent, Task, Crew, Process

>>> # Define our LLM using HF's Serverless Inference API
>>> llm = LLM(
...     model="huggingface/meta-llama/Llama-3.1-8B-Instruct",
...     api_key=get_token(),
...     max_tokens=1024,
... )

>>> # Define a tool for searching the web
>>> os.environ["SERPER_API_KEY"] = (
...     "YOUR_SERPER_API_KEY"  # must set this value in your environment
... )
>>> search_tool = SerperDevTool()

>>> # Define your agents with roles and goals
>>> researcher = Agent(
...     role="Researcher",
...     goal="Conduct thorough research on up to date trends around a given topic.",
...     backstory="""You work at a leading tech think tank. You have a knack for dissecting complex data and presenting actionable insights.""",
...     verbose=True,
...     allow_delegation=False,
...     tools=[search_tool],
...     llm=llm,
...     max_iter=1,
... )
... writer = Agent(
...     role="Technical Writer",
...     goal="Craft compelling content on a given topic.",
...     backstory="""You are a technical writer with a knack for crafting engaging and informative content.""",
...     llm=llm,
...     verbose=True,
...     allow_delegation=False,
...     max_iter=1,
... )

>>> # Create tasks for your agents
>>> task1 = Task(
...     description="""Conduct comprehensive research and analysis of the importance of observability and tracing in LLM applications.
...   Identify key trends, breakthrough technologies, and potential industry impacts.""",
...     expected_output="Full analysis report in bullet points",
...     agent=researcher,
... )

>>> task2 = Task(
...     description="""Using the insights provided, develop an engaging blog
...   post that highlights the importance of observability and tracing in LLM applications.
...   Your post should be informative yet accessible, catering to a tech-savvy audience.
...   Make it sound cool, avoid complex words so it doesn't sound like AI.""",
...     expected_output="Blog post of at least 3 paragraphs",
...     agent=writer,
... )

>>> # Instantiate your crew with a sequential process
>>> crew = Crew(
...     agents=[researcher, writer],
...     tasks=[task1, task2],
...     verbose=True,
...     process=Process.sequential,
... )

>>> # Get your crew to work!
>>> result = crew.kickoff()

>>> print("------------ FINAL RESULT ------------")
>>> print(result)
```

<pre>
[1m[95m# Agent:[00m [1m[92mResearcher[00m
[95m## Task:[00m [92mConduct comprehensive research and analysis of the importance of observability and tracing in LLM applications.
  Identify key trends, breakthrough technologies, and potential industry impacts.[00m


[1m[95m# Agent:[00m [1m[92mResearcher[00m
[95m## Using tool:[00m [92mSearch the internet[00m
[95m## Tool Input:[00m [92m
"{\"search_query\": \"importance of observability and tracing in LLM applications\"}"[00m
[95m## Tool Output:[00m [92m

Search results: Title: LLM Observability: The 5 Key Pillars for Monitoring Large Language ...
Link: https://arize.com/blog-course/large-language-model-monitoring-observability/
Snippet: Why leveraging the five pillars of LLM observability is essential for ensuring performance, reliability, and seamless LLM applications.
---
Title: Observability of LLM Applications: Exploration and Practice from the ...
Link: https://www.alibabacloud.com/blog/observability-of-llm-applications-exploration-and-practice-from-the-perspective-of-trace_601604
Snippet: This article clarifies the technical challenges of observability by analyzing LLM application patterns and different concerns.
---
Title: What is LLM Observability? - The Ultimate LLM Monitoring Guide
Link: https://www.confident-ai.com/blog/what-is-llm-observability-the-ultimate-llm-monitoring-guide
Snippet: Observability tools collect and correlate logs, real-time evaluation metrics, and traces to understand the context of unexpected outputs or ...
---
Title: An Introduction to Observability for LLM-based applications using ...
Link: https://opentelemetry.io/blog/2024/llm-observability/
Snippet: Why Observability Matters for LLM Applications · It's vital to keep track of how often LLMs are being used for usage and cost tracking. · Latency ...
---
Title: Understanding LLM Observability - Key Insights, Best Practices ...
Link: https://signoz.io/blog/llm-observability/
Snippet: LLM Observability is essential for maintaining reliable, accurate, and efficient AI applications. Focus on the five pillars: evaluation, tracing ...
---
Title: LLM Observability Tools: 2024 Comparison - lakeFS
Link: https://lakefs.io/blog/llm-observability-tools/
Snippet: LLM observability is the process that enables monitoring by providing full visibility and tracing in an LLM application system, as well as newer ...
---
Title: From Concept to Production with Observability in LLM Applications
Link: https://hadijaveed.me/2024/03/05/tracing-and-observability-in-llm-applications/
Snippet: Traces are essential to understanding the full “path” a request takes in your application, e.g, prompt, query-expansion, RAG retrieved top-k ...
---
Title: The Importance of LLM Observability: A Technical Deep Dive
Link: https://www.linkedin.com/pulse/importance-llm-observability-technical-deep-dive-patrick-carroll-trlqe
Snippet: LLM observability is crucial for any technical team that wants to maintain and improve the reliability, security, and performance of their AI- ...
---
Title: Observability and Monitoring of LLMs - TheBlue.ai
Link: https://theblue.ai/blog/llm-observability-en/
Snippet: LLM-Observability is crucial to maximize the performance and reliability of Large Language Models (LLMs). By systematically capturing and ...
---
[00m
</pre>

返回 Phoenix 仪表板后，我们可以在新项目 "crewai" 中看到来自我们多智能体应用程序的追踪信息！

![](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/crew-ai-trace.png)

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/phoenix_observability_on_hf_spaces.md" />

### 无服务器推理 API
https://huggingface.co/learn/cookbook/zh-CN/enterprise_hub_serverless_inference_api.md

# 无服务器推理 API  
_作者：[Andrew Reed](https://huggingface.co/andrewrreed)_

Hugging Face 提供了一个 [无服务器推理 API](https://huggingface.co/docs/api-inference/index)，用户可以通过简单的 API 调用，快速测试和评估成千上万的公开（或你自己私有授权的）机器学习模型，***完全免费***！

在这个 Notebook 示例中，我们将演示几种不同的方式，帮助你使用无服务器推理 API，涵盖多个任务，包括：  
- 使用开放的 LLM 生成文本  
- 使用稳定扩散生成图像  
- 使用视觉语言模型（VLM）对图像进行推理  
- 将文本转换为语音  

目标是帮助你快速入门，了解基本操作！

> [!提示]  
> 由于我们提供免费的无服务器推理 API，常规 Hugging Face 用户有请求次数限制（每小时约几百次）。如果需要更高的请求频率，可以 [升级为 PRO 账户](https://huggingface.co/subscribe/pro)，每月仅需 $9。然而，对于高流量的生产推理工作负载，请查看我们的 [专用推理端点](https://huggingface.co/docs/inference-endpoints/index) 解决方案。

## 开始使用

要开始使用无服务器推理 API，你需要一个 Hugging Face Hub 个人资料：如果你没有个人资料，可以 [注册](https://huggingface.co/join)，如果已经有了个人资料，请 [登录](https://huggingface.co/login)。

接下来，你需要创建一个 [用户访问令牌](https://huggingface.co/docs/hub/security-tokens)。一个具有 `read` 或 `write` 权限的令牌即可使用。然而，我们强烈建议使用细粒度的令牌。

> [!提示]  
> 对于本 Notebook，你需要一个具有 `Inference > Make calls to the serverless Inference API` 用户权限的细粒度令牌，同时需要对 `meta-llama/Meta-Llama-3-8B-Instruct` 和 `HuggingFaceM4/idefics2-8b-chatty` 仓库具有读取权限，因为我们必须下载它们的分词器以运行此笔记本。

完成这些步骤后，我们可以安装所需的包，并使用用户访问令牌进行 Hub 身份验证。

```python
%pip install -U huggingface_hub transformers
```

```python
import os
from huggingface_hub import interpreter_login, whoami, get_token

# running this will prompt you to enter your Hugging Face credentials
interpreter_login()
```

> [!提示]  
> 我们在上面使用了 `interpreter_login()` 方法来编程方式登录 Hub。作为替代方法，我们还可以使用其他方法，例如来自 [Hub Python 库](https://huggingface.co/docs/huggingface_hub/en/package_reference/login) 的 `notebook_login()`，或者使用 [Hugging Face CLI 工具](https://huggingface.co/docs/huggingface_hub/en/guides/cli#huggingface-cli-login) 中的 `login` 命令。

现在，让我们使用 `whoami()` 来验证我们是否已正确登录，它会打印出当前的用户名以及您的个人资料所属的组织。

```python
whoami()
```

## 查询无服务器推理 API

无服务器推理 API 通过一个简单的 API 提供 Hub 上的模型：

`https://api-inference.huggingface.co/models/<MODEL_ID>`

其中 `<MODEL_ID>` 对应于 Hub 上模型仓库的名称。

例如，[codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) 对应的 URL 是 `https://api-inference.huggingface.co/models/codellama/CodeLlama-7b-hf`。

### 使用 HTTP 请求

我们可以通过简单的 `POST` 请求，使用 `requests` 库轻松调用这个 API。

```python
>>> import requests

>>> API_URL = "https://api-inference.huggingface.co/models/codellama/CodeLlama-7b-hf"
>>> HEADERS = {"Authorization": f"Bearer {get_token()}"}


>>> def query(payload):
...     response = requests.post(API_URL, headers=HEADERS, json=payload)
...     return response.json()


>>> print(
...     query(
...         payload={
...             "inputs": "A HTTP POST request is used to ",
...             "parameters": {"temperature": 0.8, "max_new_tokens": 50, "seed": 42},
...         }
...     )
... )
```

<pre>
[{'generated_text': 'A HTTP POST request is used to send data to a web server.\n\n# Example\n```javascript\npost("localhost:3000", {foo: "bar"})\n  .then(console.log => console.log(\'success\'))\n```\n\n'}]
</pre>

太棒了！API 回应了我们输入的提示的延续。但你可能会好奇……API 是如何知道如何处理负载的？作为用户，我如何知道给定模型可以传递哪些参数呢？

实际上，在后台，无服务器推理 API 会将请求的模型动态加载到共享计算基础设施上，以提供预测。当模型加载完毕后，Serverless 推理 API 会使用模型卡中的指定 `pipeline_tag`（请参见 [这里](https://huggingface.co/codellama/CodeLlama-7b-hf/blob/main/README.md?code=true#L4)）来确定适当的推理任务。你可以参考相应的 [任务](https://huggingface.co/tasks) 或 [pipeline](https://huggingface.co/docs/transformers/en/main_classes/pipelines) 文档，以查找允许的参数。

> [!提示]  
> 如果请求的模型在请求时尚未加载到内存中（这取决于该模型的最近请求），无服务器推理 API 将最初返回 503 响应，然后再成功地返回预测结果。稍等片刻后再试，给模型一些启动时间。  
> 你还可以通过 `InferenceClient().list_deployed_models()` 来查看任何时刻已加载并可用的模型。

### 使用 `huggingface_hub` Python 库

要在 Python 中发送请求，你可以利用 [`InferenceClient`](https://huggingface.co/docs/huggingface_hub/en/package_reference/inference_client#huggingface_hub.InferenceClient)，这是 `huggingface_hub` Python 库中提供的一个便捷工具，使你能够轻松调用无服务器推理 API。

```python
>>> from huggingface_hub import InferenceClient

>>> client = InferenceClient()
>>> response = client.text_generation(
...     prompt="A HTTP POST request is used to ",
...     model="codellama/CodeLlama-7b-hf",
...     temperature=0.8,
...     max_new_tokens=50,
...     seed=42,
...     return_full_text=True,
... )
>>> print(response)
```

<pre>
A HTTP POST request is used to send data to a web server.

# Example
```javascript
post("localhost:3000", {foo: "bar"})
  .then(console.log => console.log('success'))
```
</pre>

请注意，在使用 `InferenceClient` 时，我们只需要指定模型 ID，并且直接在 `text_generation()` 方法中传递参数。我们可以轻松检查该函数的签名，以查看如何使用该任务及其允许的参数的更多详细信息。

```python
# uncomment the following line to see the function signature
# help(client.text_generation)
```

> [!提示]  
> 除了 Python，你还可以使用 JavaScript 在你的 JS 或 Node 应用中集成推理调用。查看 [huggingface.js](https://huggingface.co/docs/huggingface.js/index) 以开始使用。

## 应用实例

现在我们已经了解了无服务器推理 API 的工作原理，让我们开始实际操作，并在过程中学习一些技巧。

### 1. 使用开源 LLM 生成文本

文本生成是一个非常常见的应用场景。然而，与开源 LLM 互动时，有一些细微之处需要理解，以避免性能悄然下降。对于文本生成，底层的语言模型可能有几种不同的类型：

- **基础模型（Base models）：** 指的是纯粹的、预训练的语言模型，例如 [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) 或 [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B)。这些模型擅长根据提供的提示继续生成文本（如我们在上面的示例中看到的）。然而，它们并未针对对话场景进行微调，例如回答问题。
  
- **指令微调模型（Instruction-tuned models）：** 这些模型经过多任务训练，能够执行广泛的指令，例如“写一份巧克力蛋糕的食谱”。像 [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) 或 [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) 这样的模型即是如此训练的。指令微调模型在回应指令时比基础模型表现更好。通常，这些模型也会针对多轮对话进行微调，使其非常适合对话型应用。

理解这些细微差别非常重要，因为它们会影响我们如何查询特定的模型。指令微调模型使用的是针对模型特定的 [对话模板](https://huggingface.co/blog/chat-templates)，因此你需要小心模型期望的格式，并在查询中尽量复制这一格式。

例如，[meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) 使用以下提示结构来区分系统、用户和助手之间的对话回合：

```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{{ system_prompt }}<|eot_id|><|start_header_id|>user<|end_header_id|>

{{ user_msg_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

{{ model_answer_1 }}<|eot_id|>
```

这些特殊的标记和提示格式会因模型而异。为了确保我们使用正确的格式，我们可以通过模型的 [对话模板](https://huggingface.co/docs/transformers/main/en/chat_templating) 结合其分词器来进行检查，如下所示。

```python
>>> from transformers import AutoTokenizer

>>> # define the system and user messages
>>> system_input = "You are an expert prompt engineer with artistic flair."
>>> user_input = "Write a concise prompt for a fun image containing a llama and a cookbook. Only return the prompt."
>>> messages = [
...     {"role": "system", "content": system_input},
...     {"role": "user", "content": user_input},
... ]

>>> # load the model and tokenizer
>>> model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
>>> tokenizer = AutoTokenizer.from_pretrained(model_id)

>>> # apply the chat template to the messages
>>> prompt = tokenizer.apply_chat_template(
...     messages, tokenize=False, add_generation_prompt=True
... )
>>> print(f"\nPROMPT:\n-----\n\n{prompt}")
```

<pre>
PROMPT:
-----

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

You are an expert prompt engineer with artistic flair.<|eot_id|><|start_header_id|>user<|end_header_id|>

Write a concise prompt for a fun image containing a llama and a cookbook. Only return the prompt.<|eot_id|><|start_header_id|>assistant<|end_header_id|>
</pre>

请注意，`apply_chat_template()` 方法是如何将我们熟悉的消息列表转换成模型期望的正确格式化字符串的。我们可以使用这个格式化后的字符串，作为参数传递给无服务器推理 API 的 `text_generation` 方法。

```python
>>> llm_response = client.text_generation(
...     prompt, model=model_id, max_new_tokens=250, seed=42
... )
>>> print(llm_response)
```

<pre>
"A whimsical illustration of a llama proudly holding a cookbook, with a sassy expression and a sprinkle of flour on its nose, surrounded by a colorful kitchen backdrop with utensils and ingredients scattered about, as if the llama is about to whip up a culinary masterpiece."
</pre>

查询一个 LLM 而不遵循模型的提示模板 _不会_ 直接产生错误！但是，这将导致输出质量较差。来看一下当我们传递相同的系统和用户输入，但 **没有** 根据对话模板格式化时，会发生什么情况。

```python
>>> out = client.text_generation(
...     system_input + " " + user_input, model=model_id, max_new_tokens=250, seed=42
... )
>>> print(out)
```

<pre>
Do not write the... 1 answer below »

You are an expert prompt engineer with artistic flair. Write a concise prompt for a fun image containing a llama and a cookbook. Only return the prompt. Do not write the image description.

A llama is sitting at a kitchen table, surrounded by cookbooks and utensils, with a cookbook open in front of it. The llama is wearing a chef's hat and holding a spatula. The cookbook is titled "Llama's Favorite Recipes" and has a llama on the cover. The llama is surrounded by a warm, golden light, and the kitchen is filled with the aroma of freshly baked bread. The llama is smiling and looking directly at the viewer, as if inviting them to join in the cooking fun. The image should be colorful, whimsical, and full of texture and detail. The llama should be the main focus of the image, and the cookbook should be prominently displayed. The background should be a warm, earthy color, such as terracotta or sienna. The overall mood of the image should be playful, inviting, and joyful. 1 answer below »

You are an expert prompt engineer with artistic flair. Write a concise prompt for a fun image containing a llama and a
</pre>

哎呀！LLM 产生了一个无意义的开头，意外地重复了提示，并且未能保持简洁。为了简化提示过程并确保使用正确的对话模板，`InferenceClient` 还提供了一个 `chat_completion` 方法，它抽象化了 `chat_template` 的细节。这使得你可以简单地传递一组消息：

```python
>>> for token in client.chat_completion(
...     messages, model=model_id, max_tokens=250, stream=True, seed=42
... ):
...     print(token.choices[0].delta.content)
```

<pre>
"A
 whims
ical
 illustration
 of
 a
 fashion
ably
 dressed
 llama
 proudly
 holding
 a
 worn
,
 vintage
 cookbook
,
 with
 a
 warm
 cup
 of
 tea
 and
 a
 few
 freshly
 baked
 treats
 scattered
 around
,
 set
 against
 a
 cozy
 background
 of
 rustic
 wood
 and
 blo
oming
 flowers
."
</pre>

#### 流式传输

在上面的示例中，我们还设置了 `stream=True` 来启用从端点流式传输文本。为了了解更多类似的功能以及查询 LLM 时的最佳实践，推荐阅读以下支持资源：

1. [如何生成文本：使用 Transformers 的不同解码方法进行语言生成](https://huggingface.co/blog/how-to-generate)  
2. [文本生成策略](https://huggingface.co/docs/transformers/generation_strategies)  
3. [PRO 用户推理](https://huggingface.co/blog/inference-pro) - 特别是关于 [控制文本生成](https://huggingface.co/blog/inference-pro#controlling-text-generation) 部分  
4. [Inference Client 文档](https://huggingface.co/docs/huggingface_hub/en/package_reference/inference_client#inference)  

### 2. 使用稳定扩散生成图像

无服务器推理 API 可以用于 [许多不同的任务](https://huggingface.co/tasks)。在这里，我们将使用它来通过稳定扩散（Stable Diffusion）生成图像。

```python
>>> image = client.text_to_image(
...     prompt=llm_response,
...     model="stabilityai/stable-diffusion-xl-base-1.0",
...     guidance_scale=8,
...     seed=42,
... )

>>> display(image.resize((image.width // 2, image.height // 2)))
>>> print("PROMPT: ", llm_response)
```

<img src="data:image/jpeg;base64,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">


#### 缓存

`InferenceClient` 默认会缓存 API 响应。这意味着，如果你使用相同的负载多次查询 API，你会发现返回的结果是完全相同的。来看一下：

```python
>>> image = client.text_to_image(
...     prompt=llm_response,
...     model="stabilityai/stable-diffusion-xl-base-1.0",
...     guidance_scale=8,
...     seed=42,
... )

>>> display(image.resize((image.width // 2, image.height // 2)))
>>> print("PROMPT: ", llm_response)
```

<img src="data:image/jpeg;base64,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">


要强制每次都返回不同的响应，我们可以使用一个 HTTP 头部让客户端忽略缓存并重新生成内容：`x-use-cache: 0`。

```python
>>> # turn caching off
>>> client.headers["x-use-cache"] = "0"

>>> # generate a new image with the same prompt
>>> image = client.text_to_image(
...     prompt=llm_response,
...     model="stabilityai/stable-diffusion-xl-base-1.0",
...     guidance_scale=8,
...     seed=42,
... )

>>> display(image.resize((image.width // 2, image.height // 2)))
>>> print("PROMPT: ", llm_response)
```

<img src="data:image/jpeg;base64,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">


### 3. 使用 Idefics2 进行图像推理

视觉语言模型（VLMs）可以同时接受文本和图像作为输入，并产生文本作为输出。这使得它们能够处理许多任务，从视觉问答到图像描述。我们将使用无服务器推理 API 来查询 [Idefics2](https://huggingface.co/blog/idefics2)，这是一款强大的 8B 参数 VLM，让它为我们生成一首关于新生成图像的诗。

首先，我们需要将我们的 PIL 图像转换为 `base64` 编码的字符串，以便通过网络将其发送给模型。

```python
import base64
from io import BytesIO


def pil_image_to_base64(image):
    buffered = BytesIO()
    image.save(buffered, format="JPEG")
    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
    return img_str


image_b64 = pil_image_to_base64(image)
```

接下来，我们需要使用对话模板正确格式化我们的文本 + 图像提示。请参阅 [Idefics2 模型卡](https://huggingface.co/HuggingFaceM4/idefics2-8b) 获取有关提示格式的具体细节。

```python
from transformers import AutoProcessor

# load the processor
vlm_model_id = "HuggingFaceM4/idefics2-8b-chatty"
processor = AutoProcessor.from_pretrained(vlm_model_id)

# define the user messages
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image"},
            {"type": "text", "text": "Write a short limerick about this image."},
        ],
    },
]

# apply the chat template to the messages
prompt = processor.apply_chat_template(messages, add_generation_prompt=True)

# add the base64 encoded image to the prompt
image_input = f"data:image/jpeg;base64,{image_b64}"
image_input = f"![]({image_input})"
prompt = prompt.replace("<image>", image_input)
```

最后，我们调用无服务器 API 来获取预测。在我们的例子中，得到的是一首关于我们生成图像的有趣打油诗！

```python
>>> limerick = client.text_generation(
...     prompt, model=vlm_model_id, max_new_tokens=200, seed=42
... )
>>> print(limerick)
```

<pre>
In the heart of a kitchen, so bright and so clean,
Lived a llama named Lulu, quite the culinary queen.
With a book in her hand, she'd read and she'd cook,
Her recipes were magic, her skills were so nook.
In her world, there was no room for defeat,
For Lulu, the kitchen was where she'd meet.
</pre>

### 4. 从文本生成语音

最后，让我们使用一个基于 Transformers 的文本到音频模型，叫做 [Bark](https://huggingface.co/suno/bark)，为我们的诗生成可听的配音。

```python
tts_model_id = "suno/bark"
speech_out = client.text_to_speech(text=limerick, model=tts_model_id)
```

```python
>>> from IPython.display import Audio

>>> display(Audio(speech_out, rate=24000))
>>> print(limerick)
```

<pre>
In the heart of a kitchen, so bright and so clean,
Lived a llama named Lulu, quite the culinary queen.
With a book in her hand, she'd read and she'd cook,
Her recipes were magic, her skills were so nook.
In her world, there was no room for defeat,
For Lulu, the kitchen was where she'd meet.
</pre>

## 下一步

就这样！在本 Notebook 中，我们学习了如何使用无服务器推理 API 查询各种强大的 Transformer 模型。我们只是触及了它的一部分功能，建议你查看 [文档](https://huggingface.co/docs/api-inference/en/index)，了解更多可能的功能。

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/enterprise_hub_serverless_inference_api.md" />

### 用 Gemma, MongoDB 和开源模型构建 RAG 系统
https://huggingface.co/learn/cookbook/zh-CN/rag_with_hugging_face_gemma_mongodb.md

# 用 Gemma, MongoDB 和开源模型构建 RAG 系统

作者: [Richmond Alake](https://huggingface.co/RichmondMongo)


## 第一步：安装库

这些命令是用来安装一些软件包的，这些软件包可以帮助你使用和操作 LLMs，处理数据，并且和数据库进行交流。这些库简化了RAG系统的开发，将复杂性减少到少量的代码：
- PyMongo：一个用于与 MongoDB 交互的 Python 库，它提供了连接到集群和查询存储在集合和文档中的数据的功能。
- Pandas：提供了一个数据结构，用于使用 Python 进行高效的数据处理和分析。
- Hugging Face datasets：包含音频、视觉和文本数据集。
- Hugging Face Accelerate：抽象了编写利用硬件加速器（如GPU）的代码的复杂性。在实现中，利用 Accelerate 在 GPU 资源上利用 Gemma 模型。
- Hugging Face Transformers：访问大量预训练模型。
- Hugging Face Sentence Transformers：提供对句子、文本和图像嵌入的访问。

```python
!pip install datasets pandas pymongo sentence_transformers
!pip install -U transformers
# Install below if using GPU
!pip install accelerate
```

## 第二步：数据源选择和准备


本教程使用的数据来源于 Hugging Face datasets，具体是 [AIatMongoDB/embedded_movies 数据集](https://huggingface.co/datasets/AIatMongoDB/embedded_movies)。



```python
# Load Dataset
from datasets import load_dataset
import pandas as pd

# https://huggingface.co/datasets/AIatMongoDB/embedded_movies
dataset = load_dataset("AIatMongoDB/embedded_movies")

# Convert the dataset to a pandas dataframe
dataset_df = pd.DataFrame(dataset["train"])

dataset_df.head(5)
```

以下代码片段中的操作侧重于确保数据的完整性和质量。
1. 第一个过程确保每个数据点的 `fullplot` 属性不为空，因为这是我们嵌入过程中主要使用的数据。
2. 这一步还确保我们移除所有数据点的 `plot_embedding` 属性，因为这将被一个不同的嵌入模型 `gte-large` 创建的新嵌入所替换。

```python
>>> # Data Preparation

>>> # Remove data point where plot coloumn is missing
>>> dataset_df = dataset_df.dropna(subset=["fullplot"])
>>> print("\nNumber of missing values in each column after removal:")
>>> print(dataset_df.isnull().sum())

>>> # Remove the plot_embedding from each data point in the dataset as we are going to create new embeddings with an open source embedding model from Hugging Face
>>> dataset_df = dataset_df.drop(columns=["plot_embedding"])
>>> dataset_df.head(5)
```

<pre>
Number of missing values in each column after removal:
num_mflix_comments      0
genres                  0
countries               0
directors              12
fullplot                0
writers                13
awards                  0
runtime                14
type                    0
rated                 279
metacritic            893
poster                 78
languages               1
imdb                    0
plot                    0
cast                    1
plot_embedding          1
title                   0
dtype: int64
</pre>

## 第三步：生成嵌入

**代码片段中的步骤如下：**

1. 导入 `SentenceTransformer` 类以访问嵌入模型。
2. 使用 `SentenceTransformer` 构造函数加载嵌入模型，以实例化 `gte-large` 嵌入模型。
3. 定义 `get_embedding` 函数，该函数接受一个文本字符串作为输入，并返回一个代表嵌入的浮点数列表。该函数首先检查输入文本是否为空（去除空白后）。如果文本为空，则返回一个空列表。否则，它使用加载的模型生成嵌入。
4. 通过将 `get_embedding` 函数应用于 `dataset_df` DataFrame 的 "fullplot" 列，为每个电影的剧情生成嵌入。生成的嵌入列表被分配到一个名为 embedding 的新列中。

*注意：由于我们可以确保文本长度保持在可管理的范围内，因此不需要对完整剧情文本进行分块处理。*




```python
from sentence_transformers import SentenceTransformer

# https://huggingface.co/thenlper/gte-large
embedding_model = SentenceTransformer("thenlper/gte-large")


def get_embedding(text: str) -> list[float]:
    if not text.strip():
        print("Attempted to get embedding for empty text.")
        return []

    embedding = embedding_model.encode(text)

    return embedding.tolist()


dataset_df["embedding"] = dataset_df["fullplot"].apply(get_embedding)

dataset_df.head()
```

## 第4步：数据库设置和连接

MongoDB 既是一个操作数据库，也是一个向量数据库。它提供了一个数据库解决方案，有效地存储、查询和检索向量嵌入。其优势在于数据库维护、管理和成本的简单性。

**创建新的 MongoDB 数据库，设置数据库集群：**

1. 前往MongoDB官网，注册一个[免费的 MongoDB Atlas 账户](https://www.mongodb.com/cloud/atlas/register?utm_campaign=devrel&utm_source=community&utm_medium=cta&utm_content=Partner%20Cookbook&utm_term=richmond.alake)，或者对于现有用户，[登录 MongoDB Atlas](https://account.mongodb.com/account/login?utm_campaign=devrel&utm_source=community&utm_medium=cta&utm_content=Partner%20Cookbook&utm_term=richmond.alakee)。

2. 在左侧窗格中选择 'Database' 选项，这将导航到数据库部署页面，你可以在其中查看任何现有集群的部署规格。点击 "+Create" 按钮，创建一个新的数据库集群。

3. 选择适用于数据库集群的所有配置。选择所有配置选项后，点击 “Create Cluster” 按钮以部署新创建的集群。MongoDB 还在 “Shared Tab” 上启用了免费集群的创建。

 *注意：创建概念证明时，不要忘记将 Python 主机的 IP 列入白名单，或设置 0.0.0.0/0 用于任何IP。*

4. 成功创建和部署集群后，集群将在 ‘Database Deployment’ 页面中变得可访问。

5. 点击集群的 “Connect” 按钮，查看通过各种语言驱动程序设置与集群的连接的选项。

6. 本教程只需要集群的 URI（唯一资源标识符）。获取 URI 并将其复制到 Google Colabs Secrets 环境中的名为 `MONGO_URI` 的变量中，或者将其放入 .env 文件或等效文件中。

### 4.1 数据库和集合设置

在继续之前，请确保满足以下先决条件

- 在 MongoDB Atlas 上设置数据库集群
- 获取到你的集群的 URI

有关数据库集群设置和获取 URI 的帮助，请参阅我们的指南：[设置 MongoDB 集群](https://www.mongodb.com/docs/guides/atlas/cluster/)和[获取你的连接字符串](https://www.mongodb.com/docs/guides/atlas/connection-string/)

创建集群后，通过在集群概览页面点击+创建数据库，在 MongoDB Atlas 集群中创建数据库和集合。

这里有关于[创建数据库和集合](https://www.mongodb.com/basics/create-database)的指南
**数据库将被命名为 `movies`。**
**集合将被命名为 `movie_collection_2`。**







## 第5步：创建向量搜索索引

在这一点上，请确保你的向量索引是通过 MongoDB Atlas 创建的。

接下来，你必须做一个非常重要的步骤，那就是在 `movie_collection_2` 这个数据库的文档里，为那些用来表示电影特点的向量建立一个特殊的搜索索引。这个索引就像是图书馆里的图书索引卡，它帮助计算机快速准确地找到与你的搜索最相似的电影向量。没有这个索引，计算机就得一篇一篇地翻找，效率会非常低。所以，建立这个索引是为了让搜索变得又快又准。

点击此处了解更多关于[ MongoDB 向量搜索索引](https://www.mongodb.com/docs/atlas/atlas-search/field-types/knn-vector/)的信息。

```
{
 "fields": [{
     "numDimensions": 1024,
     "path": "embedding",
     "similarity": "cosine",
     "type": "vector"
   }]
}
```

`numDimensions` 字段的 `1024` 值对应于由 gte-large 嵌入模型生成的向量的维度。如果你使用 `gte-base` 或 `gte-small` 嵌入模型，向量搜索索引中的 numDimensions 值必须分别设置为 768 和 384。



## 第6步：建立数据连接

下面的代码片段还使用了 PyMongo 来创建一个 MongoDB 客户端对象，该对象代表与集群的连接，并允许访问其数据库和集合。



```python
>>> import pymongo
>>> from google.colab import userdata


>>> def get_mongo_client(mongo_uri):
...     """Establish connection to the MongoDB."""
...     try:
...         client = pymongo.MongoClient(mongo_uri)
...         print("Connection to MongoDB successful")
...         return client
...     except pymongo.errors.ConnectionFailure as e:
...         print(f"Connection failed: {e}")
...         return None


... mongo_uri = userdata.get("MONGO_URI")
... if not mongo_uri:
...     print("MONGO_URI not set in environment variables")

... mongo_client = get_mongo_client(mongo_uri)

... # Ingest data into MongoDB
... db = mongo_client["movies"]
... collection = db["movie_collection_2"]
```

<pre>
Connection to MongoDB successful
</pre>

```python
# Delete any existing records in the collection
collection.delete_many({})
```

从 pandas DataFrame 中将数据导入 MongoDB 集合是一个简单的过程，可以通过将 DataFrame 转换为字典，然后在集合上使用 `insert_many` 方法来传递转换后的数据集记录，从而高效完成。


```python
>>> documents = dataset_df.to_dict("records")
>>> collection.insert_many(documents)

>>> print("Data ingestion into MongoDB completed")
```

<pre>
Data ingestion into MongoDB completed
</pre>

## 第7步：对用户查询执行向量搜索

下一步实现了一个函数，该函数通过生成查询嵌入并定义一个 MongoDB 聚合流水线来返回一个向量搜索结果。

该流水线包括 `$vectorSearch` 和 `$project` 阶段，它使用生成的向量执行查询，并格式化结果以仅包括所需信息，如剧情、标题和类型，同时为每个结果引入一个搜索分数。


```python
def vector_search(user_query, collection):
    """
    Perform a vector search in the MongoDB collection based on the user query.

    Args:
    user_query (str): The user's query string.
    collection (MongoCollection): The MongoDB collection to search.

    Returns:
    list: A list of matching documents.
    """

    # Generate embedding for the user query
    query_embedding = get_embedding(user_query)

    if query_embedding is None:
        return "Invalid query or embedding generation failed."

    # Define the vector search pipeline
    pipeline = [
        {
            "$vectorSearch": {
                "index": "vector_index",
                "queryVector": query_embedding,
                "path": "embedding",
                "numCandidates": 150,  # Number of candidate matches to consider
                "limit": 4,  # Return top 4 matches
            }
        },
        {
            "$project": {
                "_id": 0,  # Exclude the _id field
                "fullplot": 1,  # Include the plot field
                "title": 1,  # Include the title field
                "genres": 1,  # Include the genres field
                "score": {"$meta": "vectorSearchScore"},  # Include the search score
            }
        },
    ]

    # Execute the search
    results = collection.aggregate(pipeline)
    return list(results)
```

## 第 8 步：处理用户查询和加载 Gemma


```python
def get_search_result(query, collection):

    get_knowledge = vector_search(query, collection)

    search_result = ""
    for result in get_knowledge:
        search_result += f"Title: {result.get('title', 'N/A')}, Plot: {result.get('fullplot', 'N/A')}\n"

    return search_result
```

```python
>>> # Conduct query with retrival of sources
>>> query = "What is the best romantic movie to watch and why?"
>>> source_information = get_search_result(query, collection)
>>> combined_information = f"Query: {query}\nContinue to answer the query by using the Search Results:\n{source_information}."

>>> print(combined_information)
```

<pre>
Query: What is the best romantic movie to watch and why?
Continue to answer the query by using the Search Results:
Title: Shut Up and Kiss Me!, Plot: Ryan and Pete are 27-year old best friends in Miami, born on the same day and each searching for the perfect woman. Ryan is a rookie stockbroker living with his psychic Mom. Pete is a slick surfer dude yet to find commitment. Each meets the women of their dreams on the same day. Ryan knocks heads in an elevator with the gorgeous Jessica, passing out before getting her number. Pete falls for the insatiable Tiara, but Tiara's uncle is mob boss Vincent Bublione, charged with her protection. This high-energy romantic comedy asks to what extent will you go for true love?
Title: Pearl Harbor, Plot: Pearl Harbor is a classic tale of romance set during a war that complicates everything. It all starts when childhood friends Rafe and Danny become Army Air Corps pilots and meet Evelyn, a Navy nurse. Rafe falls head over heels and next thing you know Evelyn and Rafe are hooking up. Then Rafe volunteers to go fight in Britain and Evelyn and Danny get transferred to Pearl Harbor. While Rafe is off fighting everything gets completely whack and next thing you know everybody is in the middle of an air raid we now know as "Pearl Harbor."
Title: Titanic, Plot: The plot focuses on the romances of two couples upon the doomed ship's maiden voyage. Isabella Paradine (Catherine Zeta-Jones) is a wealthy woman mourning the loss of her aunt, who reignites a romance with former flame Wynn Park (Peter Gallagher). Meanwhile, a charming ne'er-do-well named Jamie Perse (Mike Doyle) steals a ticket for the ship, and falls for a sweet innocent Irish girl on board. But their romance is threatened by the villainous Simon Doonan (Tim Curry), who has discovered about the ticket and makes Jamie his unwilling accomplice, as well as having sinister plans for the girl.
Title: China Girl, Plot: A modern day Romeo & Juliet story is told in New York when an Italian boy and a Chinese girl become lovers, causing a tragic conflict between ethnic gangs.
.
</pre>

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
# CPU Enabled uncomment below 👇🏽
# model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it")
# GPU Enabled use below 👇🏽
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", device_map="auto")
```

```python
>>> # Moving tensors to GPU
>>> input_ids = tokenizer(combined_information, return_tensors="pt").to("cuda")
>>> response = model.generate(**input_ids, max_new_tokens=500)
>>> print(tokenizer.decode(response[0]))
```

<pre>
<bos>Query: What is the best romantic movie to watch and why?
Continue to answer the query by using the Search Results:
Title: Shut Up and Kiss Me!, Plot: Ryan and Pete are 27-year old best friends in Miami, born on the same day and each searching for the perfect woman. Ryan is a rookie stockbroker living with his psychic Mom. Pete is a slick surfer dude yet to find commitment. Each meets the women of their dreams on the same day. Ryan knocks heads in an elevator with the gorgeous Jessica, passing out before getting her number. Pete falls for the insatiable Tiara, but Tiara's uncle is mob boss Vincent Bublione, charged with her protection. This high-energy romantic comedy asks to what extent will you go for true love?
Title: Pearl Harbor, Plot: Pearl Harbor is a classic tale of romance set during a war that complicates everything. It all starts when childhood friends Rafe and Danny become Army Air Corps pilots and meet Evelyn, a Navy nurse. Rafe falls head over heels and next thing you know Evelyn and Rafe are hooking up. Then Rafe volunteers to go fight in Britain and Evelyn and Danny get transferred to Pearl Harbor. While Rafe is off fighting everything gets completely whack and next thing you know everybody is in the middle of an air raid we now know as "Pearl Harbor."
Title: Titanic, Plot: The plot focuses on the romances of two couples upon the doomed ship's maiden voyage. Isabella Paradine (Catherine Zeta-Jones) is a wealthy woman mourning the loss of her aunt, who reignites a romance with former flame Wynn Park (Peter Gallagher). Meanwhile, a charming ne'er-do-well named Jamie Perse (Mike Doyle) steals a ticket for the ship, and falls for a sweet innocent Irish girl on board. But their romance is threatened by the villainous Simon Doonan (Tim Curry), who has discovered about the ticket and makes Jamie his unwilling accomplice, as well as having sinister plans for the girl.
Title: China Girl, Plot: A modern day Romeo & Juliet story is told in New York when an Italian boy and a Chinese girl become lovers, causing a tragic conflict between ethnic gangs.
.

Based on the search results, the best romantic movie to watch is **Shut Up and Kiss Me!** because it is a romantic comedy that explores the complexities of love and relationships. The movie is funny, heartwarming, and thought-provoking.<eos>
</pre>

```python

```

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/rag_with_hugging_face_gemma_mongodb.md" />

### 在单个 GPU 上针对自定义代码微调代码 LLM
https://huggingface.co/learn/cookbook/zh-CN/fine_tuning_code_llm_on_single_gpu.md

#  在单个 GPU 上针对自定义代码微调代码 LLM

_作者: [Maria Khalusova](https://github.com/MKhalusova)_

公开发布的代码 LLM，如 Codex、StarCoder 和 Code Llama，在生成遵循通用编程原则和语法的代码方面表现出色，但它们可能不符合组织的内部惯例，或者不了解某些特定的库。

在这个 notebook 中，我们将展示如何微调代码 LLM 来更好的理解你们公司或组织的代码风格和习惯。由于代码 LLM 非常大，按照传统的微调方式可能会消耗大量资源。但不用担心！我们会教你一些技巧，让你只用单个 GPU 就能完成微调工作。


## 数据集

对于这个例子，我们选择了 GitHub 上 Hugging Face 的前 10 个公共仓库。我们已经排除了非代码文件，如图片、音频文件、演示文稿等。对于 Jupyter notebook，我们只保留了包含代码的单元格。生成的代码被存储为一个数据集，你可以在 Hugging Face Hub 上找到，位于 [`smangrul/hf-stack-v1`](https://huggingface.co/datasets/smangrul/hf-stack-v1)。它包含仓库 id、文件路径和文件内容。


## 模型

我们将微调 [`bigcode/starcoderbase-1b`](https://huggingface.co/bigcode/starcoderbase-1b) 模型，这是一个在 80 多种编程语言上训练的 10 亿参数模型。这是一个需要权限的模型，所以如果你计划使用这个确切模型运行这个 notebook，你需要在其模型页面上获得访问权限。登录你的 Hugging Face 帐户以执行此操作：


```python
from huggingface_hub import notebook_login

notebook_login()
```

为了开始，首先让我们安装所有必要的库。正如你所看到的，除了 `transformers` 和 `datasets`，我们还将使用 `peft`、`bitsandbytes` 和 `flash-attn` 来优化训练过程。

通过采用参数高效的训练技术，我们可以在一张 A100 高内存 GPU 上运行这个 Notebook。

```python
!pip install -q transformers datasets peft bitsandbytes flash-attn
```

现在让我们定义一些变量。请随意调整这些变量。

```python
MODEL="bigcode/starcoderbase-1b" # Model checkpoint on the Hugging Face Hub
DATASET="smangrul/hf-stack-v1"   # Dataset on the Hugging Face Hub
DATA_COLUMN="content"            # Column name containing the code content

SEQ_LENGTH=2048                  # Sequence length

# Training arguments
MAX_STEPS=2000                   # max_steps
BATCH_SIZE=16                    # batch_size
GR_ACC_STEPS=1                   # gradient_accumulation_steps
LR=5e-4                          # learning_rate
LR_SCHEDULER_TYPE="cosine"       # lr_scheduler_type
WEIGHT_DECAY=0.01                # weight_decay
NUM_WARMUP_STEPS=30              # num_warmup_steps
EVAL_FREQ=100                    # eval_freq
SAVE_FREQ=100                    # save_freq
LOG_FREQ=25                      # log_freq
OUTPUT_DIR="peft-starcoder-lora-a100" # output_dir
BF16=True                        # bf16
FP16=False                       # no_fp16

# FIM trasformations arguments
FIM_RATE=0.5                     # fim_rate
FIM_SPM_RATE=0.5                 # fim_spm_rate

# LORA
LORA_R=8                         # lora_r
LORA_ALPHA=32                    # lora_alpha
LORA_DROPOUT=0.0                 # lora_dropout
LORA_TARGET_MODULES="c_proj,c_attn,q_attn,c_fc,c_proj"    # lora_target_modules

# bitsandbytes config
USE_NESTED_QUANT=True            # use_nested_quant
BNB_4BIT_COMPUTE_DTYPE="bfloat16"# bnb_4bit_compute_dtype

SEED=0
```

```python
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    Trainer,
    TrainingArguments,
    logging,
    set_seed,
    BitsAndBytesConfig,
)

set_seed(SEED)
```

## 准备数据

首先加载数据。由于数据集可能相当大，请确保启用流模式。流模式允许我们在遍历数据集时逐步加载数据，而不是一次性下载数据集的整个内容。

我们将前 4000 个示例作为验证集，其余的全部作为训练数据。


```python
from datasets import load_dataset
import torch
from tqdm import tqdm


dataset = load_dataset(
    DATASET,
    data_dir="data",
    split="train",
    streaming=True,
)

valid_data = dataset.take(4000)
train_data = dataset.skip(4000)
train_data = train_data.shuffle(buffer_size=5000, seed=SEED)
```

在这一步，数据集仍然包含任意长度的原始数据。为了训练，我们需要固定长度的输入。让我们创建一个可迭代的数据集，它可以从文本文件流中返回固定长度的 token 块。

首先，让我们估计数据集中每个 token 的平均字符数，这将帮助我们稍后估计文本缓冲区中的 token 数量。默认情况下，我们只从数据集中取 400 个示例（`nb_examples`）。只使用整个数据集的一个子集可以减少计算成本，同时仍然提供了对整体字符到 token 比的合理估计。


```python
>>> tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)

>>> def chars_token_ratio(dataset, tokenizer, data_column, nb_examples=400):
...     """
...     Estimate the average number of characters per token in the dataset.
...     """

...     total_characters, total_tokens = 0, 0
...     for _, example in tqdm(zip(range(nb_examples), iter(dataset)), total=nb_examples):
...         total_characters += len(example[data_column])
...         total_tokens += len(tokenizer(example[data_column]).tokens())

...     return total_characters / total_tokens


>>> chars_per_token = chars_token_ratio(train_data, tokenizer, DATA_COLUMN)
>>> print(f"The character to token ratio of the dataset is: {chars_per_token:.2f}")
```

<pre>
The character to token ratio of the dataset is: 2.43
</pre>

字符到 token 的比也可以用作文本标记质量的一个指标。例如，字符到 token 的比为 1.0 意味着每个字符都由一个 token 表示，这并没有太多意义。表明标记化做得不好。在标准的英文文本中，一个 token 通常相当于大约四个字符，这意味着字符到 token 的比率大约是 4.0。我们可以预见在代码数据集中的比率会更低，但一般来说，2.0 到 3.5 之间的数字可以认为是足够好的。

**可选的 FIM 变换**
自回归语言模型通常是从左到右生成序列的。通过应用 FIM 变换，模型也可以学习填充文本。详细信息可以看["Efficient Training of Language Models to Fill in the Middle" 这篇论文](https://arxiv.org/pdf/2207.14255.pdf)了解这种技术。

我们将在下面定义 FIM 变换，并在创建可迭代数据集时使用它们。然而，如果你想省略变换步骤，请将 `fim_rate` 设置为 0。

```python
import functools
import numpy as np


# Helper function to get token ids of the special tokens for prefix, suffix and middle for FIM transformations.
@functools.lru_cache(maxsize=None)
def get_fim_token_ids(tokenizer):
    try:
        FIM_PREFIX, FIM_MIDDLE, FIM_SUFFIX, FIM_PAD = tokenizer.special_tokens_map["additional_special_tokens"][1:5]
        suffix_tok_id, prefix_tok_id, middle_tok_id, pad_tok_id = (
            tokenizer.vocab[tok] for tok in [FIM_SUFFIX, FIM_PREFIX, FIM_MIDDLE, FIM_PAD]
        )
    except KeyError:
        suffix_tok_id, prefix_tok_id, middle_tok_id, pad_tok_id = None, None, None, None
    return suffix_tok_id, prefix_tok_id, middle_tok_id, pad_tok_id


## Adapted from https://github.com/bigcode-project/Megatron-LM/blob/6c4bf908df8fd86b4977f54bf5b8bd4b521003d1/megatron/data/gpt_dataset.py
def permute(
    sample,
    np_rng,
    suffix_tok_id,
    prefix_tok_id,
    middle_tok_id,
    pad_tok_id,
    fim_rate=0.5,
    fim_spm_rate=0.5,
    truncate_or_pad=False,
):
    """
    Take in a sample (list of tokens) and perform a FIM transformation on it with a probability of fim_rate, using two FIM modes:
    PSM and SPM (with a probability of fim_spm_rate).
    """

    # The if condition will trigger with the probability of fim_rate
    # This means FIM transformations will apply to samples with a probability of fim_rate
    if np_rng.binomial(1, fim_rate):

        # Split the sample into prefix, middle, and suffix, based on randomly generated indices stored in the boundaries list.
        boundaries = list(np_rng.randint(low=0, high=len(sample) + 1, size=2))
        boundaries.sort()

        prefix = np.array(sample[: boundaries[0]], dtype=np.int64)
        middle = np.array(sample[boundaries[0] : boundaries[1]], dtype=np.int64)
        suffix = np.array(sample[boundaries[1] :], dtype=np.int64)

        if truncate_or_pad:
            # calculate the new total length of the sample, taking into account tokens indicating prefix, middle, and suffix
            new_length = suffix.shape[0] + prefix.shape[0] + middle.shape[0] + 3
            diff = new_length - len(sample)

            # trancate or pad if there's a difference in length between the new length and the original
            if diff > 0:
                if suffix.shape[0] <= diff:
                    return sample, np_rng
                suffix = suffix[: suffix.shape[0] - diff]
            elif diff < 0:
                suffix = np.concatenate([suffix, np.full((-1 * diff), pad_tok_id)])

        # With the probability of fim_spm_rateapply SPM variant of FIM transformations
        # SPM: suffix, prefix, middle
        if np_rng.binomial(1, fim_spm_rate):
            new_sample = np.concatenate(
                [
                    [prefix_tok_id, suffix_tok_id],
                    suffix,
                    [middle_tok_id],
                    prefix,
                    middle,
                ]
            )
        # Otherwise, apply the PSM variant of FIM transformations
        # PSM: prefix, suffix, middle
        else:

            new_sample = np.concatenate(
                [
                    [prefix_tok_id],
                    prefix,
                    [suffix_tok_id],
                    suffix,
                    [middle_tok_id],
                    middle,
                ]
            )
    else:
        # don't apply FIM transformations
        new_sample = sample

    return list(new_sample), np_rng
```

让我们定义 `ConstantLengthDataset`，这是一个可迭代的数据集，它将返回固定长度的 token 块。为此，我们将从原始数据集中读取文本缓冲区，直到达到大小限制，然后应用分词器将原始文本转换为 token 后的输入。可选项，我们可以在一些序列上执行 FIM 变换（受影响的序列比例由 `fim_rate` 控制）。

定义好后，我们可以从训练和验证数据中创建 `ConstantLengthDataset` 的实例。

```python
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
import random

# Create an Iterable dataset that returns constant-length chunks of tokens from a stream of text files.

class ConstantLengthDataset(IterableDataset):
    """
    Iterable dataset that returns constant length chunks of tokens from stream of text files.
        Args:
            tokenizer (Tokenizer): The processor used for proccessing the data.
            dataset (dataset.Dataset): Dataset with text files.
            infinite (bool): If True the iterator is reset after dataset reaches end else stops.
            seq_length (int): Length of token sequences to return.
            num_of_sequences (int): Number of token sequences to keep in buffer.
            chars_per_token (int): Number of characters per token used to estimate number of tokens in text buffer.
            fim_rate (float): Rate (0.0 to 1.0) that sample will be permuted with FIM.
            fim_spm_rate (float): Rate (0.0 to 1.0) of FIM permuations that will use SPM.
            seed (int): Seed for random number generator.
    """

    def __init__(
        self,
        tokenizer,
        dataset,
        infinite=False,
        seq_length=1024,
        num_of_sequences=1024,
        chars_per_token=3.6,
        content_field="content",
        fim_rate=0.5,
        fim_spm_rate=0.5,
        seed=0,
    ):
        self.tokenizer = tokenizer
        self.concat_token_id = tokenizer.eos_token_id
        self.dataset = dataset
        self.seq_length = seq_length
        self.infinite = infinite
        self.current_size = 0
        self.max_buffer_size = seq_length * chars_per_token * num_of_sequences
        self.content_field = content_field
        self.fim_rate = fim_rate
        self.fim_spm_rate = fim_spm_rate
        self.seed = seed

        (
            self.suffix_tok_id,
            self.prefix_tok_id,
            self.middle_tok_id,
            self.pad_tok_id,
        ) = get_fim_token_ids(self.tokenizer)
        if not self.suffix_tok_id and self.fim_rate > 0:
            print("FIM is not supported by tokenizer, disabling FIM")
            self.fim_rate = 0

    def __iter__(self):
        iterator = iter(self.dataset)
        more_examples = True
        np_rng = np.random.RandomState(seed=self.seed)
        while more_examples:
            buffer, buffer_len = [], 0
            while True:
                if buffer_len >= self.max_buffer_size:
                    break
                try:
                    buffer.append(next(iterator)[self.content_field])
                    buffer_len += len(buffer[-1])
                except StopIteration:
                    if self.infinite:
                        iterator = iter(self.dataset)
                    else:
                        more_examples = False
                        break
            tokenized_inputs = self.tokenizer(buffer, truncation=False)["input_ids"]
            all_token_ids = []

            for tokenized_input in tokenized_inputs:
                # optionally do FIM permutations
                if self.fim_rate > 0:
                    tokenized_input, np_rng = permute(
                        tokenized_input,
                        np_rng,
                        self.suffix_tok_id,
                        self.prefix_tok_id,
                        self.middle_tok_id,
                        self.pad_tok_id,
                        fim_rate=self.fim_rate,
                        fim_spm_rate=self.fim_spm_rate,
                        truncate_or_pad=False,
                    )

                all_token_ids.extend(tokenized_input + [self.concat_token_id])
            examples = []
            for i in range(0, len(all_token_ids), self.seq_length):
                input_ids = all_token_ids[i : i + self.seq_length]
                if len(input_ids) == self.seq_length:
                    examples.append(input_ids)
            random.shuffle(examples)
            for example in examples:
                self.current_size += 1
                yield {
                    "input_ids": torch.LongTensor(example),
                    "labels": torch.LongTensor(example),
                }


train_dataset = ConstantLengthDataset(
        tokenizer,
        train_data,
        infinite=True,
        seq_length=SEQ_LENGTH,
        chars_per_token=chars_per_token,
        content_field=DATA_COLUMN,
        fim_rate=FIM_RATE,
        fim_spm_rate=FIM_SPM_RATE,
        seed=SEED,
)
eval_dataset = ConstantLengthDataset(
        tokenizer,
        valid_data,
        infinite=False,
        seq_length=SEQ_LENGTH,
        chars_per_token=chars_per_token,
        content_field=DATA_COLUMN,
        fim_rate=FIM_RATE,
        fim_spm_rate=FIM_SPM_RATE,
        seed=SEED,
)
```

## 准备模型

现在数据已经准备好了，是时候加载模型了！我们将加载量化的模型。

因为量化使用更少的位来表示数据，所以会减少内存使用。我们将使用 `bitsandbytes` 库来量化模型，因为它与 `transformers` 有很好的集成。我们需要做的只是定义一个 `bitsandbytes` 配置，然后在加载模型时使用它。

4 比特位量化有不同的变体，但通常我们推荐使用 NF4 量化以获得更好的性能（`bnb_4bit_quant_type="nf4"`）。

`bnb_4bit_use_double_quant` 选项在第一次量化后添加第二次量化，以节省每个参数额外的 0.4 位。

要了解更多关于量化的信息，请查看 ["利用 bitsandbytes、4 比特位量化和 QLoRA 让 LLMs 更易于访问" 的博客](https://huggingface.co/blog/4bit-transformers-bitsandbytes)。

定义好后，将配置传递给 `from_pretrained` 方法以加载量化的模型。


```python
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
from peft.tuners.lora import LoraLayer

load_in_8bit = False

# 4-bit quantization
compute_dtype = getattr(torch, BNB_4BIT_COMPUTE_DTYPE)

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=compute_dtype,
    bnb_4bit_use_double_quant=USE_NESTED_QUANT,
)

device_map = {"": 0}

model = AutoModelForCausalLM.from_pretrained(
        MODEL,
        load_in_8bit=load_in_8bit,
        quantization_config=bnb_config,
        device_map=device_map,
        use_cache=False,  # We will be using gradient checkpointing
        trust_remote_code=True,
        use_flash_attention_2=True,
)
```

当使用量化模型进行训练时，你需要调用 `prepare_model_for_kbit_training()` 函数来预处理量化模型以进行训练。

```python
model = prepare_model_for_kbit_training(model)
```

现在量化模型已经准备好了，我们可以设置一个 LoRA 配置。LoRA 通过大幅减少可训练参数的数量，使得微调更加高效。

要使用 LoRA 技术训练模型，我们需要将基础模型包装为 `PeftModel`。这涉及到使用 `LoraConfig` 定义 LoRA 配置，并使用 `get_peft_model()` 和 `LoraConfig` 包装原始模型。

要了解更多关于 LoRA 及其参数的信息，请参考 [PEFT 文档](https://huggingface.co/docs/peft/main/en/conceptual_guides/lora)。


```python
>>> # Set up lora
>>> peft_config = LoraConfig(
...     lora_alpha=LORA_ALPHA,
...     lora_dropout=LORA_DROPOUT,
...     r=LORA_R,
...     bias="none",
...     task_type="CAUSAL_LM",
...     target_modules=LORA_TARGET_MODULES.split(","),
... )

>>> model = get_peft_model(model, peft_config)
>>> model.print_trainable_parameters()
```

<pre>
trainable params: 5,554,176 || all params: 1,142,761,472 || trainable%: 0.4860310866343243
</pre>

可以看到，通过应用 LoRA 技术，我们现在只需要训练不到 1% 的参数。

## 训练模型

现在我们已经准备好了数据，并且优化了模型，我们可以将所有东西整合在一起开始训练。

要实例化一个 `Trainer`，你需要定义训练配置。最重要的是 `TrainingArguments`，这是一个包含所有用于配置训练的属性的类。

这些与你可能运行的任何其他类型的模型训练相似，所以我们这里不会详细说明。


```python
train_data.start_iteration = 0


training_args = TrainingArguments(
    output_dir=f"Your_HF_username/{OUTPUT_DIR}",
    dataloader_drop_last=True,
    evaluation_strategy="steps",
    save_strategy="steps",
    max_steps=MAX_STEPS,
    eval_steps=EVAL_FREQ,
    save_steps=SAVE_FREQ,
    logging_steps=LOG_FREQ,
    per_device_train_batch_size=BATCH_SIZE,
    per_device_eval_batch_size=BATCH_SIZE,
    learning_rate=LR,
    lr_scheduler_type=LR_SCHEDULER_TYPE,
    warmup_steps=NUM_WARMUP_STEPS,
    gradient_accumulation_steps=GR_ACC_STEPS,
    gradient_checkpointing=True,
    fp16=FP16,
    bf16=BF16,
    weight_decay=WEIGHT_DECAY,
    push_to_hub=True,
    include_tokens_per_second=True,
)
```

最后一步，实例化 `Trainer` 并调用 `train` 方法。   

```python
>>> trainer = Trainer(
...     model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset
... )

>>> print("Training...")
>>> trainer.train()
```

<pre>
Training...
</pre>

最后，你可以将微调好的模型推送到你的 Hub 仓库中，并分享给你的团队。

```python
trainer.push_to_hub()
```

## 推理

一旦模型被上传到 Hub，我们就可以使用它进行推理。为此，我们首先初始化原始的基础模型及其分词器。接下来，我们需要将微调后的权重与基础模型合并。

```python
from peft import PeftModel
import torch

# load the original model first
tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)
base_model = AutoModelForCausalLM.from_pretrained(
    MODEL,
    quantization_config=None,
    device_map=None,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
).cuda()

# merge fine-tuned weights with the base model
peft_model_id = f"Your_HF_username/{OUTPUT_DIR}"
model = PeftModel.from_pretrained(base_model, peft_model_id)
model.merge_and_unload()
```

现在我们可以使用合并后的模型进行推理。为了方便起见，我们将定义一个 `get_code_completion` 函数 - 请随意尝试文本生成参数！


```python
def get_code_completion(prefix, suffix):
    text = prompt = f"""{prefix}{suffix}"""
    model.eval()
    outputs = model.generate(
        input_ids=tokenizer(text, return_tensors="pt").input_ids.cuda(),
        max_new_tokens=128,
        temperature=0.2,
        top_k=50,
        top_p=0.95,
        do_sample=True,
        repetition_penalty=1.0,
    )
    return tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
```

现在，为了获得代码补全，我们只需要调用 `get_code_complete` 函数，并将我们希望补全的前几行作为前缀传递，以及一个空字符串作为后缀。

```python
>>> prefix = """from peft import LoraConfig, TaskType, get_peft_model
... from transformers import AutoModelForCausalLM
... peft_config = LoraConfig(
... """
>>> suffix =""""""

... print(get_code_completion(prefix, suffix))
```

<pre>
from peft import LoraConfig, TaskType, get_peft_model
from transformers import AutoModelForCausalLM
peft_config = LoraConfig(
    task_type=TaskType.CAUSAL_LM,
    r=8,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.1,
    bias="none",
    modules_to_save=["q_proj", "v_proj"],
    inference_mode=False,
)
model = AutoModelForCausalLM.from_pretrained("gpt2")
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
</pre>

作为刚刚在这个 notebook 中使用过 PEFT 库的人，你可以看到创建为 `LoraConfig` 函数的生成结果相当不错！

如果你回到我们为推理实例化模型的单元格，并注释掉我们合并微调权重的行，你可以看到原始模型对于完全相同的前缀会生成什么内容：

```python
>>> prefix = """from peft import LoraConfig, TaskType, get_peft_model
... from transformers import AutoModelForCausalLM
... peft_config = LoraConfig(
... """
>>> suffix =""""""

... print(get_code_completion(prefix, suffix))
```

<pre>
from peft import LoraConfig, TaskType, get_peft_model
from transformers import AutoModelForCausalLM
peft_config = LoraConfig(
    model_name_or_path="facebook/wav2vec2-base-960h",
    num_labels=1,
    num_features=1,
    num_hidden_layers=1,
    num_attention_heads=1,
    num_hidden_layers_per_attention_head=1,
    num_attention_heads_per_hidden_layer=1,
    hidden_size=1024,
    hidden_dropout_prob=0.1,
    hidden_act="gelu",
    hidden_act_dropout_prob=0.1,
    hidden
</pre>

尽管这是 Python 语法，但你可以看到原始模型并不理解 `LoraConfig` 应该做什么。


要了解这种高效参数微调与完全微调的比较，以及如何通过推理端点在 VS Code 中使用这样的模型作为你的编程助手(copilot)，或者在本地使用，请查看["个人编程助手(copilot)：训练你自己的编码助手"博客](https://huggingface.co/blog/personal-copilot)。这个 notebook 补充了原始博客内容。


<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/fine_tuning_code_llm_on_single_gpu.md" />

### 使用 Spaces 和 Gradio 创建演示
https://huggingface.co/learn/cookbook/zh-CN/enterprise_cookbook_gradio.md

# 使用 Spaces 和 Gradio 创建演示
_作者: [Diego Maniloff](https://huggingface.co/dmaniloff)_

## 介绍
在本 Notebook 中，我们将展示如何使用 [Gradio](https://www.gradio.app/) 让任何机器学习模型栩栩如生。Gradio 是一个库，允许你从任何 Python 函数创建网页演示并与全世界分享 🌎！

📚 本 Notebook 涵盖内容：
- 创建一个 `Hello, World!` 演示：Gradio 基础
- 将你的演示移至 Hugging Face Spaces
- 让它更有趣：利用 🤗 Hub 的真实案例
- 一些 Gradio 的酷炫“电池内置”功能

⏭️ 在本 Notebook 的末尾，你会找到一个 `进一步阅读` 列表，包含链接，帮助你继续深入探索。

## 设置
要开始，请安装 `gradio` 库以及 `transformers`。

```python
!pip -q install gradio==4.36.1
!pip -q install transformers==4.41.2
```

```python
# the usual shorthand is to import gradio as gr
import gradio as gr
```

## 你的第一个演示：Gradio 基础
Gradio 的核心功能是将任何 Python 函数转换为网页界面。

假设我们有一个简单的函数，它接受 `name` 和 `intensity` 作为参数，并返回一个字符串，如下所示：

```python
def greet(name: str, intensity: int) -> str:
    return "Hello, " + name + "!" * int(intensity)
```

如果你对名字为 'Diego' 调用这个函数，你将得到如下所示的输出字符串：

```python
>>> print(greet("Diego", 3))
```

<pre>
Hello, Diego!!!
</pre>

使用 Gradio，我们可以通过 `gr.Interface` 类为这个函数构建一个界面。我们只需要传入我们上面创建的 `greet` 函数，以及该函数所期望的输入和输出类型：

```python
demo = gr.Interface(
    fn=greet,
    inputs=["text", "slider"], # the inputs are a text box and a slider ("text" and "slider" are components in Gradio)
    outputs=["text"],          # the output is a text box
)
```

注意到我们传入了 `["text", "slider"]` 作为输入，以及 `["text"]` 作为输出——这些在 Gradio 中被称为 [组件](https://www.gradio.app/docs/gradio/introduction)。

这就是我们第一个演示所需要的全部内容。赶紧试试吧 👇🏼！在 `name` 文本框中输入你的名字，调整你想要的强度，然后点击 `Submit`。

```python
# the launch method will fire up the interface we just created
demo.launch()
```

## 让我们做得更有趣：会议转录工具
到目前为止，你已经了解了如何将一个基本的 Python 函数转换成一个适合网页展示的演示。然而，我们所做的仅仅是一个非常简单的函数，甚至有点无聊！

现在，让我们考虑一个更有趣的例子，这个例子能突出展示 Gradio 最初的目的：展示最前沿的机器学习模型。最近，我的一位好朋友请我帮忙处理她做的一个访谈录音。她需要将音频文件转化成一个井井有条的文本摘要。我是怎么帮助她的呢？我构建了一个 Gradio 应用！

接下来，我们一起来看看如何构建会议转录工具。我们可以将这个过程分为两个部分：

1. 将音频文件转录成文本
2. 将文本组织成章节、段落、列表等。我们也可以在这里加入摘要功能。

### 音频转文本
在这一部分，我们将构建一个演示，处理会议转录工具的第一步：将音频转换成文本。

正如我们所学到的，构建一个 Gradio 演示的关键是拥有一个执行我们想展示逻辑的 Python 函数。对于音频转文本的转换，我们将使用强大的 `transformers` 库，并通过它的 `pipeline` 工具来使用一个流行的音频转文本模型 —— `distil-whisper/distil-large-v3`。

以下是 `transcribe` 函数，它接受我们想要转换的音频作为输入：

```python
import os
import tempfile

import torch
import gradio as gr
from transformers import pipeline

device = 0 if torch.cuda.is_available() else "cpu"

AUDIO_MODEL_NAME = "distil-whisper/distil-large-v3" # faster and very close in performance to the full-size "openai/whisper-large-v3"
BATCH_SIZE = 8


pipe = pipeline(
    task="automatic-speech-recognition",
    model=AUDIO_MODEL_NAME,
    chunk_length_s=30,
    device=device,
)


def transcribe(audio_input):
    """Function to convert audio to text."""
    if audio_input is None:
        raise gr.Error("No audio file submitted!")

    output = pipe(
        audio_input,
        batch_size=BATCH_SIZE,
        generate_kwargs={"task": "transcribe"},
        return_timestamps=True
    )
    return output["text"]
```

现在我们有了 Python 函数，我们可以通过将它传递给 `gr.Interface` 来进行演示。注意，在这种情况下，函数所期望的输入是我们要转换的音频。Gradio 包含了许多有用的组件，其中之一是 [Audio](https://www.gradio.app/docs/gradio/audio)，正是我们演示所需要的 🎶 😎。

```python
part_1_demo = gr.Interface(
    fn=transcribe,
    inputs=gr.Audio(type="filepath"), # "filepath" passes a str path to a temporary file containing the audio
    outputs=gr.Textbox(show_copy_button=True), # give users the option to copy the results
    title="Transcribe Audio to Text", # give our demo a title :)
)

part_1_demo.launch()
```

赶紧试试看 👆！你可以上传一个 `.mp3` 文件，或者点击 🎤 按钮录制你自己的声音。

如果你想要一个实际的会议录音样本，可以查看 [MeetingBank_Audio 数据集](https://huggingface.co/datasets/huuuyeah/MeetingBank_Audio)，这是一个包含6个美国主要城市市议会会议的音频数据集。为了进行测试，我尝试了几个 [丹佛会议](https://huggingface.co/datasets/huuuyeah/MeetingBank_Audio/blob/main/Denver/mp3/Denver-21.zip)。

> [!TIP]
> 另外，查看 `Interface` 的 [from_pipeline](https://www.gradio.app/docs/gradio/interface#interface-from_pipeline) 构造函数，它可以直接从一个 `pipeline` 构建 `Interface`。

### 组织和总结文本
在会议转录工具的第二部分，我们需要组织上一部分转录得到的文本。

同样地，为了构建一个 Gradio 演示，我们需要一个包含我们关心的逻辑的 Python 函数。对于文本的组织和总结，我们将使用一个经过“指令调优”的模型，它被训练来执行广泛的任务。我们有许多选项可以选择，例如 [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) 或 [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3)。在我们的例子中，我们将使用 [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)。

就像第一部分一样，我们本可以利用 `transformers` 中的 `pipeline` 工具来完成这项任务，但这次我们将利用这个机会展示 [无服务器推理 API](https://huggingface.co/docs/api-inference/index)，它是 Hugging Face Hub 中的一项 API，允许我们免费使用数千个公开可访问的（或你自己私有授权的）机器学习模型！查看无服务器推理 API 的 [食谱](https://huggingface.co/learn/cookbook/en/enterprise_hub_serverless_inference_api) 部分。

使用无服务器推理 API 意味着，我们将通过 `InferenceClient`（这是 `huggingface_hub` 库的一部分）来调用模型，而不是像我们在音频转换部分那样通过 `pipeline` 调用模型（[Hub Python 库](https://huggingface.co/docs/huggingface_hub/en/package_reference/login)）。为了使用 `InferenceClient`，我们需要通过 `notebook_login()` 登录到 🤗 Hub，这将弹出一个对话框，要求输入你的用户访问令牌以进行身份验证。

你可以从 [个人设置页面](https://huggingface.co/settings/tokens) 管理你的令牌，并请尽量使用 [细粒度](https://huggingface.co/docs/hub/security-tokens) 令牌，以提高安全性。

```python
from huggingface_hub import notebook_login, InferenceClient

# running this will prompt you to enter your Hugging Face credentials
notebook_login()
```

现在我们已经登录到 Hub，我们可以通过 `InferenceClient` 使用无服务器推理 API 编写我们的文本处理函数。

这一部分的代码将被结构化为两个函数：

- `build_messages`：用于将消息提示格式化为 LLM（大语言模型）所需的格式；
- `organize_text`：实际将原始会议文本传递给 LLM 进行组织（以及根据我们提供的提示进行总结）。

```python
# sample meeting transcript from huuuyeah/MeetingBank_Audio
# this is just a copy-paste from the output of part 1 using one of the Denver meetings
sample_transcript = """
 Good evening. Welcome to the Denver City Council meeting of Monday, May 8, 2017. My name is Kelly Velez. I'm your Council Secretary. According to our rules of procedure, when our Council President, Albus Brooks, and Council President Pro Tem, JoLynn Clark, are both absent, the Council Secretary calls the meeting to order. Please rise and join Councilman Herndon in the Pledge of Allegiance. Madam Secretary, roll call. Roll call. Here. Mark. Espinosa. Here. Platt. Delmar. Here. Here. Here. Here. We have five members present. There is not a quorum this evening. Many of the council members are participating in an urban exploration trip in Portland, Oregon, pursuant to Section 3.3.4 of the city charter. Because there is not a quorum of seven council members present, all of tonight's business will move to next week, to Monday, May 15th. Seeing no other business before this body except to wish Councilwoman Keniche a very happy birthday this meeting is adjourned Thank you. A standard model and an energy efficient model likely will be returned to you in energy savings many times during its lifespan. Now, what size do you need? Air conditioners are not a one-size-or-type fits all. Before you buy an air conditioner, you need to consider the size of your home and the cost to operate the unit per hour. Do you want a room air conditioner, which costs less but cools a smaller area, or do you want a central air conditioner, which cools your entire house but costs more? Do your homework. Now, let's discuss evaporative coolers. In low humidity areas, evaporating water into the air provides a natural and energy efficient means of cooling. Evaporative coolers, also called swamp coolers, cool outdoor air by passing it over water saturated pads, causing the water to evaporate into it. Evaporative coolers cost about one half as much to install as central air conditioners and use about one-quarter as much energy. However, they require more frequent maintenance than refrigerated air conditioners, and they're suitable only for areas with low humidity. Watch the maintenance tips at the end of this segment to learn more. And finally, fans. When air moves around in your home, it creates a wind chill effect. A mere two-mile-an-hour breeze will make your home feel four degrees cooler and therefore you can set your thermostat a bit higher. Ceiling fans and portable oscillating fans are cheap to run and they make your house feel cooler. You can also install a whole house fan to draw the hot air out of your home. A whole house fan draws cool outdoor air inside through open windows and exhausts hot room air through the attic to the outside. The result is excellent ventilation, lower indoor temperatures, and improved evaporative cooling. But remember, there are many low-cost, no-cost ways that you can keep your home cool. You should focus on these long before you turn on your AC or even before you purchase an AC. But if you are going to purchase a new cooling system, remember to get one that's energy efficient and the correct size for your home. Wait, wait, don't go away, there's more. After this segment of the presentation is over, you're going to be given the option to view maintenance tips about air conditioners and evaporative coolers. Now all of these tips are brought to you by the people at Xcel Energy. Thanks for watching.
"""
```

```python
from huggingface_hub import InferenceClient

TEXT_MODEL_NAME = "microsoft/Phi-3-mini-4k-instruct"

client = InferenceClient()

def organize_text(meeting_transcript):
    messages = build_messages(meeting_transcript)
    response = client.chat_completion(
        messages, model=TEXT_MODEL_NAME, max_tokens=250, seed=430
    )
    return response.choices[0].message.content


def build_messages(meeting_transcript) -> list:
    system_input = "You are an assitant that organizes meeting minutes."
    user_input = """Take this raw meeting transcript and return an organized version.
    Here is the transcript:
    {meeting_transcript}
    """.format(meeting_transcript=meeting_transcript)

    messages = [
        {"role": "system", "content": system_input},
        {"role": "user", "content": user_input},
    ]
    return messages
```

现在我们有了文本组织函数 `organize_text`，我们也可以为它构建一个演示：

```python
part_2_demo = gr.Interface(
    fn=organize_text,
    inputs=gr.Textbox(value=sample_transcript),
    outputs=gr.Textbox(show_copy_button=True),
    title="Clean Up Transcript Text",
)
part_2_demo.launch()
```

赶紧试试看 👆！如果你在上面的演示中点击 "Submit"，你会看到输出文本是转录内容的一个更清晰、更有条理的版本，其中包含了标题和会议不同部分的章节。

你可以尝试调整 `user_input` 变量，控制 LLM 提示，看看能否得到一个总结。

### 整合所有部分
到目前为止，我们已经为会议转录工具的两个步骤编写了函数：
1. 将音频转换成文本文件， 
2. 将该文本文件组织成格式良好的会议文档。

接下来，我们只需要将这两个函数整合在一起，并为组合后的步骤构建一个演示。换句话说，我们的完整会议转录工具就是一个新的函数（我们将其创意命名为 `meeting_transcript_tool` 😀），它将 `transcribe` 的输出传递给 `organize_text`：

```python
def meeting_transcript_tool(audio_input):
    meeting_text = transcribe(audio_input)
    organized_text = organize_text(meeting_text)
    return organized_text


full_demo = gr.Interface(
    fn=meeting_transcript_tool,
    inputs=gr.Audio(type="filepath"),
    outputs=gr.Textbox(show_copy_button=True),
    title="The Complete Meeting Transcription Tool",
)
full_demo.launch()
```

赶紧试试看 👆！现在这是我们完整的转录工具演示。如果你提供一个音频文件，输出将是已经组织（并可能已总结）的会议版本。超级酷 😎。

## 将你的演示迁移到 🤗 Spaces
如果你已经完成到这里，那么恭喜你，你现在已经掌握了如何使用 Gradio 创建机器学习模型的演示 👏！

接下来，我们将向你展示如何将你全新的演示迁移到 Hugging Face Spaces。除了 Gradio 的易用性和强大功能，将你的演示迁移到 🤗 Spaces 还能为你提供永久托管、每次更新应用时的便捷部署，以及与任何人分享你工作的能力！需要注意的是，如果你长时间没有使用或更改你的 Space，它会进入休眠状态。

第一步是前往 [https://huggingface.co/new-space](https://huggingface.co/new-space)，从模板中选择 "Gradio"，然后将其余选项保留为默认设置（你以后可以修改这些选项）：

<img src="https://github.com/dmaniloff/public-screenshots/blob/main/create-new-space.png?raw=true" width="350" alt="image description">

这将创建一个新的 Space，你可以在其中填充你的演示代码。作为一个示例，我创建了 🤗 Space `dmaniloff/meeting-transcript-tool`，你可以通过 [这里](https://huggingface.co/spaces/dmaniloff/meeting-transcript-tool) 访问。

我们需要编辑两个文件：

* `app.py` —— 这是演示代码所在的地方，内容应该如下所示：
  ```python
  # app.py 的大纲：

  def meeting_transcript_tool(...):
     ...

  def transcribe(...):
     ...

  def organize_text(...):
     ...
  ```

* `requirements.txt` —— 这是我们告诉 Space 需要哪些库的地方，内容应该如下所示：
  ```
  # requirements.txt 的内容：
  torch
  transformers
  ```

## Gradio 自带“电池” 🔋

Gradio 自带了许多非常酷的功能，开箱即用。我们无法在这篇 Notebook 中覆盖所有功能，但以下是我们将要查看的三个功能：

- **作为 API 访问**
- **通过公共 URL 分享**
- **标记功能**

### 作为 API 访问
使用 Gradio 构建网页演示的一个好处是，你会自动获得一个 API 🙌！这意味着，你可以使用标准的 HTTP 客户端（如 `curl` 或 Python 的 `requests` 库）来访问你 Python 函数的功能。

如果你仔细查看我们上面创建的演示，你会看到在底部有一个链接，写着 "Use via API"。如果你点击我创建的 Space（[dmaniloff/meeting-transcript-tool](https://huggingface.co/spaces/dmaniloff/meeting-transcript-tool/blob/main/app.py)）中的链接，你将看到如下内容：

<img src="https://github.com/dmaniloff/public-screenshots/blob/main/gradio-as-api.png?raw=true" width="750" alt="image description">

让我们复制并粘贴下面的代码，使用我们的 Space 作为 API

```python
!pip install gradio_client
```

```python
>>> from gradio_client import Client, handle_file

>>> client = Client("dmaniloff/meeting-transcript-tool")
>>> result = client.predict(
>>> 		audio_input=handle_file('https://github.com/gradio-app/gradio/raw/main/test/test_files/audio_sample.wav'),
>>> 		api_name="/predict"
... )
>>> print(result)
```

<pre>
Loaded as API: https://dmaniloff-meeting-transcript-tool.hf.space ✔
Certainly! Below is an organized version of a hypothetical meeting transcript. Since the original transcript you've provided is quite minimal, I'll create a more detailed and structured example featuring a meeting summary.

---

# Meeting Transcript: Project Alpha Kickoff

**Date:** April 7, 2023

**Location:** Conference Room B, TechCorp Headquarters


**Attendees:**

- John Smith (Project Manager)

- Emily Johnson (Lead Developer)

- Michael Brown (Marketing Lead)

- Lisa Green (Design Lead)


**Meeting Duration:** 1 hour 30 minutes


## Opening Remarks

**John Smith:**

Good morning everyone, and thank you for joining this kickoff meeting for Project Alpha. Today, we'll discuss our project vision, milestones, and roles. Let's get started.


## Vision and Goals

**Emily Johnson:**

The main goal of Project Alpha is to
</pre>

哇！刚刚发生了什么？让我们来分解一下：

- 我们安装了 `gradio_client`，这是一个专门用于与 Gradio 构建的 API 交互的包。
- 我们通过提供想要查询的 🤗 Space 的名称来实例化客户端。
- 我们调用了客户端的 `predict` 方法，并传入了一个示例音频文件。

Gradio 客户端会为我们处理 HTTP POST 请求，同时它还提供了像读取输入音频文件（通过 `handle_file` 函数）这样的功能，音频文件将由我们的会议转录工具处理。

再次强调，使用这个客户端是一个选择，你完全可以直接运行 `curl -X POST https://dmaniloff-meeting-transcript-tool.hf.space/call/predict [...]`，并传入请求所需的所有参数。

> [!TIP]
> 上述调用得到的输出是一个由我们用于文本组织的 LLM 生成的虚拟会议，因为示例输入文件并非真实的会议录音。你可以调整 LLM 的提示，以适应这种情况。

### 通过公共 URL 分享
Gradio 的另一个酷功能是，即使你在本地计算机上构建了演示（在将其移到 🤗 Space 之前），你仍然可以通过将 `share=True` 传递给 `launch` 来将其分享给全世界，如下所示：

```python
demo.launch(share=True)
```

你可能已经注意到，在这个 Google Colab 环境中，这个行为默认启用，因此我们之前创建的演示已经有了一个公共 URL，你可以分享给任何人 🌎。返回 ⬆ 并查看日志中 `Running on public URL:` 的部分来找到它 🔎！

### 标记功能
[标记功能](https://www.gradio.app/guides/using-flagging)是 Gradio 内置的一项功能，它允许你的演示用户提供反馈。你可能已经注意到，我们创建的第一个演示底部有一个 `Flag` 按钮。

在默认选项下，当用户点击该按钮时，输入和输出示例会被保存到一个 CSV 日志文件中，供你稍后查看。如果演示涉及音频（如我们的例子），这些音频文件会被单独保存到一个并行目录中，并且这些文件的路径会被记录在 CSV 文件中。

现在，回到我们第一个演示，再次尝试一下，然后点击 `Flag` 按钮。你会看到一个新的日志文件会在 `flagged` 目录中创建：

```python
>>> !cat flagged/log.csv
```

<pre>
name,intensity,output,flag,username,timestamp
Diego,4,"Hello, Diego!!!!",,,2024-06-29 22:07:50.242707
</pre>

在这个例子中，我设置了 `name=diego` 和 `intensity=29` 作为输入，并点击了标记（Flag）。你可以看到，日志文件包括了函数的输入、输出（`"Hello, diego!!!!!!!!!!!!!!!!!!!!!!!!!!!!!"`），以及时间戳。

虽然记录用户认为有问题的输入和输出清单总比没有好，Gradio 的标记功能实际上能做得更多。例如，你可以提供一个 `flagging_options` 参数，让你自定义用户反馈或错误的类型，比如 `["Incorrect", "Ambiguous"]`。需要注意的是，这要求 `allow_flagging` 必须设置为 `"manual"`，如下所示：


```python
demo_with_custom_flagging = gr.Interface(
    fn=greet,
    inputs=["text", "slider"], # the inputs are a text box and a slider ("text" and "slider" are components in Gradio)
    outputs=["text"],          # the output is a text box
    allow_flagging="manual",
    flagging_options=["Incorrect", "Ambiguous"],
)
demo_with_custom_flagging.launch()
```

去试试上面的代码吧 👆！你会看到，标记按钮现在变成了 `Flag as Incorrect` 和 `Flag as Ambiguous`，而新的日志文件将反映出这些选项：

```python
>>> !cat flagged/log.csv
```

<pre>
name,intensity,output,flag,username,timestamp
Diego,4,"Hello, Diego!!!!",,,2024-06-29 22:07:50.242707
Diego,5,"Hello, Diego!!!!!",Ambiguous,,2024-06-29 22:08:04.281030
</pre>

## 总结与下一步

在这篇 Notebook 中，我们学习了如何使用 Gradio 演示任何机器学习模型。

首先，我们了解了如何为一个简单的 Python 函数设置接口；其次，我们深入探讨了 Gradio 的真正强项：为机器学习模型构建演示。

在这一过程中，我们学习了如何通过 `transformers` 库及其 `pipeline` 函数轻松地利用 🤗 Hub 中的模型，以及如何使用 `gr.Audio` 等多媒体输入。

接着，我们学习了如何将你的 Gradio 演示托管到 🤗 Spaces 上，这样你可以让演示一直在云端运行，并为你的演示提供灵活的计算资源。

最后，我们展示了 Gradio 内置的一些非常酷的功能，比如 API 访问、公用 URL 和标记功能。

### 下一步
如果你希望进一步深入，可以查看每个部分结尾的 `进一步阅读` 链接，继续探索更多功能和应用。

## ⏭️ 进一步阅读

- [你的第一个 Gradio 演示](https://www.gradio.app/guides/quickstart#building-your-first-demo)
- [Gradio 组件](https://www.gradio.app/docs/gradio/introduction)
- [Transformers 库](https://huggingface.co/docs/transformers/en/index)
- [Pipeline 函数](https://huggingface.co/docs/transformers/en/main_classes/pipelines)
- [Hub Python 库](https://huggingface.co/docs/huggingface_hub/en/package_reference/login)
- [无服务器推理 API](https://huggingface.co/docs/api-inference/index)
- [🤗 Spaces](https://huggingface.co/spaces)
- [Spaces 文档](https://huggingface.co/docs/hub/spaces)

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/enterprise_cookbook_gradio.md" />

### 基于 SQL 和 Jina Reranker v2 的 RAG
https://huggingface.co/learn/cookbook/zh-CN/rag_with_sql_reranker.md

# 基于 SQL 和 Jina Reranker v2 的 RAG

_作者：[Scott Martens](https://github.com/scott-martens) @ [Jina AI](https://jina.ai)_

本教程将展示如何构建一个简单的检索增强生成（RAG）系统，该系统从 SQL 数据库中提取信息，而不是从文档存储中提取。

### 工作原理

* 给定一个 SQL 数据库，我们提取 SQL 表的定义（SQL 导出文件中的 `CREATE` 语句），并将其存储。在本教程中，我们已经为您完成了这部分操作，表定义被存储在内存中，作为一个列表。根据此示例扩展可能需要更复杂的存储方案。
* 用户输入一个自然语言查询。
* [Jina Reranker v2](https://jina.ai/reranker/)（[`jinaai/jina-reranker-v2-base-multilingual`](https://huggingface.co/jinaai/jina-reranker-v2-base-multilingual)），一个由 [Jina AI](https://jina.ai) 提供的 SQL 感知排序模型，会根据查询的相关性对表定义进行排序。
* 我们将用户的查询和排名前三的表定义作为提示，传递给 [Mistral 7B Instruct v0.1 \(`mistralai/Mistral-7B-Instruct-v0.1`)](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)，并请求生成一个 SQL 查询来完成任务。
* Mistral Instruct 生成一个 SQL 查询，我们将其在数据库上执行并检索结果。
* SQL 查询结果被转换为 JSON 格式，并作为新提示传递给 Mistral Instruct，包含用户的原始查询、SQL 查询及请求，要求生成自然语言形式的答案。
* Mistral Instruct 的自然语言文本响应返回给用户。

### 数据库

本教程使用一个小型的开放访问视频游戏销售记录数据库，存储在 [GitHub](https://github.com/bbrumm/databasestar/tree/main/sample_databases/sample_db_videogames/sqlite) 上。我们将使用 [SQLite](https://www.sqlite.org/index.html) 版本，因为 SQLite 非常紧凑，跨平台，并且内置对 Python 的支持。

### 软件和硬件要求

我们将在本地运行 Jina Reranker v2 模型。如果您使用 Google Colab 运行此笔记本，请确保使用支持 GPU 的运行时。如果您在本地运行，您需要 Python 3（本教程使用 Python 3.11 编写），并且在启用了 CUDA 的 GPU 上运行将会 *大大* 提升速度。

本教程还将广泛使用开源的 [LlamaIndex RAG 框架](https://www.llamaindex.ai/)，以及 [Hugging Face Inference API](https://huggingface.co/inference-api/serverless) 来访问 Mistral 7B Instruct v0.1。您需要一个 [Hugging Face 账户](https://huggingface.co/login) 和一个至少具有 `READ` 权限的 [访问令牌](https://huggingface.co/settings/tokens)。

> [!WARNING]
> 如果你使用 Google Colab，SQLite 已经安装。它可能没有安装在您的本地计算机上。如果未安装，请按照 [SQLite 网站](https://www.sqlite.org/download.html) 上的说明进行安装。Python 接口代码已经集成在 Python 中，无需额外安装任何 Python 模块。

## 开始

### 安装环境

首先，安装需要的 python 模块：

```python
!pip install -qU transformers einops llama-index llama-index-postprocessor-jinaai-rerank  llama-index-llms-huggingface "huggingface_hub[inference]"
```

### 下载数据库

接下来，从 [GitHub](https://github.com/bbrumm/databasestar/tree/main/sample_databases/sample_db_videogames/sqlite) 下载 SQLite 数据库 `videogames.db` 到本地文件系统。如果你的系统上没有 `wget` 命令，可以通过 [这个链接](https://github.com/bbrumm/databasestar/raw/main/sample_databases/sample_db_videogames/sqlite/videogames.db) 下载数据库，并将其放置在你运行本 Notebook 的相同目录中。

```python
!wget https://github.com/bbrumm/databasestar/raw/main/sample_databases/sample_db_videogames/sqlite/videogames.db
```

### 下载并运行 Jina Reranker v2

以下代码将下载模型 `jina-reranker-v2-base-multilingual` 并在本地运行： 




```python
from transformers import AutoModelForSequenceClassification

reranker_model = AutoModelForSequenceClassification.from_pretrained(
    'jinaai/jina-reranker-v2-base-multilingual',
    torch_dtype="auto",
    trust_remote_code=True,
)

reranker_model.to('cuda') # or 'cpu' if no GPU is available
reranker_model.eval()
```

### 设置 Mistral Instruct 的接口

我们将使用 LlamaIndex 创建一个持有对象，用于连接 Hugging Face 推理 API 和运行在那里的 `mistralai/Mistral-7B-Instruct-v0.1` 模型。

首先，从你的 [Hugging Face 账户设置页面](https://huggingface.co/settings/tokens) 获取一个 Hugging Face 访问令牌。

在下面的提示中输入该令牌：

```python
import getpass

print("Paste your Hugging Face access token here: ")
hf_token = getpass.getpass()
```

接下来，初始化 LlamaIndex 中 `HuggingFaceInferenceAPI` 类的实例，并将其存储为 `mistral_llm`：

```python
from llama_index.llms.huggingface import HuggingFaceInferenceAPI

mistral_llm = HuggingFaceInferenceAPI(
    model_name="mistralai/Mixtral-8x7B-Instruct-v0.1", token=hf_token
)
```

## 使用 SQL 感知的 Jina Reranker v2

我们从 [GitHub 上的数据库导入文件](https://github.com/bbrumm/databasestar/tree/main/sample_databases/sample_db_videogames/sqlite) 中提取了八个表的定义。运行以下命令，将它们放入名为 `table_declarations` 的 Python 列表中：

```python
table_declarations = ['CREATE TABLE platform (\n\tid INTEGER PRIMARY KEY,\n\tplatform_name TEXT DEFAULT NULL\n);',
 'CREATE TABLE genre (\n\tid INTEGER PRIMARY KEY,\n\tgenre_name TEXT DEFAULT NULL\n);',
 'CREATE TABLE publisher (\n\tid INTEGER PRIMARY KEY,\n\tpublisher_name TEXT DEFAULT NULL\n);',
 'CREATE TABLE region (\n\tid INTEGER PRIMARY KEY,\n\tregion_name TEXT DEFAULT NULL\n);',
 'CREATE TABLE game (\n\tid INTEGER PRIMARY KEY,\n\tgenre_id INTEGER,\n\tgame_name TEXT DEFAULT NULL,\n\tCONSTRAINT fk_gm_gen FOREIGN KEY (genre_id) REFERENCES genre(id)\n);',
 'CREATE TABLE game_publisher (\n\tid INTEGER PRIMARY KEY,\n\tgame_id INTEGER DEFAULT NULL,\n\tpublisher_id INTEGER DEFAULT NULL,\n\tCONSTRAINT fk_gpu_gam FOREIGN KEY (game_id) REFERENCES game(id),\n\tCONSTRAINT fk_gpu_pub FOREIGN KEY (publisher_id) REFERENCES publisher(id)\n);',
 'CREATE TABLE game_platform (\n\tid INTEGER PRIMARY KEY,\n\tgame_publisher_id INTEGER DEFAULT NULL,\n\tplatform_id INTEGER DEFAULT NULL,\n\trelease_year INTEGER DEFAULT NULL,\n\tCONSTRAINT fk_gpl_gp FOREIGN KEY (game_publisher_id) REFERENCES game_publisher(id),\n\tCONSTRAINT fk_gpl_pla FOREIGN KEY (platform_id) REFERENCES platform(id)\n);',
 'CREATE TABLE region_sales (\n\tregion_id INTEGER DEFAULT NULL,\n\tgame_platform_id INTEGER DEFAULT NULL,\n\tnum_sales REAL,\n   CONSTRAINT fk_rs_gp FOREIGN KEY (game_platform_id) REFERENCES game_platform(id),\n\tCONSTRAINT fk_rs_reg FOREIGN KEY (region_id) REFERENCES region(id)\n);']
```

现在，我们定义一个函数，该函数接受一个自然语言查询和表定义列表，使用 Jina Reranker v2 对所有表进行评分，并按得分从高到低返回它们：

```python
from typing import List, Tuple

def rank_tables(query: str, table_specs: List[str], top_n:int=0) -> List[Tuple[float, str]]:
  """
  Get sorted pairs of scores and table specifications, then return the top N,
  or all if top_n is 0 or default.
  """
  pairs = [[query, table_spec] for table_spec in table_specs]
  scores = reranker_model.compute_score(pairs)
  scored_tables = [(score, table_spec) for score, table_spec in zip(scores, table_specs)]
  scored_tables.sort(key=lambda x: x[0], reverse=True)
  if top_n and top_n < len(scored_tables):
    return scored_tables[0:top_n]
  return scored_tables
```

Jina Reranker v2 会对我们提供的每个表定义进行评分，默认情况下，这个函数将返回所有表及其得分。可选参数 `top_n` 限制返回的结果数量，按得分从高到低，直到用户定义的数量。

试试这个。首先，定义一个查询：

```python
user_query = "Identify the top 10 platforms by total sales."
```

运行 `rank_tables` 来获取表定义的列表。我们将 `top_n` 设置为 3，以限制返回列表的大小，并将结果赋值给变量 `ranked_tables`，然后检查结果：

```python
ranked_tables = rank_tables(user_query, table_declarations, top_n=3)
ranked_tables
```

输出应该包括 `region_sales`、`platform` 和 `game_platform` 这三个表，它们似乎都是查找查询答案的合理地方。

## 使用 Mistral Instruct 生成 SQL 查询

我们将使用 Mistral Instruct v0.1 编写一个 SQL 查询，满足用户的查询需求，基于根据重新排序器得出的前三个表的声明。

首先，我们使用 LlamaIndex 的 `PromptTemplate` 类为此目的创建一个提示：

```python
from llama_index.core import PromptTemplate

make_sql_prompt_tmpl_text = (
    """
Generate a SQL query to answer the following question from the user:
\"{query_str}\"

The SQL query should use only tables with the following SQL definitions:

Table 1:
{table_1}

Table 2:
{table_2}

Table 3:
{table_3}

Make sure you ONLY output an SQL query and no explanation.
"""
)
make_sql_prompt_tmpl = PromptTemplate(make_sql_prompt_tmpl_text)
```

我们使用 `format` 方法将用户查询和来自 Jina Reranker v2 的前三个表定义填充到模板字段中：

```python
make_sql_prompt = make_sql_prompt_tmpl.format(query_str=user_query,
                                              table_1=ranked_tables[0][1],
                                              table_2=ranked_tables[1][1],
                                              table_3=ranked_tables[2][1])
```

你可以看到我们将传递给 Mistral Instruct 的实际文本：

```python
print(make_sql_prompt)
```

现在，让我们将提示发送给 Mistral Instruct 并获取其响应：

```python
response = mistral_llm.complete(make_sql_prompt)
sql_query = str(response)
print(sql_query)
```

## 运行 SQL 查询
使用内置的 Python SQLite 接口，针对数据库 `videogames.db` 运行上面的 SQL 查询：

```python
import sqlite3

con = sqlite3.connect("videogames.db")
cur = con.cursor()
sql_response = cur.execute(sql_query).fetchall()
```

有关 SQLite 接口的详细信息，请参阅 [Python3 文档](https://docs.python.org/3/library/sqlite3.html)。

检查结果：

```python
sql_response
```

你可以通过运行您自己的 SQL 查询来检查结果是否正确。该数据库中存储的销售数据是浮动点数，可能是以千或百万为单位的销售数量。

## 获取自然语言回答

现在，我们将用户的查询、SQL 查询和结果通过一个新的提示模板传递回 Mistral Instruct。

首先，使用 LlamaIndex 创建新的提示模板，和之前一样：

```python
rag_prompt_tmpl_str = (
    """
Use the information in the JSON table to answer the following user query.
Do not explain anything, just answer concisely. Use natural language in your
answer, not computer formatting.

USER QUERY: {query_str}

JSON table:
{json_table}

This table was generated by the following SQL query:
{sql_query}

Answer ONLY using the information in the table and the SQL query, and if the
table does not provide the information to answer the question, answer
"No Information".
"""
)
rag_prompt_tmpl = PromptTemplate(rag_prompt_tmpl_str)
```

我们将把 SQL 输出转换为 JSON 格式，这是 Mistral Instruct v0.1 理解的格式。

填充模板字段：

```python
import json

rag_prompt = rag_prompt_tmpl.format(query_str="Identify the top 10 platforms by total sales",
                                    json_table=json.dumps(sql_response),
                                    sql_query=sql_query)
```

现在从 Mistral Instruct 请求自然语言回答：

```python
rag_response = mistral_llm.complete(rag_prompt)
print(str(rag_response))
```

## 尝试自己动手

让我们将所有步骤组织成一个函数，并加入异常处理：

```python
def answer_sql(user_query: str) -> str:
  try:
    ranked_tables = rank_tables(user_query, table_declarations, top_n=3)
  except Exception as e:
    print(f"Ranking failed.\nUser query:\n{user_query}\n\n")
    raise(e)

  make_sql_prompt = make_sql_prompt_tmpl.format(query_str=user_query,
                                                table_1=ranked_tables[0][1],
                                                table_2=ranked_tables[1][1],
                                                table_3=ranked_tables[2][1])

  try:
    response = mistral_llm.complete(make_sql_prompt)
  except Exception as e:
    print(f"SQL query generation failed\nPrompt:\n{make_sql_prompt}\n\n")
    raise(e)

  # Backslash removal is a necessary hack because sometimes Mistral puts them
  # in its generated code.
  sql_query = str(response).replace("\\", "")

  try:
    sql_response = sqlite3.connect("videogames.db").cursor().execute(sql_query).fetchall()
  except Exception as e:
    print(f"SQL querying failed. Query:\n{sql_query}\n\n")
    raise(e)

  rag_prompt = rag_prompt_tmpl.format(query_str=user_query,
                                      json_table=json.dumps(sql_response),
                                      sql_query=sql_query)
  try:
    rag_response = mistral_llm.complete(rag_prompt)
    return str(rag_response)
  except Exception as e:
    print(f"Answer generation failed. Prompt:\n{rag_prompt}\n\n")
    raise(e)
```

尝试:

```python
print(answer_sql("Identify the top 10 platforms by total sales."))
```

试一试其他的问题:

```python
print(answer_sql("Summarize sales by region."))
```

```python
print(answer_sql("List the publisher with the largest number of published games."))
```

```python
print(answer_sql("Display the year with most games released."))
```

```python
print(answer_sql("What is the most popular game genre on the Wii platform?"))
```

```python
print(answer_sql("What is the most popular game genre of 2012?"))
```

试一试你自己的问题:


```python
print(answer_sql("<INSERT QUESTION OR INSTRUCTION HERE>"))
```

## 复习与总结

我们向你展示了如何构建一个非常基础的 RAG（检索增强生成）系统，用于自然语言问答，并将 SQL 数据库作为信息来源。在这个实现中，我们使用相同的大型语言模型（Mistral Instruct v0.1）来生成 SQL 查询和构造自然语言回答。

这里的数据库是一个非常小的示例，扩展到更大规模可能需要比仅仅对表定义进行排序更复杂的方法。你可能需要使用一个双阶段的过程，其中嵌入模型和向量存储首先检索更多的结果，但重排序模型会将结果修剪到你能够放入生成语言模型提示中的数量。

本 Notebook 假设没有任何请求需要超过三个表来满足，显然，在实际应用中，这种假设并不总是成立。Mistral 7B Instruct v0.1 并不保证生成正确（甚至是可执行的）SQL 输出。在生产环境中，类似的实现需要更深入的错误处理。

更复杂的错误处理、更长的输入上下文窗口以及专门用于 SQL 任务的生成模型，可能在实际应用中带来显著的改进。

尽管如此，你可以看到 RAG 概念如何扩展到结构化数据库，极大地扩展了其应用范围。

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/rag_with_sql_reranker.md" />

### 用 LlamaIndex 构建一个 RAG 电子书库智能助手
https://huggingface.co/learn/cookbook/zh-CN/rag_llamaindex_librarian.md

# 用 LlamaIndex 构建一个 RAG 电子书库智能助手

_作者: [Jonathan Jin](https://huggingface.co/jinnovation)_

## 简介

这份教程将指导你如何快速为你的电子书库创建一个基于 RAG 图书助手。
就像图书馆的图书管理员帮你找书一样，这个助手也能帮你从你的电子书里找到你需要的书。

## 要求
这个助手要做得**轻巧**，**尽量在本地运行**，而且**不要用太多其他的东西**。我们会尽量用免费的开源软件，选择那种在**本地普通电脑上，比如 M1 型号的 MacBook 上就能运行的模型**。

## 组件
我们的解决方案将包括以下组件：
- [LlamaIndex]，一个基于LLM的应用数据框架，与 [LangChain] 不同，它是专门为 RAG 设计的；
- [Ollama]，一个简单易用的工具，可以让你在本地运行语言模型，比如Llama 2；
- [`BAAI/bge-base-en-v1.5`](https://huggingface.co/BAAI/bge-base-en-v1.5) 嵌入模型，它的表现[相当好，并且大小适中](https://huggingface.co/spaces/mteb/leaderboard)；
- [Llama 2]，我们将通过 [Ollama] 运行它。

[LlamaIndex]: https://docs.llamaindex.ai/en/stable/index.html
[LangChain]: https://python.langchain.com/docs/get_started/introduction
[Ollama]: https://ollama.com/
[Llama 2]: https://ollama.com/library/llama2



## 依赖

首先安装依赖库

```python
%pip install -q \
    llama-index \
    EbookLib \
    html2text \
    llama-index-embeddings-huggingface \
    llama-index-llms-ollama
```

```python
!brew install ollama
```

## 设置测试书库

我们接下来要弄个测试用的“书库”。

简单点说，我们的“书库”就是一个放有 `.epub` 格式电子书文件的**文件夹**。这个方法很容易就能扩展到像 Calibre 那种带有个 `metadata.db` 数据库文件的书库。怎么扩展这个问题，我们留给读者自己思考。😇

现在，我们先从[古腾堡计划网站](https://www.gutenberg.org/)下载两本`.epub`格式的电子书放到我们的书库里。


```python
!mkdir -p ".test/library/jane-austen"
!mkdir -p ".test/library/victor-hugo"
!wget https://www.gutenberg.org/ebooks/1342.epub.noimages -O ".test/library/jane-austen/pride-and-prejudice.epub"
!wget https://www.gutenberg.org/ebooks/135.epub.noimages -O ".test/library/victor-hugo/les-miserables.epub"
```

## 用 LlamaIndex 构建 RAG

使用 LlamaIndex 的 RAG 主要包括以下三个阶段：

1. **加载**，在这个阶段你告诉 LlamaIndex 你的数据在哪里以及如何加载它；
2. **索引**，在这个阶段你扩充加载的数据以方便查询，例如使用向量嵌入；
3. **查询**，在这个阶段你配置一个 LLM 作为你索引数据的查询接口。

以上解释仅是对 LlamaIndex 可实现功能的表面说明。要想了解更多深入细节，我强烈推荐阅读 LlamaIndex 文档中的["高级概念"页面](https://docs.llamaindex.ai/en/stable/getting_started/concepts.html)。


### 加载

好的，我们首先从**加载**阶段开始。

之前提到，LlamaIndex 是专为 RAG 这种混合检索生成模型设计的。这一点从它的[`SimpleDirectoryReader`](https://docs.llamaindex.ai/en/stable/module_guides/loading/simpledirectoryreader.html)功能就可以明显看出，它能**神奇地**免费支持很多种文件类型。幸运的是， `.epub` 文件格式也在支持范围内。

```python
from llama_index.core import SimpleDirectoryReader

loader = SimpleDirectoryReader(
    input_dir="./.test/",
    recursive=True,
    required_exts=[".epub"],
)

documents = loader.load_data()
```

`SimpleDirectoryReader.load_data()` 将我们的电子书转换成一组 LlamaIndex 可以处理的 [`Document`s](https://docs.llamaindex.ai/en/stable/api/llama_index.core.schema.Document.html)。

这里有一个重要的事情要注意，就是这个阶段的文档**还没有被分块**——这将在索引阶段进行。继续往下看...



### 索引


在把数据**加载**进来之后，接下来我们要做的是**建立索引**。这样我们的 RAG 系统就能找到与用户查询相关的信息，然后把这些信息传给语言模型（LLM），以便它能够**增强**回答的内容。同时，这一步也将对文档进行分块。

在 LlamaIndex 中，[`VectorStoreIndex`](https://docs.llamaindex.ai/en/stable/module_guides/indexing/vector_store_index.html) 是用来建立索引的一个“默认”工具。这个工具默认使用一个简单、基于内存的字典来保存索引，但随着你的使用规模扩大，LlamaIndex 还支持
[多种向量存储解决方案](https://docs.llamaindex.ai/en/stable/module_guides/storing/vector_stores.html)。

<Tip>
LlamaIndex 默认的块大小是 1024 个字符，块与块之间有 20 个字符的重叠。如果需要了解更多细节，可以查看 [LlamaIndex 的文档](https://docs.llamaindex.ai/en/stable/optimizing/basic_strategies/basic_strategies.html#chunk-sizes)。
</Tip>

如前所述，我们选择使用 [`BAAI/bge-small-en-v1.5`](https://huggingface.co/BAAI/bge-base-en-v1.5) 生成嵌入，以避免使用默认的 OpenAI（特别是 gpt-3.5-turbo）模型，因为我们需要一个轻量级、可在本地运行的完整解决方案。

幸运的是，LlamaIndex 可以通过 `HuggingFaceEmbedding` 类方便地从 Hugging Face 获取嵌入模型，因此我们将使用它。




```python
from llama_index.embeddings.huggingface import HuggingFaceEmbedding

embedding_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
```

我们将把这个模型传递给 `VectorStoreIndex`，作为我们的嵌入模型，以绕过 OpenAI 的默认行为。

```python
from llama_index.core import VectorStoreIndex

index = VectorStoreIndex.from_documents(
    documents,
    embed_model=embedding_model,
)
```

### 查询

现在我们要完成 RAG 智能助手的最后一部分——设置查询接口。

在这个教程中，我们将使用 Llama 2 作为语言模型，但我建议你试试不同的模型，看看哪个能给出最好的回答。

首先，我们需要在一个新的终端窗口中启动 Ollama 服务器。不过，[Ollama 的 Python 客户端](https://github.com/ollama/ollama-python)不支持直接启动和关闭服务器，所以我们需要在 Python 环境之外操作。

打开一个新的终端窗口，输入命令：`ollama serve`。等我们这里操作完成后，别忘了关闭服务器！


现在，让我们将 Llama 2 连接到 LlamaIndex，并使用它作为我们查询引擎的基础。

```python
from llama_index.llms.ollama import Ollama

llama = Ollama(
    model="llama2",
    request_timeout=40.0,
)

query_engine = index.as_query_engine(llm=llama)
```

## 最终结果 

有了这些，我们的基本的 RAG 电子书库智能助手就设置好了，我们可以开始询问有关我们电子书库的问题了。例如：


```python
>>> print(query_engine.query("What are the titles of all the books available? Show me the context used to derive your answer."))
```

<pre>
Based on the context provided, there are two books available:

1. "Pride and Prejudice" by Jane Austen
2. "Les Misérables" by Victor Hugo

The context used to derive this answer includes:

* The file path for each book, which provides information about the location of the book files on the computer.
* The titles of the books, which are mentioned in the context as being available for reading.
* A list of words associated with each book, such as "epub" and "notebooks", which provide additional information about the format and storage location of each book.
</pre>

```python
>>> print(query_engine.query("Who is the main character of 'Pride and Prejudice'?"))
```

<pre>
The main character of 'Pride and Prejudice' is Elizabeth Bennet.
</pre>

## 总结和未来可能的提升


我们成功地展示了如何创建一个完全在本地运行的基本 RAG 的电子书库智能助手，甚至在苹果的 Apple silicon Macs 上也能运行。在这个过程中，我们还全面了解了 LlamaIndex 是如何帮助我们简化建立基于 RAG 的应用程序的。

尽管如此，我们其实只是接触到了一些皮毛。下面是一些关于如何改进和在这个基础上进一步发展的想法。

### 强制引用

为了避免图书馆员的虚构响应，我们怎样才能要求它为其回答提供引用？

### 使用扩充的元数据

像 [Calibre](https://calibre-ebook.com/) 这样的电子书库管理工具会为电子书创建更多的元数据。这些元数据可以提供一些在书中文本里找不到的信息，比如出版商或版本。我们怎样才能扩展我们的 RAG 流程，使其也能利用那些不是 .epub 文件的额外信息源呢？


### 高效索引

如果我们把这里做的所有东西写成一个脚本或可执行程序，那么每次运行这个脚本时，它都会重新索引我们的图书馆。对于只有两个文件的微型测试库来说，这样还行，但对于稍大一点的图书馆来说，每次都重新索引会让用户感到非常烦恼。我们怎样才能让索引持久化，并且只在图书馆内容有重要变化时，比如添加了新书，才去更新索引呢？

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/rag_llamaindex_librarian.md" />

### 多智能体 RAG 系统 🤖🤝🤖
https://huggingface.co/learn/cookbook/zh-CN/multiagent_rag_system.md

# 多智能体 RAG 系统 🤖🤝🤖

_作者: [Sergio Paniego](https://github.com/sergiopaniego)_

🚨 **注意**：本教程较为高级。在开始之前，你应该对以下教程中讨论的概念有充分的理解：
- [智能体教程](agents)
- [高级 RAG 教程](advanced_rag)

在本 Notebook 中，我们将创建一个 **多智能体 RAG 系统**，这是一个多个智能体协作检索和生成信息的系统，结合了 **基于检索的系统** 和 **生成模型** 的优势。

## 什么是多智能体 RAG 系统？ 🤔

**多智能体检索增强生成 (RAG)** 系统由多个智能体组成，这些智能体协作执行复杂的任务。检索智能体负责检索相关的文档或信息，而生成智能体则将这些信息综合起来生成有意义的输出。系统中还会有一个管理智能体，它负责协调各个智能体，并根据用户输入选择最合适的智能体来完成任务。

这个教程的原始概念来源于 [这篇文章](https://weaviate.io/blog/what-is-agentic-rag)。你可以在那里找到更多细节。

以下是我们将要构建的系统架构。

![multiagent_rag_system.png](data:image/png;base64,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rZCCQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCHgTIDCO6wMBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTygoAJ73JyuP+NJL8FruX2QDon5ziYtTMlDZe02dsiBMYF8xQwNwIIIIAAAgggEHoCXgLj3M32k2QeRUKv+xMdTZDUxaI/AtG8I+GDT0aBvBQY5+/PXyxuK5QggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIICANwEC47g+EEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIC8I+PsLywTGeT/7oeaT09fqDEnDJG3z1siipg2rm+crt26z3Hw9o0yZnO6b9RFAAAEEEEAAAQSyQcAiMM7dRf//guMKZ0NbTpdIlhRlMYhANO9I+OCTUYDAOIubCiUIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDgXYDAOK4QBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQyAsC/gbG5YW957U9tJP0VghtKk3SLElDfQXFuXte3LThSddj9f4DQmg7tIIAAggggAACCCAQLAEHgXHuFvpJMuFxoRYcZ4KjOwfLiXkRQCBXC/Dzl1x9+mgeAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIDcLEBiXm88evSOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggIBbgF9YzhvXQth/oWxlQ2A7JijuPUmDbYPi3D0TGBcCZ48WEEAAAQQQQACBHBDwIzDO3aUJjTPhcaESHJcsKVZSSg4wsiQCCIS2AD9/Ce3zQ3cIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACeViAwLg8fHLZGgIIIIAAAggggAACCCCAAAIIIIAAAggggAAC+UiAX1jOGye7raQpObwVcy3N/reHIZI2+9MLgXH+qDEGAQQQQAABBBDI/QJZCIxzb36ApL4hEhw3UdJDuf+ssAMEEAiwAD9/CTAo0yGAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggYCtAYJytFHUIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQygL8wnIonx273sIkbZNU1q484FXmGvpA0iB/g+LcHREYF/Bzw4QI5CmBxORk7dr3m5Z9sU5b9+zR34f+UXxigqIjolT58stVq+r1KnPeeUpNTdPWPbu08uuvtG3PbqWlpqtYkcKqWLasqlSooPIXXazY6BiFhZnbZ9490tOPv8WnSUpKTVViSqqSUlOUkpau5NRUpf33vPnwQ0R4uCLCCigqPFzRERGKKHD8zwUK8NGIvHuFsDMEQksgAIFx7g0N/C84rlAO7jBZUqyklBzsgaURQCD0BPj5S+idEzpCAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQyCcCfCo6n5xotokAAggggAACCCCAAAIIIIAAAggggAACCCCAQB4X4BeWc/8JbiPp7RzYhrl25kgaLGlTINYnMC4QisyBQN4TMMFnv/75p95b+onWb9mklLQ0mX+wT0lNVXJyshISE5WYlKSY6BidfUZxJaekaO/+P5WamqqoyEhFRkUqPCxcZp6wAmG6qPT5anVHPV1/5ZWKjozMU2AmBM4VEJeSqoTUFCWmpulYcrLiU1KUlJam5LR0qUABpaany/0NQHp6miLCwlymYelSVHiYYsLCVDgqSjER4YoKC1dMeLjCw8NkIvYIkctTlwybQSBkBAIYGOfeU04Hx02Q1CVkgGkEAQRCQYCfv4TCWaAHBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgXwpQGBcvjztbBoBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgTwnwC8s5+5TarJ7tkkqm83bmCvJhHAEJCjO3TuBcdl8FlkOgVwi8Nv+/Xr13Zn6+a8/VaxwIUVGRcmkmyUmJurosTgdO3pMcYmJSk1JVVp6mivQLCI8XBGRkYqOilJ0dJTCwsKUlpauFBOclpwkpYerc6MmqlW1qiIjInKJhOc2zRu5CdGLT0nVwcREHUhMUpwJhAsLU3RMjKKiohQREaFwV+hbuMvH/TAzmiA998OE7JmHCeIzVvHx8UpLTlbB8DAVj4pyPYpERboC5sIL8LGJXH3h0DwCISYQhMA49w5NuHFvSYWyecvJkmLNLTqb12U5BBAIXQF+/hK654bOEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBPK4AJ98zuMnmO0hgAACCCCAAAIIIIAAAggggAACCCCAAAIIIJBPBPiF5dx9olv/GxY3LRu38JGkQZI2BmNNAuOCocqcCORugcTkJM35bLnmr16ls88qrkKxsYqMilbyf2FmR48e0xETGBcXr8SkJKWkpioiPEyRrrC4SEVHRysqKtIVlGZC0ZKTjweiJSQmqWBMQQ1q31GXlC7tClDLbUdaeroSU1P1T1Kyfo9PUEKBAootUsS194wPEzSwtbYAACAASURBVBhnAvPcgXHmzxn3e2pgXFpamszDhMa5H0nGNiVFR48cUXhqis6KitKZ0VGKDY9QZLjJLuVAAAEE/BP4Z88e18CN06fWMF/rzZ6zwr+ZfI7KieC4VyV189kZBQggkF8E+PlLfjnT7BMBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAg5ARy36fFQ46QhhBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAEBPiF5RA4CX62YBJ6tv0bGFfWz/FOhi2QNDBYQXHuRgiMc3JKqEUg7wuYILNte/bo+elvqWChgipWpLBiY2MVFh6hhMRExcfH6+ixYzoWF6+4uDjFxye6wuDCI8IVHRWlmGgTGBelyIhIRUREuMCSk1OUnJLqCkI7Gp+gmtdep04NGiomKipXgSakpOpAUpJ+PRan9JgYFS5c2BWOdzwgL8r1MHs2wXEmKM6ExLkf7rA489UdFmc2nzE4zh0aZzxNUJzxSkxMdD1MeJx5HDt8WMXCCui8gjEqGB6uyDCC43LVRUSzCISIwIoRw0/qpO7sOcH+TNYQSY9LKpQNBMmSYiWlZMNaLIEAAqEvwM9fQv8c0SECCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAHhUI9ocT8ygb20IAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIEQE+AXlkPshDhop5Wk6Q7q/SldJGmQpA3+DHY6hsA4p2LUI5C3BeITEzXj40VavekbFS9eTEUKF1bBmBilpUsJiUlKSDgeGHf0WJzijsXJ1Kelpiki8nhgnOsRHanIyChFRUa6AtOOh58luYLjjhxLUIEC4RrRpYsuOe98uYPUQlk1OTVNB5MStS8+UcfCwlS0aFHFxMSceJigOBMSZ8LiTFCcOyzO7M39MPvLuFcTFGeOjIFx5r9NaJz5f+arCY4zDxMUl5CQ4ArrMyFy5s8JR47ozIhwlYqOVsHwsFzhGMrnmN4QyG8CORAY5yYe+l9wnAl0C+bxqqRuwVyAuRFAINcI8POXXHOqaBQBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgrwkQGJfXzij7QQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgfwoMcbhtp/UOp6fcUiBM0jZJZS3rnZYtljQwu4Li3M0RGOf0NFGPQN4VMEFlO/ft1fDJkxRTMFrFixVVbKFCrhC4lLQ0JSQkukLLjpnAuLh4xcXFKcEVGJeuyMhwV2hadJR5RCsq+nh4nAlRS01NcYWemcC4hIQkHTx6TPVvvkUd72qgiPDwkAZNSEnRr8fi9GdyimL/C4orWLCgzMOExrnD4jKGxJmQvIxBcd42aMxNrQmIc3/NGCJn/uwOjUtMTJR5mMA442nOQ1hCgs6NjlSJqCiFF+AjFSF9MdEcAiEkkIOBcW6FpyT1khSs4Ljk/+ZOCSF2WkEAgZwRcPrzFKf1ObMrVkUAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIBcI8OnmXHCSaBEBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQyKMCLSXNCMLePpY0WNK6IMztc0oC43wSUYBAvhFISUnRByuWa86q5TrrzOIqWriQKxgtMipKySmprrCy+PgEHT12zBVWFmeCyxKSlJaerogIExgXoShXaFyUChaMUXR0jCtILjU1zRVwlpSUouTkJB08fFSxBQtpVPfuOvuMEiHreyQpSXuOHtPhAmEq8l9YXGxsrMskOjraFZBnHhnD4kzomzncX203Z4LhzOH+6v6z+W8TJmdC45KTk12O7sA489U8kuPidGYB6byYaIWHmWxTDgQQQMC7QAgExrkbDGZw3BhJ3bkWEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgZwQIjMsZd1ZFAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBPK7gEng2SapbAAhlkoamFNBce59LGnacIX5c7ELy9xmvla6r00At8hUCCCQmwT++PtvPT5mlMIiwnRG0aIqFBur6IIxioyIVHJKspKTkl0hcSYs7tixuOOBcYnJSle6Il2BcSYsLtIVplYwxgTGRSsiIsIVeOYOO0tOTnGN++fwMbW6o55a17kjJIkOJSZpy6HDCouNVeHChV0hcYUKHQ/Qi4qKcu3LPMLCwlzhcO6vWd1MxsA4M5f5b/cjo6M7OM6E+Jk/xx07pmLJSSptzhehcVk9DYxHIM8LhFBgnNt6uKRekgoGED9ZUoyktADOyVQIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIGApQGCcJRRlCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACARVoIWlmgGZc9m8G0GBJnwdovoBMk/D1hvSATMQkCCCQawUWrv5cE+fOVvFixVQ4tqArHM0d+paSmqbExCQlJMQrLj5OcXHxrv9OTPIUGBejmJhoxUTHKDw8XOnpaUpJSXEFmyUlJyspKdkVGFe6ZCmN7NJVZxQpElJm8Skp2nLwH6UUjFVs7PGHOyzOeJhgvIxhcSYwzjwCfWQMj3OHxqWmpp6wNGFx5hEfH+8K5Is/elRnpCSrdGxBhQehn0Dvj/kQQCDnBEIwMM6NMUJSzwAGx73y33w5h83KCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCORTgcB/wjqfQrJtBBBAAAEEEEAAAQQQQAABBBBAAAEEEPh/9u4ESq67sPP97y61d/WmpbXLsmxJtowxRrYJBmzMYsIWh9hAYJgMSUhCTCZhMpAXQoJPQsjGMsm8lznz/DiZGZZgMMQEHJsEvOLY2PIuW7IWa997q+7ab917n/+3VXJLli11q7u6lm+dc12l7rv87+d/qUpXd32DAAIIIIAAAmcsYH5HtVXSeWe8xalXNIG4P2y2UFx9qATjznJ22RyBFhfYuf+Afu+LfyU75iibzSiVNNG3pOLxhBzHlgmVmdBbqVyOgnHlciWKv9WiYJwUc13FYq5i8ZgS8biSiUQUm4uCcUEgP5gInXk1X74fqFgqayxf1NuueL3+8w3Xy3WcphCs1nxtHR3VmOMq290dRfPMYqJx5t7E4sxizmu2QnGTIV4uGmcCcSbAZ4Jx5t5E48rlsvK5nJa7tgZSqabwZBAIINCcAk0cjKuD/YWk352BcFxFUlpS0JwzwagQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQaF8BgnHtO7ecGQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDQrALXS/rOWQzu/he2vUnSXWexj1nflGDcrBNzAASaWuCbd9ypP7/5f2rxooXq7+uJAmkm+mbiaJZlq3Ys+FYpl1UqV1SpVlWreVH8TQqPBeNiclxHiXhM8Xg8Cse5jquJ6FmgIAhV9WpROG5waESHjgxpYOEifeWTn9Sa5cvm3McPAu0dG9e+mq90V5e6urqOB+NMPM8sxsN13YbE4uogp4rGmWCccaxH40w4rlg0Ib+yyqMjuiDbpbTrzrkpA0AAgeYUaIFgXB3uryX9jqTkWUh+SdJ/PYvt2RQBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBKYhQDBuGmhsggACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggMG0B8/upTZIunMYefvpCRelzzR6Kq58XwbhpzDCbINAmAiaU9om/+Avd+/DD6uvpVnd3tzJd6YlAWjwux3UVHAvGVStVVT1PJljm+77CMJDJwcVjrmJuTLZtR9G4eNxV3I1JtiXf81X1qioXyyqWyioVSxoezalU9bR48RL99gc+oA++5Zo518xXPT0+OKhkT28UijPBuHooLpFIHA/GWZbV0GCcgTk5GmfszVIPxpn7UqkURePM0lUu6ZyuLsUce85dGQACCDSfQAsF4wyeeSL7gqT/LCk1Dc2KpHRULuWGAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAINEyAY1zBqDoQAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIICDp/ZJumaLEvx8Lxf14itvN6eoE4+aUn4MjMKcCo+PjeseNn1B+fEzZTEbpVCoKxbnxeHQfi8VkyZLv11Sr1VTxqlGozK95CkJfQWC6cFYUi4vqcVGTx4q+FoXmvJqqXk1+zaxrQmeBvJqnqu+ru7dfN7zj7frMf/yVOTUwY9k6PKqxWCwKw5lYXCaTUT0UZ+6Ng+u6E+d50s1E5Gb7Vo/GmXuzmLkwi5mLejCuXC6rUCgoNzKidemU+pOJKG7HDQEEEJgs0GLBuPrQzZPvX0r6nRfuk1Oc0a9I+i9T3IbVEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDgLAT4K+azwGNTBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBKQmY301tknThGW710LFQ3L+e4fpNtRrBuKaaDgaDwIwLeFHozVNg6m4K5fu+vJqvXD6vux55WH/z1a8qmUgqk05HwTQTR7MdV47rynGcKDpmtql6niqViirVqmqep5pfm9hnFDEzww6jx6ZRZrYxT6RhMPHvif+ECoKJ2FnZ8xRPpHTZay7RJ3/5A1rY16dELKFkPC7bso/vw+w1YWJtjjPjLvUdjpTLevTokLr7+qJYXDqdjhYTiovH49F9PRZ3qmDcTA7sVIG3eiyufpx6MC6aR8+LgnFmXkwwrlgsRkuiWNB52axizksDdzM5XvaFAAKtJ9Ciwbg69HTCcRVJ6WNF09abMEaMAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQAsKEIxrwUljyAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAiwrc8EIs7ttnMPafvbDOTZLuPIN1m3YVgnFNOzUMDIGzFnhuzy797x/+UPlyeSL6ZuJiXjWKthUrZR06clSHDh1VOpVSJpVSwgTj3FgUizPROMuxZdJvZttKtaJypaJqpSKv5inwAwXhRDBu4mbuLZmaTz0aZ+Jmk3/ZHwXjzL68qtx4XAsXLNQFa1err7snOl4m3aVEIql4PKFqzVM1n9dH3vYWrV667KwtTrWDWhBo6/CIhm0nisRls1mlUqloMbG4+lIPxpl9nCrqNiuDe5mdGlMzH/VgnInGmWCcmVsTiysUChoZHNQlfb3qjscaOTSOhQACLSDwxNe/Fo0yt2f3veb+2ltvu7oFhn3yEM1LzV9J+oSk5BmM/4uSPnUG67EKAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjMgADBuBlAZBcIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAKnFTC/l9r0QjDuwldY85EXvmdCcf9y2r21wAoE41pgkhgiAtMU2LZ3t/7uln/UUGFclm2pUvFUrXpRMM7E3srlsg4fGlRQ85VKTkTSYvG43FhMjuPIsZ2o/hZF3qqV42Gy2knBuNDE4urduCgbdywaF43bigJy5tthEET7qnqebMdRtrtbq89ZqQULFspx4+rKZhVPuCoXisoPD+ktl16md1/1JmXT6WkKvPJmpVpNDx44pHRvrzKZTLSYWJyJxxkLE4ozS2ThOMd3NlfROBOLM7d6MM7Mo1lMLM4spVJJ+Xw+WhYGvlZku2QbfG4IIIDASQLJSze0w5ODCcf9taQbTxOOq7ywnnkhCbgQEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEBg9gXa4Y8UZ1+JIyCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCJytwC9JuvVldvLoC183obgfnu1Bmml7gnHNNBuMBYGZF9ixd6/+7pZvaqxcUujY8ms1lcom/lZVEPhRaGwsNybPq8m23WhxYyaUFpMdBeMmAmUmGFeuVGRicWZds60JwE1EzEJNtMyOhePMb/jDMArH1W9mvSAIo2CcidWZMN2SxYt0/nnnaWDhIrmuo9D3VMmP64p16/WO11+p/p6emQeZtMeD+byeGslp3rx5JwTjkslkFIyrh+JMNM5E4uqhuLkIxtVjcROOwUT079i955kQYDUKABaLRY2Pj6uWG9VrFsxXzDY9JW4IIIDAiQJtEoyrn9SZhOP+RtKnuQ4QQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQGD2BQjGzb4xR0AAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHZFzCxsancprr+VPbNui8VML+T2iTpwpO+9fgL/zZz8c/tiEYwrh1nlXNC4ESB0fFx/eRnD+lnm5/ReLmkSrWmaujL90N5XjWKj9Wqnny/Fn3PBOJC2Sb/psAPosibCZJVqmZdT16tpjAKv5lA3LFgnInG+aH5b3Rwy9xblmzbisJrJroWM0s8oa6utObPX6CFCxcoFY/L8j0l3YTWrzhHb73iCi1ZuHDWp7AWBHr6yKDysZiy2exLgnFmvPVgnG3bx4NxU4nF1SNvM3kyZp9mfsxignHmvh6Mq1QqUTTOBOOGjh7RpX196kkmZvLw7AsBBNpEoM2CcfVZMeG4L0r6bUknP/lVJKXNy1qbTCGngQACpxeY6vspU13/9CNgDQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEOlSAYFyHTjynjQACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAmwlMVHTO/MbvSM7caibWfJ+k707a0RMvPDYfGv/+TOy8WfdBMK5ZZ4ZxITDzAqVKRVt37dATz23RT594RKEjlYO4gtBWMhFXzHVUrXmqlCcicp5fk+fV5Hs1Vb2qquaxCZWZoFw4EYczATUrlPxgosFjO7Zc11EiFlM8HlMinlQilVQmnVIqmTBbKAgDxUJPhdwRlYf3aP2yJbrxNz6vvr6+mT/pl9lj1ff1wL4DSvT0RMG4rq4updNppVIpJRKJ47E4E42LznHS8nKDnI1AnDlWfb+T7ycH46K58jyZYFypVIqCccPDwzo/mdDirkzDTDkQAgi0jkCbBuPqE2DCcV+S9PGTwnF/Jen/ap1ZYqQIIHCWArz/cpaAbI4AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwHQF+DDUdOXYDgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBJpJgA8sN9NsnDgW8/uoTZIulPTUC98yobjbTKeneYd8diO74/rrrjZ7eM2HP3K3ue9dufLsdsjWCCDQUgJf/YebtHnL/RqtJlQMu+Sm+uQmemQn4pJc+bIUWo5s25FjWQpD84QY/Sf6mgnDWbZ5bMsOrYknS9uSY9tyHFu2CcMFgcKwJimQV6moODauavGIXG9IWXtMXU5R2VSoC9ddrV/8ZdPxadwtX/X0wIGD6p0374RgXDKZjIJxrutOnJttR7E4c6vfT348OeJmvj+daJzZZvK+zf4nf+1UxzC2ZjHhuMnBuHK5rHw+r9HRUc0PfJ3b2xPNHzcEEEBgskCbB+Pqp2rCcV+W9FvHwnEVSenoRYkbAgh0gsBUf5bn/2DqhKuCc0QAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBoiAB/jNMQZg6CAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACsyzAB5ZnGfgsdv+LL6SO/vSF7U0o7nvtHIqrG915/XUnXI9Xf+azZ8HHpggg0GoC99/1f2vbpls1XiirUJYODYUaHLM1f2FaJS+mQzlHpVpcsWRa8WRKXZmEsl2uHCcmywTjTBzNsqLQmW1JgYmeKVC1XNNYoapSoahSoaCYSsrGqwq9ko4O5rV8nnTxalc9WUddXQm5sZgWLHmj3nDNH5kiW0MYTYDtcKGgp0Zy6u3rU09PjzKZjNLptOrBOMcxsbwTg3EvNzizv1NF3yav/0ohufq2pwrOnfy1+n7qwThzf3IwrlAoKJfLKV4q6tzurLoTiYa4chAEEGgdgQ4JxtUnZHI47iuS/rB1ZoqRIoDAWQjw/stZ4LEpAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAJnI9CYvwo/mxGyLQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBwegE+sHx6o7la42JJT83VwefiuATj5kKdYyLQPALPPfNdHXj+f6lUrqiQr2nr9pKcmDQwz9az26oaGrOUzlhyYyYGZyuZdNXd5UiWrUCWFEXSzPmYX+ebB6FsO1SlHChfqMm2fLOhfM+SYwU6d0VcQ2OBcsOBrrg0o2VLE0ombclylem9Rusu+d2G4ZjRbhsa0t5qLYrFdXd3R8E4s5hgXDwe1+RgXHSWx2J2J4ffTFTO3OoBt/p69ZOpr2/2V4+/+b7/knOtf68eqTPbmX2efKvvz+yjvo4JxlWrVVUqlWgpFotRMK6aG9WqbFaLuzINs+VACCDQGgIdFoybPCkfkHRLa8wSo0QAgbMU4P2XswRkcwQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEpitAMG66cmyHAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACzSTAB5abaTY6fCwE4zr8AuD0O15g9OjjGjv8l7JU0Y7tBQ0Oe1q7LiPbcTQ6HigZD+U6km3HVfNrKpd9lT1Lfs2EzEwv7thLmiVZoWQ7lhzHUsy1lEyGyiRjisUc1byKKtVQ2WxUntO99+cUc0Jd9aZupdOOxscduZnrtOz8jzRsToIw1OMHDylnO+rt7X1JMC4Wix0PxtUDcJNDcCbkZv5t4m6lUimKs82bN09mOxNuM9+rB+DMSZmve56nI0eOaOHChcf/bb5XX9c8NlE5s+/BwUGl02l1dXW9JERXd68H6sy9iceZ/Z8cjBsbHtLyVEqr+3obZsuBEECgNQQ6OBjXGhPEKBFAYCYEeP9lJhTZBwIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAAC0xAgGDcNNDZBAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBphPgA8tNNyWdOyCCcZ0795w5AkagOLZP44f/SHaY0zObxrVkSVwrVmXkuiZaVtXRkaT2DaY1NFLWgv6EliwI5LpF1XzzfWsiGGde1UwwzjJhOROMk+JuqGI5ri3by0qkuzQwP62+1KD6e8qKx+LasrmgxzaOacOGLg0sjuvIUFJ9i/+L+gYub9jEmGDcg3v3qRJPqK+v74RgXCKRUDwej+Jt9SicOdfJwbh6tG3Lli267bbbtH//fl100UV697vfraVLl0YBt/rNxOL27t2rb3/729q2bZvWrFmjG264QcuWLVOtVjsejDPxuZGREd15553auHFjNK73vOc9evWrXx1F4+q3lwvGmX2ZWF25XFaxWNTY2JhGh4a0JBHXmv6+htlyIAQQaA0BgnGtMU+MEgEEzkqA91/Oio+NEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCYvgDBuOnbsSUCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgg0jwAfWG6euej4kRCM6/hLAIAOF/D9ivZu/pzK45t05GBJF6xPa/HStIKwpkMjC7Qnv06DOU/3PbhVm3cc1QfeuUbvfr2nbGpIYegqDC3JROPMb/MtS7YC2bavXGmeNu3s0469Zd1x33YtGOjXR37x1VoWf1JL+o6qUJAefGBU3VlHixYnVPTWa9XFN8lxkw2bET8IdO+u3VI6E4XZstlstKTTaZlgnIm8mWBcPRpXH1g91mbibocOHdIXv/hFPfTQQ9F6ruvqF37hF3TjjTdGq5vIm/maibh95Stf0R133CGzndnHO9/5Tv3e7/1edBwTlzNfN7dbbrlFX//616Pgm7mtXbtWn/70p7Vq1apofyZaZ7Y3i/l3fTH78DwvWurBuFwuNxGMSya0lmBcw64tDoRAqwgQjGuVmWKcCCBwFgK8/3IWeGyKAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggMDZCBCMOxs9tkUAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFmEeADy80yE4xDBOO4CBDodIGaDmz5nI4ceEzVck3nrU1o/vyY8sEi5TPX6Qf3bNfu57cpXwz01e88oqsuXqH/9qfv0QWLH5P8UUmOpEkva5YvOQv1wOZz9PV/ekqvvXi5/v6bD2vrzkH97R/foHe+ZZ2cI99SNjakJ58oq1gItHJ1l5I912r+io83dDJMMO6u53fKznSdEIzLZDKKx+PRcnIwrh6LMwM14bYnn3xSf/RHf6RSqaRkMql8Pq8NGzboz/7sz2T2U6vVomDc3r179fu///saHh6OYnQm6LZ48WL95V/+pZYtWxYF48yxTCTuy1/+sn784x8rlUpFMbhKpaLPfOYzeutb33rc51TBOBOKM8ebHIwbHx/XyNCgBmIxXTh/XkN9ORgCCDS/SjkBhwAAIABJREFUAMG45p8jRogAAmctwPsvZ03IDhBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQmJ4AwbjpubEVAggggAACCCCAAAIIIIAAAggggAACCCCAAAIINJcAH1hurvno6NEQjOvo6efkEZBCT/nDf6zhQ5t06GBFa9fGlckmVFlwg4KFV+lzn/5zfe2fn1A6k9LwUF5vXRnqL/7uT3XR+WOyR2+X5bgv9uLCwFTUFPZeq2/fHdMnPvVFJVNxjRSlWG5cn/rgav3OF/9G+T0/VWroW9qxraQDB3xdcmmP+pfeIDf7oYbOSD0Yp1Ravb296unpUTabjUJvJupmgnG2bUeLibmZWz3UZmJx5us7d+7U5z//+ei+HoK75ppr9Ad/8AdRKM4E38y2JiT3uc99To899pjS6bQKhYIuv/zy6GtdXV1RMM7sz6x/880367vf/e7xf/f39+uzn/2s1q9ff9ynHq4z25ltzFKPxVWr1ShIZ+JzJhg3OjykRfG4LpjX31BfDoYAAs0rMLp7dzS4x7/xtTeb+5+/9bZ7mne0jAwBBBA4KwHefzkrPjZGAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQGD6AgTjpm/HlggggAACCCCAAAIIIIAAAggggAACCCCAAAIIINA8AnxguXnmouNHQjCu4y8BADpeIFRp8G81PvQTHT5Y0fLljrr7soqd/9uq9l2lv73pJn3hy7crSMe1fqGnD15m60N//I8aWNKtcNfnZKsoyZ5QDGsK7Kzscz6lnz0xpk/82sf15K6KBhbG9LYVVX3ohjfpjR//GznDD6rw7Fe09/lx7d7r66JX92n+yt+Qm3p7Q2cjCEPdvf15eYmE+vr6omCcibeZYFwymYwCcCb2ZhYTiDPL5GCc+beJtD344IP64Q9/GEXhFi1apOuuu04XX3xx9D2zjrmZGJyJxX3rW99SLpeLjvWBD3xAGzZsOL5ePUK3e/du3XbbbXr++eejY1977bW6+uqrowBdfX+Tg3HmcT0YZ2JxlUolCsaZKJ0JxuWGh7UkldS6/r6G+nIwBBBoXoF7vvD5Ewb3jltv42+ymne6GBkCCJydAO+/nJ0fWyOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggMG0B/jhx2nRsiAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAk0kwAeWm2gyOn0oBOM6/Qrg/BGQSqPfUXnwa8rnPWW6bPX1uUrOXyet+jVt3yl97UtfUf7AJv3cazO6+Opf1qo3fFSxsR9Jh78tyZfkTjCGnmS50uL3q9x9re742v/S7f/w/2n9+aEuf92rdNG7fl89fTX5O25W/vBzOnSgpqFhX+esnq/eZX8oJ/Gqhk6HCcY9sHOXxixbvb290ZLNZqNonAnGxePxKNI2ORh3qgGaYNvY2JhKpVK0vQnO1YNuk9c3sTeznom4mWOYaJwJvZlbPUhXf2yCbyYsF4vFovVMcG7yrR6uM9vXFxOo8zwvisWZsZhgnDlePpfT0lRSawnGNfT64mAINLMAwbhmnh3GhgACMyzA+y8zDMruEECgtQU236wTnhcv+JhO+Nt8vo/P5Cuc64P/fUy+Hnh+4PmB54cXBXh+5PmR58cXBXh94PWB1wdeH+oCzfb62No/vTN6BBBAAAEEEEAAAQQQQAABBNpLgGBce80nZ4MAAggggAACCCCAAAIIIIAAAggggAACCCCAQKcK3DTFE5/q+lPcPat3sgDBuE6efc4dgQmBWulhjR/4cxWLVcXjtvrnJZRMhLLSi2QNvEnF8jwN7TukTG+/egbmySk8IuUeN5UzyYpPROLMzQTjzCJb6rlEtcwlOvL8IVlBSb0DC5WMD8oavE/e+GGNj0tHj1Zkom3zB1apa/Gfy3b6Gzol5thP7j+gveVKFGWbHIxLp9PHg3Em1jY5GmfibvVbPQw3OehWj7mdKhpn9lO/+b6J7Z14q+97ckCuvr/Ja9a/ZvZRD8aZx9Vq9XgwLp/PR8G44tiYlqdTOp9gXEOvLw6GQDMLEIxr5tlhbAggMMMCU30/Zarrz/Bw2R0CCCAwuwIEPQh6TL7Cmi3owPXJ9cn1+aIA//skSDb5fw88P/L8yPMjz491AV4feH3g9eFFgdO9Ps7uT9fsHQEEEEAAAQQQQAABBBBAAAEEpiJAMG4qWqyLAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwGkECMZxiSCAgF/dpbF9n1KpkJdtWepbkFQ6E5dt+7LsQFZ6nmQWEzgrHJVqBclJS/axWJx5HIZSUJDCQAqqkl+UYhmpa6EUhFJpRGFxUAptVWuOxkbLGhmqyo056pl/qboW/6l04uc8Zn1iTHTt+cEhPTE4pO7u7igYZ+67urqUyWSUTCblum4Ui6sH40wY7lTBuFca7KnCcZO/Nnl/Zj8n/7u+7/rX67E4E4qbHIyr1WpRMK5SqahYLKoejKuOj2t1T7eWd2dn3ZQDIIBAawgQjGuNeWKUCCCAAAIIIIDATAuc/IH6de/aeMIhtty+4YR/8318Jl8QXB/872Py9cDzA88PPD+8KMDzI8+PPD++KMDrA68PvD7w+lAXmKvXx5OPe3JocaZ/zmZ/CCCAAAIIIIAAAggggAACCCBwegGCcac3Yg0EEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEzljgR9dfd49ZuWfFyqvM/SX/4SNnvC0rIoBAewgEQUHjB25SYfhphaGl3nlxZboSisVdybaijJsJx008iEmWY74gKZDcLin72onHuUcnQnE69r2gJvneRETOchTKVhiEKpeqyo1UlB/zFIsn1LPkfUr3/0rDMU14bbBQ0I+37VBPX596enqiYFw2m42CcalUKgrG1Zd6LM6E214u6jb5JMz+zXonB+NOFZCrb3eqfZ8cqKtvXw/G1aNxnudFwbhyuaxCoXA8GBevVLSmv0/9qWTDjTkgAgg0pwDBuOacF0aFAAIIIIAAAgjMtgDBOII2k68xgi4EXSZfD3MV9KiPgePz/MTz04sCPD/z/Mzz84sCvD7w+sDrA68PdYEzfX0kGDfb7yywfwQQQAABBBBAAAEEEEAAAQSmLkAwbupmbIEAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIDAaQXKj20MT7sSKyCAQNsKlHP3aOsTf6100teiRQmluhKKm2CcZcm2HVkxxzyUzDOFPRFBK48X5Ka6FFv0higG5x28V6qVFE+nJ1Y064ahFATRv802fs1XqVDReM7T3v0VJTNLdP6lX5DjLp0T21LV0+3PbpabTkexuHowrqurK4rGxWKxaDHROMcxBhOxuNNF414pCvdKJ/pKIbrJ+6zH4iZH40ws7uRg3Pj4uBY5tlb39SruOHNizEERQKD5BAjGNd+cMCIEEEAAAQQQQKARAuEB3v9rhDPHQAABBBBAAAEEEEAAAQQQmBsBgnFz485REUAAAQQQQAABBBBAAAEEEHglAYJxXB8IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIzIIAwbhZQGWXCLSQQLV8QLff+pvKxPO68oouxZIxJZKuLBOLs23ZrivbFOOsULJtKfS1d/uwcqM19S3MKgykwlhB8+bHNX9ZrxRaE7G4Y8G4MJh4XPVqqhQqyuWq+ud/zev8dZfrqmu/OGdSnu/r3q3bNRIEMpG4ycE48+9kMnlCMM5E48zNtu0oGmdup4q8TTcYNxli8n5PjsVNDsX5vi+zVCqVaCmXyyoUCjKxuLGxMV3U262l2ewpxzln8BwYAQTmVIBg3Jzyc3AEEEAAAQQQQGDOBAjGzRk9B0YAAQQQQAABBBBAAAEEEGiAAMG4BiBzCAQQQAABBBBAAAEEEEAAAQSmKEAwbopgrI4AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIDAmQgQjDsTJdZBoH0FvNJz+rd/+l0dOlLQu9+eVU9fXLGkK8cx0ThbdsyVHUZ1NFmOJdmWRg6O6ZmnRpXPe3Jsqa8/rrXr+5Sd3yWZdY8F40I/UBgECnxfXrWmatnT9h1F/fBHBb3trZfrtVf/jWTF5gQ3CEPtODqoh/buUyaTiYJxPT09UTwum81Gwbh4PB5F40wsznXdKLxWD8aZx/XlVCdwunBc/funis5N3l99PXNvYnGTg3G1Wk1mMbG4Uql0PBiXz+c1NjqqNy5fqp5EgmDcnFxhHBSB5hQgGNec88KoEEAAAQQQQACB2RLYfHP0U/rx27p3bZytQ7FfBBBAAAEEEEAAAQQQQAABBOZcwFqygc+iz/ksMAAEEEAAAQQQQAABBBBAAAEEJgT4IZ0rAQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFZECAYNwuo7BKBFhKojP9MP/vXP9T2XRVdtiGt1eemFUs4isVd2a4r20TiQiv6rb15bLmuFPoaGy5qZLAi25b6FySU6UmZFaRAUjARN5N/LHBW81WteCoXPf37Q3ltfqaiN19ziS655kuS0nOmNVYq65+felpOMhlF4kw0zgTjTDgunU5HwTizmFicWUwsbnIwzjw2t3r07VTxt9OF4yafvNl+8vrm8eRlcjCuHourVqtRKM4sxWJRhUIhise5lbJet2ypEma+uCGAAALHBAjGcSkggAACCCCAAAKdJUAwrrPmm7NFAAEEEEAAAQQQQAABBDpdgGBcp18BnD8CCCCAAAIIIIAAAggggEAzCRCMa6bZYCwIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAJtI0Awrm2mkhNBYOoCYaj84A/06N3/TYeOVtTf7+iiizLqm59QPO7KibmyXBOBC6UwNGU0hbYt17Wjf9umDmeF8gNblmO+ZL5uvhQo8INoHRM5q1Vr8qqedmwv6cGHSnIU6NJLl+qSa/4fhfbCaNx+ECg3Ph6t35XJKBmPT/18priFOeam/Qf02IGDSiaTUTDOhONMMM6E41KplBKJhGKx2PFgnOM4Mks9DlcPyJlDv1w47kyicfVt64E4s7/6Y2NiHkeWtZp8348WE4sziwnFmWBcPp+PgnHm/tKF87W8p+f4mKZIw+oIINCmAgTj2nRiOS0EEEAAAQQQQOBlBAjGcWkggAACCCCAAAIIIIAAAgh0kgDBuE6abc4VAQQQQAABBBBAAAEEEECg2QUIxjX7DDE+BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBlhQgGNeS08agEZgxgWpph5596tvK7f+xykVPy5e5Wr4ypWTGhNIcWY4bhcpcJ1SpYunuhyp6bocXReASsUCWbalclmzX0qpzUnrLlSnN67XkVYNojLWaL79S0/hoSY9uzGtkxFN2QVar167X2lffpMFxW/c++ph27N2r0HGVSGWUisU00JPVRatWavWKFTN2rqfa0WixqB89s1lV2z4ejauH49LpdBSNM8G4k6Nx9VCcCb2dvNSPMzkCd/Kxzfcmh+RODsadKhZnInH1YJy5N7G4SqUSBePMYkJxJhwXloq66txVyjQgujerk8POEUBgxgUIxs04KTtEAAEEEEAAAQSaWoBgXFNPD4NDAAEEEEAAAQQQQAABBBCYYQGCcTMMyu4QQAABBBBAAAEEEEAAAQQQOAsBgnFngcemCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCLycAME4rg0EOlugGNR0y7/+HxWP3h9F4C5ZMqpzF5bVPz+hRDoux3UUBKEcO5Qf2HriGU8/+ElZT2+rqupZ8oNAjm1pzaqk3vvWlF7/WkfJpKWgZtYPVfN8VQtl7d3r6cnnY4o7vuLzFqrS/RYl7FX6zh33yEmn9KY3v1nnrblAiXRG+WJRI8Pj2rdjqxbHfL33DVcqMUvxsyAMtfXQYf10x/NKpFIykbje3l51dXUpk8lESyKRUDwej6JxJhTnOE50f6ponPla/VaPwJ3JFVaPx9VDcZPvTbDPxOLqi4nFeZ4XxeFMMK5QKERLPRi3vr9PaxYuEH9ocSbyrINAZwk88fWvRSec27P7XnN/7a23Xd1ZApwtAggggAACCCDQmQLhgY1hZ545Z40AAggggAACCCCAAAIIINAJAltu33DCaV7wMX5V2gnzzjkigAACCCCAAAIIIIAAAgg0twB/x9zc88PoEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEWlSAYFyLThzDRmAmBJyYHtjxsP7tp/doJJdXuVxUbyyvdfOH9bYLBrVgvisnmZRlGmihZO4CL9DRI4H2Dloq+TEFfqiEG2jZQmnpEkd2TApDRbE4v+ZLlZK27Hb1b1sWaN94t1IJV/Pm9Wr7oZiefWa3XnvZ5frYr/+qzj/vPDnJlMLAbBtovFjWjt37dPf99yubO6xf/fm3KzlL0bhKraaHdjyv7cMjURTOBONMKK4ejTMRuXo0znXd00bjzjQUN3k9E4Uz/z5VMK4eijPr1GNxJhRngnGlUikKxZnFfC0TBnr9Oecok4jPxBXCPhBAoE0Fkpdu4G+x2nRuOS0EEEAAAQQQQOBUAgTjuC4QQAABBBBAAAEEEEAAAQTaWYBgXDvPLueGAAIIIIAAAggggAACCCDQqgL8kWKrzhzjRgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQaGoBgnFNPT0MDoHZE7At7S3s110P36WRXFH5YkW5XEFDubLssKg3n7NPb7vgqHr6U7JiJpJmybIkyw/lV2qqlAP5tVDm/zd7zLWVSDtykq4CSwqDUJ7ny6qUteewpe8+uVybjs5TOhnXwvk9Gi/4enjjFr3uitfrT/7kTzRvwTxVKr78wI9ic+Y/QeCrXPW189CQvn3b93RRMtSH3nqNXMeZFZPhfEH3bt2qUa+meDwexeLqwTgTj0smk9FignKO4xy/t207Cr3V42/m35Nv9Qic+drkQJwJw50clqvH4sy69ccmEmcWE43zPO/4Uo/FFYtFjY2NReE4v1zWm85dpYXZrpfse1bQ2CkCCLSsAMG4lp06Bo4AAggggAACCExLgGDctNjYCAEEEEAAAQQQQAABBBBAoEUECMa1yEQxTAQQQAABBBBAAAEEEEAAgY4SIBjXUdPNySKAAAIIIIAAAggggAACCCCAAAIIIIAAAggg0LYCN03xzKa6/hR3z+oISATjuAoQ6EwBE3bbPLhVW3c9rdx4SflCRcVyRaO5vIZGigprBb1hxQG96+IjSnfFZMUTclxLViAFfqigFsqvBVHgzY1bcuOOZFsKou/7CsoF7Trk6geblurZwflKp5JaOtAjN5bQ/Q/tUL5Q0le+/GVt2HCZCqWqQplSnKL7wATnqjWVyhXVqjU9veegvvPN/60/ePc1uuCcc2YlhmYCbYdyY7pv2zYV/ECJRELpdFomFmcW8ziVSkVfN0E5E44zcTgTj5scjZscjzPnc3IU7pWuNjOGyCAMXxKMq9Vqqlar0VKpVGRCcWYpFAoaHx9XuVDQFStX6PyBhabhxw0BBBB4RQGCcVwgCCDQAQJTfT9lqut3ACGniAAC7SRAMK6dZpNzQQABBBBAAAEEEEAAAQQQOFmAYBzXBAIIIIAAAggggAACCCCAAALNJ8DfMzffnDAiBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQmLrARAnmzG/8juTMrVhzmgIE46YJx2YItIGAF9S0f/yodhzarINHDmpkrKxisaTR0byOjhRVreR1zeqDeuN5w1rY68tNp2Xbjo4XycyrmombWSaMFj2U79VUKRX1zJ60/uWZAe0c7VVPNq2BBT0amN+rHXvHdP+Dm/Smq67U7/7XTyuT7lbSiUlWaGpxCiRFcbRKVcViRZ5XU8kP9I8//KG6BvfoDz70QcVcd1b0gzDUvuER/eS5rQptO4rC1UNxJhpngnFmSSaTUTTOdd1oMdE4czNxuHo8rj7AqQTjzDb1aFwQmBifiecFkYdZTCiuVCpFiwnFmSUKx42P65KlS3TRsqVybXtWbNgpAgi0lwDBuPaaT84GAQROKcD7L1wYCCCAgKTNNx+rsx/TWPeujbgggAACCCCAAAIIIIAAAggg0LYC1pIN/J1V284uJ4YAAggggAACCCCAAAIIINBqAvyQ3mozxngRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDgVAJ8YJnromkE7rj+uqvNYF7z4Y/cbe57V65smrExEAQQaJyACZINV8a15eCz2ndkjw4fyWk8X9LI6JgOHR2VE+vRa1YGunzxVi1K7VV3d0KJVFK2a0fdOBOKC4JQXi1QtVjWaM7Xgcp5+smutXpu15CSsaoGFvRr2dIFcmNx3fvQ8zp8eFC/+Ykbtf6Sy2SHgQbmLVAykVDgB/IDP4rEVaqeKmVPfq2m0LJ0zxOb9NCP/klf/eQnFJ+lYJxRN5G2bUeO6pHdu1UJwigGl0gkjsfi6gE5E4wzi4nK1aNxZt2pBuMmB+XMseuLmZd6LM7zPFWr1SgUZwJxx0NxxaK8clmXr1yu8wcGFDsWrmvc1cOREECgVQUIxrXqzDFuBBCYggDvv0wBi1URQKB9BQjGte/ccmYIIIAAAggggAACCCCAAAIvFSAYx1WBAAIIIIAAAggggAACCCCAQPMIEIxrnrlgJAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIDB9AT6wPH07tpxhgTuvv+6E6/Hqz3x2ho/A7hBAoJUESrWqth7doWd3Pq3Dg6MaGs7pyNFRJdMLtHjFWp27OKu+yiNyBn+sZHhU6WxGiaQr27JULtU0nsurHM5X7JxfkrfgGm3avE3btzwm38trYKBf5yxfqNFxT9+7/RF19/boo799o7LZPmXTaS3o71c6kVYQhlEkzQTSzFKr+QoDRRG2J3ft1Y++f4v+34//mhIxd9ZpH9q5S88cPCTLtqNonAnDJZPJKBKXSqWiiJxZ6uG4k6NxZhtzM0G4yVG4lxv4yaG4yQ6VSiWKxdWDcfl8XpVyWU4Q6NLly3Th0iVndIxZR+MACCDQMgIE41pmqhgoAghMX4D3X6Zvx5YIINBGAgTj2mgyORUEEEAAAQQQQAABBBBAAIHTChCMOy0RKyCAAAIIIIAAAggggAACCCDQMAGCcQ2j5kAIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAwiwJ8YHkWcdn11AQIxk3Ni7URaHsBy5IX+Hp6/3Y9tf0RDY+O6dCRnLqyA1qz9kKtufDVWrx0lQr7H9WO+/6njm65X1aYj1gCZTTv/Ct03lW/pSUXvlW5kUE9vvEBPf74wxrPHdGSgV4NLOzX5u1D+sGdD+jy112m933419TT1aOedEau7Uh2qCiaZoJxfigFoXw/iPbvOrae3ndQ//aD7+nvP/ZhJdzZD8ZtPXxEj+zdp6rvR+MyATgThTPL5FBc/bEJytW/X4/HmVCcid3VbyeH4+qROPN989j3/SiYZ+5rtZpMKK4eiyuXyyoWi9ESeFUtznbrtatWaqC7u+0vTU4QAQRmXoBg3MybskcEEGg6Ad5/abopYUAIIDAXAgTj5kKdYyKAAAIIIIAAAggggAACCMyVAMG4uZLnuAgggAACCCCAAAIIIIAAAgi8VIBgHFcFAggggAACCCCAAAIIIIAAAggggAACCCCAAAIItIMAH1huh1lsk3MgGNcmE8lpIHC2AratklfVUCmvfUODenrbszp4aI+yGTcKt2V6lunC9Rdrzdq1Wrb8XMUTKXnlknb+7Bva/fA/Rkdfdsl1Wv2GX1E81a0w9DU2OqRnntmkRx5+QGNDuxWP2fJ8S9ueP6oHH35a113/Pr3nug8rk0geG/1ELE6hpYlE3MR/wyCULMm2bD26c7fuu+P7+h+/+SsNCcY9PzikR/fvV8WfCLiZkFs9AGeCcCYQV4/E1R9P/poJzJlYnFnMdvWlPl31WJzZr3ls7j3Pi0Jx5t4sJhZnQnH1aJx8X33JpNYuGtCFS5dE++SGAAIITEeAYNx01NgGAQRaTID3X1pswhguAgjMrkB4YONUnxdnd0DsHQEEEEAAAQQQQAABBBBAAIEZFNhy+4YT9nbBx8xvmbkhgAACCCCAAAIIIIAAAggggMBcCvDD+Vzqc2wEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCYKYGpfjCP35HMlDz7eYkAwTguCgQ6WyC0LHmhr4P5nDZu2aRnt2/W0OCQ8vmS/CDUvP60VixZoMXL12ntha/RqnNXq3/egGzHjeAqXqAffu/rMvG0d/7ihxSz656hvEpR27c9p40/u0vDR3br8NGRaLvB4XHdc99Tesd736PLrnqbYk5c83p61Z1KS5aJxk287EXtuIn/yHFsmbHe+9QmPfSj7+sfPnmjErHYrE/eziETjDugmqwo4laPupl7E2ozQbjJi3EwSzwej75+qlic2S46r+gcJyJx9WVyKM7E4qrVahSLC31fThiqJ5nUmkUDWrdksVzHmfXz5wAIINDeAgTj2nt+OTsEEJj4P7em6MD7L1MEY3UEEGgtAYJxrTVfjBYBBBBAAAEEEEAAAQQQQGBqAgTjpubF2ggggAACCCCAAAIIIIAAAgg0QoA/xmmEMsdAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB2RbgA8uzLcz+z1iAYNwZU7EiAm0nENq2xmpFbd6/U49vflp79uxTPl+WHwZKxl1lMgl1dSWUScW1cGCZXvWaq7Rm3UVKZXqiWNrYWE7f+ta39Z3v/UCZdFo3/NJ79d73vlvZbHdk5deqevqJB/XYwz9RqTCqIJQy6ZT2HRzh1OQtAAAgAElEQVTSN75zj37uqqt05dvfqXK5orgb0zlLVqi/q1tBGETROEvh8cKHiaNVg0Dfv+tu7d54v27+1CcVdyeidbN52zE4pCcPHVZg2/J9P1omR97MsU3szdxMIM6E4swfNqRiMZmvmhd8E7o7ORxn9jE5PlcPxZlAnF+ryTKRPLNdGET7Wtzbq3Pmz9PCnh7FCMXN5pSzbwQ6SoBgXEdNNyeLQKcK8P5Lp848540AAqcUIBjHhYEAAggggAACCCCAAAIIINDOAgTj2nl2OTcEEEAAAQQQQAABBBBAAIFWFSAY16ozx7gRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEBgsgAfWOZ6aBoBgnFNMxUMBIGGCoShlLc8PfTcI9q7b68GR8dVrXqyrVBdmaR6silZtlQqe/I8T4mEqwvWX67XvfHdymR7NT4+ph/9yw/0V3/9JXV1dyudTqtcKuoTN35cb3v7O9WVzaqQH9dP/uXr2rtnS3RuqWRC3dm0ShVP/+OrtyvVs0Dv/+ivywstBdWK5vXN13lLV0aVtRdTcYrCaslEXDsOHdUt3/22Lpuf1id+6X1ybHvWzTYfPqLtuTFTg5OJupnFRONMJM4s5rHxMY/NOF3L0nkLF2hxNqtitapSraZi1VOlVlPV9xXV5KKbFUX36vsJAl9WKCVdNwrEZZMJ9WUy6u/KKJtKNSSON+uYHAABBJpOgGBc000JA0IAgZkX4P2XmTdljwgg0MICBONaePIYOgIIIIAAAggggAACCCCAwGkFCMadlogVEEAAAQQQQAABBBBAAAEEEGi4AMG4hpNzQAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEJgFAT6wPAuo7HJ6AgTjpufGVgi0vEA8rp9tf0Tb9zyncqUWxcuScVfpVEyyLZVLFRVLFVU8E0kLZFmBenp69XNvuk5LV6zVk088qh1bt+ju+x7Qnn0HlMlk1N2V0VvefKXWv+oSXbj+1dq3Z5t+etctqnpVObajeCwW7T+bTeuu+5/ST+5/Vh/86Ee1aNVaFcZGlUqktHbFaiUTSfl+LYqrWbKUSiRU8QN9/9779MQ9d+qvfvUjOn/5slmfAj8I9NjBwxqq1eS4bhSLM3E4s9TjcdVqNXochd+8qi5askSXrT5XyVjs+PjM90wwzqvVZEJ95maCeJYp8h37l23ZcmxLMddVzHFm/dw4AAIIdLbA6O7dEcDj3/jam839z9962z2dLcLZI4BAGwvw/ksbTy6nhgACZy6w+WaTZn/xtu5dG898Y9ZEAAEEEEAAAQQQQAABBBBAoMUErCUb+Cx6i80Zw0UAAQQQQAABBBBAAAEEEGhfAX5Ib9+55cwQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEOgkAT6w3Emz3eTnSjCuySeI4SEwCwKhLO0pHNFPH/2JDh0dUV9vVgMDvfLKVdWCQNWqicT58kNFITTP8+X5gRTWNG/egFau3qBUJqvFAwv06BNP6Utf+e/y/VAf+dD79dZr3qTx8YLG82Pat+sJjYwckuQo7rpKxF3FYo6SiZhsx9Zttz+sQ8MVffCjv66eBYs1MjykgXkDWtTXryAIlUwklO1KquD5+pf7H9TDd92p33rLG/Sm11wSBe5m+zZULOnZ0VG5ma7jgbhKpaLJiwnGmSUWBHr9+au1bulS2Q0Y22yfO/tHAIH2FrjnC58/4QTfcetts/+k2t6knB0CCDSvAO+/NO/cMDIEEGigAMG4BmJzKAQQQAABBBBAAAEEEEAAgTkXIBg351PAABBAAAEEEEAAAQQQQAABBBA4LsAfJ3IxIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQDsI8IHldpjFNjkHgnFtMpGcBgJTEYgndN+zd2vbrq3au38oisKtv2CF4q4jr2bCcFLND1TzQ/mBufdVrfryfV+uE2rVeRfpgle9Xtnubm189HF98cv/XdWKpw++/316z7t/Xl61oq2bN2rPzmei0JzjOErETDDOkeu6cl1b6VRCufGSbv/XjUr1LNK7rv9lDSxaqnQiqf5MtxzHkhuL61BuXHfe94A2P/Lvuu5Va/SOn7tCruNM5Wynve7OXE4HPF+JdFqe50VhuFKppHK5HN2bcJy5T1vSG9as0aqFC2Tb9rSPx4YIIIBAowQIxjVKmuMggEATCPD+SxNMAkNAAIG5FyAYN/dzwAgQQAABBBBAAAEEEEAAAQQaJ0AwrnHWHAkBBBBAAAEEEEAAAQQQQACB0wkQjDudEN9HAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBVhC4aYqDnOr6U9w9q3eyAMG4Tp59zr1TBY56Y3ro6bt1aHBY+w+NaP/+I1q1cpFetf4ceVVfQVAPxQWq1YIoGud5vryaLz+oqbe7W+tffaWWrLxA3/zmt/TPP7hDsZirV71qvX7j139V5fEj2vz0v2skl5NtO4rHXMXj5t4E4yaicSYIl0zENZ4v6p6fblJJPbrymrfrwvUXa37PfOWLJW3fs1tbNj8rKzeoay44X5euOU8x123ItFV9X5tGcwrTGVmWdTwOVygUVCwWo2icebwondKVa9dooKenIePiIAgggMBMCBCMmwlF9oEAAi0iMNX3U6a6foswMEwEEOh0AYJxnX4FcP4IIIAAAggggAACCCCAQGcJEIzrrPnmbBFAAAEEEEAAAQQQQAABBJpbgGBcc88Po0MAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEGgxAYJxLTZhDBeBsxWwXT2271HtObhdg8PjOjKY0779gypXy3rDFRdpyUC/SqWK/FCq+ceCcX6oaq2mWs2PvqbQ16pzzlXPgjX6+5u/rsOHDiiVTCmeSOiDN7xX2WRRu3Zul+9rIhYXi8mN24o7ttwoGufKsSzFEzF5nqeNT+zQc7vG5Sa71N3Tp7Xr1migv1eZaqBF2YyWz+vVwv7+sz3zKW0/VCrruWJJqa6uU8fixsZ04aIBveaclepOp6e0b1ZGAAEE5lqAYNxczwDHRwABBBBAAAEE5kYgPLAxnJsjc1QEEEAAAQQQQAABBBBAAAEEZl9gy+0bTjjIBR8Tn0mffXaOgAACCCCAAAIIIIAAAggggMArCvDDORcIAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAjMo8KPrr7vH7K5nxcqrzP0l/+EjM7h3doUAAs0m4Lm2HnruXg2OHtForqCh4bwGh3Paf/Co+nq79OYrL1Es5qhcrioIJT8I5fuBvJqJx00E4zy/pp5sWoEyuuW2+xQEgZLJpLxqVW9540VatCCt0Vxetm1HwTizP9e1FXNtOY4jx3w97kphqJ27Dmjn/jHlvbS2b9uusdGj+vh/ulZXX/46DcTOUToRazhhyavpudGcKsmkfN9XsVhUoVBQqVRSLpdTj+vo8nPP1dL+PiVijR9fw0E4IAIItJ0Awbi2m1JOCAEEEEAAAQQQOCMBgnFnxMRKCCCAAAIIIIAAAggggAACLSpAMK5FJ45hI4AAAggggAACCCCAAAIItLUAwbi2nl5ODgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAYK4Eyo9tDOfq2BwXAQQaJzBSzevRHf+u0XxO4/my8mMljY4XdGQop6NHh7Vm9XJddukaeV4tisOFoaIgXBSM8000ztz70dcz6ZQ2bz+ix5/eo2QyrhVLurVu9QL5QSDHMbG4mGKuicVZiplQnOPItq0oIBePx7R77yENDuU0OOaoGiT00/sfUFda+vSN79Wl6y/Siq71Cj2/cTiSakGg50dzOhyE8mo1lcvlKBg3Pj6ueBDooiWLtXrhQmWSCVkWf8LQ0MnhYAggMGMCBONmjJIdIYAAAggggAACLSVAMK6lpovBIoAAAggggAACCCCAAAIITFGAYNwUwVgdAQQQQAABBBBAAAEEEEAAgQYI8NfWDUDmEAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAp0nQDCu8+acM+5Mge0ju/XsridUqVRUKFVVLJr7snK5og4fGdZYPq/LLl2ntauXq1KtKghDBUEo38TifBNR8+X7fhSQMwG4ZDKljU/v19DQqM4/p0fJuCvbcZRKxJVKxZRMxOQ6ttwoFmfLcSwlEnEdHRrVocPDCkJbe48GGhsv675779FrX71av/2frtXiBfN13rxLlLKTDZ2osUpFjx06ojHPU7VUUlirqTvmakV/n1b0z4vOyyYU19A54WAIIDDzAgTjZt6UPSKAAAIIIIAAAq0gQDCuFWaJMSKAAAIIIIAAAggggAACCExXgGDcdOXYDgEEEEAAAQQQQAABBBBAAIHZEyAYN3u27BkBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKCDBQjGdfDkc+odIxBatjYffE67Dm+Lom/ValXlsqdStaZyqaLRXEGHjw7LtqXLLr1QC+d1q1qrSWEo30TjglC1mgnH+dG974eKx11ZTly7dh9UsTCudDqlZCKhVCqudDKmTCahmOvKtq0oGBePOSoUK9q5+4C6uzMaHavp4LCv3bv3avOzm3XtNRv0vnderkw6pfMXXKSMk2no/BSrnnaOjmq8XJYbhko6jga6u9XflZFjYLghgAACbSBAMK4NJpFTQAABBBBAAAEEpiCw+WaFk1df966NU9iaVRFAAAEEEEAAAQQQQAABBBBoLQFryQY+i95aU8ZoEUAAAQQQQAABBBBAAAEE2liAH9LbeHI5NQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgbkTIBg3d/YcGQEEEEAAAQQQaKQAwbhGanMsBBBAAAEEEEBg7gUIxs39HDACBBBAAAEEEEAAAQQQQACBxgkQjGucNUdCAAEEEEAAAQQQQAABBBBA4HQCBONOJ8T3EUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEJiGAMG4aaCxCQIIIIAAAggg0IICBONacNIYMgIIIIAAAgggcBYCBOPOAo9NEUAAAQQQQAABBBBAAAEEWk6AYFzLTRkDRgABBBBAAAEEEEAAAQQQaGMBgnFtPLmcGgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwNwJEIybO3uOjAACCCCAAAIINFKAYFwjtTkWAggggAACCCAw9wIE4+Z+DhgBAggggAACCCCAAAIIIIBA4wQIxjXOmiMhgAACCCCAAAIIIIAAAgggcDoBgnGnE+L7CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCExDgGDcNNDYBAEEEEAAAQQQaEGBJ77+tWjUuT277zX3195629UteBoMGQEEEEAAAQQQQGCKAuGBjeEUN2F1BBBAAAEEEEAAAQQQQAABBFpGYMvtG04Y6wUfE59Jb5nZY6AIIIAAAggggAACCCCAAALtKsAP5+06s5wXAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIDAnAoQjJtTfg6OAAIIIIAAAgg0XCB56Qb+Fqvh6hwQAQQQQAABBBCYOwGCcXNnz5ERQAABBBBAAAEEEEAAAQRmX4Bg3OwbcwQEEEAAAQQQQAABBBBAAAEEpirAHylOVYz1EUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEDgDAYJxZ4DEKggggAACCCCAQBsJEIxro8nkVBBAAAEEEEAAgTMQIBh3BkisggACCCCAAAIIIIAAAggg0LICBONaduoYOAIIIIAAAggggAACCCCAQBsLEIxr48nl1BBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ6CCBm6Z4rlNdf4q7Z3UEJIJxXAUIIIAAAggggEBnCRCM66z55mwR6FCBqb6fMtX1O5SV00YAgVYVIBjXqjPHuBFAAAEEEEAAAQQQQAABBM5EgGDcmSixDgIIIIAAAggggAACCCCAAAKNFSAY11hvjoYAAggggAACCCCAAAIIIIAAAggggAACCCCAAAKzIxBOcbf8jmSKYKw+dQGCcVM3YwsEEEAAAQQQQKCVBQjGtfLsMXYEEDhDAd5/OUMoVkMAgfYW2HyzTng+XPeuje19wpwdAggggAACCCCAAAIIIIBARwtYSzbwd1YdfQVw8ggggAACCCCAAAIIIIAAAs0kwA/pzTQbjAUBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSmK8AHlqcrx3YzLnDH9dddbXb6mg9/5G5z37ty5Ywfgx0igAACCCCAAAIINJ8AwbjmmxNGhAACMy7A+y8zTsoOEUCgFQUIxrXirDFmBBBAAAEEEEAAAQQQQACB6QoQjJuuHNshgAACCCCAAAIIIIAAAgggMPMCBONm3pQ9IoAAAggggAACCCCAAAIIIIAAAggggAACCCCAQOMF+MBy48054ssI3Hn9dSdcj1d/5rNYIYAAAggggAACCHSAAMG4DphkThEBBHj/hWsAAQQQkEQwjssAAQQQQAABBBBAAAEEEECgkwQIxnXSbHOuCCCAAAIIIIAAAggggAACzS5AMK7ZZ4jxIYAAAggggAACCCCAAAIIIIAAAggggAACCCCAwJkI8IHlM1FinYYIEIxrCDMHQQABBBBAAAEEmk6AYFzTTQkDQgCBmRfg/ZeZN2WPCCDQggIE41pw0hgyAggggAACCCCAAAIIIIDAtAUIxk2bjg0RQAABBBBAAAEEEEAAAQQQmHEBgnEzTsoOEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAYA4E+MDyHKBzyFMLEIzjykAAAQQQQAABBDpTgGBcZ847Z41Ahwnw/kuHTTiniwACrywQHtg41edFSBFAAAEEEEAAAQQQQAABBBBoGYEtt284YawXfEx8Jr1lZo+BIoAAAggggAACCCCAAAIItKsAP5y368xyXggggAACCCCAAAIIIIAAAggggAACCCCAAAIIdJbAVD+Yx+9IOuv6aOjZEoxrKDcHQwABBBBAAAEEmkaAYFzTTAUDQQCB2RPg/ZfZs2XPCCDQggIE41pw0hgyAggggAACCCCAAAIIIIDAGQsQjDtjKlZEAAEEEEAAAQQQQAABBBBAoGECfBiqYdQcCAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBGZRgA8szyIuu56aAMG4qXmxNgIIIIAAAggg0C4CBOPaZSY5DwQQeAUB3n/h8kAAAQQmCRCM43JAAAEEEEAAAQQQQAABBBBoZwGCce08u5wbAggggAACCCCAAAIIIIBAqwoQjGvVmWPcCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggMFmADyxzPTSNAMG4ppkKBoIAAggggAACCDRUgGBcQ7k5GAIIzI0A77/MjTtHRQCBJhUgGNekE8OwEEAAAQQQQAABBBBAAAEEZkSAYNyMMLITBBBAAAEEEEAAAQQQQAABBGZUgGDcjHKyMwQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEJgjAT6wPEfwHPalAgTjuCoQQAABBBBAAIHOEhjdvTs64ce/8bU3m/ufv/W2ezpLgLNFAIEOEuD9lw6abE4VAQReXmDzzTrh+XDduzbChQACCCCAAAIIIIAAAggggEDbClhLNvBZ9LadXU4MAQQQQAABBBBAAAEEEECg1QT4Ib3VZozxIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAwKkE+MAy10XTCBCMa5qpYCAIIIAAAggggEBDBO75wudPOM47br2Nv8lqiDwHQQCBORDg/Zc5QOeQCCDQfAIE45pvThgRAggggAACCCCAAAIIIIDA7AkQjJs9W/aMAAIIIIAAAggggAACCCCAwFQF+OPEqYqxPgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCDSjAB9YbsZZ6dAxEYzr0InntBFAAAEEEECgYwUIxnXs1HPiCHSiAO+/dOKsc84IIPASAYJxXBQIIIAAAggggAACCCCAAAKdJEAwrpNmm3NFAAEEEEAAAQQQQAABBBBodgGCcc0+Q4wPAQQQQAABBBBAAAEEEPj/2bsP8CqqrY3jb3ojIQlNeq9SpIkgSBFFRRQUsCGKCpZPvYL12ij2Bl7BAqhYQJQiTZEiRUWkhNCUqvTeIfSQnM8JBhNIcvYk5ySn/Od5eLwX1t6z1m9PJhDJKwIIIIAAAggggAACCCBgItDPpChDjd16m9tT7s8CBMb58+kzOwIIIIAAAgj4owCBcf546syMgN8K2P16it16v4VlcAQQ8C4BAuO867zoFgEEEEAAAQQQQAABBBBAIG8CBMblzY/VCCCAAAIIIIAAAggggAACCLhSgMA4V2qyFwIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAgN8LEBjn948AAAgggAACCCDgZwIExvnZgTMuAggggAACCJwvUE/Scn9kcexIcPjj3MyMAAIIIIAAAggggAACCCDgHwJrvm+UadCaPcX3pPvH0TMlAggggAACCCCAAAIIIICABwvwh3MPPhxaQwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8D6B6Z07zrW6LlyufEvrn5d0u9P7hqBjBBBAAAEEEEAAAWMBAuOMqShEAAEEEEAAAd8U6CTpJUkDJI2V5DchagTG+eYDzVQIIIAAAggggAACnivgcDi0a+8Bbd62U+VKlVDRIrEKDgpSYGCg5zadVWdnUqTgIO/qmW79UoDAOL88doZGAAEEEEAAAQQQQAABBBDwcAEC4zz8gGgPAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDAOwVOJib4zTfHeucJ0TUCCCCAAAIIIOAaAQLjXOPILggggAACCCDgtQLW30f/XVItSX9I6i9pnD8ExxEY57XPLI0jgAACCCCAAAIIeKnAtp17tHTbMS1YtU1b163URdHBurp5A9W/uKqiC0UqNCTESyejbQQ8U4DAOM88F7pCAAEEEEAAAQQQQAABBBDwbwEC4/z7/JkeAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDATQIExrkJlm0RQAABBBBAAAEPEyAwzsMOhHYQQAABBBBAoCAEbpI0PsONrQC5Ab4eHEdgXEE8atwTAQQQQAABBBBAwJ8FVqzbomJVGupk8XJa/vtOrV2+TMt+naHw03t1RcPqatO0vkoUK6LwsFB/ZmJ2BFwmQGCcyyjZCAEEEEAAAQQQQAABBBBAAAGXCRAY5zJKNkIAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDgXwEC43gaEEAAAQQQQAAB/xAgMM4/zpkpEUAAAQQQQCBHAevvpFshcbXOq7J+rv8/YXIOXzFcPVyZZqnRPsFXRmMOBBBAAAEEEEAAAQQ8WuD4iZNasOGQAsvUV2hkvE4cO6Gjx45px9btWr5wjo7tWK2W9SupXYuGKlmiuIKDgzx6HppDwFsEAko14nvRveWw6BMBBBBAAAEEEEAAAQQQQMDnBfhDus8fMQMigAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggUhACBcQWhzj0RQAABBBBAAIH8FyAwLv/NuSMCCCCAAAIIeKTAzZLGZdPZyn+C476VMoeteeQkTpoiMM4bT42eEUAAAQQQQAABBHxF4NTp0/px2Q6dia+m4LAIJSefUkhIhJIdgdq9e5+Wzp8lHViv9i1q64pL66twTCFfGZ05ECgwAQLjCoyeGyOAAAIIIIAAAggggAACCCBwgQCBcTwUCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCLhBgMA4N6CyJQIIIIAAAggg4IECBMZ54KHQEgIIIIAAAggUhID199J/l1Qrh5v7RHAcgXEF8XhxTwQQQAABBBBAAAEE/hU4euy4ZizeqDNxVRUSWUgnjx9VUECAQiIKKTU4Utu2bNOynyerUfkw3XJ9GxWNj4UPAQTyIEBgXB7wWIoAAggggAACCCCAAAIIIICAiwUIjHMxKNshgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggYAkQGMdzgAACCCCAAAII+IcAgXH+cc5MiQACCCCAAAJGAl3+DowbY1C5QlJ/SRMkOQzqPaqEwDiPOg6a8QCBEydPaeOW7UadhIeFqVL50ka1+VnkCzPkpxf3MhM4dfq0/tq0zai4fJmSioqMMKqlCAEEEHCXwPZde3T4yFGn20dGhKtC2VJO69xdcODQEc1cvEGno8srIiZWp06cUOqZZIWEBCu8cHEdOx2oJT9NUeWIg7rvtvYKCw11d0vsj4DPChAY57NHy2AIIIAAAggggAACCCCAAAJeKEBgnBceGi0jgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgh4vgCBcZ5/RnSIAAIIIIAAAgi4QmDZyC/Ttjm8ZfNP1j/bjZvYyhX7sgcCCCCAAAIIIOCFAtbfTf/979C4Woa9L/8nOG6iNwbHOXYkeF3YneG5UIaALYHPvpmiHr2tDEizK3nLQgUHB5kV51OVL8yQT1TcxobA2Ck/quv9zxitGNS/jx7rebtRLUUIIICAuwQqN71RGzabhcA6diS4qw1b++7as1+zErcoObKUIgrH6tSpU1JqStofL6IKF1VqcJQS507ULc3K6ZKLq9ram2IEEJDWfN8oE0PNnuJ70nkwEEAAAQQQQAABBBBAAAEEEChgAf5wXsAHwO0RQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQR8U4DAON88V6ZCAAEEEEAAAQSyEwhv0Ii/i8XjgQACCCCAAAIISF0lfWMTwiuD4wiMs3nKlBsLrP1rs44eO25UHxAQoAZ1ahjVOiv6c9NWHT5y1FlZ2q8XiupQ2yAAACAASURBVIpU9crl0/73iG8m657eA4zWWUWeGBjnCzMYHwCF+SbwzeQZuvWBZ43uN7BfH/XuRWCcERZFCCDgNgFPCYxbsmK1wsPCdHH1SkazHkk6plmL1mr3qWjFlCipM8ln5HA45EhNUWRMrKRA7Vo2Vbddd7mKFYkz2pMiBBA4K0BgHE8CAggggAACCCCAAAIIIIAAAp4nwF9S9LwzoSMEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEfECAwzgcOkREQQAABBBBAAAEbAgTG2cCiFAEEEEAAAQR8WcD6++m/S6qViyGXSeovaZIkRy7W5+sSAuPylduvblai7tXas++A8cyrfhqrmlUrGtdnVWiFqpRr1F7bdu4x2qd40XjtXjEjrdYXwtZ8YQajg6MoXwUIjMtXbm6GAAIuECjowLjU1FT9vn6rZizZpD9/X6z7brlWjepdbDTZ/oOHNXrmCpWs1VRHk5LSAuPS/kgREKDY2Dgt+2mSOrWopXoXVzPajyIEEDgrQGAcTwICCCCAAAIIIIAAAggggAACnidAYJznnQkdIYAAAggggAACCCCAAAIIIIAAAggggAACCCCAgH2BfjaX2K23uT3lCEgExvEUIIAAAggggAAC/iVAYJx/nTfTIuCnAna/nmK33k9ZGRsBnxTo8ndg3Jg8TGYFx1nvECs4zmMvAuM89mi8vrEHn3lNH30x3niO1597RE//313G9VkVJq5co4btuhnv8dDdXfT+q0+n1ftC2JorZ/hj7Qa16fKAkeUTD3TTkw91N6qlyPsECIzzvjOj44IR4L1ZMO5Z3bWgA+OSz6RoyaYjKlmpjt4YPFQnNi7S/93TVY3qOg+Ns8LmRk9fqMJVW2jv7l3nxrN+vlBUIe3esFwNK8aoWYNaCgjgW2o956mjE08XIDDO00+I/hBAAAEEEEAAAQQQQAABBPxRgK9u+eOpMzMCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAr4nYP3noe1c/DsSO1rU5kqAwLhcsbEIAQQQQAABBBDwWgEC47z26GgcAQTMBfj6i7kVlQj4u4D19dd1kqrkEWLp3+ut4LjJedzHpctXD1em92GN9gku3Z/NEJg9b7Gu7PqgMUSDOjW0ZPpI4/qsCvu9M0z93xlmvMfc8cPUsmmDtHpXhq0ZN+DiQlfO8Ovi5Wp+471GHfbudbsG9utjVEuR9wkQGOd9Z0bHBSPAe7Ng3LO6a0EHxp1JSdFPf2xX7bad9MviDZo95lOd3LFMD3bvosb1a+cIlZrq0Nezlim+WjNt27RRgYGBafUOR4pCQsIVdPqwIk7tULvm9RUVGeE56HSCgJcIBJRqxN+z8pKzok0EEEAAAQQQQAABBBBAAAHfF+AP6b5/xkyIAAIIIIAAAggggAACCCCAAAIIIIAAAggggIA/CPANy/5wyl4y4w+dO7ayWq1/x51zrH/Gli/vJZ3TJgIIIIAAAggggEBeBAiMy4seaxFAwEsE+PqLlxwUbSLgIQK3Shrtol4SJfX3lOA4AuNcdKpsk63AmTMpiq/VWklHjxsrbVgwWRXLlTKuP7+wevObtG7DFqP1xYvGa8fSaQoKOhvE4sqwNaMG3FDkyhkIPnLDAXnplgTGeenB0Xa+C/DezHfybG9Y0IFxDodDC3/foNOVWunUyRTt3rlbv00bq+Pbl+uBbp3VpGGdbHv/c+M2rT4WozOBUdq3e5cCg4KstLi0+sCgEIWkntCpXat1w5WNVaxInOeg0wkCXiJAYJyXHBRtIoAAAggggAACCCCAAAII+IUAgXF+ccwMiQACCCCAAAIIIIAAAggggAACCCCAAAIIIICAzwvwDcs+f8TeM+C0zh0zPY+tnn3ee5qnUwQQQAABBBBAAIFcCxAYl2s6FiKAgPcI8PUX7zkrOkXAEwSsJKm1kqq4sBkrOK6fpCku3NP2VgTG2SZjQS4EevcdqHeHf2W88r2Xn9Qj99xiXJ+xcNW6Dbq4VVfjtX3uv0Pv9O19rt6VYWvGTbi40JUzEHzk4sPx4u0IjPPiw6P1fBXgvZmv3DnerKAD46zmdu49pJnrT+qiShcr6dBhHTtxWovnTNaxrcs04MkHVaZUiQtm2LZzj779+XfVadlJG9etkyM1Rf9+AcOhwMAgBaee1KEtK9S5XVOVKlHMc9DpBAEvESAwzksOijYRQAABBBBAAAEEEEAAAQT8QoDAOL84ZoZEAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMDnBfiGZZ8/Yu8ZkMA47zkrOkUAAQQQQAABBFwpQGCcKzXZCwEEPFSAr7946MHQFgIeLHCbJPPEK/NBlvxdagXHfWe+xHWVBMa5zpKdsheYn7BCl99wjzHR5Y3rad6kT4zrMxa+PuQz/ffVIcZrf5syQpc1rHOu3pVha8ZNuLjQlTMQfOTiw/Hi7QiM8+LDo/V8FeC9ma/cOd7MEwLjTpw6rYk/rVBczVY6sG+vAoNCdOpMgBJ//l6Fjm/Wg3d3UemSZ0Pjks+c0bxFyzR32UY1v7G79u/er6OHDyggMFAOhxRgxcY5HAoMClLKiSPa9ecSde/YWuXLlPIcdDpBwEsECIzzkoOiTQQQQAABBBBAAAEEEEAAAb8QIDDOL46ZIRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8HkBvmHZ54/YewYkMM57zopOEUAAAQQQQAABVwoQGOdKTfZCAAEPFeDrLx56MLSFgAcLBEpaK6mKm3pMkNS/oILjHDsS7L4X3cTAtr4okJqaqpKXXKM9+w4Yj7dj6TSVLFHUuD69sGG7bkpcucZoXZmSxbUl4XsFBPz7rSjbdu7Rjz8vNFofERGmW2642qg2P4tcOQPBR/l5cp59LwLjPPt86M5zBHhves5ZeEJgnPV7oJXrtypxb7gKFS6io0cOKjwyWsmOEM0cP0LJBzerXOkSaYFwuw8mqWjlOrr6xq46cuCwDuzaoaDgoLSQOOtyOByyfscSGBSsIzs3at/WVep1W3uVL0tgnOc8dXTi6QJrvm+UqcWaPdM+rLgQQAABBBBAAAEEEEAAAQQQQKAABfjDeQHic2sEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBwmYDdb8zj35G4jJ6NzhcgMI5nAgEEEEAAAQQQ8E8BAuP889yZGgE/E+DrL3524IyLgIsEbpc0ykV7ZbeNFRzXT9L3br5Ppu0JjMtPbf+81zOvDNYb739uPPzwt5/Xfbd3NK63Cjds3i4rHMb0evbRe/TKMw+ZlvtlHcFHfnnsWQ5NYBzPAgJmArw3zZzyo8oTAuOsOQ8nHdPomctUstbl2r9vrxypyQqLKKSjKWHa8OdaHTu0TzHxRVSlWg2VKnWRDu3apsP79ykwMMNfA3E4ZH0RIzAgQI7UVG1euVDJx/bokXu6qmzpEvnByT0Q8AkBAuN84hgZAgEEEEAAAQQQQAABBBBAwMcE+GYoHztQxkEAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAwE8F+IZlPz14TxybwDhPPBV6QgABBBBAAAEE3C9AYJz7jbkDAggUuABffynwI6ABBLxSIFDSWklV8qH7xX/fwwqOm5oP9xKBcfmh7N/3WLJitRpdc6cxQrtWTTXtq8HG9Vbh/z4ercdefMd4TeKMUapfu7pxvT8WEnzkj6ee9cwExvEsIGAmwHvTzCk/qjwlMO7MmRQtWvmX1h2KVEh0YR0/ekQBjlSFRcUqtFC8goJCFBQspZw6pkN7durMqZMKDArKQOSQw+GQlRgXHBKsA7u2a93SXxQXHaqH77lV5QiMy4/HiXv4iACBcT5ykIyBAAIIIIAAAggggAACCCDgUwIExvnUcTIMAggggAACCCCAAAIIIIAAAggggAACCCCAAAJ+K8A3LPvt0Xve4ATGed6Z0BECCCCAAAIIIJAfAgTG5Ycy90AAgQIW4OsvBXwA3B4BLxa44+/AuJH52H+CpP6SvnPnPQmMc6cue1sCVtBJlWYdtWHzdmOQ/atmKz42xri++Y33ygrqMbmqVSqntfO+NSl1a83+g4e1edtOHTqcpMNJR5V09LiiIiNU+qJiuqh4EV1UvKjCw0Ld2kNOmxN8VGD0tm586EiSVq3bqAMHD2vfgUM6cOiIjp84qcIxhRRXOFpxhWNUvkxJ1apWUYGBVvap/csVgXFWaNKmbTu0a89+7dy9T6dOn1bxovFpz3rl8mXSnv2CuCyrTVt3aOeefWm9BQUF6aJiRVSyRFFVqVBWQUG5M8tultPJyWnnlPZj/yHt2X9QR5KOps0fH1v47JnFxqT9M7ZwtEJDQtzOYhlY7+dde/dr1559Cg8LU6XypVWxXKm056egr/x4xl01oye9N7ft3KM/N26V9bnmwKHDaf+0Ph8XiSusInGxaZ9jrXO23g/5deVnT54SGGfZHjiUpK+nJ6hYlcZKOpakM2fOSKmpSvtm2EApNSVFqSmpCgwMkgKsn7W+ZBFg/QZKDkdq2v8OCgnWqaQjSpg3S+FByWrSpL7qVS2nejUru/w9lV/PA/dBIL8FCIzLb3HuhwACCCCAAAIIIIAAAggggIBzAQLjnBtRgQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAp4vwDcse/4Z+U2HBMb5zVEzKAIIIIAAAgggkCZwaPPmtH8uHfVla+uf146bOBcaBBBAwEcF+PqLjx4sYyGQDwJWaszav0PjquTDvTLeYtHf/6efpB9ced/Vw9PSKM5dNdpb+XRcCLhHoP87w9Tv7x+m15eDB6jbzdcZle/YvVel619rVGsVDXjyAb3Q+74L6q0AqyGfjtGZlBSne9WuXlnt2zZ3Wpex4MTJU5o84ydNm/Obfl6QaBSgF10oUlUrllOtapV01RVNdGWLxip9UfFs75uXGY4dP6EPPhurVMfZV8Pva/7SyPFTjWa0+uvepb3T2uvbttDF1Ss5rXNWMGn6T1rz5yZnZWm/3qheTV3Z/FKj2mEjv9XBw0k51lrBXg/36Gq038Cho5RsBQM5uQICAvTovbcaBwQuXrZK3/34i2b8tEALlqx0tn3ar1vP0jWtm+nGdi3VtcNVCgkJNlpnFeUlMO6PtRv05bjvNeKbKdqz70C297Q+3u++5Xrjs7I2+mXhUs1PWOF0Dsu3V7dOio2JPlf74y+L0p7vz8dkn8lqBdr1uKWD7rujY1p4nJ3r4OEjSly5Rokr1mjZH+vSfmzdsSstGNLO1fiSWmrXqqlaNm2opg3ruCxYzwrwm/DDHI37fpbGTJ6ZbUvWc9Owbk3df+fN6tz+SgUHB2nQsK9kBd85uy5rUEctmzZwVpblr+f3M56rJiV50nszNTVV0+cu0NRZ8zR19q9Gn2Osua3QuOvaXJ72fri2TbNcB0tmZViQPXlSYJwV1Lfk9z/127rDKlauqo4cTZJSUqw8uLRQOCsg7p+IuLO/Of/n59IC4yQFBQfrzMkTWrV4vlYuXaSrrr9e7TvfpjXLFqlhxRhVq1gmt48w6xDwS4GAUo34XnS/PHmGRgABBBBAAAEEEEAAAQQQ8EQB/pDuiadCTwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIGBXgG9YtitGvdsECIxzGy0bI4AAAggggAACHikw99WXM/V1zbiJ/J0sjzwpmkIAARcI8PUXFyCyBQJ+LHDn39kaXxTQ/Av/vq8VHDfNFfcnMM4ViuxhKmCFj9Vpc4tpuTpd21rffvKWUf3wURPU68lXjGqtolU/jVXNqhUvqN++a4/KNDALqetw1RWa/PlAo3vu3X9Qrw/5TFafdsOisrpBiyb19dDdXdTp2lYKCw3NVJKXGVat26CLW5kFoRkNnkXRey8/qUfuMX8OsrvPdd0e1Q+z5xu1YYVtTftqsNPafQcOqVjttk7rrIJtiVNzDO6zajZv26kKl3Yw2s8q2r70B5UqUSzHeisczgpenD73N+N9syosU7K4+j/5gO7uer1ROFRuAuOsQLFX3xshKyzSznVn5+v0v5eeUFzhGKfLHvrv6/rw83FO66yCxT98oUb1aqWF1j3w9GtpYWl2riGvPKUH7+ps5GV9vP/31SF2tjeqtcLb+vbpldZHZES40ZqsiqywvP+88Lasj3k7l/Xc3HHTtXrj/c+NlvW8o5OGvfWcUW16UUE947aazFDsCe9NK5Ttux/n6YU3PtSK1etzO0raOiv88+WnH0wLlgwMtHKSc3d5Qk+eFBhnKVrhgt/PXaKDKXGKKlJCx08cV2pKqhyOVAWkpcWd/TKFFS53NjBOssIuA4OClHz8mFYnLtQfyxJUunwFXX7NDSpdtZ5i44tq+7IZaluvtIoVjcvdYbEKAT8UIDDODw+dkRFAAAEEEEAAAQQQQAABBDxWgL+c6LFHQ2MIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBgQ4BvWLaBRal7BQiMc68vuyOAAAIIIIAAAp4mQGCcp50I/SCAgBsF+PqLG3HZGgE/ELDSM9ZKqlKAs1rBcX0lTc9LDwTG5UWPtbkRsMLI7AQUHVn3s6yAJGfXNbc/YhygVbdmVS2fNTrLLfMStpbVhlboy9Avv9VTL//PJUFx599j7vhhatm0QaafzssMnhB85Oys03/9/RFj9PBzb5qWK3nLQgUHB+VYbycU7cvBA9Tt5pzDBcdO+VFd73/GqMecnktrg+MnTqrnEy/rqwkuyQs915MVzPjFe/1VKCrnjzM7NgP79VHbFpeq28Mv5Dq8qlL50vptyggVLxqfo5/dwLitO3brrv/0zfXH411dr9eIQX3TQpxyup56+T299YH7smUtl8//10/XtG5m9HylF6WkpKpPv4F675Ovba3LbbGdwLiCfsZzO2NBvzf3Hzyc9p6ZPW9xbkfIcp0VTDr+4zdVrIj9EDJP6cnTAuMsaCtAdsqsJToWEKfCJUrpdEqqUlKS04Lj5EhV2hcqHA5Zr5iAgCAFBkhHD+7X6qWLtP6PlSpVtqzqXHq5Cl9URhHRcSpVqaaSDh9R5P6latOossLDMofIuvShYDMEfEiAwDgfOkxGQQABBBBAAAEEEEAAAQQQ8HoBAuO8/ggZAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBP4W6GdTwW69ze0p92cBAuP8+fSZHQEEEEAAAQT8UYDAOH88dWZGwG8F7H49xW6938IyOAJ+JNBd0uceMO+Cf76mnKvgOALjPOAE/ayF14d8pv++OsR4aius5qbr2uRYbwXTFL34SuM933z+UT35kPUhfOGVl7C183ezgpkeef5Nffj5OOPe7Bb6c2Dc+o1bVO3ym4zJEmeMUv3a1XOsv/uxfvp8zHdGe97Z+Tp98d6AHGutYK5Bw74y2q/f473U9/FeWdZaIWcdezyuxJVrjPayW2SF1c0ZP1TxsTHZLrUTGGf3/tnVN76klqxnPDIiPNst7QTGlSlZXNt27slzey/26an+T9yf4z7uDoxLv/mYoa+rS4e2RjOdTk5Wj8f6uzx0MKebmwbGecIzboSYRVFBBsZZ9+5wV29t2Lw9t+3nuM76mPlh1GDVrlHZeH9P6skTA+MsyN17D2jWvGXalRSk4EKxioqJkQICZYXMpjqs4DgpQA4lnzihvTu2avWKRO3YtFGlS5dVjfqNFF2shELCIxUaGaXo2KK6qHId7di0RuVSN+mKhtWMz4pCBPxZgMA4fz59ZkcAAQQQQAABBBBAAAEEEPA0AQLjPO1E6AcBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMCrBQiM8+rjo3kEEEAAAQQQQMC2AIFxtslYgAACCCCAAAL+KxAoaa2kKh5C8NvffVjhljNy049jR4IjN+tYg4BdAbshX7d3ukaj3n85x9uMHD9Vdz7yonErf/02SZXKl86y3pWBcfc9/pI+GT3JuK/cFPpzYJzlZScM6IPXntGDd3XOltkK+Iur2UpJR48bHUV0oUgdWjNXgYHWp4Osr4btuhmHvP02ZYQua1jngo22bN+lxtd21559B4z6ym3Rje1aasKnbysgIOtvzSqIwDhrlsd63q5B/ftkO5adwLjc2mS1buY3H6hti0uz3TK/AuOsBlbM+lp1ajr/7Uh+9pQOYxIY5ynPeG6fj4IKjPtr0zbVv/p243dWbuez3nVLpo9U1YrlnG7haT3Z+Rzh2JHgdD5XF6z4Y702bNunTTsOaPf+JKU6AhUUFKqUM8k6fOigtm3fop17dytEAaoaX1RlK1dVWIkSCo0spLCoKIWGhys4LErRcVZoXG3t371d0fsX69rml7i6VfZDwGcE1nzfKNMsNXuK70n3mdNlEAQQQAABBBBAAAEEEEAAAW8V4A/n3npy9I0AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIOCRAtM6d/wxQAqOKVv2ioCAwIBLut3pkX3SFAIIIIAAAggggEDeBc6cPKl5A9/OuNH+a8ZNLJr3ndkBAQQQQAABBBDwWYG7JH3mYdNZwXF9Jc200xeBcXa0qM2rgJ0QLeteJzbOV3hYaLa37djjcU2a/pNRW40vqaVFU7/IttZVgXHjvpulLr2eNuopL0X+Hhj3eP9BGjh0lBGhs/DBxctW6dLruhvtlV60fNZo1a1ZNcs1R48dV3TVK4z2swKZDqyao+DgoEz1J0+d1hWd7pPVW35cQ155Sv/Xo2uWtyqowDirmXW/fpttWFVBBcZZ5544Y5SCgrIODMzPcLbLG9fTzxOG5xheOGveIrXt+lB+PEaZ7uEsMM6TnvHc4hREYNyJk6d0Wfu7tWL1+ty2bWtdrWqVtPiHLxQZEZ7tOk/sydMD4yzM08nJ2r33gPbtP6z9B5J0KvmMAgMD5Eg9o+MnT+rwseNauXqjNqzeomplyiqiSFGFFI5VaHS0QsLDFRoWodCIaBUudpGKV6ipvdvWqeiRlbry8vq2zphiBPxFgMA4fzlp5kQAAQQQQAABBBBAAAEEEPAmAQLjvOm06BUBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMBrBE4lLjngkCPOaxqmUQQQQAABBBBAAAHbAicPH9aC9wdnXLflmnETy9veiAUIIICA7wjcL+kjG+MMk2StMb3YP2cpfPDJKODtH1+m7wVX1lnBcf0kzTDZlMA4EyVqXCXw7vCv1LvvQOPtpo58T9e2aZZlfdLR44qpZhbKZW0w+JWn9HA2gVjWr7siMM4KzSlR9ypZvbn78vfAuB9/WaSrbjELwSpeNF67V2T/Snz53U/0wpsf2jqyQf376LGet2e55ucFiWp5Uy+j/e7qer0+e9d6ZWe+chOGVqZkcVWpWFZbtu/Shs3bje6fXmQF1+1cNl1RkREXrCvIwLhbb7xaoz98NctZcmNkCyWH4i8HD1C3m6/LsiI/A+OsBn6d/KmaNaqbZS+nTp9WuUbXa8++A64a3XgfZ4FxuTk/dz3jxkOdV1gQgXG5ccvtfOnr3HGW7u7JGwLjMho4HA6lpKSmBcYFBp4No0xOPqPd+w7ohzkLNWfuYpWKKarIuKIKjotTSKQVFheh0PBIhUVFK7Z4aRUrV107/lypcinr1KIJoXF5fcZY73sCBMb53pkyEQIIIIAAAggggAACCCCAgPcLEBjn/WfIBAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAh4ocDIxYYOkih7YGi0hgAACCCCAAAIIuEjg2N69Wjx8aIbdAv64ZtyE2i7anm0QQAABbxQgsCznU8MHn4wC3h7o5u7+C/IdaAXHDZA0LacmCIwryCPyv3tbQVblG19vPHhOITXjv5+tzj2fMt5rW+JUlb6oeLb1rgiMG/fdLHXp9bRxT1Zh40tq6bIGddSgTg0VKxKXFg6zdfsu/bZkpabPtT6Ms778PTDOCueLrHS5sfXGhZNVoWypLOsvva67Fi9bZbyXVdiuVVNN+ypT8Pq59W+8/7meeSXrXzv/JmOGvq4uHdpm+unElWvUsF03436efKi7nn20h2Jjos+tOXb8hAYMHK43P/jCeJ+hbz6rXt1uuqC+IAPjrGYOrpmTabb0BgsiOCv93q2aNdSccRm/jvIvW06BcVYwX8O6NVW7emVVKl9Gqamp2n/wsPYdOKRZ8xbZDvqz7vrMw3frtWcfzvKcvxw3Vd0ffdH4GXBlYU7vb097xnM7d34Hxv25aauqNutku932bZvr4mqVlJKaqhWr1mvmzwtt77H653GqUaXCBes8sSerSW8LjMvpQI6fOKlfFi3XuIlzFKVwFS5WXAHRMQotFKWwiAiFhEUqPDpa8ReVV1ypytq1PlFVgreqScOsgyRtHz4LEPARAQLjfOQgGQMBBBBAAAEEEEAAAQQQQMCnBAiM86njZBgEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAFPETiRmLAwQLrUU/qhDwQQQAABBBBAAAHXCxzctFHLvxqVceOF14ybeJnr78SOCCCAgNcIEIiW81Hhg09GAXcHrnn7/p7w4vtVUl9JszI2s3q4HBn/f432CZ7QKz34gUDzG+/Vr4uXG01qhSsdWDVHwcFBF9Tf8X/P66sJOeYhnluTU7hTepErAuPs9FS3ZlVN+PRtVSpfOlsLK0Rqysyf9faHI2UFE2W8XB0YZwVXWaFpDsfZV4N1Rk8MeNfonK5t00wv9u7ptLZG1QpZBn85XZhNQccej2vS9J+Mln/90au65YarL6jds++AStS98OdNNj2+4VdFhIddUHrDXX3Szs3k2r9qtuJjYzKVdr3/GY2d8qPJcv08YbhaNKmfbe3Xk2botgefNdqrVrVK+mPumAtqcxMYV6ZkcfW49QbVrFpRpS8qJisscunvazV81AQlHT1u1E960ZeDB6jbzdddwMy7PQAAIABJREFUsCY3gXGXNayjG9u1TAu+KhxdSGv+3KTflqyQFaxm99q+9AeVKlHsgmXnB8ZZYZCWRadrW+UYWmltZH2cDxw6Sp+MnmTcjrX/kukjL6i3PpZrt77lgneHs42rVSqn/z7SQ3VqVkk7P+sZP3DoiBYt/UMjx081fu/mFBjnac+4M5Psfj2/35t2n3nrY3rSZ++oSoWymUawPh47dO+tFavXG49+72036uN3Xsjzx2F+9GQ16UuBcdY8Z86kaNnqP/XF6KkKPhmoqNh4BcYWVmhklEIiIhQaEanI6FgVKV1BMcXLa/fahaode0SXXFzN+IwpRMBfBAJKNeJ70f3lsJkTAQQQQAABBBBAAAEEEEDA4wX4Q7rHHxENIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIeKPAyaUJ38oh+/+5em8clp4RQAABBBBAAAE/FdixNFHrfsjwzdEBmnjN2In8HtBPnwfGRgCBNAEC0XJ+EPDBJ6OAtwe6ubt/T3qtzpPUX1JaChGBcZ50NP7Vy0dfjNeDz7xmPPTssR+p9eWNMtWfOHlKkZUuN95j2FvPyQovyulyRWDcpdd1TwtdM7nGf/ymbrqujUmpUlJSNXriND3e/11ZAWfW5erAuPMbsQLjrHA/k6t3r9s1sF8fk1KX1nz81UT1fOJloz0fvfdW/e+lJy6otROodv7iWWM+VJvmjTP9tBUgFVujlVEo2uWN62nepE8yrV+9fqNqtexiNFO/x3up7+O9nNbaCWnctXyGShSLz7SnncA4KyhuwFMP6I6brlVoSMgFvf25aavad/uP1m3Y4rTv9IIuHdpqzNDXL6i3E55lWfd74n61bZH1fx/JCvizgv7sXMPffl733d7xgiWjvv1B1o/LGtTRLTdereqVy9vZNi208fH+gzRo2FdG64oXjdfuFTMuqE1YvkqNr+1utEd60a03Xq1hbz0vK6wzq8sK/Wtw9R1Ge2YXGOeJz7jRQAZF7nxv2g23tIIkp478nwpFZX2WVnDjDXf31tz5SwwmO1tyfkiiJ/aUPoyvBcZZc1mfX9Zu2Kp3P/haYWeCFB1fRMGFYxUcGanQyEiFRRZKC40rVq6yooqU0p51i1S/6EldXL2S8RlTiIA/CBAY5w+nzIwIIIAAAggggAACCCCAAALeIkBgnLecFH0igAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgh4lcDJpUsGyuHo7VVN0ywCCCCAAAIIIICALYENc2Zry2/zM65585pxE5+2tQnFCCCAgG8JEIiW83nig09GAXcHrnn7/p74dvzl76b6rR6uWRmbq9E+wRN7pScfFNi1Z79KXtLOeLKsgr6+/3Geru/+mPEeVpiSFaqU0+WKwLgSda8+F+jmrLknH+quN59/1FlZpl8/knRMz73+vmb8tEDjP35LtWtUzvTrrpghfUN3Bh/ZGjqH4k1bd6hikxuMtqtbs6qWzxp9QW33R1/Ul+MyBKgb7Xa26NlH79ErzzyUaYWdMCzr/K3nIOP18ruf6IU3P3TahfU8b1o0RRHhYU5r7YShTfl8kK6/qkWmPe0ExlnBgVaAYE7XgUNHVKSWWViitY8VQrd1yYVnZCcwbvEPX6hRvVo59jVtznxde4f5x+QD3W/Wh6//16l/bgqsMK+YalcYL03eslDBwUGZ6t8d/pV69x1ovMfdt3TQiEF9c6x3RWCcJz7jxkhOCt353hw75Ud1vf8Z41Z/nzPGaVDY/IQVuvyGe4z3/HLwAHW7+bpz9Z7YU3pzvhgYZ81mBUomrFitEV9NlZJSFFu0mAJjYhRSqJDCIiMUElFIhWLjVKJsVUXGl9T+9QtVr9hp1apW0ficKUTA1wUIjPP1E2Y+BBBAAAEEEEAAAQQQQAABbxIgMM6bToteEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEvEbgZGLCg5I+8JqGaRQBBBBAAAEEEEDAtsAf347X3jWrz61zBOi+a8dO/MT2RixAAAEEfEeAQLSczxIffDIKeHugm7v799g3Y6Nq0sMdpCY1zrZIYJzHHpVPNnb1rf+nmT8vNJrNCsbauWyaAgMDz9Xf9/hL+mT0JKP117Zppqkj33Na64qwterNb9K6DVuc3iu94PXnHlHPOzopPjbGeE1Oha6YIX1/dwYfuWTYfza5uFVXrVq3wWjLg2vmKDYm+lztmTMpiq/VWlY4V26urELoPvtminr07m+0nRVgZ+2R8Wrd+X7Nnb/E6fquN1ylbz56zWmdVbBxyw5VuswsWO+F3vdpwJMPZNrX1YFx1uaPPPemhowYY9S/VXRozVwVjimUqd7VgXFWEFOVZh21YfN2o74aX1JLi6Z+YVRrWmT1cDjpqPYfOKxmN9xjHECZtP5nFYqKzHSbjj0e16TpP5neWn/9NkmVypfOsd4VgXGe+IwbIzkpdOd7087HTLtWTTXtq8FGYzVs102JK9cY1Vqfr4a99dy5Wk/sKb05Xw2Ms+az3hOr/9yk4V9O0bE9RxUXX1QhhWMVEh2l0IhIhUZGqVBsvC4qX1URcSW1d90i1S1+WnWqVzI6Z4oQ8HUBAuN8/YSZDwEEEEAAAQQQQAABBBBAwJsECIzzptOiVwQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAa8ROJG4qGWAAud6TcM0igACCCCAAAIIIGBb4PDWLTq4ebN2LE2cdzopqawjIKDbtWMnzLO9EQsQQAAB3xEgEC3ns8QHn4wC7g5c8/b9PfnNOF3Sm44dCbM8uUl6800BO6FalsBvU0bosoZ10jCSk8+oyMVtjEO+vnhvgO7sfJ1TSFeErd1wVx9Nmfmz03udX9CiSX1ZwXYN69ZU/drVVaxInO09rAWumCH9xu4MPsrVcNkseva19/Xa4BFGW8785gO1bXHpudqFib/rsuvvNlqbXdHuFTNkhRqmX72efEXDR01wuqe1Ztfy6QoI+PfboY6fOKmoys2drrUKrECop//vLqPa5DNn1O62h41q773tRn38zguZat0RGGcneMxqZsF3n6lJg9qZ+nJ1YJy1+cCho/R4/0FGVlbRma2LFBT0b5ilycI9+w5oxeo/tfbPTdqyfZc2bdupjVu2a/O2XcYBceffJ6vAuJhqVxi/J++59QZ9MvBFp+3bObfzw8WszT31GXc6uGGBO9+bdgJJrbO0ztTksvP52AoUtIIF0y9P7Cm9N18OjEufcdvOPRr8yXjt33pA8bHxCo4trJDo6H9C4yLTQuNKVqiuQsXKavfaRWpVOUSlLypq8lhQg4BPCqz5vlGmuWr2FN+T7pMnzVAIIIAAAggggAACCCCAAALeJMAfzr3ptOgVAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDAawQcCQmFTwXqkNc0TKMIIIAAAggggAACuRYIS1VsQKNGh3O9AQsRQAAB3xEgEC3ns8QHn4wC3h7o5u7+PfHNaAXF9bPyd6zmHDsSHJ7YJD35tsCBQ0dUpFYb4yGfefhuvfbs2cCrOb8mqE2XB4zXHlg9W3GFY5zWuyJs7YkB7+qdj0Y6vZezAiuU55rWzdSqaUNdcVkDlSj2byBZTmtdMUP6/u4MPnI2v51f/3lBolre1MtoSf8n7teLfXqeq31p0Md68a2PjNZmVzRm6Ovq0qHtuV82DVB66O4uev/VpzNtu2DJSjXt0CNP/eR1cadrW+vbT97KtI07AuOsG9gJNPvui3fVvm3mMD13BMatWL1e9a68zZjx4Jo5io2JzrHeCrmc8dMCfT1phubOT5AV8uTq6/zAuNPJyQor39T4NueHKWa3MK+BcZ76jBtDOSl013vT7nkmzhiVFj5qcq39a7NqtLjZpDStJv1Z88SeMg7hD4Fx1ry79x7Qu5+M054Ne1QkNk5BsbEKLVRIIeERCo2KVKHC8SpVqYaii5XTvtVz1axqYZUtWcz4vClEwJcECIzzpdNkFgQQQAABBBBAAAEEEEAAAV8RIDDOV06SORBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBDxO4NTShJUOh2p7XGM0hAACCCCAAAIIIOAygYAA/R5Wv1Edl23IRggggIB3CxCIlvP54YNPRgF3B655+/6e9DacLGmApCUZmyIwzpOOyL96ueGuPpoy82ejoa0Atb9+m5RW++jzb2nwp98Yrcsq/Cq7ha4IWxs5fqrufORFo97sFN3V9Xr17nW76tWqluMyV8yQfgN3BR/Zmduk1k5o0VVXNNGMr98/t+2l13XX4mWrsr1Nr243adjIb3Nso+cdnTTsrefSavbuP6jida4yaVtZBaB9/+M8Xd/9MaP17ipq0aS+fp4wPNP27gqMMw3Xs5oZ/eGruvXGqzP15Y7AuF179qvkJe2MebcumaoyJYtnWX/oSJLeGPK53v9sjJKOHjfeMzeF5wfG7dl3QCXqZvbKad81v4xX9crlnd46r4FxnvqMOx3csMBd702753nsr3mKjAg36vrMmRSFlGtiVGsVbUn4XmVLlZAn9pRxCH8JjLNmts5i0LBx2r1xl4rEF1VQTLSCo6IUGhGhsMgoRRWOVenKtRQeW1JJGxaoccUoVSxzkfGZU4iArwgQGOcrJ8kcCCCAAAIIIIAAAggggAACviRAYJwvnSazIIAAAggggAACCCCAAAIIIIAAAggggAACCCDgvwL9bI5ut97m9pQjcFbg5JLFQxUQ0AsPBBBAAAEEEEAAAR8WcDiGhTdsbAUAcSGAAAIISASi5fwU4INPRgFvD3Rzd/8F/U51SLLSjvpLWplVMwTGFfQR+e/9v540Q7c9+KwxwLIfv1Lt6lVUqv41aeEoJteYoa+rS4e2JqVyRdjaqdOnVaVpR23bucfonnaLOlx1hT77Xz/Fx8ZkudQVM6Rv7K7gI7szm9Tf8sB/NWbyTJNSnd68QCEhwdq994AuqpdzoNbyWaPVvtt/cjzP4kXjtXvFjLR7/zB7vq7r9qhRH+cHfFmLvhj7ve76T1+j9e4qalCnhpZMH5lpe3cFxl3Z9UHNnrfYaBQrlM8K58t4uSMwzm541uqfx6lGlQoXzDBx2lzd/9Srxu8qI4Qcis5/ntb+tVk1WtxsvK1pwFheA+M89Rk3hnJS6K73pp3zjC4UqSPrzMJY08exwgVNP69a78W6NavKE3vKeDz+FBhnzW19Ths49Gvt3rhPReKLKDgtNC5SoRFnfxSKjVOlOpcqPLqo9q+br0vKBKtahdKuevTZBwGvECAwziuOiSYRQAABBBBAAAEEEEAAAQT8TIDAOD87cMZFAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMBHBaxv3rNz8e9I7GhRm2uBU4mLb3Mo4Ktcb8BCBBBAAAEEEEAAAY8XCJDj9rAGjUd7fKM0iAACCOSPAIFoOTvjg09GAXcHrnn7/vnz1sr6Ltbv7V6WtCqrX149XJm+Hl2jfUJB9sq9/VDgSNIxFa7e0njy/k/cr6tbXqamHXoYr8kqlCu7xa4KW7MbhGc8zD+F1SqV09SR76lyhTIXLHXVDNbG7go+sjuvSb2dEKqEaV+qYd2aGvXtD+r28AvZbm+FLh1aM1f/eeFtDRkxJsc2Vv00VjWrVlTft4dqwMDhTlu+tk2ztDM8/xo07Cv16TfQ6Xp3FuRnYNwNd/XRlJlmwVYjh7ykO266NtPo7giMs24QUKqRMfHGhZNVoWypTPVvvP+5nnllsPEerig8/11nJ9jNTsCYnX2tgD8r6C/j5anPuCvOwJ3vzQVLVhp/7itTsri2Lplqa6TqzW/Sug1bjNbMHT9MLZs2kCf2lHEAfwuMs2a3wmr/N3ys9m7ep2JxRRRwLjQuQqEREYotWkJlq9dVoSKltWv1fNW7SKpVpazRuVOEgC8JBJRqxN+z8qUDZRYEEEAAAQQQQAABBBBAAAGvFuAP6V59fDSPAAIIIIAAAggggAACCCCAAAIIIIAAAggggAAC/wgQGMej4HECDilgwcMPXhNTvvzUfevWqmGPexUcHu5xfdIQAggggAACCCCAQN4EwlJVLKBRo31524XVCCCAgM8IEIiW81Hig09GAW8PdHN3/wXxYrT+ww8DJK3N6eYExhXE0XDP8wVueeC/GjN5phFMrWqV1OGqFrLCmEyu2ztdo1HvW5mJZperwtZSUlJ13xMv6bNvppjdOBdVVsDT5sXfKa5wTKbVrprB2tSbAuN27N6r0vUzh4llxzrklaf0fz266s5HXtTI8dmHKt19SweNGNRXE6fNVad7nsjxlN5/9Wk9dHcXte58v+bOX+L0RNPrzy98adDHevGtj5yud2dBfgbGXdftUf0we77ROFmZeUJg3PJZo1W3ZtVzMzgLIjQaNhdF5wfGrf1rs2q0uNl4J8cOs9DYvAbGeeozbgzlpNBd782fFySq5U29jNqsVL60/vptklFtelFMtSuUdPS40ZrEGaNUv3Z1eWJPGQfwx8A4a/6NW3bofx+P05HtBxUfF6/A6GgFRUYqNDxMYYWiFR1XROWq1VZcqcra+sd81SmWrLrVyhudPUUI+IoAgXG+cpLMgQACCCCAAAIIIIAAAggg4AsCBMb5wikyAwIIIIAAAggggAACCCCAAAIIIIAAAggggAACCBAYxzPgcQLTO3ec65BapjdW9ep2Kt2oscf1SUMIIIAAAggggAACeRAI0Jzw+o3a5GEHliKAAAIIIIAAAv4mcJekzzxo6FRJ30jq7ywoLr1nAuM86PT8uJUJP8zRTfc+6RaBSZ8N1A1XX2G8tyvD1hwOh159b4Sef+MD4/vbLXz+sXv10lMPZlrmyhncFXxkd07T+obtuilx5Rqn5bfeeLW+HPyS4mu1zjEg6asPXtFtHdvpwKEjKlIr5z8u39iupcYMe11h5Zs6vb9VsH7+BFWpUPaC2sGffqNHn3/LaA93FeVnYFzTDj20YMlKo1FGDnlJd9yUORTQHYFxx0+cVFTl5kY9WUWbFk1R+TIl0+r3Hzysohdfabw2q8LiReNVvsxFqlC2lMZO+dF4r/MD4+yEKFo3ObFxvsLDQp3eL6+BcZ76jDsd3LDAXe/N5avW6ZK2txt1YT1Du1fMMKpNL7ITrrbgu8/UpEFteWJPGYe2M5NpYKIt1AIs3rlnnz4e+Z3WrvhLJWKLKqBQlIKiIhRqBcdFRigyurAuKl9ZpavV1/7dOxRx8A+1blhZEeFhBdg1t0Yg/wQIjMs/a+6EAAIIIIAAAggggAACCCCAgDMBAuOcCfHrCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg4A0CBMZ5wyn5WY/TO3fs+/eD2S997OiSJdWwx71+psC4CCCAAAIIIICAzwv8J7xBo/d8fkoGRAABBBBAAAEEXCMQ+E8oWxXXbJenXayguLGS+poGxaXfjcC4PLmz2EUCdsOZTG8bXShSu1fMtBV+4sqwtfQ+Z/y0QAMGDpcVIuSOa8fSaSpZoui5rV05g53go8d63q5B/fu4Y0TjPfu/M0z9/v7h7LLClCaNeEdWWFlO1/alP6hUiWJpJSbBZvMmfaLmNzr/dyfVKpXT2nnfZnnr0ROn6/aHnnM2QtqvW3M8/kA3o1o7RRXLllKXDm0zLflm8gzd+sCzRtsM7NdHvXuZBVuVqHu19uw7YLTvd1+8q/ZtMwe5uSMw7q9N21SlWUejnqyig2vmKDYmOq1+4NBRerz/IOO1VuFlDeuo/ZXNdXnjempUr5asd1f6dV23R/XD7PlG+50fGHf4yFHF1mhltNYqOv9dkt3CvAbGeeozbgzlpNBd780t23epfOPrjdu0G4Bm52NxzS/jVb1yeXliTxmB/DkwznLYtHWXhn45Ubv+3KO4mJi00LjgqEiFRIQpJDxKkdGFVLxsBZWr3lAHDx1WwN7lalWnlOIKxxg/ZxQi4K0CBMZ568nRNwIIIIAAAggggAACCCCAgC8KEBjni6fKTAgggAACCCCAAAIIIIAAAggggAACCCCAAAII+J8AgXH+d+YeP/GPN99c6UxAyl8ZG23y0P8pIjbO43unQQQQQAABBBBAAAEzgRQFlo5q0GCHWTVVCCCAAAIIIICA3wt0l/R5AStYX0se93cP1n/oYVVeenHsSLD7dem83I61CFwgcPdj/fT5mO9cKnPPrTfok4Ev2trTlWFr5984YfkqvffJ1/py3FRbPTkr/vaTt9Tp2tbnylw5g53go0fuuUXvvfyks3bd+usLlqx0GgKX3sCtN16tryfNyLafWtUq6Y+5Y879uhX61/ftoTn2f2O7lpo0/SenMz79f3fp9eceybLOChhsd9vDTvewCto0b6xZYz40qs1rkTsC486cSVFIuSbGrf06+VM1a1Q3U707AuPsPPdWMynbFikw0MqRlco2vE7bdu4xnunjd16Q9a4KCMj6W+LyEhiXkpKq4LKXGvcydtgb6nz9lU7r8xoY56nPuNPBDQvsPD923ptHko6pcPWWhl1Ie1bOVLEiZv8e125wa/renthTRiB/D4yzLNb+tVkffTZRR3cnKToySgHR0QqOCFNweLhCwiMUGR2tIiXLqGyN+jqZ7NCp7b+rcfkQVShbyvhZoxABbxJY832jTO3W7Cm+J92bDpBeEUAAAQQQQAABBBBAAAEEfFKAP5z75LEyFAIIIIAAAggggAACCCCAAAIIIIAAAggggAACfidg9xvz+HckfveIFMzA0zp3/FVSs/S7l292uSq2+veb0AqmK+6KAAIIIIAAAggg4BKBAE0Ir9/oJpfsxSYIIIAAAggggIDvC1ipMGslVSmgUa2vIX8ryUrCylNQXHr/BMYV0Ely23MCP8yeLysYyZXX9NFDdHXLy2xt6cqwtexubIVkLV+1TvMWLZMVLPTTb4nas++ArT4zFr/94mN6/IFu537KlTPYCT56uEdXDX7lqVzP4YqFlm18rdZKOno8z9udH+pmx8LZzWeP/UitL88cWJK+xk4Yl7Xm+IZfFREe5uyWef51dwTGzfk1QW26PGDc2/5VsxUfG5Op3h2BcSbhgOlN1K1ZVctnjU77v3bDs34Y9Z6uaX3uX7tl6ZCXwDhrw4tbddWqdRuMjK+6oolmfP2+01o7z2jPOzpp2FvPZdrTzvr8fMadDm5YYOddYee96XA4FFi6sWEX0k/fDtMVlzUwqrc+J13S9najWqsoectCBQcHyRN7yjgEgXFnNRL/WK/hIyYp9ciptIC4gMgIBYedDY0LDQ9TWFQhxZcoqbLV6soREacjW1epSthuNa5f2/iZoBABbxEgMM5bToo+EUAAAQQQQAABBBBAAAEE/EmAb4byp9NmVgQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEfFeAwDjfPVuvnmzazZ0eUIDjw/QhQiIj1fThRxUYHOzVc9E8AggggAACCCCAgCUQeGN4gwaTsUAAAQQQQAABBBAwErhT0hdGla4tsr52PFFSX0krXbk1gXGu1GSv3AicOn1axWq3dUnQl3X/6EKR2v/HbIWE2Pv6tSvD1uw4WPf9ZeEyTZnxs76aMM3OUj3Q/WZ9+Pp/z61x5Qx2go/u7HydvnhvgK3e3VHc/dEX9eW4qXneeuY3H6hti0vP7XM6OVlh5ZvmeV9rg5Ob5issNDTLveyG3v045gNd2fzfPl3SYBabuCMw7vruj+n7H+cZtVytUjmtnWdlpWa+XB0Yd/zESV1U72rjd1HGwK+1f21WjRY3G83j7DlI3ySvgXE9evfXZ99MMe4pccYo1a9dPcd6O4FvWQXGeeozbozkpNCd780b7uqjKTN/Nmr1sZ63a1D/Pka1/d8Zpn5//zC52jRvrFljzv3rYnliT+lzEBh3ViI1NVW/Jf6hjz+brKiUYAVFhCsoMkJBVmjcP8Fx4ZGRii1aTKWq1FRU0Qo6uGuLYpN+V7sWZqGDJs8ONQh4ggCBcZ5wCvSAAAIIIIAAAggggAACCCCAQGYBAuN4IhBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMAXBAiM84VT9MEZ5nTsGHsqWAczjla5zZUqe5lrvkHKB8kYCQEEEEAAAQQQ8GiBfevW6vTRoyrdsOHvYfUb1fHoZmkOAQQQQAABBBDwHIFASWslVcnnliZJesHVQXHpMxAYl8+nye2yFHjwmdf00RfjXaLz0N1d9P6rT9veK69ha8eOn1ChKi1khVuNHf6G6tasaruHjVt2qF7bW40Dq25u30bjhr957j55nSFjw/MTVujyG+4xmiG7QC+jxS4s+nrSDN324LN53vHYX/MUGRGeaZ+OPR7XpOk/5WnvLh3aaszQ13Pco+v9z2jslB+N7lOmZHEtnPq5SpUoZlSfXVHC8lWywtc2b9uln74dphpVKmQqdXVg3Or1G1WrZRfjnrMKHrMWuzow7tOvJ+vePubBh18OHqBuN1+XNsecXxPUpssDRjO1atZQc8YNdVqb18C4/308Wo+9+I7T+6QXWGGbkz8bJKu/rK7Z8xbLelev27DFaM/szs0Tn3GjgQyK3PneHDh0lB7vP8igi7MlB9fMUWxMdI711uetkpe0M/6c89JTD+r5x+49t6cn9pTeHIFx/x59SkqqZv6SoC8//05x4VEKigxXYHj42cC40FAFhYUqLDJShWLjVLJCVRUtX0tJSUlK3ZGg1vXKKj42xvi5oxABTxYgMM6TT4feEEAAAQQQQAABBBBAAAEE/FWAwDh/PXnmRgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDAtwQIjPOt8/SpaaZ17jRcctyXPlRweLiaPvyogkJDfWpOhkEAAQQQQAABBHxdwJGaqgXvD9appCQFhYYcSklO7hcWFTu09WefnfT12ZkPAQQQQAABBBDIo8Adf4fFjczjHnaWfyfpRUlL7SwltxoBAAAgAElEQVQyrV09XJm+Hl2jfYLpUuoQcLmAFUR0ZdcHXbLv3PHD1LJpA9t75TVs7dCRJMXVaH3uvgOefECP9bxdVgiTneuR597UkBFjjJacH46X1xky3nTF6vWqd+VtRn1YRTuWTlPJEkWN691RuHf/QRWvc1Wetm7XqqmmfTX4gj0+/HxcWkBZXq7P/9df3bu0z3GL4aMmqNeTrxjfxgom/GXix4qJjjJek164Y/devfbeiEzP24LvPlOTBrUz7eXKwLgt23ep0z1PKHHlGuN+rVBEKxzx/MuVgXFW4NuNPfoYB2dZvWxf+sO5sL4fZs+XFfBmcpkGLF7Rqad+WWj2W4Ck9T+rUFTmd83yVet0SdvbTVrKVHP3LR3UsG7NtODAU6dOa9W6DZo29zdZ72k7V3aBcZ74jNuZK6dad743Fy39Q03a32Xc6rsDHtd/7sv5HW73vWYFSl5x2b+fXz2xp3QgAuMyPyrJyWf0xgejtHrpOsVHxCgwMkKB4WEKDAlOC42z/r1/SFi4ImNiVLxUWZWsXEfJQZE6vDFBlxRP1sU18jsv2/hRpxAB2wIBpRrxvei21ViAAAIIIIAAAggggAACCCCAgHsE+EO6e1zZFQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBPJXgMC4/PXmbjYEZna5vlyKI3i9pHMJcRVbtlL5y5vb2IVSBBBAAAEEEEAAgYIW2LZokf78cUbGNlJSggJqtf9mwrqC7o37I4AAAggggAACHiwQKGnt34Fx+ZGW8L2kF9wVFJduTGCcBz9tftjamTMpiq/V2lZYU1ZMxYvGpwWXBQVZH7L2rryGrZ0fGGfd3QqLe+2/D+v2m65RXOEYpw05HA7d+uCzGjN5ptNaq+CN5x/VUw91P1eb1xky3nTztp2qcGkHoz6sosaX1NKccUMVFRlxwRrrfHfu2acSxeIVGhJivGduCpt26KEFS1bmZmnamoH9+qh3rwtDttb+tVk1Wtyc632thRkDxrLb6MChI6pw6fW2PhZqVaukp//vLnW+/kpFRoTn2GNqaqqskKdvJs/Uu8O/uqA2r4FxVlDiC73P/beHMu2fm1A262N625KpCgkJvqBXO4Fx8yZ9ossb18vy2Rwy4hv17jvQ1tl2veEqffPRa+fW2H0+Zo/9SK0vb5TlPQ8ePqL/vPC2vhw31binrALjrMVW6KMVYlYQV3aBcZ74jLvKx53vTevzgxUAaOc8Px30onrcckOW4309aYZue/BZ49ErlS+tdfMmZPr86ok9pQ9EYNyFR7tt5x79b9g47dmwSzFRUQqICFdQWKgCQ0MVFBKS9iMkLEzh0dGKLVpcpSrXUnTJKtq+bpnqxx/SxVXKKzDQ/u+vjB8yChHIJwEC4/IJmtsggAACCCCAAAIIIIAAAgggYCBAYJwBEiUIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDg8QIExnn8Efl3g9Nu7viBAvRguoL1Xxtv+vCjCgo9lyHn30BMjwACCCCAAAIIeLhASnKyFgx5T8knTpzrNEABQ9uNm/CAh7dOewgggAACCCCAQEELWOlBo9zcxA+SXpSU4Ob7pG1PYFx+KHMPOwJWYFNWAVZ29uhz/x16p29vO0vO1eY1bC2rwLiMjdzcvo3uuOlaNapXSyWLF1VwcFDaL1uBO9t37dWqdRs05NMxmjLzZ+P+f5syQpc1rOOyGTLe+PiJk4qqbO8/GGOFxj1yz62qVa2iwkJDtebPTZqfsFyjvp2mPfsOaMgrT+n/enQ1ni83ha8NHqFnX3s/N0vT1iyfNVp1a1bNcn2JulenzZGbq0GdGloyfaTR0teHfKb/vjrEqDZjkRVQaIV01ahSIe0Zu6h4kbRwn52798l6vlev36SvJpw9i+yuvAbGWfuWKVlc113ZXKUvKqaTp06nBVz9vCDRVgheen85BdDZCYyz9mvRpL7q166u2MLRaQZLVqzW4mWrbDtbC+aOH6aWTRucW2uFIoaUa2K8l3VWbz7/H91y41VpYZIpKala+5f18bJCz73+ge3nLLvAuPdHjNHDz71p3JcrC7MLjLPu4WnPuKvmdvd785vJM3TrA+Yhb9ZcvbrdpC4d2qpOjSoKDQ3W0pVrNfa7H/XRF+NtjZ1d+Jwn9mQNRmBc1se79I91+ujTCUo9fEphYeEKjAw/GxgXHKxA60doqEJCQxUaFq5CcXEqWbGqLqpyibatX6kKYfvU6OKKigjj7wXY+uCh2OMECIzzuCOhIQQQQAABBBBAAAEEEEAAAT8WIDDOjw+f0RFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8CGBfjZnsVtvc3vKEcgsMKXL9aVDHMHbMv5svdvvUFyFilAhgAACCCCAAAIIeIHAxp/mavOv8zJ1mhIQUrL92LG7vKB9WkQAAQQQQAABBApKIFDSWklV3NTANEkv5FdQXPoMBMa56TTZNtcCVlDS5Tfck+v11sLzA9TsbObuwLjze7FCtYoXjdf6jVtyFaRVrVI5rZ33baZt8zrD+T1eel33XAdqZWX/3stP6pF7brFzLLZrE1euUcN23WyvsxZYIV6H1sxNC1nL6rr/qVc1bGRmc9Mb9X/ifr3Yp6dR+dFjx1Wq/jW5ei6MbpBDkSsC4/LaQ/p66zz++m2SihWJy3JLu4FxruqrVbOGmj32IwUEZP5Wttx+vFhzJh09nqf2sguMs/at1bKztu3ck6f9c7M4p8A4T3vGczNfdmty+xxkt1/G96YVTFi9xU3asHm7K1t2upf1uWrz4u8UnkVQmCf2ZA1EYFz2x/rd7PkaP262wk5LwRHhCgwLVVBoiAKCghQQEqyg4BAFh4QoODREkdExKl62vCo3aKMd2zcp5uh6XV67rGJjCjl9bihAwFMFCIzz1JOhLwQQQAABBBBAAAEEEEAAAX8UIDDOH0+dmRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBPJdYNrNHd9VgP4THB6+sUH3uytGFi2a7z1wQwQQQAABBBBAAAH7AscPHFDC8KFKTUnJuPila8ZNfNH+bqxAAAEEEEAAAQT8SuA2SV+5YeIZkvpKWuCGvY23dOxIcBgXU4iAGwVSU1NV8pJrtGffgVzdxQpg25Lw/QUhTqab5TVs7dCRJMXVaG16uzzXff3Rq7rlhqsz7ZPXGc5v6plXBuuN9z/Pc6/pG+RHYFxenqO7b+mgEYOs13LW17jvZqlLr6dz5bHw+891af2LjdfOnb9ErTvfb1zvqkJPCowbO+wNdb7+ymxHK6jAOCvErlL50hf09ejzb2nwp9+46ihs7ZNdYJy1ybQ583XtHY/a2s8VxTkFxln7e9Iz7op50/dw93tzwZKVatqhhytbdrrXrDEfqk3zxtnWeWJPBMblfKwffjlRv85JVOHgMAVYoXGhIQoMDj4bGhdshcYFpQXHBYUEKyyykEpXqqqK9Zrq0JGjSt2xRFfUKa3i2YR5On2gKECggATWfN8o051r9hTfk15AZ8FtEUAAAQQQQAABBBBAAAEEEEgX4A/nPAsIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAII5IPA9E6diitY97QbO+H1U0uXfOlwOLrlw225BQIIIIAAAggggEAeBRYPH6pje/dm3GXPqdOpVW6cPDkpj1uzHAEEEEAAAQQQ8GWBQElrJVVx4ZA/SnqhoIPi0uchMM6FJ8tWeRbIS9DOs4/eo1eeeSjXPeQ1bC0/A+O63nCVvvnotQtmzesM52/456atqtqsU65Nz1+YH4Fx1j3ve/wlfTJ6ku2+Rw55SXfcdG226/buP6jida6yvW90oUgdXD1XQUHWpxTz6+tJM3Tbg8+aL3BBpacExt1649Ua/eGrOU5UEIFxQ998Vr263ZRlX7v27Fe15p2UdPS4C07C3hY5BcZZOz0x4F2989FIe5vmsdpZYJy1vac843kcNdPy/HhvDhkxRo8896Yr2852r5effkjP/ecep/fytJ4IjMv5yE6cPKX+73yuneu3KToiUgHhYQoIDjobGhcYmBYcZ/3vwKAgBYWGKjyqkOKKl1DVBs0UFBqmI+vmqdUlFVQ0Ptbps0EBAp4iQGCcp5wEfSCAAAIIIIAAAggggAACCCDwrwCBcTwNCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCOSzwLHExFLBcvzukCMun2/N7RBAAAEEEEAAAQRsCPw1a6a2LlyYaUWA9Gi7cRMH29iGUgQQQAABBBBAwB8FbpU02kWDz5bUV9I8F+3nkm0IjHMJI5u4SGDJitVqdM2dudotccYo1a9dPVdrrUV5DVvLr8C4Ns0ba8InbysmOuqCWfM6Q1Z4PXr312ffTMm1a8aF+RUY9+3U2br5vqds97x96Q8qVaJYjusatuumxJVrbO199y0dNGKQ9fq3fw0fNUG9nnzF/sJcrvCEwLh2rZrq20/eUmREeI5T5HdgXN/He6nf3z9yukaOn6o7H3kxl/q5X+YsMM7a+YU3P9TL736S+5vYXPlwj64a/Irzj0NPeMZtjua03N3vTYfDoT79Bund4V857SUvBda76+O3XzAKu/S0ngiMc37ya//arLcGj1bAkVMKi4iQwkIVGBSogLSguH9C4wKDFBQcfPYZCAhQdFycajW5QhFR4UrevkJtmlysiPAw5zejAgEPECAwzgMOgRYQQAABBBBAAAEEEEAAAQQQOE+AwDgeCQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQKQOB04uLuqQr4vABuzS0RQAABBBBAAAEEDAQObdmiZSO/yFwZoMULLr7ksn79+qUabEEJAggggAACCCDgrwLW31FfJ6lKHgGsgLj/elpQXPpMBMbl8XRZ7lIBK3CmSrOO2rB5u619q1Uqp7XzvrW15vzivIat5UdgXM87OmnwK08qLDQ0y1nzOkNWmx47fkJXdOppOyQtq73yKzDu4OEjiq/ZxtbzUKtaJf0xd4zTNS++9ZFeGvSx07qMBWP/n707AbOjLPcE/p7O1tkJgQAJkTWQsBMaQcQV0EBkCYTFjUUnI8qM+3KvikbnemcUUBy9qGSGUUR5uGGXTUFcWCQQwk4iaJQtYAgQAoRO0ukznMaQ7qxV3ZU+dap+/Tx5FPLVW9/7e7+uk+7O+XP+t2Pq+w5JdU3nxQ/M/Uuc9PEvx8OPzO92jaQX1jsw7rC3HxBX/r9zNhoWV+unNwPjkoTF1fZUe4ZN/vCn4vqbb09Kvs51HznpqJj/+FPx+9vvTlQnSWBcrdAlV/8mPvnVs2PhoucT1V21aMftxsQPv/WlaF22LI796BcSXVsL16u5Jfmo9xlPssc0a3rrufmTn18ep3/p39NsLfHa73z1k/GFT5yceP2qhXnZk8C4ZKO74te3xKWX3BjD+/SPysDmaOrfLyp9+kRTnz5RaapEn6amaFu2PAY0943+/fvF0qWvRqVvcxw0aVI0RWtsO2RltOzZ/bDeZLu0ikA2AgLjsnFUhQABAgQIECBAgAABAgQIZCkgMC5LTbUIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIpBBovfuun0SlkuxdHynqWkqAAAECBAgQINAzgbbW1rjzJz+O5a+83LnQ4hWVtj2OnHlNugSGnm3F1QQIECBAgACBRhSYGhEze7DxW167dnpE3NyDGpvs0rkzotq5+PjJszfZvRQmkEbgG+ecH9Nf+5Xm45tfOD3O/Mx/SXPJWmt7GrbW1rYyzvrRhXHujItThzFtbOO18KgvnnFK7LrTdhtc2tMe1le8FsD29bN+Ej+44JKNbXWDv99bgXG1Tbxr6scSh23V1n/pjFPif33lv2+0vz/8aU6887h0Pw55fu7NMWL4sI3W3tCCV1uXdfif8+OLMj9ftfvWAvPOOPX4OPXEI9cKa6uFjJ10+pcT7X/bbUbFk08vTLR2zUVnnfmp+PS0D0Tfvn0SXZ8mMG7okEHx0stLE9XtvGjUFpvHBd/9Wkw+9ODE1y59tTW++u3z4nvn/zLxNasW1vZ53v/8l/jQcUfEER/6ZOLguaSBcbX71PZ3/kWXxyVX3xh33P3ABvdYC4r76PuP7pjLoIHNcd5PZ8YZX/52or5++K0vxhmnnZBobW1RPc944k2mWNhbz81b77w3PnXm2ZmEeq56FnzvG5+N97zjwBTddl2ahz0JjEs+vrN+9Mu4+/aHYoshQyIGDoymvn1eD42rRFRXrIittt4sDnrrPrHVqJHxzDOL4k+33hOLXnw1Dnnfe2LRwsfiuEMPisGDBya/oZUE6ixQGd3iveh1noHbEyBAgAABAgQIECBAgACBVQK+SHcWCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNRR4NU5s2dVIt5cxy24NQECBAgQIECAwBoC7StXxtwrr4hn/zxv9e9UKodPmnnFDbAIECBAgAABAgQ2KFD7++kP1jJ0uuF0a0R8Pa9Bcav6ERjXjcm6pFcEHpz319jz3SemutfDf5gZE8btkOqaNRc/+9wLMWrPwxLVOOno98TFP/r3da6tBcfdfNtdcc2Nt3QEMi1c9Hyimuta9N9OOyE++7EPxQ5vGp2oRlY9rO9m9z38SHzr+xfEDb+7PXX4Vi1I7Pv/4/Nx7BHvTtRLTxfVgtU+/81zE5e58ZLz4tC3bfxHHK3LlsfAHQ5KXPdtB+wbf7xiRuL1G1tYO1/X3HRLR+DX9TffvrHlG/z9vSaMi/cd9rY4ZtI7Y/991v9yVzvLR57ymUT3mnfLZTF86JCO/Z39459v9JzUAtJq4Wif/dgHY+ftxya6x6pFaQLj7rr+whjQv398//9cHP/34qs2ep9aUNppJx4Vn/zoSTFs6OCNrl/Xgltm3RMf+MRXEgXo1RxOP3lqR3DhyBHDO8qdePq/xn9efWOiey+df1sMbB6QaG3nRbXn070PPdLxnHr2ucXx8itLY/PNhsXWo7aIPcbvtFZI5b986wfx7f/4WaL71J6RtWdl2o96nPG0e0yzvjeem9VqNa777W3xje+eH3fd+3Ca7b2xtvY8qAWvTjn8XdGnT1O3anS+qN572vuQ98f9cx/daB+1z70lj/xxo+uKvKAWIvmd834Zj943PzYfMjQqgwdFn1pwZ3tbTNht+zh+yrtjxLAhbxDUnhP/euZ/xNDhI2LHXcfE7uPGxFv226vIRHormIDAuIINVDsECBAgQIAAAQIECBAg0NACAuMaenw2T4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0OgCyx64e0KsiNuqUR3R6L3YPwECBAgQIECgSAKVqLxw5/k/uuCVRYs+U6lWz3nvZVd9sUj96YUAAQIECBAgsIkEToiIS1LWriX31ILibkp5XV2WC4yrC7ublkygFpjz+FPPdARGPfn0P+LJBQs7/vmxJ5+Ovz2+IAYM6BcvLnk5hg8bEptvNjxGbTEi9tl915i45/jYZ49dYrNhQ3MpVuvrkfmPx6w5D3b08tIrS6OtrS0qlUosX74i+vXr29HPVltu3hECttdu494IwcplQw26qVqw1sOPzo/Z982N+x56JJ5fvCReeHFJPPfCix1zWBX6VJtFLfSsFkS2x647xT577Bp7jt+520FoSblq4XrzH3syHnvymfj7Ews6fr3w4ksd52LM1qNi5x3GxtsP3Df69+uXtGSXdWkD41r2fj0Ur/Y5N//xp/65p9rn4lPRtnJlbDNqi9h29KgOm/32mtBxnnv6UQtiqgV4PTjvL3Hfw4/G3ffPjcVLXoohgwbF5iOGxW677BiHHLx/TD704I5Auzx/1EKiRu87aaMhgKt6+P1l58c73jKxRy3l/Yynaa63npv/ePb5+OMdczqCSx/68/z4x7PPxdMLF70xt1pAWu2sb7XlyBi/8/bx7oP37/g8HL3VlmnaSbU2j3tK1UAJFj/x9ML45lkXRCxZHoOHDI2m5v4xaHC/+PAHJ8WuO76pi8Bf/v5EnHXepTGo0i+GbTYg9t13XEfwqA8CjSIgMK5RJmWfBAgQIECAAAECBAgQIFAGgZ5/F74MSnokQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsAkFWufMOTyi/bpNeAulCRAgQIAAAQIEUgs0HdE8ceL11x8/5eDDZ15xa+rLXUCAAAECBAgQKJ9A7e+mPxgRryfLbPzjjn8Gxf1m40vzs0JgXH5mYScECBAgkF6gu4Fx6e/kiprAWeddGF/8t/+dGGPp/NtiYPOAxOst3LQC7e3tUa3GG0GSm/ZuyarncU/Jdl7MVbPvnxff+f4vYst+g6J5yMDoN2xgHPHe/ePtB6wOfnxm4XNx7s8uj/a+m0d14cJ48eXn4+ij3xmTD31rMVF0VUgBgXGFHKumCBAgQIAAAQIECBAgQKBBBQTGNejgbJsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBYAsvn3HVye1R+VuuqrbU1+jY3F6tB3RAgQIAAAQIEGkigKaqn9J+4/4UNtGVbJUCAAAECBAjkQeD418Li/jPBRma9tmZ6RNyQYG1ul1QXzK7mdnM2RoAAAQIE1iMgMC750ahZ/eFPc2K/vcbH+4+ZFO8+uCUG9O+fuMBVv/5DHHPa5xKvn3zowXHNhecmXm8hAQL1F6gF+F3/u1lx/gVXxNihm8XQrUbG4C2Hxu7jxsTO220bjz31bMx55PFoHzgy+rcui6cfuD+WxIr4ty//1xg7Zqv6N2AHBDYiMO/ali4rJkwL70l3aggQIECAAAECBAgQIECAQJ0FfHFe5wG4PQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFVAq1zZn/8pacXnHffL38R2x38thh7wIFwCBAgQIAAAQIEel/gE80TW37U+7d1RwIECBAgQIBAQwvU/l76g68Fxu22gS7ueu33akFx1zV0p//cvMC4IkxRDwQIECifgMC45DM/+OiPxm133dflgg9MmRTvPGi/aNl7t9htlx3WCpD7+xML4q57H44f//yyuPnW2h99kn/88FtfjDNOOyH5BVYSIJALgWXLl8evbrwtLvz5tbH9yC1i5I7bR/+tt47WaI5o6hPDhg6NyuJn47G77oinnl0Yx55wWJzwvkOib98+udi/TRDYkIDAOOeDAAECBAgQIECAAAECBAjkT0BgXP5mYkcECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIlFfj11KMPiqY+N1fb2wfUCEZP3C92ee+kiIof65X0SGibAAECBAgQ6H0BYXG9b+6OBAgQIECAQDEEjouIS9fTyt2v/ftaUNw1xWj19S4ExhVpmnohQIBAeQQExiWf9boC49a8euiQQbHlyBGxfPmKePLphcmLr7GyVudvs34VI0cM73YNFxIgUD+BWmjcTbfcHT+76OoY1ndgbL3dDjFwq21rXzXEqwufiWcfnx8LX1ochx52YJx8/BFR+5z3QaARBATGNcKU7JEAAQIECBAgQIAAAQIEyibgnSVlm7h+CRAgQIAAAQIECBAgQIAAAQIECBAgQIBAMQVqbzZM85F2fZra1hLolsDvTj21ednLix+PiC07Fxg5blzsPuW4aOrbt1t1XUSAAAECBAgQIJBMoCmqp/SfuP+FyVZbRYAAAQIECBAg0Emg9nfSH4yI3dZQuee1f659L/bqImoJjCviVPVEgACB4gsIjEs+4ySBccmrbXjl2V/7dHzu9A9lVU4dAgTqINDWtjLm/fXxuGjm9fHIQ3+L/lGJarUaK6IaW4zZIo476l3xtjfvHYMGNtdhd25JoHsCAuO65+YqAgQIECBAgAABAgQIECCwKQUExm1KXbUJECBAgAABAgQIECBAgAABAgQIECBAgACB3hKopryRn5GkBLO8dwRuOO7I3aPSdFNEZevOdxy6zTax14nvj36D/JfGe2cS7kKAAAECBAiUSaASlReqUflg88SJ15epb70SIECAAAECBDIUODYiLutU797X/n8tKO6qDO+Rm1JzZ0SX70ePnzw7N3uzEQIECBAgsDEBgXEbE1r9+70VGLftNqPikduuiIHNA5JvzkoCRRRoWxnRt09Dd1YLiGtdtiLmP/Zk3H3/n6Ma1dh1x+1i9113iMGDmqOpqamh+7P58gpURrf4e1blHb/OCRAgQIAAAQIECBAgQCBnAr5Iz9lAbIcAAQIECBAgQIAAAQIECBAgQIAAAQIECBDoloDAuG6xuSiPAtcde+y2TU3VGyKqu3feX//Bg2PXyUfGyJ13zuO27YkAAQIECBAg0DACT945KxY/9ljscfwJtaSPO5v6VU4dsOd+cxumARslQIAAAQIECORLoPb30R+MiN0i4v7XtlYLirsyomuoWr623LPdCIzrmZ+rCRAgQKC+AgLjkvv3RmDc0CGD4o5rfhq77bJj8o1ZSYAAAQIEellAYFwvg7sdAQIECBAgQIAAAQIECBDYgIDAOMeDAAECBAgQIECAAAECBAgQIECAAAECBAgQKIKAwLgiTFEPbwhcddRRQ5v7N/2qGvGONVm2HD8+xr338KgFyPkgQIAAAQIECBBILrB00aKY+6ur4qWnn+64aPTE/Wbt9eUzD0xewUoCBAgQIECAAIF1CEyJiG++9u9rQXGXFzkoblXvAuN8HhAgQIBAIwsIjEs+vU0dGFcLi/vD5TNi3z12Tb4pKwkQIECAQB0EBMbVAd0tCRAgQIAAAQIECBAgQIDAegQExjkaBAgQIECAAAECBAgQIECAAAECBAgQIECAQBEEBMYVYYp6WEvghqlTfhZRPXnN3+jb3Bzjjzwqthi3CzUCBAgQIECAAIEEAn+/5Y9R+7XmR7W96c2HX375XQlKWEKAAAECBAgQILBugb0i4v4y4QiMK9O09UqAAOAcHyoAACAASURBVIHiCQiMSz7TTRkY9+GpR8TXPjstdt5+bPINWUmAAAECBOokIDCuTvBuS4AAAQIECBAgQIAAAQIE1iEgMM6xIECAAAECBAgQIECAAAECBAgQIECAAAECBIogIDCuCFPUwzoFbjhuyrSoVM+NiEGdF+x14vtj8512okaAAAECBAgQILAegfa2tlgw5+544s5ZsWzJknWterkalY8cfukVMyESIECAAAECBAgQSCtQXTA77fel097CegIECBAgkLmAwLjkpFkHxg0dMig+ccrxccZpJ8TY0Vsl34iVBAgQIECgTgLzrm3pcucJ08J70us0C7clQIAAAQIECBAgQIAAAQKrBHxx7iwQIECAAAECBAgQIECAAAECBAgQIECAAAECRRBI+8Y8PyMpwtRL1MOvTzp6bPvKuLhSrby11vaAIUPufcunPl2NauxbIgatEiBAgAABAgQSCbS1tsaTd86Kp+6eHStefXWd11QjflNpbzp10uWXP52oqEUECBAgQIAAAQIE1hAQGOdIECBAgEAjCnzm69+Nc2f8MtHW7/vtxbHXhHGJ1hZx0cuvLI375/4l5jwwL+6858G476FH4/65j6ZqddQWm8db9tszDn37AXHy1MkxbOjgVNdbTIAAAQIE6ikgMK6e+u5NgAABAgQIECBAgAABAgTWLeDNUE4GAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUAQBgXFFmKIeNirw66lHf74ala+v7FPZb/IlVzzSOmf2xyPiCxGxw0YvtoAAAQIECBAgUAKBfzz4QPz5umujva1tfd0uqlbjM4dfduVFJeDQIgECBAgQIECAwCYUEBi3CXGVJkCAAIFNJrBiRVssfbU1Uf3hw4YkWle2RTW/xUteisUvvhSLl7wcLyxeEi+9sjQqlYhhQ4bEkMEDO4Lh3jRm6xgxfFjZePRLgAABAgUSEBhXoGFqhQABAgQIECBAgAABAgQKIyAwrjCj1AgBAgQIECBAgAABAgQIECBAgAABAgQIECi1gMC4Uo+/XM3/7phjNnvXlVcu7tx1LTiuUolPVKuxR7k0dEuAAAECBAgQ6Crw4hOPxz0/v3DdLNX4aTT1+9ykmTOf50aAAAECBAgQIECgpwIC43oq6HoCBAgQIECAAAECBAgQyLOAwLg8T8feCBAgQIAAAQIECBAgQKCsAgLjyjp5fRMgQIAAAQIECBAgQIAAAQIECBAgQIAAgWIJCIwr1jx1002B1jlzjopK+6lRjSmdS/z1tzdG3+aBMWz0mBg2Zkz06d+/m3dwGQECBAgQIEAg5wKVuOKWs7/z5pXLlo9ZvdPKhe3VlececdnV9+R897ZHgAABAgQIECDQAAJzZ0SX70ePnzy7AXZtiwQIECBAgAABAgQIECBAoHsCldEt3ovePTpXESBAgAABAgQIECBAgACBzAV8kZ45qYIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAHQQExtUB3S3zK/DKnDmj+0T71KjEMW2ty9516zlnddns4FGjYvi2Y2PgiBHRf/Dg6D94SPQbNDD6DR4cA4YMzW9jdkaAAAECBAgUWmDlihWxYunS13+1vhorXnkllr+yNLYcPz6ahw9ff++V+F1U48qV0XTp4IkTF9ww9ZhPR8SZEZUfr6z0/cHkmTOfKTSc5ggQIECAAAECBHpVQGBcr3K7GQECBAgQIECAAAECBAjUWUBgXJ0H4PYECBAgQIAAAQIECBAgQKCTgMA4x4EAAQIECBAgQIAAAQIECBAgQIAAAQIECBAogoDAuCJMUQ+bROCPHzl12tIli89PWnzrPfeK8UcelXR5PHnnrPjLTTcmXj/2gANjp0MOTbxe/Q1T8eHTWcDn14bPAx8+nQU8Pxvr+bnbMVNi1G67v7HpSiUerLZXb69U4vf92ys3VlpaFnXu6Kqjjhq6bMCAthNmznw18R86LCRAgAABAgQIECCQUEBgXEIoywgQIECAAAECBAgQIECgEAIC4woxRk0QIECAAAECBAgQIECAQEEEBMYVZJDaIECAAAECBAgQIECAAAECBAgQIECAAAECJReYnrL/tOtTlrecQH4EbjjumF9EJT6QdEfb7L1P+66T31f7OWKinyU+MeuO+Otvb0paPsYecEDsdMhhiderv2EqPnw6C/j82vB54MOns4DnZ2M9P7fac+/bJxx55EXVaH+4ub3p3kpLy4uJ/zBhIQECBAgQIECAAIGMBQTGZQyqHAECBAgQIECAAAECBAjkWkBgXK7HY3MECBAgQIAAAQIECBAgUDKBRG/yKJmJdgkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgURuC6Dx4+rLJswP6VqLZEpbJ/VGP/iHjT+hqsRvzg8Euv/GRSgBumHvO5iDg76fqoxncnXXZl7ZpEH+pvmIkPny4CPr82fCD48Okk4PnZYM/PiAsmXXrlRxP94cEiAgQIECBAgAABAr0kUF0wu9pLt3IbAgQIECBAgAABAgQIECDQ6wLzrm3pcs8J05L9hwd7faNuSIAAAQIECBAgQIAAAQIESiQgMK5Ew9YqAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgZrATVOmjGxrat83ommHqFTHViJ2iIix1Y7/rVw06dIrvpJU6oapx3w6Ir6XdH1EnDPp0is/n3S9+huW4sNnDQGfXxs+Enz4vCHg+ZnX52e1tRJNz1UjnotKPFetxnNNEc9Vo/32SZdedWHSPz9YR4AAAQIECBAgQKA3BATG9YayexAgQIAAAQIECBAgQIBAvQQExtVL3n0JECBAgAABAgQIECBAgMD6BQTGOR0ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCCFgMC4FFiWEiBAgAABAgQIECBAgEDDCQiMa7iR2TABAgQIECBAgAABAgQIlEBAYFwJhqxFAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMhOQGBcdpYqESBAgAABAgQIECBAgED+BATG5W8mdkSAAAECBAgQIECAAAECBATGOQMECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCCBwNwZUe28bPzk2QmusoQAAQIECBAgQIAAAQIECDSmQGV0i/eiN+bo7JoAAQIECBAgQIAAAQIECijgi/QCDlVLBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJC9gMC47E1VJECAAAECBAgQIECAAIH8CgiMy+9s7IwAAQIECBAgQIAAAQIEyicgMK58M9cxAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECHRDQGBcN9BcQoAAAQIECBAgQIAAAQINKyAwrmFHZ+MECBAgQIAAAQIECBAgUEABgXEFHKqWCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCB7AYFx2ZuqSIAAAQIECBAgQIAAAQL5FRAYl9/Z2BkBAgQIECBAgAABAgQIlE9AYFz5Zq5jAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOiBQHXB7GoPLncpAQIECBAgQIAAAQIECBDItcC8a1u67G/CtPCe9FxPzOYIECBAgAABAgQIECBAoAwCvjgvw5T1SIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKZCQiMy4xSIQIECBAgQIAAAQIECBDIoYDAuBwOxZYIECBAgAABAgQIECBAoPQCAuNKfwQAECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAaAYFxabSsJUCAAAECBAgQIECAAIFGExAY12gTs18CBAgQIECAAAECBAgQKIOAwLgyTFmPBAgQIECAAAECBAgQIECAAAECBAgQIECg+ALTU7aYdn3K8pYTIECAAAECBAgQIECAAAECBAgQIECAQJEFBMYVebp6I0CAAAECBAgQIECAAAGBcc4AAQIECBAgQIAAAQIECBDIn4DAuPzNxI4IECBAgAABAgQIECBAgAABAgQIECBAgACB9ALVlJf4GUlKMMsJECBAgAABAgQIECBAgAABAgQIECBAIGLujOjy/ejxk2djIUCAAAECBAgQIECAAAEChRWojG7x96wKO12NESBAgAABAgQIECBAgECjCfgivdEmZr8ECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAugQExjkXBAgQIECAAAECBAgQIECAAAECBAgQILDJBQTGbXJiNyBAgAABAgQIECBAgACBHAkIjMvRMGyFAAECBAgQIECAAAECBEovIDCu9EcAAAECBAgQIECAAAECBAgQIECAAAECBAgQKISAwLhCjFETBAgQIECAAAECBAgQIECAAAECBAgQyLeAwLh8z8fuCBAgQIAAAQIECBAgQCBbAYFx2XqqRoAAAQIECBAgQIAAAQIEeiIgMK4neq4lQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE8iIgMC4vk7APAgQIECBAgAABAgQIECBAgAABAgQIFFhAYFyBh6s1AgQIECBAgAABAgQIEFhLQGCcQ0GAAAECBAgQIECAAAECBPIjIDAuP7OwEwIECBAgQIAAAQIECBAgQIAAAQIECBAgQKD7AgLjum/nSgIECBAgQIAAAQIECBAgQIAAAQIECBBIKVBdMDvt96VT3sFyAgQIECBAgAABAgQIECBQP4F517Z0ufmEaeE96fUbhzsTIECAAAECBAgQIECAAIEOAV+cOwgECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAEQTSvjHPz0iKMHU9ECBAgAABAgQIECBAgAABAgQIECBAoE4CAuPqBO+2BAgQIECAAAECBAgQINArAgLjeoXZTQgQIECAAAECBAgQIECAQCoBb4ZKxWUxAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkFMBgXE5HYxtESBAgAABAgQIECBAgAABAgQIECBAoIgCAuOKOFU9ESBAgAABAgQIECBAgMAqAYFxzgIBAgQIECBAgAABAgQIEMifgMC4/M3EjggQIECAAAECBAgQIECAAAECBAgQIECAAIH0AgLj0pu5ggABAgQIECBAgAABAgQIECBAgAABAgS6KSAwrptwLiNAgAABAgQIECBAgACBhhAQGNcQY7JJAgQIECBAgAABAgQIECiZgMC4kg1cuwQIECBAgAABAgQIECBAgAABAgQIECBAoKACAuMKOlhtESBAgAABAgQIECBAgAABAgQIECBAIE8Cc2dEl+9Hj588O0/bsxcCBAgQIECAAAECBAgQIJCpQGV0i/eiZyqqGAECBAgQIECAAAECBAgQ6L6AL9K7b+dKAgQIECBAgAABAgQIECBAgAABAgQIECBAID8CAuPyMws7IUCAAAECBAgQIECAAAECBAgQIECAQGEFBMYVdrQaI0CAAAECBAgQIECAAIF1CAiMcywIECBAgAABAgQIECBAgEB+BATG5WcWdkKAAAECBAgQIECAAAECBAgQIECAAAECBAh0X0BgXPftXEmAAAECBAgQIECAAAECBAgQIECAAAECCQUExiWEsowAAQIECBAgQIAAAQIECiEgMK4QY9QEAQIECBAgQIAAAQIECBREQGBcQQapDQIECBAgQIAAAQIECBAgQIAAAQIECBAgUHKB6Sn7T7s+ZXnLCRAgQIAAAQIECBAgQIAAAQIECBAgQKCIAgLjijhVPREgQIAAAQIECBAgQIDA+gQExjkbBAgQIECAAAECBAgQIEAgPwIC4/IzCzshQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgQYQqC6YXW2AbdoiAQIECBAgQIAAAQIECBDolsC8a1u6XDdhWnhPerckXUSAAAECBAgQIECAAAECBLIT8MV5dpYqESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAJBATGlWDIWiRAgAABAgQIECBAgECJBQTGlXj4WidAgAABAgQIECBAgACB3AoIjMvtaGyMAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBPIoIDAuj1OxJwIECBAgQIAAAQIECBDISkBgXFaS6hAgQIAAAQIECBAgQIAAgewEBMZlZ6kSAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECJRAQGBcCYasRQIECBAgQIAAAQIECJRYQGBciYevdQIECBAgQIAAAQIECBDIrYDAuNyOxsYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIE8Cc2dEtfN+xk+enaft2QsBAgQIECBAgAABAgQIEMhUoDK6xXvRMxVVjAABAgQIECBAgAABAgQIdF/AF+ndt3MlAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECJRIQGBciYatVQIECBAgQIAAAQIECBAIgXEOAQECBAgQIECAAAECBAgQyI+AwLj8zMJOCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCDHAgLjcjwcWyNAgAABAgQIECBAgACBzAUExmVOqiABAgQIECBAgAABAgQIEOi2gMC4btO5kAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTKJCAwrkzT1isBAgQIECBAgAABAgQICIxzBggQIECAAAECBAgQIECAQH4EBMblZxZ2QoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQINIFBdMLvaANu0RQIECBAgQIAAAQIECBAg0C2Bede2dLluwrTwnvRuSbqIAAECBAgQIECAAAECBAhkJ+CL8+wsVSJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBEggIjCvBkLVIgAABAgQIECBAgACBEgsIjCvx8LVOgAABAgQIECBAgAABArkVEBiX29HYGAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjkUUBgXB6nYk8ECBAgQIAAAQIECBAgkJWAwLisJNUhQIAAAQIECBAgQIAAAQLZCQiMy85SJQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKB+AtNT3jrt+pTlLSdAgAABAgQIECBAgAABAgQIECBAgACBIgsIjCvydPVGgAABAgQIECBAgAABAgLjnAECBAgQIECAAAECBAgQIJA/AYFx+ZuJHREgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLpBaopL/EzkpRglhMgQIAAAQIECBAgQIAAAQIECBAgQIBAxNwZ0eX70eMnz8ZCgAABAgQIECBAgAABAgQKK1AZ3eLvWRV2uhojQIAAAQIECBAgQIAAgUYT8EV6o03MfgkQIECAAAECBAgQIECAAAECBAgQIECAAIF1CQiMcy4IECBAgAABAgQIECBAgAABAgQIECBAYJMLCIzb5MRuQIAAAQIECBAgQIAAAQI5EhAYl6Nh2AoBAgQIECBAgAABAgQIlF5AYFzpjwAAAgQIECBAgAABAgQIECBAgAABAgQIECBQCAGBcYUYoyYIECBAgAABAgQIECBAgAABAgQIECCQb4H1BcbNu7aly8bHT57d5Z/9Pp/OB8L58PnR+Tx4Png+eD6sFvB89Hz0fFwt4PXB64PXB68PqwTq/fo4YVp4L3q+v1VhdwQIECBAgAABAgQIECBQIgFfpJdo2FolQIAAAQIECBAgQIAAAQIECBAgQIAAAQIFFhAYV+Dhao0AAQIECBAgQIAAAQIECBAgQIAAAQJ5ERAY9/ok6h1Y4P4ChTo/EwTqCNTpfB48HzwfPB9WC3g+ej56Pq4W8Prg9cHrQ/dfHwTG5eU7EvZBgAABAgQIECBAgAABAgRCqrtDQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBRCQGBcIcaoCQIECBAgQIAAAQIECBAgQIAAAQIECDSmwJpBcmu+od7vR5fv4/Pp+l4O58P56Pzk8/nh86PzefB88HzwfFgt4Pno+ej5uFrA64PXh3q+PjTmV+12TYAAAQIECBAgQIAAAQIEiiFQKUYbuiBAgAABAgQIECBAgAABAgQIECBAgAABAgRKLiAwruQHQPsECBAgQIAAAQIECBAgQIAAAQIECBCop4DABoENnc+fQBuBNp3Pg+eD54Pnw2oBz0fPR8/H1QJeH7w+eH3w+rBKoNFeH+v5tbd7EyBAgAABAgQIECBAgACBsgsIjCv7CdA/AQIECBAgQIAAAQIECBAgQIAAAQIECBAohoDAuGLMURcECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECKwhIDDOkSBAgAABAgQIECBAgAABAgQIECBAgAABAgSKICAwrghT1AMBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAmsJCIxzKAgQIECAAAECBAgQIECAAAECBAgQIECAAIEiCAiMK8IU9UCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwFoCAuMcCgIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAIAgLjijBFPRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsJaAwDiHggABAgQIECBAgAABAgQIECBAgAABAgQIECiCgMC4IkxRDwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIrCUgMM6hIECAAAECBAgQIECAAAECBAgQIECAAAECBIogMD1lE2nXpyxvOQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBLIREBiXjaMqBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQyFxAYFzmpAoSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgGwGBcdk4qkKAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHMBQTGZU6qIAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBLIREBiXjaMqBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQyFxAYFzmpAoSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgGwGBcdk4qkKAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHMBQTGZU6qIAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBLIREBiXjaMqBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQyFxAYFzmpAoSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgGwGBcdk4qkKAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHMBQTGZU6qIAECBAgQIECAAAECBAgQIECAAAECBAgQIFAHgekp75l2fcrylhMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEjbPgSAAAIABJREFUCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAbAYFx2TiqQoAAAQIECBAgQIAAAQIECBAgQIAAAQIECNRXoJry9n5GkhLMcgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE6iPgzVD1cXdXAgQIECBAgAABAgQIECBAgAABAgQIECBAIFsBgXHZeqpGgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBOBATG5WQQtkGAAAECBAgQIECAAAECBAgQIECAAAECBAj0SEBgXI/4XEyAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQF4FBMbldTL2RYAAAQIECBAgQIAAAQIECBAgQIAAAQIECKQREBiXRstaAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQaRkBgXMOMykYJECBAgAABAgQIECBAgAABAgQIECBAgACBDQgIjHM8CBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAopIDAuEKOVVMECBAgQIAAAQIECBAgQIAAAQIECBAgQKB0AgLjSjdyDRMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAoh4DAuHLMWZcECBAgQIAAAQIECBAgQIAAAQIECBAgQKDoAgLjij5h/REgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAoqYDAuJIOXtsECBAgQIAAAQIECBAgQIAAAQIECBAgQKBgAgLjCjZQ7RAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg8LqAwDgngQABAgQIECBAgAABAgQIECBAgAABAgQIECiCgMC4IkxRDwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIrCUgMM6hIECAAAECBAgQIECAAAECBAgQIECAAAECBIogIDCuCFPUAwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECawkIjHMoCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgSIICIwrwhT1QIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAWgIC4xwKAgQIECBAgAABAgQIECBAgAABAgQIECBAoAgC01M2kXZ9yvKWEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBsBgXHZOKpCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBzAUExmVOqiABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSyERAYl42jKgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMhcQGBc5qQKEiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBsBgXHZOKpCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBzAUExmVOqiABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSyERAYl42jKgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMhcQGBc5qQKEiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBsBgXHZOKpCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBzAUExmVOqiABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSyERAYl42jKgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMhcQGBc5qQKEiBAgAABAgQIECBAgAABAgQIECBAgAABAnUQmJ7ynmnXpyxvOQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBLIREBiXjaMqBAgQIECAAAECBAgQIECAAAECBAgQIECAQH0Fqilv72ckKcEsJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgPgLeDFUfd3clQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEshUQGJetp2oECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECOREQGBcTgZhGwQIECBAgAABAgQIECBAgAABAgQIECBAgECPBATG9YjPxQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI5FVAYFxeJ2NfBAgQIECAAAECBAgQIECAAAECBAgQIECAQBoBgXFptKwlQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBhBATGNcyobJQAAQIECBAgQIAAAQIECBAgQIAAAQIECBDYgIDAOMeDAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFCCgiMK+RYNUWAAAECBAgQIECAAAECBAgQIECAAAECBEonIDCudCPXMAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFyCAiMK8ecdUmAAAECBAgQIECAAAECBAgQIECAAAECBIouIDCu6BPWHwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGSCgiMK+ngtU2AAAECBAgQIECAAAECBAgQIECAAAECBAomIDCuYAPVDgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECrwsIjHMSCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgSIICIwrwhT1QIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAWgIC4xwKAgQIECBAgAABAgQIECBAgAABAgQIECBAoAgCAuOKMEU9ECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCwloDAOIeCAAECBAgQIECAAAECBAgQIECAAAECBAgQKIKAwLgiTFEPBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAisJSAwzqEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEiiAwPWUTadenLG85AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEshEQGJeNoyoECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDIXEBgXOakChIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAbAYFx2TiqQoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgcwFBMZlTqogAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEshEQGJeNoyoECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDIXEBgXOakChIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAbAYFx2TiqQoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgcwFBMZlTqogAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEshEQGJeNoyoECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDIXEBgXOakChIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAbAYFx2TiqQoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgcwFBMZlTqogAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUAeB6SnvmXZ9yvKWEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBsBgXHZOKpCgAABAgQIECBAgAABAgQIECBAgAABAgQI1FegmvL2fkaSEsxyAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTqI+DNUPVxd1cCBAgQIECAAAECBAgQIECAAAECBAgQIEAgWwGBcdl6qkaAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQE4EBMblZBC2QYAAAQIECBAgQIAAAQIECBAgQIAAAQIECPRIQGBcj/hcTIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAXgUExuV1MvZFgAABAgQIECBAgAABAgQIECBAgAABAgQIpBEQGJdGy1oCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBBpGQGBcw4zKRgkQIECAAAECBAgQIECAAAECBAgQIECAAIENCAiMczwIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECikgMC4Qo5VUwQIECBAgAABAgQIECBAgAABAgQIECBAoHQCAuNKN3INEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECiHgMC4csxZlwQIECBAgAABAgQIECBAgAABAgQIECBAoOgCAuOKPmH9ESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECipgMC4kg5e2wQIECBAgAABAgQIECBAgAABAgQIECBAoGACAuMKNlDtECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDwuoDAOCeBAAECBAgQIECAAAECBAgQIECAAAECBAgQKIKAwLgiTFEPBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAisJSAwzqEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEiiAgMK4IU9QDAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJrCQiMcygIECBAgAABAgQIECBAgAABAgQIECBAgACBIggIjCvCFPVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMBaAgLjHAoCBAgQIECAAAECBAgQIECAAAECBAgQIECgCALTUzaRdn3K8pYTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgGwGBcdk4qkKAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHMBQTGZU6qIAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBLIREBiXjaMqBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQyFxAYFzmpAoSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgGwGBcdk4qkKAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHMBQTGZU6qIAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBLIREBiXjaMqBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQyFxAYFzmpAoSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgGwGBcdk4qkKAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHMBQTGZU6qIAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBLIREBiXjaMqBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQyFxAYFzmpAoSIECAAAECBAgQIECAAAECBAgQIECAAAECdRCYnvKeadenLG85AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEshEQGJeNoyoECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAfQWqKW/vZyQpwSwnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKA+At4MVR93dyVAgAABAgQIECBAgAABAgQIECBAgAABAgSyFRAYl62nagQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI5ERAYFxOBmEbBAgQIECAAAECBAgQIECAAAECBAgQIECAQI8EBMb1iM/FBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjkVUBgXF4nY18ECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAGgGBcWm0rCVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoGEEBMY1zKhslAABAgQIECBAgAABAgQIECBAgAABAgQIENiAgMA4x4MAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgUIKCIwr5Fg1RYAAAQIECBAgQIAAAQIECBAgQIAAAQIESicgMK50I9cwAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXIICIwrx5x1SYAAAQIECBAgQIAAAQIECBAgQIAAAQIEii4gMK7oE9YfAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgZIKCIwr6eC1TYAAAQIECBAgQIAAAQIECBAgQIAAAQIECiYgMK5gA9UOAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKvCwiMcxIIECBAgAABAgQIECBAgAABAgQIECBAgACBIggIjCvCFPVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMBaAgLjHAoCBAgQIECAAAECBAgQIECAAAECBAgQIECgCAIC44owRT0QIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQILCWgMA4h4IAAQIECBAgQIAAAQIECBAgQIAAAQIECBAogoDAuCJMUQ8ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECKwlIDDOoSBAgAABAgQIECBAgAABAgQIECBAgAABAgSKIDA9ZRNp16csbzkBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSyERAYl42jKgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA4P+zd9+BVZV3H8B/QerCVfembhRHEaxoXVhUFAcqSF2oKNa6FVfr3nvVWalUHCiCiigW1ApuFNwiVuvAhVrrqoh1kLcnvmBuSHLPSW6Se3M/5w9bvL/zjM/zNFxXAAAgAElEQVTz3OfkhuQrAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUHABgXEFJ9UgAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECiMgMK4wjlohQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAwQUExhWcVIMECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAojIDAuMI4aoUAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIFFxAYV3BSDRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAwAgLjCuOoFQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBRcQGBcwUk1SIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgcIICIwrjKNWCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUHABgXEFJ9UgAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECiMgMK4wjlohQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAwQUExhWcVIMECBAgQIAAAQIECBAgQIAAAQIECBAgQIBACwiclrHPrPUZm1dOgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBwggIjCuMo1YIECBAgAABAgQIECBAgAABAgQIECBAgACBlhWozNi9vyPJCKacAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGWEfDLUC3jrlcCBAgQIECAAAECBAgQIECAAAECBAgQIECgsAIC4wrrqTUCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBIpEQGBckSyEYRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKNEhAY1yg+NxMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUKwCAuOKdWWMiwABAgQIECBAgAABAgQIECBAgAABAgQIEMgiIDAui5ZaAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRKRkBgXMkslYESIECAAAECBAgQIECAAAECBAgQIECAAAEC9QgIjLM9CBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBolQIC41rlspoUAQIECBAgQIAAAQIECBAgQIAAAQIECBAoOwGBcWW35CZMgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoDwEBMaVxzqbJQECBAgQIECAAAECBAgQIECAAAECBAgQaO0CAuNa+wqbHwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEyFRAYV6YLb9oECBAgQIAAAQIECBAgQIAAAQIECBAgQKCVCQiMa2ULajoECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECPwoIDDOTiBAgAABAgQIECBAgAABAgQIECBAgAABAgRag4DAuNawiuZAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMAcAgLjbAoCBAgQIECAAAECBAgQIECAAAECBAgQIECgNQgIjGsNq2gOBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjMISAwzqYgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEWoOAwLjWsIrmQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAHAIC42wKAgQIECBAgAABAgQIECBAgAABAgQIECBAoDUInJZxElnrMzavnAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAoUREBhXGEetECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoOACAuMKTqpBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIFEZAYFxhHLVCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBggsIjCs4qQYJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQGAGBcYVx1AoBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQKLiAwruCkGiRAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECglgasHDq2sbbwHX7xHTjZHzTqv86m+b+yP3PeR94f3h/fHTwLOB+dD9feD89H5mOV8nFUrMK6UPl0YKwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQMEFBMb9SCrQSKBR9TeXQCOBRtX3g/PB+eB8+EnA+eh8bM7zcVZfAuMK/hFAgwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAqUkUFdgXM/+3XKmMXrwuJw/e51P9Q1hf3h/VN8Pzgfng/PhJwHno/PR+fiTQNrnQ833jcC4Uvp0YawECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIFFxAY9yOpQB+BPtXfXGkDbWbdY//YP/bPTwLePwIDq78fnI/Ox4acjwLjCv4lvwYJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRKWaBmYFzNoJ9SnpuxEyBAgAABAgQIlJ6AwLjSWzMjJkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQaEIBgXFNiKtpAgQIECBAgACBzAIC4zKTuYEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAoAYHTMo4xa33G5pUTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAiUosDUydMqS3HcxkyAAAECBAgQINA6Bdp3XKYimVnVP1wECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgRIXyPqDOf6OpMQX3PAJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAk0hIDCuKVS1SYAAAQIECBAg0FABgXENlXMfAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUIwCAuOKcVWMiQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQYgJ1BcaNHjyu1pn07N8t59/XrPM6n+obxP7IfR95f3h/eH/8JOB8cD5Ufz84H8vrfMz35bLAuHxCXidAgAABAgQIECBAgAABAgQIECBAgAABAgRKSUBgXCmtlrESIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBIpUQGCcwJ7qW1NgT3kF9gjs8v73/v9JwPnn/Kv+fnA+Nu35mO/LYoFx+YS8ToAAAQIECBAgQIAAAQIECBAgQIAAAQIECJSSgMC4UlotYyVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIFJnA1QOH5vw8ar6gnFnDz1fndYE71be6wJ2mDdzhy7f6+8356/x1/v4k4HwszvMx67pUFNnXz4ZDgAABAgQIECBAgAABAgQIECBAgAABAgQIEGiIgMC4hqi5hwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoEpAYNyPGyFrcIlAJoFM1Y8Q+6c4A4lmrZH1sT7V36/Ob+e38/sngZY6H7P2KzDOBxcCBAgQIECAAAECBAgQIECAAAECBAgQIECgNQgIjGsNq2gOBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEWEsgaGFczaKeFhq1bAgQIECBAgACBEhUQGFeiC2fYBAgQIECAAAECBAgQIECAAAECBAgQIECAQKMEBMY1is/NBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHyFhAYV97rb/YECBAgQIAAgeYWEBjX3OL6I0CAAAECBAgQIECAAAECBAgQIECAAAECBIpBQGBcMayCMRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIESlxg6uRpWX8utcRnbPgECBAgQIAAAQLFLNC+4zIVyfiq/uEiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECJS4QNYfzPF3JCW+4IZPgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEGgKAYFxTaGqTQIECBAgQIAAgYYKCIxrqJz7CBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgWIUEBhXjKtiTAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBEhMQGFdiC2a4BAgQIECAAIFWLiAwrpUvsOkRIECAAAECBAgQIECAAAECBAgQIECAAIEyExAYV2YLbroECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgaYQEBjXFKraJECAAAECBAgQaKiAwLiGyrmPAAECBAgQIECAAAECBAgQIECAAAECBAgQKEaB0zIOKmt9xuaVEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIlJLA1QOH5vwHjHv275Yz/NGDx+X8uebrpTRXYyVAgAABAgQIEGh5gXxfX9Z8vaLlh2wEBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBFpOQGBcy9nrmQABAgQIECBQjgIC48px1c2ZAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgwQIC4xpM50YCBAgQIECAAIEGCAiMawCaWwgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBMpXQGBc+a69mRMgQIAAAQIEWkJAYFxLqOuTAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECg5AWmTp5WWfKTMAECBAgQIECAAIFWI9C+4zIVyWSq/uEiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUO4CAuPKfQeYPwECBAgQIECguAQExhXXehgNAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBACwsIjGvhBdA9AQIECBAgQIBAjoDAOBuCAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAg0QGDKoKisftuaA6Ki+p+9zsf++EnA+8P54Hz8ScDzwfPB88HzYZaA56Pno+ej5+MsgWL/+qABH5ndQoAAgVYhIDCuVSyjSRAgQIAAAQIEWo2AwLhWs5QmQoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLNKVDsv9BvfAKJqr8fBNIIpKm+H5wPzgfnw08Czkfno/PxJwHPB88HzwfPh1kCNZ+PzflZW18ECBAoBoGrBw7N+Q9F9OzfLWdYowePy/lzzdeLYQ7GQIAAAQIECBAgUDoC+b6+rPl6zn/FrHSmaaQECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBlBGoGanToOSlnIK+O7pLzZ6/zqb4h7A/vj+r7wfngfHA+/CTgfHQ+Oh9/EvB88HzwfPB8mCXQUs/Hmv0KkmuZ7z/olQCB5hcQGNf85nokQIAAAQIECJSzgMC4cl59cydAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBJhcQGCfQpvomE+gi0KX6fmipQI9ZY9C/88n59JOA89n57Hz+ScDzwfPB88HzYZZA2uejwLgm/9aCDggQKFIBgXFFujCGRYAAAQIECBBopQIC41rpwpoWAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBSHQOUHkyqLYyRGQYAAAQIECBAgQIAAAQIECi8gMK7wplokQKA0BATGlcY6GSUBAgQIECBAoLUICIxrLStpHgQIECBAgAABAgQIECBAgAABAgQIECBAgEAWgdOyFP+vNmt9xuaVEyBAgAABAgQIECBAgAABAgQIECBAgEBrFhAY15pX19wIECBAgAABAgQIECBAQGCcPUCAQLkLTJ08zX8wotw3gfkTIECAAAECBIpIoH3HZSqS4VT9w0WAAAECBAgQIECAAAECBAgQIECAAAECBAgQKHGBrD+Y4+9ISnzBDZ8AAQIECBAgQIAAAQIECBAgQIAAAQItITBlUOR8P7pDz0ktMQx9EiBAgAABAgQIECBAgACBZhGoWLaLn7NqFmmdECBQbAIC44ptRYyHAAECBAgQIFDeAgLjynv9zZ4AAQIECBAgQIAAAQIECBAgQIAAAQIECLQ2AYFxrW1FzYcAAQIECBAgQIAAAQIECBAgQIAAAQJFKCAwrggXxZAIECBAgAABAgQIECBAoMkEBMY1Ga2GCRAocgGBcUW+QIZHgAABAgQIECgzAYFxZbbgpkuAAAECBAgQIECAAAECBAgQIECAAAECBFq5gMC4Vr7ApkeAAAECBAgQIECAAAECBAgQIECAAIFiEBAYVwyrYAwECBAgQIAAAQIECBAg0FwCAuOaS1o/BAgUm4DAuGJbEeMhQIAAAQIECJS3gMC48l5/sydAgAABAgQIECBAgAABAgQIECBAgAABAq1NQGBca1tR8yFAgAABAgQIECBAgAABAgQIECBAgEARCgiMK8JFMSQCBAgQIECAAAECBAgQaDIBgXFNRqthAgSKVODqgUNzfh61Z/9uOSMdPXhczp9rvl6k0zIsAgQIECBAgACBIhXI9/VlzdcrinQehkWAAAECBAgQIECAAAECBAgQIECAAAECBAgQyCIgMC6LlloCBAgQIECAAAECBAgQIECAAAECBAgQaJRA5QeTsn5fulH9uZkAAQIECBAgQIAAAQIECDSnwKuju+R0t+aA8DvpzbkA+iJAoMUEBMa1GL2OCRAgQIAAAQJlKSAwriyX3aQJECBAgAABAgQIECBAgAABAgQIECBAgEDZC2T9xTw/wFj2WwYAAQIECBAgQIAAAQIECBAgQIAAAQIEGi4gMK7hdu4kQIAAAQIECBAgQIAAgeIXEBhX/GtkhAQINI2AwLimcdUqAQIECBAgQIBA7QIC4+wMAgQIECBAgAABAgQIECBAgAABAgQIECBAoBwFBMaV46qbMwECBAgQIECAAAECBAgQIECAAAECBFpIQGBcC8HrlgABAgQIECBAgAABAgSaRUBgXLMw64QAgSIUEBhXhItiSAQIECBAgACBViwgMK4VL66pESBAgAABAgQIECBAgAABAgQIECBAgAABAnUKCIyzOQgQIECAAAECBAgQIECAAAECBAgQIECg2QQExjUbtY4IECBAgAABAgQIECBAoAUEBMa1ALouCRAoKoGpk6dl/bnUohq/wRAgQIAAAQIECLQugfYdl6lIZlT1DxcBAgQIECBAgAABAgQIECBAgAABAgQIECBAoMQFsv5gjr8jKfEFN3wCBAgQIECAAAECBAgQIECAAAECBAi0hMCUQZHz/egOPSe1xDD0SYAAAQIECBAgQIAAAQIEmkWgYtkufs6qWaR1QoBAsQkIjCu2FTEeAgQIECBAgEB5CwiMK+/1N3sCBAgQIECAAAECBAgQIECAAAECBAgQINDaBATGtbYVNR8CBAgQIECAAAECBAgQIECAAAECBAgUoYDAuCJcFEMiQIAAAQIECBAgQIAAgSYTEBjXZLQaJkCgyAUExhX5AhkeAQIECBAgQKDMBATGldmCmy4BAgQIECBAgAABAgQIECBAgAABAgQIEGjlAgLjWvkCmx4BAgQIECBAgAABAgQIECBAgAABAgSKQUBgXDGsgjEQIECAAAECBAgQIECAQHMJCIxrLmn9ECBQbAIC44ptRYyHAAECBAgQIFDeAgLjynv9zZ4AAQIECBAgQIAAAQIECBAgQIAAAQIECLQ2gdMyTihrfcbmlRMgQIAAAQIECBAgQIAAAQIECBAgQIBAaxQQGNcaV9WcCBAgQIAAAQIECBAgQKAuAYFx9gYBAuUmcPXAoTn/AeOe/bvlEIwePC7nzzVfLzev1jzfysrKeOOtf0byv/muJZdYKhZeaOF8ZV4vQ4H33n83ZnwzI+/MF1hgwVhmqWXy1jWm4Ntvv42p776dqonlll0+5p9v/lS1igg0VMCeTCdXTOdIuhGryiqQ7+vLmq9XZO1APQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKGeByg8m5f8twXIGMncCBAgQIECAAAECBAgQKGmBV0d3yRn/mgPC76SX9IoaPAECaQUExqWVav11U/4xOXrs0j3VRPfq2y/OPuX8VLWKykugfcd0IXArLt8+Hh07oUlxRo+9Jw4++sBUfZxy/Bmxf78BqWoVEWiogD2ZTq6YzpF0I1aVVUBgXFYx9QQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIIOAwLgMWEoJECBAgAABAgQIECBAoOQEBMaV3JIZMAECBRIopcC4L778It55d2qBZj5nMyuu0D4WXqkEZ00AACAASURBVGjhJmu/2BuePOXl2K73VqmGuXvvPeO80y9KVauovATSBj0ts9QyMeGhZ5sU556/3R2HHnNQqj5OPv70OKBfunC5VA0qIlCLgD2ZblsU0zmSbsSqsgoIjMsqpp4AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECGQQEBiXAUspAQIECBAgQIAAAQIECJScgMC4klsyAyZAoEACpRQYd8mVF8bl11xSoJnP2czhBx0VAw87rsnaL/aGBcYV+wqVxviKKehJOFdp7JlyGqU9mW61i+kcSTdiVVkFBMZlFVNPgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQyCAiMy4CllAABAgQIECBAgAABAgRKTkBgXMktmQETIFBggamTp1UWuMmCNycwruCkOQ0KjGta33JpvZiCnoRzlcuuK5152pPp1qqYzpF0I1bVVALtOy5TkbRd9Q8XAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUL/AlEGR84uiHXpOQkaAAAECBAgQIECAAAECBFqtQMWyXfwueqtdXRMjQKA+AYFxEYcfdFQMPOy4st0oAuPKdukLOvFiCnoSzlXQpdVYAQTsyXSIxXSOpBuxqqYSEBjXVLLaJUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFWKSAwrlUuq0kRIECAAAECBAgQIECAQB0CAuNsDQIEylVAYJzAOIFx5fruL+y8iynoSThXYddWa40XsCfTGRbTOZJuxKqaSkBgXFPJapcAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEWqWAwLhWuawmRYAAAQIECBAgQIAAAQJ1CAiMszUIEChXAYFxAuMExpXru7+w8y6moCfhXIVdW601XsCeTGdYTOdIuhGraioBgXFNJatdAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEGiVAgLjWuWymhQBAgQIECBAgAABAgQI1CEgMM7WIECg3ASuHji0svqce/bvlkMwevC4nD/XfL05vS658sK4/JpLmqzLww86KgYedlyTtV/sDQuMK/YVKo3xFVPQk3Cu0tgz5TRKezLdahfTOZJuxKqyCuT7+rLm6xVZO1BPgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTKWaDyg0k5vzhazhbmToAAAQIECBAgQIAAAQKtT+DV0V1yJrXmgPA76a1vmc2IAIFaBATG/YQiMO7l2K73VqneJ7v33jPOO/2iVLWKykugmIKehHOV194rhdnak+lWqZjOkXQjVpVVQGBcVjH1BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgg4DAuAxYSgkQIECAAAECBAgQIECg5AQExpXckhkwAQIFEiilwLhrr78qzr3krALNfM5mTjj6xPj9/oc2WfvF3vDkKQLjin2NSmF8xRT0JJyrFHZMeY3Rnky33sV0jqQbsaqsAgLjsoqpJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECGQQExmXAUkqAAAECBAgQIECAAAECJScgMK7klsyACRAokEApBcbVNeUJE5+Mvvvuklpk2A13RNcNNk5dXy6FAuPKZaWbdp7FFPQknKtp11rr2QXsyXRmxXSOpBuxqqwCAuOyiqknQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEWoPAaRknkbU+Y/PKCRAgQIAAAQIECBAgQIAAAQIECBAgQKA1CwiMa82ra24ECBAgQIAAAQIECBAgIDDOHiBAoNwFpk6eVlmqBqUSGPfRxx/GtI+mxUcffxTTp38VK67QPlZdebVYZOFFmoz++x++j/fefzc+/OjD+PhfH8UPP/wQ8803Xyy26OKxxmodYqEFF5rdd0sExn35ny/j9Tf+EZ9/8Xl8+tmnVf/7zYwZseCCC8bCCy0SCy+0cCy37PKx2iqrR5s2bZrMqVgbTvbL1Hfeji+++Dw++/zT+OyLz6rWcJbNIov8PFZdadVYdpnlmnQKH3z4QXz00bT48OMP4+uvv4527drFAu0WiF+suFIsv9wKOX0XU9BTaw3n+uLLL6re1//65OOq983iiy9R9R5Zesmlo6Kiokn3wo978q347PPPqt6vyb6srKyMny+yaPx8kZ9XnWcrLt++6n3rmlOgJfakc6TunTjjmxnxzrtTq95L//rkXzHPPPNU7d8Vll+x6vnT0pdnZET7jstUHWpNe7K19ErrnwABAgQIECBAgAABAgQIECBAgAABAgQIECgXgaw/mOPvSMplZ5gnAQIECBAgQIAAAQIECBAgQIAAAQIECigwZVDkfD+6Q89JBWxdUwQIECBAgAABAgQIECBAoLgEKpbt4uesimtJjIYAgWYSEBhXN3QSzHTPmLtTrUS3TX8THVZfc3ZtEqo06r674va7bouXX3mp1jaS8LZO664fu/fZM7pvsXWqfvIVJX2NvPeOGDrilqpwurquJGRq3z33j5132DXenvpWbNd7q3xNV72+e+8947zTL0pVW7PohZefj7+PfyAeeeLheO6FZ1K10a7dArHFJt1i6y17xPY9doi2bX9W531Z1mtWI9tu1bMq/CzN1ZD2++2+X7Sbv13e5n+Y+UOMf/ShePix8THukb/HO+9NzXtPUrDMUsvEFptuGb223zW6brBRqnvyFSUhcaNG3xXD7hwab779Zp3lyf7dY7e9Ys/d+lWNozUHxj0+4dF4cfIL+ehyXt+77z6xwAILVv27b7/9NoYMHRxJkGO+a41VO8SWm3efXfbdd9/FmAfvizvvGREPPfxgrbcn75N11lo3unfbOvbba/9oO1fbfN3kfX3mzJnxyOPj46FH/p5pTyahW902+03V+zbZm2kCH5PguRtv/Wt8PePrvONKCnp03y5War9y3trkDLzxthvy1iXnYZozOAkXG3H37XnbSwoWX2yJ6NOr7+za5giMc47UvzTJ+2/sg3+L++6/N+4dM6rO4lnvpz132zu222b7qvfTX268Lr777tu8a99p3c4NPoub+hmZd/BFViAwrsgWxHAIECBAgAABAgQIECBAgAABAgQIECBAgACBRgkIjGsUn5sJECBAgAABAgQIECBAgAABAgQIECBAII2AwLg0SmoIECBAgAABAgQIECBAoLUICIxrLStpHgQIZBUQGFe3WEMCfpJAmttGDI1zLj6z3sC2mr3ut9cBccJRJ8a8886bdQmr6v/96SdxzkVnpg4zmtVJEozTsUPHePqZp1L125DAuCQc7tKrLoqHHx+fqo+6ipJQsqMPPTZ69+pbawjVP15/Nbbu1S1TH53W6xx33Hx3zNVmrnrvS8Kzdu/fJyZMfCJ1+0mg2kP3PhqLLLxInfck++Vv94+OS668oN5wtjSddum0QRx7xAnRdYON05TPUZMEkw0acm2cf+k5me/fqecucffoO1Pdl6zjhIeeTVXb0KKGvHfr6uv1N16L7jtunmkoyRzH3PXQ7LX/8KMPY8MtO6VqIwkuu/6qIVW1jz35SJx81h8y7Y1k/S8//6pYeqmlU/VXsyjZ639/+IG4+E8XxJTXXmlQG7NuSoLYjj38hNhqy23qDY5L9t6qv1wxdV8nHH1i/H7/Q/PWPzBubBxw6L5565KQu0fHTshbN/LeO+OI4w/JW5cU1GyzkHuy5gCcI/mXJHkvnXbuyZG8n7NcyXs5CeS85vorU91WzM/IVBMooiKBcUW0GIZCgAABAgQIECBAgAABAgQIECBAgAABAgQINFpAYFyjCTVAgAABAgQIECBAgAABAgQIECBAgAABAvkEBMblE/I6AQIECBAgQIAAAQIECLQmAYFxrWk1zYUAgSwCAuPq1soa8NN9i63ikIG/i5dfeSnLEsyuXXP1teLPf7o+2q/wi0z3P/HUY3HAYftlCqjL1EG14ixhODO+mRHHn3JM6iCxtGPapvu2cem5V0S7+dvNccuNt95QFbCV5Trl+DNi/34D6r3lptuGxElnnpCl2Rh+48j4VecN67znnfemxoGH9W90KFfNDo478g9x8AGHRUVFRerxfvDhB9HvwN0zhyml7qBaYSkFxn094+vo2XurTIFtyVTH3PX3SN7Ps66sgXF/uuCqOOmsP8Sdo0Y0hDiSIMhrL/tLbLZxtqC7zz7/LA4++sBIzpRCXsn74NrLro/FFl2szmYPOvKA+NsDo1N1u+Xm3eOvV9+UtzZ5zybv3TTX4/c/Hcsvt0K9paecfWIMGTo4TXPxu/4Hxx8Hnjy7Nuvz5IB+B6bqxzlSP9MPM3+IM88/Lf56819SeTa2qNifkY2dX3PeLzCuObX1RYAAAQIECBAgQIAAAQIECBAgQIAAAQIECDS1gMC4phbWPgECBAgQIECAAAECBAgQIECAAAECBAiEwDibgAABAgQIECBAgAABAgTKSUBgXDmttrkSIJAIXD1waM7Po/bs3y0HZvTgcTl/rvl6MShOmPhk9N13l9RDGXbDHdF1g41T12cJ+Fl7rXXiralvNTq0bdONN4+bB92WeozjHxsX+/xuj9T1jS1MG4aTBJANOGzfBofn5RtnEsZ12w13xCILL5JTWllZGQce3j/uf2hMviZyXh9/3+OxUvuVa70nCWTadJuumdo75rDj47CDjqzznqcmTYj9Dt670fulrg769OobF551aarQuHffeyf69OsV0z6almmODS0upcC4gX88IkbcfXumqSZBbdtu1TPnniyBcYstungsvNBCmUPqag4yCY176u/PxIILLpRq/K+/8Vr0P7hfJPu9Ka5k3Yf8eWissVqHWpu//a7b4tiTjkrVdTK3lya8GnO1mavO+uQs6LzZuvHvTz9J1Wbyftlt59/WW9t9x81ThyreOnh4bLzhJrPby/I8Ofn40yNNYJxz5Nl61+u7776LgSceWfDQ0vo6LfZnZKo3QxMX5fv6subr6aNPm3jgmidAgAABAgQIECBAgAABAgQIECBAgAABAgQINEJAYFwj8NxKgAABAgQIECBAgAABAgQIECBAgAABAtkEKj+YlPX70tk6UE2AAAECBAgQIECAAAECBFpQ4NXRXXJ6X3NA+J30FlwPXRMg0HwCAuPyW2cJ+MnfWvqK4TeOjF913jDvDS+8/Hzs2HfbvHWFLEgThvPBtPdj+916pA5qauj4tt6yR1z3p8FzhKJ9+tmnkQQ7pQ2KSvrv0mmDuP3Gu+YIoJo5c2bs3r9PTJj4ROphJqGEt1w/LNrO1bbWe8Y/+lDsc9CeqdtraOEpx58R+/cbUO/tX/7ny9h6py2aLSwuGUypBMbdfuetcezJR2fiP+L3R8fRhx47xz1ZAuMydZin+JjDT4jDfndE3ianvvt2bLvrVk0WYDhrAEnQ2+jhY2sNZ0wCC7tuuX7esc4qGHvXQ9Fh9TXrrH/1tSmxzc5bpm5vx+16xRUXXlNn/RdffhHrblR72F1tN73+3NSYe+65Z7+U5XmSJjDOObJMTHio/sC4cy4+M/48+OrUe6AQhaXwjCzEPBvThsC4xui5lwABAgQIECBAgAABAgQIECBAgAABAgQIEChVgay/mOcHGEt1pY2bAAECBAgQIECAAAECBAgQIECAAAECRSAgMK4IFsEQCBAgQIAAAQIECBAgQKDJBATGNRmthgkQKHIBgXH5FyhLwE/+1tJXJGFxSWhcfdeMb2bEdrt2jzfffjN9wwWozBeG89///jf69OsVSZhdc1xnnnRO9Nt9vzm6euKpx6qC3rJcZ5x4duyzR/+cW24edmOceMbxqZtJArHG3ftoLLXk0rXe8857U6PHLt2bPJhrVuf33j421um4bp3jP+bEI2P4yGGp51eIwlIIjJvy2ivRY+ffZJpuEmB47eV/mSN0MGmkpQLjkv345IOTYuGFFq5zLt9880302r1nJHNujmu1VVaPe24fE/PNO98c3SXmacdx/ukXxW971x28OGjIn+OsC05LPaXE6qUJr9a6fkkjjz35SOx5QN9U7W27Vc+49rK/5NRmeZ7kC4xzjuQPnnx8wqOxx/67pVqvQhaVyjOykHPO2pbAuKxi6gkQIECAAAECBAgQIECAAAECBAgQIECAAIHWICAwrjWsojkQIECAAAECBAgQIECAAAECBAgQIECgRAQExpXIQhkmAQIECBAgQIAAAQIECDRIQGBcg9jcRIBAKxAQGJd/EbME/ORvLVvFsBvujK4bbFTnTUkQUhKI1NxXvjCck848IW66bUimYSUBYu1XXCk+mPZ+JEFIWa4k5GnSwy/E/PPNP8dtF1x2blw16E9ZmouH//ZE/GLFlaruefe9d2KTbTbMdP+Qa2+JLTbdstZ7GhrMtfIvVo7FF1si3njrjfj3p59kGk8ylmRMtV0PjBsbBxy6b6b2ClFc7IFxX331n9h2160y7cVkje4ZNiYWWGDBWolaKjAuGczAw46Lww86qs6la8h7trH7oK5z5OIrLog/XXtpqub79OobF519WZ21fffdNSZMfCJVW7OK6gtYvOLay+KiK85P1V4yrmR81a8sz5P6AuOcIz+q1neOfPvtt9H1N50zn5epFjdPUSk9Iwsx34a0ITCuIWruIUCAAAECBAgQIECAAAECBAgQIECAAAECBEpdQGBcqa+g8RMgQIAAAQIECBAgQIAAAQIECBAgQKCEBATGldBiGSoBAgQIECBAgAABAgQIZBYQGJeZzA0ECLQygamTp2X9udSiEZgw8cnou+8uqccz7IY7ousGG6euzxLwk7rRlIUnHXdaDNjnd7VWt2T4VH1hOC+/8lL07LN1yhlG/K7/wXHogUfEQgsuNPuer2d8HZdfc0lce/1Vqds597QLY48+e81R/91338Wue+0YL7z8fOq2kv1x6+DhVfV7HtA3nnjqsdT3JvP548CT66wffNOgOP28U1K1lwThnf7Hs2Kn7XaOueeee/Y9H3z4QZxwysB4+PHxqdpJih4c9XCstsrqOfWVlZWx1U5bxOtvvJa6nUIVFntg3GHH/j5G3Tcy9XSTtbpvxP2zgwZru7El37Nbbt49/nr1TbXO5+133orNt01/Js5qJGlz9VXXiB9++CFefW1KPPrEw6m9ZhU+dO+jscpKq+bc98zzE2OXPXdM1VZ9++jL/3wZ63RdI1U71Yv+cPRJcdD+h9R6X78Dd0/9vnt63HOx1JJL57ST5XlSX2Ccc+RH1vrW/85RI+KoPxyWef0LcUMpPSMLMd+mbKN9x2Uqkvar/uEiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECJS4QNYfzPF3JCW+4IZPgAABAgQIECBAgAABAgQIECBAgACBlhCYMihyvh/doeeklhiGPgkQIECAAAECBAgQIECAQLMIVCzbxc9ZNYu0TggQKDYBgXF1r0iWgJ+arSQhUisut2K88/47MX36V5mXfZ89+scZJ55d630XXn5eXHnd5ZnaTMJ1Dh5weHRcs2N0WH2tmG/e+eJfn3wck56bGEm4zoPj70/VXn1hOAcffWCMHntPqnaG3zgyftV5wzprk8CuJLgrzZWEoSWhaLVdDQnEOvOkc6NNmzZx4hnHp+m+qma9tX8Zd9w8Kn72s5/Ves9///vf2Kh7l/j3p5/kbXPF5dvH7TfeVRWIVNs1c+bMOPWcE+PGW2/I21ZSsH+/AXHK8Wfk1D7yxMOx94Dfprq/etHRhx4bndbrHGutsVYsvtgS8cWXX8TLr7wY9z80Nm645fpU7RVzYNzNw27MtO7JhG8adFtstvHm9c69sYFxyR7/9LNPU+2fmgNZ+Rcrx7jRj9c6vpPOPCFuum1IqnVLipJx/OXKG+YIx/tg2vvR/+B+MeW1V1K31XfXPeKCMy7Oqf/+h+9j3Y3WTH1mPvXQc7H0UrnBbEmDY/8+Jg48fL/UY5lVuPGGm8wOjKx+czKuVdZdIVV7a6+1TowePud5muV5UldgnHPkpyWo6xxpaBhm8j5JnpEdVlszVl1ltZh3nnnj8y8+jxdeei7uvOeOuHv0nanWv9Sekakm1UJFAuNaCF63BAgQIECAAAECBAgQIECAAAECBAgQIECAQJMICIxrElaNEiBAgAABAgQIECBAgAABAgQIECBAgEB1AYFx9gMBAgQIECBAgAABAgQIlJOAwLhyWm1zJUCguoDAuLr3Q5aAn1mtDNj3oNhn9/1i+eVWiIqKikjCa957/9047LiD47kXnkm9+bbcvHv89eqb5qif8c2M6LzZuqkDlZIGunTaIP58+fVVAV+1XZOnvBzb9d4q1djqCsP555uvx2922CxVG0cdckwcefDAvLW77rVjVaBdmuuZR16sc3533XNHHHnCoWmaaXDNI2OejPYr/KLO+28bcUscf+oxqdofev3t8euum9Zbm4SHddqkY6r2agsM2+egPWP8ow+luj8pWmzRxeOv19xUFYxX19W+Y+0BdzXrizUw7uVXXoqefbZObZIUnnTsqZG85/NdDQmM23rLHnH4QUfFmh3WirZzta3qIln3y66+OIYMHZyvy5zX33rp/aoQxOpXEl64/qbrpG4nCXi84dpbot387Wq9JwnG7H/IPjFh4hOp23x63HOx1JK5gW+HH3dI6nCu5Fzr0X27OfpLwh6T8L+GXK9M/Occc0yC8Hrs/JtUzdV1vmV5ntQVGOcc+WkJ6jpHXpz8QuywW49UazWraMftesV5p10YSdBrbVdrf0ZmwmrGYoFxzYitKwIECBAgQIAAAQIESkPgvj23XajNN3P/Kiqic2VULNcmKpeorKhYPCKWqKisWHxmVP532xEjVxnTu1emIIoeI0Zm+i9qab/+/cKHT3UB76/69wMfPtUFnJ+t+/x8cNddV+5+xx1vlsZXXUZJoMkEMn2dHhGZvk5vslFrmEALCPj8Wzu6r599/ezr5/QHks8XrfvzhfW1vsX8/Teff9Of1SoJECBAgAABAgSaTkBgXNPZapkAAQIECBAgQIAAAQIEik9AYFzxrYkRESDQPAIC4+p2zhLws+nGm8e5p14QKyy/Yq0NfvHlF5EEoL3+xmupFra2kK/kxscnPBp77L9bqjaSoo033CRuuObmmGeeeeq8pxBhOFdce1lcdMX5eceVBI898cDEmHfeefPWPjj+/tj/kH3y1iUFg6++MX6zed2hd0f94bC4c9SIVG1lLbryomtjh213qve2vvvumipIa5vu28Z1l6cLAzvrwtNj0A3XphruC09MiUUWXqSqdvrX02OtDVZNdV9SlIQo/f2eRyIJaKrvKuXAuD69+sb2fbaJd96bmtpllx17x6XnXpGqPktgXOJ85cV/rgp6rO2aOXNmDDzxiEz7eeL452PJJZbKaW702Hvi4KMPTDX+pOiBu8fH6quuUW/9M89PjF323DF1m5edd2XsvMOuOfUj770zjjj+kFRtJGF9SWhf9SsJ6UxCNZNAvIZcNw26LTbbePOcW28dcUuckDLwceTQe6PTep3n6DrL86SuwDjnyE+sdQXGXX/joDjj/FNSL33y3r/o7MvqrS+HZ2RqsGYsFBjXjNi6IkCAAAECBAgQIECgOAUe6LP9it/Hz3q3qZy5QWVUdImIPN/Rq/ywx4i7l/ELk/WvJx8+1QUELtS/H/jwqS7g/HR+NvL8fDUilouI8ZUVFY9UROXDPYaPfLo4vwozKgJNJnBaxpaz1mdsXjmB4hHw+TfdWvj63Nfnvj5P915Jqnx+8fmlkZ9fMgX9Op+dzzXOZ59/0x/XKgkQIECAAAECBJpIQGBcE8FqlgABAgQIECBAgAABAgSKUkBgXFEui0ERINCEAlcPHJrzcw09+3fL6W304HE5f675ehMOLXXTEyY+GX333SV1/bAb7oiuG2ycur4QAT/VOxs+clgcc+KRqft/66X3o02bNjn1l19zSVxy5YWp2xh+48j4VecN660vRBhO2iCj7XvsGFdd/OdU43/3vXdik23qH/ushg4/6KgYeNhxdbb7n/98Gdv13jpTIFiaQe7RZ68497T612PGNzOiQ+eV0zQXZ59yfuzVt1+q2rvuuSOOPOHQVLW3/GVYbLLRZlW1Tz79ePx2v96p7kuKjjn8hDjsd0fkrS/VwLgkcGzic0/H2Af/lneOswrWXmudGHHT3THfvPOluidLYFz3LbaO668aUm+7b7/zVmy+bfqz7O7b7otfrtMpp81Tzj4xhgxNF064+a+3iBuvuzXVXHv22TpefuWlVLW7994zzjv9opzaT/79r6rAtzRXsg6jh9+fUzrltVeix86/SXN7rTW/639w/HHgyTmvJed2cn7nu5JwxRefnBJt52o7R2ljnyfOkVzSugLjBhy2X9z/0Jh8SzX79UfHTogVl29fb305PCNTgxWgMN/XlzVfryhAn5ogQIAAAQIECBAgQIBASQqM3WXHzSrbtHk47eDbzjdfbHLUwMrx55yV6bPUFn88KW0XVXXjzzkrU7326+fiw6e6gPdX/fuBD5/qAs7Pojs/kx88+K4iKqZXRuXnlRH/qqiI9yMq3o7KytfHn3PW1TVHXBkxoyIqH6qsqBjRZt6vh29z0/3TM32RoZgAAQIEWo2Az7/pltLXP0X39U+9A/L5xecXn1/SnW1JlfPN+VZi3x/z+Tf921slAQIECBAgQIBACwtUfjApUyB2Cw9X9wQIECBAgAABAgQIECBAIJPAq6O75NSvOSAycRpVOQAAIABJREFU/R5Fps4UEyBAoIgEBMblX4zGBvzU7OG1f/4jttppi/wd/3/Fq8+8OUcgVdpgtqSJ9db+ZYwalj8Eq7FhOFmCjJLgqd8fkC7k7Lvvv4+9B/w2lVffXfeIC864uN7a5158Nnrt3jNVe2mKVltl9bjn9jF5Q8MmTHwiknVLcyWhdxus/6s0pfH0M0+lDg+87LwrY+cdfhzDn669NC6+4oJUfSRFzz76ciy26GJ560s1MC4J+Zo+/au885tVsNiii8foEfdHEliV9ip0YFzS71q/Wi31uKsHBs4ac7eev443334z1RQuPPOS2G2X3VPVZgnGTIK6ksCumteOfbeNF15+PlV/r0z8Z7Sbv93s2uv+ek2cfdEZqe6trSh5Xz84KvdXsbtuuX5M+2ha3jb79OobF519Wa11jX2eOEdyWesKjMvyvkj2dLK3813l8ozM51Co1wXGFUpSOwQIECBAgAABAgQItHqBr0aPXvrxIde/Uzlz5s/STHauueeOTY85TqBbHiy/sF0/EB8+1QX8wnb9+4EPn+oCzs+698NXH38ck/5yXZ4ndOU3FdHmb1Exc9jc7X5+d7cbbvgmzdc/aggQIECgdQj4/JtuHX396etPX3+me68kVb4+9/0N399I/37xfCnc88Xn3/T7TiUBAgQIECBAgEDzCAiMax5nvRAgQIAAAQIECBAgQIBAywgIjGsZd70SINDyAgLj8q9BYwN+avaQhFIlYTZpr9oC49KGciV9VA8Jq6/PxobhPPfCM9Frj+3TTqtJ6rbpvm1cd/ngvG1f/Zcr4vxLz8lbl6bg/pHjYo3VOuQt/fPgq+Oci8/MW9eUBWeceHbss0f/qi4OPKJ/jH0wf5BgUpsmiG/WuNPuzbqCngo5/yzv3az93nnLqOj8yw0y3dYUgXFZQtVqBsZ99913seovV0w9h/tGPBAd11w7Vf2bb70R3bbfJFVtUlQz8C35d1dce1lcdMX5qdoYdsMd0XWDjWfXZgnVrKuDp8c9F0stuXTVyx99/GH8qlunVGO5+pLrouc2O9Ram2VPnnz86XFAvwNz2nGO5LLWdo5k3de1BSnWtnjl9IxMtdEbWSQwrpGAbidAgAABAgQIECBAoHULTH/22WXnipm9oyJ6RWV0e2nYbfHvN/45e9LzLLhgLLTc8rHAUkvF3O3axc/atav637nnbxfzLrJI68YxOwIECBAgQKAkBT6fOjVeGj4sfvj227Tj/3qe72O5biNHfp72BnUECBAgUHoCPv+W3poZMQECBAgQIFC/gM+/dggBAgQIECBAgECxCQiMK7YVMR4CBAgQIECAAAECBAgQKKSAwLhCamqLAIFSEhAYl3+1GhvwU1sPaUO1kntrBsZ9++23sVqn9vkH/v8V99w+JtbtuF7e+saG4Tz08IOx38F75+2nKQt+1XnDGH7jyLxd/DDzh9jrgN/GE089lre2voLzT78oftt7z1RtnHvJWXHt9Velqm2qoqMPPTaO+P3RVc336dcrnn7mqVRd1RZcVdeNafd2KQfGZVn36k5NERi3z0F7xvhHH0q1jjWDsf796Sex/qbrpLq3trOovhu//+H7WGXdFVK3/eTfn4lll142p/6Fl5+PJBAvzXXckX+IQwYcXlX6xZdfxLob1R/iuEefvWLo8Jvrbfry86+KXtvvUlVz/0NjYsBh+6UZSrzwxJRYZOHafze7sc8T50juEtR2jmTd1+PufSxWXmmVvGtbTs/IvBgFKBAYVwBETRAgQIAAAQIECBAg0PoEvnn22R2jYua+URk7V5/dx5Mnx38+/CAWWm6FWGj55WKeBRZsfZM3IwIECBAgQKAsBKZ//HF88f578eX778eXH7wfX3/ySR3zrhjVY8RdO5UFikkSIECgDAV8/i3DRTdlAgQIECBQZgI+/5bZgpsuAQIECBAgQKCIBQTGFfHiGBoBAgQIECBAgAABAgQINFpAYFyjCTVAgECJC0ydPK2yVKcwYeKT0XffH0N90lzDbrgjum6wcZrSqprGBvzU1lHaUK3k3pqBcVnDcJ4e91wsteTSeefb2DCcO+4eHkf/8cfAppa61l5rnRg9/P5U3SfhXVvusGlMn/5VqvqaRdv32DGuuvjPqe895sQjY/jIYanrm6LwoP0PiT8cfVJV09133Dxef+O1VN1cfcl10XObHVLVpt3bpRwYd/1VQ6L7Flun8qhe1BSBcQcdeUD87YHRqcZSMzDuzbfeiG7bb5Lq3nbtFohXnn49Ve2soiSMLjmv0lxj7vp7rLn6WjmlSbDjOl07pHqPbrHpljHk2luq7h/74N/iwCP619ltMpe/3fFAbNZjo3qH1nun3eLicy6vqjnvkrPjmuuvzDuVfKGVjX2eOEdyl6C2cyTLvk5aq/mMrWuRy+0ZmXezN1NB+47LVCRdVf3DRYAAAQIECBAgQIAAgdYgcHufPvPtNnz4jOpz+ebZSb+vqIiDKytj7dYwR3MgQIAAAQIECKQV+O9/vowPX3wxPnrpxfj6009n37bubr99a9FVV71w3vW7XJO2LXUECBAgUFwCPv8W13oYDQECBAgQINCyAj7/tqy/3gkQIECAAAEC5SgwZVDk/KJoh56TypHBnAkQIECAAAECBAgQIECgTAQqlu3id9HLZK1NkwCBXAGBcXXviMYG/NTWcpYgpZphNlnDcN548d1oO1fbvFu+sWE4f7nxujjz/FPz9tOUBVkC45JxPDBubBxw6L6Zh7TYoovH+Psej4UWXCj1vfsfsk88OD5dmF3qRjMWVg+My7IH77xlVHT+5QapeiuHwLhk/ceNfiwWXmjhVCazipoiMO6oPxwWd44akWocNQPjnnvhmei1x/ap7m1IwF+3nr+ON99+M1X7w264M7puMGeAW5aAtFln3QmnHhO3jvgxPK62a6eeu8SfLrgq8o0vCZZ7ecI/ok2bNrHrXjvGpOcm5p3LicecEgfu9/s66xr7PHGO5NLWti+zPMuyBCFmaXf33nvGeadflDPYUnxG5t3wzVAgMK4ZkHVBgAABAgQIECBAgEDzCYzZZcdu0abN9ZWVFZdte8ddf0qC4iLi2IhYqflGoScCBAgQIECAQHEKfPHee/HRyy/F9I8/ik79Zv8F7lsRITiuOJfMqAgQIFCngM+/NgcBAgQIECBAoG4Bn3/tDgIECBAgQIAAgeYQEBjXHMr6IECAAAECBAgQIECAAIFiERAYVywrYRwECDS3gMC4usUbG/BTW8ubbtM13nlvaqplrhkYlyW0pjnDcP507aVx8RUXpJpTUxVlDYz74ssvYt2NOmQeThIY9sjfnogFFlgw9b199901Jkx8InV9UxRWD4xLG+yWjOPBUQ/HaqusnmpIadttSABZqgFUK8ry3s3adp9efeOisy/LdFtTBMadeMbxcfOwG1ONo2Zg3FOTJsRu++yc6t4Vl28fj46dkKp2VtFav1otpk//KtU99414IDquufYctaPH3hMHH31gqjbG3PX3WGPVDtFl8/Xi359+Uuc9ybol65cEXCYhXvVdSZsrrbhyrNE53a9tJ/Vrrr5WnU1m2ZMnH396HNAvd+7OkVza2s6RrKGqUydPS7W/sjx7awuMK8VnZCqYJi4SGNfEwJonQIAAAQIECBAgQKB5BMbuvXW7mNHu4sqoTD7pV1S0afP1xocf+c+fzT//us0zAr0QIECAAAECBEpcoCKei8o2p827/vqjSnwmhk+AAIFWLeDzb6teXpMjQIAAAQIEmkPA59/mUNYHAQIECBAgQKAsBATGlcUymyQBAgQIECBAgAABAgQI/L+AwDhbgQCBchUQGFf3yjc24Ke2lrv1/HW8+fabqbZbzcC4N976Z2y5/aap7k2K3nrp/WjTpk3e+saG4dxwy/Vx6jkn5e2nKQuyBsYlQVRJIFVDrl127B2XnntF6lsHHLZf3P/QmNT1TVFYPTAuS5jXyKH3Rqf1OqcaUrkExiUYQ669JbbYdMtULklRUwTGnXL2iTFk6OBUY6gZGDflH5Ojxy7dU92bhCQ+++hLqWpnFWUJxhx56+jotO76c7T/+Refx3obr5mq33NPuzA6rdsp75yefHBSLLvMcvHQww/GfgfvXW/bp55wRqy3zi9jlz13zDuGxOiZR16MioqKOmsb+zxxjuTS1hYY99HHH8avunXKu16zCl579u2YZ5558taX4zMyL0ozFAiMawZkXRAgQIAAAQIECBAg0LQCY3bZsVu0aZPE/S9fvaflOneJ1bbp0bSda50AAQIECBAg0MoEKioqbv6+suL4duuv/0Erm5rpECBAoOQFfP4t+SU0AQIECBAgQKCIBHz+LaLFMBQCBAgQIECAQIkKCIwr0YUzbAIECBAgQIAAAQIECBBokIDAuAaxuYkAgRIWuHrg0Mrqw+/Zv1vObEYPHpfz55qvF8PUJ0x8Mvruu0vqoQy74Y7ousHGqesbG/BTW0eNCYzLEjqV9P3CE1NikYUXyTvfxobh3H3fXXH4sQfn7ScpSIKVDtz3oFS1WYpWWH7F6LnNDqluGTr85vjDacemqq2r6LLzroydd9g1VRtJX0mfaa5NN948NumaPhQwTZtJzYZdus4OfssS5nX9VUOi+xZbp+qmnALjkn08/r7HY6EFF0plk+W9m3gn7vmuxgTGfTDt/dioe5d8Xcx+ferkaalrk8L1N10n/v3pJ6nuGXfvY7HySqvUWrvrXjvGpOcm5m0nCXFcY9UOce4lZ9VZu/IvVo5xox+vev2rr/4THTdcvd52k0DA5P145vmn5u1/nz36xxknnl1vXWOfJ86RXN7aAuP+858vY+2ua+Rdr1kFE8c/H0susVTe+nJ7RuYFaWRBvq8va75edwxjIwfidgIECBAgQIAAAQIECDSlwJhddzo9KipOqa2PZdfvHKv32LYpu9c2AQIECBAgQKBVClRExWcVMfPIudffIAnljbF7b91um5vun94qJ2tSBAgQKBEBn39LZKEMkwABAgQIECgpgZqff+/ZYYf5d7jnnq9LahIGS4AAAQIECBAoHoH1kt+vK57hNN9IKj+YlPOLo83Xs54IECBAgAABAgQIECBAgEDTC7w6OjcsY80BUW6/k1623/No+t2lhxITKLv3gsC4/Du0sQE/tfXQmMC4rGE49QUxVR9bY8NwHnni4dh7wG/zg0bExhtuErcOHp6qtimKXn1tSmyz85YFafqRMU9G+xV+kbetCy8/L6687vK8dUnBsUecEIceeESq2oYW9eyzdbz8ykupbr/gjIuj7657pKot5cC4Iw8eGIOG/DmmT/8q1VyToj367BXnnnZhqvpiC4xLE5hWfWLPPvpyLLboYqnmOuObGdGh88qpapOi+tq+9vqr6g2Bm9VJEuC3ykqrxNPPPFVnvwf0OzBOPv702a/36der3vqkMAmNG//oQ3nn8terb4otN+9eb11jnyfOkVze2gLjfpj5Q6y8zvJ512tWwTWXDorttt4+b305PSPzYhSgQGBcARA1QYAAAQIECBAgQIBA8QqM23ffef/71ee3RcRONUc578KLRIftd4hF2rcv3gkYGQECBAgQIECgFAQqK6+bt/MGvxvTe+e7K6Jy4W++nbnDTqNG/acUhm6MBAgQaC0CPv+2lpU0DwIECBAgQKCoBXz+LerlMTgCBAgQIECgZAR2jogzI+KMiEh+m61sQtQExpXMHjVQAgQIECBAgAABAkUvUFlZGR/+69OY+t60WHHZpWLxxRaJtnPNFW3atCn6secM8PsfItrOVVpjNto6Bco0MC4Jxev9v+9vnBoRJ0fEXbYIAQJRdt//ExiXf9c3NuCnth4aExiXNQxn0BV/ja237JF3oo0Nw8lyfzKYfzzzVsw777x5x1Xogq9nfB09e28Vb779ZkGaXm/tX8adt4yKtm1/Vm97Q4YOjlPOPjFVn80RqNfvwN3j4cfHpxpPzZCt+m4q1cC45D1y3Z8Gx52jRsTRfzw8lcusolv+Miw22WizvPcUW2Bc8rnsF2svm3fcswpuH3JXbNila6r6Kf+YHD12qT88rXpDb7z4brSdq22tbWdtq74BDrn2lqoAuFnXVYP+FBdcdm6qOeUrmjLpjZh/vvnrLWvs88Q5kstbW2BcUtF9x83j9Tdey7dkVa9vuvHmcfOg5Ff467+yPON2771nnHf6RTkNZrm/JZ+R+RwK9brAuEJJaocAAQIECBAgQIAAgaITGLvzzktWzlU5JiI61Rzccl02iFW2/E20aVv7N0GKbjIGRIAAAQIECBAocoHXH7z//feffnq5H4dZMXlmRdtu2w0f/q8iH7bhESBAoFUI+PzbKpbRJAgQIECAAIESEaj5+feHirbdew4f/mGJDN8wCRAgQIAAAQLFIJD8IvXLEbFWREz+318qnB4RI8ohOE5gXDFsP2MgQIAAAQIECBAg0DoE3pv2cTz33vSY8Mp78e5rL8XSC7aNrTdZPzp1XC0WXGD+mPtn9YdttA4Fsyg2gTILjEu+v7HL/wfFrRMRr/zv+xtrl8P3N4pt3xlPUQqU3ff/BMbl34eNDfiprYfGBMYl7fXss3W8/MpL+QcfURUWl4TG5buyhNnUFobz/Q/fx7obrRnTp3+Vr6uq14def3v8uuumqWoLWXTcKQNj2B1DC9lkHDLg8DjuyD/U2+ZLk1+M7XfbJnW/k596LRZYYMHU9VkLz73krLj2+qtS3dau3QLx/GOTY+65585bX6qBcScff3okwXhJiNpv9+sdEyY+kXeuswqS0Kq/3/NIJE71XcUWGJeMdf9D9okHx9+faq779xsQpxyf/PeU8l+XXX1xXHpVbmBWXXflC0hM1qTzZuvGvz/9JH/HeSpeefr1nHV67sVno9fuPRvdbhJCl4TR5bsa+zxxjuQK1xUYd8yJR8bwkcPyLcfs1+8b8UB0XDP5OFT3VS7PyNRojSwUGNdIQLcTIECAAAECBAgQIFCcAqP77LRO28o2f6uMyv8PLflxnG3nmSfW2nmXWHTlVYpz4EZFgAABAgQIEChBgc/eeiteuLXGX85UxtvRZuZWPYaP+mcJTsmQy0PgtIzTzFqfsXnlBBom4PNvw9zcRYAAAQIECBBoiMBnb78VLwz1+bchdu4hQIAAAQIECNQQSH6h+o5q/y4JkEt+Q6ZVB8cJjPM+IECAAAECBAgQIECgUAIvvvZOLLFq5/hmyRXjhZenxT9eeD6ef/z+mPfbf8VmndeILTfqFEstsVjMO0/+QIpCjUk7BMokMC4Jwur1v9VOfpZs3Wqrvuv//nynXUCAwGyBsvz+39TJ0ypLdQ9MmPhk9N03WbZ017Ab7oiuG2ycrjgiGhvwU1tHjQ2Myxp69vS452KpJZeud86NDcNJGj/46ANj9Nh7UtkmQT9333Zf3nHla+zFyS/ESWecEO998F4Mv/GuWGWlVeu8ZdR9I+OwY3+fr8kGvX7r4OGRBF/Vdf0w84dYp2uH1IF6SQDV9VfeEG3bNi5M+e777oqLLj8/2radK8aNfnz28O6654448oRDU8/1mksHxXZbb5+3vtQD45IJvvHWP2PL7bOFGfbbfd8486Rz6/UpxsC4QUP+HGddkP7XDF6a8I9YaMGF6p3n1zO+ji6br5d6rx9z2PFx2EFH1tvmiWccHzcPuzHv/quvIDn3k/O/+pU16LKu9s86+bzY+7f75B1fY58nzpFc4roC4wbfNChOP++UvOsxqyAJexx81ZA6vzZ44qnHItmDb779Zqo2awtVLYVnZKrJNXNR+47LJJ9ho+ofLgIECBAgQIAAAQIECBS7wJhde90YFbF39XG2W2KJWLv3bjHfz39e7MM3PgIECBAgQIBAyQm8cted8fGU5D+SmXN9WlFZud02d9z9VMlNyIDLQSDrD+b4O5Jy2BUlOEeff0tw0QyZAAECBAgQKGkBn39LevkMngABAgQIECgegeT7rUlI3Fo1hpT8u9P/P0wu6/dwi2d2NUYyZVDkzKVDz0lFO1YDI0CAAAECBAgQIECgNAS+nvFNTHjz82izfKeYe/5FY8b0GfHV9OnxwbvvxwtPjYvpH0yJzTutHNts2jmWWWrJqnALF4HmEqhYtktr/Tmrnf5nmKSB/LKGZfKDk2tH5H7+by5v/RAoUoGy+v7frDUQGFf3bmxswE9tLTc2MO7GW2+Ik8/6Q+q30Oa/3iKuuvjPsWAdQU8zvpkR51x0RiTtprnqCsO5dcQtccKpx6RpoqpmzdXXihE3jYwFFlgw9T2zCj/6+MO4atAVMWTo4Nn3jrx1dHRad/1a23r7nbdi823TBwVmHdBiiy4eD456OBb9+aJ13nr4cYfE3aPTZ7T23XWPOP/0i6KiIvuXJy9NfjEuvPzcePjx8bPHM3XytNn/f8prr0SPnX+TeppJMNPNfxkWq668Wq33zJw5M+4dMyp1IF9dQU+pB5SisDHv3cuuvjguveqiFL38VJIvjLIYA+Oef+m52Om326We56knnBH99x5Qb/1Ntw2Jk848IXWbtw+5Kzbs0rXe+gfGjY0DDt03dZu1FZ5w9Inx+/3nDEnMEnRZ1wAeGfNktF/hF3nH15g9Oatx58hPzHWdI1P+MTl67NI973rULOjTq2+s03HdquDRb7/9Nl5/47UY/9i4SALjslyl+IzMMr/mrBUY15za+iJAgAABAgQIECBAoNEC47bYom3bNVadOP3fn1T9ZdwSa3SIDjvuFHP9rHH/RYhGD0wDBP6PvfuAj6Ja2wD+bHqFEHrovYv0LkV67wgiCghKvQIqiAgICAoKCoggSpfekd6LtNBb6L2FnoRAIGUvZyEhbXfPJFtmd5/5fn5XyXva/8xOMsPuEwpQgAIUoAAF7Fjg/Ib1uH3kcOIVhjs7a8rVWbQiyI6XzqXZpoDSDxsqf6eAbbpw1jYmwPtfG9swTpcCFKAABShAAbsQ4P2vXWwjF0EBClCAAhSggPUFWr36MPVSPdM4+SY4TnzqS+mzXOuvLNEMGBinui3hhChAAQpQgAIUoAAFKGAXAi9evsSWY7cR5V8QLu6eiIx8AVdXT0RqnRAc/ABH924FHl1Ao2rF8V75Ukibxscu1s1FqF/ADgPjmrxSF0FxyafnAK3fhN+rf3M4QwpYVsBhnv/FstpCYFxIaAhu3rqR5EzYH7gPI34aKn2GfDfwe1QqlzQ4LHu2HEibJm2SfkwR8JO409QGxl27cRXv1a8kvWZRmDd3XowfPRFFChWDh4eHru3DRw+w67+dGDl2uO7fZQ99YThPQp6gcp1yCA9/KtsVCuQrqAtxalivMTw9PA22E6Fkx08exZoNq/D3nOlJavUFxongnxYfNsapM+Lxvdzx1f8GYfuurTh0NFCuAYC6terjz4kz9Aa8idCh9l3aSPcnCmtUq4VuH3+GyhWqwsnJyWDbiIgIXbDRwuXzsXHL+iS18QPjhGXl2mVxJ/htiJyxiXl7++DnURNQpWK1uNdK+LNw3Z6M/e1HHD2e5LMYertUe2CcsKzTrAau37xmjCXu62JN29bugZenV7Jt1BgYp9Vq0aBlbYgAQdlj3KgJaNvig2TLV69bKR0aKDrImT0Xdqz/D85OhkPCw8JCUbxiIdkpJlu3ZvEGvFOsZJKvLVo2H18PHZDivsW1dfva/6Tam+L7Ca8jb6kNXUdEIKaS81pqAyWLbO17pOSyrFLGwDirsHNQClCAAhSgAAUoQAEKUCClAhFHDvUAMOXMqhXwyZQZOSuZ77dXpHSObEcBClCAAhSgAAXsUeDGgf24tHVL4qVdi9G4lmu4ZMl9e1wz12SzAko/ZMjAOJvdavueOO9/7Xt/uToKUIACFKAABdQroOf+97qLa0z52gtWB6t35pwZBShAAQpQgAIUUI2AeOZ6CkBRAzOyi+A4Bsap5pzjRChAAQpQgAIUoAAFKGB3Ak/Dn2FT4BVEpSsAVy8fRDx7CmeNBq6ePohx8cLN6zdxbNdqlM3ljnaNayGDv5/dGXBB6hOwo8C4Rq90RVBcWQPKIhmkuD0E3qvvTOKM7EDAYZ7/xe6VLQTGjZ88Dr/9Md5sp1ffz/thQJ+vk/RvioCfxJ2mNjBO9Ne8Q2NFAV3x5yACjkJCQxWFxMVvry8MR9RM+WsSfpowWvE+iTCyDq0/RL68BZApYyZkzJAJThon3LsfjLv37uLi5fNY+e8Kg3PWFxg3auxwTJ89TXpOpUqWwdK5K3H9+jXUbFxVup0oHDlkDDq1/0Rvm47dPsDuvTsV9SmKxZ61aNIaWbMEIHOmLEifLj1eRr5E8L27un+OnTyK5av1/Z6b18PFD4wT//3zpJ8waeqviuciGoigJldXN0WBavEHUntgnJjrnn278OGn7RT5dO3UDUMHjki2jRoD48RElVzjYhfWoU1HNKrXBIULFtGdB6eDTmLtxjWYt2iOIi9D4XOJO0rpa0f0I64vJ/efTTaYLiUBnPHnJgIvB/X/VmrdSqxFuOmnnbon229KLRzpOjJnwUx8N2qw1L6YusjWvkeaev2m7I+BcabUZF8UoAAFKEABClCAAhSggFkFXh4J7BQDzWyzDsLOKUABClCAAhSgAAX0CtwPCoII7tXGxMTVaIFD2vAXVRuuX/+CdBRQiQAD41SyEZxGygV4/5tyO7akAAUoQAEKUIACphDg/a8pFNkHBShAAQpQgAIOLtDmVWDcYgmDEwC+B7DCFj+AzcA4iR1mCQWsJPA84gWuXL8lNbqHuzvy5somVcsiClDAvgRevHyJS1dvSi0qV/as8PbylKplEQXiC9x/+BjiH2OHs7MzCuXLZayMX3cwgUdPQrE58DJe+uaCZxo/vHj+HDFRkXB1dYFH2kwIf+mEwzvXIJ/nY3zavhHc3dwcTIjLtbSAHQTGNXjzHKKchJ14tmE4WUaiE5ZQwI4FHOL535QB8xO8H7VRl5oJtnTtjO0J/jvvd3SnAAAgAElEQVTx1y25/wyMS6gtgpm+HTHQklsQN5ahMJzwZ+EoV+NdhIc/tfjckguM27ZzCzr3/EjRXHas+w95cuXVtRFBcyJwTsmxccU2XZBWcseJ08fRpG19Jd2ZrDZxYNy5C2dRt3nC17zJBjPSkS0ExoklfDGoN1asWaaIZencVShXunySNmoNjIuKjkLNhlVTHP6nCCdecXr/DNi35RDc3d2lupgxdzq+/3GoVG3iomaNWmLi2N/1tq1Wr2KK179o1jJULFdZal6mCozjdeQ1t6HriPge9H6T93An+I7U3piyyJa+R5py3Ur6MvbzZeKviyRnHhSgAAUoQAEKUIACFKAABVQrEHHkSAMgZp1qJ8iJUYACFKAABShAAQcREB+aP70i0V/sabCs/pKVrR2EgMtUvwAD49S/R5yhAQHe//L0oAAFKEABClCAAuoQ4P2vOvaBs6AABShAAQpQwGYFxHvTT70KjSsquYLjbz6wvdIWg+O0tw8pfS4tycIyClAgpQKzFq1B534ij1LuiLx+AC4uznLFrKIABexGYMmaLWj72SCp9Uz4vj++6NZBqpZFFIgv0LLrV1ixPmGQiD6h64fWIkdAZgJSIIHA3XsPsfXIdUR6BcAzrR9evHgBxETrbp2802ZAjIs3juxYiXaVc+LdYgWoRwGzCJxdWzZBv0W6wdY+k17vzXOHCpJAZ16FxRW3xWcUkutjGQVMIeAQz/8YGPf2VOn7eT8M6PN1knPHVAE/8Tuu2agKLl+9LHWenj18GZ4eScO9IyIiUKNhZdWF4YhF7Q/ci3aftJJanymLEgfGiaAgERikJLxu5JDR6NS+c9y0RJhW64+a4+jxw9JTLZCvINYs3pDsvolOfp8+EWN/HSPdn6kKEwfGiX77ft0Lq9YuN9UQ0v3YSmDc/Qf3UL1hFUXnUM7subBp1fYk+6/WwDixaeL8bt6hsfT+maJwwYwlqFyhqnRXFy9f0L2eU3L8/MOvaNO8nd6mQ3/4FrPnz0hJ17h47DpcXV2l2pry+wmvI4YD48SG7NizHR9/ZvlnnYYC49T0PVLqpDVTEQPjzATLbilAAQpQgAIUoAAFKEABywu8OHm4CCLxnxbadJYfnSNSgAIUoAAFKEABCiQWuLx9G67v25v4j/vVX7ryV2pRQAUCSj+YZ2tvYFQBMadgLgHe/5pLlv1SgAIUoAAFKECBlAnw/jdlbmxFAQpQgAIUoAAF3gi0BbBIoYZNBscxME7hLlug/HFIKC5fu5XqkTQaDdxcXeHh4QZPD3ek9fWBj7dXqvtlB+YXmLloNbr0GyE9EAPjpKlYSAG7Eli0ehM++Hyw1JrGD++Pft3N+yHKqKhoHD9zXmo+oqhk0YIpCrsMf/YcZy9elRpHhGmKcQwdSuctNbBEUd5c2ZAubRqJSuuWNO88AKs27pSaxNWDa5Are1apWhbZtsDhE0HwcHdHsUJ5pRYSGhaOrQfPIfiFL9JkzoqoyChotVpoY6LhlcYPgBPuHluH9g2rIGN6vt1eCpVFigRsODCuzpuguEqKFgyIZxpLFLZhOQUcUcDun/8xMO7taW1rgXFi5ms3rkHP/t0t/to0FoYjJrR63Ur0+aqHRecWPzBOBL2179waBw8fkJ6DCK/6569FcHJyStAmJUFVndp/gpFD9IfCDRk5CHMXzpaemykKkwuMu3X7JirXKWeK7hX1YSuBcWJRC5b+g0HDvlS0vs+69MTgAd8laKPmwDgxURGYJoLTLHF82XcQ+nz2P8VDVaxVOkUhmXs3ByJbQHa9423atgHd+rwNipSdWKN6TTBl/J+y5TBlYJwYlNeRrNi/7YhB/1Hjvsf0WVOl98gUhbbwPdIU60xNHwyMS40e21KAAhSgAAUoQAEKUIACqhJ4fuTQAQ1QXlWT4mQoQAEKUIACFKCAgwucWLgAjy5feqOgmemucelbc8mSpw7OwuWrQ4CBcerYB84iBQK8/00BGptQgAIUoAAFKEABMwvw/tfMwOyeAhSgAAUoQAF7FhC/rOMUgKIpWOSxNx/oXgVA6TPfFAyXuiYMjEudnzlaf//Lnxj+6h9zHL4+XiiQJycqlimB2tXKo0blMjYRVmMOCzX3ycA4Ne8O50YB9QioLTBu255AvN9WPjBg14rpqFahlGJQpdfIa4H/Ime2LHrHUTpvxRPW02DUwJ749n9dTNWd2fphYJzZaG2y45iYGJy6cAObDl/FxVOB+LRdA5QtWUxqLQ8fh2DB5hPIWrQSnoaF6QLjdLdLGg38/NLh2M5VaFGtKEoWMxzyKDUYiyiQSMAGA+Pef/NcoUoKNvMMgOK28DwiBWtjEwqYWsDun/8xMO7tKWOLgXHi56VeAz7TBcdZ8pAJwxHzSUnQVmrWET8w7rc/xmP85HGKujMUKjVtxhSM/mWkov6mT5qJurXqJ9tGBNoNGTFIZ2SpI7nAODH2nAUz8d0oubB5U83VlgLjomOi0apjMxw9fljR8lfO/xelSpaJa6P2wDhxPRk5dhj+njNd0TqVFrdp3g4/jfwFzk7OSpti+JjvMHPeX4ra5c2dF9vX/mewTUhoCN6pVFhRv6J4/OiJaNWsjXQ7UwfG8TpiPDBObM7Pk37CpKm/Su9Tags/7tAFI779wWg31vweaXRyZi5gYJyZgdk9BShAAQpQgAIUoAAFKGAZgYjDgdOg0Vj+13lYZnkchQIUoAAFKEABCtisQPTLlzixcD5yVqqC9Pnz/+lRptxnNrsYTtzeBJR+eFC8aYkHBawuwPtfq28BJ0ABClCAAhSgAAWSFeD9L08MClCAAhSgAAUokCoB8UmQxanoQQTHDQcgguNUezAwTn1bY87AuORW2/+zDzHg844IyJxRfRgKZyRChAb9MFmq1dLpP6UoqEiq81QWKQ1Dirx+AC4uyj+El8ppsrkDC3w6YCTWbN5tVMDN1QVndi6FCKvkYXoBRw2Mm7FwNbr2HyENevXgGuTKnlVvvbUC40Z+3QNDvugqvQ5rFTIwzlry6hw3Mioah6+GImveEvhp0jQ8v3IQvbq0Rdl3jIfGibC5BRsPIG2BargffDdugeLPfbx9EHz5OMrkSYPKpYtCo+FbYdR5BtjurGwoMK72q6C3YQCqpkK7LYAlqWjPphRwNAGHeP537fQdpe9Ltfh5IMK/RAiYuQ5bDIwTFs8jnqPjp+1w6GhgimnS+2dAj097Y9RY8aja+CEbGCd6Ons+CL2//BwXLp033nEqK2ID4/YH7kO7T1oq6u3nH36FCLLSd4hgppYdmuD4KfFIX+7w9vbBtjW7kSWz/oDyDVvWof/g/yE83Py/015fYJxYzcifhuGvOan7BSUjh4zBd6O+kcKxpcA4saCg82dQv4XI65U/RFDZ+mVb4eHhoWuk9sC42JX9s3guBn//tfxCFVQOHvAdPuvSU0GLhKU7dm/Dx59/qKj9p52647uB3xtt07xDY8WhgAe3H0XmTPpf34kHNXVgXGz/vI4Y3V4I+2Gjh+DhowfGi+NV5MyeCyOHjMaLFy/QXfKXOvTr9SW+6DlAahxrfI+UmphKinIVy6p7+MMnQCrZEE6DAhSgAAUoQAEKUIACFHgr8PJIYKcYaGbThAIUoAAFKEABClBA/QJO0H7sVrrcHPXPlDN0AAGlb8zh35E4wEmh9iXy/lftO8T5UYACFKAABShAgbcCvP/l2UABClCAAhSgAAUUCYjnr+KTbvkVtUpafPTVH4lP461OZT8mbR40HQmeRxdudMik/bOzlAtYOjAudqYzJwzDJ+2apHziKmg5ZtJMDB7zu9RMVsz4Gc3r15CqtXQRA+MsLc7xlArU/aAXNu86INXs3snNyJg+nVQti5QJMDBOzouBcXJO+qoYGJc6P3trHRUdjZ2nb6F47RbYHXgZ2xbPQMTtY+jRqQ3KlSpucLkxMVos3HoM/gUr4+bVK3ByctLVa7XRcHX1gPPLEHi+uI16VUvB28vT3ui4HpUIaALKqvV9ViIgbkwqg+KE8hkA4sWo9P1nKtkhToMCVhGw6+d/saIMjANsNTBO7OHjJ4/Rf3BfbNu5RfGLpF7tBhg1ZAzuP7iPhq3rSLVXEhgnOoyIiMCsf/7Gn7OmKg7rkZlQgXwF0al9Z7Rp0Q7Pnj1DnWY1FI1Tu0Zd/DV5ltFQ4vMXz+n6VnJUrlAV8/5aCGcn/b/IIfjeXUz5a7LOyBxH9So18GHbThB7re+IjonGz7/9hCl/TVI8BeH/y+jfULL4u8hVTH8Ye/yObS0wTsz9pwmjFfv06tYXX3/xOkTPVgLjxFwDjxzE8DFDcOrMScXnQ3INxDkydNAIvFe5eqr6e/b8GYqUzaeoj9lT/0GNarWMtpk4dQJ+mTTWaF1sQfGiJbB2ySbpelForsA40TevI8a3QgSszl88D2s2rDIaDiiC4tq1ao+unbrD08MTcxbMkg7EFAFz4nuS7GHJ75FiLbZ0MDDOlnaLc6UABShAAQpQgAIUoIADCYQfORLgAu0pLbR8p5MD7TuXSgEKUIACFKCA7QpooHkcBU1x79Klb9vuKjhzOxFQ+oY9tb6R0U62g8swJsD7X2NC/DoFKEABClCAAhRQlwDvf9W1H5wNBShAAQpQgAI2IfABgAUmmumRV78o/Xu1BMcxMM5Eu2qGbqwVGCeW0rfrB/hlWD+4uOj/kKMZlmyyLhkYZzJKdkQBgwIMjFPHCcLAOLl9YGCcnJO+KgbGpc7P3lprtVocOHUZL/PWwIuIaATfCca+DUvw7NZxfN6xNSqUKaF3yRev3ERQeBpEOXnjQfBdODk7i7Q4Xb2TsytcY57jxd0gNH2/HING7e3EUdF6VBgYJ4LiRgJQlk6j31Q8w1ikInJOhQK2ImC3z/9iN8AWAuP++Hsyfhz/g9nOmYH9BqPnp32S9L9152Z06dlJatwxw8ehQ5uORmsbtakrHcR04eg1uLm5Ge1T/Bz2z+K5GP3LSISHPzVanzd3Xnwz4DvUrVVfV3v5yiXUbCy+7Rg/Pu3UHd8NFI+xlR1R0VHYumML5i+Zix27tylrnKi6SMGieL9GHdR9v74uqCz2mDTtN/w88Ufpvr29fbBj7R5kyphZqo0IVBPBYUqOBTOWQATHGTvCwkKx4t/lWLpyEY6fOmas3ODXRUCW2Nua1WohIGs26b4OHNqPQcMG4PLVy0bbCLu+n32hC1RydXXV1ZeuVkIqrE8EiG1ZvdPoGKkpMPVrV4SV1WpUFXeC7yia1rnDV+Dh4YGHjx6idDXDIdqxHTdt2ByTxv1hdBxxTRTXRplj+T+rUebdcjKluhpxTdm+ayt+nfJLis9H8Trt26OfLqzQUGii9KQAdO75kaJwzDMHL0Ccq8aOw8cC0fLDpsbK4r7ev/dX+F+P/tL1otDU52Ryg/M6IrclDx89wOmzp/Hw4QM8fPwQz56Fwy+tHzJmyIRC+Qsjb56EwYRKXmvitStew0oPS3yPVDona9czMM7aO8DxKUABClCAAhSgAAUoQIE4gc1t2qSN1kauQ7SmZ82hQ77UarXGnwLTjwIUoAAFKEABClBANQIajWaee6kyH6lmQpyIowowMM5Rd96G1s37XxvaLE6VAhSgAAUoQAEKJCPA+1+eFhSgAAUoQAEKUECRgBOAcwDyK2pluFgExw0HsMaEfSruioFxisks1sCagXFikd/1+xQjvvrcYus15UAMjDOlJvuigH4BBsap4+xgYJzcPjAwTs5JXxUD41LnZ4+t79x/gs0XIpAlbzGEPQlB+POXCNy+GuE3jmHEVz2QPSBpGMfNO/ewfNcplKjeAlfOn4c2Jhpv3xyjhZOTM1xiIvDk+gm0rlcJAZkz2iMd16QCARUFxlUEIFKRapmQ5QwAkVKi9L1nJpwCu6KAzQrY7fO/2B2xhcA4mz17LDzxqKhI7Ppvpy6Y6MbN67hx6zpCQkORPSA7smTJiry58qFB3UYJQtYsPEXdcCIY58Kl8zh56jjOnDuDJyGPERIagsdPHiMy8mVcuJQI8PHx8UU6v3QoVKAwihYujsIFCuv+zJ6P5xHPcebsKRw7cQyXr17Ek5AnEEFQItjIy9NL55Q2TVqdQ9o0fsiYPgMKFyqK4kWKI3/egnEBbik1OnH6ONZt/BeXrlzUnUf3HtxDpgyZkDVrALJlzYZa79VGtcrVUz1OSufHdpYVePDwPkSY4N4De3D+4jmI/w6+fy8unFIEsmXOmAkZ0mdEvjz5dQGJFcpWROZMWSw7UY6WQIDXEdOcEOHPwlGuxrtSYaxixEWzlqNiuUqpGpzfI1/zMTAuVacRG1OAAhSgAAUoQAEKUIACphTY0LrFj4B2IDSa6JyVKjvnqfYeNOK3n/GgAAUoQAEKUIACFLAhAadmHqVLr7ahCXOq9icgPiSo5FBar6Rv1lIgWQHe//LEoAAFKEABClCAAvYgwPtfe9hFroECFKAABShAAYsJtAcw3wyjHX7Vp3jG+68Z+jbaJQPjjBJZrcDagXFi4f/O+RWNale1mkFKB2ZgXErl2I4CygQYGKfMy1zVDIyTk2VgnJyTvioGxqXOzx5bP3/xEit3nkC6IjXw6MF9ODm74kWUBkd2rYXPs2vo8UkbZMv6OjQuMioKew4ew45jV1C1WSc8DH6IpyGPoHFyglYLaESulVYLJ2dnRD8Pxd2Lh9GpeU3kyh5gj3RckwoEVBAYJ4LiRgKobQYO8exioRn6ZZcUcBQBu3z+N2XA/AQhko261Eywn2tnbE/w34m/7iibz3VSgAIUoEBSgZiYGGihjQt4pBEF7FFg2owpGP2LuEWTO84dvgIPDw+5YgetMvbzZeKvaxzUicumAAUoQAEKUIACFKAABVQisLZNmyzO2pdXAE3c3V6e6jWQq4rtvXFSJaScBgUoQAEKUIACFLCOgAZHPUqVLW2dwTkqBShAAfUL8P5X/XvEGVKAAhSgAAUoQAEpAd7/SjGxiAIUoAAFKEABCrwRcAJwDkB+M4kcAvC9tYLjtLcPJfjgqJnWyG4VCKghMK5g3pw4u3sZNBrb+qiGvQTG3bxzD1t2HZA6azw93dGuaV2pWhZRwFQCDIwzlWTq+mFgnJwfA+PknPRVMTAudX722FqEBpy8cANH7nvAJ216PA19DA8vX0RqXbF52UxEPr6GnNky6wLhgh+HIUO+EqjbrC1CH4Xg0d3bcHZx1oXEiUOr1UL8tOnk7ILQO1fw4MYZdG/fCLlyMDDOHs8da67p7NqyCYYv0k136lnyEEFx4r7fXD+4XwRQCECMJRfFsShgZwJ2+fyPgXF2dpZyORSgAAUoQAEKxAkMGTkI+wP3oUSxd9CsYQtUrlAVbm5u0kKbtm1Atz6dpetrVa+NmVPmStc7aiED4xx157luClCAAhSgAAUoQAEK2KjAxtbNf9cCPWOn7+zqioq9+8LV09NGV8RpU4ACFKAABShAAYcW6OlRuuwfDi3AxVOAAhTQI8D7X54aFKAABShAAQpQwK4EeP9rV9vJxVCAAhSgAAUoYGaBDgD+MfMYIjhuOIC1Zh4nQfcMjLOkttxYagiMEzPdtWI6qlUoJTdplVTZS2CcSjg5DQroFWBgnDpODgbGye0DA+PknPRVMTAudX722jokLBwLNh9D1qJV8PDBfWhjIuHu6YOn0e64fPEcwp88QBr/9MhfsDACArLgyd2bCHn4AE5O8TK6tFqI2DgnjQbamBhcO3kAkeH30KdLW+TIltle6bguKwlYMTBOJNWNBFDfzEtvD2Chmcdg9xRwBAG7e/7HwDhHOG25RgpQgAIUoIBjCrTq2BSHjgYmWHyzRi1RqXxlvFOsJArkK5gkQO7mrRs4fuoY5i2ag70H9iiCGzlkNDq1lw+YU9S5HRUzMM6ONpNLoQAFKEABClCAAhSggL0LbGzTJI9W63zh1V/mOceuNXe19yD+4UEBClCAAhSgAAUoYJMCVzxKl81rkzPnpClAAQqYUYD3v2bEZdcUoAAFKEABClDAOgK8/7WOO0elAAUoQAEKUMA2BZwAnAOQ3wLTF59uEMFx6ywwFhgYZwllZWMoDYyb/dv3cQNERkUhIuIFwp9H4ObtYGzdE4gz5y8rm8Cb6s87tcIfP36TorbWasTAOGvJc1xHE2BgnDp2nIFxcvtg6sC4imVKIEdA6sOsOrdriga1KsstwopVDIyzIr6Kh46KisbBk5dw/okXXH3T4tnTUGi0MXD39oObjz+cnV3h7AJEvwjHk3t3EPUiAk7OcW+zB6CFVqsV/wMXVxc8unsL54/uRjpfN/Tu8gFyMjBOxbtvm1OzQmCcCIoTN2oNLSB2EUAhADEWGItDUMDeBezu+R8D4+z9lOX6KEABClCAAo4rkFxgXGINb28fpE+XHpGRL3En+E6KsUQ//206iHR+6VLch6M0ZGCco+w010kBClCAAhSgAAUoQAE7ENjYuvlcLdAxdimunp6o2LsvnF1d7WB1XAIFKEABClCAAhRwWIGeHqXL/uGwq+fCKUABCiQjwPtfnhYUoAAFKEABClDALgV4/2uX28pFUYACFKAABShgJoEPXwXGzTNT38l1e+jNB8z/NeeYDIwzp27K+lYaGKe9LU4V/celqzfRZ8hYrN+2V9GEalQug+1Lpylqo684JiYG5y5dw6279/H4SSgePHqCxyGhcHZ2Rvp0aZEubRrd/5YsVkD37yk9GBiXUjngSWgYzpy/gkePQ3T78+hJKJ49j0DaND5Il9ZXty+5smdF0YJ54OQkPkPvGIelzl1b01RLYJw4b0+fu4yHj0Pizt0XLyPhl8YH6f394O+XBtmyZEKRArktdt6+ePkSJ4Mu4v7DJ3j4WPwTgrCnz+Dj7YkM/n6611KmDP4oUSQ/PNzdUrX1DIyT4zN1YNyxLfNRsmhBucFVWiXOy+u37uLWnXu6672nhzt8fbzevF7ywMXlbbCXpQPj1Pi6jt3G28H3cffeQzwJCcPjkDCEP3v++jqTNROyZcmoe42b+nuk2Kc7wQ8QEvYUIaFP8TzihW7MrJkz6P7JlN4/wX5Z8pR79CQMCzceQsb85RAWHoaoqCggJgYaMQknICY6GjHRMXBycgY04k+1r25vNIBWhMWJXCsNnF1d8CIsFIf2bIWHcyQqVCiFkgVyomSRfHB2dpyfNyy5b446lgUD40q9uY9vYkHrDq8C4xZYcDwORQF7F7DL53/XTt8R34h5UIACFKAABShAAbsRkAmMM9Vih3w1DN0++dxU3bEfALmKZdU9QtL9Px4UoAAFKEABClCAAhSgAAUsLbCxRYtMWmdtcPxx89eug+zlK1h6KhyPAhSgAAUoQAEKUMCEAhoNTrmXKltiQ5uWjWLcnu9u+M/6UBN2z64oQAEK2JwA739tbss4YQpQgAIUoAAFKCAlwPtfKSYWUYACFKAABShAgVgBkVhw7lVoXH4Lkxx8Nd5wAOtNOW7QdF1iQ9xRuJHh0DFTjs2+DAuYOjBOjBYVFY06H/TEjr2Hpfnz5sqGS/tWSdcnLhQBScvWbsXWPYFYtXGHLjBJ5ihdojDqvFcBn37YHPlz5zDYRITQrdywI65G/Pv+wydlhkGzetVRqew7Bms1Gg0+69hSF5omDhEENXnGYkRFRxsdo3ihfGhUu2qSupeRkTh++gKCLlzBmfOXdWEznp4eqFu9ImpXK2+ycYxOEEDgsTP4d8tubNq5X9pNBArVr1lZ59e2SR24urroHSo0LBx/zFkqMxVdTfeOLRQFBgbff4RZi9dI9Z8xfTp0+aCpVK0lzt34E7HUeSW1eD1FW3YfxOETQXFf/eG3v6Vf0wN7fYx0foaDIP3S+OKzj1pKTfH4mfNYvm674vO2SZ33dNeWNk1qw9vLU2os2SIRIrVo1WZs3rVfUThnvRqVdHNq16wusmfNJDtcXB0D4+TIGBj32in22jZ32Tps2xNoEO/jto3Rp0s7lHmnCCwRGKfG17UAEq9tcb3ZfeCo7meoew8eGT3pxM9P5d8thg9bNkDdGhXhpvCXj4sxlqzZovv5acvuA1LXWnH9EKGupYoX0o1ZvWIZpPH1NjrX1BZotVocPnUR+86HIGPOAgh9GgZER4s8OF0onAiIexMR9/rG482f6QLjADi7uCAq4jnOBO7FyaMHUadxYzRq3R5njx1EmTxpUDBP9tROke0pkERAE1DWXJ9FF0Fx4r5d7gdO0+3NxVdhcYUAiBRGHhSggGkE7Or5XywJA+NMc3KwFwpQgAIUoAAF1CNgqcC4rJmzYse6vfDw8FDP4u1gJgyMs4NN5BIoQAEKUIACFKAABShgywIb2jT/H7T4NXYNzq6uqNL/Szg5v/3tera8Ps6dAhSgAAUoQAEKOKLAy/Bw3Dl+DNf+23MvJjIyE6D9uP7SVXMc0YJrpgAFKBArwPtfngsUoAAFKEABClDA/gR4/2t/e8oVUYACFKAABShgEYGPAFjr7wwOvFqh+AD6BlOslIFxplA0Tx/mCIwTM/138240+bifoknH3AqECE1TcohAGhESNurXv6RCTgz1/VHrhhjav5ve4Lg/5y3HZ1+PVjI9xbVHNv2jC2ARx62795C9dEOpPkQ41erZ4+NqH4eE4u/5qzDuj7nJBs4M6v0JxgzunepxZCYnQvWG//InNu7YJ1Out0YE1Hz/1ef4pG1jODmJz9QnPC5dvYn8lZtLjzF30gh0bCXnKzpVsv8F8+bEuT3LDc7Fkudu/IlY6ryS3ohkCtv3GIyFqzalpgujbbW3DQeXHj11DiMn/IUV67cb7ctQgQg9HPHV5+jesSW8PFP3IT8RJvXL1HkYP+2fVM1JNP6iWwd82aMjsmWRD45jYJwcOwPjoHvdfD5wjFTgWXzVKuVK4r/A43LQAIxZJ+5Ija9rMUcRkjl1zlIsW7tNeu3JFYrrzacdmqN98/oo925Rg32dDLqIkb/+pQuLM8XRoUV9dPuwBWDV30gAACAASURBVGpULmOK7vT2IYJv1+44jMfR6eCdPjOePX+GmOgYaLUx0OjS4l5nVItwudeBcdD9bCneZx/5LBxBRw7g9LFDyJYrN6rUb4psBUrCzz8Dbh3bhNolsyFjhnRmnT87dzwBMwTGlQAwAoD8D72mZf8QwHzTdsneKEABAHbz/C92NxkYx/OaAhSgAAUoQAF7E7BEYJy3tw9WLViLAvkK2huf1dfDwDirbwEnQAEKUIACFKAABShAAccW2NCm+UFoUS5WIeu7pVCoYSPHRuHqKUABClCAAhSggI0LXNuzG1d27Xy7Cg021l+ysr6NL4vTpwAFKJAqAd7/poqPjSlAAQpQgAIUoIAqBXj/q8pt4aQoQAEKUIACFFC/gEhEOgcgvxWnKoLjhgHYmJo5MDAuNXrmbWuuwLi79x4i67v1FE1eaWCcCFhp2fXLVAfFxZ+kCFtZPO1H1K9ZOcnclQSGKVp4vGJTBMbNW7YOPb/50aCLJQLjnj2PQLcvR2H+CpPkTsYptWhQE3Mmfg8fb68kzJWadIYIqJM52jSprdtr2aNhx75Yv22vVPnob3rhmz6d9dZa+tyNP5GUBsYpPa+koPQUWTMwLioqGoNGT9IFs5nyyJTBH+v/mYjSJQqnqFsRoCdcTH3MmTgCIixT5mBgnIyS8RCzbXsC8X7bHnKdvUoEOrZlPkoWtY0PiUa8eImP/zcMi1dvll5fagplA+PU+rq+E/wAPQaNwaqN8d4zlBqQeG1FgNs/v49K0tvT8GcYOm4qJvxpnrynl9f2w9XVxUSrSL6b+w8fY83WwwjXpEPazAF4GR2D6OhIXXActDHQxcRptRAZxBqNM5w0wNPHDxF09CAunD6JgBw5UKJ8FaTNkh2evukQkLcIwkJC4fXwKGqVzQcPdzezzp+dO5aACQPjRFCcCHRvIXIQraR4EYBIto6x0vgclgL2LGA3z/9iN4mBcfZ8unJtFKAABShAAccUMHdgnAiLWzJ7BYoVKe6YwGZeNQPjzAzM7ilAAQpQgAIUoAAFKEAB/QJbWrXKG6WJvhS/ovQnnZEmIBvZKEABClCAAhSgAAVsWOD548c48Mfv8VcQHeUWmbHx/LWPbXhZnDoFKECBFAvw/jfFdGxIAQpQgAIUoAAFVC3A+19Vbw8nRwEKUIACFKCAugU6AZitginufzUH8QH1FAXHMTBOBTuoZwrmCowTw2kCykovPG+ubLi0b5V0/eSZi9Hn27HS9UoL//rlO3Rt3yxBM7UHxs2Z9D26f/UDlqzZYnS55g6Mu3E7GM07D8CRk2eNziUlBe8UKYDty6bB3y9NguZT5yzThe/IHuGX9sDL08NoeUjoU/gVrmG0Lrbg4t6VyJc7e7L11jh3409EaWBcSs8raaxkCq0VGPckNAwden4rHQyYkjUunT4WrRrVkm4aExODYT9Pw6hf/5Zuo7RQXA9GDewJZ2eRU6H/YGCcnKyxEDN7DYwTIaGtu31t1tdP4h0wZi3q1fi6FvNauWEHOvUdatLQ2/g+9WpUwob5kxKQPQ4JRcOO/5MOVpU74xNWWSIwTowYfP8Rtu45hrthznDx8YN3mjSAxglarRYxWhEcJxK1tIh8/hz3b99A0IkjuH31CrJly4HCpcrCN2NmuHp4wc3LG75+GZAlXwncvnoWOWOu4r0ythHQmJL9YRvLC5ggMK7oq9uq7wG0smJQXCxcRwD/WF6RI1LAYQTs4vnflAHzddmtsUejLjUTbODaGdsT/HfirzvMbnOhFKAABShAAQrYnIA5A+NaNm2N//Xoj9w589ici7UnbOzny8Rft1YCu7WdOD4FKEABClCAAhSgAAUoYEWB9a2bD9e8/o3NusPL3x/lP+9pxRlxaApQgAIUoAAFKEABUwkcmTUDobdvv+1Ogz71l6ycbKr+2Q8FKEABWxLg/a8t7RbnSgEKUIACFKAABZQJ8P5XmRerKUABClCAAhSgwBsBkd5yDkB+lYjsezUPERy3KSXz0d4+lOCDoynpg21MK2CuwDilAVvJBZvoW+l3Y/8wa3hS7LgH1s5G+VLF4qah5sC47Fkz6YLPzl++LnWCmDMw7vqtuyjXoBPuPXgkNZeUFjWrVx0rZvwMjebtR3zEmJnfqSvd5b9zfkWj2lWN1i9ftw2tPv3aaJ0oqFKuJPasSj7cy1rnbvyJKwmMS815JYWlp8gagXERL17ivRafIvDYmdRMXartwqmj0a6p3Hnab9h4/Dp9vlS/qSnq+Ukb/D56oMEuGBgnJ2wsxMxeA+Nadv0KK9YnDACRE0t5lTFrtb6uV23cqQtVNeeR+Oeqp+HPUKVpV5wIumDOYWGpwLjYRZw4fQGXbz7A1duPEPwwDDFaJzg7uyE6KhIhTx7j5q3ruHM/GK7QoIB/BuTIVwDumTPDzcsH7t7ecPPwgIu7N3zTidC44ngYfAu+DwPRoOq7ZnVi5/YvcHZtwuDsIt2g9DPpIihO3He3VkFQnNiwiwAKAYix/93jCilgNQG7eP7HwDirnT8cmAIUoAAFKEABMwuYOjDO29sHnT74BJ06dEZAlgAzz95+u2dgnP3uLVdGAQpQgAIUoAAFKEABuxHY0KbFUGi1/QD4iUXlr10H2ctXsJv1cSEUoAAFKEABClDAkQVuBh7Exc0JPle1t/7SlVUc2YRrpwAFHFeA97+Ou/dcOQUoQAEKUIAC9i9wMzAQFzdvjL9Q3v/a/7ZzhRSgAAUoQAEKmEbgYwCzTNOVyXoRwXHiFx9uVtIjA+OUaFmm1lyBcQeOnELFxp9IL+LzTq3wx4/fGK0XYTQilMYShwjLOrV9MdKm8dENp+bAOKUe5gqMs2Q4j1jz5B++Rq/ObRMsv+nH/bFm8y4pEtnzrnO/7zFr0RqpPqeNHYzuHVsmqbXmuRt/MkoC46QWHK8o/nmltG38emsExvUa/BOmzFqSmmkrahu0aykK589tsI2SgDZFg+spnjd5JD5s2UBvV0rmM354f/Tr3sEU09Lbh9LgtZ+G9EXV8spDoBas2IDJMxdLr8VYiJnSeR/bMh8lixaUHt8aheL6KK6Tlj6MWavxdb11z0HUbmv+XxaeODCuz7djFZ3HKd1LSwfGiXm+jIxE8P1HePAwBA8fheFFZBScnDTQxkThWUQEQsKf4WTQFVwOuo6C2XPAM30GuKb1g5uvL1w9PODm7gk3T1+kzZgFmXIXwf2b55Eh9CTer1IqpQxsRwGkIjBOhLKJC2obACI8Si3HR68+vjJPLZPhPChgxwI2//yPgXF2fHZyaRSgAAUoQAEHFwh/Fo6z58/g1JmTOHbyKILOnkHQeWW/fCS9fwaUebcsqlaqhlZN28DHx9fBVVO/fAbGpd6QPVCAAhSgAAUoQAEKUIACFhDQHjqU4e61K/fvBZ1B3pq14OrpaYFROQQFKEABClCAAhSggLkFXoaHY+9vExIM4x6FdDVXrnxi7rHZPwUoQAE1CvD+V427wjlRgAIUoAAFKECB1Avw/jf1huyBAhSwW4HPAExVsLo/AYg2sgf7NyxFH/rEF7D115fsdcGUdSI4bjiABL8ZR98ADIwzJb1p+jJXYFzX/iMwY+Fq6Un++G0fDOwlPhut/7hw5ToKVkkaxGVskHLvFoWzszNOn7uEsKfPjJUn+PrYIX3xVc9Ouj9jYBzQpM57WD17vF7Dnt/8iD9mL1VkLIL58ufJgeu37uLytVuK2vr6eOHOsY3w9nr7PjoloVai/eOgHXB21p/HERUVDf+iNaXPnfuntiCDv+53wsYd1j5348+FgXGvNbS3D8WxKDlnFJ2gBoqLFsyLwxvnwcPdLdmqi1dvoEDlFqYaTrqfs7uXoVC+XMnWK3FSY2CcNEIqC42FmCkNjKtWoRRy58ia4lm5ubpi6k+D4eLinOI+DDUU1+5c5RqbpW9jnRqyVnK+GhtH9uvGXtf3Hz5GvkrNpL+fyI6bXF38wLgjJ8+iTL2OqelOuq01AuPiT06r1SI6OkYXGOfk9Pp7e2RkFIIfPML67QewfUcgAtJkgFe6DHBJlw6uXiIszhNuHl5w9/aFX6ZsyJizEG5fPImc0echXn88KJASgRQExomgOHFf/UFKxjNzm/MAxPy6A5imYCylz1fYv2Fc+tBHwcvPLKVSz/8YGGcWe3ZKAQpQgAIUoICKBZ5HPEdoaAhCw0IRGhaCkJAQPA1/Co1GA18fX3h5ecPXxwcBWbMjbZq0Kl6JbU6NgXG2uW+cNQUoQAEKUIACFKAABRxO4MWRwPZaaOY73MK5YApQgAIUoAAFKOAAAodn/o2wO3fiVqrVahs1WLZqnQMsnUu0roB4s6GSQ2m9kr5ZS4E4Ad7/8mSgAAUoQAEKUIAC9itweOYMhN25zftf+91irowCFEiZAAPLDLvRhz7xBZR+4NjRzp+UXYVM00p8cHQEgA2GumNgnGmwTdmLOQLjps5Zhh6DxiiapqGQotiOmncegFUbd0r1WzBvTvz1y3eoUq5kXGCICBHZd/gkPurznXQwWaYM/rh5eB1cXV0YGAfDgXFKA2lEEN/gvp3hl8Y3bk/Dnz3HiPHTMXbKHKl9FkXTxg5G945vgwSfhj+Db4H3pNvvWzMTFcuU0Fu/a/8RVG8p8hmMH83qVcfKmb8kKbT2uRt/QgyMe60RGxgnAgHzVGiCm3fuGd/geBUiRKhk0QJI4+uDoAtX8F/gcdx78EhRH+Ia1bV9s2TbdO73PWYtWqOoPxFWVb5UMeTKnhWXr93EgSOncP7ydUV9dGzVEHMniW/nSQ8lAVwMjNMf8KY0ME7RBuopDruwCz7eXqboKkkffYeMw6QZi8zSt7FO9QXGqfV1LX42Ej8jpeQQAadVy78LXx9vXLl+C2cvXjUYPBc/MK7Pt2MxeeZiRcPWea8CypYsilLFC8Hd3Q03bwfrgl3Xb9uLE0EX9PZl7cA4Q4t89jwCuw8ex9KV2+END6TNmAka3zRw8/GGu6cnXN294OHrC/8suZAuIB/uXjiC/C43UKHMO4rsWEwBIaAgME7NQXGxm/kRgHkMjDN6bjPQzTARfUzrY/SENGOB1PO/a6fvaM04B3ZNAQpQgAIUoAAFKEABRQK5imXViAa6/8eDAhSgAAUoQAEKUIACFKCApQUiDgdOg0Yj9+4zS0+O41GAAhSgAAUoQAEKpErg4uaNuBkYGNeHBhhVb+nK71LVKRtTwLiA0jfm8O9IjJuywgQCvP81ASK7oAAFKEABClCAAioVuLh5E24GHuT9r0r3h9OiAAWsJuBogVYM/DJ8qtGHPvEFlF4frHYhizfwf69yeIYB2Bp/MkHTkeB5dOFGh9QwV87h1YcjTBkYd/TUOUz8e6HikKM2TWpj8bQfDe6HCCcp+X57qT1r1agW5kwcAS9Pj2TrRaCYCAATAWcyx/K/x6FFg5oIDQvHmfOX45r8PmsJ5i2T+/1PQ77oikbvVzU6XLl3i8HZ2UlXZ6lgLyXjNKnzHlbPHp/sOtp+NghL1mwxukZRsGvFdIjALX3HwlWb0L7HYKm+REjW6R0Jg3A++WI4Zi/+V6r9N306Y/Q3vfTWDhw1UTrAbun0sRDnX/xDDedu/Pko2W8pwHhFg3p/gjGDeyttlqRehLeJcKLY4+P/DZMOPhOBfZkz+Bucg7eXJ0oUya+rURKCJupFaNOiqT+iQa3KCcYQQUTdvhyF+SsM5qYmaJM3Vzac270cLi7OCf5chDLlKtdYkaNw/7pnp7iAzNjGE/6cj/7Dk3/N6hvg0r5VEHNLfCixYmCcYwTGPXwcggzF3ld0ropiEZTY8P0qKFm0oO5cE6+fc5eu4d/Nu/Hz1LkGg9DiD6YvME7JuWqp17X4Gal03Q8VWw0f0B1d2jdDjoDMCdqKEFwRVPnH7KXJXnfiB8alKfietGmHFvXx57hvIa6T+o4LV65j6b9bMWbSzCT9qjkwTqxHhAkeC7qIOQvWwSXCCd5+/nDySws3L2+4enrCzdMLXr5+SJ8tN9JkyoXgcwdQ3C8U7xYrqHjv2IACQkATUFbf+6xEWrH4pZ0tVP559YsARKhdDAPjjJ7TDEQzTEQf0/oYPSEtUJDs87/YcRkYZ4Ed4BAUoAAFKEABClCAAtICDIyTpmIhBShAAQpQgAIUoAAFKGAOgRdHD53UalHcHH2zTwpQgAIUoAAFKEAB6wrcCzqDMyuWx01CC2xrsHSl8nfWWncZHN32BBgYZ3t75hAz5v2vQ2wzF0kBClCAAhSggIMK3A8KwukVy3j/66D7z2VTgAJ6BZQGQjFQzPDJRB/6xBdwtNeXmi61e0QWGQBdehQD49S0NQnnojQwbsqYQboOYmJi8DgkDI+ehODRk1BdkFrgsTMpWmjg+jkoW7KowbYf9hoiFcQkwpwu7l2JTEYCo7bsPog67XpKzbdv1w/w28gvk9SKoJTBY36X6mPFjJ/RvH4NqdrYIksFeykZR19gXNCFKyhavY3U+kT4zbABxn9natVmXXVhODLH3eObkDnj25CwTTv3o157ueCygnlz4tyet39Pm3i8fJWa4fK1WzLTQPilPUmCCtV27irZb6lFxysyVWBc4nHrftALm3cdkJrOvZObkTF9OqlaUVSmXkfp8EhxfTmy6R/kz50j2f5FgNM3oyfjp99nS4+f3LVBSUihGEiE5DWrV13vmCKEq8nH/aTn1K97B4jAt8SHkhAuBsY5RmDcuClz8PWoidLnliicMWEoOrdrqrdN884DsGrjTqk+9QXGqfF13anvUMxdKhcyKxYvrjcLpoxGo9rGw2YvXb2JT78ciR17D8e5xQbGhT19BhEYJ3s8Prsdfml8pcpFkO5vfy3A0HFT4+rVHhgnJip+hj13+QZ+nbIQ7lHO8PVPD5e0fnDx8oKblxfcvXx0oXEZc+aDd/oA3Dt/EKUyRKBYobxSLiyiQHyBZALjRGKyCIrTfyFUF2EnAHPfTImBX4b3hj70iS+g9Pm00vNHTVeKBM//YifGwDg1bRHnQgEKUIACFKAABSjAwDieAxSgAAUoQAEKUIACFKCA1QS0hw6lfeGEJ1abAAemAAUoQAEKUIACFDCrwIuwMOyb9FvcGFrg+YHi7/oMHz5c/IZOHhQwlwAD48wly35TLMD73xTTsSEFKEABClCAAhSwCQHe/9rENnGSFKCA5QUcLdBK6Qfm6GP4nKQPfSx/1VI24u5X5cODpmNr/GaFGx1S1gurzSagNDDO1BNp27QOFk0dY7DbZ88j4J3PeGCK6GTyD1+jV+e2UtMs+X57nAi6YLT2nSIFcHzrgiR1ag6MK/duURTOn1sXbJU1cwZERkbpAv5Onb2IG7eD0b1jS3Rq00i3JiUBYvoC40b9+je+G/uHUUsR5CcCfjw93I3Wrtm8C00/ThpalVzDNbMnoHGdanFfEutNX6wWRFCOzBG0a6nOK/EhghCL1ZA7n7q2b4a/fvkuQRdqPHeV7HdiDyXnlYy7bI25AuNuB99HtlINZKeBP8d9i24ftjBY/zT8GQJK1Zc+95I7bwpVbYnzl69LzatDi/r45/dRRmvrd+iDjTv2Ga0TBdmzZsKNw0mDrRgYJ8Wnu8blyu4YgXFKgj2F3sYFk1G3ekWDkKkNjFPj61rp9yQBdGLrQpQokl/upHsTgjbhz/n4csSvujaxgXEXr95AgcqGr1vxB0n8/VRmAmKML4b+gjvBD3Bw3Rw4OzvJNLNqjQj4PHQiCDPnrwPCouGXISOc0qSBq48P3L084erpAx+/dMicowC8/LPi4YUDKJnxJYoWzGPVeXNw2xOIFxgnLn4iKK6eDa3iIoBC4hLzZs5KA62UPv9j/4ZPDvrQR+2XD93zPwDbxEQZGKf27eL8KEABClCAAhSggGMJMDDOsfabq6UABShAAQpQgAIUoICqBJ4fOVhdA6cdqpoUJ0MBClCAAhSgAAUoYFKB/ZMnISI0JK7PGG1M6YbLVh816SDsjAIJBRgYxzNCdQK8/1XdlnBCFKAABShAAQpQwOQC+3+fhIgQ3v+aHJYdUoACtizAwC/Du0cf+sQXUPqBY0c7f1R7LSxbEOjdBKhQ+PUUGRinnq2yZmBcrarl8O+cX42Gh+3cdwQ1WonPxxs/Tm5bhOKF8xkvBPDpgJH4e8EqqdqwC7vg4+2VoFZtgXHN6lVH68bvo+H7VeHvl0ZqXaJISYCYvsC4mq0/w469h42OKRMQGNvJleu3kbdiU6N9ioLv+n2KEV99nqC2//DxEOE5MsfPQ7/AgM87Jin9+Y+5+Grk21/6ZaivbUumomaVsglK1HjuKtlvsZiUnlcy7rI15gqMW/rvVrTpPlB2Ggi/tAdenh5G6weOmoixU+YYrRMFicPZROCSCJyTPf5bPQOVy75jtHztlj1o3OkLo3WxBcmFnjEwTo7PUQLjlARiCjnxPX/rYuPBoqkNjFPj61p8fxTfJ2WP5IIkZduK1/q6bf+hzDtF0OWDplAaoCeuST8P+wLN6tWAh7ub7LA2WSdC44IuXsX0uWsQfu8p0vlngGtaP7j6esPN0wtuXt7w8fNHllwF4JkuK+6fP4h3Mr1EiUJ5bXK9nLR1BDQBZd8HMBiA+F9bOz4CMC/epBlYZngH6UOf+AJKn18qPX9Uez3Jn70wGldujYI5iurm2KhLzQRzXTtje4L/Tvx11S6ME6MABShAAQpQgAIUUKWAsZ8vE39do8pVcFIUoAAFKEABClCAAhSggF0LRBw51APAFLteJBdHAQpQgAIUoAAFHFwgaPUqhNy4AUB7PuJJyF4nJ/xWd8nKYw7OwuWbV4CBceb1Ze8pEOD9bwrQ2IQCFKAABShAAQrYmMDZNavx5Pp13v/a2L5xuhSggFkFHC3QSukH5uhj+PSjD33MeoEyYecbAYzV3j601YR9sisTCFgrMK7cu0WxdfFU+PokDGFLbkkjxk/HsJ+nSa1244LJcHVxkaod98ccrN+2V6r28v7VyJMzIEGtmgLj9AW5ySxOSYBYcuMoCQ2qV6MSBvb6WGZaiIyKQr32vaVqkwvXOXDkFCo2/kSqfZVyJbFn1d9Jaqs264r/Ao8b7SNTBn/cProBzs5OCWrVeO6mdr+NYpihwFyBcf/77mdM/Huh1Iw/at0QcyaOkKq9ePUGClRuIVUriq4fWoscAZl19cvXbUOrT7+WaivOuzvHNsDJKeF5l1zj6OgY5C7fGDfv3JPqe8Efo/FBs7oJahkYJ0UHRwmMUxKIKeTWzZuIBrUqG0VMbWCcGl/XX4+aiHGSIZIC6OLelciXO7tRK5kCEYrmlK2cTGmSmlaNaqFm5bIoVaIwShYtAG8vzxT1o/ZG4ro46e9leHjjEfz9/OHilxauvr5vQuO8dKFxWXMXgk/GHAg+dxA18rkiW5YMal8W52dlgbNrX4cI7w8CJq0Gjly08oRSNnxnALPiNVUaaKX0+R/7N7xP9KFPyl7JFm5VNPc7qFOuCQrlLKYbmYFxFt4ADkcBClCAAhSgAAUcTICBcQ624VwuBShAAQpQgAIUoAAFbFEg4ujh8dBq+9ni3DlnClCAAhSgAAUoQAGFAhrNBI9SZforbMVyCqREgIFxKVFjG7MK8P7XrLzsnAIUoAAFKEABCqhLgPe/6toPzoYCFLCmAAO/DOvThz7xBZR+4NjRzh9rXsv0jS2C4oaLz8qLAu3tQ0qfS6txTXY1J2sExrVtWgdTf/oG6dKmkbJs2LGvdLCbVIcpKDqy6R+UKl4oQUsGxr3m2H/4JCo1EXkS1jtaNKiJ5X+PSzABEZKTv3JzXL52S2pid45tRJZM6eNq7957iKzv1pNqO6j3JxgzOGm4nRrPXQbGvd3Smq0/w469h6X2eNIPX6N357ZStaIo8zt1ce/BI6l6EXRZt3pFXe3ICX9h6LipUu2SO+8NNezUdyjmLl0n1ffgvl3ww6CeCWoZGCdF5zCBcb/PXIze346VQsmeNROuBf4rFW6Y2sA4Nb6ulaxJBDWKwEZTHiXfb48TQRdS3aUI+63zXkVUr1QaImjVngLkgu8/wq9/L8W9y/eQ3i8dnP384ObjA1cPT7h5e8EnrT8C8haGb8aceBC0A5ULpEWOrBlTbcoO7FcgNjAudoVFuqH2q38X98VVbWjV4rf+5AMQ9WbODCwzvHn0oU98AaXPL5WeP2q8lOie/00ZMH9f/MkxME6NW8U5UYACFKAABShAAfsRYGCc/ewlV0IBClCAAhSgAAUoQAG7FYg4emg5tJD/tZN2K8GFUYACFKAABShAAQcQ0GCFR6myLR1gpVyi9QWUfjBPY/0pcwb2LsD7X3vfYa6PAhSgAAUoQAEKxBPg/S9PBwpQgAKxAo4WaKX0A3P0MfxaoQ991Ho1XQ1gBIAESTwMjFPfdlkyMC5TBn/MGD8UjWory0koU68jjpw8a1W87UunoUblMgnmwMC41xxrt+xB405fWHV/qlUohV0rpieZg5LwrRkThqJzu6ZxfcxatAad+30vta5jW+ajZNGCSWrVeO4yMO7tNhWr0RZnzl+W2uOti/9ArarlpGpFUd0PemHzrgNS9SIcSoREiaPfsPH4dfp8qXbDB3THsAEiW0Lu+On32Rj0wySp4s87tcIfP36ToNbWA+MqlimBQvlySa0/flHgsTPS54lod/XgGuTKnlXvONv2BOL9tj0UzyM1DcIu7IKPt1dqukjSdsT46Rj28zSpPpvUeQ+rZ4+XqlUSrpactRpf1+UbdoI4j2SOH7/tg4G9PpYpla7p2n8EZiwUP5qb7vD18cIX3TpAXCsCMttHcJoI+Zzw51IEX7mL9P4Z4JzGFy7e3nDz9IS7lze80/ohW76i8PDLirDL+1EujzfyZM9iOlT2ZFcCyQTGxb7fSnzDF8FxlWxkwSIVetabuSoNtFL6/I/9Gz4p6EMftV42Ejz/mzJgfoL3ozIwTq3bxnlRgAIUoAAFKEAB+xBgYJx9LCxysgAAIABJREFU7CNXQQEKUIACFKAABShAAbsWeH7k0AENUN6uF8nFUYACFKAABShAAQroBLTAQc/SZSuQgwIWEGBgnAWQOYQyAd7/KvNiNQUoQAEKUIACFLBlAd7/2vLuce4UoICJBRj4ZRiUPvSJL6D0A8eOdv6Y+PKkuDvxzHk5AJGwdDK51gyMU2xq9gaWDIxbN28iGtSqrHhNmd+pCxHiYc3j3zm/Jgm6Y2Dc6x2Zs2QtPv7fMGtuD0qXKIzDG+clmcO5S9dQuForqbklDjRq2fUrrFi/3WjbogXz4vSOxcnWqfHcZWDc261Ssj8nty1C8cL5jJ4PsQUibFCEDsocv48eiJ6ftNGVduo7FHOXrpNppgt0E2FNsoeS12qbJrWxeNqPCbq29cA4ESopwiWVHiJoSwRuyR6mDoybMmYQShZLGkgpOx8PdzfdNdLUh5Jww96d22LSD19LTSG1gXFqfF3nKNMQN+/ck1r/wqmj0a7p6wBJUx1nL15Fkfdam6q7JP0M6v0JRg3sCWdnJ7ONYamOg+8/wvhpCxF85QHS+6eHiy40zgtunq//8fFLh7wlysPDNwMent+Ld7O7oGDubJaaHsexIQEDgXGxq6j95r5Z+c2RZR2uAxA/AEUBYGCZYXv60Ce+gNLnl0rPH8teCZKOZvT537XTd5S+L9Xaa+L4FKAABShAAQpQgAJ2LJCrWFZdkHtsmrsdL5VLowAFKEABClCAAhSgAAXUJhBx5JD4VZZ51DYvzocCFKAABShAAQpQwCwCVzxKl81rlp7ZKQUSCih9Yw7/joRnkNkFeP9rdmIOQAEKUIACFKAABdQkwPtfNe0G50IBClhTwNECrZR+YI4+hs9O+tDHmtev+GMvADAKwJnkJhQ0Xfy+nLdH4UaH1DJvh5+HJQPjMmXwx9HN/yAgc0ZF7pqAsorqzVHMwLjXqolD1cSfTfhzPvoPH28Oduk+9QXGiQ7KN+yEwGPJXpqS9B92YRd8vL3w7HkEvPNVlRp/3Hf/w5c9Pkq2Vo3nLgPjXm+VVquFU7ZyUnssii7vX408OQOk6/t8OxaTZyYfJJi4kxFffY7v+n2q++OGHfti/ba9UuPMnTQCHVs1lKoVRUv/3Yo23QdK1deoXAbbl05LUMvAOCk6mDow7tiW+ShZNOWBcXKzVl6lJNxwzODeEKFiMkdqAuPU+rpW8r1g35qZqFimhAyVopoeg8Zg6pxlitooKW5Wrzr++X0UvL08lTRTZa0I9/tt+hLcv/YAGdOlhyYuNM4Tbp6e8MuQGTkKvQOf9NlwN2gvSmYBiubPocq1cFLWF9AElDX2PiuREDkcQCXrz1bvDMQPuiKZWWmgldLnf+zf8ElAH/qo5TJh8Plf7CQZGKeW7eI8KEABClCAAhSgAAWEAAPjeB5QgAIUoAAFKEABClCAAhYX2NiqWQUnF6fQ8j367nP18kzr5OJi8TlwQApQgAIUoAAFKEABywpooHnsXrqMv2VH5WgOKsDAOAfdeDUum/e/atwVzokCFKAABShAAQqYV4D3v+b1Ze8UoIBNCTDwy/B20Yc+8QWUfuDY0c4fa1z85gMYAeCcocEZGGeNrZEb05KBcWJGVcqVxLalU+Hm6io1wcjIKLjlqihVa84iBsa91k0uMG7khL8wdNxUc/Ib7dtQYNzvMxej97djjfYhClbO/AUi9Gbtlj1o3OkLqTbXAv9FzmxZktSq9dxlYNzrrYqKioZrzgpSeyyKbh5Zh2xZMknXf9hrCOav2CBVP+mHr9G7c1tdbc3Wn2HH3sNS7RZOHY12TUXOjNzx1/yV6PalyHY1frRoUBPL/x6XoJCBccbdRIWjBMYpCXabNnYwundsKQWopN/E1mp8XSudU9CupSicP7eUlZKiew8eocGHfXHk5FklzRTV1qtRCRvmT1LURq3FV67fxm9/LUXorcfwT+cPJ19fOHt5wc3DHe4+vvBNlx45CxZHuoB8uHF6L0pkjMQ7BXOpdTmclxUFJALjYmdXD8D3AOR/OLHcui4AEMmlDCwzbE4f+sQXUPr8Uun5Y7krwNuRpJ7/xZYzMM4aW8QxKUABClhJQANERkWKX8/wegJaQOPkBBen15+Bjo6JRow2+tWPuyJLOenHFl5EvUDY06dwcnKG5k3cslb0Ef/fRUfxo5i1b3vS1b0pTpzWLILlRV+6aWmj4eHuCW9Pb2i0gIvz67+fiI6J0v1iCfF/CY/X83XSOMPZyfn1l6KjAGd+tttKZxqHpUCqBBgYlyo+NqYABShAAQpQgAIUoAAFUiKwoXXzBE8bagwekpJu2IYCFKAABShAAQpQwLYEXnqULutuW1PmbG1UgIFxNrpx9jht3v/a465yTRSgAAUoQAEKUMCoAO9/jRKxgAIUoAAFKEABCugEPgYwS0UWMQAWvflAu8GguNg5MzBORbuXaCpKA+NibgXG9bBm8240+6S/4sWJYCQRkCR7aALKypaarY6Bca9pkwuMmzRjEfoOSRgsZbaN0NOxocC428H3ka1UA6kpdfmgKf4ePxQ9Bo3B1DnLjLapUbkMti+dprdOjecuA+PebpeS/bmwdwXy585h9JyILej5zY/4Y/ZSqfpx3/0PX/b4SFfbsutXWLF+u1S7mROG4ZN2TaRqRZGSwLcGtSpj3byJCfpW0n788P7o172D9NxSUrhtTyDeb9tDuumuFdNRrUIp6frYwhkLV6Nrf5GNK3c4SmCcklDEsUP64quenaQAUxMYJwZQ4+tayZzE9xTxvcUcR/iz5/ioz1Dpa0xK5rBxwWTUrW79oN+UzD1xmzv3HuCvef/i3IlLyOyXARofbzh7e8JNBMd5ecLLNy2y5MqHbAVL4WHwbXg+Po2aZfLB04Nv9zKFv730oSAwLnbJIglWBMep7YUknknMsZd94ToooGIBm3/+F2vLwDgVn2WcGgUoQAETC1y7fQXzl86Fm5sbNBoNXr58AR8vX7Rt3h5+adNhw7a1OH32FFxcXHRfF/+8DnLTIiYmBiVLl0bx4iXwPOK54pm9jnTTIDomEtFRMSIV7nXsmy4lTgNnZ2e4uLroxvJwd8ftu3fw346dugC4Tm274GXkS6zZtAL37t+Du3vSe7mIiOcomK8gar/XAP5+6RXPjw0oQAH1CDAwTj17wZlQgAIUoAAFKEABClDAYQT4gXmH2WoulAIUoAAFKEABCsQX0HqULutEEgpYQGC4wjGU1ivsnuWOLMD7X0fefa6dAhSgAAUoQAEHFuD9rwNvPpdOAQpQgAIUoIC0gPj7AhHKll+6hfkKRVDcklcftxn2Zk7SIzEwTprK4oVKA+O0tw8lmOPAURMxdory7IDZv32PTm0aSa03R5mGuHnnnlTt919+Bg8zBHV0bNUAAZkzJpjDmEkzMXjM71LzWv73OLRoUFOqNrbIUsFeqR1nwcqN6NDzW6m1ZcrgjwGfd5SqVVKUJ0cA2jSprbdJ/Q59sHHHPqNd+vp44f6pLchYvDbCnj4zWj9jwlB0btdUb50az93U7rdRFDMU1P2gFzbvOiDVc/CJTRDnmcyhZH+ObZmPkkULynSrq+nUdyjmLl0nVS9CCkVYoTg++3o0/py3XKrd5B++Rq/ObaVqRdHMRavRpZ9c8NlHrRtizsSEtQyMk6N2lMA4JaGI4rr/89AvpABTGxinxtd1vkrNcPnaLan1Kw2ClOo0XlF0dAymzV2GX6bNk56TkjEMBbgq6UcttVdv3MW0uStx9+I9pEuTRhca5+LtBVdPd7h6eMPL1weZcuRGzkJl8PhJCDT3j6NGiQCkS5tGLUvgPKwskILAuNgZ13v1L+I9WmoJjrsIoBAA8UyABwUoYB4Bu3j+N2XA/AS/wLhRl4TPQdbOSBiOnfjr5qFlrxSgAAUoYE6B0+dPYNKfv+oC4ZycnHQhcBEREcicKTO8vX1w5+5tXWCbCIqLf4g/i4qKQrPmzVGlalU8ffr0TU1s3evQN0OHGPPqlSvYvWsnoqIioXFy0mXFiVaRkZFIkyYNqteoiUyZM8Hd3QOXL1/CP/PmIeTJE+TLkx8xMdG4E3wnydzih9qJeebPUwDtW32EzJmyQpPgO505Zdk3BSggI2Ds58vEXzd8VZEZkTUUoAAFKEABClCAAhSgAAUkBfiBeUkollGAAhSgAAUoQAE7E/AoXZbPou1sT7kcClDAsADvf3mGUIACFKAABShAAccU4P2vY+47V00BBxNQGsCvtN7BOLlcCjikQCcAs628cvERmKWv5iCuUWdSMxft7UP8OE1qAM3QNrWBcZGRUajZ+jP8F3hc8ewOb5wHESxi7CjfsBMCj8mdertX/oWq5d811qVJvq4kMG7p9LFo1aiWonEtFeyV2nE27dyPeu17S62tVtVy2Lr4D6laUxbNW7YOH/UZKtXl6G96SQcBPgraZjAURo3nbmr3WwrRxEVKAuPuHNuILJnSS81Ayf6smPEzmtevIdWvKKrarKv0dTF+39+MnowfJ8+SGqdf9w4YP7y/VK0oGv7LnxDXfJkjub4ZGCcjBzhKYNygHybhp9/lfkTt2Koh5k6SCytMbWCcGl/XSq4HQ/t3gwi/NfcRFRWNddv+w+QZi6QDOWXnpCS4U7ZPa9adu3QNU2etxNPgMPh6eUPj6wsXT3e4eHjA1cMTXr6+SJ81O3IULoWISC1e3DqFcrlckTtHgDWnzbGtLHB2bdkEMyjSzUjShf751hffwgFUsPKSxPDi2cRcFcyDU6CAvQrYxfM/BsbZ6+nJdVGAAhTQL3DuchCm/D1RFxgXGwr3/PlzREdHw83NDa6urkkC2WJ7E6Fu9Rs0QvkK5REeHq7748TBcvpGFnWi7//+24N/V62Cu7s7nJyc8TrRTYMXL17Aw8MDrdu2RcFCheHq4oprV69g+bKlePz4sS7cTnzd2Hji6yLMrmD+QujU/lOkTyP3iyp4zlCAApYRYGCcZZw5CgUoQAEKUIACFKAABSiQAgF+YD4FaGxCAQpQgAIUoAAF7ECAH5i3g03kEihAAUUCvP9VxMViClCAAhSgAAUoYDcCvP+1m63kQihAAf0CSoOR+IskeDZRgALxBZwAnAOQ30os4hq2HIBIWZJL6zIyUQbGWWknDQyb2sA40fXNO/dQtHprhD19pmiB2bNmwtHN85HB389gu5Zdv8KK9dul+v5hUE8M7ttFqja1RUoC4xZP+xFtmtRWNKSlgr1SO87RU+dQuu6H0mt7dvk/eHq4S9ebojA0LBxpC1U3RVdxfYj9FPtq6FDjuZva/TYpomRnSgLjbh1dj4DMGaV6VrI/I7/ugSFfdJXqV6vV6s432WvivjUzUbFMCV3fv89cjN7fjpUap0blMti+dJpUrShSst6xQ/riq54is+LtwcA4OWpHCYwbO2UOBo6aKIVStGBenN6xWKo2tYFxSs5zS72u2342CEvWbJFaf533KmDTwt+lak1VFP7sOQ4cOYU9B49h76ET2HvouPT1K7k5HFg7G+VLFTPV9FTRz5HTFzB95irEhL7QBcRpvDzh4v46NM7Nwx3u3j7wz5wVOQq+A61nOoTeOIP87sEoV6q4KubPSVhewISBcbGTb/DqX0RwXHnLryZuxIsACr16PhBjxTlwaArYq4DdPP9jYJy9nqJcFwUoQAH9AokD48RzsYiICF0gm6enJ2Ji9P/4qAuMa9gQ5cqXR/jT8LfhbeJvy1/nvr0+4v+77r810DgBLi6uOLBvL7Zu2aILjIsf/ib6dnVzQ5MmTZC/QEG4uIrAuKu6wLgnjx/rwuZEGzFfY4foV4TgValQDe1bfGSsnF+nAAUsKMDAOAticygKUIACFKAABShAAQpQQJkAPzCvzIvVFKAABShAAQpQwF4E+IF5e9lJroMCFJAV4P2vrBTrKEABClCAAhSggH0J8P7XvvaTq6EABZIVMP4u84TNGBjHE4kCFIgvID55MscKJOLatfLVx26GAThpyvEZGGdKTdP0ZYrAODGTTTv3o1773oonJYJR1s2bCBcXZ71tf50+H/2GjZfqu0q5ktiz6m+p2tQWKQmM+3v8UHT5oKmiIS0V7JXacaKiouFftKZ0uMyWxVPwflXLZ1182GsI5q/YoGgPDBWvmjUeTeu+Z7A/NZ67qd1vkwEq6EhJYNzZ3ctQKF8uqd4n/Dkf/YfLXVtE4NWp7YsSfPBU3yDb9gTi/bY9pOYgip5f2QsPdzdd/aHjZ1CuQcKgNkMdyQbkPQ4JhX+RWtJz2r3yL1Qt/26CegbGyfE5SmDc2i170LjTF3IoAA5vnIfSJQobrU9tYJwaX9dKvhcIoG1LpqJmlbJGrZIriI6OwbPnEXB3d4Wbq2uK+zh/+RrEtWzp2q3Ysfewon4W/DEaHzSrq6iN2otFwMG+I6fx16zV8I52gfP/2TsPwCiqLQz/STadhN57J3RIqNJ7L0pXqgICYqEKFhQRfagooCCgVEHpNaFKl957h9AJkEAgCaQ+7oQN2WQ3O7O9/PMeJtk999xzv3tnszOZ+dbbC24+3nAT0rhX4jgvHx9kyZET+UoEwDdHEUTcu4EsT0+jed2qtj481mcGAmYQxqmrtLY4Tpyj+MsMyJiSBJydgMOc/6MwztmXMsdPAiTgjASEMO63P6ZIAjYhVkstjPPy8pKEcalFbmpGIi4+Ph7NW7ZEteo1EB0V/UoQl5QiiHN5ZYxLQhLU36vbi5xuKhUO7t+Hf7dugYdHemGch4cH2rRthxKlSsFdpUJoqBDGrcDj8HBJJqdEGCcEdGVKlsX7vZX/DcQZ1wXHTAKWIkBhnKVIsx8SIAESIAESIAESIAESIAHFBHjDvGJkbEACJEACJEACJEACDkGAN8w7xDRyECRAAgoI8PhXASyGkgAJkAAJkAAJkIADEeDxrwNNJodCAiSgiwCFcVwbJEAChhJwBXDh5c3YJQxNYGC7NQC+MLUoTl0LhXEGzooZm5lKGCdKVJpLPazRQ3rj+8+G6hzlyXOXUKlxd9kUvv10MMZ+2E92vLbA5y9iMXHqHMxcuBJvtmqIGd+PSRf2/a/zMGbir7L6+aBvF0z7dpSsWHWQpcRepuiny8BPsWzdVlnjK5A3Fw6EzEe+3DllxesKEmKtwWO+R+ite9i5chbKlCiSYT6lcqOMkvll8kHYqa0pki9dsba4dk0x30ZNnAGNlQjjls36Hzq1aSyrl0PHz6J6K/lyNrkSp1bvfIgN2/bKqqFB7UBsXz4zJTYuLh4ehWvKaiu97o4YiC+H9dcb/+OMhRj5zRS9ceqA6Kv/wdvLUyOewjh5+JxFGHf95h0UrSFfhCr396Cxwjhb3K/PXbqGsvU7y1tAACoGlMTRzYvg5iYOB+RvMc9foPugsVizaSfaNq2HtfMnY+e+o2jw1gC0b14fQl6bPWtm+QlfRYp8Yl7kbr9+OwpD+naRG243cULGt2X3YSycvx5ZvXzh5uMFVy+vZGGchwfcPD3g6eODTFmyIm+RkshRuCyePn2KxDuH0bBSQWTL4m83Y2WhxhMwozBOXVyrl998BaCa8dUqynAZQGkAiYpaMZgESCAjAg55/i/0zF2lfxfhKiEBEiABErBTAmphnEqlgqurazphnBDDadtShHEtWiBIEsZFvRLLiXj9n68mhHEqd3cc2JcsjBPyt9RiurjYWOmxVkIYV7Ik3N1VCL0eitUrVyAiIkIS3MkVxon6hTCuasUg9O76rp3OFMsmAecmULhcXumFRf+ri3Nz4uhJgARIgARIgARIgARIgARMSIA3zJsQJlORAAmQAAmQAAmQgB0QeBwaKlV5bNHChuJry+Wrd9hB2SyRBEiABIwmwONfoxEyAQmQAAmQAAmQAAnYFQEe/9rVdLFYEiAB4wgovTGK16gax5utScCRCLz9Uhb3lwUHtP7lTd9fij9RmKPPc7Oh8XpYpvVhc3TDnAYQUCp5S7qje+7i4xMgRElbdh1QXElGkqfExETkrdwCYQ/DZeddMHU8enYSHgVlm+hLiFE+/XYaLl69ITVu2ag2Qv6ami7R9HnLMGTs/2R1ULZUMZz89x9F8hdLib1M0c/sRaswYOS3sliIICHD2b36D/j7+cpuow68c/8Bvps6F7/OXZrSdv/6eahRtXyGuWLj4pCjXGM8fRatuM+0DQb36YzfJo7Wm8cW164p5lvvwE0coERIKFdKJUpUKmcTwqXVc3/KcHSnzl1GxcbdZBP4ZtQgfP6x5o2mbXp9DCE4lLMJeeG9E5vh4+2lM/xFbCwKBbWR/RqaVmKnTkxhnJwZAZxFGCdu7M5cur6i19RVc35EhxYNdII8eOwMmnQdJDunNta2ul8XDGyFW3fD5C2iVzJI8dogbraXswmBX/fBn2H/kVNSePMGtbBx8TTptUS8pogtV45s+Gncx+jeoYWi9yOibek6b6a8L9JXz9KZ36Nz2yb6wuzyebG+/jd9Ec4du4hs3v5w9fGGq5cnXN1VkjRO5eUFd08v+Pj7I1e+gshbvALi3Hzw5NphVM4Vh3JlLO0Ct0vMDlW0S74gc5/nEwc8XwMIsiC4Hi9fFv62YH/sigQcnYBDnf9TTxaFcY6+bDk+EiABEnhNQAjjpv85FW5ubhrCOPFzRkI2DWFcteqIio6Ci1A5iXfQamec9PXVA6/+wiSkcKKtWhh3cL8Qxm1NL4yLi4OHhwdat22LEiVKSnK5G6HXsWrFCjx+/DidME6dUz0y9c/qrxTGcdWTgH0ToDDOvueP1ZMACZAACZAACZAACZCAXRLgDfN2OW0smgRIgARIgARIgAQMJrBj4gSNti2Wrzb3hWMG18qGJEACJGBKAjz+NSVN5iIBEiABEiABEiAB2yfA41/bnyNWSAIkYDICFMaZDCUTkYBTERBmiAsvhXGWMAoEA/jCXKI49axRGGe769eUwjgxyvsPwiVZkhK5m5rO6e1LUa50Ma2wlNYpkgip16BenVC+THG9E/Aw/DHWb9mNaXOW4Oip8xrxuoRxi1ZuwDsfiN1H3ja0X1dMnTBSa3BUdAwinjxFgby5Up63lNjLFP2EP45EkeptZEt+xCCFRG/0kN7o1KZxhrIrESvEa0IktGTtFvwye3E6hnKEcaLR0M8maYjm5M1c+ighu6tTvbKs5ra2dk0x37IGbsIgISMUUkK5W0ayokcRT6T1lDN7VindsK8m4+dZ6deUrr7E68rPXw+Dh7t7upBjpy+gXe9PFEmhrh1YiyIF82nkEtLKDn2Hyx0u3qhWCctnT0KeXNnTtXnwKAJvD/lckcjz7xkT0a19s3S5KIyTNyXOIowTNPp+8jXmLVknD8yrqAmjB6N3lzYpv++EgOvc5Wv4ccZCLFweoiiXLta2uF+Pnzwb436cqWh8Qt449+dx6V4jUieJeBKJBcuC8cWkGRq/g7UJ49TthLT165EDJSGup4eH3pqiY56jaI12st/b7Vs3FzUDK+jNa68BQvw3ZdZyhF29B39fX7h4e8HN0wOuHh5wc3eX/rl7esLLzw9ZcuRCvuJl4Ze3BG5fPI4q2R6jXInCskWA9sqIdb8mYAFhnLqz1i+/+cpC4rjLwiMp3qJzrkmABIwm4HDn/9REKIwzem0wAQkYRCA+IR7nzp81qK26kUolpF9ucFe5S0Itf//MyJQpE9xc3YzKy8aOS0AI4377YwpUKpWGME4I0L28vKTzcELulnoTEjaxCQlb8xYtEVitGp5HRSEpTVxG1CRhnEqFgwcPYFsGwrhWbdqieIkScHdX4UZoKFavXCkJ40RbUZ+6FtFX6u9T9y36ojDOcdcwR+YcBCiMc4555ihJgARIgARIgARIgARIwKYI8IZ5m5oOFkMCJEACJEACJEACZifAG+bNjpgdkAAJ2CgBHv/a6MSwLBIgARIgARIgARIwEwEe/5oJLNOSAAnYIgEK42xxVlgTCdg+gR4AFpm5zA0vb/D+EsBhM/cjpacwzhKUDetDqcwq6Y7+JbP7wDHU69hfcUHFCufHkU1/IYu/X7q2TyKfoWBQK0VCMnUSIVxp06Qu8ufNhTw5syNbVn+IfLfvPcDN2/fw755D2LRjn856dQnjtu05hMZdBikaZ5d2TfHOWy1RuEBexMcn4OTZS9j236EUSc6Jf/+GELmIzVJiL1P18/2v8zBm4q+KeIhgv0w+6P92R5QpUQR5c+WQpFfihsK79x9KDM5duo7FqzZmKKqRK4zbe/gk3mjXT3GNqRsIqV/oofWyZS+2tnZNNd9GQVTYWOnrlEgvZIRCmJQ9W2bcC3uEsxevYuP2fSn7uvq17MbteyhcrY2iiurWqIJhA9+W9lWxXs9cuIrtew9j9ISpivL07NQKC6aOT9cmISERZet3wsWrN2Tny5UjG3744iNULl8KJYsWwvWbd3D4xDmMGP+LbMmT6Eys76v710o30abdKIyTNx3OJIzbvHM/mnf/QB6YNFHitb9gvjzSvmnopou1Le7XkU+jUCCwpUHvY8TvyEplS0qiVfGadufeA4TeuodDx8/gz7/XaMWXkTBO3UDMQe/ObdC5bRPpd7AQaapv4hfvUa6E3sKpc5fx2fe/yX49EjnvndisVwRr6JzbSrtjZy7i9zmrkPjkBTw9veDq45UsjBOCBPHPwwPuHh7w8PRCpqxZkbdoSeQpURm3Lp1CEc+HCCpXFN6e+mV9tjJe1mE4AQsK49RFijc1QhwXaHjVslqKcxZ/y4pkEAmQQEYEHO78n3qwFMZx4ZOAdQjsPbAH3ft1Nkvn2bPlQIVyFVGzWi3UrlEHlcrL+yAFsxTDpDZFICNhnKenp04JmxhEfFwcmjZvgaBq1RAdHQVAUywnjlGTkCQ9Kv1XiOakEBfp+FUIDg8dOIht/26Fh4eH9Jj6uDYuNhYenh5o2botSpQsIQniboTewJpVKxERESEJEdVCO31AKYzTR4jPk4DtE6AwzvbniBWSAAmQAAlshNUDAAAgAElEQVSQAAmQAAmQgMMR4A3zDjelHBAJkAAJkAAJkAAJZEiAN8xzgZAACTgrAR7/OuvMc9wkQAIkQAIkQALOSoDHv8468xw3CTglAQrjnHLaOWgSMIqAK4ALAEoYlUV3440AvrCUKE5dBoVxZppNE6RVKmKSI4wTZU2avkCxPEm0a9+8Plb++YNWGdfPsxZj2FeTTTBqZSl0CeMeRz5F1jINlSXTE3108yJUKV9airKU2MtU/TyLika+Ki0MkuEYC1GuMC4xMVGSg926G2Zwl18O64+vRwxU1N6W1q6p5lsRACODN2zbi1bvfGhkFs3mqV/L3h02HnP+WWvS/HKSpRZEpo1fuDwEvT4UXlfLbtO/+xSDenfS2imFcfLmwpmEcUIqlr9qS0VSQnkU5UVlxNoW9+tZf63EwFET5Q3OyCg5wjhtXZQqVghenp44ee6SQRV80LcLpn07yqC29tZo/ba9WLF8GzxjAZW3F1w9PeDm4Q4XNze4uKvgpnKHyt0dKg93+Pj5I1fBwihetRHu3L4O/2eX8Eb5gsjin8nehs16FRKwgjBOXWHbl06NcWYUx11+KYwTBy2JCpEwnARI4DUBhzz/N334Yo2/h7Tup3nOJHjOdo01kPZ5LhASIAHjCJhTGJe2MiGMG9hvMFo3F287uDkzgdTCOLWs7fnz59L5fW3COEkCJ8RvKcK45ggMqoaomCihgZP+l/Em2roALi5wV6lw8OBBbE8ljBNtRR+xcXHwFMK4Vm1QvESyMO7mjVCsWbUKjyMipOM1IYwTtYh/qetK2z+Fcc68wjl2Wyeg7/1l2uf1vcLY+nhZHwmQAAmQAAmQAAmQAAmQgB0R4A3zdjRZLJUESIAESIAESIAETECAN8ybACJTkAAJ2CUBHv/a5bSxaBIgARIgARIgARIwmACPfw1Gx4YkQAL2R4DCOPubM1ZMAtYm0B3AYjMUsfnVDeP7zZBbdsqkO4eVvi7Kzs1AwwiYSxgnxFwd+o7Aui27FBc2fuT7+OKT99K1E2KaDv2GI3jrHsU5jWmgSxgncgY2fwdHT503Jr1GW3sWxomB7Nh7BA07KZOpmQKeXGGc6GvcjzMxfvJsg7s9s2MpypYqpqi9La1dexTGhT+ORPayjRQx1xecWhgn8ldq3M0okaC+/tI+L6SDQj6oaxOvoW++OxJrNu1Umtrg+EZ1qmHT4l+hUrlpzUFhnDy0ziSME0RmL1qFASO/lQfHxFEZsbbF/Vr8Lmje4wNs23PIxCTSpzNUGGdsYZf3rkbxIgWMTWM37WcsXI3/th9FZpUnXIQ0zsMdripVsjROJaRxbpI4zs1dBU+fTMhfrCSKVqqFx5HPkHjnCOpVyI9c2bPazXhZqHwC54ODNIID+uu1XshPrixSGFy+AlBVWTNZ0eLcxT+yIhlEAiSgjYBDnv+jMI6LnQSsS8CSwjj1SIUwbuK4SciSOYt1B8/erUZAlzBOSNbUQra0xYnnxLmvhIQENG3WLFkYFxUlSdtSNvFt2r8mpXpMxAoJ3CEhjNv2rySnSy19i5OEcZ5o2ap1ijDuxo0bWLt6FSIiIuD+Shgn6tC3URinjxCfJwHrEaAwznrs2TMJkAAJkAAJkAAJkAAJkIAeAps6ddghQjIXKlxffK38Tk8yIwESIAESIAESIAEScGACvGHegSeXQyMBEsiQAI9/uUBIgARIgARIgARIwLkI8PjXueaboyUBJyegVIzEDzV28gXD4Ts9AVcAFwCUMCGJrQC+AGBVUZx6PBTGmXBmTZTKXMI4UZ4xspaQv6ZCiNrSbs+iolH/zQEmlbTpQ5mRMG7BsmD0/micvhSyn7d3YZwY6D9rNqP7oLGyx2yKQCXCuLMXr6Jcgy4GdVu1Qhkc2fSXQW1tZe3aozBOAO/7ydeYt2SdQey1NUotjBPPHzp+FtVb9TJZ/owSCZFT8MIpcHMTv/Z1bxFPIlGxkWVEdrlyZMPJf/9B7pzZdBZEYZy85eFswjghQavUpDvEa6ulN32sbXG/jnwaJYlVTSmb1cbdGsK4Gd+Pwfu93rL0MrBqfzHPX+Drn+bj7qVb8PP2gYuXJ1xUbsnSOFdXSRwnvnd1c4Obhwe8fDMha67cKFm1Ntw8PBF5cQ8aVC6CHNko17DqRJqhcxsSxqlH1+7lN0IcV8WEw738UnZfSovGw4RdMBUJOCwBhz3/R2Gcw65ZDsxOCFhDGCfQFCpQGOuXbUJm/8x2QoplmpKAIcI40X9SUhLi4+PRtFlzBAYFISomCi7Cs/zqL+uSG06HP0562MUF7inCuG3w9PSAi4urlFdsr4VxrVCseAmo3FW4KQnjVlMYZ8oFwFwkYGUCioVxZ0O6KL2Ax8pDZPckQAIkQAIk4HgEyrZaygtkHW9aOSISIIEMCDw/yk9Y5gIhARIgARIgARIgAWcgwBvmnWGWOUYSIIGMCPD4l+uDBEiABEiABEiABJyDAI9/nWOeOUoSIAGJgNLrTXk9DBcOCTg3gW4A/jYRgm0vX4OExWqPifKZJA2FcSbBaNIk5hTGiUIPHjuDGq17K67ZL5MPhDytRJGC6dreC3uEju+OwP4jpxTnNaRBRsK4F7GxKFu/M66G3jYkdbo2jiCME4OavWgVBoz81iRM5CRRIowT+So17o6T5y7JSa0R88v44fjove6K26kb2MLatVdh3PnL1xFQr5PB7NM2TCuME89bQnYopIMbF09DzuxZZY3l1LnLaNJ1MMIehsuKNyRIvN5uXTID1auUy7A5hXHy6OqTmG3bcwiNuwySlwzA8a2LUams8PHY7mbo73pjR6SPta3u1+J3Qf03++Pi1RvGItDZ3tLCuNFDeuO7sR/AxcX5TmlcuBKKH6b9DZfIF/D09gY8PeDq5goXSRT3Shrn6gY3lSpZFOriAr+sWVG2Rj14+3oh7vZJNKpRDt5enmZbD0xseQI2KIxTQ2j/6jyBqcRx4hzGEssTZo8kYPcEHPb8H4Vxdr82OQA7J2AtYZzA1qxRC8yaOscpjwnsfNkYXb4phHFVAwMRHROdUoski0vWx73axJ/bNY83xfGnSuWGQ4cOYce2bfDwEMK45BjxNTY2Fh6enmjZsiWKlSgpyeVu3LiBdWsojDN60pmABGyIAIVxNjQZLIUESIAESIAE5BKgME4uKcaRAAk4CgHeMO8oM8lxkAAJkAAJkAAJkEDGBHjDPFeIhQmIT49VsimNV5KbsSQgEeDxLxcCCZAACZAACZAACTgHAR7/Osc8c5QkQAISAQrjuBBIgATkEhB3slwEUEJuAx1xQhA3xtZEcepaKYwzcnbN0NzcwjhR8m9zl+KDzyYprr5sqWI4GDIfvj7e6drGxydg4tQ5GPfjTMV5lTbISBgncgmRVfVWvfD02eub2pT2oY53FGGcGI8QXXUbNBZnL141FIfsdkqFcb/MXoxPxk2WnV8dePvYBuTLnVNxu9QNrL127VUYJxiaUuimTRgn+th7+CRa9PjAJPtz2oXSuW0TzPvlK/h4eylaQ3fuP0CHvsNx6PhZRe3kBIvX2fULfkHRQvn0hlMYpxeRFKBPYuaIwjgx7tUbd6BjvxHyIOmIerd7e9y4fQ9bdh2QlUcfa3USW9yvo2Oe44tJMzB55iJZY1UaZElh3K/fjsKQvl2UluhQ8as27cbyJVuQ2c0DLt5ecPVwh4ubG1zd3ODi6gI3V1fEv4iFp5cKHh7uiI6OgYvKC7VbtIArnqNApgQEVSjtUEycfTA2LIxTT02HV+K4ykbOlXhzUt6A859GdsvmJGDXBJzi/F/ombtK/y5i15PK4knAVghYUxgnGMz/fREa1G1kKzhYh4UIpBXGJSUl4fnz53Bzc4OnpyfEz9o28Wh8XByaNG2GwKAgREVFvRYOJhvjkrfUnrhUjwmdnEqlwqGDB7Fj+7/w9PTSEBbGxcXB3cMDLVu2QrESJSRh3M0bN7B2zWo8fvwY7u7u8PLyQmJiol5SQkAn8lWtGITeXd/VG88AEiAB2yNQuFxe6dXE5WxIF75Rtb35YUUkQAIkQAJORoDCOCebcA6XBEiAN8xzDZAACZAACZAACZCAkxDgDfNOMtG2M0ylf+9wvo+Dtp25cppKKIxzmqnmQEmABEiABEiABJycAI9/nXwBcPgk4FwElAr4lcY7F02OlgQcm0AnAMuMGOLul23Fa8g2I3KYrem52ZoCzTKtD5utLyZWRsASwjhxU5gQhy1du0VZcS9lbN3aN8Pi6d9q3OyVOokQKH03bS5WbdiuOLecBn26tsUHfbsgsGJAhuEXroTioy9+xKYd++Sk1RnjSMI4MciY5y8wbc4S/PT7Xwh7GG4UG22NhexqSJ/OEPOkRMJ1624YCga2UlSPWr6jqFEGwdZau/YsjBM412zaiZ5DvzBa6KZLGCf6CL11FyO/mYJl67aaZLr9Mvlg4pgPMLh3J7i6uhqUU8ilxk+ejf/9Nt+g9toaDe3XFRNGD4a/n6+snBTGycLktMI4QUeskf4jJijeP8U+MuP7MXj7zZaSHFHs53I2ucI4W92vRV079x3FZ9//hv8OnZAzZFkxpYoVwvhR76Nru2a4F/YI3075E7/OXSqrrdwgMWcj3u+JD/p1RbYs/nKbOXTcDzMW48jeM8iRKRPg7Q1XlVuyNM4FSIqLQ+48WVD7jcrInSs77t17iH17juHhkxg0btMMD8NC8VaT2vD1TS9JdmhoTjA4l3xBtn6d1ZuvxHEVjZiOzi+lccuNaM+mJOBsBBz6/J96MimMc7ZlzfHaCgFrC+NaN2+L6ZNn2QoO1mEhAgYL45KSkBAfj8ZNm6FqYCCiYqJT3HDp30QnG+SSb3oQqrgkwMUFHu4eOHz4EDZvCIGbmyr5vJtLcuvYFy/g7euLdu3ao1jx4pLA7ubNm1i/dg0iIiIkYZw2oZ1acCckcWITP6uFcYGVqlEYZ6F1xW5IwNQEKIwzNVHmIwESIAESIAEjCFAYZwQ8NiUBErBLArxh3i6njUWTAAmQAAmQAAmQgGICvGFeMTI2MI4AhXHG8WNrMxDg8a8ZoDIlCZAACZAACZAACdggAR7/2uCksCQSIAESIAESIAFrEhB3npwGUNaAIva8usHbJkVx6vFQGGfAzFqoyQ/TF2DUhKmyesuVIxvun9wsKzZtUOTTKFRp1gNXQ28rbr9v3VzUDKyQYbvrN+9g9qLV+GfNJoP6UCcXApQOLRqgXbP6aFKvOrL4+ymqd2XINvw2bxm27TmkqJ0IFvKzNfN+QokiBaW2Dx5FIFeFprLyCLHe3zMmyopNG2SJfuLjE7B+627M+mslNmzba1Cd6kYVA0qiTdO60jxVq2zIy2ZypsZdBimap4XTxuOdt5RJ5uQM1NJr1xLzLWfcxsQ8DH+MeUvWYcaC5Qbt7w1qB2L78pl6Szh57hIm/PKnweI48Zo5anAvDOz5JjL5+ujtT06AkD/9PGuRNPanz6LlNEkXIySYo4b0RsF8uRW1X79lN9r2/kRWm5mTxmLAO8JBY75t/5FTqNW2r+wODm9cqFf+qS2ZElGeaH/3+CbkyZVdZ11K6z6/ewVKFy8se5zWDhTCxUGffif7tb7/2x0xccwQ5MiWRSq914dfYuHyEFnD0MdaWxJb3K9FnSfOXsTMhSvx14oQg/Zt8f5FCPe6tG2K+rWqppNTPol8hg3b92Ld5l1Yt2WXQX2IOkU/X3z8nrR/Z/bPJGuenCVIiD0nTV+MSyeuIlsmP7j4+sBN5QYkxiOgbBF07tgIWVMxexYVjTFf/Aa/zFlRrHR+lCuZH7UCjXF2OQtp+xqnHQjjBFBxPqLDq/MKlQwgfPalMK68ZPDgRgIkoI+Aw5//UwOgME7fUuDzJGAeAtYWxolRXTx6XZJwcXMeArqEcULe5uXlJQnXUm9qAZv4Gh8fjyZNm6JKYCCio6Kld6ZCB5d+EznSP+7q5orw8HBcPH8ez58/R1KSi9Sfq4v4mgj/zJlRukwZ+PpmgoeHuySMC163VhLGqVQqrfWlrVX8LMYSFxeHqhWDKIxznqXNkToYAZ3CuIA6oxxsqBwOCZAACZAACdgegXN7JmkURWGc7c0RKyIBEjAvAd4wb16+zE4CJEACJEACJEACtkKAN8zbykw4TR1KL9az9U++dZqJc+SB8vjXkWeXYyMBEiABEiABEiCB1wR4/MvVQAIkQAIkQAIkQAIaBLoAWKKQiTAujQOwVWE7q4RTGGcV7E7bqRBiHTl5DoeOn4UQ1zyOfIqIJ0/x8NFjSXQiJEtCciJkcFkyZ0LBfHlQtUIZVCpXEsUK5U8nWTEEZGxcHE6cuYRDx89I4rcnT5/B18cbQnSlcnODt7cXsmfNjLy5cqBMiSIoX6Y4fLy9DOnK7toIedzZS1dx+MQ5nDhzEeGPIxHxJBKPIp4gNjYObm6u0piyZckMfz9fiVP50sVRuXxpVChTQnrMUTdbWLv2xvbG7Xs4de4yzl++Lu1nQook1ojY1zzc3aX9Sojb8uXJIa2fUsUKQyUEPgo2IfXZf/S0JBg8fOIs7j8Ix70HjxD2MDwlS7HC+aV+CuXPgwa1AiVhU0DJonBxMc+fV8V+dPTUeezcdwS7DxzH7XthUj237oal1FQgby6ppry5c+CNapVQv1YggioFSFy4kYC5CYjfv/+s2SytU7Gf3rpzX9ofC+TLLckKxX7SvkV9ZM3sb+5StOa3xf1aFCpubL98/SaOnDwvvd7cvf9Q+v0Y/viJtO+K3xNenp7IltUfRQrmQ/XK5VC9SjkImaq7u0oWy7i4eFy9cRu374bh9r0HyfNz9z6u3biDazduS4LL5y9ikTWzeJ/kJ82XeJ9UpXxplC9TAl6eHrL6ccagm3fDMP6HOUBkLHwz+cHVywM+vu7o+XYLlC5WSAOJmOcfpi+Hj4s7/LN4okqVkpIMl5tjEbATYZwaunjT0vHVeQal9kJxTmOZY80eR0MCZiHg8Of/1NQojDPL+mFSEtBLQKkw7sP3P0HJ4qWQhCQkJiQiLi4W10Kv4fipYxC5DNn2bDqAggU03/sakodt7IeAEMb9OvsXScAmxGriuFbI23QJ49THvuKrJIxr1gxVqwbi2bNnmufRxLtT9V0O6tNrqX9OEk8nSf16eHggMVE8kCT+/0o8J766IDY2FokJCfDy8cKdW3ewbs1qRISHw93DQ5IbphXaqcmnfpzCOPtZj6yUBHQRoDCOa4MESIAESIAErEiAwjgrwmfXJEACNkGAN8zbxDSwCBIgARIgARIgARIwOwHeMG92xOxAkwCFcVwRNkeAx782NyUsiAQcksDDiAg8iIjQOzY3VzeUKlJYbxwDSMAUBC5cv47ExERZqUoVKQI31+Sbp7mRgL0S4PGvvc4c6yYBEiABEiABEjADAXGry2kAZWXm3v/qBu7NMuNtIozCOJuYBhZBAiRAAiRgQgLixtGEhETFAjoTlqA1lZDJKZXimbsm5icBeyFgq/u1vfBjncDhk+cxacoi5HT3gVcmb7j7e6NV82qoV6NqCp57YY/wy/yVSFRlQ1JYGJ48C0f79g3QuskbROhgBOxMGKemb4g47iyA8qmUHg42kxwOCZiEgFOc/5s+fLHG9ait+zXUgBc8Z7vGz2mfNwlpJiEBJyagVBi3ceVWBJQup5XY9RvX0K3PW7h7/64ioisXrUVg5WqK2qiD4+LiEB7xCOGPwxEREY6Hjx7iWdQz+Hj7IEvmLMjsnxmZ/bMgc2bxIROZ4W4hIX7E4wjcvnMLkU+f4OnTp8k1+fggT648yJkjl/RPiMfMtUU+jcSlKxfw+MljhEeES1+fx8TAz88vmYd/ZuTPV0CS/wmxmaW3jIRxqbmI423xwQqpv4o5r1OnDurUq4/nL56/Fr6JQaR8BkMqc1xqiZz0Gyf5AekDG1xcUpoIkZz0f7VADknw9vHBlcuXEbxuLSIjIyXRnJeXVzphXGpRXOp6Ra2Blaqhd9d3LY2Y/ZEACWRAQN/7y7TPu5wN6aLxhjWgzigCJgESIAESIAESMDMBCuPMDJjpSYAEbJ4Ab5i3+SligSRAAiRAAiRAAiRgEgLH/1oo5XlyI3Sn+Np8+Wp+fKxJyDKJDgIUxnFp2BwBHv/a3JSwIBJwSAJdh4/Emu2aF6LqGujlDcEokDu3Q3LgoGyHwJWbN1GufUfZBf3752y8UaWK7HgGkoAtEuDxry3OCmsiARIgARIgARKwEoHOL2VxS2X0feBlzFcANsqItdmQpDuHlZ6XttmxsDASIAESIAESIAESIAESIAESSE1AfDDQhu0HMGvOKhT0ywK/3Nnhm9MP5UrmR4nCBRB6+wGOXryBRO/s8Hj+AndPnUQk4jBh7AAUzM+/RzrKajofHKQxlID+r3UXdjRGYd9485WwvoKMujsBWCEjjiEk4KwEnOL8H4Vxzrq8OW5bIWBKYZwYk5Ck1W6qTP7295xlqF2jToZInkQ+wemzJ3H67CmcOX8GZ8+fxp17dxAV9UwRykrlK6NenQaoGVQLVSsHSWI5U2zPnz/Hlu2bsHPPdhw4vB83boXqTevrmwlFCxeVpG11a9XHGzXrIk/uPHrb6Qo4cfo4/t2xBbv27sSxE0dk5RE1NKjTEM0atUCbFm2hUrnLamdsUGphnCRuAxAbGytJ3Dw8PDJMLyRsefLkQe06deHj6yO9aU7OkdoMp+v7NKlT/eVJ7ZJTtxQqOXGsdvzoUZw5cxrx8fGSME5IB1ML4lLL7FJnFzVRGGfsSmF7EjAPAQrjzMOVWUmABEiABEjApAQojDMpTiYjARKwQwK8Yd4OJ40lkwAJkAAJkAAJkIARBLyqBqV8LpIRadiUBPQRUHpjHtelPqJ83mgCljz+PXvlKp7HvlBUc+kiReDr7a2ojbbgF7GxOHPliqI8Kjc3VCxVSlEbBpMACWgn0HnYcKzbIfl59W4Xg9ehUN68euOMCYhPSMDJixeNSZHS1l2lgrenJ7w8PaXXq6z+/ibJyyTmJXD5xg2U7yDuuZC3bf1jFupUrSovmFEkYOMEePxr4xPE8kiABEiABEiABMxNQJxzPf1SGFc2g44OvXxOiOJCzF2MJfJTGGcJyuyDBEiABEiABEiABEiABEjAWgTEtQDrtvyHBQuDUSR7DmQvVgQeefLgObwAVzf4+/nB5fEDhB7aj9sPwvBml6bo0qYxVCo3a5XMfk1MwEGEcWoq4rzFWwC+fCmEy0gcdxZA+ZeCOaXXopmYPtORgE0ScJrzfxTG2eT6Y1FORMDUwjiBru/gnti2c6tsitvX70GxosV1xk//Yxr+9/NE2fnkBgpZ2ieDh+Odbr3h7WXY9b2Pwh9hxh/TsHj5IsXyOm11Vg+sgZ7d+qBFk1Z6xWnq9kIO9/NvP2LnfzvkDl1rXN7ceTHsg5Ho1KErXF1djcqlr7E2YZxoIwRtaoGc+Dn196lzJiQkwN3DE97e3lC5uYpAiP8lIUlDG5fcJrU8LnUW8RZUtNL+ZlT0LUSAUc+evsoBSRiXWhanrjHtY+rHKYzTtxL4PAlYhwCFcdbhzl5JgARIgARIQBEBCuMU4WIwCZCAAxKw5A3zDoiPQyIBEiABEiABEiABuyPAG+btbsrstWClF+lRGGevM21HdVvq+Pd5bCyy1KytmMzXQwZj9Lv9FLdL22DOylUYPOFbxXmubdqAvDlzKm7HBiRAApoEbE0Yt+PQIbQYOMhs0xRQrBgqlS6FRjWqo35QEArny2e2vpjYMAIUxhnGja0cgwCPfx1jHjkKEiABEiABEiABgwmIm66X62h95OXjQhS33uDsNtiQwjgbnBSWRAIkQAIkQAIkQAIkQAIkYFICQhq3dfcRzP9rLfxV3shTuCi8cxeQ9AUxYffw4MZVhD19jCZNa6JX51bwy+Rj0v6ZzLoEHEwYp4apFseNeyWG0wZZnONYaV367J0EbJKA05z/ozDOJtcfi3IiAuYQxg0f+xGWr1kqm+L5I1czFLZN/OkbzJwzXXY+pYHZs+XA5O+mokGdhrKbCkHYoqULIWqLinomu53cwCXzVqJmtVoZhsc8j8HoL0dgTbBp30o1b9ISP383Db4+vnLLVRynTRgnBG2Cqy75muhEPKeOi42NTRbMpRG+pfyclCSJ5HRvGQvjRDvRl7u7uyTQE9+r+5YzYBFLYZwcUowhAcsToDDO8szZIwmQAAmQAAkoJkBhnGJkbEACJOBgBCx1w7yDYeNwSIAESIAESIAESMBuCfCGebudOnsrnMI4e5sxJ6jXUse/hgrj/Hx9cPPfrfDy8DB4NsSFDZXe6oRLoTcU57i6MQT5cuVS3I4NSIAENAk4mzAu7fy3b9gQY/q/h8plStv90jh75SqaD3xf1jg+6fkOhvXuJSvW0kEUxlmaOPuzJQI8/rWl2WAtJEACJEACJEACFiYg7m45DaBsmn6PvfxZiOLWWrgei3RHYZxFMLMTEiABEiABEiABEiABEiABKxOIj0/A+Ss38NeyDbh45ho8kCxNiEMScuTPgbfaNUTd6pXg4+1l5UrZvakJOKgwTo1JnMvo9NLlIcRx5dKwO/tKJqf0ejRTTwHzkYAtEXDK83+hZ+7ydcCWViFrcRoC5hDGvf/xe9iwJVgWw0IFCmP3pv0ZxppbGKfufPrkWWjdvK3euhMSEzDu28+w8J/5emMNDdAnjLtz7w76D+2D02dPGdpFhu0CSpXFP/NWIEvmLGbJr00Yl7ojIVvTt+mLUcvl9OXR93xqgZ22nNoEdyInhXH6yPJ5ErB9AoXL5ZVejFzOhnTReKMaUGeU7VfPCkmABEiABEjAzglQGGfnE8jySYAEjCZgqRvmjS6UCUiABEiABEiABEiABExCgDfMmwQjk+gnoPTCHP1/tdXfJyNIIEMCljr+NVQYJ4qf/dU49Gyn/2ISXQPdsHsPOn70sUErgcI4g7CxEQmkI+Dswjg1kKruDGQAACAASURBVMHduuJ/wz6Bu0plt6tk3/ETaNjvXVn1f/h2D0waPkxWrKWDKIyzNHH2Z0sEePxrS7PBWkiABEiABEiABCxM4E0AK1L1efzl90IUt8bCdViku3OzoXE+ukzrwxbpl52QAAmQAAmQAAmQAAmQAAnYKYH4BEDlZqfFJ5cthAPPX8ThaugtHDl5AUlIQulihVGudFH4+njB1dXVrsfH4jMm4JIvyJGvsxJj6wzgyzTiOHGuYxXXBgmQQAoBpzr/px41hXHcA0jAOgRMLYyLj49D9YZV8Sj8oawBjft0PPr17J9hrKWEcaKITau2oUypgAzrGfXlcCxZsVjW+AwNykgYd+fubbTp0kI2Y0NraNaoBWZNnSOJz0y96RLGib7EP10SNlPXYYq+1HzEh4Kn3iiMM/VsMR8JWJ4AhXGWZ84eSYAESIAESCCFAIVxXAwkQALOSmBDpw4NxNirvN1zu/iapXBhZ0XBcZMACZAACZAACZCAUxHgDfNONd3WHCyFcdakz741CFj6+NcYYVzJwoVwcuUKgy+eaDZgIHYdPmLQCqAwziBsbEQC6QhQGPcayRtVqmDpTz8gexbzfIqouZcfhXHmJsz8JGB+Ajz+NT9j9kACJEACJEACJGCTBMRdOacBlAVw8mWFQhS3WvgEbLJaExRFYZwJIDIFCZAACZAACZAACZAACZAACZCA3RBwcGGceh7E+Y0ur8Rx4hzHWQDlHfn8ht0sQBZqCwSc7vyfGjqFcbaw/FiDMxIwpTAuPiEeEyZ9jbl//SEb5en9F+Dn559hvCWFcUFVqmHZgtU6Jc0hm9dj0CcZC+5kDz6DQF3CuBcvXqBzrw44cVp8lpD5t28+n4he3fuavCMhjJs262eoVKoU1mrxWkxMDKKiohAXF4fEpCQk6+rEf9V/Ckt+RIi1Uz+aNur1G08XKTb15vKqpehT9KFum5w3edPsN7U0LzmbeETIvD09PeHn5yeNJbXojsI4ky8bJiQBixOgMM7iyNkhCZAACZAACbwmQGEcVwMJkICzEtjYqYPGWYwGYz93VhQcNwmQAAmQAAmQAAk4FQHeMO9U023NwSq9+dD0Hy1mzdGzb5siYOnjX2OEcQLc2l+nolnt2ooZHjt/HrV6vKO4nboBhXEGo2NDEtAgQGGc5oKoFxSIkBnToXJzs7uVQmGc3U0ZCyaBdAR4/MtFQQIkQAIkQAIk4KQEOr68/2T8y7ELUdxKZ7iRmsI4J13pHDYJkAAJkAAJkAAJkAAJkAAJOCkBJxHGqWc3tTjus1dSfCedeQ6bBFIION35P/XIKYzjXkAC1iFgqDBOiLESEhMkqdf1G9ew78B/WLF2GU6fPSV7IHJlZJYUxoniVy5ai8DK1dKN4/nz56harwKiop7JHqOhgbqEcZ9/8ykW/jNfUdq8ufOicKGiuHP3Nm7cClXU1tc3Ew7vPAEfbx9F7fQFaxPGiTbPnj2D4BwbGwuVyg2e7qpkOZuLC5ISRUQSXFxf3ZaQBAjXm0uquxTS/izVkdYq9+oxtfRNbYiTbo5IlU/yyLm+VBwnAi7i66tN1CH6TEhMwovYWOlRtTTOw8NDksaJf2phXFDl6ujd9V19SPg8CZCADRKgMM4GJ4UlkQAJkAAJOA8BCuOcZ645UhIgAU0Clr5hnvxJgARIgARIgARIgARsgwBvmLeNeXCCKsTNiEo2pfFKcjPWyQlY+vjXWGFc45o1EDz9N8Wz1u+LL7E4OERxO3UDCuMMRseGJKBBgMK49AtiRJ/emPDhULtbKRTG2d2UsWASSEeAx79cFCRAAiRAAiRAAk5KoCKAk840dgrjnGm2OVYSIAESIAESIAESIAESIAESIAEnE8alnvDKAI5zBZAACcDpzv9NH75Y4wOMW/drqLEMguds1/g57fNcMyRAAsYRUCqMM663160HvfsBPh0mfLH6t4yEcUJoVqFsRZQuWQaFChZGUmIiIh5HIDziEf7bv0exIE1UM/i9oRj9ydh0hYVsXo9Bn/TXX3CqiErlK6NKpUCUL1sB2bNmx8NHD3Dn3h0cPX4YO//boTOXNmGckPG17txMdv8D+w3GBwM+gr+ff0qb6JhoTJkxGb//Kf865u+++gE9Ohv+gdfaCk4tjBNiNbG9ePECUVFRENK12rVroW7tmvD28UVCgjC0qT9CKa0h7pUQTt1JigVONibNwNTt1d+nzZlssIPK1QWhN25g45Z/EXr9OlQqFTJlygRXV1cK4wzEz2YkYCkC+t5fpn3e5WxIF403rAF1RlmqVvZDAiRAAiRAAk5LgMI4p516DpwEnJ6ApW+Yd3rgBEACJEACJEACJEACNkKAN8zbyESwDBIgAYsRsPTxr7HCOAHm4D+LUbFUKdmMbt8PQ/GWrWTHawukMM4ofGxMAikEKIzTvhiubAhB/ty57GqlOIowTnxK6fLNW5CYKH2Eqt6tXaOGyJwpk944BpCAPRDg8a89zBJrJAESMJKAUgG/0ngjy2NzEiABErAsgaQ7hzWuw7ds7+yNBEiABEiABEiABEiABEiABEiABMxL4HxwkEYHAf0lLQY3EiABEnB4AhTGOfwUc4A2TsAawrhPhozAR4OGQS0K04corTBOyNe6dOyG5o1bIU/uPBk2v3TlImbPn4klKxbr6ybleZE/eNnmdPEfjhqCNcErZeUJKFUWs6bNQaEChXXGh0eEY+uOzZg1dwZEnak3bcK4wcMGIHjTOln9L1uwGtUDa+iMXRuyGkNHDpKVq2TxUti6dqesWLlBqYVxasFadHQ0YmNjUbxYMXzx2aeI9MqPo6FPpeviXF1t661xQgKQ3d8TtYt74fie7Zj8yzSI+v38/CThXVJSkrS+4+LiEFS5Onp3fVcuGsaRAAlYgACFcRaAzC5IgARIgARIwFgCFMYZS5DtSYAE7JWApW+Yt1dOrJsESIAESIAESIAEHI0Ab5h3tBnleEiABPQRsPTxrymEcT3btcXsr8bpG1rK81/++hsmzZkrO15bIIVxRuFjYxJIIUBhnPbF8NWQwfj03X52tVIcRRhnV9BZLAmYmACPf00MlOlIgARskYBSMZJtXSVvi0RZEwmQgF0ToDDOrqePxZMACZAACZAACZAACZAACZAACeghQGEclwgJkICzEqAwzllnnuO2FQKWFsat+GstgqpUUzT81etXYtX6FahaKRBtW7RHsaLFFbUX8q4Jk77CHwtmyWqXPVsOHN19Kl1su64tceL0cVk5Zk75Ey2ayPug6ITEBKwNXo1vJn2FR+EPpfxphXGXr15C47b1ZPUthHwfDx6uN/atd9rh8LFDeuNEwJFdJ5Eje05ZsXKCtAnjYmJiEBsXh6YN6qHfh5/i9513cDo0HJ4ebjZgUhZ/un/95/j4hCS4uLmge+3CqOT7ECNGj8G9+2Hw9/dPJ4wLrFQNfbq9JwcLY0iABCxEgMI4C4FmNyRAAiRAAiRgDAEK44yhx7YkQAL2TMDSN8zbMyvWTgIkQAIkQAIkQAKORIA3zDvSbHIsJEACcghY+vjXFMI4Ma5rmzYgb079F088jYpGsRYtIL4as1EYZww9tiWB1wTsXRg3ok9vBBQrJg1IXGgW8/wFYl68QPiTJ9h99CiERM2QrWj+/Di3bo0hTa3WhsI4q6FnxyRgMgI8/jUZSiYiARKwXQIUxtnu3LAyEiABKxCgMM4K0NklCZAACZAACZAACZAACZAACZCAxQhQGGcx1OyIBEjAxghQGGdjE8JynI6ApYVxAnCTBs3wZrtOaN6kJVRuKoswj4p6hrLVS8ru68rJm+lqq1q3QorQTV+igf0GY+zwL/SFaTz/7NlTTJryPXbv3YHff/kTpUuWSXl+2u+/4Mdp/9ObT8ju9m45BC8vL72xW3dsxrtDeuuNEwFzpi9A4/pNZcXKCUotjHNxSRaxCWFcXFwcWjRpiI79PsGsXXdwKywSXh5uclJaNCYhMQnxiUloVbUAaud8irFjxuD23Xspwjh1MWI8FMZZdGrYGQnIIkBhnCxMDCIBEiABEiAB6xKgMM66/Nk7CZCA9QhY+oZ5642UPZMACZAACZAACZAACaQmwBvmuR5IgAScjYClj39NJYwb8967GDd4kN7p+n3pUnz8/SS9cfoCjBHGiU/se/T4MR4+fix9DQuPwNOoZ/Dx8ka2zJmR1d8PWfz8pa+Z/fzg4e6urxyLPB8RGYnQO3dx/9Ejqe5c2bJJoqx8uXJCfYGJKQtJSEzE48hIPIiIkPoTXyOeRMLV1RU+3l7w9faGr5c3cmXPhqIFCsDLw8OU3WvNJWq6dusW7j18iLsPHyIxMQmF8+ZF4Xx5kSdHDrNwUDKoF7GxOHPlCh5KzJ5AzNmTZ2JteSGbvz+yZs6M3NmzoUKpUmblJWRpN+7ew50HYdKceXl6ws/XB/ly5UKZokWhcnt9wZG9C+MO/rMYFUuV0jlNkVFR+P6PPzF5/gIlUynFRh7YZ7L9//b9MFy9dQvhkU8Q/vgJHj15goSEBGT190cWf39pfZQuWgQF8+RRXKe6AYVxBqODrey7ho/AfC0tsXbNV739Zebxr/3NGSsmARJQTIDCOMXI2IAESMCRCVAY58izy7GRAAmQAAmQAAmQAAmQAAmQAAlQGMc1QAIk4OwEQs/cVfp3EWdHxvGTgEkIWEMYpy68do06mPbDdOTIrv/Dlw0dbFJSEp4+e4qIx+Ho2KOtbOHb2UOX4evjq9Ftw9Zv4Or1q7JL+XTYZ+je6R1kyZxFdhtdgV37vIX9h/bqzdOmRTv89tNMvXEi4OatG6jTvIas2A/f/wTDh46SFSsnKLUwTlxjK+YpOjoacfHxaNG4ATr2G4bp22/hetgTeLurkIQkuCBZLJd+E78+dD0npxoRoy2H7rxqYVy76gVRN9czjB3zKe7cvZ8ijBPjEdcpUxgnlz/jSMA2CRQul1d6cXE5G9JF441qQB3TvSDa5tBZFQmQAAmQAAlYnwCFcdafA1ZAAiRgHQKWvmHeOqNkryRAAiRAAiRAAiRAAmoCj0NDpW+PLVrYUHxtuXz1DtIhARIgAWcgYOnjX1MJ44SM6tqmjcjk46NzmuITElC6TVsI+YyxmxxhnJB1HT9/HsfOncfJixdx4sJF3Lp/D0+johV1H1SuLJrWqoW6gYGoUbGCJEpTsgkR0YwlSyHGr28rV7w4WtatkxIm5HZrtm/H4uAQbNi9R2tzwb5KQABa16uHId27acjA9PUnnhd9CDYnLlyQ/h07dw6hd+/hQXi4nOYpMUXz50e18uXQpkEDNK1VUxJhmWrbe/w4lm3ajOVbtmZYV5UyZdCpeTO8+2ZHZPHzw+pt23D5xk29ZeTLmRM9WrfSG6ct4Mbdu1i9bTv+3b8fm/7TfwGTOkeDatXQos4b6N2+nUlYiXW9+t9/sSg4BDsOHcpwLO+0bYPB3bqiakAAHF0YpwYxYeYsiH9KtssbglEgd24lTVJihdxw03//Ycvefdi45z9cu31bVp78uXOh+RtvoHvLlqgbWDXDNlExMZi5dBkSk5Iv2zhz+Qr+DgmR1Y+QTb7dprXe2FZ166Js8WIpcSL/7bAHetv5+/piQOdOWuPOX7uGs1eu4tzVq5Jc0V2lQqkiRfDeW2+atB99RVpi3/1jxUo8fvpUXynS883fqI0KJeV/6q9oM3XRYuk1XN8mLtYb1LWLJK/Ut1li7aatwVLrSt/Yrfk8j3+tSZ99kwAJWJiA0hujjL0K3sLDY3ckQAIkII/AudnSXTopW5nWh+U1ZBQJkAAJkAAJkAAJkAAJkAAJkAAJ2CEBl3xBPM9nh/PGkkmABIwnQGGc8QyZgQQMIWBNYZyoN3u2HJg9bQ4CK1czpHypzaPwhzh34SyuXr+C23dv49btm5IM7dadW7IFcWk71yaMe3dIb2zdsVlxndUDa6BB3UaoUK4SypUpj+zZsivKEfM8BmUCX1+TllHj+m80wKD3PpCVX8jZevbvJiu261s9MGn8T7Ji5QSlFcaJNkIYJ67tatKoAXoPGoZtF57gwZNouLu6yklp0ZhEJCExCahWPBfyutzH2DFjcPdesjDO/dWHblMYZ9EpYWckYBYCFMaZBSuTkgAJkAAJkIA8AhTGyePEKBIgAccjYOkb5h2PIEdEAiRAAiRAAiRAAvZFYMfECRoFt1i+mheO2dcUsloSIAEDCVj6+NdUwjgx3CmfjsbALp11jlzIu7qNMM0HUOkTxv0wdx6+mPargbOgu5mQs302YIAkQpIj3hGZ7oSFoVgLeTIyIX1b8ctkqYBtBw7io++/x6XQG7LHUS8oEPMmfIN8uXLJarPr8BE0GzBQVqzSoO6tWmHcoPdRJH8+pU1T4i9eD8WYX6YgeNcuRTnEPA3t0QMTZ/8hq52QdF3ZIE+0pU4oZFM/zpuPWcuWy+ojo6DPBvTHh++8jcyZMhmUS0gFP/j2O8WSv1qVK2Hf8ROy+7wYvA6F8uaVHW9IoJDdtRg4SHbTg/8sRsVSpfTGRzyJRN6GjfTGpQ7YvWC+JEFUsgkx5Kp//8X4Gb8r2ne19SHm5+vBgyH2a22bEK5V6dRFSXmKYyePGinJBdVbQNv2suV3z4++Fh6IC9+EWPHn+Qtw7Pz5dHUIMeeehQuM7kfOAC2174pPVS3RqrVsSWqfDu3x+5dfyBmCFKN0/q9t2oC8OXV/erEl127aQVpqXcmGa4VAHv9aATq7JAESsBYBCuOsRZ79kgAJ2BQBCuNsajpYDAmQAAmQAAmQAAmQAAmQAAmQgJkJUBhnZsBMTwIkYLMEKIyz2alhYQ5OwNrCODXe/duOIm9uedfaxcfHYdd/O7F2w2rsP7gXd+/fNfksaRPGTfjha8ye97vRfRUqUBgN6jZEzWq1USOoJnJk132Nkujs2Ikj6NCjjdH9GpOgeZOWmDVljjEpNNqmFsYJsZrYYmJiEBcXh3r16mHUyJHwlPFhnyYryIhEly9fxtixYxEWFkZhnBEc2ZQEbJEAhXG2OCusiQRIgARIwGkIUBjnNFPNgZIACaQhYOkb5jkBJEACJEACJEACJEAC1iXAG+aty5+9kwAJWI+ApY9/TSmME9KtC+vXQeXmphVgvd59cfDUKZPA1SeMGztlKibPfy0fMkmnqZLkzJYNf47/Cs1q19abWqkwbt63EyRR3OJgZQIzdSFClvb3pEloUqum3tpCdu/Gmx99ojfOmIBvhn6AkX37KE6xZOMm9B77meJ2hjRQIowTEqifFyyEWGOm3EoWLoTVU6egeMGCstOK/fe9L8Zh+ZYtstsYE2jPwjgx7god31Qkcdu9YB6qlS8vG9m127fRZdgInLp0SXYbOYHjPxgi7UPqC8nUbZQKw+T0lTbGFMK4w2fOou/nn2fI3hLCOGvsu0IcKFdcKV677+7YrvN3aLq5mb9A9utQ45o1EDz9N51LwNJrN20hhgjjDFlXhuwDlmrD419LkWY/JEACNkCAwjgbmASWQAIkYH0CFMZZfw5YAQmQAAmQAAmQAAmQAAmQAAmQgOUIUBhnOdbsiQRIwLYIUBhnW/PBapyHgK0I49q3fhNTJ+m+XkfMSOTTSMz441fM/3suoqKemXWStAnjVq1bgY8//cDk/XZq3wXv9R6AgNLaP6x1286t6Du4p8n7VZKwemANLFuwWkmTDGOFMG7qzMlQqVRwdXWFuFZNCOPi4+NRt25djBo1Cl52Kozz8PCQxiM2IcALqlwdfbq9ZzJ2TEQCJGA5AhTGWY41eyIBEiABEiCBdAQojOOiIAEScFYClr5h3lk5c9wkQAIkQAIkQAIkYCsEeMO8rcwE6yABErA0AUsf/5pSGCdYLfnpB7Rv2DAdNiGKE8I4U23WFsapx7Hof9/jraZNMhyWEmGcENFl8cukSGqlrXMhHrq8IQSZM2XKsDZLCONEAR++3QPff/KxdCGMnG3GkqX45H+T5ISaJEauMC7mxQsMmfCtwTI/fcWKeds483cEli2rLxTRz5+j+8hR2PTfXr2xpgqwd2GcUmnllQ0hEGtDzrbn6FF0/OhjPI2KlhOuOKZnu7aYNe5LDWmcrQvjog8fxP/mzMXX02foHa+5hXHW2nfPXL6CwC5d9Y5fHbBtzh+oXbmyrHgl6/nP8V/j7Tattea1xtpNW4gSYZwx60oWWCsF8fjXSuDZLQmQgDUIUBhnDerskwRIwOYIUBhnc1PCgkiABEiABEiABEiABEiABEiABMxIgMI4M8JlahIgAZskMH34Yo2/h7Tup3ktXfCc7Rp1p33eJgfFokjAjggoFcb9M3c5ypZJ/lBRIcVKTExEYmICoqKjEHozFNdDryJ403rsP6T8Or0l81aiZrVaWult+ncjxnw1Eo/CH1qErjZhXGxsLOq1qIm79++apYYmDZrhp4lTkCVzFo38K9Ysw7CxH5qlT7lJy5etgOBlm+WG641TC+Pc3d2la/zUwjghWKtXr57dCuP8/PygFsaJcVEYp3cpMIAErEJA3/vLtM+7nA3povGGNaDOKKsUzk5JgARIgARIwJkIUBjnTLPNsZIACaQmYOkb5kmfBEiABEiABEiABEjAugR4w7x1+bN3EiAB6xGw9PGvqYVx1StUwK75c9MBfGf0GCzfssVkYG1FGCcGdHjpPyhfooTOsSkRxpkM0MtEXw0ZjE/f7ZdhSksJ40QRv30+Fu+++abeIVqyJnUxcoRxsXFxaDPkA+w6fETvGIwJELUcXvIPsvr7Z5im6/CRWLNd8wJSY/qV09behXG1eryDY+fPyxmqFPPs0AGo3Nz0xgtpX/uh5r+A7YcRwzC0R4+UemxdGCfkoXLXqDmFcdbed6t17Y5Tly7pXUciYESf3pjw4VC9sfcfPULhps31xqkD7u/aoVUgaq21m7ZwJcI4Y9aVbGBWCOTxrxWgs0sSIAFrEaAwzlrk2S8JkIBNEki6c1jp66JNjoNFkQAJkAAJkAAJkAAJkAAJkAAJkIA2AueDgzQeDugPF5IiARIgAWcgQGGcM8wyx2jLBJQK4zau3IqA0uX0Dmnzto3oP1TZhya/2a4Tfv5uWrrcM/78Fd9P/lZvn6YM0CaME/nXhqzG0JGDTNmVRq5iRYph3u+LULhgkZTH/1gwC9/8b5zZ+pST2FzCOJVKJX2wsqMI4/z9/SEkeGI8FMbJWVmMIQHrEKAwzjrc2SsJkAAJkAAJKCJAYZwiXAwmARJwIAKbOnXYIYaTuVDh+uJr5Xd6OtDoOBQSIAESIAESIAESIIG0BHjDPNcECZCAsxKw9PGvqYVxYt6EME6I49Tbtdu3IWQwptxsSRhXq3Il/PvHbOkiD22btYRxfr4+uBi8PkPxmCXlbKKeM6tXI1f2bDqXgpAgle/QEU+jok25XPTmkiOMG/XTZExdtFhvLlMEdGzcCH//MElnqoVr16H/V1+boitFOexZGBefkIBM1WrIHm/R/Plxbt0avfHi9a16t+4WW7N7Fy1E1YAAqS5bF8bphZcqwJzCOGvvu+J1Q9QgZ5O77pS8BnRp3gwLvpuYrntrrt20xSgRxsnhqI5Ju66UtLV0LI9/LU2c/ZEACViRgFIxEm8gteJksWsSIAHzE6AwzvyM2QMJkAAJkAAJkAAJkAAJkAAJkID1CFAYZz327JkESMC6BCiMsy5/9k4C5hLGCbLLVi/BiM8+lg1Zm5Rs9fqV+Gj0ENk5TBWoSxiXkJiA0V8Ml8Zmrs3XNxP2bT2MzP6ZpS6m/v4zfpqm+/pIc9WROq+lhHHx8fGoW7cuRo0aBS8vL0sMzeg+Ll++jDFjxiAsLAxCGOfh4ZGSMy4uDkGVq6NPt/eM7ocJSIAETEeAwjjTsWQmEiABEiABEjAbAQrjzIaWiUmABOyEwPOj/IRlO5kqlkkCJEACJEACJEACRhHgDfNG4WNjEiABByBgqeNfcwjj3mraBIv+933KLJhD1mNLwjgx0B1z56BmpYpaV561hHGimC8HvY+x/XVfmGFJYZyoZ8KHQzGiT2+de2ivMWOxdNNmi+/B+oRxyzZvRs9PxyqqSwjyypcsiWdR0Th16ZKitiI4rXhRneDmvXso2aqN4nymaGDPwrjFwSHo98WXsjE0rVUL635L/+muqRPEvHiBer36KJ7fkoULIVe27LgYGooH4eGyaxKBzd+ojTXTpkptKIxLRvf86GGdDG1h3711/z5KtGwte56PLluKssWLZRjfdfhIrNm+XVbOVVN+Qcu6dTRirb120xZOYRzA419Zy5lBJEACjkGAwjjHmEeOggRIwEQEKIwzEUimIQESIAESIAESIAESIAESIAESsEkCFMbZ5LSwKBIgAQsQoDDOApDZBQlkQMCcwjghy6r0RllERT2TPQfXT9+Bi0vy52RFPI5A5TfKym6rLTB7thwokK8ACuQviOBN62Tn0iWMEwmSkpLw66yp+HHq6+t+ZSeWGTj0/Y8xYuhoKXreoj8xbuLnMluaJ8ycwjj1fMfExECsmXr16tmlMO7Bgwfw8/OThHFijYhxURhnnvXIrCRgLAEK44wlyPYkQAIkQAIkYAECFMZZADK7IAESsGkClrph3qYhsDgSIAESIAESIAEScAICvGHeCSbZtob4lcJylMYrTM9wEpAEOEpvpDcImxJhXNH8+VG1bABWbNmqt6+za1ejWIECiIiMRN4GjfTGC4nYj/Pm641TBxgjjBMiryoBAShXvDiKFiiAxMREhD95goePH2P7gYO4dvu27DrUgSP79sE3Qz/Q2s6awjghCRKyIF2bPmFcULmyCCheHKUKF4a3p6c0n4LTsXPncfDUKcWcRL49CxdobXf11i2UbddBcU5TNMhIGBf9/DkKN22Gp1HRsroSQq+fR4+S1r96i09IwNKNmxQJy7o0b4YF301M1+ewST9g+j/m+zTPjAZpr8K4Y+fPo1aPd2TNnzpo5rgv0bt9uwzb/Lr4b4z4cQW0kQAAIABJREFU8SdZecXrzuSRI9GlRXN4pvrETSETGzR+Arbs2ycrjwg6tnwpAooVozDuFTFdwjhb2ndbDByEHYcOyZrjbz/6EMN799IZK8aVrbamAE5XsFh3t7f9Cw93d40Qa6/dtPVSGEdhnKydg0EkQAKOQkDpcW7ynRPcSIAESMBBCVAY56ATy2GRAAmQAAmQAAmQAAmQAAmQAAlIBCiM40IgARJwdgKhZ+4q/buIsyPj+EnAJATMKYwTBXbo0QbHThyRXeuBbceQJ3ceKX72/JmYMEnZJehVKgWicb0mCKpaDRXLVYKvb6aUvnu//zZ27N4mq5aMhHHqBLv27sSU6T/h8DF51znJ6jhV0KEdx5ErZ26sCVmFD0cOltVcCPIG9HlfVqySoIIFCqF187ZKmmQYe+HqOUydORkqlQqurq6SYC06Ohrx8fEawjjxuC1vQgp36dIljB07FmFhYfD3908Rxom6hTCuWpUa6NNN9wdZ2/L4WBsJODuBwuXyStfhuJwN6aLxahRQZ5Szs+H4SYAESIAESMDsBCiMMztidkACJGDjBCx1w7yNY2B5JEACJEACJEACJODwBCiMc/gptrUBKv3rK29YtrUZdMB6LHX8q1QYN/vrcWjy3gC9xD98uwcmDR+GXxb+hU9/1i0sE4nqBQXivbfeQq8xY/XmVQcoFcZVKVMGvdq3Q/uGDZAvV64M+zl39Sqm/LUI81avkV2PyL9v8V9a440VxgkxlJC0PQgPl12POrBk4UI4tWqlznZphXFCnNarbVu81aypJNRTf9KhtgT3Hj7Eko2bMHryz4rqurdzO7L4+aVrM/LHyZi2eLGiXEKG9PnAgQgsG4CyxYsjW+bMktjt/LWr2LB7DybO/kNWvoyEcbOWLceH38n75MyJH32IT3r11Mntys2bKNe+o6yaRND1zRuRJ0eOlHghNszXsLHs9urAPh3ao0WdN1CxVCkI8aMQTl0MDUXIrt34ZeFC2TI8exPGiX3vz5WrMHXRItljFMxyZsuGSyHr4ZVK7JYWunjtLNmqjaz9UjDf+sdsiHWmbRPSyk8m/YCZS5fJmtuhPXrghxHDJNnlkbNnpYvLxLb3+Am9r7fqDoTY8LMB/fX2V6pIEY391VJiLyX96BLG2dK+u3DtOvT/6mu9vEVARmJN8bx4bev40ceycg3u1hWTR43UiLWFtZu2eCXzLWvgr4L0sVSSy9yxPP41N2HmJwESsCECyu5+AJTG29BQWQoJkAAJ6CZwbjY0zkeXaX2YuEiABEiABEiABEiABEiABEiABEjAYQm45AvidVYOO7scGAmQQEYEKIzj+iAB6xAwtzCubPWSiIp6JntwB7cfQ+5cycK4mo2q4u79u7LbThr/E7q82V3n9YimFsapCzt55gTm/vUHVq5dLrtWOYGzpsxB8yYtIcR0Pft3k9MEtWvUwd9z5F1XJyuhmYKEMG7K7z+lCONENzExMYiNjZWEcaNHj4aXlxcSEhKk+RTX/ElfhbjpVU3q6wC1Xberjk9bvq7H1XF6n0/Vv2gjZHdphXHu7u5SveI5IYwLqlydwjgzrSOmJQFzE6AwztyEmZ8ESIAESIAEMiBAYRyXBwmQgLMTsNQN887OmeMnARIgARIgARIgAWsT4A3z1p4Bp+ufwjinm3LbH7Cljn+VCuPOrl2Nqp27QkjV9G2hWzYhqGt3vUKlpT/9CFGHKYVxf4dswD8bNqB6hQro3KwZShUprK9cjefFxQ1ChDZ1kTyBmRBM3dy6WWsfhgjj2jaojzH935MEXyo3Nynvo8ePMWHmLMxYslTRWKIPH5Qu1NC2nbp0CZ9PnYZiBQqgc/NmqFmxos5YXZ2u+ncbuo+U/8FiJ1euSDcf8QkJyFSthqJxVShZEv/8OAnFCxbU2c6rapCsnLqEcbFxcSjesrXeNSw6eaNKFWyZPVMvv/EzfpctshPj69CoUcoYJs9fgLFTpsoakzpo1ldfole7djrbdB42HOt27JSV0xaFcUO6d0PpIkWkC5KeRkfjcWSkJFi8++ABNv23V9a40gYJ2aWQXma0zV21GoO+mSAr/4bfp6Nh9eoZxor9O3+jJrLy6RJB7jt+Ag37vSsrh1rqKSs4VZClxF5K+tEmjLO1fTciMhJ5G7zel/Vxv7ZpA/LmzKk1bOjE7zB7+Qp9KaTnd8ydg5qVKmrE2uLaVTLfsgb+KojCOCW0GEsCJEACJEACJEACJGBJAhTGWZI2+yIBEiABEiABEiABEiABEiABErA2AQrjrD0D7J8ESMBaBCiMsxZ59uvsBMwpjHv85DEq1Q5QhFgtjHv27CnK1Sglu+38mYvRoE7DDOPNJYxTdxqfEI9z58/i0NEDOHzsEPYf2odH4Q9ljyFt4Ocjx6F/n/dx5txptOrUVHaeC0euSbI1W97SCuPE9YxCGBcfH4+6detKwjhPT08NYZypx6MWzanFc0rzi/Zubm7phHEeHh7Sh9uK65CFAE8I4/p21/9htUr7ZzwJkID5CVAYZ37G7IEESIAESIAEdBKgMI6LgwRIwNkJWOqGeWfnzPGTAAmQAAmQAAmQgLUJUBhn7Rlwuv4pjHO6Kbf9AVvq+FepMO7cujWYv2YtBn49Xi/Eovnz49rt2xnGCVHXhfXrsHLrvyYVxuktTkbA06ho5KxbT0ZkcsizQwdS5G6pGykRxgkef333HWpVrqS1X3HRxXvjvsLi4BDZdV3fvBF5cuSQHW9IYL3efXHw1ClZTbfP+TPd+I6eO4fab/eU1V4ECWHWgb8Xw0fPRUDGCuP2HD2KJu8NkFXXwX8WS4I/fVvEk0jkbShPHDWsdy9M/OjDlJRCBiakYHK39b/9iia1amYYbu/COLks5MaJ162D//wNP1+fDJs0GzAQuw4f0Zu2fcOGWPLTD3rjRMCnP/+CXxb+JSv27vZtyJrZXyPWloVxYp8tX6KEJHgslDevdPHY06goXLpxAxeuXUflMqXx08gRKeNRIhDTJoyztX1XDOzt0Z9ixZatsuZ3xhefo2/HDuliExITkad+A4jfT/o2sZaF5DXtJ87a4tpVMt+px610XeljZs3nefxrTfrsmwRIgARIgARIgAQsT4DCOMszZ48kQAIkQAIkQAIkQAIkQAIkQALWI0BhnPXYs2cSIAHrEqAwzrr82bvzEjCnMG7DlmC8//F7iuBeOXkTKjcVrl67goZt6shue+lYKISoK6PN3MI4bX3fu38PB4/sx9YdW7AmeKXs8YjAd7r2wrdf/g9CRFexVgCiop7Jar/4z6V4o2ZdWbHWCtIljIuLi0sRxgnpnRDIGby5uABJMm55EHFiSxUrriHTJ5ITMSqVSqswTt1WjIfCOINnkA1JwOoEKIyz+hSwABIgARIgAWcmQGGcM88+x04CJCAIWOqGedImARIgARIgARIgARKwLgHeMG9d/k7Yu4y/nmpQefWXVCckxSFbjICljn8NEcZFxcSgSLPmsoQ1+oAJGZaQYi3dtNlmhHHiwoYnz54h/MkT1O/TDw/Cw/UNQ3r+4Z5dyOSTXjKlRBjXul49rPhlcob9Xbl5E+Xad5RVkwjavWA+qpUvJzteSWB8QoLEZ+SPk7F8yxZZTbVJzH5d/DdG/PiTrPYiaO6Eb9C9VUu98cYK4ybO/gPjZ/yutx8hFwvbtTOdmElXw+ItW+H2/TC9eatXqIBd8+dKcdHPnyNbbfkXjDWoVg0bZ87Q2weFca8R5cyWTeItRFsZbUrmYtrYMejf6S298yAC/g4JQd/Pv5QVGzJjOhrVqK4Ra2vCOLF+OzVrijb166FYgQKyxqUOUiIQ0yaMs6V9Vz2mtdt3oMvw11K8jIC0rFsHq6b8ki7k0OnTqNurjyyWX7w/EJ8N0PwkV1tdu0rm25h1JQuclYJ4/Gsl8OyWBEiABEiABEiABKxEgMI4K4FntyRAAiRAAiRAAiRAAiRAAiRAAlYhQGGcVbCzUxIgASsSmD58scb1qK37NdSoJnjOdo2f0z5vxdLZNQk4BAFzCeNu3ApFh+5t8Cj8oWxO2bPlwNHdyR/Eu+/gf+jWt5OstjWr1caSeSv0xhojjIuOiUZAUHEUK1IM03+ejYBSZfX2lzbg5q0baP5mY9nit5ZNW+P3X/6Q0gweNgDBm9bJ6jNv7rxY808IcufKIyteV9DJMyfw+fhPcevOLSxbsArFi5YwKl/qxmmFceK56OhopBbGeXp6ahXGqX9ppL05IfXj4ntdNy9k9FzaAWYY6+ICD3d3XLx4EWM/+wwPw8Lg5+8Pd3d3KY0QysXGxlIYZ7JVw0QkYDoC+t5fpn3e5WxIF403rAF1RpmuGmYiARIgARIgARLQSoDCOC4MEiABZydgqRvmnZ0zx08CJEACJEACJEAC1iZw/K+FUglPboTuFF+bL1/dwNo1sX+HJkBhnENPr30OzlLHv4YI4wTRz6dOw4/z5hsN9/a2rcieJYvFhXFCcnbq0mVcDL2Om3fvIfTOXVy/cxuhd+/JFsSlHbylhHGi35x168kW9mkTS8mdOCEHPHP5Ms5euYo7Dx7g3sOHuPvgAW7du49bYWEGsdImjHtn9BjZwjkh8zq1eiVUbm56h2GsMK7xu/3x37FjevsRwrjlkzMW/aVO8s6YsbLY5c+dC1c2hEhNdx85iqb9B+itRR2wZtpUNH+jtt54CuOSEYk53DV/HgKKFdPLbNfhI2g2YKDeOBHw5aD38UblyrJi9xw7hm9+nykrdu6E8ejeqpVGrK0J47SJ3GQNDoASgZi2fmxp31WPOebFCxRq0lT2a3fY7p3w9/XVQCbWx7ezZsvCeHLlCpQqUlgj1lbXrrHzLQuIjQfx+NfGJ4jlkQAJkAAJkAAJkICZCCTdOaz0vLSZKmFaEiABEiABEiABEiABEiABEiABEjA9gfPBQRpJA/rrdFyYvnNmJAESIAErEqAwzorw2TUJADClMC4+IR7nL5zDjj3b8MOU7xXz7dW9D775/Dup3Y7d2yAEb3I2IXHbHvyf3tDOvTrg4JEDeuNEwNlDl+Hr8/papMinkahQs3RK2+FDR+Hdnv3h65tJVj510Jfffob5i+fIapOax9/LF+HTcfI+fFMkF0K75QtXI1MmP1l9pQ66H3YPv82eplHn6r+DUaViVcW5dDVILYwTYjWxxcTESIK4OnXqYPTo0fDw8JAEcnj1vBSUlPynInUb8YHX4nmRQZvcLUNxXFKSlEfKmPr7/7N3H+BRFH0YwN9UQigiCAioNEF6kd57l470XpQiXXoVlN6RJkqVCEhHQi+CFCmiIiEEKQHpEHoLKd83SxJy4S43e3X37t3n8Ynk/jM785u5Sza5fRN7HnHeBJ+PPY8o8RSBcb6+OHv2LIaJwLg7d5A8eXLlc2Jcom8x/qKFiqNDC8M/ZGozSHZEAQpYJMDAOIvY2IgCFKAABSjgWAEGxjnWm2ejAAW0J+CoG+a1N3OOiAIUoAAFKEABCringN/HRU39MST3BOGs7SWg9sY87kt7rQT7jRNw1PWvpYFxF69eVQJ9rDnaN6iP+SNHKF2s3r4DbYcMle7uwrZAZEyXTrr+5f/fdLHr8BHlPPtPHMfVm7ek28oWOjIwrmybtjh+OkhqaGoD4479cxqrt23HriNHcObCBalzqCkyFhgnG+4kzjPssy4Y0VUurMuawLioqCj4Fy2uZmp2qY0Nw5q/ejX6TJgkdQ4RNHduyy/w9PQ0W8/AOKBwrlz4fsxXyPthdrNeomDa0mUYOnOWVK29iqYPGohuzZoadM/AuFccWnvuxl+knuPGY+Ea83/9V7RZOWUSGlSubLDG+Rs2wrnQy2a3VdG8efDb8mVv1Gl17zIw7vVS8frX7PZmAQUoQAEKUIACFHApAQbGudRycjIUoAAFKEABClCAAhSgAAUokECAgXHcEhSggLsKMDDOXVee89aKgNrAuKoVq+P99z5QArHEf5GRERDvN71x4xp+PbjPqmmtXroeJYqWVPq4cPE8Kn1SVrq/lYvXoFTxMkbrHzx8gNHjh2PdpjXS/ZkLjBMdibC4wX2Hon6dRngr5Vtm+xZeX3zZFb9s22S2VhQM6TccXTv1UGrvP7iP0tWK4cmTx1JtRVGO7DnRrdMXqF3jEyT1S5poO/Eesr9OncTmbRvxw7I3/0CnrQPjgs8HYdaCafD29lbetylsYgPjypQpowTGJfH1RbgIjIs9YgLcxD+VoLeY8LjYf4uPCT8XG9xmNEwuJmwuNhQuYXtz0GIMfn5+CD57FsMZGGeOi49TQFMCDIzT1HJwMBSgAAUoQAHjAgyM486gAAXcXcBRN8y7uzPnTwEKUIACFKAABbQiwBvmtbISLj8OBsa5/BLrb4KOuv61NDBOiKoJmjK2AkcCVqBQrld/odBegXH3Hz3C1CVLIcK2Hj15ateN4MjAuPo9e2H7wUNS85EJjIuIjMTi9Rswa8UKqTAiqRObKDIWGFe4SVPpcLoFo0aiXf16UkOwJjDu3sOHyFDRMKxJ6qQ2Lnr4+2H4+vjgm+8WYuz8BVK91ylfHmtnTJOqVfM8DtmyGR9kyCDVr6VF+44dQ83Pu1naXHW7CX374IuWLeDt5SXddtjMWZi69M0wLukObFAoQhNFeGL8g4FxrzS09tyNv0YHTvyBal0+k9oBLevUxqKxY+Jqz1+5grz1G0q1nTF4ILo2NQwUFA21uncZGPd6WXn9K7XFWUQBClCAAhSgAAVcRoCBcS6zlJwIBShAAQpQgAIUoAAFKEABChgRYGActwUFKOCuAgyMc9eV57y1IqA2MM5e4xbhZts37IGX56v3pUVERiB7gfelTyfC24b2H4G6teor4W2RUZFK6NyJk8cwaeYE3A27I92XKJQJjIvfYa1qddDwk8bIn68g0qVNB28vb+VhEVh249YNnDsfgqUrFmHXvh3S49gQ8AsKFywSVz/3+9mYOH2cdPvYQmHTskkrZM+WQxlb2nfSwdPDE7du31TG9u+FEGz4ZX2iRvYMjBPBa+IQgXEvX75E2bJllcA4Hx8f5d/iUeXGhZg6EfCmHDEBcrHtlc/GC5ETj3uIYENTtTFASv/x20kKxwXGBQdjxIgRuH37NlKkSAFfX1+lP/G4GH/RQsXRoYXhexclT8EyClDATgIMjLMTLLulAAUoQAEK2FKAgXG21GRfFKCAHgUcdcO8Hm04ZgpQgAIUoAAFKOCKArxh3hVXVZNzYmCcJpfFvQflqOtfawLj9vx+FLW7dbdooUoVKoi9i36Ia2uPwLhNe/ehxzfjcDsszKIxqm3kyMC4FgMGYv3uPVJDNBcYd+rcOXQbMxbHTwdJ9WdtkbHAuLTlyksH+pmbT/zxWRMYd+G//5CnXgNrp2t1++v79uDtlCkxYMo0zA4IkOqvW7OmmD5ooFStOwfGdWzUEHOHD5Nyil/UZfRXWL5ps+p2tmzQv11bfNO7l0GXDIx7xaG15278RYqMikKW6jWlvi6lSOaPa3v3wMf71Zst561ajb4TJ0lto9Cd25E+TZo3arW6dxkY93qpeP0rtcVZRAEKUIACFKAABVxGgIFxLrOUnAgFKEABClCAAhSgAAUoQAEKGBFgYBy3BQUo4O4Coaevq31fqruTcf4UsImAVgLjEoajicnVa1YLf/3zp+p5ioC0J08eq24Xv4HawLiEJ8uQPgPSpHkHF0MvWjSWbFmyYe+WgwbdPnn6BMUqFrKoP6swANg6MO7shTOYOX8qvL29lWA1ccQGxpUpUwaDBw9WAuPCw8NNDl20syToTekwYficBUDxA+NGjhyJO3fuIHny5Mq4X52CgXEWsLIJBTQlkDlvBuUFyiMosKnBN6q5y8q96VxTs+FgKEABClCAAjoTYGCczhaMw6UABWwu4Kgb5m0+cHZIAQpQgAIUoAAFKGCRAG+Yt4iNjdQLqH1jzqvf5PKggB0FHHX9a01gXFRUFAo2boJzoZdVS6yYOAGNq1WNa2frwLgpS5Zi+KzZqsdlTQNHBsZ1HDESAVsCpYabWMDab3/8gaqdP5Pqx1ZFxgLjZIPdxBhOrF6FvB9mlxqObL+Z0qfD+a2GnieCglCmdVup89izKDYwTs2aj+35BQZ0aC81LHcOjBNA2xbMQ8VixaSsYosa9+mHLfv3q2pj62IGxr0Wff7HcQNerT13E679iNnfYvLiJVJbYvuC+ahQrKhSW/Pzbth37JjZdjXKlMbG2bOM1ml17zIw7vVy8frX7BZnAQUoQAEKUIACFHAJgTMLYfDz6Fx1DK9rXGKSnAQFKEABClCAAhSgAAUoQAEKUCBGwCNjUb7PiruBAhRwSwEGxrnlsnPSGhDQQmBc9849Majv0Dc0Ro0bjiUrXv+hZUdyWRsYZ+1Yv50yH3Vr1X+jmyPHDqFZ+8bWdq+6va0D44LPB8UFxnl6eirjSRgYJ8LkDALjoqNfB72pnoHtGoiQOiXkzsMDfn5+OBscjFGjRuH27dtIkSIFfH19lSA7BsbZzpw9UcBZAgyMc5Y8z0sBClCAAhQAwMA4bgMKUMBdBbY2aVBRzL1wqzZ7xcdUmTO7KwXnTQEKUIACFKAABdxKgDfMu9VyO3OyDIxzpj7PbSDg6OtfawLjxMAXrlmLnuPGq1rFtKlT4/zWLfCN+atzorEtA+N+CtyKDsNHqBqTLYodGRgnzIW9zGEqMC7o/AVUaN8ej548lenGZjXGAuPSlisvPY6jKwNQIGdOqfFYExh34MQfqNbFsWF6xiYVGxinJthtzvCh6NSokZSRmn5DtmzGBxkySPVraZEIxBLBWI46UiTzV0II1cyr+mefY//xE44aotHzMDDuNUvCwDitPXcTLuDfISEo3ryl1P7p1aolJvXvh3sPHiJDpcpSbZaNH4emNaobrdXq3mVg3Ovl4vWv1DZnEQUoQAEKUIACFNC9AAPjdL+EnAAFKEABClCAAhSgAAUoQAEKqBBgYJwKLJZSgAIuJcDAOJdaTk5GRwLODozr0LozRg0e8yqAK8Fx+84tVKhdBk+ePHa4qDMD4z6pWQ9zpi4wOedNgRvQc4Dj3jMoBmLvwDgRsCYC4yIiIlC6dGkMHjwYXl5eSmBc3N6Ijlb+wlDcTjGyZwzQYgLmDNrEK4gNdRMfxSHOExcGF1MXv63y2KtCQLTx9IRfkiQ4e/YsRo8ezcA4hz9LeUIK2F+AgXH2N+YZKEABClCAAiYFGBjHzUEBCrirwLYmDQwCHCoOHe6uFJw3BShAAQpQgAIUcCsB3jDvVsvtzMkyMM6Z+jy3gYCjr3+tDYx7+OQJ0pWroGoVR/fojsGdOhq0sVVgXNiDB8hYqYqq8SQsFoF2mTO8i8wZM2Ltzl3SfTkyMK7vxEmYt2q11NhMBcaVbdMWx08HSfVhrEgEbWXJmEkJ2tp/4rh04JuxwDg1YUHbFsxDxWLFpMZtTWDcn8FnUbJlK6nz2LMoNjCu/bDhWLl1m9SpxvXuhX7t2krV6j0w7kjAChT86FWAYOi16yjWvLn0XowFKpwrF/Ys/gFJkySxuZlUhxYUMTDuNVrCwDitPXeNLW/hJk1x5sIFsyufKX06/Bu4BT/v2Im2Q978q8PGOjD1tUjUqnm+mx2chQXG9q6arwEJ19vCYWi2Ga9/Nbs0HBgFKEABClCAAhSwqQAD42zKyc4oQAEKUIACFKAABShAAQpQQOMCDIzT+AJxeBSggN0EGBhnN1p2TIFEBZwZGDewzxB079zTaFhc7KDXb16LPoO/cPgqOiswrnSJslg4axGSJ0+R6Jx/WrMCg0d96TAXWwfGnb1wBjPmTYG3tzc8PT2VoLb4gXEDBw6Cj483Xrx4ETdHJdAt9l8xIW9mAWLC3RIGEhqExIlOYvuLDaFL2L+Hx+ugOqU8Wtm3fn5+OBsSogTG3bl9GylSpIBPzB8HF4+/fPkSRQsVR4cWXcwOlQUUoID2BBgYp7014YgoQAEKUMCNBBgY50aLzalSgAIGAo6+YZ78FKAABShAAQpQgALaEOAN89pYBzcYxWiVc1Rbr7J7lruzgKOvf60NjBNrNXDqNMxaESC9bKE7tyN9mjQG9bYKjJv54woMmjZdeiyisHj+/KhVrixKFyyIj/PkgQhCiz3q9+yF7QcPSfWnp8A4ERQnAuPUHCK4qFnNmihVsCCK589nsIbTly3HkBkzpbozFhhXqmVrnAwOlmq/YuIENK5WVarWmsC4KzduIEftT6TOI4q+6d1Lula20NfHG92aNYO3lxd6jZ+A735eI9W0T5vWmNC3j1StmgCpkC2blYBAex77jh1Dzc/l/1ro0ZUBKJDzVWCcOAIPHECj3n1VD7Fd/XpYMGqkVLseX4/DD+vWSdVWKVkClUuUkKpVU1Tu48LKa1f84/Cff6FSx05S3fRs2RKTv+wnVRu/yFHBXtacR2vPXWPIU5cuw7CZs6T8RSjitKVLIb5Omjta1qmNRWPHmCzT6t61Zr3NmejtcV7/6m3FOF4KUIACFKAABShgmQAD4yxzYysKUIACFKAABShAAQpQgAIU0KcAA+P0uW4cNQUoYLnA3P4BBn/AuE7HSgadbVm01+DfCR+3/MxsSQEKCAFnBMYVLlgEk8ZMRc4PPzK7CCKYq3231th3YI/Z2sQKmjZqgctXQnHkmNz7W50RGNeiSSuMGTYOvr6+UnMNDjmDL77sinPnQ6TqrSmyV2CcCFcTgXHiePr0qRKwVrp0aQwYMBDe3l4IF4FxMSFuSmCcbFCc6DBe+JuHCHmLCX1TvujEBL4p/ytKEf0qMy4mYE7GKi4w7uxZfDVmjBIYlzJlyrjAONFHeHg4ihUuwcA4GVDWUMCBAua+v0z4uEdQYFODb1hzlx3owOHyVBSgAAUoQAH3FGBgnHuuO2dNAQoAjr5hnuYUoAAFKEABClCAAtoQ4A3z2lgHjoICFHCcgKOvf20RGBdyKRQFGjWWQjIVZmOL+RiSAAAgAElEQVSrwLjstWrj6s1bUmMRRfNHjoAIikr41/ZiO3DVwDg1oUHColerlhjdozv8/fyM2lobGFenew/sPvK71Lp1a9YU0wfJ/V7amsC4x0+f4p2y5aXGJIruHzkEP8k3Nkl3Gq9w+KzZmLJkqVTTFrVrY/HXpkOj4nfiaoFxYm5qrOJbzB46BF2amH8tGzVnLib+sEhqLb7q0R2DOnWUqrW2SE1gXPfmzTBt4ADVp3RUsJc159Hac9cY8qWr15Crbj0p/yGdO2H89z9I1RoL5IzfUKt715r1loLRURGvf3W0WBwqBShgqYDaAH619ZaOi+0oQAEKOEUg+tpxg/fhO2UQPCkFKEABClCAAhSgAAUoQAEKUMBOAsFbihr0nLsLRK4FDwpQgAIuL8DAOJdfYk5Q4wKODIyrU6MumjdphTIly8LL00ta5tnzZ5gycwK+X/addJvYwmTJkuObERPQsG5jtOvaSjp4LmFgXERkBBYsmosfli3E3bA7qseRWAMRZtetYw9ky5pddb/Pnz/HkhU/4Lsl820+LjGYHNlzom2LDvi0YTMk9UuqenymGpy9cAbT505WwuJi348swtUiIiJQqlQpDBgwAN7e3gaBcW/0FT/czUTQm8Gn4wXIKX0pD8Z+iH6VHKd8Cx77yZhfS5ns2wN+fn44e/Ysxo4di9u3b8Pf318Ztzi8vLyU+TAwzmbbhh1RwGYCDIyzGSU7ogAFKEABCthPgIFx9rNlzxSggLYFHH3DvLY1ODoKUIACFKAABSjgPgK8Yd591pozpQAFXgk4+vrXFoFxYtyywWoHli1BsXz53lhuWwTGPXzyBOnKVZDeSpu+nYXqpUsnWi87L9HJnd/2I7m//xv9Xbt1C9lq1pYaV53y5bF2xjSztX0nTsK8VavN1omCwHlzUblEcYPasm3a4vjpIKn2Azt2wJgveiRaa21g3IAp0zA7IEBqPKLo7sEDSJbU/Jt1rAmME+eRbS9q9yz6HqULFZKeg9rCqUuXYdjMWVLNcmfLhpNr5PaHKwbGRURGosZnXXHw5Ekpr/hF+xYvQsmCBRJtJ5574jkoc1QsVgzbFsyTKbW6Rk1gnJrgxfgDc1Swl7Xn0dJz19TCVunUxaI9aqq/FMn8cX3fXnh7mX7zqVb3rrXrbfWTR0Md8PpXQ4vBoVCAAvYSUBuMxBtI7bUS7JcCFNCEAAPjNLEMHAQFKEABClCAAhSgAAUoQAEK2EmAgXF2gmW3FKCA5gUYGKf5JeIAXVzg5F8n0KDlJzabZZrU7+CtlCmRInlKZP4gC/LkyovcOfMgf96CSJM6jVXnOXrid/Qa0A3Xb143248IimvdrC26dfoCb6d6W6nv0f9z/LJtk9m2ouDsiYtKGFjCQwTHHfr9IHbv24nNWzdaFdLWrmVHdGn3Od5/7wOpMSVWJMa1e98uBPy8XDoUz1R/Yr2qVKyG6lVqomA++7zH88y//2Du97ORK2duJVgt7F4Yzl/8F1FRUUpgXP/+/eHt7YPw8BevhvlGOFz8T8ULeIurexX89ioTLn4wnPh0/CC4uNy4mM/H/Mpdaa60VApeNTH89b2Hpyf8kiRBSEiIEhh369YtZM+aHRkyZFLaiTld/i8UJYqUQocWXaxeY3ZAAQrYToCBcbazZE8UoAAFKEABuwkwMM5utOyYAhTQuICjb5jXOAeHRwEKUIACFKAABdxGgDfMu81Sc6IUoECMgKOvf20VGLf94CElNC6xo2jePPht+TKjJbYIjAu5FIoCjRpL76UHRw4hia9vovWuGhiXtlx5PHryVMpq/9LFKJ4/f6K11gbGLd+0GV1GfyU1HlE0beAAdG/ezGy9bGhUpvTpcH5r4Bv9Ne7TD1v27zd7HlEw/PPPlP/sdWw98Bsa9u4j3f3hgB9ROFcus/WuGBgnJn399m3l9UB2n8dCpU2dGsdWBuDdd94xaffHmTMo3aqNWdvYglsHfkXKZMmk6y0tVBMY17JObSwaO0b1qRwV7GXtebT03DWFvHj9BnQb+7XqNTDVoHfrVpjYr2+i/Wl171q73jZD1EBHvP7VwCJwCBSggL0FGBhnb2H2TwEK6EqAgXG6Wi4OlgIUoAAFKEABClCAAhSgAAVUCjAwTiUYyylAAZcRYGCcyywlJ0IBhwg8e/4Mf536EyH/BiMo+DROBf2Nhw8fIpl/Mrz1Virk/DAnypQsh8rlq8LXzPtdrR1wdHQ0rl2/qgTYXb9xDTduXsfV61dx9dp/uPLfZeX8jx4/VMLzUr2VCmnSvIM8ufIhf578yseUKVJaOwSj7UV43LnzITj1z18IOhuE+w/u4cHDB7h3/x5evgyHl+erP7ApxpQ8eQolUO+jHLmUMeXKkUv5nL2Pv878ge27tqJzm65KYNyF0POYv/hbPHv2DGXLlkW/fv3g7e2N8PDwV0NJGBgnPhcb/BZ/sDF1r+LiYo74bU1NLEHQnMH5TLQxCIwbMwZ3w8KUAMBC+YogKjIK5y6GYPGKhfi4YFF0bt3V3qTsnwIUUCHAwDgVWCylAAUoQAEKOEuAgXHOkud5KUABZws4+oZ5Z8+X56cABShAAQpQgAIUeCXAG+a5EyhAAXcTcPT1r60C4yKjotBuyDA8ePzY5JJ1a94UtcuVM/q4LQLjfj12HDU+l3sTQvmiRbDjuwVmt5crBsapWXMB9PjY7/D2evWGGlOHtYFxJ4ODUapla7PrEb9gUv9+6NWqpdE2/16+jNFz5mHNzp1SfZoKjFuw+mf0njBRqg9RtH3BfFQoVlS63ljhjTt30H/yFOw/8Qcm9OmNVp/UUcpCr13DR5/Uk+67W7OmmD5ooNl6Vw2MExPf8/tR1O7W3axBwoJShQoqa+nr42O0rXi9e7dCRekwuhplSmPN9Gnw8fZWPZb4DVZt247Rc+bC29sLp9ave6OvI3/9jYodOkqdI0fmD4z2Ya6xo4K9rD2Plp67pkzv3r+PTJWrmiOXfvzgj8tQJE+eROu1unetXW9pJB0U8vpXB4vEIVKAAtYKMDDOWkG2pwAFXEqAgXEutZycDAUoQAEKUIACFKAABShAAQokEGBgHLcEBSjg7gKhp6+r/b2Iu5Nx/hSgAAV0KyAC4/Ye2I3+3Qcpc/j3YgjGT/8aT58+Rfny5dG3b194+/i8CoyTDYYTHXl4QITFiTbRIgQu5v9joTw8PCCC/uIfBuFyrwsNzmu0nYcH/Pz8EBISgrExgXG9u/VDySJllHP8E/w3vv1uBgoXKIIubbrpdq04cAq4s0DmvBmUFxKPoMCmBq8cucuaf8O5O8Nx7hSgAAUoQAFbCDAwzhaK7IMCFNCjgKNvmNejEcdMAQpQgAIUoAAFXEngfmioMp2TK5ZXEh9rrdmwz5Xmx7lQgAIUMCXg6OtfNeFhWTNlwpnNG+2yeLYIjNt+8BBEwJvMIRuWVKVTFxw8eVKmS9z5bT+S+/u/UXvt1i1kq1lbqo865ctj7YxpZmv7TpyEeatWm60TBYHz5qJyieJxtSLUL335ilJtRVHozu1InyZNovXffLcQY+ebD+ATnfwy51tULVXSoL/wly+RqXIV6QCu2MYiiKtM4cLInyMH/JL4IuRSKH4/dQoBWwKl5ycKTQXGBV+8iEKNP5XuK0Uyf+xdvAj5PvxQuk1s4dPnz/H92nUYO39+nMP4Pr3Rt20bpUS84SZd+QqqjFZPnYJ6lUyv9bF/TqN2t27SfYZs2YwPMmRQPTc1DfYdO4aan8u/mejoygAUyJnT5CnGLfweY+bNVzMEpbZ782aYNnCAyXbthw3Hyq3bpPtt36A+5o0YDvFGK7XHH2fOYNS3c7Hz8OG4ps//OP5GN6fOnUOxZi2ku7+0Yxvefecd6XpR6KhgL2vPo6XnbmLAagIbE+tH9mua6EOLe9fa9Va1iTVazOtfjS4Mh0UBCthDQO2NUeq/ebLHqNknBShAARsLnFkIg9fDXHXevMaz8SnZHQUoQAEKUIACFKAABShAAQpQwGkCHhmL8ud8TtPniSlAAWcKMDDOmfo8NwUoQAHHCpz59x98v2wBcmTPCQ9PT4Tdu4sLF8/j5cuXKFWq1KvAOG9vJTBOCWsTgU0iBC7+MGPC4QwC4OJ9Lq5dTBtRF//9iLHtYuveCKZL+N7FhEFzMYFx586dwzfffIPbt28jS+YsSJ/2XfgmSYL79+/h0uWLKFa4BDq2/MyxwDwbBShgEwEGxtmEkZ1QgAIUoAAFLBNgYJxlbmxFAQroX8DRN8zrX8x2Mwg6fwHPw1+o6vCjLFmQLGlSVW2MFb8ID8fp8+dV9ePt5ZXojcKqOmMxBSigCYGHT57g38uXVY1FhJm8nTKlqjYspgAFtCWwb9zXBgOquWYD3zimrSXiaChAATsJOPr615UC40RYWIFGjaVXZvuC+ahQrKjR+nsPH6L/5Cmqgsf0EhgnJvx+1eq4HRYmZTWyW1cM7dLZaG1EZCRm/rgCw2bOkupLFBkLjBOfHzx9BmYs/1G6H1sWmgqME+dQExoo6kVo3IAOHdCufj2zQXui/vyVK9iwZ68y94RrEj8wTtR2Gf0Vlm/arGrqo3t0R5tPPlFC8cTxMiICIkxr+rLlqva3aKvHwLjIqCg06NnbIGxNFnDR2DFoWcd42KPaYDtxThFw2Lt1a1QsVhSenp6JDuPZixfYd/QYlmzYiI17975Rayww7vL168hZp67s9FA0bx5s/26B0Z/hief2jTt3kC51avj6+MT16ahgL1ucR0vPXVOLsm7XbrQc+OqvylpzfNWjOwZ16ijVhRb3ri3WW2ryGi7i9a+GF4dDowAFbC3AwDhbi7I/ClBAlwIMjNPlsnHQFKAABShAAQpQgAIUoAAFKGChAAPjLIRjMwpQQPcCDIzT/RJyAhSgAAWkBc5eOIPpcycr7wtUAtuio5VwuIiICJQsWfJVYJyPj/I55YgJe4v7BboIb0sQ6BZ784xSEz/cTdQZC4uLDZcTYXQxf6RY+RjTb+znxHmU/4/tN15/fn5+CAkJwbhx43D37l34+/srQXdiTmJuIgCvRJFSaN/c+HuKpcFYSAEKOEWAgXFOYedJKUABClCAAq8EGBjHnUABCrirgKNvmHdX54TzVhMcEL+tmpsUE7NetG49un/9jerluLh9KzKkTau6HRtQgALaFBDBCSI8Qs0xsGMHjPmih5omrKUABTQmwBvmNbYgHA4FKOAwAUdf/6q57hOhvGc2b7SLxertO9B2yFDpvi9sC0TGdK8CsGIPEXCUvFgJ6T5EsNe43r3RpHo1JWxYBEuFXLqEI3/9jZFz5koHqsWeUE+BcfV79sL2g4ekrUQQUYcGDZAlU0alzX83b+L46dOYtnQ5jp46Jd2PKDQVGPd3SAiKN2+pqi9bFScWGCf2Q8UOckFMCccjwsaK58+H9GneQYZ33kEy/6S4HXYPV2/eROj169i4Zy9OnTtnchoJA+N2HT6CT3p8YdG0xX5/L/27OHPhgkXtRSM9BsaJcYsgvo+bNlf9nBZtjwSsQKFcHxk1q9O9B3Yf+V21Z47MH6BF7dp4P3165edX77z9Nl5GvMTVW7dw/fYdHPvnH7NhfsYC454+f47UpcuqGo8IjevevDlyZcuKJD6+OKu8Bv6Fn7ZuU7xmDB6Irk2bxvXpqGAvW5xHS89dU4tiyZoZ6yto0wZke+896bXX2t61xXpLT16jhbz+1ejCcFgUoIA9BBgYZw9V9kkBCuhOgIFxulsyDpgCFKAABShAAQpQgAIUoAAFrBBgYJwVeGxKAQroWoCBcbpePg6eAhSggCoBERg3Y94UeHl5xf0x2WfPnsUFxvXp00cJXosLjFPVu5XFMYFwcb3E/jvB50UonAiMO3funBIYd+fOHSRPnhy+vr5KAJ4IjHvx4gXy5MyHnl36WjkoNqcABZwhwMA4Z6jznBSgAAUoQIEYAQbGcStQgALuKuDoG+bd1TnhvNUEB8RvK25CvrJ7F/x8fS2mjIqKQsHGTXAu9LLqPowFB6juhA0oQAHNCJRq2Rong4NVjUcEPvwbuEX5KyY83Fdg2aZNGDbrWymAlZMnokzhwlK1LHKMAG+Yd4wzz0IBCmhPwNHXv2qu+7QeGCdWs2ybtjh+Okj1worr2EdPnqpuF7+BngLjRsz+FpMXL7FovtZamQqME4NpMWAg1u/eY9G4rGmUWGCc6Ldxn37Ysn+/NaewqG3CwDgRipi1Ri2Lgs8sGkCCRnoNjBPTOPznX6jUsZNqBrE3jv4UgDSpUr3R9kRQEMq0bqu6T1s0MBYYZ81roKkxTRs4AN2bN4t72FHBXrY6j1aeu4mt+edfjcHSjZss3halChXE3kU/qGqvtb1rq/VWhaCxYl7/amxBOBwKUMCeAgyMs6cu+6YABXQjwMA43SwVB0oBClCAAhSgAAUoQAEKUIACNhBgYJwNENkFBSigK4G5/QMMfh9Sp2Mlg/FvWbTX4N8JH9fVZDlYClCAAhRQBE6fP43pM8fD29c37j62+IFxAwYMcF5gnOQaxQbGnT17FmPGjMHdu3eVwDgfHx+lBxEa9/LlSzSu1xS1q9SV7JVlFKCAIwTMfX+Z8HGPoMCmBt+w5i470BHj5DkoQAEKUIACbi3AwDi3Xn5OngJuLeDoG+bdGjve5NUEByQ0Wzh6FNrUs/yHP1sP/IaGvftYtBQMjLOIjY0ooEmBfy9fRr4GjSwa24FlS1EsX16L2rKRawhMWrQYI7+dIzWZ1VOnoF6lilK1LHKMAG+Yd4wzz0IBCmhPwNHXv2qu+/QQGNdv0mTMXbnKKQurp8A4EcomwtmccSQWGHfjzh3kb9jI6vA+tfMyFxh3/fZtlG3bDldv3lLbtVX1CQPjRGeL1q1H96+/sapfSxvrOTBOzHnG8h8xePoM1dOvUrIENs6eBW8vrzfaqrnmUH3iRBqYCowbPms2pixZarNT6T0wTkvPXVOLsuf3o6jdrbvFazZn+FB0aqT+5yZa2rsMjAN4/WvxU4ANKUAB/QkwME5/a8YRU4ACdhSIvnZc7euiHUfDrilAAQpQgAIUoAAFKEABClCAArYVCN5S1KDD3F3AvwBtW2L2RgEKaFSAgXEaXRgOiwIUoIAdBW6F/ouNh/fBKyoqLjBOBKxFRkbi/fffR6VKleDp6an8O/aI++bYw0OkscHkL41iHk9s+AZto9X9+kmpjmkjwuFu3bqFnTt34vnz5/D19Y077dOnT5ErZy5UKF0ZPt6vP29HVnZNAQpICjAwThKKZRSgAAUoQAFnCjAwzpn6PDcFKOBMge1NGuwT53/rg8wVxMdCrds4czhuc241wQEJUXJk/gB/r1sb90MutWjVP/sc+4+fUNtMqWdgnEVsbEQBTQqIG93FDe+WHP3btcU3vXtZ0pRtXERAzQ3wDIzT3qLzhnntrQlHRAEKOEbA0de/aq779BAYd/PuXeRr0NDhgWNid+gpMC4yKgpVO3fB4T//cszGjneWxALjRNmmvfvQtP+XDh2XucA4MZjgixdRrm07h+4tY4FxEZGRKNasBc5cuOBQI3EyvQfGiTdgib21ed+vqu0GdGiPsT2/MNqu1/gJ+O7nNar7tKaBqcC481euIG/9htZ0bdBW74FxWnrumloU8ZzOWqMWboeFWbRuV/fsQppUqSxqq5W9y8A4BsZZtIHZiAIU0KuAunemgzeQ6nWhOW4KUEBOgIFxck6sogAFKEABClCAAhSgAAUoQAF9CjAwTp/rxlFTgALWCzAwznpD9kABClCAAhSgAAUoIC/AwDh5K1ZSgAIUoAAFnCbAwDin0fPEFKCARgSe/8G/sOzIpVATHGBsXJu+nYXqpUurHvLJ4GCUatladbvYBgyMs5iODSmgOQERhHDq3DmLxpU2dWqE7tim/BUWHu4pwMA4fa87A+P0vX4cPQUoYL2Ao65/1Vz36SEwTsj/FBiIDsNHWr8IKnvQU2CcmFrIpVAUaNRY5SytLzcXGCfOEHjgABr17mvVyXJnyyYdqiZC70+tX2f2fEdPnULjvv0tDnYye4IEBcYC40TJsX9OK+F1jj70HhgnvO49fIiizZrj6s1bqvl+mjwJDatUfqOdCPwSwVuL1q1X3aelDUwFxon+uoz+Css3bba0a4N2rhAYJyakleeuqUUZOnMWpi1dpnrN6lasgJ+nTVXdLraBVvYuA+MYGGfxJmZDClBAjwIMjNPjqnHMFKCA3QQYGGc3WnZMAQpQgAIUoAAFKEABClCAAhoQYGCcBhaBQ6AABZwiwMA4p7DzpBSgAAUoQAEKUMBtBRgY57ZLz4lTgAIUoICeBBgYp6fV4lgpQAF7CDjqhnl7jF2PfaoJDjA2vyolS2DL3Dmqp95xxEgEbAlU3S62AQPjLKZjQwpoSsAWARb7ly5G8fz5NTUvDsZxAgyMc5y1Pc7EwDh7qLLPRARGq9RRW6+ye5ZTAHDU9a+a6z69BMZFR0ejQa/e2H7wkFVbqV39erh49Sr2Hz8h1Y/eAuPEpERAkQgqsuYomjeP8j333JWrpLqRCYwTHf1x5gz6TJikBD2pPUTIlQj2ylqjllTTUoUKYu+iH6Rqwx48QI+vv8H63Xuk6q0pMhUYJ/rctHcfmvb/0pru0b5BfVy5cQO7j/wu1Y8rBMaJiZ4ICkKZ1m2l5pyw6OSa1RBhhMaODXv2oMuo0Xj05KlFfatplFhg3JNnz1C1UxeIP8hg7eEqgXHCQSvPXWNrIl7vSrdqo3q5AiZNRKOqVVS3S9jA2XuXgXEMjLN6E7MDClBATwIMjNPTanGsFKCA3QUYGGd3Yp6AAhSgAAUoQAEKUIACFKAABZwowMA4J+Lz1BSggCYEQk9fV/t7EU2Mm4OgAAUoQAEKUIACFHBNgcx5M3iImXkEBTY1+EY1d9mBrjljzooCFKAABSigIQEGxmloMTgUClDAKQKOumHeKZPT4EnVBAeYGv7RlQEokDOn9Oyu3ryF7LVqS9cbK2RgnFV8bEwBzQhM/GERRs2Za9V4erduhYn9+lrVBxvrV4CBcfpdOzFyBsbpe/10OHq1b8xRflHCgwL2FHDU9a+a6z69BMaJdXn6/DlGz5mLWSsCVC9TimT+mDVkMFrUro36PXtJB8/pMTBOhOst27QZn381RrWTaDC0S2cM6NgB81etxpAZM6X6kA2ME52J8YlgthW/bMGW/fsT7V+sW/NatTCkcydkTJcOp86dQ7FmLaTGVL9SJayaOlmqNrZo7c5dmLJ4iU1CuRKeWMzlixYt0LlxY2RKn87kuH7esQPdx36tOqDs1R4fgha1a+HTfv2xed+vUnN3lcA4Mdnvfl6DXuMnSM07fpF4HTwc8CNSpUhhtO3127cxefES6QBFtQOoVqoUOjdpBLFnEzvuPXyIsfMXWD0OVwqM09Jz19ja5W/YCOdCL6vaEmGHfoO/n5+qNqaKnbl3GRjH61+bbGJ2QgEK6EVAbQC/2nq9OHCcFKCAmwucWQiDn0fnqnPczUU4fQpQgAIUoAAFKEABClCAAhRwZQGPjEX5PitXXmDOjQIUMCnAwDhuDgpQgAIUoAAFKEABLQkwME5Lq8GxUIACFKCA2wkwMM7tlpwTpgAFEgg46oZ5wr8SUBMcYMqsTb26WDh6lDTpyG/nQAT8WHMwMM4aPbalgHYECjdpijMXLlg1oLSpU+PSjm3w8vS0qh821qcAA+P0uW6xo2ZgnL7XT4ejZ2CcDhfN1YfsqOvf8JcvkbJEKSnO/Dly4Niqn6Rq1RYFHjiARr3lg36v7tmFNKlSmT3NwZMn0XboMIhwcnOHCNHq0qQJvmzfDqnfekspbz1oCNbs3GmuqfL4vcMHkTRJkjdq79y7h/eqVJPqo2mN6lg2fpzZ2uGzZmPKkqVm60TBvsWLULJggURrL1+/js9Gj8G+Y8ek+hTjHNy5M/Jkz6bUL1yzFj3HjZdqu3fRDyhVqKBUbfwiEQL4R1AQbt4Nw+17YQh78BDJ/f3x7jvvIGumjCicOze8vbzimmw98Bsa9u4jdZ4uTRpj9tAhUrUJi4798w++W7MWG3bvVh3cFr8vEQxXp3x51CpbFlVKloCvj4/UeMTaCfvtBw9J1Xds1BBjv+gR9/zpOGIkArYESrUN3bkd6dOkkaq1tOjoqVMo366DdPO/161FziyZpevjF7YdMhSrt+9Q3fab3r3Qv13bRNs9ePwYKwO3YvnmzTh+Okj1OeI3qFGmNOpWrAjx8f1331XV198hIRBh7DsOHVK9P8WenDrgSzSoXDnunCKEUYQxmjvE6+ntA4mHPCbWhyPO4+znbsL5qw3Nb9+gPuaPHGFuKVQ/7oy964j1Vg3h4Aa8/nUwOE9HAQpQgAIUoAAFnCzAwDgnLwBPTwEKUIACFKAABShAAQpQgAIOFWBgnEO5eTIKUEBDAgyM09BicCgUoAAFKEABClCAAmBgHDcBBShAAQpQwIkCDIxzIj5PTQEKaELAUTfMa2KyGhiELQLjxDQubt+KDGnTmp3RoydPka1mTdU3sCbsmIFxZqlZQAHNCwSdv4CPP21qk3HuWfQ9ShcqZJO+2Im+BBgYp6/1Sjha3jCv7/XT4egZGKfDRXP1IfP613YrLILGTpwOwunz/+LvkHM4GXQG9x89UsLG3n4rJXJny4ZKxYspQV1JfH1td2Kd9RQVFYWTwcEI+ve8Egj159mzuHL9BvySJEGqlCmQ/f33UbJAATSqWiUuUE/LUxRhcSI0TuYY2a0rhnbpLFOaaM2lq9dwIigIJ8+cwc2wMNx/+FAJtgt78EAxe/rsGd5KkQJvJU8eZ1oo10co+NFHVgexieA4EX7255lgXL5xA1dv3YS/nx8ypUuP995NjwpFiuCTihXwdsqUVs+THagTEK9Bf58NwbHT/yDkUijuPXgIEcolwiT9kyZV9od4jqVMlkxZn3SpU0MEdBbM9RFyZc0qHR6Y2O2OAWoAACAASURBVKiio6NxLvQyREha6PXrePz0KSIiIuDh4QERHOrj7Y2333oL6VOnVp7r+XPm0MXzXN1KmK525nPXVnOwRz9a2Lv2mJcW++T1rxZXhWOiAAUoQAEKUIAC9hNgYJz9bNkzBShAAQpQgAIUoAAFKEABCmhPgIFx2lsTjogCFHCMAAPjHOPMs1CAAhSgAAUoQAEKyAkwME7OiVUUoAAFKEABuwgwMM4urOyUAhTQkQBvmHfsYtkqMG5I504Y1b2b2cHPX70afSZMMltnrsCawDhxI+Td+/dx+9495cZd8f/Pnr9QbugWN+2KG3hTif9PkRIpUySHl6enueE45PF7Dx8i9Np13Lx7VxmzuLlYhB5kTJdWufnX1oe4mVic5879+8rHW2H38OjJY/j7JVVuKH5bcUqpfBR2vj4+th7CG/2Jtbv431XF4ObdO0jimwRZ38uELBkzauKGeBFIEXzhIsIePsDde/cR9vAhxJhFUECs1wcZMiB3tqzw1Mi+svuiJXKCcQu/x5h5820yhJ4tW2Lyl/1s0lfCTrjvbMd6/fZt/HfzFsRHEV6QNVMmJRxBhLhYejAwzlI5bbTjDfPaWAc3GgUD49xosfUyVV7/6mWlOE4tChw9dQrl23WQHhpDpqWpWEgBClCAAnYQ4PWvHVDZJQUoQAEKUIACFNCwAAPjNLw4HBoFKEABClCAAhSgAAUoQAEK2FyAgXE2J2WHFKCAxgXm9g8weD9qnY6VDEa8ZdFeg38nfFzj0+PwKEABClCAAhSgAAU0JmDu+8uEj3sEBTY1+IY1d9mBGpsSh0MBClCAAhRwPQEGxrnemnJGFKCAOgHeMK/Oy9pqWwXGpUjmj4vbtyG5v7/JIUVERuKjT+ri6s1b1g4bMoFx4jx/nDmDv86exckzwQg6fx4Xr15Vfe4aZUqjYrFiKPvxxyicOxe8vbxU9fEiPBzzVq2GmL+5I2/27KhVrmxcmQht27h3LwK2BGLrgd+MNhf2hXPnRp3y5dGjRXPV4xNBdH8GBytGf4eE4K+zIfjv5g08evLU3HANHi+aNw+qlSqFckWKoESB/EiWNKmq9qaKhdvGPXuxftdurNm502SfsQ6dGzdGo6pVFIdZKwIgDM0dJfLnR7kiH5srM/r48dNB2HrgAHYePgIRWCBziLFWL10an1SogCbVq8HH21ummVITeu0aft5h2iF+RzXLlkG+Dz98o+8Hjx/j77MhOHPhAv69fFnZm2K92jeoj+zvvy89FmsL8zdshHOhl63tRmmfNnVqXNy+VfX+576T47dm34U9eIBV27Zj2cZNOBkcbPSEYv2K58uHjo0aKK9liR0hl0Kxad++uJJNe/dJP/fqVqyAEgUKJNq/CODs3LiREvTIw/4Cf/64XDnJg8uhv4qPNdZsqGj/s/IMbizAwDg3XnytTp3Xv1pdGY7L3gLLN23GtGXL8V769GheqyZqly+nKgQ8+OJFNOzVR9U1/sPfDzsk6NzeduyfAhSgAAX0KcDrX32uG0dNAQpQgAIUoAAFrBWIvnZc7c+lrT0l21OAAhSgAAUoQAEKUIACFKAABRwmELylqMG5cneB7f8CusNmwxNRgAIUkBdgYJy8FSspQAEKUIACFKAABawXYGCc9YbsgQIUoAAFKGB3AQbG2Z2YJ6AABTQuwBvmHbtAtgqME6OeOXgQPm/6qckJbNizB82/tE0QubnAuLo9emLn4cM2x8yR+QOM7fkF6lWsCE9PT6n+r926hWw1a0vViqCktTOmKbV7fj+K3hMmqArUKl+0CJZ8PRYZ06WTOt/kxUswYva3UrVqikQg2rDPPsNnnzaBv5+fmqYGtcKg/+QpSrCZmiNT+nRoUasWpixZKtWsY6OGmDt8mFRtbJEIhxs7/zur95kY68iuXdGm7idSe+rnHTvQZvBQqbFO6t8PvVq1jKs9f+UK5q5chTk/rTTafuPsWRABiY44Tp07h2LNWtj0VDsXfmdx8F/8gXDfvbksluw7EUS4eP0GDJ05U1UApQi+/LpXTyRNksTo/vhh3Tr0+HqcTfdOws6OBKxAoVwf2fUc7NxQwO/jonyjGDeFIwTU3pjHfemIVXHzc/D61803gBtP/+sF30H8F/+oUrKEEkIuwsgLfPQRUiZLZvD43fv3laDzVdu3QwTOqTkaVqmMnyZPUtOEtRSgAAUoQAG7CPD61y6s7JQCFKAABShAAQpoVoCBcZpdGg6MAhSgAAUoQAEKUIACFKAABWwgwMA4GyCyCwpQQJcCDIzT5bJx0BSgAAUoQAEKUEC3AgyM0+3SceAUoAAFKOBOAgyMc6fV5lwpQAFjArxh3rH7wpaBcSL46uwvm+Ht5WV0EuXbdYAI2bLFYS4wTgRRiUAqex2Fc+XCikkTkO2998yeQm1g3JJvvlaC4gK2BJrt21iBCGv7adIkVC1V0mz7oTNnYdrSZWbrLC1Imzo1fhgzGtVLqwshi4yKwsCp00wGm1k6HlPt1ATGPX3+HN3Hfo2VW7fZdBj1K1XCD2O/QnJ//0T7tSS460V4uBLGIAICEzscGRhnLCDCWtBuzZpi+iDLQym570yvgNp9V6d8ObQeNAQng4MtWtb8OXJg1dTJRl9jGRhnEanmG/GGec0vkasMkIFxrrKSLjQPXv+60GJyKqoEZK8HsmbKhPCIl3j4+LGqEOKEg9m2YB4qFiumaowspgAFKEABCthDgNe/9lBlnxSgAAUoQAEKUEC7AgyM0+7acGQUoAAFKEABClCAAhSgAAUoYL0AA+OsN2QPFKCAPgUYGKfPdeOoKUABClCAAhSggF4FGBin15XjuClAAQpQwK0EGBjnVsvNyVKAAkYEeMO8Y7eFLQPjxMhFwI8Ivkp4iKA4ERhnq8PZgXFiHiIMbfuC+ciTPVui01ITGCf6TJUiOc6FXraKSoTG/bs1EG8lT55oP/YOjIs9+YqJE9C4WlWpOYW/fInPRn9l80C2xE4uGxj3382b+LRvf4tDsMwBiJCsHd8twNtvpTRZqja4q3rpUmgzeKhUgKIjA+Ny162Pi1evmiNR9bjY99f37TUZWplYZ9x3ttt3IlDz3yuXrQr0EGtVpWQJbJk7541lY2CcqqeFbop5w7xulkrvA2VgnN5X0AXHz+tfF1xUTklKQDYwTqozM0W1ypXF+pkzbNEV+6AABShAAQpYLcDrX6sJ2QEFKEABClCAAhTQlQAD43S1XBwsBShAAQpQgAIUoAAFKEABCqgUYGCcSjCWU4ACLicQevq62velupwBJ0QBClCAAhSgAAUooB2BzHkzeIjReAQFNjX4RjV32YHaGSVHQgEKUIACFHBRAQbGuejCcloUoIC0AG+Yl6aySaGtA+OK58+P/UsXvzG21oOGYM3OnTYZs+hEC4FxYhyZ0qfDqfXr4O/nZ3JuagLjbAb0/45G9+iOwZ06JtqlowLjxCCOr16JfB9+aHaKjhxT7GBkAuOu3LiB0q3b4nZYmNk5WFNQt2IFrJ46BR4eys9m3jjUBMaVKlQQ/5w7Jx3c5ajAuD+Dz6Jky1ZSTC3r1MbhP/+SDpfbtmAeKhYrJtV3/CLuO9vtO9X4iTTY/cNClClc2KCCgXG2FNZOX7xhXjtr4eIjUfvGHONfjF0cidNzrACvfx3rzbNpR8CRgXEnVq9C3g+za2fyHAkFKEABCri1AK9/3Xr5OXkKUIACFKAABdxI4MxCGPw8Oled4240e06VAhSgAAUoQAEKUIACFKAABdxNwCNjUb7Pyt0WnfOlAAUUAQbGcSNQgAIUoAAFKEABCmhJgIFxWloNjoUCFKAABdxOgIFxbrfknDAFKBAjsLVJg4rifwu3arNXfEyVOTNtHCBg68A4MWQRGCeC42KPi1evInfd+jadjVYC48SkhnTuhFHdu5mcn7MC41Ik80fIll/wdsqUJsfmyJAsEV62+/uF8PT0NDmevUePolbX7jbdKzKdmQuME8+Tqp064/jpIJnurK6ZMXggujZtarQfNYFxagfiqMC4r+bOw/jvf5Aa3sopk3D8n9OYsmSpVH2XJo0xe+gQqdrYIu67VxLO2neJLZYIixOhcfEPBsap2t66KeYN87pZKr0PlIFxel9BFxo/r39daDE5FYsEHBUYt3zCOHxavbpFY2QjClCAAhSggD0EeP1rD1X2SQEKUIACFKAABbQnwMA47a0JR0QBClCAAhSgAAUoQAEKUIAC9hNgYJz9bNkzBSigbQEGxml7fTg6ClCAAhSgAAUo4G4CDIxztxXnfClAAQpQQFMCDIzT1HJwMBSggAMFtjVpYBDgUHHocAee3X1PZY/AuMbVqmLFxAlxqAOnTsOsFQE2RdZSYJyY2L3DB5E0SRKjc3RWYJwYzMhuXTG0S2eT9o4MjBOD2Ld4EUoWLGB0PC/Cw/Fh7U9wOyzMpntFpjNzgXG9xk/Adz+vkekqriZT+nTI/v77uHL9BkRooppDhP1d2rEdyZImfaOZ3gPjoqOj8WHtOrh685YUya0Dv+Kfc+dQuaPpfRy/I2F3be8e+Hh7S/XPffeayVn7ztxC7Vz4HcoV+TiujIFx5sT0+ThvmNfnuulw1AyM0+GiueqQef3rqivLeckKOCIwbuHoUWhTr67skFhHAQpQgAIUcIgAr38dwsyTUIACFKAABShAAacLmAqMC95S1GBsueocN/g3H6dP/A3B/cHnR/z9wNcHvj7w9eG1AF8f+frI18fXAvz6wK8P/PrArw+xAs7++pi7CzycfjHOAVCAAhRwggAD45yAzlNSgAIUoAAFKEABCpgUYGAcNwcFKEABClDAiQIMjHMiPk9NAQo4VYA3zDuHX01gXNZMmfBxntxYu3OX2cEGbdqAbO+9h3sPHyJDxcpm679s3w5Tliw1WxdbYE1gnAjyKpAzJ/Jky4ZM6dPjybNnyjhFsNuW/fvx6MlT6XHEFm6cPQs1ypQ22s6ZgXG1ypXF+pkzTM4nscA4ER5VOHdu5M2eHVnfew9RUVEIe/AAd+7fx97fj6oOQRODGNChPcb2/MLoeAK2BKLjiJGq7W3RILHAuJPBwSjVsrX0afq1a4uBHTsgVYoUcW3EHhv33UJMXbpMup85w4eiU6NGb9TrPTDuRFAQyrRuK+UQu38jIiORoWIl6efmL3O+RdVSJaXOwX1nyOSMfWduoSb264verVvFlTEwzpyYPh/nDfP6XDcdjnq0yjGrrVfZPcvdWYDXv+68+py7ELBnYFzubNkwb8Rwk2HlXAEKUIACFKCAMwV4/etMfZ6bAhSgAAUoQAEKOE6AgXGvrJ0dWMDzM1Ao/rOegToM1Im/H/j6wNcHvj68FuDrI18f+fr4WoBfH/j1gV8fLP/6wMA4x/3MgWeiAAW0ITC3f4DBHzCu07GSwcC2LNpr8O+Ej2tjFhwFBShAAQpQgAIUoIBeBMx9f5nwcY+gwKYG37DmLjtQL3PlOClAAQpQgAK6FWBgnG6XjgOnAAWsFOAN81YCWthcbWDcwq9GoWrnz8yerVerlpjUvx9mLP8Rg6ebDiwTHZUvWgSdGzdG2yFDzfYbW6A2MK5O+fJoVqsmapUtCxGEZuoQwVRHT53C4OkzlY+yhwi8+7pXT6Pl1gbGiZvdRUjb7bAw2eHE1eXI/AFOrV9nsl3CwLjCuXKhbf16qF+pIjKmS5fo+c5cuICZP67Akg0bpccl+j8c8OMb9dHR0fj402YQfao5xPwGduiAvDk+RK6sWZE0SRIl/O/YP6fxU2AgVm7dJtVdYoFxrQYNlgpJFCfa/cNClClc2OQ5V2/fIb3PxbqfXLP6jb70Hhg3Yva3mLx4idS6zB85Au0b1Fdqu44ZK73XElvP+CfmvntzGeyx78RrbpaMmXDp2lXp0L/4I+vWrCmmD3r9M8mHT54gON5rxbxVPyvPd5ljSOdOEEGE5o4iefPCy9PTXBkft6EAb5i3ISa7ogAFdCHA619dLBMHaUcBewTGievDzz/9FJ992gS+Pj52HD27pgAFKEABClguwOtfy+3YkgIU0I2A2gB+tfW6geBAKUABChgTSBgkl/CGej4Ow/sWusAjviN96BN/P/D5w+cHXx9eC/D1ka+PfH18LcCvD/z6wK8P/PoQK8Cvj879+sifClCAAhRwdQEGxrn6CnN+FKAABShAAQpQQFsCDIzT1npwNBSgAAUoQAGjAgyM48agAAXcVYA3zDtn5dUGxgVt2iAd7BW6czuKNmthNuhs9dQpEOOwZWDc8FmzcfbSJdQoUwYNKlfCO2+/rQpYBBLV79kLh//8S6pdyzq1sWjsGKO1lgTG1a1YAUO6dEaBnDnh7eWl9Hv3/n2Im+rnrXozRCyxQT49fhSeJoKPfgrcipVbt6J4/vz4tHp15MySWWq+sUUicGvQtOmYtSJAql3a1KlxZdeON2pPBAWhTOu2Un3EFjWtUR1zhg83GQD4Z/BZlGzZSqpPUwFjwRcvolDjT6X6GP75ZxD/mTsqdewkva8u79yBdGlSG3Sp58C4qKgo5KjzCa7evGWOSXn84vatyJA2rfL/W/bvR+M+/aTaiYCy/3bvQhJf30True+M89hq3/Vp0xpdmzZF5owZ4OHhAfF6EXrtOtoOHaYqkFMEvK2faTp4dNKixRj57RypvSG+3tSrVFGqlkWOFeAN84715tkoQAHnC/D61/lrwBE4V0CEtZ8LDcWpc//i77NncTI4WPke8dGTp9IDE9/3F8mTF8Xz50ODKpXxce7c0m1ZSAEKUIACFHCWAK9/nSXP81KAAg4UMAj6kTivQRCQRD1LKEABCuhagIEFzg0soD/947+AMFCIgULx9wNfH/j6wNeH1wJ8feTrI18fXwvw6wO/PvDrg+VfH3R98c7BU4ACFJAQYGCcBBJLKEABClCAAhSgAAVsJsDAOJtRsiMKUIACFKCA/QQYGGc/W/ZMAQpoW4A3zDtnfdQGxp3ZvBFLN27C518ZD0eLP4usmTLh4tWriU4sU/p0OPvLZqzbtdumgXG20Nx1+Ag+6fGFVFdVSpbAlrnGA4vUBMYJjx/Hj0epQgWNnlcEbnUeNRoBWwKlxiWKLu3YhnffeUe6Xm2huKE/bbny0s0eH/s9LgQvttHsgAAMmDJNuo829epi4ehRidbbIjBu/Pc/4Ku588yOSwThhWzZjKRJkpitVRN8tm7mdNQuV86gT2sC48oXLYIPP/gA2d57D2neeksJarx19y7+DjmHsIcPMGvIYOTPkcPsHCwtOPbPPyjXtr1U86J58+C35cviap88e4Y0ZQwtEuto4+xZqFGmdKLn4r4zzmPtvhOvh3OGDUOWTBmNnuDew4eo3LEzzly4ILUXcmT+AKfWrzNZy8A4KUbNF/GGec0vEQdIAQrYWIDXvzYGZXcuIxD+8iUePHqEB48f497DR3H/HxkVhWRJ/ZDCPxmSJ/PHu2nSIGO6dC4zb06EAhSgAAXcR4DXv+6z1pwpBdxYgIFxbrz4nDoFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQwB0EGBjnDqvMOVKAAhSgAAUoQAHtCDAwTjtrwZFQgAIUoAAFTAowMI6bgwIUcFcB3jDvnJW3JDBOBDdlqV4DIijM2mNc717o164tVm/foZnAuIjISOWm9JBLoajUsZPUFBOGW8VvpCYwrk758lg7I/HgtPNXriBv/YZS4xJFB5YtRbF8eaXrZQujo6OVm/jDHjxAhfYdcTssTKrpnd/2I7m/v0Htp/36Y/O+X6XaiyIRXCgCCRM7bBEYV/2zz7H/+Amz42pSrRp+nDjebJ0ouHT1GnLVrSdVO7RLZ4zs1tWgVk1gXIpk/vi0Rg3Ur1QJFYoVhZ+vr9R57VU0bOYsTF36OgQusfN807sX+rdra/E+aVe/HhaMGpnoVLjvjPNYu+8m9e+HXq1aJmq/fNNmdBn9lfRWe3r8KDw9PY3WMzBOmlHThbxhXtPLw8FRgAJ2EOD1rx1Q2SUFKEABClCAAhTQgQCvf3WwSBwiBShgrQAD46wVZHsKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAV0IRB6+rra34voYl4cJAUoQAEKUIACFKCAPgUy583gIUbuERTY1OAb1dxlB+pzRhw1BShAAQpQQEcCDIzT0WJxqBSggE0FeMO8TTmlO7MkME50PnzWbExZslT6PKYKr+7ZhTSpUjk0ME4EnV26dg1/h4TgwpX/cOXGDeU/EeR16dpVi4LwHBkYJyzTlisvPc7AeXNRuURxi9ZKhMCdOvcvQkIv4cr1Gwi9dl0xCr1+QzogLuGJjQXGqZmPTBCYOKe1gXFPnz9H6tJlpdyqlSqFL9u3k6p9GRGBT3p8IVXbvkF9zB85wqBWTWCcTHCX1EBsUBQVFYXM1WtK75uTa1Yjd7ZsBmdWEzImwvKu7N6VaEge953xhXXEvgs6fwEff9pUemeFHfoN/n5+RusZGCfNqMnC+6GhyrhOrlheSXystWbDPk0OlIOiAAUoYGMBXv/aGJTdUYACFKAABShAAY0L8PpX4wvE4VGAArYUUHtjlPJGVR4UoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQQG8CDIzT24pxvBSgAAUoQAEKUMC1BRgY59rry9lRgAIUoIDGBRgYp/EF4vAoQAG7CfCGebvRJtqxpYFxF69eRe669a0adPxQotXbd6DtkKHS/V3YFoiM6dJJ199/9Agb9+zFul27cOjPP6XD1mRP4OjAuLJt2uL46SCp4akJjBNhZrsOH1EC/PafOI6rN29JnUNNUcLAuPCXL5GyRCnpLmTnY21g3NFTp1C+XQfpcdmjsH6lSlg1dbJB13oNjDvy19+o2KGjFFPWTJlwZvPGN2pv3Q3DB9WqS/UhitbPnIFa5YyH/nHfmWZ0xL579OSpEnwpezAwTlZKf3X7xn1tMOiaazbwBlH9LSNHTAEKWCDA618L0NiEAhSgAAUoQAEK6FiA1786XjwOnQIUUCvAwDi1YqynAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClBAlwIMjNPlsnHQFKAABShAAQpQwGUFGBjnskvLiVGAAhSggB4EGBinh1XiGClAAXsI8IZ5e6ia79PSwDjR86f9+mPzvl/Nn8RExZGAFSiU6yPlUXsFxoVcCsX473/AT4GBFo9TpqGjA+Pq9+yF7QcPyQwNMgFrIlBv6pKlmL96tc3D9BIOMmFg3O2wMLxfVT4E7O91a5EzS2azc7c2MG7rgd/QsHcfs+exZ0GZwoWx+4eFBqfQa2DcwKnTMGtFgBTXl+3b4etePY3WVurYCYf//Euqn5Z1amPR2DFGa7nvTBM6at/5fVxUah1FEQPjpKl0V8gb5nW3ZBwwBShgIwFe/9oIkt1QgAIUoAAFKEABnQjw+lcnC8VhUoACthBgYJwtFNkHBShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACmhdgYJzml4gDpAAFKEABClCAAm4lwMA4t1puTpYCFKAABbQmwMA4ra0Ix0MBCjhKgDfMO0ra8DzWBMbt+f0oanfrbtHASxUqiL2Lfohra+vAuJcREZix/EeMmP2tReNT28jRgXEtBgzE+t17pIZpLjBu09596PHNOIgALUccCQPjRKhfgUaNpU+dWHBU/E6sDYxb8csWdBo5Snpc9igsnCsXDgf8aNC1HgPjIqOikKV6Tek9Jl4bxGuEsUOEzonwOdnD1H7hvjMt6Kh9x8A42V3s2nW8Yd6115ezowAFTAvw+pe7gwIUoAAFKEABCriXgJtf/6YQf38GwCL3WnXOlgJuK8DAOLddek6cAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKOAeAnP7Bxj8PqROx0oGE9+yaK/BvxM+7h5KnCUFKEABClCAAhSggK0EzH1/mfBxj6DApgbfsOYuO9BWY2E/FKAABShAAQqYEGBgHLcGBSgQIyCScv5yJw3eMO+c1bYmMC4qKgoFGzfBudDLqge/YuIENK5WNa6dLQPjIiIj0az/AGzZv1/1uCxt4OjAuI4jRiJgS6DUcBMLjJuyZCmGz5ot1Y+tihIGxqkJdkuRzB+3D8itq5p+OzZqiLnDhxlMUW0wma184vfjqOAue4w9fp8HT55ElU5dpE8zZ/hQ+Hh7G60POn9BCYOUPX6eNhV1K1Z4o1zN/uC+A+wRVPh+1erSIYKJBUVOWrQYI7+dI7UlVk+dgnqVKkrVssgxAm5+w7xAFjfM/+wYbZ6FApoW4PXv0OGaXiAOjgIUoAAFKEABClDAOgE3vf59C0BfAL0BiB9Cj7ROka0pQAGdCDAwTicLxWFSgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShgmQAD4yxzYysKUIACFKAABShAAcsEGBhnmRtbUYACFKCAFQLR0a/eC+rh4WFFLxY0jYgEvL0saOj8JgyMc/4acAQU0IhAQwBjAYyJCZFQ++Z6jUxDfhjbmzTYJ6rf+iCzkuxTqHUb+castFjAmsA4cdKFa9ai57jxqs6fNnVqnN+6Bb4+PnHtbBkYJ8YjxuXIw9GBcWrmaCow7qfAregwfIQjmZRzJQyMC7kUigKNGkuP4/kfx6Vq1QSCGQuMG7fwe4yZN1/qXPYqcpXAuP6Tp2DOTyvtxZRov81r1cSSb75+o4b7zjSbo/Zd7rr1cfHqVal9wcA4KSZdFrnpDfOeAKYA6A5gBoDBulw8DpoCthXg9S+vf227o9gbBShAAQpQgAIU0JiAm13/pgLwJYCeAFICeAmgBQDH/tBeY3uAw6GAGwmo/Z22g99M5EYrwalSgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShgFwEGxtmFlZ1SgAIUoAAFKEABCpgQYGActwYFKEABCthNIDoqGoiOQnR0BKIjXyIq/Bki+dfuOwAAIABJREFUI14CURGAeHunhxe8vH3h5ZME8PaFh4c34Onl+CA5uwnYrmMGxtnOkj1RQOcC4tXzHwB5AJz+/yvpVwDWAFD7JnvdMTz/47jLz1FLi2JtYNzDJ0+QrpyS8Sd9jO7RHYM7dTSot1Vg3I5Dh1Dvi17SYzFWmCl9OmTJmAnvv5seK7duk+rL0YFxfSdOwrxVq6XGZiwwLuzBA2SsVEWqvakiEfyXOcO7yJwxI9bu3CXdV8LAuOu3byNrjVrS7e8fOQQ/X1+z9dYGxs1duQr9Jk02ex57FjgquMuec4iIjESGipXw6MlTe54m0b6NhY1x35kmc9S+y9+wEc6FXpbaFwyMk2LSZZGb3TAvguIm/j879QsAfgBeAPAHEKXLxeOgKWBbAV7/2taTvVGAAhSgAAUoQAEKaEzATa5/UwMYCKDH/4PSk8dbgmsA3nOH329pbNtxOBRwloDa3/MyMM5ZK8XzUoACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoYJEAA+MsYmMjClCAAhSgAAUoQAELBRgYZyEcm1GAAhSggHGBqMgoREeGI/rFQ4Tf+w+RT28j6uFFeDwLhU/kHXhFP4GHZ6Ty3u/oaC9ERCdFpGdqRCV9Dx4pssIreQb4pMwIr2Sp4eHlB08vce80DwbGcQ9QgALxBBoBWBvv3yJAboyrB8cxMM6xzwFrA+PEaAdOnYZZKwKkBx66czvSp0ljUG+rwLiGvftg64HfpMciCutXqoSKxYuhRIH8yJ8jB3y8vePa+31cVKovvQXGzfxxBQZNmy41t9ii4vnzo1a5sihdsCA+zpMHKZKJjJtXR/2evbD94CGp/hIGxj14/Bjpy1eUaiuKLu3YhnffecdsvbWBcau2bUe7ocPMnkcUiPC8Pm1aS9WqKcqSMSMaV6tq0OTnHTvQZvBQqW4m9e+HXq1aStXaq2j/8ROo/tnn9upeqt+VUyahQeXKBrXcd6bpHLXvnBEYt2rqZOU1n4d2BNzkhnnxw45xAHrHBMXFLoBIJRU30vNwnMBoladSW6+ye5YnEOD1L7cEBShAAQpQgAIUoIDLCrj49W9aAIMAdAWQzMgifhrzey2XXV9OjAIUMBBgYBw3BAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAm4hEHr6utrfi7iFCydJAQpQgAIUoAAFKOAcgcx5Myh/uNEjKLCpwTequcvy/jXnLAnPSgEKUEBbApEvIxD5LAwv715AZNgZeIYdRXKfC0DKSCBJFJAEgLfnq/88Yv4YsPiKEhEJREQB4dHAC0/giSeePsmIFymKwPudgvBLlwOeydLAy0d04L4HA+Pcd+05cwoYERAvoiIkLk+Cx8TnvooJk3O5Hy4zMM6xzwVbBMaFXApFgUaNpQbesk5tLBorcg8ND1sExl2+fh0569SVGocoypopkzKWUoUKmmzjqoFx2WvVxtWbt6St5o8cgXb168Ej9nu7BC2tCYyLjIpCsqLFpccSMGkiGlWtYrbe2sC4XYeP4JMeX5g9jyioWKwYti2YJ1VrbZHeAuP6TpyEeatWWzttq9o3qVYNP04cb9AH9506UnvsO2cExv00eRIaVjEMD1QnwWpbC7j4DfOCSwTF9QGQNIHdCwAieTXK1qbsL1EBtddOMT/UoqqDBHj96yBonoYCFKAABShAAQpQwPECLnr9mx7AEACfGbnujUW+CuB95S/M8aAABdxFQG0Av9p6d3HkPClAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFNC4AAPjNL5AHB4FKEABClCAAhRwMwEGxrnZgnO6FKAABWQFIiMiEfnwBl5cPY6oGweQMuIkPNKGv7rtObkvkDQFkDQDkOQ9wCsN4OUPePiIDFIgOgKIegZEPgReXAOeXwWehQFPngNPI4F7Pnj0Igc8MlSBz3vF4JXqA3j7iLbudzAwzv3WnDOmgBkBkcK1xkTNqZjguHWudLMNA+Mc+5ywRWCcGLFsYNiBZUtQLF++NyZpi8C47QcPKeOQPa7v24O3U6ZMtNwVA+MePnmCdOUqyDJh07ezUL106UTrZddfdHLnt/1I7i8ycl4fhZs0xZkLF6TGVKVkCWyZO8dsrbWBcWrai8HcO3wQSZPYP/jYHsFdZjEtLHgZEYGMlSrj0ZOnFvZgu2bcd9ZZ2mPfOSMwbsXECWhcrap1GGxtUwEXvWFeGI0F0C8mFM6Y2RQAA2yKyc5kBNQGFDAwTkbVtjW8/rWtJ3ujAAUoQAEKUIACFNCIgItd/2YAMBRAZwB+Zog/TeR3XBpZHQ6DAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSggHoBBsapN2MLClCAAhSgAAUoQAH7CTAwzn627JkCFKCAbgVePArDy8tHEHVlO1J6/gGkDgeS+wAp0gApcgHJCwNJcgK+uQCPTIBHcgDeADxj5izuyY34/x8ZfwFE3QYizgHhwcDjv4HHp4EHV4DHz4BHPnj8OAeiM9aBb5by8HkrPTxju9CtnrqBMzBOnRerKeAGAiKk4B8AeRKZq0sFxzEwzrG72laBcTJhbUXz5sFvy5cZnaAtAuOWbNiIrmNEPov5o0aZ0tg4e5bZQlcMjAu5FIoCjUQWh9zx4MghJPH1TbTY2sC4LqO/wvJNm+UGBOBIwAoUyvVRovVqAt86NmqIucOHGfQXERmJDBUrSYedbZ0/F5WKF5eeg6WF9gjusnQs5trtPXoUtbp2N1fmkMeNBYVx38nT22PfOSMwbsGokWhXv578xFlpdwEXu2FeeI0G8CWAZIngvYgJkouyOzBPkFCAgXHa3xO8/tX+GnGEFKAABShAAQpQgAIWCLjI9e97AMQPUTsCSPwHxq+M/gPwgSv9wSMLlp5NKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABFxVgYJyLLiynRQEKUIACFKAABXQqwMA4nS4ch00BClDAHgKRUcCL68GIOLcBSe9thc+7T4GU3kCqzMDbJYDkJYCkRQHPzDF/QNwTiI4EIO57NnEfrocXAHH/ZzgQfQcIPw08PgLcPwSEBQEPHgN3fXHPqzx8sjdAksxF4eMjwufc42BgnHusM2dJAZUCn/4/MG61RJu///8C+xWA9Xq+AYeBcRIrbcMSWwXGRUZFod2QYXjw+LHJ0XVr3hS1y5Uz+rgtAuO++W4hxs5fIKUzsltXDO3S2WytKwbG/XrsOGp83tXs3EVB+aJFsOM786bWBsZ9G/ATvpwyVWpMoihFMn+snT5dGZ+xY9+xY+g5bjzOhV6W6tNYYJxo2GrQYKzduUuqj0zp0+G3ZUuRIW1aqXpTRSeCgtB7/ASEXr+BXd9/h4+yZDEotUdwl1UDTqSxWIOFa9baq3tV/TasUhk/TZ5k0Ib77jWHM/adrQLjJi9eghGzv5XaD92aNcX0QQOlalnkGAEXuWFeYI0AMMhMUFwsqviCJ0LleDhegIFxjje35Iy8/rVEjW0oQAEKUIACFKAABTQtoPPrXxH6Jq572wHwUQEtvrdfo6KepRSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAU0LzC3f4DB+1HrdKxkMOYti/Ya/JuP0yf+huD+4PMj/n7g6wNfH/j68FqAr498feTr42sBa78+eAQFNjX4hjV3Wd5QqfmrDA6QAhSggA0Fwl+E48W/vyL631VI6X8KSBUJpHkXeKc88HY1IGkRwCMVEB0NRL+MySYSQXCeMYFwsf8vBhUbICc+xv4nynwAJUAuHAgPAR7uBW7tAu4EA/ci8PT+B4jI3BK+H9WAX/IUNpyddrtiYJx214Yjo4ATBcQL6j//D43LIzmGv2KC4zboMTiOgXGSq2yjMlsFxlk7HFsExg2fNRtTliyVGkr35s0wbeCARGsjIiORvFgJqf6K5s2D35YvM1p77dYtZKtZW6qfOuXLY+2MaWZr+06chHmrZHIkgcB5c1G5RPG4PrcfPAQR8CZz5Mj8AU6tX2e2tEqnLjh48qTZOlFw57f9SO7vb1D7d0gIijdvKdU+flGbenXxce7cSqjai/BwnLlwATsOHYYIjFNzmAqMW7RuPbp//Y10V/lz5MDuRd8jZbJk0m1iC6/fvo1JixYbrOuBZUtQLF8+g770EhgX/vIlMlWugkdPnqq2sFeDWwd+NVgb7jvAmfvOVoFxC1b/jN4TJkptm9zZsuH46pXw8hTXrDy0IPDnj8uVYTy4HPqr+FhjzYaKWhiXijEMBTAEQHLJNi8AiC+C4gcjPBwvwMA4x5tbckZe/1qixjYUoAAFKEABClCAApoW0On1b9b/X7/+j73zAG+qesP4myZtOmhLy95DUKaAgIDIFmQ5UAQBQf0rSwRZgqKyBURBBEUQRQQBQRAQ2RtkKMPBkCUCUjbdK2nGn5Mm0kKbnNtmtu95nlhsvvOd7/zOyW1vb+4vowG8AEDpp7tdAiBEc0rPw7x6HVkcCZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACVAYR6FNxldBboUu7E9hWMb9RGEWjy88vtwhwOMjj488Pt4hoPTng1cI465dj8aly9ekzh7CwwqgUsUyUrF3Bx09fhbiZmaZVvm+sggLVX7zt0xuxpAACZCAtxBIuHUTqt8+R2DCNmgKJwKRwUCJ1kDRTkDgA+kfHm42AGajVQ4n3iMuHuLGezWgEvd22h5iVuK94EIsZ5PFiX62h/ieClBp0vsZrwHR24FL3wM3/oX5hgbJ/o1hrNkXYWXu9xZELquDwjiXoWViEvB1Al0ALFM4CZ8Ux1EYp3CVcxmel4Rx36z5EX3HjZciUiQyEoeWLUWxQoWyjBfisd5jxuLQ8RNS+XxJGHf6/AU8+MyzUvMSQZvmzkGz+vWyjI+Jj8ewDz/CknXrpfNlJYwTnet37YajZ85I53FmYHbCuJi4eNzfsaMi6ZkQUg1/6UV0eqwVggMD7ZZpMplw6PhxfL9pC2YtWXJPrC8L47buP4COA16XXqatX36BKhXEPajy7bsNGzH8o2nSHRZOnoQuj7fJFM9957l95yxh3NL1G/Dyu+9J7wN7wtCklBTExiegVLGi0vkY6BwCgQ/VE39A8KU2EsA7AJSa7T8GMNSXJprHalUqKvC1fZmXlovnv3lpNTkXEsgHBMR5dmKKvCw7MECLavdVzAdkOEUSIAESIIG7CfjI+a+4ID8GgPJP+LgzYfE7/ffcASRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiSQ1whQGEehU8Y9TaERhUYZ94NSoQ/3D/cP988dAnz98Ocrf77eIZDbnw9eIYx7ZcB4zF/0o9S5QNEikbj292ap2IxBMbHxiCzbUrrf9MlDMWRAbt4XKT0UA0mABEjAIwQSbl4HDs5ESOp2+BXWA4ULA2WeA4q0BTRFrKI3cY+tkMOpALMZMNn+X239vvU5ywzE/bXieSGGE1+FKE7825hBIGewfk90196+7zoNiPsNuLgYuPoncMsPiaa6MNUagLDyNTzCxV2DUhjnLtIchwR8joA4mB4DUC0Hlf9++2A8DsAa64E4Bync14XCOPexFiPlJWHcr0ePoumLL0sDrFm5Msa81h+tGzWENiDAwuLo6dPYvG8/JsyZK51HBPqSMM5gNKJA/QbS8wsNCcakN95A5zatEREWBqPJhNPnz+PAH39i9GezcSM6WjqXCMxOGDdn+XIMnjJVUS5nBWcnjBP5P/x6Ad6b9anioQS3/3XqhAfKl0fxIoUtckI/Pz9cvXETl29cx8lz5/Hdxo12+fmyMK7f+AlYsFr82HHchMDx4pZNUFmk0/It6tp13NeuvXSHJ5o3w/fTMwvmuO/uxeeufecsYdzOgwfRtm9/6X0gAju3bo1uHdqhbIkSEMfEY6fPYMfBg//JLw8uWwrxM4LNfQR85IZ5AWT47T9mCENhWA7o6AAE3/njRw4ysEtuCVAYl1uC7uvP81/3seZIJEACuSQg/pZQptVjikTjYsgrO7dbzrHZSIAESIAE8hcBLz//FZ8aNxaAkL2Ji/05bf8CKOcL16NyOkH2IwESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESyL8EKIyj0Cbj7s+t0IX9KQzLuJ8ozOLxhceXOwR4fOTxkcfHOwSU/nzwCmHcy/3HYcHitVJnDRTGSWFiEAmQAAnYJZAghB8HpiEkdRv8Ig1A0RJA+Z5AkaaAXwCAOJgS45CSpIPaT4PA8HAgIAKAFjCZAJO4n1MFqIQ4LqPwQdyTa06Xy2UUxlnEcVZ5nPhqtsrkLP39gaTz6dK4SweAWyokGuvCVG8owsqKDzbPm43CuLy5rpwVCTiJwHO3hXHLc5FLiOPEzT5yBp9cDJSbrhTG5Yae8r55SRgXl5iIYk2bK4cAQMi9EpKSc9RXdPIlYZyo99GevXDo+AnF880tJzFgdsI4wb92584QEjB3N3vCuMTkZFR4vG2u9kdO5+MucVdO68uun06vR2kFwoSB3bvjw+FDc1RGo+4v4LeTJ6X7Xt21AwVDQ/+L5767F5279p2zhHGxCQko3izzH8GlN0Q2gQeWLEbtKuIeaTZ3EfDyG+YFhhG3zfbv5FAUZ8P4MYCcHezctRB5fxwK43xrjXn+61vrxWpJIN8S2LDnZ3R6Y7Di+c+fMB7dO8gLsBUPwA4+QUDI1tft3uOw1gB/DX5fscLy9ys2EiAB3ybgpee/4oOKxIcOPXvXBf6cwhbCue9z2pn9SIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESMAXCFw4fkXp+1J9YVqskQRIgARIgARIgARIwEcJlKtewiL4oTAumwWcPnkohgzo7qPLy7JJgARIIHsC8XEJUB34BCEJ6+EXoQeKFgcq9ASKPQwgEVePH8WJA0dx+vd/EH09AWqNGiXLF0GNBjVQ+/HWUAWVAIxC+ib+ziGEb5YfJ9avtr992ORwQgwnBHJWQVxGaZxNImdWAUZ/IOU68O8y4N/9MEerkaRqCFOj4QgrViZPLieFcXlyWTkpEnAWAXFQPQ2gUi4T/na7vxDH/ZjLPC7pTmGcS7BmmzQvCePEJJUIiJxJ2teEcUOnfojZ3y1zJgLpXNkJ40SCzfv24cnXB0nnclagPWGcGGP3ocNo06evs4aTzuMucZd0QZKBm/buw1MD5ddx/eez0bKBOOdQ3j74aj7GfDZbuuPXE8ejW/vMUgbuu8z43LXvlByvo/f9jODAwGzXWak40NGGoTDOESHnP++lN8yLib5225A/OZeiOJFHB0DYPcQfRdg8R0DpG3MyfhKC56rOvyPz/Df/rj1nTgI+ReDVMWPx7dqfFNfcrsmjWPXJDMX92CFvEejw2gBsO/CL1KQubduCwhHiA5zYSIAEfJmAl53/1rz9SW/vARCyZme1iwDKp78RgI0ESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAE8i4BCuPy7tpyZiRAAiRAAiRAAiTgiwQojHOwahTG+eK2Zs0kQAKOCBiNKsTtnoOwq99CUygFKFIIKP88UKwu0uKuYNfKzdj03RFci0pAYDAQEh4KY5oRiXHJUJk0aP5MTXR89WlE3FcdMPpZpXHivs6MwjghiBPvDbfK4sR90ipxr7T4nu37VoGc2QQYjYBB9AkAUm4BF5cBUb/DHK1BYnBb+Ld6F4FajaOp+dzzFMb53JKxYBJwN4HnASx10qBHbh+ox3mLOG5D56ebi3nV6dFzh/hasFw5J02TaewRyGvCuK37D6DjgNfdvui+Joy7dusWajzdCQlJyW5nZU8YJ4p56+MZmLHoW7fW5UgYJ4pZvmkzer09yq11uUvc5exJ9R47Dot+XCudNu7APmgDAqTjMwYePXMG9bt2k+7boWlTrJwx/Z547rs7SNy175wpjFv80zq8MnqM9D5wFEhhnCNCzn/ey26YFxMUorj3xa+kTprtJwAGOykX0+ScgFJhAYVxOWftrJ48/3UWSeYhARJwCYHk1FREPvJojnNf3rENkeHhOe7Pjr5PgMI4319DzoAElBLwkvPfOgDGA+iotH6JePE7vGc+qUSiOIaQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgLMIUBjnLJLMQwIkQAIkQAIkQAIk4AwCFMY5oEhhnDO2GXOQAAl4G4HkqJMw7xyGkILXgMIhQNmngOINoE+OxopZP2Djsr9QvFwY6rVpiBqNa6Fg8UiYDGZcu3gTR3f/jp9X78Z9VQqi1+heKFXzQasTTtyHe5cwTkjixMPPD1CL52yiuLQMIjnr901GQGcE9AbA6A8kXQHOrwCunkPqjSDoH56MkEpNoFYrvd/X2+hnrofCOO9eH1ZHAl5AwA/AKQCVnFiLEMeNBSBv93Hi4LZUGzs/nemA3nzUuy4YhSnvJpDXhHFifv3GT8CC1Wvcuti+JowTcJauX4+X3x3tVk5iMEfCOBEzbvbnmPzlV26rrX/XLvh45AiH483/YRVemyj8Qe5p7hJ3OXM2KTodIho1lk75fLu2WPD+ROn4uwPNZjMqte+AqGvXpXNc2bEdEeFh98Rz36Ujcde+c6YwTqfXo/azz+GfqCjpfWAvkMI4p2BUlMRLbpgXNb8KYCqACEUTsB+su50zOP2PIWweJqD0D0gUxnl4wW5LLHj+6/k1YAUkQAJ2CPy0azc6DxmaY0bzxo5BzyefyHF/dvR9AhTG+f4acgYkoJSAh89/6wGYAKCt0rol4y8CKG99A4BkF4aRAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgG8SoDDON9eNVZMACZAACZAACZBAXiVAYZyDlaUwLq9ufc6LBPIvgdT4WCQfmIuIpO+hilQDZZoCRZsAhUJxdv9RTB+2HLWa1cDTA59EoZJFoNaYofIzWu7ZNJkCYUzT4MSBv7BtyWY0alsd9Ts8Ao2fGjCJe6GzEMapzIAqDfrYW7h+4SqS4uKhS0lBWmoaoDLDXxuAsEIFUKxUYQRGFoTKqAF0JsDkD8SdBs7/BFyPRoy+EYKavYnAomXz1OJRGJenlpOTIQFXEegGYIkLkh++nVOI435yQW6HKSmMc4jIJQF5URgXEx+PB5/pjBvR0bliNmvU2xg4abJUDl8UxgnR1tOD3sCmvfuk5phd0ItPPWmRNO0+JA4hjpuMME5k+X7zZgyd+pHidaxQqhRmvDUSqXodug5703FBAN7t28fykGnHzp5Fz7dG4a9z52TCcxXjLnFXroq8q/P6PXvwzBtDpFMunDwJXR5vIx2fVeCIadMxc7H8j8Wvxo9Dj44dshyT+843hXFiMU+dP49He/ZCQlJyrvaT6ExhXK4RKk7g4RvmRb1CFPcBgEjFxTvuMAvAIMdhjHADAXGuo6QpjVeSm7HyBHj+K8+KkSRAAm4mICTsQsae09a6USOs/Uz8qsCWXwlQGJdfV57zzs8EPHT+2/D2hf3xAFq7mL343f07F4/B9CRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiTgUQKzhy3J9AHGHf7XIlM96+bvyPT/fJ58Mm4I7g++PjLuBx4feHzg8eEOAR4feXzk8fEOgdz+fFCdWN8l0y+sVR8d4fZfoF/uPw4LFq+VGrdokUhc+3uzVGzGoJjYeESWbSndj8I4aVQMJAES8AECRhOQ9Oc6BByehMCSeqDk/UDploA2FAhR48bVBJw7G406zR5AQLAGSEsFzOLHgxoQUjiNH8xmf0BVALoUA2DUQatVWTVxNlmcAGH9kaL2Q1pKEn5fvw87Vv6Cf/+Jhz4VMBpVlhA/tcrimFNrgBKlA1G3cUU06fQwwooVApKNgMEM3PgduLAbuJqG6LJ9EdLgBWi1gT5AW65ECuPkODGKBPI5AT/hRQFQyUUcDt02fo5ztziOwjgXraaDtHlRGCemfOnaNfQfPxFb9u9XDLZm5cqY/d67qF+jOgIfqifV3xeFcWJiyampGPvZbEWyLRuQ0JBgzHz7LXRr3x5PDRwkLZ6TFcbZ6vvqhx/w/aYt+PXoUbtrIURxLz39FAb26I7gwEDMXf493pgi3D+O24y3RqBfly6OA60RKTodZn+3DDMWfatYaCczSNWKFdGvy3Po+eQTlrlkbEJoJoR1Mm3qsKEY1KO7TKjTYv733mgsWScvTLiyczsiwsJyNf7OgwfRtm9/6RyPN34Ea2bNzDZevC6471y/72p2egZnLlyUWrfofT/f81rIquPp8xcw7MOPcnTsz5iPwjipZXFqkIdumBdzEKK4KQAKOXVCd5KlAQi+7SY1uCg/05JAfiDA89/8sMqcIwn4IIGklBQUatwk15Vf2rYFhSMicp2HCXyTAIVxvrlurJoEckPAzee/zQGMBpD53bW5mUD2faMAiE95E58ox0YCJJC/CSgV8CuNz990OXsSIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAGPE6AwjkKbjJswt0IX9qcwLON+ojCLxxceX+4Q4PGRx0ceH+8QUPrzgcK4bE4ZKIzz+LkUCyABEnAigaRrF5C4/QMU8/8VKBYBlGkGhJUB1EZAbQaCA4HQIECnA9IMgFoNqP0Afw30ScmIOnYR8TeTcV/9+1CgeDHApBL2N8As7uUUzSaNswrj/NVIiE3C2tm78evWMyhZLgzhkQUQEhGEgkXCUSCyAMxmM5ISUhBzMxlpqalo1b4SylUtAsTrAJMaSEkGLu8FLpxCTFxJaFtNRHC5mhnGciIgD6SiMM4SRoKdAAAgAElEQVQD0DkkCfgmAWEAWuzi0oU4TrxJf52Lx7GkpzDOHZTvHUOfloawBo2kBhcitYPLlkrFKg1av2cPnnljiHS3qO1bUahgQbvx4neK+atWYcDESVJ5hXBs0As98Oqzz8Bfo7H0KdKkKRKSkh32b9WwAdbN/izLuJsxMSjdqrXDHCKgy+NtsHCy43rfnTkLHy34Rirnzq/no2GtB+3G7v3tN/Qa9Q6irl13mFOI4np37ozhL72IyPBwS/wLI9/Gii1bHPYVATH79yJIq5WKzRh0Izoaf5w6jRsx0bgRE4vE5GREhoehWKHCqH7ffbi/fLlMOZUwEswFe6XNYDRi/e49FrnYpr37lHbPFC9eX+2bNsETzZtDCAiza0peK5+9OwqvPPNMrupS2lmJBKx5/frYOPdzpUPcEy+OY6VatpJ6rYrOYg9f370LKlVGwXXWZXDfpXNxxb5r1P0F/HbypNT6xx3YB21AgFSsCFq9fTvmLPseQiaotAlh44qPp+G+MmWUdmV8Lgi4+YZ5UWlPANMBFM5F2TJdZwEYJBPIGBIgAbsEeP7LDUICJOB1BNbs2IGuw97MdV1zRr9nkZ+z5U8CFMblz3XnrPM3ATed/z5mFcXl3mwqv1zdAHwnH85IEiCBPEwg0wcUS8zT8R/qJZIwhARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgATcReBuYdxr07pnut7B55dkul5EPtwfGV+bfH3w9ZFxP/D4wOMDjw93CPD4yOOjM4+PFMZlc2ZAYZy7Tpk4DgmQgKsJGAxGxB5ehcInpgKFzEDZB4EiNQGtf7osziKHA6BWpUviLA8VEKBBSmIC9i77DT99ewJxt9LQ7vn70a5PY4QWKwykWc/nLQIG8bD+v9kMqMwwQw290R9mkwqBAUYgQMjlxEAiTjzEB49b+xj0QFwSkKoHjOb0h8EPiLsInNsD3NLhepGeKNj0VQQEhbgamVvyUxjnFswchATyAgFx8DwFoJIbJiNsK0Ict96VY1EY50q6+Tu3ED4d+eskjp45gz9PncLRM2dhMBpQICgYRQtFQoi6OjRtapGqyQik8irN5NRUHD5+Asf/Pos/T5/Bbyf+QmxCAgoEByMiPAxCotTi4fpo9+ijisRNnuAlZHIVHm8rLRDbMu8LNKn7UK5KFfK4v86dw5ETf+HP06cRHReP2IR43IqNQ1paGtTid2kAEWHhCCsQYpHtVa90H2rd/wCqV66EsJC88btsriD6eGfuO+9bQCETFMezw8eP40ZMDOITExESFISbsbHQqNUICgy0vBZLFC6M+8uXt7wmgwMDvW8i+aAiN90wL0gKUdw04YR1A9Y0AMEADG4Yi0OQQF4nwPPfvL7CnB8J+CABJeJ0e9NzlkjbBxGyZAAUxnEbkED+I+Di89+2VlGc3CfEOA//JQDi0zzERX42EiABEqAwjnuABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEggTxOg0IVCl4wbnMIrCq8y7gceH3h84PHhDgEeH3l85PHxDgF3/3ygMC6b0xFnCOMuXrqKK1dvIi4+EXFxiUhJ1SEyIgwlihdGiWKFUbRIJDQaIU9yTbsUdR1//3MJ0TFxuBWd/jAajYgoGGZ5REaGocr95VG2dHHXFOCDWZNTUvHP+cuIunId129EW24qrlSxNCpWKI3QAuLeT7asCOh0ehz762/cuBlj2WfRMfGWPR8cHGjZ85ER4ShWNBK1atyPwMAAQnQzgZToK0jc9D6KmA8AhUKA8o8AoYXSZXF+QhKnAjR+gMb6VXxP4weTGjiy4S8snHoQMBih1apgMPuh56gmqNO+GlRGFWCyvQc0oxzfKoTzA8xqFWL+vYkTu85CG6TG/Q3LIrxoOKA3pkvhLKGm9K96AEYTYDABJvFVBej0wMUDwNUruBlbHMEdP0JQySqwOOp8vFEY5+MLyPJJwL0EetwWxn3rxiEP3TaBjgPwkyvGpDDOFVSZkwTyJ4Hp3yzEqE9mSk8+Zv9eBGm10vEMJIGsCHDfcV+QQM4JuPiGeVFYNwAfAyiW8yoV9/wUwEDFvdiBBEggOwI8/+XeIAES8BoCQhZd+NGmTqvnwpZNKFaokNPyZUwUExeP81cu4+qNm4iOi7MIk8uXKolyJUt6hSw56tp1nLt0CdHxcYiOjcOtOOs167AwFAwLQ2RYGB6oUB5linv2mnWKTofzUVG4evMmrt68heCgQFStUBEVy5S2yKhz2iiMyyk59iMB3yXgovPfjlZRXH0PkekOYKmHxuawJEAC3keAwjjvWxNWRAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkkE8IUBiXzULnRBgnBGPfr9qKbbsOYuuOX5CQmOxwG5UuVRTlypRAnVoPoE3Lhmj2aF2EhYY47JdVgNFowoYte7Fp236s37QX585HSeURNbRr3Rg9urRDs0cfkupjC9qz7zfs++VPh31UKhX6vNwJBcND74mNunwdfx4/i+N//Y0L/16BRq1BWFgIRg17GVqt6+VigtvGrfuwZPlGLPl+Y7ZzEYK/x5o/jCGvd0e9OtVw7Xo0Fixe63DuIuC5To+hYvlSWcbu+vkIDhw8KpXnlV5PoXChgnZjDQYjPv1iGXS6NIc5H6hcDk93bO4wLqsAsVY//LgDW7YfwIYt+6RztGxWH+3bNMb/ej5pERfKtvMXL2PZyi1S4SJ/zeqV7okV8sbf/zyNEyfP4czfFyFYiSYkdoUi7XO1JXujfzdF0jvxOhTHBZkmjgXPd24jEyodYzYD8X/tgnb3SAQWMgJFSwClagMB/oAGgNrPKozLII4TAjmtGgmxKVgx7Vcc3RuFKlUCIG6F+fuCHs2fr4XmL9ZCgL+/RSSXuYn3hKoAIYvTAJf/jMKqTw/j8M/REPfStOlcHu1710aBiGBAbwJEd5MxXTxnsArjhEhOPAxmQGzj62eAq2dhvpWGW9WGIaL+s1Br/KUZeGsghXHeujKsiwS8koDfbfnFqdvSuHt/uLm23F9vpx8LYIMzh6Ewzpk0mYsEfJfAoMlTsOfwEdSpWgVd27ZF8/r1oA2QP/9bu3MXnhs6TBpAuyaPYtUnM6TjGZg3CXDf5c115ay8n0DshQuWIn9bvKiF+NpuxeqdTq5aiOKmA3C3YUT81UJ8woT4iwYbCZCAcwjw/Nc5HJmFBEjACQRWbtmKHiPfckKm9BSfvjMKrz77jNPyxcTHY/nGTVi5dSt2Hzqcbd4ikZF4tE5tDOrRA41q18L1W9FYuFbu+uazrR9DhVJZX9+0NxGjyYRNe/diy7792PjzXvwTJXfNulSxoni8cWN0a9cOTeoqu2a9dP16RF2/4ZBvWEgI+jzXOVOc+PvE4nXrsGD1mmz716teDY/Uro23e7+KiDD71ze3//Irjvz113+5PvjqKyQkOX7fgOgw/KUXLRI9e61gaKhT95JDaAwgARKQJuCi89+nb19Ffw+AsgOjdNVSgZcAlBNX9qWiGUQCJJAfCFAYlx9WmXMkARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARLwSgIUxmWzLEqEcUePn8WEqV9KS6Ec7YTuz7VF75c6oXmTuo5CLc8L6dXKNdsw+v05OH32olSf7IIaN6yFie+9Jj32a0On4PMvV0iNeXDXQotozdaEbO7jz5Zg1dodWfaPi9qVY3meVEEA/jx2Bn3fmCQtbLPlFdIzIW5b/oOcwOzbLydYhHxZtT6D3se8BaukSt65/guHUr/YuARElLHcf+uwNXmkDnZvnOcwLmOAEMV98PE30utuL/not3pj6Os9EB5WwGENy1ZuxvMvj3IYJwLufv2ePfcvZs1ZhplzvpPqby9o7fKP0bFtE+k846fMw5hJc6XixWt+xzq5WKmEANJ0ybi16ysUv/wNUDAQKFoOKFwW8PcDNCpAc5cwTgjkxPe1alw7F4sFE3+FOSkR5ctrYTKa8M+FNNTvVA3NetaENsAvC2GctbIAP6TEp2LdrCPYsvwCSpfWIDbWCHWgP159ty7ub1AK0BkBgyldFifeWm6RxFklckYTkGZKl8YlxALXzgA3YnDFrz4inxgHbXhRWQReG0dhnNcuDQsjAW8l0BPAQg8V98vtcYU4LnuzroLCKIxTAIuhJJCHCbT43yvY//sfmWb4fLu2aFq3Lh6qVg1VK1a4RyB34fJlHDp+AvNWrMTOgwcV0Znx1gj069JFUR8G5z0C3Hd5b005I98gsHPSxEyFtl2xWuWkyoVx5VMAJZyUT2mazwC8rrQT40mABBwS4PmvQ0QMIAEScAcBIYsT0jhntab16mLzF7m/BmQwGi1is7dnzJCWkNnm0LhOHZQoXBgrtshd3/x64gR0a5/19c2suIjaVm3bhvGfz8GZC7m7Zi3kduNeew2Cm0yr+sRT0mK61COHLCnPXryIYR9+hE175T8YS0jtFk+Zgoa1Hsy2rF5vj8LyTZtlys5xjG0OOU7AjiRAAi4h4MTzX3He/KxVFJf9Accls8gyaQ8AS9w3HEciARLwAQIUxvnAIrFEEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiCBvEmAwrhs1lVGGJeYlIzRE+dYpGeuaPpbB+Dvr7Gb+tz5KHTqPtwiPnNmmzRmAN4a+hJUKvv3b+ZEGHfl6k30HjgR6zb9bLdkVwvjps36FsPfmeFMbNnmygvCOLPZjI9mLsKI92Y6ldn9lcpi3YpPUKliGbt5cyKM0+n0GDv5C0yZvsBu7lIliyLq8nWpefV8vj0WfjFeKlYEVX+4C06cPCcV//XnY/BSjyekYmWDUm5cxK0fRqJU0BmgUDhURcoCYZGAvwqwyOHUgBpWeZwK8BMSORUQqMblM/FYNOUwtMZklC6jRUqSAZdvAK1erY2H2pWDnxC8mYTpzQzYDhXiLaF+APzVuHT0Fr59/zBSo5NwXyUt4mMNuBoNPNOvBmq3KgOV3ggYjemyOCGNM1ilcRZxnHgIoZwZSNYDty7AfP0qomPDEPDETBQoXQ0ODk+yiDwWR2Gcx9BzYBLwVQLi6HoKQCUPTkCI48YA2JSbGiiMyw099iWBvEMgK3HX3bMLDQlG4YIR0BvSEHVN7vf1rAiJPKd+WovI8PC8A5AzyREB7rscYWMnEsg1ASfeMG+rRYjiZgEomevicp4gDUCw+CyPnKdgTxIggWwI8PyXW4MESMDjBOKTklC0STOn1/HPpg0oUaRIjvPGxMfjmcFD7hGw5zihg45KhHH/REWhy9DhOHrGudesx78+AG++7PiatRJhXPKhX/HJt4vx9oxPcozu/TcGYdiLvbLsT2FcjrGyIwn4PAEnnP+K34XFp168K95q4CVA/gVQXlzR95J6WAYJkIB3EKAwzjvWgVWQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAnkQwIUxmWz6I6EcTGx8Wj/7Bs4cPCoy7aNI2Hc7r1H0PG5wUhITHZJDUJcNX/2aLvSOKXCOCGL6/HKu1I1u1IYN37KPIyZNNcl3LJK6uvCuJQUHfq+8T4WfbfeJcxCCwRj+7o5qFenWrb5lQrj2j7WCM+/PEpKptj7pU6Yt2CV9NySr+1FUJDWYfzZc/+icu1ODuNsAbGXdiI8rIB0vKNAk9mMhDO/wLBhCMILGaEuUgSqyBJAcCCg8QP8hTDOD1ALSRzSBXLi30Igp/VDfIIJP8w6jitHr6F0GQ3iYgzQliyEjoMeRInywUCqMb0EIYtTWd8Lar4jnTux8yqWzziGyBAjipfwtwjjbsSq8ESf6qj+aFGodEIIZ0p/a7lIJaRxNlmc0QSkiYcZ0BuA2BswXr8MQ4wRt+qMQrG6HaDW2BdqOuLj6ecpjPP0CnB8EvBJAuIOxG+8oPIDt2sYm1NxHIVxXrCCLIEEvICAjLjLWWVOGTIYg3u+4Kx0zOPDBLjvfHjxWLpPE3DCDfO2+QvL/mwApb0AyOcAXvOCOlgCCeRVAjz/zasry3mRgI8QWL5pM4T0y9ntk7dGom+X53KU9sqNG2jffwD+Oif3IUU5GuSuTrLCuJ+PHEGnNwYjIck116x7PvkEvhhj/5q1EmHcE82bYe3OXblGtPPr+WhY68F78lAYl2u0TEACPksgF+e/QhTXHcA7AKp4GQDxh9XFXlYTyyEBEvA8AQrjPL8GrIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESCCfEqAwLpuFtyeMS0xKRuPWr0iJqHKzr+wJ4zZs2Yf2zw7KTXqpvh9PGYrBr4n3pWbdlAjj2rV+BKJu2eYqYdynXyzHwOFTZctwSpwvC+P0+jQ83ul17Nxz2CkssktSulRR/Ln/O0QUDMsyRIkwrnHDWpbXp6xMcdG8CejZ+z3p+f24bDqeaNfUYfyM2Usw5K3pDuNEQM/n22PhF+OlYmWDDIY03DiwChHHpkJVMAD+hQrDr2AhQKuxyuLUVkGcEMZZH+Kt6OKhUcEUHIDzp1Kx5ZszSLyagIKlCqBVn+ooWzkQfqk6QMjhVMIWl8X7QLVqnD0Ug5UzTqCAWo9iJQJw5ZIOpsBgPDu4OkpXDAJSDemCOCGLEyn+k8WJf1uFcUIopzfClJAIw83rUEUn4GKBNij79Cj4BztPrifL1JlxFMY5kyZzkUC+ISCO0KcAVPKSGe+/XYcQx21WUg+FcUpoMZYE8i4Bd4m7ShUrimOrVyFI61j4nHdpc2Y2Atx33Ask4BkCubhh3lawN4niRE1pAIIBGDxDlKOSQL4gwPPffLHMnCQJeC+BrsPexJodO6QKfKdPb7z/xTyp2Ea1a2HH/K+kYjMGpRkMaPHy/3Do+AnFfXPTQUYYt2nvPjw10PXXrD8cPhQDu2d/zVqJMC43TDL2bV6/PjbOFR7hzI3COGcRZh4S8D0COTj/FR/l1hOAsJRW9sIZ/wugvPjoNzu11QIgztvFubLe+tXevy8AOKlgrsUAVFUQf1VhfgWpGUoCJJCBAIVx3A4kQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIk4CECFMZlA96eMG7gm1Px6dzlLl+y7IRx585HofYj3aRlWLkt9NDuRahbO+v3XyoRximtwxXCuL9O/YNq9Z9TWkqu431ZGDf07en4+LMluWYgk+DZp1pixaKsZX5KhHEyY2WMWb9yJj77YjnWbfpZqqus3O3RNq9g74E/pHJuWTMbj7V4WCpWNsioS8alDTNR5OoKqCIDERARAXVoOKD1twrj/ACNHyDehm4RxvkBfuZ0YZxf+v8bA7W4dtWI2OsGhBf3R8my/lDp9IDBGmcpxpwujjNneD9ogBqxcWb8NPssLvx6FZERKsQlqfBQx/Jo0qkkgoQlTm+0iuKsbzE3mdLz2sRx4t8WYZwBpqQU6KOjYb4Vj6umaijVfRoCQgvLovDKOArjvHJZWBQJ+AKBF2/fnLPAywoV4rgxALbI1LWp89M7RVx42XLNxNfaL4h7odhIgATyGwF3iLtCQ4Kx+5sFqFqxYn7Dy/lmQ4D7jluDBDxDIAc3zNsKbQtgLoCynqk821HnAOjvZTWxHBLIiwR4/psXV5VzIgEfIBATH48SzVtKVVq5XFnsXrAAJVrIxYukf29YDyE3V9Imzv0C4uHu5kgY909UFB5+vhsSkpLdUtq+xYvwUNWsr1l7QhgnJr1p7hw0q18v0/wpjHPLduAgJOCVBBSe/75qFcVV8MrJpBclLuB866C+FwAsUjCHbwC8pCCe+e3Dan1b0veOAp4bAUxREO/q/A8CeEZBPb/fvrS4WkG8q/O7Wmjo6vwKUN4TSmFcbuixLwmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAnkggCFcdnAy04Yd+SPk6jbRLwf0fUtK2FcSooODVu9hD+PnVFUwP2VyqJY0UI4deYCrt+IVtS3XetHIIRaWTVfEsYZDEY0aPEixBq6u/mqMC4nkrbQAsF4sEZli9BQ6T4V67J/29doWL/mPUuUk1pk11ns7+TkVHTuOUK2C5Ku/YzgoMBs469eu4USlR+Xyle0SCSiTm2ARiPMbc5r+oQYXPp+FIqmHYRfRDACCoZBXaAAVNqAO8I4tQoQDyGME8OLfwtZnJDGqQAEqIAgIZjTACYjoEsDTKr050SzfLX9j612IZADTFp//HNChx1LLiAlOhWVGxZFgw7FEBFiBJINEM44izFOCOLEQ3wuuUE8xL+tsrg0E6AzwJiaAn1sHEy3EnAztRSKPj8LQYXLOA+WBzLZhHHV2isWkN4N3FH1rn6jLvPbXwHyIZ+MBFz9+nV0PHD180IcNxbAZpmBUo8cUvr6kEnLGBIgAR8h4Gpxl5DFbZk3D7WrPOAjRFimOwhw37mDMscggXsJKLxhXiTwVlGcqE0PQAugj1Vm56wlFwaYvhmSMX9msq7kk9V5mlL+d9fnaF8wv31CSvk44u2K53n+6wqqzEkCXkJg6foNePnd96SqeadPb7zXry/a9u2PnQcPSvX5eOQI9O/aRSpWBB07exb1ujwvHe/MQHvCuBSdDk17vYSjZ5RdsxaSvaKRhXD6wgXciFZ2zfrxxo9gzaysr1l7ShjXtF5dbP5COI7vNArjnLkLmYsEfIuAxPmvOJ8UoriRALz9Yvd5ADIyO/EhR0L2LNtOA1DyR1vmt0+WfMgnIwF3vr6UXudVep1c9pjCOBIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARLIdwQojMtmybMTxg18cyo+natM7tO6RQPUe6ga6jz4ALTaAFyKuoaLl65iw5Z9doVaWQnjPvl8KQaPnCa1UYW4a+aHb6Jb58ct49rav5euoffAidi0TdzTJNeO/7oc1apUvCfYl4RxK9dsVyQEkyMjF+WLwrjklFQUv6+NRfwm04RYcNZHI3BfhdL/hQtJ39IVm9Crz2iZFJaY5zu3wdL5k+6Jd7UwrvmjdVHsvtbS8129dBqe6tAs23l9/e2P+N9r46Xm/fawlzFpzACpWCVBKTf+xaUlg1BUewnqyCBow0OgCQ6GShuYQRgnJHF+gD/SJXEWgZzfHWGcynRHIife7qkSMjk/QGV772cGeZytOPGU2WSJM/kHICYG0CebEF5EjWA/PZAipHMiRljlrHI4mzAuDVZ5nBkQsrg0E8y6NIswLi0+EYaYRNyMLYhCXWcjtHTle1R1Svh4OpbCOOkVcPUbjZnf/lKQj2/xkX5huThQ/JIpfghutDcOhXEuXgWmJwEvJ+BKcVf3Du0hbtq/r4y333Pp5YuUB8vjvsuDi8op+QQBiRvmbfPwZlGcrcY5APpTGHfP1nOl0E0M5sr8FMbdy9fRsUWp0M3VQj1H9bryeZ7/upIuc5OAhwg8O3go1u3eLTX6noXfoH6N6pizfDkGT5kq1efhmjWx+5uvpWJFUN9x4/HNmh+l450ZaE8Y9+mSpRj+keQ165BgTH/zTXRp+zi0AXeuWV+6dg39x0/Elv3y16x/W7EcVSvee83aU8I4wTv50K/wE9fOrI3COGfuQuYiAd8iYOf8N8gqihsFoLiPzKongG8lav0GQC+JOFvIKQBVFMQzv31Y5ONZPkqFfXlp/7v6fQQKDhMMJQESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIIH8RYDCuGzWOzthXFjJptJCqe7PtcUXs95BSLB472fW7czfF7Fi9TZMnvb1PXnvFsalpupRrnpHXL/h+NPWK5YvhV0b5qF0qaJZDmwymTDwzQ8xe973Ujt+8Gvd8fGUoffE+pIwrlGrl3Hg4FGp+dqCmjxSB71f6oRaNSqj6gMVYDabLbK/Ldt/wZz5K+0K/zIO5IvCuM+/XAGxvjJt6oRBGD6oJ1RCJpZFO3vuX1Su3UkmlSXm8umNKFG8cKZ4VwvjhPDujREfYeac76TqfKFreyyal70Q7smuQ7F2g9wNRUcPLEONavdJjSsbJN6ZmRB1Ble/HYDC4THwF8K4MC00QcHw0wYD/mpAYxPECUkcAI2QwdkeVoGcWFKxrpav1u+JGNWdG18sNWVcerPFLAdYv8BfPGkG9OkCuHRZHKxfrcI4g/h/M2C0CuPE/wuJXJoJJp0OxtRk6BOSoY9JQcwtf0Q8OwsF76uT7Z6T5eTJOArjpOm7+o3GzG9/KcjHt/hIv7DcFLj39hF/DIBtWY1HYZybVoHDkICXEnC2uCs0JBh9n3sO/bp2Qelixbx01izL0wS47zy9Ahw/vxKQEMY9A2DWbSlYSS9nJDT3wQDEXy2UCrMcTc2VQjQxNvNnvwIUxlEY5+j1KfM8z39lKDGGBHyAQExcPEq0aClVaZHISFzYvNEiCjsfdRlVnnhSqp8IOr1uLcqWKOEw/sqNG6jweDuHcXcHdGjaFEKmXqNypf9k6tdu3sLuw4fw+bLv8etRueul2QnjUvV6VG7fETeiHV+zrlCqFLZ+OQ+limV/zXrI1A8xd7ncNeuB3bvjw+H3XrPOjTBO1CjaP1FRilmLDuc3b0Txwneua0Zdu46o69f+y/XK6DE4c+GiVO7vp09DsUKRdmODg4JQo1IlqXwMIgEScC+BLM5/CwB4/fal+2EAiri3mlyNJg5aFaxX9B0lUiosOwmgqqOkGZ5nfvuwyId8MhJw5+trrILXsQhVGq8wPcNJgARIgARIgARIgARIgEgt83QAACAASURBVARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIIP8QoDAum7XOShiXkJgMIYyTbTH/7kDB8FCp8PiEJHwyeylGvz/nv/i7hXFffrMavQdOlMq39cfZaNX8YbuxN2/FokiFx6Ty3V+pLE4d+eGeWF8RxglRnBDGKWn9X+2MTz4YDn9/YdW6t82auwyD3vxQKqWvCeP0+jSUqdpBSk4opHo718+13BBjr4m9PeGDL6V4rfx2Kp55MvMNOe4Qxh367QTqN5P/8Oukaz8jOCjwnjmJ13N4qWZSc32oVhUc3iPzwdxS6TIFxV04jqtLXkNkwWRoI7XQhmqhCQ6GWhsCiH0tBHFqqyxOfBVLaPl/qzROZU4XyNlkcUIc52d1R1kkctaHZVTbvbXW5y3SOHO6GE78O5MgzlqmSWWVxFklckIkJyRxNlmckMelGWDSJcOYmgJdQipSY3RIilEjpMNUFKraGCpRn482CuOkF47CMvuoyId8pF9MHgz8+fYPinEAtmasgcI4D64IhyYBLyCQmJyMY2fO4reTJ3Hw2DEcPX0GR8+cUVSZuDm/4YM10bJBA/To2AFhISGK+jM4/xHgvst/a84ZewcBO8K4bgCmAyjuHZU6rGIugH7WKArjMuPyZSEdhXEUxjl88SsI4PmvAlgMJQFvJLD4p3UQci+Z1r9rF3w8csR/ofW7dpM+r506bCgG9ejucJiZi5dgxDTx65J8e/+NQRjS84Vsr9vN/m4Zhk6Vu76ZnTDu61Wr0X+C3DXrDXNmo8XD9q9Z34qNRamWctesK5cri6Or7r1mrVQYV7ViRUx6YxCa1a+H4MD0a31JKSn4YctW9B4r/pQp3/Ys/Ab1a1TPtkOH1wZg24FfpBJe2rYFhSMipGIZRAIk4H0EbOe/SWlpWH3mbyw6cVKYNe1bIL1vGqIi8aaJRZKlibgXJGNFmFKhFfPbh7sQQE8X8nd1fq6v/cVzNR9Xr6+CrclQEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABWQIUxmVDKith3Nlz/6Jy7U6ybLF2+cfo2LaJdLwIFGMMHjkNV67exK87F0KtviPhatGhL3buOewwX6cnWuCHxXJv9B/+zgxMmyUnq7p1YTsiI8IyjZ9TYVzRIpGoW7sKKt9XFhUrlLJIt5JTUnHh4hX8cSxdErB+xSfQagMczlcmYOzkLzBusrhvUa69O+IVTHi3v93gvCyM2733CJq1E/e9Om6/712CWjXvdxgYHROPQuUyS+Cy6/TmG70wdcKgTE/nRhjXvEldy167r0JpFIoMR6pOj2vXb+GPo2cQHROH2dPfwoM1KlvGq/5wF5w4ec7hfETAqiUf4emOze+JXblmOzr3vHOTkL1kn340AgP6dJEaT2lQrBDGLbYJ4wKhDfW3CuMKQOXvD2j8ALWQxFlFcUK+prZK4oQA0CKLM90rjbMJ5ERBlltq75LFiW/ZFFYWWZwqgzjODAgxnOhjEcRlkMWZzICQxFkeQhxntgjjjLokGFOToUvQITVGj8QYP4R2+ACFqj1KYZzcpqBQzD4n8iGfjASUWii9bf/IHRU8F7Xn9tDi0+O3ixIojPPcQnBkEvBmAsmpqYhLSEBsQgLiEhIREx+PhORki6s4LKQACgQHITQkBGWKF0dEWObzU2+eF2vzbgLcd969PqzO9wlkIYzrDWAygMI+NDvxVwwt0jX3olEYl3nxKIwD+irYz0r3z918HQ2V3/I74uGJ53n+6wnqHJMEnEDgqYGDsGnvPqlMP332KR5r1PC/2EnzvsT4z+98MJi9JPWqV8PPi4Sfw357dvBQrNu921HYf8/PGzsGPZ98wm68M4Rxbfr0xe5Djq9ZP9WiBZZNk7tm/dbHMzBjkdw16ys7tiMiPPPfBJQI476eOB5d2raFOpsPw1qweg36jZ8gzX3J1A/wzGOtso2nME4aJQNJwOcJ2M5/l586gyV/nUSqQVz49rmWBkCYNC1X9SWaOHj3kIizhVAYZx+Wr/P5C0A1BftB6f7xtvyuFrr5en4FW4GhJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACsgQojMuGVFbCuMtXbqDUA+1k2aJ0qaL4aOJgPNWhOQIDcyc+EzK1kGKPSo39+cdvo98rz0rFfrtsPXr2Hi0Vu2XNbDzWIvMnwCsRxlUsXwrPd34cT3Vohnp1qmb7yfZSxSgMatq2N/bs+02qV2iBYFw6tQFhoSF24/OyMG7CB19i9PuOb2oRrOKidkEl7A0SrUzV9rgUdd1hZMP6NbF/29eZ4pQI40RdYq8JeWKLJvUUvf4+/mwJhr493WGNIqD7c22x+KuJ98T26jMai75bL5Xj2t+bIQSKzm7CYBR/8RSiFvVHZHgCAiK1CAr1h39wINSBoVD5awGNGlALSZxVGicEccJRaXmI74v/F2trgsXQIf5pkchZq737q20SGfVJ4t/iId5ObpHHif+xCuQMJutzqnRBnBDGmaz/tgrjzGl6GHUJMKYkIzUhzSKMi7ulQaHnZiCyUj0K4+Q2jrcJreQOGHfmxvrtrzP5kI/ckcC7osRdrmNTjxyyiOPYSIAESIAESIAESIAE8jYB2w3zK0+fxXcnTyNBr/fFCZ8QnzOQoXClQi5Hc/Zl4ZqYmy/Xn9XfKZSuL4Vu9ne4q/k4en158nme/3qSPscmAYUEbsXGolTLx6R7xR7Yh8CAO9d/fz95Cg27y/ty/lq7BhVKlcp2PIPRiBLNWyAhKVmqJpHr6OofoFGLiz7Zt9wK44RwO/IRuWvWs0a9jd6d5a5ZL12/Hi+/K3fNev3ns9GyQeZr1kqEcalHDtllZDabUbb147gRHS3FfsqQwRjc84VsYymMk8LIIBLIEwRs57+/X7+BxSdO4ujNW744L/GDx/6bZTLPyteFX6zf/i5Vyufuv584eg0sFm+9cRSU4Xlvy6+Uj7cJ71xdv4KlZSgJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkIAsAQrjsiGVlTBOvDncL7y+LNtMcc8+1dIirqpTqwpq1ayMkOAgRXl27jmMFh36SvUZ/04/PNqotlSskKiNmTRXKnbRvPF4oWv7TLFKhHEHdy1EvTpKPkhWqiyHQUpkeyLZyCEvYsq4gQ7z5mVhnKxgT4jZ1nwnJ1cTQJ9/eRSu33B8c4WQLf77V2bhmhJhXFavX4cLag1QKoZMuvYzgoPEB2ynN51OjyIVHkNCouMbeIQ8cfXSabKlKY6Lv3we5xcOQJHg6/CPEMI4NQJCtFAHhsHPPwjw11jFcAA0VlGc2g9Qme/I4YQ8ziaLswjjxPOiFBFj9VT9Jwy0yuCsT1sKtgjjhAjOJoyzfU9I5GzfV6X/W0jijBmFcSaY0lJg1MXDkKyDLsGI1NhUxMSFoESPz1GwbBXFTLypw18/T7WUU639cqVlUbhmnxj5kE9GAvlNqKf0eOKp+E0ApqYeObTNUwVwXBIgARIgARIgARIgAfcR+P3bRZbBPl63/tzqs+fK6YxG+xYT95WmZKSTAKpm6KBUKOZoLF8Wrom5+XL9FMbdu36O9qvS/Z+fhXE8/3W0m/g8CXgRgYU//og+Y8dLVdTl8TZYOHlSplilkrHJg9/AkF49sx3vxN/n8NBzXaTqEUHzJ4xH9w6Zr+Nm1Tm3wrjdhw6jTR+5a9aj+/dD49py16x//u03TJgjd83664nj0a195rk6UxgnuL0w8m2s2LJFiv87fXrjvX7ZM6EwTgojg0ggTxCwnf/+tG//n0tOnip3LjYu3AcnJq6tlQdwUbJ2Xxd++Xr9rhZ+Kc2vVOi2BEA3yb0mwvJbflfvT6Xrq1R4p2BpGUoCJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJCBLgMK4bEhlJ5yq9Ug3/HnsjCzfbOPqP1QNrVs2RLNHH0LjhrUcCuQ+/GQhRrw3M9fj5ibBrI9G4PU+mW9M8AVh3B9HT6N2Y/kPpD139EdUKFfSIaq8KowzmUxQF3zY4fxdHWCOP5RpCHcJ48SgT3YdirUbdktNceW3U/HMky3/i92641e0fuo1qb6rlnyEpzs2l4rNSVBq3A2cXTgCEWnHEBCpRWCYGgHBGvgHhULlHwKVfwAgBHHiNm2NChByOMtDZRXGWWVxFmmcVSIn5HCW58U3rd+z6JiycDJZviVkcKZ0IZwtxCy+ZxXGie9ZnrPK4gxIF8eJ76XpYEpLhjE1EfrkNKQkGpEWrUO0rijK9foMBYqVywkWr+lDYZz0UuQ34ReFd/a3BvlIv3S8MlDcKD8WwAFRXeqRQ0pf3145KRZFAiRAAiRAAiRAAiQgRyDwoXq23+eH3z77fw9AmFxPr4gSv7sGi19jrdUoFWY5moQvC9fE3Hy5fgrjKIxz9PrMyfM8/80JNfYhAQ8TeGLAQGzZv1+qCiGLE9K4u9uIadMxc7HwnjhudapUwf4lwtGRdVMiZhMZovf9jODAOx9wlF3e3Arjpn+zEKM+8ew1649HjkD/rpmvWTtbGDf6088wdf7XjhcSAIVxUpgYRAL5ikCG899Wtycuros86mMAxAHwf5I1U/hlH1R+43McQA3JvSPClorPnlQQ723589v6KhX2KVhahpIACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACcgSoDAuG1LZCeNeGTAe8xf9KMtXKi60QDAGD+iOfv97FiVLFMmyz8jRMzF1xkKpfK4KGjeqL0a/1TtTel8Qxu3YfQgtO/aTxmKKOwiVkGI5aHlVGBcTG4/IsncEaI44uOp53c39CAjw/y+9O4Vxq9buwDM93pSa2vOd22Dp/En/xQ58cyo+nbvcYV/xur9+bisCAwMcxuY0QJ+ciL9/+BAFLq+DJlKLoDANAkPU0AQHwc8/2PKAxv+ONE4tZHAmqyzOKoQT3xNNfNWIr+J/xPesUjnrPy0x4tZp8RDCN8u/rR4g8VUI4mzP/yeJU6XHCDmcSQUYRV8hl1MBBgPMackWYZwhJQW6ZANSEozQR6ciVlUZlV6ajqCI4jlF4xX9KIyTXgalQinHB/DMQzO//aUgH9/iI/3CcnOg+OV5PIDDGcelMM7Nq8DhSIAESIAESIAESMDDBDLcMG+r5G0A4hHq4dJkh/8KwKvWYArjMlOjMA7oK7uRACjdP3fzdTRUfsvviIc7n+f5rztpcywScCKB67eiUbb1vQK47Ia4snM7IsLudd/uPHgQbfv2l67s2OofUKls2Szj1+zYga7D5K5VFYmMxL9bN0uNm1th3DufzMS0bzx7zfq9fn0tkraMzdnCOCViPArjpLYeg0ggXxHI4vxX/JAR4rhGPgRCfHrbRYl6KfyyDym/CcWOAagpsW9sId8B6Kog3tvyK93/SoVrvr5/FCwtQ0mABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABGQJUBiXDanshHEnT59H1XqdZfkqjntr6EuY+N5rUKuFrOlOe7n/OCxYvFZxPmd2GDG4Fz4YPyhTSl8QximRf91fqSxOHflBClteFcb9/c8lVKr1tBQDVwZFX9yOiIJ3brZxpzAuNVWPohUfQ0JistQUE6/uQUhwEIxGE0re3xbXb0Q77Dewb1fM/FDuRh+HybIJMKTpcWn3d1AdnAl1QaswroAa2mB/+PkHQWV9GMx+MBqM8Av0hybQDyo/s9UJJwRywg2ngslsRopOj+QEHdKSjTAZAT8/M1RqP/j5+8E/WIOAIDW0QRr4+6vTJXRCBGcTwAlhnHgIa5xFGCceVpGc7Tnb/xuMgEGXLowzCFmcHrpkI1LijTBEpyIusgmqdh8LTfC9N2PllJUn+lEYJ02dwjL7qMiHfKRfTG4OFHtT/FI1DsDRrMamMM7NK8LhSIAESIAESIAESMDDBLK4Yd5W0QgA7/qAOM5grTE1B8IvR/R9Wbgm5ubL9Wclns9vwjVfF9I5en25+nme/7qaMPOTgBsIfL1qNfpPmCg1UquGDbBu9mdZxurT0lCqZSskJMldX5o4aCCGv/RilrkWrF6DfuMnSNX0cM2a2P3N11KxuRXG9R47Dot+9Ow162Ev9sL7b2S+Zu1sYdyc5csxeMpUKaYUxklhYhAJ5CsCds5/H7NeN3nEB4DMs577OipVqTBLqfCL+e2vgFKh2HEANRwtaobnleYX1wQfVJDf14Vxrq7f1ftf6fpm3D9CgqmkKY1XkpuxJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJJCvCFAYl81yZyeME+H9h0zGnK9WumyjPNWhGRZ/NdEioLK1J7sOxdoNu102pkxiXxXGzV/0I14ZMF5mimjdogE2r8n6Jo+7E+RVYdyh306gfrNeUrxcGeRJYZyY15C3pmPGbPHeSMdtxaKpePaplvjl0DE0bPmS4w4ADmxfgAb1lLwPVyptpiCzyYyrf2xH0rpR8A9TIShCi8ACagQGq6EJ0EIVEAyjSQ29wR9GVSCS45MRGBGCsEg/qNRCFGcG1CpAo0L8TR0O7L6Bfy+nQq31h59GLTxyFtGc2WiCv58JQVoVIiL8UaJkICJLaBEWGYigAgGAyg/QG9MFcWaVVRhnFccZbfI461ejVRZnSIXZkAJjmg6pSUbokoQwLg2GWD0MtfqiwmM9LXPw5UZhnPTqUYhmHxX5kI/0i8mNgeLmBXGH7Ql7Y1IY58YV4VAkQAIkQAIkQAIk4AUE7Nwwb6vubQDiEeoF5WZXwhzx52EK4+7BQ2Ec0FfBvqWQzj4spXwUoHd6KM9/nY6UCUnAMwTa9u2PnQcPSg1etWJFDOn1QraxfcbKXZMUCWpWroyDy8Sh5N6mROzWuXVrfPvBZKn6leT9euIEdGvfLlPeZwcPxbrdnr1m7Q5h3MIff4TsWlIYJ7X1GEQC+YqAxPlvm9tAhDypkReDEVfyywC44qBG8QPoLQXz2AegsYL4zwH0UxCvNL+rhVz5Lf+fAGopWK9lt68ndlEQr1RI523585Iw0dXvU1CwLRhKAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAvmLgM8J48TymOMPKV6li5euoly1jtL97Anjrt+IRrtnBuHIHyel8ykNfLxVI2xcNeu/bi069MXOPYeVpnFqvK8K45SI3Z7v3AZL50+S4qYk77dfTkCPLplvqLAN0mfQ+5i3YJXUmNt/moMWTevZjY2NS0BEmRZS+Zo8Uge7N4oPhr7Tdv18BM3bi3vyPNs8LYw7/PtfqNe0pxSELs+0xrIFk/HexM8xcepXDvvcX6ksTh35wWFcbgPEuzPj/jmGc4sGo2BwPAIjAhEYqkZQATX8AzRQa4OgSzbArI2A9r66iP79IK5fS0PZGoVQIFwI46wPfxVi44w4fCwFfiFaFC0VhMAgNcxmQKczIinRgPgbqYi5pUdKYhpS41Kg0aWicKQG5SsEoVT5EEQWCYYKflZpnBkwCXGcGfhPGCe+ZwYMepgNqYAxFaa0VKTpDUhNNiI1wYjUOB2S4jUo8syHKFKtkUVY58uNwjjp1XP1G42Z3/5SkI9v8ZF+YbkoUJhWxR2xp+zl39D56ebi+To9eu4QXwuWK+eicpiWBEiABEiABEiABEjAmwhI3DBvK3eUVRxXwJvqt9aSBiCAwrh7VobCOArj7L1c794fjl7aviCM4/mvo1Xk8yTgQwSu3ryJ8m3aeqziP35YgQfKl79n/AWr16Df+AlSdT3VogWWTftQKja3wrg2ffpi9yHPXrN2hzBu+abN6PW2+LXUcaMwzjEjRpBAfiOg4Pz38dtX5McBaOCljGYDGOCgNiHPFnJ12ab0/ID57ZMdCWCKLHwA392+jtdNQbyr84vaxRiybT2ADrLBVjauzD/X+jci2ZL2AnhUNhiAUmGi0vy5ERq6+n0ECjAxlARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgATyFwGvEMb1HzIZc75aKU0+LmoXwkJDpONF4C+HjqFhy5ek+3z60QgM6JP9h5gmJaegZ+/RWLXW4rlwSdu0+lO0adnQkvvpbsOwZt0ul4wjm9RXhXFffrMavQdOlJpmu9aPYP3KmVKxnhDGbVkzG4+1eNhufbkVxv32xyk81KSHFANXBnlaGCfmVv3hLjhx8pzUNBOu7EbdJi/g9NmLDuOnjBuIkUNedBjnjABdYjzO/DgTIRdWQxUeiOAwDYJCNQjQ+sFf64/UJCNU2lAE1XgEplv/4MTW09AWL4PKNQPShXHiPyozoPEDgjSAGoDJJNSd1udUgJ/1ARUM8ENcCnDjqh5XLqXiwqlY+OtS8ESnkggL9Qf0pvSu4iEEcSKV5WEEjEIWpwcMKTAbdTDo05CmNyEl0YjkeBMMMcmI9a+CKi9NRXChEs7A49EcNmGcrYhq7Zf7uALPozg5OAnkJwLiB8gCL5qwOIovs97QZFcUZ6t5Y+enM91A0HzUu140HZZCAiRAAiRAAiRAAiTgKgIKbpi3lfA2AGHp8DZxnNKb212FlHmzJsAblvPOzuD5b95ZS86EBLyawLwVKzFw0mSP1ThuwGsY+cr/7hl/5Zat6DHyLam6GtWuhR3zHX+gkUiWW2Hcc0OHYe1Oz16zdocwTgl/CuOktimDSCBfEcjB+W8b63WW9DcIeU8zACgL4Ir3lMRKSIAEvIQA//7iJQvBMkiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABPIfAa8Qxr0zfjYmfTRfmv7JwyvxQOVy0vEiUIjdnunxpnSfpfMn4fnO4j2Z2Tej0YS581di2qxvce58lHRu2cCHalXB4T3fWsL7vjEJX3z9g1TX1i0a4LEWzv8A4qaN66Bh/ZqZanht6BR8/uUKqboO7lqIenWqScU6M2jZys14/mW5T4B/sEZl/LFPfICq4+YJYdxP389Ah8ftf9hsboVxFy9dRblqHR0DsEZ8MH6QdKxsYECABq/36QqNRtjJ0puSdZw+eSiGDOguO1y2cZ98vhSDR06TyjP6rd4YP2WeVOyFEz+hbOniUrG5DRLHqSuHNiB503ioCqgQVFBrkcZpA/2g1WqgTzHCrNIguHp9qIwp+HvbH0hASdRqGAKV2iaFQ7oUTojjxMP6bahsfjPxfREjHn7pcjl/P5jgj5sJJiTeSEWpMBW0/gCMQhJnzWGRxonvGQBTGswmIYtLhdmQClOaAWlpJuhTTUhOMCA53gDE62Co8TIqPP4SAgKDc4vG4/0pjPP4ErAAEvBFAuJIK6RslbygeHEE//62AnSMtSbpkiiMk0bFQBIgARIgARIgARLIUwRycMO8bf7iD3tCHuct4rg0AOIPE+LGeTbvI7BTYUnNFcYz3D0EeP7rHs4chQRIAECrV3pj72+/eYxF1YoV8duK5feMv2nvPjw1UO4aXOVyZXF0ldx15NwK4wZMnISvfpAbq1XDBmjZwPnXrJs8VAcP18x8zbrqE0/hnyi56/WpRw45XG9PCeP+3boZRSIjHdbHABIgAe8mkIvz38dvz2wsAG8Sx30KYKB3E2d1JEACHiBAYZwHoHNIEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEhAEvEIY9/FnSzD07enSK7L9pzlo0bSedLwI/OyL5Xh9+FTpPptWf4o2LeXeg2kwGLF+8158OncZtuz4RXoMmcBrf29G0SKRUCLVe3/0axg1/N5PopcZT2mMLwjjNmzZh/bPyt1QEVogGPGXd0th8IQwTkZkmFthXGJSMkJLNJViIIJSru9DYGCAdHxOAz0hjLt67RZKVBbvyXZea9msPrat/dx5CSUyxV/+G38veQ8haWegKRiEAuEaaIPV0AaqYUg1wGTyQ4HaTeCHNPy98RdEpxXFQ4+EQx2QjRBOSOOEIU48bQnJ8D5QIZazfEuVLpAT8jihFEo1AkYTYBbCOFX68+LfxjSLLM4ijDOmAAY9TIY0GAxm6PUmi9AuMc4AfWwqklKDUabbNERWqgPVf7I6CQBeGkJhnJcuDMsiAe8m0AvANx4uURz0hS1Y3LB0Iie1UBiXE2rsQwIkQAIkQAIkQAK+TyAXN8zbJv/u7T9FjfQScdxcAP18f1U4AxLwWgI8//XapWFhJJC3CFy+fh0V27b3+KSOfL8c1e6rmKmOA3/8ieYvy1/v/WfTBpQoUsThXHIrjBvz2Wx88JXcB8GNG/AaRr4iPweHxdsJyCvCuAtbNqFYoUK5QcG+JEACXkDACee/ba3XYZxv3VTOR8jSSwC4qbwre5AACeRhAhTG5eHF5dRIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgAS8m4BXCOMWLl2HF/uOkSb15hu9MHWCnADMlvTJrkOxdoOcCEz0ObhrIerVqSZdky0wKTkFvxw8hp/3/459v/yJfb/8gYTEZMV5bB1+2fENHq5bHZ9+sRwDJYV37hRS+YIw7sDBo2jU6mXpNbh+bguKFI5wGO8JYdz4d/rhvZGv2q0tt8I4kVwVJi9k3LPpSzzaqLZDXrkN8IQwTtT8dLdhWLNuV27L/6//onnj8UJX9958lKbX499t38B86AsYgzUILhSIICGM+lQcDAAAIABJREFUC1bDpDMAai1C63WACik4tWorbqVG4OEmYdBoNYDZDxASOIskDukSOJtHzvJ9IYZTWaRwaWlC8GaALiUNMBoQEKhFUEgA1BohiTNbJXFIF8iZhUDOCJj16cI4ow5mQyrMBgMMRkCvN0OvM0KXbERirB6q+FToSrdBxU7DERQe6bT18GQiCuM8SZ9jk4BPEhBH4FMAKnmoevGD4IfbR/HRORXF2eqmMM5DK8hhScBHCOjS0vDPlcvY/ssBnLxwAbfiYpGiS4VWE4A6VargsfoNUK5kSRiNJpy88A92HzmMUxfOw2Q0Izy0AGpWqoR61aqhaoWKCNYGws9PHD7zbjMLCbP1V2y90QidwQi90QCDyYw0oxEm6/PiV3iNWg2NnwoBajW0Gg00qvR/5wUZc95dYc6MBPIWASfcMG8D8p5VHBfiQUJpAIIBiBvn2UiABJxLgOe/zuXJbCRAAnYIzFm+HIOnyH/gmKtgvtevL97p0ztT+hvR0SjzWBvpIScMfB1vvvySw/jcCuM+X7YcQz6QY9a8fn1snOueD1HKK8I4WfGfw4VmAAmQgEcJOPH8t93tiYgP8HnYoxMCZgAY4uEaODwJkIB3EaAwzrvWg9WQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAnkIwJeIYxTKvQS6xN9cTsiCoZJLdUfR0+jduPuUrG2oJh/d6BgeKiiPlkFi5vYT5+9gO27DmLFmm3YueewopxL50/C853b4PDvf6Fe057SfeOidiEs1PX3TPqCMC4mNh6RZVtKs5vxwTC80b+bw3hnCeOUMGzdogE2r/nMbm3OEMYpESyOebsPxt5+uLp5Shi3+qed6NR9uNOmF395N0ILiPuJ3deEIyLm3HFcXDkOQYZ/4B8ZjKAQNQJDNP9n7z7g2yjvN4A/Ou3llcTOhpCELEYICRTKCJsyGqCETRltocyyKavMAi2rg0IphVIolLApBCiUDf8wAgTITsgky0ns2Na+9c/vbCWy48SSLcmS/NyHQx7vveP7ns56rdxjQI3DXdUX3vHHw4YElrz8PJYtM7DnAZVWoBwMB2C3A4pDUt4AuwTIyYcmTN2EpqpIRFQrJC4WU5BI2GF3OODxOWGoEZg2Gyp6e+HxKNYxVnCcobWExCUD42KAnoCh6dA0QNMMJOIGYlEdsYiOeF0UobAHA35yK2p22c/KqCuFjYFxpTCLHAMF8iogLwQfz2uLzY3JP/Z/aeOjpDt/m432GRiXDUXWQYHSE5Dgs+9ra/Hs/97CZ3O+hWYYVk6xputQVRWxeBzxRAIetwfVFZVQNQ0r1tZC13W4nE44XU7YFTukHsWmYMiAgTjlsB9hz512gtvpLCkwCYGTDOaEpiOma4jrBsKqiqimIWEYUOU1t80GXV6zt4zcNA04FMUyVUzAZVfgURQEXC54HHa4FDs8djvsdqU5I7pUXnSX1MxzMBQofoEs3jCfxOju4Li/Ajiv+GeGI6BAwQlw/VtwU8IOUaB0BQ44+2eYNuPrbh/g8O0G49sX5W81tN4kME6C49LZBtRU44tnpqAiuO33t7saGPflnDnY+9T037Ou/fB9lPlz/551qQTGffPC89hx++3SmXKWoQAFClggB+vf7g6OSwAYAGBdAbOzaxSgQH4FGBiXX2+2RgEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAgU0CBREYp6oaXL1+kNG03Hr9ebj+qp+ldcyJZ16DZ154K62yUugHE3bGtLf/sUX59z/6EhOPOAeTjtwfj/zlN+hVVZ52ncmCL099H8ecfHnax91/91W44JwTIMFzlYMmoikUSevYHx2yN15++l44nRLy1Pnt38/9F9ff8gAcDjvmfbnljQqZhJ19/v7jGL/b6M53pgtHDhp1BL5fUZtWDTsOG4y5Xzzf4Q3y2QqMu/3uR3HdLQ+k1Tcp1LTqAwT8Ww8cW1Nbh77DDk2rvn333g0fvPHwFmUfePhZXHD579KqQwq98+pfccB+49Mu317BVavX4VdX3w15nt1126/w05OPbFWsuwLj4vEE+gw5OO3n3rYQzjz1aPzjQcnbyf8mwW5L334SsWl/haPCDnfQAbfXCYcZR9kOo+De5VjAU47IjNex+MNp6DekDFX9/YBNriEOwCaBbwpMw4CuqtB0A5pmg6bbAYcXDn8llGAv2P0B2L0KFL0e8e9mYNX8RgT6DUR1jQ0wVUDXWwLjtObPjTigazAMHboKqJoJNWEgHm8JiwtpMOrj0IYejaE/vgiesor84+WoRQbG5QiW1VKgNAUku2cegGF5Ht7LACSEIytBccm+MzAuz7PI5ihQJAIr167F/c/8G8vW1aI84IfT5YKkm8XjcYTCEYRDYUTiceiaDsM0rPWaw26Hw+mE2+WC2+2CoigwDBOaBKepCcC049xjf4KDJ0yA09G1tXF3M8qdVxKiF9V01MfjqIsnEJFAOEWB2+OBy+WCw+GA3Qp9s1s+yV36LkF6yV1C9mSXID6xikajMFQVXruCSpfL2oMupxUwZ2dwXHdPPdunQEkJ5OCG+aSP/LLlSgC5TyJpPSMqAPklnVZSE8XBUKB7Bbj+7V5/tk6BHiWwfPVqDD/iqIIZ8+dT/o2dhw9v1Z/Jl12OV957P+0+jtphB7zylz9jYE3NFsdIIPuTr07FuTffknZ9/7jtVpx8hGQUbd50w0Df/SeiKZzee9aH/XBvPHffvV1el09547+46S/N71m3F65XKoFxT/3+dzju4IPSniMWpAAFClMgh+vfIzaO+CYAE7ph5HcBuKob2mWTFKBAYQowMK4w54W9ogAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhToAQIFERgnzhKiJmFqmWyXX3Qabr/xArhcznYPq6tvxHmX3pFRWJxUdOM15+CmjXvbbep/P8JRky+xvlzdpwr33H4JTj7+cNjtcg9T+tuIccdh/sJlaR3wzD/vxORjD7bKnvqz6/HUs2+kdZwU+tlPJ+HhP1/fYfBZexV+MWMOrrv5Afz37Wmbvm02Tt+iaLEExp1wxq/x7Iv/S9vu2ivOxq3X/9IKHGhvk/C5k866Bh9/8nVadf7r77fi1BNa31CRPPDJZ17HaT+XLJb0tsMO2gsvP32PFYiQuiUSKv751Ku4/tYHUbu2Lq3KthYYN2feYoyeMDmtOqRQMODDx289ip3HZJ5jE4nG8NCjL+DG3/51UyibBMZdcfHprdrvrsA46cRl19yL+/7yVNoeWyv49isP4sD9u+Pfbjf3KLJ6EZa+/HsY676Cs8wFl9sGr1tHr932hWPEUTBdVbBF1yAy/XlEl86Ev9IDu5xnNjtM2GGaDhg2FwxXEPayGijBatj9QSguG2A0wZZYD4TXwqyvxYaVDahbo8JwVKLfDhUI+LXmoDizJTBOwuI0FaahwdAB3QBU1YSqGlATOuJRHbGwhkSjClXpg+2PvwFlO4zv1PWsyxOXowoYGJcjWFZLgdIUOHVjWNy/8ji0VwH8BsBXuWiTgXG5UGWdFChugbiawEvvv4tXPv4Q1b0r4ff54HS5obaEmYVCYTRJYFwkingiAbnJ3WFX4LTC4pxwu93W7wUkKE1C0VS1ORAtFk/A6/HiN2f9HEMHDCjK15KGaSKu69iQULE6GkPMZoMvGLTGnrpLYJysX5OBcfKxBMYlt7aBcYZhQHYJjUvuCbHVNISammDXNfR2udDL7YLP7oAzw997FPcZyd5TgALZFtiwdKlV5VdPPnGAPP7ouZfey3YbLfV1R3Dc/QAuytF4WC0FeqIA1789cdY5Zgp0k8ADT0/BZb+X7JvC2K4/9xzInrr99ZlncMmdv8+og32qqjD50EMwduQIDBkwELV1dZizaBGmvPEGFixN7/3pZIPtBcbJ98687no8/Xr671mfecwkPHhD596z/nLOHNx4/wN4a9rm96xjX275nnUhB8adevWv8fxb6b1Pfd6JJ+C+q5nHlNFJz8IUKCCBPK5/JTjuZgBd+6t+mdklAAwAsC6zw1iaAhQoUQEGxpXoxHJYFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAKFL1AwgXGPPfkKzjpP/j1jZtu4XUfirNN/jJHDt8ewoQOhaTrmLVgKCby6649PpB2cldrq5+8/jvG7jd6iI6mBcclv7rLTcNx87bn40SF7bxHg1d5IJBxryE4/Trtf097+B34wYWerqnfe/xwHHX1eRkDSr8suPA0H7j9+q+FnyQqj0Tje+eBzPPL4y3jxlXe3aKeYA+OeePo1/PQcyT1Jf5OgvluvPw9Dhwy0/lq93GC/ZNkqPDnlddxw24PpVyQJL9sIjPto2gzse9jPM6pvwrjROPyQvTFm5A6IJ1R88dUcvPDKO5Agu0y2rQXGSR37Hf4LfPh/6efESGjcNZefhbNO+zH61vTqsBsLFy3HC/95F/f8+V9bPB8KLTDuq6/nYdy+cq9i5zcJmVw5/42MAyY73+KWR5owUP/1m1jy/G3weHU4fEDvag8q9jgStoH7Akpz+KYtvBrq6jkwIxI8aMLm8gFOP2yeAJRAL8DthWIzgPg6ILIMZuNS6LWrsXZZFOtqDaxd70RM86Hf4ACGjnSgLGg2h8VJMpwExpkaoOswDQnysEHXDfm0JTBOwuI0xCI6EiEVagioPuBc9N3vJEBxZ5Oj2+tiYFy3TwE7QIFiEZD02nkbX05knsqa+QinApAU2/RfAGTeBhgY1wk0HkKBEhaQdda8pUtx15OPwev3ojwYgM/ng2J3IBaPIxqNIhQOIxyJIhKJQNatEgZnd9jhdrngcUtgnAtOhxMOh8OSUlUNqqZbQWihaAwH7b4HfjHpGHhcrUO3C501pumoSyTwfTgC0+NBIBCwwvGaA/Jc1i5jluA4CYqTkLjkngyLk8dkWJyMNzU4LhkaJ54SFCde8Xjc2iU8TvZwYyPKFRv6ez3w2u1wbiVUvdAt2T8KUKB7Bd67/bZWHTj8uZc2J1rmpms3AbgCgD831beqVQXgA6DloS02QYFSF+D6t9RnmOOjQIEJ7HP6TzF91uy0enXkfvvhoRvT/+NPyfXXuBNOwtq69P7I05ABAzDnlZdb9UeOHXTwoWn1MReFthYY997nn+PwczN7z/qwH+6NX512GiZOSOM963gc7332OR576WW8/O6W71kXW2Dc+bf9Fo++8GLaU/Tk7+7ETw5p/oNybbe6hgYrAL53ZWXa9bEgBSiQP4FuWP8euXF0sgbOV3Dc7wD8On+ibIkCFChgAQbGFfDksGsUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQp0XeCBy59q9/2Q8+85pdW/g25bjt+nT+rZx/Oj9fOIzw8+P/j82CzA6wOvD6nPB14feX3M5PqYLFswgXGJhIqhu07KOPCq6y/ZW9dw5GH74NVn/9Bute0FxiULSljWGacehcnHHIyRO26PPr0rkbxBW0Lsvlv8Pb6dtRDX3fIXzF+Y3l9vlzpXf/cmfF7Ppv4cOukCvPXupxkPe8dhg3HaiUdg0MAa9O/XB316VSKhqlixshYrV6/DZ9NnQkLVtrUVc2DchoYmVA46IGO35AGjR+6A5d+vRlMo0qk6thUYt+z71dhu9FGdqrerB20rMO7/Pv0GPzzk7E41cfpJR2DP8Tuhb01v9OvbGwG/F7Vr6/H9yjVW6J4EEn4zc8FW6y60wDjp6Jg9TsDsuYs65SEH3XD1z3HLdb/s9PFZO1CLYP6TNyH63UfwBlUMGNMf/p0OgFk1BnAGJR8OUOywSSCcBLzBBtjsG0cgQW8JmOoGoHEZUD8Pev0yNH7fiOWLgUVLXaht8MNX5sbAwXbssIMN/fubcLoMQJddbQmLk6A4wDBMGPJl3bTC4jTdQCJuIBGTwDgdibAKI2QAvUdixzPugCPQN2sEhVIRA+MKZSbYDwoUvMApAJ7McS9fByDJutNz3I5VPQPj8qHMNihQPALReBxPvfk6Pv52BioryxEMBOD1eGCYQCyeQCzWHBgXCkcQCUcg5Q3dgMPZHBhn7W4nnE4XXE6nFZjWHH6WsILjmsIx2Gx23H7eeRjaf+CmdXohC6m6gfpEHKuicYQVBWVlZfB4PJt2CYqTkDgJi5OguGRYnPwOIrnL+JK/k5CPJSgu+ZgaICc3mcvn8ijBcbJLUFwsFrPC+iRETj6ONTWhl8OOGrcbXrtSFI6FPMfsGwV6mkA33DCfJJa/TiLBcRLolsvtfgAX5bIB1k2BHiLA9W8PmWgOkwKFILBkxUqMPPrHaXflkVtuxqlHSSZPZtvFd9yJvz37XNoHffLUkxg7ckSr8idefmW7oWlpV9qFglsLjJMqjzz/Arz9SebvWQ/fbjBOPuIIDKqpQb8+fazgM1VTsaK2FqvWrsPnM2fiqanbfs+62ALjbnvob5A9k+2KM8/AIXvthaqKcqxZtx5zFi/Cmx9Pw1vTplnVtGeQSf0sSwEK5EagG9e/8g9OJDhu99yMbFOtMQD9AGzIcTusngIUKHwBBsYV/hyxhxSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABCnRBgIFxzXgMNGKgUerTiIFGDDRKPR94feD1gdeHzQK8PvL6mM/rY7KtggmMkw5JYNlPz5Gsiu7bPnvvcUwYN7rdDmwrMK69AySkzeNxbzMYa1sjvfDcE/Dnu65qVWT6V7MxYf+fdgtQMQfGCdjJZ1+Lp597s1vsthUYJ4GCzqo9u6Vf2wqMkw79+MTL8MrrH+S9b4UYGPfnh6bg4ivv6rTFnOnPWWGShbDF18zG/Cn3wB+fjYETtodr+7Ewff0ATxVMuxewOZqD4qz0OAmN0wE9AZsWgi26Clg9A00L5uGrLxyY9lUlmhJuVNfYMWKkiZE7mujfD3B6TEmDk2Q4tCTEwTQljELCKgBda/6WamXJGVBVA3EJjItqUKMaEo064B+I7Y++EMER+7f0pVXwfiFQdqkPDIzrEh8PpkBPEVA2voSZB2BYjgb8hmSa5isoLjkGBsblaDZZLQWKUECCyhatWoHbHn0YHq8bleVl8Pn9VgicZhiIxeJWaFlYAuMiUUQiEcSswDgTTqfdCk1zu2R3w+VuDo+TEDVd16zQMwmMi8USqA+FceQP98HPj5oEh13CkAt3i2kavg9HUKtq8LUExXm9XsguoXHJsLjUkDgJyUsNitvW6MRcykpAXPIxGSCXfEyGxsXjccgugXHiKfOgxGLo53aiyuWC3VZar88L96xgzyhQ/ALdeMN8Eu9WAJflMDhObalbK/7Z4ggo0G0CXP92Gz0bpkDPFPjTk0/hqnvuTXvwy956E9W9qtIunyz434//D5Muujjt4675+c9w4/nntSo/Y+48/OCUU9OuI5sFtxUY98Xs2fjhad3znnWxBcZleh6kM4cMjEtHiWUokH+BAlj/Hr3xjfUbcxwcdzuA6/KvyxYpQIECE5CQyky2TMtnUjfLUoACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQIGsC2wtMO7Isw9o1dbUR99t9Tm/T5/UE4LnB58fqecDrw+8PvD6sFmA10deH3l93CyQ7s+Hts+bggqMk+CsQyadj/c+/CLrL8zTqfD8X0zGX+65eqtFMw2MS6fNbZVZ+PVLGDpk4BZF7rjnH7j25r90tfqMjy/2wLg58xZj9ITJGY87GwdsKzBO6j/rvJvx2JOvZKOpjOroKDBu5aq12PPAM/D9itqM6u1q4UIMjFtTW4e+ww7t1NAkhFLCKAtjkxA4G0KLPkPDR/egZogTjoGjYDiDgMMPKB7A4WoJjZNUNw0w9ebQN1ODYkSBNXNQ9+23+PAjYNq3vTF4kB3jx6nYcbiJigoTsEkiXEsyHEwrIE7C4SR/TjdN62Ndb35UNQNqQnYdiZgBLaJCDevQnDUYfPgvUDlW/hh6aW4MjCvNeeWoKJBlgZMBPJXlOqU6SdCVG4Y+yUHdHVbJwLgOiViAAj1GQNM0vPDeu3jpw3fRu1clygJ+KxjN6XJB1XQrrCwajSEUDlthZREJLoslYJgmHA4JjHPAZYXGueD1euB2e6wgOV03rICzREKDqiZQ3xiCz+vHvRdfjOqKzG/wz9eENCUSWBoKo9GmINgSFufz+SwTt9ttBeTJnhoWJ6FvsiUf0+2rhMPJlnxMfiyfS5ichMapqmo5JgPj5FF2NRJBLxvQ3+OGXZFsF24UoAAFti1QADfMJzuYy+C4PwNIPw2GJw0FKNBWgOtfnhMUoEBeBfY65TR8NXduWm3uNXZXvPvoI2mVbVtI1rFVe++T9rEDaqqx8LWpW6zxfnP/X/D7R/+Rdj3ZKritwDhpQ/okfcv3VmyBcfUNjeh3wIFZZWJgXFY5WRkFsiZQQOtfCY6TcKZxWRvc5oriAPoBqM9B3aySAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgQEEIMDCueRoY6MNAn9QnZLqBNsljeP7w/OH5s1mAzx8GBqY+H3h95PWxM9fHgg6MkwHVb2jEDw48E/MXLsvrC/pDDtgTU5/7o3XD+da2fAbGPXjfNfjlz36y1b6cf9mdePDvz+XVqNgD4wTr0l/fiz88kIvclW1PRUeBcYuXrsQOO/84r/MpjXUUGCdlJGhvzwPOQFMokrf+FWJgnAz+uFOvxIuvtP7hkw7KX/9wLc49+7h0iuavjKkj9P71cNuXwdl3BxjOQHNInARO2OyAItdCCX2ToDgJk2hJfDMN2BJNsG1YgujSJfjgYxdsHj9220VD7ypDsuisQ5KbHGrCBsPYHBynGyZ0CYrTTaiqAS1uQI3r0CIaEmEdursGgw86ExXjj4FNKpR+WI+ltTEwrrTmk6OhQA4EJIFnHoBhWaz7fwBu6K6guOQ4/nv8Me/Jx+WDt9tfHseednoWh8iqKECBYhJYs349rvjzvVAcCirKyuD3+eD2euB0OKFqKtSEaoXESVhcOBxpDoyLqxJJDKcVGCdhcU4rTM3rkcA4NxwOhxV4lgw7U1XNOm5DYxinHPYjnHroYQVJ1BBPYE5DIxSfD4FAwAqJ8/ubA/RcLpc1LtkVRbGCA5KPXR1MamCc1CWfJ/dUx2RwnIT4yceRcBjlagIDZL4YGtfVaeDxFCh5gQK6YT5pfRuAywB4s4ivAvC0/AIli9WyKgr0CAGuf3vENHOQFCgcge+WL8eYScem3aE7L70El5x+Wtrl2xY8+cqr8OLb76R9/Mf/ehy7jx7dqrysa488/wJMm/F12vVko2BHgXHSxsV33Im/PZvf96yLLTBOnH5x08144j/Z++NhDIzLxhnOOiiQfYECXP/KP0KR4LjdsjzaW1r+MFGWq2V1FKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABQpDoG1gXNugn8LoJXtBAQpQgAIUoAAFKNBTBAo+ME4mYsmyldj3sJ/j+xW1eZmXcbuOxP9eeQCVFWXbbC9fgXFXX3oG7rjpwi3+enxq5zRNh4TGPfzYi3kxkkZKITCuuwIJOwqME9/uCLNLJzBO+vbJ599i0kmXo3ZtXV7Ot0INjPvPax9g0klyP3Fm27olb6NXVXlmB+W6tBZB6IPfwG1fDkev/jBd/s0hcRIaZ4XEJbeW4LiWT22GBlukFqEly/DFdBt85T6MGKbB69ky2M2wAidgBcbJrukmdNk1A5pqQk0Y0CUsLqohHjag+wdj8CFnoGrskS2tlWZYnAyOgXG5PslZPwWKXuAkAP/O0ijkjtQbAXyUpfqyUk3sy+mpP2yyUicroQAFikvgtY8/wkMvP4fK8nIEfF4rHC0Z+qbpBuLxBGKxKCLRCCKRqPV5PNFeYJwHHo8bHrcHdrsdpmlA0zQr2CyhqkgkVCswbkCfGtxx3vmoCAYLCiqqaZhTvwGa1wefr3lPhsWJhwTjpYbFSWCc7NneUsPjkqFxuq5vspSwONmj0agVyBcNhVChqRjg88Keg/5ke3ysjwIU6D6BArxhPolxO4BLshgc98eW+roPmy1ToDgFuP4tznljrylQtAL3/vNxXPvHP6Xd/xnPP4uRQ4akXb5twaemvoazb/hN2sdfceYZuO3ii7YoH45GccLlV+DtTz5Nu66uFnzyd3fiJ4ccvM1qNF23QuMefSF/71kXY2DcvCVLsOtxx3d1SjYdz8C4rFGyIgpkVaCA17+TWt4nylZwXBhAXwChrAKyMgpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIFIsDAuAKZCHaDAhSgAAUoQAEKUMASKIrAOOloQ2MIF13xezzx9Gs5nbrLLjwVt994AdxuV4ft5CMw7v67r8IF55zQYV+SBV74zzs485c3oSkUSfuYzhYshcA4GfuiJSswdu+Tu2RW3acKE8aNhpwT6WzpBMatqa3D8LHHdKlfOw4bjEcfuBH7HPqzdLqFdAPjpLL1dQ0491e/xfMvS+ZMbrdCDYyTgIo+Qw7OaI4mH3swnvnnnbkF60ztahMa370BXmUpHL37wnT6ALsTsIIeJCBOtx6aP24JjDM1QMLi9ATMulWYM70Jn82qwOhRNuw4XIPHJUVNwAbYJCTONGGYNutrhmE0B8ZpEhZnQlcNa9cSGtSIgUjIhKtmNAYffibKhu/XPCLTaOlP9sMwOkOW7WMYGJdtUdZHgZISkAvffADDujgqeaFyTaEFxSXHxMC4Ls4uD6dAkQssXrESl9z9OyhOO4JBP7weCX3zwOVyw25XIEFlEvQWjcWswLhYLG6Fv2lWYBzgdDjgdDrgdDnhdrngcbutsDkrMM5VUAZsAAAgAElEQVQwoBvNQWeqpkPXDUSiMTSGIjhkz71x8eTj4bDbC0IwoemYv2EDGu0OBMvKrNA82SU0Th4lLE52GVeuguJSIbYWGicBcRLAJ4Fx8iihcbFYDKGGBgxyKKjxegvCk52gAAUKU6CAb5hPgt0B4FdZCI6LA/ABMApzJnpMr97LcKQTMyzP4tkV4Po3u56sjQIUSENgwokn49sFC9IoCQwZMABzXnk5rbJbK7S2rg6DDj407ToG1FRjwdRXoSjKFsfEEgnc9/gTuPmBB9OuL1lw4oQJ+OM1V+Pvz72APz/1VFrHv/HQg5Dj0tleeucd/OLGm9AUzv171sUYGCeGz/z3Tfz0mmvT4eywDAPjOiRiAQp0i0ARrH+PaQmOG5sFIElDvTUL9bAKClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAgUrsHTWKutOa24UoAAFKEABClCAAhQoBIHtxvSzAnhss187odUL1VH7XFUI/dvUh2df/B+uv/UBzF+4LKv9krCv235zPg498Adp17t6zXr89u5HcP9Dz6R9TDoFgwEfrrj4dFx47omoqixL55BWZVauWos7730Mf35oSsbHpnPAYQfthXPPPg7HHn3AFsXPv+xOPPj359KpBp+//zjG7zY6rbK5LvTtrIU4/vSrOnVeicOD912DZ158CxdfeVdaXU0nME4qWrx0JU7/xQ34+JOv06o3tdCF556A22+80ApWqBy05Vy1V2EmgXHJ4+U5Kefbl1/PzbiPHR0gz4VfnX8yzj3rJxg4oLpV8SnPv4mTzkrvJop777gMl15wSkfNder7F135+4yuAf+Zci+O/lFLAFqnWszRQVoCDe/dAHdiFtzVvWG6/IDiaG5Mgtok9c0KizMAQ8LjNEBXASMB6Dr0hiYsnRvG59/60W+gE2NGJuB2mVa2XPJQKzBODjdMGLoBQ28OizOswDgNWtSAFjcRNQIoG7E3Bk08Ee6a0c0BddK2FV4nGwPjcnQWsFoKUKBwBY4H8GwXuvfhxmNvApD7lNcudJKBcV3A46EUKAGBp15/A799+CH061uNqspyKyBNQt8kHM1mU6C1BL7FYzFEY3HEEwlommqFv8lrxebAOCfsDjvcLidcLpcVHOewO5pDjNEcWJyQ152ahnXr67G6dj1qqvvivksvxY6DBna7om4YWN7YhO81Hb5AAIFAYFNgnITnyS5jdDgceQmLS4K0FxongXHimAyNk+C4SESC/GKIbajHqGAAPkfLeqLbZdkBClCg0ASK4Ib5JNnvN/7NhIsAeLpgeA+AK7pwPA/tukCm/zCnNH/x1HXHfNXA9W++pNkOBShgCcxfshS7HPeTtDWuOPMM3HaxvDzo2nbA2T/DtBnpv/f3wT//gT123nmrja6srcUDT0/B82/9D4tXrNhm56SeC085GZMPPcRaW5569a+t49LZPnv6Keyy447pFLXKrFq7Fnf94zGrb7nYDtlrL/z8+OMw6YAt3wcddfSkDi2SfUonbE2MxCqd7bpzfoEbfnluOkXxynvv4+wbbuhysF46Y0irQyxEAQpkVaCI1r/HtQTH7dIFgDCAvhvXwKEu1MFDKUABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABChS0AAPjCnp62DkKUIACFKAABSjQ4wSKJjBOZkZuVn7vwy/wl4efwfMvdy334rQTj8AF50zGDyZs/R/Zd3Q2NDSG8Pqb/4dXXv/A2ptCnftL6RKOdcPVP8c5Zx2H8rJAR812+H3p15NTXsdjT76Cz7+c3WH5bRX40SF7Y9KRE/GjQ/fG4IHybzzb36664U+464+Pp9XWrM+eweiRO6RVNh+FItEYbrj1Qdx7/5NpNbfLTsNxz28vxcEH7GGVl4C+bAfGSb3axrCA3//hn7julgfS6peEvt17x6WbwvjCkSgCffdN69gjD9sHrz77h7TKti306fSZ+Osjz+P5l9/u9HNA6pRgOAlUk74ccsCecLmc7fbn1Tc+xNEnXJpWXx/647XW8yoX2+m/+A3+NeW1tKqW5/i6JW9vdUxpVZLDQo3T/wHb8ufgr3ICbg9Mm9KSzWYD5GMrME63AuKaQ+MMmJIAZ1Ngs9lhjzRgxnQVdREPdhymweVsCYxD86NkzZm6CcMEDM1oDopL6EjEDOhxEzHTj6oxP0Dv3X+MsiG7AIq3pU0Ji5P2S3ub85Hch755G33EM7xBubSnnKOjQLoCci2YCaAzKbsftdzg07UXzOn2tIvlGBjXRUAeToEiFpCgtAvvuAPvf/YZKsvLUFZWBn/A1xyQ5nLB7nDAaAmMS8QTSKgqJLBMwrFNeU0KwOV0wOlwQlEUKzTO5XLA5XACig26qiOhJhCLxCDrvmgkiroNDYgmVPTr1x/nn3giTjrowG4XDCVUfLVuHTzlFVZQnATGJYPi3G73psA4uaE/ueer021D48Re9mRgnDxGo1ErNE72QCyK7QMBOO2l/zo+X3PAdihQSgJFdMO8sMuF7HYAFwOQX1RkusUB+KzkUm7dJcDAuO6Sz7xdrn8zN+MRFKBAFwVefPsdnHxl+n887Z1H/469x47tYqvAH//1JK6+976063ng+utw9nHHplV+4bJlWLB0GdbV16O2vh6qpqG6shJ9+/TG+NFjUN2rqlU9e51yGr6am94fhVr4+lQMrKlJqx+phRpCITz92ut44pVXMH1W196zPuyHe+PoiRMhj4P6bv096wknnoxvFyzosK9Bvw9rP/ygw3Kvf/gRjv3VJR2WkwISKijhgulu6zdswOP/eQV/e/a5tEPuUuveb/zuePNvD6XbHMtRgAJ5FCiy9a+8Hj+m5X2lXTvJdF3LGrqTh/MwClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAoUtsLXAuKmPvttux488u/UfQmxbjt+nT+qJw/Oj9fOIzw8+P/j82CzA6wOvD6nPB14fe9b1saNXx0UVGJc6GFXVMHP2d5j+1Wx8+fVcrKmtQ/2GRtTVN8Jht2NDQxMqyoOoqAiiV1U5aqqrsPvYUZgwbgxG7rg97Fm+eVn6s2jJCqxYWYsVq9Zi2fLV+H7lGixeshKLl65AwO9DLJ5AZUXQ6teggTUYt+tI7LbrCOw0ahg8HldHc9Wp78tN8TO+mY/PvpiJeQuWWj4NDSGsXVcPv9+L9XUNVp/Kgn5UVpRZThKIttsuIzBqxJCCDbjqFEYHB0nQ3ouvvIt33v8cy1esseZQAgn61fRG35re2Gn0UJx43CHYdecdW9WUq8C4ZCPLv1+DGd/Os873Gd/Ox4xv5llhB9sN6ocB/autuZp87MEYOmRgLlgyqnPx0pWY/uVsfDFjTqvnpJxn8jwMh6PWc1KCEeW8G7bDIOs5MHbnEehb0yujtrqzsFxrqganHyxx6QWn4N47LuvOLm+z7fDyL6F+dhsC/hBMtws2RQFkt9kkqbPlWEl+k8Q3wwrvNGGDzTBhahrWr4jj21lOeP02DO6fgN2ePEwC40wra840TCssTk0Y0OImTFVBTPGicvg4VO58MMp2GAtHIHmzUbLNnpGbxsC4gn1qsGMU6G6BEwBMybAT/9dyQ8//MjyuW4szMK5b+dk4BbpVYENTEw6/4EKEmhoR9Pvh83qtoDiHy2U9Op1OedUJXdegaRriasIKKtM1FYapy0tTKDabFRZnpcdZmTw262tW0JyqIaFq0DUpK0FnBlRNRULXUVZRhcmHH4prf5r+jdy5wJK+zK/bgEan0wqGk7A4v9+PZFCcPIqDw+FoHmebTQLkcr0lQ+Os1/amac2F7DIXycC4WCyGcDiMhvp6jPR5UeVxW+F23ChAAQqkChTZDfPJrsvF904AF2189GQ4o5IGU7i/EMpwMEVYnIFxxTNpXP8Wz1yxpxSgQIkIfD5zFvbNYD1cP+1jeN3uLo0+Eovhm3nz8fmsmZi/ZCnqGxohgXIScCe/D6hraEBFWRBlfj8qy8pQXVWFnYcPx64jR2DkkCFwOdv/Q1Nd6lQBHbx89WrMXLAQ85YsQWMoZNmIxboNG6yx+zwe9KmqQr8+vbHTsGEYvt121r+J4EYBChSmQJGuf+WXeZJSeiOAXTKUDQPoAyCa4XEsTgEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUKAoBBgYx8Ce1BOVgT09K7CHgV18/vP5v1mA1z9e/1KfD7w+5vb62NGL5KINjOtoYPw+BfIlkOvAuHyNg+2kL/DQoy/gl5fcnvYB0z94wgqsLNTN0GJYP/VKeOPfwhFwwuawA5IXl+ywBD0kw+NM+VjyOGyAakBvCuPrGcDcRV4M3y6G6qqElTXXki1nhXgYOqBrJkxDQdx0wt2rH6qG7Y7y4XvC229HOIPVgK3lIGlUbuftQeESDIwr1GcG+0WBbhWQq+FMAKPT7MUnLTfwvJlm+YIqxsC4gpoOdoYCWRdQraA3FYa8MIQJXdeharp14/M7n3+Gux55BB63B36fzwpMk3A0xe6A3eGA3W63QsfkGAnzjsfjiCcS0FQVmq4112mFmEm3mwOOm1+6Ssxcc2ix9bLS+p8Jw2gOO4upKlxuLybsNhaXnnwiqisr4Xa64XG5oNiUTXVIrW4Ja8vhDdj1sRi+WLseZZWVVlicz+ezdgmKc7lc1mMyLK69wLhsTlh7AW/JsLhkO8nAOGseVdUKjJN5kcC4SCRi7e5IGMOCQTizHNSfzbGyLgpQoHsEivSG+SRWZ4Lj4gB8LYmm3YPes1tlYFxxzD/Xv8UxT+wlBShQYgKTLroY//1Y/vZEx9vECRPwxkMPdlyQJShAAQpQYJNAka9/Oxsc92sAv+NpQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUKCUBB64/KlW/x61o6Cc5Ng7KsfvM3An9XnCwJ3cBu7Ql76pzzdef3n95fV3swCvj4V5fcx0XmyzXzuh1QvWUftcVUqvxzkWCuRMgIFxOaMt2Ip33/c0fPn13LT6N3rkDpj12TNple3OQuElnyL8/s0o84dguhwwJWQjGa4hoRtKc1AcTBtskggniXKS+RGLYdkCFTMXB+B1xlHmVeGwm9BMwO5wwuFyw+4JwFVRjbIBIxDcYSxclYPgLusNxROU9I7mdLnN6XTdydAtbTMwrlvY2SgFCl1g8sawuHR+eHy6cSA3AXij0Ae0rf4xMK6YZ499p8C2BeYtW4J/vvoqQrFYc+ibhIupCSu0LRKPYXXtWqxevRY+rxd+rxduCYxzOK2wOAmNs9kVSPSbHBtPxBGLx5GIx6FqKgzdgGE2B8Y1b1bqsLxK3RT4JuFmm15mSlqPBMZJXWoCDpcL1X2qMWrEUFSWlVvt+X0BuN0euFxuJDQViVAIpx9yEIYOGJiTqdYMA/Pr6lGn2K2QuGAwCK/Xa+0SFpfck4Fx0on2Qt1y0rmtVCqmMh/JwDgJjZPAOJlbCYsLh8OoX7cOYysrUOZy5rNrbIsCFCgCgRn/esLqZcOype/L42HPvTSxCLrdtovyo0ZugL8QgCeN/t8N4Mo0yrFI9gUYGJd901zUyPVvLlRZJwUoULICsqadeOZZiCdUHPSDPTH5sEMxbtSotNeKumHgjof/jtse+lvaRndeegkuOf20tMuzIAUoQAEKACWy/pVfrR7X8geLdk5jXpsA1ACIplGWRShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQoUhQAD45qnKdPgEgYyMZAp9QnO86cwA4mSc8T54fykPl95/eb1m9fvzQLddX3MtF0GxhXFsoKdLEQBBsYV4qzkrk9ffT0P4/Y9Ne0G7v7tJbj8osK/kcY0dDTNmorojEfgV9bD5rRZAR1W7EZqcJzNBtO0NYfFmUA4pME99EhgwIHQ41HosYhEccDh8sDu8cHh8kFxB2BzBeHweKG4fc1hc7L18KC45EnEwLi0n04sSIGeIiBX3ZkbA+NGb2PAn2/8ngTFvVYKKAyMK4VZ5Bgo0L7AguVL8acp/8b6cJMVQByPq0gkVCswTsLeYrEY1qxeB0PT4fU0h6Q5XS44nE7Y7XbYFbv1YtQKeUvENwWTaW0C40x5YZoSCZPy8tUKkZPXs/Jt0zCsuhKqCsVuR7CsDEO33w59+lTD7nAhEAzC5XYgFo4gVLceB42bgKP23w9Bn7yGzf4W1TRMW7kavooK+P1+a5ewOAmPEwsJipPdsrDbN3Wgu0LjJCxOtmRgnMyj7BIWJ3s0GkUoFLL2akPH4GAAirWY4EYBClCgtYBn3PhSuDjILzd+D+CCDoLj4hvLyQ8S+U0Kt/wKMDAuv96daY3r386o8RgKUKBHC0TjcVTu9cNWBn2qqnD8IQdjr7FjsfvoURgyYAAU6w//NG+yDp63eAk++vJL3P/vf2PB0mUZGX4+5d/YefjwjI5hYQpQgAIUaBYokfWvvG7/ycZ17W8AdBQcdwWAezj/FKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABUpFINPAuLZBO6XiwHFQgAIUoAAFKEABCuRHgIFx+XFmKxQAA+N61klw6a/vxR8eeCrtQX8/9zUM6F+ddvnuLGhoCSTWLURozptQV82ATasH1AhspgqYGkwJ27C7obg8UHxVcFbuAM/2+8A1YALgClopHKbZfP+zDTZAsaI6WvbmkUmYx+a7wkvh/vCuzxgD47puyBooUGICctPNc1sZ0xcbvy5Bca+W0pgZGFdKs8mxUGBLge+WL8efpjyFxlgUpl2BrmmIxiT8LQHD0K2gscaGRqiqBkVxWLvDKUFpTihWYFxzQJkExsXicUhYnJSVYyUArjnEzLSyiJtfkLa8/DRTX3fK61QThmFaN8pLWJ0E0/Xv1xfDhw1DTXVfOBx2mLqKeKgJe44cg8P3/iGqystzOqWrQiF8U9+AXr16tQqM83g8VmBcMihOQuMkJC4ZFNcdgXHJsLhmR6M59K/lUVUlCDBhBQBGIhE0NTVBa9iA3fr0hjMlpCCnmKycAhQoKoESuWE+aZ5OcNxdAK4qqkkqjc4yMK7w55Hr38KfI/aQAhQoMIH2AuPa66KEyAW8XoSiUaytq+v0KH642254+5GHO308D6QABSjQ0wVKbP2bDI67ceP7WDttZW4bAfQFEO3pc8/xU4ACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACpSHAwLjSmEeOggIUoAAFKEABChSLAAPjimWm2M+iF2BgXNFPYdoDiEbjqBl6CJpCkbSOOeygvfDGi39Oq2yhFJIACFNLwNTiMBJNMONh6PGwFZ4Bmw2K2weHJwjFUw6bw2UFyMGmABIUZ7NZ+RyybT0KjiFxbeeagXGFcvazHxQoCAG5SM4EMLpNb77a+LkExf2nIHqZ5U4wMC7LoKyOAgUosKGpCW9/+gk+nTMLTbEo4gkNCVOHrptQ1YQVPqYlVOi6Zn1PAuJMKNZrS0M3rJA3CSSLJ6SsClXTYFrBb1ZicUsssQlTl3ji5lek1itTef2q2KzgNQldc8ruciMQ8KF37z6oru4Dr8sFm67C43BjzODtcfCee6J/de4DnzXDwLe16xByOhEMBrcIjJP+JgPjFEXZFBiXSVhcMuQtm6eE1CnzI7sExsljMjAuHo9boXESGLd+bS3GVVai3OPOZvOsiwIUKBGBErthPjkrEhx3N4DzAbS9+MUB+OTHWolMYbEMg4FxhT1TXP8W9vywdxSgQIEKpBsYl63uf/j4Y5iw09YygbLVCuuhAAUoULoCJbr+ldfyx2/8pawEx41pZ/YuBfCH0p1VjowCFGgjIO9hZ7JlWj6TulmWAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABCuRMYOmsVZn+u9Sc9YUVU4ACFKAABShAAQpQYLsx/azwHtvs105o9UJ11D5XUYcCFEhDgIFxaSCVSJEpz7+Jk866Nu3R/PvR23HS8YemXT7rBTUdcNi7Xq0VwtG8yUeZhFR0vfHSr4GBcaU/xxwhBTIQOA7A8ynlZ2z8WP7R/MsZ1FF0RRkYV3RTxg5ToNMCcmP7/CXfYca8ufhoxucw7UDMcMEwFXjcLjgddiQ0FfFYc4icqmtQVQ26qiGhJpCQjyWoTALlJOgYZnOQmgnoRnMGj2JX4HDY4XY64XI54XZ54PZ64Pd54fW45QgYpgGnqSLcUItY3TKMGdgfF5xzGyorKzs9tkwPTOg6Pv5+Jdzl5VZgXCAQgM/ng9frhdvt3hQWJ6Fx1hhT9q21lYuAOGsN0LIeSH1MDYyz5kpVIYFx0WjUCoyrq6vDcI8b/QL+TGlYngIU6AECJXrDfHLmJDjuHgDntQmO+x2AX/eA6S2kIWb6D3P4Vw7yO3tc/+bXm61RgAIlIpDPwLiTfnQ4HvvtbSUix2FQgAIU6B6BEl//yhpq8sZw9N+0CY6rB9AXQKJ71NkqBSiQZwH+/iXP4GyOAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKECB7hFgYFz3uLNVClCAAhSgAAUoQIH2BRgYxzODAl0UYGBcFwGL6PBDJ12At979NO0eh1Z/aAVTcKPAtgQYGMfzgwIUaBGQG2tmAhgN4JuNX5OguJdasjpLEun144+ZKAPb7dTT35XHiu22K8lxclAUoED7Ao/84ybMmfshNiTciJgBOLyVcLjLobhdABzQYYNps0NR7LDbbJDMMgmIk//kaxIMZ1PkYwWKabOCjaHYYFcU2O0KFAmGMwyYprbxnkUDajyOSGMTEpFaONT1CCqNCNgjCHpNjB45EceeLDk++dtCCRUfr1yFil69WgXGeTweKzDO4XA0j01RNgU2pwY3Jz9ODXGTr3UmNE6OaRsKnfq19toQW9klOC41MC4WiyEUCmHDhg3obejYoaLcmj9uFKAABVIFSvyG+eRQJTjuXgC/bAmOiwPwWT+UuOVLgDcs50s683a4/uX6N/OzhkdQgAKWQL4C4/bYeWdMfeAvCPrl5Qs3ClCAAhTorEAPWf/K6/sTWoLj5D2uVQAuAPBiZ914HAUoUFQC/P1LUU0XO0sBClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoEBnBRgY11k5HkcBClCAAhSgAAUokAsBBsblQpV19igBBsb1jOletGQFhu4yKe3B/uynk/D3+29IuzwL9lwBBsb13LnnyCnQRuDYjVFHt2z8mgTFvVDKQXHJcb9x/DGtbiCYeO31PCkoQIEeJPDhO/djwczn0BSOIRwDVq83sa5RQe9qH6KqE6sb7IhqLjg9Prg8XgT8bgQDDtjtTtgkME7C0Ww2K+hMsQGGhJ7BQCKmoTGcQDQcQTQchhNRBF0JmGoUa9eFMKgXsMtQB8qDdgQCbjicTvTpvy/2OfA6IE/BZhLAtiYcxjf1DaiorER5eTn8fj98Ph+SgXF2u4TltQ6M29rpIfW1F/qWWn5bQXLJY9sLnGv7tWQ9ycA4eWwbGBcOh9HQ0ABXNIIdyoIoc7t70JnNoVKAAukI9JAb5pMUqcFx9wG4Jh0jlsmKAG9YzgpjTirh+pfr35ycWKyUAj1BIB+BcTsPH443H34IlWVlPYGUY6QABSiQU4Eetv5NDY67ruWPIuXUl5VTgAIFIcDfvxTENLATFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEK5FqAgXG5Fmb9FKAABShAAQpQgAKZCDAwLhMtlqVAOwIMjOsZp8XNd/wNN23c093ef/1v2O+H49ItznI9WICBcT148jl0CrQW2AXANz0JhYFxPWm2OVYKbCkwb9bzWLnoMURjcYRDGuYvjMLuBGp6KZi9IIH1jTb4/DY4nBIGp8DjcaAsYAdsCgzYACskTeqV+xDlAxOKYiIeMxAKa1BsuhwIXbXBbjOww2AX1jcaaKgzsOc4PwYOcMPjUQCbA/6KAzFy7K/yNk3S2wXr12N5QrPC4srKyqzAONklMM7lciE1MM4aZUuYXdvgNwmVky0Z4JYslxxMsrzUlwx/03V9i7Emv5cMqZPjpM62W7I+qSNZRgLjEokE4vG4tUciESswLtGwAUOCQfQL+PNmy4YoQIHiEOhhN8ynTsqJAKYUxyyVRC/fy3AUEzMsz+KdF+D6l4FxnT97eCQFerhALgPjgn4fzj/pJFx+5hko83Md18NPNQ6fAhTIkkAPXv+OBTAjS4yshgIUKGwBBsYV9vywdxSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABCnRR4IHLn2r1fsiRZx/Qqsapj77b6vO23+9i8zycAhSgAAUoQAEKUKCHCXT0+rLt922zXzuh1QvWUftc1cPIOFwKdE6AgXGdcyumozRNx4ARP0Lt2rq0uj1wQDWWznoVyfCGtA5ioR4rwMC4Hjv1HDgFerwAA+N6/ClAgB4usGHtV2hccydsiOO7hWGsq1MxYqQfit2ODU0GPC4TDjugKC5ouoZYTEdMtUHXJMhM8uJafoVhA2wmoNhtsNttcDps8HhM+D1OOJ12aGoc8YSJYNBKnsP7HzbAaTex/35l8PnsaGqyw+E/BgOHn563GTFME1+tWo0GxY6KiootAuOcTuemwLhkAFxqEJwEucnnst6IRqNWOFuvXr0gx0lwm3wvGQAng5Kvq6qK2tpaVFdXb/pcvpcsKx9LqJzUvW7dOvh8PgQCgS2C6JLuyYA6eZTwOKm/bWBcY916DPJ6MbSyIm+2bIgCFCgOgR58w3xxTBB7SQEKZF2A69+sk7JCCvRYgVwExg2oqcaVZ52JU488ChIax40CFKAABbInwPVv9ixZEwUoULACDIwr2KlhxyhAAQpQgAIUoAAFKEABClCAAhSgAAUoQIRcdHwAACAASURBVAEKUIACFMiGAAPjsqHIOihAAQpQgAIUoAAF0hVgYFy6UixHgS4KPPToC/jlJbenVcuzj/8Oxx9zUFplWahwBGbPXYQxe5yQdoduvvZc/ObXv0i7PAv2bAEGxvXs+efoKdCTBXjDfE+efY6dAkCk8Xs0rbkOitmAWTOb0L+/C4OH+OFwSGhZAmvrPfh+nQ/r62PoU+VG/z4GHI4INF2+b2sOjJPbkCQwzibBchIYB7gcJiIxF+YujMHtC6Cmtw+V3nWoKo/B5XRh7pwwvpzeiPHjA6jp50Lteg8q+12Gypo98jYtEhg3bfn3iLvcqKysbBUY53a74XK5rPC2ZCicjDU1MC4Z2jZ37ly89NJLWLFiBXbaaSccddRRGDBggBXgltwkLG758uV45plnsGDBAuy4446YPHkyBg4cCE3TNgXGSfhcfX093njjDUyfPt3q19FHH41dd93VCo1LblsLjJO6JKwuFoshEomgsbERG9avR3+3CztWVebNlg1RgALFIcAb5otjnthLClAgewJc/2bPkjVRgALA2ro6fD1vPr6YPRvTZ83CzAULsXjFioxohm83GOPH7IQfHzARR0/cHw5ZUHOjAAUoQIGsC3D9m3VSVkgBChSeAAPjCm9O2CMKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAWyKMDAuCxisioKUIACFKAABShAgQ4FGBjXIRELUCA7ArpuIBSOpFVZMOCD3IjPrfgEGhpDaXfa5/XA6XSkXZ4Fe7YAA+N69vxz9BToyQK8Yb4nzz7HTgFA1+NYPudGxJpmonZVFKPG+NBvgA+GqWF1fR8sC43EugYVH0ybjznfrcWJR+yIo/ZWEfSuh2k6YJo2QELjbBIaZ4MCA4qioyHaCzMXV+K75TG8/sFC9KmpwunH7oqBrq/Rv3ItwmFg2scbUBa0o28/NyLqGAzZ5SbYHZ68TYtuGHh/yVLA57eC2YLBoLX7fD5IYJyEvElgXDI0LtmxZFibrClXr16Nu+++G5988olVzuFwYNKkSbjgggus4hLyJl+TELf77rsPr7/+urUWlTqOOOIIXHLJJVY7Ei6XXKNOmTIF//rXv6zAN9lGjBiBq666CkOGDLHqk9A6OV52+Ty5Sx2qqlp7MjCuoaGhOTDO48YIBsbl7dxiQxQoFgHeMF8sM8V+UoAC2RLg+jdbkqyHAhTYmoCs05oiETQ0NWFDU1PLYwhN4TDcLhfK/H4EfD6UBQIYMnAAvG43MSlAAQpQIA8CXP/mAZlNUIAC3S3AwLjungG2TwEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgQE4FGBiXU15WTgEKUIACFKAABSjQRoCBcTwlKEABClCAAkUgwMC4IpgkdpECFMiJAG+YzwkrK6VAEQloWDn3RtSu/BKJmIZhI9zo3duJkNEXIf8xeOW9hVi6aAFCEQOPPPs59t9lMP5wy9EY1e9LQN8AwA4g5T4kmw7Yq/HxnO3xrxe/we67DMIDT32G+YvX4Y83TMYRB42EvfZpBJ3r8fWMGCJhA9sNDcBTfhh6Dz4vr24SGPfOosVQ/IFWgXF+vx8ul8va2wbGJcPipKMS3Pb111/juuuuQzQahcfjQSgUwvjx43HrrbdC6tE0zQqMW758OS6//HLU1dVZYXQS6NavXz/ceeedGDhwoBUYJ21JSNy9996L//3vf/B6vVYYXDwex7XXXouDDz54k097gXESFCftpQbGNTU1oX79OtQ4nRjdu1defdkYBShQ+AK8Yb7w54g9pAAFsivA9W92PVkbBShAAQpQgAIUKBYBrn+LZabYTwpQoAsCDIzrAh4PpQAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQoHgEls5alen7IsUzOPaUAhSgAAUoQAEKUKDoBLYb088mnbbNfu2EVi9UR+1zVdENhh2mAAUoQAEKFJsAA+OKbcbYXwpQIFsCvGE+W5KshwJFKmCqCK25AXWrZ2L1qjhGjHDBH3Qj3mcyjOr9ceNVv8UT/5kBn9+LuvUhHLydiTv+dAt2Gt4IZcNU2OyOzXlxpiEpajArDsMz7zpx4ZV3w+N1oT4COBuacOVJQ3HR3XchtOwjeNc/je8WRLFypY6x48pRNWAyHMFT8oqYDIyD14eKigqUl5cjGAxaQW8S6iaBcYqiWLuEucmWDGqTsDj5+uLFi3HbbbdZj8kguAMPPBBXX321FRQngW9yrATJ3Xjjjfjyyy/h8/kQDoexxx57WF8LBAJWYJzUJ+UffvhhPP/885s+r6qqwvXXX48xY8Zs8kkG18lxcozsybC4RCJhBdJJ+JwExm2oW4++LhdG9arKqy8bowAFCldgw9KlVue+evKJA+TxR8+99F7h9pY9owAFKJA9Aa5/s2fJmihAAQpQgAIUoEAxCHD9WwyzxD5SgAJZEsj0xijrH6pyowAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgALFJsDAuGKbMfaXAhSgAAUoQAEKlLYAA+NKe345OgpQgAIUKHABBsYV+ASxexSgQM4EeMN8zmhZMQWKRMBEdN0f0bT+baxZFcegQXaUVQbhHH4+EpX744833YTb750Kw+fCmGoVJ01QcMoN/0ZN/zKYS26EgggApXmspgZDCULZ/kp8OqMRF/7sPHy9JI6aaicOGZzAKZP3w77n3QV73TSEZ9+H5YuasHS5jp12rUTv7c6Bw3toXs0M08S7CxdBdbtRWVlpBcZJeJsExnk8HisATsLeZJeAONlTA+PkcwlpmzZtGl599VUrFK5v37445phjsMsuu1jfkzKySRichMU9/fTTaGhosNo68cQTMX78+E3lkiF0S5cuxUsvvYRFixZZbR922GGYOHGiFUCXrC81ME4+TgbGSVhcPB63AuMklE4C4xrq6tDf68HIqsq8+rIxClCgcAXeu/22Vp07/LmXeINo4U4Xe0YBCmRRgOvfLGKyKgpQgAIUoAAFKFAEAlz/FsEksYsUoEC2BBgYly1J1kMBClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoEBBCzAwrqCnh52jAAUoQAEKUIACPU6AgXE9bso5YApQgAIUKCQBBsYV0mywLxSgQD4FeMN8PrXZFgUKUyC64VnE1j2BUEiFP6CgstIBT++RwJCfYeFi4Il77kNo5Uzstbsfu0w8GUP2OQvOxv8Ca54BoANwNA/MVAGbA+h3AmJlh+H1Jx7D1H/8HWOGm9jjBztjpyMvR3mlBv27hxFaMw+rV2pYX6dj+6G9UTHwGtjdO+cVSALjPl68BI02BRUVFdYeDAat0DgJjHO5XFZIW2pgXHsdlMC2xsZGRKNR63gJnEsGuqWWl7A3KSchbtKGhMZJ0JtsyUC65McS+CbBck6n0yongXOpWzK4To5P7hJQp6qqFRYnfZHAOGkv1NCAAV4PRjAwLq/nFxujQCEL8Ib5Qp4d9o0CFMilANe/udRl3RSgAAUoQAEKUKDwBLj+Lbw5YY8oQIGcCTAwLme0rJgCFChGgTkPo9V1cdQv0OoP5/D79Ek9r3l+8PmRej7w+sDrA68PmwV4feT1kdfHzQL8+cCfD/z5wJ8PSYFC+/lYjGt29pkCFKBANgQYGJcNRdZBAQpQgAIUoAAFKJAtAQbGZUuS9VCAAhSgAAU6IcDAuE6g8RAKUKAkBHjDfElMIwdBgS4JaNHP0LTyt4hEEnC5FFT1csPjNmHz9YWtZj9EYr2w/vvV8FdUobymF+zhz4GGryTlDLC5mkPiZJPAONmhAOVjofnHonbRatiMKCpqquFxrYNt3QdQm9agqQlYuzYOCW3rXTMEgX6/hWKv6tI4Mj1Y2v56xUosj8WtULbUwDifz7cpME7C2lJD4yTcLbklg+FSA92SYW7thcZJPclN1yVsr/WWrDs1QC5ZX2rJ5NekjmRgnHycSCQ2BcaFQiErMC7S2IhBPi+GMzAu01OE5SlQsgK8Yb5kp5YDowAFOhDg+penCAUoQAEKUIACFOhZAlz/9qz55mgp0MMFbspw/JmWz7B6FqcABSjQvQIM9GCgR+oZWGiBDjw/eX7y/NwswOcnA8lSnw+8PvL6yOsjr49JAf584M8H/nzYLNDRz8fuXX2zdQpQgAL5F3jg8qda/aGII88+oFUnpj76bqvP234//z1mixSgAAUoQAEKUIACxSzQ0evLtt+3zX7thNZ/2Wyfq4p5/Ow7BShAAQpQoCgEGBhXFNPETlKAAjkQ4A3zOUBllRQoMgE9sQSN31+JaDgExWZDZR8PfH4XFEWHTTFg8/UCZJeAs/BaQAsDdh+gtITFycemCRhhwDQAIwHoEcDpBwLVgGEC0XqYkXWAqSCh2dG4IYb69Qk4nHaU9x6HQL9bgNb/ziXnihK6tmjdesxYtx5lZWVWYJw8BgIB+P1+eDweOBwOKywuGRgnwXDtBcZtq7PtBcelfi21Pqmn7efJupNfT4bFSVBcamCcpmlWYFw8HkckEkEyMC7R1ISh5WUYVBbMuSkboAAFikOAN8wXxzyxlxSgQPYFuP7NvilrpAAFKEABClCAAoUswPVvIc8O+0YBClCAAhSgAAVyJ9D2hvqRR05v1djcqeNbfc7v0yf1hOD5wedH6vnA6wOvD7w+bBbg9ZHXR14fNwvw5wN/PvDnA38+JAW66+dj23bbBi3mbsXNmilAAQp0rwAD47rXn61TgAIUoAAFKECBnibAwLieNuMcLwUoQAEKFKUAA+OKctrYaQpQIAsC/z3+mPekmvLB2+0vj2NPOz0LtbIKClCgmAQMI4ymlTchXPctTNOGil4u+ANuOF0OQLFZMW4SHNf8gROw2eULAAzAEQCCuzd/3PBFc1AcWr5naICuNofI2ewwocA0TMSiCTTUxxFqVOF0uVHe/zj4qs7IO5kEr60Lh/G/Bd+hvLIS5eXlVmBcMBi0AuO8Xq8VGJfck2FxEty2tVC31EFI/VKubWBcewFyyePaq7ttQF3y+GRgXDI0TlVVKzAuFoshHA5vCoxzxePYsaoSVV5P3o3ZIAUoUJgCvGG+MOeFvaIABXIvwPVv7o3ZAgUoQAEKUIACFCgkAa5/C2k22BcKUIACFKAABSiQPwEGxjHQJvVsY6ALA11Sz4fuCvRI9oHt8/rE69NmAV6feX3m9XmzAH8+8OcDfz7w50NSIN2fjwyMy9/vGNgSBShQWAIMjCus+WBvKEABClCAAhSgQKkLMDCu1GeY46MABShAgZIQYGBcSUwjB0EBCnRBIPbldLMLh/NQClCgyAViDe9h/ozfw+fR0bevG96AGy4JjLPZoCh22Jx2+RCQK4XSHIIWawrD4Q3A2XcfKwxOXfU+oEXh8vmaC0pZ0wQMw/pcjtE1HdFwHE0NKpaviMPj74/h426H3TGgWwSjCRVTZ8+Bw+ezwuKSgXGBQMAKjXM6ndYuoXF2uxg0h8V1FBq3rVC4bQ10W0F0qXUmw+JSQ+MkLK5tYFxTUxP62hUMrayAy27vFmM2SgEKFJ4Ab5gvvDlhjyhAgfwKcP2bX2+2RgEKUIACFKAABbpLgOvf7pJnuxSgAAUoQAEKUKB7BcyV/Pcv3TsDbJ0CFKAABShAAQpQgAIUoAAFcinAwLhc6rJuClCgkAUYGFfIs8O+UYACFKAABShAgdITYGBc6c0pR0QBClCAAiUowMC4EpxUDokCFMhIgDfMZ8TFwhQoOYFEbCWmPncu/K4QfrhnAE6PE26PAzYJi1MUKA4HFEmMs5mAogCmjuUL69CwQUNldRCmAYQbw+jV24XeAysA09YcFtcSGGcazR8nVA3xcBwNDQn8580Qho/cA/sfdne3eaq6jvfnL0S9YUBC4lID4+Rzj8fTKjBOQuNkUxTFCo2Trb2Qt84GxqVCpNbbNiwuNShO13XIHo/HrT0WiyEcDkPC4hobG7FTRRkGBIPt9rPb4NkwBSjQrQK8Yb5b+Xti4+9lOOiJGZZncQpkLMD1b8ZkPIACFKAABShAAQoUpQDXv0U5bew0BShAAQpQgAIU6LIAA+O6TMgKKEABClCAAhSgAAUoQAEKUKCABRgYV8CTw65RgAJ5EVg6a5WZl4bYCAUoQAEKUIACFKAABdIQ2G5MP+tGY9vs105o9UJ11D5XpXE4i1CAAhSgAAUo0BUBBsZ1RY/HUoACpSDAG+ZLYRY5Bgp0XkCNzsNbL/4Kq2vDOOrQIMorXXB6HLDbJTROgeJ0QJHfVthssNltgGJD/apGzPpmA0IhFXYFqKxyYcSYSgR7BwAp2xIYZ+oGTMOAoetQExoSMRULv4vg1f+GccjBe2D3iXcBNmfnO9+FIw3TxHdr1+GT5d/D7/dbgXHl5eVWeFwwGLQC41wulxUaJ2FxDofDCl5LBsbJx8m9vW50FByX/H57oXOp9SXLyaOExaUGxmmaBtklLC4ajW4KjAuFQmjcsAH7DhqAcrebgXFdOE94KAVKTYA3zJfajBb8eDL9hznNiazcKJBDAa5/c4jLqilAAQpQgAIUoEABCXD9W0CTwa5QgAIUoAAFKECBPAjMedh6l3rTNvLI6XlolU1QgAIUoAAFKEABClCAAhSgAAW6R8DWfzz/nVX30LNVClCgmwUYGNfNE8DmKUABClCAAhSgAAVaCTAwjicEBShAAQpQoBsFGBjXjfhsmgIUKAgB3jBfENPATlCg2wTiTZ/i0zevwcIlcUwY78PQHXxwuu1wuhxQHA4oEhJn2gDJirPbYHM4AFNHY10E9eviUBSgqo8b/nKvFAAMAEZzuBn0loAzTUciriIWUfF/n4QwZ1YcBxw4FmMPvAeAr9vG3hiN4T/ffAu7x2OFxElonATGSXCcz+ezAuNkl7A42SUsLjUwTj6WLRn61l74W0fBcamDl+NTy8vHqXtqYFwyLC6RSFhBcbJHIhGEw2ErPM4Rj+EHAwfALfPFjQIUoECLAG+Y56mQZwEGxuUZnM11LMD1b8dGLEGBnigQV1UsXrUS73z6CeYuXYr1DRsQjcfgdriw28iROHjCntiuf3/ouoG5Sxfjgy+/wLylS2DoJsqDAew8bBjGjx6NUUN2gM/tsdaNpbwl162y/E/oOuKy5tc1aIYJVdchAe3WWhmAQwLYFRtcdru1PnXYmj/uKDy9lP04NgpQID8CXP/mx5mtUIACFKAABShAgUIRYGBcocwE+0EBClCAAhSgAAUoQAEKUIAC+RBgYFw+lNkGBShQiAIMjCvEWWGfKEABClCAAv/P3nnAt1Gf//+jvT0TO3uQTcJOgEJYIezRlFmgQJllt0CBtlBKgW4KnZT+KJRVyh6FMFrIIAQCCRBISEK2Mx3HS9bW6U7//3POBdux45Nj2ZL1uReHZOl73/F+TpIf5563SIAECpcAhXGFG3uunARIgARIIAcIUBiXA0HgFEiABHqVAAvmexU/ByeB3iWQTiNc+xo+mf0HVG9PoKzMhkmTfCjt54LTaYfNYYfFLhK4NCAF3yI0s1pht1v1n61ih7OkoWpWWGzykDwuD2nQVE1vI5KzVDIFJalgzeoYPlwQgw0aDjxwMPaf9lekrRU6A1XTEAyF9PZ+nw9upzPrbGTMpZu34NMtW+F2u3VhnIjjRBgn4jiPxwOXywWHw7FTGGez2SC7UdxuCORksh2J48xI44xjDUGc9GfcFyZyX2eZSkFVVX0XWZzsIooTYVw4HNaFcXJ7YEU/DC0uZhF+1s8iDkAC+UWABfP5Fa8+MFsK4/pAEPvaEpj/9rWIcj0ksGcEJM/aVFOD59/5Hz5evgQpTdMlZylVhaIoiCcSSCSTcLvcqCgphZJKYfP2Gj0fczoccDgdsFlter5mtVgxcvAQnH/CSThk0iS4HI49m1yOHS0SOF0Ql1IRV1NIqBoiioJYKoWkpkGRvxtYLFBFfL5j7um0BrvVqjO1pgGnzQq31Qq/0wm33Qan1Qa3nmNbIYo9SuRyLOicDgnkOQHmv3keQE6fBEiABEiABEiABDIkQGFchsDYnARIgARIgARIgARIgARIgARIIK8JUBiX1+Hj5EmABPaAAIVxewCPh5IACZAACZAACZAACXQ7AQrjuh0pOyQBEiABEiAB8wQojDPPii1JgAT6JgEWzPfNuHJVJGCWQDK2Bsu+eA7Bze8gHlUwdIgdQ4d74PaJKM0Gi82ui8rstjRiCQtmL0jgqzWKLoFzOTRYrBbE44DVbsHIER4ce7gH5SUWKEkpJQdSKRVqIoVQYwyfLAqjoUFBoH8Ao8ZNxLj97kJtyIq5n3yKNRs3Im2zw+XxweNwoLI4gEkjh2PUsGFml9Kldo3RKN7+cjmSVutOaZwhjvN6vbo0ToRxbaVxhihOCtrb7sZEWkrg2k5OnmspkmsrjGtPFidSAkMYJ7cii0skErowTnYRxYk4Lh2L4qi9RsLXA9K9LkHnQSRAAr1GgAXzvYa+UAemMK5QI5/D62b+m8PB4dRIoBcIbNm+HX957t/YUFuDYr8PDsmhLNDzrHAkikg4gmgiATWlQktreu5nt9lgdzjgcjrhcjkhuaGmpfVcLakkgbQN3/vWmZg+ZQocdnsvrKr7hpQPcpHoxVIqGhIJ1CeSiIoQzmqFy+2G0+nU5eotxepGfiyzMPJauTXE5yLiE1axWAyaosBjs6LU6dT3gNOhC+ZsFlHMcSMBEiCBPSPA/HfP+PFoEiABEiABEiABEsg3AhTG5VvEOF8SIAESIAESIAESIAESIAESIIE9IUBh3J7Q47EkQAL5SODBm59udT3qKZce02oZMx+d3ernts/n45o5ZxIgARIgARIgARIggd4j0Nnvl22ftyx745xWv7BOmHpr782eI5MACZAACZBAgRCgMK5AAs1lkgAJdEiABfM8OUigsAlEtRSe/e8TiG6fp0vg9h/UiL0q4ijr54LL64TNbtOL323WNFTNisVfKnjt3TiWrEoiqVigahpsVgvGjnTj9OkeHHaQDW63BVpK2qeRUlQkI3Fs3Kjg87UOOG0qnOUVSBQdC5d1JJ5/cw5sXg+OPOYYjB47AS6vD+FoFA31IWxasxIDHSpOn3q4XoyfjU1Lp7GyehveX7MWLo8HIokrKSmB3++Hz+fTd5fLpRfCizROZABSDC+37Unj5DFjMyRwZuZtyONaFtS3lMYZxfWGNE6K7EUOJyKDSCSi74YwbmJZKcZW9BfPATcSIAESaEVg8VNP6j8HN1TNldsTXnjlaCIigSwSoDAui3DZddcIMP/tGjceRQJ9kUBCSeKVubPx2vx5qOhXCp/XC4fTBWWHzCwcjiAkwrhoDIlkEilVhd1m1fNCl9OxI0906Pmh5G6KoupStHgiCY/bgzsvuRyjBg/WJXP5tkmenFBVNCYVVMfiiFss8AYCO2XqhlRd8mQjRzby5JbrbSuMExm97JLPGruI0EUgFw6FYFNT6Od0otzlhNdmh8P2dX6dbww5XxIggd4nwPy392PAGZAACZAACZAACZBAbxBIb1mU6d+le2OaHJMESIAESIAESIAESIAESIAESIAEukRgxczJrY6bcAUvFe4SSB5EAiSQdwQojMu7kHHCJEACJEACJEACJJDXBCiMy+vwcfIkQAIkQAKFQoDCuEKJNNdJAiTQEQEWzPPcIIECJmBzYP6aj/G/9+egIRhGPB5FiSOM8f3qcdyEWvTvZ4fN7YZFarTTgNxoiobtNRo21loQUx3Q1DRcdg1DKoDBg2ywOoB0GrosTk2pQCKGFVV2/G9Ff2wKFcHjsqO8vASrqx1Y9mUVDppyMK64/FKMGT0aNrcHaU2O1RCKxrGmahNmz5uHQHAbLj3peLizJI1LpFJYsGYtVtc36AXwIowTUZwhjROJnCGNs9vtnUrjzAoBWraTonn5uT1hnCGLkzZSSC+F9SKKE2FcLBbTRXGyy2O+tIbDRoyAz5UdwV4Bv1q4dBLoUwTcB07OP3NJn4pAwSwm08I8npcFc2r03kKZ//Yee45MArlEQPKur6qq8Lt/PQaPz4PigF+Xh1ttdsQTieY8S8Tc0Rii0ShisYQugxOhusjM3S4RxjnhsDsgOaJsipKCklL1fC0ci+PYgw7GFd+ckbU8Nls84ykV9ckkNkWiSLvdel4s+bCRE4skTtYsuXNLmbohVJd5tcxt5eeWea4hjWspQ5dcVnaRx8keaWpCsdWCQR43PDYbHC3E7NlaN/slARLouwSY//bd2HJlJEACJEACJEACJNAeAQrjeF6QAAmQAAmQAAmQAAmQAAmQAAn0ZQIUxvXl6HJtJEACuyNAYRzPDxIgARIgARIgARIggZ4kQGFcT9LmWCRAAiRAAiTQRQIUxnURHA8jARLoMwRYMN9nQsmFkEBmBKwWbIxsxqyPZ6EhGEU4mkAwGEFdMA5rOopjRmzCcRO2o7jMA4tDJGkWWCyARU1DTaSQiGtQU2nId9M57Fa4vDbY3HZoFiCtpaEoKiyJODZss+DFz4di6fZyeN1OVPQrRiii4uNFK3DoIYfhzjvvRHn/ciQSKlRN1WVz8j9NUxFPqlhXXYfnXnkJk9xpnD99Guw2W2brNNm6PhzB3JUr0aikIAXwUhRvCONEHud2u/XdKIpvWRwvxfCG/E2K5FtuRqG8PNZSECcF823FckYRvbQ17rcsphfxgLEbsjiRFzQ1NelCAzUex5F7jURFwL9L3yYxsBkJkECBEGDBfIEEuveXSWFc78eAM2hDgPkvTwkSIAEhEEsk8PR/38T8JYtRWlqMgN8Pj9sNLQ3EE0nE483CuHAkimgkqrfXVA12R7MwTt9dDjgcTjgdDl0q3iz3TuriuFAkDovFhl9efTVGDRqSF/mZompoSCawNZZAxGpFUVHRzjxYcmHJkyUPFlmciOIMWZyRDxv5bdu8t2V+K3mubJLnyn25NQTpIooz8lzJe+V+PBRCgXby2wAAIABJREFUud2GSpcLHps1LzjyFUYCJJB7BJj/5l5MOCMSIAESIAESIAESyCYBCuOySZd9kwAJkAAJkAAJkAAJkAAJkAAJ9DYBCuN6OwIcnwRIoLcIUBjXW+Q5LgmQAAmQAAmQAAkUJgEK4woz7lw1CZAACZBAnhGgMC7PAsbpkgAJdDsBFsx3O1J2SAJ5QUDEbstrV2Ll+iUIhqQYPoFoPIHGYBh1DVGkUxFMHbYFp+xbA6/fAYvTBZvdAosGaGoaWioNNSVF3oDdaYHdaQOsFmj68yq0eATrq+14belgLKvtB6/HjcGVxbA7XJi3YA3CkRgeuP9+TJ48BZFYEmk0F47LrSbCuWQKsXgCqWQKSzZsxfNPP47bTp2GCSNGZKVIXIrVq4NNeG/VKkRUDS6XC16vFyKLk13uezwe/XGjUF6kAO0VybcskG8rhdvdyWEUzxuyOKOAXoroRT4gBfSyJxIJiChO9kgkglAohHgkgkOGD8OYygpx+HEjARIggd0SYME8T5AeIkBhXA+B5jDmCTD/Nc+KLUmgrxKQPGvt1s2499GH4fa4UFpcBK/Pp0vgUpqGeDyhC7kl1wpHY3reFdeFcWk4HDZdmuZyyu6C09UsjxOJmqo252wijIvHk2gIR3DK4VNx+anfzJr4vLtiFE+lsCkSRY2SgneHKE7yX9lbyuJa5r+SD7eUp3eW60pbyW2N25Z5r9wXcZyR70rOK8I4+VniYI3HMdDlQJnTCZuY7LmRAAmQQAYEmP9mAItNSYAE8pXAXRlOPNP2GXbP5iRAAiTQuwQojOtd/hydBEiABEiABEiABEiABEiABEgguwQojMsuX/ZOAiSQ+wSqvtya6XWpub8ozpAESIAESIAESIAESCBvCQyfOFC/sNmy7I1zWv2iOmHqrXm7KE6cBEiABEiABPKFAIVx+RIpzpMESCBbBFgwny2y7JcEcp+AoqWwObQda6qXY2vNVjQ0xRGNxtDYGMb2hiiSiTCmjdqKI0bXo6JEhd3rhdVqw04jmfwVI51G2gJIzbbI41QlhUQsii83ePHGl5VY11iC4oAXlf2LUdmvBGs2NmHeh0tx5FGH4/s/vBU+bxHcNgdgSYstDhrQLEdLJBGNJvRi+5iq4d+vvw5/7Qbcdv634bDbswJXS6exqb4B7361EmmrVRcBGKI4kca1LZgXKYDsUjSv/2HHYoFRNG9MMBNhnBxjSOOkkN4QxgkP2aVoXsQFhrxACud1cVwohP0HD8KkIYNht1qzwoadkgAJ9C0CLJjvW/HM4dVkemEODTA5HMy+MjXmv30lklwHCXSdgORWL82ZjVfmzUa/8lIU+ZtzPYfTCSWl7si74ghHIrqsLCrisngSki/a7SKMs8OpS+Oc8HjccLncukhOVbUdku8UFCWJhqYwvB4f7r/hBlSUlHV9wlk+MpRMoiocQZPFisAOWVxLYbrkxbK3laUbOXAm02spSTeOM8RxkgOLNE5RFJ2jIYyTW9mVaBTlFmCQ2wUb895MsLMtCRQ8Aea/BX8KEAAJFAIB/v2lEKLMNZIACXRKYPnDO76dbEfL8acs6vQYNiABEiABEiABEiABEiABEiABEiCBfCVgGTSZ11nla/A4bxIggT0iQGHcHuHjwSRAAiRAAiRAAiRAAt1MgMK4bgbK7kiABEiABEggEwIUxmVCi21JgAT6EoE3z5pxtKzngAsunC23JcOH96XlcS0kQAImCUhRdn0ihBVbl2FTzQZsqwkiFI6hobEJ1dsbYXMU44DhGg4euBIDPBtRVOSCy+OG1W7VvXEiitO0NJSUhmQ0jsagii2J0Xh3/Th8tb4ObkcSlf3LMGRwf9gdTsxdsBbbttXie9ddi4n7T4E1raGyvD/cLhc0VYOqSYF4ComkgkRcgZpKIW2xYM7ipVjw9st45Mbr4MySME6QSbH6qprtWFhVhYSW1oviXS7XTlmcUTjvdDohuxTOG9K4lgX0Bv7OhHEtnzcK5Q1RnMRGZAZGwbyI4kQQt1MUF41Cicdx8PChGFNZCccOcZ3J0LMZCZBAARNgwXwBB79nl86C5Z7lzdF2Q4D5L08PEiABg8C2ujr88M/36zltSVERfF6vnuM67A4oKQVKUtElcZJ3RSLRZmFcQkEaaTh0YZzI4hzNeaJbhHEuPSeU/M3I3SSnleMamyI4/4STcMHxJ+RkAIKJJJYHm2D1euH3+/W815ClS75rSNINMXpbQXpXF2WI44zj2+bCBkdDHCfydLkfjURQrCQxWOJFaVxX8fM4Eig4Asx/Cy7kXDAJFCIB/v2lEKPONZMACexCgMI4nhQkQAIkQAIkQAIkQAIkQAIkQAKFRIDCuEKKNtdKAiTQkgCFcTwfSIAESIAESIAESIAEcokAhXG5FA3OhQRIgARIoOAIUBhXcCHngkmABHYQeOusGa0KCI7+yR1kQwIkUMAEYqkkVm5fg2XrlmBbbSPq6oOo2d4It7c/Bg4bh70GBlCaWAhb7Ttwp7fDG/DB5bbDarEgHkshFAwjnu4Hx4gzofSfhqXLV2H1ik+hKmFUVpZhxNAKNIYUvDRzIYpKinHJNdciEChFwOtF/7IyeF1eaOn0ziJ7KRBPpVSkNUCK0j9fvxFvv/os/u/qy+By2LMeqQXr1uPLrdWwWK26NE6kAG63W5fESRG9SAFkN8RxbaVxcoxsIoTrTBon7YwCeZEMGLswkF2K40UWZwjjwuEwEvE4bJqGA4cOwd6DB5kaI+vQOAAJkEDeEGDBfN6EKt8nyoLlfI9gH5o/898+FEwuhQT2kMAb89/H3199AaXFxfB7PTvzO8npUqqGRCKJeDyGaCyKaDSm/yxC812FcW643S64XW49Z0ynm4XfIjZLKgqSSUUXxg3uX4lfXX0NSgKBPZx59x4eS6WwvKERKY8XIkaX3ZDFSa5ryNENSZzZ3DbTWbaUxxl5saqqO1lKPmzkxJIfx8JhlKQUDPZ6YBODPTcSIAES6IQA81+eIiRAAgVAgH9/KYAgc4kkQAKdE6AwrnNGbEECJEACJEACJEACJEACJEACJNB3CFAY13diyZWQAAlkRoDCuMx4sTUJkAAJkAAJkAAJkEB2CVAYl12+7J0ESIAESIAEdkuAwjieICRAAoVKgAXzhRp5rpsEOiBgsUDRVCzZvBpfrF6I+sYmVNcE4Q9UYuy4vTF27/0wcPBIRDZ/gjXv/R3bV8yDJR3WO9PgQ/mYQzD6qKswaO/pCDbU4rNF8/HZZx8jFKzBoMoSVFaUYfnqOrz21nwcfOgUnHHBZSj2F6PY64PdagOs6WZpmqZBUdOAloaqanr/dpsVSzZtxf9eewkPXnEBXPbsC+NWbqvBwo2bkFRVfV4iABCBgOwtRXHGfaOY3mgj7aWgXorrja2tOM4ohpfn5b4UxYssziiOb1kYH4/HEY2KsCAKTUliYKAIB40cjsqiIp7SJEACJJAxARbMZ4yMB3SNwJwMDzs6w/ZsTgKmCTD/NY2KDUmgTxNYt3kLfnDfb2B12BAI+OBxi/RNxOAu2GxWPRcT0VtM8q9YFPF4Qpe/pXRhHOCw2+Fw2OFwOuByOuHeIRLXhXGSy2nNojMlper5bDQWR1M4iuMOOQw3nH0W7DvE4r0NOZlSsbKxEU02OwJFRbo0T3aRxsmt5LeyG3mtGQn6nqypI2mcCOJEwCe5sdyKRF1y43AwiKF2Kyo9nj0ZlseSAAkUCAHmvwUSaC6TBAqbAIVxhR1/rp4ESGAHAQrjeCqQAAmQAAmQAAmQAAmQAAmQAAkUEgEK4wop2lwrCZCAEHjw5qdb/XvIKZce0wrMzEdnt/q57fOk2HcIyHVGa9at1utvOtsq+leiuKi4s2Z8vgAJbNq8EbF4rNOV+/0BDKwc2Gm7PWkg18VVbVxvqovBg4bA6/GaastGJNBVAjwnzZHLpfcRczNmq0wJdPb7ZdvnLcveOKfVbycTpt6a6ZhsTwIkQAIkQAIkkCEBCuMyBMbmJEACfYYAC+b7TCi5EBLYMwJWK2JKEnWxMDbV1WLJqmXYWr0BAZ9dF7f5iodg74n7Yuy4cRgydC84XR4o8RjWffQvVH38b33sIfvPwKipF8PpKUI6raKpsQ5ffrkUCz+ej6a6KjgdViiqBavWbseHHy/BjLPOwGkzLoDP5d4x92ZZHNIWNCvimv+f1tKABbBarPhkXRXee/NV/O17F/eIMG5tbR0+2bwZCbVZ4CYiN0MAJ1I4o4C+5f2Wj0lhvcjiZJfjjN0IliGLk37lvtxKMbwuF1AUfZeieCmGl1spjIeqotTtxrgBldh78CC9T24kQAIk0BUCLJjvCjUeQwIkkM8EmP/mc/Q4dxLoPgJPv/kWfvHw3zFwQAXKSot1QZpI3ySXs1isSO0QviXiccTiCSSSSaRSyg6ZeXqHMM4Bm90Gl9MBp9Opi+PsNvuOixA1aFoaSSWl53a1dQ2orqlDZcUAPHDjjRg7dEj3LaaLPamaho1NIWxKqfD6/fD7/TuFcSLPk90QorfNY7s4pKnD2pPGGTmyIY2T3Fgk6pInxxsbMCHgh7cHhPKmFsBGJEACOUuA+W/OhoYTIwES6D4CnVfDtB6L/7DQfezZEwmQQA4SSG9ZlOn7Yg6uglMiARIgARIgARIgARIgARIgARIggfYJrJg5udUTE66Qq6y5kQAJkEDfJ0BhXN+PsdkVLv/qS5x4xnRTzb9z7kX4xZ2/MdWWjQqLwPCJ5iRww4YMx7y3F2QVzsy3X8M1N11paow7b7sbl110ham2bEQCXSXAc9IcuVx6HzE3Y7bKlACFcZkSY3sSIAESIAES6AUCFMb1AnQOSQIkkBMEWDCfE2HgJEig1wikLRYoaRVbw0EsWrEUy1YvR11tHcLhGFQtjfIyL4YN6o+BQ8dj3N4HYOReo1BWXgmrza7POaFoeP2lpyDCtJO/dT4cVmMpaSiJKFav+gqLPpqF+poqbNveoB9XWx/CnPe+wImnn4YpRx0Hh82J8uISFMk3nFhEGtd83YLujmv+H2w2K2Suc79YigVvv4p/3ngtXA5H1rmtqxNh3BakYNEL/Q2pm9xK0bwI4VruwkF2EQZ0JIuT44wieKM/EcXJ3lIUJ0XxUhAvRfBpVYUtnUax242xAyoxftBA2G22rK+fA5AACfRtAiyY79vx5epIgAR2JcD8l2cFCZCAiNKu+9WvMPfjj1FaXISioiL4/N5mQZrkcXY7tB3CuGQiieQOkbcIxNNpDVJt7nTY4bA7dDG4SOOcTjucdodYzqEqKpJKEvFoHNFYHLFoDPWNQcSSCgYOHIRrzj0X3z52Wq8HIpxU8FltLdzFJbooToRxhijO5XLtFMa1Jz7P9uTbSuOEveyGME5uRaYu0jjZ/fEYRvj9cNh2/kEi21Nk/yRAAnlIgPlvHgaNUyYBEsiUQKZiJBaQZkqY7UmABPKKAIVxeRUuTpYESIAESIAESIAESIAESIAESCBDAhTGZQiMzUmABPoMgXwSxgWbgtiwsSpr7IcNHY7iouKs9Z/rHX+5fClOPus4U9M876wL8Ouf32eqLRsVFgGzoqeBlQOxYNanWYXz2puv4rofXmVqjJ/e9nNcfpE5uZypDtmIBNohwHPS3GmRS+8j5mbMVpkSoDAuU2JsTwIkQAIkQAK9QIDCuF6AziFJgARyggAL5nMiDJwECfQKgbTViqZUFMs3r8Nny5dgw4ZNCIfjUNMa3E47fD4X/H4XfB4nKiqHYJ8DjsLY8ZPg8RXrsrSmpiCeeeY5PP/Sa/B5vTj7zNNx+umnIhAo0tejppJYsvhDfPrxu4hFGqGlAZ/Xg01b6/Cv5+fgG0cdhcOPPxnxeEIvrh8xaBjK/EXQpAg/bYEFab0YXzaRoyU1Da/Omo2qRfPw8C03wmlvltZlc1tTW4fPq7dBs1r1AvVmSUBal7vJLptxK4I4EcVJlZXH4YA8K/MX0Z2IBGQ3iu2lj5byOUMUJ4XvaioFi0jy5Li0pvc1sKQEI/qVo6K4GA6K4rIZcvZNAgVFgAXzBRVuLpYESAAA81+eBiRAAo2hEE689jqEQ00I+Hzwejy6KM7udOq3DocDko2qakoXeieUpC4qU1MKtLQKSQOtO3K85oRVMj+L/pgumlNSSCopqClpKzmkBiWlIKmqKCopw9knHo+fXHRxrwZC5rKyvhFNDocuhhNZnM/ngyGKk1vhIDJ0yWPbbpLXZntrKVmX+xIL2SUWhjBO5OqRSATBhgaM93pQ5nbpOTc3EiABEmiPAPNfnhckQAIFQIDCuAIIMpdIAiRgngCFceZZsSUJkAAJkAAJkAAJkAAJkAAJkED+EaAwLv9ixhmTAAl0D4F8Esbd/5ff4Y9/u797Ft5OLzdcdSNuvv7WrPWf6x1TGJfrEcqP+eWS6Ilyrvw4ZwppljwnzUU7l95HzM2YrTIlQGFcpsTYngRIgARIgAR6gQCFcb0AnUOSAAnkBAEWzOdEGDgJEuhxAuk0ELYoWPDVQmzctBG1jSEkkwqsljT8PjeKAx5YrEAsrkBRFLhcdkyYeDAOPeJU+AIlCIWa8PYbr+E3v/09/EVF8Hq9iMeiuO7aq3Hc8SfDHwggEg7h3TeewsYNK/T1edwuFAW8iCUU/O2RmfAU98c5l1wOJW2BlkygvLQfRg8erlvWvlbFQReruV1OrKnejmdffA5T+nlx3ZlnwNZO4Xp3g1y+rQarg02AzbazQF2kcYYwTu4LH/lZ5mm3WDC6oj8GBgKIJpOIpVKIJhUkpLBdVcUjsGOz6IXsRj8iErCkAbfdrgviAm4XSn0+lPl9CHg8PSLH62527I8ESCD3CbBgPvdjxBmSAAl0LwHmv93Lk72RQK4SUHTRW3OeJtmlnrelVATDYcxa+DF+98gjcLvcuvhchGkiR7Pa7LDZ7RARuORqckxSUZBIJJBIJpFSFKTUVHOfugBcVi/JaxriKNPl4PKI1vxz8/9ENt4sO4srCpwuD6YcsD9uPO9cVJSWwuVwwe10wmoRuXhzH7K5RNaWRVF4QzyOT7bXoai0VJfFST4vu4jiRIIut4Ysrj1hXHfGvT3BmyGLM8YxhHFG/i3COImLCOOi0ai+u6IRjA4E4LDtKrjrzvmyLxIggfwlwPw3f2PHmZMACZgmQGGcaVRsSAIkUAgEKIwrhChzjSRAAiRAAiRAAiRAAiRAAiRQuAQojCvc2HPlJEACzQSqvtya6b+L9Dg6CuOyi5zCuOzyLZTec0n0RDlXoZx1+bNOnpPmYpVL7yPmZsxW2SIwfOJA/SJwy7I3zmn1i+qEqYVr+M0WbPZLAiRAAiRAAm0JUBjHc4IESKBQCbBgvlAjz3UXPAGnEx+tXojVG75CPJHSC9PdTju8HgdgtSAeSyAaSyChpJBKabBYNBQXl+AbR87A4GHj8PniT7Bm5QrMfm8+NmzaAp/PhyK/D8ceczgm7rM/9p64HzZtWIX3Zz2LpJKEzWqD0+HQ+w8EvJg17wu8O28Zvn3JJRgwchwiTY3wuDwYN2yUXrivqildriYl9x6XCwlVw6tz38PiOW/hN5deiDFDh2Q9hKqm4dOt21CXSuniACnyFzmc7HJfdilUl1td/KYkMWnQIEwZtRfcDsfO+clzIowTaUGzVKBZiGcRI9+On0QQYLNa4LDb4ciiGCDr0DgACZBAXhBorKrS5/nZv548Rm5PeuGVOXkxcU6SBEiABPaQAPPfPQTIw0kgDwh8tWE9Hn/9dYTj8Wbpm8jFlOa8LZqIo7pmO6qrt8Pr8cDn8cAlwji7Q8/5RBpnsVn1PFSOTSQTiCcSSCYSUFIKNFWDlm4Wxhm5nGStktkZwjeRm+30hAPNwjjpS0nC7nSion8FJowbhdKiYn08n9cPl8sNp9OFZEpBMhzGhccdi1GDs5PzpjQNK+sbUG+16ZK4QCAAj8ej7yKLM3ZDGCfrbE/q1pOngjCVeBjCOMnJRRgnsRVZXCQSQUNtLfYvLUGR8+tcvCfnyLFIgARylwDz39yNDWdGAiTQ7QQyLYxq+Wtrt0+GHZIACZBAbxFY/rDY3b/exp+yqLemwnFJgARIgARIgARIgARIgARIgARIIOsELIMm8+98WafMAUiABHKRAIVxwA1X3Yibry9c/wiFcbn4ysy/OeWS6Ilyrvw7f/r6jHlOmotwLr2PmJsxW2WLAIVx2SLLfkmABEiABEjABAEK40xAYhMSIIE+SYAF830yrFwUCeyWgJSvb4jU4P1P3kX19gaUlgRQWVkCJZ6EFI8nkyJDU6Gmpbhdg6KoUFQpik+hvLwSw0dNhscXwMDK/vhk8Rf4/QN/hqqmceH552D6tCMRCkUQCjdh0/rFaGioBmCD026Hy2mHw2GD2+WA1WbFKzM/RnV9At++5HIU9x+Ihvo6VJZXYkBpmV5U73a5EPC7EVFUvDHvQ3w86y1cdexUHHnA/j1SsF4XjWFZYyPsPv9OQZwUpbfcpUBddoem4bAxozB+8GBYxRTAjQRIgARymMCcX97banYnvvAK37hyOF6cGgmQQPcRYP7bfSzZEwnkKoFVG6vwp2f/jbpICBarBYmEgmSyWfotsrd4PI5t1bXQUio87mZJmsPphN3hgM1m02XnYn/TJW/J5vyvWRTeWhgnEvCWJejyy5QujdPBWPT7UqGe1jS9r6SiwGqzIVBUhFEjhqN//wrY7E74AwE4XXbEI1GE6+tw7IFTcOpRRyLg9WYFcSyVwodbquEtKdHF77KLLE7kccJCRHGy6yxayMx7SxonsjjZDGFcS3m7xCUWiyEcDut7haZiWMDPnDwrZw47JYH8JcD8N39jx5mTAAlkTIDCuIyR8QASIIG+SIDCuL4YVa6JBEiABEiABEiABEiABEiABEigIwIUxvHcIAESKFQCFMZRGEdhXKG++rt33bkkeqKcq3tjy972nADPSXMMc+l9xNyM2SpbBCiMyxZZ9ksCJEACJEACJghQGGcCEpuQAAn0SQIsmO+TYeWiSGD3BJwuvLdsNlatX4mNm+t0KdzECcPgtNugpEQMB6RUKWpPQzWK25OqXqBtt6UxcvQkTNjnML3QfdEnn+G++/+MZELBt885A6edehKUZAIrly/ChnVf6qI5KTJ3OUQYZ9tRfG6F1+NCMBTDzP8ugqd4AE456zxUDhgMr8uNMl8RbDYL7A4nqoMhvPXefCxf+AFm7DMWJ37jENhbFK1nM9TrgkFsUVS4vF4oikgGmovRRTAgtyIOkFuvBZg6dixGVvSH1WrN5pTYNwmQAAl0CwEWzHcLRnZCAiSQhwSY/+Zh0DhlEugCgTUbN+JPzz6NpngMaZsVaiqFWFzkb0lomqrndk3BJihKClarXd/tDhGlOWDVhXHNgjIRxsUTCV0WJ23lWBHANUvM0mh2me0Qx4kpLi169q83aScydBHGiaxOxHSDBg7AmNGjUVkxAHa7DWlVQSIcwiHjJ+LEww5HWXFxF1Zs/pCt4TC+aAiivLy8lTDO7XbrwjhDFCfSOJHEGaK43hDGGbK4Zo5as/Rvx62Ro0t+Ho1GEQqFkAo24oD+/eBgXm7+hGBLEigAAsx/CyDIXCIJkIBBgMI4ngskQAIkAIDCOJ4GJEACJEACJEACJEACJEACJEAChUSAwrhCijbXSgIk0JIAhXEUxlEYx/eE7iCQS6Inyrm6I6LsozsJ8Jw0RzOX3kfMzZitskWAwrhskWW/JEACJEACJGCCAIVxJiCxCQmQQJ8kwIL5PhlWLooEdktgu9KEBUtmo7q2HpurG7B5cw1GDh+AfSaOgJJU9YL2ZlGcFGRr+n1FUaGkVKhaCiVFRZi43+EYNHwCnn76GfzntTfhcNixzz4TceXllyIeqsHyJR+gIRjUi+2dDjucTrkVYVyzNE6EcG6XE6FwFHPeX4oYinH4tOOx98R90a+4H8LRGFZvqMKK5ctgCdZi2oQxOHDsaDjs9h6JblJVsbQxiLTXpxfIG3K4SCSiF6NLUbrcH+D14PBxY1GZ5aL+Hlk0ByEBEigYAiyYL5hQc6EkQAJtCDD/5SlBAoVDoDEUwrsfLcBHy79EKB5DIplCMi0i9DQUJanLx1JJBaqa0p8TQVwaVtG/QdMF6s1iuURS2ipQUimkdfGbCOJ2CONEGqem5f86WIvcWiywWi26eE1yX8lhHU4X/H4v+vXrj4qK/vA4nbCoCtx2FyYOG4HphxyCQRUVWQ9OStOwpKYWYYcDgUBgF2Fcc65u03eRoRvCuExkcYbkrTsXI31KfGQXYZzcGsI4ydUlPxdhXN32GhxYWopit6s7h2dfJEACeU6A+W+eB5DTJwESyITAXZk0/v9tM22fYfdsTgIkQAK9Q4DCuN7hzlFJgARIgARIgARIgARIgARIgAR6hwCFcb3DnaOSAAn0HoEHb3661RfonHLpMa0mM/PR2a1+bvt8T878/r/8Dn/82/1ZG/KGq27EzdffmrX+c71jCuNyPUL5Mb9cEj1RzpUf50whzZLnpLlo59L7iLkZs1WmBDr7/bLt85Zlb5zT6hfWCVML9xe2TGGzPQmQAAmQAAl0lQCFcV0lx+NIgATynQAL5vM9gpw/CWRIwGrHp5s+wYatq1FbH0JNbRCbNtcinoxj6iGTMKiyDLFYAmoaXwvj1DSSUkyfUvXHkFYxcsReKO4/Fg8+/BS2VW+Bx+2B0+XCt88+HQF3FOvXrYaqolkW53DA7rTCabPCrkvj7LBZLHC6HHqR96LFa/DV+hDsbj+KiksxbvxYVJaVwJfUMCDgw9DyElSUlWW40D1rXheL46toDB6/v31ZXFMT9h5QiQNGDEeR17tng/FoEiABEuhhAiyY72HgHI4ESCBnCDD/zZlQcCIk0GMEYokEVq5fg8VfrcD7ixcibQPimhNa2qpLzB12G5IpBYl4s0ROUVNQlBRUJYWkkkRS7ouoTIRy6WY5nC5SS0PUA0QvAAAgAElEQVSXq8tmlVzXboPL4YDT6YDL6YbL44bP64HH7ZIjoKU1ONIKIsEaxOs3YOKQQbj2yntRWlraYyxEjD5/0xa4iot1YZzf74fX64XH44HL5dopixNhXEtZ3O6EcdkQxAkQo9+Wty2FcXqsFGVnvi7CuPr6eoxxuzDQ7+sxphyIBEgg9wkw/839GHGGJEACJEACJEACJJANAukti1pdh5+NMdgnCZAACZAACZAACZAACZAACZAACfQWgRUzJ7caesIVsPTWXDguCZAACfQkAQrjvqZNYdxSnHzWcaZOv/POugC//vl9ptqyUWERyCXRE+VchXXu5cNqeU6ai1IuvY+YmzFbZUqAwrhMibE9CZAACZAACfQCAQrjegE6hyQBEsgJAm+fNWOOTKR42PCj5Hb/71yYE/PiJEiABLJDQLFbseCruahtrEFjMIK6+jBq64PYvHU7Skv8OObw/eFw2BCPJ6HpBfBpqKoGJaXtFMZJAX1xwAsNPjz7ynvQNA1utxtKMoljj5iEAf29aAyGYbVadWGc9Ge3W+GwW5sL0OVxp10qwLFu/Ras29yEsOLF6lWr0dS4HVd/9wQcffChqHSMgNflyA6I3fQaU1L4qjGIhNutiwGi0SgikQhisRiCwSCK7TYcvNdeGFxWqgsBuJEACZBAvhFgwXy+RYzzJQES6C4CzH+7iyT7IYH8JPDIP+/C8hXz0Jh0IZr2w+4phd1VDKvLCcAOFRakLTZYrTZdcp5OQxfEyX/ymIjhLFa5b4U1bZGHAatFz3FtNiusIobTNKTTKQAalEQC0aYQktEa2JU6BKxN8NuiCHjS2Hv80fjWeb/pUZDhpIL5W7aipLy8lTBO8nkRxoncXV+b1aoL42RrKYsz7reUuMljXZHGyTFtRXQtH2tvDGEru+TpLYVx8Xgc4XAYjY2N6Kep2KukWI8fNxIgARIQAsx/eR6QAAmQAAmQAAmQQGESoDCuMOPOVZMACZAACZAACZAACZAACZBAoRCgMK5QIs11kgAJtCWQT8K4hx75K351/71ZC+KPbrodV192Xdb6z/WOv1xOYVyuxygf5pdLoifKufLhjCmsOfKcNBfvXHofMTdjtsqUAIVxmRJjexIgARIgARLoBQIUxvUCdA5JAiSQUwTin/IblnMqIJwMCWSJQEMyjE/WfIDGcBChcBzhphgaQxHU1AWxfXs9xo4aiikHjoWipJBSpdAdekG2LoxTRRont6r+uM/rwfLVNfhsyQa43U4MG1SE8aP6Q9U0vVje6XDAYRdZnAUOEcXZpPDeogvknE4HqjZWo7YuiNomG5KaC+/Pmw+/F7j12tNx4MRJGOafiLSiZolE+92mNA1rG4PYpqWhpFKQwnMRxoVCITg1DZMGDcSoigr43K5ditt7dKIcjARIgAT2gAAL5vcAHg8lARLoEwSY//aJMHIRJJAxgXmz/oJVS19AKBJHJA5U16VR22RFvwovYooD1UEbYiknHG4vnG4P/D4XAn47bDYHLCKMEzmaxaLnglYLoIn0DBqS8RSaIknEIlHEIhE4EEPAmURaiWF7bRhDy4F9R9lRHLDB73fB7nCg/6AjMHXa7WJky3gdXTlABGzbIhF80RBESWkpiouL4fP54PV6dQG8COOac/bWwriOxpL+2pO+tWy/O5GccWx7wrm2jxn9GMI4uW0rjBPJuwjenbEo9ioKoMjl6gomHkMCJNAHCTD/7YNB5ZJIgARIgARIgARIwAQBCuNMQGITEiABEiABEiABEiABEiABEiCBvCVAYVzeho4TJwES2EMC+SSM62ipCxZ+iHO/e4ZpEs8+9iIOnXKY6faF0pDCuEKJdHbXmUuiJ8q5shtr9p45AZ6T5pjl0vuIuRmzVaYEKIzLlBjbkwAJkAAJkEAvEKAwrhegc0gSIIGcIsCC+ZwKBydDAlkjsLqhCsvWL0YikUAklkQ0KrdxBINRbKupR1M4jCkHjse4UUORSCb1AnhNS0MVWZwqEjUVqqrqAjkpJne7PVi0ZDPq6hoxZkQx3E47rDYbPC4nPB4H3C4H7DYr7DsKz202C1wuJ7bXNaJ6Wz20tBUbt2toCsXx3tw5OGi/UbjmuydgYP9+GF2+PzxWd9ZYtNdxUyKBT6tr0KQoSMZiSKdSKHLYMaysFMPKyvV1iSSAGwmQAAnkMwEWzOdz9PJy7nMynPXRGbZncxLImADz34yR8QAS6BMEvvryRWxZ+xhi8QQi4RRWro7B5gAqy61YtiqJuiYLvD4L7A6RwVnhdttR5LcBFis0WCDmdJGnQ+5D7qRhtaaRiGsIR1KwWlQ5EKpigc2iYa9hTtQ1aQjWazjkQB+GDHbB7bYCFjt8JdMwfv/v9xhXme2qujpsTKZ0WVxRUZEujJNdhHFOp7OVME5f5Y7ct634TaRyshkCN6OdsRijvfzNwJC/yd8R2m7Gc4akTo6TPttuRn/Sh9FGhHHJZFL/24bsInoXYVwy2IiRgQAG+n09xpYDkQAJ5DYB5r+5HR/OjgRIgARIgARIgASyRYDCuGyRZb8kQAIkQAIkQAIkQAIkQAIkQAK5QIDCuFyIAudAAiTQmwSqvtyqX8GTj1u+COO21VRj67at2FazDZFIGMOGDsfovcagpLgka9hTagqbNm9E9bZq1GzfptcteTwelJf1w7gx41EUKNo5dm8I45pCTVi15is0BhtR31Cv38ZjMQQCARQXlaC4qBiDBw3BmFFj9S/tLLRNzpeqDesRDDaiobEeDcEGPYYGm5KSUoweORqDBg7OKpot1VuwbdtWVNdU69eUyfVxfp8fI4aNxJDBQ1uNnUuip74q5wo2BfXX9fbaGv11069ff/01MqBiwM7rE7N1QjSfk+vQ0Nigv17lvJTrD0tLylBaUqq/nw0bMlx/3XLblUBvnJN8H+n4TIzFY9iwsUp/LW2v3a5/SbScv0OHDNM/f3p742ckMHziQL3g2LLsjXNa/aI6YeqtvR0fjk8CJEACJEACfZ4AhXF9PsRcIAmQQCcEWDDPU4QE+j6BtMWK5Vu/wvptq/Q/vEtxdTyuIJZMIR5LoDEYwbbt9ZB/m5hy4N6oKC9CMpXSi+JVKXrX0kilRByn6reqmobTaYfF5sT6qq2IRkLwej1wu1zweJzwuh3w+Vxw2O2wWi36P3o4HTZEogmsq9qCoiIfGptS2FqvoqpqI5YvW44Tpk3GGScfDJ/XgzH9J8Fn69ki72hSwbrGRoTicdjTabhtNlQWFaHM74OtAP/Rpu+/KrhCEihMAiyYL8y49+KqM70wh2bWXgxWoQzN/LdQIs11kkBrAo3bP0PTtl/DggTWrI6gtl7BuPE+XXreGNLgdqZhtwFWqxNyAWI8riKuWKCmRGQmqfGOjzQLYEkDVpsFIkV32C1wu9PwuR1wOGxIKQkkkmkEArp5DnPnBeGwpXHUkUXwem0IhWyw+2ZgyJgLeyxEIoP/bGs1glYbSkpKdhHGORyOncI4QwDXUgQnIjf5WfL6WCymy9nKy8shx8nfFuQ5QwAni5LHFUVBTU0NKioqdv6sXwyxo63cF6mc9F1bWwuv1wu/37+LiM7gbgjq5FYX2SvKLsK4pvo6DPV4MKo0exen9ljQOBAJkEC3EGD+2y0Y2QkJkAAJkAAJkAAJ5A2B5Q/rhved2/hTFuXN3DlREiABEiABEiABEiABEiABEiABEsiUgGXQZF5nlSk0ticBEugTBCiM6ziMImZ67a1XTcX5mCOOxfixE3a2FanSf954Gc+9/AyWLlvSbh8ibztg3wNx3tkXYPrRx5sap7NGMtYrr7+Ip1/4ly6n62gTydR3L7gM3zrtTKyvWoeTzzqus67158876wL8+uf3mWrbttHnSxfj3Tn/w3sfzMVnn39iqg+fz4+jpx6D46ediFNPPA12+fbSDrZM4mV0cdJxp+jyMzNbV/q/6LxL4PN2XselairmzJuFue/Pwez33sWGTVVmpoSBlQNx9BHTMOPUM3HolG+YOqazRiKJ+8/Ml/HsS09j7fq1HTaX8/f8c76DC865SJ9HXxbGzV8wD198+Xln6Fo9f+G5F8PvD+iPyTWBjz/9qH4dZWfbuNHjMe2o6TubyXV9b73zBl567QXMmvtOu4fL62SfvffF9GOOxyXfuQx2m72zYTp9Xq4rfG/+HMx6792MzkmRbh1z5LH661bOTTPCR7mm8Yl//xPRWLTTeUmDE6efjJHD9+q0rbwHPvHMY522k/dDM+/BIhd74dXnOu1PGvQr74+zZ5y7s21PCOP4PrL70Mjr7+133sQb/30dr7/1nw4bG6+nC865ECefcKr+evrHE/8HRUl2GvsD9j2oy+/F2f6M7HTyOdaAwrgcCwinQwIkQAIkUFgEKIwrrHhztSRAArsSYME8zwoSIAESIAESIAESKAwCLJgvjDjn0CopjMuhYHAqzQSY//JMIIHCJBBt2oTQttthTQfx5dIQBg1yYthIH+x2kZYlsb3BjU21XtQ1xNG/zIVB/TXY7VGkVHne0iyMk081EcZZRCwnwjjAaU8jGndixeo4XF4/Kvt5UeqpRVlxHE6HEyuWR/DpoiZMnuxH5UAnaurcKB14E0orD+6xQIgw7sONm5BwulBaWtpKGCffNOh0OnV5myGFk7W2FMYZ0rYVK1bglVdewebNmzFp0iSceuqpGDx4sC5wMzaRxW3cuBHPPfccVq1ahbFjx+Lss8/GkCFDkEqldgrj5MKqhoYGvPXWW1i0aJE+r9NOOw377befLo0zto6EcdJXswg/rn8bbFNTExrr6jDI5cTYstIeY8uBSIAEcpsA89/cjg9nRwIkQAIkQAIkQALdTYDCuO4myv5IgARIgARIgARIgARIgARIgARymQCFcbkcHc6NBEggmwQojOuYblcEPyKkeeaFp/HL39+zW2Fb21Ev+c7l+NGNt8Ptdncp3HX1tfjlffeYlhkZg4gYZ+L4ifj4k49MjdsVYZzI4R74632YO3+OqTE6aiRSspuuuwVnzTi3XQnVV6tW4PgZx2Q0xgH7HYQXn3oVNqttt8fJ9VfnXXo2Fiz8wHT/IlSb9fo8lBR3/GWdcr68+d+ZuP8vv92tnM3MoJMPmIJbvv8jHDrlMDPNd2kjYrKHH38Iv3nglxkf/81TzsCrM18ydZzEccGsT0217Wqjrrx2Oxpr1ZqVmH76URlNRdb41suzdsa+els1Dpl2gKk+RFz2yF8f19u+/+F7+Om9P87o3JD4//E3f8WAygGmxmvbSM71d+f+D7//02+xfOWyLvVhHCQitltu+BGOm3bCbsVxcu6N3n+Y6bF+dNPtuPqy6zpt/7/Zb+Py677baTuR3M17e0Gn7V55/SV8/7ZrO20nDdr22Z3nZNsJ8H2k85DIa+muX/0U8nrOZJPXsgg5//bIX0wdlsufkaYWkEONKIzLoWBwKiRAAiRAAoVHgMK4wos5V0wCJNCaAAvmeUaQAAmQAAmQAAmQQGEQYMF8YcQ5h1ZJYVwOBYNTaSbA/JdnAgkUJgFVTWDj8p8hHlqKmq0xTJjoxcDBXmjpFKob+mNDeDxqgwre+3Allq/ZjnNPHotTD1MQ8NQhnbYjnbYAIo2T72i3WGCFBqtVRTBWjqXrSrFmYxxvvrca/SvLcOG39sMQ5+cYVLodkQjw4fxGFAVsGDDQhagyESP3vQs2e9culuxK9FRNw9z1VYDXp4vZAoGAvnu9XogwTiRvIowzpHHGGIasTeRu1dXVuO+++7BgwQK9nd1uxze/+U1ce23zBUVy4ZU8JhK3Bx54AG+++aZ+0ZT0cfLJJ+MHP/iBPo7I5Yxv4Xz22Wfx1FNP6cI32caNG4dbb70VI0eO1PsTaZ0cL7v8bOzSh1x0JbshjAsGg83COLcL4yiM68ppwmNIoE8SYP7bJ8PKRZEACZAACZAACZBAhwQojOPJQQIkQAIkQAIkQAIkQAIkQAIkUEgEKIwrpGhzrSRAAi0JUBjX8fmQqeBn+tHH4dqbv4ely5Z06SSbMHZv/P1Pj2D40BEZHf/BR+/j8usvyUhQl9EALRpnIsOJxWO47c4fmhaJmZ3TCdNPwgO/+jN8Xt8uhzzx78d0wVYm25233Y3LLrpit4c8+czjuOOeH2XSLZ5/4hUcfNAhHR6zYVMVrrz+0j2WcrUd4NYf/BjXXH59qy847WziW6q34KIrz8tYptRZv+09n0/CuGgsilPOOi4jYZus+a2X34W8no0tU2Hcn377V9xx74/x0n9e6ApiiAjyoT/8A0celpnorqGxAdfcdCXkPaU7N3kdPPSHR1BeVt5ht1f94HK8+b+ZpoaddtR0/PPBJzttK69Zee2a2eb/92MMGTx0t03v/MXtePzpR810h+9deg1+cvNPd7bN9PPk8ouuNDUO30d2j0nVVNzzm7vwz6f+YYrnnjbK9c/IPV1fTx5PYVxP0uZYJEACJEACJNCGAIVxPCVIgAQKnQAL5gv9DOD6SYAESIAESIAECoUAC+YLJdI5s04K43ImFJyIQYD5L88FEihUAilsWfEz1Gz5FMl4CqPHudCvnwNhbQDCvhl4bc5qVK1dhXBUwyPPL8RR+w7DH+4+DRMGfgqojQDkW1lbfKxZVMBWgfnLR+Cpl7/AQfsOxYNPf4yV62rxx5+ejZOPHQ9bzTMIOOrw+eI4ohENw0f54S4+Af2GXd2jQRBh3Ky162D1+VsJ43w+H5xOp763FcYZsjiZqIjbPv/8c9x+++2IxWL6NwOHw2FMnjwZ99xzD6SfVCqlC+M2btyIm2++GfX19bqMToRuAwcOxK9//WsMGTJEF8bJWCKJu//++/HOO+/A4/HoMrhEIoGf/OQnmD59+k4+7QnjRBQn47UUxoVCITTU1aLS4cDe/Tq+UKtHwXMwEiCBXifA/LfXQ8AJkAAJkAAJkAAJkECPEqAwrkdxczASIAESIAESIAESIAESIAESIIFeJkBhXC8HgMOTAAn0OIEHb3661fWop1x6TKs5zHx0dquf2z7f4xNuZ8AFCz/Eud89w/RUnn3sRRw65TDT7TMR/Ezaex+sq1q3x9K2Iw47Ck89/IzpOc55fzYu/t75ptvvaUOzMhwRkF1x/Xe7LM/rbJ4i43rmsRdRUlzSqqlcG3XlDZfiv7Pe6qyLVs/PeWM+Rg7fq91jRMh0xAmHZtTfD6+/Dddf9YMOj/lo0QJccs2Fe3y+dDTA2TPOxe/ufcCUNG7jpg04+6IZ2Lpta0Zr7GrjfBLG3fyT7+OFV5/LaKkiajvpuFNaHZOJMK68rB+Ki4oyltS1naRI4z569xMEAkWm5r9qzUpces1FkPM9G5vE/fG/P41xY8a32/1zLz+DW+640dTQsrYlC1bAZpVrUNvf5L3goCP3RV19rak+5fVyzre+vdu2008/yrRU8d+PPo/DDpm6s79MPk9+etvPYUYYx/eRT3cbL7ke9ubbf9Dt0tLdDZrrn5GmXgxZbtTZ75dtn7cse+OcVr+wTph6a5anyO5JgARIgARIgAQojOM5QAIkUOgEWDBf6GcA108CJEACJEACJFAoBBY/1fztRMENVXPl9oQXXjm6UNbOdfYKAQrjegU7B90dAea/PD9IoEAJpBWEt/0U9dVLUb01gXHjnPAFXEj0PxtaxVH42a2/wJP/WQyvz4P6ujCmD0/jV3+6G5PGNMHaOBMWm/1rX1xaE4sa0iUn4LnZDlx3y31we5xoiAKOYAi3fHsUrr/vdwhveB+eumewZlUMW7ao2P/AYpQNPhv2QM9d9CjRNoRx8HhRUlKC4uJiBAIBXfQmUjcRxlmtVn0XmZtshqhNZHHy+Lp163Dvvffqt4YIbtq0abjtttt0UZwI3+RYEcn97Gc/w6effgqv14tIJIKDDz5Yf8zv9+vCOOlP2j/88MN48cUXd/5cVlaGO+64AxMnTtx5khriOjlOjpHdkMUlk0ldSCfyORHGNdbXYYDTiQnlZQV6knPZJEACbQkw/+U5QQIkQAIkQAIkQAKFSSC9ZVGmf5cuTFBcNQmQAAmQAAmQAAmQAAmQAAmQQF4SWDFzcqt5T7gClrxcCCdNAiRAAhkSoDCuc2CZCH467818i+efeAUHH3RIpwd8vnQxTj/3pE7bdWcDMzKcLVs349RzTjQtaurq/I6fdiL+70+P7iJFq2+oh4idzIqiZPzJB0zBc0+8vIuASq6tOu/Ss7Fg4QempylSwn898izscn1cO9ucebNw8VUXmO6vqw3vvO1uXHbRFbs9vCnUhOO/eXSPyeJkMvkijHvupX/jlp/elBH+7199E2667pZdjslEGJfRgJ00/uENP8L13/t+p11WbVyPk848LmsCQ2MCInqb+fzb7coZRVh46LQDO52r0eDtl2dh/NgJHbZfsXI5TvjWNNP9nX7yDPz5d3/rsH2wKYh9v9G+7K69g1Z9VqVfR2psmXyemBHG8X1kIBbM2r0w7pe/vwd/f/RB0+dAdzTMh8/I7ljnnvRBYdye0OOxJEACJEACJNBDBCiM6yHQHIYESCBnCbBgPmdDw4mRAAmQAAmQAAmQQFYIuA+czAvFskKWnbYhkGlhHs9LnkJZJ8D8N+uIOQAJ5CiBNGK1f0So7l1s25rA0KE2FJUG4BhzDZKlR+GPd92FX94/E5rXiYkVCr49xYrzf/pvVA4qQnr9z2BFFIC1eW3pFDRrANYRt+CjxU247rKr8fn6BCorHDhuWBLnn30kjrj6d7DVf4jIsgewcW0IVRtVTNqvFP2GXwm75/geZaSl05i9ei0UlwulpaW6ME7kbSKMc7vdugBOZG+yiyBO9pbCOPlZJG0ffvghXn/9dV0KN2DAAMyYMQP77ruv/py0kU1kcCKLe+aZZxAMBvWxzj33XEyePHlnO0NCV1VVhVdeeQVr167Vxz7hhBNw9NFH6wI6o7+Wwji5bwjjRBaXSCR0YZxI6UQYF6yvxyCPG+PLSnuULwcjARLIfQLMf3M/RpwhCZAACZAACZAACXQnAQrjupMm+yIBEiABEiABEiABEiABEiABEsg1AhTG5VpEOB8SIIGeIkBhXOekMxH8dN6b+RYiixNp3O62WDyGk8+cjrXr15rvuBtadibDkeuPzr5oBkRm1xPbPXf8Ehedd8kuQ33w0fu66C2T7e7bf4GLz7+01SFPPfsEbr/7NtPdiBBr9uvzUFkxoN1jNmyqwolnTM+6mMsY/PXn3sY+E/ftcP4/vP0HeP6VZ02vrzsa5oMwbvnKZTjxW8dmtFwRGD70x3/sIh2UTnpLGCfn44fvLEJxUXGHa5HrBWecdwpkzT2xjRk1Fq899xY8bs8uwwlzs/P4zc/vw7fP6li8+PDjf8e9v73L9JKE1ZIFK9qNn3Ty/ofv4YLLzzXV30nHnYKH/vCPVm0z+TzpTBjH95HOxZPzF8zD+ZedYype3dkoXz4ju3PNmfZFYVymxNieBEiABEiABHqBAIVxvQCdQ5IACeQUARbM51Q4OBkSIAESIAESIAESyDoBFsxnHTEHaCZAYRzPhJwjwPw350LCCZFAjxGINT6PeO2TCIcV+PxWlJba4e43Hhh5GVavA578/QMIb1mKbxzkw75Hn4eRUy+Bo+ltYNtzAFQAO75FNa0AFjsw8BzEi07Am08+hpn//Acmjknj4EP3waRTbkZxaQrqmocR3vYVqrekUFevYsSofigZ8mPYXPv02JplIBHGzV+3Hk0WK0pKSvQ9EAjo0jgRxsk3Q4qkraUwrr0JirCtqakJsVhMP16Ec4bQrWV7kb1JO5G4yRgijRPRm2yGkM64LxdwiVjO4XDo7UQ413IzxHVyvLGLoE5RFF0WJ3MRYZyMFw4GMdjjxjgK43r0/OJgJJAPBJj/5kOUOEcSIAESIAESIAES6D4CFMZ1H0v2RAIkQAIkQAIkQAIkQAIkQAIkkHsEKIzLvZhwRiRAAj1DgMK4zjlnIvjpvLfMWjz72Es4dMo3OjxIREgiROrprTMZzh33/AhPPvN4RtMSgdjwYSOxZetmiAgpk00kT4vmfg6vx7vLYb/9w6/w14f/lEl3mPvmBxgxbKR+zMZNGzD1hEMyOv7xh/6Fo4+Y1u4xXRVz7TViL/Qr748169agrr42o/nIXGRO7W3/m/02Lr/uuxn11x2Nc10YFw6HcNKZx2V0LkqMXnv2Lfj9gXYR9ZYwTiZz8/W34oarbuwwdF15ze7pedDR+8jv//xb/OmhB0x1f/aMc3HfL/7QYdtzv3smFiz8wFRfRqPdCRb//NAfcN+ff2OqP5mXzK/llsnnye6EcXwfaaa6u/cR+fLkQ489KOP3S1PB7aRRPn1Gdsd6u9IHhXFdocZjSIAESIAESKCHCVAY18PAORwJkEDOEWDBfM6FhBMiARIgARIgARIggawSYMF8VvGy868JUBjHsyHnCDD/zbmQcEIk0GMEUrGPEdryC0SjSTidVpSVu+B2pWHxDoCl8khE4+Wo21QNX0kZiivLYYssBIKfieUMsDibJXGyiTBOdliB4v2R8u2PmrXVsGgxlFRWwO2shaX2PSihbQiFgO3bE7q0rV/lSPgH/gJWW1mPrVkGkrE/37wFG+MJXcrWUhjn9Xp3CuNE1tZSGidyN2MzxHAthW6GzK09aZz0Y2yqKrK91pvRd0uBnNFfy5bGY9KHIYyT+3KRjCGMC4fDujAu2tSEoV4PxlAY16PnFwcjgXwgwPw3H6LEOZIACewhAfNfNd48UKbt93B6PJwESIAEepYAhXE9y5ujkQAJkAAJkAAJkAAJkAAJkAAJ9CwBCuN6ljdHIwESyD0CVV9uzfS61JxZxIKFH+Lc755hej7PPvYiDp1ymOn2mQh+THdqsuEdt96FKy7+Xrute1M+tTsZztJlS3DK2cebXCHwvUuvwXVXfh9FgaKdx0RjUfzxb/fjoUf+arqfX931O5x/9nd2aS9foHnmd+6heUsAACAASURBVE7H50sXm+5Lzo9/P/q83v6Cy8/FBx+9b/pYWc9Pbv5ph+0fffJh/PzXd5rqT0R4P//Jvfjmyd/Sr0Uzti3VW/CjO2/G3PlzTPUjjd75z1yMGTW2VXu5huy4bx6NVWtWmu6nuxrmujDu+luuxn/eeMX0ciVWb7zw352iwfYO7M3X7LSjpuOfDz7Z7nrWb1iHo04y/55odCJ9jh09DnLd4YqVyzHvg7mmeRkNZ70+D6NGjm513CeLF+KMC0431dfuzqOmUBP2OXScqX5aNvrxTXfgqsuubfe4i648z/Tr7uPZn6GyYkCrfjL5PNmdMI7vI81Ydxf/l/7zAm788fUZx787Dsinz8juWG82+xg+caB+wbVl2RvntPpFdcLUW7M5LvsmARIgARIgARIAQGEcTwMSIIFCJ8CC+UI/A7h+EiABEiABEiCBQiPAgvlCi3ivrTfTC3O+NtP02pQ5cF8nwPy3r0eY6yOBjgmoyfVo2nQLYpEwrBYLSvu74fU5YbWqsFg1WLzlgOwiOItsB1IRwOYFrDtkcXI/nQa0CJDWAC0JqFHA4QP8FWJmA2INSEdrgbQVyZQNTY1xNNQlYXfYUNzvQPgH3i2XBPRomOSCubW1dVhcW4eioiJdGCe3fr8fPp8Pbrcbdrtdl8UZwjgRw7UnjNvdxNsTx7V8rGV/0k/bn42+jccNWZyI4loK41KplC6MSyQSiEajMIRxyVAIo4qLMLSo/W8+7VHoHIwESCCnCDD/zalwcDIkQALZIcC/v2SHK3slARLIMwLLH0ar98PxpyzKsxVwuiRAAiRAAiRAAiRAAiRAAiRAAiRgnoBl0OSe/Ydn81NjSxIgARLIKgEK4zrGm4ngp20vIpEaNngYNmzegEgknHEMLz7/Utx9+y/aPe53f/w1/vJ/f8yoT5HrXHPFDZg4YSLGj90bHrcH22trsOizhRC5zjtz/muqv93JcK656UrMfPs1U/08/8QrOPigQzpsK8IuEXeZ2USGJlK09rauCLHuueNXkGu9br/7NjPD6232m7Q/XnzqP3A4HO0eI9dlfWP6ZNTV13ba57Ahw/HcEy/rQqT2Nrn262e/vB1P/PuxTvuSBpdddAXuvE2usft6e++Dubjwim+bOr5lo5uuuwUH7HcQ9h63N/qV90ewKYily77Af2e9jcf+9Yip/nJZGPfUs09kFHdZ8JMPP4MjDztqt2vfU2GcnOP1DfWmzp+2E9lrxF6YPXN+u/O7454f4clnHjcVN2kk8/jHXx7bRY63ZetmXHrNRVi+cpnpvs4983z89u7ft2qfUlPY9xsTTL9nfjTrMwyobC1mkw7ffvctXHnDJabnYjQ87JCpO4WRLQ+WeY3ad6ip/ibtvQ9mPr/r+2kmnycdCeP4PvJ1CDp6H+mqDFNeJ/IZOX7MBIweNQZulxuNwUZ8vuQzvPTai3h15kum4p9vn5GmFtVLjSiM6yXwHJYESIAESIAEhACFcTwPSIAECpXAm2fNOFrWfsAFF86W25LhwwsVBddNAiRAAiRAAiRAAgVFgAXzBRXu3lwsC5Z7kz7HbkWA+S9PCBIgAU2LILTlLkTqlyCdtqCk3Amf3wWH0w5YLbrGTcRxzXccgMUmDwDQALsfCBzUfD/4SbMoDjue01KAqjRL5Cw2pGFFWksjHksi2JBAuEmBw+lC8aAz4C27uMcDIReV1EYieGfVGhSXlqK4uFgXxgUCAV0Y5/F4dGGcsRuyOBG3dSR1a7kI6V/atRXGtSeQM45rr++2gjrjeEMYZ0jj5Nt0RRgXj8cRiUR2CuOciQTGlpWizOPuccYckARIILcJMP/N7fhwdiRAAt1CgH9/6RaM7IQESCDfCVAYl+8R5PxJgARIgARIgARIgARIgARIgAQyIUBhXCa02JYESKAvEaAwruNoZiL4MXq54rtX4eLzLsGQwUN3Xv+zafNGXH/rNfjs809MnzrTjpqOfz745C7tY/EYDjpyX9NCJelg8gFT8Pc/PqILvtrbvly+FCefdZypuXUkw1m9dhWOPe1IU33ceO0P8YNrbu607ZnfOV0X2pnZPnnviw7X9/JrL+IHP7rOTDddbvPeWx9i+NARHR7/zAv/wm0/+6Gp/p9+5DkcfugRu20r8rADpk401V97wrCLr7oAc+bNMnW8NCov64d//u1JXYzX0TZ8YvuCu7btc1UYt3TZEpxy9vGmmUjDO275GeQ139nWFWHc8dNOxA1X3YgJ4/eG3WbXh5C4/+HB3+Pxpx/tbMhWz69bslmXILbcRF544BH7mO5HBI+PPfQv+Ly+do8RMeal116MBQs/MN3nx7M/Q2VFa+HbDbdea1rOJe9rJ04/eZfxRPYo8r+ubMsWrt5ljSLCO/Fbx5rqrqP3t0w+TzoSxvF95OsQdPQ+8sWXn+O0c040FSuj0eknz8Cv7/odRPTa3tbXPyMzgtWDjSmM60HYHIoESIAESIAE2hKgMI7nBAmQQKESeOusGa0KCI7+yR2FioLrJgESIAESIAESIIGCIsCC+YIKd28ulgXLvUmfY7ciwPyXJwQJkIAQiAfnYOXi38LrVjFggAsevwtOEcZZLLBabbA4bHIXkE8wa7MELR6KwO7xwzFgqi6DU7bOBVIxOL3e5obSNp0GNE3/WY5RUypikQRCQQUbNyfg9g3CmAN/CZt9cK8EIpZUMHPZcti9Xl0WZwjj/H6/Lo2Tb4uVXaRxNpswaJbFdSaN250UbncL3Z2IrmWfhiyupTROZHFthXGhUAgDbFaMKi2B02brFcYclARIIHcJMP/N3dhwZiRAAt1GgH9/6TaU7IgESCCfCVAYl8/R49xJgARIgARIgARIgARIgARIgAQyJUBhXKbE2J4ESKCvEPh/7J0HeBTF38e/13KXHhJI6L33Kl2KCCiIiBRBRUXBXrHwWkFsKNhQLCh/UQERVARBkN57h9B7hySkXZLrL7NwkIQkt5vcJVe+8zxHQu43M7/5zNxudrP7WQrj8p9JJYKfju064cN3PkalipXzbDAlNQVCgHboyEFZSycvyZeouHbDagx5dKCsNkRQu9Yd8NM3v0Kv1+dbxx0ynInffo7xE8e5zEuIx9Yt3gyDwfUDLJes+A+PPi3vYaJTJv2M2zrlL7178f+exZ9zZ7vMrzABX43/FnfdcXeBVQc9fK8skVaPbnfg+y/kycDe+2QMJv/0rayUd67bh6jIKCnWmGFE/VY1ZdUTQUKitHTeKghBU0HFl4VxA/oOQu8BPXDy9AnZXPr16Y/PPpwoK16JME5w/mrCd5LoMa8irvsb+cbzitbz5hU7EFsmLkdz8xfNw1MvjZCVvwha/PcK1K5Zp8D4rTs2o9/9fWS3+flHX+Geu+7NET/nnz/x/GtPy2pDyPqEtC97EddKCqmmEOIVpvwy+Tfc2q5TjqozZk/DKJnCxznT/0GzJuJByjmLkv1JfsI4bkduMM1PGPfjz5Px7ri3ZU+9+OyPf//zAuMDYR8pG1gxBlIYV4yw2RUJkAAJkAAJ5CZAYRzXBAmQQKAS4A3zgTrzHDcJkAAJkAAJkECgE+AN84G+Aopt/CsU9tRZYTzDSUA2AR7/ykbFQBLwawLmrLOYP/txhAalo33rMOgMOugNWqiELE6thlqrhVoY41QOQDyl0mHDqcNJSEm2olRsOBx2wJhqREzpIJSuGAU4VFdlcdeEcQ771e/NFitMRhNSUsyY+186atW9BZ16jC8xthabDSsPHsZlux1CEpddGCf+Ly5qzC6ME9I4UcSTOp1yt7wkb4UVxmUHkb3d3LK47KI4m80G8TKZTNIrKysLRqMRQhaXmpqKhlERqBAefj3fEoPNjkmABLyOAI9/vW5KmBAJkID7CVAY536mbJEESMAHCVAY54OTxpRJgARIgARIgARIgARIgARIgAQKTYDCuEKjY0USIAEfJTBp5PQcfw/pNaxLjpHMn7I8x/9zv+8Nw96weT0GPdxPdiozf/oDbVq1kx3vDsFP9s5mzZmJl994QXb/x3afka41yl6++OZTfPrVJ7LbmPXzHNzSonWB8e6Q4cgVGfXu2QdfT/hOVv6nTp9Ehx4F5+5s6LknXsTIZ1/Nt920tFTc2b+7IiGYnCSHDHgAH44ueD4yszJRt0V1Oc3h/bfH4YFBQ2XF/jXvD7ww6hlZsdN+mIkObW+VYtdvWov7Hukvq54Ievm5UXj28eddxvuqME4IxzZv34RFS/51OUZnQMP6jTD7l78RbAiWVUeJMK5b5+748eupBbZ7/OQxdLpD/rbs798WoGmjZjnafPv9NzB1ujw5Yaf2nfHz9zNkjbXXgO7YE79bVuzg/vfjozE5rwFNSLwkCd/kFDEP82f9lyN038F49LznNjnV84x5fNhTeH3kWzneE9ttsf12VYRccdf6fdBqtDeFFnV/wu1ITqT5CeOGP/sI/lu20NVUXX9/9aINqFyxSsDvI2UDc0Ogq98vc7+vil8wMMcvrPU65L+zd0N+bIIESIAESIAESAAAhXFcBiRAAoFKgDfMB+rMc9wkQAIkQAIkQAKBToA3zAf6CuD4SSDwCPD4N/DmnCMmgbwIWDIPYPFfz+P8RSN6dw9HZKkg6AxaaDRCGqeGWqeFWvy1XqWCSqMC1CpcPpeKvbuSkZ5ugUYNlIoOQp0GpRBeOgwQsdeEcQ6bHQ67HXabDRazFeYsCw4fycA/i4y4vdstaNH5E0ClK5GJsTscOHIpARtOnUZoaKgkjIuMjJTkceHh4ZIwLigoSJLGCVmcVquVxGtOYZz43vnKawCuxHHO9/OSzmVvzxknvgpZXHZhnNVqhXgJWVxmZuZ1YVx6ejpSk5PRsVIFROr1FMaVyApjpyTg3QR4/Ovd88PsSIAE3EKAwji3YGQjJEAC/kLAcXaL0u2ivwyd4yABEiABEiABEiABEiABEiABEggAAvvnt8wxynrDoQqAYXOIJEACJAAK41wvgqIKfnL3cPDwAdx+t/znQO/fevQmIZVcMZvou0nDppg707UEq6jCOCUiIyGeevIxeZIzi9WKB4ff53qirtzPPujeIfj43QkFxm7ftQ19B/eS1Z6coFo1amPe7wtdSsM2bF4HMW9yipDetWp+i5xQbNq6UbY88POPvsI9d13N4ctvP8OEiR/L6kMEbVu9BzHRMS7jfVUYJyRfRmO6y/E5A2KiS2P+7P8ghFVyi7uFcaLf+rfUkp13dmGgM+cuvdrj6PGjsobwydhPMbDfYFmxSsSYQtQlhF25S59Bd2Dnnh2y+ovffBihIaHXY7//3zd4f/y7surmFSQ+10vmrszxVpuuzXHuwjmXbQ7oOwjj3/88z7ii7k+4HcmJNT9hnJLPhVjTYm27KoGyj3TFwV3vUxjnLpJshwRIgARIgAQ8SIDCOA/CZdMkQAJeTYA3zHv19DA5EiABEiABEiABEvAYAd4w7zG0bJgESMBLCfD410snhmmRQDETMKVtxMb//g+Hj5vQqmUIalQPgU6vgS5IC7VWC7WQxDlUEJfUi+9VWi3gsCE1KQOXE0wQD8GNLqNHaGSwCADsAOxX5WawXROcWW0wmyzIyrBg3YZ07NtrQpeuTdG0q7jIL6SYR3yju9TMLMzdtRsag0GSxAlpnBDGCXFcSEiIJIwTLyGLEy8hi8sujHM+AdgpfctL/uZKHJd98KJ+9njxffZXdmGcUxZnNpslUZx4ZWRkwGg0SvI4rSkLbSpWgF7MFwsJkAAJ5CLA418uCRIggQAgoFSMxBtIA2BRcIgkEMgEKIwL5Nnn2EmABEiABEiABEiABEiABEjA/wlQGOf/c8wRkgAJ5E2AwjjXK6Oogp/cPQgplZDZyC15CePkSrlEH9klYQX1WVQZzvadW9F3SG+5w/JIXI9ud+D7L6a4bHvSDxMx7rMPXMbJCfhvznLUqVXXZeh3UybhgwljXcZ5MuDdN97HQ0OGSV2MeH4YFi1xLRIUsXJEfM685a7N/ERP7hy/ks+u0n7/nDYXLZq2UlTNE8I4JVK13MI4i8WCmk0ryx7DgtmL0aBeQ1nxR48dQZfeHWTFiqDcwjfxs4nffo7xE8fJamPmT3+gTat212OVSDXz62DT8u2Iiy0rvX3h4nnc0qWZrFwmffo9evW4K89YJWvyrdfG4LGhI3K0w+1ITqx5bUeUruu8RIp5TV4g7SNlLfQiBlEYV0SArE4CJEACJEACxUGAwrjioMw+SIAEvJEAb5j3xllhTiRAAiRAAiRAAiTgeQK8Yd7zjNkDCZCAdxHg8a93zQezIYESIeBwID1hHrYu/xznL5kQHa1Bw4ahKFVaj6AgLTQ6LVRaIYFzAA4HIIRmajW0WrX0f7Www6kcsNnVUGnEj8TPxY/ssNvsUoyQnFnNVljMFhw5nIn1GzKhgR3Nm1dA065fw6GOlYZus9uRkpYmxYeFhsIQFORxJKLPPWfOYtvZczAYDJIwTojjhDBOiOOCg4Oh1+uh0+muC+M0Gg3EyymHcwrkRLL5iePkSOOcdZ2CONGe83vBRHwvsbRaYbPZpJeQxYmXEMUJYVx6erokjBNfm8eWRqXIyOs5eRwmOyABEvApAjz+9anpYrIkQAKFI0BhXOG4sRYJkICfEqAwzk8nlsMiARIgARIgARIgARIgARIgARKQCFAYx4VAAiQQqAQojHM980UV/OTVg1yplrSP2noUwYbg682I63xqNaviOvFrEfN+X4jGDZq4jC+qDGfZyiV45KkHXfbjyYBbWrTGrJ/nuOzCZrfhgcfuw7qNa1zGFhQwbsx43Nf/flltfPjpe/j2x69lxXoq6KVnXsHzT74kNT9gaF9s2rpRVld5iavyqyh3bfuyME7JvGfn5Alh3ENP3I8Vq5fJmsfcYqzEpAQ079hIVt28tkUFVbTarKjRuJLsttcv3YryZcvniN+5ZweEEE9OefWF/8PTw5+TQlNSU9C4bcESxyEDHsD0Wb8W2PQX475G3979pJj/li3E8GcfkZMKdq7bh6jIqDxji7o/4XYkJ9a8tiNK1/Xyf9agerUaLuc2kPaRLmG4IYDCODdAZBMkQAIkQAIk4GkCFMZ5mjDbJwES8FYCvGHeW2eGeZEACZAACZAACZCAZwnwhnnP8mXrJEAC3keAx7/eNyfMiARKgoA58wjid/2OlDNLkJVhQaWKWlSqEgxDqBClaaDSaCVRmVbjQKZJheUbTDhwxCJJ4PQ6O1RqFbKyALVWhWpVg3Fb+2DERKlgMdul4VitNthMVqQlZ2LrlnRcvmxBeJlw1KjTAHWajEZCmhort27DkVOn4NBooQ8ORbBOh7jIcDSsVgU1Kst/EmZh+CVnZGDR3n0wq9XXpXFOcVxISIgkjRPCuNzSOKcoTojecr+ceWSXwOXOTbyXXSSXWxiXlyxOSOKcwjjxVVxEajKZJGGceAlRnBDHOTIz0Kl6NYQWg3SvMMxZhwRIoOQJ8Pi35OeAGZAACXicAIVxHkfMDkiABHyJAIVxvjRbzJUESIAESIAESIAESIAESIAESEApAQrjlBJjPAmQgL8ROLH3nNK/i3gNgg2b12PQw1elPnLKzJ/+QJtW7eSESjFFFfzk1ZFcqZaom1sYp1SGs2n5dsTFlnU53qLKcP74exZeev2qsKmkSsP6jTB/1n+yuhfyrq53dYTRmC4rPndQ75598PWE72TXffmNFzBrzkzZ8Z4IfOLRp/F/L70pNd2tTyccOnJQVjeTPv0evXrcJStW7tr2ZWHcj19PRbfO3WXxyB7kCWHcEy88hn8Xz5eVS25h3NFjR9CldwdZdUNDwxC/6ZCsWGeQkNGJ7ZWcsvCvpahXu36OUCF2bNSmrqzPaOeOXTH122lS/UVL/sWI54fl260Yy79/LMatPdsWmFr/uwdiwgdfSDEfffo+vvnxK5dDcSWtLOr+hNuRnFOQ13ZEyboWreXex+Y3yYG2j3S52IspoEqDcirRlSp+wcAcv6jW6/BqMaXAbkiABEiABEggcAlQGBe4c8+Rk0CgE+AN84G+Ajh+EiABEiABEiCBQCOQfOKENOTt037pIr7eMXvOikBjwPGSAAkEJgEe/wbmvHPUJJCbQIbdipn//YyMS6slCVzT8smoHpuF6NJ66EOCoNFqYLc7oFE7YLOrsWOvBfOWZmH3ITPMFhVsdjs0ahVqVzOgT7dgtGuhgcGggt0q4h2wWmwwG7Nw6pQFO4/qEKSxISgmFqaI26BXV8Osf1dAExKMW7t0Qc3a9aAPCUV6RgYuJ6Xh9JGDKKezoU+H9tB7SH5mdzhw8PwFrDlyFPrgYAhJXFRUFMLCwhAaGiq99Ho9goKCJGmcEMVpNBrpa17SOPEzZ3FK4OSsOqc8zimKy/5VCPuELM75ErI4i8UiyeGEMM5oNEovpzCuQXQp1I4tA+lqCxYSIAESyEaAx79cDiRAAgFEQOmNUfzVKYAWB4dKAoFEYN9k5Nge1u21JZCGz7GSAAmQAAmQAAmQAAmQAAmQAAkEGAFV+ZY8zxdgc87hkgAJXCVAYVz+K6Gogp+8WlYiUsots1Eqwzmy6xS0Gq3LpV5UGc4PP3+PsePecdmPJwOUCONEHouXL8JjzzysOKWY6NJYsWAtIsIjZNd99OmHsGSFPJmd7EYVBmYXxilZg39Om4sWTVvJ6i0QhHFi/pfPX4PIiEhZTJxBnhDGvfh/z+LPubNl5ZFbGLd951b0HdJbVt3CCP669GqPo8ePymp/5k9/ok2rmwVuSgRpzm3dqHdexozZV+VxeZW7e/XDlx9/DVf5CbHcng0HpOs7732gD7Zs3+xyLG+8/DZGPPJkvnFF3Z9wO5ITbV7rUsm+TIkIUUm7g/vfj4/GjM+RrC/uI10u+GIIoDCuGCCzCxIgARIgARLIjwCFcVwbJEACgUqAN8wH6sxz3CRAAiRAAiRAAoFKYMUH7+UYes/Zc3jhWKAuBo6bBAKMAI9/A2zCOVwSyIuARoe1RzZh8ZoVuJySjqysDETp0lG3dBJur5eAMqW10BgMUAkHmgMQX+wWOy5dtONUggqZNh3sNgf0WjsqxgIVymug1gEOByRZnM1qA0yZ2H9Ci8X7y+B0WgSC9VrExETh8Hkd4veeQItWt2D4Y8NQq2ZNaAzBcNhFXTvSMrJw5MRpLF+9GuEpFzDsju4weEgaZ7JaseHIURxOuixJ4YQwTojinNI4IZFzSuO0Wq1LaZxcUVz2OCGFE//PSxjnFMWJGKcsTojihDAuMzNTEsWJl/hZqMOOdlWrIlQfxDVPAiRAAjcR4PEvFwUJkEAAEaAwLoAmm0MlARLInwCFcVwdJEACJEACJEACJEACJEACJEACgUSAwrhAmm2OlQRIIDsBCuPyXw9FFfzk1XLHHm1w8vTVh5W7KrmFcUqkNcUpw/ny288wYeLHrobj0feVCuNSUlPQuG1dxTkJYdiqf9chLCxcdt1BD9+LDZvXyY73RGB2YZxcsZvIY8nclahVo7aslOS2WxgBmawEsgUp+ewqbXtA30EY//7niqp5Qhj3xruv4deZP8vKI7cwbuOWDRj40D2y6lauWAWrF22QFesMqn9LLRiN6bLqLJi9GA3qNbwpdv6ieXjqpRGy2lj411LUqVkXLTs1QWJSQr51xLyJ+ROCSyHxKqiINqtVro46LarJzqFe7fr5xipZk2+9NgaPDc05dm5HcqLNazuiVKp6Yu85WXOrZN+blzDOF/eRssB4OIjCOA8DZvMkQAIkQAIkUBABCuO4PkiABAKVAG+YD9SZ57hJgARIgARIgAQClQBvmA/Umee4SYAEePzLNUACAU5ArcIp4xks27QMl1MykJ5hQkqKEYkpWVA7MtCl6mncXu8SIqODodIJSZoKKhWgsjlgM1lhyrLDZnUAKkCnVUMfooHGoIVdBTjsDlgsNqhMWTh5QYU/dlbCnksxCDEEIbZ0JNKMNmzash9tWrfD22+/jZgyMTCZbLDZbZJsTvxjt9uQZbbh2PlE/D7nTzQ0ODCkW1doNRqPTFxSuhErDx5EssWKoKAgSRbnFMYJeZzBYJBeQiin0WiufxVPoRSiN6f8Tfw/e3FK4MTPsgvihBgut1jOKYsTsc7vhSROvIQ0zmKxXH85ZXEZGRlITU2VxHG2rCzcWr0aYsPDbmrbI9DYKAmQgM8R4PGvz00ZEyYBEig8AQrjCs+ONUmABPyIAIVxfjSZHAoJkAAJkAAJkAAJkAAJkAAJkIBLAhTGuUTEABIgAT8lQGFc/hNbVMFPXi136dUeR48flbWacgvjjhw7jK69O8qqK4KO7T4jPdjSVSmqDOenaT/inQ/edNWNR99XKowTIiohpCpM6denPz77cKLsqsOffQT/LVsoO94TgdmFcUpkXnOm/4NmTVrISilQhHECxtRvp6Fzx66yuIggTwjj3n7/DUydPkVWDrmFcfsO7EXPft1k1RWSxG2rd8uKdQYpEWPOmTEfzRo3v6n95JRkNGlXT1a/H47+BM0aN3M5pvVLtqB8uQpYtnIJHnnqwQLbfmfUu2jSqCn63d/HZQ6C0dZVuwq85rKo+xNuR3JOQ17CuAsXz+OWLs1czpcz4OC249KDoF2VQNxHumJSHO9TGFcclNkHCZAACZAACeRDgMI4Lg0SIIFAJcAb5gN15jluEiABEiABEiCBQCXAG+YDdeY5bhIgAR7/cg2QQGATEGK3fQkHcfD4bqSkZSLdaEJGlgnJKelIvJwBh9WIDpXPolfjiwgJ00EVpIdGq4LKDthtDtitDtisdknwpg1SQRukAdQq2KX3bbBnGXH8vBbz9lRAfEJphAQbUCEuElqdHqs3HEG6MROfffopWrZsBWOmGQ5cdXqIr3YhnDNbkZllgtVsxe6T5zBr+lS81rsr6lWt6hEZmhC0nU9JxapDh2C02aULSUJCQiBkceIlvg8ODpZ+LoRyQhwnLsgU8rjs0rjsKhlgWQAAIABJREFU8jgxntxSuIJWnchBYuBw3CSMs1qtMJvN0stkMkGI4sTLaDQiLS0NWUYjWlepjFpxscLhx0ICJEACeRLg8S8XBgmQQAARGK1wrErjFTbPcBIgARIoGQIUxpUMd/ZKAiRAAiRAAiRAAiRAAiRAAiRQMgQojCsZ7uyVBEig5AhMGjk9xwN0eg3rkiOZ+VOW5/h/7vdLLvMbPW/YvB6DHu4nO5WZP/2BNq3ayY4vquAnr46KIoxTIp0Sfe9ctw9RkVEux1tUGc7fC/7Cc6885bIfESDESiMefkJWrJKgShUro1ePu2RVmT7rV/zf6FdkxeYX9PlHX+Geu+6V1YboS/Qpp3Rs1wkd2siXAsppU8S0btnmuvhNiczrx6+nolvn7rK6CSRhnFjHKxasRUR4hCw2Sj67grfg7qoURRh39twZtO3W0lUX198/sfec7FgR2LxjIyQmJciqs/yfNaherUaesfc+0Adbtm922Y6QONapWRcffvpevrHVq1bH8vlrpffT09PQoHXtAtsVQkDxeRw77h2X/T80ZBjefeP9AuOKuj/hdiQn3ryEcWlpqWjYpo7L+XIGbF6xA7Fl4lzGB9o+0iWQIga4+v0y9/uq+AUDc/zCWq/Dq0VMgdVJgARIgARIgARcEaAwzhUhvk8CJOCvBHjDvL/OLMdFAiRAAiRAAiRAAnkT4A3zXBkkQAKBSoDHv4E68xw3CdwgYLFbcSbtEo6c34dzF8/hcmoWMjIykZycjkuXM2A2paNrjXPoWDMJsVE2aENCoFZrcN1IJv6KL+RmKiFGk76FzWKFKTMDe0+GYMHeOBxLjkJkeAjiykQirnQUjpxKxer1e3Brp/Z4/uVXERoSAYNGB6gcwhYHOwBJjmYyIyPDBIvFikybHTP++QdhCSfx2pD7oNNqPTKNdocDp5MuY+mBg3Co1ZIUzimKE9I4IYwTL4PBIEnjtFqt9BLSOFGEHM4pj3MmqEQYJ+o4pXF2u5DxCXmeXeIhXkIUl5mZKb2EKE68JHFcWhqaViiPhhUrQCvjqcIegcdGSYAEfIIAj399YpqYJAmQAAmQAAmQgOcINBH313muee9t2XF2S47r8L03U2ZGAiRAAiRAAiRAAiRAAiRAAiRAAsoJ7J+fU5ZRb3jAPWMrYM95KF8trOHnBALus0BhnOsVXVTBT149FEUYp1SGU5CIKXtuRZXhrFq3Eg8Ov881UADtWnfAjCmzZMV6Imj/wX3ocU9XtzS9auF6VKlU1WVbn3zxEb76/guXcSLgledH4ZkRz8uKLWxQrwHdsSd+t6zqH787AYPuHSIr1peFcS88NRKTp34HozFd1lhF0JABD+DD0Z/Iivc2YZwcYVr2gW1bvQcx0TGyxpqZlYm6LarLihVBBbX97Y9fFyiBc3YiBH41qtXApq0b8+33saEj8NZrY66/P2Bo3wLjRaCQxq1YvczlWP436Rd07dStwLii7k+4HcmJNy9hnM1uQ/VGFV3OlzPgm88m487uvV3GB9I+0iUMNwRQGOcGiGyCBEiABEiABDxNgMI4TxNm+yRAAt5KYFH/vitEbpGVq3QSX5s+8KC3psq8SIAESIAESIAESIAE3ECAN8y7ASKbIAES8EkCPP71yWlj0iTgdgJCSJZkSsP+c/E4ffEkLlxMQVp6Ji4np+L8pWRodJFoVsWOW8odRNngU4iI0EMfbIBaq5ausheiOLvdAYvVDnNGFpJTbDhrqomlx+vgwPFEGHRmxJWJRsUKZaDVBWHlhqO4cCEBjz/zNBo0bQW1w464mDIw6PWw2+wQF30ISZzJbIEpywKb1QqHSoUVO/Zgw6K/8OOLzyDIQ8I4AVdI2g5dvITNJ07AZHdIMji9Xn9dFucUyAlhnHgJqZxTGidilQrjsgvlRN/Ol5gXpyzOYrHAbDZLojghiLsuisvIgCUrC7dUqYRacXHQXRPXuX2RsEESIAG/IcDjX7+ZSg6EBEiABEiABEigcATuATAWwLsAxN1sASNRozCucAuGtUiABEiABEiABEiABEiABAKTgPh73flLSThx+hwql49D6ZgoaK/9HdCniFhtgPbqg6/8vQSoME78ub7/lfMb7wB4C8Bf/j7PHB8JyCAQcOf/KIxzvSqKKvjJq4eiCOOUynAmT/wfunft6XKgRZXhKKkvkjmw9Zj0wM3iLhmZGejV/3YcPX7ULV03adgUf06bC61WV2B7U6dPwdvvvyGrz+IQ6g0dMRgr10q3v7osuSVbBVXwVWGc+Ix8/+UU/Dl3Nl56/TmXTLIHTPthJjq0vdVlHW8TxoljlqoNy7vM2xnw+9S/0LplG1nx+w7sRc9+BcvTsjd0ZNcpaDV5PwBYaVsFJTj122mSAM5Zvp78JT7+/ENZY3IVtG/LEYQEhxQYVtT9CbcjOfHmJYwTEd36dMKhIwddTZn0fsd2nfDr5N9cxirZxw3ufz8+GjM+R5tK6pfkPtIlCDcFUBjnJpBshgRIgARIgAQ8SYDCOE/SZdskQAK+QCBrG5+w7AvzxBxJgARIgARIgARIoKgEeMN8UQmyPgmQgK8T4PGvr88g8ycB9xDItJpx8NIRxB/bjQsJyUhMSsHFS8kwhJRBucp1UL1cOEqZNkOTsAQGxyWEhIdCb9BCrVIhK9OKtJR0ZDlKQ1f1XljKdMWefYdweP822CzpiIuLRtVKsUhOs+DP+ZsRERWJR556GuHhpRAeEoIy0dEI0YfA7nBIkjQhSBMvq9UGhx2ShG3n8VNY9PdMfP/ko9Dr8r7AyD0krray4dhx7D13Hiq1WpLGCTGcuNBRSOKCg4MliZx4OcVxuaVxoo4oQgiXXQqXX465RXHZOZhMJkkW5xTGpaenw5SVBY3djuaVKqJ+hfKy+nAnH7ZFAiTgmwR4/Oub88asSYAESIAESIAE3EZA3Ei9B0B9AHuvHLKNATA7EMRxFMa5bQ2xIRIgARIgARIgARIgARIggQAgcPrcRWw/bcSG+NM4dXA3yoZr0b1DczRrUAvhYSEI0hUsFAkARF43xAATxonzG/2uieIaAYi/cn6jYSCc3/C6hceEvJFAwJ3/ozDO9TIsquAnrx6KIowT7fUa0B174ne7Th6QZHFCGueqKJHZ5CXDsdqsaNy2HozGdFddSe9P//F3tG/TUVasO4NefXskZv4x3Z1N4unhz+HVF/6vwDZ3792F3gN7yO5378aDCAsLlx2vNPDDT9/Dtz9+LataaGgYdqzZK13j5qr4qjDurdfGQIjxxPV39z3SHxs2r3M11OvvC2nV0nmrIDgVVLxNGCdyffTph7BkxX+yxvro0OF4+zXxPCXX5fNJE/DZ1zmFWfnVciVIFHPS4tbGSExKcN2xi4j4TYdyzNP2XdvQd3CvIrcrJHRCRueqFHV/wu1ITsL5CeNefuMFzJoz09V0XH9/wezFaFBPHA7lXwJlHykbWhEDKYwrIkBWJwESIAESIIHiIEBhXHFQZh8kQALeTIA3zHvz7DA3EiABEiABEiABEnAfAd4w7z6WbEkWAXmPdLvRVGdZrTKIBIpAgMe/RYDHqiTgTwRUKljsNuw+cxi7Dm9GUnIqzl9MQVh4HGrXqY/a9ZugXIVqMJ7ZiiOrvsOl/auhcly9SNCOUMTUao2anZ5A+frdkHI5Adu3rMX27ZuQlnIR5eOiEBcbjX2HEzFv4Vrc0qYV+t3/KCLDIhEZEgqtWgOoHdJFWw4hjLM5ALsDNptdal+rUWP36XNYPO9PTBp+P/RazwvjDl64iM2nTsNss0l5CQGckMKJV3ZRnPN7IZRzvu+UxwlRnJDdOUtucZxTEifeF9/bbDZJmCe+Wq1WCFGcUxaXlZWFjIwM6WW3mFEuPAItqlVBXESEP61CjoUESMDDBHj862HAbJ4ESIAESIAESMAXCIgbqv/IlqgQyIk7ZPxaHEdhnC8sTeZIAiRAAiRAAiRAAiRAAiTgLQR2HTyJMjVbICu2MnbuOYcDO3dgx9r/YDBfwq0t6qBr22aIKxMDg961dMNbxuTveQSIME6IsPpemcvRABpnm9N7r/z/T3+fY46PBBQQCMjzfyf2nnMoYORVoRs2r8egh8W0ySszf/oDbVq1kxcMoKiCn7w6KqowTqn0bNPy7YiLLVvgmIsqwxGNP/XSCMxfNE8WWyH6+fu3BS7zctXYrr078ea7o3D67GnM+vkv1KhWM98qcxfMwbOvPOmqyUK9P2PKLAjxVX7FZrehUZu6soV6QkD141c/Qastmmj47wV/YfwX46DVarB8/trr6f017w+8MOoZ2WP95rPJuLN7b5fxvi6MEwM8cuwwuvZWJjMcOvhhjH3zwwL5eKMwbvLU7/Dex+JXQ3ll94YDiAgv+FrDjMwMtOzURPZaf/nZ1/DsEy8UmMAb776GX2f+LC/JfKLEdl9s/7MXpaLL/BJ4762P8OB9D7nMr6j7E25HciLOTxg35ZfJGPPR2y7nwxkgZI9Tvp6a7+8G6zaugViDR48fldVmXlJVX9hHyhpcMQdVaVBOHMNCFb9gYI5fVOt1eLWYU2F3JEACJEACJBB4BCiMC7w554hJgARyEuAN81wRJEACJEACJEACJBAYBHjDfGDMsxeNUumFOdIfSlhIwJMEePzrSbpsmwR8gIBajUyLGYmZ6TidmIDdh+Jx7vxJhIdqJXFbaGRF1G/QGLXr1EHFStURpA+GJSsTxzZOw4lNM6QBVmzaFzU6PISg4Ag4HDakJidi79492LxpLVITTyBIp4bFpsKho5ewftNu9O3fD3f1vR+hesM1QFdlcXCocFURd/Vfh90BqAC1So2tx05g1b9/45vHHyoWYdzRhERsPXMGJttVgZsQuTkFcEIIJwRxTkmc8/vsPxOCOSGLEy9Rz/lyrginLE60K74XXy0WiySKE1/FS8jihCjOKY2DzYZSBgPqlI1D/QrlpTZZSIAESEAJAR7/KqHFWBIgARIgARIgAT8lIA6khCSufq7xiZ+NuSaTU3oO12tR7ZuMHGOp22uL1+bKxEiABEiABEiABEiABEiABEjAWwhkZGZhw9FkqCs2Q1BINDKNmUg3GnH21Bns3LgcxrP70KlZdfTo2ALl4mIlgQeLdxBQlW/pr39AvfsKYWEDaZqLdPwVCX5D8ad175gBZkECXkEgoM7/OYlTGJf/2iuq4CevlosqjPt5xk94673/k/2B6dS+M76e8B3C8xE9ZWZl4oPx70K0K6fkJ8OZMXsaRr3zspwmpJh6tetj9i9zEBYWLruOM/DCxfP4evJETJ0+5XrdOTPmo1nj5nm2dfzkMXS6Q74oUGlCMdGlsWTuSkSXis636nOvPo2/58t3tA66dwjGjRlfqOu7du/dhU+++BAr1954RvmJveeu57bvYDx63nOb7GEKMdOvP8xEzeq18qwjrlv7Z+Fc2UK+/ERPshOSEViUz+7nkybgs6/Hy+jlRogrGaU3CuN27N6Ou++7U/Y43xn1LoY9OLzA+F9+m4o3x46S3ebvU/9C65ZtCoxfvHwRHnvmYdlt5hU46qU38OSjN0sSlYgu80tg1cL1qFKpqsv8irImnY1zO3IDc37bkX0H9qJnv24u5yN3wIC+g9CoQWNJPGo2m3HoyEGsWLMcQhinpPjiPlLJ+IozlsK44qTNvkiABEiABEggFwEK47gkSIAEAp0Ab5gP9BXA8ZMACZAACZAACQQKAd4wHygz7TXjVHqBnr9eyOg1E8JEAB7/chWQQGAScKhUsDhsOJeegi379yD+8D4kJiQiPT0TNrsDMdEhqFy+DMpVqos69ZuhWvUaiI6Jg1qjlYCZLHb88+evEPK0O+8ZAp3aydEBiykDhw8dwJaNy5B08QQuXLos1UtISsOKVbvQs89daNXpdug0QYiJjEJEcAigEtK4q7s9yR139R9oNGqIXFfu2oMNi/7G/158Gnpd0Z6+KmfGjyUKYdxZWKGSJG5OqZv4KkRtQgiX/SU4iFdQUJD087xkcaKeNC5pjFclcc5XdlGckMWJi1aELM5hs0HjcCDSYEDtsnGoW74ctBredCJnDhlDAiRwMwEe/3JVkAAJkAAJkAAJkIBE4N4rN1PPzofF7mviOHHXl9JzuV6Hl8I4r5sSJkQCJEACJEACJEACJEACJOAjBExmM5bsOAtrdG1oxQO1LCbodMGwONS4cCEB29ctBZIOoVfHhrj1lmaIjAjzkZH5d5p+KIy768qMCVFc3vYcoP81+b1/TyxHRwLKCQTM+T8nGl8QxqWkpuD0mVM3zeaGzevx7ri3Zc/yW6+NQdtWN4vDKlaohMiIyJvacYfgJ3ejRRXGnTh1HLf2bCt7zCKwetXq+PSDL1GvTgMYDFcf0JmYlIBVa1di7Mejpe/llvxkOMkpyWh3eysYjelym0KtGrUlidOdPXoj2BBcYD1xjdTO3dsxb+Hf+PHnyTfF5ieME9dQ3XN/b+yJF6fv5ZVXnh+F5auWYsv2zfIqAOjetSe+/3JKvoI3IR0aPGyA7PZEYOeOXTH8ocfRrnUH6Vqygoq4Tkz08duf07Foyb83hWYXxgmW7bq1xLkLNyRyrhILDQ3D+Pc+Q/s2Ha9/VowZRmlOPv7iI2zfudVVE9ff93ZhnGB5+92dcfL0CUVjWjZ/DULEdYx5FG8UxonrD+/o1w1CICi3fPLeZxh4z315hs9dMEe2NFA0ULliFaz4dy006oKvZUxLS0XDNnXkpphn3LzfF6JxgyY3vTfzj+l49e2RhW5bbFuXz18rq7479ifcjtxAXdB2RAgxlaxrWRMoM8jX9pEyh1UiYRTGlQh2dkoCJEACJEACVwlQGMeVQAIkEOgEeMN8oK8Ajp8ESIAESIAESCBQCPCG+UCZaa8Zp9KbDCmM85qp899EePzrv3PLkZFAfgQcajVSrRnYd+YYtu/bjZMnTyM9PQs2hx2GIC1CQ/UIC9MjNDgIsXEV0ahZJ9Su2xDBoZHSRXGpqSn47bffMevPeQgNCcGAe/ugT5/e159ka7OasXvHemzbtBSZxmTYHUBoSDBOn0vEtFkr0LZTJ7TvfieyskwI0upQtXxlRIdFwO6wS9I4FRzX78oXcjSz3Y6/ly3HiS2rMfmVFxGkvSqt82Q5kpCInecvwK5Ww2azSa/skjfRt7j4ThQhiBOiOLHTDtbpIH4qdvhCdJdbHCfayC6fc4rixMWNNqsVKiHJE/UcdqmtclFRqFo6BrGRkdBRFOfJKWfbJBAQBHj8GxDTzEGSAAmQAAmQAAm4JiAO3/YAqF9AqF+I4yiMc70YGEECJEACJEACJEACJEACJEAC+RFIN2bgv83HYC1VC7qQMGRlpEt/x9MFh8GuDcHpk6exY9VctKyix6DeXVE6OoowS5iAHwnjel1BKURxLQtAKswgDf1BeF/Cy4bd+yeBgDn/55w+XxDGffrVJ/jim089tuKee+JFjHz21Zvad4fgJ3ejRRXGifb6DumtSNCVPQchOEpJTVUkictePz8ZjoiZ9MNEjPvsA8XzJGRkQ/rfjxrVayG2TCzKlI6FWqXGxUsXcP7ieRw+ehBz/vmrwJzzE8a99/FoTJ76neycmjVpgdm/zMHJkyfQpXcH2fVE4Ng3P8TQwQ/nW+eB4fdh9bqVitoUwWLO7rmrP8qVLY+42LKIKRUDs8WMCxfPS68du7fjz7n5PefmanfZhXHi/+MnjsPEbz9XnIuoIERNOl2QIqFa9o68XRgncl2zfhXuf2yQIj6PDh2Ot197N8863iiME4kq2cY5BzZkwAPo1eMu1K1dT1oHe/ftxvxF8/DrzJ8V8SpIPpe7ocJ+dkQ7Yvuye8P+PMV0hRFwZs9NCC9HvfSGrHErYS3kpo8NHZFnu4VlEUjbkZ9n/A9vvfe6rHlxd5Cv7SPdPX53tkdhnDtpsi0SIAESIAESUEiAwjiFwBhOAiTgdwR4w7zfTSkHRAIkQAIkQAIkQAJ5EuAN81wYxUyAwrhiBs7uXBPg8a9rRowgAX8i4HAA6SoLNhzYjFOnTyEhOQ1mswVqlQNhoQZEhgdDpQYysyywWCzQ67Wo1+AWtOnYG6HhURBPfFy0YB7GfTwBYRERCAkJQVZmBp55+knc3v1OhIWHw5iehqULfsWpk/sldMEGPSLCQ5BpsuCbH+cjOLIMBj7yGCwOFexmE2JKlUbNClUky9oNVRwksZpBH4Qj5y9h5h+/o1XpEDxzbz9oXDxx1R3zte/CRRxOSRU2OAipm3gJaZyQxImX+F7wEd+LPLUqFWrGlkG58HBkmM3ItFqRYbbAZLXCbLNBsslJRSVJ95zt2O02qByAQauVBHHhBj1KhYYiOiwU4cHBxSLHcwcvtkECJOAbBHj86xvzxCxJgARIgARIgASKhcCAK8K432X0tOvKgdwYAH/54g3YFMbJmGGGeCWBpORUnL+YICu36KhIlI2NkRXLIBIgAf8icOb8RaSkprscVEiwAVUrlXcZxwAS8DcCp85eQFq6UdawKpUvi/CwEFmxDCKBQCMgfjddvPkozOFVEBwRBVNmJuxWC3Q6LQyRsTCa1di6ch5qBF/GY4N7QR8UFGiIvGq8fiCMu+PaeYhWMsCKcxsFm2VkNMIQEvBjAgFx/m/SyOk5rkftNaxLjimdP2V5jv/nfr8455/CuJy0hZjpjXdfK84puN5XQTIcY4YRrTo3hdHo+njb3cnnJYxbtnIJHnnqQUVdrViwFtWqVJfqCNGcEM4pKYv+WiaJtPIqu/buxF0Deyppzm2xuYVxBw7tR/e+OT/zbuvMRUO+IIwTQ3hh1DP4a94firDM/uVvtGp+y011vFUYZ7VZ0eXODoWW/ymCky04Jro01i/ZAr1eL6uJKb9MxpiP3pYVmzvo7l798OXHX+dbt2OPNoUe/8yf/kCbVu1k5eUuYRy3I1dxF7QdEfug2+66FecunJM1N+4M8qV9pDvHraQtV79f5n5fFb9gYI5fWOt1uNnwqyQBxpIACZAACZAACbgmQGGca0aMIAES8G8CvGHev+eXoyMBEiABEiABEiABJ4Edv/4ifZty8oT0yK8es+d0Jh0S8CABCuM8CJdNF44Aj38Lx421SMBnCQQFYePhzTh88gCyTFZJXmYI0iIkWAeoVcjKNCEj0wSTRUjS7FCp7IiMjELbW/uiQuU62LljK44c3I/lq9bi5OmzCA0NRURYKG7r0h4NGjVF/QZNcPrkIaxZNlN6EqpGrUGQTie1Hx4egmWrd2Hp6njc98gjKFutDoypyQjWB6NO5Row6A2w2aySXE0FFYL1ephsdvy9chV2rFiIccMeRK1KFT2O3ma3Y9u5C0i0WqHRaiVZnJDDiZdTHmc2m6XvJfGbxYyG5cujVY3qMOh01/MT7wlhnMVqhRD1iSKEeCph5Lv2P/E0XY1aBZ1WC51G4/GxsQMSIIHAJsDj38Cef46eBEiABEiABEggBwGh9d5zRRpXXyaXnddu2J7ji+I4x9ktSs9Ly8TCMBLwDIGHXxiNqb//I6vxrh1aYenv38iKZRAJkIB/EajR9m4cPXFG1qAcZ7fIimMQCfgTgbjG3XExIUnWkN577Sm88fwwWbEMIoFAJHD+YiKWbjsJS0h5BEdGwWQyAXabdHgYGlkadm0otq2Yg0HtKqNpg1qBiKjEx7x/fsscOdQbfuNxXiWenLwEelw779BaXjjir8jiGvriOQqZ42MYCbiDQECc/6Mw7sZSee6JFzHy2Zv9I+4S/GRflF16tcfR40dlrdP9W48i2BB8U2xWVhY639nO62Q4ItENm9dh0MP3yhqfO4NyC+OEKEgIg5TI68a++QGGDn7kelpCptX/wb7YvnOr7FRr1aiNeb8vzHPeRCNfT/4SH3/+oez23BWYWxgn2n3u1afx9/w/3dWF7HZ8RRh3KeEiOt3ZXtEaqlyxCv77e/lN8++twjgxaWJ99x3SW/b8uSNwxpRZaNe6g+ymDh89JH2eC1PGv/85BvQdlG/Vt99/A1OnTylM0zi84yR02a73LKgRd+5PuB0pWBgn5mHFmuV46PEhhZrXolQqSBjnTfvIooyxqHUpjCsqQdYnARIgARIggWIgQGFcMUBmFyRAAl5NgDfMe/X0MDkSIAESIAESIAEScDsBQ/OW4gIRFhLwNAGlN+ZxXXp6Rtg+ePzLRUACgUPAARVOGi9izdalOH/pMkpFhSMuLgqWLDOsdjvMZiGJs8HmgCRCs1hssNjsgMOKmJg4VKnREsGh4SgXVwZbd+zChM8mwmZz4MEhA9Gt661ISzMiLT0Vp4/vwOXL5wFoEKTVQh+khU6ngUGvg1qjxpz5m3A+yYT7HnkMkWXK4XJSIuJi4lC2VDTsdgcMej3CwwwwWmxYsHo9Ni1biCdu64BbmzWVBHeeLokZmYhPToY2NOy6IE7c+JH9JYRx4qWz29GuVg3UrVAB6mLIzdNjZ/skQAKBQYDHv4ExzxwlCZAACZAACZCASwIDAcx0GZUzwCfFcb4mjNt74CiyhIChiEWr1UAfFASDPgghwQbElIqCRuOUuBexcVb3KIGhz72NX2YvkNVH53YtsHz2d7JiGUQCJOBfBLxNGHc5JVW2wK6gmRD7L3GOPCw0GGXLlHbbvstd+1clq0g8TKZRvZr5VimJnGJLR8PhcOBS4mUlQ/F4bPUqFVAqMsKt/SgRxo199Um8+cKjbu2fjZGAtxPYumuftL1tUKe6rFRT04xYuukALpjCERFXDlaLeGCUAw67DSERUQDUOL9jAQbf2R5lYkrJapNB7iPgw8K426+J4toqpCHOacxSWIfhJBCIBPz+/B+FcTeWta8J40Tm8xfNw1MvjSj2z6YrGY5IaO6COXj2lSeLNbfswjghehv8SH9s2rpRdg5CXjXth5lQq3OeAy6MqGro4Icx9s38pXBvjh2FX36bKjs3dwTmJYw7c/Y02t3eyh3NK2rDV4RxYlAzZk+Y8WOtAAAgAElEQVTDqHdeVjS+x4c9hddHvpWjjjcL40SiQpgmxGnFUV5+bhSeffx5xV216dq8UJLMdYs3o0L5/B8y/N+yhRj+7A1RpNzEevW4C5M+/V5uONwpjBOdcjtSDhuWbSuQ/3ufjMHkn76VPUfuCPSFfaQ7xlmUNoosjCtK56xLAiRAAiRAAiRQOAL17/zd83dhFS411iIBEiABjxDgDfMewcpGSYAESIAESIAESMBrCfCGea+dGn9LjMI4f5tRPxgPj3/9YBI5BBKQSyBIj1Xxy3Ho+EGcOpMoSeEa1KuMIK0GFqsQwwFWmx1WmwM2u/hqg9lsg81mg1bjQLWaDVGvUTuER0Rgy9btGP/pRJhNFtw3sB/u6n0HLGYTDu7bgpPH9kqiOY1GA71OCOM00Gq10GrVCAnWIyUtE/P/24LgyLLo1X8w4spWQIjegOjQCGg0Kmh1QTifkoaFq9Zi3+Z16NuoNnq2bQ2tRiN3pEWKO5aSgrMWG/QhIbBYLJIYLjMzE+LpvuKrEMeJryEqoEPt2qgWW+amiw2LlAArkwAJkICHCfD418OA2TwJkAAJkAAJkICvEBDXwu0BUL8QCe+4dkP335COpr27+JIwLstkRnC1dh4DKmQs9WpVQ9f2rdClfUs0qV+Lx/Qeo134himMKzw71iSBQCLgbcK4MRO+x+grL3eXxvVqoX7tapJ47a7bby1QwJZf357evxY05tSDqxAeFnJTSEnl1LVDK2RkZmHD1t3unqoitTd65Ai8M9K9oggK44o0JazsxwTE30f3HDqF/7Yex+E9m/HYoDvQskkDWSNOvJyCGYt3oVz9tkhPS5OEcdIhoUqFqKhS2LHyb9zTsT6aNKgtqz0GuY+ADwrjbrt2XqF9ISjEA2joC+cjCjE2ViEBdxPw+/N/FMbdWDK+KIwTv0s8PfJxSRxXnEWODEfkUxjRVlHGkV0Y98U3n+LTrz5R1FxBUqnvpkzCBxPGKmpv8sT/oXvXnnnWEUK7N98dJTEqrpKXME70/fOM/+Gt914vrjSkfnxJGGez23DvA3dj+86tihjNmf4PmjVpcb2OtwvjxPZk7Mfv4MefJysap9LgAX0HYdzYCdColV/HOfrDt/C/X39Q1GX1qtWxfP7aAuukpKagcdu6itoVwZ9+8CXuvXuA7HruFsZxO+JaGCcmZ/zEcZj47eey56mogQ8NGYZ333jfZTMluY90mZyHAyiM8zBgNk8CJEACJEACniBAYZwnqLJNEiABbybAG+a9eXaYGwmQAAmQAAmQAAm4nwBvmHc/U7aYJwGlNw9S4M+F5HECPP71OGJ2QAJeQ+CSJRUbdi/H+YQknDl/GWfOXES1KmXRqEFVWMw22O1OUZwdVqtdksZZLDZYrDbY7FZERUSgQZP2KF+lHqZP/w1z5/0LnU6LRo0aYMRjw5CVdhH7dq/D5ZQUqNUaBOm0CAoSX4Uw7qo0TgjhDPogpKVnYMWaPchEJNp37Y76DRqjdGRppGdk4vDJE9i/Lx6qlAR0rVcLzWvXhE6rLRaOZpsNe5JT4AgJhUqlui6HMxqNyMjIkKRx4vuyIcFoX6c24iIjiyUvdkICJEAC7iTA41930mRbJEACXkpgtMK8lMYrbJ7hJEACXkxA3AnyexHyE+I4sQ0R4jivLRTG5T81QsIz9rUn0btbB78Qxz02cizmLV7tci2KczbxK2fnKfBxWbkYAiiMKwbI7KLQBPYeOIquA56QVf/lJx7AK08NlRXLIOUEAkUYl5tM7eqVMfKJB/DIoD7S+Xk5paTkbCK3lAMrEREeelOaJZVT53YtIPqmMC7nlIx99Um8+cKjcpYTY0jA5wmIv3tuPZ6KctUbYdzE75B5bBOeHjYQLRu7lsYJ2dyMRRsRWasjLl04f52F+HlYaBguHN2JFtUi0K55fenvjCzFR8CHhHHdroje3gHQoQh0BgKYVYT6rEoCgUYgIM7/ndh7Tul1qcW+DoT8S0jAPFV8URgnWGRmZeKBxwZhy/bNhUYTE10aTz72DN77WN6fu+QK40RC+w/uwzMvP4FDRw4WOj+5FZ3CuA2b12PQw/3kVpPixr//OYTIKr8ixEz9htyFnXvEKX15JTQ0DMvmrUbZuLL5Vli4ZAFeev15GI3p8hotQlR+wjjR5Nhx7+CHn4smsR/75od4673/k5WhLwnjxID2HYxHz3uEr1d+EaKyf/9YCoPBIFXydmGcc2TTfv8Fr495Vf5AFUS+PvItPD7sKQU1coauWL0MDz1xv6L6jw0dgbdeG+OyTt8hvRVLATct34642Pw/37k7dbcwztk+tyMupxeC/TsfvInEpATXwdkiKlesgrFvfiBdAzzi+WGy6r749Mt44amRsmJLYh8pKzEvCarSoJx0YkQVv2Cg1/+i6iXMmAYJkAAJkAAJeIwAhXEeQ8uGSYAEvJQAb5j30olhWiRAAiRAAiRAAiTgIQK8Yd5DYNlsbgJK/97BK0i5hjxOgMe/HkfMDkjAOwiotdh2eitOnjuMhKQ0XExIwekzCcgyZ6FD64YoHxeNzEwTbA7AarsmjLM5YLZaYbXapJ/BYUO1qtURWaY2Jk3+FRfOn0WwIRhBej3uG9AH4YYMHD92GDYbrsridDpog9QI0qihlaRxWmhUKgTpdbBYLNiy4wgOHE+D1hCGiMhSqFO3NuKioxBqtqNseCgqxUQhNjq6WPklZmbhQEYmgsPC8pbFpaaiftk4NKtaBREhIcWaGzsjARIgAXcR4PGvu0iyHRIgAS8mwPMvXjw5TI0EvIyAOP8q7nSrWcS8tl+pL+7Gm1vEdtxafd9k5Nge1u21xa3te6KxkpLHdGzdDLO+H4e4MsV7HsLdDLvf9zQWr9ooq9mLuxejTEwpWbHFHURhXHETZ39KCKzdvBMd7pYnVXpxxBB8OvolJc0zVgGBQBXGOREJcdzE919F905tXFIrqf2rSIzCOJfTIwWMHjkC74wcIS9YZlRc4+64mJAkK5rCOFmYGOQnBKw2G1buPYOG3e7B6s1Hsez3Kcg6uwNPDh2AVs0aFjhK8fCt35buQHTtdjh9/Nh16bTDYYNOZ4DGnIJg01n06NAMoSHBfkLMt4ahKt/SW6+zEoK4D4soihOTEQ9ALFSl5z99ayKZLQm4l4Bfn/9zoqIwDvBVYZyYw8vJl/HS689h2colild/j2534L03P8SlhEu4s//tsuorEcaJBsXDNX+a9iO+/+lbxbIeOQnVqlEbQwc/ggH3DJIe5nn73Z0V9dOtc3f88NVPLoW9Bw8fkNpWUtq17oBff/gNGrUm32oXLp7HpB++khh5onRq3xn3DxwKMdf5FZvdhvFfjMOkHyYqTkHwn/DBF2jSsCmqNCgnq76vCePEoMZ99oFiPk8Pfw6vvnBVoucrwjiR6+ZtmzD6wzexJ363rPl0FSTWyNuj3sWt7Tq5Ci3w/YzMDNRrWUNRG1O/nYbOHbu6rPPlt59hwsSPXcY5AxrWb4T5s/6THS8CPSWME21zO+J6KoRgdfrvv2Lewr9dygGFKG7QvYPx6NAR0vXNP8/4SbYQUwjmxD5JbinOfaQYiy8VCuN8abaYKwmQAAmQgN8ToDDO76eYAyQBErhG4N/+faWzf83uf3C5+BpVpQrZkAAJkAAJkAAJkAAJBAAB3jAfAJPsHUNUesGet17I6B00mUWRCPD4t0j4WJkEfI6ARavGhgMrkZB8EckpRiQmpSMhKQVnzl1CqagwdGnfFDqdBllZZtgdgM3ugM1mh8Uq5HFXhXEWmxWR4SGwIxQz56yC3W6XnqJpMZtxW8eGKFsmBMkp6dLNEUIYJ9rTatXQadXQaDTQiJ8HaQGHA8eOn8WxM6lIt4Tg8KHDSE2+hCcf7oHOt7RBnK4qQvS6YmecabHiQHIKTAYDbDabdBGi0WhEZmYmUlJSEKnV4Jbq1VEhuhT0uuLPr9iBsEMSIAG/JcDjX7+dWg6MBEjgBgGef+FqIAESUELgPgAzlFQoIHbblQelj/EWcRyFccpmNbZ0NBZOn4hmDesoq+hF0RTGedFkMBW/JUBhnPdMbaAL45wz8dX7r+LpRwYWODEUxt3A07ldCwgeG7a656Zpd30iKIxzF0m2QwKuCTgcDmzccxTm6p1hyrLhwrkLWL9wFjLO7MQTD/RH6xaN8m3k8LHT2GeMgFUdioQL56HWaKS/e4qi1uigs2fCdH4f+tzWymvlzK4J+XaEFwrjhChuLABldpr8p0Gcw5jp27PE7EmgRAj47fk/J01fEMZ98+NX+OjT9z22AF578XU89dizN7W/dOViDHtqqKx+Pxz9CYYMeMBlbK8B3WWLmA5tP4GgoCCXbYrfUab9/gs+mDAWRmO6y/jqVavj/0a+he5de0qxR48dQZfeYrfjujw2dATeek2cxlZWrDYrlq5YgumzfsGK1cuUVc4VXa92fdzW+XZ0v62nJCpzlonffYHxX34ku+3Q0DCsmL8GsWXiZNURQjUhDlNSZkyZBSGOc1XS0lLx1z9/Yvacmdi5Z4er8ALfF4IsMbddOnZF+XIVZLe1ccsGjHpnJI4eP+qyjmD33OMvSEIl3bXr4Jp3bCRL1icEYkvmrnTZR1EC3P3ZFbKyrr064NyFc4rSOrD1mHR9ZGJSIpp3LFgw7Wy4z519MfGTb1z2I7aJYtsop/w5bS5aNG0lJ1SKEduU5auW4vNJEwq9HsXn9LknX5RkhQVJE2UnBeCRpx5UJMeM33QIYq26Klt3bEa/+/u4Crv+/kvPvILnn1T2oA93r8m8kuV2RN4UJiYlYO/+vUhMTEDi5URkZBgRFRmFMqVjUadmXVSvllNMqOSzJj674jOstBTHPlJpTiUdf10YV9KJsH8SIAESIAESIAESIAESIIHAIbCwf98cNxB0fv3NwBk8R0oCJEACJEACJEACAUyAN8wH8OQX79B5w3Lx8mZvBRDg8S+XBwkEFoHL5nRsPbIOyekpSEvPQnpqJpLTjLiYmIJLl5JQu0YltGpeGxaLVZLDiXsbhBBOEsbZhDROfLVJPw8NCca+wxexffdJGAxBqFw+AnVrlIHNbodGI2RxOui0Qhangk6I4jQaqNUqSSAXFKTDiVPnkZCYgoRUDcx2PdasXouwEODVp/ugeYOGqBzWAA6LrVgnyGq342hyCi7YHbBYrdLTcYUwLi0tDUF2OxqWL4casbEINehdPpG2WBNnZyRAAiRQCAI8/i0ENFYhARLwNQI8/+JrM8Z8SaBkCagBHABQ041pCHHcaADz3Nim4qYojFOMDOFhIdi3ajYqlI1VXtkLalAY5wWTwBT8ngCFcd4zxRTG3ZiLt18ajjEvP57v5FAYdwMNhXF5L5Oxrz6JN1941Hs+4MyEBDxM4NylZCw+lIWy1RsgLTkFxkwzNi+fC+OpHXj3lSdRsfzNwpHT5y7iz1V70KjTPTh28CAcdhtunIByQK3WQGvPQvLJXejfoy3Kx5Xx8CjYfF4EvEgY1waAsCJ1deNMxQMQlhKl5z7dmAKbIgGfJeC35/+cM+ILwjifXT3FnLjVasGqtSshxESnTp/EqTMnkZKaiorlK6Js2XKoXqUG7ujeK4dkrZhTlLoTYpxDRw5i956diD8Qj+SUy0hJTcHl5MuwWMzX5VJC4BMWFo5SUaVQp1Zd1K/bEHVr1ZV+5s8lMysT8fv3YMeuHTh6/DCSU5IhRFBCbBQSHCJxioyIlDhERkShTExp1K1THw3rNUTN6rWvC9wKy2jX3p1YsOgfHDl2WFpHFxMuIrZ0LMqVK48K5Sqg663d0LFdpyL3U9j8WK94CSQkXoKQCa7buAYHDx+A+P+FSxevyymFkC2uTCxKx5RBjWo1JUFi65ZtEBdbtngTZW85CHA74p4FYcwwolXnprJkrKLHmT/9iTat2hapc+4jr+KjMK5Iy4iVSYAESIAESIAESIAESIAECkOAN8wXhhrrkAAJkAAJkAAJkIDvE+AN874/hz4yghUK83TXE2YVdsvwQCDA499AmGWOkQRuEDh8+QTij++AyWSSbnrIyBBfs5CSkoELF5OQmp6OVs3rok6NSjCZzbA7HLDbHbAJWZxNSNRssNlskkBOCOAMhmBs2X0GiYnJqFU1EoYgLdQaDYL1QQgO1sGg10GrUUMryeLU0GhU0OuDcCkxGecvJMHuUOPUJTtS07KwauUKtGhSA0893APlypRGzZimCFYbinX6Uk0mbDt/EakWC8yZmXBYrYjQaVE5uhQqR8dI41KrVMWaEzsjARIgAU8R4PGvp8iyXRIgAS8ioPSmSf6i50WTx1RIoIQIDAYw3QN9b73SphDH/eOBtl02SWGcS0R5BrRv1QTLZ38HnU5buAZKsBaFcSUIn10HDAEK47xnqimMyzkXy2Z9iy7tW+Y5QRTG3cBCYVzen2EK47xn28ZMiodApsmMOSt3oVS9zkhKuAS1RgeTVYVtq+YjLOMEnnx4ACqUuyqNEw+aWrNpB1bsOIYOdw9F4oVEpKckQaVWSw/aUgl3l8Mh/Z3UlpmK84e3YmjfLqhSsXzxDIa95CDgBcI4IYobC6CbB6ZGnLv4zQPtskkSCBQCfnn+b9LI6Tn+HtJrWJcc8zl/yvIc/8/9fqBMPsdJAiRAAiRwMwHxMGEHHNcFj2REAv5I4Lspk/DBBHGIJq8c2HoMBkPxXrssLzPviXL1+2Xu93kxjvfMHTMhARIgARIgARIgARIgAb8nwBvm/X6KOUASIAESIAESIAESyJMAb5jnwiABEgg0Ajz+DbQZ53gDmYBDpca+cwdw/MIhSfpmNpuRlWVBptmKrEwTklOMuHApCWo10Kp5fcTGRMBstUo3N9iENM7ugNUqxHE26avN5kBQkBYqTRCOnziHDGMaQkKCYdDrERwchBCDDqGheui0WqjVKkkYF6TTwJhhwrETZxEREYrkVCvOJdlw4sQp7Ivfhx5dW6LfnbcgNCQYtco0RKgmtFinLMNswbHkZKRlZUHrcMCg0SAuIgLRYaHQCDAsJEACJOBHBHj860eTyaGQAAnkR4DCOK4NEiABpQTEgd8BADWVVpQZvwXAmJISxznOblG6XZQ5LPeHlaTQJvtofvv2Awzq0939A/Rwi/4ijNuyMx579h+RRata5Qro1La5rFgGkYA7CFAY5w6K7mmDwricHCuWi8W+VbMRFhpyE+CS3L+mHFiJiPCbz/eXVE6BJIybt3gVEpNSZH3g2rZsjDo1qsiKZRAJ+AMBIUbYfegUtl0yICwyBumpl2EICYfFocPiP/4Hy+UTqFwhThLCXbichtI1GqH73QORmpSCpPNnodFqpL+jiuJwOCBuflZrtEg9dwwJp+IxYnAvVKlEYVxxrpX983NKU+sNl6alOIsQxYnjfk8dSB4GUAeAvTgHxb5IwM8I+OX5Pwrj/GyVcjgkQAIkQAIkQALXCbw5dhQ2bF6PRg0a4+4770G71h0QFBQkm9B/yxZi+LOPyI7v2qkb/jfpF9nxgRpIYVygzjzHTQIkQAIkQAIkQAIkQAI+QIA3zPvAJDFFEiABEiABEiABEvAAAd4w7wGobJIESMCrCfD416unh8mRAAmQAAmQAAmQgMcI8PjXY2jZMAmQgPcQUCpGKu4bSL2HFDMhARLITmAIgGkeRiLEcaMBzPdwPzmapzBOOe2uHVph6e/fKK9YwjX8RRhXwhjZPQkUSIDCOO9ZIBTG3TwXkz4chScf6n/TGyUlZxOJUBgn7zMzeuQIvDNyhLxgRpEACbiFQEqaETMW70C5+u2RmHAJDrsF+uAwpNv0OHr4AIzJCYiIjkHN2nVRvnxZJJ8/jZTEBOlhWdeLwwFxEkqtUsFht+PE7o2wGC/i2WEDUalCnFvyZCPyCJSgME6Y6sYC6Ckv00JHDQbwW6FrsyIJkICTgN+d/6MwjoubBEiABEiABEjAXwnc+0AfbNm+Ocfw7u7VD21vaYfGDZqgVo3aNwnkTp85hZ17duDXmT9j3cY1itCMffMDDB0sXzCnqHE/CqYwzo8mk0MhARIgARIgARIgARIgAX8jwBvm/W1GOR4SIAESIAESIAESkEeAN8zL48QoEiAB/yHA41//mUuOhARIgARIgARIgASUEODxrxJajCUBEvBRAhTG+ejEMW0SKGECagAHANQshjzE3Q1CHLegGPqCPwvj7rr9VvTvfZuE0W63w2S2ICMzC2npRmzZuQ8r1m9BWnpGoTCf3DIflcr7luSBwrhCTTUrkYAiAhTGKcLl0WBfF8YJQZhKpYLd4YDJZEa6MQN7DhzBinVbC82tY+tmWPXX5JvqKxXGVa9SAS0a1yt0Hs6KBn0QfpjwFoJ0Oq/JqVWT+rBYrdix96Ci8c2at0R2fHhYCHp2aSc7XgQO7d8LvW/vqKgOg0mABIpGwGq1YdPuIziYHAJdeCQy0lOhctihD41CUFg0NBodNFrAZjIi+eI5WE1ZUGs02Tp1wOFwQBjjtDotks6fwcHtq1EqPAjPDLsPlSmMK9oEKaxdAsI4IYobA+BOhakWJvwwgDrisLcwlVmHBEggBwG/O/9HYRxXOAmQAAmQAAmQgL8SyEsYl3usoaFhiCkVA4vFjHMXzhUahWhn7X+bUCqqVKHbCJSKFMYFykxznCRAAiRAAiRAAiRAAiTggwR4w7wPThpTJgESIAESIAESIAE3EOAN826AyCZIgAR8igCPf31qupgsCZAACZAACZAACbiNAI9/3YaSDZEACXgvAQrjvHdumBkJeDuB+68I434txiS3XLvB/B9P9unPwriv3n8VTz8yMF98QgIxddY/ePGdCYrFcSv++B6d2jZ3y9SYzGbs2X8ElxIvI/FyCpKSU5GSmo6QYAOioyIQXSoScaWj0aRBbQi5TmELhXGFJQecPncRR46fRlJyijRH4mWz2VAqMgKlxBxFRaBuzaqoXKFs4TvxwZrFtXZ9CY03CePEuj187NS17crVdSvENTGlIhFTKkpat0L8VaViuWJDXJw5+bowznJyI7Ta7OKhq9NksVixeuN2vDL2C2zbvV/x3J3etgAVysbmqKdUGOdq/6o4qTwqeGNOBY0rrnF3XExIkjX0RwffLYnyAqmI37mOnDgtbYfE7zgpaemwWq2ILR2NcrGlUb5sGWmbJCSJ7i5CFnz4+Ckkp6RJ/Yr+tVqt1G/Z2BiUjysDIfELxOItv9948xwlJafht0VbUKZmK6QZ06R1C7sd0kpVA3abDXabHWq1BpDWrzjlpAIcQhYn3F0qaHRamNJSsWXNUhg0FrRu3QxNalVGk3o1oNEILxFLcRAoRmFcs2vH8XcVx7iu9THkijBuRjH2x65IwN8J+OX5vxN7zyn9u4i/zzPHRwIkQAIkQAIk4OME5Ajj3DXEN195B8MffsJdzbEdAFUalJNOr7j/bCDxkgAJkAAJkAAJkAAJkAAJkEA+BHjDPJcGCZAACZAACZAACQQWgeQTJ6QBb5/2Sxfx9Y7Zc1YEFgGOlgRIIFAJ8Pg3UGee4yYBEiABEiABEghUAjz+DdSZ57hJICAJKL0xiteoBuQy4aBJIE8C4m7+A1ekcTWLmc+mK/2NBvCvO/vdN1myGVwvdXsJP513F0/JY5as3oTbBz2laPAzvvkA993dXVGd7MEnTp/DnwuWY/GqDfh32TrZ7XTt0Ap3dm2PYYP7SLKygooY19Zd+66HvP/Fj7LFeK89/ZAkQiuoREWE4/EH+10POX7qLGbOXSxrLGIMjerd/FESEpcdew8i/uBRHDp2EkIwExoSjEeH3I2aVStJbbujH1dJ2mx2/LtsLRatWI8Fy9bi6IkzrqpI71csF4s7urbH/f3ucCkU3HvgKP5ZslpWu2EhwQXKD/NqZMvOeCxds1lW+y0a10O3jrfIii2OtZs9EXfMt5x1JWvweQQZMzIx6adZsDuublKF/PHXPxbIaq5+7eoYOqCXy9je3TqiQZ3qLuPsdjsWrdiABUvXKFq3QhonPpM9u7TDHV3bQa12n7ymJHPyV2GccyGI7ePAx0fhr3+Xu1wb2QP+mjIefXt2zlHHU/tXRYnlCvbGnAoajy8K44R4cN2WXS6nSUjcRjxwD8R+X0nZf/g4fp+7GCs3bMMymfsjsT2qUaUi2rZsjNtvbY3WzRpCp9Mq6RZiu7N64w7MWbgCqzZskyVWFMK4SuXLSn137dAS3Tq2lra7BQnsfpn9/+ydB2BTVRuG37Rpugd777I3FJEhS2QKiAxFGYIiCIqIiuNXBBX3BGQqAgIO9p4CIrKXIHuWKatlFQod+Tm3pCZtmpybJs1N8p7/R6D5zne+85yTm3svvU+X4tyFS3ZrE+cxA5/pYleGJ3KJnDJNsKlVtYJMqEVMTpzfyBSVU2skU4u9GCFa3fHPUWw6fA35ipfF9Zs3gJQU4YNTpHBCEHdfEZd2cXX/a4owDoC/Xo/kxNvYv20j9u7aikcefRRtO3fDwd1bUbtUBMqVKmqvBL7uZAK6wjGuus8nRHHiur29k0u2l+7oPVlceQDCUMhGAiTgHAJedf/PhITCOOdsDmYhARIgARIgARLQDoGcEsYVKlAI65ZuRFBQkHYm7wWVUBjnBYvIKZAACZAACZAACZAACZCApxHgA/OetmKslwRIgARIgARIgASyR2DdRx9aJGg1e76rvnEse4WyNwmQAAk4mQCvf50MlOlIgARIgARIgARIQOMEeP2r8QVieSRAAs4kQGGcM2kyFwn4HoEeAKa5adpb7o0rHkBf7ozxKYyzpFj94W7Yc+CINNovhg3Gq/27S8ebAoVs69PvpmLc1Nmq+2bsMGxIXwx5/mlERoRZzdXthbfxy4KV2R7HVgLjuf9Eg78uXIkn+78tNd5Xw4fgleefSo89evI0Rv/wK0b98IvV/kunj1JEVqJlZxx7xQkB05ylv2PYZ+Nx+Pgpe+E2X29Qpzo+fGMAmtSvbTVOSLL5tz0AACAASURBVGF6DhomPcaJLQtRslhh6Xg16/9i764YPXKozdw5uXfNC8nOeqvZV9JgMwQKuWHlJl0d7S7Vb9SHr+OlPk9kGSvEO4tXb8C7n45TdRyzllBI7D584wV0aNk4W+I4LdTk7cI4sX5CWFioRktpGajoM/7Tty1kn+JrWpSzabEmW29YTxTGDXjrE+nzkW3LpiGmeiWpY9bClevx2XdT8de2v6XibQUJkVv/np3xbLcOKF+mhM18d5OSMOGnufhm0kxp0authEJeN/CZrorYM2/uqEyhxWq3wZnzF6XmeDd2s13x3ZLVG/Boz8FS+Ua81g/iPFC25eT5ja2acnqNZPnYixPH2iXrdiA+JRdC8xTArdu3kJqSCqMxFTrFFpd2m0nI5dKEcVAEgX7+/ki6lYADO7dg3+7tKFKiJBq0ao8iZasjKndenN29Es2rF0G+vLnslcDXnUjABcK4qgDeB/CYE8tUk+ppADPVdGAsCZCAFAGvuf9nmi2FcVLrziASIAESIAESIAEPIpATwrjQ0DAs+HkJypYp50FkPKNUCuM8Y51YJQmQAAmQAAmQAAmQAAl4FQE+MO9Vy8nJkAAJkAAJkAAJkIBdAnxg3i4iBpAACXgpAV7/eunCclokQAIkQAIkQAIkkAUBXv9ya5AACfgQAQrjfGixOVUScAEBPwCHAES7ILdsSiGOew/ACtkO1uIojLOkMuidzzF68q/SSD9/92W89oJ4fliuCYHEF+N+wtAPR8l1kIwqV7o4lkz/FtEli2XqoUYYJjlcprDsCuPu3L2L4V9MxCdjptgsISeEccdjz6Jjn9eyLdzKOJGP3hqIN198RhGHmLf4a9eRu2IzafTfffQGBjzTRSpecA0qmSbYk2kbFvwAIbiz1tyxd83rcEQY58i+kuFkLcbdwrgr8dfQtd+bWLNhm6NTsNrvobo1Mef7z5Avj3qJjVZq8gVhnFi8EV9OxPB7v2Tb8Fefx3uvPm8RrkU5mxZrssWYwjjg5OlzGPTOF1i0ar3sdpSOE/LVtbMnZBn/55ZdeO7VD7Ite7U2wPuv98e7rzyX6SVPEcbl9PlNVovkjjWS3mASgZeuxGPR7zuQoMuFyAKFcTclFSkpSYo4DsZUKDeajEaI0z2dzh9+OuBm/BUc2LUVR/btReFixVD1gQaILFgUweG5ULh0Rdy4dh0hV3ahWUwZBAUaJKpgiDMIOFEYJ0RxQujeUTgCnVGbAzmOAigPINWBvuxCAiRgm4DX3P8zTZPCOG55EiABEiABEiABbyPgamGckMXNmjoPlStW8TZ0mpgPhXGaWAYWQQIkQAIkQAIkQAIkQAK+RYAPzPvWenO2JEACJEACJEACJMAH5rkHSIAEfJUAr399deU5bxIgARIgARIgAV8lwOtfX115zpsEfJKAeJhTTVMbryY3Y0mABDyTQE8AUzVQ+uZ7NYhjlEPiOArjLFdw2Ofj8cHX30sv68/jPsKTHVpIxd9OvIN+Q0fip9lLpeLVBoWHhWDNrPGIqV7JoqvWhXGtmtbDk/3flhK0uVoYt37zTjzaczBu3LylFr9U/DNPtMPkr4ZlksY90f8t/LZwlVSOZg3r4PffxknFrv5zKx55YoBUbNFC+RG7bTH8/MTz8JbNXXvXvAq1wjhH95UULCtB7hTGibHb9XoFQgbkiib2xrIZo1GlQhnp9FqqyVeEcfOWrcXjz74uvUZCFiekceZNi3I2LdZkC7KvC+P+3n8YDz32nMs+R20J46bPWYoeLw2Tfg+oDfRkYZy7zm8yMnbXGqlda3vxFy7F4fcNu/HvDX/ow6IQGhEB6Pwg5LqpRiGOE9YwI5Ju38alc6dxYM9OnDt5AkWKFEOFmjEIz1cAAUEhMISEIjwqLwqWqYpzJw+ieOpJNKpdzt7wfN1JBJwgjBMXXSMAdHKjKM5EozuAGU5CwzQkQAKZCXjF/b+xr860+AE6bfs0tZjpkslrLf6e8XVuDBIgARIgARIgARLQKgFXCuMeb98ZL78wBCWLl9Lq9DVbl73zy4yvu8vArlmALIwESIAESIAESIAESIAESMB1BPjAvOvYMjMJkAAJkAAJkAAJaJEAH5jX4qqwJhIggZwgwOvfnKDMMUiABEiABEiABEhAOwR4/audtWAlJEACJEACJEACmicgrEqHAERrpNJN9+oQ4riVjtRjPLfd4sFRR3LkVB9XymPeHDkan34n7wH8a+Fk1I+pZnfqd5OS0LLbi1i3cYfd2OwECLHTnjW/IFdkRHoaLQvjGtSprojiZAVtrhTGLVuzEW26D8oOfqm+X48YgsF9n7KIXbhyPTo8M0Sqvwi6sn8Nckf9t8ZZdXz53S8w6odfpPIOG9IXI17rlynWnXvXvBg1wrjs7CspWFaC3CWMO3byDGq2eEr6PeTo/ISQcseK6ShbqrjdFFqryVeEcYtX/amIA2XbK88/ha+GWx53XPn5KltXxjgt1mRrLr4sjPvn4DHUb9/bpcejrIRxrhaRiTX3VGGcO89vzN8r7lwjR48/9vrt2XcEx89cxslzcbhw5QZSjX7w9zcgJTkJ167G48zZUzh/6QICoEPZ3HlRrExZBBYoAENIGAJDQ2EICoI+MBThuYQ0rgquXDiL8Cvb0LphDXtD8/VsEDi4JMaid8W+UPtMuhDFievuzhoQxYm5HAVQHkBqNrCwKwmQgG0CXnH/j8I4bnMSIAESIAESIAFvJeBsYVxoaBh6PvkMej7VG4ULFvZWbC6fF4VxLkfMAUiABEiABEiABEiABEiABBwlsLzzY6t1gD6iWLFGOp2frkb3Ho6mYj8SIAESIAESIAESIAGNE0hOTMSGr74wr/JKq9nz82q8bJZHAiRAAk4hwOtfp2BkEhIgARIgARIgARLwCAK8/vWIZWKRJEACJEACJEAC2iLQC8AUbZUEIY57D8AqNXVRGJdGSwjDhFhDtp3esRRC0mavDRn+Fb6eONNemFNe79S2GWZP+iw9l5aFcWon7Cph3PHYs6jxSDeXSm7M57p9+U+oXa1i+peEECl/1ebS4/887iM82aGFTXxGoxEFq7fExctxUpj3rfsNlcqVzhTrzr1rXowaYZzUhM2CzPeV2r6meHcI424n3sGDbZ9RpIs50cT+2LZsGkKCg7IcTos1+YowbsnqDXi052DprWBNfqVWzvZstw7o062D9JjWAksVK4xCBbL+1gu1NY0ZORQDe3fNVk3Z6eyrwjghF63TqqfLj0fWhHHiM1y8z13dPFEY5+7zG9OauHuNXLk3xN6/cCkOl69cw5W4G7iTlAw/Px2Mqcm4lZiIawm3sPfACRw/cArlihZDcJ68CIiMgiE8HAFBQTAEBsMQHI7IfAWRv2RFXDpzGHmv78XDDWq6smyfzp0NYZyQso0A0OWeQ1LIo7TSxAMs07VSDOsgAS8m4PH3/yiM8+LdyamRAAmQAAmQgI8TSLiVgIOH9+Of/Xuxe+8uHDi4HwcO71dFJU/uvKhdIwYN6z2ETu27ICwsXFV/BmcmQGEcdwUJkAAJkAAJkAAJkAAJkIDmCdzZuSPOCGMuzRfKAkmABEiABEiABEiABBwmkHjtGjZ/N9q8/6lWs+eXcDghO5IACZCABxLg9a8HLhpLJgESIAESIAESIAGVBHj9qxIYw0mABHyBQD8A41VMdCIA0Ue2Mb9tUuRDPuYEPP39JXtccGacEMcNB7BSJimFcYAjwqekU1ug1/vbROyI6Co8LATVKpZVBGKOyKA2LfoRD9auqtRFYRzw1fAheOX5p6yuk6OCq3Kli6NAvjw4dCxWWspmKqB1s/oQkjLzNuCtTzBu6myZtyu6tn8Ev47/2Gbsrn8OoVaLp6Xy1apaATtWZPZMuHvvmhfvSC1SkweUtRBrkp3myPFD7XijPnwdL/V5Ir2bmj2jdqys4vs+3RETP/9flum0WJOvCOM+Hv0j3v74O+mlHvfJW+jfs5NFvFo5m/RgNgIH930KX48YkmWE2ppKlyiCBnWqZ6u0YUP6IrpkMYdy+Kow7tvvf8bgYV86xExNJ2vCuMd6v4oFK/5Qk8ahWE8Txmnl/EbAdvcaObTgDnQSst6UlFRFGOfnl+YTS0pKxoXLcVi2dgvWrtuGwhF5EZIrL/S5ciEgRMjigmEICkFgaDii8hdBvuLlce7oXhRPOYyH6lIa58Ay2O3igDBOiOLEdfWTdpPnfMBhAKK+5wFMUDG82vsrzG8bLvmQj4q3n0tCpe7/URjnEvZMSgIkQAIkQAIkoGECtxNv4/r1a7h+4zqu37iGa9eu4WbCTeh0OoSHhSMkJBThYWEoXKgoIiMiNTwTzyyNwjjPXDdWTQIkQAIkQAIkQAIkQAI+RSBx5/bjAEr51KQ5WRIgARIgARIgARLwMQIJly5h2yTz76vS7Ws1e14VH8PA6eYsgXUqh2uiMp7hJKCaAK9/VSNjBxIgARIgARIgARLwOAK8/vW4JWPBJEACridAYZltxuRDPuYE1D5w7Gv7x/VHrKxHEA+Ovg9gua0ifF0YF3/tOmJa9cDx2LPSa9WjcxtMGyXQZt1u3U5EweotFPGbTBPSqtEfDkWZkkXTw5OTU/Dz/BXoOWiYTAol5skOLfDzuI+UP1MYZ1sYp0ZyI0R+oz54Hd06tkSgwZC+HqfPXUDf1z7EinXi7SbX9q37DZXKlU4P/nPLLjTq2FeuM4DbJzYiKPC/GjJ2/PCbH/DuZ+Ok8mUUkYlOWti75sVTGAeYr9PRk6dRtn5HqfU1D2rbvCEqlyuNlNRU7Nl/BKvWb1Gd48D62agQXTJTPy3WJIr0BWHc9RsJqNykC86cvyi9nqt+HYvmDz1gEa9WziY9mI1AZwvjnFHTip/HoEXjBx1K5avCuPINH8fh46ekmeXPm1sR+9WpUQlVKpTBnTtJEJ+lx2PP4LdFq7MUsWYUxp27cAlFaraWHlcECuFr/TrVEVOtIkqXKIor8VeV986+Q8cxfc7SLHN5mjBOK+c3WlgjVRvERcHivOrPrX9j9vy1CEUQIvPlhy48AoawUAQGByMgMARB4eHIXbAEchUug3+P7ES0/jTq1q7moop8N60KYZyWRXGmBewBQFifKSyzvaXJh3zMCai9f6l2/7jzACt1/y9233mjO4vk2CRAAiRAAiRAAiRAAiRgTqBE5UI68XflP2wkQAIkQAIkQAIkQAIkQAIkkJMEbu/cvkUHWH7nTE4WwLFIgARIgARIgARIgARcTiD+5An8PXOG+ThbWs2e79h3CLu8Wg7gJQTUfmMO/43ESxZey9Pg9a+WV4e1kQAJkAAJkAAJkIBzCPD61zkcmYUESMCrCPia0ErtA3PkY3u7kw/5aO2A+BeA9wD8bl7YgUmwuB9doe12rdWdqR61QpsxI4diYO+uVueVcOs2Zsxdhk+/m6pKFieS7fn9F1StGG2T17ipszHgrU+kmH72ziC89kIP6HTWb/mrlTGd27UchQrkVSQoZ85dSK+h18vvSUtd5v/4JQrkzW2z/tCQYAsOOSX2UjPOV8OH4JXnn7K6l0rUeTRLMY15h9IliuCPuZNQtFB+qzxSU1Px0jufY+yUWVLrnVGSlJKSisI1W0nVIgZYNmMUWjWtn+VYtVt2x869B6VqObtrGQoXyGcRq4W9a16QmvWWmrRZ0NLpoyBkjdlpYv237d4PozHtkPrXtr/x2vvfSKUUYw97xb4ssELZkoiKCFdyiuOKWCPZJuSEC6Z8ieiSxSy6nDr7L9r1fAV7DhyRTYVnu3XA91++myleizWJIn1BGPfCmx9j/LQ50msoAm8cWY+w0BCLPmo/X1UNmEUwhXGZ30vO4Komh5r37rZl0xBTvZJF+gNHTqBS4y7SQ4rP5MF9u2V5viOOp1t37VPOz8b8+JtF3ozCuEkz5uH510dKjS2kr/Mmf4GHG2b97d63E+9g9Z9bMGn6fCxatd4irycJ48R7WSvnN1pYI6kNkgNBQgK9+8BRTPt5KfSJfgiNyg2/qEgYQkIREBwMQ3AIQsKjkKdISUTkL4ELh7agStR11KhcLgeq870hdIVjsvo+q6r3aAwHIMy8Wv5erKMAhNQulcI4u/tXrfBL7f1R5re9BL7Gx+6GzIEAq/f/TONSGJcDK8AhSIAESIAESIAESIAEpAlQGCeNioEkQAIkQAIkQAIkQAIkQALOJpC4a/tcGJV/FGQjARIgARIgARIgARLwUgLndu3E4WVmP0lYh/mtZs3nOaCXrrdGpkVhnEYWgmX8R4DXv9wNJEACJEACJEACJOD9BHj96/1rzBmSAAmoJkDhl21k5EM+5gTUPlDra/tH9QHIhR023HvwfQSA1WIMXxDGtWxSDx1aNlaQChHIlfhriLt6Tfl9+dqNuHHzlmrc7R5phIVTv7LZ725SEorVbislAHuobk2smzMBfn5+NnMO+3w8Pvj6e6l653z/GR5v0yxTbIsnB2LV+i1SOS7uXYV8eXJJxZqCckrspWacrIRx38+cj76vfSg1v9W/jbUpmRFJLsddRb4qzaXylStdHIc2zLWI/d8nY/HRqMlS/Qc80wXfffSG1djT5y6geExbqTzi/bF85miLWC3uXTXrLTVxsyBnCOMyjimEcQ07PCtVipAZij0q2y5ejkOBai1kwyGOL0unf5tJDmZKII6B7Z95Bes27pDOmVEyqMWaTJPxZmGckEJ9OmYKhn8pTsPkm9gT6+dNytSBwrg0JCt+HoMWjR37+YHivSneDzItK/miTF9nxmRXGCfOpVo/PUiqJGuffbY6njx9TtnfU39brIRlFMa9/fF3+Hj0j1JjD3r2SXz7wWtSsSLIJP7cvGOv0seThHFaOr/RwhpJL3oOBAoh4qHjp/HN2F8QmOyP8Nx5oI+Mgj4kBIaQEASGhCnSuHzFyyA0T2FcPLwVNfMmonL50jlQnW8NYUUYV/MeASGKa+8hJHre8+L+dL9WXxNyqb3/RD62NzX5OJePlg4hFvf/TIVRGKelJWItJEACJEACJEACJEACFMZxD5AACZAACZAACZAACZAACbiNQOKuHV/BaHzFbQVwYBIgARIgARIgARIgAZcTOL52DU5t2mg+zmetZs+3/gSGy6vhAD5CgMI4H1loT5omr389abVYKwmQAAmQAAmQAAk4RoDXv45xYy8SIAGvJuBrQiu1D1ySj+3tTz7ko/UD5J/3Chx+YBJ+Ny+0QtvtWq8b7hDaZISyY8V01KpawSar9Zt3ovHj4tlr+2336pmoXqmc3cC4q9eRp1JmCZy1jq8P6InP3skscNGyME6IYMqWKo4yJYsiT65IZa0vXLqCv/cdUSR/Yz95E9UqllWmq0YglpUwrmnnflKSrI6tm2LuD5/bXR8R8Nr73+DL8dOlYq/sX4PcURHpsXsOHEH1h7tJ9c2fNzfO715uVTI4cfpc9Bv6kVSen0a/j+6d2ljEanHvqlnvjBNXs6+koEkEuVIYN2vRanTt96ZEFWkh/6z9za5oZuP2PWjQvo90zoz7Ros1mSbj6cK4xJMb4afzQ6oxFbduJyqy03P/XsLqP7fix18W4sz5i9LrZgr88ev38MwT7TL1c8fn6+C+T+HrEVkLE91RE4VxWW+pbcumIaZ6JYuAKb8uQu9XhItYrl3YsxLiM0xNE/v97Y/HoE6Nyhay1GcGD0+XydnLJ87bxPmbmpaSkorx02Zj7NTZeGNgL/TsklnGWqx2G+n34d3YzQgI0NssYcnqDXi052CpMke81g/DhvTNFKul8xstrJEUzBwMMhqN2L7nAH6cuRS4kYKovPngFxGBgLAwBIYEIyA4DGFRuVCgWFmE5C6EK0e2oHq+u6hUrlQOVun9Q5kJ44QhVIjiWnrQrI8CKA8g9X7NFH7ZXjzyIR9zAmrvf6vdP1o8lCj3/wCsEcVRGKfFJWJNJEACJEACJEACJOC7BCiM892158xJgARIgARIgARIgARIwO0EEndufwHAWLcXwgJIgARIgARIgARIgARcRmDf3Dm4dPBAen6jDs+1njX/B5cNyMQkAFAYx12gOQK8/tXckrAgEiABEiABEiABEnA6AV7/Oh0pE5IACXg+AQq/bK8h+ZCPOQG1D1z62v7R7BExphzwYjug7n33GYVx9pdq8bRv0LZ5Q7uBH3z9PYZ9Pt5uXHhYCK4d+gM6nc5urAiQFZM8WLsqNi36MVNOLQnjxNyf7NASQsjWtEEMggINUgxEkBqBmDVhnBAvhZaxv45irHGfvIX+PTtJ1TZ9zlL0eGmYVOyqX8ei+UMPWMRWbtIV+w8fl+q/ZclUPFCzcqbYNt0HYdkaix+GlWU+sfciwkMtXtfi3lWz3tnZV1LgJYJcKYx76X+fYcyPv0lUAbRsUg/LZ46Wiq3dsjt27j0oFdv36Y6Y+Pn/0mO1WJOpOE8XxkktiIogIeo6tX0xAg2Zj7fukLNRGPeuitVzTeiAtz7BuKmzpZJbE8bNXvw7ujwv//MWH6pbEx+//SLq1a5qVXoqVcj9IDXHHtHl2W4d8OZLzyC6ZDE1w9iMlT0vE0lyQhintfMbLayR0xbbiYmENO7A0ZOY9NMiJFy8iVy58yIgMgoB4aEwBIfAEBKKsKjcKFiiLIJzFcKlw1tRLf9dVC1f2olV+HYqXeGYh++9Ld8GIH73tNYDgLkBU63QSu39G+a3vUPIh3w84hgSXbQCHq3fGeWKpcl/2/ZpalH3kslrLf6e8XWPmCSLJAESIAESIAESIAES0AwBe+eXGV+X+xdSzUyPhZAACZAACZAACZAACZAACXgDgds7tzbWwW+dN8yFcyABEiABEiABEiABErBO4NrpU4iPjcW5XTs33L1xo5hRp+veeta8DeRFAi4kQGGcC+EytWMEeP3rGDf2IgESIAESIAESIAFPIsDrX09aLdZKAiSQQwR8TWil9oFR8rG9EcmHfHLoUJXtYVYA+Mx4bvvv2c6UQwncIbQxTe2X8R/hifYtpGbaqGNf/Llll91YIbda8ONXduNMAU++8DYuXo6zG1+0UH6c3rE0U5yWhHHWRG52J3Y/QI1AzNo46zbuQNPO4lBtv73/en80fKCG/cB7xgmx5u99MUEq9qfR76N7pzYWsV+M+wmvf/CtVP9hQ/pixGuWc7h+IwGR5RtL9RdjixoyNi3u3eyutxQQJwa5UhhXvuHjOHz8lFS1P3w1DH2ebC8VO+XXRej9ygip2NIliuDYpgXpsVqsyVQchXGWSzrhs7fxfPfHra6zOz5fKYzzfGHcjj0HENNKuIvUNSEvfKxVEzSoUx01q5RHxbKloNf7q0ryzaSZeOU9+XMoU/JypYsrslohXa1ZpQJKFiskLe7NWKDWhHFaO7/Rwhqp2lQ5HHzm/EWM/mEOrpyOQ+6o3NBHRSIgPPy+NC5EkcYVKlkeYfmK4cKhrWhSJgBFCubN4Sq9a7iDS2KUCW0+AIxeCOw86pHz6w1gilnlFJbZXkbyIR9zAmrvf6vdP5o9qFQqWQ2P1GmH8sXTpPMUxml2qVgYCZAACZAACZAACXgFAQrjvGIZOQkSIAESIAESIAESIAES8G4Cxu3bI+/44ap3z5KzIwESIAESIAESIAESEAQCUxGli4m5RhokkAMEKIzLAcgcQh0BXv+q48VoEiABEiABEiABEvBkArz+9eTVY+0kQAJOJkDhl22g5EM+5gTUPnDpa/vHyYcnp6QTorjh4ll5kc14brva+9JOKcKRJO4Q2gip27RR7ytiE5mWmpoK/6IPyIS6NMZ4bnum/BTGpSH5fOw0DP1wlEv520s+euRQvNi7q0VY7JnzKPlAO3tdldeF8ObQhrkWsXOXrkGn54ZK9V86fRRaN6tvEavVvUthXNoy3U1KQmCJelLrK4J2rpyhiJhk2qFjsajwUCeZUCXmxpH1CAsN0WRN5pOgMO4/Gl3bP4Jfxn2UpRjLHZ+vFMZ5vjAu7up15KnUTPrYYSuwZZN6eLhhHTR6sBZiqleCv7+fzbyLVq1H+15Dsj22OM9r+3BDNLs/dvkyJaRzak0Yp7XzGy2skfRiuinwwqU4fPPDbFw8fhF5onLBPyoKhrAwBAQFwxAagrDI3ChcugLC8xXH5QPrUL9sJIoVyuemaj1/WJMwzjSTin3R/N6fxXVxQw+anTAHlwGQfL9mtUIrtfdvmN/25iAf8tH64UO5/zf21ZmbzAulME7ry8b6SIAESIAESIAESMCzCVAY59nrx+pJgARIgARIgARIgARIwGcI3Nm1fa/RiCo+M2FOlARIgARIgARIgAR8kIBOh38Ca8ZU9cGpc8ruIaD2wTyde8rkqL5GgNe/vrbinC8JkAAJkAAJkIAvEuD1ry+uOudMAiRgg4CvCa3UPjBKPrbfPuRDPlo9wC4E8D6AHeYFUhiX9XI9260DPnv3ZeSOipBe0/hr15G7onPkKdKDWgm8E7sJhoAAi1cojEvD8caHo/DZ2GnZwZvtviNe64dhQ/pmytO0cz+s22jxFs1yrMN/zUXZUsXTX+/9yghM+XWR3dqEHOfKvjUICNBbxGp171IYl7ZMFy/HoUC1FnbX1xSQcGwDQoKDpOKTk1MQULyuVKwIOrV9CYoVLqDJmswnQWFcGo1aVStg3ZyJEO/9rJpaYZyQVtaLqSa9Z6wFtm/RCI+3yfrzUm1N2SrmfucVP49Bi8YPOpRKvD/F+1SmifOL77/0fGGcmKsaaZoMGxFTukQRvDGwF7p3apPlcezYyTOIrv+YbErpuCb1a+O1/j0Uqaqfn21pnZq5343dnOlzN2NRS1ZvwKM9B0vVau08QmvnN1pYIymYbg4Sx42vJ87GhRP/Ik/uvPCPCIc+NBSG4GAEhoQiNDIKRcpUQlBUIdw4vhl1SoWiVNGCbq7aM4e3Iowzfb+VOMES4jh5M697EfQGMOV+CRSW2V4L8iEfcwJq73+r3T/uPTJYjm5x/2/sqzMt5ZbI+wAAIABJREFUvh+VwjgtLRVrIQESIAESIAESIAHvI0BhnPetKWdEAiRAAiRAAiRAAiRAAl5JIHHHtgnQ6cQ/BrCRAAmQAAmQAAmQAAl4KwGjcWJQ7TriAT82EsgJAhTG5QRljqGaAK9/VSNjBxIgARIgARIgARLwPAK8/vW8NWPFJEACriRA4ZdtuuRDPuYE1D5w6Wv7x5XHKpnc4p7zXAAjAOy11oHCOOsYixbKj9hti+3KQjL2dpUcQ2axzWPiDqxBrkhL0R2FcWmEZMVqapmriR86oCc+fWdQpi6Tf1mIZ4cIr6P99vWIIRjc9yklUAi/cldqihs3b9ntKPqIvp6ydymMS1upQ8diUeGhTnbXVwQIMdj1w+ulYk1BamRXf//+M6pVLKvJmswnTWEc0O6RRpg59kOEhWYtixPM1MrZxowcioG9u6raY2qD1dbU9+mOeOaJdmqHsYivXa0CAg0Gh3KoeQ95kzBOzeeWWrDiWDbx83fwZAfrssyeg4bhp9lL1aaVihdSxFmTPlWOdVk1rQnjtHh+4+41klpsDQRduBSHryb8ggsnLiNP7jzQK9K4EBiC036FReVC6aoPICg8L64c3ogaRfUoV7KIBir3rBJsCONME2l+/7q5vsZndgpAGXEJAECt0Ert/Rvmt70ZyId8tHS4sHv/L3bfebXfl6ql+bEWEiABEiABEiABEiABLyNQonIhReRusrl72fQ4HRIgARIgARIgARIgARIgAa0TuLNzWzcjdDO1XifrIwESIAESIAESIAEScJyADsanAmvV+dnxDOxJAqoIqP3GHP4biSq8DHaUAK9/HSXHfiRAAiRAAiRAAiTgOQR4/es5a8VKSYAEcoSArwmt1D4wSj62tyH5kE+OHKgkBhH/tvEhgP3WYg9MgsX96Aptt0ukdG+IWnlMdqsd8Vo/DBvSV1Wa7X/vR53WPVX1cUUwhXFpVL8aPgSvPJ8mVTO19r2GYNEqdTItZ69RVsK4uKvXkadSM6nhHqpbE+vnTVJi/9yyC406yu3VzYunoG6tKpnG0OrepTAubak279iLeu16S+0NIbw8vUOdRKl8w8dx+Ljwj9hv6+ZMRON6tTRZk3n1vi6ME8eZkW8OhF7vb3dR1X6+alEYlxM12QLpq8I4ISyt3KSL9PHD7ma0EvDhGwPw9qDe0OksvzXieOxZiPe5K9uyGaPQqql1d5TWhHFaPL9x9xq5cm84O/eZ8xfx7aRZuBR7Gfly5YEuXRoXDENwMKLyFkCx8tUQlqcI/j2wEdULApWiizm7DJ/IpyscY+/7rISlcjiAehoG0gPAdArj7K4QhW62EZGPc/nY3ZAuDLB5/880LoVxLlwBpiYBEiABEiABEiABElBNgMI41cjYgQRIgARIgARIgARIgARIwFkEjIBu84svtIooUWLp5cOHULv3s9AHBTkrPfOQAAmQAAmQAAmQAAlohEBgKvLpYmIua6QcluH9BCiM8/419rgZ8vrX45aMBZMACZAACZAACZCAQwR4/esQNnYiARLwXgIUftleW/IhH3MCFA5q71gofvDh+wAO2SqNwji5hVs87Ru0bd5QLhjAH5t2okkn8dy1exuFcWn8rQnjmnbuh3Ubd7h1gbISxomiHn/2dcxbtlaqvgt7ViJ/3tx4c+RofPrdVLt9SpcogqMb52eS7oiOWt27FMalLev6zTvR+HG5Y4tY52ObFtjdD+YBEeUa4cbNW1J9dq6cgZpVymuyJvMJ+KowTnxmffq/QahcvrTUeoogCuOkUWUZ6KvCOAFk6659aP7EC9LHEEdof/K/l/DGwF6Zuk6aMQ/Pvz7SkZTSfTYs+AEN6lTPFK9GGJd4ciMCDQabYy5ZvQGP9hwsVZc1qbFWz2/cuUZSMDUUdOLUOXz7/WxcPxuP3Llywy88HP4hITAEBSIwLBzhufKgeLkqyFW4DE7v24iq+ZJQrVwJDc3AM0qREMaZJtISwAgAdTU4syMAylEYZ3dlKESzjYh8nMvH7oZ0QYDU/T/TuBTGuWAFmJIESIAEtEpAByQlJwGmnxkkvgnczw96P71ScUpqClKNKfdOd4VLOfNjC3eS7+DGzZvw8/OHyd1uFDnuq5eVP4tE5ipm43+ZlLj7wRltzUajEaK/aEZjCoICgxEaHAqdEdD7B9yvLxlKXKba0ur10/nD3+/+D0hISQb80+bFRgIk4FkEKIzzrPVitSRAAiRAAiRAAiRAAiTgVQRWdH5snRFobJpU2RYtUSSmjlfNkZMhARIgARIgARIgAZ8noMPaoJoxzXyeAwHkJAEK43KSNseSIsDrXylMDCIBEiABEiABEiABzybA61/PXj9WTwIkQAIkQAIk4A4CwlgxxR0DZzFmKoBf7z/QblMUZ+rvC8K40SOHYuAzXZQp301KwkOPPYdtu/erWrbwsBAIOVJ0yWJS/Xb9cwi1WjwtFevKIArj0uhaE8Y91vtVLFjxhyvx281tSxg3d+kadHpuqN0cImDaqPfRo3MbyIqxPhj6At4Z/KzV3FrduxTGpS3X3/sPo0bzp6T2hZAICpmgmia7h0TOzYunoG6tKpqsyXzOauZkPLddDS6HYkd8ORHD7/1yRStaKD+6tGuOx9s0Q8MHaqgegsI41cgydfBlYZyAcezkGbTpPgiHj5/KPswsMlzcuwr58uTK9OrytRvR+ulBLhv3wdpVsXHh5EyyVTXCuIRjGxASbPsHkmdXGKfl8xt3rZHLNoULE5+/eBnfT1+MQ3uOoUBUXujCQuEfGgyDEMeFBCMkPBIFS5RBkXI1ceXCOQTH70PT2mUQHBTowqq8K7UKYZxp4i3uX2c/qDES4p7ENI3VxHJIwBsJePz9P9OiUBjnjduTcyIBEiAB6wRiz53AzNk/wWAwKNeyd+/eQVhIOLo+1g1RkbmwfM0S7Dv4D/R6vfK6+JUmcjMiNTUV1WvVQpUqVXE78bZqxGlKNx1SUpOQkpwqrHBp2jfFEqeDv78/9AF6ZaygwECc+/c8/lr3hyKA69m1D+4m3cWilfNw8dJFBAZmvs5JTLyNcmXKoXmj1sgdlUd1fexAAiSgHQIUxmlnLVgJCZAACZAACZAACZAACfgcgRWdH3vv3q2K4aaJhxcqjNq9+/gcB06YBEiABEiABEiABLycwMtBtWJGefkcOT1tEVinspwmKuMZTgKqCWS+/i2E2r2tP1CmOjk7kAAJkAAJkAAJkAAJaIUAr3+1shKsgwRIgARIgARIwBMI+AEQUrZoDRQrRHGz7j1u8979mqRL8gVh3BghjOvdNZ1J7JnzqNrsCdy4eUuakwisVK40tiyZgrDQELv9Tp39FyXqPGo3zhTw6TvOF5wYAvR4sfcT0Ov9Lepo8eRArFq/Rao2IZsS0ik1LafEXtkdp9/QjzBx+lypqT3SqC6aN6orFasmqFHdmhACGmvt1u1EhJZpKJWuY+um+PCNF1C5yX/73FbHw3/NRdlSxa2GaHXvZne9pUA6MeivbX+jYQe5f0MZ3PcpfD1iiNToatdHrQBNjezq4J9zUL5MCWixJnOYni6Ma92sPvz8xCnHf81P54eoyDAUyp9XOUYXyJcb1SqVRdUK0ZlkVlIb634QhXFqaFmPVfMeerZbB3z/5bvZHzSbGQa89QnGTZ0tlWXbsmmIqV7JZmz8tev4dMxUjJ06S/W5lkwRrw/oic+yOG86cOQEPvzmB8yct1wmleqYRVO/xqOPPGTRT40wzprIN2MR2RXGaf38xh1rpHqhNdLh5Ol/MeGn+fj36EXkiohQpHH60BAEBAciICgUIeFhyF+sJIqXr434q9egu/Q3mlQtjFyRERqZgbbLcEAYZ5pQy3t/EM+NaEUcdxRAeQDingAbCZCAawh4xf2/sa/OtPgBxm37NLWgtWTyWou/Z3zdNWiZlQRIgARIwJUE9h3eg9ETv1GEcOLekpDAJSYmokD+AggNDcP5f88pwjYhijNv4mvJycno8NhjaNCwIW7evHk/xhSXJn2z1cSYJ0+cwJ/r/0BychJ0fn6KK070SkpKQkREBBo3aYr8BfIjMDAIx48fw4zp03Ht6lWUKRWN1NQUnL9wPlNt5lI7UWd0qbLo1qkHCuQvBJ3FJ50ryTI3CZCADAF755cZX7d9VJEZkTEkQAIkQAIkQAIkQAIkQAIkoJLA6k6dSifrUo6Zd6s7YCCCozL/BDuVqRlOAiRAAiRAAiRAAiSgEQIp8CsSWqvWOY2UwzJIgARIwC0EeP3rFuwclARIgARIgARIgARylACvf3MUNwcjARIgARIgARLwfAI9AUx18zTEIzDC8CEeWN+fnVqM57Z7zOM0zhDaLFuzEW26q5e0dW3/CH4Z95FdIc/NhFsIL9tIeklun9iIoECDdHx2AtUI487vXoGC+fOoGi6nxF7ZHed/n4zFR6MmS81t5JsD8PagnP8Bos+9+gF++HmBVI3vvfo8Rnw50W5snRqVsHXptCzjtLp3s7vedsE4OUCNMO6lPk9g1IevS1Vw/UYCIss3looVQRf3rkK+PHLfx6hGUmieW4s1mQPydGFc0qktmcSf0htAZaAzPl9VDmk3XIs12Sqawrj/6Agx7/Q5SzHmx9+w//Bxu2stGyAEvvvW/WYz/PS5Cxg/bQ6+nzkfFy/Hyaa2G2dN8KlGGHdq+xIUK1zA5jjZFcZ5wvmNAJCTa2R3YTUccOhYLMZPmY+bF24gPCQUuvBw6IMDoQ8KQkBQMELCw5GnUFEUq1ATiUlG3Dn7D+qUCEDJYoU1PCv3lnZwSYxFARX72jFdZF1uq3svietw51ul1SMS9yZ+Ut+NPUiABCQJeMX9PwrjJFebYSRAAiTgRQQOHT+AsT+MUoRxJinc7du3kZKSAoPBgICAgCz/nUNI3Vq1bosH6j6AhIQEhUpGsVxWqEScyP3XXxuweMECBAYGws/PH2lGNx3u3LmDoKAgdO7aFeXKV0CAPgCxJ09g7pzZiI+PV+R24nV744nXhcyuXHR59Oz2HPJEqPvBN1601JwKCWiSAIVxmlwWFkUCJEACJEACJEACJEACJJCRwPLOj/0FoL7p6yUaNESpxk0IigRIgARIgARIgARIwBsI6DAvqGbM494wFc6BBEiABLJLINP1b/0GKNXE8ieOZncM9icBEiABEiABEiABEnATAV7/ugk8hyUBEiABEiABEvBQAn4ADgGIdlP94smauQCGZVcUZ6rf14RxYt5qhBrm6/zFsMF4tX93u0uvK2wpI7DV4c/536PhAzXs5nRGgBph3Nldy1C4QD5Vw+aU2Cu74wiBzUv/+0xqbs0a1sHvv42TinVm0Nq/tqNZl/7OTImxH7+JF3p1tplTi3s3u+vtVIgSydQI417s3RWjRw6VyAoYjUb4FakjFSuC/pg7EY0erCUV//f+w6jR/CmpWBFkEplpsSbzSVAYJ72k0KKcTYs12SJKYZx1OhcuxWHTjj34c8su5de23dnyHCP59Fb4+4vTcfvt6MnT2LBlNzZs3a2Mffj4Kfudsoho27whFk/7xuJVNcK4dXMmonE928fk7ArjPOH8JiNeV6+RwwuukY479x3BpB8XIPX6HUUQpwsJhj4wTRpnCApEYGgYchcohGLlqsEYnAvXT+9HdOAF1KlZRSMz0FYZThTGmSbW+t4fhDjuATfO9CiA8vfuD6S6sQYOTQLeSsBr7v9RGOetW5TzIgESIIGsCWQUxol7eImJiYqQLTg4GKmpWZ8+KsK4Nm1Q54EHkHAz4T95mw5AmvctrZn/Wfm7Djo/QK8PwJZNG/H76tWKMM5c/iZyBxgMaNeuHaLLloM+QAjjTirCuKvx8YpsTvQR9dprIq+Q4DWo+xC6dexhL5yvkwAJ5CABCuNyEDaHIgESIAESIAESIAESIAEScJzA8k4d+0NnTP+uwICQENR7cRD89HrHk7InCZAACZAACZAACZCARgj4dQiqVWuhRophGSRAAiTgVgK8/nUrfg5OAiRAAiRAAiRAAi4mwOtfFwNmehIgARIgARIgAe8iIJ48meaGKYknZObfe+zmPQB7nTm+LwrjkpNT8MiTA7Bu4w7VKIU8TEjEbLX2vYZg0ar1Urnfe/V5DL/3KyeaGmHcwT/noHyZEqrKyimxV3bH2bHnAGJayT9Edu3QH4gID1XFIrvBYo8WqdUaFy/HZTdVev8Le1Yif97cHrd3s7veTgMomUiNMK5H5zaYNup9ycyAmmPL4L5P4esRQ6Ryj/hyIobf+yXTMkoUtViTaR4UxsmsaFqMFuVsWqzJFlEK4+T2m1jX3f8cwso/NmPG3GWqJW6ndyxF0UL55QbLEBV/7Tq27PwHy9ZsxI+/LsSNm7ek85QuUQTHNi2wiFdzjPnorYF466XeNsfLrjDOE85v7AF39hrZG0/rrwuJw6ad+/D9lIUITdHDPzgI/iHB8BfSuPviuKCQEETlzYfC0RURmrck4v89hagb/6DlQ3LSWK0zcGZ9LhDGmcpztzhOXNhMdyYr5iIBElAIeM39PwrjuKNJgARIwPcICGHcd99/qwjYhFjNXBgXFBSkCOPMRW4mQiIuOTkZLVu3Rp0H6uJWwq37gjhjuiBOd98YZ4QRpj+b+ouc/no9tm7ehN9Xr4LBkFkYZzAY8Gi79oguVw4Bej1iY4Uwbg6uxsUpMjk1wjghoKtQthL693rR9xaZMyYBDROgME7Di8PSSIAESIAESIAESIAESIAE/iOw9rHHou7oEW/OpEyzh1HswXrERAIkQAIkQAIkQAIk4IEELh8+hLs3b6JI7dr/BNaMqeqBU2DJJEACJOASArz+dQlWJiUBEiABEiABEiABtxHg9a/b0HNgEiABEiABEiABzybgB+DQvYexo3N4GsJO8a6zRXGmOfiiME7M/dyFS6jwUCdVshDRLzwsBP+s/Q3FixTMchuMnTILA9/+VHqbrJk1Hk0bxEjHWws8f+EyXh72Bf7YtBOfv/syenZpmylMjTBu1sRP0fnRh1XVlFNir+yOk5KSilwVm0ivfetm9bHgx68QEJC9HyD68/wVeOfTsdD7++PQhrl22b45cjQ+/W6q3TiZgHaPNMLCqV/ZDdXi3s3uetudtJMDNm7fgwbt+0hlLVe6uNReMCX7asIMvDria6ncIij+4FpERYTbjE+4dRuFarSUfj98MPQFvDP42fScWqzJVJwamZPx3HZpro4GqhHziTGSTm2BXu/v6HCq+mlRzqbFmmxB9UVh3Adff49hn4+HkN++8/Kzqver+Dx+6+Mx+HysvIv5799/RrWKZdG0cz8IQdqPXw9Hp7bNVO13ESxkcW17vIw/t+yS7pvxOPFw1xewZsM2qf4ZZZvWOs1dugadnhsqlW/Ea/0wbEhfi1itnd9oYY2kYGo8SKzrqj+346epi5ErKBT+IUHwCwpKE8YZDPAPNCAwJARhUblQqGRZ5C1RCTdu3EDque1oWr0YckdFaHyGOVeeC4Vxpkm0ufeH4QBs28WdP+WjAMoDSHV+amYkAZ8l4JX3/2L3nRc/jIKNBEiABEjABwiYhHF6vR5+fn6ZhHFCDGetpQvjWrVCjCKMS7gvlhPxOrvkhDBOHxCALZvShHFC/mYupku6e1f5WhshjCtbVrnfHnsyFvPnzkF8fLwiuJMVxolihDCuVrUY9Hriv3uVdotkAAmQgGYIlKhcSDmw2D+6aKZkFkICJEACJEACJEACJEACJOBtBJZ37jgJMD5nmldASAgeHPAi/A0Gb5sq50MCJEACJEACJEACXk3AmJqKzd+Nxp0bN+BvCLiakpQ0PDA0akLTKVMSvXrinBwJkAAJSBLIeP2rDwpCvRcH8fpXkh/DSIAESIAESIAESEArBHj9q5WVYB0kQAIkQAIkQAIeSODpe7K46TlY9+J7D30PAyBvslBR3IFJsHgqqEJb18tyVJRnNdTZ8pi1f21Hsy79VZdVq2oFbFjwA4KDAq32PXDkBCo17iKdV0jo/lowGVUrqncR3rqdiAk/zcV7X4xPFz4JYdxrL/TINH7Xfm9i1qLVUnW92LsrRo+Uk5aYEuaU2MsZ4zw98B3MnLdcioUIerZbB0z64h2LB9xkOwuhzf8+GYsV6zald5GRU+365xBqtRCHney33yZ8gi7tmttNpMW964z1tjtxJwbsOXAE1R/uJp3x3K7lKFQgr1T81l37ULdtL6lYEfTN+6/i5eds1zJu6mwMeOsT6Zx/zJ2IRg/WSo/XYk2m4iiMk15WOPvzVX7krCO1WJOtefmiMM5cbFqnRiV88/5rqB9TTdXyX467inxV7H8+mZKe3bUMhQvkQ+2W3bFz70Hlyz06t8H7r/dHyWKFVY0tzonEuZFMK12iCI5tEg7n/9rzr4/EpBnzZLorMdYkb+LrV+Kv4YtxP+GTMVOynUtL5zdaWCNpoBoPTEpKxqdjZ+DArsPIHRwBv5Bg+AUFwi9Ar0jjxPdtBAQGISQiAvkLF0OhMlWR5B+Caye2o0b+JFSuoP4aQ+NIslWernCMq59FF+K4EQCyZwRXN8un7knjflbXhdEkQAI2CHjV/T/TPCmM454nARIgAd8hIIRxY38YBX9/fwthnPi7LSGbhTCuzgNIuJUAnVA5iTNokzNO+f3+F+7/C5OQwom+JmHc1s1CGLc6szAuKQkGgwFt27VDdHRZRS53KvYk5s2Zg6tXr2YSxplymlbO9HfT7xTG+c6e5ky9kwCFcd65rpwVCZAACZAACZAACZAACXgUgVVdHi2eYtQfAZBuiCvVpClK1G/gUfNgsSRAAiRAAiRAAiTg6wTObN2Ko6tXmmNISfHXVWr767zDvs6G8ycBEiABQcDq9W/jJijRoCEBkQAJkAAJkAAJkAAJeBABXv960GKxVBIgARIgARIgAS0R8ANw6J4wLieetl8C4F1XieJMUCmMSyMx8tvJeOfTsar3mhCIff+lWCbrrVHHvvhzi7zrT0jj3nqpN3o/0R4F8+exW8/Rk6cxd+lafDl+Oi5ejrOIz0oYp1ZsYksyJuQmqampyJcnV/rYOSX2csY4azZsw8NdX7DL2TygdbP6GNKvO5o1iFEetLPVbifegRjjh58XYN6ytZlCZYRxolP5ho/j8PFTquq0Fnzz6J8IDQmWyqO1veuM9ZaauJOCYs+cR8kH2klnE5KltbMnWF2f5OQUnL94GQXy5YYhIEB58LJG86cgpHSybfLXw5TjirX2y4KV6PbC27KpIIRJhzfMg7//f/tfizWZJkRhnPTSqhbGicz58+aWHyCLSEOAHof/mmdVwEphXLbx2k0gZJFCGinTti2bhpjqlSxCzYVxphc6tGyMN198BnVrVZGSrO49cBTVHn5SpgQlJvn0VuUYZC4jM3V+tX93DOjVRTlWyTQ1wsyWTeph+czRFmk/GjVZEcKqaYJPvZhqKFe6OP69eAVbd+/DnCW/p0t/ZXNlJZ/T0vmNFtZIlqcnxJ05fxHfTpyNi8f/RURoKHTBQfAPNMDPYIB/QIDyKyAwEEHh4YjKmx+Fy1RCeKFonD28GzVzX0Xl6BJ2z189gYMzaswBYZypzLb3/jA8h8RxR8Wlwz3hfKozGDEHCfg4Aa+7/2daTwrjfHxnc/puI5CckowDB/dna3y9Xki//BGgD1CEWhERkQgLC4O/n3+28rKz9xIQwrjvvv8Wer3eQhgn7mkHBQUp9/WF3M28iXt8ogkJW8tWrVG7Th0kJiTAmCHOFjVFGKfXY+vWLVhjQxjX5tF2KBMdjYAAPU7FxmL+3LmKME70FfWZahFjmf/ZfGwxFoVx3ruHOTPfIEBhnG+sM2dJAiRAAiRAAiRAAiRAAponsLzTY2OhQ/p3EYqf1lXvxUHwN6Q75DQ/BxZIAiRAAiRAAiRAAr5MICUpCZvHjELS7dvpGHTQTWg5e15/X+bCuZMACZBARgK8/uWeIAESIAESIAESIAHPJsDrX89eP1ZPAiRAAiRAAiTgVgJPAZjh4gqW3XvAexiA7S4eR0lPYVwa5ZSUVLTrNRjL1mxUjX3cJ2+hf89OVvtt3L4HDdr3UZ1TdOjRuQ3q1qyCgvnzolCBvAgLDcbFy/E4c+4CTp45j3lL19oURmUljBvx5UQMv/dLTXtjYC8ISUqe3JGK2GT/4eNYvnYTVqzbpKQxF5/llNjLWeO0eHIgVq3fogaHEiskL907tUGxwgVQuGA+RZp3924Szv57EecuXMbWXf/gp9lLbeaVFcZ9MmYK3vpojOoazTs880Q7/Pj1e9I5tLZ3nbXe0gCyGXjrdiJCy6j7QTtCGvdSnydRqVwpBBoMOHj0JDZu/xsz5i5XhJBjRg7FwN5dlcrU8DBN5fnuj6NLu+aoWiEaBoMeu/YewqzFqzF+2hxVs81KPqfFmsTEKIyTX161cjb5zPYjrx36AxHhoZkC1dZk/j6xP6rzIwpUa5FJ4JrVKPaks86vznpGVwjjTCMJadszXdvh0UceQuniRRAZEZZehDhOHjoWi03b9+DNj0ZLy9KEuHXp9FFKHmsyMtMADepUR88ubdG0QQyKFipgISSMu3odB46cwKKV6/Hpd1OlUX/2ziC8PqCnRfzP81fgqQH/k87hzMCshHFiDK2c32hhjZzJXAu5du07jPGT5yH12h0EBgbBLyQoTRgnJBDil8GAAIMBhsAghOXKhUKlyqJgdA2cObIXJQMvI6ZyKQQH8rmGHBTGmbbNo/f+IMRxtV28j8Q9i59dPAbTk4AvEPC6+3+mRaMwzhe2L+eoRQIbt2xAtz5dXFJantx5UbVyNTxYpx7q122I6lVquGQcJvU8AraEcYGBgVlK2MRMk5OS8EjLVoipUwe3biUAsBTLCVGbEUblq8p/hWhOCdEpEjohONy2ZSvW/L4aBoNB+ZpJTpd09y4MgQa0btsO0WWjFUHcqdhTWDBvLuLj4xUhokloZ486hXH2CPF1EtA+AQqJHwCXAAAgAElEQVTjtL9GrJAESIAESIAESIAESIAEfILAoi6PFgkw6s+YT7b6U08jV8lSPjF/TpIESIAESIAESIAEPJ3AiT/WIfavDRbTSNEFFGo7a9a/nj431k8CJEACziTA619n0mQuEiABEiABEiABEsh5Arz+zXnmHJEESIAESIAESMArCPgBOAQg2kWzWX7PW/ZuToniTHOgMO6/1bx0JR5Vmj4hLXox3webFv2IB2tXtbo12vcagkWr1rto22SdNithnJDitek+yKn1eLIwbvvf+1GntaX8xalwbCSTFcYdO3kG0fUfy1ZZq34di+YPPaAqh5b2rhoZ2VfDh+CV58Xz/e5tD7TpiW279zutiFEfvo6X+jyh5EtOTkH5hx7H8dizTssvkyh/3tyI3bYYQVZEM1qsScyJwjiZlU2LUStnk89sP5LCOPuMXBXhSmFcxprDw0JQqlgR3Lx1y+Hj128TPlHkl6LZkpFlHFscv4oWyo8z5y86dK4n8p3ZuRRFCua3SP3Xtr/RsMOzrloem3ltCeO0cn6jhTVyy+K4eNDFazZizuw1CLwL6IOD4BdogL8hADp/f+gC9PDXB0AfEAC9IQAh4RHIX6wEytRqhnNnTyLi5hE0qFIMUWYCRxeXq8n0bhDGmTi0u+fUEBZnV4njjt4TxpW/J6FP1SR4FkUCnkHAK+//jX11ptEcf9s+TS1WY8nktRZ/z/i6ZywdqyQB7RJwpTAu46yFMK5fnwFo21KcdrD5MgFzYZxJ1paYmAg/Pz9YE8YpEjghfksXxrVE7Zg6SLidIDRwyv9sN9FXB+h0CNDrsXXrVqw1E8aJvmKMu0lJCBTCuDaPokx0mjDu9KlYLJg3D1fj45VrGSGME7WIX+Z1ZRyfwjhf3uGcu9YJ2Du/zPi6vSOM1ufL+kiABEiABEiABEiABEiABLyAwPJOj30DHV7WBwWdqNXzmVIhefN6waw4BRIgARIgARIgARLwfgK34uKwfdIEpKakmE/2g1az5w/z/tlzhiRAAiSgngCvf9UzYw8SIAESIAESIAES0AIBXv9qYRVYAwmQAAmQAAmQgIcS6AZgpgtqX3n/gfHNLsgtndJ4brvFg6PSHd0QqFZoM2bkUAzs3VWq0o3b96BB+z5SseZBQkKye9VMFCqQ+fuEzl24hLpteimCkpxsWQnj4q5eR55KzZxaiicL4wSIj0f/iLc//s6pTGSSyQrjRC4hoxFSGkea2J9ndy6DXu+vqruW9q4nCuPeHDkan343VRVzW8HmwjgRt3nHXtRr19tp+WUS/f7bODRrWCfLUC3WRGGczMqmxaj9fJXPbD+Swjj7jFwVkZPCuOzOoVK50ti1agYMAQFKKjUysuyO/UKvzhj78ZuZ0ogH3Ss26uywAC87ddkSxmnl/EYLa5QdxlruO+6n+fhr7U5E6gOhE9I4QwD89Po0aZxeSOP8FXGcf4AegSFhKFK6LEpVr4er128i9dwONKpaBPnz5NLyFF1S28ElMRZ5K/a1a71wSR0AhMFlOIBaLhhA3Lv4xQV5mZIEfIWAV97/ozDOV7Yv56lVAjkpjDMxEMK4j977DFGRUVrFwrpcTCArYZyQrJmEbBlLEK+lpqYiJSUFj7RokSaMS0hQpG3pTfwx478mmX1NxAoJ3DYhjFvzuyKnM5e+JSnCuEC0btM2XRh36tQpLJw/D/Hx8Qi4L4wTddhrFMbZI8TXScB9BCiMcx97jkwCJEACJEACJEACJEACJOAggRUdO+aHHn1azpr3yZ1dO34yGo3dHUzFbiRAAiRAAiRAAiRAAjlIYNukCUi4dMl8xIt37qZGd1i48EYOlsGhSIAESMBjCPD612OWioWSAAmQAAmQAAmQgAUBXv9yQ5AACZAACZAACZCAQwT8ABwCEO1Qb+udVgN4Vzh/nJjT4VQUxv2H7uuJMzFk+FeqWTaoUx1rZo9Pl5iYJzhw5ATqtu2FGzdvqc7raIeshHEiX+9XRmDKr4scTZ2pn6cL48SE1AhznAVOjTBu4vS56Df0I4eGHjqgJz59Z5BDfbWydz1RGHf05GmUrd/RIe7WOmUUxomYMT/+hpf+95nTxrCV6MM3BuB/L9sXamqtJgrj5LcHhXHyrLKKLFCtBS5ejpNK9Gy3Dvj+S3Eq6N6m5vNv27JpiKleyaJgZ8sxbdHYvXomqlcqlx6SUzKyooXyY/8fsxEeFmK1vF8WrES3F97O8YW0J4zTwvmNVtYoxxcnBwa8nXgHI76civNHziA8OAS6oEDo9P5p0jg/P0UcJ/7s5+8Pf4MBQaFhyJW/AMrWqg9/QyCuH96AJjVKIm9u3xKIaEgYZ9ol7e/9QYjjajpx2xy9J7sXB0uPkcI7ce5MRQLZJeC19/8ojMvu1mB/EsgeAXcI40TFxYuWwOJZKxAZEZm9CbC3RxJwRBgnJmo0GpGcnIxHWrRE7ZgYJNxOgE54lu+fXSpuuCz8ccqXdToEpAvj1iAw0ACdzk/JK9p/wrg2KF0mGvoAPU4rwrj5FMZ55E5j0SRgnQCFcdwZJEACJEACJEACJEACJEACHk0gYefOwnoY/zHC6Hs/gsujV47FkwAJkAAJkAAJ+BqBY7+vwuktWyymrQMGtZw9f7SvseB8SYAESMARArz+dYQa+5AACZAACZAACZBAzhPg9W/OM+eIJEACJEACJEACXkPgSQA/O2k2a+49T/MegA1OyueUNBTG/YdRPLjU6bmhmLdsrWq2L/V5AkLqZK1t3rEXHXq/Ki2SUT14hg62hHEHj55ExUadsztEen9vEMYlJ6co0rhJM+Y5jYu9RGqEcZeuxCN/1UfspbT6+o4V01GragWH+opOWti7niiME+ycKWe0JowTx6shw7/GN5NmOry+Mh2feaIdvv/iXfj7C3+C7aa1miiMs7di/71OYZw8q6wiKYzLPsOsMsz94XN0bN3U4uWckJEJWdyqX8eiQnTJLCeXkpKKWi2exp4DR1wHwEpmGWGcu89vtLJGObowOTjYoWOx+Hz0z9Bdv4PA4GAg0AA/fz/oFFHcfWmcnz/89fq0z3CdDuG5cqFS3UYIDg1C0tk9aFa3MoKDAnOwavcOpUFhnAlIh/v3CZwljhP3MH51L22OTgIeScBr7/9RGOeR+5FFexEBdwnjBMIWzVph4qjJ0OnMDF9exJZTyZqAM4RxtWrXxq3b//0QHEUWl6aPu9+EBM5yb4m9ptf7Y9u2bVi3Zg0MBiGMS4sRv9+9exeGwEC0bt0apaPLKnK5U6dOYdECCuO4n0nAmwhQGOdNq8m5kAAJkAAJkAAJkAAJkICPEri7c1vPVOim+uj0OW0SIAESIAESIAES0DyBq6dOYff0aZZ16rBtc+UaDw4fPjxV8xNggd5KYJ3KiTVRGc9wEnA6AV7/Oh0pE5IACZAACZAACZCAUwnw+tepOJmMBEiABEiABEjAtwiIJ1kOA4jO5rSFIO4trYniTHOiMM5ydeOvXUdMqx44HntW9bL/NPp9dO/Uxmq/K/HX0G/oSMxZIryBrm22hHFi5F8WrES3F952ShHeIIwzgZi7dA2eGTwcN27+9yCcUyBZSaJGGCe6t+81BItWrVdVTrnSxXFow1xVfawFu3vveqowLuHWbTTq2Bc79x7M9hpYE8aZkk74aS76v/FRtsewluCzdwbh9QE9VefWSk0UxskvHYVx8qyyiqQwLvsMM2YQwrYZ332IRg/WypTc1TKyBnWq45fxH0PUYK+t/GMzWnZ70V6Yzdc7tGyMTm0fRs9Bw6TyyAjj3H1+o6U1koLqgUHzVvyJ2b+uQqS/AbrgIPgZAqDz94efvz90fjr4+/kh+c5dBAbpYTAE4Nat29Dpg1C/VSv4IRFFw1IQU7W8B87csZI1LIwzTeix++K4Go7NML3XfgBVFJ8HGwmQgCwBn7j/F7vvPI8LsjuCcSTgRALuFMaJaUwdPwNNHmrmxBkxlScQyCiMEz/kITExEf7+/ggMDIT4u7UmvpqclITmj7RA7ZgYJCQk/CccTDPGpTVzT5zZ14ROTq/XY9vWrVi39ncEBgZZCAuTkpIQYDCgdes2KB0drQjjTp86hYUL5uPq1asICAhAUFAQUlPtP8YjBHQiX61qMej1xLOesCyskQRIIAOBEpULKUcTak25NUiABEiABEiABEiABEiABDRJIHHHtgnQ6Z7XZHEsigRIgARIgARIgAR8mEByYiK2ThiPuwk3zSlcTdIlV2k3a7H6J4B8mCWn7nQCar8xh/9G4vQlYEJHCPD61xFq7EMCJEACJEACJEACrifA61/XM+YIJEACJEACJEACXk2gM4BZ2Zjhn/f6DgfgekOYA0UemGT5AHmFttsdyJKzXdQKbcaMHIqBvbuqLlIInoTkwpG2c+UM1KyStXhh1qLV+GTMFKdIpDLWFx4Wgpef64Z+PTrZFZwsWPEHerz0brblaN4kjBM8z124hE9GT8Hoyb86svx2+7RsUg/9ejyOjq2b2o01DxD7pmu/N1X1+eR/L+GNgb1U9bEV7K6966nCOMFSCCjf+3xCtveTLWGcGGfD1t14+d0vnHZcqVSuNL4eMQQtGj/o8P7RQk0Uxskvn9rPV/nM9iOvHfoDEeGhmQLV1uToZ779CuUifFEYt2PPAYz8djLmLVsrB0kyqnSJInh38HN46vFWMAQEWO01e/Hv+Hj0j0477pkGaVK/tjJ2s4Z1JKtNC1u3cQee6P8WLl6OU9VPBIu9+0Kvzli2ZiMe7TlYqr8aYZy7zm+0tkZSYD0w6PNxM7Fj4z7kDQsDgoPhp/dPk8bpAGNSEgoUjEL9BjVQIH8e/PvvZWzasAuXr93Gw4+2wOWLsejUvD5CQ4M9cOaOl6wrHKP177N6/L44rprjs0SXe9K42dnoz64k4GsEvPr+n2kxKYzztW3N+WqFgLuFcW1btsPYryZqBQfryCECDgvjjEakJCfj4UdaoFbt2ki4fStd5JT5JDrNIJf20INQxRkBnQ6GAAO2b9+GlcuWwt9fDz8/P+Xrot29cwfBoaFo374DSpcpowjsTp8+jcULFyA+Pl4RxlkT2pkEd0ISJ5r4u0kYV7t6HQrjcmhfcRgScDYBCuOcTZT5SIAESIAESIAESIAESIAEnE7g9s7tW3TAA05PzIQkQAIkQAIkQAIkQAIOE0hNScGB+fNw6ZDZT3TX6Vq3mjVvucNJ2ZEEnEOAwjjncGQWNxDg9a8boHNIEiABEiABEiABErBDgNe/3CIkQAIkQAIkQAIk4DAB8eTJPwAqOZBhw/0HvDUpijPNxxOFcXeTkhBYop70kkz+ehh6P9FeOt48cOL0ueg39CPVfYUQbPnM0Xb7bdn5D8b/NAdzlvyeLWlb0UL50a5FI7R9uCEeaVw3S7GKtYIux13FlF8XYdy02Tgeq/7nKQmpytrZE9JTL171J9r1esXu3EXAhM/exvPdhQtBfcuJca5dv4kZc5dhym+LsG33fvVFmvVo3aw+OrRsAvF78SIFHcqVcOs2wqIfUtX3+OaFKFW8sKo+MsE5vXdzYr1l5p2dmL/3H1akSsvXblT9fhfv8W8/eA2Pt2lmswTxoOTS3//CiK8mOrxnq1Usi3dfeU4RGvr7+2Vnykpfd9dU/eFu2HPgiN15CNnm9cPr7cZlN+DzsdMw9MNR0mmST291yjrIDKj281Ump2xMwrENCAkOyhSutqbsfObL1morrnzDx3H4+CmpVIOefVJ5X7u7if0o9qVM27fuNwiZpLV26uy/WLRyPRat+hMr1m2SSWc1RuQfNuQ5dGrzMPR6f6k8QvIrPieECFf82dHWtnlDvD2oD+rHOO5nEpLQ/m98jN8WrpIqo1fXR/HRWwNRuEA+Jf6PTTvRpJPczyf/YthgvNpfvdzYHec3WlojqYXxsKBbtxPx2diZOPL3ceQOC4cuNAT+4v2TmoyKlUqiS8dmyBURlj6rmwm38Na73yE8MhdKly+CymWLoF5tx/e9h+FSyvUAYZxSJoDH7t9XqO4AZ3EBU0UxeLCRAAnYI+D19/9MACiMs7cV+DoJuIaAu4VxYlaHd55UJFxsvkMgK2GckLcFBQUp9+zMm0nAJn5PTk5G80ceQc3atXEr4ZZyZip0cJmbyJH5637+foiLi8PhgweRmJgIo1GnjOenE7+nIiIyEuUrVEBoaBgMhgBFGLdk0UJFGKfX663Wl7FW8Xcxl6SkJNSqFkNhnO9sbc7UywhQGOdlC8rpkAAJkAAJkAAJkAAJkIA3Erizd0dFJOEvI4y5vHF+nBMJkAAJkAAJkAAJeCoBHXTxWyeOm5xw+fIrOqPxy5ZzFgz11Lmwbq8ioPab9bT+k2+9anE4GdsEeP3LHUICJEACJEACJEAC2iTA619trgurIgESIAESIAES0DyBrgB+VVnlxvsPdK9W2c8t4Z4ojHMLqBwY9MSpc9j+937s2HMAFy7HIf7qdcRdvY4r8deQJ1ckhCwsKjIckeFhyBUZjuhSxVCzSnnUqFweBfPncUqFQvay98BRHDx6Etdu3IQQikSEh0JI5QwBAYrMJ3/e3ChcMC+qVohGudIlpGUuTinQjUmEiGP3vsPYuusfHDoWq6yN4HPpSjxCQ4KVdRLrInjlioxAgXy5IcRbNauWR8WypVRJ/Nw4TYeG1sLedahwN3USD0cKoZSQ7sWeOY8bCbeUhzB1Oh3u3k1CQIAeuaMilT0UXbIYqlUqqxwD1LYLl+KwfvNOrPlrG/YdOo4Ll67g/MXL6bI6IUgrlD8vCuTLgwrRJdGsYR00erBmurRI7Xgy8VqsSaZuxpAACagjIM5ZjsWewdnzl3Dm/AWcOX9ROd6JX9dvJEDIqoICAxEZEYbcURHKOU1M9UqoUbkcypUurjzw7WgT5ywnT59Txjx7/iLEuc3JM+eV31NSUpTP66iIcOWcShxbq5Qvg1pVKyif10UK5nd02Ez9xPnUvsPHlfMqIQz9e99h5TyqaOECKFIwHxrXq62IOcX83dnccX6jlTVyJ3dXjH36/EW8//lk4PpdhIaFwy/IgJDQAPR4uhXKly5uMeTRk6fx+djZCNEFICIqEDVrlsVjrZq4oizN5vQQYZyJn/iesI737zOoNfuJexqzNLsQLIwEtEPA6+//mVBTGKedTcdKfIuAWmHcoP6voGyZcjDCiNSUVCQl3cWJ2BPYvXcXRC5H2oYVW1CsqOV5oSN52MdzCAhh3JhJ3ygCNnGdLe5JCnlbVsI4MTOTRE4RxrVogVq1auPmzZvKfcv0Jv5oesrB9GXzvxvFy0ZlXIPBgNRU8QWj+P998Zz4XdwHvQvxwy+DQoJw7sw5LFowH/FxcQgwGBS5YUahnWl8869TGOc5+5GVkkBWBCiM494gARIgARIgARIgARIgARLwCAKJO3e2BlKXekSxLJIESIAESIAESIAEfIaAX5ugWrWWLevSsWHrWfMc+1dUn2HFieYgAQrjchA2h3I+AV7/Op8pM5IACZAACZAACZBA9gnw+jf7DJmBBEiABEiABEjAxwiIR13+AVBJct6b7z/AvVIyXhNhFMZpYhlYBAmQAAnkGIHU1FTlAU1/f8eFTM4uVos1OXuOzEcCJEACJEACriawfc9BfPbtDOQLCEFQWDACIoLRpmUdNKpbK33ofy9ewTdT5yJVnxvGixdx7WYcOnRogrbNG7i6PE3l9zBhnImdI+K4/QCqmCk9NLUOLIYENELAJ+7/jX11psX3o7bt09QC/5LJay3+nvF1jawVyyABjyWgVhi3fO5qVCxf2ep8T546gSef6YTzF86r4jF3xkLUrlFHVR9TcFJSEuLiryDuahzi4+Nw+cpl3Ey4iZDgEERFRiEyIhKREVGIjIxERHgkAgICHBpHbaf4q/E4e+4Mrt+4hhs3bqTVFBKCgvkLIl/e/MovIR5zVbt+4zqOHDuEq9euIi4+Tvk98fZthIeHp/GIiESRwkUV+V92xOiO1m9LGGfORQjYhBDO/Hex5g0bNkTDRo2ReCfxP+GbKCbdHWdmjjOXyCmfOGlfUERzOl16FyGSU/5vEsjBiOCQEBw7ehRLFi3E9evXFdFcUFBQJmGcuSjOvF5Ra+3qddDriWcdRcV+JEACLiBg7/wy4+tmWkoXVMOUJEACJEACJEACJEACJEACJOAEAnd3buuZCt1Uker/7N0JnJVlvQfw/xmGYZTFblhKaaam4ZYLKIhL4JIoam6ltmkmXbW8VyWXrLTsttzKzLyZS1FZ2u26oYm5JdhioLjmAriFKRiLO4swzLm8hxk4MzIz58ycdc739SMwc57l/3yfd96zzJzfNC1bFvWNjQUY1RAECBAgQIAAAQLdEaiL9HENu+x6VXf66kOgyAIC44oMbPjiC3j+W3xjMxAgQIAAAQIEchXw/DdXKe0IECBAgAABAm0EPrYqLO7/cjCZvqrN1yPithzaVmyT9NwZ+b4uXbFrURgBAgQIECBAgAABAgRqTSAJYP3DlOlxxcQbY9OB74iBGw2O/u8aGNtt9d74wGabxJwXF8SDs5+P5vUGR8Oyt2Le3x+N12NF/Ne5n49N37tRTXDNnDy8zTq3Gb827qKKAJL30R/REli/Qw51HxUR1+fQThMCtSpQE6//CYyr1dPbuitFoJCBccmakpC0UfvnF/7224nXxqgRe3ZK8trrr8VjTzwajz3x93h85uPxxMzHYu5Lc2Px4jfzotxx+51i7z1Hx8jhu8cuOw3PBMsV4li2bFncOeX2uOcvU2L6jGnx/Atzuhy2f/8Bsflmm2dC2/ba/cOxx8i9YuONNu6yX0cNHnns4fjj1DvjT/feEw898kBO4yQ1jN5zTHxkn7Fx8NhDor6+NIF62YFxmeC2iFi+fHkmxK2hoaHT2pMQto033jhG7blXrN9//cyD5tVjZCfDdfTvdkNnfeepNUuutWcSJZc8j3n4wQfj8ccfi6ampkxgXBI6mB0Qlx1mlz16UpPAuJxOQ40IlFxAYFzJyU1IgAABAgQIECBAgEApBJY9OOPkN+bNvfSRa66OzfbcKzYdMbIU05qDAAECBAgQIECgrcApjbsM/ykUAhUqkO8b8/xSnQrdyFovy/PfWj8DrJ8AAQIECBCoEAHPfytkI5RBgAABAgQIVJVA8prrY6sC47btpOr7V92WBMXdWlUr66BYgXG9YRetgQABAgQIECBAgACBWhZ4a/ny+P2df42rfj053j94wxi8xfujYeONY1k0RtT1iUEDB0bq1QUx5/5p8eKC+XHEx/ePjx+8b9TX96kJtl4SGNe6V8nrFkdGxHmrAuE6C457IiK2XxUwl+/PotXEOWGRNS9QM6//CYyr+XMdQJkFCh0Ylyzns6d8Ou6+566cVzbllr/EFptv2WH7S392Sfz3Rd/OebxcGyZhaaefMiE+dcxxsV7jerl2a9Nu0cuL4qc/uySuue7qvMPr1jXhbsNGxKePOT7G7ndQl8Fprf2TcLiLfvKDuOevU7u1htZOQzYaEmd88cw46rCjo66urkdjddV5XYFxSZ8koK01QC75OPvf2WOuXLky+jb0i/XWWy/q+9QlDSP5Lx3pNrFxq/tkh8dlj5I8BE16rfvBaDJ3EgS4+M03WsaITGBcdlhca43tP9f6eYFxXZ0JbidQHgGBceVxNysBAgQIECBAgAABAkUWuP2oj46Kuj53p5ub+yVTvWeXYbH1AWMzL5w4CBAgQIAAAQIESiLgzfIlYTZJDwTy/SE9TyZ6gK1r8QQ8/y2erZEJECBAgAABAjkKeP6bI5RmBAgQIECAAIF2Asmbrq/rQOWBVZ9PguJu6U1qAuN6025aCwECBAgQIECAAAECtSqQhMbd9ecH4le/uTkG1a8XG2+2eay30SaZiIal81+KBc8/G/PfeDX2239kfOZjB8XAAevXDFUvC4xr3bfW4LjzW4Lh1rWfyWscN9TMRlsogdwFaub1P4FxuZ8UWhIohkAxAuMmnPufcd1N/5dzuTMfeLbTwLZvX/jNuHzipTmPl2/Dwe/cMH74nR/H6D3H5Nw1CQi7+v9+HUltixe/mXO/XBv+7pc3xMhdd++0+dJlS+Ps874UN00u7EOpA/Y7MC76ziXRf/3+uZabd7t1BcYlAW2Ja0fha8kkyW2t7ZYvX746YK5d4Nuaj9PpLt4P3XlgXDJfMlffvn0zAXrJv1vnzmXBSVuBcblIaUOg9AIC40pvbkYCBAgQIECAAAECBIosMOX44xvfevPV5yPiXdlTDd5qq9ju8COjrr6+yBUYngABAgQIECBQ2wJ1kT6uYZddr6ptBauvAgGBcVWwSUrsXMDzX2cIAQIECBAgQKC8Ap7/ltff7AQIECBAgEBVCyTvdXksIrZtt4qHVn2cBMXdXNWr66B4gXG9cVetiQABAgQIECBAgACBWhRoaloZM595Pn5z7R9i9uPPRUOsDoZYEenY8L0bxpGHjom9dtsx1l+vsaZ4emlgXOseJq9lHLUqyyMJjtuu3cY+0RIml+/Po9XU+WGxNSdQk6//zXl8nutAzZ3qFlwJAsUIjDvptBPjD3dOzml579tks/jz7dM6bVvswLjWyS/94RUx7oBDuqx7ZfPKOP9bX4lf/++vumzb3QZdBcbNfWlujD/1+Hjsib93d4pO+22z9bbxv7+8Pt6xwTuKMv66AuOyJ0rC1ro6umrTGi7X1Thd3Z4dYLeuMdcVcJeMKTCuK1m3E6h8gc22G5K5GHV9Rar8taiQAAECBAgQIECAAIEaELjtyEO2i1TdXRGpjbOXO3DIkPjQ0cdG3/Vr5zd11cB2WyIBAgQIECBQIQKpSL2SjtQnG3fZ5Q8VUpIyCHQmkO8P5vgeifOpIgU8/63IbVEUAQIECBAg0MsFPP/t5RtseQQIECBAgEApBI6IiOuzJnp41b+ToLibSjF5qed48spo83r00HEzSl2C+QgQIECAAAECBKCHtI8AACAASURBVAgQIFBZAk0rI+r7VFZNeVaThCose2tFPDvnhXjg0VmRjnR8cIvNYrsPbh7912+Murq6PEfsPc1T7xnem3/OKlnbxyLivHbBcclrHTf2nl20EgI9Fqip1/9atQTG9fi8MQCBbgkUOjCuqWlF7DZml1j08sKc6jn/nAvihE+P77RtqQLjkiJuv/HuGLr1Np3Wc9Z5E+J311+T0/q626izwLi5816Mgz8+Nmfj7tbwkX3GxhU/npgJPiv00VFgXDJX8n9HIWyFrqMQc7X6NDc3tylPYFyhd8t4BEovIDCu9OZmJECAAAECBAgQIECghwK3HnHEJnV16dsi0m1+g1ND//7xwXGHxOAPfKCHM+hOgAABAgQIEKhtgRfumx6vzpkT23/s48k7ne6r65s6vt8Ow56sbRWrryIBgXFVtFlK7VzA819nCAECBAgQIECguAKe/xbX1+gECBAgQIBAzQkk78p5LCK2jYhHV60+CYqbFNE2VK03qQiM6027aS0ECBAgQIAAAQIECBAg0JVALw+Ma11+8vrGx1uC45LXOJ6IiO178+sbXe272wlkCdTc63+taxcY5+uAQHkEChkY17SyKf7re9+IX/zmZzkv5rFps2LgwEGdti9lYNzwnXeNa6+a1GGA8a133BInn955wF3Oi++kYUeBcW+99VZ87DOHxSOPJb9LqPjHN7/67fjMsZ8t+ERJYNwlV1wU9fX1a6xbg9eWLl0aixcvjhUrVkRzOh2r4+qSP1vfvrD6M0nodPZn27da+8AzlWmbfaRaeiZzJnO09l097uqj7bzZoXmrR0s+kwRd9+vXLwYOHJhZS3bQncC4gp82BiRQcgGBcSUnNyEBAgQIECBAgAABAoUQuOnQQwc2NtT9Ph3x4fbjvWvo0NjqgAMjCZBzECBAgAABAgQI5C6wZOHCePL3N8Ub8+ZlOr1nl2HTP3Tu10bmPoKWBCpCQGBcRWyDIgol4PlvoSSNQ4AAAQIECBBYK+D5r7OBAAECBAgQIFAUgcNXvf/kglUjJ0FxN9TCG6kFxhXlPDIoAQIECBAgQIAAAQIECFSoQI0ExrXqZwfHfaUlFL9Cd0ZZBEomUHOv/7XKCowr2TlmIgJtBLobGJcEY61sXpkJ9frH88/F36b/Na6/+dp47Im/5yycaxhZKQPjkuJvuPrmGLbTrm9bx7Jly2KXvXeIxYvfzHmN3W3YUWDcV795Tvz6f3+V17BDNhoSm71v85g778V4/oU5efXt339AzLjnkVh/vfXz6tdV43UFxiV93nzzzUicly9fHvX1faJf3/rV4WypVKSbkxbpSNW1hLelI5Kst1RWllv7jzN1tE+Va/lca+hba0Jc5s0RWeNlcuTqVkUcN0ekkr9bjqSOZM6Vzel4a/nyzGdbQ+MaGhoyoXHJ/62BccN32i2OO/pzXZG4nQCBChQQGFeBm6IkAgQIECBAgAABAgRyF7jtqMN/FZH+TPse9Y2NMfSQQ2PDrbbOfTAtCRAgQIAAAQI1LPCPP/8pkv/bH+nmut0OvOGG+2uYxtKrT2BqniWPzrO95gTKIuD5b1nYTUqAAAECBAj0QgHPf3vhploSAQIECBAgUCkCH4qIRyulmFLUITCuFMrmIECAAAECBAgQIECAAIFKEaixwLhs9p0i4uFK2Qd1ECijQM29/nfphGva/ALjcSeMacM/eeKUNh+3v72Me2VqAr1CIN/AuEIt+uTPfTHOOSPJi+366CwwLgk022HbD8UHtxoa79t0s0g3N8crr74SL7+yKP467S95B6Ql1Zxy4qlx9unnvq2wW++4JU4+fXzXBWe12HH7nWLnHYfF9tvuEIP/bXAsXLQg5r40Nx58eEbc89eOfxx/XYFxSRjfuI99JOf5//2EU+KLn//PGDRw0Jo+S5YuiYt/+sO47Oc/yXmc73z9+/GJj30q5/a5NMwOjEuC1ZLjrbfeisWLF0cSujZq1O6x16iRsd76/WPlyiShrfVXKLVPiGsJhGuddE0KXC5VrKNNdv/Wf7cfc3WCXdTXpWLO88/HbXf+Meb84x9RX18fAwYMiLq6OoFx3eTXjUCpBLp6fNn+9qxcylKVaB4CBAgQIECAAAECBAgURuC2Iw8fH6n0jyKiza8D+NDRx8Y7t9yyMJMYhQABAgQIECDQCwWam5pi7oMPxD/vmx5vvf76ulb4ZjpSJxx43Y3X9sLlWxIBAgSqTsDz36rbMgUTIECAAAECFSLg+W+FbIQyCBAgQIAAAQK9VCA9d0abN4720mVaFgECBAgQIECAAAECBAjUqMDMycPbrHyb8ZlYDAcBAgR6vYDAuF6/xRZY4QLlCIw7/Qtfiv88+YxoDQrriqh9YFwSvvbxw4+JA/Y9KDbeaONOuz/1zOy48leXx++uv6aradbcnow/+do73tb+P876Qtw0+Yacxtlm623jiksmxvs22azD9i+/8nLcNfWOuOIXP42kzuxjXYFxp5zx+Zh8++9zmv/aqybFbsNGdNj25lsnxalnnpzTWFttuXXcdfM9ObXNtVF2YFxrwNqSJUti+fLlseUWW8TXvnJOvN743nhwzhvR3NwcdXWV9dB45cqIwYP6xagtG+Phv0yJH/7okkjqHzhwYCbwLp1OZ87vFStWxPCddovjjv5crjTaESBQAgGBcSVANgUBAgQIECBAgAABApUjcPsxH920eWX8NpVO7ZFU1W/AgId3/8/T0pGOnSunSpUQIECAAAECBCpDoGnZsnjhvunx4gMzYsXSpessKh1xR6q57vixN9wwrzKqVgUBAgQIJAKe/zoPCBAgQIAAAQK5C3j+m7uVlgQIECBAgAABAt0XEBjXfTs9CRAgQIAAAQIECBAgQKDyBQTGVf4eqZAAgeIICIwrjqtRCeQqUOrAuOt/c3MM33nXXMvLtJt0yw1x4y3Xxy47DotDxn40tth8y7z6J+Fd//W9r8fPrroip36D37lhPPjnv7+t7aFHHxiPPPZwTmNcfvHPY+x+B+XUdmXzyrh58qT45ve+HoteXpjp0z4w7ulnn4p9D9k7p/GSQL7TTpnQZdsjP3VozHjo/i7bJQ0e+NOjseHgd+XUNpdG6wqMW7p0aSxfsSL2H713nPAf58Rl98yNx+a8HP0a+lRAknLyO43WhtY1rUxHqk8qjh21WezYf2F86ewvx0v/mh+DBg16W2DcsB13jeOPOTEXFm0IECiRgMC4EkGbhgABAgQIECBAgACByhK4/aiPfikdqfNX9kkNG/e7G2cve3BG8usEzoyIzSurUtUQIECAAAECBMoj8K/H/h6zbp0czU1NHRWwMJ2O0w+8ftJvylOhWQkQIEAgFwHPf3NR0oYAAQIECBCoZQHPf2t5962dAAECBAgQIFBaAYFxpfU2GwECBAgQIECAAAECBAiUVkBgXGm9zUaAQOUICIyrnL1QSW0KlDowLlHeb/RH4ohDj4oD9jsw6vvUlwR+8eI3Y9vdtsp5rmce/efbattlrx3WBLp1NdC/n3BKnDvha101a3P7m2++Ed+7+Lvx53unxmU/+nl8cKuha26/5LIfxQ8u+e8ux0vC7u698/5obGzssu1dU++Iz33huC7bJQ0mXnpV7Pvh/XNqm0uj7MC4VGp1EFsSGLdixYoYu9+YOPyE0+OKP82NF+a/Ho0NfXIZsqRtVjano6k5HQftskmMetcbce6XvxwvzntpTWBcazHJegTGlXRrTEYgJwGBcTkxaUSAAAECBAgQIECAQG8UmHLYYe8YM2nSq9lrS4LjUqk4JZ2O7Xvjmq2JAAECBAgQIJCrwGv/fD4e+vVV626ejl/WN6e+tN+NNy7KdTztCBAgQKB8Ap7/ls/ezAQIECBAgEDlC3T1/Dfq+k4Ye+21L1f+SlRIgAABAgQIECBQ6QIC4yp9h9RHgAABAgQIECBAgAABAj0REBjXEz19CRDoDQJzHp+X7g3rsAYC1SZQjsC4VqNRI/aMS75/aWw4+F1FY0un0/HGm2/EK6++HId/4pCcA9+euP/p6L9+/zZ1jRm3Rzz7j2dzrvWcM74Sxx71qXjHBu/IuU9HDY8+/siYdv+9XY5z8NhD4ycXXt5lu6TBP194PvY8YERObf/jpNNjwqln5dQ2l0bZgXF1dXWR7NOSJUtiRVNTjN13dBx+whlx6ZQX4h/zX4v1+tZHOtKRitXBcm8/kruPjm7LpZqkzbrG6Hjc1sC4Q3fbNPZ695tx7pfPibnz/rUmMC5ZTxKEJzAuV3/tCFSmwGbbDclcXHp6hanM1amKAAECBAgQIECAAAEC7QSWPfjgoZFqPj7ScXj2Tc/88c6ob1wvBr3nvTHove+NPg0N7AgQIECAAAECvVMgFTf++Qff223lW8vf27rAVMRvVqabf3jQ9Tc/1DsXbVUECBCoPQHPf2tvz62YAAECBAgQaCewjue/EamrmtMrf+T5r7OFAAECBAgQIECgEAJPXpl5l86aY+i4GYUY1hgECBAgQIAAAQIECBAgQKAiBVLvGe696BW5M4oiQKDYAgLjii1sfALrFihnYFxS0eB3bhhXXjIxhu20a7e3aNHLC+PJWU/Es/94Jl6c92K88OI/M2FoL8x9IeeAuPaTrysw7nNfOC7umnpH3nXuNmxEjN5rn9hhux1ju6Hbx+B3Ds5rjKXLlsbQYVvk1OfDe4yOk0/8Yk5tk3C2T48/Jqe2Rx/5ifjeBRfm1DaXRu0D45I+SWDc8hUrYr99RsdxJ58Rd896LRa8tiT61tXlMmRJ2zRHOprTEbtu+e4YkvpXnPvlL8e8l1YHxvXt2zdTi8C4km6JyQgURUBgXFFYDUqAAAECBAgQIECAQKULLH7wwff0ieajIhWHNS17a8xfLvx+m5L7v/vdscEmm8Z6//Zv0dC/fzT0HxB9118v+vbvH/0GDKz05amPAAECBAgQ6KUCK1esiBXJbydK/l+2NFYsXhzLFy+Jdw0dGo0bbNDxqlMxJdIxaWXUXdd/l13m3nbUYadFxNciUpetTNVfMu7aa1/qpWSWRYAAgZoX8Py35k8BAAQIECBAoCoFPP+tym1TNAECBAgQIECg5gQExtXcllswAQIECBAgQIAAAQIEalpAYFxNb7/FE6hpAYFxNb39Fl9GgXIHxrUufdrdD8aQjYbkJNHUtCL+9Nd74uY/TIpp990b8/41L6d++TRaV2Dcf33/G3HlLy/LZ5h1tn3fJpvF6L3GxMhdR8WI4SNjw8Hv6nTMhx55IA77xME9nrcnAxyw34FxxcUTezJEm77ZgXFJsFpyLF26NFasWBF77713nHXmmdGvsbFg8xVzoKeffjrOPffcmD9/vsC4YkIbm0AZBATGlQHdlAQIECBAgAABAgQIVJbAn044fvyS11+9IteqNt7hQzH0kENzbR4v3Dc9nr7rzpzbbzpiZGy57345tzd+51R8+GQL+Prq/HzgwydbwPWzuq6f2x52eLx72+3WFJ1KxWPp5vS9qVRMbWhO3ZkaPnxh9opuOvTQgW/169f08WuvXZrzgw4NCRAgQKDqBTz/ra77d4/PPT73+NzrYx19Fbg+uD7U8vXB89+qf0hqAQQIECBAgACBXiUgMK5XbafFECBAgAABAgQIECBAgEAXAgLjnCIECNSqgMC4Wt156y63QKUExn103BHx4+/9pFOO1994PX76s/+JX/32F7F48ZtFpVtXYNyNv78+TjvniwWf96iPfjxOPO7zsc0H175XJXuSu++5Kz57yqcLPm8+A+42bERce9WkfLp02jYJjPvx5T+M+vr6qKuri3Q6nQmMa2pqir322ivOOuusaKzSwLiGhobMepIjCcAbvtNucfwxJxbMzkAECJROQGBc6azNRIAAAQIECBAgQIBAhQrcduRhV0cqPpFreUN23Kn5g+MOTn49wOpfEdDF8c/p0+KZP97VVbM1t286YkRsue/+Obc3fudUfPhkC/j66vx84MMnW8D1s7qunxvtsOO92xxyyG/S0fxEY3Pdw6nhw1/L+cGEhgQIECBQMwKe/1bX/bvH5x6fe3zu9bGOvgpcH1wfavn64PlvzTx0tVACBAgQIECAQFUICIyrim1SJAECBAgQIECAAAECBAgUSEBgXIEgDUOAQNUIXDrhmtWpOi3HuBPGtKl98sQpbT5uf3vVLFShBCpUIN/AuP/9xXWx7dDtM6tJQrGam5ujuXllLF6yOOb8c078Y86zMfn2W2La/ffmveLf/fKGGLnr7uvsd/sfb4svf/3MWPTywrzH7U6HdQXGLV++PPYeOzLm/Wted4bsss9+oz8SF3774njHBu9o0/b6m66NM879jy77F7PB9tvuEJOvvaNgU7QGxvXt2zdSqdSawLgkYG3vvfeu2sC4gQMHRmtgXLIugXEFO2UMRKCgAl09vmx/e04hBwWt0GAECBAgQIAAAQIECBCoEIFbP3ngoNRb/XZNRXp4pFK7Rjp2jYj3dVReOuKSA6+blPMrWbcdddiEiPhBzstNxw/HXj8p6ZPTYfzOmfjwaSNQGV9fo5PvPUTEPc5P52cFnp9rSnJ+Vtn5GTFx7HWTPpfTgweNCBAgQKBmBTz/rbL798p4/uLxYY5XDI+ffX15fuf1z46+ClwfCnx98Pw3x3smzQgQIECAAAECBEopkJ47o80bR0s5t7kIECBAgAABAgQIECBAgECxBWZOHt5mim3Gh/ekFxvd+AQIVISAwLiK2AZF1LBAvoFxt91wV2zzwe26FLvj7tti/Kmf7bJddoMjDj0qLvrOJW/r89Of/09894ffymusnjZeV2BcMubNt06KU888uafDd9h/i/dvEb+87OrYbNP3r2nzs6uuiG/+9/lFmzOXgYsVGFdfXx91dXW9JjBu0KBBkYTgJWGKAuNyObO0IVAeAYFx5XE3KwECBAgQIECAAAECvUTgrsMPH9xU17xzRN3mkUpvmorYPCI2TWf+Tv1m7HU3fiXXpd521GGnRcRFubaPiAvHXjfpS7m2N37nUnz4tBOohK+vaRGxdFVg3Bjnp/OzAs/PNSU5Pyv1/EwvS0XdonTEokjFonQ6FtVFLEpH871jr7vpqlwfP2hHgAABAgRaBTz/XXsuePxTqY9/OqyrEp7fefyc4+XU15evL89/vT7c0VdBx9cHz39zvMRqRoAAAQIECBAgUAECAuMqYBOUQIAAAQIECBAgQIAAAQJFExAYVzRaAxMgUOECAuMqfIOU1+sFihUYl8BdO+l38aWvJG/5zO1YVyjZpFtuiP88+wu5DVDAVh0Fxq1sXhlnf21CZm3FOvr3HxB/u2tGbDBog8wUP77sorjwku8Va7qcxi1VYFxTU1PstddecdZZZ0VjY2NOtZW70dNPPx1f/vKXY/78+ZEExjU0NKwpacWKFTF8p93i+GNOLHeZ5idAIEtAYJzTgQABAgQIECBAgAABAgQIECBQeQKjI2JKS1m7R0QSHucgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgSoVEBhXpRunbAIECBAgQIAAAQIECBDISUBgXE5MGhEg0AsFBMb1wk21pKoSKGZgXBKWteMe28bixW/mbPKPx+ZGKpXKtH/l1Vdipz22zbnvuhoOfueGscl7NolN3rtpTL799zmP1VFgXDJAOp2O/7nix/GDH3835/HybXjqSafFl049O9Ptl1f/PM7/9lfzHaKg7YsZGNe630uXLo3knNl7772rMjBuwYIFMXDgwExgXHKOJOsSGFfQ09BgBAomIDCuYJQGIkCAAAECBAgQIECAAAECBAgUTCAJiBvRMloSHLdPwUY2EAECBAgQINAqMDVPiiTQ1UGAAAECBAgQIECAAAECBAgQIECAAAECBLolIDCuW2w6ESBAgAABAgQIECBAgECVCAiMq5KNUiYBAkUTmPP4vHTRBjcwAQIdChQzMC6Z9LBPHBwPPfJAzjsw/e6HYuONNs60v/JXl8d/fe/rOfdNGu6847DYd+/9Yvguu8aHttsx+vcfsKb/cSd9Mqb++e6cxussMK51gD/de09cfOmFMeOh+3MaM99G9099ON79ro3ipltvjP8485ScuicBeZ8//qSc2ubTaNNN3hfjDjgkny6dtp317JPx48t/GPX19VFXV5cJWFuyZEk0NTW1CYxLPl/JRxIK99RTT8W5554b8+fPj0GDBq0JjEvqTgLjdt15RBx/zImVvAy1ESDQgcBm2w3JJJiujjF1ECBAgAABAgQIECBAgAABAgQIFEsgCaNJQuKyj90jIgmRcxAgQIAAAQKFE8j3u6++R1I4eyMRIECAAAECBAgQIECAAAECBAgQIECgZgSevDLavB49dNyMmlm7hRIgQIAAAQIECBAgQIBA7Qmk3jPcz1nV3rZbMQECESEwzmlAoDwCxQ6M23a3rWLx4jdzXtx9Ux6Kjd69OjBu5D67xLx/zcu57/cuuDA+fsSxkYR4resodGBc6xyPPv5I/OI3P4sbbr4u51pzaXjFxRPjgP0OjCSY7tPjj8mlS4wasWf8duK1ObUtZ6MkMO7iyy5cExiX1LJ06dJYvnx5JjDu7LPPjsbGxli5cmVmP5PguMzfWcFNrWFy69rv1vbt19jR51vbdXl7u+CoJOyufWBc3759M/UmtyWBccN32k1gXDlPNnMT6IGAwLge4OlKgAABAgQIECBAgAABAgQIEMhDIAmGG9GuffLrX/bNYwxNCRAgQIAAga4FBMZ1baQFAQIECBAgQIAAAQIECBAgQIAAAQIECPRQQGBcDwF1J0CAAAECBAgQIECAAIGqEhAYV1XbpVgCBAooIDCugJiGIpCHQDED41597dXYcdQ2eVQT0RoY9+abb8R2I7bOue+vLr8mRu85ptP2xQqMa520aWVTPDnzibj/wekx46H7Y9r9f4tFLy/MeQ3tG371zPNj/PEnxeNPPhYHHbV/zuPMeuC5TNhaJR/tA+OSgLUkMK6pqSn22muvTGBcv3792gTGFXo9rUFzrcFz+Y6f9O/Tp8/bAuMaGhqiubk5ExiXBOAlgXGfPXZ8vsNrT4BABQgIjKuATVACAQIECBAgQIAAAQIECBAg0OsFklf2k3C4dR27R0QSJucgQIAAAQIECiMgMK4wjkYhQIAAAQIECBAgQIAAAQIECBAgQIAAgU4EBMY5PQgQIECAAAECBAgQIECglgQExtXSblsrAQLZAgLjnA8EyiNQzMC4P9w5OU467cS8FvbMo/+M+j718exzz8SYg/fMue9TD82JJKirs6PYgXHrmvulf70U9z0wLe6aemfcNPmGnNeTNPzU0Z+Jb53335EE0X1o921i8eI3c+p/zc//L/YYuVdObcvVqKPAuBUrVqwJjEtC75IAuW4fqVREOoe3PCTtkiOrbRIG11WQXNKmvr5+nYFxrX2T9QiM6/YO6kig7AIC48q+BQogQIAAAQIECBAgQIAAAQIEakAgCYQb0cE6kyC5fWvAwBIJECBAgECpBHL47mmbUlq+k1qq8sxDgAABAgQIECBAgAABAgQIECBAgAABAr1BQGBcb9hFayBAgAABAgQIECBAgACBXAUExuUqpR0BAr1F4NIJ17T5edRxJ4xps7TJE6e0+bj97b3FwToIlEugWIFxz78wJw479uBY9PLCnJc2+J0bxoN//num/d/u+2sc89mjcuo7ctdR8btfXt9l254Exi1ZuiS2Gb5lbPH+LeLSi66Mbbbetsv52jf45wvPxwFH7Jtz8NuB+4+Ly370s8wwp5zx+Zh8++9zmnPIRkPipv+9NTZ698Y5te+o0aOPPxJfveCceGHuC3HtVTfGlpt/oEfjZXduHxiX3LZkyZLIDozr16/fOgPjWu802r85Ifvzyb87evNCZ7e1X2CnbVOpaOjbN2bPnh3nfuUrsXD+/Bg4aFD07ds3M0wSKLd8+XKBcQU7awxEoHACXT2+bH+7N0MVzt5IBAgQIECAAAECBAgQIECAAIFsgeS7gkkoXGfH7hGRhMo5CBAgQIAAgZ4LCIzruaERCBAgQIAAAQIECBAgQIAAAQIECBAgQCBHgfTcGfm+Lp3jyJoRIECAAAECBAgQIECAAIHyC8ycPLxNEduM7zDjovzFqoAAAQIFFBAYV0BMQxHohkAhA+OaVjbFzFlPxtS/3B3fv/i7eVfzmWOPj29+9TuZflP/fHckAW+5HEmI25TJf+2y6cc+c1jc98D0LtslDZ64/+nov37/NW1ff+P12GHkB9d8POHUs+Jznx4f/fsPyGm81kbnfesr8atrJubUJ9vjt9ddHeec/6Wc+iWNkkC76349KQYMGJhzn9aG/5r/Uvzkykva1Dnpt5Nj5w/tkvdYHXXIDoxLgtWSY+nSpZmAuD333DPOPvvsaGhoyATIRcvtmUbp1d8qau2TTj5OpTIPnNcV7tZpcFw6nRknM2L2v1vnSeZt9/nWeZImdUlgXENDzJo1K76SBMYtXBgDBgzIfC6pKxk7qX/4TrvFZ48dXzA7AxEg0HMBgXE9NzQCAQIECBAgQIAAAQIECBAgQKAQAkkQ3IguBvpjROxXiMmMQYAAAQIECKz+3mgeh1+qkweWpgQIECBAgAABAgQIECBAgAABAgQIECDQVkBgnDOCAAECBAgQIECAAAECBHqzgMC43ry71kaAQGcCAuOcHwTKK5BvYNx+oz8Sm27yvkwgVvL/ypVNsaKpKV56aW7c89epPVrM//3qxhgxfGRmjGefeybGHLxnzuP97y+ui91322Od7V97/bX4+ne+GjfcfF3O43UVGJcMlITFnXP6ufHRcUfEBoM26HLsxOuLXzopbrnt5i7bJg2+fMZX46TPfSHT9tXXXo1R++8aixe/mVPfpNFWW24dJ3/ui3HQAQfHeo3rddqvubk5Hvn7Q/H7226Kn1915dvaFjowbuYzT8SPL/9h1NfXR11dXeZcag2M22OPeRJXSgAAIABJREFUPTKBcf0aGmJ5EhjXerQEuCUfZoLeWsLjWj9O/m7/udbgtnWGybWEzbWGwrXv3xV0UkNjY2PMnDUrviowrisutxOoKAGBcRW1HYohQIAAAQIECBAgQIAAAQIEalRgdERMyXHtyav/9+bYVjMCBAgQIECgYwGBcc4OAgQIECBAgAABAgQIECBAgAABAgQIECiZgMC4klGbiAABAgQIECBAgAABAgTKICAwrgzopiRAoCIEBMZVxDYoooYF8g2MKxZVEm52+6S7o09dn8wUTSubYssPbZrzdEl427kTvhaHHPjRTHjbyuaVmdC5Bx66P7538Xdj0csLcx4raZhLYFz2gAfuPy4OP/jI2GH7HePd73p31Pepz9ycBJa9NP+leOqZ2fGrqyfGXVPvyLmOSdfcEjvvOGxN+0t/dkn890Xfzrl/a8PE5hNHfTK23GKrTG3v2vDdUZeqi/kL/pWp7elnZ8ekW27s1KiYgXFJ8FpyJIFxK1asiD333DMTGNe3b9/Mx8mtmTcutLRLAt4yR0uAXGv/zGezQuSS21NJsGFHbVuAMuNn98tReE1g3MyZ8bWvfS0WLFgQAwcOjIaGhsx4ye1J/cN32i0+e+z4HEfVjACBUggIjCuFsjkIECBAgAABAgQIECBAgAABAp0LTIuIETki3R0R++bYVjMCBAgQIECgYwGBcc4OAgQIECBAgAABAgQIECBAgAABAgQIECiZgMC4klGbiAABAgQIECBAgAABAgTKICAwrgzopiRAoKIE5jw+L9+fS62o+hVDoFoFKiUwrn04WuJ56NEHxiOPPZw3bRKQtnjxm3n3y+6Qb2Bc+8mGbDQkBg/eMJ6b81y3atni/VvElMl/bTPs4iWLY9fRO3VrvB5hREShA+NmPftkXHzZhVFfX58JVkuO1sC4PfbYI84555xMYNzy5cs7LD3p152gt8yA7cPnugGUHRh33nnnxcKFC2PAgAGZuldPITCuG6y6EKgogc22G5K5QK2+SjkIECBAgAABAgQIECBAgAABAgQKJbBPRPwxz8FGRsT0PPtoToAAAQIECLQVyPcHc3yPxBlEgAABAgQIECBAgAABAgQIECBAgAABAnkLPHlltHk9eui4GXmPoQMBAgQIECBAgAABAgQIEKgWgdR7hvs5q2rZLHUSIFBQAYFxBeU0GIGcBSohMO6UE0+Ns08/9201n//tr8Yvr/55zmspZMOeBsb1tJb/+cFlcciBH33bMNPuvzeOPv7Ing6fd/9CB8bNfOaJNYFxdXV1mXraB8YlYXJtAuPS6bVBb3mvoHAdkpC6TMhdKhWNjY0xa+bMOP/882PBggUxcODAaGhoyATZCYwrnLmRCJRLQGBcueTNS4AAAQIECBAgQIAAAQIECPR2geTXpYzKc5FJwNx+efbRnAABAgQIEGgrIDDOGUGAAAECBAgQIECAAAECBAgQIECAAAECRRcQGFd0YhMQIECAAAECBAgQIECAQAUJCIyroM1QCgECJRUQGFdSbpMRWCNQ7sC4z37qxDj/nAtWB3C1OxYsnB8fPmiPWLz4zZLvWDkD4w4ee2j85MLLO1zzzbdOilPPPLmkJsUOjEsC1pLAuKamphg1alScc8450adPn0xg3JpzI53O/IahNWfKOs6ZNigtAXNt+mQ1aA11S/5OjmSeNWFwLe2y+2ZuW90wIulTVxeN/frFrFmz4utf/7rAuJKekSYjUBoBgXGlcTYLAQIECBAgQIAAAQIECBAgUFsC+0REEv7WnWNkREzvTkd9CBAgQIAAgYyAwDgnAgECBAgQIECAAAECBAgQIECAAAECBAgUXUBgXNGJTUCAAAECBAgQIECAAAECFSQgMK6CNkMpBAiUVEBgXEm5TUZgjUA5A+POOu3LccqJp64zLK61wBt/f32cds4XS75j5QqMGzViz7jyxxNjwICBna75t9ddHeec/6WSuRQ6MG7Ws0/Gj376g6ivr4+6urpMUFt2YNxZZ50dffvWx1tvvbVmjZlAt9aPWkLeugRoCXdrH0jYJiQuGaR1vNYQuvbjp1Jrg+oyzdOZ87axsTFmzZ6dCYxbuGBBDBw4MPr27ZspK7l9xYoVMXyn3eKzx47vslQNCBCoPAGBcZW3JyoiQIAAAQIECBAgQIAAAQIEql9gWkSM6OYypkREEjjnIECAAAECBLonMDXPbqPzbK85AQIECBAgQIAAAQIECBAgQIAAAQIECBAIgXFOAgIECBAgQIAAAQIECBCoJQGBcbW029ZKgEAicOmEa9r8AuNxJ4xpAzN5YvLWj7VH+9spEiDQM4FyBMbtvOOw+N4FF8bWH/hgl8UnwVzHn/ypmPrnu7ts21mDjx9xbDz/zzkx7f57cxqnHIFxxx71ybjgK9+OhoaGnGqcOfvJ+OKXToqnnpmdU/ueNCpWYFwSrpYExiXHkiVLMgFro0aNijPPPCvq6/vE8iQwriXELRMYl2tQXDJgVvhbKgl5awl9y9zptAS+Zf6ZNI306sy4loC5XKzWBMbNmhXfuOCCTGDcoEGD1gTGJWMsX748dt15hMC4XEC1IVBCga4eX7a/PbmGOAgQIECAAAECBAgQIECAAAECBHousG9E3NXDYUZGxPQejqE7AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBRZID13Rps3jhZ5OsMTIECAAAECBAgQIECAAIGSCsycPLzNfNuMD+9JL+kOmIwAgXIJCIwrl7x5CawWKGVg3LgDDoljjvpk7DFyz+hT1yfnLVi6bGn84OLvxs+uuiLnPq0N+/cfEN/62nfj8EOOjONO+mTOwXPtA+OaVjbF5RMvjZ9fdWUsenlh3nV01iEJszv5hC/EFptvmfe4y5Yti19e/fO44peXFbyupJitttw6PnPsZ+Njhx8d6zWul3d9HXWY9eyTcdGl38+ExSXBa8mRhKs1NTXF7rvvHmeeeWbU19e3CYx721jZ4W4dBL21+XRWgFxmrMyNrX+lVyfHZR6Ct36y5dtSHY6disbGxpg1a1Z885vfjAULFsT666+fqTs5+vTpk1mPwLiCnTYGIlAwAYFxBaM0EAECBAgQIECAAAECBAgQIEAgL4FpETEirx5vb5z8qql9ejiG7gQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQZAGBcUUGNjwBAgQIECBAgAABAgQIlFVAYFxZ+U1OgEAZBQTGlRHf1AQi4qFHHojDPnFwwSwGv3PD2GDQoBg4YFBs9r73x7ZDt4tttt42dthuxxj8zsE9mue+B6bHf5x5csz717wux0mC4j519Gfi5M99Mf7tHf+Waf+FCf8et9x2c5d9kwazHnguEwbW/kiC4+6d/tf449Q74/d/uKlHIW3HfeKEGH/cv8emm7wvp5o6a5TU9cepd8U11/4651C8jsZL9mvf0fvHR/YdGztuv1OPa1vXAE8+/Vhc+rNLYujW22SC1V5+5eV45rmno7m5ORMYN2HChKiv7xvLl7+1uvvbwuGyP5UV8Lam3ergt9WZcNnBcMmns4Pg1uTGtXy+JbM50z3TM9NgdZe2v9coVVcXjf36xezZszOBcfPnz48tN98yhgx5b6ZfsqbnX5gTI4btHp89dnxRHA1KgED3BATGdc9NLwIECBAgQIAAAQIECBAgQIBATwT2jYi7ejJAVt+RETG9QGMZhgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSKICAwrgiohiRAgAABAgQIECBAgACBihEQGFcxW6EQAgRKLCAwrsTgpiNQ5QJLly2NR/7+cMx+emY8MfPx+PsTj8brr78e/dfvHxts8I7Y+gNbxx4j94p99t4vGhoairradDodc+e9mAmwm/fS3HjpX/PixXkvxotzX4h/vvB8Zv433nw9E573jg3eEYMHbxjbDt0+dth2h8zfgwYOKkp9SXjcU8/Mjr8/9kg8MeuJePW1V+K111+LV159JVasWB596vpk5k1qGjBgYCZQ74NbDc3UNHSroZnPFft45MkH4/a7/hAnfvqkTGDcs3Oeict+8T+xdOnS2HPPPeOMM86I+vr6WL58+epS2gfGJZ9rDX7LLral3eq4uJYju29HC2sXNNdmvg76tAmMu+CCWPTyy5kAwJ22HxbNK5vjqedmxy+uvjJ22XF4nPipk4pNanwCBPIQEBiXB5amBAgQIECAAAECBAgQIECAAIECCUyLiBEFGmtqRIwp0FiGIUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEiCAiMKwKqIQkQIECAAAECBAgQIECgYgQExlXMViiEAIEyCcx5fN6abJ8ylWBaAgQIECiRQBIYN+XPf4wJp5ydmfHp52bHdy76r1iyZEnsvffecfrpp0d9376rA+NyDYZLBkqlIgmLS/qkkxC4ln+3LiuVSkUS9Jd9tAmXW9uwzbzr7JdKRWNjY8yePTu+2RIY958nnxEjh+2RmeOxmY/G/1zxo9j5Q8Ni/KdPLpGsaQgQKKTAZtsNyVxIWq4mhRzaWAQIECBAgAABAgQIECBAgACBmhLYNyLuKvCKR0bE9AKPaTgCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6KHAk1dGm3fuDB03o4cj6k6AAAECBAgQIECAAAECBCpXIPWe4d6LXrnbozICBIooIDCuiLiGJkCAQIUJPPn0Y/Gzqy6PrbbcOlJ1dfHyK4vi2eeeiRUrVsTuu+++OjCuvj4TGJcJa0sCm5IQuOx1tITDtQmAy/rcmn4tfZJ2yedaj9Z+re3eFkyX1TbTp33QXEtg3FNPPRXf+ta3YsGCBfH+zd4fG71r42jo1y9effWV+Mfzz8WuO4+IEz7x+QrbAeUQIJCLgMC4XJS0IUCAAAECBAgQIECAAAECBAh0LTAtIkZ03SyvFlMjYkxePTQmQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgaILCIwrOrEJCBAgQIAAAQIECBAgQKCCBATGVdBmKIUAgZIKCIwrKbfJCBAgUFaBWc8+GRdd+v2oq6tbHQiXTmfC4ZqammLkyJGrA+P69s18LnO0hL2tCYxLwtvaBbq1RsFl2mSHuyXt1hUW1xoul4TRZbqkV//dMm7r55J5Mv9uHTdrvMbGxpg9e3Z8+9vfjkWLFsX666+fCbpL1pSsLQnAGzFs9zj+mBPL6m1yAgS6JyAwrntuehEgQIAAAQIECBAgQIAAAQIEsgX2i4g7i0QyMiKmF2lswxIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBANwQExnUDTRcCBAgQIECAAAECBAgQqFoBgXFVu3UKJ0CghwIC43oIqDsBAgSqSCAJjPvRT38Qffr0yQSrJcfSpUvXBMaddtppmeC1NYFxpVxbSyDcmilbP273+SQULgmMe+qppzKBcQsXLowBAwZEQ0NDJnwuWddbb70V2269fZw6/vRSrsBcBAgUSEBgXIEgDUOAAAECBAgQIECAAAECBAjUtMC0iBhRJIF7ImJ0kcY2LAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAh0Q0BgXDfQdCFAgAABAgQIECBAgACBqhUQGFe1W6dwAgS6KXDphGvS2V3HnTCmzUiTJ05p83H727s5rW4ECBAgUEaBx595PC66+DtR39AQSfBacmQHxp155pnlC4zL0aU1MG7WrFlxwQUXxKJFizKBcX379s2MkITGrVixIo489ONx0L6H5DiqZgQIlEKgq8eX7W9ffZVyECBAgAABAgQIECBAgAABAgQI5CuwX0TcmW+nPNsn31mcmmcfzQkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgyALpuTPavHG0yNMZngABAgQIECBAgAABAgQIlFRg5uThbebbZnx4T3pJd8BkBAiUS0BgXLnkzUuAAIHyCcyf83Tc9Lep0ae5eU1gXBKwtnLlyth0001jzJgxUVdXl/m49Vjz4DgJmEuno8NvGrXc3tnq2vRN5/ftp0zrlj5JONz8+fPjzjvvjGXLlkVDQ8OaaZcsWRJDtx4aHx61T/StX/v58qmbmQCBVgGBcc4FAgQIECBAgAABAgQIECBAgEBpBKZFxIgiTzU9IkYWeQ7DEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECeAgLj8gTTnAABAgQIECBAgAABAgSqSkBgXFVtl2IJECiggMC4AmIaigABAgQIECBAoEsBgXFdEmlAgAABAgQIECBAgAABAgQIEOixwH4RcWePR8ltgDERMTW3ploRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQCkEBMaVQtkcBAgQIECAAAECBAgQIFAuAYFx5ZI3LwEC5RYQGFfuHTA/AQIECBAgQKC2BATG1dZ+Wy0BAgQIECBAgAABAgQIECBQHoFpETGiRFNPj4iRJZrLNAQIECBAoJoF8g1YHV3Ni1U7AQIECBAgQIAAAQIECBAgQIAAAQIECJRXQGBcef3NToAAAQIECBAgQIAAAQLFFRAYV1xfoxMgUPkCcx6fl678KlVIgAABAgQIECBQKwKbbTcklaw184eDAAECBAgQIECAAAECBAgQIEAgZ4H9I+KOnFsXpuE+ETGlMEMZhQABAgQI9FqBfH8wx/dIeu2pYGEECBAgQIAAAQIECBAgQIAAAQIECBAonsCTV0ab16OHjptRvMmMTIAAAQIECBAgQIAAAQIEyiyQes9wP2dV5j0wPQEC5REQGFced7MSIECAAAECBAisW0BgnDODAAECBAgQIECAAAECBAgQINA9gWkRMaJ7Xbvda3pEjOx2bx0JECBAgEBtCAiMq419tkoCBAgQIECAAAECBAgQIECAAAECBAiUVUBgXFn5TU6AAAECBAgQIECAAAECJRYQGFdicNMRIFAxAgLjKmYrFEKAAAECBAgQIBARAuOcBgQIECBAgAABAgQIECBAgACB/AX2j4g78u9WkB77RMSUgoxkEAIECBAg0DsFBMb1zn21KgIECBAgQIAAAQIECBAgQIAAAQIECFSUgMC4itoOxRAgQIAAAQIECBAgQIBAkQUExhUZ2PAECFSsgMC4it0ahREgQIAAAQIEalJAYFxNbrtFEyBAgAABAgQIECBAgAABAj0UmBYRI3o4Rne7T4+Ikd3trB8BAgQIEKgBAYFxNbDJlkiAAAECBAgQIECAAAECBAgQIECAAIFyCwiMK/cOmJ8AAQIECBAgQIAAAQIESikgMK6U2uYiQKASBC6dcE2bn0cdd8KYNmVNnjilzcftb6+ENaiBAAECBAgQIECgegS6enzZ/vZU9SxNpQQIECBAgAABAgQIECBAgACBsgp8JCJuL2sFEftERNvvLpa5INMTIECAAIEKEhAYV0GboRQCBAgQIECAAAECBAgQIECAAAECBAj0doH03Bn5vi7d20msjwABAgQIECBAgAABAgR6kcDMycPbrGab8eE96b1ofy2FAIGOBQTGOTsIECBAgAABAgRKKSAwrpTa5iJAgAABAgQIECBAgAABAgRqSeDeVYFxu5d5wX+JiL3KXIPpCRAgQIBApQrk+8Y8P8BYqTupLgIECBAgQIAAAQIECBAgQIAAAQIECFSBgMC4KtgkJRIgQIAAAQIECBAgQIBAtwUExnWbTkcCBKpcQGBclW+g8gkQIECAAAECVSYgMK7KNky5BAgQIECAAAECBAgQIECAQFUIfGRVWNztFVLpvhFxd4XUogwCBAgQIFBJAgLjKmk31EKAAAECBAgQIECAAAECBAgQIECAAIFeLiAwrpdvsOURIECAAAECBAgQIECgxgUExtX4CWD5BGpYQGBcDW++pRMgQIAAAQIEyiAgMK4M6KYkQIAAAQIECBAgQIAAAQIEer3AtIgYUSGrnB4RIyukFmUQIECAAIFKEhAYV0m7oRYCBAgQIECAAAECBAgQIECAAAECBAj0cgGBcb18gy2PAAECBAgQIECAAAECNS4gMK7GTwDLJ0Ag5jw+L9+fS6VGgAABAgQIECBAoGgCm203JJUMnvnDQYAAAQIECBAgQIAAAQIECBAg0KHA/hFxR4X57BMRUyqsJuUQIECAAIFyC+T7gzm+R1LuHTM/AQIECBAgQIAAAQIECBAgQIAAAQIEqlDgySujzevRQ8fNqMJVKJkAAQIECBAgQIAAAQIECOQmkHrPcD9nlRuVVgQI9DIBgXG9bEMthwABAgQIECBQ5QIC46p8A5VPgAABAgQIECBAgAABAgQIlExgWkSMKNlsuU00PSJG5tZUKwIECBAgUDMCAuNqZqstlAABAgQIECBAgAABAgQIECBAgAABAuUTEBhXPnszEyBAgAABAgQIECBAgEDpBQTGld7cjAQIVIaAwLjK2AdVECBAgAABAgQIrBYQGOdMIECAAAECBAgQIECAAAECBAh0LXDAqmC227puVpYW+0XEH8sys0kJECBAgEBlCgiMq8x9URUBAgQIECBAgAABAgQIECBAgAABAgR6lUBHgXEzJw9vs86h42a0+djtfLJPCOeHr4/s88H1wfXB9WGtgOuj66Pr41oB9w/uH9w/uH9oFSj3/eM24yPVq57YWwwBAgRyFBAYlyOUZgQIECBAgAABAiUREBhXEmaTECBAgAABAgQIECBAgAABAlUuMC0iRlToGqavCrMbWaG1KYsAAQIECJRDYGqek47Os73mBAgQIECAAAECBAgQIECAAAECBAgQIEAgBMatPgnKHVhgfoFC2ZcjgToCdbLPB9cH1wfXh7UCro+uj66PawXcP7h/cP/Q/fsHgXFeDCFAoNYELp1wTZtfYDzuhDFtCCZPnNLm4/a315qX9RIgQIAAAQIECPRMoKvHl+1vl+reM2+9CRAgQIAAAQIECBAgQIAAgd4r8JGIuL3Cl7dvRNxd4TUqjwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAr1eoH2QXPs31Ls92rzRlk+0eS+H88P5kX2R9PXh6yP7fHB9cH1wfVgr4Pro+uj6uFbA/YP7h3LeP/T6J/gWSIBAzQsIjKv5UwAAAQIECBAgQKCkAgLjSsptMgIECBAgQIAAAQIECBAgQKAXC0yLiBEVvr7pETGywmtUHgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBHq9gMAGgQ3ZJ7lAG4E22eeD64Prg+vDWgHXR9dH18e1Au4f3D+4f3D/0CpQbfePvf4JvgUSIFDzAgLjav4UAECAAAECBAgQKKmAwLiScpuMAAECBAgQIECAAAECBAgQ6KUCB6wKYrutSta2f0TcVSW1KpMAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUJECAuMqclsURYAAAQIECBDotQIC43rt1loYAQIECBAgQIAAAQIECBAgUEKBhyJipxLO15Oppq8KtxvZkwH0JUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQWC0w5/F5aRYECBAgQIAAAQIEKkVgs+2GpJJaMn84CBAgQIAAAQIECBAgQIAAAQIE1giMjYg/VJnH/hFxV5XVrFwCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQMUJCIyruC1REAECBAgQIECgpgUExtX09ls8AQIECBAgQIAAAQIECBAg0InAfRGxa5UJ3RsRe1RZzcolQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECFScgMC4itsSBREgQIAAAQIEalpAYFxNb7/FEyBAgAABAgQIECBAgAABAh0IjI2IP1SpzkdWhcbdWaW1K5sAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUBECAuMqYhsUQYAAAQIECBAg0CIgMM6pQIAAAQIECBAgQIAAAQIECBB4u8CMiBhWBJjmiFjeMm5DRNQVYY6/RcSoIoxrSAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBArxe4dMI16exFjjthTJs1T544pc3H7W/v9UAWSIAAAQIECBAgUFCBrh5ftr89VdDZDUaAAAECBAgQIECAAAECBAgQqF6BAyPi1gKX//dV4XAXRMT1EdH6TcPk9ZijIuK8iNi+wPN9JCLuLPCYhiNAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9HoBgXG9fostkAABAgQIECBQUQIC4ypqOxRDgAABAgQIECBAgAABAgQIVJHAjIgYVqB6n1w1ztcj4v+6GO8Tq4Lkzo+IrQs0798iYlSBxjIMAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBmBATG1cxWWygBAgQIECBAoCIEBMZVxDYoggABAgQIECBAgAABAgQIEKgygQMj4tYC1PxoRFwQEdfnMVYqIo6MiPNW9dshj34dNT1gVWjcHQUYxxAECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgZoREBhXM1ttoQQIECBAgACBihAQGFcR26AIAgQIECBAgAABAgQIECBAoMoE7ouIXXtQcxIU942IuKEHYyRdj1j1x9d7GBx3b0Ts0cM6dCdAgAABAtUoMDXPokd1h2eDAAAgAElEQVTn2V5zAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBGhCY8/i8dA0s0xIJECBAgAABAgSqRGCz7YakklIzfzgIECBAgAABAgQIECBAgAABAjUscGBE3NrN9T/SEhR3Yzf7d9QtCY47f1UA3Ye6Oe4BEXFHN/vqRoAAAQIEqlUg3x/M8T2Sat1pdRMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEiiggMK6IuIYmQIAAAQIECBDIW0BgXN5kOhAgQIAAAQIECBAgQIAAAQK9VOChiNgpz7U93BIUNynPfvk2T4LjzouIHfPsOD0iRubZR3MCBAgQIFDtAgLjqn0H1U+AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqAABgXEVsAlKIECAAAECBAgQWCMgMM7JQIAAAQIECBAgQIAAAQIECBCIOCgiJucBkYTLfSMibsqjTyGaHt4SHJdPsN3YiLi9EJMbgwABAgQIVImAwLgq2ShlEiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQqWUBgXCXvjtoIECBAgAABArUnIDCu9vbcigkQIECAAAECBAgQIECAAIG3CyQBcLmEsD0YEReUISiufcWHRcT5OdY8PSJG2nQCBAgQIFBDAgLjamizLZUAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUGiBSydc0+bnUcedMKbNFJMnTmnzsdv5ZJ8Qzg9fH9nng+uD64Prw1oB10fXR9fHtQI9vX9IFfoBsPEIECBAgAABAgQIECBAgAABAlUicFBETO6i1iQo7hsRcXOFrSkJjjtvVYDdzl3UNTYibq+w2pVDgAABAgSKJSAwrliyxiVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1ICAwDiBNtmneU8DXfQXGJZ9PgnMcn1xfVkr4Pro+uj6uFYg3/sHgXE18KTEEgkQIECAAAECBAgQIECAAIF1CjwUETt1YPNAS1Dc7yvc7qMRcX4nwXHTI2Jkha9BeQQIECBAoFACAuMKJWkcAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEANCgiME+iUfdoLNBJolH0+5Bvo4/xx/jh/1gr4+nH/6v51rUBP7x8ExtXgkxRLJkCAAAECBAgQIECAAAECBOKgiJi8DocZLUFxt1SZ0aEtwXG7rKPusRFxe5WtR7kECBAgQKA7AgLjuqOmDwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQEZAYJxAm+wvhZ4GuugvMCz7fBKY5fri+rJWwPXR9dH1ca1AvvcPAuM8cSFAgAABAgQIECBAgAABAgRqUeChiNgpa+H3twTFrStErjs+n8+z0xV5tu+o+SEtwXHDsho8HBE7F2h8wxAgQIAAgUoWEBhXybujNgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAlQjMeXxevj+XWiUrUyYBAgQIECBAgEA1Cmy23ZBMVpzAuGrcPTUTIECAAAECBAgQIECAAAECPREYFxG3tAxQ6KC41rry/cZgoV+jaR8cd2BE3NYTNH0JECBAgEAVCJT7/rcKiJRIgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQlYDAuK6E3E6AAAECBAgQIFBKAYFxpdQ2FwECBAgQIECAAAECBAgQIFBJAg9FxPJVQfrfiIhbi1RYpQTWHBwR50dEfUTsXKS1GpYAAQIECFSKQKXc/1aKhzoIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgS6ISAwrhtouhAgQIAAAQIECBRNQGBc0WgNTIAAAQIECBAgQIAAAQIECFSwwBYRsU1ETC5yjZUWWJMExz0REc8Wed2GJ0CAAAEC5RSotPvfclqYmwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoJsCAuO6CacbAQIECBAgQIBAUQQExhWF1aAECBAgQIAAAQIECBAgQIAAgYyAwBonAgECBAgQKL3A1DynHJ1ne80JECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgR6scClE65p836QcSeMabPayROntPnY7XyyTwjnh6+P7PPB9cH1wfVhrYDro+uj6+NagZ7eP6R68WNxSyNAgAABAgQIECBAgAABAgQIlEtAYFy55M1LgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOiGgMA4gTbZp01PA130FxiWfT4JzHJ9cX1ZK+D66Pro+rhWIN/7B4Fx3XiQrwsBAgQIECBAgAABAgQIECBAoAsBgXFOEQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAFQm0D4w75cJPtMnkcPs1bd4vw8f5kf3l7evD10f2+eD64Prg+rBWwPXR9bGQ10eBcVX05EKpBAgQIECAAAECBAgQIECAQNUICIyrmq1SKAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgQqCLQJfsrwOBVwKvss8H1wfXB9eHtQKuj66Pro9rBUp9/yAwzrMWAgQIECBAgAABAgQIECBAgEDhBQTGFd7UiAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqUkBgXE1uu0UTIECAAAECBAgQIECAAAECRRYQGFdkYMMTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqBUBgXG1stPWSYAAAQIECBAgQIAAAQIECJRSQGBcKbXNRYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAXCwiM68Wba2kECBAgQIAAAQIECBAgQIBA2QQExpWN3sQECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEepeAwLjetZ9WQ4AAAQIECBAgQIAAAQIECFSGwOV5lvHvebbXnAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBGhEQGFcjG22ZBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhUn4DAuOrbMxUTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFAjAgLjamSjLZMAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgeoTEBhXfXumYgIECBAgQIAAAQIECBAgQIAAAQIECBAgQODtAlPzRBmdZ3vNCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUBYBgXFlYTcpAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUGCBdJ7j+R5JnmCaEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQHgFvhiqPu1kJECBAgAABAgQIECBAgAABAgQIECBAgACBwgoIjCusp9EIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKgQAYFxFbIRyiBAgAABAgQIECBAgAABAgQIECBAgAABAgR6JCAwrkd8OhMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUKkCAuMqdWfURYAAAQIECBAgQIAAAQIECBAgQIAAAQIECOQjIDAuHy1tCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoGgGBcVWzVQolQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEOhEQGOf0IECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgVwoIjOuV22pRBAgQIECAAAECBAgQIECAAAECBAgQIECg5gQExtXcllswAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgdoQEBhXG/tslQQIECBAgAABAgQIECBAgAABAgQIECBAoLcLCIzr7TtsfQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqVEBgXI1uvGUTIECAAAECBAgQIECAAAECRRX4fJ6jX5Fne80JECBAgACBtwsIjHNWECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQKwUExvXKbbUoAgQIECBAgAABAgQIECBAoMwCAmvKvAGmJ0CAAIGaFHD/W5PbbtEECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEer+AwLjev8dWSIAAAQIECBAgQIAAAQIECJReQGBN6c3NSIAAAQIE3P86BwgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6JUCAuN65bZaFAECBAgQIECAAAECBAgQIFBmAYE1Zd4A0xMgQIBATQq4/63JbbdoAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAr1fQGBc799jKyRAgAABAgQIECBAgAABAgRKLyCwpvTmZiRAgAABAlPzJBidZ3vNCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUBYBgXFlYTcpAQIECBAgQIAAAQIECBAg0MsF/p+9O4+1s7zvBP4719c7iyEswTCgssVmSTCY2JCQMbvBAcKadqaNMlKAtH9USdPOjKpREtMo0miUaTUjdQRM1WVmmhBDMDAGAwZMRxA7XAyYxWQjhIADJAECGGwD94wvMXDP9Xbee973vMvzsVRFqZ/3eX6/z+/xOb73+nwjMK7hA9YeAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgX4JCIzrl7RzCBAgQIAAAQIECBAgQIAAgZQEBMalNG29EiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEChQQGBcgbi2JkCAAAECBAgQIECAAAECBJIVEBiX7Og1ToAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCBfAYFx+XrajQABAgQIECBAgAABAgQIECAwIiAwzj0gQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAXAYFxuTDahAABAgQIECBAgAABAgQIECDQISAwzoUgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAXAYFxuTDahAABAgQIECBAgAABAgQIECDQISAwzoUgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAXAYFxuTDahAABAgQIECBAgAABAgQIECDQISAwzoUgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAXAYFxuTDahAABAgQIECBAgAABAgQIECDQIXB1Ro8rM663nAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBRAQExiUyaG0SIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFA/AYFx9ZuZigkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSERAYFwig9YmAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQL1ExAYV7+ZqZgAAQIECBAgQIAAAQIECBAgQIAAAQIECBDYVmBlRpQFGddbToAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgVIEBMaVwu5QAgQIECBAgAABAgQIECBAgAABAgQIECBAIGeBdsb9/IwkI5jlBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAiUI+DDUOW4O5UAAQIECBAgQIAAAQIECBAgQIAAAQIECBDIV0BgXL6ediNAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoCICAuMqMghlECBAgAABAgQIECBAgAABAgQIECBAgAABAj0JCIzric/DBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhUVUBgXFUnoy4CBAgQIECAAAECBAgQIECAAAECBAgQIEAgi4DAuCxa1hIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUBsBgXG1GZVCCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAgZ0ICIxzPQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQaKSAwLhGjlVTBAgQIECAAAECBAgQIECAAAECBAgQIEAgOQGBccmNXMMECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE0hAQGJfGnHVJgAABAgQIECBAgAABAgQIECBAgAABAgSaLiAwrukT1h8BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBRAUExiU6eG0TIECAAAECBAgQIECAAAEChQpckXH3azKut5wAAQIECBDYVkBgnFtBgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAjBQTGNXKsmiJAgAABAgQIECBAgAABAgRKFhBYU/IAHE+AAAECSQp4/01y7JomQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0HwBgXHNn7EOCRAgQIAAAQIECBAgQIAAgf4LCKzpv7kTCRAgQICA9193gAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBRgoIjGvkWDVFgAABAgQIECBAgAABAgQIlCwgsKbkATieAAECBJIU8P6b5Ng1TYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKD5AgLjmj9jHRIgQIAAAQIECBAgQIAAAQL9FxBY039zJxIgQIAAgZUZCRZkXG85AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEShEQGFcKu0MJECBAgAABAgQIECBAgACBhgsIjGv4gLVHgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoF8CAuP6Je0cAgQIECBAgAABAgQIECBAICUBgXEpTVuvBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAoUEBhXIK6tCRAgQIAAAQIECBAgQIAAgWQFBMYlO3qNEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMhXQGBcvp52I0CAAAECBAgQIECAAAECBAiMCAiMcw8IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMhFQGBcLow2IUCAAAECBAgQIECAAAECBAh0CAiMcyEIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMhFQGBcLow2IUCAAAECBAgQIECAAAECBAh0CAiMcyEIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMhFQGBcLow2IUCAAAECBAgQIECAAAECBAh0CAiMcyEIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMhFQGBcLow2IUCAAAECBAgQIECAAAECBAh0CFyd0ePKjOstJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgEQGBcYkMWpsECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNRPQGBc/WamYgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEEhEQGJfIoLVJgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgED9BATG1W9mKiZAgAABAgQIECBAgAABAgQIECBAgAABAgS2FViZEWVBxvWWEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoBQBgXGlsDuUAAECBAgQIECAAAECBAgQIECAAAECBAgQyFmgnXE/PyPJCGY5AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLlCPgwVDnuTiVAgAABAgQIECBAgAABAgQIECBAgAABAgTyFRAYl6+n3QgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqIiAwLiKDEIZBAgQIECAAAECBAgQIECAAAECBAgQIECAQE8CAuN64vMwAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJVFRAYV9XJqIsAAQIECBAgQIAAAQIECBAgQIAAAQIECBDIIiAwLouWtZi9kkMAACAASURBVAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1EZAYFxtRqVQAgQIECBAgAABAgQIECBAgAABAgQIECBAYCcCAuNcDwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEGikgMK6RY9UUAQIECBAgQIAAAQIECBAgQIAAAQIECBBITkBgXHIj1zABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBNAQExqUxZ10SIECAAAECBAgQIECAAAECBAgQIECAAIGmCwiMa/qE9UeAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgUQGBcYkOXtsECBAgQIAAAQIECBAgQIBAoQJXZNz9mozrLSdAgAABAgS2FRAY51YQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINBIAYFxjRyrpggQIECAAAECBAgQIECAAIGSBQTWlDwAxxMgQIBAkgLef5Mcu6YJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQINF9AYFzzZ6xDAgQIECBAgAABAgQIECBAoP8CAmv6b+5EAgQIECDg/dcdIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgkQIC4xo5Vk0RIECAAAECBAgQIECAAAECJQsIrCl5AI4nQIAAgSQFvP8mOXZNEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEGi+gMC45s9YhwQIECBAgAABAgQIECBAgED/BQTW9N/ciQQIECBAYGVGggUZ11tOgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBUgQExpXC7lACBAgQIECAAAECBAgQIECg4QIC4xo+YO0RIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6JeAwLh+STuHAAECBAgQIECAAAECBAgQSElAYFxK09YrAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgQIFBMYViGtrAgQIECBAgAABAgQIECBAIFkBgXHJjl7jBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBPIVEBiXr6fdCBAgQIAAAQIECBAgQIAAAQIjAgLj3AMCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBHIREBiXC6NNCBAgQIAAAQIECBAgQIAAAQIdAgLjXAgCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBHIREBiXC6NNCBAgQIAAAQIECBAgQIAAAQIdAgLjXAgCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBHIREBiXC6NNCBAgQIAAAQIECBAgQIAAAQIdAgLjXAgCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBHIREBiXC6NNCBAgQIAAAQIECBAgQIAAAQIdAldn9Lgy43rLCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIREBgXCKD1iYBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAvUTEBhXv5mpmAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBRAQExiUyaG0SIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFA/AYFx9ZuZigkQIECAAAECBAgQIECAAAECBAgQIECAAIFtBVZmRFmQcb3lBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKEVAYFwp7A4lQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEchZoZ9zPz0gygllOgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEA5Aj4MVY67UwkQIECAAAECBAgQIECAAAECBAgQIECAAIF8BQTG5etpNwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEKiIgMK4ig1AGAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0JOAwLie+DxMgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBVBQTGVXUy6iJAgAABAgQIECBAgAABAgQIECBAgAABAgSyCAiMy6JlLQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECtREQGFebUSmUAAECBAgQIECAAAECBAgQIECAAAECBAgQ2ImAwDjXgwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBRgoIjGvkWDVFgAABAgQIECBAgAABAgQIECBAgAABAgSSExAYl9zINUyAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgDQGBcWnMWZcECBAgQIAAAQIECBAgQIAAAQIECBAgQKDpAgLjmj5h/REgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIVEBgXKKD1zYBAgQIECBAgAABAgQIECBQqMAVGXe/JuN6ywkQIECAAIFtBQTGuRUECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDRSQGBcI8eqKQIECBAgQIAAAQIECBAgQKBkAYE1JQ/A8QQIECCQpID33yTHrmkCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECzRcQGNf8GeuQAAECBAgQIECAAAECBAgQ6L+AwJr+mzuRAAECBAh4/3UHCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBopIDAuEaOVVMECBAgQIAAAQIECBAgQIBAyQICa0oegOMJECBAIEkB779Jjl3TBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBJovIDCu+TPWIQECBAgQIECAAAECBAgQINB/AYE1/Td3IgECBAgQWJmRYEHG9ZYTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgFAGBcaWwO5QAAQIECBAgQIAAAQIECBBouIDAuIYPWHsECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE+iUgMK5f0s4hQIAAAQIECBAgQIAAAQIEUhIQGJfStPVKgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoEABgXEF4tqaAAECBAgQIECAAAECBAgQSFZAYFyyo9c4AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXwFBMbl62k3AgQIECBAgAABAgQIECBAgMCIgMA494AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgVwEBMblwmgTAgQIECBAgAABAgQIECBAgECHgMA4F4IAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgVwEBMblwmgTAgQIECBAgAABAgQIECBAgECHgMA4F4IAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgVwEBMblwmgTAgQIECBAgAABAgQIECBAgECHgMA4F4IAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgVwEBMblwmgTAgQIECBAgAABAgQIECBAgECHwNUZPa7MuN5yAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQSERAYl8igtUmAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQP0EBMbVb2YqJkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgEQGBcYkMWpsECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNRPQGBc/WamYgIECBAgQIAAAQIECBAgQIAAAQIECBAgQGBbgZUZURZkXG85AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEShEQGFcKu0MJECBAgAABAgQIECBAgAABAgQIECBAgACBnAXaGffzM5KMYJYTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFCOgA9DlePuVAIECBAgQIAAAQIECBAgQIAAAQIECBAgQCBfAYFx+XrajQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBiggIjKvIIJRBgAABAgQIECBAgAABAgQIECBAgAABAgQI9CQgMK4nPg8TIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBVAYFxVZ2MuggQIECAAAECBAgQIECAAAECBAgQIECAAIEsAgLjsmhZS4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAbQQExtVmVAolQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEdiIgMM71IECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgkQIC4xo5Vk0RIECAAAECBAgQIECAAAECBAgQIECAAIHkBATGJTdyDRMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIQ0BgXBpz1iUBAgQIECBAgAABAgQIECBAgAABAgQIEGi6gMC4pk9YfwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQSFRAYl+jgtU2AAAECBAgQIECAAAECBAgUKnBFxt2vybjecgIECBAgQGBbAYFxbgUBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAo0UEBjXyLFqigABAgQIECBAgAABAgQIEChZQGBNyQNwPAECBAgkKeD9N8mxa5oAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBA8wUExjV/xjokQIAAAQIECBAgQIAAAQIE+i8gsKb/5k4kQIAAAQLef90BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQaKSAwrpFj1RQBAgQIECBAgAABAgQIECBQsoDAmpIH4HgCBAgQSFLA+2+SY9c0AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgeYLCIxr/ox1SIAAAQIECBAgQIAAAQIECPRfQGBN/82dSIAAAQIEVmYkWJBxveUECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAoRUBgXCnsDiVAgAABAgQIECBAgAABAgQaLiAwruED1h4BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBfgkIjOuXtHMIECBAgAABAgQIECBAgACBlAQExqU0bb0SIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKFBAYFyBuLYmQIAAAQIECBAgQIAAAQIEkhUQGJfs6DVOgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIF8BgXH5etqNAAECBAgQIECAAAECBAgQIDAiIDDOPSBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBcBgXG5MNqEAAECBAgQIECAAAECBAgQINAhIDDOhSBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBcBgXG5MNqEAAECBAgQIECAAAECBAgQINAhIDDOhSBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBcBgXG5MNqEAAECBAgQIECAAAECBAgQINAhIDDOhSBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBcBgXG5MNqEAAECBAgQIECAAAECBAgQINAhcHVGjyszrrecAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFEBATGJTJobRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUD8BgXH1m5mKCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIREBgXCKD1iYBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAvUTEBhXv5mpmAABAgQIECBAgAABAgQIECBAgAABAgQIENhWYGVGlAUZ11tOgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBUgQExpXC7lACBAgQIECAAAECBAgQIECAAAECBAgQIEAgZ4F2xv38jCQjmOUECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECJQj4MNQ5bg7lQABAgQIECBAgAABAgQIECBAgAABAgQIEMhXQGBcvp52I0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgIgIC4yoyCGUQIECAAAECBAgQIECAAAECBAgQIECAAAECPQkIjOuJz8MECBAgQIAAAQIECBAgQIAAAQIECBAgkEVg3bXR8X3p2ZdHx7/N9/t8Rt8n98Ofj9H3weuD1wevDx8IeH30+uj18QMB7w/eH7w/eH94T6Bq749Zvla2lgABAgQIECBAgAABAgQIEChWQGBcsb52J0CAAAECBAgQIECAAAECBAgQIECAAAECBPojIDCuP85OIUCAAAECBAgQIECAAAECBAgQIECAAIGIEOgh0GP0H4SqBTq4n+6n+/mBgD+fAslG/3nw+uj10euj18f3BLw/eH/w/vCBwK7eH30TgAABAgQIECBAgAABAgQIEKiOgMC46sxCJQQIECBAgAABAgQIECBAgAABAgQIECBAgMD4BQTGjd/OkwQIECBAgAABAgQIECBAgAABAgQIECCQUWDsB+pnLRrq2OHJZXM7/rvf5zP6Qrgf/nyMvg9eH7w+eH34QMDro9dHr48fCHh/8P7g/cH7w3sCZb0/jj13bNBixi+jLSdAgAABAgQIECBAgAABAgRyEBAYlwOiLQgQIECAAAECBAgQIECAAAECBAgQIECAAIHSBQTGlT4CBRAgQIAAAQIECBAgQIAAAQIECBAgQCAdAYFxAm1G33aBLgJdRt+HsgI93qvB+V6fvD59IOD12euz1+cPBLw/eH/w/uD94T2Bbt8fBcal8z0OnRIgQIAAAQIECBAgQIBAfQQExtVnViolQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEdiwgMM7tIECAAAECBAgQIECAAAECBAgQIECAAIG+CbTXD2X9vnTfanMQAQIECBAgQIAAAQIECBDoVUBgXK+CnidAgAABAgQIECBAgAABAvkLCIzL39SOBAgQIECAAAECBAgQIECAAIErMhJck3G95QQIECBAgMC2Alk/mOdnJG4RAQIECBAgQIAAAQIECBAgQIAAAQIECIxbQGDcuOk8SIAAAQIECBAgQIAAAQI1EBAYV4MhKZEAAQIECBAgQIAAAQIEkhPwYajkRq5hAgQIECBAgAABAgQIECBAoA8CAmv6gOwIAgQIECAwRsD7rytBgAABAgQIECBAgAABAgQIECBAgAABAoULrLs2Or4fPWvRUOFnOoAAAQIECBAgQIAAAQIECJQl0Jo512fRy8J3LgECBAgQIECAAAECBAgQGCPgi3RXggABAgQIECBAgAABAgQIECCQv4DAmvxN7UiAAAECBHYl4P13V0J+nwABAgQIECBAgAABAgQIECBAgAABAgR6FhAY1zOhDQgQIECAAAECBAgQIECgRgIC42o0LKUSIECAAAECBAgQIECAQOMFBMY1fsQaJECAAAECBAgQIECAAAECBEoQEFhTArojCRAgQCB5Ae+/yV8BAAQIECBAgAABAgQIECBAgAABAgQIECheQGBc8cZOIECAAAECBAgQIECAAIHqCAiMq84sVEKAAAECBAgQIECAAAECBATGuQMECBAgQIAAAQIECBAgQIAAgfwFBNbkb2pHAgQIECCwK4GVu1ow5vcXZFxvOQECBAgQIECAAAECBAgQIECAAAECBAgQCIFxLgEBAgQIECBAgAABAgQIpCQgMC6laeuVAAECBAgQIECAAAECBKouIDCu6hNSHwECBAgQIECAAAECBAgQIFBHAYFxdZyamgkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINClQHv9UNafC3e50PqB0gAAIABJREFUs2UECBAgQIAAAQIECBAgQKB8gSeXze0oYvbl4TPp5Y9FBQQIECBAgAABAgQIECCQuIAvzhO/ANonQIAAAQIECBAgQIAAAQIEChHI+sEA36MpZAw2JUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQDECAuOKcbUrAQIECBAgQIAAAQIECFRDQGBcNeagCgIECBAgQIAAAQIECBAgMFrAh5HdBwIECBAgQIAAAQIECBAgQIBA/gIC4/I3tSMBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQqIyAwrjKjUAgBAgQIECBAgAABAgQIFCAgMK4AVFsSIECAAAECBAgQIECAAIEeBQTG9QjocQIECBAgQIAAAQIECBAgQIDAdgQExrkWBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQaLCAwLgGD1drBAgQIECAAAECBAgQIBAC41wCAgQIECBAgAABAgQIECBQPQGBcdWbiYoIECBAgAABAgQIECBAgACB+gsIjKv/DHVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBbQTWXRsdPw+etWiIEgECBAgQIECAAAECBAgQaKxAa+Zcn0Vv7HQ1RoAAAQIECBAgQIAAAQJ1E/BFet0mpl4CBAgQIECAAAECBAgQIECgDgIC4+owJTUSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgo4DAuIxglhMgQIAAAQIECBAgQIBArQUExtV6fIonQIAAAQIECBAgQIAAgYYJCIxr2EC1Q4AAAQIECBAgQIAAAQIECFRCQGBcJcagCAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECOQrIDAuX0+7ESBAgAABAgQIECBAgEC1BQTGVXs+qiNAgAABAgQIECBAgACBtAQExqU1b90SIECAAAECBAgQIECAAAEC/RG4OuMxV2ZcbzkBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIlCAgMK4EdEcSIECAAAECBAgQIECAQGkCAuNKo3cwAQIECBAgQIAAAQIECBDYRkBgnEtBgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQyCLTXD7UzLLeUAAECBAgQIECAAAECBAjUSuDJZXM76p19efhMeq0mqFgCBAgQIECAAAECBAgQaKKAL86bOFU9ESBAgAABAgQIECBAgAABAjsSOCwijomImxIjuiAiHo2IpxLrW7sECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAoREBgXCGsNiVAgAABAgQIECBAgACBiggIjKvIIJRBgAABAgQIECBAgAABAgRGCQiMcx0IECBAgAABAgQIECBAgACB1AQeioi3ImJxRCxrePOfjoivRcTglpC8OQ3vVXsECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDom4DAuL5RO4gAAQIECBAgQIAAAQIEShAQGFcCuiMJECBAgAABAgQIECBAgMAuBATGuSIECBAgQIAAAQIECBAgQIBAagLnjgqKG9oaHPd/G4Zw3taguBO29jXS820N61E7BAgQIEBgrMDKjCQLMq63nAABAgQIECBAgAABAgQIECBAgAABAgQIvC8gMM5lIECAAAECBAgQIECAAIEmCwiMa/J09UaAAAECBAgQIECAAAECdRUQGFfXyambAAECBAgQIECAAAECBAgQ6EXgoYg4btQGTQmOGxsUN9LiwxExpxcszxIgQIAAgZoItDPW6WckGcEsJ0CAAAECBAgQIECAAAECBAgQIECAAIGIdddGx/ejZy0a+XGzXwQIECBAgAABAgQIECBAoJkCrZlz/TurZo5WVwQIECBAgAABAgQIECBQQwFfpNdwaEomQIAAAQIECBAgQIAAAQIEehY4NyKWbWeXByNicUTc0vMJ/d3g/Ij4WkQcv51jR3q9rb/lOI0AAQIECJQiIDCuFHaHEiBAgAABAgQIECBAgAABAgQIECBAIC0BgXFpzVu3BAgQIECAAAECBAgQSF1AYFzqN0D/BAgQIECAAAECBAgQIFAlAYFxVZqGWggQIECAAAECBAgQIECAAIF+CjwUEcft4MA1W4Pjbu5nQeM464KtQXFzdvDswxGxo98bx3EeIUCAAAEClRYQGFfp8SiOAAECBAgQIECAAAECBAgQIECAAAECzRAQGNeMOeqCAAECBAgQIECAAAECBLoTEBjXnZNVBAgQIECAAAECBAgQIECgHwIC4/qh7AwCBAgQIECAAAECBAgQIECgigLnRMStuyhsJDjuqi2hazdVrIHPRMRXuwiDG+lxecVqVw4BAgQIEChKQGBcUbL2JUCAAAECBAgQIECAAAECBAgQIECAAIH3BQTGuQwECBAgQIAAAQIECBAgkJKAwLiUpq1XAgQIECBAgAABAgQIEKi6gMC4qk9IfQQIECBAgAABAgQIECBAgECRAg9FxHFdHDCybnEFguMu3BoU103ND3cRKNdF65YQIECAAIHaCAiMq82oFEqAAAECBAgQIECAAAECBAgQIECAAIH6C7TXD2X9vnT9m9YBAQIECBAgQIAAAQIECCQj8OSyuR29zr48fCY9melrlAABAgQIECBAgAABAgSqKuCL86pORl0ECBAgQIAAAQIECBAgQIBAPwTOiYhbMxw0EsI2Ehy3NMMzeSwdCYr7WkR8LMNmI70tz7DeUgIECBAgUHeBrB/M8zOSuk9c/QQIECBAgAABAgQIECBAgAABAgQIEChRQGBcifiOJkCAAAECBAgQIECAAIHCBQTGFU7sAAIECBAgQIAAAQIECBAgkFnAh6Eyk3mAAAECBAgQIECAAAECBAgQaJjAQ1sC4I7L2NNIcNxVWwLcbsz4XNblF20NivtoxgdH6puT8RnLCRAgQIBA3QUExtV9guonQIAAAQIECBAgQIAAAQIECBAgQIBAjQQExtVoWEolQIAAAQIECBAgQIAAgcwCAuMyk3mAAAECBAgQIECAAAECBAgULiAwrnBiBxAgQIAAAQIECBAgQIAAAQIVF1gYEbeNs8ZHImLxdoLjrsi43zVj1l8cEV+NiKxBce9tc05ELM9Yg+UECBAgQKDuAgLj6j5B9RMgQIAAAQIECBAgQIAAAQIECBAgQKBGAgLjajQspRIgQIAAAQIECBAgQIBAZgGBcZnJPECAAAECBAgQIECAAAECBAoXEBhXOLEDCBAgQIAAAQIECBAgQIAAgRoIPBQRx/VQ50hw3FVbAt6+t3WP8QTWjHyf5r2guGN7qGX1lrC4+T0871ECBAgQIFBXgfG8/9a1V3UTIECAAAECBAgQIECAAAECBAgQIECAQEkC666Nju9Hz1o0VFIljiVAgAABAgQIECBAgAABAsULtGbO9Vn04pmdQIAAAQIECBAgQIAAAQIEuhLwRXpXTBYRIECAAAECBAgQIECAAAECDRdYGBG35dDj2q3Bcddn3OuyiPhqRByT8bntLR/p5fYc9rEFAQIECBCom4DAuLpNTL0ECBAgQIAAAQIECBAgQIAAAQIECBCooYDAuBoOTckECBAgQIAAAQIECBAgMG4BgXHjpvMgAQIECBAgQIAAAQIECBDIXUBgXO6kNiRAgAABAgQIECBAgAABAgRqKvD9iJhf09rfK3vVlrC4k2reg/IJECBAgMB4BQTGjVfOcwQIECBAgAABAgQIECBAgAABAgQIECDQtYDAuK6pLCRAgAABAgQIECBAgACBBggIjGvAELVAgAABAgQIECBAgAABAo0REBjXmFFqhAABAgQIECBAgAABAgQIEOhR4OwtgXHLe9yj7McXbgmMu73sIpxPgAABAgRKEliZ8dwFGddbToAAAQIECBAgQIAAAQIECBAgQIAAAQIEQmCcS0CAAAECBAgQIECAAAECKQkIjEtp2nolQIAAAQIECBAgQIAAgaoLCIyr+oTUR4AAAQIECBAgQIAAAQIECPRT4L6IOLmfB+Z41v0R8Ykc97MVAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECOxAoL1+qA2HAAECBAgQIECAAAECBAg0VeDJZXM7Wpt9efhMelOHrS8CBAgQIECAAAECBAgQqI2AL85rMyqFEiBAgAABAgQIECBAgAABAn0QODsilvfhnCKOGKn9jiI2ticBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIdAoIjHMjCBAgQIAAAQIECBAgQKDJAgLjmjxdvREgQIAAAQIECBAgQIBAXQUExtV1cuomQIAAAQIECBAgQIAAAQIEihK4PyJOKmrzgvb9/pawuJML2tu2BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIDBGQGCcK0GAAAECBAgQIECAAAECTRYQGNfk6eqNAAECBAgQIECAAAECBOoqIDCurpNTNwECBAgQIECAAAECBAgQIFCUwFlbAuNuL2rzgvY9e0tg3B0F7W1bAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEBgjIDDOlSBAgAABAgQIECBAgACBJgsIjGvydPVGgAABAgQIECBAgAABAnUVEBhX18mpmwABAgQIECBAgAABAgQIEChSYFVEzCvygBz3Xh0R83Pcz1YECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsAOBdddGe/RvzVo0xIoAAQIECBAgQIAAAQIECDRWoDVzrs+iN3a6GiNAgAABAgQIECBAgACBugn4Ir1uE1MvAQIECBAgQIAAAQIECBAg0A+BsyLi9n4clMMZZ0fEHTnsYwsCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ2IWAwDhXhAABAgQIECBAgAABAgRSEhAYl9K09UqAAAECBAgQIECAAAECVRcQGFf1CamPAAECBAgQIECAAAECBAgQKEtgVUTMK+vwLs9dHRHzu1xrGQECBAgQIECAAIF3BW79t+fsMbBx0sejFSe0o3XgQLT3bbda+0TEvq12a5/haG865/qlhy2/5DPtLGQLr1+a6WeP9t+5Lh8+owX8+dr5feDDZ7SA189mv36uuPjiQ8+44YansvwdxVoCBAgQIECAAAECeQsIjMtb1H4ECBAgQIAAAQIECBAgUGUBgXFVno7aCBAgQIAAAQIECBAgQCA1gUwf2kgNR78ECBAgQIAAAQIECBAgQIBA0gJnRMSdFRc4MyJWVLxG5REgQIAAAQIECJQscOelnz747Zh4yUB7+MR2tOZGxOE7L6n9/MLrbzpA4M7OlfjwGS0gsGzn94EPn9ECXj+9fvb4+vlkRBwYESvbrda/tKJ978IlS39Q8l+3HE+AAAECBAgQIJCYgMC4xAauXQIECBAgQIAAAQIECCQuIDAu8QugfQIECBAgQIAAAQIECBColIDAuEqNQzEECBAgQIAAAQIECBAgQIBAxQRWRcS8itX0XjmrI2J+RWtTFgECBAgQIECAQIUEbr/o/E+1Bwbu7bakwalT45Nf/kp75Te/kelniQv+8j91e8S761Z+8xuZ1tt/51x8+IwW8Odr5/eBD5/RAl4/K/f62Y6It1rR2tCO9ivtiF+1WvFcROvpaLd/vPKb3/jbsRW3I95sRfvudqt1/cCUN5ac/b/u2JDpLxkWEyBAgAABAgQIEBinQHv90MjfX/0iQIAAAQIECBAgQIAAAQKNFHhy2cj/JuEHv2ZfHpn+HUkjUTRFgAABAgQIECBAgAABAgRKFvDFeckDcDwBAgQIECBAgAABAgQIECBQaYEzI+KOilZ4VkTcWdHalEWAAAECBAgQIFAhgdeXLfvwff/4d8+0h4cndlPWhEmT4pQ///cC3XaBJXBq50B8+IwWEMi18/vAh89oAa+fO74Pr7/4Ygz9z2t28Q7d3tiKgduiNXzdpOl73XTqP/zDxm7+/mMNAQIECBAgQIAAgfEICIwbj5pnCBAgQIAAAQIECBAgQKAuAgLj6jIpdRIgQIAAAQIECBAgQIBASgIC41Katl4JECBAgAABAgQIECBAgACB8Qisioh543mwwGdWR8T8Ave3NQECBAgQIECAQM0FNqxZM3NCDF8SrfhMtOPUR6/7Tvzmpz95v6vJu+8eexx4UOy2//4xafr0mDh9+rv/OWna9JgyY0bNu1c+AQIECBAg0ESBV37+83h0yXXxzubN3bb3xuS348BTly59pdsHrCNAgAABAgQIECCQRUBgXBYtawkQIECAAAECBAgQIECgbgIC4+o2MfUSIECAAAECBAgQIECAQAoCAuNSmLIeCRAgQIAAAQIECBAgQIAAgV4EzoiIO3vZoIBnz4yIFQXsa0sCBAgQIFBngZUZi1+Qcb3lBGohsHHNmvOjNfz5aMeFowt+8fHH47Xn18ceB/6r2OOgA2PybrvXoh9FEiBAgAABAgTGCmx48cX47XPPxqvPPRevrn8u3vj1r3eA1Lp54fU3XkCQAAECBAgQIECAQFECAuOKkrUvAQIECBAgQIAAAQIECFRBQGBcFaagBgIECBAgQIAAAQIECBAg0CkgMM6NIECAAAECBAgQIECAAAECBAjsWmBVRMzb9bK+rFgdEfP7cpJDCBAgQIBAvQTaGcv1M5KMYJZXR+C7l1469bIlS94cXdHGNUN/3GrFn7TbcUx1KlUJAQIECBAgQKB4gU2vvRrPr10bLzy6Nt546aX3D/zoZb//s70PP/y/TDl+7v8ovgonECBAgAABAgQIpCSw7tro+H70rEVDKbWvVwIECBAgQIAAAQIECBBITKA1c65/Z5XYzLVLgAABAgQIECBAgAABAtUV8EV6dWejMgIECBAgQIAAAQIECBAgQKA6AqdHxIqKlHNmhWqpCIkyCBAgQIDAuwIC41yEJASWX3T+qTEw8Hftdutvzrnhxv82EhQXEX8REb+XBIAmCRAgQIAAAQI7Efjts8/GC489GhtefCHmfO7z7638WUQIjnNzCBAgQIAAAQIEchMQGJcbpY0IECBAgAABAgQIECBAoAYCAuNqMCQlEiBAgAABAgQIECBAgEAyAgLjkhm1RgkQIECAAAECBAgQIECAAIEeBe6LiJN73KPXx++PiE/0uonnCRAgQIBAQwUExjV0sNr6ncDtf3TW9Hhz+rfa0b4iIlqtgYE3Tv7TL/1k4rRpH2VEgAABAgQIECDQhUArHor2wNenHH/8zV2stoQAAQIECBAgQIDADgUExrkcBAgQIECAAAECBAgQIJCSgMC4lKatVwIECBAgQIAAAQIECBCouoDAuKpPSH0ECBAgQIAAAQIECBAgQIBAVQTO2BLWdmfJxZwZEStKrsHxBAgQIECgqgIC46o6GXX1LLD8ovNPjYGBf4qIg0ZvduAJc+OIsxf2vL8NCBAgQIAAAQIpCbRarf/9drv1H6Yff/z6lPrWKwECBAgQIECAQH4CAuPys7QTAQIECBAgQIAAAQIECFRfQGBc9WekQgIECBAgQIAAAQIECBBIR0BgXDqz1ikBAgQIECBAgAABAgQIECDQu8CqiJjX+zbj2mF1RMwf15MeIkCAAAECaQgIjEtjzsl1ufziCxZHq/XV7TU+8/gT4siF5yRnomECBAgQIECAQK8CrWi93IrhL006/sSRUN645bzzpp13yy1v9Lqv5wkQIECAAAECiQp8LCIeSbH39vqhrN+XTpFJzwQIECBAgAABAgQIECBQU4Enl83tqHz25eEz6TWdpbIJECBAgAABAgQIECBAoDkCvjhvzix1QoAAAQIECBAgQIAAAQIECBQvcHpErCj+mO2ecEZE3FXS2Y4lQIAAAQJ1EMj6wTw/I6nDVBOu8Z7Pf37Kptdf+U5EXDCWYcqeM2LWp8+LGYcckrCQ1gkQIECAAAECOQi029dMOeHEK5dfcuFNrWjvuXHz8HkX3HzzaznsbAsCBAgQIECAQEoCF0bEX0XEVRGxJCKyfq+2tlYC42o7OoUTIECAAAECBAgQqKxAu92O53/1Uvz82V/GwTP3j30+NCMGJ0yIgYGByta83cLefidicEK9albtNgIC41wKAgQIECBAgAABAgQIECBQPQEfhqreTFREgAABAgQIECBAgAABAgQIVFtgVUTM63OJqyNifp/PdBwBAgQIEKibQNYPIfoZSd0mnFC9t1944X7tCe3lETFnbNsHzj0xDjvt9BgYHExIRKsECBAgQIAAgeIEfrzijuee+8EPDvzdCa3H32kNnrFoyZLnizvRzgQIECBAgACBxgmMfK/1sYg4KiIe3/KXqsURcX0KwXEC4xp3lzVEgAABAgQIECBAoHSBZ3/5Yjz07IZY9cSz8YsfPRof3n0wzvrk8THn6CNi992mxaSJE0uvUQHpCAiMS2fWOiVAgAABAgQIECBAgACB+gj4MFR9ZqVSAgQIECBAgAABAgQIECBAoBoCp0fEij6XckZE3NXnMx1HgAABAgTqJiAwrm4TU+92BZZdesGxg+2B29rR3hpa8rtlg5Mnx1EXXhR7H3oYOQIECBAgQIAAgZwEXn76Z/HIP/+fzt3a8XQMDJ+5cMnNP8npGNsQIECAAAECBFIQuCgibhjV6EiA3FVND44TGJfC1dYjAQIECBAgQIAAgf4KrP3RM7Hv4SfExv0Ojkce+2X88JGH4+H77ogpm38VnzrhI3HaSXNi/30/FFMmT+pvYU5LUkBgXJJj1zQBAgQIECBAgAABAgQIVFxAYFzFB6Q8AgQIECBAgAABAgQIECBAoJICqyJiXp8qWx0R8/t0lmMIECBAgECdBQTG1Xl6an9fYPnFn/mnaMUfjSaZvu++ccwll8XUvfYiRYAAAQIECBAgkLPAEzd+L15c98TYXV9qtdvnnn3DTSPfm/OLAAECBAgQIEBg1wIj/yZ9JCTuqDFLR/5/i7eGyWX9Hu6uTy1pxbpro6OXWYuGSqrEsQQIECBAgAABAgQINE3gjTc3xqqnXomBg+bEpGl7x5sb3ozXN2yI9b94Lh5ZfU9sWL8u/vWcQ+PsU06IA/bfLwYHJzSNQD8VFGjNnOuz6BWci5IIECBAgAABAgQIECBAIE0BX6SnOXddEyBAgAABAgQIECBAgAABAr0JnBoRd/e2RddPnxYR93S92kICBAgQIJCuQNYPG/oZSbp3pdKd37NgweDgRw5/YMNvfn3cSKH7fmRWzDr/gpgwcWKl61YcAQIECBAgQKDOAj9aflusX/Pg2BY2TJjQOvHM625cV+fe1E6AAAECBAgQ6KPAxRFx/Q7Oe3RrcNz3IjrD1vpYX25HCYzLjdJGBAgQIECAAAECBAhsR2DT5s2x4uH18fbeR8bg5Knx1lubYuLEqfFWeyBeeOHX8dD9d0W89ONYdMox8amPz4k999iNI4FCBQTGFcprcwIECBAgQIAAAQIECBAgkEnAh6EycVlMgAABAgQIECBAgAABAgQIEHhfYFVEzCvYY3VEzC/4DNsTIECAAIGmCAiMa8okE+9j45qhP46Iv33iphtjt/32j4NPOjlxEe0TIECAAAECBPoj8IvVq+Knd60Ye9gzgxOHP37Gt29+oT9VOIUAAQIECBAgUGuBkX+X/lhEHLWTLhoRHCcwrtb3VPEECBAgQIAAAQIEaiHw+oY34o4HfhZv73VETJy2W2x84/WY0GrFxKm7xfDgtHj2mWfj4X+5OeYeMjk+++nTYp+9Z9SiL0XWU0BgXD3npmoCBAgQIECAAAECBAgQaKaAwLhmzlVXBAgQIECAAAECBAgQIECAQPECp0XEXQUfc3pE3F3wGbYnQIAAAQJNEViZsZEFGddbTqBwgc1rHvjccLT+sfCDHECAAAECBAgQILBdgV+tWxcjwb3t4eH3f78dMdTesOmT59522yZsBAgQIECAAAECuxS4dEtg3Hd3uSpibUQsjogbIyLr/xhIF9sXu0RgXLG+didAgAABAgQIECBQhMCmzZvjp08/29XWhxx0QEyfNrWrtUUueumVV+POB56KzbsfElP3mBGb3nwzht9+KyZOHIwpe+4XGzYPxIP33hKHTX05vvAHi2LypElFlmPvhAUExiU8fK0TIECAAAECBAgQIECAQOUEBMZVbiQKIkCAAAECBAgQIECAAAECBGoksCoi5hVU7+qImF/Q3rYlQIAAAQIECBComMDGNWvOiRi+tWJlKYcAAQIECBAgkJzASGjc4zfe0Nl3K25YuGTpJclhaJgAAQIECBAgkF1g5N+mP7YlNO6oLh99ZGtw3NI6Bse11w/VLuyuy7lYRoAAAQIECBAgQKBxAktuWRGXXfkfu+rrrxf/WXzp8n/T1dqiFz3/4m/irjXPxFvTZsbUPWfEpk2bIobfefdLqOl77hPDg9Njzcql8dmTD47jjj6i6HLsn5jAk8vmdnQ8+/LwmfTE7oB2CRAgQIAAAQIECBAgQKB6Ar44r95MVESAAAECBAgQIECAAAECBAjUR+DUiLi7oHJPL3Dvgkq2LQECBAgQIECAwHgENj364Ox4K+5rR3uv8TzvGQIECBAgQIAAgXwFnrrn7njm+/eP3fTLC69f+jf5nmQ3AgQIECBAgEAjBS6LiOsydlbL4DiBcRmnXODyl3/7ajz18+d6PmFwcEJMmTw5dps+NT687z4xYcJAz3vagAABAgQIECBAoBoC1918R/z+F/+yq2L+69f/LL58RXGBcQ+uXffu3zuP/sihXdXz6msb4q4f/DBe2LR77LH/AfH2W29Hu92O9vA7MW2PGRExEM8/fGv8wbmfiH0/5J8ddIVqUVcCAuO6YrKIAAECBAgQIECAAAECBAj0VUBgXF+5HUaAAAECBAgQIECAAAECBAg0UGBVRMzLua/VETE/5z1tR4AAAQIECBAgUFGBN9cgnkRvAAAgAElEQVQMrW5FfLyi5SmLAAECBAgQIJCkwNrvfDteeuqnW3tv/f3k1uCfnrpkyetJYmiaAAECBAgQIJBNYOTfpz8WEUdle+zd1Q9HxOKIuCki2uN4vq+PCIzrK/dOD1v8rWvi61v+L+9fH519RBx15O/FsbMPj/PO/NS7/+kXAQIECBAgQIBAPQWqEBg3PDwcj/34F3HHg0/HTx57IL7w2XNi7seO7gr0Ny//Nr5959o44KiT4vXXXns3MO7dL5tarZgxY694+N6b4sJTjoqPHX1kV/tZRKAbAYFx3ShZQ4AAAQIECBAgQIAAAQIE+isgMK6/3k4jQIAAAQIECBAgQIAAAQIEmidwakTcnXNbpxewZ84l2o4AAQIECBAgQCAPgY0PPnB1tFpX5LGXPQgQIECAAAECBPITeGfz5lj7nX+Og0/6RHzo8MOvmXLCiVfmt7udCBAgQIAAAQKNF7h0S2Dcd3vociQ47utbg+N62KbYRwXGFeubZfeiAuPG1nDkoQfHV774h/HvPnt+TJw4mKVEawn0VWAkQGbWKRfHK6/uOvf8pBOOjaV//62+1ucwAgTyE3j8h0/FaZd+sasN//yLfxh/8Sef62qtRfkKfOErfxW33Pn/drnppImD8cS918fuu03b5VoLsgtUITDurbffiQeffjUOOPTY+M///er/z959QEdVrVEA3jOT3khCD733HoqANJFepAqIKCAgiCgIiEpHEbGggFQpUqX3LkVAWkKvoddAQif0lPs4g+ElZJI5N5k++67Fej7y33P+8507dyZhZgdPLuzDJ51aI7ik8dA4ETY3f8NepCvwJm5G3HgFIP7ex9sHEecPo1weP1QuWxQaDT82rP4K4RmGBBgYx+uCAhSgAAUoQAEKUIACFKAABShgewL8yY/t7Qk7ogAFKEABClCAAhSgAAUoQAEKUMD+BPYAqGiitvcCqGSisTgMBShAAQpQgAIUoIANCzw/ENIhDpo/bbhFtkYBClCAAhSgAAUo8J+AFsoHbmXLzyIIBShAAQpQgAIUoICUgHiP+mkA+aWqky86+OJLIjhuZRrHMenpJ6dCSThg4YahJh2fg6kXsFRgXHxnIjhu3Hf9Uac6/2lf/W7xDEsIiOAYXfYKUlMVLZgXx7elJeNTahoWUYACZhL4N+QwqjbtLDV6767t8MvQPlK1LDKtQJ02n2DTdvG2QONH5NFNyJg+wHghK1QL2EJgXExsLP45fg3FazfDjpDz2LJwOp6GH0L3Dq1QvkzxFNcUF6fgr82HEFiwMq5evACtVquvV5RYuLp6QPf8PjyfhaNu1TLw9vJU7cMTKJCSgCYomJ9F5yVCAQpQgAIUoAAFKEABClCAAhSwEQF+k24jG8E2KEABClCAAhSgAAUoQAEKUIACFLBrgRoAtppoBbVMOJaJWuIwFKAABShAAQpQgAKmFnh04ECQC5RjChS+29/UuByPAhSgAAUoQAEKmEFAA83dGGiKe5ctG26G4TkkBShAAQpQgAIUcESBNgDmm2hhBwAMs5XgOAbGmWhXTTiMpQPj4lsf/11/fNKxtQlXwqEoYBoBBsaZxpGjUMAeBBgYZw+7BDAwzjb2yRYC4xRFwd5j5/E8bw08exqLiOsR2L1+ER5fO4yP27dExXIlksU6e+EqTj7yQ4zWG7cibkCr04m0OH29VucK17gneHbjJJq8VZ6hg7ZxyTlUFwyMc6jt5GIoQAEKUIACFKAABShAAQpQwM4FGBhn5xvI9ilAAQpQgAIUoAAFKEABClCAAhSwGYE9ACqmsRvxa0T5K8jTiMjTKUABClCAAhSggK0KbGrVKl2sEr0WsZoeNQcP7KsoSntb7ZV9UYACFKAABShAAQokFdBoNHPcy5R7nzYUoAAFKEABClCAAlICWgBhAPJLVcsVieC4oQBWyZWbp4qBceZxTcuo1gqMEz0P7tMFw/p2S0v7PJcCJhdgYJzJSTkgBWxWgIFxNrs1iRpjYJxt7JMtBMYJies372HTmafIkrcYou7dx6MnzxGydSUeXTmE4f26I3tQ5iRgV69HYun2YyhRvRkunD4NJS4WL6PixKFAq9XBJe4p7l0+gpZ130BQ5oy2gc4uHEaAgXEOs5VcCAUoQAEKUIACFKAABShAAQo4gAAD4xxgE7kEClCAAhSgAAUoQAEKUIACFKAABWxCoAaArWnspJYJxkhjCzydAhSgAAUoQAEKUMBcAutbNhsFKF9Co4nN+UZlXZ43q0Ejfus3DwpQgAIUoAAFKEABOxLQNvUoW3alHTXMVilAAQpQgAIUoIA1BdoCmGeGBva/GFMEx602w9hGh2RgnFEiixdYMzBOLHbLokmoWSXY4uvmhBRIToCBcbw2KOA8AgyMs4+9ZmCcbeyTrQTGPXn2HMv/OYKAIjVw59ZNaHWueBajwYHta+Dz+BK6f9gK2bK+DI2LjonBzn2HsO3QBVRt2gG3I27j4f070Gi1UBRAI2LjFAVanQ6xTx7gxtn96PBOTeTKHmQb6OzCYQQYGOcwW8mFUIACFKAABShAAQpQgAIUoIADCDAwzgE2kUugAAUoQAEKUIACFKAABShAAQpQwGYE9gComMpu9gKolMpzeRoFKEABClCAAhSggI0LrGnVKotOeX4B0HjEt5qneg3kqlLVxjtnexSgAAUoQAEKUIACiQQ0OOhRJrgsVShAAQpQgAIUoAAFpAS0AMIA5JeqVl8UCmCYtYLjlPBQRX3LPMMcAtYOjMueNRNObl8MH28vcyyPY1JAtQAD41ST8QQK2K0AA+PsY+sYGGcb+2QrgXHiefromSs4cNMDPunS4+GDu/Dw8kW04opNS2Yg+u4l5MyWWR8IF3E3ChnylUCdpq3x4M593LkRDp2LTh8SJw5FUSA+IKzVueDB9Qu4deUEurZtiFw5GBhnG1ed/Xdxak3iYOwiXfSXHA8KUIACFKAABShAAQpQgAIUoAAFrCjAb86tiM+pKUABClCAAhSgAAUoQAEKUIACFHA4geoAtqVyVTXTcG4qp+RpFKAABShAAQpQgAKWEtjQ8p3fFaBH/Hw6V1dU6tkLrp6elmqB81CAAhSgAAUoQAEKmE6gh0fZ4ImmG44jUYACFKAABShAAYcWaAdgrplXKILjhgJYY+Z5Eg3PwDhLaqc8l7UD40R3E74fgO4ftLQdFHbi1AIMjHPq7efinUyAgXH2seEMjLONfbKVwDihcT/qEeZvOoSsRavg9q2bUOKi4e7pg4ex7jh/NgyP7t2CX2B65C9YGEFBWXDvxlXcv30LWm2CjwMrCkRsnFajgRIXh0tH9yL6USQ+7dQaObJltg10dmH3AgyMs/st5AIoQAEKUIACFKAABShAAQpQwAEFGBjngJvKJVGAAhSgAAUoQAEKUIACFKAABShgVYHNAGqp7GAHgGoqz2E5BShAAQpQgAIUoICdCGxo1TiPoujOANDFt5z7zWoQf3hQgAIUoAAFKEABCtilwAWPssF57bJzNk0BClCAAhSgAAUsL6AFEAYgvwWmDnkxhwiOW2uBucDAOEsoy82hNjBu6BddodFoEKcoePbsOR4+eoxjYeewbdd+uQkNVL1ZsQy2L5ua6vN5IgVMKcDAOFNqciwK2LYAA+Nse3/iu2NgnG3sky0FxsXExGLf0XM4fc8Lrr7p8PjhA2iUOLh7+8PNJxA6nSt0LkDss0e4F3kdMc+eQqt79XYDAAoURRH/AxdXF9y5cQ2nD+5AgK8benZqg5wMjLONi84BumBgnANsIpdAAQpQgAIUoAAFKEABClCAAg4nwMA4h9tSLogCFKAABShAAQpQgAIUoAAFKEABKwtUBLBHZQ81AWxTeQ7LKUABClCAAhRILKD2ubQGASlgKYENLd+ZrQDt4+dz9fREpZ69oHN1tVQLnIcCFKAABShAAQpQwPQCPTzKBk80/bAckQIUoAAFKEABCjikwHsvAuPmWHBloQCGAVhtzjkZGGdOXXVjqw2Mi768Fy4uCQM3Xs4XHR2DHXsPot+I33Dg6Cl1TQC4emAtsmXJpPo8S55w70EUjoedx+2793Hn7n3cunMPz55Hw9/PB+kD/RHo76dfQ5ECuaHVirxH6x6PnzzF+UvXEB5xExE378DXxwu5smdFzmxZkD4gnermHj1+gguXw3HxSjju3o9C3lzZUKRAHv26rXWIwJiLV8NxI/I2rkfcwrPnz5EpQyCyZsqAfLmzw8vTQ3VrjhQYd+feA/1+CRtx3Yp9z5MzCLlzBKXKRjWmkROuXo/EuYtXcefefX1/4k9sbCwC0vkhwN9Pf20Vzp9bf81a83jy9BkuXL6G65G39NeauK6KFsyLfLmyG7wfmrpXe3FK67qfR0fr76v6P7fvIfL2XTyIeghvL08E+qdDQDpf/XUh/tc/nS/cTPBvhbYUGCf2+eyFKy+fY/57TIhAK/G4TR/w8jlG3HfFfdzZDlsJjBPPMUdPnsXN2/dw+674cx9RDx/Dx9sTGQL99fcu8RxUokh+eLi7WWSbLNmTLQXGCdw796Lw14ZQZMxfHlGPohATEwPExUH/gV8tEBcbi7jYOGi1OkAj/lZ58W2OBlBEWFyc/r91ri54FvUAoTs3w0MXjYoVy6BUgZwoVSQfdDrrv5azyEXEScwqwMA4s/JycApQgAIUoAAFKEABClCAAhSgQKoEGBiXKjaeRAEKUIACFKAABShAAQpQgAIUoAAFUhTY+iIATjaEZi+ASvSkAAUoQAEKUCDNAuKdsWoO/huJGi3WplpgQ7NmmRSdEpFwgPy130b2CiJnmAcFKEABClCAAhSggL0KaDQ45l4muMT6Vs0bxrk92dFg7roH9roW9k0BClCAAhSgAAUsICCSCsJehMblt8BcCafY9+L/DAWwzpTznpyqT2p4dRRuKPLpeFhTwFSBcfFrEAFerbsNwLJ14p/+5Y9l03/CO/USv1VgzpK1uHbjptFB/Hy80f2DlgbrTp65oA95O3H6PG7evgtXVxcUypcb3d5vbnRcUXD4xGksXbsVG//Zgz37j0qdI4LZGr9dDW9Xq4hWjWvrw47UHiJ8b1foEaOnaTQadG3fDP5+vq9q9x44hul/rcSUOUuTPb9ujTfw2UdtUad6JaOBKGLdk2YvwZ8LDedIinCcKuVL4dsvu+tDtGQPEWS2YOUmqfIGtaroA3jiD7GnsxevwYwFqxB5606yY3z4bmN0atMEb1Ysk+I80+av0IdUiUMExn39/e9SfYmiUd98arS2ZJECqF+rstE6UxTcvf8A85dtwKLVf2Pbrv3JDin27c2KpdG763v6/ROhgjMXrpJqoVWj2vrgKrVHbGwc1m35Fxu27cbaLf/qAw1ljuxZM6F+rSp4r3l9VH+jrMwpr2rSch/5Z/cB/XUmro/kjvKli6JqhdIY1PsjfViUKQ57c4pfs+z9VlyjIlj0wJFTOHT8tP7PlfAb+uAtNYewF/ey6m+UwxvlSkjda0Xw5YSZixCnvHw5cuzUOYhrROYQ97cOrRoaLW1U+00UK2T8XijuNRu27cHazTtVPR7EY0/cE+vVrKy/r6QmoDQtjwu1+20UzEDB3zv2Yf+Rk6++8t1v06Svjy8/+UAfLJjSIZ4zZV8HiNDVBSs2YdP2PVi3ZZf0csS1KV4HvNu0DsQ9zJSHtXqytcA4Eai4/9hZ7D59HxlzFsCDh1FAbKzIg9OHwomAuP8i4l5+A/Lf3+kD4wDoXFwQ8/QJToTswtGD+/B2o0Zo2LItTh3ah3J5/FAwT3ZTbhvHcnIBTVAw32fl5NcAl08BClCAAhSgAAUoQAEKUIACtiPAb9JtZy/YCQUoQAEKUIACFKAABShAAQpQgAKOIyDeIfuv5HJqvgiX2yZZyzIKUIACFKAABZIXYGAcrw6bFFjf6p3PoODX+OZ0rq6o0qcvtDqdTfbLpihAAQpQgAIUoAAFjAs8f/QI1w8fwqV/d0bGRUdnApQP6i1eMcv4maygAAUoQAEKUIACTi3wPgBrvWYSv8RLBMetN8UOMDDOFIqmHcPUgXGiOxHIk7V0XelwF3HOpB++ThLeku+NptKBUkr4/8MHn0dH60PefpwwSx9K9PohQob2rU35IXXwWBhGjPlDdfDd63OJ8Ljh/T5G1/bN4eXpIb15Pb4ahYl/LpaqD1k3C8GliuqD0/p/OzbZYDdDg4kwm8VTR8PP1zvJly9dvY5u/Ufqw71kj4mjvtLvowiyM3akJnhG7O3IsTMgrls1x8cdWuDHQZ/Bx9vL4Gl+Baupul7VzC1qm9atjuUzflZ7mqp6EdYogs36jfhV9VpEoF7WzBmwUDLAb874EfrwNtlD9LZk7WYMHj0Jp89flj3NYN3LcMIeqFG5nNQ4qbmPnLlwGZ8N+klVOJQIhFoweRQqB5eU6stQkb05iTWovd+OGj8TX40cn2qj5E4U99ohfbrqw0NTuteK8NBiNVqbfP6EA479th8+7fRusnOIoLjVf+/EoB8m4sjJM2nqRYTYibBOcY9RExyXmsdFavY7tYtr2/1r/LViY2pPlzov4esGQyeIULafJ83BL5PnSo2XUtHnXdqhb/f2yJYlbcFx1u4pNc/bacYzMoB4zblm237cjQ2Ad/rMePzkMeJi46AocdDo0+JevhVGhMu9DIyD/jWKeL9B9ONHOHlgL44fCkW2XLlRpV4TZCtQCv6BGXDt0EbULpUNGTMEmHsJHN9JBBgY5yQbzWVSgAIUoAAFKEABClCAAhSggF0IGP8XLLtYBpukAAUoQAEKUIACFKAABShAAQpQgAI2J7AFgAiDS+n450VYXOJfL25zy2BDFKAABShAAbsRYGCc3WyVczW6vtU7+6CgfPyqs5Yug0INGjoXAldLAQpQgAIUoAAFHEzg0s4duLBd/Gjvv0ODDfUWLa/nYMvkcihAAQpQgAIUoICpBbQAwgDkN/XAKsYTwXFDAGxQcU6SUgbGpUXPPOeaIzBOdKp23KFfdMWQL7omWmRqAm1CDp1A+54DUwylSikwTgQ2DRg5Th8SY8ojU4ZArJs7FmVLFJYaVm1gXMTNO2jb42vVQWGimZJFCmDD/PHIkim9vjcRqvLHvOXo2u87qV5fL2pWvyYWTfkBOp24dSV/qA2eqf1mBbTvOSjVAUsiWGnnij8QkM4vSVP2Hhh39/4DNO7QG/+GHE7Vnqk9SU1g3PlL19CsU99U71tyvY386hMM6Pmh0XBCNfeR2Kv79OFQ/Ub8ppbkVf0PA3uhf48Oqs+3J6f4oK3U3G9FqKUI8zTXIe61f/42FPVqit9XmvSwdmDc7bv30brbAGzZGWJSAhH6uOSP0ciYXi7cSs3jIi37ndpFWjswToTViR5MfcwaOxzvt2yQqmFtoSe1z9u9u7ZL1VrVnnTz9l2s2rwfjzQBSJc5CM9j4xAbG60PjoMSB/2bYRQFIstWo9FBqwEe3r2Nkwf34czxowjKkQMlKlRBuizZ4ekbgKC8RRB1/wG8bh9EreB88HB3U9sS6ymQRICBcbwoKEABClCAAhSgAAUoQAEKUIACtiPAwDjb2Qt2QgEKUIACFKAABShAAQpQgAIUoIBjCVQCYOxXVIsa8aEEHhSgAAUoQAEKpF2AgXFpN+QIJhb4u0WLvDGa2HMJhy37YUf4BWUz8UwcjgIUoAAFKEABClDAkgJP7t7F3om/J5wyNsYtOmOjeWvuWrIPzkUBClCAAhSgAAXsUECkz/xpA33vedHD0NQGxzEwzgZ28LUW1Aa7RV/eCxcXndGFLFu3Fc079zNaF18gwuJEaFzCQ02gjQh6Gjl2BgaNnmh0zuQC4+49iEK7Ht9g3ZZdRsdIbcHiqaPRomEto6erCYyrUbkctu3ab3TMlApEyNvSaT/i2fPn6NZ/JP5cuDpN48kEiqkJnklTMwlOrlW1vD64z83VNdGQ9hwYFx5xE2+/+wlEEJalDpn9Fb1s33MAjTp8nqogQ5m1fPhuY0z/ZXCKoXFq7iNN61bHig0JQu5lmjBQ8+/K6agcXFL6bHtzSsv91tyBcfHoCyePQqvGtZPsgTUD48TcjT/oDREOaI4je9ZMWDd3HIoXzmd0eDWPi7Tst9FGkimwVmBcXFwchvw0Gd/+Oi21rRs9TwRdfvtlD6OhqvED2VJPap63fxnaB5YKjBNWIjh3885DuBGlg4uPP7z9/ACNVh+CG6eI4DhAAwXRT57gZvgVnDxyAOEXLyBbthwoXCYYvhkzw9XDC25e3vD1z4As+Uog/OIp5Iy7iGrlChrdVxZQwJgAA+OMCfHrFKAABShAAQpQgAIUoAAFKEABywkwMM5y1pyJAhSgAAUoQAEKUIACFKAABShAAecT2AqgRjLL3gagpvORcMUUoAAFKEABswkwMM5stBw4tQLrWr4zVAMMiT/fKzAQFT7ukdrheB4FKEABClCAAhSggA0JHJg5HQ/Cw//fkQaf1lu0fLwNtchWKEABClCAAhSggC0KaAGEAchvI82JXwAmguM2pqYfJTxU7c+lUzMNz5EQMFdg3OpNO/TBOLKHCBUR4SIJDzWBNiLwTITUyRyGAuOePnuOas0+QsihEzJDpKnmr0kj8W6TOimOoSYwLk3NJDh59rjhmDJnGXbsPZjmIUVw0el/l8HTwz3ZsdQEz6S5oQQDDOr9EYb3+zjRkPYaGBcdHYMqTTtZ5LpNCCYTGCeCFxu072XKrTM41phhffB5l3bJzqPmPmKqZkUw4eaFxsMrxXz26JSW+62lAuOE7ZHNf6FEkcQvm6wVGHfu4lWUqdPObOGJ8deur48X9m+YgwJ5cqZ4Oat5XKRlv1P7mLJWYFzvIb/g16nzUtu29Hk9PmyF30d+KVVvSz2ped62dGBcPOaR42dw/uotXAy/g4jbUYhTtNDp3BAbE4379+7i6rXLuH4zAq7QoEBgBuTIVwDumTPDzcsH7t7ecPPwgIu7N3wDRGhccdyOuAbf2yGoX7W01H6xiAKvC5xaE5zor4p0AT+TzsuEAhSgAAUoQAEKUIACFKAABShgZQF+c27lDeD0FKAABShAAQpQgAIUoAAFKEABCji0QCUA4kMGhg7xtb0OvXoujgIUoAAFKGBZAbUfzOO/kVh2f5xytvWtmg2GoohPE/oLgPy130b2ChWd0oKLpgAFKEABClCAAo4mcDVkH85uSpQrsqve4uVVHG2dXA8FKEABClCAAhQwg8AHAGaaYdy0DCn+TVf84odNagZhYJwaLfPWmiswbs3fO9Gow+fSzYsALxHklfBQE2gjPREAQ4Fxn3z9AybMXKRmmDTVnty+GIXz5052DGsExqVpQQZO/mnw5/ji4/bJDqsmeMbUvV0OXYMcQZlfDWuvgXFqH7+mcjQWGHf+0jWUfrut2cOx4tcTun42ypUsYnB55rqPGLPcsmgSalZJHBLz+jnO4PT6/daSgXFVypfC9mVTodWKzN2XhzUC4548fYZKDT/EkZNnjF02Jvl60YJ5EbJuFrw8PZIdz1yPC0PPr6lZlDUC4yz9nGTsPircbK0nNf1YKzBOuD2PjkbEzTu4dfs+bt+JwrPoGGi1GihxMXj89CnuP3qMoycv4PzJyyiYPQc802eAazp/uPn6wtXDA27unnDz9EW6jFmQKXcR3Lx6GhkeHMVbVcqk5nLmOU4uwMA4J78AuHwKUIACFKAABShAAQpQgAIUsEkBfhjKJreFTVGAAhSgAAUoQAEKUIACFKAABSjgQALbAFR/bT3i72o60Bq5FApQgAIUoIAtCDAwzhZ2gT0kEVBCQzPcuHThZuTJE8hbsxZcPT2pRAEKUIACFKAABSjgAALPHz3Crt/GJFqJewwCai5ffs8BlsclUIACFEiLQDcAk1QMMAWAOEf24PgpS9GHPgkF7P3xJXtfMGWdCI4bCiBRMnByEzAwzpT0aRtLbeBU9OW9cHHRGZ30+3Ez8PX3vxutiy+YOOorfNyhRaJ6SwXaqAlAkV6QkUIR6LN/wxx4uLsZrHSEwDhfHy/cO7UtUVhTwsVawz1+/s5tm+KPnwe9asceA+OOnjyLkm+1MdUlqWqclIKOUhuOVTBvTmTOmB5h5y4h8tYdVf3Ur1UZa+eMNXiOue4jxhqsUbkcti6enGyZszhZMzBO4P+7cjoqB5d8tQ/WCIyzxv28y3vNMOXHb5K9/sz1uLDXwLizF6+gQOVmxh7WJv/6qR1LUChfLoPj2mJPap63rRkYlxBUURTExsbpA+PiwyOjo2MQcesO1m3di63bQhDklwFeARngEhAAVy8RFucJNw8vuHv7wj9TNmTMWQjhZ48iZ+xpvFmRoXEmfyA4+IAMjHPwDebyKEABClCAAhSgAAUoQAEKUMAuBRgYZ5fbxqYpQAEKUIACFKAABShAAQpQgAIUsCOBGi8C47a+1m8lAHvtaA1slQIUoAAFKGAPAgyMs4ddcsIenx0IaatAM88Jl84lU4ACFKAABShAAYcX2D9jGqKuX3+1TkVRGtZfsmKtwy+cC6QABSiQsgADy+iTUMDeA8vYf8rXs7l9rHm/FcFxwwGsT6kJBsZZc4sSz22OwLgHUY9QrEYrXL0eKb3QTQsmoPabFRLVWyLQJiYmFnkqNlbVq2hSBIaUKloAfr4+OHnmAv4NOaw65EoElongMkOHNQKGpDdLReHpf5eiQJ6cBs9QEzyjYkrp0odnd8Db6+UvqrHHwLjOfYZj+l8rpddrysKUAuN++2M+Ph/8s9R0IlRw7Ih+aNusLtzd/h+eeCU8Al36fosN28RTitxxfNtCiCDG1w9z3Udkuoq9ui/ZwERncVITGCeuh3Ili6B4oXzImys74uLicPvufdy6cw+bd+7D+UvXZNgT1Qzo+SG+/7rnq7+zdGBcakO/GtauimIF8yI2Lg5HTpzBpu3q3yp3cvtiFM6f26CZuR4X9hoY17H3MMxcsErV9SXuNxXKFEOu7KdPGcUAACAASURBVFlx/tJV7D1wDKfPX1Y1RvsWDTB7nHjZnPSwxZ7UPG/bSmBcShvy+MlT7Nh3GIuXb4U3PJAuYyZofP3g5uMNd09PuLp7wcPXF4FZciEgKB9unDmA/C5XULHc/0MoVW04i51SgIFxTrntXDQFKEABClCAAhSgAAUoQAEK2LgAA+NsfIPYHgUoQAEKUIACFKAABShAAQpQgAIOISDe/SlC4sQhwuNqOcSquAgKUIACFKCAbQkwMM629oPd/CfwdH/IZGg0XQlCAQpQgAIUoAAFKOB4Amc3bcDVkJBXC9MA39ZdvHyQ462UK6IABSigSoCBcSlz0Yc+CQXMHbhm7+OruvmYqfhfAENe5GptTjj+yalI9PPowg1DzTQ9h5UVMEdgXPcB32PSrCWyLejros5sh4+3V6JzLBFooyb8RDQnAo0WTBqF+rUqJ+pVhI6IgKt5y1LMSkx0Tt5c2RC2YylcXHRJrNISGCfGffjoieoAu+Q2TITiiJCl1BwLJ49Cq8a1DZ6q1l4Mkj1rJnRs0wRFCuRBUOaMuHztBg4cPYVp85cj6uFjVS0umvIDWjZ6S3/O8bDziHr4SP/fcYqCKk06SY0lroeN8383WhuUJSNyZstitE62IDziJrKVqS9b/qqu8dvV8H7LBihRJD/y586h//sbN29j265Q/D5zEfbsPyo1ZnKBcU+fPUeu8o2krj1xnf6zdKp+Tw0dIizs04E/YsLMRVI9fd6lHcYM65OkNi33EdGjOFITVCbOCz+4HlkzZ0jSk6M5pbRBxgLjypYorH9MN6tfA9myGL4W4scX96FfJs/FtPkrpK4JUSTG379hzqt6cV2FHDoBRXn5ckSEffYd/qvUeOK+P7h3F6O1hQvkhr+fr75O7b1c3G9XzPz51eMzfjJxr2vcoTeOnDxjdP74AhFIKoJJDR1peVyo2W/pZl8rFIGzV8MjXv3tB58NkQ5jWz7jZ2TOEJji1CIsVNwHxSFsxX1LzSFCCPv36JAkEHLMlHnoM/QXNUPh3O4ViL/XJNxvW+tJ9KbmedseAuPEmkRw8KGTZzFr/lq4PNXC2z8QWv90cPPyhqunJ9w8veDl64/02XLDL1MuRITtRXH/ByhdrKCqfWYxBTRBwfwsOi8DClCAAhSgAAUoQAEKUIACFKCAjQjwm3Qb2Qi2QQEKUIACFKAABShAAQpQgAIUoIBDC9QEsOW/FYrgOPW/MtWhebg4ClCAAhSggEkEGBhnEkYOYmqBZwdDjyoKipt6XI5HAQpQgAIUoAAFKGB9gciTJ3Bi2dJXjSjAlvqLl7/8pD4PClCAAs4rwEC0lPeePvRJKGDvgW7m7t+W7qQ7AQwD8LdoioFxtrQ1L3sxZWCcCEH6YfxMDP1ZXOLyx5sVy2D7sqlJTrBEoE25uu31gWMyhwgHO7BxbpIgn/hzRQDRVyPH44ff/5QZTl+zbPpPeKdejST1akOGROjWj4M/R53qlRDo76cf70bkbXz85Uis2PCPdD/xhW9Xq4gR/bujXMki+kC72Ng4fWhc7Xd7SIWBxY/z6/Av8NlHbQ3OryZ4RqxveP+P8V7z+nBzdU0yXti5S2jQvpeqYK92zeph7u/fJhlLBErpsleQMhPhTse3LZSqNWVRakKJfhjYC30/bp8k4Ci+r3HTF6DXwB+l2kwuMO6Pecv1wYkyx98LJ+Ctqik737pzDxmLGw4cfH2OgnlzImzn/3/OEP91tfcRsaejB/ZCzSrB8PL00A/z6PETLFr1Nzr2Fk9n8sfeNX+iQpliSU5wBCdZhdcD4+YuXQfxp1LZEni3aR0UypdLdih9nbjPfjFsDMRjQObIlCEQEUc2JlsqAuOqNu0sMxR6d20HEYIle0TeuoPMJevIlkM8F66d81uS8NT4AUQoZpMPe2Pbrv3SY147uE4frvn6ofZxITvh6/ste56xujptPsGm7XJvGYw8ugkZ0wcYG/LV17/8dixGT5glXS8C6ZrWrZ5s/epNO9D4g97S4xm6rmyxJ7EgNc/b9hIYJ9YlnvfDzl/BrxP+gnuMDr6B6eGSzh8uXl5w8/KCu5ePPjQuY8588E4fhMjT+1Amw1MUK5RXep9ZSAEGxvEaoAAFKEABClCAAhSgAAUoQAEK2I4AA+NsZy/YCQUoQAEKUIACFKAABShAAQqYUGDoso9z69yUwohVikCjKRKnUTYNaTTl1a9rHb6qq6pAkcGNp6j6Hprjp7yZTuqzG8ATALWMXepO6mOM5dXX6ZOqx1ckgPuKgvsaDW4qGlzWKsolBZpLccDGoY2n3JLeABZSgAIUsF0BVa/vXnx+TNXrO9tdNjuzZQElNDTdMy3u2XKP7I0CFKAABShAAQpQIPUCz6KisHvcb68GUIAne4uX9hk6dGhc6kflmRSgAAXsXoCBaClvIX3ok1DA3IFr9j6+Ld4Qd7xoaujJqdicsLnCDUNtsVen6kltYNzTi7ug1WgRp8Th8ZOnuH33PsJv3MTfO/Zhxl8rcfW6+OdVdceMMUPw4buNk5yU2kAbERxVokh+fbBbruxZ9QFZUQ8f4fT5yzh19iLKFC+E30b0RXjETWQrU1+62Sk/foMu7zVLsf7ho8cIKlMPIthH5ujctin++HlQklI1gXH9enTA0C+6vgq3SjiY2KM33/lIOhRPnJtSGM7xsPMoXrO1zNL0NcP7fYxBvT8yWG/q4Bm14Ux5c2XDud0rkvRmD4FxTT7og1WbtkvvQ3KPsYQDmCIwrmbLblJhVs3q18TSaXLhdH2H/4qfJ82RWuvtE1teBSbGn6DmPjJ73HC0facedDqtwfmmzV+Bj74YIdWLKFo05Qe0bJQ0G9/enRICyN5vpdEkCsX91a9gNYnKlyXRl/fqgy8NHeYMjBMhg627DZDu89jWhUYDqHaFHkGVJp2kxxTXdPsWDSz+/CrdoGShOQPjClVtrn99IHMkFzT6+rn12n2KDdvE2x2NHyIQ9cr+tYkKbbEn0aCpn7eN61iuQoRRhh45iRnz1gJRsfDPkBFaPz+4+vjA3csTrp4+8PEPQOYcBeAVmBW3z+xFqYzPUbRgHss1yZnsWoCBcXa9fWyeAhSgAAUoQAEKUIACFKAABRxMgB+GcrAN5XIoQAEKUIACFKAABShAAQo4q8CI1V1qx8Vp6mk1qKkARQB4JrRQoPl+SOPJX8f/HQOnUr5S6GMWHxEU9wiA0V8VSn+z+EvfHp3NH9CUHtx48mFpIBZSgAIUsF2BbSpbq6GynuUUUC3w5MC+6hpo1V6bqufhCRSgAAUoQAEKUIAC1hPYM34cnj64/6qBOCWubIMlKw9aryPOTAEKUMDqAgxES3kL6EOfhAL2Huhm7v6tfkNLroHggkDPxkDFwi8rGBhn/a1SGxhn6o4zZQjE5dDVcHdzSzK0mqCnSuVKoHXjt9GkTjXky51dqs3FqzejVdcvpWpF0aNzOw2Gsr0+wJffjsXoCbOkxjUUFCNOVBMYF7JuFoJLFU12vp8mzka/Ef8P7E6pMbEfEUc2pti7bNiVGESE2Y0e2MvgeOYInuk+4HtMmrVEyl4UPTi9Hb4+XonqbT0wLiYmFoFFa0qHEopgvLAdS5MNzIpffFoD40Q4oXe+qlL2E0d9hY87tJCqnbNkLd7/dLBU7aYFE1D7zQqJatXcR5TwlENMRaBQllJ1IcIJZY6fBn+OLz5un6jUEZxSc7+V8TJWI/zvRz3E7Tv3UblJJ+l9iDqzHT7eiR/n8XOZMzDu029GY/yMhcaWpf963RpvYP28cVK15eq2lw4BFSGnIuz09UPN48Ja+52wZ3MFxl2PuKUPeZU9/l05HZWDSxotX/P3TjTq8LnRuviCi/tW6QNuxWGLPcX3aY7nbWkkCxSKe8zJsxcxdfYqPIp8iIDADHBN5w9XX2+4eXrBzcsbPv6ByJKrADwDsuLm6X0omek5ShTKa4HuOIW9CzAwzt53kP1TgAIUoAAFKEABClCAAhSggCMJMDDOkXaTa6EABShAAQpQgAIUoAAFKOBEAt+t75o1JlpppIFG/PrI2gB8Ulp+jvSF7lfIWzfmvzq3JSFjVX1P3KK84Td+JjfnkpCxqnaD46fMRR/6JBTg4yvl60GNjwaIbVS6e5EyOcqcUXXTYjEFKEABClCAAlICTw+EdgcwQaqYRRSgAAUoQAEKUIACdilwcuUK3L9yBYBy+um9+7u0WvxWZ9HyQ3a5GDZNAQpQwDQCDERL2ZE+9EkoYO7ANXsf3zR3JfOMsgHAaCU8dLN5hueoagWsHRg3efTX6Nq+ucG21QTaGAt6MjTBZ4N+wthpf0mRvd+yAWaNHS5Ve/biFRSo3EyqVhRdDl2DHEGZE9WbMjBu8859qN26h1Q/MoFxfYf/ip8nzZEar1fnNvhtRF+DteYIngk5dAIVGnSQ6k0U7Vs7C+VLJw7bs/XAuONh51G8ZmvpNYrrVly/xo60BsZt27UfIkxQ5hje72NUrVBaphQ79h7EkJ8mS9XOHjcc7VskXqup7yPvfvwVFq7cJNXP4D5dMKxvYhNncZICMlAkwviOnDyLsLMXcfnaDVy8eh0XLl/Dpas3pAPiXh/WWoFxhao2x+nzl6Uopv0yGJ3aNJGqnblgFTr2HiZVKwIjz+1ekaTW1I8LqWbSUGSuwLila7egxUf9pToTz4/XD62HVqs1Wh8bG4fcFRrh6vVIo7WiYP7EkWjTtI6+1hZ7il+EOZ63pYAsXCT2bdy0Jbh95Q4C/QPh4p8Orr6+/4XGeelD47LmLgSfjDkQEbYPNfK5IluWDBbuktPZi8CpNcGJWi3SBaref28v62SfFKAABShAAQpQgAIUoAAFKEABexLgN+f2tFvslQIUoAAFKEABClCAAhSgAAVeCQxf3W0BFEX6nYNBAfnwRv6Gr85XE6gkTmJgWcoXH33ok1CAj6+Urwdb8vH3yoi3irUVDV+HouyAVrs5ziVuXYn0JcSnnHlQgAIUoAAFKJBGgacH9/8CRemdxmF4OgUoQAEKUIACFKCAPQhoNGM8ypTrYw+tskcKUIACZhZgIFrKwPShT0IBew90M3f/Zr5dpWp4ERQ3FMAecbYSHqqkahSeZHIBawbGtW7yNv6aOBIajeGPZpg70EaEW4nwJplj3Hf90bOj9FttkLlkHelwow3zx6NO9UqJ2jBlYFx4xE1kK1NfZpmQCYxTE1Zk6cA4sUi/gtUQ9fCx1HpXz/oVDWtXTVRr64FxagLHxMIendsJL08Pox5pDYz7ccIs9P9W3S8JNdqUygJDj1NT30e+/v53fD9uhlRnhgLjnMVJCghAdHQMNv6zB3+t2Ihtu0KlA7Zkxxd11giMex4dDfdcb0i3eWDjXJQpXkiqPuzcJRR+s4VUbXLrN/XjQrqZVBaaKzBuxJg/MPjHSVJdNatfE0un/ShVK4o69BqM2YvXStV/3asTvhvwMtjVFnuKX4SzBMaJ9UbcvINfpy1G5PlIpPcPgM7fH24+PnD18ISbtxd80gUiKG9h+GbMiVsnt6FygXTIkTWj1H6zyLkEGBjnXPvN1VKAAhSgAAUoQAEKUIACFKCAfQgwMM4+9oldUoACFKAABShAAQpQgAIUoMBrAmO29G8e9ejeEkMwLlpX+Hj4w8vdDz7u6fT/G+CdCYHeWehIAQpQwOkEYuNiER37FM9jniI69jkeP4/Co2f38ejZA/h6BKBglrJJTTTKdsRpF8V6xs4t6V/yrtOhccEUoAAFKEABEwk8PRi6FAqamWg4DkMBClCAAhSgAAUoYMsCGizzKBPc3JZbZG8UoAAFLCTAQLSUoelDn4QC5g5cs/fxLXTbkppmJYDhABKlcjEwTsrOIkXWCowrW6Iwti2ZAl8fr2TXae5Am2I1WuPE6fNSzpsXTkStquWlakWRmnCb+RNHok3TOonGNmVg3L0HUQgoXFOqd5nAODWBMdYIjCtUtTlOn78stV5D9rYeGLds3VY079xPan0y+xk/UFoD4778dixGT5gl1Ze5iob17QYR0pbwMPV9RE3gm6HAOGdxMrbH4r70w/g/8fvMhdIBj8bGTO7r1giMi7x1Rx8cKnvIBjuK8WJiYuGas6Ls0LgcugY5gjKb9XEh3UwqC9U8p0Ye3YSM6QOkZuo95Bf8OnWeVO3QL7piyBddpWpF0Q+//4kB342Tqv+4QwtMHPWVvtYWe4pfhJrn/1+G9kHvru2k1m+rReJxPGbKYkRcuIH0gRmg8/OFi7c33Dw94e7lDe90/siWryg8/LMi6vwelM/jjTzZ+T5rW91Pa/XFwDhryXNeClCAAhSgAAUoQAEKUIACFKBA8gIMjOPVQQEKUIACFKAABShAAQpQgAJ2JRB261j52BhNNwVKpzWHpmmeRj/S95/OKwOC/PMhW0A+/X/zoAAFKEABkwgoGmim61yUyYUyFA8Zsvaj7LpY7eLYuLiuw5r+ccQkM3AQClCAAhSggAMLPDkQulcDVHDgJXJpFKAABShAAQpQgAL/CSjAPs+ywfKfdKUcBShAAccVYCBayntLH/okFLD3QDdz92/tO6UCYCmAYQCOGmqGgXHW3qL/z2+NwLjGb1fDvAnfwsc7+bA40aGpg55eVxdhPiIMROY4umUBihfOJ1Oqr+nYexhmLlglVf/7yC/R48NWiWpNGRj3+MlTeOerKtWLTMDY0rVb0OKj/lLjWSMw7q3W3bFlZ4hUf1N/GoiP2r2TqNbWA+OmzV+Bj74YIbW+SuVKYPeqGVK1aQ2MU3PNSzWUiqL+PTrgh4G9Ep1p6vvI7zMWouc3o6W6MxQY5yxOKQEtX78N3fqPlL7/SmGnUGSNwLiwc5dQ+M0WUq2L4NQHp7dL1cYXqXn+Orx5PkoWKWDWx4Wq5lNRbK7AuA69BmP24rVSHYlANxHsJnvMWrQGH3w2RKq8VePaWDh5lL7WFnuKX4SzBcaJdUfcvINfJv+FiAu3kD4wPVz0oXFecPN8+cfHPwB5S1SAh28G3D69C6Wzu6Bg7mxS+84i5xBgYJxz7DNXSQEKUIACFKAABShAAQpQgAL2JcDAOPvaL3ZLAQpQgAIUoAAFKEABClDAaQVORRyrEqsofQHNq3c4Xrh5DNGxz/Uhcd7u6ZzWhgunAAUoYBkBZfmGI3+6Pnz2oKGYT1GUce6emkED3p5y3zLzcxYKUIACFKCA/Qk8PRB6HkAe++ucHVOAAhSgAAUoQAEKpELggkfZ4LypOI+nUIACFHA0AQaipbyj9KFPQgFzB67Z+/jWvD/OB/AtgBOGmjg5FSJM7tVRuGGoNXvl3CLV7+cpGPrij6UOEej03YBP4OKiMzqlqYOeEk6oKAq02cob7SG+4PyelciTM0i6/tNvRmP8jIVS9cP7fYxBvT9KVGvKwLiYmFi45pTL6JYJjFu9aQcaf9Bbam3WCIxr8kEfrNokF740Z/wIvNe8fqK12HpgnJpgt9ZN3saCSd9L7ZWacQ25qXGXaigVRZYIjJuxYCU69R4u1Z2hwDhncUoO6Iff/8SA78ZJ+ZmqyBqBcXv2H8UbjTtKLSF71ky4sl8utCx+wEJVm+P0+ctS429bMgXV3yibqNacz69STaksMldgXIP2vbBuyy6pbmaPG472LRpI1Yqixas3o1XXL6Xqa1Quh62LJ+trbbGn+EU4Y2CcWPvV65H4beoi3Lx0CxkD0kPzKjTOE26envDPkBk5CpWET/psuHFyF0plAYrmzyG19yxyHgFNUDA/i+48282VUoACFKAABShAAQpQgAIUoICNC/CbdBvfILZHAQpQgAIUoAAFKEABClDA2QWORhzNp4N2iKIo7zu7BddPAQpQwJoCUU/vYtPROVASf/7lhqJo+gxpMll8aIYHBShAAQpQgAL/CWxo0bSi1kX7oEL3XrtdvTzTaV1caEMBClCAAhSgAAUo4OACGmjuupctF+jgy+TyKEABCsgIMBAtZSX60CehgL0Hupm7f5l7jqlr5gEQCTphKQ3MwDhTs6d9PEsFxjWsXRU/fNMLxQrJZ0WbM9BGTYiaUL56YC2yZckkDf7eJwMxb9l6qfpx3/VHz46tE9WaMjBODKwJCpbqRSYwbv3WXaj/Xi+p8awRGKcmbOf3kV+ix4etEq3F1gPjps1fgY++GCHl36x+TSyd9qNUbVoD42q27IZtu/ZLzWWuIksExv21YiPadv9aagmGAuOcxckQ0Nyl69C+5yApO1MWWSMwbvueA6jevKvUMvLmyoZzu1dI1cYX+RWshqiHj6XOObBxLsoUL5So1pzPr1JNqSwyV2CcmsfjX5NG4t0mdaQ7/2PecnTpKzKUjR8J79W22FP8Cpw1ME6s/8LlcPz2x2I8uHYXgQGB0Pr6QuflBTcPd7j7+MI3ID1yFiyOgKB8uHJ8F0pkjEbJgrmMbz4rnEaAgXFOs9VcKAUoQAEKUIACFKAABShAAQrYgQAD4+xgk9giBShAAQpQgAIUoAAFKEABZxU4duPYAA0wUrzn01kNuG4KUIACtiJw4eYxHLi4JZl2lMU+T3w69Gk95omt9Ms+KEABClCAAtYUWN/yHSXh/DW+HmjNdjg3BShAAQpQgAIUoIBlBJ57lA12t8xUnIUCFKAABShAAQrYtcAHAGba0AriACx48b6EYcaC4uJ7ZmCcDe3ef62YMzAue9ZMaNW4Npo3qIWqFUqrXry5A21kQ9RE42d2LUP+3Dmk16Am8O3HQZ+hb/fEvwtSzfkh62YhuFTRFHuTXatMYNzGf/agbtueUhbWCIx7o3FH7Nl/VKq/OeNH4L3m9RPV2npg3KJVf6N1twFS66tSvhR2rpgmVZvWwLh3On6BFRv+kZrLXEWWCIxT428oMM5ZnF7f49t37yNDsbfStPXi/pQrexbkzhEEsQ+yhzUC4w6fOI3StdtJtShz3319IDXPj3tWz0TFssUTDaHmfCU8VGod5iwyV2Bc8879sGzdVqnWZ4wZgg/fbSxVK4rUhKvVr1UZa+eM1Y9tiz3FL1rNmn4Z2ge9u8o9BqRRrVx4PfIW/pizGmFHziGzfwZofLyh8/aEmwiO8/KEl286ZMmVD9kKlsHtiHB43j2OmuXywdODP/a28tbZxPQMjLOJbWATFKAABShAAQpQgAIUoAAFKEABvQA/cM8LgQIUoAAFKEABClCAAhSgAAVsQmDYqi7vaaCpMLjxlM/CIo6VioEyFoqmmk00xyYoQAEKUEAvcOdRBA5c3Iz7j28ZEjmu1cY2HNhw2iVyUYACFKAABZxdgIFxzn4FcP0UoAAFKEABCjipgOJRNljrpGvnsilAAQpQgAIUoICsgHi9FAYgv+wJZqwTQXGLAAyRDYqL74WBcWbclVQOrTYwToSaaLWJX75rNVr4p/NB1kwZIIJvMmcMRMmiBVCicH5oNKn/2IW5A21ylGuAq9cjpeQO/T0PpYoWlKoVRR16DcbsxWul6qf9Mhid2jRJVMvAuMR0aoJnMpesg8hbd6TsV8/6FQ1rV01Ua+uBceu27EKD9r2k1lcwb06E7VwqVZvWwLhu/Udiyhy5ud6uVhG1q1WU6ktNUbWKZVCpXIlEp5j6PpLWwDhncXp9336ZPBdfDBujZjv1e9nwraoQwYciFNPXx+vV+eIxIB4LMoepAuM+79IOY4b1kZkSl6/dQK7yjaRqRZHaUDY197lTO5agUL5cZn1cSC80lYVqAuMijmzUvxaROdQ8Hsd/1x+fdGwtM6y+ZsaClejUe7hU/fstG2DW2Je1tthT/CKcPTBOOFy8cgOTZy/HjbORCPDz04fGuXh7wdXTHa4e3vDy9UGmHLmRs1A53L13H5qbh1GjRBAC0vlJXQssclwBBsY57t5yZRSgAAUoQAEKUIACFKAABShgfwKp/5dL+1srO6YABShAAQpQgAIUoAAFKEABGxUYsarrEAUYKtorFBQ8oXi2yt0Zcm6jm8W2KEABpxdQoOB8xBEcv7Yb0bHPX/e4p1HiWgxq8scWp4ciAAUoQAEKOLUAA+Ocevu5eApQgAIUoAAFnFjAo2ww34vlxPvPpVPASQT0/56n4lBbr2JollKAAnYq0AHAn1bu/cXbE7D4RQ/iHnUiLb0o4aFiLB42IKA2MC768l64uOgs0rmpg55eb7pCgw4IOSR3KS+b/hPeqVdDet1Vm3bGvyGHpeoNjc3AuMR0soFxMTGxcM0pH0T278rpqBxcMtFkagLj1ASySV0MEkW7Qo+gSpNOEpUvS64dXIegzBmN1qc1MO6bURMwcux0o/OIgu8G9MDXveTXIDVoMkWmvo+kNTDOWZxe3w41AZ3i3D9+HqQP0kwudNQagXGfdnoXY7/tJ3U5Poh6hHSFqkvViqLIo5uQMX2AVP3jJ0/hnS9x0GVKJxoa29SPC6nG01CkJjDu+qENyJIpvdRsX40cj1HjZ0rV9u7aDuK5SPYY+vMUiNdYMkfCsW2xp/g1MDDupUTYuUuYNHM5HkZEwdfLGxpfX7h4usPFwwOuHp7w8vVF+qzZkaNwGTyNVvDs2jGUz+WK3DmCZC4H1jiYwKk1wYlWVKQL+O8gDrbHXA4FKEABClCAAhSgAAUoQAEK2J8Avzm3vz1jxxSgAAUoQAEKUIACFKAABRxGYOHCVrpTngHiHSWv3j3nonPD28Xawcudv43OYTaaC6EABRxS4PHzKOw+sxr3Ht98fX1xUPDN4CZTRjnkwrkoClCAAhSggIQAA+MkkFhCAQpQgAIUoAAFHFCAgXEOuKlcEgUo8LqA2mAkvkeV1xAFKJBQQCuyCQDktxKLuIctBTA4rUFx8f0zMM5KO2lgWmcOjGveuR+WrdsqtRkj+nfHwM87S9UqiqIPCop6+FiqfveqGahUrkSiWgbGJaaTDYzb+m8oarX6WMpdFN06vhnpA9Ilqrf1wLjIW3eQuWQd6TV+/3VPDOj5odH6tAbGjZ+xEJ9+M9roPKKgVtXy2LxwDVlgoQAAIABJREFUolRtWotMHYyV1sA4Z3FKuG9qw9PWzR2LejUrp7j11giM69mxNcZ911/qkhTPA9ps5aVqRdE/S6egWqWyUvWHT5xG6drtpGpFkaGgV1M/LqSbSWWhmsA42ZBM0crvMxaip+R9q0blcti6eLL0CtS8xhg9sBf69RDZ0LbZU/yiGRj3/+0/cPwMps5YgbgHz/QBcRovT7i4vwyNc/Nwh7u3DwIzZ0WOgiWheAbgwZUTyO8egfJliktfQyx0DAEGxjnGPnIVFKAABShAAQpQgAIUoAAFKOBYAnwzjmPtJ1dDAQpQgAIUoAAFKEABClDAbgSGrurqpVWwBBrUe73pygUaIat/XrtZCxulAAUo4MwChy79g3ORSX6rfJRGiSs1qMkfF5zZhmunAAUoQAHnFWBgnPPuPVdOAQpQgAIUoIBzCzAwzrn3n6ungJMIMDDOSTaay6SAmQTeBzDLTGOnNKy4dy0HMATAUVPOz8A4U2qmbSxnDowbM2Ue+gz9RQqwaMG8OLZ1ATQa4x8j2bIzBG+17i41rih6cmEXPNzdEtUzMC4xn2xgXJMP+mDVpu1S9gXz5kTYTpGFmfhQExiXKUMgIo5slJrPlEUiME4Ex8kc2bNmwtGtC+Dv55tieVoD4/YfOYngeuLpSu64H/YP/Hy95YrTUGXqYKy0BsY5i1PCLQs7dwmF32whvYtPL+6Cu1vie+LrJ1sjMO79lg0wa+xw6XWouR993qUdxgzrIzW2muft5MIZTf24kGo8DUVqAuNO7ViCQvlySc0WevgEytd/GdQmc8iG0d29/wCBRWrJDKmv2bH8D1StUFr/37bYU/xCGBj3/y0VrxV2HziOP2auhHesC3SeHtB5eUInQuP+C47z8PKCf4aMCMpfBN4ZcuPujcvwjzqGum/KhUNKX0AstGkBBsbZ9PawOQpQgAIUoAAFKEABClCAAhRwUgHj/9LnpDBcNgUoQAEKUIACFKAABShAAQqYT2Dhwla6U54BmwFUTziLTuuCygUaI5NfDvNNzpEpQAEKUMDkAlduh2H/xc2IjYuBi9Yl1s8nU62e1YfKvYPd5N1wQApQwIkFtqlcew2V9SyngLQAA+OkqVhIAQpQgAIUoAAFHEqAgXEOtZ1cDAUoYFiAgXG8MihAgdQKaAGEAcif2gFSed4KAINMHRQX3wsD41K5K2Y4TU3wjJg++vJeuLjozNBJ0iHNHWgTcugEKjSQD4rZsmgSalYJNrp2NUFGNSqXw9bFk5OMycC4xCQygXFqQ6k6t22KP34Wt7nEh6Io0GYrb3Sf4wvCD65H1swZpOtNUfhOxy+wYsM/0kOJwMP188YhR1DmJOfExMRi1uI16NxHPghrzvgReK95/URjxcbGIaBIDUQ9fCzVV/1albFixi9wdXWRqk+uaP7yDRj4wwS46HQGAwBNfR9Ja2Ccszgl3K+t/4aiVquPpfY5uXvi6yeruc9GndkOH28vg/PvCj2CKk06SfWWXMhkcif/Mnkuvhg2RmpsUXT31FajwY6PHj9B1tJ1pR9nI/p3x8DPOyfpwdSPC+lFprJQTWDcoik/oGWjt6Rmio6OgVuuSlK1omhY324Y3KeL0fqfJs5GvxG/Ga2LL3h8/l94erjr/68t9hTfJwPjEm+puJ9v2hGK2X+uRoCHN3ReHtB6eLwMjHNzg87dDe5eXvDxD0DW3AWQIVdRREVFIS48FDVL5UCgv5/0NcJC+xVgYJz97h07pwAFKEABClCAAhSgAAUoQAHHFWBgnOPuLVdGAQpQgAIUoAAFKEABClDAZgVGrOo6XQE6JmzQw9ULVQo2hb9XRpvtm41RgAIUoEDyAlFP7iDkwkaUzf0W/L0y7Yn1iG1Q0r/kXZpRgAIUsKAAP7BsQWxOlbIAA+N4hVCAAhSgAAUoQAHnErh36ZJ+wQfnzq4p/rf+4uVqA62dC4yrpQAF7FmAP3+x591j7xSwrsB7L8Li5liwhdUABouXaOaY8+RUJLofFm4Yao5pOKYKAWcOjFMbytK0bnUsn/FzirpHT55FybfaSO9AcmE+DIxLTGgsMO7S1eto3rkfDhw9JW2/eOpotGhYy2C9X8Fq0oFMasKJpJszUvj7jIXo+c1oVcNlyhCId5u8jTIlCiFvzuyIvHUHJ06fx7xl63H6/GVVYxkKjBMDvPfJQP14socI7Zv600BoNOo/nrX/yEl8M2oCNmzb/Wo6JTzpc4qpg7HSGhjnLE4Jr4F1W3ZBBLzJHLKhbNWadcGOvXIvVVIKjDty8gxKvdVWpjV9jZqAyH0Hj6Niww+kx/51+Bf47KOUe5n452KI5wfZ45+lU1CtUtkk5aZ+XMj2k9q61t0GQDz2ZI6eHVtj3Hf9ZUr1NY06fI41f++Uqvf18cKNwxvh5emRbP2z58+RM7iR/h4rcxgKSbTFnsRaGBiXdEfFa8kfJszFyYOnEejpB62XJ7Qe7tC6uuhD41w8PODq7gEvPz9kCsqBrPlKIFrnhfsXQlE6UzSKFbZ0JrjMVckacwhogoLVv9gxRyMckwIUoAAFKEABClCAAhSgAAUoQAHwm3ReBBSgAAUoQAEKUIACFKAABShgUYHhK7v0hUbzY8JJfT0C8GahZvB087FoL5yMAhSgAAXMKKBRtj/J9Kx2sCY42oyzcGgKUIACCQX4gWVeDzYjwMA4m9kKNkIBClCAAhSgAAUsIrBt5LeJ5qm3eDnfk2UReU5CAQpYQYA/f7ECOqekgAMIaAGEvQiMs0SSwBoAg8wVFBe/FwyMs72r0pkD48Ru9Bn6C8ZMmSe9MT0+bIUxw/rAzdU1yTkHj4WhyQe9cfV6pPR4F/auRO4cQUnqGRiXmGTkV5/gq08T/X5NfYGiKNi8MwTNO/eVDngT54nwtCv71xjcR/H1HOUaSO+jCBAKWTcbhfLlMrjv4RE34ePlBT9fb+nrwlihCCLKXLKOsTKzfT25wLgtO0PwVuvuquatX6sy+nRrj1pVgqHViqe95I8nT59BzDFt/gosW7c1SaG9BMY5g1PCzQk7dwmF32whfV1sWTQJNasEG6y/e/8BPhv0E2YvXis9XkqBcSJsMneFxtJjlS9dFFsXT4a3l2eSc2JiYnE98hYyZwzU31vE/al07XYQoXSyx/Qxg9Hx3SYGy/9asRFtu38tOxTy5sqG0zuXQadL+riyt8C4rv2+w9S5y6TXvnDyKLRqXNtg/e279xEXF4eM6QP0X1+x4R+80/EL6bGrlC8FETiaJVP6JOfcvH1XH5y5afte6fHmTxyJNk0T389tsSexIAbGGd5W8brvtymLEXn+Bvy8vaHx9IDO3Q1aNzfoXF31f1zd3eHh6wv/DJkQlK8ofLPmx7XTh1Am8B6K5c9l9PlP+oJioc0KMDDOZreGjVGAAhSgAAUoQAEKUIACFKCAEwrwzYlOuOlcMgUoQAEKUIACFKAABShAAWsJjFjZpZGi0awE/h9g7uHqhdrF34O7S9I3IFmrT85LAQpQgAKmElCWF8tSopmpRuM4FKAABYwI8APLvERsRoCBcTazFWyEAhSgAAUoQAEKWESAgXEWYeYkFKCAbQjw5y+2sQ/sggL2JtAOwFwzN70OwGAAoWaeRz88A+MsoaxuDmcPjLt87QZylW+kCu3NimXQp9t7KFmkgD4w5njYeWzdFYovvx2rapz3WzbArLHDDZ7DwLikLEUL5kW1SmUQ6J8OT589w6HjpxFy6LiqoLj4UYf3+xiDen+U7H5VbdoZ/4Yclt5PEUDXv0cHiCAhd3c3iOvq8PHTWLJmiz4sqkXDWvqAIVMezTv3MxiaZso5khsrucA4UV+nzSeqwpLi5yiYNyfat2iAHEGZEZQloz7M6fnzaFy7EYnwiFvYd/CY0ZAwewmMcwanhNeOCFJzzVlR+tIUIYyjB36Gd5u+jYB0foiNjUPYuYvYFXoE34yaABGYqOZIKTDu8ZOn8M5XVc1wEKFxn3Zqg6IF88DdzQ2nzoreDmPu0vX63sZ/1x+fdGytH1NNwFZ8E13bN9eHnZUonB9ubi44eDQMi1b/jUmzlqjqM6XwOXsLjFP7WkVAffnJB6hb4w2kD0yHG5G3ceL0eazfuhsbtu3WO8bfL8T1VbR6S5w+f1naV9zzfxz0GUoXL4gCeXLi4pVwhB4+ib7Df1V1fWbPmgnn96yEq6tLorltsSe11/MvQ/ugd1fxrYxzHAePn8ak6csQd/8Z3N09oPXyeBkY5+ICrfjj5gZXNze4uXvAJyAAWfMUQJb8pXH1zFHkdr+F4GJ54Onu5hxYTrpKBsY56cZz2RSgAAUoQAEKUIACFKAABShgkwIMjLPJbWFTFKAABShAAQpQgAIUoAAFHE9gyIqPSuq02j0AXiXD6bQuqFmkNdJ5ZXC8BXNFFKAABSjwn4Dmt2JZin1ODgpQgAIWEOAHli2AzCnkBBgYJ+fEKgpQgAIUoAAFKOAoAgyMc5Sd5DooQAEJAf78RQKJJRSgQCIBLYAwAPnN5LIewCBLBcXFr4GBcWbazTQMqzaEJfryXri46NIwo/yplgq06dxnOKb/JX6Ho2WPw5vn60PnDB0MjDPfXogwqnO7V+gDyZI7vho5HqPGzzRZE03rVsfyGT+bbDwx0MFjYShb5z2Tjik7WEqBcaGHT6B8/Q6yQ5m0zp4C4xzd6fWNrdCgA0IOnVC93+LxGvXwserzEp6QUmCcqEttb8k1Nfbbfvi007v6L4uwvEJvNsf5S9fStAa1J4tAs0shq+GRTACVpZ5f1fadXP26LbvQoH0vUw2nHyfh/WL24rXo0EvkJ1v2mPD9AHT/oKXBSW2xJzUBiM4WGCc2cfWWXViyeAvcnwMunh7QurtB5+YKjU4HjasLdC6ucHF1hYubK7x8/ZApRy7kK1sL4dcuwu/hGVQpngP+fj6WvQg5m8UEGBhnMWpORAEKUIACFKAABShAAQpQgAIUMCrAwDijRCygAAUoQAEKUIACFKAABShAAVMIDFnZNadOg+UAysSPV7lAY2T1z2OK4TkGBShAAQrYsoBG061Y5mJTbLlF9kYBCjiEAD+w7BDb6BiLYGCcY+wjV0EBClCAAhSgAAVkBRgYJyvFOgpQwAEE+PMXB9hELoECFhZoC2CeGebc+CIjYwgA8UvrrHYo4aFq74tW69XRJ2ZgHHDn3gOUeqsNrl6PtNh2D+vbDYP7dEl2PgbGmW8rFk35AS0bvZXiBGv+3olGHUz3u93MERgnFvD197/j+3EzzIeVzMgpBcaJU0RPojdLH/YUGOfoTq/vfa+BP2Lc9AWWviT08xkLjBvw3Tj88PufJustYWCcGHTP/qN4o3FHk40vM9DmhRNRq2r5ZEvtLTBOPE+nL1pLZunSNQnvF3FxcWjeuR9WbPhH+vy0For92TBvfLIhvLbYEwPjjO/6xNnL8e/WA0jn4g6NCI1zc4XWxeVlaJyLCI3T6YPjdK4ucPfyQba8BZCn1Bu49+Ah4sL3o1qJbMiUQqCt8Q5YYWsCp9YEJ2qpSBfwM+m2tknshwIUoAAFKEABClCAAhSgAAWcToDfnDvdlnPBFKAABShAAQpQgAIUoAAFrCdw4PLO4GPh+0Mu3jyOYtnfQOGsyb+hx3pdcmYKUIACFDCDgOKiQZlCmYsfNsPYHJICFKBAvIDaD+bx30h47ZhNYEPLd7aJwdPlzFVd/G/p9u+bbS4OTAEKUIACFKAABShgfQEGxll/D9gBBShgMQH+/MVi1JyIAg4hoAUQBiC/CVfzN4BB1g6Ke/VDaQbGmXBr0zYUA+Ne+oUcOoEKDTqkDVPy7Lo13sCa2b9BpxMPdcMHA+MkMVWWtWlaB/MnjjR6VkxMLPJUbGyyEEFzBcY9fvIUddp8gn9DLPt2AmOBcQJYzTVsdEMkC+wtMM6RnV7fshuRt1GwajNEPXwsuZumKzMWGHf24hUUqNzMZBO+HhgnBh4/YyE+/Wa0yeZIaaBvv+yBbz7rlOJc9hYYJxbTsfcwzFywymSGr98v7t5/gJK1LBMemylDII5s/guZMwamuB5b64mBccYvvydPn2HYz3/i+pmr8PX0gsbDHRoX3cvQOK1WHxwn/lur00Hn5gYPbx8EZMqMAmUrQ+fmjgend6JG6dzIEOhvfDJW2IUAA+PsYpvYJAUoQAEKUIACFKAABShAAQo4mQA/DOVkG87lUoACFKAABShAAQpQgAIUsKbA8Yij/0DRVIt8cAWZ/HJYsxXOTQEKUIAClhbQKNuLZS6hD83hQQEKUMBMAvzAsplgOWzqBZ4eCFV7XaZ+Mp5JAQpQgAIUoAAFKGA1AQbGWY2eE1OAApYXUPt9Lt+javk94owUsCWBNgDmm6ihLQCGANhpovFMMozCwDiTOJpiEAbG/V/xrxUb0bb716ZgTXaMsiUKY/28cciYPiDFedSEbYWsm4XgUkVTHE8TFCy1LhFiE3FkY4q1G//Zg7pte0qN16tzG/w2oq/BWjXBM1KTGSkSQX3Lpv8ETw93qeFmLVqDDz4Tt8+0H+YKjBOdPXr8BM069cWm7XvT3qjkCAsnj0KrxrVTrBahe+I6njp3meSoaS+zx8A4R3UytJtzlqzF+58OTvtGqxzBWGCcGM6UYWSGAuMURUGfoWPw69R5KrtXV/7hu43xx0+DUgwkFSPaY2DcqbMXUaRaS3UgKVQbul8cPXkWtd/tgchbd0w2z+sD+fp44e8FE1GhTDGpOWypJzXP278M7YPeXdtJrdHRisLOXcKP4+ZD8+AZ3D09AXc3aHVaaPRBcf+Fxml10Lm4vHysajTwDQhA0YrV4OntgehrR1CrYjHp1yuO5udo62FgnKPtKNdDAQpQgAIUoAAFKEABClCAAo4gwDfjOMIucg0UoAAFKEABClCAAhSgAAXsQODYjWMDNMD3dtAqW6QABShAATMJKMBXxbMUH2Wm4TksBShAAX5gmdeAzQkwMM7mtoQNUYACFKAABShAAbMIMDDOLKwclAIUsE0B/vzFNveFXVHAFgXEe9RPA8ifxuZEQNxXthYUF78mBsalcXdNeDoD4xJj7go9gnrteiLq4WMTKr8cSgRszfx1KLw8PYyOzcA4o0SqCtSGxcUP3n3A95g0a4mquQwVmzMwTsz39Nlz/DRxNgaNnqi611pVy+P3kV9i8uyl0mFWmxdOhDhP5li6dgs+/HyoWR5Tr89vj4Fx8WtwNCdD14YITWv4/mdYt2WXzKWTbE2nNk1w/vI1bNu1X2ocmcA4EbxYrVkXHDh6SmrMlIoMBcbF14vH2cdfjkzzHIYGGD2wF/r16CA1tj0GxomFmTLY1dD9QswRHnET73T8AiGHTkhZqikqWjAvVs/6FXlyBqk5zWZ6YmCc/LYt27ADixdsQjqdGzSeHtC6uUKj00Gr00Gj1UCn1SLm2XO4e7jAzc0Vjx8/gcbFA5Xr1YMWT5HdJxbBJQrJT8hKmxVgYJzNbg0bowAFKEABClCAAhSgAAUoQAEnFmBgnBNvPpdOAQpQgAIUoAAFKEABClDAUgJHI47m0yqaMwD4fail0DkPBShAAdsUUM7fPlzp4Pl/umkU7BvUZMpk22yTXVGAAnYqwA8s2+nGOXLbDIxz5N3l2ihAAQpQgAIUoMD/BRgYx6uBAhRwIoGhKteqtl7l8CynAAVsWKAlgEVp6G/Hi3PFPWRLGsYw26knpyLRz6MLNww121wcWE6AgXFJnS5dvY5+I37DolV/yyEaqfL18cLIr3qixwctodVqpcZkYJwUk1TRz0N6o1fnNnBx0UnVJyyKiYnF6Al/4ptRE1Sfm/AEcwfGxc917UYkxk1bgEWr/8b5S9dS7LlSuRL47KO2eLfJ29BoNGjdbYD0NX/o73koVbSgtIkIYBo1bibGTV8gfY6aQhEI2O395mhWv2aS00wdjCXuC8JK5hjcpwuG9e0mU6qvcSSn5Bb9+MlTDPxhAsZMmSftEl8o7qUTvh+A9i0aoEH7XtLBczKBcWKOu/cfYMiPk9N8naYUGCfm2bnvED4b9JNJwunEeCKEbMywPqhTvZK0qakfF9ITm6BwxYZ/8P6ng9IcQplcYJxoUVynw3+Zih9+/9MEHb8c4tNO7+LbL3vAz9c7VWPaQk8MjFO3dT9OnIf9u44jg48P4OkJrYvuZWicBlCio5E5iz8qVymNzJnS48aNW9i98yBu3X+CtxrVwa3IS2hRuzK8vT3VTcpqmxXQBAXzMwA2uztsjAIUoAAFKEABClCAAhSgAAWcTYDfpDvbjnO9FKAABShAAQpQgAIUoAAFrCBwIuL4LEVR3rfC1JySAhSgAAVsRCBOicPZGwdxInxvdGxcjCs0uBjnHVRgaM2hMTbSItugAAXsX4CBcfa/hw63AgbGOdyWckEUoAAFKEABClDAoAAD43hhUIACFKAABShAgUQC4v3px0T2RypcdgIYYqtBcfHrYWBcKnbWzKf8OGEW+n87VnqWmCv7oNPJhZ5JD5pMYam32uLISfE7FlM+RIjQg9PbjZWp/rqY+9tfp0mHaL0+QaYMgejfo4M+yMrH20vV/GJPxN7IHMe3LdQHBqV0+BWsJhWukzdXNpzbvSLFsUTY0ZvvfCTTGr76tCNGfvWJwVq1wTNt36mLKXOW4qdJs42uRVwT77dsiD5d30O+3Nmlek2p6NTZi5g6d5n+T9TDx6rGE7182qkNvhvQQ9V5aS0+c+EyTp+7jJu37yLyf+zdCZyN9R7H8e/YjS1LthFFyr4UkYRkCcmukiUVLUrayy17kqS4qRBCShIiWxIRibGUbUJkX8a+ZZ259zk1MszM+T/nnJl5znk+5/Xymnvz+2/v3/+ZyTTn69ARnT9/QXnz5FSBvHlUuUJp5bs2V7wlbq3f1jjAakfkTF1XMJ/tLR47flITpszWp5NmaMWaDbbHXz6gQe1qalK/lqyPhSPyJzpXoD+PzPz+J93bvpvR3gf85xm90qWDUe3lRaHg5O3Qi39ZrTZP/Ue79h7wVirrGXqifUuPZe6cOTz19z/xmiZNn+d1rFVweusSZc6U0ajWKvp1wya9OWS05ixYavt5L1Qgr4b0fVHNG9ZOcr3Y2FjNmr9EvQeP8PlZKFeyuN547jFPUKLdr8uBfi6McQNUePDwUX365Qx9NG6y13DMhJasVe1WLZjs/e/p3HfgkN4bMcGzjt3P/XHrPt2xtV7u0sGnz5kJ7T019/TtvMVq3OE5oy4OH9hdnds2N6oN1SIr5G/gh59r869blStrNoVlCVdaK7w25oJKlrperZrVVs7sWS8d/+Sp03rtjWHKliOnit4codLFI3T7reVClcd15yIwznUt58AIIIAAAggggAACCCCAAAIOFiAwzsHNYWsIIIAAAggggAACCCCAQCgIRO1fd8fFWFk/1M0LAQQQQMDFAmcv/KVZa0YrJvbiZQphHXo0Hm72DgEX23F0BBAwFiAwzpiKwpQSIDAupaRZBwEEEEAAAQQQSF0BAuNS15/VEUAAAQQQQMBxAq0lfWlzV0v/CYr73ua4VCknMC5V2FnUTwErwGPZqnX64acVivx1g/ZHH9a+6EM6cPDwpZmtoDUrIM4Krqp1+62qefstKln8BoWF8baTxPjtBsY917mNZ6ozZ89p6/Zd2r5rn/7cucfz68ixE54AtIj8eXXjDdepRtWKypA+vZ+dv3r4hQsXZQWx/bphs7bt2K3jJ07p+MlTynVNdh06ckyZMmZQtqxZlDd3ThUpVEBlS97ouRNOvwfLV69XlUbmwWZ2A7gSaoQVpLNm/SYtX71Ov/+xXYePHpcVlGYF3GUJz+zxzJkjm7Jny6KcObJ7+msFZFUse7Pn2UqO/gb8wgRgwlB2ss5mBQeui9rieaZW/rZRR4+fUNbwcOXKmd0ThHl39cpqVKe6MmbIEABNe1NYoW6btu7QL6vWafuuvTpx6rQuXLjgeZ7PnTuv9OnTKdc1OTx388brr1O5UsUvBdrZWcn6mrJo2Sr9sGSF1v++VfujD2nvgYOXAsqswDwr6DHftblV4sbrVbt6Zc/nuIL5rrWzTMjW7ti9T2s3bpEV7HnsxEnP5xHr84YVKmd9ngjPnMnz9blg/jwqW+JG3VS0iNJZoV02Xtbn/lVro/Tjzyu1+Jc12r3vgOffAS4PPLTCAq11CuTLozsql1fN229VpfIlk+1zlRP3ZIPUNaU79x5Qn3dGS8fPKUvWbEqTKYPCs6RXu4fu0c1FC8dz2PLnTr3z4WSFh6VX9msyqmLF4mp6Ty3XWIX6QQmMC/UOcz4EEEAAAQQQQAABBBBAAIFgEuC/3AVTt9grAggggAACCCCAAAIIIBCEAuv3rZ0qhTUNwq2zZQQQQACBAAus2b5Qfxz47fJZN71x74gSYWGyG/IU4J0xHQIIhIjAQpvn4KdSbYJRbl+AwDj7ZoxAAAF3C5w+c0bb9+wxQihaqFCqvMHOaHMUIYCA6wQIjHNdyzkwAggggAACCCQuYP1s+jpJpQyRlv0TFPedYb0jygiMc0Qb2ESABKwwoYsXY2wHzwRo+aCfxtfAuKA/uAMP0LBtV83+wcof9f6ywqrmT/rIeyEVCCDgl0BMTIxiY6W0adP4NQ+Dk1fACm6zG0CXvDuSnLin5D6zk+eP/C1KA4dM0LXpw5Upa2alz55ZDetXVo0qt1za9r4Dh/T+2CmKSZdLsQcO6NjJw2rSpJYa1bnDyUdjbzYECIyzgUUpAggggAACCCCAAAIIIIAAAsksQGBcMgMzPQIIIIAAAggggAACCCDgZoHfD66rfOGClrvZgLMjgAACCPwrcOb8Kc36dYxiY2Mu/cPYMLXuee+Ir3BCAAEEEEAgFAUIjAvFrnImBBBIToGPJ01StwEDjZYY91Z/ta5fz6iWIgQQQCC5BdZ8Nt6zxLEd23+0PtafPI2A6uRGZ34EEEAAAQQQcKpAq/+HxU0y2Nwv/6/youwSAAAgAElEQVTpJWmOQa1jS2L3RPKXIjm2O2wMgZQRIDDOf2crFKjafR119ux51a1ZRQ80qadby5VUWJjZ252swMO+73+i3u+OMN7MoB7d9MITbY3rKUQAAQQQQMDNAlb44+wFv2jE6Km6Lts1ypYvt7Jcm02li0foxiKFtH13tFZt2qGYzLmV4cxZ7V37m47rvPp176zrIvK5mS4kzh41s1K8c5TsJLN/SQuJ03MIBBBAAAEEEEAAAQQQQAABBJwpwB/OndkXdoUAAggggAACCCCAAAIIhITAhn3rP4lV7KMhcRgOgQACCCAQEIGV277Xnwc3XJorTFr3RuMRZQMyOZMggAACCCDgMAEC43xryM59+xR95IitwRF58ypf7ty2xiRWvHbzZp2/cMHWXCWLFlXmjBltjaEYAQSuFvhw4pd6fuA7RjRj+vXVgw0bGNX6UrR+yx86e/6cL0O9jsmYPoNK31jMax0FCCAQfAKZbqnEz2IFX9vYMQIIIIAAAggETsD6d6F1/w+MK5XElCv+/3tWUNyswC2bejMRGJd69qyMgFMECIzzvxN/nTmr8KJ3xJsob55cat24ju64rYIqlS+pooUjlCZNmks1Vshc1JY/tWjZKg355Att2rrD1kZ+nf+FypUsbmsMxQgggAACCLhZ4Oy5c5oxb4nGjZ+p63PnUe6i1ytD/vw6o0xSmrTKni2bwo5Ga/uKZdodfUDNW9dV63vvVrp0ad3MFhJnJzAuJNrIIRBAAAEEEEAAAQQQQAABBEJMgB9SDLGGchwEEEAAAQQQQAABBBBAwCkCvx39LWfaM2kOSfxNYk7pCftAAAEEnCBw8sxRfbd2vGIV++92YmNv63HfSOsNQrwQQAABBBAIKQEC43xr512PPKqf1/xqa3CtypU1Z/hHtsYkVBy1bZsqtGhle54Jbw9Qi7p1bI9jAAIIxBdwSmDc6TNnlKta9WRtz+GlPyk8U6ZkXYPJEUAg5QUIjEt5c1ZEAAEEEEAAAUcJtJA0OZEdrfz/P7eC4r511I793AyBcX4CMhyBEBAgMM7/JiYUGJfQrFaIXNYsmXXy1F86cPCwzwvfWaWiFk0d6fN4BiKAAAIIIOBWASs07vvFKzX2s+nKni6z8he5QZnzFZIUq78O7FP0jq06cOKo6tStqvatGipb1nC3UoXUuQmMC6l2chgEEEAAAQQQQAABBBBAAIEQESAwLkQayTEQQAABBBBAAAEEEEAAAacI9Pm20yNhMWE5apW6PzxX1nz9nLIv9oEAAggg4ByBxb9P1YHjO//dUFhYnx73Du/pnB2yEwQQQAABBAIjQGCcb46+BMZZKy37fIIqlLjZt0X/GfVM/7c0cvLXtucYP6C/WtWrZ3scAxBAIL4AgXHcCAQQCHYBAuOCvYPsHwEEEEAAAQT8ELB+Jn2dpFJXzLH6///fCoqb7sfcjh1KYJxjW8PGEEgxAQLj/Kc2DYzzf6W/Z1j27aeqckuZQE3HPAgggAACCLhK4MKFi4r6Y4c++2q2Nq3fpgwKU2xsrM4rVnki8qjFfXfpztvKKzwzf2lSqFwMAuNCpZOcAwEEEEAAAQQQQAABBBBAIJQECIwLpW5yFgQQQAABBBBAAAEEEEDAAQJ9ZnReLqmytZXcWQuoctF6ypIxhwN2xhYQQAABBJwi8MeBX7Vm+4+XthMmrXmj8YiKTtkf+0AAAQQQQCBQAgTG+Sbpa2Bc28b36pPe1vuPfXtFHz6s6+r4FvpGYJxv5oxC4EoBAuO4EwggEOwCBMYFewfZPwIIIIAAAgj4IdBc0uUp/Gv+P5f1jZpv/JjTsUM3jlTs5Zsr0SjSsXtlYwggkLwCBMb575uSgXFtmt2jCcP4+0/97xozIIAAAgj4LHDhopQurc/DnTDQCog7c/a8tm7fpZW//a5YxermokVU+uYblCU8k9KkSeOEbbKHAAuEFazEe9EDbMp0CCCAAAIIIIAAAggggAACCPgqwB/SfZVjHAIIIIAAAggggAACCCCAwFUCA+Z1znHujI5I8vx5MywsTPfd8oTSpUmPFgIIIIAAApcEzpw/pZlrRsUTuRhzIaJ3k9F7YEIAAQQQQCAUBGa3bFrLOkfFh9otsD5eU6RIKBwrxc7ga2CctcGtc2apYN68Pu31rU9GqfeHH/k0lsA4n9gYhMBVAgTGcSkQQCDYBQiMC/YOsn8EEEAAAQQQ8FHA+vmAdZJKSfrt/3NYQXHTpPihaj7O7chhBMY5si1sCoFUESAwzn/2lAqMq3prWX33xTBlyxru/6aZAQEEEEAAAQQQcJkAgXEuazjHRQABBBBAAAEEEEAAAQQQcLQAgXGObg+bQwABBBBAAAEEEEAAAQSCS6D3jE4PhSnss7hd58laUDVLtgyuQ7BbBBBAAIEUEZi/fqKOnj5waa2wMD31xr0jfEtoSZEdswgCCCCAAALmAnNaNo29vLpW99fNB1MpfwLjXn6ko/o83cW24l9nz6pwnbo6ceq07bHWAALjfGJjEAJXCRAYx6VAAIFgFyAwLtg7yP4RQAABBBBAwEeBZpL6/H+sFRQ3JZSD4uJ8CIzz8aYwDIEQFCAwzv+mpkRgXLmSxbVwynDlzJHd/w0zAwIIIIAAAggg4EIBAuNc2HSOjAACCCCAAAIIIIAAAggg4FgBAuMc2xo2hgACCCCAAAIIIIAAAggEn0DfGZ3HxUrt4nZeutDtKlGgcvAdhB0jgAACCCS7QNSeFVq/++dL68RKw3o2HvF0si/MAggggAACCKSAAIFx/iH7ExiXLUu4ts6ZI+ujnde46dPVuZf1vmbfXgTG+ebGKASuFCAwjjuBAALBLkBgXLB3kP0jgAACCCCAgI8C5ST95uPYoBxGYFxQto1NI5AsAgTG+c+anIFx2bKG65lHHtArXTooe7Ys/m+WGRBAAAEEEEAAAZcKEBjn0sZzbAQQQAABBBBAAAEEEEAAAUcKEBjnyLawKQQQQAABBBBAAAEEEEAgOAX6zOi8X1LeuN3fXfoBXRN+6f8G56HYNQIIIIBAsgicOHNEW/atVu5sEUdWb//25u4Nx0Qny0JMigACCCCAQCoIEBjnH7o/gXHWyu+/+rKeaN3aeBMxMTEq36KlNm/fYTzmykIC43ymYyAC8QQIjONCIIBAsAsQGBfsHWT/CCBgINDLoObyErv1NqenHAEEEEhdgdg9kbGpuwNWRwCB1BYgMM7/DiRHYFyhAnn16tMPq32re2WFxvFCAAEEEEAAAQQQ8E0gamaleANLdhLvSfeNklEIIIAAAggggAACCCCAAAIIBEyAP5wHjJKJEEAAAQQQQAABBBBAAAF3C/Sa+sT1adLFbItTSJ82g+675Ql3o3B6BBBAAAEjgZiw2BvL5iv7h1ExRQgggAACCASBAIFx/jXJ38C4iHx59fu3M5QubVqjjXy3dKnue7qrUW1iRQTG+cXHYAQuCTglMO7suXPKUbVasnbm6LKlypQhQ7KuweQIIJDyAgTGpbw5KyKAQIoL2A1G4mdUU7xFLIgAAikpQGBcSmqzFgLOFJg6e4GaP/qS0eY+ePNldelo/pedGE0aIkUHDh7WmvWbFPnrBi1fvV5ro7Zo6/bdtk53U9HCuq1iGTW9p5aa1K+pdOnM/huBrUUoRgABBBBAAAEEXCZAYJzLGs5xEUAAAQQQQAABBBBAAAEEgkKAH8YJijaxSQQQQAABBBBAAAEEEEDA+QK9Zzx+d5hiv4/bab4cRVT9pibO3zg7RAABBBBIfYHYi61KFyg/OfU3wg4QQAABBBAIjACBcf45+hsYZ60+cdBANa1d22gjjZ7qovnLfjGqTayIwDi/+BiMwCUBpwTGJdWSep0f16LIlUZdu6NiRc0fNdKoliIEEAgNAQLjQqOPnAIBBJIUIDCOC4IAAghcJkBgHNcBAQRiYmJ04uRpI4isWcKVNm0ao1qKpNjYWI/t0eMn/v51zPp4UidOnlLGDBmUPVsWWaY5smVV0SIRypwpI2wIIIAAAggggAACARYgMC7AoEyHAAIIIIAAAggggAACCCCAQAAECIwLACJTIIAAAggggAACCCCAAAIISL1ndOoUprARcRZF85ZVxSJ3QYMAAggggIBXgdgw9SyTr0wfr4UUIIAAAkkLLLQJVMtmPeUIGAsQGGdMlWBhIALjbitbVovGjvG6kd82bdJtD7TxWuetwJ/AuHPnz+vQ0aM6ePSo5+OBw0d04tRJhWfKrFw5cihn9my6Jlt2z8cc2bIpQ/r03raTIr//19mz+nP3bu07eFD7Dh5SeOZMKnlDURW9rpDSpU0b8D1cjInR0ePHFX3kiMfJ+njk2HGlSZPGs3aWzJmVJVNm5c2dSzcUKqRMGTIEfA9XTmjtaduuXR6DvQcPKiYmVkUKFFCRggWUP08ehYWl7n+OP3vunNb/8YcOesyO6cjx4zp20rpbmZQre3blzJFD+XLnUtmbbkpWr9NnzujP3Xu058ABRR85LClMWcPDVSh/PpUsWjTe2gTGBebaWs/Gtj27tedAtI6dOKFrc+XUdfnzq3CBAp5nxe4rbj6rjxcuXtRNRYropuuLeO5SoF886/ZFnfKs2995aI4gMC40+8qpEEAgngCBcVwIBBBA4DIBAuO4DggggAACCCCAAAIIIIAAAqEsQGBcKHeXsyGAAAIIIIAAAggggAACCASrQOr+hHqwqrFvBBBAAAEEEEAAAQQQQACBqwR6T+80ICws7JW43yhb6A7dVOBWpBBAAAEEEPAqEBYWNr5UvtLtvRZSgAACCCQtwBuWuSGOESAwzr9WBCIwztrBwjGjVbV8uSQ306lXb42fPsO/DUsyCYyzwrrWREVp9cYoWUF1v/6+Sbv279OJU6dtrV+pdCnVvf123XnrrapSrqxP4U9fzJql3Qeiva6bPUsWdW7VMl7d4pWrNGHmTH067ZtEx1t7rFahgl7r9JhyZs/udZ3LC6wAPcvm199/9/xavXGjtu/dp+jDVtCY+euGiAhVLlNa99aqpbq3V7W9j6RWWrpmjb6a+50mz/s+yX1VLFFCLevX06PNm+mabNk07YcftGXHTq+HKHjttWrTqKHXuoQKduzdq2k/LND8Zcs0d8lS4zlqVa6se6rfoQ5N7guIlRX69d2SpfpyzhxNnD0nyX1Yaz/z0IO6p3p1DZ/0lZ4f+I7Rvsf066sHGzYwqg1kUb3Oj2tR5EqjKe+oWFHzR400qo0r8vX5tMy//fFHz7M5e/FPia75cNMmeuqB+1XuppuS3JcVDDdr0WJZIX4LV6xIsLZ4kcKqXaWKej31pO17w7MuhcKzbutyh3jx0e3bPSdcPWG8529RaTB5mt1A6xAX4ngIIBBCAnz/JYSayVEQQMB3gY0jFe/zYYlGkb5PxkgEEEAAAQQQQAABBBBAAAEEHC4QVrAS70V3eI/YHgIIIIAAAggggAACCCCAgHsE+EO6e3rNSRFAAAEEEEAAAQQQQACBZBXoO6PzpFipVdwiVYo1UKFcxZN1TSZHAAEEEAgRgbDYRaXzla0ZIqfhGAggkHoCvGE59exZ+QoBAuP8uxKBCoxrUbeOJrw9INHN7I2O1g31AxM25S0w7p0xn+qN/37gH0wCo7NlCdd/Onf2hLqFZ8pkPH/Jxk20bfduo/ozq/5+w/OWHTv0wjuDbIWQReTLqwkDBngN7ovbiBXCZYVxJcfrwYYN1fPJJ3R9REGfp9/053a99v4QzVy0yNYcVp+eadNG/Ud+YjTOcvtj9iyj2rgiKyhu0KdjNeKrybbGJVT8n86d1LXtQ8qRNatPc63dvFld+vXX8rVrbY23Qv5O/vWXcThgqAbG+fJ8+mL+UseH1bvLU0qTJs1VfbJCER99o6fx54lrc+XSFwMHqPottxj1nGf9b6Zgf9aNmu2iooX9+8U77T2Tp/EzWS7qP0dFwGUCfP/FZQ3nuAggkLAAgXHcDAQQQAABBBBAAAEEEEAAATcJEBjnpm5zVgQQQAABBBBAAAEEEEAAAacLhG2Y1druD/A4/UzsDwEEEEAAgaATKNVwEm8YCLqusWEEEEAAgSsF+s7ovDJWuvSu0Nql7lfOLPmAQgABBBBAwKtAWFjYplL5St/stZACBBBAIGkBu/+9g+/HcKOSTYDAOP9oAxUYZ+1i/TdTVey66xLcUJ+PPjYO8PJ2Im+Bcd2HDNXgseO8TePz71thTaP69FK9atWM5rATSHU6crmGfDbBE5Tm6+vNZ7vqhQ7tvQ6ftXixmj/7nNc6fwr6PvO0rKAsu68v58xVh+7/sTvMp3o7IVKxsbF6b9x4WXcskK/iRQpr2tAhiT4/ia31/vjP9Op77wdyK4nORWCcZD2fA8d8ql7DPvTJvE2jhhrRq6fSpU3rGX/6zBnPXEMnfO7TfANfeF5dH2rjdSzP+t9Ewfyse22yCwsIjHNh0zkyAu4V4Psv7u09J0cAgcsECIzjOiCAAAIIIIAAAggggAACCLhJgMA4N3WbsyKAAAIIIIAAAggggAACCDhdgMA4p3eI/SGAAAIIuEKAwDhXtJlDIoAAAiEv0HdGp9diwsKKFLqmWMcLMRcy3Fa0vjKkyxTy5+aACCCAAAIBEThUOn+ZPAGZiUkQQMDNArxh2c3dd9jZCYzzryGBDIx7pk0bvfPi81dt6OTp07qh/j06ceq0f5v9Z3RqB8bFHWLC2wPUom4dr2eyExjXuFZNzVj4o9c5vRUsHDNaVcuXS7IsJUKkrA1YgVYDnuumNGnSeNu25/c/+nKSnnt7oFFtIIpMQ6T+OntWXfq9qc9nzgrEslfNkS1LuOYM/1i3liplNP+bI0aq78fDjWoDUURgnNSoRg3NXLTIL873X31ZT7RurT0HDqhFt+e1OirKr/k2TJ+mooUK8awbKAbrs25wNFeWEBjnyrZzaATcKsD3X9zaec6NAALxBAiM40IggAACCCCAAAIIIIAAAgi4SYDAODd1m7MigAACCCCAAAIIIIAAAgg4XYDAOKd3iP0hgAACCLhCgMA4V7SZQyKAAAKuEVi/b90ZSRldc2AOigACCCAQCIGzpfOXIWU0EJLMgYC7BXjDsrv776jTExjnXzsCGRhn7WTvgh+UM0f2eJsaOflrPdP/Lf82etlopwTGWVuKnDRRZW68Mcmz2QmMCxRSrcqVNWf4R0lOl1KBcdYmhr3eXY82b+71eCm5p7jNmIRInTt/Xvd2eVqLIld6PYM/BdZeIr+cqJzZ4z9DV86Z0qF61voExvnT2X/HWsGAnw0YoMd69lL04cN+T9q6fj2Ne6s/z7qBZDA+6wbHcm0JgXGubT0HR8CNAnz/xY1d58wIIJCoQOyeSLufF9FEAAEEEEAAAQQQQAABBBBAIGgEomZWirfXkp0UFjSbZ6MIIIAAAggggAACCCCAAAIIhKgAgXEh2liOhQACCCAQXAIExgVXv9gtAggggEDSAuv3rYuR+I/B3BMEEEAAAVsCsaXzl0ljawTFCCCAwNUCdt+Yxw8wcouSTWBuy6YLrclzFC5S0/pYoW27ZFsrFCcOdGDcm8921Qsd2l+iuhgTozJNmmnb7t0B43NSYNztFcpr/icjlSZN4v96lRqBcRb23OEfq2bl+D9QfnkTUjKczQrJWj9tmvLmzpXoPdh/6JDKNG2mE6dOB+yumExkEiL18ruDNXTC5ybT+V3T7O7a+uKdgYnOE7Vtmyq0aOX3OnYnIDDOrljK1S8eN1aVy5ROdEGe9b9pgu1ZT7kbFJwrERgXnH1j1wgg4JMA33/xiY1BCCAQqgIExoVqZzkXAggggAACCCCAAAIIIICAJUBgHPcAAQQQQAABBBBAAAEEEEAAAecJXBUYV7L6y87bJTtCAAEEEEAgxAQ2/hT/jVUExoVYgzkOAggg4HKB9fvW2X2jiMvFOD4CCCCAgCVQOn8Zgpu4Cggg4K+A3X8P5fOOv+KM9ypwZlWk3XvpdU43FAQ6MO7aXLm0Zda3ypghg4dvxsIf1er5FwJK6aTAOOtgC8eMVtXy5RI9Y2oFxtWodKu+GzE80X2lZIiUtYl+XZ/Riw93SHQ/7V/rrklzvwvoXTGZzFuI1Ffffad2r3Y3mepSjRWQV6Z4cZ08dVprN2+2NdYqXjR2jG4rW/aqcRcuXtSd7TpodVSU7Tn9HUBgnL+CyTe+Rd06mvD2AJ51L8TB9Kwn320JnZkJjAudXnISBBDwKmD3z7l8/8UrKQUIIBDMAgTGBXP32DsCCCCAAAIIIIAAAggggIA3AQLjvAnx+wgggAACCCCAAAIIIIAAAgikvACBcSlvzooIIIAAAgiIwDguAQIIIIBAKAsQGBfK3eVsCCCAQPIJEBiXfLbMjICLBHjDsouaHSxHJTDOt07ZCYyzwr4GfTrW60Jj+vXRgw0beupM5jedN25hfwLjrCCviiVLqnSxYrqhUCHFxMTo8LFjOnj0qBb8slzbdu/2er4rC17q+LD6PvN0ouNSKzDO2tDpyOVKkyZNgnvzFhhXqXQplSxWTDcVKaLMGTPqyPHjHqfVG6O0fO1a207WfD+NH5fguK27dqnUfU1tzxmIAUmFSJ0+c0ZF6tbTiVOnjZaqf0c1vffKyypaqNCleivkbdKcuXrkjR5Gc1hFrevX07i3+l9VP3X+D3rwpdT5S+kIjDNuX4oXWkGdO79PPGyRZ/3vlgTTs57ilygIFyQwLgibxpYRQMBXAb7/4qsc4xBAICQFCIwLybZyKAQQQAABBBBAAAEEEEAAgX8ECIzjKiCAAAIIIIAAAggggAACCCDgPAEC45zXE3aEAAIIIOACAQLjXNBkjogAAgi4WIDAOBc3n6MjgAACPggsivraMyr6xO4frY89Go+o5cM0DEEAAQQsAd6wzD1wnACBcb61xCTQLW7mVV9N0i2tWntdqGzx4lo+8XNFrt+gO9t38FpvOm/cRHYD4yqWKKH2Te5Tk7tqqWDevEnuZ+PWrRry2QR9Ou0br/uOK7Dm//nzzxKt9ycw7oaICM+8vgTZWeP+/G6O8ufJk+DergyRssKU2jdurBb16noC9cLCwhI9076DB/XlnLl6ZfB7xk5W4b4fF+iabNmuGvPSoMH67+ef25rLCv97/fHHdWupkipVrJhy5cjhCXaL2rZVsxf/pP4jPzGaL6kQqRFfTVbXtwYYzdP/2a56rn27RN3+2LlTpZs0M5orsd7V6NDRdljfHRUr6tHmzVT2puIqccMNio2N1c59+zR/2S8aOflrrd282WhPBMZdzVS8SGFt3r7DyM9bkXWfrWfV1/m2zZ2tAtdey7OeBHQwPeve7gu/LxEYxy1AAAEXCfSyeVa79TanpxwBBBBIHYGNI+N/P7pEo8jU2QirIoAAAggggAACCCCAAAIIIJACAmEFKyX+H+tTYH2WQAABBBBAAAEEEEAAAQQQQACBfwUIjOM2IIAAAgggkAoCBMalAjpLIoAAAgikmACBcSlGzUIIIIBASAh8vWJovHP0aDyCHywKic5yCARSRYDAuFRhZ9GkBAiM8+1+2AmM2zpnlt4f/5mGTvAe7DXrow81bvp0TZw9J8mNdX2ojZ5t21bFGjQ0PoC3wLgvZs3WxNmzdVvZsmpVr55uur6I8dxWoRWoZQWhmZzTqr82Vy7t/P67RNewGxhXsmhRWeFjNStXUnimTJ55T/31l6bM+16devW2dZbF48aqcpnSCY6xgsJeH/pfFS1USK3q11PVcuWUJk0aW/NPnf+DHnzpZeMxv035+qp+XLh4UVkrVzGewyq0QgknDhqoYtddl+i4TLdUMpozsRCpc+fPq1iDRoo+fNjrPFYo27yRw7369fnoY+MgO+t8TWvXvrT28rVrZQXG2Xl1btVS7770otKnS5fgsA8nfqnnB75jNCWBcX8zWb3u+eQTqly2jDJnzKiYmBht2r5dzbp28ynY8cn7W+vpNg96nkMrpPHsuXP6bunPavX8C0Z9iSuyQiut8MqEXjzrf6sEy7Nuq/EuLiYwzsXN5+gIIIAAAggg4EoBAuNc2XYOjQACCCCAAAIIIIAAAgi4VoDAONe2noMjgAACCCCAAAIIIIAAAgg4UIDAOAc2hS0hgAACCIS+AIFxod9jTogAAgi4WYDAODd3n7MjgAAC9gUIjLNvxggEEEhUgMA4LofjBAiM860ldgPjTp7+S+Wat/C6mBXMs3v/Aa91VoBYlsyZAxoY53VRg4ITp07r2jtrGFT+XXJyxS9KlzZtgvV2AuPG9Ouj1vfco7SJBLd9Ou0bPdGnr/G+Ph/4tprXudu43pdCK8TMCjMzeS0YPUq3Vygfr3TVxo2q9lA7k+GemuJFCuuXLz6/FKaX2EB/A+N+WrVKdR7rbLSv5RM/V7mbbvJae+TYcRW4698QuKQGPN+hvSc4MO7Vb/gIWb9MX6899qh6PvVkkuUExkl2nk8rrLJl3bqeYLcrX1t27FCZps1N2+O5x9bzaYUfJvQaP32GrYDIeSNH6M5bbzFe35dCnnUpJZ51X3rjxjEExrmx65wZAQQQQAABBNwsQGCcm7vP2RFAAAEEEEAAAQQQQAAB9wkQGOe+nnNiBBBAAAEEEEAAAQQQQAAB5woQGOfc3rAzBBBAAIEQFiAwLoSby9EQQAABBERgHJcAAQQQQMCOAIFxdrSoRQABLwILbQrVsllPOQK2BQiMs03mGWA3MK5g3rxq3OUZzfv5Z98WvGxU/Tuq6Zv/DvUEyxVr0NB4Piu0qVW9esb1dgpjY2N17ORJHT52TDUffkTRhw8bDT/40yJlDQ9PsNZOINWZVZFJrmftr3Dd+sb7GvBcN3Vr19boDHaLLly86NnHS4MGa/K8eUbDvx32gercXjVe7Qeff6EXB71rNN4qGtOvrx5s2MBrvb+Bcf1HfqI+H33sdZ1sWcJ1YNGPCYaIJTTYuusmYYq3lS2rRWPHXJri7kc7acnq1V73Y84zKf8AACAASURBVBVYe/pjzmxlz5IlyXoC4+wFxnl7Pps801Vzlyw16lGbRg01um+fRGv/OntWOW+/w2guq2jKkPfU8M47jevtFPKs/62VUs+6nd64uZbAODd3n7MjgAACCCCAgBsFCIxzY9c5MwIIIIAAAggggAACCCDgXgEC49zbe06OAAIIIIAAAggggAACCCDgPAEC45zXE3aEAAIIIOACAQLjXNBkjogAAgi4WIDAOBc3n6MjgAACPggQGOcDGkMQQAABBIJGgMA431rlS2Dc7MU/qdmz3Xxb8LJRVlicFRqX0oFxVsjZ2s1btGn7n9q5d5+279mrP/fs1va9+4yD2K48fEoFxlnrtn3lNeOAtv907qQ3nnjcp16d+usvrd+yRRv+2Ko90dHad/Cg9kZHa9e+/dp14IBPVgkFxtk5zw0REVo7bYrSpU3r9Uz+BsaZBrRZIVKTBw/2up+4gravdTeyi8iXV3/MnuUZdvrMGeWqVt14jRcf7qB+XZ/xWk9gXGAD4/oNHyHrl8nLW2CcNUfl+x/U2s2bTabTF+8MVLO7axvVXlnEs24WxpgSz7pPDXTpoDWfjfec/NiO7T9aH+tPnkZAtUvvAsdGAAEEEEAAAXcJxO6JjHXXiTktAggggAACCCCAAAIIIICAmwSiZlaKd9ySnRTmpvNzVgQQQAABBBBAAAEEEEAAAQScKEBgnBO7wp4QQAABBEJegMC4kG8xB0QAAQRcLUBgnKvbz+ERQAAB2wIExtkmYwACCCCAQBAJEBjnW7N8CYy7GBOjMk2aadvu3b4tKskK/lr3zVSlTZMm2QPjzl+4oO9/XqZJc7/TopWRnvUC/UrJwLgeHwzTwNFjjI5gNzBuxbr1mjRnrr5ftkwbt241WsNOUUKBcabBbNY6ds7jT2BcTEyMwivdZudoyVJ7ZlWkZ97fNm3SbQ+0MV4jasZ0XR9R0Gs9gXGBDYz7et73euiVV726WwUmgXGP9eylz2Z8azTfxEED1bS2eWAcz/rfrE571o2aTVE8gUy3VOKNUtwJBBBAAAEEEEDARQIExrmo2RwVAQQQQAABBBBAAAEEEHChAIFxLmw6R0YAAQQQQAABBBBAAAEEEHC8AIFxjm8RG0QAAQQQCEUBAuNCsaucCQEEEEAgToDAOO4CAggggIAdAQLj7GhRiwACCCAQbAIExvnWMV8C46yVPvj8C7046F3fFpU0+OWX9NQD93vGWwFuxRo0NJ5r/ID+alWvntf6oydO6N1Px+rjSZN04tRpr/X+FKRkYNzgsePUfchQo+2aBKxduHhRY6ZO09AJE7R5+w6jeX0tSigwrmLL1sbhdMN79lCHJvcZLe9PYNyR48dVoJZ5+JbRhnwoOv7Lz8qQPr1+XBGp+o8/YTzDXytXKCzMe4YSgXGBDYybvfgnNXu2m1GfTALjur41QCO+mmw0n0lgHM96Xv0xe1Y8T6c960bNpiieAIFxXAgEEEAAAQQQQMBdAgTGuavfnBYBBBBAAAEEEEAAAQQQcJsAgXFu6zjnRQABBBBAAAEEEEAAAQQQCAYBAuOCoUvsEQEEEEAg5AQIjAu5lnIgBBBAAAFJvWd07mVBlCpYpafnY0QVXBBAAAEEEPAqQGCcVyIKEEAAAQSCWIDAON+a52tgnL8hO3sX/qCc2bN7Np0cgXHTFyxUlzf7K/rwYd9gbI5KycA4KwCv24CBRjv0Fhi3dvNmPdmnryLXbzCaz9+ihALjrr2zhnGg36yPPlTtKrcZbcOfwLitu3ap1H1NjdZJzqK45+SbBQt0/wsvGS1VvEhhrZ06xaiWwLjABsb98MtyNXzyKSN7k8C4F94ZpGFfTDSaz1tgHM+6FJHv6sA4pz3rRs2mKJ4AgXFcCAQQQAABBBBAwF0CBMa5q9+cFgEEEEAAAQQQQAABBBBwmwCBcW7rOOdFAAEEEEAAAQQQQAABBBAIBgEC44KhS+wRAQQQQCDkBAiMC7mWciAEEEAAAUl9ZnSOvRyiReWuuCCAAAIIIOBVgMA4r0QUIIAAAggEsQCBcb41z9fAOGu1Zwe8reGTvrK98OOtW2nIq69cGhfowLhBn47V60P/a3tf/gxIycC4cdOnq3OvPkbbTSow7qdVq1Tnsc5G8wSqKKHAONNgN2sPKyd9qdI3FjPajum8CYVIrdywQXe0bW+0TnIWxQXGjf1muh7vbdbzu6tW0cwPhxlti8C4wAbG2XmmTALjug8ZqsFjxxn1MqnAODv7MlrMoIhn3QDpspLLQ1TtjaSawDjuAAIIIIAAAggg4A6BjSMV7+cjSjSKdMfBOSUCCCCAAAIIIIAAAggggIArBcIKVgpz5cE5NAIIIIAAAggggAACCCCAAAIOFCAwzoFNYUsIIIAAAqEvQGBc6PeYEyKAAAJuFCAwzo1d58wIIICA/wIExvlvyAwIIIAAAs4TmN2yaS1rVxUfarfA+nhNkSLO26SDd+RPYNy6LVtUqfUDtk93ZehXIAPjvpg1Wx1ff8P2nvwdkJKBcZPmfqf2r3U32nJigXEb/tiqmg8/rBOnThvNE6iihEKkrr2zhvE+lk/8XOVuusloO/4Exi1euUp1O6VsmF5Ch4oLkbIT7Na6fj2Ne6u/kZGdecf066sHGzYwmjeQRfU6P65FkSuNpryjYkXNHzXSqDauqGTjJtq2e7fRmDOrkg4kWPbrb6rV8RGjuUwC43p8MEwDR48xmi+xwDie9X/5EgqHdNqzbtRsiuIJEBjHhUAAAQQQQAABBNwhQGCcO/rMKRFAAAEEEEAAAQQQQAABBP4WIDCOm4AAAggggAACCCCAAAIIIICAcwQIjHNOL9gJAggggICLBAiMc1GzOSoCCCDgIgEC41zUbI6KAAIIBFCAwLgAYjIVAggggIBjBOa0bBp7+WZqdX/dMXsLho34Exhnne+ex5/UwhUrjI9aq3JlzRn+Ubz6QAXGHT52TAXvutt4LwkVXpsrl4oUyK8iBQvq63nfG8+VkoFx1r4eeuVVo70lFhhXvV17Ra7fYDRHQkXZsoTr+oIRKlyggBatjDQOfEsoMM5OYJd1d6w7ZPLyJzBuTdTvqtrmIZNlkrUmLjBuzNRperJvP6O16t9RTd/8d6hRLYFxkp375y0wbvnatarRoaORvUlgXM9hH+rtUaON5kssMI5n/V++hALjnPasGzWbongCBMZxIRBAAAEEEEAAAXcIEBjnjj5zSgQQQAABBBBAAAEEEEAAgb8FCIzjJiCAAAIIIIAAAggggAACCCDgHAEC45zTC3aCAAIIIOAiAQLjXNRsjooAAgi4SIDAOBc1m6MigAACARQgMC6AmEyFAAIIIOAYAQLj/GuFv4Fx0xcsVOsXXjTexFeD31XjWjXj1QcqMG7IZxP0yuD3jPdiFd5Wtqwa3Fld1cqX1y2lSskKQot7NXmmq+YuWWo0XzAFxllBcVaIlJ2XFbR0/z336Pby5XVb2TLKlzv3peHvjRuv194fYjRdQoFxt7dpq9VRUUbjJ7w9QC3q1jGq9Scwbue+fSre8F6jdayiN5/talxrWpghfTo9ef/9Spc2rb767ju1e7W70dCyxYtrxZdfGNUSGBfagXE86/Efg4QC45z2rBs9uBTFEyAwjguBAAIIIIAAAgi4Q4DAOHf0mVMigAACCCCAAAIIIIAAAgj8LUBgHDcBAQQQQAABBBBAAAEEEEAAAecIEBjnnF6wEwQQQAABFwkQGOeiZnNUBBBAwEUCBMa5qNkcFQEEEAigQPSJXZ7ZFkdNu8v6+EbjjxcGcHqmQgABBBBAIFUECIzzj93fwLgLFy/q5nsbywp98/aywnp+/3aGJwDr8legAuOKNWhotI+4tT/u8YY6NLlPYWFhCW49VAPjuvTrr1FTpnhr16Xf7/pQG/Xq8pTCM2VKcIy/gXGNnuqi+ct+MdrPk/e31nuvvGxU609g3MnTp5Wneg2jdayio8uWKlOGDMb1dgut4ELrPpq8rNDD6MWLTEpFYFxoB8bxrMd/DBIKjHPas2704FIUT4DAOC4EAgi4QKCXzTParbc5PeUIIIBA6grE7omMTd0dsDoCCCCAAAIIIIAAAggggAACyScQNbNSvMlLdlLCP8yQfFtgZgQQQAABBBBAAAEEEEAAAQQQuEKAwDiuBAIIIIAAAqkgQGBcKqCzJAIIIIBAsgsQGJfsxCyAAAIIhLRA6fxl+EGikO4wh0MAAQTcJUBgnH/99jcwzlp98Nhx6j5kqNeNvNXtWT3Xvt1VdYEIjDt+6pTy3lnT6x7iCqZ/MFT1qlVLsj5UA+Oqt2uvyPUbjKxefqSj+jzdJclafwPjXho0WP/9/HOj/VhFh5YsVpbMmb3W+xMYZ01uOt6q/WH0J6pWoYLXPflasHztWtXo0NF4+K7585QnZ06v9QTGhXZgHM96/EcgocA4pz3rXh9aCq4SIDCOS4EAAi4QsBuMxPd9XXApOCICbhYgMM7N3efsCCCAAAIIIIAAAggggEDoCxAYF/o95oQIIIAAAggggAACCCCAAALBJ0BgXPD1jB0jgAACCISAAIFxIdBEjoAAAgggcJUAgXFcCgQQQAABfwQIjPNHj7EIIIAAAk4TIDDOv44EIjDu0NGjiqhdx+tGdv/wvXJfc81VdYEIjNv053aVa97C6x7iCo4tW6qMGTIkWR+qgXHX3llDJ06dNrJaNHaMbitbNslafwPjxk+foU69ehvtxyoa/PJLeuqB+73Wmwa+JRYi1aLb85q5aJHXdayC1x/v7PmVXK8jx4+rQK3axtMPevEFPd3mQa/1BMaFdmAcz3r8RyAYnnWvDy0FVwkQGMelQAABFwgQGOeCJnNEBBAwFyAwztyKSgQQQAABBBBAAAEEEEAAgeATIDAu+HrGjhFAAAEEEEAAAQQQQAABBEJfgMC40O8xJ0QAAQQQcKAAgXEObApbQgABBBDwW4DAOL8JmQABBBBwtQCBca5uP4dHIFACC21OVMtmPeUIGAsQGGdMlWBhIALjrInf+mSUlq5ek+hm7rilol599JEEfz8QgXE/rohU/cefMMKoUelWfTdiuNfaUAyMO3PunK6pWs3r2eMKTq74RenSpk2y3t/AuNVRUbq9TVvjPVmFA194Xl0fapPgmC07dqjXsI80ed48ozkTC5EaPukrPTvgbaM5rKK5wz9WzcqVjOsTKtx38KBeeGeQFq1cpQHdntVD9za6VFasQUNZz4rJq3iRwvptytcKCwtLspzAuNANjONZv/rqB8uzbvKMU/OvAIFx3AYEEHCBAIFxLmgyR0QAAXMBAuPMrahEAAEEEEAAAQQQQAABBBAIPgEC44KvZ+wYAQQQQAABBBBAAAEEEEAg9AUIjAv9HnNCBBBAAAEHChAY58CmsCUEEEAAAb8FCIzzm5AJEEAAAVcLEBjn6vZzeAQCJcAblgMlyTx+CxAY5x9hoALj/NlFIALj5i5ZKivgzeRlhWmtnTrFa+ndj3bSktWrvdZZBQd/WqSs4eEJ1pZs3ETbdu82mufMqkivdV/P+14PvfKq1zqr4D+dO+mNJx6/VHvs5Enlq2Ge4bl93lzly507ybXeHDFSfT/2HsBnTfLtsA9U5/aq8eY7d/68ImrfrROnThudKa6o/h3VdEfFiipbvLgyZcygTX9u1y9r1+rzmbNszZNYiFTUtm2q0KKV8VzZsoRrwZjRKnPjjcZj4gpPnzmjT76eor4ff3zJ4a1uz+q59u0uzWX13Oq96euVRx9RzyefUJo0aRIcYj13bV97TT+v+dVoyjH9+urBhg2MagNZVK/z41oUudJoSus+zB810qg2riiQz+fytWtVo0NHo/XbNGqo0X37JFnbc9iHenvUaKP5Jg4aqKa1a/OsJ6EVLM+6UcMp0tHt2z0KqyeMv8v62GDyNLuB1igigAACwSLA91+CpVPsEwEEklVg40jF+3xYopH3758k64aYHAEEEEAAAQQQQAABBBBAAIFkFAgrWCnpvxksGddmagQQQAABBBBAAAEEEEAAAQQQiC9AYBw3AgEEEEAAgVQQIDAuFdBZEgEEEEAg2QUIjEt2YhZAAAEEQlqAwLiQbi+HQyClBHjDckpJs45XAQLjvBIlWRAqgXFWWFi55i2MMeYO/1g1K1dKsP7I8eN64Z1BtoLHgiUwzjrwdXXqKfrwYSOrHk8+oe6dHkuw9sLFixry2QT9Z8hQo7msooQC46x//up77+v98Z8ZzxPIwsRCpKw17IQGWvVWaNxLHTuqQ5P7vAbtWfV/7NypaT8s8Jz9yp5cGRg34duZerRHT1tHb1G3jno++aSKXldI6dKmVWxsrLbv2asvZs9W7w8/sjVXcgfGbdy6VWfPnb9qT+1ee02bt+8w2usNERH64p2BV9VmzJBeJYsWTXCOUA2M41m/ut3B8qwbXXaKtLB/v3gK90yexhunuBcIIBCqAnz/JVQ7y7kQQMCWAIFxtrgoRgABBBBAAAEEEEAAAQQQCHIBAuOCvIFsHwEEEEAAAQQQQAABBBBAIKQECIwLqXZyGAQQQACBYBEgMC5YOsU+EUAAAQTsCBAYZ0eLWgQQQACBKwUIjONOIIBAAAR4w3IAEJkiMAIExvnnGCqBcVZ4WdbKVYwxrGCv/s8+q5b16ipn9uy6GBOjTX/+qWW//qYewz40DlSLWzCYAuOaPNNVc5csNbZ65dFH1LFpU10fUdAzZtf+/Ypcv16Dx47X8rVrjeexChMLjPtt0ybd9kAbW3MFqjipECnrPtTq+IhPS7Vp1FC3lS2jfLnzqECePMoSnlnRh49o9/792r53r775YYHWbt6c6NxXBsZZQYYFatX2aS/WICswbdf+fTpx6rRPcyRnYNzpM2eUq1p1n/ZlOujw0p8UninTVeWhHBjHsx6/3cHyrJveabfXERjn9hvA+RFwlQDff3FVuzksAggkJkBgHHcDAQQQQAABBBBAAAEEEEDATQIExrmp25wVAQQQQAABBBBAAAEEEEDA6QIExjm9Q+wPAQQQQCAkBQiMC8m2cigEEEDA9QK9Z3TuZSGUKlilp+djhPmb4l2PBwACCCCAgAiM4xIggEAABHjDcgAQmSIwAgTG+ecYKoFxlkL1du0VuX6DbRArPM7XEK24xYIpMO6N/36gd8Z8atvJGuCvVWKBcdbcD770sqbO/8GnffkzKKkQKWveFt2e18xFi/xZwqexVwbGWZO0feU1TZ43z6f5/B1EYJx0ZlVkkoxWgGKNDh2NqK1AwdF9+yRZ23PYh3p71Gij+SYOGqimteMHCvKsx6cLpmfdqOkuLyIwzuUXgOMj4C4Bvv/irn5zWgQQSESAwDiuBgIIIIAAAggggAACCCCAgJsECIxzU7c5KwIIIIAAAggggAACCCCAgNMFCIxzeofYHwIIIIBASAoQGBeSbeVQCCCAAAL/CKzft87uG0WwQwABBBBAgMA47gACCARCwO6/h4YFYlHmQCAhAQLj/LsXoRQY9/zAd/ThxC/9A/FxdDAFxlmhbFY4W2q8kgqM23fwoMo2a+53eJ/dc3kLkdobHa3q7Tto9/4Ddqf2qz6hwLgNf2zVLa1a+zWvr4MJjAu+wDie9fi3PZiedV+fUzeNIzDOTd3mrAi4XoDvv7j+CgCAAAKXC8TuibT7eRFABBBAAAEEEEAAAQQQQAABBIJGIGpmpXh7LdlJ/LxV0HSPjSKAAAIIIIAAAggggAACCISqAIFxodpZzoUAAggg4GgBAuMc3R42hwACCCDgpwCBcX4CMhwBBBBwqUDp/GX4QSKX9p5jIxBAAbtvzOPzTgDxmSq+wNyWTRda/yRH4SI1rY8V2raDyIZAKAXG7T90SGWaNkvxwDGLO5gC4y7GxKjOY53085pfbdyUwJQmFRhnrTB9wUK1fuHFwCxmOIu3EClrmqht23Rn+w4percSCoyz9vLc2wP10ZeTDE8XuDIC44IvMI5nPf79D7ZnPXBPb2jORGBcaPaVUyGAQIICfP+Fi4EAAghcJkBgHNcBAQQQQAABBBBAAAEEEEAglAUIjAvl7nI2BBBAAAEEEEAAAQQQQACBYBUgMC5YO8e+EUAAAQSCWoDAuKBuH5tHAAEEEPAiQGAcVwQBBBBAwBcBAuN8UWMMAghcIcAblrkSjhM4syrS7r103BlSY0OhFBhn+X0xa5Y6vt4jxSmDKTDOwtn053aVa94ixZ28BcZZG5q1eLGaP/ucX3srWbSoNm7dajRH8SKFtXbqFK+1y9euVYvnXlD04cNeawNRkFhg3JFjx1WtbTtt2707EMsYz0FgXPAFxvGsx7/ewfasGz+cLi0kMM6ljefYCLhTwO6fcwnsd+c94dQIuEaAwDjXtJqDIoAAAggggAACCCCAAAKuFCAwzpVt59AIIIAAAggggAACCCCAAAIOFyAwzuENYnsIIIAAAqEpQGBcaPaVUyGAAAII/C1AYBw3AQEEEEDAFwEC43xRYwwCCFwhwBuWuRKOEyAwzreWhFpgXGxsrJp2fVZzlyz1DeSfUR2a3OcJ5FoUudJonmALjLMONXjsOHUfMtTofIkVVSpdSreVLasPJ35pNI9JYJw10aqNG9VtwEBZIW12X4NffknN7q6tG+o3MBp6e4XyWjB6lFHt4WPH1KXfm5o6/wejen+KEguMs+b8Y+dOVW3zkE6cOu3zElbvqlWooKETPjeag8C44AyM41n/93oH47Nu9HC6tIjAOJc2nmMj4E4Bvv/izr5zagQQSESAwDiuBgIIIIAAAggggAACCCCAQCgLEBgXyt3lbAgggAACCCCAAAIIIIAAAsEqQGBcsHaOfSOAAAIIBLUAgXFB3T42jwACCCDgRYDAOK4IAggggIAdgUVRX3vKo0/s/tH62KPxiFp2xlOLAAIIXCbAG5a5Do4TIDDOt5aEWmCcpXD6zBn1GvahcRDW5XLZsoRr6Guv6sGGDdXkma7GwXPBGBhnheuNmz5Dj/fu49Pl6d7pMb30SEd9/OUkvfb+EKM5TAPjrMms/VnBbBO+namZixYlOb/VtwcaNNBrjz2qgnnzau3mzap8/4NGe2py11368t13jGrjir6e970GjflUq6OibI0zKbbO8vSDD+qxFi0UkS9vokN+27RJD73yqjZv32EybbyaVx59RFb/Rk2ZqucHmp2dwLjgDYzjWf/7+gfrs277AXfJAALjXNJojokAApZAL5sMduttTk85AgggkDoCG0cq3vejSzSKTJ2NsCoCCCCAAAIIIIAAAggggAACKSAQVrBSWAoswxIIIIAAAggggAACCCCAAAIIIGAgQGCcARIlCCCAAAIIBFqAwLhAizIfAggggICTBAiMc1I32AsCCCDgfIGvVwyNt8kejUfwg0XObxs7RMCpAgTGObUzLt4XgXG+Nd9OKNqu+fOUJ2dO3xZKYtTBI0dU6O66xvNOHfK+GtxZ3Wv9ktWr1b77f7R7/wGvtVZIV6eWLfXiwx2UK0cOT33bV17T5HnzvI61Co78vESZM2ZMsNYKLrMCzLy9rD1EL046GM2aY/bin9Ts2W7epvP8fr+uz3jOlNRrx9696tyrjxauWGE0Z+v69fTqY4+pVLGinvqRk7/WM/3fMhq7YPQo3V6hvFHt5UVWCOCqDRu0/9BhRR85rMPHjitreLjy58mjGyIKqmLJkkqXNu2lIXaMOrVsof92f832nqwBK9at04jJX2va/Pk6ceq0T3NYg6xguEY1aqhB9eq6u2oVZUif3miuU3/9pV7DPtJ/P//cqN6a++3nn1OZG2/01I/9ZrpxYOBXg99V41o1jdaxW3T23DnlqFrN7jBb9UeXLVWmDBmuGhPI59NOUGHnVi094ZRJvd4eNVo9h31odM7pHwxVvWpJG/KsB++zbnQJXFZEYJzLGs5xEUAAAQQQQMD1AgTGuf4KAIAAAggggAACCCCAAAIIuEqAwDhXtZvDIoAAAggggAACCCCAAAIIOFyAwDiHN4jtIYAAAgiEpgCBcaHZV06FAAIIIPC3AIFx3AQEEEAAATsCBMbZ0aIWAQS8CBAYxxVxnACBcY5riSM2ZAWNrVy/Qev/2KLfNm3W6g0bdfTECU/YWM4c2VWyaFHddVtlT1BXxgTCpBxxiBTYRExMjFZHRWnDlj884XZrfv9dO/fuU6aMGXVN9mwqdt11qlqunJrXuftSoF4KbMvnJaxAPSs0zuTV48kn1L3TYyalSdb8uXuPVm7YoNUbN2r/4cM6evy4J9ju8LFjHrPTf/2lHNmyKUfWrJdMK5S4WeVvvln5cuf2a33rTs9YsFALVqzQrv37Pb07d+G8ChcooOvy5VP5EiXUsm4dFSlY0K91GBz8Ajzrwf2sB/8NDNwJCIwLnCUzIYAAAggggAACwSBAYFwwdIk9IoAAAggggAACCCCAAAIIBEqAwLhASTIPAggggAACCCCAAAIIIIAAAv4LEBjnvyEzIIAAAgggYFuAwDjbZAxAAAEEEAgiAQLjgqhZbBUBBBBwgACBcQ5oAltAIHQEFto8Si2b9ZQjYFuAwDjbZAxAICQFlq9dqxodOhqf7YfRn6hahQrG9RQigIAzBHjWndGH1NoFgXGpJc+6CCCAAAIIIIBA6ggQGJc67qyKAAIIIIAAAggggAACCCCQOgIExqWOO6sigAACCCCAAAIIIIAAAgggkJAAgXHcCwQQQAABBFJBgMC4VEBnSQQQQACBFBMgMC7FqFkIAQQQCAkBAuNCoo0cAgEEEEAgEQEC47gaCAS/wPjpMzR43HgVypdPDzS4Rw1r3Kmc2bMbHyxq2zY169pN23bvNh5z/JeflSF9euN6ChFAwH8BnnX/Dd0+w5rPxnsIju3Y/qP1sf7kaQRUu/1ScH4EEEAAAQQQcIVA7J7IWFcclEMigAACCCCAAAIIIIAAAgi4UiBqZqV45y7ZSWGuhODQCCCAAAIIIIAAAggggAACU7sPVQAAIABJREFUCDhIgMA4BzWDrSCAAAIIuEeAwDj39JqTIoAAAm4UIDDOjV3nzAgggIDvAgTG+W7HSAQQQAAB5wsQGOf8HrFDBLwJ9Bs+Qtavy193V62iurffrkqlS6nczTcre5Ys8X7/0NGjWr0xSl/OnSsrhMrOq9ndtfXFOwPtDKEWAQQCIMCzHgBEpvAIZLqlEm+U4i4ggAACCCCAAAIuEiAwzkXN5qgIIIAAAggggAACCCCAgAsFCIxzYdM5MgIIIIAAAggggAACCCCAgOMFCIxzfIvYIAIIIIBAKAoQGBeKXeVMCCCAAAJxAgTGcRcQQAABBOwIEBhnR4taBBBAAIFgEyAwLtg6xn4RuFogoRCphJxuiIjQuQvndfzkSZ04ddpnyjnDP1KtypV9Hs9ABBDwTYBn3Tc3Rl0tQGActwIBBBBAAAEEEHCXAIFx7uo3p0UAAQQQQAABBBBAAAEE3CZAYJzbOs55EUAAAQQQQAABBBBAAAEEgkGAwLhg6BJ7RAABBBAIOQEC40KupRwIAQQQQEBS7xmde1kQpQpW6en5GFEFFwQQQAABBLwKEBjnlYgCBBBAAIEgFiAwLoibx9YR+EfANEQqEGAN7qyuqUPeD8RUzIEAAjYFeNZtglGeqACBcVwOBBBAAAEEEEDAXQIExrmr35wWAQQQQAABBBBAAAEEEHCbAIFxbus450UAAQQQQAABBBBAAAEEEAgGAQLjgqFL7BEBBBBAIOQECIwLuZZyIAQQQAABSX1mdI69HKJF5a64IIAAAggg4FWAwDivRBQggAACCASxAIFxQdw8to7APwIpGSK1ctKXKn1jMewRQCAVBHjWUwE9RJckMC5EG8uxEEAAAQQQQACBKwQ2jlS8n48o0SgSIwQQQAABBBBAAAEEEEAAAQRCViCsYKWwkD0cB0MAAQQQQAABBBBAAAEEEEAgyAQIjAuyhrFdBBBAAIHQECAwLjT6yCkQQAABBOILEBjHjUAAAQQQ8EWAwDhf1BiDAAIIIOB0gdktm9ay9ljxoXYLrI/XFCni9C2zPwQQSEQgpUKkxg/or1b16tEHBBBIJQGe9VSCD8FlCYwLwaZyJAQQQAABBBBAIAEBAuO4FggggAACCCCAAAIIIIAAAm4SIDDOTd3mrAgggAACCCCAAAIIIIAAAk4XIDDO6R1ifwgggAACISlAYFxItpVDIYAAAq4XIDDO9VcAAAQQQMAnAQLjfGJjEAIIIICAwwXmtGwae/kWa3V/3eE7ZnsIIJCYQEqESI3s1VPt7mtMExBAIBUFeNZTET/EliYwLsQaynEQQAABBBBAAIFEBBILjIuaWSneiBKNIuP9f34fn8svBPeD5+Py+8DnBz4/8PnhXwE+P/L5kc+P/wrw9YGvD3x94OtDnEBqf30s2UlhfJMAAQQQQAABBBBAAAEEEEAAAQScIUBgnDP6wC4QQAABBFwmQGCcyxrOcRFAAAGXCBAY55JGc0wEEEAgwAIExgUYlOkQQAABBBwhQGCcI9rAJhAIiEByhkiVLFpUH73xuqqWLxeQvTIJAgj4LsCz7rsdI+MLEBjHjUAAAQQQQAABBNwhQGDc331O7cAC1idQ6PLPOATqEKhz+X3g8wOfH/j88K8Anx/5/Mjnx38F+PrA1we+Pvj+9YHAOHd8v4NTIoAAAggggAACCCCAAAIIBIcAgXHB0Sd2iQACCCAQYgIExoVYQzkOAggggIBHgMA4LgICCCCAgC8C0Sd2eYYtjpp2l/XxjcYfL/RlHsYggAACCCDgJAEC45zUDfaCgH8CyREiVbxIYT3eqpU6t2qpDOnT+7dBRiOAQEAEeNYDwsgkkgiM4xoggIALBHrZPKPdepvTU44AAgg4S+DKILkr31DP7yv28o7ho7DLPbgf3A+ej38F+PzA5wc+P/4rwNcHvj7w9YGvD3ECfH3k66OTvj4660/j7AYBBBBAAAEEEEAAAQQQQAABdwm4MjDuwoWL+nXdJuNOly9zk9KlS2tcb6dw/catOnP2rNGQYjcU0jU5shnV2i3asWufog8esTUsokBe5c+X29aYxIp/W7dZ5y9csDVXqZuLKnPmjLbGJGfxnr3R2rv/oK0lypUurvTp09kaQzECCISGAIFxodFHToEAAgggEF+AwDhuBAIIIICAPwKl85eJ94YIf+ZiLAIIIIAAAqktQGBcaneA9REInMCFixe1eft2rd28Rb/9/rtWR0Vp+dq1OnHqtPEi2bKE69ZSpXVb2TJqendt3VKypPFYChFAIGUEeNZTxtkNqxAY54Yuc0YEXC8QL+jHQIPv+xogUYIAAqEjQKALgS6X32YCPQj0uPw+8PmBzw98fvhXgM+PfH7k8+O/Anx94OsDXx/4+hAnEGxfH0PnT/KcBAEEEEAAAQQQQAABBBBAAIHgE3BlYNwPP67Q3Y2fNO7WojkjdWe1isb1poVnzpxT5rzVTMvV742n9J+XHjGut1NYvd6jWrLsVztDVLtmZc2f8ZGtMQkVb/x9m0pVbmV7nkljB6hVszq2xyXXgHtbddPMuT/Zmn72lKG6p475HbA1OcUIIOBoAQLjHN0eNocAAggg4KMAgXE+wjEMAQQQQMAjQGAcFwEBBBBAIJQECIwLpW5yFgQSFjh3/ryOnTihYydP6sjxE5f+98WYGGXJnEnZwrMoa5Zw5c+dWwXz5oURAQSCVIBnPUgbl4rbJjAuFfFZGgEEUkqAwLiUkmYdBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSuECAwzuBKEBiXONKqxRNUsfzNBoqJlzz53Fv6eNTXtueYOKa/7m9Rz/a45Bhw8NBRXXuD/fC6R9rdp1HDeiTHlpgTAQQcLkBgnMMbxPYQQAABBHwSIDDOJzYGIYAAAgj8I0BgHFcBAQQCILDQ5hy1bNZTjoCxAIFxxlQUIoAAAggggAACISVAYFxItZPDIIBAwgIExnEzEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBIJQEC4wzgCYxLHKlDm3v16ce9DBQTLjkQfVj5ivkW+uakwLhPJ8xQxyd723bIljVcB7Z+r0yZMtgeywAEEAhuAQLjgrt/7B4BBBBAIGEBAuO4GQgggAAC/ggQGOePHmMRQOAfAd6wzFVwjACBcY5pBRtBAAEEEEAAAQRSRODo9u2edVZPGH+X9bHB5Gl2A61TZJ8sggACCARAgO+/BACRKRBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8EWAwDgDNQLjkkbaFTVLEQXzGkheXdJv4Ci90e8jn8Y6KTDunmbPaO78n306x7dfva9G9av7NJZBCCAQvAIExgVv79g5AggggEDiAgTGcTsQQAABBPwRIDDOHz3GIoDAPwK8YZmr4BgBAuMc0wo2ggACCCCAAAIIpIjAwv794q1zz+RpYSmyMIsggAACKS/A919S3pwVEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDwCBAYZ3ARCIxLGum1Fzqqf88uBpLxS/7666zyFaurEydP2x5rDXBKYNyB6MPKV6yeT2ewBnVoc68+/biXz+MZiAACwSlAYFxw9o1dI4AAAggkLUBgHDcEAQQQQMAfAQLj/NFjLAII/CPAG5a5Co4RIDDOMa1gIwgggAACCCCAQIoIEBiXIswsggACzhDg+y/O6AO7QAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEHChAIFxBk0nMC5ppGxZw7V70xxZH+28xnw2XY881cfOkHi1TgmMGzXuGz32dF+fz2ENPL1/iTJnzujXHAxGAIHgEiAwLrj6xW4RQAABBMwEes/o7ElCLlWwSk/Px4gqZgOpQgABBBBAQBKBcVwDBBAIgABvWA4AIlMERoDAuMA4MgsCCCCAAAIIIBAsAgTGBUun2CcCCARAgO+/BACRKRBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8EWAwDgDNQLjvCN9MOhldenc2nvhPxUxMTEqWamlNm3ZYTzmykKnBMbd3fhJ/fDjCp/PYQ38ZuJg3dewhl9zMBgBBIJLgMC44OoXu0UAAQQQsCewft86u28UsbcA1QgggAACISlAYFxItpVDIZDSAnb/PTQspTfo4vXKS/rVTecnMM5N3easCCCAAAIIIICARGCcWv3/7wP4iruAAAKuEOD7L65oM4dEAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQcKIAgXEGXSEwzjtSoYi82rZ2htKlS+u9WNKc75eqQfOuRrWJFTkhMG7vvoMqeNM9fp3DGtz2/oYaP7KP3/MwAQIIBI8AgXHB0yt2igACCCBgX4DAOPtmjEAAAQQQkAiM4xYggEAABHjDcgAQk2mKZpL6SrK+EW6FSNjtVTJtK/mmnduy6UJr9hyFi9S0PlZo2y75FmNmBBBAAAEEEEAAgVQXcGlgXBpJgyQ9Jel9Sa+meiPYAAIIpISA3T/TE9ifEl1hDQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAFXCBAYZ9BmAuMMkCR9/dlANb+vtlFxvSZdNG/BL0a1iRU5ITBu+OgpeqJbf7/OETf41P6fFJ45U0DmYhIEEHC+AIFxzu8RO0QAAQQQ8F2AwDjf7RiJAAIIuFmAwDg3d5+zIxAwAd6wHDDKgE9kvTl8naRSktZL6i1pshuC486sirR7LwOOz4QIIIAAAggggAACyS/gssA4KyjubUlPS7J+0OWspHBJMckvzQoIIOAAAbt/ziUwzgFNYwsIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACoSFAYJxBHwmMM0CSVLVyWf08f4zX4l/XblKFO9p4rfNW4ITAuLsaPa6Fi1d626rR70+Z8I6aNb7LqDY5io4cPa4/t+/VvgOHdPDQUeXLm0ulbi6qiILXKiws+X5u7/RfZ7Tlj506euyEjh0/6fmVLm06FcifR/nz5VbBAtcqW1br54p52RHYtfuA/ti2S4ePHNOhw3//unjxonJek93zK1eu7Cpx0/UqXCi/nWmpDaAAgXEBxGQqBBBAAAHHCRAY57iWsCEEEEAgKAQIjAuKNrFJBJwuwBuWnd2h5tbfPXPZFq0AuT6hHhxHYJyzLyW7QwABBBBAAAEEAiXgksA4KyjO+lsVn/0nKC6O7x1JLwfKknkQQMDxAnz/xfEtYoMIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACoSpAYJxBZwmMM0D6p2TJvNGqVqVckgM6Ptlbn06YYT5pIpWpHRi3e88BFSrR0O9zxE3QunldffnpW7bnO3v2nD4YMUkXLlz0OrZMqWJqVL/6pbpz585r6owFGj9xlmbO/SnB8VZY260VS6pxgxrq+sQDSpcurdd1kiqIiYnR4qVrNO3bhVq0ZJVW/RrldT5rD9cVyq9iNxRS7ZqVVKdWFZUuWTRekJ0Vcjdq3Dde54orsM6SOXNGr/UborZqxuzFXuvuqXO7ype9yWvdilUb9MOPK7zWWQW33Vpad9WoZFR78WKMZs9bornzf9asuUu09c/dRuMKReRVg7p36KHWDVSz+i1GY+KKPvtylnbvifY6Jnu2LHrysZYJ1m38fZvWb9wqyzn60BGlT5dONxe/Xo8/Yr13NLRfBMaFdn85HQIIIOB2AQLj3H4DOD8CCCBgT2BR1N/ZQdEndv9ofezReEQtezNQjQACCFwS4A3Lzr4M1t9MYoXElbpim9Y/6/1PmJzdHjr7xJIIjHN8i9ggAggggAACCCAQEAEXBMZZQXHdJGW+AuysJOtvAYwJCCSTIIBAMAj0srlJu/U2p6ccAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAH3CBAYZ9BrAuMMkP4padWsjiaNHZDogD17oxVxcwPzCZOoTO3AuA9HfqUuL7wdkLPETXJi7yJlzWL9HK35y05wnRX6Nv3LwZ7Jv1+wXF1eGKBNW3YYL1brzlv12ci+iiiY13hMXKEVTjd8zBS9P+xz4zCzpBYpen2EunRurfYPNlKe3Nfol8h1qlr7YeN9mYQbWpO90P09Df5ggtd5rUC0Dwe/6rWuc9c3NfLTqV7rrIKnOrXSsHdfSbLWCgr8+pv56vHmx7Z6mdCkd1Qtr35vPCWrzyavYuWaGPcy9njkpSmtuzBl+gK9M2RcgoGBlW8ppeULx5lsIahrCIwL6vaxeQQQQAABLwIExnFFEEAAAQTsCHy9Ymi88h6NR1iBQrwQQAABXwTsho3x+cYXZf/GtJA0OZEp1v4THDdFkt1e+rerZBxNYFwy4jI1AggggAACCCDgIIEQDozrK+n5f0LhEhIfJOklB7WCrSCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQsgIExhm0lsA4A6TLSjavmaobi16X4CAr1Krv25/YmzCR6tQOjKte71EtWfZrQM4SN8lX495Wy6Z325rTbmDchFH91OX5ARo/cZatdeKKs2UN1+TPBqpe7arG4xcvXa3Hnu7rd6BZQgv2+c8TeuOVx2QFp+UqfJdOnDxttK93+z+n559+yGutaShaoYi82rnRu+nNtzQ3dpgx6T3de8+die5x65+71azNi/pt3Wav57BT0L9nF736/MMKC0v6/bKmNtbacYFxK1ZtUNvHXk/SgMA4O92iFgEEEEAAAWcKEBjnzL6wKwQQQMCpAgTGObUz7AuBoBSwGzJGYFzKt9kyXyepVBJLh1RwHIFxKX/JWBEBBBBAAAEEEEgNgRAMjOsl6UVJWZLwPPtPkFxMapizJgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAJuEyAwzqDjBMYZIF1W0u2pNnpvgPWXC8d/nTx1WgWL32Mc6OVt1dQMjNu5a78Kl2rkbYu2f79VszqaNHaArXF2AuPyXptL1+TIahxYlthGrNC4nVGzlCN7Vq97/ezLWWrXqYfXOl8L4gLjrPHtO/cwDsJr0qimpn3xbpLLbtm6U8UrNDPe2qbVU1S8WOFE66MPHlHeonWN5zuxd5GyZglPsH7RklW6t1W3gD1PVy7y8EONNfrDHkmGxtkJjLt4dLn6Dxqj/7F3FuBNXX8Y/mgLlBq02KAwGMNl2HAZxWW4DWe4w3B3d3d3iru7bjjsj8NwKxQptIUKf05GuqaNnBtpkvY7z5MnQL7zO+e85yalSe57B42cY3D9FMYZRMQACZAACZAACdg8AQrjbH6LOEESIAESsCkCFMbZ1HZwMiRg7wSOKFxAKYV5xs1DoO5XYZyvRKkrAIYB2CyuSSGRt8kIhXE2uS2cFAmQwDcCb969x/PXr6R4eCVOjJRJk0plGSIBEiCBuEggFgnjBgHoY0AUp95i8aULIZVjIwESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESiAECFMZJQKYwTgJSlMjrB4fg5emh8a9zF21E+z/GKC+mo4c1hXEz5/uic8/xUmsZ1KcVRoxbKJUVoXdPjsLDXd8FmjVLKRHGSU9CIjhyUAcM6NVCb9LSsjgxeGRh3NoN+9CgRX+J2QNCevf28RE4ODjozM9ZuAEdussL/OZN6482v9fSWW/3/lOoXLuL1PwqlSuKXRuna80qqSM1mI6QED8KAaSupkQYV7OqDzZvPyw1HQrjpDAxRAIkQAIkQAI2TYDCOJveHk6OBEiABGyOAIVxNrclnBAJkAAJWJpAPAB/f5XGZZcc6PI3cdwWexTHURgnucvfYuHh4fgQFARHBwe4ODvrvaiJsspMkwAJaCPQashQrNy+QwpOqQIFsGee4QsDSRVjiARIgARiIYFLK1eoVvXu4YOj4r7Chi32JqgWX7boB8DwlQP/3b9PAMQV8MJj4XZySSRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRgkwQojJPYFgrjJCBFiYwb3gW9uzWN+NewsHBkzlsT9+4/UV5MRw9rCuOKlPkdZ85elVrLs9t7UaVOV1y4fEMqv2bxaPxWp7xUVoSsJYwTwrUH13bAM4mmGFA9cbHXQihm6RZZGPfq9Vsk/6Gs9JDXz21A1szpdeZ/rdsNO/eekK5Xu3ppbFihWyQ4ZPQ8DB+7QKrezIm90bFNvWhZwTVP0QYI+BAoVcfU0LljK5A/TzatZZQI45TMg8I4JbSYJQESIAESIAHbJEBhnG3uC2dFAiRAArZKgMI4W90ZzosESIAELEpAvPm5TuEIdimOozAu+i5/+fIFtx48wIkLF3Dy4iU8femHF69f49Xbt/Dz99fo4O7qAg83N3i4uiFDmjTInyM78mTNgp8yZ0aalCkVHkKMkwAJRCXQYtBgrN65SwpMyZ/zY9/8eVJZhmyLwDM/Pzx79UrRpHJlyoT4Tk6K+jBMAiTwLwHnfD8LQbI9tT4ABgBwVzjpKQC6K+zDOAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgAkEKIyTgEdhnASkKJEUyb3w8NoOJEyYQPXI1p1HUaNBD+WF9PSwljDu/sOn+CFnNam1FCucGyf2LcLoiYsxYPhsqT41q/pg06oJUlkRspYwTowdWdYWdcJiv8W+W7pFnYMSmd+SOUPQvFFVrVMMDAqGa8riiqcf4v8nnJwctfbzqdIWR46fl6p5+9JmZMyQViMbFPQJhcs0x5W/b0vVUIcyZ/weKVMkxc3bD/DST/NkO0OFKpUril0bp2uNURhniJ7+x6+f0JQLZq/sa28nDpgGgL1JgARIgARiNQEK42L19nJxJEACJGB2AhTGmR0pC5IACZCAPRAQ74X9DSC7EZO9BGCY+OgBwBcj+sdoFwrj/sN97Nx5rNixA3tOnIwmhjNmU4TMqH39eqhdvhwSu7kZU4J9SCDOE6AwLm4cAjW7dsPu4/IXyRJUts2cjvJFi8YNQFwlCZiZgB0J43oCGARA+1UC9XP5BMAFQLiZ8bEcCZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZCAHgIUxkkcHhTGaULq80czjJuyzCC5FQuGo3H9yqpc8fItcfLMZb19ZOuqi1hLGDdl1mp07zfZ4PpFYOKobujRubFK8JW7aAOpPiL05tFhJEksd/FmawrjqlQojh3rp0Zb19NnfvDOUkl6vSIopGZFC+XGz3mzIcMPafDa/y0eP3mJ/12/h5XrdumsFVUYN3byUvQbOlNq7JZNq2PhTPH95+ht36EzqFCjk1SdyKHTB5egcIFc0fp9/hyChMmKSNXLkN4bd6+Icx0127Q5a9CtzySpGu5uLpg+oRca1KkQIW4UHR89foHWnUdi78HTUnVE6H9/+SJ71gzR8hTGSSPUGqQwzjR+7E0CJEACJGCbBIZtbzNUzCx76kJDVPfehWxzopwVCZAACZCATRGgMM6mtoOTIQESIIGYJFD3668NviYMKMRx4neQ6G+mmlDU3F0pjAOOnD2LEXPn4+TFi+bGG1GvZa1aaF6jOgrkzGGxMViYBKxBYPm2bRgwXe5zr7UTxqFY3ryKpklhnCJcdhl+/fYtvEuXVTz3ZtWrYd6QwYr7sUPsItBu+AjsPHbc4KISxHfCpQ0b4O4q/GFsdiCM6w1ggJGiOPUGTwHQnbtNAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiQQswQojJPgTWGcJqS///RFzkL1DJL7KWcmXDq5GmcvXEMhn2YG87J11YWsJYwrWKqpak0y7cb5jciSKR2+fPmC77NXUQnQZNrKhSPQqJ6ccM1UYZwQgb16/RYv/fxlpqaREZK3mxc2Reu3YOlmtOkySqqeEJttXj0RZUoV1JkPCvqEA0f+xIKlW7B99zGNXFRh3MXLN5GvRCOpsXWJ2URnIQUUckClbeSgDhjQq0W0bucuXkOBX5pKlevdrSnGDe+ikQ0O/ox0OX6V2iexrqO7FyCNdwqt44WHh6NzrwmYvWC91Hy6dWiIKWOjf9ebwjgpfDpDFMaZxo+9SYAESIAEbJPA8O1tvkSeWe0Cmv+nsc1Zc1YkQAIkQALWJkBhnLV3gOOTAAmQgNUIxANwC0BGE2cgLGRCHLfNxDoW6R6XhXF3Hz1CqyFDcfqS/gsKmRN8rkyZ0LZeXZU8zsnR0ZylWYsErEJg/OIlGDxzltTYvpMmoppPKamsOkRhnCJcdhlesW07Wg8dpnjuQvz16OABOCdIoLgvO8QeAlU6dMTBM39KLejxwf1I5ukplY3tIRsWxnUAMMZEUZzYvk8AhB0wPLbvJddHAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAArZGgMI4iR2hME4T0uMbuzBpxkopmdb+rbOxZOU2rF6/Ry/pPzo2RPdOjZE2W2WJHfk3Yg1h3J17j5ApT02pOUaVqfUcMFXFTaZVrVQS29ZNlonCGGFc9Sq/YFCfVsidMzOcnP49YUpI44aNnY+Z83ylxlWHwt7+BQcHB40+/YfNwphJS6TqdGmopNrLAAAgAElEQVT3G6aN7ymVFaGTZy5DsDxz9qqqT1RhnJChpcpUUUqsJvo/v7MPKVN4RRvfWBlascK5cWLfomj1Zs33Raee46XWeXD7HJT+pYBGduGyLWjdeaRU/wPbZusV8Kn3O/kPZaXq6RIDGsvI0KAF8mXHX0eWG4rZ/eMUxtn9FnIBJEACJEACWghQGMfDggRIgARIwBgCFMYZQ419SIAESCDWEPgNwBozreYCAGHEsQlx3O46NVTWpryNmhwW90nSpTPTMu2jzIHTZ9Cgd28EfAy0yoSfHTkETw8Pq4zNQUnAnAQojDMnzbhZq2rHzth/+rRRi988bSoqlShuVF92ih0EKIwzbh9tUBgnRHHiin9JjFtRtF7TAHQzUy2WIQESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESUECAwjgJWBTGaUISwrgPH4OQNX9tg/TSeKfA4ycvDeZunN8IV5dENi+Mmzh9BXoNFN99Ndz692yBUYPF927/bUdPXECpym0Md/yW8H94CJ5JDJ/MpEQYJ/Zj7ZIxEFIzbU3I1pq3G4oVa3dJz/PprT1I9V0yjbyosWz1Dqka+XJnxfnjciI9dcGwsHDMXbQBsxduQJ8/mqFpgyoaY7XpMgoLlm6WGl+I+YSgL3K7ffchMuetJdVfW+jt4yNI7OGm8VCjlgMNihPVHYL9TiFhwgQa/X2qtMWR4+cNzqlmVR9sWjXBYE4ElEgMXz84BC9PzePRWGGcENDlypERGTOkRbq0qVTCwYAPH3HrzkPcuHUfeX/KokgiKLVYGwxRGGeDm8IpkQAJkAAJmEyAwjiTEbIACZAACcRJAhTGxclt56JJgARIQE1AXI3kJoCMZkQixHFDAWw3Y03FpfbUqfElcqdS/QcqrmGvHaatXIU+k6dYdfoUxlkVPwc3IwEK48wIMw6W8vP3R9qy5Y1eeeOqv2LhMPEjlS2uEqAwzridtyFhXCsA4qp2nsatRGuvT19rugAIN2NNliIBEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEpAkQGGcBCgK4zQhCWGcd+oUqFizM/YeNO5q5JErVipXFLs2TleJ5dJmqyyxI/9G1i4Zjfq1jf+Cu/RAkYL5SzTGhcs3pLqePrgEhQvkisiGhIQiabrSCPgQKNV/+fzhaPKbYR5KhHFCjCYEafranXuPkClPTak5itCfh5ehYP4cGvnOvcZj5jxf6Rotm1ZH3+7NVQIxc7TN2w+jVqNeUqWEcG7ssM4a2VnzfdGpp/jetHFtu+8U/FqxhEbnlD+Wx0s/f4MF69YsC99lYzVygUHBcE1Z3GBfEZgzpR/atTQscxTZlet2oUnrwVJ192+djbI+BTWySoRx4rlQr1Y5VKtcEj/+kEZqzNgeojAutu8w10cCJEACcZMAhXFxc9+5ahIgARIwlQCFcaYSZH8SIAESsHsCDQCstsAqxBU4hOVG7uomZp5AXBXGjZw3H+Jm7UZhnLV3gOObiwCFceYiGTfrLN2yFe2GjzBp8W9On0SihAlNqsHO9kuAwjjj9s4GhHFCFDcOgJdxK9DbawaALhaoy5IkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAISBCiMk4BEYZwmJLUwbufeE/i1bjcJgvojQhYnpHG2Loy7efsBsuaXE3G5u7ngzaMjcHR00Fj87+2HYemq7VLM1CI9Q2FzC+PEeB6pS0qL7bSJxKbOXo0/+uoX02lbV+aM36NmVR+VgC5v7qxI/30qxIsXzxCCaI+/efseXt+XluonRGZC7he5Va7dBbv3n5Lqry3UrUNDTBnbPeKhB4+eIX2OqlL1lswZguaNNLNHjp+HT5W2Uv2HD2iH4kXySGWPn7qIIaPnSWVXLBiOxvU1BYZKhHFf3p+TGicuhSiMi0u7zbWSAAmQQNwhQGFc3NlrrpQESIAEzEnAL+CxqtzxG1t8xP2gqnOPmLM+a5EACZAACdg8AfFG+s2v17jIaKGZijcnh8W0OC4uCuO2HzmKut17WGgblZWlME4ZL6Ztl4ClhXHnr13DtTt3pQCk9/ZGifz5pLIM2QaBim3b48jZsyZNZsOUyfj1l5Im1WBn+yVAYZxxe2dFYZwQxYmr0yU1buYGe4UAcAEQajDJAAmQQGwnIOTsSprSvJLazJIACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZBAnCJAYZzEdlMYpwlJLYwLCwtH5rw1ce/+EwmK2iMZ0nvj1sXNKrGarQvjxk1Zhr5DxMWSDbd2LWtjzpR+0YJbdhxBzYY9DRf4lnh1/yCSeiXWm7eEMK5gqaY4e+Ga1Dy1CeO27z6GavX/E6ZJFdISEuK9KhWLo3TJAihZLB+yZEonXUoI1oRoTaZ9fHECLomcVdGPgUFw+66ETDedGXFc372yNeLx9ZsPoF6zvlI1n9zcjdSpkmtkJ0xbjt6Dpkv1t1RoxsTe6NSmnkZ5CuNMo01hnGn82JsESIAESMA2CVAYZ5v7wlmRAAmQgL0QyPFdTuXWeHtZHOdJAiRAAiRgiEBDAKsMhUx8XIjjxEnqO02sI9U9rgnjbvzzD/LUrivFxlAoW4YMSJ0iOcTnULcfPsCTFy8NdYn2OIVxipGxg40SsLQwzkaXzWmZgcDzV6+QvnxFkys1qFwZS0YON7kOC9gnAQrjjNs3Kwjjmny9LqC4ol8y42Ys3Ut8YaaLdJpBEiCB2Ezgi8LF8X1fhcAYJwESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAFdBCiMkzg2KIzThKQWxol/nTZnDbr1mSRBUXtk+oRe6Ny2vupBWxfG5ShYD9du3JNa666N01GpXNFo2YAPgfBILX8F9sWzB+P3xtX0jmkJYVzl2l2we/8pqbVqE8bd/ecxMuauIdVfSahUifzo2aWJiq2Dg4PerpNnrkKP/lOkyp/YtwjFCudWZcW6xfr1tYZ1K2L1+j16M/f/tx3p0qZSZbr3m4wps1YbnEu+3Flx/vjKaLk+g6dj/NTlBvtbMjCsf1sM7ttaYwgK40wjTmGcafzYmwRIgARIwDYJUBhnm/vCWZEACZCAvRCgMM5edorzJAESIAGLEBBv+N4EkNEi1TWLnv36VyGO22XJseKSMO5zSAjy16uP2w8eGoXU3dUF7erVQ8XixfBzjhxImCCBRp3A4GD88/gJhJRu8ebNOHjmT4PjUBhnEBEDdkKAwjg72SgbnObCjZvQadRos8zM/9QJuDj/e/EttrhFgMI44/Y7BoVxQhQnvrCjeUU646ZtqFcIABcAoYaCfJwESCBOEKAwLk5sMxdJAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRgiwQojJPYFQrjNCFFFsa9efseXt+XlqCoPeL/8BA8k3ioHrRlYdyNW/eR7ec60uv8+OIEXBJp/9J8tfrdsX33MalaFcoUwZ7N4iLNupslhHF1mvTGxq2HpOaoTRgnOjZtMxgr1lrmfLvMGb/H+uXj8FPOTDrn+Pe1u8hV+F8ZoaE2fkQX9OraVBX7o+9kTJ2tW+5Wu3pp1Kzqg8atBuktu2TOEDRvVFWVyV+iMS5cvmFoGtAmZROdfm8/DEtXbTfY35KB3t2aYtxwTZEehXGmEacwzjR+7E0CJEACJGCbBCiMs8194axIgARIwF4IUBhnLzvFeZKATRM4onB2pRTmGbcsgUZfhXHRr6hhuTHPARgGYIclhohLwrhlW7eh7bDhRmHs16olujRqBM/E/35WJNOEOG7Bho2YtWatzrg5hHGfPn/G/+7exas3b/D67Tu8ef8e7z58UEmTvDw84Jk4MVIm9UKuzJnhHEVyJ7MOc2bE3B48fYYXr1/j9du3SOHlhWwZMiB1iuSIFy+eOYeKVsueOJkKQsgLBV+/N2++HRdvERT8CYnd3eHp4YEkHu5IIv7s7gEPdzc4Grj4kcx8YpswLjw8HLcePMDTl3549fYt3rx7hzcBAUjg5IRknkng6ZEYSZMkVh2/gqktNGs+v0xZf/k2bXHs3HlTSkT0XTdpAqr7+JilVtQib969x/1nT/Hc7xX8372DV+LESO+dGulSp7YJSd2TFy9x7/Fj+L9/B/+37/D63TuEhYV9e857qH4eZPkhPdJ+951F+MgWFc+tf548Uf0cePLyJb58ATKnS4fM6dOZxJHCONkd0MzFgDCuAQBx9byUxs3QqF4zAXQ2qic7kQAJxEYCFMbFxl3lmkiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABOyCAIVxEttEYZwmpMjCOPFIxx7jMHvBegmSmpEOreti1qQ+Ef9oy8K4URMWY+CI2dJrXDx7sM7szHm+UvIwdYEXd/chRXIvnfUsIYxTInvTJYy7d/8JhFDMkm33pumoWLao1iG+fPmC7zJWwEs/f4NTqFqpJLatm6zKpc1WWSUv1NXmTx+AKhWKwztLJb11G9atiFWLRuLDx0C4pyppcA4icObQUhT6OWe0rBLJoNRARoQojDMCmoEuFMYhN4DL5ifLiiRAAiRAAtYkQGGcNelzbBIgARKwfwIUxtn/HnIFJGADBHjCsg1sgglTcABw86s0LqMJNYzp+tfXTkMB7Dams64+cUUYFxIaiqxVq0EIdZQ0d1cXrJs4EaULFVTSTSP79OVLdBw1GruPn4hWw1hh3MNnz7Dl0GEcPHMGe0+ekp5bqQIFULF4MTSrXk2R5ErI1uas80VoWJjBsXL8+CMqlSgekfscEoKthw9j9c5dWhmIoOCcN1s2VClZEh0b/AYnR0eD48gE7ImTej2C15Vbt3Hzn39w/d49fAwKQiJnZ5QtXDjacSiO5wvXr+PyzZu4eP0Grt29qxIxKW0VihWFODaK58uHvNmySvG/df8Bth35z3+67fAR/HX1qtTQVUv9gkI//aQ3KwSCrWrXQmI3N1XuwdOnWL9vv1R9cYznzKj8JVqI9jYfOIgDZ/7EjqNHEPAxUGq8n3NkR7kiRVDVpxTyZcsm1SdyyF6fX4oXqqWDeH3MULGyOUqpatQpVw4rx40xWz0h4fPdsxcbDxzQK7VL7uWF4nnzqMSiRfLkxsvX/li+Xe4iW7XLlcUP3t6K5xwWHo69J09i/6nT2HPipPRz3ztlClQoVgwNKlVCifz5FI178uJFnL58xWAf8fxtWaumSkypbuI1Y82uXVi4aTP8/LV/Lp4p3ffInz07+rRsoZIx6muH/vxL9fqnbuMWLZJ+zvZs3gxJDIgexdzFa1BsbW8fPFAt7eKqFSrDYqUNW5QKrQ2hEaI48cWGmDYUhgBwARBqaIJ8nARIIM4Q4PsvcWaruVASIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAFbI0BhnMSOUBinCSmqMO7q/+7gpyK/SZDUjFw9sw45s/8Y8Y+2LIzLkq8Wbt15qHiN5uiwYMZAtGpWQ2cpSwjj2v8xBnMXbZSavi5hnOi8YOlmtOkySqqOsaET+xahWGHhnYreOvcaDyHoM9Tc3Vzw9vER3L77CFnz19Ybv31pMzJmSIscBevh2o17OrOi5ptHR3Di9CWUqtzG0BSgzjs6inMiNZtPlbY4cvy8wRqWDFAYZ366cVQYF0+cVwRgCIBBADabnywrkgAJkAAJWJMAhXHWpM+xSSDmCQhRt6p9AUJDQhH6OVR1Hx4WjrDQMISH/3fOmFN8Rzg4OsDRyRHxE8RHPEcHiH8TJzuzkYCaAIVxPBZIgATMQIAnLJsBopVLNAGw3Epz+PPruEIct8cc48cVYdyKbdvReugwxcgubVyPrD/8oLiftg5CmNZ17FgNoY1SYZwQoE1cugzz128weU4D2rRGl8aNIoRc+goqkToJ6dvGqf9e/EYIfcSabz+Q/+yq5M/5sXTkCKROkcLoNdobJ7FQIaZaumUrJi9foVWm1Ov35hjRuVMEk6odO2P/6dNGM9LVUQibxDjVSpWCg0P0z4LU/RZt2oSOI0ebffzIBc+sXoU8WbOo/mn9vn1o0re/1Hjje3RHl0YNpbIiJERxy7Zuw+gFC3WKrGSLieN/QNvWisRx9vb8kmUhk5vnux5dx46TiUpnXp04BjcX4Ysyvgk5png+9ps6VVpCph6tWN68SJUsGTbslxMcLhk5Ag0q678AWOSViLltPngQw+fMVfTaqo2GkNsN69AB4nVXpnUZM1b658/JlctV8jfx2jZmwUJMX7VaZoiIzNzBg9C8hu6LvjXt1x++e/cpqqk0HHzhnNIudpM/Mnqkxlwrbthirje+hGVvJoBUVoIxC8B/PyytNAkOSwIkYFME+P6LTW0HJ0MCJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJBCXCFAYJ7HbFMZpQooqjBOPlqnaHoeOnpWg+W+k9C8FcHD7HI28rQrj/r52F7kK15dem7mD2lhFHsMSwjhZ0ZqYhz5hnHh8z4FTqFSri7mxRNQrXCAXTh1YrFUysHPvCfxat5vU2P/7yxeHj51Dp57jdebTeKfAo+u7VI/3HTID46Ys01v7/PGV2H/oT1XWUGveqCqWzBEOreitRoMe2LrzqKESFn2cwjjz441jwjhxMoQ4kUEc5LkAXAOQ81+1CBsJkAAJkEBsIkBhXGzaTa6FBLQT+BL+BUIUFxoShtBPIQgLCcPn4M8I+RSq+nN4aDjiIR7Cw8MjCnwJD4eDkyMg/lcYDyppnFMCRyR0SQinBE5wjC/+7qQSygmBHCVycffoozAu7u49V04CZiSg9L0GcwkczLiEOF9KWJRuAshoRRJCHCfex9pryhziijAub516uH5P98VVtDFcN2kCqvv4mII3Wt9/njxBw159cPHGDdVjssI48X/bKctXoP+06Wadj5CDbZk+DT+mTau3rlKh1dJRI1WiOCHJM6a5u7pgzfjxKFuksKLu9sZJLdZbs2sXhIgp4GOgzvVGFcYVqN8AV2/fVsRHSThv1qxYNX4sMqRJo7VbbBHGCYY1unTFkxcvleAxmO3Y4DeM6/4HnBwdDWbt5fllcCFGBHxatMTpS5eN6Km7y+rx41CrbBmjawrBWa1uf5h9XrompEQYJ36G1Ove0+zP/eGdOkK8xhh6n0OpMO7Nu/do0Lu3YumemlWdcuUwf9gQuDg7R8NHYZzRh7iqowWEceLzVfFlg9Smzcyk3iEAhC0y1KQq7EwCJBDbCPD9l9i2o1wPCZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZCA3RCgME5iqyiM04SkTRi3ZccR1GzYU4Lmv5EtayahepVfNPK2KowbPnYBhoyeJ702SwSf3tqDVN8l01ra1oVxYtLXb/6DkeMXYfX6PZbAg+2+U/BrxRLRan/4GAj3VCWlxlw0azB8N+3H3oOndeY7ta2HGRN6qx7fd+gMKtTQfxHt8SO64PipS9i++5jBOfguG4u6NctqzbXtOhrzl2wyWEMEyvkUQlmfQlJZJaGSxfJCyPkitx9/qo57959IlfnyPvZeKV4KgJZQHBHGiZOua3xd/lAAP0XCUPvr3+UOamMBsx8JkAAJkIBVCAzb3ka85iN76kIqE252b/P/v8QqC+OgJBDXCXwBwsKEIC4UQQFBCHofjPDPYYgHBzgndEaCBAng5OQER0dH1U0tfVOfEC3kEuqbqCNuISEhCA0NRVBQEEJCQ+Dk7ARnt4RwdndWieSEVC6eAx0+ce3QozAuru0410sCFiHAE5YtgjXGizYFoP9qHTEzpTPf3tcyShwXF4RxSmRM6i1rUq0qFgzVfvEUU7c16NMnTF62HO8CAjCyS2ckiB9fb0mR7zhylNHyNUPzFXK2PfPmIn/27DqjShgm9/JCEnc33H7w0NDQeh8X87qzexcSu7lJ1bE3TlVKlsSiEcNUe7tx/wGDa4xpYZyYkNjLvfPmIvuPGaLNLzYI47YdPoJ6PeQ/Nza4SVEC5YoUwfIxo+Dp4aG3qz08v5SuXSb/+MULZKxURSaqKFO7XFmsGjdWUR91+JmfHyq376hYMGrUYN86yQrjTly4gJpduxktXzM0R/Fzb/6QwXqlcUqEcaUKFMCRs/IX8tM1vxGdO6lkdlEbhXGGdlT/42YUxlUFMBuAdruoadNU2ltcCbKD0k7MkwAJxHoCfP8l1m8xF0gCJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJGCrBCiMk9gZCuM0IWkTxoWGhuGHXFUhpG+GWhrvFPjn6nY4OWle+d1WhXFZ8tXCrTumnXxjiImhx+dO7Y+2LcTFo6M3exDGqWf96PELzF28EQuXbcFLP39Dy5Z+vFuHhpgytrvWfMWanfVK4NSdalcvjY1bD+kdM7Lo8GNgENy+iy6pi1ygVIn8OH/xOgI+BBpcy+sHh+Dlqf3ElgHDZ2P0xMUGa4jAqMEd0L9nC6msqSEK40wjGAeEcdW/EhLSoDxRSF0DkBOA0i8QmwacvUmABEiABGKUwP+e/83X+RglzsFIwDIEvoR/QWhIKIIDPuHjm4/48vkL3N3cET9+fI2bEMY5ODhECOPEn9WyODGzqMK48PBwiJuQxqlvnz9/VgnkAgICEB4vHIkSO8PZwxnxneOr5HFscYMAhXFxY5+5ShKwMAGl/w+lndTCG2JkeQcANwFkNLK/ubuJq4yI97n2KSkcF4Rx6/bsRbP+A5RgwZVNG5E5fTpFfSwR/hwSgl87dsKxc+ctUT6ipnfKFDi3bq1OsZUSoZU5Jzq0Ywf0bWn4swR75CSYuzg7S4v1rCGME3sp5nl18ybVXCM3exfGbTl0CL/1/PfiS5ZsebNmxaEli5AoYUKdw9j688tSfOas88Uf48ZLle/fuhVGL1golRWhl8ePwsPVVTovgiGhofD5vQXO/U98PBRzTUYYt/fkKVTv3MXik5rQszs6N2yocxwlwjhzTVbIQ2/v2okk7u4aJSmMM42wGYRxtiSKUz2FAbgACDWNDHuTAAnEQgJ8/yUWbiqXRAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkYB8EKIyT2CcK4zQhaRPGicSEacvRe9B0g0QnjOyKnl2aRMvZojDuyt+3kbtoA4NrsnSgRNG8EMehtmZPwrjI879z7xFOnL6kuh0/ddEkKV+VCsWxY/1UrXxmzfdFp55yJ0UY2kf/h4fgmeQ/qVv56h2x//CfhroZfFzf/orOM+f7orPkGkr/UgAHt4uLfFu+URhnGuNYLIwTJzKIE2jz6SBUB8BG0+ixNwmQAAmQgK0ToDDO1neI8yMBwwRCP4ci6H0wAl4GIIFjAri5uSFhwoSqmxDEiZuTk5NKHOfo6KgSxqlvalmcuFfL4sSIkcVxamlcWFiYShQnxHGfPn1S3YQ8TtwCPgTAycURrkld4ZRQiOOEO4YtNhOgMC427y7XRgIxRoAnLMcYaosP1AzAUouPomwAIY4bAmC/TLe9dWocEbnE36f7RdznaRz9cxGZOracaTd8BJZu2So9xTrlymHluDHSeUsGe0+ajOmrVltyiIjaNcuUxpoJ2j+nsJbQSoiCbu3coVNkp558XOBkLWGcYNyvVUsM6dBe4zi0Z2HcnYcPkbOG9gtgWeLJ1qJWTcweqFtaaevPL0swETVLNvsdf129KlX+wf69qNG5Ky7euCGVXz5mNOpVKC+VVYdGzpsPcYvpZkgY98+TJyj4WwMEfDR84S9zzP3UqhXIly2b1lLWEMaJiQxq1xYD2rTWmBOFcabttgnCuIoA5gH43rQZmL33XACaP6jMPgQLkgAJ2CkBvv9ipxvHaZMACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACdg/AQrjJPaQwjhNSLqEca9ev0XyH8oaJOr3zwEkS5okWs4WhXFDx8zHsDEx/wV2bRCf3NyN1KmSR3vIXoVxURfy5u17/Hnub+zedwpLVm5DwAf5L+dnSO+Nu1e0n5R28/YDZM1f2+BxaShQrHBunNi3SCM2cfoK9Bo4zVBXg4/rkiiqO56/dB0/l5Q/mfDdk6PwcHc1OK6pAQrjTCMYC4VxVb4SEaK4n/WQuQYgp3CFmEaPvUmABEiABGydAIVxtr5DnB8J6CYQFhqG4IBgfHj9EfgMeHh4wNnZOeImRHFCEidkcUIUp5bFCTmc+iaqq6Vx4s9CFKe+jyyQE9I48XdxL8Rx4iZEccHBwQgKClJJ5MSfPwR+QAL3BHDxTASnhE4atbmXsYsAhXGxaz+5GhKwEgGl7znE0zLPtgCEGEG2iTeQRR/Zxvr6SSnlI8vdnDkhjhPvg+2TKRp84ZzS41KmrE1k0pYtDz9/f+m5rBg7GnXLKxMNSRdXEFy/bx+a9O2voAcgBGs5M2XCh4+BuHr7tqK+Inxs2RIUzJUrWj9rCa3ERAa3b4f+rVvpXEtc4WRNYZyA/+b0SSRKmDBiH+xVGPfp82cUbtgY1+/dU/z8MKWDPimYLT+/TFmzvr4Pnj5Fll+rSZUvkic3Di9ehHGLFmPIrNlSfar7+GDdpAlSWRH6+84d/FzvN+m8OYP6jo2gT59Qsmlzxa/nmdJ9jxReSXHrwQNFP4RZxsMAACAASURBVP/EuioUK4qtM7RffM9awjgxrxfHjiCxm1sEegrjTDsKjRDG2aooToD4DOC/H1C60awE0EgBuesAsivIs75+WORDPpEJxOTzS+nvudref1HwUsAoCZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZCAmgCFcRLHAoVxmpB0CeNEauT4RThx+pJOqiWK5sWAXi20Pm5rwjhx0nrG3DVw7/4TiaPE8pGZE3ujY5t60QayRWGcT5W2OH/xOpbMGYra1UsrhiNkcVXqdMXxUxel+355f05nVonYTFeREQPbY2DvlhoPX7x8E/lKKPnus/bqV06vRa4cGXXOPywsHJ5pS0lL9CqVK4qtaycjfnwnaX7agms27MXA4bPh5OSImxc2RYso4apvf0yapB13jkXCuEpffSDDABSQ2I66X4VxGyRyjJAACZAACdg5AQrj7HwDOf04SyDkUwgC/AIQ/O4T3F3dVZK4RIkSqW7iz2pZXGRJnIODg4YoTh888Xu2EMkJQZz6Xi2QU9+rpXGfPn2CuAlhnJDIffz4EZ/DPyORVyI4ezjDwYHnl8XGA5XCuNi4q1wTCcQ4AXOcsKxUWEZhnP5ttjSfGD/IIg0oxHHDAezRN4nYKowLCQ2Fe8HCivj/s3c3UiWPfmEcRUVMDAcGByNdufII+Ch30Roh+JnSpzcypEkTMXJoWBh89+xFi0GDpWdTr0J5LB8zOlremkKrSiWKY/O0qVrXEJc4KRHGeadMgZ8yZ0b2DBngnTIlPgYF4c379xD7uPPYMenjKjJ0IZASx5m62aswbtWOnWg5eIj0c0IEk3t54Zf8+SEkXP7v3+P8//6Hc/8T152Rb6LGvT27EN8p+mditvr8kl+d8uT0VavRe9JkqY5j/+iGbk0aq6RpBeo3kOojQs+PHkYSd3epfNthw7Fs6zaprLlD+oRxM1evQc+Jk6SGFMLQyb16oV7FCkiYIEFEn8cvXqD98JHYf1r8d0CuXdzgi2wZMkQLW1MYd2LFcvyc4z93F4VxcnupK6VAGGfLojj18oREu70EEQrL9EMiH/KJTCAmhW4ST1+Ycnya4/0XmTkyQwIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkEIUAhXEShwSFcZqQ9AnjJHDqjNiaMM5cMjBTmETuW6xwbpzYtyhaOVsUxuUv0RgXLt9QzbXJb5UxfGA7pP8+tSIU6zcfQL1mfaX6ZEjvjbtXturM9ug/BZNnrpKqpSt0cv9iFC30k8bDSkVu2mqnSO6F53f2qmQJ+lqjlgOxer3e8w41urdsWh0LZgw0WFfbmOcvXceAYbOx9+B/JzhoE75RGGfSIYVYIIyr8E0UV0iShDjLKycApV8clizPGAmQAAmQgC0RoDDOlnaDcyEBOQKfAj/h3fN3CA/6Ag8PD5UgzsXFRSWLS5gwIeLHj6+6RZbFqX+PMfT7TNQZCDmcaOp79Z/F34VMTkjjQkJCVKI4tTBO3Itb0KcgOLk5wjWZK4Ssji12EDh2Y6NqIX4BT46K+8FV55eKHSvjKkiABKxAQOn7DtrelKMwTv/G2RofKxxm0YY8+fW/NsKSdFDbZGKrMM7P3x9py5aX5v+Dtzeub9f9Pr50IROD89dvgBDzyLTRXbvgj6ZNdL7PfvfRI+SoXlOmlCpzf98efJcsmUbeVKGVEA+9evsWYj+UNiHpuro5+sViRJ3YxkkfG0PCuColS6J+pYqoVLw4hDhKVxMiwb+uXkXfKdNU97KtZ/NmGNmlc0T8/cePuHHvXsTf56xbjzW7dkmV69eqJYQI0FDLnyMHHL/9PrV+3z406dvfUBfV4+N7dEeXRg2jZcPCw5Gndh3cfvBQqo4I1S5XFnMHD47G9OjZc6jTvbsi+d6SkcPRoHLlaGPb6vNLGpIRweJNmkpL965s2ojM6dOpfjfPWLkKnrx4KTWiPhFb5ALP/PzwQwVxzSFlTTznGlapjJyZMuLHtGlVnV+8eo1j589BPB9kn1+65hn8+TMyVf5V6nVT/Ow6sHABhCxSWxPvYfwxfgLm+a6XWmTnhg0xoWf3aFlThHFCmpgsSRJcj/S6ITWZb6HV48ehVtkyEV3EcfDk5YuIvwsRpOxze/3kSUiZ1Evv8C6JEiFnRt0XUlMyd1vMSgjjagGY8fVHrbIvT8T8YkO+vuyLH3qhEkObIpySKG+S0Ir1oxOITcIy7q9193eozAZEyijNKyzPOAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAnEHQIUxknutZA6mdrmTOmLWtVKR5QJDv6MRCn+u2K8TH1zzEMIvCaO6qYxXPHyLXHyzGWZKSCuCOMGDJ+N0RMXSzFxd3PBrYub4eio7GT1jj3GQYjRZNvDazuRNk1KjbitC+PUk+3RuTE6tK4LIXeTaXMWbkCH7nInbVUoUwR7NovvVWtvBw7/hXLVO8gMqzPz+fUZxI/vFO3xJq0HY+U6uRN1tBXv1LYeZkzobXBuh46eRZmqMhfw/q9UpXJF0b1TY5T+5WeDIoWgoE84dOwsFi3fis3bD0ebD4VxBrdIccCOhXHlvoniiihcdD0AcmfMKCzMOAmQAAmQgO0RoDDOOnvy7Mkz+L9+IzV40mRe+C71d1JZpaGw0DDcuHZTulvW7Fng6OQonVcSvHPzDj59+qykCzJk/AGJXBIp6qMtLGRnt2/cUVTHydERWXJkUdTHHOHgD8F49eA1EsVPBDc3N5UkztXVVXWfIEECODk5qW5C0CbkcOp7U8eOLIwTtcTf1Tdx0rVaGqcWx3369EklkfsY+BHhCcPhnswNDk7Kfg83dc7sbxkCG89O1yg8uOp8/VZ1y0yDVUmABGIHAQrjAFsTus3Hv3OSbUrnL1s3JnInvr1vpvGmf2wVxt28fx+5a9WR5lqmcCHsnD1LZ/7a3XvSUiyZQYXUSkh0IrfPISH4sVIVKUlQsbx5sX/BPIPvrQ+fMxejFyyUmRLWThyPGqX/+5xQdDJGaFW11C/o17oVfsqcGeJ3CNFev32LkfPmY846X6m5qEOB5/6KtsbYwkkWRFRh3MDpMyCO7wrFiqFGaR8k8/SULaXKCeFb9c5dcPqS3OeuQoy1eMRwnWOMX7wEg2fqfu5E7ug7aSKq+ShzL5tDGLf7+AnU7Kr52bM+aN2aNMaYbl31yhhL/d5S6rkqxhHixIsboh/7tvj8UnQwKQwrkVhGFUb2nTIVU1cI55PhJoRuG6dONhicvmo1ek8ynItcaJQQdTZprPO1d/badeg+foLBsUVAlzBuyeYtaD9ipFSN3XNnw6dgQb1Z8frrXbqsVD1dok6lwjghrxzVpYtKvJg0SRLV2EJaee7v/6Fqp06KhItj/+gG8ZzU1ap06IiDZ/6UWt/jg/sVv2ZKFbajkB5hXAMA4glhmTdkzc9oHoB2kmUpjNMPinzIJzKBuCbsk3wZYYwESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESEAJAQrjlNAyMTt/+gC0bl4zoooxwjgTp6Dq3rBuRaxapPklaArjNMmKE9S/z14Fj5/IXUm9eaOqWDJniOLtWbdxH377vb90v+kTeqFz2/oaeXsRxqknXaxwbjRtUAU+JX9GmtQpkShRwoj1+L95j+s3/8H23ccwbsoyaS7jR3RBr65NdeaFDM0lZTHpelGDtauXxoYV47X2X75mJ5q1Vb736mI71k9FlQrFpeZWvnpH7D8s94X8yAUzZ/wejetXVskGU6dKjuRJPSFO+BLHztPnr/DXub+xYq1+6R2FcVJbpChkh8K4Mt9OeDXmyXQNQE7hAFEEiWESIAESIAG7JUBhnHW2rnG1Jrj410WpwQsVL4TFGxZJZZWG/jzxJ1rUaSndbfnWZchfKL90XjYo5GL50imv27VfV7Tp2lp2GJ25DSs3YEjPoYrrHL50CCm+S6G4n7EdQj6FwO/+KySMlxAuLi6qm1oWlzBhQsSPH19DFieEceJm7hZZHqeWxoWFhSE0NFQliRP7KW5BQUEqkdyHwA+A8xe4JXeHg4P552Pu9bGefgIUxvEIIQESMCMBpe89aPsholRYZmkhGuub8QCJoVLHv44j/iN4SIwXW4VxZy5fQanfW0gj/a1SRSwdpVvQs3zbNrQZqluaJT3Qt+CRJYtROPdPGt1OXLiAsq3aSJX6a+1qlZDNUHvz7j1S+WhK4HT16d6sKUZ37aLxsBKhlXfKFFg5ZgyK5MmtdQghXW41ZChW75S/yM39fXvwXbJksYpTVDg/58iOzOnT48e0aZEqWTKEhIbiTUAArt25g0cvXqBVrVpo9GsVQ1ut6PEDp8/g146dpPoYkinagzDuj3HjpWWF4jj+e8tmJEr43+eD2kBNXLoMQt4n227u2IZ0qVPb/PNLdj3G5KYsX4F+U6dJde3TsgWGdfzvYlvHz19AudZyr49igGdHDsHTw0PvWLW7dcfOY8ek5iNCC4YOQZNqVfXmzSGMK9+mLY6dO29wXtV9fLBukpycTolw79nhQ/BMrMlOiTBOCFEHtmsLD1dXrWu4dOMmCjdsZHB96kD7+vUwpY/ui5tRGCeNUhXUIowTbzKOAaD5w1ZZ2ZhOi9+pEggPoeTAFKLpB0U+5BOZAIVxki8sjJEACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACegmQGFcDB4dFMbphy3kbGmzVZbekbVLRqN+7fLSeSXBcxevocAvugVkUWttWjUBNav6KBlClX3z9j28vpc7kUjkCxfIhdMHl2iMY2/CuKiQUiT3QprUKfD46Uu89PNXzFB0eHxjF7xT6xcq1GjQA1t3HjWqftTnbuQiDx8/R7rsvxpVV3T68Pw4XF0SSfVXelxKFZUMURgnCUpBzI6EcWW/it6EFVHObKidQT0A6xXgYZQESIAESMDOCVAYZ50NpDBOk7uxwjhXN1cc/98xCFmasU2IGqqWqIb7d+8rLnHo4iGkTBUzwrjQkFC8evga+AR4uHsgUaJEqpuQxol7IYsTN0dHR5UkzhKiuMiAdEnjhCBOLY0T90IaFxwcjPcB7+Cc1BmuXtpPklYMnx2sRoDCOKuh58AkEBsJHFG4qFJa8sKWMk9BHZFtpyDP+vphKeWjAH2MR4UlZ2jwhXMqcVxsa7uPn0DNrt2kl9Wmbh1M79dXZz4mhHGjFyzE8DlzDc7Z3dUFL48dlf7/74+VKuPJC8MXICqYKxeOLdP8jEeJMK5KyZLYOHWy3vnfffQIOar/d/EqQ4s9vnwZCuTMoRGzd05iMVVL/YJaZcugYrHi0cRMhpiY8nhoWBjeBQTg1v0H8GkhJ1IXQrsTK5brHNYehHG5atbC7QcPpdCN7NIZPZs3M5j18/dH2rLynwMvGTkCDSpX0qhri88vgws3IVCkYWNcvHFDqoJ4LRKvSeomRIqpfUoj4GOgVP/FI4ajYRXdn+uL50KqUj7S9X7w9sbVLZvg5Oiod3xThXGBwcHwKir3cdeM/v3Quk5tKR5rdu3C7wMHS2V3zZmN0oUKamSVCONOrlyO/Nmz6x1LyENXbt8hNZ9KJYpj87SpOrMUxklhjAiphXEbb93B2hu3EPD5s7ICtpF+A8BLwVQoRNMPi3zIJzIBCuMUvLgwSgIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkoJ0AhXExeGRQGKcfti0J4/oOmYFxU5ZJHx3vnhyFh7txJ6iXqdoeh46elR7rn7+3If33qSPy9i6Mk164jmD7VnUwe7Luk8zU3RYs3Yw2XUYZNdztS5uRMUNanX2z5KuFW3fkToSJXKRKheLYsV73F/C1DThm0hL0HzbLqHWY0onCOFPoae9rB8I4ccaMuOK93JkzuhFdA5Dzq3ROXI2ejQRIgARIII4QoDDOOhtNYZwmd2OFcaLKqGkjUaN+DaM38uiBY+jQuINR/WNKGCekdm+fv0XQ62C4ubrBzc0tQhjn7OwMcROyOCcnpxiRxalhaZPGCWFcaGhohDRO7G1gYKBKGvfu4zt4fe+J+M7xjeLNTrZBgMI429gHzoIESCCCQFsAho1S/wGbD0D0kW2sr5+UUj6y3K2R2wtgfPCFcwetMbilx9x36hSqdeoiPUzzGtUxd/AgnfmYEMaVadkaJy9eNDhnIYzbMFm/mC1ykcb9+kPIrQw175QpcHf3Lo2YuYVWonjyEiWlJU3axEVxhZOh/dL2uPh95f7Tp7hy6xbuPXqMR8+fq273nzzF/adPpLlHrm3vwrgXr18jXbkK0jivbNqIzOnTSeVbDx2GFdu2y2Xr1IYQfEVutvj8klqMESEhKfyplpzcTLzGPT96BI4ODhojKeFdoVhRbJ0xXedMr929h3x1xbWE5JohAZ26iqnCuGPnzqN8G7n/tg1u3w7F8uSRWsCJixcxYq6cb3jJyOFoUFlTtmduYdzCjZvQadRoqbkXyZMbhxcv0pmlME4KY0To0soVqj9P2bnr3pY799J9CgvTb0FUVj6m0m8BeCoYjEI0/bDIh3wiE6AwTsGLC6MkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQALaCVAYF4NHBoVx+mHbijBOnDSfLsevEPORacZIvyLXnTXfF516jpcZSpWZPKY7/ujYMCIfl4VxabxT4NrZDXB3czHI758HT5EhVzWDuagBMcaj65oncEXNdO83GVNmrVZcW4juhPBOaevQfSzmLNygtJtJeQrjTMKntbMNC+OEIG4EgFJmWvVvANaZqRbLkAAJkAAJ2AkBCuOss1EUxmlyN0UYl/7H9NhxYrtKlGZMa17rd5w9JS8GjzxGTAnjPgV9xrObz+Dh6qESxQlhnFoUlzBhwghhnGCgvhnDwpg+UaVxYWFhELfPnz9D7Ku4DwoKUknjxC3UKQSJUyWGo5M9noNrDKHY14fCuNi3p1wRCdg5AaXCMgrj9G+4pfnY4uEmRHFDAZwRkwu+cC5WXkjh7N//Q4mmzaT5V/fxwbpJE3TmLS2ME5/9uPxcUHq+lgoGXzinUdoSQqviTZri3P/EdTwMt6jCuLjEyTCdfxNvAwKw9dBhbDpwAKcuXTJKCqdvLHsXxh0/fwHlWreRxYmg82elf9detnUb2g4bLlVbG0dbe35JLcTI0MSlyzBw+gyp3q21yPVEx22Hj6Bej55SNUTo6eGD8EqcWGteiZhNFPA/dQIuzs4GxzZVGDd52XL0n6ZbdGdwAmYITOnTG+3ra8r0zC2MUyJ1pTDODJuqpYRzvp/VbyqKJ5Uw5npYZiSLVBX/dxVfwAiWrE4hmn5Q5EM+kQlQGCf5wsIYCZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZCAbgIUxsXg0UFhnH7YtiKMO3P2KoqU+V36yIi6r9IdvwXv3X+CH3+qLt0tX+6sOH9cfKf03xZXhXFC5LZ/62xkzZxeml2WfLVw685D6bwIdmpbDzMm9NbbZ/f+U6hcu4uiuiJ85/IW/PhDGsX9QkPDIKRxC5ZuVtzX2A4UxhlLTnc/GxTGFQYwCkBpM65WnJWYE0CsPCnWjJxYigRIgARiHQEK46yzpRTGaXI3RRgnKs1bMxfFfYRLV1m7dvUa6pbTPPFXSYWYEMaFhoTh1UM/xPvkoBLDCVmcq6sr1KI4cR8/fnw4OTnBwcEh2vSNFekp4aCWxol7cQsNDVXdhCxOLYwLDg7Gx48f8ebtGyRJmxiJPBJJiweUzIVZyxOgMM7yjDkCCZCAIgIUxunHZWt8FG2uhcPbAAiz0fnI48RWYdzdR4+Qo3pNaaTF8ubFwUULdOYtLYx78/49UpUy51uf0kvXCL7/8zQSxI8f8W+WEFpV79wFe0+ekppgVGFcXOJkCNCt+w8wZuEirNml/6JChuoYetzehXFbDx9G/R69DC1T9bghMVXUImcuX0Gp31tI1f7B2xvXt2/VyNra80tqIUaG8taph+v37kn13jpjOioUKxotG/AxEMlLlJSqIULzhw5G02raL9al5LhI7uWFRwf2SY1rqjBuwLTpmLRsudRYlgoNatcWA9q01ihvbmHcX1evomQzue87GHpeVunQEQfP/CmF4/HB/Ujm6SmVje2hSMI49VL7ARA3dztZ+yIArSTnSiGaflDkQz6RCVAYJ/nCwhgJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkIBuAhTGxeDRQWGcfti2IozrPWg6JkyT/6L4o+u7IORlprQcBevh2g25L/GLcSKLxuKiMK5Y4dxYu2SMYu79hs7E2MlLFW3VljWTUL3KL3r7vA/4iMTe+jNRC2TO+D1uXtikaC5Rw5u2HULzdkMR8CHQpDoynSmMk6GkLGNDwjghihsBoKyyFUilGwBYK5VkiARIgARIIFYQGLa9zVCxkOypCw1R3XsXihXrspdFUBinuVOmCuOK/lIEC9bpFlroOi76de6Hbeu3G33YxIQwLjAgCE+vP4VnEk+VLM7FxUV1E6K4BAkSqO7VsjhtwjijF6elozb5nFoWp46rhXFhYWEICQlRCePE/gphXGBgoOr2KV4wPL094ejkaM7psVYMEaAwLoZAcxgSIAFZArYmRJsPQMxJtsW1+ctysVROXChBvNE7DMBVbYPEVmGc/7t3SO1TRpqru6sLXh47qlPwa2lh3L3Hj5G9Wg3p+Voq+OzIIXh6eESUt4TQqkGv3th88JDUEqIK4+ISJ12AQkJDMXXFSgyaMVOKoakhexfGLdu6DW2HCVem4VanXDmsHDfGcPBbQsnxKF5j/I4f06hta88v6YUrDN68fx+5a9WR7uV/6gRcnJ215mt3646dxzQ56ipcrkgRbJ81Q+vDS7dsRbvh4qMnw61grlw4tmyJ4SAAU4VxrYcOw4ptxr9nIzVJA6EezZpiVFfNC5GZWxh39fZtFKgvPqIz3CiMM8zImIQWYZy6jLhS3UA7EMeFfptjsMT6KUTTD4l8yCcygdgkjFN9HqigKc0rKM0oCZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACcQtAhTGxeB+UxinH7YtCOPCwsKROnNFvPTzlzoy8uXOivPHxfc7TWvDxszH0K832TZ+RBf06tpUFbdFYdyGLQcxZtISXLh8Q3ZJUrlSJfJjUO9WKP1LAal81NDRExdQqnIbRX39Hx6CZ5L/Tt7S1dmnSlscOX5eunafP5ph7LDO0nldwafP/FQSvBnz1plcS1uBCmWKoG2LWqhZ1Sfawz/+VB337j+RGlebcE6qYywO2YAwTojixIms5S2E+Q6ALADCLVSfZUmABEiABGyQwPDtbYQsIaLVLqB58qMNTjlWTYnCOM3tNFUYJ6ptOrgRWXKI/9LItRfPXqB0XnlphraqlhbGhYeFw++BH8IDv6gkce7u7kiUKJHqJmRx6ptaGCfmqE3qJkfEPCkhjBOyOLUwTkjjxP4KcZyQxX38+BGv/V8hRcYUSOia0DyDskqMEqAwLkZxczASIAHDBOKacM3ehXSGd9RyiTUARgK4pm+I2CqMCw0Lg1sBZZLwy5s2IEv69FpxWVoYd/7aNRRr/O/nKtZsMSGMazFoMFbv3CW1zKjCuLjESRsgcVzX79FLWpglBdlAyN6FcZOXLUf/adOlUDSvUR1zBw+SyorQ81evkL58Rel8wF9nEN/JKSJvCWGcKc8v6YUoDI5dtBhDZ82W7jV/6GCd2TlrfXHxhvznu48O7ENyL69o9ZSI3ZSIBJXUXTJyBBpUrqQxNyVCPGmgCoMxIYy78/AhctaoJTUzCuOkMCkO6RHGqWv1AyBu7oqLx1yHuQDaSwxHIZp+SORDPpEJxCZhnMbngRKvFfEkMoyQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAlIEIiTwrgzZ6+iSJnfJfCYN7Jy4Qg0qvffl5I/fw5BwmRFzDuIRLXWzWtCyOsit8q1u2D3/lMSvYGX9/YjeTJPqaySkN+rN0iRoZx0lx3rp6JKheLSeZngs+evVMI42TZqcAf079lCNq4zd/bCNRQsJX+iUsO6FbFqkTgPDVDC7bc65bFm8WiD8+07ZAbGTVlmMCcCJ/cvRtFCP2nNCmHcjt3HsXXnUZPkcWKfBWdd40hNFIDS51yxwrlxYt8iqfJC2tZv6EyprAgd3jkPQoBnrvbu/QesWrcbS1dthzieTGmVyhVF9SqlUKl8UXyf5judpXIXbYArf982OJS7mwvePz1mMBfXAlYUxv0MYAQA+Rc74zanAYC1xnVlLxIgARIgAXslQGGcdXeOwjhN/uYQxtWoXwOjpv37u5dMmzp6GhZMXyAT1ZmxtDAu5FMIHlx9iMRuieHq6qq6CVmckMcJWZwQxYmbo6Oj6qZu1pLGCVmcaGphXGhoKMRNyOLELSgoCB8+fFDdHNzjIUnKJIjnwHPPTDoIrdCZwjgrQOeQJEAC+ghQGKf/+LA1PtY4mlcDGA7gpr7Bd9epUUo8nrdRk8PiPkm6dNaYq0XH/LFSZTx58VJ6jAVDh6BJtapa8yu2bUfroeL6FuZpR5YsRuHc/312cfz8BZRrreyCMuaZiWaVmBDGdR49Bgs2bJSaflRhXFzipA2QEnZSgCVC9i6MG7NwEYbNniOxZJnZlQAAIABJREFUUqBN3TqY3q+vVFaElAjf3F1d8OzIYThF+j1WSf8qJUti49TJBuem5BiJ+vwyWNzIQK6atXD7wUMje5vWbc6ggfi9Zo1oRZZu2Yp2w8VHUYZbdR8frJs0wXAQgKnCuPJt2uLYOfkLgElNSmEoJoRx4mez+Bkt0yiMk6GkPCMhjFMX7f9NHOemfBSL9wgBkEBiFArR9EMiH/KJTIDCOIkXFUZIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgAT0E4iTwjgeFCRAAjFP4NXrt7j/8CkeP3mJJ09f4uHj57j/8BkePnquOvH/tf87JEnsjiRJ3JHUKzFyZvsR+fJkRd6fssA7dYqYn7AdjxgYFIxLV27hr/N/4+btB/B/8x7v3n1QyQVdXROpWHsmcYeHuys8k3ggZQov/JQzk4p1tiw/IEGC+Ha8evuZuhWEcUIUJ864lDtDxDSUdwBkARBuWhn2JgESIAESsDcCFMZZd8cojNPkbw5hnKh4+NIhpPjO8O8kHz98hE+e0hD3pjRLC+Pev3qPl3f9kDRpUg1hnLOzs0oYpxbFCWmckMSpRXHWEMapZXHiPjw8XCWKU9+HhISohHHBwcEIDAxEQEAAAoLfI1Xm1HB0cjBlC9jXCgQojLMCdA5JAiRAArZPoBmApTY0TfE+17pv76/pFcWp57ynTo1/zbffWqn+A21oOeaZipABCSmQbCtTuBB2zp6lNR4YHIw7D/ULjwr+1lB2KEQVxl26cROFGzaS7m+pYEwI4/4YNx5z1vlKLSGq0CoucYoKaN+pU6jWqYsUN10h75QpkD61N9J+lxJrd++RqmXvwri5vr7oNna81FqFMFKII2WbEuGbqBl0/mzE77Di70r6ywrjTHl+ya5bSe5/d+4if736SrqYNVuqQAHsmRddGLhx/wE06iMnBzQkLIs8YVOFcXW798D2I0fNykBpsZgQxj3z88MPFf67kKG+ORriX6VDRxw886fUMh8f3I9knua/8KDU4DYWUiCMU8+839fr3wl5nK2J4+YDEMJoNhIgARKISkDj910JPLzKiwQkRkiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEhAhgCFcTKUmCEBEiABEiABMxOIQWFc3m8nslY18xL0lRNnbq6JwfE4FAmQAAmQgI0QoDDOuhtBYZwmf3MJ49r90Rad+3Q2uLlrlqzByH6jDOYMBSwpjAsPC8fTO8/g8NkB7u7u0YRxQhKnFsY5ODhECOOUyOLUkjdD61TyuKgpROPiJoRx4l4tjBP7LKRxQhjn98oPKTOnQCK3RErKM2sDBCiMs4FN4BRIgARIwLYICPurkLJltIFpCVHcegDCcCQlilPPOS4I49bs2oXfBw5WtE2nVq1AvmzZFPVRh5OXKImAj4FSfaMK4x49f45MlX+V6itCo7qaJg7TNlCC+E5oX78+nBwdIx62NaFVXOIUdY9qdu2G3cdPSB8jIljdxwelChZAoZ9yIVemTIjv5BTR3zmfuIaL4WZOYdy6SRNUc1LS1u/bhyZ9haPIcBvfozu6NNIUN67bsxfN+g8w3PkbLzFH2SYkkjlr1JKKJ/fywqMD+zSytvb8klqIwtCo+QswYu48hb3MG7+/bw++S5ZMo+jek6dQvbPc62imdN/j6uZNUpMyVRjXceRoLNokN5aQnJYuVEhqXkpCJfLlRcFcuTS6dBkzFvPXb5Aqc3LlcuTPnl1v1lrCOPEcFM9FNsAIYZwam3hBFvI4WxHHhQBwARDKfSUBEiCBKAQojOMhQQIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAJWIkBhnJXAc1gSIAESIIG4TSAGhHFCFDcUQLUYJn3nqywuCwBxMi0bCZAACZBAHCNAYZx1N5zCOE3+5hLGubq54sjlw3BxFedGam9hoWEoV6A8Xjx7YfJBYElhXGhIKO5fvg93Fw+VMM7NzQ0uLi5IlCgREiZMGCGLE9I4IYmLfNO1MEsI4sRY6rqR7yML40JDQ1XSOLHPQUFBKmGcv78/3FO7wSOZh8n7wAIxS8Av4LFqwOM3tqjsFoOqzj0SszPgaCRAAiRAAjZGoCmAZVaekzj5XZhbxPtr14yZS1wQxj189gyZqyi7TkXVUr9g/eRJxiCFKcK4D4GBSFa8pPS4b8+cgnOCBNJ5Y4O2JrSKS5wi75nSY/kHb28sHjEcRfLk1rn11hDGrZkwHjXLlFZ0OJoqjFMiBsuWIQMubvCVnt/hv/5CpXYdpPLaatva80tqIQpDuWrWwu0HDxX2Mm985oD+aFVbU+x35vIVlPq9hfRA/+zdjVTJkxvMmyqMGzJrNsYtWmxwHBEY1rED+rSUX4NUUR2h2CKMe7B/L1ImTWoKiljT1wRhnJrBQAB9bEQcJ6yU7WLN5nAhJEAC5iJAYZy5SLIOCZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACSgkQGGcQmCMkwAJkAAJkIA5CFhQGJcLwHAANcwxTyNqNAKw2oh+7EICJEACJBALCFAYZ91NpDBOk7+5hHGi6sCxA9Gg+W86N/jAzgPo2rKbWQ4ASwrjPgV+woMrD+Hl6aUhjHN2dlYJ45ycnODg4KC6CVmcaOr7yH+OLHETjxsjjRN9ItcW9SP/m7YxwsPDIW5CHBdZGBccHIwPHz7g7du3cHCLh6TeSRHP4d/5s9kXgRzf5eTG2deWcbYkQAIkYAkCDgBuAshoieISNcVJ75u+XgxhsLGiOPUYcUEYJ9aarWp1/PPkiQTa/yLje3RHl0YNFfURYVOEcaK/rMBLZA8tXoiiefIonqPSDrYotIornCLvlRLpmej37MgheHroF1XLcvw5R3acWLFc56EzfvESDJ45S+rQWjVuLGqXKyuVVYdMFcZduXULBX+Tfz6/O3MKCSVljHN9fdFt7Hip9VQoVhRbZ0zXyNri80tqMZKhq7dvo0D9BpJpy8WK5c2Lg4sWaAzg5++PtGXLSw86onMn9Pq9ucG8qcK4Oet88cc4uWOqVIEC2DNvjsE5mSMQW4RxsuI/czCz9RpmEMaplzjomzjO1YprDgEgrqQRasU5cGgSIAHbI0BhnO3tCWdEAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiQQRwhQGBdHNprLJAESIAESsC0CFhDGCVHcUAA1hdfDSqu9AyDL1xNqw600PoclARIgARKwMgEK46y7ARTGafI3pzAuZaqU2H92HxydHLVucsMqjXD5/GWzHACWEsYJAVuAfwBe3vWDZxJPJE6cGK6urnBxcYFaGOfo6BhNGKdrUaKeNulb5Lw+kZy6rzbhXNR/U9dRC+PEfVRh3MePH/Hu3Tt8wid4eXvC2dXZLPvBIjFLgMK4mOXN0UiABEjARgk0AaDbnGS5SYuT3bd8ddgOAXDVHMPEFWGcEnlPZK6bpk1B5RIlFKE2VRhXu1t37Dx2TGrMgW3bQNws3WxRaBVXOEXe26VbtqLd8BFS261NTKatozWEcfOGDEaz6tWk1qEOmSqMCw0Lg1uBQtJjrps0AdV9fKTyPi1a4vQlud+1h3XsgD4tW2jUtcXnl9TCJUMj582HuNlC0yYKE8I4IY6Tad4pU+C87zokcXfXG1fyM2fJyBFoULmSRr0L16+jaCPxXw259vL4UXi4Wt7TFVuEcVc2bUTm9Onk4MbylBmFcWpS1hbHzQXQPpZvG5dHAiSgjACFccp4MU0CJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACZiNAYZzZULIQCZAACZAACcgTMKMwLvtXQdywryey1raiKE698MYAVslTYJIESIAESCC2EaAwzro7SmGcJn9zCuNE5WmLp6Fs5TLRNlmI4oQwzlzNUsI4fAFePHyJoFdBKlmch4eHShgnbkIYlyBBAkQWxon1CHGbaFHFbw4ODqp/Vwvc1Dk1A3Ve1FPL38LCwqIhUj8m6omb6CdqRm3qeqKGOiOEcZ8/f4bYZ3ELDAxUCePeB76HZ+ok8EjmYa4tYZ0YJEBhXAzC5lAkEHsJCJm+kqY0r6Q2s8oJiP9k3ASQUXlXk3psBSAkHGYRxalnEleEcR+DgpC+fAUEfAxUvAmD27dTCZ4cv/3/0lABU4Vx83zXo+vYcYaGiXh877y5+KXAz9J5bcHnr16hx4SJOHb+AsZ264pGv1bRiNmi0CqucIq8EaPmL8CIufOk9loct/1btzKYNZcwbsKSpRg0Y6bB8USgff16mNKnt1RWHTJVGCfq1OzaDbuPn5Aat1jevDi4aIHB7Nm//0aJps0N5tSBAwvno3i+fDb//JJekIGg+N04e7Ua+OfJE3OVNKnO1L690a5ePY0adbv3wPYjR6XrZsuQAdtnzUCalCmj9RFiwlU7dqLtsOHS9bQJ48LCw/HdL6Wkf2YJQeSGKZMR38lJelxtwXV79mLorNlwcnLE1c2bokViizBu9fhxqFU2+ntnJsGz084WEMapSQi5cS8AljcZarIPAeACINROt4TTJgESMD8BCuPMz5QVSYAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESECKAIVxUpgYIgESIAESIAHzEjCDME6I4sRJzXVsQBQn4NwBkEV4Q8xLitVIgARIgATsiQCFcdbdLQrjNPmbWxiXO39urN4Z3Y3bo01P7Nm2x2ybbylh3JfwL3h08zG+BH1BkiRJognj4sePHyGMUwvgIovghMhN/F2I3YKCglRytqRJk0L0E+I28ZhaACdgiH8PCQnBy5cvkSJFioi/i8fUWfFnIZUTtV+9egUXFxe4ublFE9GphXFqQZ24F/I4UT+qMM7/rT/cU7gjWZqkZtsTFoo5AhTGxRxrjkQCsZgAT1i2780VFt6VMbiEHV/fyxoM4KIlxowrwjjBbsryFeg3dZpRGIvkyY0BrVujTOFCEcJiXYVMFcbd+Ocf5KldV3qe7q4uOLxkMXJmVO4wDAwOxsKNmzBi7twIMdGYbl3xR9MmGuPbojAurnCKvBEDp8/AxKXLpI6NDr/Vx+TewtWjuwm5lVuBQlL1fs6RHSdWLNeZVSLwE8Ktc75rpSWMYlBzCOOmrliJvlOmSq1XhE6vXom8WbPqzTfq0xcb9x+Qrul/6gRcnJ1t/vklvSADwUs3bqJwQ/PJ602dl3gtP7x4kUaZub6+6DZ2vKLSyb28ULd8OeTJmgU/eKfBS39/XL93D+v27MHtBw8V1dImjBMFmg8YiLW75d/HaV6jOuYMGmjwZ5S2yV24fh1DZs7G/tOnIx4OvnAuWtSWhXFKnovGSCsVbaodhN8+eKCa5cVVK3zEfaUNW45YaNrWEMcJe2lnC62HZUmABOyPAN9/sb8944xJgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARiCQEK4/7P3p3AyVHVax9/eu/p2Sf7RgKBACEJBAOIiAZlXwRZXFDB3Qt4US5XvSKKKCoo4ut+gSvuIIoSRAVUdpElYYckECAJWcg6e+9dXW9OTSrMhGSme6Z7evsVn6J7Zk6dOuf7r+7OdE09XSWFZBoIIIAAApUlMILAOBPKdrkkc2Wjt4xmba50HM0Lesto6gwFAQQQQMAVIDCutMcCgXED/QsdGGd6N4FxJjjOXdauXqvjDju+oIUvWmCcbeuVp1fKn/WrtbV1QGBcKBRSMBh0wtvcUDgT0tY/MM4NbVu+fLkWLVqkdevWac6cOTr55JM1ZcoUJ8DNXUxY3Jo1a/T73/9eK1as0KxZs3TWWWdp6tSpymQyOwLjTPhcR0eH7rzzTi1ZssQZ1ymnnKIDDzzQCY1zl90Fxpm+TFhdIpFQLBZTd3e32ju2KjKmXuP2GFvQutDZ6AgQGDc6zuwFgSoX4ILlyi2weZ/rhW3vL+WfzJX/nP8q6cvFCopzh1NLgXE90Zj2Ov74HcFo+ZdEmrvPPnr/iSfoLQcdpPn776dQMPiGbkYaGGc6fOfHPqGHnsw9I9CExn3uIx/Ruae+SxPGDB1K/PKaNVp0z70yAVqb29sHzKFSAuNqxal/cX5525/1qcu/ltOhawKtltx8026PBxNu9YnLvqolzy/Nqb+hAuNu+tsd+sil5ikrt2WwQLtoPK7O7h5NmTB+R2eFCIzbsGWLZhyb++/GxvBP/+97OmTOAW+YlAnbu+xHP9Z3f7n7EL2dN/rEmWfoh5d88Q19lWMgY25VHLrVZT/+ia762Q1DN5RknseeW3RrXkGCpuPPXHlVXqF9L93xV02dMGHHmMxz4LSjj81pjMVotLvAuPsWL9bxnzovr10ed8Rb9JkPflALD1ngBOkPtsSTSd332GL9YtFtuu3ee9/QtNIC486/4hu64U+35uz126uu1BnHHL3L9u1dXc77PWNbW3Pur9Ia3vfNKwYM+fhbFnmKPAfz4WL/Lam+yPsx3aclRSRlRmFf7AIBBMpfgPdfyr9GjBABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgSgUIjKvSwjItBBBAAIHyFhhGYJwJijN/9P++8p7ZG0aX74UQxf7DYvof/ADCB5/+Ajx+Bz8e8BnE52u3f9L56RmHXFhhL1uVPVwC4wbWrxiBccedcpyuuf67O3Z01WXf1q+uzf0i9lyOsGIFxpkLclcsWaGwv84JZmtsbHTWSCQiExhnQt5MYJwbGueO1Q1rMxdEb9iwQVdffbUeeeQRp53f79epp56qCy64wGlu9mG+Z0Lcvve97+mOO+5wLqQ2fZx44on67Gc/6+zHhMu5F1jffPPN+s1vfuMEvpll33331ec//3ntueeeTn8mtM5sb1bztbuaPtLptLO6gXFdXV3a2rFV9WPqNX76uFy4aVNmAgTGlVlBGA4ClSlQ7N9rK1OlMkZ9tqTfFnmod2z7J8tXJC0p8n6c7mspMM7M999PPaV3fPTjBaM1gVbjW9vU1tIsr8errt5ePfbsszn3f9/Pb9CbD5z3hvaPPP2MFn7kozn307/h2SedqEPnztGEMWM1aexY1UfqtLm9Q+s2btTq117Tbffcq2dXrNht35UUGFcLTv0LZY6tt537kZyPCxNweNn55+mYw9/shBsmUik9++KL+vu/H9bX//fanPsxDYcKjBtOuNWZxxyj9590gvaYNEkmgO25F1fo3sWLdeNf/+aMbfHNNzkhjWYpRGCc6efCb12p6/5wS15z/8ZnLtSRB8/X/nvNVHdvr55cvlw/+O1v9cCSx/Pq55k//VGzZkx/wzbVGhhnfj/e+8STtG7jppycPvSuU3T9Vy/LqW3/RvkcG2a7az7/OZnAwv7Ley/+3C5D0/IezDA22F1gnOnqpPMv0N2PPJp3r/tM30PvP/FETZswQZPGjXOCz9KZtNZt2qTXNm/R4uee2/E4213nlRYYd8W118ms+Sz//eFzdczhhzuv4Ru3bNWyla/o7w89rH88/LDTza4M8um/nNuWIDDO5TAfNmaC40ygWzGXH0n6z2LugL4RQKBiBHj/pWJKxUARQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEqk2AwLhqqyjzQQABBBCoCIE8AuMqNSjOrQOBSoMfkfjg01+g2H9YT/+DH2/4FNDnD4//zOlt9pTDKuJ1uVoGSWDcwErmExg3dfpUHTDvAN11+11DHg53PnKHps2Ypu6ubh2+71uGbP+xT39MP/tR32Mil6WYgXHLH3tBkUBkQGBcfX29gsGgs+4cGOeGxZlxm+C2p59+Wl/60pcUj8cVDofV29urBQsW6Otf/7pMP5lMxgmMW7NmjS6++GK1t7c7YXQm0G3SpEm68sorNXXqVCcwzuzLhMRdc801+uc//6m6ujonDM7U7ZJLLtHRRx+9g2tXgXEmKM7sr39gXE9Pj7a2b1W4JayJe03IhZs2ZSZAYFyZFYThIFCZAsX+d31lqpT/qL2SXpC0d5GGeqekL49WUJw7h1oLjDPzvuFPt+r8K75RpDLm1+3uAuNML2d89r/01wceyK/DArSupMC4WnDqX1ITSDjhbQuHVeXG+oh6on0B2MNZhgqM6+zp0cS3HzWcrne7zSM3/lYH7WdOPRQuMO7lNWt0wKnvLug4c+nsjGOO1m+vunKXTas1MO7xpUt1xAfPyYXHaXPzd7+jU4/K/xjq6O7WpIXvyHk/h86dqwd++fMB7Z9a/oLefPYHcu6jkA0HC4zL17CQ46q0wLi7Hvq3Tv3Pwn4oCIFxhTyi3tDX1yX9VxGD49Lb+84UdRZ0jgAClSBgPuwwnyXf9vn0TVsEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBGpKgMC4mio3k0UAAQQQKBeBHALj5m4bq/mjWXOFVb6hYuUyTTOOfMde7Au76X/wowMffPoL8Pgd/HjAZxCf5zc8V06vRTUzFgLjBpY638C4b3z/Cp172oeHPF7O+dQ5+sLln9cvfvoLfefyqwdtf8hbDtF7znmPPvcfnxuyX7dBsQPjwr6wWlpa1NzcrMbGRifozYS6mcA4r9frrCbMzSxuUJsJizPfX7lypa644grn1g2Ce8c73qEvfOELTlCcCXwz25ogucsuu0xPPPGEIpGIotGoDj30UOd7DQ0NTmCc6c+0v/766/XHP/5xx9dtbW269NJLdcABB+wwc4PrzHZmG7O6YXGpVMoJpDPhcyYwrr2jXZHWOo3fc3zO5jQsHwEC48qnFowEgQoWKPbvtRVMU9ZDf7+kG4swwr9v+yfNZZIeKULfQ3ZZi4FxBuWiq76tn978+yF9it1gsMC41zZv1lvPOVfrNm4q9jAG9F9pgXHV7rRz8ee++3StWP3qqB4TZmdDBcaZNoef/UE9uXx5wcZWjMA4M7gf/PZGff671xRsnEN1NK6tTYt/d6Mmjh27y6bVGhh36Q9+qKt/8cuheHb8fNOD96upvj7n9v0bHv+p83Tf4sU5b/vCX/6s6ZMnD2j/lR/9WN++YWCQXM4djqDhYIFxplszJjO20V4qLTCuo6tbk47KPTgwF08C43JRGnGbYgbH/VBSYVMERzxdOkAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgdgQIjKudWjNTBBBAAIEyEhgkMG7+tmGaoLh3ldFwRzIUApUG18MHn/4CxQ42oP/Bjzd8CuhDYNxIXjqHvy2BcQPt8g2Mu/ORO3Tq20/Tyy++PGQR7n/mPr37HaerfUv7oG1/8PPvK5lMlUVgnAldW/bYcgXsgFpbW53AOBPeZgLjwuGwEwBnwt7MagLizNo/MM58bULaHn74Yf3lL39xQuEmTpyo0047TfPmzXN+ZtqYxYTBmbC43/3ud+rq6nL29d73vlcLFizY0c4NoVu9erUWLVqkV155xdn3cccdp4ULFzoBdG5//QPjzH03MM6ExZk6m8A4E0pnAuM6OttVP6Ze42cQGDfkgVyGDQiMK8OiMCQEKk+g2P+urzyR8h+xV9ILkvYu4FD/KenLpQqKc+dx15mn3WfuN+8x/e3m9qAPfqiAUyzfrqxsVt+87np947rrSzrIwQLjzMCWr1ypI885Vz3R2KiNs9IC46rdaefC//PhR3TyBZ8etePB3VEugXG//ctf9bGvmPzLwizFCowzv6+d/fkv6Na77ynMQIfo5R/XX6cj33TwbltVY2Cc+X14n5NOzjnw8oQj36pbv///hl2P//397/XZK7+d8/bfvvi/dOEHzh7QPpZI6KTzL9DDTz2dcz+FaDhUYJzZx4XfulLX/eGWQuwu5z4qLTDOTOwTX71cv/7z7TnPcaiGBMYNJVTQn18h6b8k1RWw17SksKRsAfukKwQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQyFGAwLgcoWiGAAIIIIBAIQV2ERh3+Lb+TVDccYXcTxn0RSDa4EXAB5/+AsUONqD/wY83fAroQ2BcaV6BCYwb6J5vYNxdj96pP910q758kckWGXyZOn2q1q5eO2ijCZMm6B+L/667/vL38giMy9pa8dQKZeO2WlpanLWxsdEJjTOBccFg0Alp6x8Yt6sJmgCA7u5uxeNxZ3sTOOcGuvVvb8LeTDsT4mb2YULjzIXtZnED6dz7JvDNBMsFAgGnnQmcG/ACadvOPsz27moC6tLptBMWZ8ZiAuPM/rp7ulU/tl7jp48bqoz8vAwFCIwrw6IwJAQqT6DY/66vPJHyH/H7JN1UoGGahCKTqPSvAvVXkG4STyzJ97gsyH5L3cnfHnxQ517ypVENZOs/56EC40zbx559VmdcdLE2tw8eBF0oy0oMjKtmp13V9T++9nX9YtFthSp5Tv3kEhiXTKV00BlnaeW6dTn1OVSjYgXGmf129fbq9M9cpIeefHKoYYzo5z/98qX6yLtPG7SPagyMM89bbzv3Iznb/eTSL+mjp7875/Y7NzTH3P6nnJrz9vP3208P3/ibN7SPxuN6z8X/rbsfeTTnvkba8LdXXakzjjl60G4yluWExt3wp1tHuruct6/EwLgXVq3SgaefmfMch2pIYNxQQkX5+TclfbaAwXHf395fUQZLpwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggsHsBAuNtwq7HAAAgAElEQVQ4OhBAAAEEECiBgBsY9+jTm3Ttzcv0yFMbSzCKUdklgWiDM+ODT3+BfC8g5/jh+Cnb44fAuFF5jX3DTgiMG0gynMC4eCyut89bqGhvdMRFvPgrF+uj539Ef1t0R9kExq1+4VVFN0edULb+gXGRSGRHYJwJa+sfGmfC3dzFDYbrH+hmvueuO6OZftzFsqw3mLp99w+Q21Vf7vdMH25gnLmfSqV2BMb19vb2BdRFe9Q4rkHj9iAwbsQHcQk6IDCuBOjsEoHqEyj271XVJ1baGZl/aLwoae8RDsMExH2x3ILi3DnVamCcmf/La9bovK9foQeWPD7CEue3+dsWvEnXf/UyTZ88ecgN27u6dMEV39Ctd5u8weIulRoYZ1Sq0WlX1e7o7ta8088ccYjgDy/5ov7zm9/K6YDKJTDOdGQCm976oXMKEsJYzMA4M1YTcPeZK68qSvheY31EN199td5x2KFD+lZjYNwl3/+Brvnlr4acu9vg5Tv+pikTxufcflcN55/5Hi175ZWc+1j650Xaa+rUN7RPpFL63q9+rct/8tOc+3IbLjzkEH3/i1/Q/93yJ/3wxhtz2v7Oa38qs10uy6J77tEnLvtqQR5fQ+2vEgPjzJx+f9ffdc4XLxlqejn9nMC4nJiK1ci8OH2mAMFxSUkRSX2fDsGCAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAKjJkBg3KhRsyMEEEAAAQReF3AD40xQ3I9+87yeWLqlWnkItBq8svjg01+g2MEG9D/48YZPAX0IjCvNyzqBcQPdhxMYZ3q45orv6Wc/+tmIi/ivpQ+qta21fALjbFub1mzSays2qKmpyQmMM7cNDQ2qr69XOByW3+93wuLcwDgTDLerwLjBcNxQuQEvcPbrT7H9+zNtdv7a3c79vhsWZ4Li+gfGZTIZJzDO1DkWi8kNjOuN92rM1Da1TGgZcQ3pYPQEHlj+R2dnm3vW3W9uv3LKdQtHb+/sCQEEqkyg2P+urzKukk/nTEl/GMEoHty27VclFT/pawSDrOXAOJftkaef0VU33KA7HjTZfsVZDj/oQL37ne/U6Ue/U1MnTMh7J3/8xz919c9/oSeXL89726E2MAFXn37/+/XxM854Q3BTpQVaVZPT7uq2duNGnfe1K/SPhx8eqrRv+PncffbRT758qQ6Zc4DCBy/IaftcA+NMZy+uWq2Lv3P1sMbWfzDFDoxz9/XL2/6sS3/4oxEH8Ln9nXDkW3XVRRdp1ozpOdlW2uNrqElZ2axmHHt8zp7z99tPD9/4m6G6HfLnV1x7ncya6/LNz1yo/zr3nN02N3X5ye9ulnk+Wblu3aDdHjp3rj599vt11rHHOO8ffOAL/+Nsl8vy2O9u1LxZs3Jp6rR5bfNmfefnv3DGVozlmMMP18fPPF2nHnXUG7q/8FtX6ro/3JLTbh/6za/0ptmzB21r5rLncSfk1J95/bz3htzeB7v9vvv10S9/ecTBegTG5VSaYjf6tqT/lBQewY6+K+m/R7A9myKAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwDAECIwbBhqbIIAAAgggMFIBNzDO7Wf2ib8/ett9c4HrW0fad5ltTyDa4AXBB5/+AsUONqD/wY83fAroQ2BcaV6NCYwb6D7cwLi1q9fquMOOH1ERzzj7dH3tmq85ffxt0R363H98Luf+7nnyHk2YND7n9rk2NMFrPZ09WvrwMrW2tKq5udkJjGtsbHQC4+rq6pzAOHd1w+LMBdm7C3Ub8CJm2067nQPjdhUg5263q753Dqhzt3cD49zQuHQ67QTGJRIJRaPRHYFxaU9a46ePU6QpkisN7cpA4I+LfzBgFF855bp8/51cBrNgCAggUCYCxf53fZlMsyqGYZ7rn5M0eOLJrqdqUscuK/egOHfoBMa9XkQTxvbHv/9Ddz/y6IiD2fbfay8ddegheuebD9MR8+erpbGxIA+Mxc89p+tu+aMW3X33iAJxpkwYr5Pe9jad8Na3OmMMBgK7HN+Wjg5NfecxOY39Pccdq19965tDtr30Bz/U1b/45ZDtTIP7fn6D3nzgvJza9m9UDU6DTdr8HnLDrbfqgiuG9jb97Dllii784Af08TNOV8Dvd7oed+TbcjqGzPHx15/8OK8aLLrnHv3vzX/QfYsX57WdaWweO7d877uaOW2as+3fHnxQp3/mopz6+fGll+hjp5+eU1u3UTyZ1K9u+7O+/fOfa93GTXlt6zY2j6X/+fjHnCC+fJZKfXztbo4btmxxAuNyXS6/4Hx94WMfzbX5btsteX6p3vqh3QfA7bzh+044Xr/4xhU57felV1/VitWvytRqU0eH0pmMxre2auK4sVow+wCNH9M2oJ/Dz/5gzq8fL93x12EFiHb19up3f7tDv779dpm5j2Q57oi36JSFC2Vup02cuNuuLvn+D3TNL3+V066evOX3zuN4sKW9q0uTj3pnTv2ZILvbf/zDnNqaRls7O/WrP9/uBNwNFfi3q07ftuBN+vt11+a8v0preN83Bx77x9+yqJzf3/BKMi90F0qqG4Z1UpJ5Ay47jG3ZBAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEhilAYNww4dgMAQQQQACBkQjsIjDOvWDg2G39muC4w0fSfxltm++FEMW+sJv+Bz848MGnvwCP38GPB3wG8SEwrjSvxATGDXQfbmCc6eU/P3yh7rnznmEX8pZ//EH7z93f2b5cAuPMWFKJlJ564GnVBeucsDg3MK6hocEJjQsEAs5qQuN8Pp8TANd/3R3IYKFwgyEOFkTXv083LK5/aJwJi9s5MK6np0fBlqDGTh0rf8A37Pqx4egLEBg3+ubsEYEqFij277VVTDfqU3vPttyim/Pc67+3B8X9M8/tStqcwLhd85swm38/9bSeXLZMm9rb+8KC2s26VZ09vWppbFBrU7NamxrV2tSkyePHa87ee2v2zJmaNWO6IuFw0eu6at16Pb50qTPGje3t6uzuVnuXWbvU1tysWDyu5sZGNTc0qKWp0QngOmi/fXXgvvtqwpgxRR9fueygmp02t7friWXL9eyKFXrmhRf07IqXlLEyaqiLOEFWc/fZxwkGNKF7uQRtF7pmqXRaz7y4Qo8//7w2d3Sou7dX9XV12tLZKb/Pp7pw2DlWJ40dq1kzZuiAvWeOymNnd/N8cdVq/euJJ/TA44/rlbXrZHy3dHbsCNYb19amsS0tzuPnTbP319sWLHBszWOMBYHFzz2vI885N2eIjocfUl0olHP7XTWMJRJ65oUXtfj552SO346ubplAOfOaFamrc14PzPN/U32981o1vq3veeHA/fbVfnvuuduw0BENqow2XrNhg55b8ZJeWLXKef4xNsbCPAeZoFTzWm0e15PGjXVew/eZPt15bqrmpcIC49xSmOC4K81bottu8/0H1vck/Vc115S5IYAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIFBuAgTGlVtFGA8CCCCAQE0IDBIY587/aEmXS3pLhYMQqDR4AfHBp79AsYMN6H/w4w2fAvoQGFeaV28C4wa6jyQw7uEHHtbH3/OJYRVy/qHz9Zs//3rHtuUUGGdlLC19bJnS0bRMSFz/wDjzdTgcHhAYZ0LjzOL1eneEH+wqBGG4gXH9gfv3u3NYXP+gOMuyZFZTX7MmEglFo1GZsLju7m6N3XOsWsY3lySsYVgHDBs5AgTGcSAggEABBUwIfz5Lvu3z6Zu2uxcw74c8J2l2jkiPbA+K+3uO7cuqGYFxZVUOBoMAAmUokLEs+fr93lmGQ2RIZSBw6n9eqLseMtmxQy8LDzlEd17706Eb0gKBAgtUaGCcqzCc4LikpIikbIEp6Q4BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBHYjQGAchwYCCCCAAAIlEMghMM4d1bHb7piLlw8vwTALsUsC0QZXxAef/gIElg1+POBTQT4ExhXiJTT/PgiMG2g2ksA4E1B2ypHv0qqXV+VdiGuu/66OO+W4HduVU2CcnbW1cc1GvfLMStXX1zuBcc3NzU54XGNjoxMYFwwGndA4Exbn9/ud4DU3MM7cd9ddwQwVHOf+fFehcwNeEO2+p3zT3tSif2BcJpORWU194/H4jsC43t5edXZ1aq/5e6muIUxgXN5Hbmk3IDCutP7sHQEEECiBwFnbwuJ+n8N+H93+vtidObQt2yYExpVtaRgYAggggMAoCJhAwIUf/oiSqbTe+ebDdNZxx+rg/ffP+fd2K5vVt67/P11x7XU5j/bKiz6rz37ogzm3pyEChRJ46jd9HyLR9erq+83tcbcsWliovkexHxMcd5WkT0sK57DfqyV9Lod2NEEAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgQIIEBhXAES6QAABBBBAIF+BPALj3K5N6sjlkg7Ld18lbk8g2uAFwAef/gIEog1+POBTQT4ExpXm1ZfAuIHuIwmMMz3d/Mvf62tf+FpexWwb26Z7nrzbCVxzl3IKjDNjivXG9fg9jysUCDkhcSY0zgTGmeC4SCTiBMaZ1YTFmdWExfUPjDP3zeKGvu0q/G2o4Lj+qGb7/u3N/f5r/8A4NywulUo5QXFmjcViikajTnhcSintNW9P+QP+vOpG49ILEBhX+howAgQQQGAUBcx7Ic9tC4ybPcg+F2/7mfkAhb+N4riKtisC44pGS8cIIIAAAhUgEE8m1Xr4EQNGOq6tTWcec7QOP+ggvWn2/tpzyhTnvQd3MSFzL6xcpX898YR+dNNNWrH61bxmuvjmmzR3n33y2obGCBRSIHzwgnzP/xVy94Xqyzwovy3pgiGC45Lb2kUkZQu1Y/pBAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHdCxAYx9GBAAIIIIBACQSGERjnjvLY7cFxby7BsAfb5bmSflVmY2I4CCCAAAIlEnh+w3P5BvyVaKTVtVsC4wbWc6SBcb09vTpsn/z+yXXh/1yoT332kwMGUm6BcSaA7dUX1+jVZa8qHA47gXEmOM4ExpnguLq6OoVCISf0zg2M8/l8MqsbDucGyJmJ7i44LpfQOHdbNyDO9OfeN+M0982tCYqzLMtZTVicWU1QnAmM6+3tdQLjzO2UfSerdWLrjjFV1yO8umdDYFx115fZIYAAAjsJnCHplt2oPL7t+yYo7i/VpEZgXDVVk7kggAACCOQrsKvAuF31YULkGurq1BuPa3N7e7672dH+iPnzdffPrh/29myIQCEEqiQwzqXIJTjuO9s+p+LzhbCjDwQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQGFyAwDiOEAQQQAABBEogMILAOHe0x227Yy6gzS/FpHhzfUnSvnx6fPGA6RkBBBCoBIHLb/+keW3S7MmHXebcTjmsEoZdNWMkMG5gKUcaGGd6u+qyb+tX1+aeiXv/M/dp7PixAwZSboFxZnDR7qie+tfT8lieHaFxbnBcJBJxQuNMYNzOoXFuUJwJett5dSfdPwRu5weX+Vn/ILmdA+N2FRZnQuLcwDhza8LiTG1NYJxZTVCcCY5LZBLad8EshepCVfOYrqWJEBhXS9VmrgggUOMCHknPmV8VdnJ4cvv7XH+uRh8C46qxqswJAQQQQCBXgVwD43Ltb6h2D/7qFzpkzpyhmvFzBIoqUGWBca6VCY67WtL5knZ+Ay4pKcJ54qIeVnSOQLkJOOcD81jybZ9H1zRFAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKC2BAiMq616M1sEEEAAgTIRKEBgnDuT47fdMX9cWw6JPOdI+nWZEDMMBBBAAIESCHzt9k/a/Xd7xiEXlmAUtbtLAuMG1r4QgXErX16lk484OaeD6l1nnaJv/fBbb2hbjoFxdtbW+lXrtfzxF1QXrpMJiWtpaVFDQ4Pq6+udNRQKKRgMOqFxJijO5/M5t7sKjTPfcxc3BC4XNDc8zg2K63+bzWZlwuLc1YTFpdNpJxzO1DYajTqrGxg3fs/xmjRjomRiaFgqToDAuIorGQNGAAEEhitwuqQ/9tv4qe3va9023A4rYTsC4yqhSowRAQQQQKBYAqMZGPe+E47XL75xRbGmQr8I5CxQpYFx7vzNG4HflXTeTsFxV0n6n5yRaIgAApUuMOB8YA6T4Z37HJBoggACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBALgIExuWiRBsEbPN3bhlJXsnjU3zjYwokn5F//AlSeAo+CCCAQN4CBQyMc/d9wrY7Jjju0LwHU7gNXpK0L58eXzhQekIAAQQqTYDAuNJWjMC4gf6FCIwzPf7H2efpwXseHLK4N/3tRs07eN4b2pVjYJwZZDqV0YtPvaDNa7Y4oXAmMM4ExbmhcSZEzg2N8/v9Q4bG5RoU17+dCYUzX+8qMM4NijNt3LA4U1MTGBePx52gOLOa73mCHu0zf2+F6kJD1okG5SlAYFx51oVRIYAAAgUWMBeHPydptqRntr+PtUhSvheZF3hYxevujjNPW2h6n/+BD91rblumTy/ezugZAQQQQACBMhUYrcC4Q+fO1V9/8mM11kfKVIJh1ZJAlQfGuaU0wXHXmLdPtwfHJSWZB2C2lmrNXBGoYYF8f5cnMK6GDxamjgACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIFBYAQLjCutJb1UqYCU7ld1ym/x1k+Vpe4e2PP1TtTW8Jm/bMVLL22VnM/J4/ZKHv2+r0kOAaSFQcIEiBMa5Yyx1cNyHJP2m4GB0iAACCCBQEQIExpW2TATGDfQvVGCcCYszoXGDLXMOmqOb7/zdLpuUa2CcGWxPV6+WPrZUyd6kgsGgExbnBsaZ8LhwOOysJlDO5/PtuPV6vU7Qmxv+Zr7uv7ghcOZ7/QPiTDDczsFyblicaeveNyFxZjWhcel0esfqhsXFYjF1d3c7wXHJdFL7vmmWmsY0vaHv0j4i2Xs+AgTG5aNFWwQQQKBiBd697RNZvrZt9OYDD/5UzUFxboXuPPO0ARfQL7zk0ootHgNHAAEEEEBguAKjERg3d5999Pfrr1VrU9Nwh8l2CBRUoEYC41yz/sFx35P0xYJi0hkCCJSrAIFx5VoZxoUAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBQ9QIExlV9iZngyAVsJTpXy7PlJvkDPnkmfFjty36ttuZuqe14eeoPUHrjvfK3zpO3fs+R744eEECgJgSKGBjn+p247Y65APeQUQZ9SdK+fHr8KKuzOwQQQKBMBAiMK20hCIwb6F+owLisldXnzvu8enp6dlvgD3z0bL39mLfv8uflHBhnAto6N3dq6ZJlyiQyCoVCikQiMmFxZjX36+rqnO+bQDkTHGfC4Ux4XP/QuP7hcQZh51C4wR4ZZgxmccPizK0bGJfJZJRKpZzV1NMExZk1Go069YjFo9pr7kxNnjFJIr+9tE9AI9w7gXEjBGRzBBBAoDIE5kl6pjKGWphREhhXGEd6QQABBBCobIFiBsY11kd0/vvep4s/fK6a6usrG4rRV5VAjQXG9a/deyXdXFXFZDIIILA7AQLjODYQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEECiRAIFxJYJnt5UhYGd6ZKc2KZ0JKrH6JkUiKXkmnK3OF3+ntuYuWW3vUdb2K9T9ByUCb1J4yrsrY2KMEgEESi4wCoFx7hxNcNzlkhaM4qTP3hYad9Mo7o9dIYAAAgiUiQCBcaUtBIFxA/0LFRg30qqWc2CcmVs2a2vrhq16/pHn5JHXCYVzg+JMaJwJjDNrOBx2QuP8fr+zmtA4s5hwODc8zrXKJzDObOOGxpmgODcwzoTFmdXUMR6PO6sJijOrCY3rjfZqj/320B6z9pDP5x1pmdi+xAKbe9Y6I3hw+aKjzO2XT/nf+0o8JHaPAAIIIIDAiAUIjBsxIR0ggAACCFSBQDEC46ZMGK/PfeTD+sBJJ8uExrEgUG4CNRwYV26lYDwIIFA8AQLjimdLzwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIDCpAYBwHCAKDCGTa/63s1ttktbxb0XWPqKGuXd4JZ6l7xc0a09qpdOvHlOxepYj1jGLBw9W4x7F4IoAAAjkJjGJgnDuek7bd+eooBce9tC0wbl+TP5ITBo0QQAABBKpGgMC40pYyn8A4M9K2sW0jHvDkqZN1852/G9DPo/96VB8982N59V2IsVx21Vd09ElH79gvgXG5l8CEtG1YvUEvP/uyrHTWCYMLhUI7wuLcADkTGGdWEyrnhsaZtvkGxvUPlDP7dlcTGGdWExSXTqeVSqWcoDgTEOcGxZn7iVRCM+fupUkzJsvv7wuuY6kOgQMmzvFUx0yYBQIIIIAAAhKBcRwFCCCAAAII9Alsbm/X0y+8qMeXLtWS55/Xcyte0sp16/Li2Wf6HlpwwBy966iFOmXh2+XfHmSfVyc0RmCUBAiMGyVodoMAAqUUIDCulPrsGwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKCmBQiMq+nyM/mhBNIb7pK/81Yl285Qz/qlagyuVrblFCXW36XWlg4lGj6gxMZ/q7E+plj9aWqadPBQXfJzBBBAwBEoQWCcK3/ytjsmOO5NRS7F2dtC424q8j7oHgEEEECgzAQIjCttQfINjCvEaOsb6vXYS48O6Go4gXGFGMvlV39VZ37wzB1dERiXv+qKp1Zo7UvrnAA4EwRnguHC4bATEldXV+eEyJnVDY7bOTTObGMWEwjXPxRudyPZOSjOhMWZoDizmvqZsDg3MK63t1eJZEK2x9aes2do2t7TctpH/gpsUUoBAuNKqc++EUAAAQQKLUBgXKFF6Q8BBBBAoJoEzHsCPbGYunp61NnTs/22Vz3RqELBoJrq69UQiaipoUF7Tp2iulComqbPXKpcgMC4Ki8w00MAASNAYBzHAQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACJRIgMK5E8Oy2MgSSmxbL3nSDfONPUs+mVxXRM8pEFird8YSam6OKB49XdtMdCjW1yprwadU1T6qMiTFKBBAouUAJA+PcuZ+y7Y94LyticNxL2wLj9pWULTk2A0AAAQQQGDUBAuNGjXqXOyIwjsC4kR6Br616Ta88t1JW2pK5cNsEwJlQOLP2D4pz75tAOffnbnicCYozgXPusnNwnBsSZ35u7luWJRMUZ24zmYwTFOeGxSUSCcViMWdNZ9JqGdesmXNnqmVMy0inyvZlKkBgXJkWhmEhUFkCJiQ/nyXf9vn0TdsaFyAwrsYPAKaPAAIIIIAAAjUrQGBczZaeiSNQSwIExtVStZkrAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQFkJEBhXVuVgMOUmkOxYLq3/X3nbDlVvV0KN9mKlA7Nk9a5WpNGjpGe+fD13Kx3eT+GZ/y2fP1BuU2A8CCBQpgJlEBjnypjgOHNx9MFFoHq/pN8VoV+6RAABBBAoUwEC40pbGALjCIwb6RG4ae0mrXp+tRMY5wa5uQFwJhDOBMS5IXHu/f7fMwFzJizOrGY7d3XH5YbFmYA4c9/cptNpJyjO3JrVhMWZoDg3NC5rW6primjKnpM1be9pTp8s1StAYFz11paZITCKAlywPIrY7GpwAQLjOEIQQAABBBBAAIHaEuhcvdqZ8JO//fVR5vaEWxbdV1sCzBYBBGpIgPdfaqjYTBUBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgvAQIjCuvejCaMhNIda+RteaHCkYalLJaFNSrkgKyrbQ8Pr9SCSkU6FC37wi17H1O/qO3s1LWkm0uePeYC+q9+ffBFgggUJECZRQY5/q9a9sdExw3v4CgL0maJSnfPxYu4BDoCgEEEEBgNAUIjBtN7Tfui8A4AuNGegRuXrtZq5aulrJyQtzcUDdza4LaTCBc/9WEyJk1GAw6399VWJzZzmxvFrc/ExRn1v5BcSYsLpVKOWFxVtaSvFJdQ50TFDd15lSnf5bqFyAwrvprzAwRGAWBfN+DIIl0FIpSq7sgMK5WK8+8EUAAAQQQQKBWBe775hUDpn78LYv4faNWDwbmjUD1C/D+S/XXmBkigAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIlKkAgXFlWhiGVR4Cqd7NSrz8AzU2pZVVo8w17j6fX9msR7ItebK9kp1Wl+/tat3rpEEH7Vwgn9qgbHyd7FSPbKtbSvfIzlryeP2SPJLXL2+gQQqNlzc8WZ7QRCdIjgUBBKpPoAwD41zkU7dleVxWwOC490m6ufoqyIwQQAABBHYlQGBcaY8LAuMIjBvpEbhpzSatfXGdPLZHlmU5a/+QN9O/CXoziwlwM0FxZgmGg30RwdsvEds5OM700T98zg2KMwFxGSsjW7YTNmd7bAVDQbWOb9WEqePVPKbZCaRjqR0BAuNqp9bMFIEiCnDBchFx6To/AQLj8vOiNQIIIIAAAgggUOkCBMZVegUZP7LK97MAACAASURBVAII5CHA+y95YNEUAQSqX2DZ9QM/THX/T5g/Bn594ef49D8eOD54fPD8wPOjK8DrA68PvD68LsDrI6+PvD7y+sjrY59Auf/7oPp/w2eGCCCAAAIIIIAAAggggAACCFSOAIFxlVMrRloCgUyiW9EXfqTm5pjkCcmyzPXvJsCt7+85/N60sumEYpET1TDlyO0jtKXEK7JjK6XGQ+QJNCvb9bjs3mWys34pOFHyN8sjv2yP6adv9TiXyFtSNi2P1aVscqNsKypfeKI8LQfJWze1BALsEgEEiiVQxoFx7pRP2x4cd9AIDZZKmvN6fMgIe2NzBBBAAIGyFrj89k9+1Qxw9uTDTPioZk85rKzHW22DIzCOwLiRHtPrX3lNW9dslc/rkwl1M6sJjTMhcWY199PptHPfBMB5fV5NnD5BzWOblYwnlU6mlUqklU6lZaUt91dn5zdej8fzej9ZyxlqIOh3wubC9XVqaK5XY3OjIg0R+QOExI20lpW6PYFxlVo5xo1AWQlwwXJZlaO2B0NgXG3Xn9kjgAACCCCAQO0JEBhXezVnxgjUsIBzPjCPJd/2eXRNUwQQQKD0AuV+QT/jI5Co/6OEQB4CefofDzw/8PzA88PrAjw/8vzI8+PrArw+8PrA6wOvD67Azq+Ppf8NnBEggAACCCCAAAIIIIAAAggggIArQGAcxwICgwhYmbh6nv+eWiLrJY9ftm1tf+fXI3mDTsibspbSzWcoOP5IybaknielnvsV27JJ2ciBCgQs+YJt8tbvLW/dJMnf5PRlNrWdyxfdDxO0nYvonR94LSnTrUzPGimxWpnYy/IFm+Qff5Q84WnUDAEEqkCgAgLjXOXTtwfHzRsB+1nbQuNuGcH2bIoAAgggUGECz294Lt+gjgqbYXkOl8A4AuNGcmSaELg1y9Yq2ZOU3+d3wuJMOJxZ3fC4VCrl3Ddt05m0ps2apn0PnKVAMLBj187PUmll0pkd3zPhch6PCV83iy2v1yuP1yt/wCe/n3C4kdSt2rYlMK7aKsp8ECiJQL7/DnXfnC3JYNlpdQsQGFfd9WV2CCCAAAIIIIDAzgIExnFMIIAAAggggAACtSmwc6DGfictGQCx/K8LBnzNz/Hpf0BwfPD46H888PzA8wPPD68L8PzI8yPPj68L8PrA6wOvD7w+uAKlen3ceb8EydXm+x/MGgEEEEAAAQQQQAABBBBAoLwECIwrr3owmjITyKRT6nr222oNrpC3LiRZlmzbI9tcRugJSR6fZGdk1x0q78QzpNgyeTruVKx9o7o6bYUbx6hx2tHyjzlCtjci27IkmdWkxZnJ7nz9omd7fpxX8njl8XjksROyY68q3bVEduwF+VsOlXf8cfJwKWOZHS0MB4H8BCooMM5MzDzjnLY9OO7A/GbqtF66LTBuzi6e9IbRFZsggAACCFSCAIFxpanSf5x9nh6858FR3fmESRN0z5N3D9jn048/rbNP+sCojsPs7KofX6mTzzh5x35N0NlB0+bnNI5Zs2fp1nv+lFPbfBvd/4/7df6HLsh5s38tfVCtba05ty9Uw2hXVJtXblEkFNkREJdMJtV/NYFxZrW9tvY7eD9NmzlNXi+/nBaqBvQjERjHUYAAAgUQIDCuAIh0URiBu8487T7TU/Me099ubg/64IcK0zG9IIAAAggggAACCJSlAIFxZVkWBoUAAggggAACCBRdgMA4Am36H2QEuhDo0v94KFWghzsG9s/zE89Prwvw/MzzM8/Prwvw+sDrA68PvD64Arm+PhIYV/S3FtgBAggggAACCCCAAAIIIIAAAnkLEBiXNxkb1JJA1rK0ccl31Ox5QpHWBilry1ZQtrex79bjl+ysvCbcLdQkj9Upq/c1bViXkOVp0pjpB6t+xvuU9Y2RsklJ2V3wmWsYd77A3r2u0ev8zM6mJKtL2egKWV2PyRtsVGDah+Xx+rdvm+91kLVUReaKQHkKVFhgnItonqzevT04bl6esu+R9Ic8t6E5AggggECFChAYV6GFY9g1LdC+vl3xrQlF6iIyYXsmGC4ejyuRSDi3JjjO3HqDXs1eMFsTp02Q12t+Z2VBoHACBMYVzpKeEKhhgXzfKCX5tIYPltGaeuKJJfkel6M1NPaDAAIIIIAAAgggUEABAuMKiElXCCCAAAIIIIBABQnY63n/r4LKxVARQAABBBBAAAEEEEAAAQTyFCAwLk8wmiOAAAIIIIAAAggggAACCCAwCgIExo0CMruobIGVD16jSPw+jZ/cIHnDynpbJV+rbF9Ytrzy2Cn5PN3y2D1K9XZq89p2JTNBBQNSZPKRap71UXlDY2RbqdcD43Z7eZj7A/fWXK/okW1nJSsh247Lk9kqq+Nfkp1WcM9PyVZAHo9pxzVnlX2kMfpaE6jQwDi3TMMJjlsqaQ5PVrV2pDNfBBCoVQEC42q18sy7UgWstKVNKzerzlfn/H7phsNFo1HFYjEnNM7cb2hrcMLiWse2VOpUGXeZCxAYV+YFYngIVIZAvm+SEhhXGXWt6FESGFfR5WPwCCCAAAIIIIBAzgIExuVMRUMEEEAAAQQQQKCqBAiMq6pyMhkEEEAAAQQQQAABBBBAAIGdBAiM45BAAAEEEEAAAQQQQAABBBBAoPwECIwrv5owojITWPXoz2VtuE1TJnkVCDXK8o+T/I3yBPzyWgl5st1Sukuxni5tfi0qKzJPbZOnq3PlvfKFWjVx/qcUbJ2rbDYjmeA3eSWPVx55JNuW+U/KOvf7Qt/svhvnUkWP5ITBmSZp2eluyeqRnY3J6nhIHm9IoZkXOP2xIIBAZQlUeGCci22eoE7f9sR1maS5OVTgzG3t/phDO5oggAACCFS4AIFxFV5Ahl9zArGumLrWdqs+Ur/LsLie3m5NnjlFM2fvpfrG+przYcLFF3hged+vCZt71t1vbr9yynULi79X9oAAAlUqQGBclRa2kqdFYFwlV4+xI4AAAggggAACuQsQGJe7FS0RQAABBBBAAIFqEFh2/cBPed7vpCXVMC3mgAACCCCAAAIIIIAAAggggMAuBTyTF/DBnBwbCCCAAAIIIIAAAggggAACCJSJAIFxZVIIhlG+Aq89/1f1vPBLjW+NKxgOK+utlzfUqIA/K2W6ZaXi6ulKav26TqX9M7T32z+rxgavVv7rx+retFLTDzxazXscJflb+sLgPD7JG5Tk6wuFM0FxtiXbtraHxW0PkHNITIPt76XZGdmZbtmpLtl2Uh5PVFbnMvlb5ykw5XR5CI0r34OIkSGwC4EqCYxzZ2aeqM4wuQ5DBMctlTRnezomxwUCCCCAQBULEBhXxcVlalUnkE6mtWX1VgXsgCzLUiwWUzQaVTweV1dXl4L1Qc2at4/GTByjQDBQdfNnQuUh8MfFPxgwkK+cch1/WFQepWEUCFSiAIFxlVi1Kh8zgXFVXmCmh8AwBZLptFa+tl73PPqIlq9era1dnYonEwr5g5q/3346+pDDNH3yZFlWVstXr9QDTzyuF1avUtay1dzYoLl7760Fs2dr/z33UiQUltdb3R8uZTsfvOV8BJdSlqVkxlLKyiiTtZW2LGW3/9z8IuH3+eT3ehT0+RTy++X39N33uB/SNcyasRkCCCAwlACBcUMJ8XMEEEAAAQQQQKC6BAiMq656MhsEEEAAAQQQQAABBBBAAIHBBQiM4whBAAEEEEAAAQQQQAABBBBAoHwECIwrn1owkjIV2LR2hdY+9D1NaVql+gavsrZPHl9AAb9HqVRSmzentHZ9Ut7QFM188/s1ee+DZfWs0Lpnb9OyRx/RtL0maOoBhynUPEvy1fdFwHn8ktfnhMbZsuVRVrbdt/YFyJnLHbaHyZmLH7LmZykpm5CycdlWXF6fLb83KTu2WZkpZyvSajKYWBBAoFIEqiwwzmV3g+Mu2x4Mt6tymGC5P1VKnRgnAggggMDwBAiMG54bWyEw2gJZK6ut69qV6kwpk8kokUg4gXE9PT2yfbam7TNNk/aYqHAkzIX1o12cGtsfgXE1VnCmi0BxBQiMK64vvQ9DgMC4YaCxCQJVLGCCz9Zu2qQ//PMfemzZs8pks865w4xlKZ1OK5FMKplKKRwKa3xLq9KZjNZt3uQEfAcDASfI2+f1yfTj9Xi155SpOvu4E3TYnDkKBaor5NuEwDkBcRlLCSujpJVVNJ1WPJNRKptVOms+qMsjyzZnW/sWc67V7/U6pl5bCvq8Cnu9aggGFfb7FPT6FPb55PN5ZSL2CJGr4gcbU0OgBAIExpUAnV0igAACCCCAAAIlFCAwroT47BoBBBBAAAEEEEAAAQQQQGDUBQiMG3VydogAAggggAACCCCAAAIIIIDAbgUIjOPgQGAIgZWvbtKvfvpjjfW8oHn7SBNbLNWH08pkLK1bn9RrG5KaMG26Dlh4tlrGTFU6tll2pkNdax7VPbf/W/6AR3MPnqwJ0/dTsH6yPD6/kwnn8ZrLEExonKfvYgSPZFuWbDsjZdNOaFzWXOjgRMrZzvfsbFoez/ZgOWUUCPilTKei/ilq3efT8vpMfywIIFAJAlUaGOfSm2uxztz2BGaC4w7YqR5Lt4fJ5XsBdyWUlTEigAACCGwXIDCOQwGByhBIRBNau3ydktGkEsm4MllLwUhQYyeP1bhJYxUKh+Txmn/asSBQXAEC44rrS+8I1JjAV/Ocb77t8+ye5ghIBMZxFCCAQH+B9Zs360e/v0mvbtmk5oZ6BYJB5xxhMplUbzSmaG9UsWRSVsZS1s465xD9Pp/8gYBCwaBCoaC8Xq9zDtEEf6fSKcn26VPvPkNHH3KIAn5/RYObEwcmRC+esdSRTKo9mVLMBMJ5vQqFwwoGg/L7/fI5oW8+x8ddzcRNkJ67mpA9s5ogPmMVj8eVTadV5/OqNRh01sZgwAmY85lztSwIIIDACAUIjBshIJsjgAACCCCAAAIVJkBgXIUVjOEigAACCCCAAAIIIIAAAgiMSIDAuBHxsTECCCCAAAIIIIAAAggggAACBRUgMK6gnHRWbQLpTFaL7nhMP7nhL9q0YbPGNHq1xxifxjWlZaXiWr8hpkP39+ii89+kwNgjlOjcomw2IZ+iim95Vn+6+SkteymhQ+eFNW/eWI2bMkXhpjGyPUF55JPt8W4Pi/PIo6xkpWVbCdl2XMpmnIs9bBMa5/XL4wnItrPORSN9/7OcgLiwP6NEQspMP08tY2dWWwmYDwJVK1DlgXFu3cyT1VmSvrJTcNzpkm6t2uIyMQQQQAABERjHQYBAZQikEiltWbdViWhc8kr+oF/NY5vV2Nwor8+EnLMgMDoCBMaNjjN7QQABBBAojQCBcaVxZ68IlKNAMp3Sovvv1e0PPajxY1tVH4koEAwpvT3MrLc3qh4TGBeLK5lKKWNZ8vu8CjhhcQGFQiEFgwEnKM2EoqXTfYFoiWRKdeE6feUjH9fMKVP6zj1W2JK1bSUtS52ptDbEE0p4PIo0Njpz77+awDgTmOcGxpn7/ee7c2BcNms+oCvrhMa5a8rYZjLq7emRz8pobDCoMaGgIj6/AvwuXGFHDsNFoLwEnvrNr50Bdb26+n5ze9wtixaW1wgZDQIIIIAAAggggEAxBOz1S/jg1GLA0icCCCCAAAIIIIAAAggggEBZCCz/64IB49j/E31Xt7IggAACCCCAAAIIIIAAAggggEDpBAiMK509ey5zgVQ6o2UvbdbDT6zWa69t0roNm7RxU6e2bO1SPJ5QOp1SIimd9c46feP8qfJojPPJ9LLT8mS2KtH5ku6+c6WWPBN33gXbf2+/DtivXpOnj1Vja6u8/jrZ5op8Z7Ek25KspGyzvZ2RPLayVlZWxpLH65MvEJHtDchkxtnmQg87K69XCnhNm4Qy409Q8/SzVIHXgJT5kcDwECiOQI0Exrl45mnwPduD42Zvu79U0hxJ/LFccQ4vekUAAQRKLkBgXMlLwAAQQACBihIgMK6iysVgEUAAAQTyFCAwLk8wmiNQpQImyOyF1av1nd/+QnX1dWpubFAkEpHX51cimXTOMfZGo4rG4orFYorHk04YnM/vUygYVDhkAuOCCvgD8vv9jlI6nVE6YzlBaL3xhN75pkP1iVNPUzgYrCjFRMZSeyqltdGY7HBYDQ0NTjheX0Be0FnNnE1wnAmKMyFx7uqGxZlbNyzOTL5/cJwbGmc8TVCc8Uomk85qwuPMGu3uVrPXo8l1YdX5fAqYk7AsCCCAwDAFwgcv4EKpYdqxGQIIIIAAAgggUIkCBMZVYtUYMwIIIIAAAggggAACCCCAQK4CBMblKkU7BBBAAAEEEEAAAQQQQAABBEZPgMC40bNmTxUmsHpdh+57ZJWCoXrnogNzEYK5YKCzs0Pd3d3OBRvJlKWJY+t14vwuzWzboEQyK9uKyU5uUCq6QY8/tkWxaFbrN1lauy6p8W3SvjPrNH3PBrWNb1RdfVA+55Pqs7KzWdm2WbfHyDmhcLZSqYzzvWA4LI/PBMZ5lLGyTs6SuVbB7zNZcwllI/PVOPcL8nrJX6qwQ43h1qhAjQXGuVXuHxz3JUmLarT8TBsBBBCoegEC46q+xEwQAQQQKKgAgXEF5aQzBBBAAIEyEyAwrswKwnAQKJFAPJnUjX+/Qw89+5RaW5vV2NCgunBYWVtKJFNKJPoC43qjMcWiMZn25oOl/IG+wDhnDQUUCAQVDAScc5d94WcpJziuJ5qQx+PTN887TzMnT5UbpFai6ea027SVVUcqqdfiSUW9XjU1NSkcDu9YTVCcOT9rwuJMUJwbFmfm5q5mR/3nas6pmqV/YJz52oTGme+ZWxMcZ1Zz3jeRSDhhfSZEztxP9PRojN+nCaGQ6nzeinDMCZtGCCAwqgIExo0qNztDAAEEEEAAAQRKLkBgXMlLwAAQQAABBBBAAAEEEEAAAQSKKEBgXBFx6RoBBBBAAAEEEEAAAQQQQACBYQoQGDdMODarboFs1tZTS9fryaWbnQsuzIUI5gIFc2GCuRjBXHhgLsJIJBPq6s1qesMqLZy+RFY2IyvVIyu5RVayVy+/2K1A0KeGxoCWLo1pxStx2RlLrc0+TZ0a1MTJdWpuCSpS71cwaPbjk/NR0x6Pc9GCuY1H04r1ptTUEpI/EDAZcspYlnORiNfnUSBgPuXeUtQzU41zvqhQpKG6i8PsEKgSgRoNjOtfvYMkPVUl5WQaCCCAAALbBS6//ZNfNXdnTz7sMud2ymHYIIAAAgggMKQAgXFDEtEAAQQQQKCCBQiMq+DiMXQECiRgzvm98to6XXHD9QrXhdTa3KRIfb0TApfJZpVIJJ3QsqgJjIvFFYvFlHAC4+y+84CBgEJBs4YUDPWFx5lzl5aVcULPTGBcIpFSR29UJx3xVn385FPl9/kKNPridJPIZLQ2GtOmdEaR7UFxdXV1Mqt7TtbMu39InDln2z8obrCRGXPT1gTEubf9Q+TMfTc0LplMyqwmMM54mjp4EwlNCgXUZs4Nmw/5YkEAAQTyECAwLg8smiKAQKUKOOcD81jybZ9H1zRFAAEESi9AYFzpa8AIEEAAAQQQQAABBBBAAAEEiidAYFzxbOkZAQQQQAABBBBAAAEEEEAAgeEKEBg3XDm2q2qBeCKth5as0qp1vU5gnPvp9ea++8n05gKDWDyu3mhGezSv1bEz/i2vYsqk4s5tZ3tCTz/VpTFjQ9p733oFfF51dmW0bm1Ca9Yk1NOZVsay5fd71NjoU3OTXw0NAUXqfaqv96u+yaeW5oAymaxWvtSrxia/xowLy+NcDOFVJp3eHhrnVSjkVU9ynCKzPqnmyfOqujZMDoFqESAwrloqyTwQQAABBPoLfO32T9r9vz7jkAsBQgABBBBAYEgBAuOGJKIBAggggEAFCxAYV8HFY+gIFEjAfAjVn+67V4sevFdjx7SqqaHeCUYLBINKZywnrCweT6g3GnXCymImuCyRUtY25xFNYJxfQSc0Lqi6urBCobATJGdZWSfgLJXKKJ1OqaO7V5G6el1z4YUa39JWoNEXvpueVEqre6Pq9njVuD0sLhKJOCahUMg5L7tzWJwJfTOLe5vrqNzzuu6t2c4NjjPnek1oXDqddhzdwDhza9Z0LKYxHmlyOCSf15vrLmmHAAIIiMA4DgIEEKgBgQHnA3OYLwm8OSDRBAEEKk9g2fUa8Hy430lLKm8SjBgBBBBAAAEEEEAAAQQQQACBHAU8kxfwPl+OVjRDAAEEEEAAAQQQQAABBBBAoNgCBMYVW5j+K1Kgoyuuf/5rhTp7Ms6FCX6/3/kEe/cT6M0FBObijmgspu7etGY0rdKJe94jvzelaDyjTDKpbDajRx7pVktLUAce3KxwxGcuY5Bt2YpFLXV1pdXVmVFXV0bRnrRSCdOn+fsRW+ms5PdL48YFNGN6WK++mlQykdXcA+tlWR7VN4Xk93qUSqVlro8IBnyKploUnnmu2mYcUZHmDBqBWhMgMK7WKs58EUAAgdoQIDCuNurMLBFAAIFCCxAYV2hR+kMAAQQQKAeBO848baEZx/wPfOhec9syfXo5DIsxIIBACQQ2bt2q//7hNfL6vWppalJ9JKJQXVgBf0DpTFrpVNoJiTNhcdForC8wLpmWLVsBJzDOhMUFnHOWdWETGNd37tKcr3TDztLpjLNdZ3dUZx93gj5w7HElmOnQu+xKprSsq1veSEQNDQ1OSFx9fV+AXjAYdOZlVvMhXua8rHs7dM+Dt+gfGGdauqFx5ra/oxscZ0L8zP1YNKrmdEpTTL0IjRtpGdgegZoRIDCuZkrNRBGoZQEC42q5+swdAQR2CBAYx8GAAAIIIIAAAggggAACCCBQSwIExtVStZkrAggggAACCCCAAAIIIIBAuQsQGFfuFWJ8JRFYs75Tdz3wouQJKBwOOxdimAsSzGIuHDCfNm8+WT4eT6i9K6GZzS/qlL3vU8Bn6eWVCT36aLsmjvdr7rwGNbaE1NQckifkkyw5wXB9i62MZSudyjphcMlERomE1Xc/mVU6mVVHe9oJjjOhcMtfiGv/fUNKprPaa896TZxSp3TSci6WCAa96km2qW7mh9U2480lMWOnCCCQnwCBcfl50RoBBBBAoDIECIyrjDoxSgQQQKDcBAiMK7eKMB4EEEAAgUII3HnmaQMuoF94yaWF6JY+EECgAgX+9tC/dO1tt6i1uVkNkTonHM0NfctY5rxgSolEXLF4TLFY3Pk6mdpVYFxY4XBI4VDY+aAr2+77gCsTbJZKp50PmjKBcVPGTdC3zjtfLY2NZaUVz2S0rKNTmbqIIpG+1Q2LMx7mfGz/sDhzDtSshV76h8e5wXHm3K9racLizBqPx51Avnhvr1oyaU2J1MlXhPEUen70hwACpRcgMK70NWAECCBQdAEC44pOzA4QQKASBAiMq4QqMUYEEEAAAQQQQAABBBBAAIFCCRAYVyhJ+kEAAQQQQAABBBBAAAEEEEBg5AIExo3ckB6qTCCbtbXspY269+GVzoUK7gUKfRde2M5qLhgwFwlEozG1d8Y1q2WZ3r3/A0okLT32WI/Wv5bUjD1COuhNTU7QXFe3pVjcViDg06QpdQoEPbLNn87ZJoDO/RT7rOys+aZHsWhaW7ekFO/NKJnKaswYvzZuTOnlF3u1dmNKc/aL6C1HtPYlydke+QMedSUnqHn2p9Q8aU6VVYTpIFCdAgTGVWddmRUCCCBQ6wIExtX6EcD8EUAAgeEJEBg3PDe2QgABBBAobwEC48q7PowOgdESWLluvT579VXyBnxqbKxXXdiEvoUVDIbk83mdD6kyQW/xRMIJjEskkk74W8YJjJMCfr8CAb8CwYBCwaDCoZBz7tI5b5nNysr2BZ2lM5YsK6tYPKHu3piOOewtuvCsM+X3+UZrqoPuJ5Wx9GJnp7p9fjU2NTmheWY152LNrQmLM6uZV7GC4voPcHehcSYgzgTwmcA4c2vOB5sPEevt6tI0v1cT6urKwpNBIIBAeQsQGFfe9WF0CCBQEAEC4wrCSCcIIFDpAgTGVXoFGT8CCCCAAAIIIIAAAggggEA+AgTG5aNFWwQQQAABBBBAAAEEEEAAAQSKK0BgXHF96b0CBVLpjB5/Zo2eXr7FuWDDrO6n2buBcc6nycfj6u2NanNHVPPantHJ+zyol1emtHJVQvMPblLa8ujfD3XJsmyFQl4lYpYaW4M65viJamwOKpvJOjpORpzJjjN3bFt+v0erV8f0xOJOWem0fD6P7KwUqfepod6r1euSeu21pE48pk1TpoWVtT3yyFZneoYmLLhIkZYpFajOkBGoPQEC42qv5swYAQQQqAUBAuNqocrMEQEEECi8wOaetU6nDy5fdJS5/fIp/3tf4fdCjwgggAACCIyuAIFxo+vN3hAoV4Eb77hT37j+Wk2aOF5trc1OQJoJfTPhaB6PV5ntgW/JRELxRFLJVEqZTNoJfzOfPNUXGBeQz+9TKBhQMBh0guP8Pr/zIVdSVubDsMz5TRMct2VrhzZs2qoJ4yfqexddpFnTppacxspmtaa7R2szliINDWpoaNgRGOeeizVzNOdjRyMszgXZVWicOQdsHN3QOBMcF4uZIL+EEp0d2r+xQRG/v+SmDAABBMpbgMC48q4Po0MAgYIIEBhXEEY6QQCBahGw1y/J93mxWqbOPBBAAAEEEEAAAQQQQAABBGpAYPlfFwyY5f6fkKcGps0UEUAAAQQQQAABBBBAAAEEEChrAQLjyro8DK4UArF4Sg88+rJWr486FyuEQqE3BMa5nyjf3d2jLR29OmLKYh029t9a8kRKrWOCmje/SU8+EVUsbmv/eU1qau67cMDr8Ssc9joXO9jOe2Pm70TcxDgnL875KpPKKpWy5PFknW90dKb0zBPdGtvm0x4zQlr05y1qa/Rq4VGtCoa8iscsJUPzNeXwixUMN5SCjX0igECeAgTG5QlGcwQQQACBihAgMK4iysQgEUAALuMBQQAAIABJREFUgbIVOGDiHP6QqGyrw8AQQAABBPIVIDAuXzHaI1B9AiYo7dPf+pbuf+wxtTY3qampSfUNEefDqgLBoHx+v7LbA+NSyZRS6bRMYJllWbLtrHPOMBjwK+APyOv1OqFxwaBfQX/AnHSUlbaUSqeUiCUUiycUj8XV3tmleCqtSZMm6/z3vlfve+c7Sg7bm0rryS1bFG5ucc69msA4NyjOnId1PJwAPc+oBsYZmJ1D44y9Wd3AOPecsAmNM2tDIq4ZDQ0K+Lwld2UACCBQvgIExpVvbRgZAggUTCDfYCTe9y0YPR0hgEA5ChAYV45VYUwIIIAAAggggAACCCCAAAKFEiAwrlCS9IMAAggggAACCCCAAAIIIIBA4QQIjCucJT1ViUB3b0J33LNU3THtCIzz+XzOBQpmyWZNmFvKuSigq6tHnV1dOm6vBzQxvVir1no176AmjRkbUsbjlTfikVJe2ZmgfCHJG5JkefrWHcvrf0Nn230hch5zjYHPUjZpy0r75PVmlPUk5bE88trSk0926bHHuvXWw5u0x4ywtmzNyDv2HZpxxGd2jLNKysE0EKhaAQLjqra0TAwBBBCoaQEC42q6/EweAQQQGLEAgXEjJqQDBBBAAIEyEiAwroyKwVAQKJFAZ0+Pjr/g0+rt6VZjfb0idXVOUJw/GHRunZA0eWRZGWUyGSXTKeccpJVJK2tbymZNLpzHCYvr+/yprCSP8z0naC6dUSqdkZUxbU3QWVbpTFopy1JTS5vOOv5YXXLOuSWafd9uzVhebO9UdyDgBMOZsLj6+nrnA7vM1+bWOPj9/r557rS452eLOQk3NM7cmtXUwqymFm5gXCKRUDQaVVdHh/aL1KktHOKcbDGLQt8IVLgAgXEVXkCGjwACuQgQGJeLEm0QQKBmBAiMq5lSM1EEEEAAAQQQQAABBBBAoCYFCIyrybIzaQQQQAABBBBAAAEEEEAAgTIXIDCuzAvE8EZfYGtHVLf/83llFXIuUggGgzKBceYiBXORQP/AuPaObiWiW3TCtDtlbVymQCSiA+Y0KljnVyab1Wud47X0JZ9eXNmtgD+rIw9t0awZCQUDKckOOOFw26/wcC7w6FuzMrlxm7fU6aHHerRyQ1YTxrXo4NkBTWpaq6YGWz1dWT30r3aF/B7NO6hBHT0hRfZ6r6YedMbog7FHBBAYlgCBccNiYyMEEEAAgTIXIDCuzAvE8BBAAIEyFyAwrswLxPAQqAyBr+Y5zHzb59k9zWtZgMC4Wq4+c68lgbQT9JZ2zh+ac36WZSmdsdTV26t7Fj+m7/zsZwqHwqqPRJyANBOO5vX55fP7nfOPJhDNbJNKp5VMJpVMpZRJp5WxMn19OiFmRtR27pvPtzLbOB9Ble37uu9/5hxmX9hZIp1WMFSnQ+YfpIve/16Nb21VKBBSOBiU1+Pd0YfpNWTC2ny+opWsI5HQ45u3qqm11QmLi0QizuqegzW3bljcrgLjCjmwXYXPuWFx7n7cwDinjum0Exhn6mIC48yHiZk1FItq78ZGBXxvDLgr5HjpCwEEKleAwLjKrR0jRwCBnAUIjMuZioYIIFALAgTG1UKVmSMCCCCAAAIIIIAAAgggULsCBMbVbu2ZOQIIIIAAAggggAACCCCAQPkKEBhXvrVhZCUS2LC5R3+5e5n8gTonLM5cuOEGxpkhORdtpFLOp8hv7ehRuvtVHdn6FwUT69Q6rkEzZ0Vky9bm6HQ9vnaWurq71d4V03f/7yEdtO8kXXnxwdpv8nJ5PWnJdi/AMH9HZ67wyEpeW2s276G7/p1VuKFNv7ltiZat3KwvffpkvfUAvyboXjVFMnru+bg2rE9q5t4hZUL7aM+3fUF1rVNKpMZuEUAgXwEC4/IVoz0CCCCAQCUIEBhXCVVijAgggED5ChAYV761YWQIVJAAFyxXULGqfagExlV7hZkfAtILr67SL//yF/UmEjvOHybTKSe0LZZMaMOmzdqwYbMidXWqr6tTyATG+QNOWJwJjfP4vDLRb+bcYzKVVCKZVCqZVDqTVtbKKmv3Bcb1LX3nEk1EmRsaZ8LNTFScuziBcaavdEr+YFDjx43X/vvOVGtTs7O/+kiDQqGwgsGQUpm0Ur29+tAx79TMKVOLUk7z4Vovtneo3etzQuIaGxtVV1fnrOYcrLu6gXFmELsKdSvK4HbTqTE19XAD40xonAmMM+eGTVicOT/csWWLDmptUVPQfDgYCwIIIPC6QOfq1c4XT/7210eZ2xNuWXQfPggggECVCvD+S5UWlmkhgEB+Asuud35Z37Hsd9KS/Dqg9f9n707g5Ljqe9H/aum9e3r2kUb7vnuVLfAqbGMTbBODbTAQksdiJwRuIMkNvAsE20Dywg2BG/Je8oKTl7xAgm1ssDHe8CZ5wbIty5K1Wvsykkaz977UdvnXqERLyNa0ND3q5dcfim71nDp1zvdf2EhH51cUoAAFKEABClCAAhSgAAUoQIEaElC6l5cuT9fQyDlUClCAAhSgAAUoQAEKUIACFKBA/QkwMK7+asoZnaHA/oPDeGL1DgSCYTcsTjYrSGCct0HB3bRRKCCTzaJvIIFAYReu6foFrPQw2roimDRZQw5TgUkfxbe+/xj279+HObO68a8PrIM9lMQ/fvsTuPmGONTUs1DVwNHRHg2Mc4pAeBJWbVqKv/6Hp3DukilY++Z+rHr5LXz8yin47j99G8XeZ9GUexyHD5rYsaOAeQtCmLRwJeLL/vvoDhG+KECBmhBgYFxNlImDpAAFKECBMgUYGFcmGJtTgAIUoMBxAgyM4w1BAQqMgwA3LI8DIrsYHwEGxo2PI3uhQDUL7DiwD9+/78cYzKSgqAoKBQPFouEGxknYWz6fx5HeAdimhVDw6IOq/H7oRx9Wpamau7bnhrwVC8eCycwTAuPkQVWlW9BlNdANjXNxFPez/AvQsW23r6JhQNU0xJqaMGfmDHR0dELT/YjGYvAHdOQzWaSHBnH1BRfhhiuvQCwcrghzzjTx8qFehJubEYlE3EPC4iQ8TtZfJShODlmHlcN7na3QOAmLk5cXGCd1lEPC4uTI5XJIp9Pu0WlbmB6LQuXabEXuHXZKgVoVWPXX3zpu6O974CH+BY5aLSbHTQEKnEqAf/5yKiH+nAIUaAgBBsY1RJk5SQpQgAIUoAAFKEABClCAAhQ4KsDAON4KFKAABShAAQpQgAIUoAAFKECB6hFgYFz11IIjqRKBvT3DeHL1DoQjUXeTgoTGeYFx3lPl5WnyshngcF8Szcpb+NC8JzF0eARNLX50dvmgdN+E4OyP445PfQn/ct+v3JmFIiFcFM3hz77wu7jhjtvh7PkWdCUNQD86cxu2bULtvAHPrmvGZ7/wt9h+YBhqIIAuXxG/d7EPX/3BfyAc05Fa+1WMHOnDlm0WZs8NYc6F1yEw978xMK5K7iEOgwJjEWBg3FiU2IYCFKAABWpNgIFxtVYxjpcCFKBAdQkwMK666sHRUKBGBbhhuUYLV4/DZmBcPVaVc6LAbwvsOnAA37/vv5DM5+BoKizTRC4v4W9F2LblBo0lE0kYhglV1d1D90lQmg+qGxg3GlAmgXH5QgESFidt5VwJgBsNMXMwmmV2NDhO4occ52hg3OiYpJ1tO25gnITV+fx+dE+ehHlz56KrcxJ0XYNjGSikU1ixcAned8mlaI3HK1rSw+k03hxOoK2t7bjAuGAweOyBXbIGK+uxEhLnBcWdjcA4Lyxu1NEeDf07+i7rwlJHCQDMZrNIpVIwEyM4v6MdPlWtqCE7pwAFakuAgXG1VS+OlgIUOCMB/vnLGfHxZApQoF4EGBhXL5XkPChAAQpQgAIUoAAFKEABClBgLAIMjBuLEttQgAIUoAAFKEABClCAAhSgAAUmRoCBcRPjzKvUkMDu/UN4+qXdCIcjblCc92R79ehf+JcNAl5g3KEjCbRgG25e+CTyyYy7waOjy4fQtCugzPkUfvLvj+J7f/MfGBgexrJ5wPXnteHaT38DU85dAXvrl6BZhwAlCHc3iFN0N3soU34fhwsX4Rv//at4+pevoqXdwcrFOt539SW49A/uQsB4E5mN/4DB3kHs2mOjY1IIcy66CaG5n64hZQ6VAhRgYBzvAQpQgAIUqEeBux+54y6Z1+LuFXe671NW1OM0OScKUIACFKiQAAPjKgTLbinQWALcsNxY9a7q2TIwrqrLw8FRYFwFRlIpPPPKGryydTNS+RwKRRNFx4JlOTCMohs+ZhYNWJbp/kwC4hyoEv8G27LdkDcJJCsUpa0BwzThuMFvEhB3NDBOQuMsR/7bHbsi74oCVVWOrWf65EFY/gCi0TDa2zvQ2dmBkN8PxTIQ1ANYMn0mrlmxAt2dneM6/5N1Zto2NvYNIO3zIRaL/VZgnLf+KmuxsgbrBcaVExbnhbyN52S8h4dJjSQwTt69wLhCoeCGxklg3GB/Hy5oaUE8GBjPy7MvClCgxgUYGFfjBeTwKUCBcgTc9cAyXuW2L6NrNqUABShw9gQYGHf27HllClCAAhSgAAUoQAEKUIACFJh4AQbGTbw5r0gBClCAAhSgAAUoQAEKUIACFHg7AQbG8d6gQImAbALYvrsfz768B9FozN1g4fP53NA4eXmbBGRjh2wGkMC4Lv923DT3CcDIwbB1tLToCEcD0LouRy7+fjz/81Xo2fAiFs7TMGv5e9G1+BpoqeeA/icBxwJU/9HAOANwTCA0C5jxKWzbcABrHn4IMe0wzrtoDrrO/xAioSSc/T9GdmA/hoYtDA1ZaGmPoWvZbQjOuIW1pAAFakiAgXE1VCwOlQIUoAAFyhbY3Lup3KCOsq/BEyhAAQpQoP4EGBhXfzXljChwFgTK/f+hylkYIy/ZIAIMjGuQQnOaFCgRyBUK2L53F9a/tQ0vrn8NjgbkbT9sR0Uw4IdP11A0DRTyoyFyhiUPqTJhGSaKRhFF+SxBZRIo54yGw7lBag5g2bZ7JVVToesaAj4f/H4fAv4gAqEgIuEQQsGAnAHbseFzDGQSfcgP7ceSqd343B3fQktLy4TVq2hZeKnnEALxuBsYF41GEQ6HEQqFEAgE3DVY7ygNi3unwLhKBMQJiNdv6XtpYJz3MDEJjMvlcu4a8dDQEOYFA5gcjUyYKS9EAQpUvwAD46q/RhwhBShAAQpQgAIUqISAc2htuX8uXYlhsE8KUIACFKAABShAAQpQgAIUoEBFBLY9uvy4fhfdDv59q4pIs1MKUIACFKAABShAAQpQgAIUoMDYBRgYN3YrtmwAAdt2sG1XH55zA+OiblicHLJhQTYoeIFx8iT5dDqNQ31JzGnuwXun/xJ25ggKho6WtgAiUQW6Bigdy4HYfOSTedjwIRyNAqk3gNQWQA0Cil92dowGxsEaDYyzcoC/E+haAbsQQDaVQDAUhY5+OAO/gp1PIp1WMDRYgGE4aGpvR8uSP0Kg69IGqBCnSIH6EWBgXP3UkjOhAAUoQIHfFmBgHO8KClCAAhQ4HQEGxp2OGs+hAAVOECh3Yx7/AiNvoYoJPHnLTauk8/j0GVfK+3m/94mKXYsdU4AC1Sfwr/92F7ZuewEjxQCyThR6qAV6IA41IA+S0mFBgaNoUFUNmrsGCTcgTv4j30kwnKLKZxWqo8jXgKpAU1VomgpVguFsG46sLcKGUSggm0yhmO2DbgwipiYR1bKIhRwsXrgSH/zotycUKV008NKhw2huazsuMC4YDLqBcfKwLnduququwcqrNCzO+1wa4uat1ZY7EenjxCC60u9Odg2xlUOC40oD4/L5vLtGPDIygnbbwuzmuFs/vihAAQqIAAPjeB9QgAIUoAAFKECBxhRgYFxj1p2zpgAFKEABClCAAhSgAAUo0CgCDIxrlEpznhSgAAUoQAEKUIACFKAABShQSwIMjKulanGsFReQwLjN23uxas1oYJzf73c3LHibFmTDgGwOkMC4TCaD3r4hTG1Xcd289Qj0P4KhdADxFh9i8QD8fg2qYgI+HxAIA44CGDnAsQElCGiSKOcH/JMAPQoUegAz4W4EgZkHFBvwBWQnCFAswCnmAUeDaSpIJgsYHiy4GxCik+aiffndUAItFffhBShAgfETYGDc+FmyJwpQgAIUqD4BBsZVX004IgpQgAK1IMDAuFqoEsdIgaoXYGBc1Zeo8QaYX7e23Puy8ZA4YwrUocALz/7f2LHpAaQyeWTyQO+gg4GkivbOMHKGD70JDTnTD18wDH8whGgkgFhUh6b5oEhgnITIKYobdKYqgC2hZ7BRzJtIZorIZbLIZTLwIYeYvwjHyKF/II1pbcA5c3TEYxqi0QB0nw8d3Zfjsqu+KolsEyIt66lHMhm8OZxAc0sL4vE4IpEIwuEwvMA4eVjXiYFxbzc46e9koW+l7b3Qt5P14Z17ssC5E7/z+vEC4+T9xMA4WSNOJBLw57KY3RRDUyAwIa68CAUoUP0CDIyr/hpxhBSgAAUoQAEKUKASAgyMq4Qq+6QABShAAQpQgAIUoAAFKECBahFgYFy1VILjoAAFKEABClCAAhSgAAUoQAEK/EaAgXG8GyhQImBZNja91YvVr+xxNy3IE+59Pp8bGCcbF2STgDxJXo5sNou+/n40xaK4+TIH1pZvI5NOI9YcRJMExgVHN3QoqjJ6KLInTAUU3X1zTBOKhMm1XgQEZgBDzwOZ/XAU/+h+DdsGHGs0YM6R/6hwLBvFgoFUooCRYQOOqqNryQcQX/hpQFFZSwpQoIYEGBhXQ8XiUClAAQpQoGwBBsaVTcYTKEABClAAAAPjeBtQgALjIFBuMNfEJOeMw8TYRe0KMDCudmvHkVPgTATe2vwgDu3+d+TyBWTSJrbvzEHzAV1tKrbsKGIwqSAcUaD7JAxORTCooykqD5tSYUMB3JA0GYH8q0o+OFBVB4W8jXTGhKpYciIsQ4Gm2Jg93Y/BpI3EkI0VF0QwdUoAweDoumSk+SosPO8LZzKdss6V0e4YHMSBoumGxTU1NbmBcXJIYJw8sKs0MM6d5dEwuxOD3yRUTl5egJvXzhuQ117688LfZB33xJf3My+kzntI2IntvP6kD6+NBMYVi0UUCgX3kDViCYwrJkYwKxbD5GikLB82pgAF6leAgXH1W1vOjAIUoAAFKEABCryTAAPjeH9QgAIUoAAFKEABClCAAhSgQD0LMDCunqvLuVGAAhSgAAUoQAEKUIACFKBArQowMK5WK8dxV0RAAuM2bDmIF17bd2zDghcaVxoYJ5sScrkcBgcH3XC3T9+yDLte+gES2x/DosVxxJr8vwmM0zWosklBQuNkh4Ts61CBZF8a2ayFthkzoQWakenbAzOXQFN7FJquuxtB3MOWk+zRTQmmhVzOQDZVwLYdeQxkW/CBO74JPb6wIh7slAIUqJwAA+MqZ8ueKUABClDg7AswMO7s14AjoAAFKFBLAs9ve9Adbn/q4Gp5//qNP1hZS+PnWClAgaoSYGBcVZWDgxEBBsbxPqBAYwqM9L+B5JG/gYICdu3MYGDIwIKFEXfNcCRlI+h3oGuAqvphWibyeQt5Q4FlOqPPlBpNi3PXFWV9UdUUaJoCn64gGHQQCfrg82kwjQIKRQexmJs8h9UvJODTHFx5RRPCYQ2plAY9chOmzvvEhBXCdhy8cbgXCVVDc3PzbwXGycO6vMA4LwCuNAhO1mHl1xLuJuuxEs7W1tbmPuRLgtvkZ14AnExKvjcMA319fejs7Dz2a5fvaFv5LNeUvgcGBtwHh0Wj0d8KovPcvYA6eZfwOOn/xMC45NAgpoVCmNPSPGG2vBAFKFDdAgyMq+76cHQUoAAFKEABClBgvAW23uMmvB97Lbx+7Xhfgv1RgAIUoAAFKEABClCAAhSgAAWqRkDpXs4Hc1ZNNTgQClCAAhSgAAUoQAEKUIACFGh0AQbGNfodwPkfJyCBces392D1K3sQCoXcDQXypHtv44JsEpBDNgbIpgDZoJBMZvG7V09Hdtdj2P78vbjk0jZ0dfnh8/ug+nQoigZVQuPU0T8Tk4A56CpGepPYtH4YDlT4fArk2lOmhDBjfgsU7TeBcY4t17TcTR6mBMZlikgni3h6dQpFpQu33/U9IDSVlaQABWpMgIFxNVYwDpcCFKAABcoSYGBcWVxsTAEKUKDhBR587fvHGXz9xh/wLxY1/F1BAAqctgAD406bjidWSoCBcZWSZb8UqG6BbLIHqSNfheoksHlTCt3dfkyfFYGuS2hZEf3DQfQMhDE4nEdHawDdHTZ0PQvTkp8ro4FxRx9EJWuLss6oaYBfd5DN+7FtZx6BcBRd7WG0hAbQGs/D7/Nj29YM1q1NYvnyKLom+9E3GETL5D9DS9fFEwYmgXEvH+hBwR9AS0vLcYFx8qAuv9/vhrd5oXAy19LAOC+0bdu2bXjooYdw8OBBLF26FDfccAOmTJnirtN6L1nDPXDgAO6//37s2LED8+fPx6233oqpU6fCNM1jgXESPjc8PIwnnngCa9eudcd144034txzz3VD47zX2wXGSV8SVpfP55HNZpFMJjEyOIjugB/zW1smzJYXogAFqluAgXHVXR+OjgIUoAAFKEABCoy3AAPjxluU/VGAAhSgAAUoQAEKUIACFKBANQswMK6aq8OxUYACFKAABShAAQpQgAIUoECjCTAwrtEqzvm+o4BtO9iwpQdPv7jjWGCct3FB1yX8bXSDhvck+VQqjb7BBOZ0JLDAeRybX9mIuYuasWxxCL6ADl/QB1VRocoODlWedn/0SfaaBqto4PD+JHr2Z2FbDlra/Jg+M4pIS1hi5UbHKQF1lu0elm3BLJoo5IvYvy+PJ59KY9bcLnzwi38FLb6YlaUABWpMgIFxNVYwDpcCFKAABcoSYGBcWVxsTAEKUKDhBRgY1/C3AAEoMJ4CDIwbT032NS4CDIwbF0Z2QoGaE7CsAg5svRP51Cb0Hc5h0ZIwJk8Jw3ZM9A53YH96IQYSBp5/eTu27urHR94/HzdcYiAWGoTj6HAcxV0ndJcMFQUqbKiqhUSuDZv2tGDXgTwef34nOrpa8YkPnoup/g3obulHJgO8/NIImmIaJk0OIGsswaxz7oKmByfM0LJtrN67DwhH3GC2WCzmHuFwGLLu6j2oywuN8wbmhbVJuFtvby++853vYM2aNW64nKzT/u7v/i4+97nPuc0l5E2+kxC3733ve3j88cch50kf73//+/HFL37RvY6s6cr38rrvvvvwox/9yA18k9eCBQvwpS99CbNmzXL789aBpQ/5tXd468KGYRwLjJOHirmBccEAFjAwbsLuLV6IAtUuwMC4aq8Qx0cBClCAAhSgAAXGV4CBcePryd4oQAEKUIACFKAABShAAQpQoLoFGBhX3fXh6ChAAQpQgAIUoAAFKEABClCgsQQYGNdY9eZsTyEggXFvbu3B489t/a3AONlU4G00kA0Csjkgm81heCSJQm4Ys4wnoR1+HZGWCC68MIqmZh8CEhin61C10Y0IgANFVaH5dECTMDgLZs5wNy9oAQ1awAdABQwHju2430sb+SzXK+aLyGVNrF2bwZaNOSxYMgnX3v5lBDrfzdpSgAI1JsDAuBorGIdLAQpQgAJlCTAwriwuNqYABSjQ8AIMjGv4W4AAFBhPgbvK7Kzc9mV2z+YUABgYx7uAAo0qYOLQtjvRd2gdinkTcxcE0N7uQ9qehHTkJjyyaif27d6BdNbGv/7kNVx5znT8r2/ciEWT1wHWCADNXVc89lIsQOvES1tn4kc/exMXnjMN//hfr2L7ngH8/V/eivdfvRBa372I+QaxYX0e2YyNGXOiCMavQ/v0z05oESQw7tnde6BGoscFxkUiEfj9fveQELjSwDgvLE4GKsFtGzZswFe/+lXkcjkEg0Gk02ksX74c3/zmNyH9mKbpBsYdOHAAf/7nf46hoSE3jC6fz2Py5Mn4m7/5G0ydOtVdX5XrSEjcd7/7XTz99NPuGrCs9RYKBXzlK1/BNddcc8zHXZs9ITBOguLkeqWBcalUCsODA+jy+bC4vW1CfXkxClCgegUYGFe9teHIKEABClCAAhSgQCUEGBhXCVX2SQEKUIACFKAABShAAQpQgALVKsDAuGqtDMdFAQpQgAIUoAAFKEABClCAAo0owMC4Rqw65/yOAlt3HMbPntiAQCDoPnleNi1477KhwNskIJ3IU+vTmQyGRzJAcifm5B5BBAOYNieOWbNCCEX90H0SGCebOgDHtt2tHUVDleg491AcG6piw7IVQHGg6Rr8fhW6YsM2rdF2jg3LsFAsGOg5UMALL6YAW8HMuc247EOfRHTOzawqBShQYwIMjKuxgnG4FKAABShQlgAD48riYmMKUIACDS/AwLiGvwUIQAEKUKCuBRgYV9fl5eQo8PYCjoH0kb/EUO8m9B4uYMECPyKxAAodt8LuvBJ3fumv8MOfr0c4EsLQYBrXzHDwf33/G1g6Lwl15FEomv6bvDjHlhQ1OM3X4f7nfPj8X3wHwZAfw1nAl0jhL26bg//2nb9Fev+LCA3ei107cjh0yMJ5F8TROuVW6LGPTWilvMA4hMJobm5GPB5HLBZzg94k1E3WXuUhXXLI2qu7hno0qE3C4uT7PXv24Fvf+pb77gXBXXXVVfjyl7/sBsVJ4JucK0Fyd955J9atW4dwOIxMJoOLL77Y/S4ajbqBcdKftL/nnnvw4IMPHvt1a2srvva1r2HJkiXHfLzgOjlPzpHDC4uTdWEJpJPwOQmMGxkaxCS/H4vaWifUlxejAAWqV2D9j37oDi6xf99qeb/ugYdWVu9oOTIKUIACFKAABShAgfEScA6tLUl8H69e2Q8FKEABClCAAhSgAAUoQAEKUKA6BLY9uvy4gSy6HUp1jIyjoAAFKEABClCAAhSgAAUoQAEKNK4AA+Mat/ac+dsIHDg0hHsffg0OdDcoTg7ZeCBH6ZPuZXPj8L+lAAAgAElEQVSBbBqQp8+n0hkMJ9LoMDdiUfhNRH0j6GrPo31SDP6ADlUdDZpTYQOqimfWFPHYszkkEgZ01YKuA4UiYDsK5swM4jMfbcLUyRrMogPblsNGsWghOVzA+vVZHDpsYdbcJiy7/H2IT1uBUNdFrCcFKFBjAgyMq7GCcbgUoAAFKFCWAAPjyuJiYwpQgAINL8DAuIa/BQhAAQpQoK4FGBhX1+Xl5CjwDgIOcgN/j9TgMzhyuIBp0zQ0tcTgm/fHKLZcib+/6y789XcfhR32Y0mngdsuUvGxv/wxurqb4Oy9EyqyANTR/h0TthqDOvMv8Mr6JD7/6c9iw94Cujp9eO/0Ij526xW4/LN/C23oZWS2fA8Hdqew74CFpee2oH3GHdBD105opWzHwXM7d8MIBNDS0uIGxkl4mwTGBYNBNwBO1ly9dVcJiSsNjJNfS0jbyy+/jF/84hduKNykSZNw00034ZxzznF/Jm3kJeu1EhZ37733IpFIuNf6yEc+guXLlx9r54XQ7du3Dw899BB2797tXvu6667DypUr3TVgr7/SwDjvIWJyPQmLkzVhCYyTUDoJjEsMDaE7FMTC1pYJ9eXFKECB6hcIXrCcG6Wqv0wcIQUoQAEKUIACFBg3AQbGjRslO6IABShAAQpQgAIUoAAFKECBKhRgYFwVFoVDogAFKEABClCAAhSgAAUoQIGGF2BgXMPfAgQ4UWAkmcVPH1uHw31pd4OAt2nBe8q9vMumAW8jg7dJIJcroFjM4YJFrVjccQS57fchEuhHNB6Az68BGN3soKoKkkkHL641sOpVA0NpwDQdaKqDmVN8uO6yIC4+T4WmAbYNOLYDwzCRThkYGApDi8+HPz4NkZCKSRd+GnCUY5sYWE0KUKB2BBgYVzu14kgpQAEKUKB8AQbGlW/GMyhAAQo0sgAD4xq5+pw7BShAgfoXYGBc/deYM6TA2wnkRn6C/MAPkU4biERVtLToCLYvBGZ9Gjv3AD/8u+8hfWgT3n1hBOes/ChmXfZJ+JJPAkfuB2AB0Ee7dgxA0YHJH0a+6To8/sN/x6P/9i9YMs/Bxe9ahqXX/zniLSasXfcgfeQt9B4yMThkYeacdjRP/R/QAssmtEgSGPfSnr1IKiqam5vdIxaLuaFxEhjn9/uPe1CXF9Z24iBlXTWZTCKXy7nnS+CcF+hW2lbOl3YS4ibXkNA4eRiXvORnXv/yLoFvEiwnDwyTdrLuW/ryguvkfO+QtWDDMNxzZSwSGCfXSycSmBIKYgED4yb0/uLFKFALAgyMq4UqcYwUoAAFKEABClBg/AQYGDd+luyJAhSgAAUoQAEKUIACFKAABapPgIFx1VcTjogCFKAABShAAQpQgAIUoAAFKMDAON4DFDhBoFA08dxL2/D6xh53I0DppoUTw+JkM4F8J+1ks0Aub7hBbyvfNQtzu4Poe/GrCNnb4Y+FoesaFFUBbAeq40AxbGRyCnKmDssBVNiIRxxEmhQYjgrZTCFhcaZpI5ssImO1oftdf4ZQ+9LRTSFw3BA6vihAgdoUYGBcbdaNo6YABShAgXcWuPuRO+6SFou7V9zpvk9ZQTIKUIACFKDAKQUYGHdKIjagAAUoQIEaFmBgXA0Xj0OnwBkKmLlXkTr0V8hmi/D7VbS2BRAMOFDCk6B0XYFsvg2DPb2INLci3tUGLfMakHhDUs4AxX90PfBoYJyExkEF4ufBjJyHvt29UOwcmrs6EfQPQBl4HkbqCFIpoL+/4K4ztnfNQnTyX0HVWs9wJuWdLtfecPAQDuQLbihbaWBcOBw+tvbqPazLC3UrDY7zguFKA928MLeThcbJg768l2VJ2N7xr9LQOO+z119pS+876cMLjJPPxWLxWGBcOp12A+OyySSmhUOYx8C48m4QtqZAAwgwMK4BiswpUoAC7npgGa9y25fRNZtSgAIUOPsCDIw7+zXgCChAAQpQgAIUoAAFKEABClCgcgIMjKucLXumAAUoQAEKUIACFKAABShAAQqcrgAD405XjufVrYBtO9j01kE8/OQGWJbtBsJJMJxsNJDD27wgn3Vddw/5LJsLbMuCYZqQh9GvvGQhJvl2Y2j938Nn7kcoFoLu092fOTZgFkxYBRtW0YLt2NB9KnxBHVpAh6JJCJ0Dy7SQTZtI5SLovvhPEJl6iXsdvihAgdoXYGBc7deQM6AABShAgd8W+MYjd0iq8bHXzRf9CZkoQAEKUIACpxRgYNwpidiAAhSgAAVqWICBcTVcPA6dAmcoYBX3ItnzF8hl0lAVBS0dQYQjfqiqBUW1oYTbADkk4CzTD5gZQAsD6tGwOPnsOICdGV1ctIuAlQV8ESDa6T6kCrlhONkBwFFRNDUkR/IYHixC92mIt1+A6ORvTPgDqCR0bffAINYPDKKpqckNjJP3aDSKSCSCYDB4bH3VW2P1HtrlkZ8sFO7EcpysTel3J66pvt0a64kBchIUVxoYJw8Nk8C4QqGAbDYLLzCumEphTrwJ05piZ3in8HQKUKDeBBgYV28V5XwoQIGTCBy3HjgGIf5ltzEgsQkFKFB7AlvvcZ/6fOy18Pq1tTcJjpgCFKAABShAAQpQgAIUoAAFKDBGAaV7Of+cb4xWbEYBClCAAhSgAAUoQAEKUIACFKi0AAPjKi3M/mtSYHA4jYeeWI+ewyPuhgAvLM57Lw2Kk89eoNzohgIHjm3DsS1cd/UyhAq7kNr0T9CL2xEMK9B9fkBVoNjO6B4PE244nKYDqq5AUVRYEhZnmMhmTeTsbrQt+300z7icYXE1eTdx0BQ4uQAD43hnUIACFKBAPQowMK4eq8o5UYACFKi8AAPjKm/MK1CAAhSgwMQLPH7LTSvlqud//BPPyXvzjBkTPwhekQIUOKsCtp1B6tBdyAxthOMoaG7zIxINwOeXRUEFsqoowXGjH3yAoskXAGxAjwKxC0c/J14fDYrD0Z/J4qJljIbIKRocqHBsB/lcEYnhAtJJAz5/APHuDyHc+gcTbiChbQOZDJ7esQvxlhbE43E3MC4Wi7mBcaFQ6NhDuWSd1QuLk3XWsTw4S/qXdicGxr1TyNzJ+i69lpzrne8FxnmhcYZhuIFx+XwemUzmWGCcv1DA/NYWtIaCE27MC1KAAtUtwMC46q4PR0cBCoyLAAPjxoWRnVCAArUuwMC4Wq8gx08BClCAAhSgAAUoQAEKUIAC5QgwMK4cLbalAAUoQAEKUIACFKAABShAAQpUVoCBcZX1Ze81KmCaFl5/cx+efnEbCgXD3SAgmxVKA+O8kDj5Tj6X/lqmLe1V1cGyhd3oiORh9TwIp/8F+LQkVL8GVVFkLwggmx8kOE4eNmg78h8UCxaKBRV6yzloXvwRhNuX1agkh00BCrydAAPjeG9QgAIUoEA9CjAwrh6ryjlRgAIUqLwAA+Mqb8wrUIACFKDAxAs8cctNx22gX/mVr038IHhFClDgrAvkE6uwff3/RDhoYdKkAELRAPwSGCfrhKoGxafJR3kelRsiJ2uS+VQGeigK36TL3DA44/BqwMzBHw6PNpS27lOp7NEHWTkOLNNCLlNAKmHgwMECgpFuzLvgr6HpU86KQa5o4NEtW6GHw25YnBcYF41G3dA4b21VAuNkrdULdDtVaNw7hcK900TfKYiutE8vLK40NE7C4k4MjEulUpikqZjT0gy/pp0VY16UAhSoXgEGxlVvbTgyClBg3AQYGDdulOyIAhSoZQEGxtVy9Th2ClCAAhSgAAUoQAEKUIACFChXgIFx5YqxPQUoQAEKUIACFKAABShAAQpQoHICDIyrnC17rnGB4UQGjz69ETv39kOeIC8v2UwgmxYkDE42MnibGLzP8mu/3+9+Ly9NU5Er2pg1tRkzuuM4tPlxZLf9C2Z15uEP+t2NH4oqG0IUWJYDq2jDtBxkRjJIYBHOuembaO7oqnFJDp8CFDiZAAPjeF9QgAIUoEA9CjAwrh6ryjlRgAIUqLwAA+Mqb8wrUIACFKDAxAswMG7izXlFClSjQDF/CI8+8IeI+NO4dEUUvqAPgaAORcLi5OFTuu4+ZMp9upSqAo6FAzuHkBgx0dIZg2MDmWQGbe1+tE9tBhxlNCzuaGCcI0+ichwUDROFTAGJRBE//2Ua8xZejCuv+85ZIzEsC6u378SwbUNC4koD4+TXwWDwuLVWWX+Vl6zBeuFuJwt5O93AuFKI0n5PDIsrDYqT9WE5CoWCe+TzeWQyGUhYXDKZxNLmJkyJxY6N96xh88IUoEDVCTAwrupKwgFRgALjL8DAuPE3ZY8UoEANCjAwrgaLxiFTgAIUoAAFKEABClCAAhSgwGkLMDDutOl4IgUoQAEKUIACFKAABShAAQpQYNwFGBg37qTssJ4Etu44jAcfXQfDtCAbBLyXbFaQUDgJipMNDPLZOyQwTg5p47bTNPgDEWi6gp6DB7Hp6X/Guzo3Y9ZkBeGwhM5JOwmMAyzTQaFgY+e+LDZnlmHJyo9h6ZK5aI0HEY0EEAkHEQj464mYc6FAwwowMK5hS8+JU4ACFKhrAQbG1XV5OTkKUIACFRPoT/W4fb+w7aH3yPtf3vj/rqrYxdgxBShAAQpQYIIEGBg3QdC8DAWqXMDIvYWnfvYF9PZlcMO1McRb/PAFdWiahMapUH06VInbUBQomuI+bGr4cBKb3xxBOm1AU4GWVj8WLGlBrD0KSNujgXGOZcOxbdiWBaNoopg3sHNXFr94MoP3XnMxLlz5t4DiOytCtuNgV/8A1hzoQSQScQPj4vG4Gx4Xi8XcwDhZTy1da5UgNy8wTj57x8kmcKrgOO/nJwudK+3PayfvshZcGhhnmibkkLC4XC53LDAunU4jOTKCy6dNQTwQYGDcWbnDeFEKVLcAA+Oquz4cHQUoMC4CDIwbF0Z2QgEK1IuAc2htuf9crJepcx4UoAAFKEABClCAAhSgAAUo0AAC2x5dftwsF90OpQGmzSlSgAIUoAAFKEABClCAAhSgAAWqWoCBcVVdHg7ubAuYpoVHn9mIDVt6IJ9LNx/IhgUJiysNjJNNDXKUbnCQn/t0HX7ZMKD6sL/nMLavfwb24OuY3ZrElDYTft1C0QAGkip29fmwuTeOUOdizJw5B0G/hnBIR3NTBG2tTZg2pQNd7RG0t4TR0hw920S8PgUocJoCDIw7TTieRgEKUIACVS3AwLiqLg8HRwEKUKDqBZZMWsq/SFT1VeIAKUABClBgrAIMjBurFNtRoL4FCqlX8Mov/wd27i3gouVhzJkdhi+gwefXoeo6VAmJcxTIX6mXz4quA46F5FAWwwMFqCrQ2hFAJB6SBoA838oeDTeDdTTgzLRQLBjIZw38ak0aWzcX8J6rzsN5V/0dgPBZA07m8vj5mxuhBYNuSJyExklgnATHhcNhdz1VDu+hXN7DuLygOPm1vLzQt5OFv50qOK508nJ+aXv5XHqUBsZ5YXHFYtENipMjm80ik8m44XF6IY93TZ2CgNSLLwpQgAInCDAwjrcEBSjQAALlBiPxz30b4KbgFCnQyAIMjGvk6nPuFKAABShAAQpQgAIUoAAF6l+AgXH1X2POkAIUoAAFKEABClCAAhSgAAVqT4CBcbVXM454ggWO9Cfxs8fX4XBf0t18IRsHSjcmeIFxXlhc6btscnAD43y+o+86AsEQhhNZ7Nq1E30HdyCfPIh8ZgiFoo2cGYDib8WMOQswfepkhIM+2Pbo9aQP2RjhOBYCPhXN8TCmdDWhuzOC9tYIVJV/t26Cbw1ejgJnJMDAuDPi48kUoAAFKFClAgyMq9LCcFgUoAAFakSAgXE1UigOkwIUoAAFxiTAwLgxMbERBepbwHGQHngErz/3v9DbX0Brq4alSyNoaQ/A79eh+XQouoTAOYDjSDIaHFWFrqvur1VJh1McWLYKRZOv5Hv5yoZt2W4bWbs0iyaMooFdO3N4eU0OGmxccMEUnHfV/wNH7XSNLdtGIpVy20cjEQT9/orbyzU3HTyEdYcOIxgMuoFxEhwngXESHBcKhRAIBNw1UAmNK31Yl7cWK9+dGBh3YnDcWELjvHO8gDiZvPfZW/91LU0TlmW5h4TFySFBcRIYl06n3cA4eb+gsx3T4vFjY6s4Ji9AAQrUlAAD42qqXBwsBShwegIMjDs9N55FAQrUqQAD4+q0sJwWBShAAQpQgAIUoAAFKEABCrgCDIzjjUABClCAAhSgAAUoQAEKUIACFKg+AQbGVV9NOKIqFNi4tQe/ePpNFIqWu1FAXqWbE2QTgxylmxrk1xIY5x0SHCebGvx+H4LBMGRnRzKVxUgygWwm627s0HUNwaAfwYAOxzbdTRuqqrlhc3I97102MMhGBdMoIhzS0NkWxpzpzehoDVehHodEAQqcTICBcbwvKEABClCgHgUYGFePVeWcKEABCkycAAPjJs6aV6JAHQvcVebcym1fZvds3sgCDIxr5Opz7hT4jUAxtwtb3rwfiYNPI581MG2qjmkzQghGJChNg6Lp7nqgrjnIFRQ8t6aAt3YZbghcwGdDURXk84CqK5g1M4SrLw2hrVmBUbTdi5imBatgIjWSw+tr0xgeNhDriGHOgiVYcO5dGEipWP36Ouw6cACOpiMQiiDk86ErHsPSWTMwZ/r0ipZrJJvFk5u3oqiqx0LjvOC4cDjshsZ5D+MqDY3zguJkffTEwxtwaQjciZOQn5UGyZ0YGHeysDh37fVoYJy8S1hcoVBwA+PkkKA4CY5zcllcOXsWIhMQulfR4rBzClCgYgIMjKsYLTumAAWqR4CBcdVTC46EAhSoAgEGxlVBETgEClCAAhSgAAUoQAEKUIACFKiYAAPjKkbLjilAAQpQgAIUoAAFKEABClCAAqctwMC406bjiY0kYJo2Vr38Fn61difks2zckFfp0+29gDgJjZNgt9IAOQmNk1/L93KMbnyQX+tuP4771+gct19vA4J8lv69DRHSQj7LIZsY5OfexoVcLgtdtTB1UgwXnTsFPl1rpPJwrhSoSQEGxtVk2ThoClCAAhQ4hQAD43iLUIACFKDAmQgwMO5M9HguBShwVIAblnkrVI0AA+OqphQcCAXOqkDWNnHfL/8D2f4X3BC487pHMLszj9b2AAJhPzRdg2070FQHlq1i/WYDjzyTx8YdRRQNBZZtQ1MVzJ8VxAeuCeGSC+XhUwpsU9o7MA0LxUweBw4Y2LDbB79mwd/WiULT1Qios/CTx1dBC4dwxXveg7nzFyEQjiCdzWJ4KIWeXdsx2WfhA5ddikCFws9sx8H23iN4cdduBEIhSEhcc3MzotEoIpGIe8jaqqylyvqprIN6D+E6WWicfOe9vHXasRTYC4/zguJK3701V1l39dZeDcNww+EkMC6TybiHFxi3pLUF8zs7oIzlwmxDAQo0lMDIvn3ufN/4zx++R95/54GHVjUUACdLAQo0kgD//KWRqs25UoACbyuw9R4c98/DhdevpRYFKEABClCAAhSgAAUoQAEKUKBuBZTu5VwirdvqcmIUoAAFKEABClCAAhSgAAUoUGsCDIyrtYpxvGdNIF8w8OjTG7FxW4+7cUM2DJQ+0b40JM4LiBsNhvO5mxy8wDh5l8Pb5FAaCOcFwcm7t8nhxM0OJ/5MxiEbFmSjQrGYx8wpTVhx3jREwv6zZsULU4ACpxZgYNypjdiCAhSgAAVqT4CBcbVXM46YAhSgQDUJMDCumqrBsVCgZgW4YblmS1d/A2dgXP3VlDOiQNkCmg8v7XoVT724CsOJNPL5LJp9aSxsH8J7Fw2go12HFgxCkQw0B5A327DR32fjwICCnOWDbTkI6DamdgJTujWovtEHUUlYnGVaQCGHbft0PLWtAz2pJoQCOtramrGz14ctm/fhwosuxu2f+RTmzZ0LLRiCY8u5NlLZPHbt68FzL7yAWOIIPvU71yJYodC4gmliza7d2Dk07K6bSmCcBMV5oXESIueFxnlrqO8UGjfWoLjSdt6Duk4WGOcFxXkP9pKwOAmKk/XXXC7nBsXJId9FHBuXzJyJSIDrsGX/74EnUKABBFb99beOm+X7HniIG6caoO6cIgUaVIB//tKghee0KUCB4wUYGMc7ggIUoAAFKEABClCAAhSgAAUaSYCBcY1Ubc6VAhSgAAUoQAEKUIACFKAABapdgIFx1V4hjq+qBFLpPH762Drs3j/gBsZ5L9lwIMFvsonBC4iTkDj5XPqdFypXuslBzpOj9OVtVni7yXtBddJOXqZpIpVKuZsWisUCFs3twMXnTkUwoFeVHwdDAQr8RoCBcbwbKEABClCgHgUYGFePVeWcKEABCkycAAPjJs6aV6JAHQtww3IdF7fWpsbAuFqrGMdLgXEWUBUcyBzEs68+i+FEFulsAYlEBoOJPFQni/fM7MF7F/Uj3hqC4pMHTSlQFECxHFgFE4W8Dct0AAXw6SoCYQ1aUIetAI7twDAsKIU89h9R8OCGadjU34Zw0I/O9jhSGQuvrt2Gd624BF//+tfR1tGGQsGCZVtu2Jz8l21byBct7OkdxP0P/RRLgw4+ds1V0DVtnCFGuxtKZ7B6+3aMGKb7oC0Ji/MC4yQ8LhgMuoesrco6qvfuPXjLC387cU1VvvfWS0sD4kofwOVNqHT91fssIXFyyLqvBMV5hxcWl81mkUwm3TVYK5/HFbNnoTMWPfbgr4pgsVMKUKBmBRgYV7Ol48ApQIHyBfjnL+Wb8QwKUKAOBRgYV4dF5ZQoQAEKUIACFKAABShAAQpQ4G0FGBjHm4MCFKAABShAAQpQgAIUoAAFKFA9AgyMq55acCQ1IjA4nMa9D7+KI/2pYxsQvE0H3gYGLzQuEAi4mxokNE4+y7v3a++zt9GhdPonBsZ5Gx28Nl5gnPdr2bwgGxa8J9xrmoorV8zEkvmT3c0lfFGAAtUnwMC46qsJR0QBClCAAmcucPcjd9wlvSzuXnGn+z5lxZl3yh4oQAEKUKBhBBgY1zCl5kQpUEkBbliupC77LkuAgXFlcbExBepOQILdtg5sx/a9G5FI5ZDOFJDNFzCSSGNwOAvHzOCy6Ydw/Tl9CEd9UPwBaLoCxQZsy4FtOrBM2w140/0KdL8GqAps9+cW7HwGe3t1PLJpCrYMtCMcCmJKVxy6L4AX1uxCOpPD9777XSxffhEyuSIcjP4rUt5tCZwrmsjlCzCLJjbuP4yf/Nf/jy/fcBUWzZxZkTA0WevsTSTx/I4dyFi2u24aDochYXFyyOdQKOR+7z2US9ZQvYdweWujJ66RlobEneom8tZbvXVYefcC4+ThXMVi0T0KhYK77ipHJpNxH9qVz2SwYsZ0zOvqlAw/vihAAQqcVICBcbwxKECBBhJw1wPLeJXbvoyu2ZQCFKDA2RNgYNzZs+eVKUABClCAAhSgAAUoQAEKUGDiBRgYN/HmvCIFKEABClCAAhSgAAUoQAEKUODtBBgYx3uDAqchsHPPETz27CYUiqZ7thcY521ckDA4OYLB4G+FxMn3XqCcfD7Zy9ug8HY/k80P0kaedi8veZcn3cumBXnKfbFoYsGcTlyxYg7CIf9pzJCnUIAClRZgYFylhdk/BShAAQqcTYHNvZvKDeo4m8PltSlAAQpQoEoEGBhXJYXgMChQ2wLl/v9QZr7Udr2revQMjKvq8nBwFJgQAcM2cTDVj129W3G47zCGk3lkszmMjKTRP5xFsZDGVXMO4/K5Q+hstqCHw1BVDccSyeTfao4DR4H7gCgJj7MME4VcFpv3h/HY5i7sGWlGPBZGV0ccXe3N2HUgiRde3oQrrrwUX/jvX0Ik3ISg5gMUR9LiYANww9EKRWSzBRiGiZxl48e/+AWiA/vx5Y/dBt/brF+eKZrtOOgZGsYzb22Ho6rueqkXFCehcRIYJ4esr0ponLfeKqFx8pL10RMfxFVOYJz04YXGSVCctx4rHnJIUFwul3MPWXOVww2OS6Vw3pRuLJ06BbqqnikDz6cABepYgIFxdVxcTo0CFKAABShAgbEInAtgw1ga1lsb59Dacv9cut4IOB8KUIACFKAABShAAQpQgAIUqGOBbY8uP252i27nM7bquNycGgUoQAEKUIACFKAABShAAQrUiAAD42qkUBwmBShAAQrUlwAD4+qrnpwNBShAAQocL8DAON4RFKAABShwOgIMjDsdNZ5DAQqcIFDuxjwGxvEWqpjAk7fctEo6j0+fcaW8n/d7n6jYtdgxBShQvQISTDZUSGHb4S3o6duPI30JpNI5DI8k0ds/As0Xx/kzbFw8eTsmhQ6gqSmAQCgIVVfdv2UvQXG27cAwbRSzeYwkLBwqzMUzexfgrb2DCPqK6OpoxdQpHdB9fqxesxtHjgzgDz//OSw57yKojo2utg4EAwHYlg3LttyQuELRQCFvwDJNOIqCVes3Yc2TP8O//unn4a9QYJxUSULadvT147V9+1CwHUgYXCAQOBYW5wXISWCcHBIqJ8Fx0k6OcgPjSgPl5NreIXWRQ4LiDMNAsVh0g+IkIO5YUFw2CyOfx8UzpmFeVxd8R4Prqvdu48goQIGzLcDAuLNdAV6fAhSgAAUoQIGzLPBBAN8E8A0AP5HfAp7l8UzY5RkYN2HUvBAFKEABClCAAhSgAAUaTkDWtnr7h7Cv5zCmd3ehva0Z+tE1s5rCMC1AH31IFF+1J8DAuNqrGUdMAQpQgAIUoAAFKEABClCAAvUvwMC4+q8xZ0gBClCAAlUowMC4KiwKh0QBClCAAuMmwMC4caNkRxSgAAUaSoCBcQ1Vbk6WApUSKHcTIgPjKlUJ9ntMIL9ubbn3JfUoQIE6FMiZRWzv34UtezbiyMAIBocS6OsfQTDcgcnTF2D25BhaCq9BG3gaQacf4VgEgaAOVVGQz5lIJdLIO+3wzbwZRsdV2LR1B3ZuWwfLSKOrqxUzp3ViJGXgp4++hqbmOD75x59DLNaCWDiMjtZWhANh2I7jhqRJQFACxykAACAASURBVJocpmnBseGGsG3YewBPPnwffvDZTyPg0ytegTV79mLz4V4oquoGwUkwXDAYdEPiQqGQGyInhxccd2JonJwjLwmEKw2Fe7uBnxgUV+pQKBTcsDgvMC6dTqOQz0OzbVwwbSoWT+ke0zUqjsYLUIACVS/AwLiqLxEHSAEKUIACFKBAZQXkz1o3AVgMYPOvf8t2N4AHGiE4joFxlb2x2DsFKEABClCAAhSgAAUaWaDncB/e6MlgzZYeHNi+EZNiOq697AKcv2QeYtEw/D5fI/Nw7hMkwMC4CYLmZShAAQpQgAIUoAAFKEABClCAAmUIMDCuDCw2pQAFKEABCoyXAAPjxkuS/VCAAhSgQDUKMDCuGqvCMVGAAhSofgEGxlV/jThCCtSAQLnBXAyMq4Gi1voQGRhX6xXk+CkwTgKKAsO2sPHgTry58zUMjSTR25dANNaF+QsWY/7iczF5yixkDr6OXc//M/q3vQDFSbsXtxFB27wVmHvlH6F78TVIDA/gjbUv4Y03XkUq0YfurmZ0dbZi685BPPLES7j4XRfhQx//NOLROOLhCHRVA1QHbmiaBMZZDmA7sCzb7V/XVGzsOYynHvkp/vH2jyOgVz4wbvuRPrx2oAdFy3LHJQFwEgonR2lQnPdZAuW8n3vhcRIUJ2F33uvE4DgvJE5+Lp8ty3ID8+TdNE1IUJwXFpfP55HNZt3DNoqYHGvChbNmoKupaZxuAHZDAQo0ggAD4xqhypwjBShAAQpQgAKnEPgQgAdL2kiA3DfqPTiOgXH83wUFKEABClCAAhSgAAUoUCmBN7fvR8fcC5HvnI4Nmw7jrQ3rsf6lXyJY7McVFy7AVe8+H10dbQgG/JUaAvulABgYx5uAAhSgAAUoQAEKUIACFKAABShQfQIMjKu+mnBEFKAABSjQAAIMjGuAInOKFKAABRpYgIFxDVx8Tp0CFKDAaQg8v21071B/6uBqef/6jT9YeRrd8BQKUIACIsDAON4HVSfAwLiqKwkHRIGJFVBV5IwiBnNp9AwOYOOOLTjcux+xiO4Gt0XiU7F4yTmYv2ABpk6bDX8gBCOfw55X/hP7Xv2xO9ap592EOZf9AfyhJjiOheTIIDZv3oTXXn0JycF98PtUGJaCHbv78fKrG3HTLR/CjTd9HJFA8OhcR8Pi4CgYjYgb/W/HdgAFUBUVr+/Zh+cffxj/9Id/MCGBcbsHBvH6wYMoWKMBbhLk5gXASSCcBMR5IXHe59LvJGBOwuLkkPO8wyuuFxYn/cpneTcMww2Kk3c5JCxOguK80DhYFlqCQSyY1IXFU7rdPvmiAAUoUI4AA+PK0WJbClCAAhSgAAXqVEB+IyUhcYtPmJ98d/fRMLly/wy3aqm23nP8n0cvvH5t1Y6VA6MABShAAQpQgAIUoAAFalMgm8tjze4RqFPPhz/cilwmh3Qmg0MHDmLDK88hc2grrjx/Nq67/EJM7uqErmu1OVGOuiYElO7lXECtiUpxkBSgAAUoQAEKUIACFKAABSjQCAIMjGuEKnOOFKAABShQdQIMjKu6knBAFKAABSgwjgIMjBtHTHZFAQpQoAEEHnzt+8fN8us3/oB/sagB6s4pUqBCAuVuNuQ/bypUCHb7GwEGxvFuoEBjCjiKAsOxcDidwNptm7Bl51YMDgwinc7Bsh20tYYxvbsDk6ctxILF52PW7DlobeuCqukuWMGw8Yuf/ggSnvb+D34MPtVzdGAUsti54y2sfeVZDPXtw5H+Yfe8gaEUVj3/Jt73gRtx0ZXvhU/zoy3ejKZQGFAkNG70X3tudtzof0HTVMhYV7+5CWuefBj/9qefQ8Dnq3jR9gxKYNwhmFDcEDcv1E3eJahNAuFKD3GQw+/3u9+fLCxOznPn5c5xNCTOO0qD4iQsrlgsumFxjmVBcxzEg0HMn9SFhd2ToWvcSFPxG4AXoECdCjAwrk4Ly2lRgAIUoAAFKFCuwM0AHnibkzYeDY776Wk8/KPccVS8PQPjKk7MC1CAAhSgAAUoQAEKUIACsm5YLOLp9Ydgts6HLg+fMgrw+UIwHBVHjgzgjV89AwztwPWXL8UVF5+PeFOUbhSoiAAD4yrCyk4pQAEKUIACFKAABShAAQpQgAKnJcDAuNNi40kUoAAFKECBMxNgYNyZ+fFsClCAAhSobgEGxlV3fTg6ClCAAtUmwMC4aqsIx0OBmhZgYFxNl68+B8/AuPqsK2dFgXcScFQVSTOLrQf34I2tG7F/fw/S6Twsx0bQryMSCSAaDSAS8qOzayqWnX8l5i9cilAk7oalJZMJ3Hvv/fjJTx9BJBzGrTd/AB/4wA2IxZrcy1pmERvXv4x1rz6DXGYEtgNEwiH0HB7Ef/5kFd595ZW49Nr3I58vwK/7MLN7OlqjTbAd2w2NU+DA+xemhKMVbRsPP/sc9q19Aff8xZ/Cr4+G1lXytWtgEBt6j8BWVViW5R6lIW9ybQl7k5cExElQnMTdhXw+yLcyfgm6OzE4TvooDZ/zguIkIM4yTSgSkifnObbb1+TmZsxsb0NnPA4fg+IqWXL2TYGGEGBgXEOUmZOkAAUoQAEKUODUAvLbt00AFr9D07oIjmNg3KlvBragAAUoQAEKUIACFKAABcZHIJ3J4pev7YHZMg++cBT5bNpd8/KForD1MHr292D98z/H8hkBfOSGq9De2jw+F2YvFCgRYGAcbwcKUIACFKAABShAAQpQgAIUoED1CDAwrnpqwZFQgAIUoEADCTAwroGKzalSgAIUaEABBsY1YNE5ZQpQgAJnIMDAuDPA46kUoMCJAneVSVJu+zK7Z3MKAAyM411AgcYScBwgrRhY89ZrONBzAAMjKRSLBlTFQTQSRDwWgqICubwBwzAQCOhYtORivOvyGxCJNSOVSuLJxx7Bt//n3yHa1IRwOIx8LovPf+6zeO+170c0FkMmncIzj/0IB/Zvc3FDwQCaYmHkCgb+6V8fRSjegQ9/8jMwHAV2sYC2lnbMnTLDTVn7TVQc3GC1YMCPXb39uO/B+3FRexifv/lD0FS14kXbeqQPOxNJSYODhLrJIaFxEhInh3wWH/ks49QVBXM7OzA5FkO2WETONJEtGiiYJoqWBTdNzn0pbuie149tW1AcIKjrbkBcLBhASySC1mgEsVBoQsLxKo7JC1CAAlUjwMC4qikFB0IBClCAAhSgwNkXuPXXgXH3j2EYb/76N3J3A/jZ0WzwMZxSPU0YGFc9teBIKEABClCAAo0mcODQEaTSmTFNe1r3JMSi4TG1ZSMKNJJAoVjErr09Y5ryjKmT3Yc3ne3X0EgST722G8XYDISamlHI5WCbBnw+HcF4JzJFFa+vfgRzQsP4zEevR8DvP9tD5vXrTICBcXVWUE6HAhSgAAUoQAEKUIACFKAABWpagIFxNV0+Dp4CFKAABWpVgIFxtVo5jpsCFKAABcYiwMC4sSixDQUoQAEKeAIMjOO9QAEKUIAC9SzAwLh6ri7nRoGTCPj9eGXna9i5/y3kC6YbXhb06wiHfICqIJ8rIJsroGBISJoNRbERjzfj3VfchCnTF2DD+texa/s2PPf8S9jfcwiRSARN0Qiufs+lWLLsPCxeci569u/Ai8/eh6JRhKZq8Pt8bv+xWBjPvvAmnnlhC2775CcxadYCZJIjCAVCWDB9DoKBICzLdMPVFCgIBQIoWDYeXv081q96At/+1Ccwb9rUipfVsm2sO3wEg6YJTdfdsDgJh5PDC48rFovuZzf4zShiaXc3LpozG0Gf79j45GcSGGeYJiSoT14SiKdIIt/RX6mKCk1V4NN1+DSt4nPjBShAgcYWWP+jH7oAif37Vsv7dQ88tLKxRTh7ClCAAhSgAAUaWEBivTf9OjRu8RgNNhwNjnuoFoPjnENrj/6udIyzZTMKUIACFKAABShwhgJd51yLvoGhMfXyrS//Mb76hU+NqS0bUaCRBH7yyNP48B/+n2Oa8vfu/jN88faPjaltpRv19g3imXX7YYS7EYo3o1AoALbl/lYqEm+HrUewbtVD+Mgl03HeknmVHg77bxCBbY8uP26mi27/zeO8GoSA06QABShAAQpQgAIUoAAFKEABClSdAAPjqq4kHBAFKEABCjSCAAPjGqHKnCMFKECBxhVgYFzj1p4zpwAFKHA6AgyMOx01nkMBClCAArUiwMC4WqkUx0mBMxdwoGB/pg8vvv4MevuH0dIcQ1dXM4x8EaZto1iUkDgLlgM3CM0wLBiWDTgm2tq6MGPOcoQiMUzu6sDr69/E333vH2BZDj7xsQ/jmquuQCqVQSqdRM/e9Rge7gWgwa/rCPh1+HwaggEfVE3FQ4++it6hAm775GcQ75iM4aFBdLV1YVJLK2zbQTAQQCwaRMaw8NgLL+PVZ5/AH119Ga44/zw34K7Sr8FsDltGRqBHoscC4mQzS+khgXFy+Gwbl8ybg4VTpkCdgLFVeu7snwIUaAyB4AXLK/8P08ag5CwpQAEKUIACFKhtgQ8DuK/MKdRkcBwD48qs8jg337RtFwrF4jj3OtpdwO/H0oVzKtI3O6UABShAAQqciUA5gXHf/NJn8bUvfvpMLsdzKVCXAvf9/Je47Y++Mqa5ffeuP8Of3lG5wLjX39zqrt8tWTB7TONJpjJ45tW3cKQQQ1PXZJiGPFzJgWNbCDc1A1DRu/4xfPT9l6KjrWVMfbIRBd5JgIFxvD8oQAEKUIACFKAABShAAQpQgALVJ8DAuOqrCUdEAQpQgAINIMDAuAYoMqdIAQpQoIEFGBjXwMXn1ClAAQqchgAD404DjadQgAIUoEDNCDAwrmZKxYFS4MwF/AE8v+U57Ni7HQcODrqhcEsWTYdf12CYEgwHmJYN03Jg2fJuoVi0YFkWdM3BrLlLsWjZJYg1NWHt62/gO9/9BxQLBm778Idw4w2/A6NYwPata7F/z2Y3aE7TNAR8EhinQdd16LqKcCiARCqHR3+5FqH4JFx/y0fRNWkKwoEgWiNN0DQFus+P3kQKTzz/Era+9ivctGw+3vfuFdA17cwNxtDDnkQChwwLgXAYhmG4wXC5XA75fN59l+A4eQ8rwGXz52NWZwdUVR1Dz2xCAQpQoDoEGBhXHXXgKChAAQpQgAIUOOsCEqK7CcDi0xjJegB3A3gY7u+mq/vFwLizV59sLo/InMsqOoDMrhcRDgUreg12TgEKUIACFChXgIFx5YqxPQV+W6AaAuNkLXHTjgP45et7sXPTa/jMR34Hy89dMqZyDQ4n8OOn3sTkxe9GOpVyA+Pc3z4pCpqbW7B+9cP44OWLce6S+WPqj40o8E4CDIzj/UEBClCAAhSgAAUoQAEKUIACFKg+AQbGVV9NOCIKUIACFGgAAQbGNUCROUUKUIACDShw9yN33CXTXty94k73fcqKBlTglClAAQpQoFwBBsaVK8b2FKAABShQSwIMjKulanGsFDgzgX4jiTUbn0PvwBAO9g7j4ME+zJoxCcuWzIRRtGDbXlCcDdO03dA4w7BgmBYs20RzUxOWnHspumcswn/91734+SOPw+fTsWzZEtzxmU8hn+rD1o2/wnAiAVXV4Pfp8PvlXQLjRkPjJBAuGPAjlc5i1YubkEMcl151LRYvOQft8Xakszns3L8P27ZugZIYwFWL5uGC+XPh0/Uzm/wYzy5aFjaNJOCEI1AU5Vg4XCaTQTabdUPj5POkcAiXLpiPrnh8jD2zGQUoQIHqEWBgXPXUgiOhAAUqJuCuB5bxKrd9GV2zKQUoUOUCt/76rw3cfwZjlOA4+WeIBMdV7YuBcWevNAyMO3v2vDIFKEABCry9gIQ2Lbz8Zowk06dkeveFy/DQv/3dKdud2ICBcWWTVdUJn/nzb+KRp1445ZhkHWjL6gcQi4ZP2ZYNyheohsA4WSN8fW8Sk2cvw7f/4Z+R2/MqPvepD2P5OacOjZOwuR8/+Qri8y5H/5HeYwDyfTQSxZHdG3DhrCZccsFid02OLwqciQAD485Ej+dSgAIUoAAFKEABClCAAhSgAAUqI8DAuMq4slcKUIACFKDAOwowMI43CAUoQAEK1KPANx6547ine9980Z/U4zQ5JwpQgAIUGGcBBsaNMyi7owAFKECBqhJgYFxVlYODoUDlBFQd63pex/7DOzEwlELfQAI9BweQL+Zx2Yql6O5qRS5XgOUApnU0MM5yUDRNmKblfgfHwqyZsxHvmI9/vOdHONJ7CKFgCP5AALfd+gHEglns3bMTloXRsDifD7pfhV9TobuhcTo0RYE/4INhGFi7fhfe2puCHoyiKd6CBQvno6u1GZGijUmxCKa1NaOztbVyJifpeTCXx1vZHELR6MnD4pJJLJ7UhfNnzkBTmBugJrQ4vBgFKDBuAgyMGzdKdkQBClSvwHHrgWMYJncljwGJTShQpwLyv//tAOae4fze+PX5Ehz38zPsZ1xP33oPjvvn4cLr145r/+zs1AIMjDu1EVtQgAIUoMDEC0hYkzb14jFdePH82di8qvx8XQbGjYm3ahtde9vn8NTzr4xpfH0bn0JHW8uY2rJReQLVEBhnWhZWbz6Ipdd8EC+8thvP3v//IX9oPT77+7fiovOXvuOE5EFV9z6zHq3zL0HP3j1QVdVt7zgWfL4gtGICocIhXHfZ+YiEQ+XhsDUF3kZA6V7OP+fj3UEBClCAAhSgAAUoQAEKUIACFKgSAQbGVUkhOAwKUIACFGgsAQbGNVa9OVsKUIACjSLAwLhGqTTnSQEKUGB8BRgYN76e7I0CFKAABapD4PFbblopIzn/4594Tt6bZ8yojoFxFBSgQEUEDF3FmrdWY2CkDyOJDAaH0hgYSuDg4X60NEfxnkvPg8+nIZ8vwnYAy3ZgWTYMU8LjRgPjDMtEPBaGjQjue+h5yKayYDAIo1jE1ZcvxaSOMEYSaXfDhwTGSX+6rsKnq9A0DZp879dlJwj27D2EPQeTSBth7NyxE8mRfnz2/7gOKy9+F7p8MxEO+Cri8E6d5gwTb40kUAgGYVkWstksMpkMcrkcEokE4rqGi2fPxpTWFgR8Ez++CQfhBSlAgboVYGBc3ZaWE6MABX4jwMA43g0UoEA5ArcB+HE5J7xD23UA7q6W4DgGxo1TVc+gGwbGnQEeT6UABShAgYoJMDCuYrR10zED46qjlNUQGOc4Dl7ZtBvF2StRyFs4cvgIXn7iJ8ge3IA/+r1bsOLCZW+LtXNPD7ZmmmCqEQwc6YWqae4aobxUzQefnUOhdys+cPVFDB2sjluuLkbBwLi6KCMnQQEKUIACFKAABShAAQpQgAJ1IsDAuDopJKdBAQpQgAK1JcDAuNqqF0dLAQpQgAJjE2Bg3Nic2IoCFKAABY4XYGAc7wgKUIACFKhHgSduuem4DfQrv/K1epwm50QBChwVGP7f7N0JXFRl2wbwaxZgGBYBcQMRFfc1FTPTzKVyzywz2yxbrNT2L982t960fXm1LDErrSy3NPel1MxMBfcFUcMdWRTZRNaZz2cMAx2Yc2bmwJmZ6/x+fr5f3Oc+9/N/HgZFuCjIwY6/tyAjJxPZOXnIybqEjOyLSD2fibS0dDSJikDH9k1QWFhkCYcT368hvmnMEhhXLELjxO/Flv/uZ/RF/NFU7Np3EgaDN+qFBaJZVA0Um0zQ6URYnBe89CIsTgMvERSn00Gr1VgC5Ly9vXDiVDLOnc/EuSwdCkw+2PzHn/A3AmNH34n2LVuhnn9LmAuLK3XvikwmJGZkIsVkRmFREfLy8iyBcdnZ2fA2mdAqrA6iataEn8EHGg1/MH2lbg4fRgEKOF2AgXFOJ2VDClBAfQIMjFPfnnAiCqhZQAsgAUAjJw4pguMmAljmxJ6yWzEwTjaZ029gYJzTSdmQAhSgAAWcIMDAOCcgunkLBsapY4PVEBgnJM6mZWDdkTzUbtgS2RmZuHipALEbluLiqd1465VnUDes1nVgp8+m4udN+9H61sE4dvgwzKZi/PvJGjO0Wh30pjxknNyLIb07I6xWDXWgcwqXF2BgnMtvIRdAAQpQgAIUoAAFKEABClCAAm4kwMA4N9pMLoUCFKAABVxHgIFxrrNXnJQCFKAABaQLMDBOuhUrKUABClDgXwEGxvE0UIACFKCAOwowMM4dd5VrokD5AkcvnMDB47uRn59v+UaO3Fzxex4yM3ORkpqOrJwcdGzfDE2jIpBfUACT2QyTyYxiERZXLELUilFcXGwJkBMBcAaDL+L2ncH58xloXL8aDN56aHU6+Pp4w9fXCwYfL+h1WugtYXFa6HQa+Ph4I+18BpJT0mEya3EqzYSs7Dxs+n0jOrSNwqhHe6NOjVA0qn4DfLWGSt3OrPx87ExORVZhIQouXYK5qAiBXnrUCwlGvZDqlnVpGRRXqXvCh1GAAsoJMDBOOVt2pgAFVCPAwDjVbAUHoYDLCNwPYK4C0+643FMExy1XoLfNlgyMs0mkeAED4xQn5gMoQAEKUMAOAQbG2YHmYbcwME4dG66WwLhL+QVY8vteBDfvjvRzadDqvJBfpMHOTSvgn3sCzzx6L8LrXAmNEz+UafP23di4+xi6DhqO8ynnkZOZDo1Wa/mhVBoRG2c2W/5NsfhSFpKP7sDwu3ogsm6YOtA5hcsLMDDO5beQC6AABShAAQpQgAIUoAAFKEABNxJgYJwbbSaXQgEKUIACriPAwDjX2StOSgEKUIAC0gUYGCfdipUUoAAFKPCvQFr2acv/88ehJT3E7+MGfrmRPhSgAAUoQAFXF2BgnKvvIOengHQBs0aL+LMJOJ5yxBL6VlBQgLy8QlwqKELepXxkZF5ESlo6tFqgY/sWqFk9EAVFRZZv2CgWoXEmM4qKRHBcseX34mIzvL310Oi8cfzEWeRezIbR6AuDjw98fb1hNHjBz88HXno9tFqNJTDO20uHi7n5OHYiCYGBfsjIKsLZ9GKcOHEK8Qfj0btnNO7udyP8jL5oXKMV/HR+0hfohMrcgkIcy8hAdl4e9GYzDDodagUGIsTfDzoBw4sCFKCAGwkwMM6NNpNLoQAFyhNgYBzPBgUoIFdA/MUvAUAjuTdKrI8DMKmqguPMSXFyXxclLotltgQYGGdLiG+nAAUoQIGqEKiMwLhl6zbhfHqmpOV1jm6DplGRkmpZVDkCDIyrHGdbT1FLYJx4zdh35BR2phngX606crIuwGAMQKHZC+sWfYPCCydQL7yWJRAu5UI2QqNa445BQ5GVnon05CTo9DrLvzmKy2w2QwNAq9Mj6+wxnDt1ECPv74/ICAbG2ToPfHvFAodWRJcpaP6k5ajxogAFKEABClCAAhSgAAUoQAEKUKAKBRgYV4X4fDQFKEABCniuAAPjPHfvuXIKUIAC7izAwDh33l2ujQIUoIDyAi1rt+IXEinPzCdQgAIUoEAlCTAwrpKg+RgKUIACFKAABSigMgEGxqlsQzgOBSighIDcYCR+3leJXWBPCriewAMAflB4bBEcNxHACoWfU6Y9A+MqU7vss/ILCmCof7OiA1w6tgUGH29Fn8HmFKAABSjgXgKVERjnXmKetxoGxqljz9USGCc0MrMv4sd1u1GnRRecP5cGs6kQPr7+yCn2QeLRBFzMOIfAkOpo1KQZwsJqIyP5NDLPn7P8YKmrl9kM8QkbrUYDs8mEE/u2ofBiKp59bCgiwmupA51TuKwAA+Ncdus4OAUoQAEKUIACFKAABShAAQq4sQAD49x4c7k0ClCAAhRQrwAD49S7N5yMAhSgAAXsF2BgnP12vJMCFKAABQAGxvEUUIACFKCAOwkwMM6ddpNroQAFKEABClCAAtIFGBgn3YqVFKCAywowMM5lt46DU6BKBbQAEgA0qoQpYi8/QwTHrayEZ4GBcZWhLP8ZPYY8hY1bdki68ZZO7bBp8UxJtSyiAAUoQAEK2BJgYJwtIb6dgXHqOANqCowrKirG9n1/43CGEV4B1ZCbkwWN2QQfvyB4+4dAp/OCTg8U519ERupZFOXnQavTlYI0w2w2QyTG6b30SE8+g8O7/kBwgDfGPDYM9RgYp45D58JTMDDOhTePo1OAAhSgAAUoQAEKUIACFKCA2wowMM5tt5YLowAFKEABNQswME7Nu8PZKEABClDAXgEGxtkrx/soQAEKUEAIMDCO54ACFHCCgPgmQDmX3Ho5vVnr4QIMjPPwA8DlU4ACFKAABSjgsQIMjPPYrefCKeBJAgyM86Td5lop4FyBBy8Hxn3v3JYVdosDMAnAciWfycA4JXXt7+0KgXHpGVk4dvIMziSnISMzGzVDg1EvvDYi69aBn9FX9uJL+h07mYSi4mI0jYq0/DL6GmT3snVDQWEhzqVnXPl1PgOp5y8gKzvHMndIUDUEVwtAcFCg5fegagHw9vKy1dLht+deykPiiTNITjuP5NRzMPj4oGFkOBrUC0NwtUCH+zvaICMrGwcPH0P6hUyLm9gvMXO1QP8rXtUCLXvfokkDaLUiY1O9F62dszciVCzx5Bkkp57HmeRUiJwfJd9vr53anc5kRTsiApQys3Ouvl6lpV+w/O/iYpPlNUu8Rl557QpEVGRdy/uko5c7BsaJ83IgIRHnL2RefR3LLyhEUKA/qocEWfzCa9dE88b1VfMalpSShtNJqRC/Z+fkWj4mNG/cwDJrVV9qCYxT475W5kxqCowTZzI9Ixs/rYlDjUYdkX0xG0VFRYDJBI14oxYwFRfDVGyCVqsDNOK/ik/PaCA+gJjNJsv/1nnpkZ+dhbjNv8GgK0SnTu3QtnE9tG0eBZ1O3X++qOr3Sz6/YgEGxvGEUIACFKAABShAAQpQgAIUoAAF1CfAwDj17QknogAFKEABDxBgYJwHbDKXSAEKUMADBRgY54GbziVTgAIUcKIAA+OciMlWFPBcAX7DsufuvepWzsA41W0JB6IABShAAQpQgAKKCmScOGHpv+uH73qI3/suXLJR0QeyOQUoQIGqE+DnX6rO8O7bWQAAIABJREFUnk+mgKsLiISChMuhcY0qeSHbLz9P/PCQVc58bvxMS0LD1atZf5FPx0stAkoHxn2/aKUl6M3WFejvh2ceGXK1TAQULV37O2b9+AtW/Lq53Nsfv38Qnn38PrRt0aTCRxQVFWP5r39g2tfzsH5zrNXaJg3r4bZunfD2f56RHZx2ITMLO/cdws69h7D7wGHLr1NJyZbgHTlXxxtaoHf3zri1cwd07tDarkA8a88T61+8agMWrvgN85euK3ekAH8jOrRpjqcevgdD+veCXq/DJzFzIYLvbF03tW+NWzu3t1Vm9e2xuw9a9mft71uxdcc+ST3ErH163IxBvW/F0IG3w8tLL+k+pYtoXVb4j227sCVur012jUaDkQ8NRlBgwNXahL9PQLyGxHy/GKnn0st9v41u2wJvPP8YWjRpaPM5Ugsq+0weP5WEeRW8b5aeu1/PLmjd/Po/ImRm5Vheew4eTsSRYychzqIIeHv8gUFoVD/iagsRDCdsd+yNx56DRxC7+wCOHjuF02dTpfJY6mqGhqBDm2bo36sr+vXqagmdlHKJ13URRCcuERj3+jufS7nNUvPuG8/arG3TvDH69rz5ap0jZ9Dmw/4p2HPwMH5euUH2a9jA27vh9m6dcO/A2+x6vXfk3IhAux+XrME3Py21fPyydok97tSuFZ586C6IWSvj+vWP7ZazWXJN/t8syR9L/zP6EUsIa0WXeI156uG7JS2lqva1ouGqaia1BcaJ17Ed+4/ir8OZqFGvMbJysoHiYkug6D//pyQi7spfRK684UpgHACdXo+ivEs4GLsF+3Ztx+0DBqD/kPtxaPd2dGgQiCYN6ko6IyyiQEUCmrBoS4YhLwpQgAIUoAAFKEABClCAAhSgAAWqXoCBcVW/B5yAAhSgAAU8UICBcR646VwyBShAAQ8QYGCcB2wyl0gBClBAQQEGximIy9YU8BwBfsOy5+y16lfKwDjVbxEHpAAFKEABClCAAk4V2Djl7TL9+ixcwm+ccqowm1GAAioS4OdfVLQZHIUCLijwMIA5VTT3tsvPFcFxq53xfAbGOUNRuR5KB8ZFdR6ExBNnJC3AnHQlTHBv/BE8NXaK5OAwcc+rYx7F5FdHQasVeYtlr83bd+OR5ydInkME5CyIeRfdbpIWfvbuZ9/itSmfSVqjnCIRiDbhpZGWID2jr0HOrWVqRfjO8+M+tIRIybnq1qmJB+/ui/c+ny3pticfHIyYD96QVFtSJMLhJn4UgzUb/5J137XFYtZJrzyNR4cOsHoGHGou42ZaX4816rV38cXshZIUY1fNgQh/EwGM//3kK0tYoZzrq4/GQYRIOnJV1Zl0JIzp6PFTmDZrHqbO+snq0ld+P/VqgFpW9kXU7dBXcgCXHMsuHdtaAt263nhDhbcFNummyPNLHipCJJd889HVGew5g1LXvWt/guWsikBORy7xev/WK09j5EN3y3q9t+fciCDBr+Yuwdi3/ydrH557fJhlf30NPo4s1ea99z/zOn76Za3NOkcKSv68UV6Pqt5Xa3NV9Uz2nDVH9kjKvRdzL2HFxh24UBwMv+q1kHspF6ZiE8xmEzTiszGW/yMy4q4ExYlLhJNqdToU5l5E/M5tOLA7DuGR9dGlz50Ib9wWQSGhOLN7LW5rG44aocFSxmANBcoVYGAcDwcFKEABClCAAhSgAAUoQAEKUEA9AgyMU89ecBIKUIACFPAgAQbGedBmc6kUoAAFPEiAgXEetNlcKgUoQAEFBBgYpwAqW1LA8wT4Dcuet+eqXTED41S7NRyMAhSgAAUoQAEKKCLAwDhFWNmUAhRQpwA//6LOfeFUFHAVAZF6lQCgURUOLILjJgBY48gMDIxzRE/5e9UUGFd8ejvemfYt3nxvul0Lf3hIP3z98QTo9TrL/bmX8iy95IZOlTz844kv4cWRD9icZezbU/HBdOXyHUWA3ez/TUSfHjfbnKV0QXGxCS9N/LjcEClZzSQUywmME3vz5P+9jbmLnZJLeXW6wX17YM7USfD3M0qY2HkltC7fUm5YV3pGFoY8OVZWkFXppw+983Z888kEWaFbJa8XVXkm7Qljyi8owMQPYyBCKyu6SgfGiTC+kOY9nXf4rXQaNugOzPp4fLl74A6BcSJ07dUp0/DRl9871VK83q/6YSrat24mqa/cczPwjltw31OvYee+Q5L6X1vUpnlj/DzrA0TVr2vX/VJuqsrAOLXsa2kntcwk96xJ+fOTlPNgqybt/AUs+20HLmqCUa1WGAqKTSguLrQEx8FsguWTMmYzNBoRFqeDVgPkXDiP+F3bceTAPoRFRKD1jV1QrXZd+AYEI6xhc2RnZsF4fhd6RkfB4ONtawS+nQLlCjAwjoeDAhSgAAUoQAEKUIACFKAABSigHgEGxqlnLzgJBShAAQp4kAAD4zxos7lUClCAAh4kMGnZSPHTuNEirJP44mq0CO/kQavnUilAAQpQwFEBBsY5Ksj7KUAB8WWxMhU0MutZTgHJAgyMk0zFQgpQgAIUoAAFKOAWAgyMc4tt5CIoQAFpApZ/D5Rxya2X0ZqlFKCAiwoMBzBbBbNvvTyDeI2yKziOgXEq2MEKRlBTYNzA27th2bpNDoF9NnksRo8YijPJqbjzkZfsDsUpGeLoliU2g3GUDowrmWX+jHdx78DbJPkUFBZixAuTnB7IVtHDpQbGnUpKwV0jXnZ4b8qbRQQabVg0AyFBgZKsHC2idcXWcgLjenbtiPWbYx3dErzz+hi8OuZRyX3UcCblhjH16dEZw55+HXvjj9hcZ2UHxomBbunUDstmf4Jqgf7XzefqgXEZWdl4YNQbWLV+i017ewsWznwf9/S3Hewn59yIELojx07aHcZYspbbu3XC2p8+t3dpNu+rqsA4Ne1rCZKaZpJz1qQG7to8DBILUtLS8dvm3UjO1kHvHwS/wEBAo4XZbIbJLILjAA3MKLx0CWlJpxC/dyeSjh9DeHgEmrWLRkCNWvAyGOFt9ENAUChqR7VG0vFDqGc6jm4dmkicgmUUuF6AgXE8FRSgAAUoQAEKUIACFKAABShAAfUIMDBOPXvBSShAAQpQwIMEGBjnQZvNpVKAAhTwQIEDyfvlBnV4oBKXTAEKUIAC1wowMI5nggIUcIKA3D+HMjDOCehsYV2AgXE8GRSgAAUoQAEKUMCzBBgY51n7zdVSgAIUoAAFKOCQgBZAAoBGDnVx3s1/XW4lguPW2tPSnBQn9/PS9jyG98gUUFNgnMzRrZYH+Bsx78t38egLE5F6Lt3hlsMG3YEfv5hSYZ/KCowTQ+z97Se0bm77JaEyZyrBkRIYd/JMMjr2He6UvaloUwb1vhWLv/4QGo3y/7xF64qt5QTGOfwO+08D8TpwMm4FggIDbLZUy5mUE8bUpWNbS1Bcdk6uzfWJgqoIjBPPLe/105UD4/LyC9Bt8BOI3X1Qkr0jRT99OQX33XlHhS3knBtHZrn23k2LZ1pCAZW4qiIwTm37KlzVNpOcs1bZgXEl53DvgSNIPH0Ox5PSkXI+GyazFjqdN4qLCpGZcQGnz5zE2bQUeEGDxiGhiIhqDJ9ateBt9IePnx+8DQboffwQECxC41rhfMoZBJyPRd+uNyhx1NnTjQUOrYgus7rmT0L5P5C6sSeXRgEKUIACFKAABShAAQpQgAIUcIYAA+OcocgeFKAABShAAZkCDIyTCcZyClCAAhRwKQEGxrnUdnFYClCAAqoRYGCcaraCg1DAlQXkfmMev4DRlXdb5bOvHnLXrxpAHxgR0U2j0WpueOhhlU/M8ShAAQpQgAIUoAAF7BUoysvD5o8/LH37+T4Ll4Ta24/3UYACFKAABShAAQ8QeATAtypbpwiOmwBgnZy5GBgnR6vyat0tME4JuW0rZuPGdi3LbV2ZgWEiKEoE9Wi1Ik/S+vXb5u24begoJSgq7GkrMK4yw2/EoJ9NHovRI4Yq6kDrK7wVWVdFYJyYadL/PYXxLz1Z4f6r6UzKCWOSe6irKjBOzLnmx89wx603lRnZlQPjRr/+HqZ/u0DuFthdH79pIZo1ql/u/Uqem4qGFmFx4mOREldVBMapbV+Fq9pmknPWqiowTrgVFBYiJS0d585n4nx6NvILi6DVamA2FSE3Lw+ZF3OxL/4YEuNPokndCPhWD4VXtSB4BwTAy2CAt48vvH0DUK1GbdSs3xxppw8jNGsfenVRJiBRifch9qx6AQbGVf0ecAIKUIACFKAABShAAQpQgAIUoMC1AgyM45mgAAUoQAEKVIEAA+OqAJ2PpAAFKECBShNgYFylUfNBFKAABdxKgIFxbrWdXAwFqkqAgXFVJc/nliuQv3NHuhnmYBJRgAIUoAAFKEABCrivQF5mJrZ+Pq30Ak/2Wbgk0n1XzJVRgAIUsCnwFIAvbVb9WxADQNwj9WL/iqXoQ5/SAq7+/iX1dcGZdSI4biKAtVKaMjBOilLl1zAwzrb5vQNvw/wZ75ZbWJmBcWKIP5d+jZuj21idJ7+gAPWiByD1XLrthTm5wlZgnD3BYXXr1ESjBhE4eSYZiSfOyJo4wN+Is7vXwM/oK+s+qcW0/leqImt79l3qHtiqyzi0EdUC/csts2c2pc6knDAmW+u+9u1VGRjXu3tnrJ5b5vNAcNXAOCX3qLw9bdGkIXas+R4GH2+rJVUxU8kgGxfF4NbO7eUeR5v1lR0YVxWGatxXZ85UlYFxpQ+Y2WxGcbHJEhhXErRbWFiElHPpWLVhGzZsjEVYYCiMwaHQBwfDyyjC4nzhbTDCxy8AQTXDUaNeUyQd3Yd6xYchghJ5UUCKAAPjpCixhgIUoAAFKEABClCAAhSgAAUoULkCDIyrXG8+jQIUoAAFKGARYGAcDwIFKEABCrizAAPj3Hl3uTYKUIACygkwME45W3amgAcJMDDOgzbbVZaatzMuEUADV5mXc1KAAhSgAAUoQAEKyBe4mJaG2JkzSt2oOdBn4eJW8jvxDgpQgAJuI8DAsoq3kj70KS3g6oFuSs9flS+MIjjuLQCrKxqCgXFVuUXlP5uBcbb3pWZoCFL2lp+LWFFgnAjS6tCmOVo1jULDyLowmUw4fyET59Iz8Nvm7bJD0MS0r455FO+8Psbq4N8tXInhz423vSgFKioKjNu57xA69H5I8lNfGTUcrz83AkGBAVfvuZh7CW99PBPvT58juc+M91/HyIfullwvp5DWZbXKs7YnlE3OPlRUu33lHHS8oYXVErWdSSVDq+QExonQqFbNotCicQNUDwnChYwspGdk4e/jp7Fs3Sa7tiY9fj2CqwVevdcVA+OKiorRoNNAnD6bKstABEy1bdEYgQH+iD9yDH/G7pEd6PnVR+Pw+P2DrD5XyXNja6EfTXgRLz31oK0y2W+vzMA4Ne6rGmcSmyjnrKklMK6iw5d7KQ9/bN+DhUs2wA8GVKtRE5qAQHj7+8HH1xdePkYYAgIQUjsSwWFRSD6yE430p9Cpg/XAXtkHnTe4tQAD49x6e7k4ClCAAhSgAAUoQAEKUIACFHBRAQbGuejGcWwKUIACFHBtAQbGufb+cXoKUIACFKhYgIFxPCEUoAAFKCBHYNOhRZbytOwzv4vfxw+M6S7nftZSgAIUKCXAwDgeB9UJXNoZt00D3Ki6wTgQBShAAQpQgAIUoIDTBC4cP4Y9c38o3W9bn4VLbnLaA9iIAhSggOsJMBCt4j2jD31KCygduObq/dXwCvgngAkAfis9TPxMlPl8dLP+cWqYlTP8I6DmwLgmDevhcOJJp+yVCG6rUzPU7n5ndq1CWK0aVme5NjCufetmGDHsTgzu2x3htWtWOP/Bw4n4eMYPmPXjL5LXKfrvWPP9dfVmsxmtetwH0VPOJZxfe3YEWjdvhOaNG8DX4GMJiNq+6wC+X7QScxdXmAV59VEVBcYNfepVLFj2q6SxNi2eCRGyVN710y9rIQKFpFwi/OrAxvlSSmXV0Pp6rvKsHQmME2GNoSFBss90yXQLYt7DkAG9rO6t2s6knDAmWYcVgK3AOBFG9sDgPuh2U3vo9bpy2+dczMXa37fi0RcmIjsnV/IYy+d8iv63db1afyAhEdk5Fy3/v8lsRpc7H5PUS7yOr/3xc5u1YbVroF547at1cs5g7Ko5iG57fcig3P0Rs8778l307XlzmXlFSNWT//e25NdVcXPDyHAk/PGz1b2RO1fpYcSMDSLCcezUGVn7WdJjzIihmDZ5rM39kFsgQvlOJ6Vcve2R5ydI/ti95JuPUCs0pMJH+hl9LR/vxCXXrzL2VY0zybVyhcA4sSYRzrc7/ijm/LgS+jwt/IJCoA2qBm+jH7x8feHta4QxIAjVw+sjsGYkUhK2oVVQFm5o2UTusWa9hwpowqI1Hrp0LpsCFKAABShAAQpQgAIUoAAFKKA6AQbGqW5LOBAFKEABCniCAAPjPGGXuUYKUIACnivAwDjP3XuunAIUoIA9Aotip5a5bfzAGH5hkT2QvIcCFBACDIzjOVCdQN6uuJ9hxmDVDcaBKEABClCAAhSgAAWcJpC0aycOr1r5bz8NlvRZsIR/BnSaMBtRgAIuKMBAtIo3jT70KS3g6oFuSs+vppfAzQAmAbCkQzEwTk1bc/0saguME0Fhb73yNDq1b2UJLjOZTEj4+wQGDH8BiSfOyMYUgTbPP3k/oiLrQqPRIL+gAKs3/IW7Rrwsq5cIaBNBbdauH35eBfHrpvatcd+gO9A0KlJWbxE+9vKkT/BJzFxJ94kQrZS9a6+rjdtzEB37DpfUo6Ro2KA7EPPBmxAhONauXfsT0P6OByX1LC8wLv7IMbS49V5JPSa+PBITXh5ps7broMfxZ+wem3WiIHnPWtSqUXGAkKRGpYpobV3MmrWcsC7RVZzF9954DvcOvM0SFicuEeqzffcB9HlgjKxgqw/Hv4CXn37oumHVeCblhkTJObOlA+Mu5eVbAhdFaJYI0+vT42bLa62cS/h1v+cppJ5Ll3TbrI/H47Fhd1qtFa/xurrSfpaUvQGQcs5geYFxHXo/hJ37DklarzjDO9f+gEb1I6zWi9f816Z8hvc+ny2pnyha/PWHuKvP9T9P0p5zI94nRj86FPUj6lg+Lop5jp86iwdGv4GtO/ZJnkmEAIowQKWvO4aNxrpN2yQ9JnXfOtSoHiypVhSpcV/VOJOwknPWXCUwTqzL8ufMxFP4dPpP8CnSISCkOvTVgqA3GuFtNMLH6G8JjatRLwp+1cOQeng72oXmoWXThpLPGQs9V4CBcZ6791w5BShAAQpQgAIUoAAFKEABCqhP4LrAOPWNyIkoQAEKUIAC7i/Qot98BiK4/zZzhRSgAAU8RuBA8n7T5S+U5sc2j9lxLpQCFKCA/QImczEWx5X5acn54wfGGOzvyDspQAEPF2BgnIcfADUuP2/Xjo9hNr+oxtk4EwUoQAEKUIACFKCAcwQSN6zHyb+2lG72fp+FS/7jnO7sQgEKUMAlBRiIVvG20Yc+pQWUDlxz9f5qfBH84/JQE+Nn4rfSwzXrH6fGWT12JjUFxv305RQMHXi7JcDm2uvIsZNo0uVuyfvUpGE9LJj5Hto0b2z1nm/nLcOIF0WuobRr46IY3Nq5vbRiO6qyc3IR2KSb5DsLT26DXq8rU//pzLl4ccLHkns8et9AfPPJhArrnREY9/anszDu/S9sziWC8I5vXyYpvGrZuk2485GXbPYUBctmf4IBt98iqVZqEa2tS1mzlhPW9eLIBzDx5acQGOBn9QFyzqNoIAIjp00ee10vNZ5JOWFM1y6o+80d0LhBPUTVr4vqwdWQl1+AlLTz2HPgCNIzMjH93VfLfS2UeuavrRPv08JRyjXltdF47dkRVktdITAuKSUN4e36SlmqpSbmgzcgAjQrunIu5iKsXR/JAYiP3z8IX3007rqWcs7N7d06Ycb7b6BBvTCro13IzELXQU/g4OFESWsVH2cTNv8sqdaRIqUC49S4r2qcqWTv5Jw1VwqME+sToYlxe+PxzdyVQHYxgkJrQBsYCC9/f/gYfeHl6w//oGDUimgMY0gdnD+yDW1rFKBFkwaOHG3e6wECDIzzgE3mEilAAQpQgAIUoAAFKEABClDAZQQYGOcyW8VBKUABClDAnQUYGOfOu8u1UYACFPA8gQPJ+/MAyPsxnZ7HxBVTgAIUoACA/MJcLN/9VWmLlPEDY2oThwIUoICdAhNl3ie3XmZ7llMAyNsZ9wyA6bSgAAUoQAEKUIACFHBfgQM/L0LaofirCzRr8ETfBUukfYex+7JwZRSggGcLMBCt4v2nD31KC7h6oJvS86v21TS6CTBmINCp2ZURGRinrq1SU2CcOaniMMF+Dz2HVevLBFCXi/nwkH6YM/Wtct9+KS8fxoZdJG+GEqFj4uEipCQzOwfn0zNx852PIfVcuqSZso9sgr+fsUztXSNexi9rfpd0vyj6+69f0DAyvMJ6OQFdIiBJBCVde0k9Y0PvvB3zvnxH0vzHTiah4U13Sqod9+ITeOuVpyXVSi2itXUpa9ZyAuNiV81BdNsWFW7Doy9MxOz5yyVtVf/bumL5nE9d4kzKCWMK8Ddi2KDeGNy3B3p0iYbBx1uShzOKRMBbekYW5ixYgZcnfSKp5evPPYbJr46yWusKgXELl/+Ge0dK/1kHF//eDKOv7Z/9+J+3p+L96XMkGdatUxOndqy8rlbOuZES4iU3TLX49HZotVpJa7C3SKnAODXuqxpnKtk3Z581e8+DUveJP4/FHz2Omd8tw8XUHASHhMKrWhC8Avzg7WuEt9EP/kEhqB3ZGL7BdZB2eDva1CxA66YNlRqJfd1AgIFxbrCJXAIFKEABClCAAhSgAAUoQAEKuI0AA+PcZiu5EApQgAIUcGUBBsa58u5xdgpQgAIUuFbgQPL+cwCqU4YCFKAABShgSyA77wLW7vuudFnC+IEx/3xrja27+XYKUIACFKCA+gUu7dx+qwbajeqflBNSgAIUoAAFKEABCtgrkHnqJC6cOIGkXTs3F2RnR5g1mof6Lli82d5+vI8CFKCAGwgwEK3iTaQPfUoLKB245ur91fySuAbA++akuN/UPKSnziY1zEv43NKpHTYtnimLKqrzICSeOCPpHluBcRM/isGkj8S7qu3LVmCc6NC21/3YG3/EdjMAC2e+j3v695RUe22RCIHbG38UCUeP4+SZZBw/fRbHTp7BidPJkgPiru1pLTAusEk3ZOfkSprxsWF3YtbH423WOhoYl3spD35RXW0+RxT07t4Z/xn9iKTawqIi9L5/jKTax+8fhK8+GiepVmoRra1LWbN2dmDcjO9+xtP/mSJpq7p0bIvNv5TNqFfrmVRLGFNRUTHijxyzvDaeSU7D2ZRzOJOciqTkNJw4fRanz6ZKsi9d5OqBcc+P+xBTZ/0kad1SPvaUNDp6/BQa3zxYUl9RdDJuBSLCapWpd/a5OZCQiFY9hkqeSWo4nuSGVgqVCoxT476qcaaSLXH2WXPkTCh5r3iNmzZrEc6fSkdIUAj0QdXgFRDwT2ic0RIaV6d+U/jXiEBKwnZ0j/JCeO1QJUdibxcUOLQiuszUzZ+ExgWXwZEpQAEKUIACFKAABShAAQpQgAJuJcDAOLfaTi6GAhSgAAVcVYCBca66c5ybAhSgAAWsCRxMOZBgNpubUIcCFKAABShgSyD9YjI2HJxfumzb+IExN9m6j2+nAAUoQAEKuIqAOS6uWr4WGa4yL+ekAAUoQAEKUIACFLBfwMeEIE10dKb9HXgnBShAAbcRYCBaxVtJH/qUFnD1QDel51fjC6MIipsIYKsYzpwUZ1bjkJ4+kysFxi1Y9iuGPvWqpC2TEtrz6AsTMXv+ckn9Fn31Pu7uJy0wrrCwCGt/34qfflmLjVvi7ApYsjXUtYFxBYWF8InsbOu2q29fN286brvlRpv1jgbGbd2xD50HjrD5HCULBvftgZ9nfeC0R9C6fEpr1s4OjFu9YQv6PvicpP20Fhin1jNZlWFMx04mYe7i1Vi1/k/8GbtHkq2cIlcPjJPzcXLa5LEYM0J64FqtNndIDg5d8+NnuOPWsl8i5OxzI0JHRSCm1MuVA+PUuK9qnKnkLDj7rEk9Y1VRl5KWjk9nLURqYiqqBwVDFxQEb39/eBl84e1nhH+1EIQ1bIaAGvVwLn4jbm5cDRF1alTFqHymSgUYGKfSjeFYFKAABShAAQpQgAIUoAAFKODRAkxz9+jt5+IpQAEKUIACFKAABShAAQo4X+BAyr7fYdZI/yob54/AjhSgAAUo4CICKZknsPnwL6Wm1awZP3BGHxcZn2NSgAIUoAAFJAnk74rbZzajlaRiFlGAAhSgAAUoQAEKuKSARoP9Pu2iW7vk8ByaAhSggPMFGIhWsSl96FNaQOnANVfv7/xXKPs7LgXwFoAdpVswMM5+UCXvlBNOckundti0eKascaI6D0LiiTOS7jEnxVVYt+LXzRgw/AVJvaQExskJspISGJeRlY33PpuNz7+dDxG6o+R1bWBc6rl0iOAhqdehPxahaVSkzXJHA+Pk7JnNYewssOfcVvQoWpevY81azvtZ7Ko5iG7bosKdlhP4Zi0wTq1nsirCmESw5TvTvsHGLWU+XNv5nlb+ba4eGNey+1AcPJwoyeW3+V+gZ9eOkmpF0R3DRmPdpm2S6n/8YgqGDSr7Oq/EudGERUuaRxS5cmCcGvdVjTOVHAYlzprkg1YFheJj/ScxC5FyLBnVQ0KhCwyA3s8P3r6+8DH6wa9aEMKjWsAQVAfZiVvRsYEfGtStXQWT8pFqFGBgnBp3hTNRgAIUoAAFKEABClCAAhSggKcLMDDO008A108BClCAAhSgAAUoQAEKUMBJAhOXPNVCozN1qV+9xXMmmFrVDW6EsOAoJ3VnGwpQgAIUcEeBY2n7sfP4+qtLM2swY8KAmKfdca1cEwUoQAEKeK5A3o7YGdBoRnquAFdOAQrGmsAgAAAgAElEQVRQgAIUoAAFPEDAbI4xdOgoAoB4UYACFKAAwEC0ik8BfehTWsDVA92Unr+qX1PNAH4GMAnAPmvDMDCuqrfI+vNdKTDu1z+24/b7RkmClBIY9/y4DzF11k+S+tkKjFuyeiOeGjsFImCkMq5rA+MS/j6BZrfcI/nRUkN+HA2Mm7NgBR55foLkuZQobN+6GXas+d5prWldPqU1a2cHxu2NP4K2ve6XtJ/WAuPUeiYrM4wp7fwFvDzpE3y3cKUkR0eLXD0wToRxSn1t37d+Hlo1k/41oCNenIRv5y2TRPz5lP9g1KP3lqlV4tx4SmCcGvdVjTOVHDglzpqkg1+FRSlp6fh4xk9IOXYO1UOqQ28JjTPC2/fKL/+gYDRsfSMMAaE4f3gLbqirR5P64VU4MR+tFgEGxqllJzgHBShAAQpQgAIUoAAFKEABClDgXwEGxvE0UIACFKAABShAAQpQgAIUoIBTBN5aNnIsgPdKmjWo0Qrt6/d0Sm82oQAFKEAB9xTYdWIDElPLfH/Ns+MHxnzmnqvlqihAAQpQwFMF8nfG3m+GZq6nrp/rpgAFKEABClCAAp4goIH5AZ/2HX/0hLVyjRSgAAUkCDAQrWIk+tCntIDSgWuu3l/CS45iJeLPdm8DOGjtCfEzIcLkrl7N+scpNggbyxdwpcC4TVt34ta7pf28ESmBcWPfnooPps+RhFZRYNx7n8/Gq5OnSerjrKJrA+PkBLsF+BuRdXiTpFHk9H3ywcGI+eCNMn0/iZmLlyZ+LOlZShU5OzBOjgmtAWcHxh05dhJNutwt6bhYC4xT65msrDCmsynncGO/4Th9NlWSoTOKXDkwzmw2QxveUTJD4talaFAvTHL9s2+8j8++mS+p/q1Xnsa4F58oU6vEuZETWiY1fFTSAsspumPYaKzbtE1Si9R961CjerDNWjXuqxpnKg2pxFmzuVEqKBCvlf+buQBpJ86hRnB1aK6GxvnC29cXQaG1ENG0DfyrhyM5fgva1gZaNIpQweQcQQ0CmrBofi+6GjaCM1CAAhSgAAUoQAEKUIACFKAABS7/xDP+JZ3HgAIUoAAFKEABClCAAhSgAAWcIvDfpU/0NGu0v5U0CzLWQK+W0n76qVMGYBMKUIACFHA5gQ3x85Gek/zv3GZz9/F3zvzd5RbCgSlAAQpQgALlCJgBzdYxz/QJjIxcee5wAjqMeBx6g4FeFKAABShAAQpQgAJuJuBjQg1NdPQ5N1sWl0MBClDAXgEGolUsRx/6lBZw9UA3pee393XIkfvED354C0BCRU0YGOcIsfL3ulJg3Ja4vehy52OSUKQExr3+zud4Z9o3kvqVFxj3w8+r8NCYcZJ6OLPo2sC4hL9PoNkt90h+hDlJWnCjnHA0a4Fx//3kK4z/4EvJcylR6OzAOFqXv0vWrJ0dGCfCeyI69JN0VKwFxqn1TFZGGFN2Ti663zMSO/cdkuTnrCJXDowrKiqGV71OkilO71yJ8No1Jdc/OPpNzF28WlL9tMljMWbE0DK1SpybqM6DkHjijKSZXDUwTo37qsaZSh8CJc6apEOmgqJjJ5Pwv68WIuvMBYQEh0AbEACd0Qhvgw98/AMQEFwd9Zq0QnBYFE4d2ILWNQrRpkmkCibnCFUtwMC4qt4BPp8CFKAABShAAQpQgAIUoAAFKPCvAAPjeBooQAEKUIACFKAABShAAQpQwCkCH89/0TfH92JuSTMNNLgrejS0Gq1T+rMJBShAAQq4n8CSHdNRbCq6ujBTUUHwxMHfZrjfSrkiClCAAhTwVIE1Q+7aaAZuLVl/4zt6Izy6o6dycN0UoAAFKEABClDAPQU02GBoF93TPRfHVVGAAhSgAAUoQAFFBB4B8K0ine1ragIw7/IPYp9kKyiupD0D4+yDrqy7XCkwbuuOfeg8cIQkGimBcW+8Ox1Tpn4tqZ+1wLjzFzIR2rKXpPvLK6oZGoLIurVRPyIMC5b9KrnXtYFxSSlpCG/XV/L9l45tgcHH22a9o4Fx076eh+fe/MDmc5QscHZgHK3L363KCIyT428tME6tZ7IywpjkhGSWt8tNGtZDZN06yMjKRuzug5LedV05ME4sUBMWLWmdoujIlsVoVD9Ccr2cQMUPxj2P/3vm4TK9lTg3TbvejcOJJyWtwVUD49S6r5521iQdMpUUnU09h6++X46EvX+jVlAoNP5+0Pn5wlsExxl9YQyohtqRUQhv0g7nU5Lge+EAenSIgq/BRyUr4BhVIcDAuKpQ5zMpQAEKUIACFKAABShAAQpQgALWBRgYx5NBAQpQgAIUoAAFKEABClCAAk4TeGvZSPHjOpuWNOzRfChC/Gs7rT8bUYACFKCA+wgUFRcg7tivSM06ZSoszhfpomfGD4yp6z4r5EooQAEKUIACwJohd00wAxNLLALqhKHDiMdIQwEKUIACFKAABSjgXgLPG9pHT3WvJXE1FKAABShAAQpQQDEB8W9CCQAaKfYE6Y1FUNwCABOkBsWVtGZgnHTkqqhkYJz9gXEfz/gBL0/6RNa23dShNfr36goRZBXdtgUC/I1X7+/30HNYtX6LpH7XBsZlZuUgqFl3SfeKoqRdq1GnVqjNekcD435csgYPjHrD5nNEgQjPe/nphyTVyilqEBGGewfeJueWCmtpXT6PNWs5gVixq+ZY3i8quhwNjFPrmVQi+Ku0Y+6lPNRueweyc67+fFub7xPi9WnYoN7oeuMN6Bzd2hKEptFc+ba6bTv346YBj9rsIQpcPTAuokM/nD6bKmmtu3+di7YtmkiqFUXDnxuP7xaulFQ/6+PxeGzYnWVqlTg3rhwYl7J3reVjiZRLjfuqxplKLJU4a1L2SU01x08lY8Z3S5B8NBXBgYGW0Di9nxFevj7wMvjBGOCPmhH1Ua9pB1zIyIQmbQ+6tw5DcLVANS2Ds1SiAAPjKhGbj6IABShAAQpQgAIUoAAFKEABCtgQYGAcjwgFKEABClCAAhSgAAUoQAEKOE3graUjv4MGV7/S8YbIWxFVs63T+rMRBShAAQq4oYDZPH9R3GdTNBpEjRsw42c3XCGXRAEKUIACHizw6z33NCzSFP9dmqDTqNHwDQr2YBUunQIUoAAFKEABCriXQDG04X7t2ye516q4GgpQgAIUoAAFKKCYwHAAsxXrLq3x5Z/xgIWXS8UPejgo7RbrVeakONGLl8oEGBhnf2CcnGAXse1ffTTOEvZTErh07VFwJDCuuNgEfcSNkk/Xgpj3MGRAL5v1jgbGrf19K3rfP8bmc0RBz64d8dv8LyTVVmURreXpqy0wTq1nUukwJjlBeWKHb+/WyfKaVS/c+g+/rYrAuCYN6yFhs/wvk3H0DN7Ybzhid0v7I9Dirz/EXX2kh3d2HfQ4/ozdI+mdylpvJc6NKwfGnd29BrVrVpfkqcZ9VeNMJZhKnDVJG6WyooS/T+DLb5cgJyUbAUY/aAICoPf1gd5ggJfBF8aAAFSvUxcRzdohr9CM/DP70THSC/UjwlS2Eo6jpMChFdFl2jd/EvyedCXB2ZsCFKAABShAAQpQgAIUoAAFKCBBgH85l4DEEgpQgAIUoAAFKEABClCAAhSQJvDW8pEvwIyrP2q4VrVIdG0ySNrNrKIABShAAc8U0GiealmrZYxnLp6rpgAFKEABTxBYPeSuPwHcXLLWyC5d0eBW6d9Y4QlGXCMFKEABClCAAhRwWQENFhvaRd/tsvNzcApQgAIUoAAFKFC5AloACQAaVe5jrz5NhLuJVJbxjgbFlXRkYFwV7aSNxzIwzr7AuKzsi6jW9FbJm7rqh6no0+Pqp76t3udIYJxo2LL7UBw8nChpJhEGtfanz23WOhoYJ+d+MUxu4p/wNfjYnKuqC2gtfQccDeu69klJKWkIb9dX0gBdOrbF5l9mlalV65lUOozptSmf4d3PvpXkdlOH1vjzl1nQasUfRaxfnhQYd/fjr2Dxqg2S7P479hm8+cLjkmrNZrPl40h2Tq6k+r+WfQOxN6UvJc6NKwfGndm1CmG1akjyVOO+qnGmEkwlzpqkjVJh0c4DRzDzm19gysq3BMRpjL7Q+1wJjfM2+MDHzx8hteogokkbmH2DkXXqIBr5pKBju1YqXA1HUkKAgXFKqLInBShAAQpQgAIUoAAFKEABClDAMQEGxjnmx7spQAEKUIACFKAABShAAQpQoJTAxF8eb6TV6o6U/CfxE4T7t30CPl6+dKIABShAAQpYFTB5meu1rt76FHkoQAEKUIAC7iqw+p7BT0Nj/qJkfV5GIzqPeQ5avd5dl8x1UYACFKAABShAAQ8S0A4ytG+/1IMWzKVSgAIUoAAFKEABRwQeBjDHkQZ23iuC4pYAmABgn509rN7GwDhnajqvFwPj7AuMS/j7BJrdco/kjcg7vgU+3t4V1jsaGDfixUn4dt4yyTPtXPsD2rVqWmG9nHCtJx8cjJgP3ijTr6ioGCEtekgORPp1/nT06nqj5DVUVSGtpcurLTBOrWdS6TCmh58dj+8XrZS0ce+/+RxeGTW8wtqqCIyrGRqClL1rJa2hdJGjZ/CTmLl4aeLHkp7boklD7N8wD+LrQG1d6zfHotfQZ2yVXX37pWNbYPAp+3FEiXPjyoFxh/5YhKZRkZJM1bivapypBFOJsyZpo1RYZDKZ8NfOA/jq26XwK9ZD52uAzugLnQiN+yc4zmA0Iii0BsIaNYdfaH1cSD6JoOz96H1LexWuiCM5W4CBcc4WZT8KUIACFKAABShAAQpQgAIUoIDjArY/Y+v4M9iBAhSgAAUoQAEKUIACFKAABTxI4K1lT8UB5g4lS24TcQsa127nQQJcKgUoQAEKSBbQmDe1rNVa+o+pl9yYhRSggIcKTJS5brn1MtuznAJXBDbcdVdQvh4XSntE9eyFiJs6k4gCFKAABShAAQpQwAUFzh1OQEFODsI7dNjv0y66tQsugSNTgAIUoAAFKECBqhDQAkgA0KiSH/4LgHHODoorWQMD4yp5NyU+joFx9gXGbfgzDj3vfVqScvebO2DDwhk2ax0NjPvfVz/ihfEf2XxOSUGAvxFLv/0EYj5rlwgzeubVd3A48aSkntYC48SNQ596FQuW/SqpR906NbFt5WyE1aohqb68org9ByFCok6cTsbvP8egWaP6DvW79mZa/ytiy9rRsK5r7ZNS0hDerq+k/ezSsS02/zLrulo1nkmlw5i6DX4Sf2zbJclt46IY3Nq54lAjZwXGmc1maMM7SppLFCXtWo06tUIl14tCR89g7O6DuLFfxQF6pQdav+BL9OgSbXNGOa/55X0cUeLcuHJg3IKY9zBkQC+b9qJAjfuqxplKMJU4a5I2SqVFxcUmrPsjDt/NXo5ggx90RgO0BsOVwDhvb+h8vOFjNMI/KBh16jdGaGQLZGdnw5QUhx5tIxASFKjSlXEsZwgwMM4ZiuxBAQpQgAIUoAAFKEABClCAAhRwrgAD45zryW4UoAAFKEABClCAAhSgAAU8XuC/S0e+aNbg6o+grGYMxW0tH/B4FwJQgAIUoIAVAbPm2ZZ1Wn5GGwpQgAJOEjDL7MN/I5EJxnL7BVYPGTwTMD9R0sHLaMRNo8ZA5+1tf1PeSQEKUIACFKAABShQ6QJmkwlbP5+G/Oxs6Ly9MooLCyf6+AXN6PHtt3mVPgwfSAEKUIACFKAABVxL4MHLYXHfV+LIywGMByAtSUbmYPEzUebz0c36x8nswHIlBRgYZ19g3Kr1WyDCfqRcTRrWQ8Lmn22Wygl0yj6yCf5+xjI99xw8jBtuk/81R4/eNxAd2jS3hKrl5xfg4OFErN74F0RgnJyrvMC4mT8sxshXJktu1aZ5Y/yx5CsEBvhJvqekUISJvTP1G3z2zfyr925d/i06tW8lu1dFN9AakGrtaFjXtfvgjMA4NZ5JpcOYOvR+CDv3HZL0frBw5vu4p3/PCmvlhGa+/txjmPzqqHL7BTbphuycXEmzyQkEK2no6BksLCyCd+RNkuYTRYN634ol31Qc3rkv/ija9Bomued/xz6DN194/Lp6Jc6N2gLj5AQ8jhkxFNMmj5XkqsZ9VeNMJZhKnDVJG6XiIrFf703/AfG7DiPENxBaoy+0Bh9ovfSW0Di9wQAvHwOMgYGoGRaBOlGtUagzIvNYHG6oWYiWzSo7G1zFmG46miYsml9n5aZ7y2VRgAIUoAAFKEABClCAAhSggOsJ8C/prrdnnJgCFKAABShAAQpQgAIUoICqBSb/8kStYq32LICrf+e8vdVDCPQNUfXcHI4CFKAABSpdwFxsMFVvE9TmQqU/mQ+kAAXcVYCBce66s26wrnX3DqhXbNYfAXA1Ia5B9x6IvLmLG6yOS6AABShAAQpQgAKeI3B6+3Yc/XVt6QUXF+s0LfrPW3zYcxS4UgpQgAIUoAAFKCBbQAsg4XJgXGUkCKwAME6poLiSlTMwTvYZUOQGEQKWX1B4Xe9hT7+Gw4knJT2zYWQ4RJDQtZePtxdaNGlotUdU50FIPHFGUn9zUsVhglt37EPngSMk9Xp4SD/MmfpWhbVvvDsdU6baFxiX8PcJNLvlHkmziKL1C75Ejy7RVusvZGbh+XEf4ruFKyX3sxYYJ25u2+t+7I0Xn16v/Ku8wLj0jCzUv3GA5EAoMbk4T/8Z/QiGDOgFo6+hwsWYTCZs33UA85auw6cz515Xq0RgHK2lWzsa1nXthjojME6NZ1LpMKbhz42X/Bpze7dOWPn9VOj1Oqvve8vX/YEHRr8h+X3aVmBcRId+OH02VdKLVoC/EbGrvkPTqEir9eJ8+BuNZQInnXEGX5r4MT6Juf71pbyhRz16Lz6Z9BK8vbyuK9m1PwF3PvKi5DWLBse2LUX9iLDreilxbtQWGCcCR0XIo9Rr/ox3ce/A26yWn7+QCfExo0b1YMvb1bivapxJWClx1qTuqZrrxGvX/2IWIjUxGYF+ftD4GqDz8YbW2xs6Ly/LLy8fHxgCAhAUWhNhUS0QUKcRzhzejXYhGWjZKBJarfjrHy93FGBgnDvuKtdEAQpQgAIUoAAFKEABClCAAq4qwMA4V905zk0BClCAAhSgAAUoQAEKUEDFAm8tf3ItzJrbxYi1g+qjbUQ3+BuCVDwxR6MABShAgcoW0EAzq0Xtlk9U9nP5PApQwK0FGBjn1tvr+otbfc9d06HBMyUrET+Bu/OY56Dzvpoh5/qL5AooQAEKUIACFKCAGwsUFxZi62dTUXjp0tVVaqCZ0Xvh4qfdeNlcGgUoQAEKUIACFHCGwAMAfnBGowp6rAIwHkDF6VxOGoKBcU6CdKBN7qU8+EV1daCD7Vsv/r3ZarCXuwbGFRUVw6teJ9sw/1SIkKP333we9w26HcHVAlFcbELC38exJW4vRHBd6rl0yb1EYXmBcZ9/Mx9j3rg+1E9WczuLywuME+3e/exbvDblM9mdhZvo26xRfdSpGYraNatbglXOppzDmeRUxB85jrmLV1fop1RgHK2v305r1s4I6yr9JGcExqnxTCodxvTB9DkY+/ZUye+D9/TvibGjHkF02+aW9zkRbLlj7yF8v2glZs9fLrmPKLQVGNd10OP4M3aP5J41Q0MwdtRwdOnYFj4+3jh5Jhl7DhzGohXrLYGZYvbS4abOOIPiGZEdB0ieURTe0qkdXnrqQbRp3tjy2nUgIREbtsThPzL2QfSpKABViXOjtsC4SR/FYOLlX3IuETjau3tnVA+phuTU8xChuas3/IU1G/+ytCkJqFXjvqpxJmGmxFmTs6dqrt114DC+/HoxTJn58PExQGs0XAmM0+uhFb+8veHl7Q1vHwP8g4NRp0Fj1G50A04f2Yf6PucQ3bIBfH34NRBq3mN7Z2NgnL1yvI8CFKAABShAAQpQgAIUoAAFKOB8AQbGOd+UHSlAAQpQgAIUoAAFKEABCni8wKSlTz6s1WgGtqh745JmdW5S+gu/Pd6bABSgAAVcUUCvx41NQ1vFuuLsnJkCFFCtAAPjVLs1HEwILLt3QLiXWX+6tEbbBx5EcP0GBKIABShAAQpQgAIUcAGBY79vxIk/N5eZtFjjVaf/ggXJLjA+R6QABShAAQpQgAJVJaAFkACgkUIDrAYwrrKC4krWwMA4hXZTRlsGxlnHEkFtU6Z+LUly0Vfv4+5+PcvU3thvOGJ3H5R0f+kiEYKWnZMr+77SN5QXGCf6trh1CE6fTXWovz03VxQYl3MxF2Ht+ji8bnvmUiowjtbX74YrBcap7UwqHca0av0W9HvoOXveheDoa5atwDgRJilCJZ11Dep9K5Z889HVds4IjBPNHn/pLXz901JnjSm5z57ffrSEzlm7lDg3aguMc+TslodcEhin1n31lLMm+Z3ABQqXr9+CRQvXw6cA0PsaoPXxhs7bCxqdDhovPXR6L+i9vKD39oIxIBA1IyIR1b4nks4cR2DOEXRpFYGgQH8XWClHlCPAwDg5WqylAAUoQAEKUIACFKAABShAAQooK8DAOGV92Z0CFKAABShAAQpQgAIUoIDHCxxI3rcY0Nzl8RAEoAAFKECBUgLmJS1rtx5MEgpQgAJOFmBgnJNB2c75AqvvuetTaPC83mA41n74ow2MoaHOfwg7UoACFKAABShAAQo4XSA3PR1xM2fAVFxcuvd/+yxcMt7pD2NDClCAAhSgAAUo4F4C9wOYq8CS1gKYAGCrAr0ltzQnxcn9vLTk3iysWICBcdZ9HA2Me+7NDzDt63lVcvzKC4wTw6zesAV9H7QvGMqRxVQUGCf6btyyAz2GPOXII+y6V6nAOFpfvx2uFBintjOpRPBX6R1KSklDeLu+dr0POXqTrcC4Fb9uxoDhLzj6mKv3KxUYl56Rhba9hlVqIOek/3sK4196slwbJc6N2gLjhHv1FmUDWx09LKUD49S4r2qcSYmz5ug+qu3+L75bgj837EQ1vQ80IjTO2wtavf5KaJxehMbpLMFxOi89fIz+CG/YGA3adkZGVg5MSTvQrXU4alYPVtuyOI8dAodWRJe5q/mT4Pek2+HIWyhAAQpQgAIUoAAFKEABClCAAs4U4F/OnanJXhSgAAUoQAEKUIACFKAABShwncChlP1dis3YTBoKUIACFKBAiYBOg67NarX6kyIUoAAFnCwg9xvz+G8kTt4AtrMtsGbw4JrQ47HeCxa/m79rx3dms/kh23exggIUoAAFKEABClCgqgViZ87AxbS00mOk5heYGg1aujS7qmfj8ylAAQpQgAIUoICKBbQAEgA0cuKMvwIYV9VBcSXrYWCcE3dWZisGxlkHczQwLjn1PJp0HYzsnFyZO+J4eUWBcaL7/731KT768nvHHySjg63AONHqp1/W4v5nXpfR1fFSJQPjaF12f1wtME5NZ7IywpjGvf8F3v50luPvVDI72AqMKyoqRoNOA50WxKZUYJxYduzug7ix33CZAvaV9+7eGSu++x90OvFHROuXEudGbYFxYuUjXpyEb+ctsw/Syl2lA+PUuK9qnEmJs+a0DVVJo0t5+Zj00WycPXIaAb5GaAw+0Oh1V0LjtFpLcJz431qdDjpvbxj8/BFcsxYat78ZOm8fZB3ejO431EdoSJBKVsQx7BVgYJy9cryPAhSgAAUoQAEKUIACFKAABSignAC/GUo5W3amAAUoQAEKUIACFKAABShAgX8EDqYcmGM2mx8mCAUoQAEKeK5AwtkdqBkYgRD/Wt+1qNWycr7a1HO5uXIKeKoAA+M8dedddN0Xd+4M08O83wwzf6y2i+4hx6YABShAAQpQwDME/v5tHU5t21ZmsRrgud4Ll0zzDAGukgIUoAAFKEABCtgtMAzAj3bfXfbG9QAmAOr6YXUMjHPS7trRhoFx1tEcDYwTXb9ftBIPPzvejl1x7BZbgXGie2WHQ40ZMRTTJo+1ubCZPyzGyFcm26xzVoHSgXG0/nenXDEwTkyvhjNZGWFMefkFaNtrGA4nnnTWu5ekPrYC40STOQtW4JHnxR9dHL+UDIwT01VG8GX71s2weu401Khe8T+LKnFu1BgYd+jocTTvNsTxw/FPh2sD49S2ryULdfez5rQNVVGjhL9P4INpP0KTlQ8fX1/AxxtanRYaS1DcP6FxWh10ev2VMEiNBgHBwWjRqRt8/QwoPLMXPTu1hK/BR0Wr4ihyBRgYJ1eM9RSgAAUoQAEKUIACFKAABShAAeUFGBinvDGfQAEKUIACFKAABShAAQpQwOMF9qXsi9KaNUcA8O+hHn8aCEABCniiQErmCWw+/Itl6d56w+K8SwVPT7z7y1RPtOCaKUABRQUYGKcoL5srIVCwM3a4CZrZSvRmTwpQgAIUoAAFKEABxwUyTp7E7u/nlG2kQezWljfcNHHiRJPjT2AHClCAAhSgAAUo4LYC4msDDl/Onmrk4Ao3A3hNbUFxJWtiYJyDu+vA7QyMs47njMA4s9mM/g8/j1XrtziwQ8Bjw+5E4skz2Lhlh6Q+UgLjRCMRJvTcmx8i9Vy6pL4lRQ0jw/HZ5P8gLz8fdz/+iqR7J748EhNeHimpdl/8UQx75nUcPJwoqd6RosoIjKP1lR1y1cA4MXtVn0klgr+svd9s3bEPnQeOcORdCgH+Rox74QmMfXuqpD5SAuNEo2defQdfzlkkqWdFRUoHxolnb4nbiz4PjEF2Tq7D817b4N6Bt+HbTyfC6Guw2VuJc6PGwDgB4czwNGuBcWra19Ib785nzeYBd9GCxWv+wMJ561BN5w2NrwFaby9odDpodTpotBrotFoU5RfAx6CHt7cXcnMvQaM34OY+faBFHur6FyO6dVMXXT3HFgIMjOM5oAAFKEABClCAAhSgAAUoQAEKqE+A36ivvj3hRBSgAAUoQAEKUIACFKAABdxSYH/y/lc1wDtuuTguigIUoAAFyhUoLC7A2n3fIa/wYuma2PEDY24kGwUoQAEnCzAwzsmgbFc5Ank7YmdAo5H2HWeVMxKfQtcv4YkAACAASURBVAEKUIACFKAABSgAoCgvD9tnfImCizmlPTIKNUWtBi5YfoZIFKAABShAAQpQgAIVCgwBsMABoz8u3zsRwHoHeih2a/xMlPl8dLP+cYo9i42tCzAwzrqLMwLjRGfh++Z70/FJzFzZR1AEL01/51U8dE8/9HvoOcnBc1ID40rmi/n+Z8xbug4iLKqiSwTFPX7/ILzw5AOWsKLp3y7A6Nffk7SuzyaPxegRQyXViqJLefmY9vU8fPTl97ID7aQ8pEWThhj96L149L6BkoKXpPS0VSPOAq3LhlyNeu1dfDF7oS06y9tjV81BdNsWFdYmpaQhvF1fSf26dGyLzb/MklRb1WdSieCv8hb+Z+weDH9uPBJPyP90zT39e2Lq26/g1JkU3DTgUUm2UgPjioqK8f702RCvzY5clREYJ+Y7cfosXvnv/7Bg2a+OjHv1XvHxYMprYzDqkSHQarWSeipxbtQaGCdAflnzOx5+dpzDQX3lBcapZV+v3Xx3PWuSDrmLFn3wxVzs2HIAof7+gK8vtHrdldA4DWAuLESt2kG4ucsNqFWzOpKTz+GvzbtwLvMSeg24A+dST+Ce226Gn5+vi66eY5cIaMKi+b3oPA4UoAAFKEABClCAAhSgAAUoQAGVCPAv6SrZCI5BAQpQgAIUoAAFKEABClDAEwQOpOz7HWZNN09YK9dIAQpQgAJXBLb/vRqn0g+X5igqNpk6TBr01V4aUYACFHCyAAPjnAzKdpUncGln3DYNwDDVyiPnkyhAAQpQgAIUoIBNAVNxMeKXLEZawqF/azWavn0WLF5t82YWUIACFKAABShAAc8WEF+fvh9AxQk51o02A5ig1qC4kpEZGFf1Bzy/oACG+jcrOsilY1tg8PG+7hlte92PvfFHbD5bBOVkHd5UYZ3oI/pJuZ55ZIgliK2ia8rUryUHE636YSr69KjY8I9tu/DAqDdw+myqzRHFep8ePgT/Gf0IqgdXs9Tf9/RrmL90nc17RUFu4p/wNfhIqi1dlHouHbsPHLYEtKWdz0DOxVyEBAWids1QtGoWhaZRkWV6vjp5Gt77fLak5/z4xRQMG3SHpNrSRSIkavmvf1iC1lat3yL7/tI3tGneGANuvwV39emOjjfY87Lq0OPL3EzrKxxj356KD6bPkQR7YON8iKC/iq7zFzIR2rKXpH69u3fG6rnTJNVW9Zlcvu4PDHzkRUmzznj/dYx86G5JteUVXcy9hNff+RxTZ/0kqc8tndpZXq/639bVUr8v/ija9Bom6d4pr43Ga8+OkFQrig4dPY6ZPyy2/MrOyZV8nygUr63PPjYMk18ddfU+Z5/BawcSH5ve/nSW3cFxNUNDMHbUcDz18N3w9zPKWq8S56ZD74ewc1+pz+9VMFHe8S3w8b7+Y7+sRcgsPpeegW/nLcMXcxbaFXrY/eYO2LBwhs2nVuW+ljdcVc6kxFmzuQkuXCACZN+fPhdH9iQixD8AGj8jdHodYCpC8xb1ce/gnggO9L+6QvHnsdfGfY6AasFo2DQcLRuHo3OHNi4swNGFAAPjeA4oQAEKUIACFKAABShAAQpQgALqEWBgnHr2gpNQgAIUoAAFKEABClCAAhRwe4GElP1ti8zYJf7d2O0XywVSgAIUoACSMhLx15HlZSQ0Zrw57s6YyeShAAUooIAAA+MUQGXLyhHI37ejOQrxpxnm4Mp5Ip9CAQpQgAIUoAAFKCBFQAPNhe0xX3x98dy5FzVm80e9F/0yVsp9rKEABShAAQpQgAIeLjAUwDyZBiJRSQTF/SrzviopZ2BclbDzoVUkIAJCYncfxP5DR7Hn4BHs2BuPjKxs+BuNCAkOtIRh9era0RK6VNkhO3JJRHhJWLs+kgObNi6Kwa2d28t9TJl6ER538Egi4vbEY8+Bw0jPyMKFzCyIkLCCgkLodFpLfUhQNQQG+FnC9lo1jcINrZqidbNGlv/mihet1btr7n4mjx4/hV37ErDv0FFLSFj8kWPQ63SoFuiPeuG10b51M9w78DY0blCv0jdJ2B85dtLyWnrs5BlkZV9EVs5FS8ileE0QIaUB/n6oWT0YkXXroHXzRpaZNZqq+VJL8X68ded+rN8ci7g9B5GSlo7ktPOWgM6Sq2FkOERAnJize+cOltfM5o0bVNnMlb6pTn7gyTPJlvBCETKYmZ2DzKwcy8cBESrn7eUFo6/B4h1WO9TyMaJJw0joRWiXjEuN+6rGmWSQekzpqbOpeOuDr4GsAvj5B0Br8IbRzwsPP9gHTRuWfU0Vr8UfTF8Io8YLgUE+aNeusSX8lpdrCzAwzrX3j9NTgAIUoAAFKEABClCAAhSggHsJVM1njd3LkKuhAAUoQAEKUIACFKAABShAARkCB1IOjITZbPtHGsroyVIKUIACFFCfwIWLqdh0aBGKTIWlhtPsMO2oc+PEiRNN6puYE1GAAm4gMFHmGuTWy2zPcgrIE8jbubMvYFop7y5WU4ACFKAABShAAQooK6DtZ2jfftWqewd37btg8WZln8XuFKAABShAAQpQwC0ExNem7wfQQuJqtv4TFLdWYr0qyhgYp4pt4BAUkC3wwfQ5GPv2VMn35Sb+CV+Dj+R6Fv4rQGueBgq4r4DZbEZxsUl2UJn7irjHytS4r2qcyT122/5VxO09hPf/9wNqeBlh8PeFV6Av+vXuiG6d/g3YTU49j09n/wyTPgTm1FRk5qRj0KDu6H9bF/sfzDtVIcDAOFVsA4egAAUoQAEKUIACFKAABShAAQpYBBgYx4NAAQpQgAIUoAAFKEABClCAApUucCD5wKdZl849v+PYr+jYsDf8DUGVPgMfSAEKUIACyglk5p7D74cWorC4oPRD8jQ6tBrXL+Zv5Z7MzhSgAAUoQAHXFijYGTvcBM1ssYqivDzoDQbXXhCnpwAFKEABClCAAi4soIX5Ee/2Hee48BI4OgUoQAEKUIACFKgKgXsvh8XNl/DgbZdrxA/1WC2hVrUl5qQ4s2qH42AUcDOBUa+9i9//2okObZrh/rv6oGfXaPh4e0te5S9rfsddI16WXN//tq5YPudTyfXuVEhrd9pNroUCFKAABSjgPAGTyYRVG7Yh5uvFiAgIQkCt6vCrEYCWjcPRKLIuTpxJw87DJ2HyrQ7vvHyc3bcXWSjE26+PRER4LecNwk6VKnBoRXSZ5zV/kt+TXqkbwIdRgAIUoAAFKEABClCAAhSgAAWsCDAwjseCAhSgAAUoQAEKUIACFKAABSpdYNLSpwbptdpFxeZincHLD92b3ws/n8BKn4MPpAAFKEAB5wtkX0rHxkMLUVCUV7q5SWM2Dxp358zlzn8iO1KAAhSgAAXcSyBvZ9wz2WeTpu+Z+wMiu96CiE43udcCuRoKUIACFKAABSjgGgKjDO2jv3CNUTklBShAAQpQgAIUUI2A+Lr0/ZcD41pUMFHs5beJoLiVqpnagUEYGOcAHm+lgEyBroMex5+xe8rc9cDgPuh+cwdEt22BFk0aXBcgd/xUEmJ3H8SX3y3C+s3i5Uf69dnksRg9Yqj0G9yoktZutJlcCgUoQAEKUMDJAvkFBVi27k/M+W4F6lcPRfWG9eFduzbyYAC0OgQGBECTkYYTsVtxJi0Vdw+9HUMH9IJer3PyJGxXWQIMjKssaT6HAhSgAAUoQAEKUIACFKAABSggXYCBcdKtWEkBClCAAhSgAAUoQAEKUIACThB4a+mTHaHRbC/dytfb3xIaZ/QOcMIT2IICFKAABapKIDvvAn6PX4j8oktlRjCbNU9MuHPGrKqai8+lAAUoQAEKuJLAmiGDboZWt95sMvmIucPad0CT3n0ADf9Zz5X2kbNSgAIUoAAFKODSAgyLc+nt4/AUoAAFKEABClShwD0AFpbz/B2X/7sIinOrHy7EwLgqPG18tMcJWAsxuxYhwN+IGtWDUVBQiNNnU+02En2ObVuG6sHV7O7hyjfS2pV3j7NTgAIUoAAFlBcQoXG//rEDs79fikC9L2pHNoBvrboAzLiUmoy0k4lIzc7AbbffhOH39oP4sxUv1xVgYJzr7h0npwAFKEABClCAAhSgAAUoQAH3FeB3lrjv3nJlFKAABShAAQpQgAIUoAAFVCswafnItzVmvFF6QD+fQNza7B74MjROtfvGwShAAQrYEhCBcRsOzkdhcf7VUo0Zb467M2ayrXv5dgpQgAIUoAAFgA2PPmrIz8k4CaBGaY/qjRuj5eB7oNXryUQBClCAAhSgAAUooKCAFuZHvNt3nKPgI/6fvXsP0qus7wD+O5vb5kq5k0DaImgSAlhhAS+jHUZsiSExAdRaq6U6UbSt0kr/sRq2OFOdeumFqRfSsVbpdDrcghAMRcdWlBFcQC6aoGDLxXAp0RhCCCS7pywU2M1lec/us/s+7zmfzDCIPOd3nt/n9+x58+7m/aI0AQIECBAgQKCuAoN/Jv2uiDhmtwZve+afB4Pivl7HxgXG1XGqespVoJUQs1R7/8zq8+Ij5/5BqnIdV4d1x43MhgkQIECAwIQL7NrVHxvvvT8uufQb8ZMf/XdMjSLKsoydUcZBhx8UZy0/NV5/8itjxvTuCd+bG6YVEBiX1lM1AgQIECBAgAABAgQIECCQQkBgXApFNQgQIECAAAECBAgQIECgssCF17z/01GW5w+9sHvKjHj10UvjwFlzK9dzAQECBAjkIfDo4w/cdsPGKwc/DDQtovzi6mVrPpDHzuyCAAECBAh0hsD6s5YtjqLrmxHFYUN3PHvu3Dj+7e+IKTP817c7Y5J2SYAAAQIECHSSQBHFL8so3tl9wgnf6KR92ysBAgQIECBAICOBMyPi8iH7+eEz/3swKO6qjPaYbCsb1kQ5tNjCpX3JaitEgMDeBSYqxOyIuYfET753ZUzvntbYUbBu7Og1ToAAAQITKbCrP2LypIm8Y/J7DQbE7XhqZ/zsvgfjljvujjLKWPCy34jFC46MmTO6o6urK/k9FWyfQDGvx2fR28fvzgQIECBAgAABAgQIECBAYJiAN+kOBAECBAgQIECAAAECBAi0TWBvoXFFUcTiw18bC+ae2LZ9uTEBAgQIjFagXPvkoU+97RvXrnljDJTnfOyMi99RFMM/MDPayq4jQIAAAQJNErj2zDOP6Ooq10eUi4f2PXXmzFiwdFkcePTRTeLQKwECBAgQIEAgucCDN98UW+67L45969sGv3Fxc9eU4pxpx524IfmNFCRAgAABAgQINENg8M+j3xURg/9BoTueaXkwKG5tRH1/RiQwrhkHW5d5CUxEiNnsWTPi+9d8JY55xcvyan6Cd8N6gsHdjgABAgQIECDQAQIC4zpgSLZIgAABAgQIECBAgAABAo0REBjXmFFrlAABAgQIECBAgAABAnkKXHj1+y6KiD/ZfXeHzJkfpxy1JKZO7s5z43ZFgAABArsJFH+/+LDF52EhQIAAAQIE0ghctXz57O6pXVeXEb+9e8WDFy6Ml//ukhgMkPOLAAECBAgQIECgdYHtjz0WG66+Kh5/6KFnL5p3wok3Hf/Rj7+69QpWEiBAgAABAgQI7EVgZURc+Mz/PxgUd0Wdg+Ke711gnK8DAhMvMN4hZoNhcf91xZp41bELJr65zO7IOrOB2A4BAgQIECBAIAMBgXEZDMEWCBAgQIAAAQIECBAgQIDA/wsIjHMUCBAgQIAAAQIECBAgQKDtAhde/b7BwLjB4Lhhv2ZMmxNLjj+n7fuzAQIECBAYUaCMojh38aGLL+ZEgAABAgQIpBdYf/bKf4ko37175cnd3bFw2fI46OWvSH9TFQkQIECAAAECNRT4nxu+E4N/7f6rHOg6eckVV/yghi1riQABAgQIECAwUQLHR8QdE3WzHO4jMC6HKdhD0wTGM8TsXWe/OVb/+ao4+jfnN411r/2ydgwIECBAgAABAgR2FxAY50wQIECAAAECBAgQIECAAIF8BATG5TMLOyFAgAABAgQIECBAgECjBS686txXRdfA5RFx5PMQx89/fbz8sFc12kXzBAgQyFqgKL8zOYoPLTj02Nuz3qfNESBAgACBDhdYf9bKVVGUfxcRM4a2cvzb3xEHHHVUh3dn+wQIECBAgACB8RMY2LUrNt16Szxw803x1Nate7vRtjKK9yy57MpLx28XKhMgQIAAAQIECNRVoNzUV9a1N30RyE0gdYjZ7Fkz4oN/+Nb44z96W8yfd2hu7bZ1P6zbyu/mBAgQIECAAIGsBDau6xm2n0WrwmfSs5qQzRAgQIAAAQIECBAgQIBAEwW8OW/i1PVMgAABAgQIECBAgACBTAU+df379nt6R/HPEeXKKMrvndnz4WuKiL+O8MPlTEdmWwQINERg18DOZzud3DXl+Y7LMuKjxx527KcaQqBNAgQIECDQdoHrfu8t8wf649+Ksnjd4GamzZr1w9d8+LwyypCy3fbp2AABAgQIECCQm8CuHTviwZtvip/f0hc7n3xyr9srI/6jGOg65/Qrrngot/3bDwECBAgQIECAQGcICIzrjDnZZT0Etj2xPe7YcE/ceufGuPm2u+L2H/007tjw00rNHXLQAfGaE4+L095wSrz77KUxZ/bMStc3ZTHrpkxanwQIECBAgACBlxYQGPfSRlYQIECAAAECBAgQIECAAIGJFhAYN9Hi7keAAAECBAgQIECAAAECLylw4dXv+9BAMema3jO+8LM7H7nzqEnRdUFZlu96yQstIECAAIGkAo8/+Yu499E7477HfhyvmHtiLJp3chRF8bX+GPir4w497t6kN1OMAAECBAgQaEngurPfcn4ZxQX9k4oTl/77lT/ZcWvfByLiLyLiyJYKWESAAAECBAgQqLnAI3fdGXdfuy4Gdu3aV6ePlWX82ZLL115ScwrtESBAgAABAgQIjLOAwLhxBlaeQAsC25/cEVu2Ph5bfvV4bNm6LX65ZWs8/sT2KIqIObNmxayZ058Nhvv1ww+L/feb00JFS/YlwNrZIECAAAECBAg0T0BgXPNmrmMCBAgQIECAAAECBAgQyF9AYFz+M7JDAgQIECBAgAABAgQIEIiIjY/c9br+sjw/olgBhAABAgTGT6Asy9i05Wdx76O3x/9uffCFG00qJvWfuuDMpa99+ZuuG7+7q0yAAIExCfRWvLrq+orlLScwfgLfXrHi105du3bL0DsMBscVRXywLOPY8buzygQIECBAgACB/AV+9cD9cdvXvrr3jZbxlckDxfmnXXnl5vw7sUMCBAgQIECAAIHcBQTG5T4h+yNAgAABAgQIECBAgACBsQgIjBuLnmsJECBAgAABAgQIECBAgMD4CAiMGx9XVQkQIECAAAECBAgQIEBgnATufuyuk/p3Fe8vo3xPRLzwvvbeR26PHTu3xwGzDo0DZ82LqZO7x2kHyhIgQKBeAv0Du+IX2x6Ozdseise2/Tw2b3s4dvU/vdcmyyg+ecGyL320XgK6IUCgRgJlxV78jKQimOWdIbDj1luXRzFwTpSxcuiO7/3W9TG5e3rMmXd4zDn88Jg0dWpnNGSXBAgQIECAAIGqAkVcecNn/ubk/qeePvz5S4uIS/rLgc+9+fKv31a1nPUECBAgQIAAAQIEdhfYsCaGfT964dI+SAQIECBAgAABAgQIECBAoLYCxbwef86qttPVGAECBAgQIECAAAECBAh0moA36Z02MfslQIAAAQIECBAgQIAAgWcF7thyx/6Tnpz0zugaeGuUxRuuu/OrsW3Hlhd0ZnfvHwfMOixmdx8Q3VNmxLQp02Pa5BnRPWV6TJ86myIBAgQIRMSGTTfFj39+U6sW90RRfnL1GWu+3OoF1hEgQGCCBQTGTTC42+Ut8MStt86bFANnRxErdu146tTvfvbTwzY885BDYr8j5sf0/fePqTNnxtSZs2LKjOkxZebMmDbLe6a8p2t3BAgQIECgvgL9O3fGzu3bn/trx5Ox84kn4ukntsfBCxdG93777bvxIr4dZaztj67LZp5wwqb1Z684LyI+HlF8sb+YfNHSSy99uL5qOiNAgAABAgQIEJhoAYFxEy3ufgQIECBAgAABAgQIECDQTgGBce3Ud28CBAgQIECAAAECBAgQIDBcQGCcE0GAAAECBAgQIECAAAECHS9wyQ8uet3PHr7zu602cvDsw+MNC89qdXk8uvX+uOHutS2vP2TO/Hj9gpUtr1d/ZCo+fIYK+Poa+TxU9fnpI7fFHfffMFLR/ijKa8r+rn+84C1fur7lB5uFBAgQaI+AwLj2uLtrBwh85z3nrNq+dcvFrW71sOOOj4XLlre6PB68+aa455ut/1Zh/imvjqPeeJr6+xDgM/LR4MNnqIDnz8jngQ+foQKd/vw8ZsXKOOSYxS+0VBRxVzlQ3lgU8Z9TB4rri56ex4b2e9Xy5bOfmjZt19suvfTJln/TYSEBAgQIECBAgACBFgUExrUIZRkBAgQIECBAgAABAgQI1EJAYFwtxqgJAgQIECBAgAABAgQIEKiJgMC4mgxSGwQIECBAgAABAgQIEGiywCeuef+flmX5D60aHDz7iHjDwjNbXS4w7iWkBLqNDMSHz1CBqoFuic7PYIDS0xGxrSiKzWUMPFxE130DUd7z4wdunLTxob7Ve5nS4Ies/6mrf/LnP7bi8w+0/MC0kAABAu0VEBjXXn93z1hg/Vkr/jWK+P1Wtzj3lb81sGDpGYM/R2zpZ4kP3PT9uPdb32y1fMw/5ZQ46o1vanm9+iNT8eEzVMDX18jngQ+foQKen531/Dz0uFfeuGjZskvKGPhx90DXD4uenl+1/JsJCwkQIECAAAECBAgkFhAYlxhUOQIECBAgQIAAAQIECBDIWkBgXNbjsTkCBAgQIECAAAECBAgQaJhASx/yaJiJdgkQIECAAAECBAgQIECgwwQuvPr9r4yIZRHlyRFxYkTMG7GForx+9RlrfqfVNj9xzarTyrK4vtX1EcV1q5d96fRW16s/shQfPsMFfH2NfCKq+fSuf+8BXTsnbY6IxyKKGyLiu1EO3LB6+ZoftPoMs44AAQIZCQiMy2gYtpKXwLXvXDKneGraSUWUPVEUJ0UZJ0XEr+9rl2XERUsuW/uhVrtYf/aKj0TEZ1pdH2V87vTL1w5e09Iv9Udm4sNnmICvr5EPBB8+QwQ8Pzvs+Rnx5dMvW/veln7zYBEBAgQIECBAgACBCRIoN/VV/b70BO3MbQgQIECAAAECBAgQIECAwNgFNq7rGVZk0arW/sODY7+zCgQIECBAgAABAgQIECBAgMC+BATGORsECBAgQIAAAQIECBAgUDuB3ivOPaSYOnBSV8QJA2XM7Yo4qIw4qIg4ePDvz4TL3bJ62cVntNq4wLKRpfjwGS5QLbDM+Wnv+Rm8+yeu+sCCj7/lC3e3+ky0jgABAhkLVP1gnp+RZDxMWxt/gW+uXHngrq6BV0V0HRlFOb+IODIi5pfP/r245PTLrvzLVnex/uwV50XE37a6PiI+e/pla89vdb36I0vx4bObgK+vkY8EHz4vCHh+5vr8LHcU0bW5jNgcRWwuy9jcFbG5jIEbT7/sqq+2+vsH6wgQIECAAAECBAhMhIDAuIlQdg8CBAgQIECAAAECBAgQaJeAwLh2ybsvAQIECBAgQIAAAQIECBDYt4APQzkdBAgQIECAAAECBAgQIECAAAECBAgQIECAQB0EBMbVYYp6IECAAAECBAgQIECAAAECBAgQIECAQIcICIzrkEHZJgECBAgQIECAAAECBAiMSkBg3KjYXESAAAECBAgQIECAAAECBMZVQGDcuPIqToAAAQIECBAgQIAAAQIECBAgQIAAAQIECEyQgMC4CYJ2GwIECBAgQIAAAQIECBAgQIAAAQIECBCIEBjnFBAgQIAAAQIECBAgQIBAnQUExtV5unojQIAAAQIECBAgQIAAgU4VEBjXqZOzbwIECBAgQIAAAQIECBAgQIAAAQIECBAgQGCogMA454EAAQIECBAgQIAAAQIECBAgQIAAAQIExl1gw5oY9v3ohUv7xv2ebkCAAAECBAgQIECAAAECBNolUMzr8Vn0duG7LwECBAgQIECAAAECBAgQ2E3Am3RHggABAgQIECBAgAABAgQIECBAgAABAgQIEKiDgMC4OkxRDwQIECBAgAABAgQIECBAgAABAgQIEMhcQGBc5gOyPQIECBAgQIAAAQIECBBIKiAwLimnYgQIECBAgAABAgQIECBAYEwCAuPGxOdiAgQIECBAgAABAgQIECBAgAABAgQIECBAIBMBgXGZDMI2CBAgQIAAAQIECBAgQIAAAQIECBAgUGcBgXF1nq7eCBAgQIAAAQIECBAgQGB3AYFxzgQBAgQIECBAgAABAgQIEMhHQGBcPrOwEwIECBAgQIAAAQIECBAgQIAAAQIECBAgQGD0Ar0VL626vmJ5ywkQIECAAAECBAgQIECAAAECBAgQIECgjgIC4+o4VT0RIECAAAECBAgQIECAwL4EBMY5GwQIECBAgAABAgQIECBAIB8BgXH5zMJOCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKADBMpNfWUHbNMWCRAgQIAAAQIECBAgQIDAqAQ2rusZdt2iVeEz6aOSdBEBAgQIECBAgAABAgQIEEgn4M15OkuVCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKABAgLjGjBkLRIgQIAAAQIECBAgQKDBAgLjGjx8rRMgQIAAAQIECBAgQIBAtgIC47IdjY0RIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQI4CAuNynIo9ESBAgAABAgQIECBAgEAqAYFxqSTVIUCAAAECBAgQIECAAAEC6QQExqWzVIkAAQIECBAgQIAAAQIECBAgQIAAAQIEeG0ClgAAIABJREFUCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEGiAgMK4BQ9YiAQIECBAgQIAAAQIEGiwgMK7Bw9c6AQIECBAgQIAAAQIECGQrIDAu29HYGAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjkJLBhTZRD97NwaV9O27MXAgQIECBAgAABAgQIECCQVKCY1+Oz6ElFFSNAgAABAgQIECBAgAABAqMX8CZ99HauJECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEGCQiMa9CwtUqAAAECBAgQIECAAAECITDOISBAgAABAgQIECBAgAABAvkICIzLZxZ2QoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIZCwiMy3g4tkaAAAECBAgQIECAAAECyQUExiUnVZAAAQIECBAgQIAAAQIECIxaQGDcqOlcSIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJNEhAY16Rp65UAAQIECBAgQIAAAQIEBMY5AwQIECBAgAABAgQIECBAIB8BgXH5zMJOCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKADBMpNfWUHbNMWCRAgQIAAAQIECBAgQIDAqAQ2rusZdt2iVeEz6aOSdBEBAgQIECBAgAABAgQIEEgn4M15OkuVCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKABAgLjGjBkLRIgQIAAAQIECBAgQKDBAgLjGjx8rRMgQIAAAQIECBAgQIBAtgIC47IdjY0RIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQI4CAuNynIo9ESBAgAABAgQIECBAgEAqAYFxqSTVIUCAAAECBAgQIECAAAEC6QQExqWzVIkAAQIECBAgQIAAAQIECBAgQIAAAQIECBBon0BvxVtXXV+xvOUECBAgQIAAAQIECBAgQIAAAQIECBAgUGcBgXF1nq7eCBAgQIAAAQIECBAgQEBgnDNAgAABAgQIECBAgAABAgTyExAYl99M7IgAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoLlBWvMTPSCqCWU6AAAECBAgQIECAAAECBAgQIECAAAECERvWxLDvRy9c2oeFAAECBAgQIECAAAECBAjUVqCY1+PPWdV2uhojQIAAAQIECBAgQIAAgU4T8Ca90yZmvwQIECBAgAABAgQIECBAgAABAgQIECBAgMDeBATGORcECBAgQIAAAQIECBAgQIAAAQIECBAgMO4CAuPGndgNCBAgQIAAAQIECBAgQCAjAYFxGQ3DVggQIECAAAECBAgQIECg8QIC4xp/BAAQIECAAAECBAgQIECAAAECBAgQIECAAIFaCAiMq8UYNUGAAAECBAgQIECAAAECBAgQIECAAIG8BQTG5T0fuyNAgAABAgQIECBAgACBtAIC49J6qkaAAAECBAgQIECAAAECBMYiIDBuLHquJUCAAAECBAgQIECAAAECBAgQIECAAAECBHIREBiXyyTsgwABAgQIECBAgAABAgQIECBAgAABAjUWEBhX4+FqjQABAgQIECBAgAABAgT2EBAY51AQIECAAAECBAgQIECAAIF8BATG5TMLOyFAgAABAgQIECBAgAABAgQIECBAgAABAgRGLyAwbvR2riRAgAABAgQIECBAgAABAgQIECBAgACBigLlpr6q35eueAfLCRAgQIAAAQIECBAgQIBA+wQ2rusZdvNFq8Jn0ts3DncmQIAAAQIECBAgQIAAAQLPCnhz7iAQIECAAAECBAgQIECAAAECBAgQIECAAAECdRCo+sE8PyOpw9T1QIAAAQIECBAgQIAAAQIECBAgQIAAgTYJCIxrE7zbEiBAgAABAgQIECBAgMCECAiMmxBmNyFAgAABAgQIECBAgAABApUEfBiqEpfFBAgQIECAAAECBAgQIECAAAECBAgQIECAQKYCAuMyHYxtESBAgAABAgQIECBAgAABAgQIECBAoI4CAuPqOFU9ESBAgAABAgQIECBAgMDzAgLjnAUCBAgQIECAAAECBAgQIJCfgMC4/GZiRwQIECBAgAABAgQIECBAgAABAgQIECBAgEB1AYFx1c1cQYAAAQIECBAgQIAAAQIECBAgQIAAAQKjFBAYN0o4lxEgQIAAAQIECBAgQIBARwgIjOuIMdkkAQIECBAgQIAAAQIECDRMQGBcwwauXQIECBAgQIAAAQIECBAgQIAAAQIECBAgUFMBgXE1Hay2CBAgQIAAAQIECBAgQIAAAQIECBAgkJPAhjUx7PvRC5f25bQ9eyFAgAABAgQIECBAgAABAkkFink9PoueVFQxAgQIECBAgAABAgQIECAwegFv0kdv50oCBAgQIECAAAECBAgQIECAAAECBAgQIEAgHwGBcfnMwk4IECBAgAABAgQIECBAgAABAgQIECBQWwGBcbUdrcYIECBAgAABAgQIECBAYC8CAuMcCwIECBAgQIAAAQIECBAgkI+AwLh8ZmEnBAgQIECAAAECBAgQIECAAAECBAgQIECAwOgFBMaN3s6VBAgQIECAAAECBAgQIECAAAECBAgQINCigMC4FqEsI0CAAAECBAgQIECAAIFaCAiMq8UYNUGAAAECBAgQIECAAAECNREQGFeTQWqDAAECBAgQIECAAAECBAgQIECAAAECBAg0XKC3Yv9V11csbzkBAgQIECBAgAABAgQIECBAgAABAgQI1FFAYFwdp6onAgQIECBAgAABAgQIENiXgMA4Z4MAAQIECBAgQIAAAQIECOQjIDAun1nYCQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAh0gEC5qa/sgG3aIgECBAgQIECAAAECBAgQGJXAxnU9w65btCp8Jn1Uki4iQIAAAQIECBAgQIAAAQLpBLw5T2epEgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAg0QEBgXAOGrEUCBAgQIECAAAECBAg0WEBgXIOHr3UCBAgQIECAAAECBAgQyFZAYFy2o7ExAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMhRQGBcjlOxJwIECBAgQIAAAQIECBBIJSAwLpWkOgQIECBAgAABAgQIECBAIJ2AwLh0lioRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQAMEBMY1YMhaJECAAAECBAgQIECAQIMFBMY1ePhaJ0CAAAECBAgQIECAAIFsBQTGZTsaGyNAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBnAQ2rIly6H4WLu3LaXv2QoAAAQIECBAgQIAAAQIEkgoU83p8Fj2pqGIECBAgQIAAAQIECBAgQGD0At6kj97OlQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQIAGBcQ0atlYJECBAgAABAgQIECBAIATGOQQECBAgQIAAAQIECBAgQCAfAYFx+czCTggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgY4F9BcZtXNczbNcLl/YN+2f/ns/QA+F8+PoYeh48HzwfPB9eFPB89Hz0fHxRwOuD1wevD14fnhdo9+vjolXhs+gZf5/C1ggQIECAAAECBAgQIECgWQLepDdr3rolQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgVEKCIx7Dq7dgQXuL1Bo6JewQB2BOkPPg+eD54Pnw4sCno+ej56PLwp4ffD64PVh9K8PAuNG+Q0ElxEgQIAAAQIECBAgQIAAgXEQEBg3DqhKEiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBzBHYPktv9A/X+fZRDTwOfGPZZDufD+fD18aKA54Pnw9CvB89Hz0fPR8/H5wW8Pnh98PrwokC7Xx+b805fpwQIECBAgAABAgQIECBAID8BgXH5zcSOCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKCDBNr9gX33F6gz9MtFoIpAlaHnwfPB88Hz4UUBz0fPR8/HFwW8Pnh98Prg9eF5gaqvjx30Vt1WCRAgQIAAAQIECBAgQIBA7QQExtVupBoiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAuAgLj6jJJfRAgQIAAAQIECBAgQIAAAQIECBAgQIAAgWYL9FZsv+r6iuUtJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQBoBgXFpHFUhQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE2itQVry9n5FUBLOcAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIH2CPgwVHvc3ZUAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIKyAwLq2nagQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIZCIgMC6TQdgGAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMCYBgXFj4nMxAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQK5CgiMy3Uy9kWAAAECBAgQIECAAAECBAgQIECAAAECBAhUERAYV0XLWgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEOkZAYFzHjMpGCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAgREEBMY5HgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1FJAYFwtx6opAgQIECBAgAABAgQIECBAgAABAgQIECDQOAGBcY0buYYJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQINENAYFwz5qxLAgQIECBAgAABAgQIECBAgAABAgQIECBQdwGBcXWfsP4IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQINFRAYFxDB69tAgQIECBAgAABAgQIECBAgAABAgQIECBQMwGBcTUbqHYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEHhOQGCck0CAAAECBAgQIECAAAECBAgQIECAAAECBAjUQUBgXB2mqAcCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBPYQEBjnUBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJ1EBAYV4cp6oEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgT0EBMY5FAQIECBAgAABAgQIECBAgAABAgQIECBAgEAdBATG1WGKeiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYA8BgXEOBQECBAgQIECAAAECBAgQIECAAAECBAgQIFAHgd6KTVRdX7G85QQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEgjIDAujaMqBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSC4gMC45qYIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIIyAwLo2jKgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEguIDAuOamCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSCMgMC6NoyoECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBILiAwLjmpggQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEgjIDAujaMqBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSC4gMC45qYIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIIyAwLo2jKgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEguIDAuOamCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSCMgMC6NoyoECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBILiAwLjmpggQIECBAgAABAgQIECBAgAABAgQIECBAgEAbBHor3rPq+orlLSdAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAaAYFxaRxVIUCAAAECBAgQIECAAAECBAgQIECAAAECBNorUFa8vZ+RVASznAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB9gj4MFR73N2VAAECBAgQIECAAAECBAgQIECAAAECBAgQSCsgMC6tp2oECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECGQiIDAuk0HYBgECBAgQIECAAAECBAgQIECAAAECBAgQIDAmAYFxY+JzMQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECuQoIjMt1MvZFgAABAgQIECBAgAABAgQIECBAgAABAgQIVBEQGFdFy1oCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBDpGQGBcx4zKRgkQIECAAAECBAgQIECAAAECBAgQIECAAIERBATGOR4ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNRSQGBcLceqKQIECBAgQIAAAQIECBAgQIAAAQIECBAg0DgBgXGNG7mGCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDRDQGBcM+asSwIECBAgQIAAAQIECBAgQIAAAQIECBAgUHcBgXF1n7D+CBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDRUQGBcQwevbQIECBAgQIAAAQIECBAgQIAAAQIECBAgUDMBgXE1G6h2CBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBB4TkBgnJNAgAABAgQIECBAgAABAgQIECBAgAABAgQI1EFAYFwdpqgHAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgT2EBAY51AQIECAAAECBAgQIECAAAECBAgQIECAAAECdRAQGFeHKeqBAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIE9BATGORQECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAHQQExtVhinogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGAPAYFxDgUBAgQIECBAgAABAgQIECBAgAABAgQIECBQB4Heik1UXV+xvOUECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIIyAwLo2jKgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEguIDAuOamCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSCMgMC6NoyoECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBILiAwLjmpggQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEgjIDAujaMqBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSC4gMC45qYIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIIyAwLo2jKgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEguIDAuOamCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSCMgMC6NoyoECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBILiAwLjmpggQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEgjIDAujaMqBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSC4gMC45qYIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAGwR6K96z6vqK5S0nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAGgGBcWkcVSFAgAABAgQIECBAgAABAgQIECBAgAABAgTaK1BWvL2fkVQEs5w1RP+WAAAgAElEQVQAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgfYI+DBUe9zdlQABAgQIECBAgAABAgQIECBAgAABAgQIEEgrIDAuradqBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhkIiAwLpNB2AYBAgQIECBAgAABAgQIECBAgAABAgQIECAwJgGBcWPiczEBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABArkKCIzLdTL2RYAAAQIECBAgQIAAAQIECBAgQIAAAQIECFQREBhXRctaAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQ6RkBgXMeMykYJECBAgAABAgQIECBAgAABAgQIECBAgACBEQQExjkeBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjUUkBgXC3HqikCBAgQIECAAAECBAgQIECAAAECBAgQINA4AYFxjRu5hgkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAg0Q0BgXDPmrEsCBAgQIECAAAECBAgQIECAAAECBAgQIFB3AYFxdZ+w/ggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAg0VEBgXEMHr20CBAgQIECAAAECBAgQIECAAAECBAgQIFAzAYFxNRuodggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQeE5AYJyTQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNRBQGBcHaaoBwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE9hAQGOdQECBAgAABAgQIECBAgAABAgQIECBAgAABAnUQEBhXhynqgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBPQQExjkUBAgQIECAAAECBAgQIECAAAECBAgQIECAQB0EBMbVYYp6IECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgDwGBcQ4FAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUAeB3opNVF1fsbzlBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSCMgMC6NoyoECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBILiAwLjmpggQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEgjIDAujaMqBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSC4gMC45qYIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIIyAwLo2jKgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEguIDAuOamCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSCMgMC6NoyoECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBILiAwLjmpggQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEgjIDAujaMqBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSC4gMC45qYIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIIyAwLo2jKgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEguIDAuOamCBAgQIECAAAECBAgQIECAAAECBAgQIECAQBsEeives+r6iuUtJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQBoBgXFpHFUhQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE2itQVry9n5FUBLOcAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIH2CPgwVHvc3ZUAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIKyAwLq2nagQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIZCIgMC6TQdgGAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMCYBgXFj4nMxAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQK5CgiMy3Uy9kWAAAECBAgQIECAAAECBAgQIECAAAECBAhUERAYV0XLWgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEOkZAYFzHjMpGCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAgREEBMY5HgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1FJAYFwtx6opAgQIECBAgAABAgQIECBAgAABAgQIECDQOAGBcY0buYYJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQINENAYFwz5qxLAgQIECBAgAABAgQIECBAgAABAgQIECBQdwGBcXWfsP4IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQINFRAYFxDB69tAgQIECBAgAABAgQIECBAgAABAgQIECBQMwGBcTUbqHYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEHhOQGCck0CAAAECBAgQIECAAAECBAgQIECAAAECBAjUQUBgXB2mqAcCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBPYQEBjnUBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJ1EBAYV4cp6oEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgT0EBMY5FAQIECBAgAABAgQIECBAgAABAgQIECBAgEAdBATG1WGKeiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYA8BgXEOBQECBAgQIECAAAECBAgQIECAAAECBAgQIFAHgd6KTVRdX7G85QQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEgjIDAujaMqBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSC4gMC45qYIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIIyAwLo2jKgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEguIDAuOamCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSCMgMC6NoyoECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBILiAwLjmpggQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEgjIDAujaMqBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSC4gMC45qYIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIIyAwLo2jKgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEguIDAuOamCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSCMgMC6NoyoECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBILiAwLjmpggQIECBAgAABAgQIECBAgAABAgQIECBAgEAbBHor3rPq+orlLSdAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAaAYFxaRxVIUCAAAECBAgQIECAAAECBAgQIECAAAECBNorUFa8vZ+RVASznAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB9gj4MFR73N2VAAECBAgQIECAAAECBAgQIECAAAECBAgQSCsgMC6tp2oECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECGQiIDAuk0HYBgECBAgQIECAAAECBAgQIECAAAECBAgQIDAmAYFxY+JzMQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECuQoIjMt1MvZFgAABAgQIECBAgAABAgQIECBAgAABAgQIVBEQGFdFy1oCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBDpGQGBcx4zKRgkQIECAAAECBAgQIECAAAECBAgQIECAAIERBATGOR4ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNRSQGBcLceqKQIECBAgQIAAAQIECBAgQIAAAQIECBAg0DgBgXGNG7mGCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDRDQGBcM+asSwIECBAgQIAAAQIECBAgQIAAAQIECBAgUHcBgXF1n7D+CBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDRUQGBcQwevbQIECBAgQIAAAQIECBAgQIAAAQIECBAgUDMBgXE1G6h2CBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBB4TkBgnJNAgAABAgQIECBAgAABAgQIECBAgAABAgQI1EFAYFwdpqgHAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgT2EBAY51AQIECAAAECBAgQIECAAAECBAgQIECAAAECdRAQGFeHKeqBAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIE9BATGORQECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAHQQExtVhinogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGAPAYFxDgUBAgQIECBAgAABAgQIECBAgAABAgQIECBQB4Heik1UXV+xvOUECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIIyAwLo2jKgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEguIDAuOamCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSCMgMC6NoyoECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBILiAwLjmpggQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEgjIDAujaMqBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSC4gMC45qYIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIIyAwLo2jKgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEguIDAuOamCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSCMgMC6NoyoECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBILiAwLjmpggQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEgjIDAujaMqBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSC4gMC45qYIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAGwR6K96z6vqK5S0nQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAGgGBcWkcVSFAgAABAgQIECBAgAABAgQIECBAgAABAgTaK1BWvL2fkVQEs5wAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgfYI+DBUe9zdlQABAgQIECBAgAABAgQIECBAgAABAgQIEEgrIDAuradqBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhkIiAwLpNB2AYBAgQIECBAgAABAgQIECBAgAABAgQIECAwJgGBcWPiczEBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABArkKCIzLdTL2RYAAAQIECBAgQIAAAQIECBAgQIAAAQIECFQREBhXRctaAgQIECBAgAABAgQIECBA4P/Yuw8wu6pyf8DflPQeAoEQQCkhoUgLeqWqV1GMiqCCCiKWqNiu/Xotf4LitWMviBVFpCnFgDRpXjUkQCCBNExCSCWk9zJz/uzBQIZkMmfP7FPm7Pc8jw+aWXut9b3fmn0ynD0/CRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKDLCAiM6zKtslECBAgQIECAAAECBAgQIECAAAECBAgQIEBgFwIC4xwPAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqUkBgXE22VVEECBAgQIAAAQIECBAgQIAAAQIECBAgQCB3AgLjctdyBRMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDIh4DAuHz0WZUECBAgQIAAAQIECBAgQIAAAQIECBAgQKDWBQTG1XqH1UeAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgpwIC43LaeGUTIECAAAECBAgQIECAAAECBAgQIECAAIEaExAYV2MNVQ4BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAs8ICIxzEggQIECAAAECBAgQIECAAAECBAgQIECAAIFaEBAYVwtdVAMBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAjsICIxzKAgQIECAAAECBAgQIECAAAECBAgQIECAAIFaEBAYVwtdVAMBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAjsICIxzKAgQIECAAAECBAgQIECAAAECBAgQIECAAIFaEBAYVwtdVAMBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAjsICIxzKAgQIECAAAECBAgQIECAAAECBAgQIECAAIFaEBiXsoi041NObzgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSyERAYl42jWQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJC5gMC4zElNSIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECFMJF/wAACAASURBVBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgWwEBMZl42gWAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIZC4gMC5zUhMSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgGwGBcdk4moUAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKZCwiMy5zUhAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMhGQGBcNo5mIUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQOYCAuMyJzUhAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEshEQGJeNo1kIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQuYDAuMxJTUiAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFsBATGZeNoFgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECGQuIDAuc1ITEiBAgAABAgQIECBAgAABAgQIECBAgAABAhUQGJdyzbTjU05vOAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBLIREBiXjaNZCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgcoKFFIu7zOSlGCGEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQGQG/DFUZd6sSIECAAAECBAgQIECAAAECBAgQIECAAAEC2QoIjMvW02wECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECFSJgMC4KmmEbRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKdEhAY1yk+FxMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUK0CAuOqtTP2RYAAAQIECBAgQIAAAQIECBAgQIAAAQIECKQREBiXRstYAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgS6jIDAuC7TKhslQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEdiEgMM7xIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgJgUExtVkWxVFgAABAgQIECBAgAABAgQIECBAgAABAgRyJyAwLnctVzABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBfAgIjMtHn1VJgAABAgQIECBAgAABAgQIECBAgAABAgRqXUBgXK13WH0ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEciogMC6njVc2AQIECBAgQIAAAQIECBAgQIAAAQIECBCoMQGBcTXWUOUQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIPCMgMA4J4EAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoBQGBcbXQRTUQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQILCDgMA4h4IAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoBQGBcbXQRTUQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQILCDgMA4h4IAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoBQGBcbXQRTUQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQILCDgMA4h4IAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoBYFxKYtIOz7l9IYTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgGwGBcdk4moUAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKZCwiMy5zUhAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMhGQGBcNo5mIUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQOYCAuMyJzUhAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEshEQGJeNo1kIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQuYDAuMxJTUiAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFsBATGZeNoFgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECGQuIDAuc1ITEiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBsBgXHZOJqFAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECmQsIjMuc1IQECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDIRkBgXDaOZiFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDmAgLjMic1IQECBAgQIECAAAECBAgQIECAAAECBAgQIFABgXEp10w7PuX0hhMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAbAYFx2TiahQABAgQIECBAgAABAgQIECBAgAABAgQIEKisQCHl8j4jSQlmOAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEClRHwy1CVcbcqAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkK2AwLhsPc1GgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECVCAiMq5JG2AYBAgQIECBAgAABAgQIECBAgAABAgQIECDQKQGBcZ3iczEBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAtUqIDCuWjtjXwQIECBAgAABAgQIECBAgAABAgQIECBAgEAaAYFxabSMJUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgywgIjOsyrbJRAgQIECBAgAABAgQIECBAgAABAgQIECBAYBcCAuMcDwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEalJAYFxNtlVRBAgQIECAAAECBAgQIECAAAECBAgQIEAgdwIC43LXcgUTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQyIeAwLh89FmVBAgQIECAAAECBAgQIECAAAECBAgQIECg1gUExtV6h9VHgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIKcCAuNy2nhlEyBAgAABAgQIECBAgAABAgQIECBAgACBGhMQGFdjDVUOAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLPCAiMcxIIECBAgAABAgQIECBAgAABAgQIECBAgACBWhAQGFcLXVQDAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQI7CAiMcygIECBAgAABAgQIECBAgAABAgQIECBAgACBWhAQGFcLXVQDAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQI7CAiMcygIECBAgAABAgQIECBAgAABAgQIECBAgACBWhAQGFcLXVQDAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQI7CAiMcygIECBAgAABAgQIECBAgAABAgQIECBAgACBWhAYl7KItONTTm84AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEshEQGJeNo1kIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQuYDAuMxJTUiAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFsBATGZeNoFgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECGQuIDAuc1ITEiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBsBgXHZOJqFAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECmQsIjMuc1IQECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDIRkBgXDaOZiFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDmAgLjMic1IQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBLIREBiXjaNZCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkLmAwLjMSU1IgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBbAQExmXjaBYCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhkLiAwLnNSExIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIVEBiXcs2041NObzgBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSyERAYl42jWQgQIECAAAECBAgQIECAAAECBAgQIECAAIHKChRSLu8zkpRghhMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUBkBvwxVGXerEiBAgAABAgQIECBAgAABAgQIECBAgAABAtkKCIzL1tNsBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhUiYDAuCpphG0QIECAAAECBAgQIECAAAECBAgQIECAAAECnRIQGNcpPheXQmDapdHqXI4aG60+m/N1PtufO+fD98f258H9wf3B/eE5AfdH90f3x+cEvD94f/D+4P1hm0C1vT+W4mdqcxIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0FpAYJwTQYAAAQIECBAgQIAAAQIECBAgQIAAAQIECNSCgMC4WuhijdUg0EOgx/ZHutoCHZxP59P5fE7A96dAsu2/H9wf3R/dH90ftwl4f/D+4P3hOYH23h9r7Md55RAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoSgGBcVXZFpsiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEUgoIjEsJZnjpBZ7/C/Ujx0xqtej08aNb/W9f57P9gXA+fH9sfx7cH9wf3B+eE3B/dH90f3xOwPuD9wfvD94ftglU6v3x+es+P2ix9D95W4EAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAfgQExuWn1yolQIAAAQIECBAgQIAAAQIECBAgQIAAAQK1LCAwrpa720VrExgn0Gb7oyvQRaDL9uehUoEe2/Zgffcn96fnBNyf3Z/dn58T8P7g/cH7g/eHbQLFvj8KjOui/8LCtgkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBLqkgMC4Ltk2myZAgAABAgQIECBAgAABAgQIECBAgAABAgSeJyAwzpGoOoHCwklpz2XV1WBDBAgQIECAAAECBAgQIECgLQGBcc4GAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgfIJCIwrn7WVCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgdIJpA3m8hlJ6Xph5n8LCIxzFAgQIECAAAECBAgQIECglgUExtVyd9VGgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQbQJ+GaraOmI/BAgQIECAAAECBAgQIECAAAECBAgQIECAQEcEBMZ1RM01JRGYdmm0Oo8jx0wqyTomJUCAAAECBAgQIECAAAEC1SBQN2y0Z1GroRH2QIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUNMCHtKp6fYqjgABAgQIECBAgAABAgQIECBAgAABAgQI5EZAYFxuWl39hQqMq/4e2SEBAgQIECBAgAABAgQIZCcgMC47SzMRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQaEtAYJyzQYAAAQIECBAgQIAAAQIECBAgQIAAAQIECNSCgMC4WuhijdQgMK5GGqkMAgQIECBAgAABAgQIEChKQGBcUUwGESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOiUgMC4TvG5mAABAgQIECBAgAABAgQIECBAgAABAgQIEKgSgXEp95F2fMrpDc+zgMC4PHdf7QQIECBAgAABAgQIEMifgMC4/PVcxQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAuUXEBhXfnMrEiBAgAABAgQIECBAgAABAgQIECBAgAABAgQI5ECgsHBSIQdlKpEAAQIECBAgQIAAAQIEciowffzoVpWPGhueSc3pWVA2AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBA6QU8nFN6YysQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjkUEBgXA6brmQCBAgQIECAAAECBAjkSEBgXI6arVQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGKCwiMq3gLbIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAWBQTG1WJX1USAAAECBAgQIECAAAEC2wQExjkLBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBMonIDCufNZWIkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQyJGAwLgcNVupBAgQIECAAAECBAgQyKGAwLgcNl3JBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECFRMQGFcxegsTIECAAAECBAgQIECAAAECBAgQIECAAAECBAjUosC0S6OwfV0jx0yqxTLVRIAAAQIECBAgQIAAAQIEWgTqho32LKqzQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKDEAh7SKTGw6QkQIECAAAECBAgQIECAAAECBAgQIECAAAECBPIlIDAuX/1WLQECBAgQIECAAAECBPIuIDAu7ydA/QQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAuUQEBhXDmVrECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI5EZAYFxuWq1QAgQIECBAgAABAgQIEIgIgXGOAQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHSCwiMK72xFQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBHIkIDAuR81WKgECBAgQIECAAAECBAgIjHMGCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECJRBQGBcGZAtQYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkD+BwsJJhfxVrWICBAgQIECAAAECBAgQyIvA9PGjW5U6amx4JjUvzVcnAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBA2QU8nFN2cgsSIECAAAECBAgQIECAAAECBAgQIECAAAECBAjkQUBgXB66rEYCBAgQIECAAAECBAjkV0BgXH57r3ICBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHyCwiMK7+5FQkQIECAAAECBAgQIECAAAECBAgQIECAAAECBHIgIDAuB01WIgECBAgQIECAAAECBHIsIDAux81XOgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQNkFBMaVndyCBAgQIECAAAECBAgQIECAAAECBAgQIECAQAkExqWcM+34lNMbTiBCYJxTQIAAAQIECBAgQIAAAQK1LCAwEiXzNAAAIABJREFUrpa7qzYCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFqExAYV20dsR8CBAgQIECAAAECBAgQIECAAAECBAgQIECgIwKFlBf5jCQlmOHFC0y7NFqdx5FjJhV/sZEECBAgQIAAAQIECBAgQKCLCdQNG+3fs3SxntkuAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBA1xPwkE7X65kdEyBAgAABAgQIECBAgAABAgQIECBAgAABAjsKCIxzKqpGQGBc1bTCRggQIECAAAECBAgQIECgDAIC48qAbAkCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHcCwiMy/0RAECAAAECBAgQIECAAAECBAgQIECAAAECBGpCQGBcTbSxNooQGFcbfVQFAQIECBAgQIAAAQIECBQnIDCuOCejCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECHRGQGBcZ/RcS4AAAQIECBAgQIAAAQIECBAgQIAAAQIECFSLgMC4aumEfYTAOIeAAAECBAgQIECAAAECBPIkIDAuT91WKwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQKUEBMZVSt66BAgQIECAAAECBAgQIECAAAECBAgQIECAQJYCAuOy1DRXJgKFhZPSnstM1jUJAQIECBAgQIAAAQIECBAoh8D08aNbLTNqbHgmtRzw1iBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMilgIdzctl2RRMgQIAAAQIECBAgQIAAAQIECBAgQIAAgZoTSBvM5TOSmjsC1VeQwLjq64kdESBAgAABAgQIECBAgEB2AgLjsrM0EwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIH2BPwyVHtCvk6AAAECBAgQIECAAAECBAgQIECAAAECBAh0BQGBcV2hSznbo8C4nDVcuQQIECBAgAABAgQIEMiZgMC4nDVcuQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAhUVEBhXUX6LEyBAgAABAgQIECBAgAABAgQIECBAgAABAhkJCIzLCNI02QkIjMvO0kwECBAgQIAAAQIECBAgUH0CAuOqryd2RIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgULsCAuNqt7cqI0CAAAECBAgQIECAAAECBAgQIECAAAECeRIQGJenbld5rdMujVbnceSYSVW+Y9sjQIAAAQIECBAgQIAAAQIdF6gbNtqzqB3ncyUBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBogQ8pFMUk0EECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAlQsIjKvyBuVpewLj8tRttRIgQIAAAQIECBAgQICAwDhngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDpBQTGld7YCgQIECBAgAABAgQIECBAgAABAgQIECBAgEDpBQTGld7YCkUKCIwrEsowAgQIECBAgAABAgQIEKgJAYFxNdFGRRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECFS5gMC4Km+Q7REgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJFCYwratRzg9KOTzm94XkWEBiX5+6rnQABAgQIECBAgAABAvkTEBiXv56rmAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoPwCAuPKb25FAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBHAgUFk4q5KBMJRIgQIAAAQIECBAgQIBATgWmjx/dqvJRY8MzqTk9C8omQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAovYCHc0pvbAUCBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEcCgiMy2HTlUyAAAECBAgQIECAAIEcCQiMy1GzlUqAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBxAYFxFW+BDRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNSigMC4WuyqmggQIECAAAECBAgQIEBgm4DAOGeBAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQPkEBMaVz9pKBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECORIQGJejZiuVAAECBAgQIECAAAECORQQGJfDpiuZAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgYgIC4ypGb2ECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFaFJh2aRS2r2vkmEm1WKaaCBAgQIAAAQIECBAgQIBAi0DdsNGeRXUWCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECJRYwEM6JQY2PQECBAgQIECAAAECBAgQIECAAAECBAgQIECAQL4EBMblq9+qJUCAAAECBAgQIECAQN4FBMbl/QSonwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoBwCAuPKoWwNAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB3AgIjMtNqxVKgAABAgQIECBAgAABAhEhMM4xIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFB6AYFxpTe2AgECBAgQIECAAAECBAgQIECAAAECBAgQIECAQI4EBMblqNlKJUCAAAECBAgQIECAAAGBcc4AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgTIICIwrA7IlCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE8idQWDipkL+qVUyAAAECBAgQIECAAAECeRGYPn50q1JHjQ3PpOal+eokQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAou4CHc8pObkECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIE8CAiMy0OX1UiAAAECBAgQIECAAIH8CgiMy2/vVU6AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFB+AYFx5Te3IgECBAgQIECAAAECBAgQIECAAAECBAgQIECAQA4EBMbloMlKJECAAAECBAgQIECAQI4FBMbluPlKJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKLuAwLiyk1uQAAECBAgQIECAAAECBAgQIECAAAECBAgQKIHAuJRzph2fcnrDCUQIjHMKCBAgQIAAAQIECBAgQKCWBQTG1XJ31UaAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBtAgLjqq0j9kOAAAECBAgQIECAAAECBAgQIECAAAECBAh0RKCQ8iKfkaQEM7x4gWmXRqvzOHLMpOIvNpIAAQIECBAgQIAAAQIECHQxgbpho/17li7WM9slQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDoegIe0ul6PbNjAgQIECBAgAABAgQIECBAgAABAgQIECBAYEcBgXFORdUICIyrmlbYCAECBAgQIECAAAECBAiUQUBgXBmQLUGAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJB7AYFxuT8CAAgQIECAAAECBAgQIECAAAECBAgQIECAQE0ICIyriTbWRhEC42qjj6ogQIAAAQIECBAgQIAAgeIEBMYV52QUAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgc4ICIzrjJ5rCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAgWoREBhXLZ2wjxAY5xAQIECAAAECBAgQIECAQJ4EBMblqdtqJUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqJSAwLhKyVuXAAECBAgQIECAAAECBAgQIECAAAECBAgQyFJAYFyWmtnOdUREPJTtlF1jtsLCSWnPZdcozC4JECBAgAABAgQIECBAgEBETB8/upXDqLHhmVQngwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECJBDycUyJY0xIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJlFUgbzOUzkvK15/SI+HJEfCkiro6ItL0q304zXklgXMagpiNAgAABAgQIECDQBQUKhUIsXro8Hp+/KPYdNjSG7DYwGhsaor6+vmtVs7UporGha+3ZbksuIDCu5MQWIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIPCsgF+GchgIECBAgAABAgQIECBAgAABAgQIECBAgACBWhBIG0LmM5LydT2xnhoRh0TEIxFxYURck4fgOIFx5TtkViJAgAABAgQIECBQrQLzFz0ZD85fF/98dH48MXNK7NmvMU454eg46tCDol/f3tG9W7dq3bp9EWhXQGBcu0QGECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMhMwC9DZUZpIgIECBAgQIAAAQIECBAgQIAAAQIECBAgQKCCAgLjKohfxNJnRMS1241LAuS+VOvBcQLjijgZhhAgQIAAAQIECBCocYGHZ86L3Q88JjbusW88NHVRzHhockz+v1uj5+alcdIxB8crXnpUDN19t+jZo3uNSyivFgUExtViV9VEgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQrQIC46q1M/ZFgAABAgQIECBAgAABAgQIECBAgAABAgQIpBEQGJdGq/xjk8+kkpC4Q563dPJnF/47TC5tD8tfRZErTrs0WtUycsykIq80jAABAgQIECBAgACBWhNYv2Fj/HP2yqgfflR07z04NqzbEGvXrYuFTyyIhybcGesWTouTj9o/Xn3iMbHX0D2isbGh1gjUkwOBumGjPYuagz4rkQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoLICHtKprL/VCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAgWwE0oaN+YwkG/c0s7wpIq5p44Ip/w6O+2NE67C1NAtUy1iBcdXSCfsgQIAAAQIECBAgUB0CmzZvjtsnL4ytg0dEY49esWXLpujWrVdsKdTHkiVPxYN/vyNi+awYc+JhcdKLj4oB/ftWx8btgkCRAgLjioQyjAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAnBPwyVCfwXEqAAAECBAgQIECAAAECBAgQIECAAAECBAhUjYDAuKppRZsbST6XmhoRh+xiqzURHCcwrvoPox0SIECAAAECBAgQKLfA2nXr49aJc2LroIOiW+++sXH92mioq4tuvfpGc2PvmD9vfky+54YYvV+POOt1r4ghgweWe4vWI9BhAYFxHaZzIQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGiBQTGFU1lIAECBAgQIECAAAECBAgQIECAAAECBAgQIFDFAuNS7i3t+JTTG96GwFueDoy7qgidhyPiwoj4U0SkDQMsYvrSDhEYV1pfsxMoFAox/bG5kfyzvddeQ4fEoAH92xtWM1/fsHFTzJm3oKh6evboEfvvt3dRYw0iQIAAAQLVKLBp8+b419z5RW1tv+F7RZ/evYoaW8pBy1eujtsmzo7N/faLXv0HxqYNG6J565bo1q0xeg7YI9Ztro/7774xDui1It77tjHRo3v3Um7H3AQyExAYlxmliQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAi0KSAwzuEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAol0Dy2dTUp0PjDilywYf+HRx3XVcMjissnNR+mlWREIbVpkBTU3OsXbc+GhrqW0JCBIIU1+eHHp0ZR77y7UUN/sC5b4qffO1/ihpbC4N+feWN8a6PJ3mbxb22zJsQjY0NxQ02igABAgQIVJnA1TfeHme+/7NF7eo7F34iPja2uL8/FDVhJwYtfnJZ3PHAvNjSe1j0GjAwNm3aFNHc1PIjT58BQ6K5sU88cNd1cdZx+8aRhx7UiZVcSqD0AtPHj261yKix4ZnU0rNbgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIKcCHs7JaeOVTYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCokMCZEXFlyrW7ZHCcwLiUXa7h4UuXrYi7/n5/3H7vfTFn3oJYsHhpPLFwcaxZu36HqofvtUe8cN+9Y/QRo+LYIw+NIw8dESP2368lVM7rGYEHp86Io085uyiOsWefHj/75ueLGlsLg3515Q3x7o9/qehSBMYVTWUgAQIECFShwJU33Bpv/cDnitrZxeM+ER9/X+kC4+5/eFr07NEjDj14/6L2s3rNurjjvhmxZFO/6D90r9i6ZWsUCoUoNDdF7/4DI6I+Fk++Kd722uNj990GFTWnQQQqISAwrhLq1iRAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMirgMC4vHZe3QQIECBAgAABAgQIECBAgAABAgQIECBAgACByggkn09NjYhDOrD85Ii4MCKuj4hCB64v6yUC48rKXXWLrVq9Nn7622vj2vF3xMTJj3Zqf/369o53vuV1cfYZp8ZLjj4s6ury/TGvwLi2j5PAuE59q7mYAAECBLqYQDUExjU3N8fUWU/ErffPjcemToz3nnVqjD7i0KIkl61YFVfc9nDsdchLY+2aNS2BcS0/5tTVxcCBg2Ly3dfH6SceEkccOqKo+QwiUAkBgXGVULcmAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAXgXy/ZsEee26ugkQIECAAAECBAgQIECAAAECBAgQIECAAAEClRV4y9OBcVd1YgtJcNy4fwfHdWKa0l4qMK60vtU6+/oNG+OS3/4xLvjWT2PN2vWZb3P//faOT7zv7DjvrNdHn969Mp+/K0woMK7tLuUhMC4J0xl54pti5eq17R7Xlx5zeFz3q2+3O84AAtUm4JxXW0d23M97P/nluPG2e9vdaPdujfHo3ddEEv7qlb1ANQTGbdnaFPfPXR177X94fP0Hl8SGOffFh959Zox+UfuhcUnY3BW3TIgBB50YS5csfhYo+fO+ffrGktkPxTEv7B/HHX1I7gODsz89ZsxKQGBcVpLmIUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINC+gMC49o2MIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQyFYg+YxqZkQc2MlpH3z6+iQ47oZOzpPp5dMujcL2E44cMynT+U1WvQI3//Xvcd7HxsWTTy0v+SaT4Jkff/Wzcc6bXlvytaptAYFxbXckD4FxSYhOw/AXF3UsDxmxfzxyV2fySYtaxiACmQs455mTZj7hKW/9UNx2z4Si5n1yym2x+26DihprUDqBagiM29rUFHc/siAOe+Xpce/E2fHXq34ZGxdOjvPPfUsce9RhuyyoubkQf7hjcgwecVzMnzsn6uvrW8YXCk3RrVvPaNi8KnptWhivPuGo3AYFpzsRRldSoG7YaM+iVrIB1iZAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMiFgId0ctFmRRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEqk7grRFxRUa7eiAiLqyW4DiBcRl1tYtN8+NfXx0f+tzXy7rrd7z5tXHZ979U1jWrYTGBcW13QWBcaxuBcdXwHWsPHREQGNcRtfJeIzCuvN5trVYNgXGFQiEmTJ0dm/d/WWza2BRLFi2Jf/zl6li/4KH4wDlvjpccc3ibWI/NmR/T1vWPrfV94qkli6O+oSFJi2sZX9/QLbo1b4hNi6fFG/7zWKGD1XHk7GIXAgLjHA8CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECpRcQGFd6YysQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjsKFAfETMi4sAMcZLguHERcWOGc6aeSmBcarIufcHWrU3xmYu+F9/52e/LXofAuPbJx559evzsm59vf2CNjBAY17qRAuNq5GDnsAyBcdXfdIFx1dGjagiMSyQWLV0Zt83aGHvuf2isWbkq1m3YHBPvvCHWPTE5vvTp82P4sKE7gM1f9GT88Z6pcfjJp8ecmTOj0NwUz0TFJa9C1Nc3RGPzxlg57+F486tfGsOG7l4d6HZBoA0BgXGOBgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHSCwiMK72xFQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBHYu8LaIKEXK1v1PL5cEx/25EvAC4yqhXrk1P/qFb8YPfnllRTYgMK59doFxuzbaMm9CNDY2tA9ZRSMEaVVRM2ylZALOecloM5tYYFxmlJ2aqFoC4zZs2hzX3f1wDBr1slj+1NKob+gWm7bWxQP3jI++6x+P8897S+y91zOhcVu2bo2/3Tc57po8J0447dxYtmRZrF21POrq66NQiKhLYuMKhahvaIimDatj8WP3x7lvfHnsN3xYp6xcTKDUAgLjSi1sfgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLJM2ZeBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBCojUB8RMyLiwBItPykiLqxUcFxh4aRCieoybZUIXH3j7XHm+z9bsd0IjGufPm+BcfMXPRm33zOhfZiI6NWrR5z1hlOKGltNgwRpVVM37KVUAs55qWSzm1dgXHaWnZmpWgLjku/ZKbOeiAeW9oy+A3aLtatXRM/e/WJLoVvcdu2vYsuKx2PfvYe2BMItWbEmhhxweJxy2pmxevmqWL54YTQk4a3JFyP5R6Hlgb76hsZYvWhOPPXEo/G+t42J/fYRGNeZs+La0glMHz+61eSjxnomtXTaZiZAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMi7gMC4vJ8A9RMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEKivw9oi4vMRbSILjxkXE+BKv02p6gXHl1C7/WrPmzIsRx5+RycJ7DBkc+w3fM6Y/NjfWrF1f9JwC49qnyltgXPsiXX+EIK2u30MVtC/gnLdvVOkRAuMq3YFn1q+WwLhkL6vWrIsrbpscex1yfCx7amkUmrdEj159Y21Tj5j92IxYt/Kp6D94tzhwxMgYNmzPWLl4fqxa9lTU12/3+F6hEElsXH1dXRSam+PxKRNiy7on4yPvPjP22XtodaDbBYHnCQiMcyQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIlE9AYFz5rK1EgAABAgQIECBAgAABAgQIECBAgAABAgQIECCwo0B9RMyIiAPLgDPx6TWS4LibyrBWCIwrh3Jl1kiCfI499dx4YMr0Dm1gxP77xqfOf0ccPvLAGHnQC2Jg/34t8xQKhVi4ZGlMmzU3Lv/jzfHrK2/c5fwC49rnFxjXvlFXGyFIq6t1zH47IuCcd0StvNcIjCuvd1urVVNg3NatTXHflH/FzJW9o1u/AbF+7eqoKzRHjz4Do3vfwdHQ0C0aGiOaNq2LlU8uiq2bNkZ9Q8N2pRVa/i6YJMY1dmuM5YsXxMwH741B/brHh9/91thXYFx1HDq72EFAYJxDQYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKB8AgLjymdtJQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKB0AkkIWJpX2vFp5jY2vcDZTwfG/S79ZR2+YlJEXBgRf+7wDEVcKDCuCKQuOmT87X+L1537sQ7t/tJvfSHOO/P10di4fUDIzqea+8TCuPiSy+MHv7xypwM6ExiXhBHN+NfjsWDx0nhq+cpYvmJVrFi1Jrp3a4whuw2MwQMHxG6DBsShB+8fgwb071CtaS5KQlbmzl8YCxYtjUVPPhVbt26N3r16xh5DBsdhIw94NlQvmfPBqTPi6FOS20b7r6wC41auXhOPzpzT4tTitXJ1rN+wMQb07xuDBvRrMdpv+F5xyIgXRn19koPplQhs2Lgp5sxb0NLTxU8ua+npISP2jwP2G17U98DOFKspSGv776MVK1e3nI0Vq1ZHQ0NDy/dPci6Sfx5x6EFl+T6qhlOXfG88Pn9RS78Tj6Tnew0dEsP32iP23XvPathip/dQjvuBc97pNpV8gmoJjNu0eXNMmfZYLF22MpatSP6zKtasXR99+/SKIYMHttx7kvfSw0cdGD17dC+5S7JAOfdUTYFxSe3LV66JP9wyKXY/8NhYs25Ny99nork5Wh7Qq49obmqK5qbmqK9viKhL/rTw9I8ldUlqcBQKzS3/vaFbY2xaszom/e2O6NmwJV7ykqPiiIP2jSNGHRANDf6OUZZDbJFUAgLjUnEZTIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBTAgLjOsXnYgIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBKBJLftE/z8hlJGq3Sj02SD2Y8HRp3YOmXarXCfU//ryQ88OYs1512aUvyw7OvkWM5jcBoAAAgAElEQVSSfDqvWhI44bT3xP9NfChVSf369o5br/hR/Mcxh6e6LhmcBLu9/YOfjwemTG91bdrAuCTg7Jo/3xG33TMhrr/lrpZAmWJexx55SLz6ZS+NN77mZXHMi0YVc0nRY5KaLv/jzXHp5X/a5X6SkLGPvPusOOdNp8asOU+UJTBu4uRH48+33xu33v3P+Of9U4qqKenza15+XJz26pPjzNe/Krp1ayzquqwGJSE9P/zlVbG1qandKQ87+IAY88oT2hz3u2tvagkUbO/Vv2+fOP+db2417O5/PBC/vWZ8/OKK69u8PDlXJ7z4yPjix9/bbphaMk8SPpa8kiCtz331R+1t69mvf+3zH2l37ItGHRSnvuK4dsclA5Lvm2vH3xF3/G1iqu+jow8fGa866SXx3rPfGAe+YJ+i1to2qDO92DbHtFlz4pEZs+PRmbNj6bIVLWfz4ANeEO9/xxnP7qWj62zctDmu+fPt8btrb45b7vpHm7Ulzued9fp411tf3yoEclcYWZzpzVu2xEOPzIrEIKl/3foN0atXzzjl5P+IV5744qJ6UY77gXPe8XtOmnNeVMN3Muj2e++L+x+e9uxXvvK9XxT9PvrfH3pnDBq46/DVgf37tfp+3NU+Fy5ZGldef1vcds8/4+a//r3okpL38uQ+dNZpp7SEOGb5qtSeqi0wrlAoxP1TH4t/zFwVu+97UKxeuyaiqSnJg2sJhUsC4v4dEffMDwz//rOWwLiIaGhsjK0bN8SjE/8eUx68L171utfFmDe/LaZPvi+OeWH/GPHC4Vm2zVwEMhWoGzbav2fJVNRkBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBHYU8JCOU0GAAAECBAgQIECAAAECBAgQIECAAAECBAjUgoDAuK7fxXdExGUVKmPC0+smwXF/yWJ9gXFZKFbvHElQXBIYl/b1wK2Xx1GHHZz2smfHJ4FHX/7Oz+Oi7/7i2T8rNjAuCYr75R9uaLn+yaeWd3gPyYWvf9VJccEnx3Y6OC7Zx2cu+n785qo/p9pPEsh25KEHx70THizqurFnnx4/++bnixq7bVASDjfu2z/bZfBVMRMmYTwXfvoDcd6Zr4v6+iQXs/SvBYufjOFHv7aohZJe3vCbi9sce8BLT4vZjy8oaq7CwmeCMWfNmRf/9cVvpQowSpyuvORrcdzoF7W5Vv8RJxUdzFTUhp83KAn4u+5X397lpUlQ3E8uuyYu+u7PO72X5Hv3/31ibNHBcR3pRVJMct/44013xjd/fNkOgZPJ15PQvvtueu6ttyPrPDh1Rpzz4S+2BLEV+9pjyOC44sdfiVeccGy7l3TmTK9YtTp+8fvr45s/+e1O732f/fB58dXPfXiXeyjn/cA5T3/P6cg5b/fQtTHgbed/Lv5w/a0dvbyo67bdS9sanISyffunv4uLL7m8qPl2NehjY98enzr/nNh7z84Fx1V6T9UWGJeYJ6GU4++6P1Y0DYo+uw2N9RvWR3NTcxQKzVHXkhb3zI+uSbjcM4FxEXV1dVHf0BBb1q+LaQ9MiEcmT4q993tBHP+aN8TeBx0RAwcPiQWTb41XHrF37D5kUKf7bwICpRAQGFcKVXMSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQaC0gMM6JIECAAAECBAgQIECAAAECBAgQIECAAAECBGpBQGBc1+9ikqY0IyIOrGApSXDcBRFxS2f2IDCuM3rVf+3ZH/pC/P5P6bIFL/nG5+J955yRSXE33HpPnHbeJ1rmKiYw7uFps2LMOf8V8xc9mcn62yb56HveGt++4OPR2NiQet6//m1ivPHdn+x06FYxC6cJjEuC9cZ+6qLU/W1vH6ef+vK47PsXRt8+vdsb2umvdyZc6/mLpwkPa5p/X0uA0ae//L0O1/D1L3w0PvPBc3d6faWDtG6/97444z2fyvTMJuGHV13ytXjNy49r1yxNL7YFTk2c/Gic8+EvxMzZ89qcvzOBcVvmTYiv/+g38YWv/7jd/bc14Off/mK8522n7fL6jp7p3117U3zwf762y57tKjCuEvcD5zx9YFxHznlHD2ylA+OSsLpkD1m/Lvv+l1r+PtGRVzXsqRoD4xLLpctWxI133B/r6gbFgKHDYnNTczQ1bWkJjotCc7T88FooRF1dEhbXEPV1EWtXLItpD94Xsx6ZEsP22ScOf/HxMWDP4dGr36AYtv+oWLNqdfRe9mC8YvQB0bNH9460zDUESiogMK6kvCYnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0CIgMM5BIECAAAECBAgQIECAAAECBAgQIECAAAECBGpBQGBcLXQxIkkK+k0VlPLPp/cwrqPBcQLjqqCDJdpCoVCIAQefnCo06ujDR8b9t/wu0x09OHVGXH3j7XHc6BfF6151YptzX/eXu+L0d38q07W3n+zVL3tpXPGTr8SgAf2LXuMvd/49Tj37o0WP7+zAYgPjnli4JN74rk/GA1Omd3bJnV7/olEHxZ3XXhKDBxZv1ZGNdDRca2drpQkpO+3VJ8f1t9zdkS23uub/bvhly7l+/quSQVo//NVV8ZHPf6PTtbU1QTGhaWl6kYT3/e/3fxVf/MZP2t1zZwLjkmuTsK7Ovm654odxysn/0eY0ac/0ZT+4MN736a+03CPbe7UVGFep+4FzXnxgXGfOeXvnoq2vVyowrrm5OS741iVx0Xd/0dGtt3td8r1w0X9/MBoakvzm9l/VtKdqDYxLFJcsXR53/G1yLF7TEI19B0af/v0j6uoj+ftkcyEJjkse3CvElg0bYunCJ2Laww/EwrlzYu+994mRR42OfrsPjW49e0f33n2i38AhsecBh8fCudNj3+a5cdIxI9pvlBEEyiwgMK7M4JYjQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDIpYDAuFy2XdEECBAgQIAAAQIECBAgQIAAAQIECBAgQKDmBATG1UZLk5SKGRFxYJWU84+n95EEx93akf0UFk5Key47soxryigwbdacOOTkt6Ra8dqffyPOeO0rUl2TxeA/3vTXeNN7P5PFVLucIwnE+9v1v4hePXu0u1YSLvXi1ya5kOV7FRMYN2/B4jj21HPjyaeWl3RjSajan375rairK93H9GnDtW74zcVt1pwmpCwruFeccGzccdWOQWeVCtJKQtdKGdK0zW3C+N/Ei486NJNenH7qy+NPN99ZVEs6ExhX1AJFDOrXt3f86x/Xx+67Ddrp6DRnevhee0TvXj1j5ux5RawcsbPAuEreD5zz4gPjOnPOizocOxlUqcC4j19wcXz30t93dNtFX/fB894SP/rf/y5qfDXtqZoD47ZhPvzIrJg9/6mYu3B5LFm2JpoL9dHQ0D2atm6JVStXxPwF82LR0iXRLerioMFDYp8DDooeQ4dG9959o0efPtG9Z89o7NEn+g1KQuMOi2VLFkS/ZRPj1BOOLKpfBhEotcD08aNbLTFqrP8T41Kbm58AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCC/AqV7Ej2/pionQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEyi+QNpjLZyTl71GxK74zIn5d7OAyjUuC4y6IiNvSrCcwLo1W1xj7899fF2M/dVHRm03Ci+be9+doaEiyEMv3mjVnXow4/oyyLVhMKNv6DRvjqFe9veggp6w2397eNm7aHCed/t5IwuzK8frhVz4TH3rXmSVbKk241utfdVJUW2BcAvPXq38aLz++dfBIJYK0ktC1M97z6ZL1avuJk3vF1DuvigH9++50vVKF91VDYFxS8Eff89b43pc/tdPa05zptM16fmBcpe8HznnxgXFpev38c57m2u3HViIwLk0YWkfr2v663/3wy3H2Gafucqpq21Oa/Vw87hPx8fe9PQuq1HNs3rIllixdHk8tWxXLlq+JTVu2Rn19XRSat8b6jRtj1br1MWXanJg9bV6MGL5P9NptSHQbMDC69+sX3Xr2jO49ekX3Xv1iwO57xh4vGBVL58+MIaunxH8ef1TqvbiAQNYCAuOyFjUfAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgbYF/DKU00GAAAECBAgQIECAAAECBAgQIECAAAECBAjUgkAWgXHvj4ifpsD4WUQk1xT7Mv+updL6FOue5bgkOG5cRNxazKQC44pR6lpjzv3o/4vfXnNT0Zs+/51vjh9/9bNFj89i4KbNm+PoU86JR2fOzmK6oudoL2Tmkxd+Jy6+5PKi58tqYHuBcR/8n6/FT35zTarlknCvA1+4T8xbsDhmP74g1bX9+vaORZNviT69e6W6rtjBacK1qjUw7mXHHRN3XnNJq5LLHaTV0dDFJJiqoaEhHpnxr1izdn2xbWsZ940vfDQ+/cFzd3pNrQfGJUU/9vfr4oAXDN+h/jRnOhV4RDw/MK7S9wPnXGBcYeGkZ4/xY3OfiIOOOz3tse70+On3XhsHH7DfTuepxj11lcC47UELhUI0NTW3BMbV1z8TKrxly9ZY8tTyuPnOCXHnXRNjWP8h0XvQkGgcNCi69U7C4npF9569o0effjFwj71j930PjoWPTYl9m2bGiS8RGtfpg2+CTgkIjOsUn4sJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIpBIQGJeKy2ACBAgQIECAAAECBAgQIECAAAECBAgQIECgSgUExj0TXpenwLtKHsUkOO5LEfGXXW1CYFwlW1SatU86fWzcO+HBoif/w0//N856wylFj89i4GVXj493/tcFqabaY8jgSEK6Ruy/b/z/du4Ezq7x/h/4d9bMTDKRTSKJiCWy2Sto7fuuaq2t/NUPLa0WrVpqbYtSqtQaa6g1dqGWomKXUGsiNAiJiD1il8zfGU3MyEzuucm9d27mvs/rldcrzPc8z/N9P889Fzk+730wPZ74z/PxxH9eyHqMN8beHlVVlXPdl8/Ap0yLnFdg3JPPjo9VN9sj0xBzfp6EeR150N7RpXP9nL/38SefxgmnD49TzhmRepzzTzky9ttj+9T12RRmY12sgXFJvzPfeHxOiE7y14UO0vrR3ofGzXf+OxV98rm58LSjY63VVpqz5iQM6JGxz8ZPfnl06lDB5HPY2meoFALj/nDYz+P3v96noM+PpoFxxfA8cM4FxjUNjNv74OPj0mtuTfUcml00dODSsfoqy0X/xXvHxNfeiMeefC4mTJyU1Rh77LBlXH5W8o+5c1/FuKaFMTBuXhvyyaefxejHn46RN90XHaMmFlm0Z5TVd47qTh2jQ21tVHWoi5r6+ui2WP/o2meZmPrSkzGg8vVYY9UVs9pnxQRyKSAwLpeaxiJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwbwGBcU4IAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0B4EBMYJjGuLc/xQRCTJXP9qOvm44dHsPA7eakxbrM2ceRAYtPb2WQWvTBozKvr16ZWHlbQ85MyZs2Loejtmtcadttk4LjrtmKjvVNds0PseGhPb7n1IfDTjk9TrTwJmkqCZ715HnXxOnHjmxanHSQoX790zjvjl3rHy8oNixSEDoq62Jqa+/W489PjTcfnI2+PWux9INd68AuN23v/wuO7We1KN88CNw2OdNVZptfbqm++KXX9+ZKqxkkCf5++/NlVttkXFEhi3dP++jUuf+NrkbFtorJ/y1D+jd68ec+59/sWJ8dGMjxv/elZDQ6z1w5+mGjc513dddXbG2j6LLRpL9F2sse6ZcS/FShvtmvGepGCHrTaMEWee0Hg+W7pmfPxJrLf9fpGEkaW5brjo1Nhuiw3mKi3mwLjkGZJ8NpZdql80NEQ8O/7luPP+R+LeB59I0/KcmiR478UHb5jrnmzOdFYTRkTTwLhieB4458UdGPfGm9PijSlvzTlmSThr2jC2my45LXr16DbPI9qxrjZWGDKgsWbS5KnRf7WtszrSJx35izjsgD2bhW0mA/z1givjkONOz2qs/z5yc8x+js++sRjXlKytvQXGJT199dXM+M+4l2PEVbdH5Wfl0bFLtyjvskhU13WMqtraqK6ti7r6LtG975LRuWf/eOvFx2L5LtNj5eUGZrXPignkWqCszzDvouYa1XgECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEviPgJR1HggABAgQIECBAgAABAgQIECBAgAABAgQIEGgPAgLjBMa15Tl+MCKOj4jG5CmBcW25Ffmdu/PAdbMKUPty0mNRWVmR30U1GX3UPQ/G1nv+OvV8h/5sjzj16F9FWVnLf2z88quvx1o/3CemvfNeqjFbCkL75NPPYrGVNs3Kba3VVorrLzw1ei3acrjOU8+9GN/bdPdUa2otMG7cS6/E0PV2SjXGcYfuF8ceul/G2rW33SceeuLpjHVJwdSn72q1v1QDtFKUTbjWNpusG7dc1nqIULYhZcn+n/L7g2KDtYbNCVD7+JNPG0P59j44eUSmvx4bdVmsvspyLd4wa9asqFh89VSDzU843+4H/j6uvPGfGcdPwuhefvim6JkhBOqe0Y/HJj8+ION4ScFB++wSf/vDb+aqzXYvUk0WEautPDQev33EnPJs5tlr560bA9cGD1hyrukavk6OO+60C+KE04enXUpj3bgHRs41XjZnOqvJmgTGFePzwDnPdjdbr//uOc/VyJvucmDc/cBjqYab9uzdsWj3rqlqk6Lf/fHMOOWcbz+bmW5MAum23Wy9Vstuu3t0bLPXwZmGmfPzg/fbLU4/7pBm9cW4pmSB7TEwLukreQa8OPH1OOOcq6PDVxVR3617VC7SJSrr6qK6ri461HVqDI1bdIllomP3PjFtwuOxSo/PYrlBS6feZ4UEci0gMC7XosYjQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMLeAwDinggABAgQIECBAgAABAgQIECBAgAABAgQIEGgPAgLjBMYVwzke/fUijhs3PP7VdDGDtxpTDGuzhgUUyCa8J5kqCZOaPuGBBZw1u9t/edQp8fdLrk110+K9e8aEh26M2poO86z/89mXxeF/OivVmEnRK4/dEkv26zOn/l8PPh4b75wuLCu5acO1V4tRl/8tajpUtzpnLgLj/njGRXH0Kedm7CsJA3v18VszOiUD3Xr3A/HDvZoH7LQ2wa2X/TW23mSdjPNnW5BNuFYuA+MuP+uE2PVHm0dFRXmLS77oqpvj/w79Q+p2rrvgz7Hj1hu1WJ/NZzHbwLgk4LDjMmunWuff/3RYHLj3zqlqV9po13hm3EsZa1ccsmw8/a+r5qrLJsit6c0Dl14iVhgyIAYs2S/6L947ysvL46MZH8eEiZNi/MuvxirLD2oWUJfNPA1TMn+3HXnS2XHSWZdk7Ht2wVXnnhi7bLtps/pszvR3J0qCwpJAu6T/3r16xJdffhXvf/hRPDf+5Xh9ylux3x7bx547bRXF+DxwzlMfm8j2nKcfed6V+QyMG7T29o2f0zTXbtttHv84+48ZSzff7Zdx5/2PZKxLCpJ/Rnh97O3NaotxTckC22tgXNJbEr455plxccmVt0d8NDO69Fg0yjt3jqpOnaJDXW1U1XaKTl26Rq9+y0Zdt97x7kuPxUqLfhFDBy6Vap8VEci1gMC4XIsajwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMDcAgLjnAoCBAgQIECAAAECBAgQIECAAAECBAgQIECgPQgcl2UTLdXvFxHnZzFOUvuzLOqNP2+sbH2yoC9s6bCBEb/YJmKNwd/MKzCusP75mu2D6R9F18EbpB5+6f5947+P3Jy6PheF2YS5nHzUL+N3B+6Vcdpp77wXvVZsHuA0r5uu+PsfYvftt5hTcsLpw+PYv6R/tD5w4/BYZ41V5rmuXATGbbDj/nH/w2Mz9r/zDzeJa847KWNdUvDKpCmx9Pd/mKr26IP/L074bTZfIamGjWzCtXIZGJcpPCwJvVlspc0iOU9prr8c8+s49Gd7tFiazyCtfz/yZKy/Q/J1lPl69t5rYvnBy2QujGgMy0tC89JcH730QHTqWNesNJsgt++vukLsvM0m8cNN141lllw8zZRzarKZJ9OeJ4NOmjw1+q+2deo1HHnQT+NPhzcPmMzmTCcTbbvZeo1hg1tutHZ069I51dzF+Dxwzue9dQtyzlMdihRF+QqMe/Otd6LPKpunWME3JQ/dcnGsOWzFjPWj7nkwtt7z1xnrZhckYalJ0GRyFeOaZq+zPQfGJT0m35/jXn41hl9+a3w8bUZ07dYjqhbpElX1HaO6ti6q6zpGpy7dYrH+y0Zt197x9oTHY8WeX8QKg5ZOvdcKCeRKQGBcriSNQ4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKB1AYFxTgcBAgQIECBAgAABAgQIECBAgAABAgQIECBA4BuB/SPivCwwLvjfPWlvMf68pbL1SeveFnV3RsQpDVPG/KstJjdnfgQ+nD4jugxeP/Xgi/fuGa+PvT11/YIWTp32bvReebPUw4wffX0MWqZ/qvq9Dz4+Lr3m1lS1P9tzhzj35CPm1KYNYkpuWG3lofH47SMyzrOggXGffPpZdFxm7YzzJAWbrf+DVMF6Se2XX30Vm+36i1Tj7rPrtnHhaUenqs2mKJtwrUIGxiU9/PhnR8S1t9ydqp1jDtk3jv9N8rUw95XPIK1sAg7vvOrvUVVZmaqfU88dEXfc+3Cq2omP3hJLLdGnWW2ug9xaW0g+5kkCqpKgqjTXVhuvHbeNOKNZaS7PdEtrKNbngXM+7xOTJrAwzZlbkJp8BcbdcPu9scP/HZZqaT17dIs3//PPKC8vz1g/c+asWHL1reONN6dlrE0Krjr3xNhl228CY4txTbObaO+BcbP7TPbtrIuuj3dffy+6dekWlV0Wiar6+v+FxtU1hsb1XnJQdFq0X7z14uOx/jJV0XexHqn2WhGBBRUYP2pYsyGG7BveSV1QVPcTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQaEXAyzmOBgECBAgQIECAAAECBAgQIECAAAECBAgQIEDgG4FsA8sExs375OTbpxjPbRIUd1xEPJosrmHKmIZiXKQ1zZ9AQ0NDlPddLaubCxlo8+9Hnoz1d9gv9fpmTX4iysrS/XHxxVffEvscckKqsb8b+lbWp3mAxLwGufysE2KPHbbMOM+CBsY9OvbZ+ME2e2ecJ58F222xQdxw0ak5nyKX4Vq5Dg878qSz46SzLknVc1sFxm25x0Gpg91SNTIfRU/e9Y9YZflBze7M9V60tqx8zHPOpdfFgUf+OZXE91YYHGPvvKJZbS7PdEuLKNbnQT4D40r9nKc6jCmK8hUY94e/XhjHnJouwzrb75I9DzomLh+ZLsz2yIN+Gn86/IBGiWJc0+wtKpXAuKTft95+L864aGRMmzgtunfpGhVdukR1p05RVVMb1R3rotMi3aLP0oOjftEl4p1x98eayy4S/XovmuI0KyGwYAIC4xbMz90ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEshFI938AZDOiWgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAwikgMG7e+1ZsPsV0ym6JiCRNa2zTRQmMK6Ytys1aOg9cNz6a8UnqwT5/7ZGorqpKXb8ghTfecV9sv89vUw2x1morxYM3X5SqNil6eMwzsdYPf5qqfun+feO/j9zcWPv5F19EzZJrprovKXrijhExbKWhGesXNDBu1D0PxtZ7/jrjPPksWGeNVeKBG4fnfIpchmvlOjzs1HNGxGF/PDNVz20VGLfqZnvEk8+OT7XGfBXdN/L8WH/NVZsNn+u9aG3t+Zjn+lH3xo77HpaKq+nzY/YNuTzTLS2iWJ8H+QyMK/VznuowpijKV2DcwceeHmcMvzLFCiKOO3S/OPbQ9GGxfz77sjj8T2elGvtne+4Q5558RGNtMa5pdhOlFBiX9DztnffirxeMjLdemRrdu/WIis71UdmxY1TX1kaHuo7RcZEu0XeZoVHTpXd8NPHRWG2pjrHU4oul2nNFBOZXQGDc/Mq5jwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgED2AgLjsjdzBwECBAgQIECAAAECBAgQIECAAAECBAgQINA+BYotEO2CiEjWlPYqtfWndclXXUNE3BARx0fEsy1NIjAuX/RtN+6gtbePCRMnpV7ApDGjol+fXqnrF6Tw4qtviX0OSXILM187/3CTuOa8kzIX/q/iv6++EQPW/FGq+vpOdTF9wgONtUmoSa8VN011X1I0+ak7ok+vRTPWL2hg3IjrRsVevzo24zz5LPjeCoNj7J1X5HyKXIZr5To87OxLro1fHHVKqp7bKjAuOa/JuW3L67YRZ8RWG6/dbAm53ovW+svHPKMfeyrW3W7fVKRNnx+zb8jlmW5pEcX6PMhnYFypn/NUhzFFUb4C4/Y86Ji4fOTtKVYQjYFuSbBb2iub877TNhvHteef3Dh0Ma5pds+lFhiX9P3W2+/F6edfHW+98k5079Y9KhtD4+qiuvabX526dI2lV1g9aup7xLsTHo6VF6+MgUv2TXtM1BHIWkBgXNZkbiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAw3wIC4+abzo0ECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAOxMotcC1hT2Qri2P31UR8ceIeEdVJgMAACAASURBVKGlRYwbHkmY3Jxr8FZj2nKt5s6hQBJ6lIQfpb2uOvfE2GXb9IFpacdtqe7Uc0bEYX88M9UQ++y6bVx42tGpapOiN996J/qssnnq+i9eezSqqirjxf++FoPXSR9k8+Wkx6KysiLjPAsaGPfXC66MQ447PeM8+SwoxcC4S665JX56cLpQw7YKjCvrMyyf255q7PYWGPf0CxNi5Y13S9X74r17xutjmwdl5TswrlifB/kMjCv1c57qMKYoyldg3JZ7HBR33PtwihVEXH7WCbHHDlumqk2KRt72r9hpv9+lql9/zVXjvpHnN9YW45pmN1GKgXFJ72+8OS3+Nvy6ePu1d2LRrt2jbE5oXG1U19ZGlx69ot+gFaNT974xddzDsdJiEUMH9Eu194oIzK9AWZ9h3kWdXzz3ESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEgp4CWdlFDKCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXYvIDBu3ltcbD5tcSCvjIgk7ejFeU0uMK4ttqYwc+550DFx+cjmYUbzmjnbYLYF6eKPZ1wUR59ybqohfr7XjnHOSYenqk2Ksglsqu9UF++9cF9j8Fs2wW7JfdMnPJBqTdmMu+/u28UFpx7VbNw//PXCOObU81LNla+iUgyMu/rmu2LXnx+ZirQtAuO+/PKrqO7//VTry2dRewuMe/LZ8bHqZnukImvpOZDN82ebTdaNWy7LLgyyWJ8H+QqMc85THcVURfkKjNtgx/3j/ofHplrD1eedGD/+Yfpg2guvvCn2/U2SeZz52m6LDeKGi05tLCzGNc3uoFQD45L+X5k0Jf524ciYPvn96Na1W5TX10dFXV1U13SIDp3qo75r91hi4PLRtc8y8frzD8cKi34ZKw7sn3nzVRCYTwGBcfMJ5zYCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECWQgIjMsCSykBAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDeBfaKiEvzPkv6CWZFxDURcXymoLjZQwqMS4+7sFWef/kN8bPfnZh62T17dIs3xt4eVVWVqe+Z38KzL7k2fnHUKalu/38/3iYu+euxqWqTomwCm5L6WZOfiLKyshj/8qsxZN0dU88z843Ho7y8PGP9ggbGnXXxNXHQ778JwWmrqxQD46679Z7Yef90QYVtERiXnIWyPsPa6kjMmbe9BcbdM/rx2OTHB6RyXbx3z3h9bPNQzmyeP/MTGFesz4N8BcY556mOYqqifAXGbb/Pb+PGO+5LtYbkuzz5Tk97ZROutsWGa8btV5zZOHQxrml2z9n0dPpxh8TB++2WlmuhqHtz2jtx4RW3xYvP/Dd6dekRZZ06RkXH2qhOguPqaqOufpFYrP8y0XfgKvHuW1Oi9v3nY4NVl4namg4LRX8WuXAJCIxbuPbLagkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBBZOAYFxC+e+WTUBAgQIECBAgAABAgQIECBAgAABAgQIECBAoD0KJElRL0bEgCJoLgmKuy4iklStZE2pL4FxqakWusJnx70cK260S1brvuxvx8eeO22V1T3zU3zVTXfGbgcclerW7bbYIG64KH1g2kuvTIqBa22fauwkJO+tZ+5qrM0m6Cmpf/eFe6Nbl84Z51nQwLhsrJJ+Dv3ZHhnXlG3BUv36xE7bbJztbRnrszHPFK61zA+2jYmvTc44Z1LQMGVMxrqFITCu36pbxhtvTsvYS1Jw/G/2j5o8BO7sscMW0afXos3WkOu9aK3BfMwz4rpRsdev0gVUthSkmMsz3VLfxfo8yGdgXKmf81Qf8BRF2QTGJd+LyfdJmmv/w06MC664IU1p/P1Ph8WBe++cqjYpuuSaW+KnB5+Qqv4nO24ZI878prYY1zS7iVIPjEscXn19apx/+U0x9eVp0bVz58bQuMqOdVFV2yGqajpGXX2n6NlvyVhi0Krx/gcfRtnbT8f6K/SJrotk/meuVIdFEYH/CQiMcxQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI5F9AYFz+jc1AgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQTmDPiLgsXWneqhoiYuTXox8XES8syCwNU8YkY7nakcDMmbOi65D146MZn6Tuaun+fWP8A9dHVVVl6nsyFd7/8Ng4+e+XxjabrDMnKOaOex+OLfc4KNOtjT8fOnDpeP7+a1PVJkX/evDx2HjnA1LVNx37w+kzosvg9VPdlxSNH319DFqmf8b6BQ2Mu+vfj8Zmu/4i4zxJwYZrrxb/uvbcVLXFUJTLcK1ch4e1RWDcwKWXiBcfTBe8lOzf6lvuGU/8J92jf/RNF8baq69ckG3P9V60tuh8zHPSWZfEkSedncpps/V/EP+88qxmtbk80y0tolifB9kExjnnqY5XzouyCYx78z93xmI9u6dawxEn/r3xOz7NdfB+u8Xpxx2SprSx5rjTLojjT7sgVX3TsYtxTbObEBj3jcSL/30tzrv0ppjx1kdRX9cxyurro7K2Q1TW1ERVTW3U1ddH996LR7/Bq8RnXzbE55Ofi9X6V8WS/fqkOg+KCMxLYPyoYc1+PGTf8E6qI0OAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgTwJezskTrGEJECBAgAABAgQIECBAgAABAgQIECBAgAABAgSyEihPsg4iYkBWd+WuOAl3S1KFjlnQoLjZSxIYl7vNKaaRtt/nt3HjHfdltaQjfrl3nHjEgVnd01rx5SNvjz0PSo5pxE923DJGnHlC4++ffmFCrLzxbqnn+OzVh6NDdXWq+rMvuTZ+cdQpqWq32HDNuP2KMxtrk4C9yn6rp7ovKbrpktNi283Wy1i/oIFx2dyfLOaTiQ9FbU2HjOsqhoJchmvlOjxsYQiMy+bz/afDD4gjD/ppQbY913vR2qLzMc+62+0box97KpXTIfvvHqcde3Cz2lye6ZYWUazPg3wGxpX6OU91GFMUZRMYN/mpO6JPr0VTjBqRzXfu+muuGveNPD/VuElRNnt/yu8Pit8ekGQ5F+eaZjctMO7b7X/y+Zdi+CU3x6zpnzcGxJXV1UZlh29C46prOkSHjp2iW6/e0W/gitFQ2zWmv/5CDOjwVqy2yvKpz5BCAi0JCIxzLggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgUTkBgXOGszUSAAAECBAgQIECAAAECBAgQIECAAAECBAgQINC6wE8iYkQbACVBcTdFxLER8Wwu5xcYl0vN4hnrn/c9HFvsflDWC7r2/JNjp202zvq+2Td8/Mmn8ZsTzojzRlw/Z4ymgXFffTUzqpZYI/X4N1x0amy3xQap6tfedp946ImnU9V+N0Rr1c32iCefHZ/q3iQsLgmNy3RlE/C07+7bxQWnHtVsyMSq29AN4qMZn2SaqvHn91x7Tmy0dvrgu1SD5qkol+FauQ4Pa4vAuJ49usVbz9yVWvuM4VfGwceenqp+rdVWigdvvihV7YIW5XovWltPrucZ+8y4GLZ58vWe7mrpOZnLM93SKor1eZBNYJxznu585boqm8C48aOvj0HL9E+1hDFPvxCrbfFNUFuaK20Y3fsfTo9uQzZMM2RjzeibLoy1V1+58ffFuKbZjQiM+3ZLk+fGI08+Hxdeekt0nFkZFbU1UVFXGxVJaNz/guNq6uqiS49Fo8+AIdGxx5Lx/tRJ0eWj52Kzdb6X+mwoJPBdAYFxzgQBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBwgkIjCuctZkIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRaFiiPiBcjYkCBgW6OiKNzHRQ3uweBcQXezQJN19DQECtvvFs8M+6lrGc8789Hxv4/2T7r+x5/6vn4yS+PjgkTJzW7t2lgXPKDrff8dYy658FU46+zxirxwI3DM9Y+9uRz8f2t/1/GutkF/77hglj3+9+GjvzfoX+Ii65KPmrprjTBNwsaGJesZOf9D48kwCzNtXjvnvHY7ZdFn16LpilvtSYJ3DngiJPjtTemRuI0eMCSCzReSzfnMlwr1+FhuQqMSz6D5X1XS2035al/Ru9ePVLVJ5/rlTbaNVVtUvTdgMTUNzYp/OzzL+LEMy+O8y+/IbbfcoM49+Qj5hom13vR2jpzPc+eBx0Tl4+8PTXLK4/dEkv269OsPpdnurWFFOPzwDmf97FpmDIm9bnKV2E2gXHXXfDn2HHrjVIt5csvv4rq/t9PVZsUHf+b/eOYQ/bNWP+Xcy+P3/7hbxnrZhd8MvGhqK3p0PiXxbim2esUGNd8S2fOnBV3jx4Tl192W3St6RgVdTVRXlPzTWBcdXVUdKiODnV10alL1+i95LLRo//Q+Oijj2LWlDGxwUr9oluXzqnPiEICswUExjkLBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAonIDCucNZmIkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQaFlg96/D4q4oIM5tEXFMRDyVjznHDY+GpuMO3qrtQ03y0Wcpj3n1zXfFrj8/cr4Idttu8zjuN/vFskstkfH+R8c+GyeeeUncevcDLdZ+NzDutPOuiN+ccEbGcWcXjL3zivjeCoPnWZ9NkFIy0Mf/fTDqamvmjHnOpdfFgUf+OfWaNlv/B3HNeSfFIp07tXjPJ59+1hh4k4yb5tp39+3iglOPmqt0+D9ujP1++6c0QzTWrDhk2Rh904XRub5j6ntmF0556+046cxL4u+XXDvn3kdvuzTW+N7yWY+V6YZchmvlOjwsV4FxiUHngevGRzM+ycTR+PNsgppmzZoVvVfePKa9816qsZOiEWeeEMlnMdsrmevmO/8dh//prDlhkFtsuGbcfsWZcw2V671oba25nOdvF14Vvz7mtNQsA5deIl588Ia56nN5pltbTLE+D5zz1o9PMQTGZfP9+Iu9d46z/nRY6s9DNgGw9Z3qYurTdzX77v3uRJ9/8UUsMWzr1M+29ddcNe4beX6zYYpxTckCBcbNfaySgL8/n/OPGPfUhOhW2znK62qjvKZDlFdVNobGVdbURFWHmqjr3Dl69ukXvZdZIb6sqIsPXxkTK/f8MpYbXOgM79QfDYVFLlDWZ5h3UYt8jyyPAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBg4Rfwks7Cv4c6IECAAAECBAgQIECAAAECBAgQIECAAAECBAgszALlEfHi14FxhUgmGBURR+crKG72JgiMW5iPY7q1f/XVzBi0zvYx8bXJ6W5ooWq7LTaINYetGMsNWiaGDlwq+vRaNF59Y0qMe+nVeG78y3H3A4/F/Q+Pnef43w2Me/Otd6LPKpunXlPPHt3i1sv+Gquvstxc9yQ9HnXy2XHKOSNSj/ezPXeIc08+oln9f199Iwas+aPUYySFSXDUZX87PlZabmDU1nRovDcJ8Lrr34/GocefkTrwJrmvtcC49z6YHkuuvnXq0LFkrKEDl47fHbhX7Lj1RvMM5klqkzCwx596Pq655e44Y/iVc/UvMG7eR+KYQ/aN43+zf6tF/VbdMt54c1qqc5WEKT1xx+UxaJn+LdYngX6d6urmhAEef9oFcdzXv7K5Dvh/O8XP99wxlh+8TMbb3nnvg7jt7tFx1sXXxJPPjm9WvzAFxn3+2iNRXVU1V78fTp8Rv/z9KXH5yNszWjQtSJ4dyTPku1chAuOK9XngnLd+hIohMC4JHU3CBtNe155/cuy0zcYtlr/7/oeN3xuLdu/a+PMkTPJHex+aduhYa7WVYuTwU2Kxnt3nuuftd9+P3Q/8feM/V6S9rjr3xNhl202blRfjmpIFCoxreVeT78i/XTAypk2cGp07doyy2pqo6FAd5dXVUVFV1firqkOHqKmvjy49ekafZYZGfe8BMXnCf2KVbh/EcgP6R3l58q9pLgLpBQTGpbdSSYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGB+BQTGza+c+wgQIECAAAECBAgQIECAAAECBAgQIECAAAECBHIhsFtE/CMXA81jjDu+zm86JiLG5HmexuEFxhVCue3nePqFCbHyxsnxbbvru4FxyUoOOOLkOPeykVkt6s+/PyjWXWOVxvC6Dz+a0Rhi9dcL/pExsO67k4wffX2LoVw/2GbveHTss1mtaXZxEh73wfQZWYXENZ2otcC4pObkv18aR5z496zXlQSQJeMOHrBk9O7ZozGgJwlVSQL7knCrJPTvyhv/Oc81C4ybN3umwLi1t90nHnri6dR7l4QjHnbAno2hSh06VMekyVPj6ecnxPWj7o1nxr0UO2y1YWPYUnIlgWf9hm2ZVZjg7IWsv+aqsfXG60Tf3j1jsUW7R7eunRvHmzz17Xh98tT414NPxJ33P9LquhemwLikiST4cqkl+kSnjnXxyqTJ8diTz8WEiZNS70vTwg9f/Pec0L6mf78QgXHF+jxwzls/SsUQGDc/4ZJJ6Ohm6/8gundbJKZOezdemDAx/nnfI3OeC7P7mjlzVgxdb8esPk/Jc+7Uo38VKy8/MJZdaol49fUpMebpcfGbE7ILWl28d8+Y+OgtUVVV2WwDinFNyQIFxrX+OXnq+Qlx3sU3xqwPP48OHWqivK7mm8C4ysooT35VV0dVdXVUd6iJTl27Ru+llo3FBqwcb7z0bCzZ4Z0YttxSUduher6e6W4qTQGBcaW577omQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAorIDAuMJ6m40AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQOBbgfKIeDEiBuQJ5Z8RcXShguJm9yAwLk+7WYTDXnrNrbH3wce32cpaCox7+dXXY9k1tyv4mnbaZuO49vyTW5z3vBHXx88PP6nga0omnFdg3IyPP4k+q2w+X8FgC9qMwLh5C2YKjEuC/pLAv1xd2262Xtx0yWlzhvvrBVfGIcednqvhU4+zsAXGpW4sQ+Hxv9k/kj1v6SpUYFwxPg+c89YPTjEExt1x78Ox5R4H5epj0DhO074uH3l77HlQkndc2Ouckw6Pn++1Y4uTFuOaBMbN+3zcdu/Dcf3Ie6PDFxGVtTVR3qE6KqqroqyiIsqqKqOisioqq6qisroq6uo7R89+/WOZ720YUya/Gp1nvBRrLd8vunTuVNhDaLaFVkBg3EK7dRZOgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCwEAkIjFuINstSCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLtTGDXiLgyDz3d9XXmxrER8Wgexk49ZMOUMQ2pixUutAL7H3ZiXHDFDW2y/pYC45KFFDrsqmePbvGfu6+M3r16tOjw6Wefx8C1tos33pxWcKd5BcYli7n/4bGxwY77F3xdAuPmTZ4pMG7UPQ/G1nv+Omf79t3AuK++mhk/+umhkcxTyKsUA+NWHLJsPPHPEVFdVdUidaEC44rxeeCct/7pK4bAuPc+mB7dh26Y00dE075mzZoV2+/z27j5zn/ndI55Dbbh2qvFnVf+PSorK1osK8Y1CYzLfDzOvfymeOi+J2ORyg5RloTGVVdFeWXlN6FxlUloXEVjcFxFVWV0qOsUfZdeNpZa6QfxwfQZMWvK2Fh3hb7Rs3vXzBOpKFmB8aOGNet9yL7hndSSPQ0aJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQyLeAl3PyLWx8AgQIECBAgAABAgQIECBAgAABAgQIECBAgACBlgTKI+LFiBiQQ557IuLotg6Km92PwLgc7mwRD5UESx32x781hrQV+motMK6hoSF22u93cf2oewuypPuvvyDW+8H35jnXdbfeEzvvf3hB1tN0kkyBcUnt1TffFbv+/MiCrk1g3Ly5MwXGJZ+7pdbYJmchhN8NjEtWN+PjT2K97feLJ58dX7CzUYqBcU//66pIQuNauwoZGFdszwPnvPWPXjEExiWr2/vg4+PSa27N2TPiu329/+H0WHHDXXL2rJvXQpPw12f+dXX0WrTbPPsptjUJjMt8/JLg3uNPuyzefOmNqK+ti7KaDlFWWfFNaFx5eWNwXPL78oqKqKiujpqOnaJrz16x7PfWjIrqDjF9woOx/spLRo9uXTJPpqIkBQTGleS2a5oAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKCNBATGtRG8aQkQIECAAAECBAgQIECAAAECBAgQIECAAAECJS6wS0RclSODJBXr2Ih4MEfj5WQYgXE5YVxoBrn46ltin0NOKOh6WwuMSxbx4fQZsc1eB8fox57K65qG/+X38X+7/SjjHEmI3Y9/dkQkwXGFvNIExiXrGf6PG2O/3/6pYEsTGDdv6kyBccndI64bFXv9Knn0L/jVUmBcMurUae/Gdvv8Jh4d++yCT5JihFILjLv9ijMj6XleV6ED44rteeCct3w6iiUwbvzLr8aQdXdM8elOV9JSX8+Oezk2/vEBMe2d99INMh9V9Z3q4p5rzo3VV1ku1d3FtCaBcam2LF7872tx6llXRdn0z6NDbW1Eh+ooryiPssaguP+FxpVXREVlZVRUlEeUlUV9164xdI11o7ZjTXw5+ZnYcI3loramQ7oJVZWUgMC4ktpuzRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECLSxgMC4Nt4A0xMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIESlAg+TOqCRExYAF7TwLijii2oLjZPQmMW8DdXQhvf+iJpxtDx16YMDHvq99vj+3jkP13j0HL9G91rs+/+CIOPOLPcdFVN+d8PUm4zA0X/SU2Xmf11GN/8ulnsekuB0biNL9Xzx7d4ncH7hWHHv/XVEOkDYxLBksCcHb5+ZEF2T+BcfPevjSBcckIPz/8pDhvxPWpzsK8iloLjEvu+eqrmXHimRfHsX85f4HnyTRAKQXGpQmLS7zaIjCu2J4Hzvncn5xiCYxLVnb1zXfFrj8/MtPHO9XPW+tryltvx4/2PjSe+M8LqcbJpmjowKXjthFnxFJL9MnmtiiWNQmMS79tN945OkZec3csUlEdZbU1UV5dFWUVFVFeURFl5WVRUV4eX33+RXSoqYzq6qr45JNPo6yyJtbcfPMoj89i8U4zY9gKg9JPqLJkBATGlcxWa5QAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAIBATGFcEmWAIBAgQIECBAgAABAgQIECBAgAABAgQIECBAoMQEdoyI6xag59Ff33tcRNy7AGPk7dZxw6Oh6eCDtxqTt7kMXHwCM2fOihtuvzd+/+dzYsLESTld4OK9e8bP9twxfrrLD6N3rx6px7746lviiBP/HtPeeS/1PfMq3GrjteO0Yw+eZ1hda/e/+/6Hsdevjo1R9yR5j9ld222xQZx94u9i6tvvxvc23T3VzdkExiUDfvrZ53HWxdfEaeddkTOvpgtNgnkO/H87xf/78TZRV1uTqodsinIZrrXMD7aNia9NTjV9mvCm6269J3be//BU46UNjEvC3E4557I46uRzUo3bWtG8AuNm35MENZ101iVx4x33LdBcrd2cnIlf7L1zrLrikLlKcr0Xra0hm3kGLr3EfD3j1lptpRj+l9/HkGWXSuWYyzOdasImRcXyPHDO5965NM+cbPd7QepvvvPf8ZNfHh0fzfhkQYaJefWVhK6ecPrw+PPZly3QHE1v/uVPfxx//N0B0bm+43yNWQxrEhiX3dadeu6VMfbh56NHp04RtbVRXlnxTWhcWUTDl19Gr8W6xJprrRy9enaPqVPfiUcefCre+fDT2GjrTeOdaa/FDhuvGR071mY3qeqSESjrM8y7qCWz2xolQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoKwEv6bSVvHkJECBAgAABAgQIECBAgAABAgQIECBAgAABAqUpkPz51HMRMXQ+2k8Spo4t1qC42f0IjJuPnW2HtyQBP7fe/UBjMNqtd4+e7/CxJJRp0/W+Hz/edtNYc9iKUV5ePl9aSfDRJVff0hh49cab0+ZrjG02WTd+/+t9YvVVlpuv+2ff1NDQEOdffkMc9se/pQrXSQxOOfpXkYR6JdeL/30tBq+zQ6o1HLzfbnH6cYekqm1alOzfbfeMjguuuCHuuPfhrO9vesOKQ5aNrTdZJ360+fqx2srz8+hLP/3b774fPVfYJNUNu2y7aVx17omt1q600a7xzLiXMo5V36kupk94IGNd8lnYes9fZ6xLCk4+6pfxuwP3SlWbFI1/+dUY/o8bG39lG9iUrP+XP90l/nT4Aanme/X1KTH8HzfF1TffmTpQr6WBk3mTM/HDTdeLjdddPbp0ri/YXrQ2UTaBcTPfeDzuGf14/OXcy+PuBx7LaJcExf3fbj+Kn+y4VVRUpH+O5fJMZ1xkKwXF8jxwzr/ZoLTPnPnd7/m97533PohLr7k1zh0xcr6eDeuvuWrcN/L8jNNPnfZu/PWCfzTOk+3zbvbgSTjlYQfuFf369Mo4X5qCtlzTbXePjm32OjjNMuP8U46M/fbYPlVtey1KQv5OOefKeOnpidGtU32UdayLisqKiFlfxZChS8ZO220YXTt3mtP+jI8/iSOOPjvqF+kaSw/qG8st2zd+sOqK7ZVHXwsoIDBuAQHdToAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgs8rXSAAAHXpJREFUQCCFgMC4FEhKCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEciawc0Rck+VoSVpTEhR3T5b3tUm5wLg2YS/qSZOAtCToZ/RjTzWGtSUBSG+9/V5jiFxdbU3j7+vqaqKmQ3Us3rtXLNF3sVh1xcGNgRy9e/XIeW9J4NoDjz4Z9z88NiZOmty4jmRNs4NnevboFj26dYnFenaPYSsOifXXHNYYVrdIkwCRXCzqyy+/irv+/WhjMNsrk6bEK5MmxwfTZ0T/xRdrdEiC4nbYaqO8h6xl6iUJi3rhpYkx5ulx8fTzE+K9D6bH+x9Oj3ff/zC++OLLOeFX3bosEp3rO0b3rovE8oOWiZWXHxQrDB7Q+PdchRFI9uqlVybF0y+81Hiepn/0cUyf8XF069K5cb+Sz1h9p47Rs3vX6L9471hhyIDGz1tZ2fy9OpF8bsY+My6e+M8L8dobb8YH0z+K9z/8KN5594PGUKvkM5V8bpIwuC6LdIp+fRaL760wOFZabtlYeom+8x0AmS/NbALjGqaMmbOMKW+9HS9NfL3R/qWJkxqtO9bVRt/ePaPvYovGmsNWiqWW6JOvZRd03GJ4HjjnBd3y+Zps0uSp8ey4lxu/+z/8aEZ8OH1G43dBEipXXVXV+N2ffNf2WaxH4/fEwKX7R2US2pXFlZyDJ58dH/9+ZGyMfuw/MXnqtMbv86ahsIv37tk4T/LPEklo43o/WDWGrTSkcQ35uIpxTfnoc2Ef8/U3p8UJp14cMf2L6NipPsprqqOuY1X8ZPfNY9DSSzRr7+VXX49TzxkZdWVV0blLh1hllWUbw05dBFoSEBjnXBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDIv8D8vfWc/3WZgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAoP0JJH829VxEDE3Z2qP/C4q7K2V9UZQJjCuKbbCI+RBIgl4qKsrnOzxrPqZ0CwECRSQwv4FxRdSCpRAgEBHJ93m2AXT5hivGNeW752Ief8wz4+OUv/0jFq2qi5pOtVHVuTa23Gy1WHeN781Z9tRp78YZl90Qsyq7RcO0afHhjPdi223Xj602XquYW7O2NhQQGNeG+KYmQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAoGQGBcSWz1RolQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECLS5wE5fh8Vdm2IVj31dc1xE/DNFbdGWNEwZ01C0i7MwAgQIECDwHQGBcY4EAQIESkNg1qxZccd9j8UFF98Y/eq7RH2v7tFx0fpYbtm+MaD/4vHa5LfjyQmTYlZt96j+7PN489lnYnp8GX88cr/o17dXaSDpMrXA+FHDmtUO2Te8k5paTyEBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB7AS8nJOdl2oCBAgQIECAAAECBAgQIECAAAECBAgQIECAAIH5E0j+XOq5rwPjhs7j9ie+/lkSFHf7/E1RXHcJjCuu/bAaAgQIEJi3gMA4J4QAAQKlI/D5F1/ErXc/FCMuHxVLdu8R3ZdeMqoXWyw+i5qI8oroXF8fZR+8Ha898WhMfntabL/zJrHz1htFZWVF6SDpNJWAwLhUTIoIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI5ERAYFxOGA1CgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQQWCHiBjZSs3Yr/9+EhR3W3tSFBjXnnZTLwQIEGj/AgLj2v8e65AAAQJNBZLQuHtGj43LrrglOlfWxmL9l4raXotHREN8Om1qvD1pYkz76IPYeJPvx547bRn1neoAEphLQGCcQ0GAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgcAIC4wpnbSYCBAgQIECAAAECBAgQIECAAAECBAgQIECAQKkKJH8m9VxEDP0OwFNf/3USFHdLe4QRGNced1VPBAgQaL8CAuPa797qjAABAq0JfPXVzBj/30lxxXV3xITnX4nqKIuGhob4MhqiR98escMPN4h1Vl8p6mprIBJoUUBgnINBgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoHACAuMKZ20mAgQIECBAgAABAgQIECBAgAABAgQIECBAgECpCmwfEdc3af4/X/8+CYq7uT2CjBseDU37GrzVmPbYpp4IECBAoJ0JCIxrZxuqHQIECiPw1cyIyorCzJWnWZKAuM8+/zImvvZGjH3mxWiIhhi0dP9YbtBS0bGuJsrLy/M0s2Hbk0BZn2HeRW1PG6oXAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBohTwkk5RbotFESBAgAABAgQIECBAgAABAgQIECBAgAABAgTajUDy51HPRcTQiHjm666SoLibIpqHqrWbbiNCYFx72k29ECBAoHQEBMaVzl7rlAABAgQI5FpAYFyuRY1HgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYG4BgXFOBQECBAgQIECAAAECBAgQIECAAAECBAgQIECAQD4FtouIE76eIAmKu6E9B8XNRhQYl8/jZGwCBAgQyJeAwLh8yRqXAAECBAi0fwGBce1/j3VIgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQ9gIC49p+D6yAAAECBAgQIECAAAECBAgQIECAAAECBAgQINCeBVaMiGfac4Pf7U1gXCnttl4JECDQfgQExrWfvdQJAQIECBAotIDAuEKLm48AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAUBQTGleKu65kAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCDvAg1TxjTkfRITECBAgACBHAkIjMsRpGEIECBAgEAJCYwfNaxZt0P2De+kltD+a5UAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKCwAl7OKay32QgQIECAAAECBAgQIECAAAECBAgQIECAAAECBEpEQGBciWy0NgkQINBOBJZbf+d4YcLEVN00TBmTqk4RAQIECBAg0L4FBMa17/3VHQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQHEJCIwrrv2wGgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXYiIDCunWykNggQIFAiAp9+9nl88cWXGbutqqqMutqajHUKCBAgQIAAgfYvIDCu/e+xDgkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBIpHQGBc8eyFlRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECLQjAYFx7WgztUKAAAECBAgQIECAAAECcwkIjHMoCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBROQGBc4azNRIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUAIC44ZHQ9M2B281pgS61iIBAgQIECBAgAABAgQIlKpAWZ9h3kUt1c3XNwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQMEEvKRTMGoTESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIlIKAwLhS2GU9EiBAgAABAgQIECBAgMBsAYFxzgIBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB/AsIjMu/sRkIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRKSEBgXAlttlYJECBAgAABAgQIECBAIATGOQQECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE8i8gMC7/xmYgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAoIQGBcSW02VolQIAAAQIECBAgQIAAAYFxzgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBAggIjCsAsikIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRKT6BhypiG0utaxwQIECBAgAABAgQIECBQKgLjRw1r1uqQfcM7qaWy+fokQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAouICXcwpObkICBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFSEBAYVwq7rEcCBAgQIECAAAECBAiUroDAuNLde50TIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgUXkBgXOHNzUiAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFACAgLjSmCTtUiAAAECBAgQIECAAIESFhAYV8Kbr3UCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGCCwiMKzi5CQkQIECAAAECBAgQIECAAAECBAgQIECAAAECBEpBQGBcKeyyHgkQIECAAAECBAgQIFC6AgLjSnfvdU6AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFB4AYFxhTc3IwECBAgQIECAAAECBAgQIECAAAECBAgQIECAQDsWGDc8Gpq2N3irMe24W60RIECAAAECBAgQIECAQKkLlPUZ5l3UUj8E+idAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMi7gJd08k5sAgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgVISEBhXSrutVwIECBAgQIAAAQIECBAQGOcMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMi/gMC4/BubgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAoIQEBMaV0GZrlQABAgQIECBAgAABAgRCYJxDQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCD/AgLj8m9sBgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgRISEBhXQputVQIECBAgQIAAAQIECBAQGOcMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECiAgMC4AiCbggABAgQIECBAgAABAgQIECBAgAABAgQIECBAoPQEGqaMaSi9rnVMgAABAgQIECBAgAABAqUiMH7UsGatDtk3vJNaKpuvTwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgYILeDmn4OQmJECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKAUBgXGlsMt6JECAAAECBAgQIECAQOkKCIwr3b3XOQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQOEFBMYV3tyMBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECJSAgMK4ENlmLBAgQIECAAAECBAgQKGEBgXElvPlaJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKLiAwLiCk5uQAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgFAQExpXCLuuRAAECBAgQIECAAAECpSsgMK50917nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEChRcQGFd4czMSIECAAAECBAgQIECAAAECBAgQIECAAAECBAi0Y4Fxw6OhaXuDtxrTjrvVGgECBAgQIECAAAECBAiUukBZn2HeRS31Q6B/AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBvAt4SSfvxCYgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAoJQGBcaW023olQIAAAQIECBAgQIAAAYFxzgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB/AsIjMu/sRkIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRKSEBgXAlttlYJECBAgAABAgQIECBAIATGOQQECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE8i8gMC7/xmYgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAoIQGBcSW02VolQIAAAQIECBAgQIAAAYFxzgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBAggIjCsAsikIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRKT6BhypiG0utaxwQIECBAgAABAgQIECBQKgLjRw1r1uqQfcM7qaWy+fokQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAouICXcwpObkICBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFSEBAYVwq7rEcCBAgQIECAAAECBAiUroDAuNLde50TIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgUXkBgXOHNzUiAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFACAgLjSmCTtUiAAAECBAgQIECAAIESFhAYV8Kbr3UCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGCCwiMKzi5CQkQIECAAAECBAgQIECAAAECBAgQIECAAAECBEpBQGBcKeyyHgkQIECAAAECBAgQIFC6AgLjSnfvdU6AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFB4AYFxhTc3IwECBAgQIECAAAECBAgQIECAAAECBAgQIECAQDsWGDc8Gpq2N3irMe24W60RIECAAAECBAgQIECAQKkLlPUZ5l3UUj8E+idAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMi7gJd08k5sAgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgVISEBhXSrutVwIECBAgQIAAAQIECBAQGOcMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMi/gMC4/BubgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAoIQEBMaV0GZrlQABAgQIECBAgAABAgRCYJxDQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCD/AgLj8m9sBgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgRISEBhXQputVQIECBAgQIAAAQIECBAQGOcMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECiAgMC4AiCbggABAgQIECBAgAABAgQIECBAgAABAgQIECBAoPQEGqaMaSi9rnVMgAABAgQIECBAgAABAqUiMH7UsGatDtk3vJNaKpuvTwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgYILeDmn4OQmJECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKAUBgXGlsMt6JECAAAECBAgQIECAQOkKCIwr3b3XOQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQOEFBMYV3tyMBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECJSAgMK4ENlmLBAgQIECAAAECBAgQKGEBgXElvPlaJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQKLiAwLiCk5uQAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgFAQExpXCLuuRAAECBAgQIECAAAECpSsgMK50917nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEChRcQGFd4czMSIECAAAECBAgQIECAAAECBAgQIECAAAECBAi0Y4Fxw6OhaXuDtxrTjrvVGgECBAgQIECAAAECBAiUukBZn2HeRS31Q6B/AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBvAt4SSfvxCYgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAoJQGBcaW023olQIAAAQIECBAgQIAAAYFxzgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB/AsIjMu/sRkIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRKSEBgXAlttlYJECBAgAABAgQIECBAIATGOQQECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE8i8gMC7/xmYgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAoIQGBcSW02VolQIAAAQIECBAgQIAAAYFxzgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBAggIjCsAsikIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRKT6BhypiG0utaxwQIECBAgAABAgQIECBQKgLjRw1r1uqQfcM7qaWy+fokQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAouICXcwpObkICBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFSEBAYVwq7rEcCBAgQIECAAAECBAiUroDAuNLde50TIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgUXkBgXOHNzUiAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFACAgLjSmCTtUiAAAECBAgQIECAAIESFhAYV8Kbr3UCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGCCwiMKzi5CQkQIECAAAECBAgQIECAAAECBAgQIECAAAECBEpBQGBcKeyyHgkQIECAAAECBAgQIFC6AgLjSnfvdU6AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFB4AYFxhTc3IwECBAgQIECAAAECBAgQIECAAAECBAgQIECAQDsWGDc8Gpq2N3irMe24W60RIECAAAECBAgQIECAQKkLlPUZ5l3UUj8E+idAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMi7gJd08k5sAgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgVISEBhXSrutVwIECBAgQIAAAQIECBAQGOcMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMi/gMC4/BubgQABAgQIECBAgAABAgQIECBAgAABAgQIECBAoIQEWguMGz9qWDOFwVuNafbXfs6n6YFwPnw+mp4HzwfPB8+HbwU8Hz0fPR+/FfD94PvB94Pvh9kCbf39OGTf8C5qCf13D60SIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAi0jYCXdNrG3awECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLtVEBg3Dcb29aBBeYXKNT0ESNQR6BO0/Pg+eD54PnwrYDno+ej5+O3Ar4ffD/4fpj/7weBce30P3BoiwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoKgEBMYV1XZYDAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQHsV+G6Q3Hf/h3o/j4ame88nmv1ZrvPhfPh8fCvg+eD50PTz4Pno+ej56Pk4W8D3g+8H3w/fCrT192N7/fd6fREgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoSwGBcW2pb24CBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGSEWjr/2Hf/AJ1mn7YBKoIVGl6HjwfPB88H74V8Hz0fPR8/FbA94PvB98Pvh9mC2T7/Vgy/6KvUQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIFFPj/WbzGY7BvLVEAAAAASUVORK5CYII=)

# 1. 安装依赖

首先，让我们安装依赖：

```python
!pip install -q git+https://github.com/huggingface/transformers.git#egg=transformers[agents]
```

```python
!pip install markdownify duckduckgo-search spaces gradio-tools langchain langchain-community langchain-huggingface faiss-cpu --upgrade -q
```

让我们登录来调用 HF 推理 API

```python
from huggingface_hub import notebook_login

notebook_login()
```

# 2. 创建我们的多智能体 RAG 系统

在本节中，我们将创建我们的 RAG 系统中所涉及的每个智能体。

我们将拥有 3 个由中央智能体管理的智能体（请参见图片了解详细信息）：

* **🕵💬 网络搜索智能体**：它将包含 [`DuckDuckGoSearchTool`](https://github.com/huggingface/transformers/blob/main/src/transformers/agents/search.py) 工具和 [`VisitWebpageTool`](https://github.com/huggingface/transformers/blob/main/src/transformers/agents/search.py) 工具。正如您所看到的，每个智能体可以包含一组工具。
* **🕵💬 检索智能体**：它将包含两个工具，用于从两个不同的知识库中检索信息。
* **🕵💬 图像生成智能体**：它将包含一个提示生成工具，并附带图像生成工具。

💡 除了这些智能体，**中央/协调智能体** 还将访问 **代码解释器工具** 以执行代码。

我们将使用 [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) 作为每个组件的语言模型（LLM），该模型将通过推理 API 进行访问。根据智能体的不同，可能会使用不同的 LLM 模型。

> _注意：_ 推理 API 托管的模型根据不同的标准进行选择，已部署的模型可能会在没有通知的情况下更新或替换。了解更多信息，请访问 [此链接](https://huggingface.co/docs/api-inference/supported-models)。

```python
from transformers.agents import HfApiEngine

llm_model = "Qwen/Qwen2.5-72B-Instruct"
llm_engine = HfApiEngine(llm_model)
```

让我们深入了解每个智能体的详细信息！

## 2.1 网络搜索智能体 🔍

**网络搜索智能体**将使用 [`DuckDuckGoSearchTool`](https://github.com/huggingface/transformers/blob/main/src/transformers/agents/search.py) 来进行网页搜索并收集相关信息。这个工具充当搜索引擎，根据指定的关键词进行查询。

为了使搜索结果可操作，我们还需要让智能体能够访问 DuckDuckGo 检索到的网页。这可以通过使用内置的 [`VisitWebpageTool`](https://github.com/huggingface/transformers/blob/main/src/transformers/agents/search.py) 来实现。

让我们探索如何设置并将其集成到我们的系统中！

以下代码来自原始的 [让多个智能体在多智能体层级中协作 🤖🤝🤖](https://huggingface.co/learn/cookbook/multiagent_web_assistant) 示例，因此可以参考该教程了解更多细节。

### 2.1.1 构建我们的多工具网络智能体 🤖

现在我们已经设置了基本的搜索和网页工具，接下来让我们构建一个 **多工具网络智能体**。这个智能体将结合多个工具，执行更复杂的任务，并利用 `ReactJsonAgent` 的能力。

`ReactJsonAgent` 特别适合用于网络搜索任务，因为它的 JSON 动作形式只需要简单的参数，并且能够在单一动作的顺序链中无缝工作。这使得它成为搜索相关信息并从特定网页检索详细内容的理想选择。相比之下，`CodeAgent` 的动作形式更适合涉及多个或并行工具调用的场景。

通过集成多个工具，我们可以确保我们的智能体以更加复杂和高效的方式与网络进行交互。

让我们深入了解如何设置这个智能体，并将其集成到我们的系统中！

```python
from transformers.agents import ReactCodeAgent, ReactJsonAgent, ManagedAgent
from transformers.agents.search import DuckDuckGoSearchTool, VisitWebpageTool

web_agent = ReactJsonAgent(
    tools=[DuckDuckGoSearchTool(), VisitWebpageTool()],
    llm_engine=llm_engine
)
```

现在我们已经创建了第一个智能体，接下来让我们将其包装为一个 `ManagedAgent`，这样中央智能体就可以使用它了。

```python
managed_web_agent = ManagedAgent(
    agent=web_agent,
    name="search",
    description="Runs web searches for you. Give it your query as an argument.",
)
```

## 2.2 检索智能体 🤖🔍

我们的多智能体系统中的第二个智能体是 **检索智能体**。该智能体负责从不同来源收集相关信息。为了实现这一目标，它将利用两个工具，从两个独立的知识库中检索数据。

我们将重用在其他 RAG 示例中使用过的两个数据源，这将使检索器能够高效地收集信息，以供后续处理。

通过利用这些工具，检索智能体可以访问多样化的数据集，确保在将信息传递给系统的下一步之前，能够全面收集到相关的信息。

让我们探索如何设置检索器，并将其集成到我们的多智能体系统中！

### 2.2.1 HF 文档检索工具 📚

第一个检索工具来自于 [Agentic RAG: 使用查询重构和自我查询加速你的 RAG 🚀](https://huggingface.co/learn/cookbook/agent_rag) 示例。

对于这个检索器，我们将使用一个包含各种 `huggingface` 包文档页面的数据库，所有文档都存储为 Markdown 文件。这个数据集将作为检索智能体的知识库，用于搜索和检索相关的文档。

为了使我们的智能体能够轻松访问这个数据集，我们将执行以下步骤：

1. **下载数据集**：我们首先获取 Markdown 格式的文档。
2. **嵌入数据**：然后我们将使用 **FAISS 向量存储** 将文档转换为嵌入，以便高效地进行相似度搜索。

通过这种方式，检索工具可以根据搜索查询快速访问相关的文档片段，使智能体能够提供准确和详细的信息。

让我们继续设置这个工具，处理文档检索！

```python
import datasets

knowledge_base = datasets.load_dataset("m-ric/huggingface_doc", split="train")
```

```python
from tqdm import tqdm
from transformers import AutoTokenizer
from langchain.docstore.document import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores.utils import DistanceStrategy

source_docs = [
    Document(page_content=doc["text"], metadata={"source": doc["source"].split("/")[1]})
    for doc in knowledge_base
]

text_splitter = RecursiveCharacterTextSplitter.from_huggingface_tokenizer(
    AutoTokenizer.from_pretrained("thenlper/gte-small"),
    chunk_size=200,
    chunk_overlap=20,
    add_start_index=True,
    strip_whitespace=True,
    separators=["\n\n", "\n", ".", " ", ""],
)

# Split docs and keep only unique ones
print("Splitting documents...")
docs_processed = []
unique_texts = {}
for doc in tqdm(source_docs):
    new_docs = text_splitter.split_documents([doc])
    for new_doc in new_docs:
        if new_doc.page_content not in unique_texts:
            unique_texts[new_doc.page_content] = True
            docs_processed.append(new_doc)

print("Embedding documents...")
embedding_model = HuggingFaceEmbeddings(model_name="thenlper/gte-small")
huggingface_doc_vector_db = FAISS.from_documents(
    documents=docs_processed,
    embedding=embedding_model,
    distance_strategy=DistanceStrategy.COSINE,
)
```

现在我们已经将文档嵌入到 FAISS 向量存储中，让我们创建 **RetrieverTool**。这个工具将查询 FAISS 向量存储，以根据用户的查询检索最相关的文档。

这将使检索智能体能够在收到查询时，访问并提供相关的文档内容。

```python
from transformers.agents import Tool
from langchain_core.vectorstores import VectorStore

class RetrieverTool(Tool):
    name = "retriever"
    description = "Using semantic similarity, retrieves some documents from the knowledge base that have the closest embeddings to the input query."
    inputs = {
        "query": {
            "type": "string",
            "description": "The query to perform. This should be semantically close to your target documents. Use the affirmative form rather than a question.",
        }
    }
    output_type = "string"

    def __init__(self, vectordb: VectorStore, **kwargs):
        super().__init__(**kwargs)
        self.vectordb = vectordb

    def forward(self, query: str) -> str:
        assert isinstance(query, str), "Your search query must be a string"

        docs = self.vectordb.similarity_search(
            query,
            k=7,
        )

        return "\nRetrieved documents:\n" + "".join(
            [
                f"===== Document {str(i)} =====\n" + doc.page_content
                for i, doc in enumerate(docs)
            ]
        )
```

```python
huggingface_doc_retriever_tool = RetrieverTool(huggingface_doc_vector_db)
```

### 2.2.2 PEFT 问题检索工具

对于第二个检索器，我们将使用 [PEFT 问题](https://github.com/huggingface/peft/issues) 作为数据源，正如在 [使用 Hugging Face Zephyr 和 LangChain 的简单 RAG 架构处理 GitHub 问题](https://huggingface.co/learn/cookbook/rag_zephyr_langchain) 中所示。

同样，以下代码来自该示例，因此可以参考该教程了解更多细节！

```python
from google.colab import userdata
GITHUB_ACCESS_TOKEN = userdata.get('GITHUB_PERSONAL_TOKEN')
```

```python
from langchain.document_loaders import GitHubIssuesLoader

loader = GitHubIssuesLoader(repo="huggingface/peft", access_token=GITHUB_ACCESS_TOKEN, include_prs=False, state="all")
docs = loader.load()
```

```python
splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=30)
chunked_docs = splitter.split_documents(docs)
```

```python
peft_issues_vector_db = FAISS.from_documents(chunked_docs, embedding=embedding_model)
```

现在，让我们使用相同的 `RetrieverTool` 来生成第二个检索工具。

```python
peft_issues_retriever_tool = RetrieverTool(peft_issues_vector_db)
```

### 2.2.3 构建检索智能体

现在我们已经创建了两个检索工具，接下来是时候构建 **检索智能体** 了。这个智能体将管理这两个工具，并根据用户的查询检索相关信息。

我们将使用 `ManagedAgent` 来集成这两个工具，并将该智能体传递给中央智能体进行协调。这样，中央智能体就能够控制和调度检索智能体，从而确保系统在执行任务时能够有效地检索并提供信息。

```python
retriever_agent = ReactJsonAgent(
    tools=[huggingface_doc_retriever_tool, peft_issues_retriever_tool], llm_engine=llm_engine, max_iterations=4, verbose=2
)
```

```python
managed_retriever_agent = ManagedAgent(
    agent=retriever_agent,
    name="retriever",
    description="Retrieves documents from the knowledge base for you that are close to the input query. Give it your query as an argument. The knowledge base includes Hugging Face documentation and PEFT issues.",
)
```

## 2.3 图像生成智能体 🎨

系统中的第三个智能体是 **图像生成智能体**。该智能体将有两个工具：一个用于优化用户查询，另一个用于根据查询生成图像。在这种情况下，我们将使用 `CodeAgent` 而不是 `ReactAgent`，因为一组动作可以一次性执行。

关于图像生成智能体的更多细节，你可以参考 [Agents, supercharged - Multi-agents, External tools, and more](https://huggingface.co/docs/transformers/en/agents_advanced) 文档。

让我们深入了解这些工具如何协同工作，根据用户输入生成图像！

```python
from transformers import load_tool, CodeAgent

prompt_generator_tool = Tool.from_space("sergiopaniego/Promptist", name="generator_tool", description="Optimizes user input into model-preferred prompts")
```

```python
image_generation_tool = load_tool("m-ric/text-to-image", cache=False)
image_generation_agent = CodeAgent(tools=[prompt_generator_tool, image_generation_tool], llm_engine=llm_engine)
```

🖼 同样，我们使用 `ManagedAgent` 来告诉中央智能体它可以管理该智能体。此外，我们还包含了一个 `additional_prompting` 参数，以确保智能体返回生成的图像，而不仅仅是文本描述。


```python
managed_image_generation_agent = ManagedAgent(
    agent=image_generation_agent,
    name="image_generation",
    description="Generates images from text prompts. Give it your prompt as an argument.",
    additional_prompting="\n\nYour final answer MUST BE only the generated image location."
)
```

# 3. 添加中央智能体管理器来协调系统

**中央智能体管理器**将负责协调各个智能体之间的任务。具体来说，它将：

- **接收用户输入**并决定由哪个智能体（网络搜索、检索、图像生成）来处理。
- **委派任务**给合适的智能体，依据用户的查询类型。
- **收集并综合**来自各个智能体的结果。
- **将最终输出**返回给用户。

我们将所有已经开发的智能体作为 `managed_agents` 添加进来，并在 `additional_authorized_imports` 中加入必要的代码执行器导入。这样，中央智能体管理器不仅能协调任务，还能确保系统能够根据不同的需求动态选择合适的智能体并执行复杂的操作。

```python
manager_agent = ReactCodeAgent(
    tools=[],
    llm_engine=llm_engine,
    managed_agents=[managed_web_agent, managed_retriever_agent, managed_image_generation_agent],
    additional_authorized_imports=["time", "datetime", "PIL"],
)
```

现在，一切都已设置完毕，让我们测试多智能体 RAG 系统的性能！

为此，我们将提供一些示例查询，观察系统如何在智能体之间委派任务、处理信息并返回最终结果。

这将帮助我们了解智能体协作的效率和有效性，并在必要时识别出优化的方向。

让我们开始运行一些测试吧！

## 3.1 示例：触发搜索智能体

```python
manager_agent.run("How many years ago was Stripe founded?")
```

## 3.2  示例：触发图像生成智能体

```python
result = manager_agent.run(
    "Improve this prompt, then generate an image of it.", prompt='A rabbit wearing a space suit'
)
```

```python
>>> from IPython.display import Image, display
>>> display(Image(filename=result))
```

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">


## 3.3 示例：触发检索智能体以访问 Hugging Face 文档知识库

```python
manager_agent.run("How can I push a model to the Hub?")
```

## 3.4 示例：触发检索智能体以访问 PEFT 问题知识库

```python
manager_agent.run("How do you combine multiple adapters in peft?")
```

🏁 **最终思考**

我们已经成功构建了一个多智能体 RAG 系统，该系统集成了网络搜索、文档检索和图像生成智能体，所有智能体都由中央智能体管理器协调。这个架构使得任务能够无缝分配，处理效率高，并且能够灵活地处理各种用户查询。

🔍 **深入探索**

- [智能体指南](agents)
- [高级 RAG 指南](advanced_rag)
- [更多指南](https://huggingface.co/learn/cookbook/index)
- [了解更多关于 Agentic RAG](https://weaviate.io/blog/what-is-agentic-rag)

感谢你跟随我们完成这一旅程！希望这个系统能为你带来更多灵感，帮助你构建更智能、更高效的应用。

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/multiagent_rag_system.md" />

### 使用大型语言模型作为评审者清理现有的偏好数据集
https://huggingface.co/learn/cookbook/zh-CN/clean_dataset_judges_distilabel.md

## 使用大型语言模型作为评审者清理现有的偏好数据集

_作者：[David Berenstein](https://huggingface.co/davidberenstein1957) 和 [Sara Han Díaz](https://huggingface.co/sdiazlor)_

- **库**: [argilla](https://github.com/argilla-io/argilla), [hf-inference-endpoints](https://github.com/huggingface/huggingface_hub)
- **组件**: [LoadDataFromDicts](https://distilabel.argilla.io/dev/components-gallery/steps/loaddatafromdicts/), [UltraFeedback](https://distilabel.argilla.io/latest/components-gallery/tasks/ultrafeedback/), [KeepColumns](https://distilabel.argilla.io/latest/components-gallery/steps/groupcolumns/), [PreferenceToArgilla](https://distilabel.argilla.io/latest/components-gallery/steps/textgenerationtoargilla/), [InferenceEndpointsLLM](https://distilabel.argilla.io/latest/components-gallery/llms/inferenceendpointsllm/), [GlobalStep](https://distilabel.argilla.io/latest/sections/how_to_guides/basic/step/global_step/)

在本教程中，我们将使用 **distilabel** 清理数据集，利用大型语言模型（LLMs）作为评审者，通过提供 AI 反馈来评估数据的质量。[distilabel](https://github.com/argilla-io/distilabel) 是一个用于工程师的合成数据和 AI 反馈框架，帮助快速、可靠且可扩展地构建基于经过验证的研究论文的管道。查看文档 [这里](https://distilabel.argilla.io/latest/)。

为了评估响应，我们将使用与 distilabel 集成的 [无服务器 HF 推理 API](https://huggingface.co/docs/api-inference/index)。该服务免费但有请求限制，允许你通过简单的 HTTP 请求测试和评估超过 150,000 个公开模型，或者使用你自己的私有模型，推理任务在 Hugging Face 共享基础设施上进行。如果需要更多计算能力，您可以使用 [Hugging Face 推理端点](https://huggingface.co/docs/inference-endpoints/guides/create_endpoint) 部署自己的推理端点。

最后，为了进一步整理数据，我们将使用 [Argilla](https://github.com/argilla-io/argilla)，它允许我们对数据质量提供人工反馈。Argilla 是一个为 AI 工程师和领域专家提供的协作工具，帮助他们为项目构建高质量的数据集。查看文档 [这里](https://docs.argilla.io/latest/)。

## 开始

### 安装依赖

为了完成本教程，你需要通过 pip 安装 distilabel SDK 和一些第三方库。

```python
!pip install "distilabel[hf-inference-endpoints]"
```

```python
!pip install "transformers~=4.0" "torch~=2.0"
```

让我们进行必要的导入：

```python
import random

from datasets import load_dataset

from distilabel.llms import InferenceEndpointsLLM
from distilabel.pipeline import Pipeline
from distilabel.steps import (
    KeepColumns,
    LoadDataFromDicts,
    PreferenceToArgilla,
)
from distilabel.steps.tasks import UltraFeedback
```

你需要一个 `HF_TOKEN` 才能使用 HF 推理端点。在此 Notebook 中直接登录以使用它。

```python
import os
from huggingface_hub import login

login(token=os.getenv("HF_TOKEN"), add_to_git_credential=True)
```

### （可选）部署 Argilla

你可以跳过此步骤，或者将其替换为任何其他数据评估工具，但如果缺乏数据质量，模型的性能将受到影响，因此我们建议你查看你的数据。如果你已经部署了 Argilla，可以跳过此步骤。否则，你可以按照 [此指南](https://docs.argilla.io/latest/getting_started/quickstart/) 快速部署 Argilla。

同时，你需要将 Argilla 作为 distilabel 的附加组件安装。

```python
!pip install "distilabel[argilla, hf-inference-endpoints]"
```

## 数据集

在这种情况下，我们将清理一个偏好数据集，因此我们将使用 Hugging Face Hub 上的 [`Intel/orca_dpo_pairs`](https://huggingface.co/datasets/Intel/orca_dpo_pairs) 数据集。

<iframe
  src="https://huggingface.co/datasets/Intel/orca_dpo_pairs/embed/viewer/default/train"
  frameborder="0"
  width="100%"
  height="560px"
></iframe>

```python
dataset = load_dataset("Intel/orca_dpo_pairs", split="train[:20]")
```

接下来，我们将打乱 `chosen` 和 `rejected` 列，以避免数据集中的任何偏差。

```python
def shuffle_and_track(chosen, rejected):
    pair = [chosen, rejected]
    random.shuffle(pair)
    order = ["chosen" if x == chosen else "rejected" for x in pair]
    return {"generations": pair, "order": order}

dataset = dataset.map(lambda x: shuffle_and_track(x["chosen"], x["rejected"]))
```

```python
dataset = dataset.to_list()
```

### （可选）创建自定义步骤

步骤是 distilabel 管道中的一个模块，用于操作、生成或评估数据等任务。提供了一组预定义的步骤，但你也可以创建 [自定义步骤](https://distilabel.argilla.io/latest/sections/how_to_guides/basic/step/#defining-custom-steps)。与之前章节中的数据预处理不同，你可以使用自定义步骤来打乱列。这个步骤应该放在一个单独的模块中，以便导入并在管道中使用。在这种情况下，管道将首先使用 `LoadDataFromHub` 步骤加载 `orca_dpo_pairs` 数据集，然后应用 `ShuffleStep` 步骤。

```python
<CopyLLMTxtMenu containerStyle="float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"></CopyLLMTxtMenu>

# "shuffle_step.py"
from typing import TYPE_CHECKING, List
from distilabel.steps import GlobalStep, StepInput

if TYPE_CHECKING:
    from distilabel.steps.typing import StepOutput
    
import random

class ShuffleStep(GlobalStep):
    @property
    def inputs(self) -> List[str]:
        return ["instruction", "chosen", "rejected"]

    @property
    def outputs(self) -> List[str]:
        return ["instruction", "generations", "order"]

    def process(self, inputs: StepInput) -> "StepOutput":
        outputs = []

        for input in inputs:
            chosen = input["chosen"]
            rejected = input["rejected"]
            pair = [chosen, rejected]
            random.shuffle(pair)
            order = ["chosen" if x == chosen else "rejected" for x in pair]
            
            outputs.append({"instruction": input["instruction"], "generations": pair, "order": order})

        yield outputs
```

```python
from shuffle_step import ShuffleStep
```

## 定义管道

为了清理一个现有的偏好数据集，我们需要定义一个包含所有必要步骤的 `Pipeline`。类似的工作流也可以用于清理 SFT（监督微调）数据集。接下来，我们将详细讲解每个步骤。

### 加载数据集
我们将使用刚才打乱的数据集作为源数据。

- 组件：`LoadDataFromDicts`
- 输入列：`system`、`question`、`chosen`、`rejected`、`generations` 和 `order`，与加载的字典列表中的键相同。
- 输出列：`system`、`instruction`、`chosen`、`rejected`、`generations` 和 `order`。我们将使用 `output_mappings` 来重命名列。

```python
load_dataset = LoadDataFromDicts(
    data=dataset[:1],
    output_mappings={"question": "instruction"},
    pipeline=Pipeline(name="showcase-pipeline"),
)
load_dataset.load()
next(load_dataset.process())
```

### 评估响应

为了评估响应的质量，我们将使用 [`meta-llama/Meta-Llama-3.1-70B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct)，并应用 `UltraFeedback` 任务，根据不同维度（如有用性、诚实性、遵循指令的能力、真实性）来评判响应。对于 SFT 数据集，您可以改用 [`PrometheusEval`](../papers/prometheus.md)。

- 组件：使用 `InferenceEndpointsLLM` 的 `UltraFeedback` 任务
- 输入列：`instruction`、`generations`
- 输出列：`ratings`、`rationales`、`distilabel_metadata`、`model_name`

根据你的使用场景并为了提高结果，你可以使用任何 [你选择的其他 LLM](https://distilabel.argilla.io/latest/components-gallery/llms/)。

```python
evaluate_responses = UltraFeedback(
    aspect="overall-rating",
    llm=InferenceEndpointsLLM(
        model_id="meta-llama/Meta-Llama-3.1-70B-Instruct",
        tokenizer_id="meta-llama/Meta-Llama-3.1-70B-Instruct",
        generation_kwargs={"max_new_tokens": 512, "temperature": 0.7},
    ),
    pipeline=Pipeline(name="showcase-pipeline"),
)
evaluate_responses.load()
next(
    evaluate_responses.process(
        [
            {
                "instruction": "What's the capital of Spain?",
                "generations": ["Madrid", "Barcelona"],
            }
        ]
    )
)
```

### 仅保留必要的列

我们将去除不需要的列。

- 组件：`KeepColumns`
- 输入列：`system`、`instruction`、`chosen`、`rejected`、`generations`、`ratings`、`rationales`、`distilabel_metadata` 和 `model_name`
- 输出列：`instruction`、`chosen`、`rejected`、`generations` 和 `order`

```python
keep_columns = KeepColumns(
    columns=[
        "instruction",
        "generations",
        "order",
        "ratings",
        "rationales",
        "model_name",
    ],
    pipeline=Pipeline(name="showcase-pipeline"),
)
keep_columns.load()
next(
    keep_columns.process(
        [
            {
                "system": "",
                "instruction": "What's the capital of Spain?",
                "chosen": "Madrid",
                "rejected": "Barcelona",
                "generations": ["Madrid", "Barcelona"],
                "order": ["chosen", "rejected"],
                "ratings": [5, 1],
                "rationales": ["", ""],
                "model_name": "meta-llama/Meta-Llama-3.1-70B-Instruct",
            }
        ]
    )
)
```

### （可选）进一步的数据整理

你可以使用 Argilla 进一步整理您的数据。

- 组件：`PreferenceToArgilla` 步骤
- 输入列：`instruction`、`generations`、`generation_models`、`ratings`
- 输出列：`instruction`、`generations`、`generation_models`、`ratings`

```python
to_argilla = PreferenceToArgilla(
    dataset_name="cleaned-dataset",
    dataset_workspace="argilla",
    api_url="https://[your-owner-name]-[your-space-name].hf.space",
    api_key="[your-api-key]",
    num_generations=2
)
```

## 运行管道

下面，你可以看到完整管道定义:

```python
with Pipeline(name="clean-dataset") as pipeline:

    load_dataset = LoadDataFromDicts(
        data=dataset, output_mappings={"question": "instruction"}
    )

    evaluate_responses = UltraFeedback(
        aspect="overall-rating",
        llm=InferenceEndpointsLLM(
            model_id="meta-llama/Meta-Llama-3.1-70B-Instruct",
            tokenizer_id="meta-llama/Meta-Llama-3.1-70B-Instruct",
            generation_kwargs={"max_new_tokens": 512, "temperature": 0.7},
        ),
    )

    keep_columns = KeepColumns(
        columns=[
            "instruction",
            "generations",
            "order",
            "ratings",
            "rationales",
            "model_name",
        ]
    )

    to_argilla = PreferenceToArgilla(
        dataset_name="cleaned-dataset",
        dataset_workspace="argilla",
        api_url="https://[your-owner-name]-[your-space-name].hf.space",
        api_key="[your-api-key]",
        num_generations=2,
    )

    load_dataset.connect(evaluate_responses)
    evaluate_responses.connect(keep_columns)
    keep_columns.connect(to_argilla)
```

现在我们来运行管道，清理我们的偏好数据集。

```python
distiset = pipeline.run()
```

让我们检查一下！如果你已经将数据加载到 Argilla 中，你可以在 [Argilla UI 中开始标注](https://docs.argilla.io/latest/how_to_guides/annotate/)。

你可以将数据集推送到 Hub 以便与社区共享，并 [嵌入它以探索数据](https://huggingface.co/docs/hub/datasets-viewer-embed)。

```python
distiset.push_to_hub("[your-owner-name]/example-cleaned-preference-dataset")
```

<iframe
  src="https://huggingface.co/datasets/distilabel-internal-testing/example-cleaned-preference-dataset/embed/viewer/default/train"
  frameborder="0"
  width="100%"
  height="560px"
></iframe>

## 总结

在本教程中，我们展示了使用 distilabel 构建清理偏好数据集管道的详细步骤。然而，你可以根据自己的使用场景自定义此管道，例如清理 SFT 数据集或添加自定义步骤。

我们以一个偏好数据集作为起点，并通过打乱数据来避免任何偏差。接下来，我们使用一个模型通过无服务器的 Hugging Face 推理 API 评估了响应，遵循了 UltraFeedback 标准。最后，我们保留了必要的列，并使用 Argilla 进行了进一步的数据整理。

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/clean_dataset_judges_distilabel.md" />

### 用 Hugging Face Zephyr 和 LangChain 针对 Github issues 构建简单的 RAG
https://huggingface.co/learn/cookbook/zh-CN/rag_zephyr_langchain.md

# 用 Hugging Face Zephyr 和 LangChain 针对 Github issues 构建简单的 RAG

_作者: [Maria Khalusova](https://github.com/MKhalusova)_

本 notebook 展示了如何使用 [`HuggingFaceH4/zephyr-7b-beta`](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) 模型和 LangChain 快速构建一个针对项目 GitHub issues 的简单 RAG。



**什么是 RAG**

RAG 是一个很流行的方法，用来解决强大的 LLM 不知道具体内容的问题，因为具体内容不在其训练数据中，或者当它看到它之前时产生幻觉。这样的具体内容可能是专有的、敏感的，或者，就像这个例子中一样，是最近的和更新的。

如果你的数据集是静态的和不需要定期更新的，那么你可能会考虑微调一个大模型。但在大多数情况下，微调模型花费巨大并且重复去微调的话(比如，处理数据漂移的时候)，可能会导致“模型偏移”。这种情况模型行为的变换就不是设计的那样了。

**RAG (检索增强生成)** 并不需要模型微调。相反， RAG 通过提供检索到的额外的相关内容喂给 LLM 以此来获得更好的回答。

这里是一个简单说明：

![RAG diagram](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/rag-diagram.png)

* 额外的数据通过独立的嵌入模型会被转化为嵌入向量，这些向量会储存在向量数据库里。嵌入模型通常都比较小，因此在常规偏差上更新嵌入向量相比于微调模型会更快，便宜，和简单。

* 与此同时，由于不需要微调，给了你极大的自由度去切换选择你自己的更强的 LLM，或者对于更快速的推理去切换更小的蒸馏模型。

让我们用开源的 LLM ，嵌入模型，和 LangChain 快速构建一个针对项目 GitHub issues 的简单 RAG。


首先安装相关依赖：

```python
!pip install -q torch transformers accelerate bitsandbytes transformers sentence-transformers faiss-gpu
```

```python
# If running in Google Colab, you may need to run this cell to make sure you're using UTF-8 locale to install LangChain
import locale
locale.getpreferredencoding = lambda: "UTF-8"
```

```python
!pip install -q langchain langchain-community
```

## 准备数据


在这个例子中，我们会从[PEFT 库的仓库](https://github.com/huggingface/peft)加载所有的 issues（包括现在开放的和已经关闭的）。

首先，你需要获取一个 [GitHub 个人权限 token](https://github.com/settings/tokens?type=beta) 来访问 GitHub API。

```python
from getpass import getpass
ACCESS_TOKEN = getpass("YOUR_GITHUB_PERSONAL_TOKEN")
```

下一步，我们将会加载  [huggingface/peft](https://github.com/huggingface/peft) 仓库中所有的 issues:
- 默认情况下， PR 也被认定为 issues，这里我们要设置 `include_prs=False` 来排除 PR。
- 设置 `state = "all"` 意味着我们会把开放和已经关闭的 issues 都加载了。

```python
from langchain.document_loaders import GitHubIssuesLoader

loader = GitHubIssuesLoader(
    repo="huggingface/peft",
    access_token=ACCESS_TOKEN,
    include_prs=False,
    state="all"
)

docs = loader.load()
```

个人仓库的 issues 内容可能会长于一个嵌入模型可以最为输入处理的长度。如果我们想要嵌入所有可用的内容，我们需要把文档分割成适当大小的块。

最普通直接的切块方法就是定义一个固定的块大小，以及判断块之间是否加入重叠。保存一些块之间的重叠允许我们去保存一些语义上下文。

其他方法通常更复杂，会考虑到文档的结构和上下文。例如，人们可能希望根据句子或段落来分割文档，然而，固定大小的分块在大多数常见情况下都表现得很好，所以我们将在这里采用这种方法。


```python
from langchain.text_splitter import RecursiveCharacterTextSplitter

splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=30)

chunked_docs = splitter.split_documents(docs)
```

## 创建嵌入和检索器

现在所有的文档都设置成立合适的大小，我们可以用他们的嵌入创建一个数据集了。

为了创建文档块嵌入，我们将会使用 `HuggingFaceEmbeddings` 和 [`BAAI/bge-base-en-v1.5`](https://huggingface.co/BAAI/bge-base-en-v1.5) 嵌入模型。在 Hub 上有许多其他的嵌入模型可用，你也可以查看 [Massive Text Embedding Benchmark (MTEB) Leaderboard](https://huggingface.co/spaces/mteb/leaderboard) 关注表现最好的模型。

为了创建向量数据库，我们将会使用 `FAISS` 库。这个库提供高效的相似度搜索和稠密向量的聚类，正是我们需要的。FAISS 目前是大规模数据集上 NN 搜索最常用的库之一。

我们通过 LangChain 的 API 来获取嵌入模型和 FAISS 向量数据库。

```python
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings

db = FAISS.from_documents(chunked_docs,
                          HuggingFaceEmbeddings(model_name='BAAI/bge-base-en-v1.5'))
```

我们需要一种方式，来返回给定无结构的查询所需要的文档。针对这个，我们会使用 `as_retriever` 方法，使用 `db` 作为支柱：
- `search_type="similarity"` 意味着我们会执行查询和文档之间的相似度搜索
- `search_kwargs={'k': 4}` 指示我们指定返回的最高的 4 个结果


```python
retriever = db.as_retriever(
    search_type="similarity",
    search_kwargs={'k': 4}
)
```

向量数据库和检索器现在设置好了，下一步我们需要设置好链中的下一块 - 模型。

## 加载量化模型

针对本例，我们选择 [`HuggingFaceH4/zephyr-7b-beta`](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), 一个小而强大的模型。

随着每周都会出好多模型，你可能会想要替换这个模型到最新的最好的模型。最好的方式是查看 [Open-source LLM leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)。

为了推理更快，我们将加载模型的量化版本：

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

model_name = 'HuggingFaceH4/zephyr-7b-beta'

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=bnb_config)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```

## 设置 LLM 链

最后，我们有了所有的需要设置的 LLM 链的部分。

首先，使用加载的模型和他的tokenizer创建一个文本生成的流水线(pipeline)

下一步，创建一个提示模板-这个应该遵循模型的格式，所以如果你替换了模型检查点，确保使用合适的格式。


```python
from langchain.llms import HuggingFacePipeline
from langchain.prompts import PromptTemplate
from transformers import pipeline
from langchain_core.output_parsers import StrOutputParser

text_generation_pipeline = pipeline(
    model=model,
    tokenizer=tokenizer,
    task="text-generation",
    temperature=0.2,
    do_sample=True,
    repetition_penalty=1.1,
    return_full_text=True,
    max_new_tokens=400,
)

llm = HuggingFacePipeline(pipeline=text_generation_pipeline)

prompt_template = """
<|system|>
Answer the question based on your knowledge. Use the following context to help:

{context}

</s>
<|user|>
{question}
</s>
<|assistant|>

 """

prompt = PromptTemplate(
    input_variables=["context", "question"],
    template=prompt_template,
)

llm_chain = prompt | llm | StrOutputParser()
```

注意：你也可以使用 `tokenizer.apply_chat_template` 转换列表消息为合适聊天格式的字符串（字典也行  `{'role': 'user', 'content': '(...)'}`）

最后，我们需要将 LLM 链与检索器(retriever)结合起来创建一个 RAG 链。我们将原始问题以及检索到的文档上下文传递到最后生成步骤：

```python
from langchain_core.runnables import RunnablePassthrough

retriever = db.as_retriever()

rag_chain = (
 {"context": retriever, "question": RunnablePassthrough()}
    | llm_chain
)
```

## 比较结果

让我们看看对于特定领域库的问题不同的 RAG 的生成的回答。

```python
question = "How do you combine multiple adapters?"
```

首先，让我们看看仅仅通过模型自身不加检索内容能得到什么答案:

```python
llm_chain.invoke({"context":"", "question": question})
```

可以看到，模型将这个问题解释为关于物理电脑适配器的问题，而在 PEFT 的背景下，“适配器”指的是 LoRA 适配器。
让我们看看添加 GitHub issues 的上下文是否有助于模型给出更相关的答案：


```python
rag_chain.invoke(question)
```

我们可以看到，加入检索的信息后，同一个模型能够对于特定库的问题给出更准确、更相关的答案。

值得注意的是，将多个适配器结合用于推理的功能已经被添加到库中，人们可以在文档中找到这些信息，因此在下一个迭代的RAG中，包含文档嵌入可能是有价值的。

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/rag_zephyr_langchain.md" />

### 使用 Stable Diffusion 进行图像插值
https://huggingface.co/learn/cookbook/zh-CN/stable_diffusion_interpolation.md

## 使用 Stable Diffusion 进行图像插值


_作者: [Rustam Akimov](https://github.com/AkiRusProd)_

这个 notebook 展示了如何使用 Stable Diffusion 来对图像进行插值。使用 Stable Diffusion 的图像插值是通过基于扩散的生成模型，从一张给定的图像平滑过渡到另一张图像，创建中间图像的过程。

以下是一些使用 Stable Diffusion 进行图像插值的不同应用场景：
- 数据增强： Stable Diffusion 可以通过生成介于现有数据点之间的合成图像，来增强机器学习模型的训练数据。这可以提高机器学习模型的一般化和鲁棒性，特别是在图像生成、分类或对象检测等任务中。
- 产品设计和原型制作： Stable Diffusion 可以通过生成具有微妙差异的产品设计或原型变体，来辅助产品设计。这对于探索设计替代方案、进行用户研究或在投入物理原型之前可视化设计迭代非常有用。
- 媒体制作内容生成：在媒体制作中，如电影和视频编辑， Stable Diffusion 可以用来生成关键帧之间的中间帧，实现更平滑的过渡并增强视觉叙事。与手动逐帧编辑相比，这可以节省时间和资源。

在图像插值的背景下， Stable Diffusion 模型通常用于在多维潜在空间中导航。每个维度代表模型学到的特定特征。通过在这个潜在空间中行走并在不同图像的潜在表示之间进行插值，模型能够生成一系列中间图像，这些图像显示了原始图像之间的平滑过渡。 Stable Diffusion 中有两种类型的潜在：提示潜在和图像潜在。

潜在空间行走涉及沿着由两个或多个点（代表图像）定义的路径在潜在空间中移动。通过仔细选择这些点和它们之间的路径，可以控制生成图像的特征，如风格、内容和其他视觉方面。

在这个 notebook 中，我们将探索使用 Stable Diffusion 进行图像插值的示例，并展示如何实现和利用潜在空间行走来创建图像之间的平滑过渡。我们将提供代码片段和可视化来展示这个过程的效果，从而更深入地理解生成模型如何以有意义的方式操纵和转化图像表示。

首先，让我们安装所有需要的模块

```python
!pip install -q diffusers transformers xformers accelerate
!pip install -q numpy scipy ftfy Pillow
```

导入模块

```python
import torch
import numpy as np
import os

import time

from PIL import Image
from IPython import display as IPdisplay
from tqdm.auto import tqdm

from diffusers import StableDiffusionPipeline
from diffusers import (
    DDIMScheduler,
    PNDMScheduler,
    LMSDiscreteScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
)
from transformers import logging

logging.set_verbosity_error()
```

让我们查看一下 CUDA 是否可用




```python
print(torch.cuda.is_available())

device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
```

这些设置用于优化在启用 CUDA 的 GPU 上 PyTorch 模型的性能，尤其是在使用混合精度训练或推理时，这在速度和内存使用方面可能有益。

来源：https://huggingface.co/docs/diffusers/optimization/fp16#memory-efficient-attention


```python
torch.backends.cudnn.benchmark = True
torch.backends.cuda.matmul.allow_tf32 = True
```

### 模型

我们在这个项目中使用了 [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) 模型和 [`LMSDiscreteScheduler`](https://huggingface.co/docs/diffusers/en/api/schedulers/lms_discrete) 调度器来生成图片。尽管这个模型已经不是最新的技术，但因为它的速度快、对内存的需求小，而且有很多社区成员基于这个版本改进的模型，所以还是挺受欢迎的。当然，如果你想尝试其他模型或调度器来生成图片，也是可以的。


```python
model_name_or_path = "runwayml/stable-diffusion-v1-5"

scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)


pipe = StableDiffusionPipeline.from_pretrained(
    model_name_or_path,
    scheduler=scheduler,
    torch_dtype=torch.float32,
).to(device)

<CopyLLMTxtMenu containerStyle="float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"></CopyLLMTxtMenu>

# Disable image generation progress bar, we'll display our own
pipe.set_progress_bar_config(disable=True)
```

这些方法旨在减少 GPU 消耗的内存。如果你有足够的显存，可以跳过这个步骤。

更详细的信息可以在这里找到：https://huggingface.co/docs/diffusers/en/optimization/opt_overview  
特别是，关于以下方法的信息可以在这里找到：https://huggingface.co/docs/diffusers/optimization/memory


```python
# Offloading the weights to the CPU and only loading them on the GPU can reduce memory consumption to less than 3GB.
pipe.enable_model_cpu_offload()

# Tighter ordering of memory tensors.
pipe.unet.to(memory_format=torch.channels_last)

# Decoding large batches of images with limited VRAM or batches with 32 images or more by decoding the batches of latents one image at a time.
pipe.enable_vae_slicing()

# Splitting the image into overlapping tiles, decoding the tiles, and then blending the outputs together to compose the final image. 
pipe.enable_vae_tiling()

# Using Flash Attention; If you have PyTorch >= 2.0 installed, you should not expect a speed-up for inference when enabling xformers.
pipe.enable_xformers_memory_efficient_attention()
```

`display_images` 函数将图像数组的列表转换成 GIF，保存到指定路径，并返回 GIF 对象以供显示。它使用当前时间来命名 GIF 文件，并通过打印出来处理任何错误。


```python
def display_images(images, save_path):
    try:
        # Convert each image in the 'images' list from an array to an Image object.
        images = [
            Image.fromarray(np.array(image[0], dtype=np.uint8)) for image in images
        ]

        # Generate a file name based on the current time, replacing colons with hyphens
        # to ensure the filename is valid for file systems that don't allow colons.
        filename = (
            time.strftime("%H:%M:%S", time.localtime())
            .replace(":", "-")
        )
        # Save the first image in the list as a GIF file at the 'save_path' location.
        # The rest of the images in the list are added as subsequent frames to the GIF.
        # The GIF will play each frame for 100 milliseconds and will loop indefinitely.
        images[0].save(
            f"{save_path}/{filename}.gif",
            save_all=True,
            append_images=images[1:],
            duration=100,
            loop=0,
        )
    except Exception as e:
        # If there is an error during the process, print the exception message.
        print(e)

    # Return the saved GIF as an IPython display object so it can be displayed in a notebook.
    return IPdisplay.Image(f"{save_path}/{filename}.gif")
```

### 生成参数


* `seed`: 这个变量用于设置一个特定的随机种子，以便复现结果。
* `generator`: 如果提供了种子，这将设置为一个 PyTorch 随机数生成器对象，否则为 None。它确保使用它的操作具有可复现的结果。
* `guidance_scale`: 这个参数控制模型在文本到图像生成任务中遵循提示的程度，值越高，对提示的遵循越强。
* `num_inference_steps`: 这指定了模型生成图像所需的步骤数。更多的步骤可以导致生成更高质量的图像，但生成时间会更长。
* `num_interpolation_steps`: 这决定了在潜在空间中两点之间插值时使用的步骤数，影响生成动画中过渡的平滑度。
* `height`: 生成图像的高度，以像素为单位。
* `width`: 生成图像的宽度，以像素为单位。
* `save_path`: 生成的 GIF 将保存的文件系统路径。 

```python
# The seed is set to "None", because we want different results each time we run the generation.
seed = None

if seed is not None:
    generator = torch.manual_seed(seed)
else:
    generator = None

# The guidance scale is set to its normal range (7 - 10).
guidance_scale = 8

# The number of inference steps was chosen empirically to generate an acceptable picture within an acceptable time.
num_inference_steps = 15

# The higher you set this value, the smoother the interpolations will be. However, the generation time will increase. This value was chosen empirically.
num_interpolation_steps = 30

# I would not recommend less than 512 on either dimension. This is because this model was trained on 512x512 image resolution.
height = 512 
width = 512

# The path where the generated GIFs will be saved
save_path = "/output"

if not os.path.exists(save_path):
    os.makedirs(save_path)
```

### 示例 1：提示插值

在这个例子中，我们将通过在积极提示和消极提示之间进行插值，来探索这两个提示定义的概念之间的空间。这样做可以让我们看到一系列逐渐融合这两种提示特征的图像。具体来说，我们会修改原始提示的嵌入向量，逐渐添加一些小的变化，从而创建出一系列新的提示嵌入。这些新的嵌入将被用来生成图像，这些图像会平滑地从一种提示的状态过渡到另一种。



![Example 1](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/sd_interpolation_1.gif)

首先，我们需要对积极和消极的文本提示进行标记化并获得它们的嵌入。积极提示引导图像生成朝向期望的特征，而消极提示则使其远离不希望出现的特征。

```python
# The text prompt that describes the desired output image.
prompt = "Epic shot of Sweden, ultra detailed lake with an ren dear, nostalgic vintage, ultra cozy and inviting, wonderful light atmosphere, fairy, little photorealistic, digital painting, sharp focus, ultra cozy and inviting, wish to be there. very detailed, arty, should rank high on youtube for a dream trip."
# A negative prompt that can be used to steer the generation away from certain features; here, it is empty.
negative_prompt = "poorly drawn,cartoon, 2d, disfigured, bad art, deformed, poorly drawn, extra limbs, close up, b&w, weird colors, blurry"

# The step size for the interpolation in the latent space.
step_size = 0.001

# Tokenizing and encoding the prompt into embeddings.
prompt_tokens = pipe.tokenizer(
    prompt,
    padding="max_length",
    max_length=pipe.tokenizer.model_max_length,
    truncation=True,
    return_tensors="pt",
)
prompt_embeds = pipe.text_encoder(prompt_tokens.input_ids.to(device))[0]


# Tokenizing and encoding the negative prompt into embeddings.
if negative_prompt is None:
    negative_prompt = [""]

negative_prompt_tokens = pipe.tokenizer(
    negative_prompt,
    padding="max_length",
    max_length=pipe.tokenizer.model_max_length,
    truncation=True,
    return_tensors="pt",
)
negative_prompt_embeds = pipe.text_encoder(negative_prompt_tokens.input_ids.to(device))[0]
```

现在让我们来看看生成随机初始向量的代码部分，该向量使用正态分布生成，其结构符合扩散模型（UNet）预期的维度。这允许通过可选地使用随机数生成器来复现结果。创建了初始向量后，代码通过每次迭代增量地添加一个小步长，在两个嵌入（积极和消极提示）之间进行一系列插值。结果存储在一个名为 "walked_embeddings" 的列表中。

```python
# Generating initial latent vectors from a random normal distribution, with the option to use a generator for reproducibility.
latents = torch.randn(
    (1, pipe.unet.config.in_channels, height // 8, width // 8),
    generator=generator,
)

walked_embeddings = []

# Interpolating between embeddings for the given number of interpolation steps.
for i in range(num_interpolation_steps):
    walked_embeddings.append(
        [prompt_embeds + step_size * i, negative_prompt_embeds + step_size * i]
    )
```

最后，让我们根据插值嵌入生成一系列图像，然后显示这些图像。我们将遍历一个嵌入数组，使用每个嵌入生成具有指定特征（如高度、宽度以及与图像生成相关的其他参数）的图像。然后我们将这些图像收集到一个列表中。一旦生成完成，我们将调用 `display_image` 函数，以在给定的保存路径上将这些图像保存并显示为 GIF。


```python
# Generating images using the interpolated embeddings.
images = []
for latent in tqdm(walked_embeddings):
    images.append(
        pipe(
            height=height,
            width=width,
            num_images_per_prompt=1,
            prompt_embeds=latent[0],
            negative_prompt_embeds=latent[1],
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale,
            generator=generator,
            latents=latents,
        ).images
    )

# Display of saved generated images.
display_images(images, save_path)
```

### 示例 2：针对单个提示的扩散潜在插值
与第一个示例不同，在这个示例中，我们是在扩散模型本身的两个嵌入之间执行插值，而不是在提示之间。请注意，在这种情况下，我们使用 slerp 函数进行插值。然而，这并不妨碍我们在一个嵌入中添加一个常数。

![Example 2](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/sd_interpolation_2.gif)

下面呈现的函数代表球面线性插值（Spherical Linear Interpolation）。这是一种在球面上进行插值的方法。这个函数在计算机图形学中常用于平滑地动画旋转，并且也可以用于机器学习中高维数据点之间的插值，比如生成模型中使用的潜在向量。

该函数的来源是 Andrej Karpathy 的 gist：https://gist.github.com/karpathy/00103b0037c5aaea32fe1da1af553355。  
关于这种方法更详细的解释可以在：https://en.wikipedia.org/wiki/Slerp 找到。


```python
def slerp(v0, v1, num, t0=0, t1=1):
    v0 = v0.detach().cpu().numpy()
    v1 = v1.detach().cpu().numpy()

    def interpolation(t, v0, v1, DOT_THRESHOLD=0.9995):
        """helper function to spherically interpolate two arrays v1 v2"""
        dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
        if np.abs(dot) > DOT_THRESHOLD:
            v2 = (1 - t) * v0 + t * v1
        else:
            theta_0 = np.arccos(dot)
            sin_theta_0 = np.sin(theta_0)
            theta_t = theta_0 * t
            sin_theta_t = np.sin(theta_t)
            s0 = np.sin(theta_0 - theta_t) / sin_theta_0
            s1 = sin_theta_t / sin_theta_0
            v2 = s0 * v0 + s1 * v1
        return v2

    t = np.linspace(t0, t1, num)

    v3 = torch.tensor(np.array([interpolation(t[i], v0, v1) for i in range(num)]))

    return v3
```

```python
# The text prompt that describes the desired output image.
prompt = "Sci-fi digital painting of an alien landscape with otherworldly plants, strange creatures, and distant planets."
# A negative prompt that can be used to steer the generation away from certain features.
negative_prompt = "poorly drawn,cartoon, 3d, disfigured, bad art, deformed, poorly drawn, extra limbs, close up, b&w, weird colors, blurry"

# Generating initial latent vectors from a random normal distribution. In this example two latent vectors are generated, which will serve as start and end points for the interpolation.
# These vectors are shaped to fit the input requirements of the diffusion model's U-Net architecture.
latents = torch.randn(
    (2, pipe.unet.config.in_channels, height // 8, width // 8),
    generator=generator,
)

# Getting our latent embeddings
interpolated_latents = slerp(latents[0], latents[1], num_interpolation_steps)

# Generating images using the interpolated embeddings.
images = []
for latent_vector in tqdm(interpolated_latents):
    images.append(
        pipe(
            prompt,
            height=height,
            width=width,
            negative_prompt=negative_prompt,
            num_images_per_prompt=1,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale,
            generator=generator,
            latents=latent_vector[None, ...],
        ).images
    )

# Display of saved generated images.
display_images(images, save_path)
```

### 示例 3：多个提示之间的插值

与第一个示例中我们从一个提示移动开来不同，在这个示例中，我们将对任意数量的提示进行插值。为此，我们将取连续的提示对，并创建它们之间的平滑过渡。然后，我们将这些连续对的插值组合起来，并指示模型基于它们生成图像。我们将使用第二个示例中的 slerp 函数进行插值。


![Example 3](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/sd_interpolation_3.gif)

再次，让我们对多个积极和消极的文本提示进行标记化并获得它们的嵌入。

```python
# Text prompts that describes the desired output image.
prompts = [
    "A cute dog in a beautiful field of lavander colorful flowers everywhere, perfect lighting, leica summicron 35mm f2.0, kodak portra 400, film grain",
    "A cute cat in a beautiful field of lavander colorful flowers everywhere, perfect lighting, leica summicron 35mm f2.0, kodak portra 400, film grain",
]
# Negative prompts that can be used to steer the generation away from certain features.
negative_prompts = [
    "poorly drawn,cartoon, 2d, sketch, cartoon, drawing, anime, disfigured, bad art, deformed, poorly drawn, extra limbs, close up, b&w, weird colors, blurry",
    "poorly drawn,cartoon, 2d, sketch, cartoon, drawing, anime, disfigured, bad art, deformed, poorly drawn, extra limbs, close up, b&w, weird colors, blurry",
]

# NOTE: The number of prompts must match the number of negative prompts

batch_size = len(prompts)

# Tokenizing and encoding prompts into embeddings.
prompts_tokens = pipe.tokenizer(
    prompts,
    padding="max_length",
    max_length=pipe.tokenizer.model_max_length,
    truncation=True,
    return_tensors="pt",
)
prompts_embeds = pipe.text_encoder(
    prompts_tokens.input_ids.to(device)
)[0]

# Tokenizing and encoding negative prompts into embeddings.
if negative_prompts is None:
    negative_prompts = [""] * batch_size

negative_prompts_tokens = pipe.tokenizer(
    negative_prompts,
    padding="max_length",
    max_length=pipe.tokenizer.model_max_length,
    truncation=True,
    return_tensors="pt",
)
negative_prompts_embeds = pipe.text_encoder(
    negative_prompts_tokens.input_ids.to(device)
)[0]
```

如前所述，我们将使用 `slerp` 函数对连续的提示对创建平滑过渡。

```python
# Generating initial U-Net latent vectors from a random normal distribution.
latents = torch.randn(
    (1, pipe.unet.config.in_channels, height // 8, width // 8),
    generator=generator,
)

# Interpolating between embeddings pairs for the given number of interpolation steps.
interpolated_prompt_embeds = []
interpolated_negative_prompts_embeds = []
for i in range(batch_size - 1):
    interpolated_prompt_embeds.append(
        slerp(
            prompts_embeds[i],
            prompts_embeds[i + 1],
            num_interpolation_steps
        )
    )
    interpolated_negative_prompts_embeds.append(
        slerp(
            negative_prompts_embeds[i],
            negative_prompts_embeds[i + 1],
            num_interpolation_steps,
        )
    )

interpolated_prompt_embeds = torch.cat(
    interpolated_prompt_embeds, dim=0
).to(device)

interpolated_negative_prompts_embeds = torch.cat(
    interpolated_negative_prompts_embeds, dim=0
).to(device)
```

最后，我们需要根据嵌入生成图像。

```python
# Generating images using the interpolated embeddings.
images = []
for prompt_embeds, negative_prompt_embeds in tqdm(
    zip(interpolated_prompt_embeds, interpolated_negative_prompts_embeds),
    total=len(interpolated_prompt_embeds),
):
    images.append(
        pipe(
            height=height,
            width=width,
            num_images_per_prompt=1,
            prompt_embeds=prompt_embeds[None, ...],
            negative_prompt_embeds=negative_prompt_embeds[None, ...],
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale,
            generator=generator,
            latents=latents,
        ).images
    )

# Display of saved generated images.
display_images(images, save_path)
```

### 示例 4：针对单个提示在扩散潜在空间中的循环行走

这个示例来自：https://keras.io/examples/generative/random_walks_with_stable_diffusion/

假设我们有两个噪声成分，我们称之为 x 和 y。我们从 0 移动到 2π，在每一步中，我们将 x 的余弦和 y 的正弦加到结果中。使用这种方法，在我们的移动结束时，我们得到了与开始时相同的噪声值。这意味着向量最终转变成它们自己，结束了我们的移动。




![Example 4](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/sd_interpolation_4.gif)

```python
# The text prompt that describes the desired output image.
prompt = "Beautiful sea sunset, warm light, Aivazovsky style"
# A negative prompt that can be used to steer the generation away from certain features
negative_prompt = "picture frames"

# Generating initial latent vectors from a random normal distribution to create a loop interpolation between them.
latents = torch.randn(
    (2, 1, pipe.unet.config.in_channels, height // 8, width // 8),
    generator=generator,
)


# Calculation of looped embeddings
walk_noise_x = latents[0].to(device)
walk_noise_y = latents[1].to(device)

# Walking on a trigonometric circle
walk_scale_x = torch.cos(torch.linspace(0, 2, num_interpolation_steps) * np.pi).to(
    device
)
walk_scale_y = torch.sin(torch.linspace(0, 2, num_interpolation_steps) * np.pi).to(
    device
)

# Applying interpolation to noise
noise_x = torch.tensordot(walk_scale_x, walk_noise_x, dims=0)
noise_y = torch.tensordot(walk_scale_y, walk_noise_y, dims=0)

circular_latents = noise_x + noise_y

# Generating images using the interpolated embeddings.
images = []
for latent_vector in tqdm(circular_latents):
    images.append(
        pipe(
            prompt,
            height=height,
            width=width,
            negative_prompt=negative_prompt,
            num_images_per_prompt=1,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale,
            generator=generator,
            latents=latent_vector,
        ).images
    )

# Display of saved generated images.
display_images(images, save_path)
```

## 下一步
接下来，您可以探索各种参数，如指导比例（guidance scale）、种子（seed）和插值步骤数（number of interpolation steps），以观察它们如何影响生成的图像。此外，尝试使用不同的提示和调度器来进一步优化你的结果。另一个有价值的步骤是实施线性插值（`linspace`），而不是球面线性插值（`slerp`），并比较结果，以更深入地了解插值过程。

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/stable_diffusion_interpolation.md" />

### 开源 AI 指南 (Cookbook)
https://huggingface.co/learn/cookbook/zh-CN/index.md

# 开源 AI 指南 (Cookbook)

开源 AI 指南 (Cookbook) 是一系列 Notebook 的合集，里面展示了如何利用开源工具和模型来开发 AI 应用和解决各种机器学习问题的实际技巧和方法。

## 最新 Notebook

查看最近添加的 Notebook：

- [使用 LLM 作为评判者🧑‍⚖️进行自动化和多方面的评估](llm_judge)
- [创建一个合法偏好数据集](pipeline_notus_instructions_preferences_legal)
- [使用 SetFit 进行零样本文本分类的数据标注建议](labelling_feedback_setfit)
- [通过引入语义缓存到 FAISS 中以增强 RAG 系统的性能](semantic_cache_chroma_vector_database)
- [用 LlamaIndex 构建一个 RAG 电子书库智能助手](rag_llamaindex_librarian)
- [使用 Stable Diffusion 进行图像插值](stable_diffusion_interpolation)
- [用 Gemma, MongoDB 和开源模型构建 RAG 系统](rag_with_hugging_face_gemma_mongodb)
- [使用 PEFT 进行提示微调](prompt_tuning_peft)
- [使用 TGI 的消息 API 从 OpenAI 迁移到 Open LLMs](tgi_messages_api_demo)
- [通过推理端点使用 TEI 自动嵌入](automatic_embedding_tei_inference_endpoints)
- [用 Hugging Face Zephyr 和 LangChain 针对 Github issues 构建简单的 RAG](rag_zephyr_langchain)
- [用 🤗 transformers, 🤗 datasets 和 FAISS 嵌入多模态数据进行相似度搜索](faiss_with_hf_datasets_and_clip)
- [在单个 GPU 上针对自定义代码微调代码 LLM](fine_tuning_code_llm_on_single_gpu)
- [使用合成数据和 LLM 作为裁判评估 RAG](rag_evaluation)
- [使用 LangChain 在 HuggingFace 文档上构建高级 RAG](advanced_rag)
- [使用 Cleanlab 检测文本数据集中的问题](issues_in_text_dataset)

你还可以在指南 (Cookbook) 的[Github 仓库](https://github.com/huggingface/cookbook)中查看 Notebook。

## 贡献

开源 AI 指南 (Cookbook) 是社区和大家共同努力的成果，我们非常欢迎每个人都来参与贡献！


查看指南 (Cookbook) 的[贡献指引](https://github.com/huggingface/cookbook/blob/main/README.md)了解如何添加你的“食谱(教程)”。


<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/index.md" />

### 使用 Hugging Face 生态系统（TRL）对视觉语言模型 (Qwen2-VL-7B) 进行微调
https://huggingface.co/learn/cookbook/zh-CN/fine_tuning_vlm_trl.md

# 使用 Hugging Face 生态系统（TRL）对视觉语言模型 (Qwen2-VL-7B) 进行微调

_作者： [Sergio Paniego](https://github.com/sergiopaniego)_

🚨 **警告**：本 Notebook 资源需求较大，需要强大的计算能力。如果你在 Colab 中运行，它将使用 A100 GPU。

在本教程中，我们将演示如何使用 Hugging Face 生态系统，特别是 [Transformer 强化学习库 (TRL)](https://huggingface.co/docs/trl/index)，对 [视觉语言模型 (VLM)](https://huggingface.co/blog/vlms) 进行微调。

**🌟 模型与数据集概述**

我们将使用 [Qwen2-VL-7B](https://qwenlm.github.io/blog/qwen2-vl/) 模型，基于 [ChartQA](https://huggingface.co/datasets/HuggingFaceM4/ChartQA) 数据集进行微调。该数据集包含各种图表类型的图像，并配有问答对，非常适合增强模型的视觉问答能力。

**📖 其他资源**

如果你对更多 VLM 应用感兴趣，请查看：
- [多模态检索增强生成 (RAG) ](https://huggingface.co/learn/cookbook/multimodal_rag_using_document_retrieval_and_vlms)：我将带你了解如何使用文档检索（ColPali）和视觉语言模型（VLMs）构建 RAG 系统。
- [Phil Schmid 的教程](https://www.philschmid.de/fine-tune-multimodal-llms-with-trl)：一个深入讲解如何使用 TRL 微调多模态 LLMs 的精彩教程。
- [Merve Noyan 的 **smol-vision** 仓库](https://github.com/merveenoyan/smol-vision/tree/main)：一个关于前沿视觉与多模态 AI 主题的 Notebook 集合。

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)

# 1. 安装依赖
让我们从安装微调所需的核心库开始！🚀


```python
!pip install -U -q git+https://github.com/huggingface/trl.git bitsandbytes peft qwen-vl-utils trackio
# Tested with trl==0.22.0.dev0, bitsandbytes==0.47.0, peft==0.17.1, qwen-vl-utils==0.0.11, trackio==0.2.8
```

登录 Hugging Face 以上传你的微调模型！🗝️

你需要通过 Hugging Face 帐户进行身份验证，以便直接从本 Notebook 保存和共享你的模型。


```python
from huggingface_hub import notebook_login

notebook_login()
```

# 2. 加载数据集 📁

在这一部分，我们将加载 [HuggingFaceM4/ChartQA](https://huggingface.co/datasets/HuggingFaceM4/ChartQA) 数据集。该数据集包含图表图像及其相关的问答对，非常适合用于视觉问答任务的训练。

接下来，我们将为视觉语言模型 (VLM) 生成一个系统消息。在这种情况下，我们希望创建一个系统，能够作为分析图表图像的专家，并根据图表提供简明的回答。

```python
system_message = """You are a Vision Language Model specialized in interpreting visual data from chart images.
Your task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.
The charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.
Focus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary."""
```

我们将把数据集格式化为聊天机器人结构进行交互。每次交互将包括一个系统消息，接着是图像和用户的查询，最后是对查询的回答。

💡有关此模型的更多使用技巧，请查看 [模型卡](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct#more-usage-tips)。

```python
def format_data(sample):
    return {
      "images": [sample["image"]],
      "messages": [

          {
              "role": "system",
              "content": [
                  {
                      "type": "text",
                      "text": system_message
                  }
              ],
          },
          {
              "role": "user",
              "content": [
                  {
                      "type": "image",
                      "image": sample["image"],
                  },
                  {
                      "type": "text",
                      "text": sample['query'],
                  }
              ],
          },
          {
              "role": "assistant",
              "content": [
                  {
                      "type": "text",
                      "text": sample["label"][0]
                  }
              ],
          },
      ]
      }
```

出于教育目的，我们将只加载数据集中每个部分的 10%。然而，在实际应用中，通常会加载整个样本集。

```python
from datasets import load_dataset

dataset_id = "HuggingFaceM4/ChartQA"
train_dataset, eval_dataset, test_dataset = load_dataset(dataset_id, split=['train[:10%]', 'val[:10%]', 'test[:10%]'])
```

让我们看一下数据集的结构。它包含一个图像、一个查询、一个标签（即答案），以及一个我们将丢弃的第四个特征。

```python
train_dataset
```

现在，让我们使用聊天机器人结构来格式化数据。这将使我们能够为模型适当地设置交互。

```python

```

```python
train_dataset = [format_data(sample) for sample in train_dataset]
eval_dataset = [format_data(sample) for sample in eval_dataset]
test_dataset = [format_data(sample) for sample in test_dataset]
```

```python
train_dataset[200]
```

# 3. 加载模型并检查性能！🤔

现在我们已经加载了数据集，接下来让我们加载模型，并使用数据集中的一个样本来评估其性能。我们将使用[Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct)，这是一款能够理解视觉数据和文本的视觉语言模型（VLM）。

如果你在寻找替代方案，可以考虑以下开源选项：
- Meta AI的[Llama-3.2-11B-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct)
- Mistral AI的[Pixtral-12B](https://huggingface.co/mistralai/Pixtral-12B-2409)
- Allen AI的[Molmo-7B-D-0924](https://huggingface.co/allenai/Molmo-7B-D-0924)

此外，你还可以查看一些排行榜，比如[WildVision Arena](https://huggingface.co/spaces/WildVision/vision-arena)或[OpenVLM Leaderboard](https://huggingface.co/spaces/opencompass/open_vlm_leaderboard)，找到表现最好的VLM模型。

![Qwen2_VL 架构图](https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/Qwen2-VL/qwen2_vl.jpg)

```python
import torch
from transformers import Qwen2VLForConditionalGeneration, Qwen2VLProcessor

model_id = "Qwen/Qwen2-VL-7B-Instruct"
```

接下来，我们将加载模型和分词器，为推理做准备。

```python
model = Qwen2VLForConditionalGeneration.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

processor = Qwen2VLProcessor.from_pretrained(model_id)
```

To evaluate the model's performance, we’ll use a sample from the dataset. First, let’s take a look at the internal structure of this sample.


```python
train_dataset[0]
```

我们将使用没有系统消息的样本来评估VLM的原始理解能力。以下是我们将使用的输入：

```python
train_dataset[0]['messages'][1:2]
```

现在，让我们来看一下与该样本对应的图表。你能根据视觉信息回答问题吗？

```python
>>> train_dataset[0]['images'][0]
```

<img 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让我们创建一个方法，接受模型、处理器和样本作为输入，以生成模型的答案。这将帮助我们简化推理过程，并轻松评估VLM的性能。

```python
from qwen_vl_utils import process_vision_info

def generate_text_from_sample(model, processor, sample, max_new_tokens=1024, device="cuda"):
    # Prepare the text input by applying the chat template
    text_input = processor.apply_chat_template(
        sample['messages'][1:2],  # Use the sample without the system message
        tokenize=False,
        add_generation_prompt=True
    )

    # Process the visual input from the sample
    image_inputs, _ = process_vision_info(sample['messages'])

    # Prepare the inputs for the model
    model_inputs = processor(
        text=[text_input],
        images=image_inputs,
        return_tensors="pt",
    ).to(device)  # Move inputs to the specified device

    # Generate text with the model
    generated_ids = model.generate(**model_inputs, max_new_tokens=max_new_tokens)

    # Trim the generated ids to remove the input ids
    trimmed_generated_ids = [
        out_ids[len(in_ids):] for in_ids, out_ids in zip(model_inputs.input_ids, generated_ids)
    ]

    # Decode the output text
    output_text = processor.batch_decode(
        trimmed_generated_ids,
        skip_special_tokens=True,
        clean_up_tokenization_spaces=False
    )

    return output_text[0]  # Return the first decoded output text
```

```python
# Example of how to call the method with sample:
output = generate_text_from_sample(model, processor, train_dataset[0])
output
```

尽管模型成功地提取了正确的视觉信息，但它在准确回答问题方面存在困难。这表明微调可能是提高其性能的关键。让我们继续进行微调过程！

**移除模型并清理GPU**

在我们继续下一部分的模型训练之前，让我们清理当前的变量并清理GPU，以释放资源。

```python
import gc
import time

def clear_memory():
    # Delete variables if they exist in the current global scope
    if 'inputs' in globals(): del globals()['inputs']
    if 'model' in globals(): del globals()['model']
    if 'processor' in globals(): del globals()['processor']
    if 'trainer' in globals(): del globals()['trainer']
    if 'peft_model' in globals(): del globals()['peft_model']
    if 'bnb_config' in globals(): del globals()['bnb_config']
    time.sleep(2)

    # Garbage collection and clearing CUDA memory
    gc.collect()
    time.sleep(2)
    torch.cuda.empty_cache()
    torch.cuda.synchronize()
    time.sleep(2)
    gc.collect()
    time.sleep(2)

    print(f"GPU allocated memory: {torch.cuda.memory_allocated() / 1024**3:.2f} GB")
    print(f"GPU reserved memory: {torch.cuda.memory_reserved() / 1024**3:.2f} GB")

clear_memory()
```

# 4. 使用TRL进行模型微调


## 4.1 加载用于训练的量化模型 ⚙️

接下来，我们将使用[bitsandbytes](https://huggingface.co/docs/bitsandbytes/main/en/index)加载量化模型。如果你想了解更多关于量化的内容，可以查看[这篇博客文章](https://huggingface.co/blog/merve/quantization)或[这篇文章](https://www.maartengrootendorst.com/blog/quantization/)。

量化能够显著减小模型的存储需求并提高推理效率，特别适用于需要部署到资源有限的设备上的场景。

```python
from transformers import BitsAndBytesConfig

# BitsAndBytesConfig int-4 config
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

# Load model and tokenizer
model = Qwen2VLForConditionalGeneration.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
    quantization_config=bnb_config
)
processor = Qwen2VLProcessor.from_pretrained(model_id)
```

## 4.2 设置QLoRA和SFTConfig 🚀

接下来，我们将为训练设置配置[QLoRA](https://github.com/artidoro/qlora)。QLoRA使得大语言模型的高效微调成为可能，同时与传统方法相比显著减少内存占用。与标准的LoRA方法通过应用低秩近似来减少内存使用不同，QLoRA通过量化LoRA适配器的权重，进一步降低内存需求。这不仅减少了内存占用，还提升了训练效率，使其成为在不牺牲质量的前提下优化模型表现的理想选择。

```python
>>> from peft import LoraConfig, get_peft_model

>>> # Configure LoRA
>>> peft_config = LoraConfig(
...     lora_alpha=16,
...     lora_dropout=0.05,
...     r=8,
...     bias="none",
...     target_modules=["q_proj", "v_proj"],
...     task_type="CAUSAL_LM",
... )

>>> # Apply PEFT model adaptation
>>> peft_model = get_peft_model(model, peft_config)

>>> # Print trainable parameters
>>> peft_model.print_trainable_parameters()
```

<pre>
trainable params: 2,523,136 || all params: 8,293,898,752 || trainable%: 0.0304
</pre>

我们将使用监督微调（SFT）来优化模型在当前任务上的表现。为此，我们将使用[TRL库](https://huggingface.co/docs/trl/index)中的[SFTConfig](https://huggingface.co/docs/trl/sft_trainer)类来定义训练参数。SFT允许我们提供标注数据，帮助模型根据接收到的输入生成更准确的响应。这种方法确保了模型能够针对我们的具体应用场景进行调整，从而在理解和响应视觉查询方面实现更好的表现。

```python
from trl import SFTConfig

# Configure training arguments
training_args = SFTConfig(
    output_dir="qwen2-7b-instruct-trl-sft-ChartQA",  # Directory to save the model
    num_train_epochs=3,  # Number of training epochs
    per_device_train_batch_size=4,  # Batch size for training
    per_device_eval_batch_size=4,  # Batch size for evaluation
    gradient_accumulation_steps=8,  # Steps to accumulate gradients
    gradient_checkpointing_kwargs={"use_reentrant": False},  # Options for gradient checkpointing
    max_length=None,
    # Optimizer and scheduler settings
    optim="adamw_torch_fused",  # Optimizer type
    learning_rate=2e-4,  # Learning rate for training
    # Logging and evaluation
    logging_steps=10,  # Steps interval for logging
    eval_steps=10,  # Steps interval for evaluation
    eval_strategy="steps",  # Strategy for evaluation
    save_strategy="steps",  # Strategy for saving the model
    save_steps=20,  # Steps interval for saving
    # Mixed precision and gradient settings
    bf16=True,  # Use bfloat16 precision
    max_grad_norm=0.3,  # Maximum norm for gradient clipping
    warmup_ratio=0.03,  # Ratio of total steps for warmup
    # Hub and reporting
    push_to_hub=True,  # Whether to push model to Hugging Face Hub
    report_to="trackio",  # Reporting tool for tracking metrics
)
```

## 4. 训练模型 🏃

我们将使用[trackio](https://huggingface.co/blog/trackio)来记录我们的训练进度。让我们将 Notebook 与W&B连接，以便在训练过程中捕获重要信息。

```python
import trackio

trackio.init(
    project="qwen2-7b-instruct-trl-sft-ChartQA",
    name="qwen2-7b-instruct-trl-sft-ChartQA",
    config=training_args,
    space_id=training_args.output_dir + "-trackio"
)
```

现在，我们将定义[SFTTrainer](https://huggingface.co/docs/trl/sft_trainer)，它是[transformers.Trainer](https://huggingface.co/docs/transformers/main_classes/trainer)类的包装器，并继承了其属性和方法。这个类通过在提供[PeftConfig](https://huggingface.co/docs/peft/v0.6.0/en/package_reference/config#peft.PeftConfig)对象时，正确初始化[PeftModel](https://huggingface.co/docs/peft/v0.6.0/package_reference/peft_model)，简化了微调过程。通过使用`SFTTrainer`，我们可以高效地管理训练工作流，确保我们的视觉语言模型微调过程顺利进行。




```python
from trl import SFTTrainer

trainer = SFTTrainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    data_collator=collate_fn,
    peft_config=peft_config,
    processing_class=processor.tokenizer,
)
```

该训练模型了! 🎉

```python
trainer.train()
```

让我们保存结果 💾

```python
trainer.save_model(training_args.output_dir)
```

# 5. 测试微调后的模型 🔍
现在我们已经成功微调了我们的视觉语言模型（VLM），是时候评估它的表现了！在这一部分，我们将使用来自ChartQA数据集的示例来测试模型，看看它在基于图表图像回答问题方面的表现如何。让我们深入探索并查看结果吧！🚀




让我们清理GPU内存，以确保最佳的性能 🧹

```python
clear_memory()
```

我们将使用之前相同的管道重新加载基础模型。

```python
model = Qwen2VLForConditionalGeneration.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

processor = Qwen2VLProcessor.from_pretrained(model_id)
```


我们将把训练好的适配器附加到预训练模型上。这个适配器包含了我们在训练过程中所做的微调调整，允许基础模型在不改变其核心参数的情况下利用新学到的知识。通过集成适配器，我们可以增强模型的能力，同时保持其原有结构。

```python
adapter_path = "sergiopaniego/qwen2-7b-instruct-trl-sft-ChartQA"
model.load_adapter(adapter_path)
```

我们将使用之前从数据集中选取的样本，这是模型最初无法正确回答的样本

```python
train_dataset[0]['messages'][:2]
```

```python
>>> train_dataset[0]['images'][0]
```

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```python
output = generate_text_from_sample(model, processor, train_dataset[0])
output
```

既然这个样本来自训练集，模型在训练过程中已经接触过它，这可能被视为一种“作弊”方式。为了更全面地了解模型的表现，我们将使用一个未见过的样本来进行评估。



```python
test_dataset[10]['messages'][:2]
```

```python
>>> test_dataset[10]['images'][0]
```

<img 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```python
output = generate_text_from_sample(model, processor, test_dataset[10])
output
```

模型已经成功地学习了根据数据集中的查询作出响应。我们达到了目标！ 🎉✨

💻 我已经开发了一个示例应用来测试该模型，你可以在[这里](https://huggingface.co/spaces/sergiopaniego/Qwen2-VL-7B-trl-sft-ChartQA)找到它。你可以将其与另一个展示预训练模型的Space进行对比，预训练模型可以在[这里](https://huggingface.co/spaces/GanymedeNil/Qwen2-VL-7B)找到。

```python
from IPython.display import IFrame

IFrame(src="https://sergiopaniego-qwen2-vl-7b-trl-sft-chartqa.hf.space", width=1000, height=800)
```

# 6. 比较微调模型与基础模型 + 提示策略 📊

我们已经探讨了如何通过微调VLM将其适应特定需求的过程。另一种值得考虑的方法是直接使用提示（prompting）或实现RAG（检索增强生成）系统，这在另一个[教程](https://huggingface.co/learn/cookbook/multimodal_rag_using_document_retrieval_and_vlms)中有详细介绍。

微调 VLM 需要大量数据和计算资源，这可能会产生一定的成本。相比之下，我们可以尝试使用提示，看看是否能在没有微调开销的情况下实现类似的结果。

清理 GPU 内存以确保最佳性能 🧹



```python
>>> clear_memory()
```

<pre>
GPU allocated memory: 0.02 GB
GPU reserved memory: 0.27 GB
</pre>

🏗️ 首先，我们将按照之前相同的管道加载基础模型。

```python
model = Qwen2VLForConditionalGeneration.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

processor = Qwen2VLProcessor.from_pretrained(model_id)
```

📜 在这种情况下，我们将再次使用之前的样本，但这次我们将包括系统消息，如下所示。这一添加有助于为模型提供更多上下文，从而可能提高其响应的准确性。

```python
train_dataset[0][:2]
```

让我们看看他是如何表现的！

```python
text = processor.apply_chat_template(
    train_dataset[0][:2], tokenize=False, add_generation_prompt=True
)

image_inputs, _ = process_vision_info(train_dataset[0])

inputs = processor(
    text=[text],
    images=image_inputs,
    return_tensors="pt",
)

inputs = inputs.to("cuda")

generated_ids = model.generate(**inputs, max_new_tokens=1024)
generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]

output_text = processor.batch_decode(
    generated_ids_trimmed,
    skip_special_tokens=True,
    clean_up_tokenization_spaces=False
)

output_text[0]
```

💡 正如我们所看到的，模型在没有任何微调的情况下，通过使用预训练模型并结合附加的系统消息，成功生成了正确的答案。这种方法在某些具体应用场景中，可能成为微调的有效替代方案。根据不同的任务需求，采用提示（prompting）或RAG（检索增强生成）等方法，可以在降低计算成本的同时实现类似的性能。

# 7. 继续学习之旅 🧑‍🎓️

为了进一步增强你在使用多模态模型方面的理解和技能，以下是一些推荐的资源：

- [多模态检索增强生成（RAG）指南](https://huggingface.co/learn/cookbook/multimodal_rag_using_document_retrieval_and_vlms)
- [Phil Schmid 的教程](https://www.philschmid.de/fine-tune-multimodal-llms-with-trl)
- [Merve Noyan 的 **smol-vision** 仓库](https://github.com/merveenoyan/smol-vision/tree/main)
- [使用 AutoAWQ 对 Qwen2-VL 模型进行量化](https://github.com/QwenLM/Qwen2-VL?tab=readme-ov-file#quantize-your-own-model-with-autoawq)
- [使用 TRL 优化视觉语言模型的偏好](https://huggingface.co/blog/dpo_vlm)
- [Hugging Face Llama 指南：针对 VLM 的 SFT](https://github.com/huggingface/huggingface-llama-recipes/blob/main/fine_tune/sft_vlm.py)
- [Hugging Face Llama 指南：PEFT 微调](https://github.com/huggingface/huggingface-llama-recipes/blob/main/fine_tune/peft_finetuning.py)
- [Hugging Face 博客：IDEFICS2](https://huggingface.co/blog/idefics2)

这些资源将帮助你加深对多模态学习的理解和技能。继续探索、学习并尝试更多的技术，你会在这个领域走得更远！

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/fine_tuning_vlm_trl.md" />

### 在 HF 空间中的互动开发
https://huggingface.co/learn/cookbook/zh-CN/enterprise_cookbook_dev_spaces.md

# 在 HF 空间中的互动开发

_作者：[Moritz Laurer](https://huggingface.co/MoritzLaurer)_

像 Google Colab 或 Kaggle Notebooks 这样的服务使得人们可以在浏览器中的 Jupyter notebooks 里轻松地访问计算资源。然而，这些服务也存在一些局限性：
- GPU 不稳定，训练任务可能在完成前被取消。
- GPU 选择仅限于几个单一 GPU。
- 没有原生支持通过本地 IDE（如 VS Code）连接到云端 GPU。

HF JupyterLab Spaces 克服了这些限制。使用 HF JupyterLab Space，你可以：
- 在浏览器中使用 JupyterLab 完成所有开发工作。
- 动态切换 CPU 和各种 GPU，除非你希望它们停止，否则硬件不会停止。
- 通过 SSH 将你的本地 IDE（如 VS Code）连接到云计算资源，进行完整的远程开发。

本教程将引导你完成设置自己的 JupyterLab Space 的步骤。

## 在 HF JupyterLab Spaces 中进行互动开发

### 创建你的 JupyterLab Space
要创建自己的 HF JupyterLab Space，请访问 [空间创建页面](https://huggingface.co/new-space?template=SpacesExamples%2Fjupyterlab)，然后点击 `Docker` > `JupyterLab`。HF JupyterLab Space 本质上是一个 Docker 容器，内含预配置的 JupyterLab 复制品，运行在 Hugging Face 的云基础设施上。以下是配置 JupyterLab Space 的一些建议：

- **选择正确的所有者**：如果你将 JupyterLab Space 用作企业组织的工作一部分，请在 `Owner` 下拉菜单中选择该组织的名称（例如，下面图像中的虚拟“enterprise-explorers”）。任何计算费用将会计入该企业组织的账户。
- **访问控制**：如果你只想让团队中的某些成员访问 JupyterLab Space，可以点击 `Everyone` 并限制对 JupyterLab Space 的访问，仅限于预定义的资源组。资源组是企业 Hub 的功能，它允许你将对特定仓库（如模型、数据集、Spaces）的访问限制给更小的团队成员群体。请参阅[文档](https://huggingface.co/docs/hub/en/security-resource-groups)了解如何创建你的第一个资源组。

<div style="flex justify-center">
    <img src="https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/enterprise-jupyterlab-creation-1.png" width="450">  
</div>

- **选择硬件**：你可以选择从免费的 CPU 到 A100 GPU 等各种硬件。设置 Space 时，我们建议选择免费的基础 CPU。等到你需要更强硬件时，可以切换到付费硬件（可查看可用硬件和价格 [这里](https://huggingface.co/pricing)）。
- **持久存储**：将持久存储附加到 Space 上非常重要，这样你创建的所有文件（代码、模型、数据）在 Space 暂停或重置时也能被保存。以后你也可以在设置中增加磁盘空间。所有持久数据都存储在 `/data` 目录中（[文档](https://huggingface.co/docs/hub/en/spaces-storage)）。
- **设置密码**：创建 Space 后，登录 JupyterLab 时需要密码。该密码通过 `JUPYTER_TOKEN` Space 密钥来定义。如果没有设置密码，默认密码是“huggingface”。建议设置一个强密码。
- **开发模式**：开发模式是为企业 Hub 用户提供的一项功能，它允许你通过 SSH 连接到任何 HF Space。启用此功能后，你可以通过 VS Code 进行远程开发，使用 Space 的云硬件（该功能也可以在后续启用/禁用）。有关预览文档，请参见[此处](https://huggingface.co/dev-mode-explorers)。
- **私有 Space**：作为额外的安全措施，建议将 Space 设置为私有，这样只有企业组织的成员（以及特定资源组成员）才能看到它。

<div style="flex justify-center">
    <img src="https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/enterprise-jupyterlab-creation-2.png" width="450">
</div>

设置好 JupyterLab Space 后，你可以点击 `Create Space`。Space 会开始构建，几秒钟后，你会看到 JupyterLab 的登录界面。此时，你可以使用之前定义的密码登录。

<div style="flex justify-center">
    <img src="https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/enterprise-jupyterlab-login.png">
</div>

### 使用你的 JupyterLab Space

现在，你可以在浏览器中使用自己的 JupyterLab Space 进行工作！你可以在左侧的文件浏览器中创建自己的目录结构，存放 .ipynb 笔记本或其他文件和数据集。如果你启用了持久存储，所有文件都会永久保存在 Space 的默认 `/data` 目录中。

<div style="flex justify-center">
    <img src="https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/enterprise-jupyterlab-first-notebook.png">
</div>

### 动态切换 CPU 和 GPU

与 Google Colab 等服务类似，你可以动态更改 Space 的硬件配置。我们建议先在免费或升级的 CPU 上进行初步工作，如数据清洗、设置终端或测试 API。一旦代码配置完成，你只需点击 Space 右上角的 `Settings`，即可切换到适合计算密集型推理或训练任务的硬件。更改硬件时，Space 会自动重启，所有环境变量会丢失（类似于 Google Colab），几秒钟后，你将在新硬件上获得一个全新的清洁环境。你的存储和保存的文件（如代码、数据等）当然也会随之迁移到新硬件上。下图显示了当前（2024 年 6 月）的可用硬件，未来会进行更新。

图像左下角显示的是 `Sleep time settings`，你可以设置在空闲时硬件运行多长时间。这是相较于 Google Colab 的一大优势。如果你想节省费用，可以设置 Space 在空闲 15 分钟后进入休眠模式；但如果你需要长达 48 小时或更长的训练运行，你可以防止 Space 进入休眠模式，任由它持续运行。你还可以手动暂停 Space，这样就不再为硬件付费。

<div style="flex justify-center">
    <img src="https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/enterprise-jupyterlab-hardware-options.png">
</div>

在设置下方，你会看到其他选项，如扩展存储、重置 Space 等。如果在创建 Space 时没有设置密码，你也可以在这里创建一个名为 `JUPYTER_TOKEN` 的密钥，替换默认的“huggingface”密码。

> [!TIP]
> 如果你在多日或多周的使用过程中，存储缓存积累了文件，当你收到持久存储已满的警告时，可能会认为存储配额还未达到，重新设置 Space 为出厂设置可以清空缓存，有时会有帮助。

### 自定义你的 JupyterLab Space

记住，你的 JupyterLab Space 本质上是一个预配置的 Docker 容器，因此，如果你熟悉 Docker，也可以根据自己的需求进行自定义。例如，你可以进入 Space 的 `Files` 部分，向 `requirements.txt` 文件中添加新依赖，或者你可以将默认的容器镜像更换为另一个镜像（例如，如果你需要预安装特定版本的 CUDA 和 PyTorch）。

<div style="flex justify-center">
    <img src="https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/enterprise-jupyterlab-files.png">
</div>

## 开发模式：通过本地 VS Code 在 HF Spaces 上开发

如果你不喜欢在浏览器中使用 JupyterLab，尝试使用 `开发模式`。`开发模式` 允许你通过 SSH 将本地 IDE（如 VS Code）连接到任何 Space 的硬件上。[HF Pro/Enterprise](https://huggingface.co/pricing) 订阅用户可以为任何 Space 启用 `开发模式`。

启用 `开发模式` 后，你会在 JupyterLab Space 窗口的左下角看到一个弹出窗口。要通过 SSH 连接到本地 VS Code，首先需要在本地安装 [VS Code Remote - SSH 插件](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-ssh)，并将你的 SSH 密钥添加到 [HF 个人资料](https://huggingface.co/settings/keys) 中。点击 `Connect with VS Code`，应该会打开你的本地 VS Code 窗口，并与 Space 建立远程连接。任何支持通过 SSH 进行远程开发的 IDE 都可以使用类似的过程。

<div style="flex justify-center">
    <img src="https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/enterprise-jupyterlab-devmode-popup.png" width="450">
</div>

当你通过 SSH 连接到 Space 时，默认目录是空的 `/app` 目录。你需要切换到 `/data` 目录，这里保存了所有持久化的文件（代码、数据、模型等）。`/data` 目录是唯一一个保证在会话间持久存储文件的目录。如果你希望修改基础 Docker 容器，也可以访问 `/HOME/user/app` 目录。

> [!TIP]
> `/data` 目录中的持久化文件目前不会自动备份，因此建议定期备份重要文件，以避免数据丢失。




就这样，你可以在浏览器中运行 JupyterLab Space，动态切换多个强大的 GPU，并从本地 IDE 连接到硬件。

整个教程就是在一个免费的 CPU 空间中编写的，我们邀请你在自己的 JupyterLab Space 中跟随其他 Enterprise Hub Cookbook 的教程一起操作。

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/enterprise_cookbook_dev_spaces.md" />

### 使用 Cleanlab 进行主动学习标注文本数据
https://huggingface.co/learn/cookbook/zh-CN/annotate_text_data_transformers_via_active_learning.md

# 使用 Cleanlab 进行主动学习标注文本数据

作者: [Aravind Putrevu](https://huggingface.co/aravindputrevu)

在本 Notebook 中，我重点介绍了如何使用[主动学习](https://arxiv.org/abs/2301.11856)来改进一个经过微调的 Hugging Face Transformer 文本分类模型，同时保持从人工标注者收集的标签总数较低。当资源限制使得无法为整个数据集获取标签时，主动学习通过选择数据标注者应该投入精力标注的样本，旨在节省时间和成本。

## 什么是主动学习？

主动学习帮助优先选择需要标注的数据，以最大化基于标注数据训练的监督学习模型的性能。这个过程通常是迭代进行的——在每一轮中，主动学习告诉我们应该为哪些示例收集额外的注释，以在有限的标注预算下最大限度地提高当前模型的性能。[ActiveLab](https://arxiv.org/abs/2301.11856) 是一种特别有用的主动学习算法，尤其在来自人工标注者的标签存在噪声，并且我们应该为以前标注过的示例（其标签看起来可疑）收集更多注释，而不是为尚未标注的示例收集注释时。收集这些新注释以扩充我们的训练数据集后，我们会重新训练模型并评估其测试准确率。

![ActiveLab thumb.webp](data:image/webp;base64,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)

在本 Notebook 中，我考虑了一个二分类文本分类任务：预测特定短语是礼貌的还是不礼貌的。

与随机选择相比，使用 ActiveLab 进行主动学习在为 Transformer 模型收集额外注释时效果要好得多。无论总的标注预算是多少，它始终能产生更好的模型，错误率约减少 50%。

接下来的部分将介绍如何使用开源代码来实现这些结果。

## 设置环境

```python
!pip install datasets==2.20.0 transformers==4.25.1 scikit-learn==1.1.2 matplotlib==3.5.3 cleanlab
```

```python
import pandas as pd
pd.set_option('max_colwidth', None)
import numpy as np
import random
import transformers
import datasets
import matplotlib.pyplot as plt

from cleanlab.multiannotator import get_majority_vote_label, get_active_learning_scores, get_label_quality_multiannotator
from transformers import AutoTokenizer, AutoModel
from transformers import AutoModelForSequenceClassification
from transformers import TrainingArguments, Trainer
from datasets import load_dataset, Dataset, DatasetDict, ClassLabel
from sklearn.metrics import accuracy_score
from sklearn.model_selection import StratifiedKFold
from scipy.special import softmax
from datetime import datetime
```

## 收集并组织数据

这里我们下载我们需要的 Notebook 数据。

```python
labeled_data_file = {"labeled": "X_labeled_full.csv"}
unlabeled_data_file = {"unlabeled": "X_labeled_full.csv"}
test_data_file = {"test": "test.csv"}

X_labeled_full = load_dataset("Cleanlab/stanford-politeness", split="labeled", data_files=labeled_data_file)
X_unlabeled = load_dataset("Cleanlab/stanford-politeness", split="unlabeled", data_files=unlabeled_data_file)
test = load_dataset("Cleanlab/stanford-politeness", split="test", data_files=test_data_file)

!wget -nc -O 'extra_annotations.npy' 'https://huggingface.co/datasets/Cleanlab/stanford-politeness/resolve/main/extra_annotations.npy?download=true'

extra_annotations = np.load("extra_annotations.npy",allow_pickle=True).item()
```

```python
X_labeled_full = X_labeled_full.to_pandas()
X_labeled_full.set_index('id', inplace=True)
X_unlabeled = X_unlabeled.to_pandas()
X_unlabeled.set_index('id', inplace=True)
test = test.to_pandas()
```

## 文本礼貌性分类

我们使用的是 [斯坦福礼貌语料库](https://convokit.cornell.edu/documentation/wiki_politeness.html) 作为数据集。

该数据集被结构化为一个二分类文本分类任务，目标是分类每个短语是礼貌的还是不礼貌的。人工标注者会被提供一个选定的文本短语，并对其礼貌性进行标注：（不礼貌为 **0**，礼貌为 **1**）。

我们在标注数据上训练一个 Transformer 分类器，并在一组保留的测试样本上衡量模型的准确性。由于这些测试样本的标签来自五个标注者的共识，我对其真实标签有较高的信心。

至于训练数据，我们有以下内容：

- `X_labeled_full`：我们的初始训练集，包含 100 个文本示例，并且每个示例有 2 个标注。
- `X_unlabeled`：一个包含 1900 个未标注文本示例的大型数据集，供我们考虑让标注者进行标注。
- `extra_annotations`：当请求某个示例的标注时，我们可以从中提取的附加标注池。

## 可视化数据

```python
# Multi-annotated Data
X_labeled_full.head()
```

```python
# Unlabeled Data
X_unlabeled.head()
```

```python
# extra_annotations contains the annotations that we will use when an additional annotation is requested.
extra_annotations

# Random sample of extra_annotations to see format.
{k:extra_annotations[k] for k in random.sample(extra_annotations.keys(), 5)}
```

# 查看一些来自测试集的示例

```python
>>> num_to_label = {0:'Impolite', 1:"Polite"}
>>> for i in range(2):
...     print(f"{num_to_label[i]} examples:")
...     subset=test[test.label==i][['text']].sample(n=3, random_state=2)
...     print(subset)
```

<pre>
Impolite examples:
</pre>

不礼貌的示例：

|     |                                                                                                              文本                                                                                                              |
|----:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 120 | 和浪费我们的时间一样。我只能重复一句话：你为什么不通过添加你心爱的马其顿的内容来做一些建设性的工作？ |
| 150 | 与其告诉我我关闭某些 afd 是多么错误，也许你应该把时间花在处理当前的 afd 积压上 <url>。如果我的决定这么错误，为什么你没有重新打开它们？ |
| 326 | 根据 CFD，这本应已经转移到 <url>。为什么没有被转移？ |

礼貌的示例：

|     |                                                            文本                                                            |
|----:|:--------------------------------------------------------------------------------------------------------------------------:|
| 498 | 你好，我提出了取消保护 tamazepam 页面 <url> 的可能性。你有什么想法？ |
| 132 | 由于某些编辑，页面的对齐方式发生了变化。你能帮忙吗？ |
| 131 | 我很高兴你对整体外观感到满意。在我标注所有街道之前，文字大小、字体样式等都可以吗？ |

# 辅助方法
以下部分包含了本 Notebook 所需的所有辅助方法。

`get_idx_to_label` 旨在用于主动学习场景，特别是在处理标注和未标注数据的混合时。它的主要目标是根据主动学习分数确定应选择哪些示例（来自标注数据集和未标注数据集）进行额外标注。

```python
# Helper method to get indices of examples with the lowest active learning score to collect more labels for.
def get_idx_to_label(
    X_labeled_full,
    X_unlabeled,
    extra_annotations,
    batch_size_to_label,
    active_learning_scores,
    active_learning_scores_unlabeled=None,
):
    if active_learning_scores_unlabeled is None:
        active_learning_scores_unlabeled = np.array([])

    to_label_idx = []
    to_label_idx_unlabeled = []

    num_labeled = len(active_learning_scores)
    active_learning_scores_combined = np.concatenate((active_learning_scores, active_learning_scores_unlabeled))
    to_label_idx_combined = np.argsort(active_learning_scores_combined)

    # We want to collect the n=batch_size best examples to collect another annotation for.
    i = 0
    while (len(to_label_idx)+len(to_label_idx_unlabeled)) < batch_size_to_label:
        idx = to_label_idx_combined[i]
        # We know this is an already annotated example.
        if idx < num_labeled:
            text_id = X_labeled_full.iloc[idx].name
            # Make sure we have an annotation left to collect.
            if text_id in extra_annotations and extra_annotations[text_id]:
                to_label_idx.append(idx)
        # We know this is an example that is currently not annotated.
        else:
            # Subtract off offset to get back original index.
            idx -= num_labeled
            text_id = X_unlabeled.iloc[idx].name
            # Make sure we have an annotation left to collect.
            if text_id in extra_annotations and extra_annotations[text_id]:
                to_label_idx_unlabeled.append(idx)
        i+=1

    to_label_idx = np.array(to_label_idx)
    to_label_idx_unlabeled = np.array(to_label_idx_unlabeled)
    return to_label_idx, to_label_idx_unlabeled
```

`get_idx_to_label_random` 旨在主动学习的上下文中使用，其中数据点的选择是随机进行的，而不是基于模型的不确定性或学习分数。这种方法可以作为基准，与更复杂的主动学习策略进行比较，或者用于在不确定如何为示例打分的场景中。

```python
# Helper method to get indices of random examples to collect more labels for.
def get_idx_to_label_random(
    X_labeled_full,
    X_unlabeled,
    extra_annotations,
    batch_size_to_label
):
    to_label_idx = []
    to_label_idx_unlabeled = []

    # Generate list of indices for both sets of examples.
    labeled_idx = [(x, 'labeled') for x in range(len(X_labeled_full))]
    unlabeled_idx = []
    if X_unlabeled is not None:
        unlabeled_idx = [(x, 'unlabeled') for x in range(len(X_unlabeled))]
    combined_idx = labeled_idx + unlabeled_idx

    # We want to collect the n=batch_size random examples to collect another annotation for.
    while (len(to_label_idx)+len(to_label_idx_unlabeled)) < batch_size_to_label:
        # Random choice from indices.
        # We time-seed to ensure randomness.
        random.seed(datetime.now().timestamp())
        choice = random.choice(combined_idx)
        idx, which_subset = choice
        # We know this is an already annotated example.
        if which_subset == 'labeled':
            text_id = X_labeled_full.iloc[idx].name
            # Make sure we have an annotation left to collect.
            if text_id in extra_annotations and extra_annotations[text_id]:
                to_label_idx.append(idx)
            combined_idx.remove(choice)
        # We know this is an example that is currently not annotated.
        else:
            text_id = X_unlabeled.iloc[idx].name
            # Make sure we have an annotation left to collect.
            if text_id in extra_annotations and extra_annotations[text_id]:
                to_label_idx_unlabeled.append(idx)
            combined_idx.remove(choice)

    to_label_idx = np.array(to_label_idx)
    to_label_idx_unlabeled = np.array(to_label_idx_unlabeled)
    return to_label_idx, to_label_idx_unlabeled
```

以下是一些实用方法，帮助我们计算标准差、选择之前标注过该示例的特定标注者，以及一些用于文本示例标记化的令牌化函数。

```python
# Helper method to compute std dev across 2D array of accuracies.
def compute_std_dev(accuracy):
    def compute_std_dev_ind(accs):
        mean = np.mean(accs)
        std_dev = np.std(accs)
        return np.array([mean - std_dev, mean + std_dev])

    std_dev = np.apply_along_axis(compute_std_dev_ind, 0, accuracy)
    return std_dev

# Helper method to select which annotator we should collect another annotation from.
def choose_existing(annotators, existing_annotators):
    for annotator in annotators:
        # If we find one that has already given an annotation, we return it.
        if annotator in existing_annotators:
            return annotator
    # If we don't find an existing, just return a random one.
    choice = random.choice(list(annotators.keys()))
    return choice

# Helper method for Trainer.
def compute_metrics(p):
    logits, labels = p
    pred = np.argmax(logits, axis=1)
    pred_probs = softmax(logits, axis=1)
    accuracy = accuracy_score(y_true=labels, y_pred=pred)
    return {"logits":logits, "pred_probs":pred_probs, "accuracy": accuracy}

# Helper method to tokenize text.
def tokenize_function(examples):
    model_name = "distilbert-base-uncased"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    return tokenizer(examples["text"], padding="max_length", truncation=True)

# Helper method to tokenize given dataset.
def tokenize_data(data):
    dataset = Dataset.from_dict({"label":data['label'] , "text": data['text'].values})
    tokenized_dataset = dataset.map(tokenize_function, batched=True)
    tokenized_dataset = tokenized_dataset.cast_column("label", ClassLabel(names = ["0","1"]))
    return tokenized_dataset
```

`get_trainer` 函数旨在为文本分类任务设置训练环境，使用的是 DistilBERT，这是一种轻量级且速度更快的 BERT 模型的蒸馏版。

```python
# Helper method to initiate a new Trainer with given train and test sets.
def get_trainer(train_set, test_set):

    # Model params.
    model_name = "distilbert-base-uncased"
    model_folder = "model_training"
    max_training_steps = 300
    num_classes = 2

    # Set training args.
    # We time-seed to ensure randomness between different benchmarking runs.
    training_args = TrainingArguments(
        max_steps=max_training_steps,
        output_dir=model_folder,
        seed = int(datetime.now().timestamp())
    )

    # Tokenize train/test set.
    train_tokenized_dataset = tokenize_data(train_set)
    test_tokenized_dataset = tokenize_data(test_set)

    # Initiate a pre-trained model.
    model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=num_classes)
    trainer = Trainer(
        model=model,
        args=training_args,
        compute_metrics = compute_metrics,
        train_dataset = train_tokenized_dataset,
        eval_dataset = test_tokenized_dataset,
    )
    return trainer
```

`get_pred_probs` 函数通过交叉验证对给定数据集执行样本外预测概率计算，并额外处理未标注数据。

```python
# Helper method to manually compute cross-validated predicted probabilities needed for ActiveLab.
def get_pred_probs(X, X_unlabeled):
    """Uses cross-validation to obtain out-of-sample predicted probabilities
    for given dataset"""

    # Generate cross-val splits.
    n_splits = 3
    skf = StratifiedKFold(n_splits=n_splits, shuffle=True)
    skf_splits = [
        [train_index, test_index]
        for train_index, test_index in skf.split(X=X['text'], y=X['label'])
    ]

    # Initiate empty array to store pred_probs.
    num_examples, num_classes = len(X), len(X.label.value_counts())
    pred_probs = np.full((num_examples, num_classes), np.NaN)
    pred_probs_unlabeled = None

    # If we use up all examples from the initial unlabeled pool, X_unlabeled will be None.
    if X_unlabeled is not None:
        pred_probs_unlabeled = np.full((n_splits, len(X_unlabeled), num_classes), np.NaN)

    # Iterate through cross-validation folds.
    for split_num, split in enumerate(skf_splits):
        train_index, test_index = split

        train_set = X.iloc[train_index]
        test_set = X.iloc[test_index]

        # Get trainer with train/test subsets.
        trainer = get_trainer(train_set, test_set)
        trainer.train()
        eval_metrics = trainer.evaluate()

        # Get pred_probs and insert into dataframe.
        pred_probs_fold = eval_metrics['eval_pred_probs']
        pred_probs[test_index] = pred_probs_fold

        # Since we don't have labels for the unlabeled pool, we compute pred_probs at each round of CV
        # and then average the results at the end.
        if X_unlabeled is not None:
            dataset_unlabeled = Dataset.from_dict({"text": X_unlabeled['text'].values})
            unlabeled_tokenized_dataset = dataset_unlabeled.map(tokenize_function, batched=True)
            logits = trainer.predict(unlabeled_tokenized_dataset).predictions
            curr_pred_probs_unlabeled = softmax(logits, axis=1)
            pred_probs_unlabeled[split_num] = curr_pred_probs_unlabeled

    # Here we average the pred_probs from each round of CV to get pred_probs for the unlabeled pool.
    if X_unlabeled is not None:
        pred_probs_unlabeled = np.mean(np.array(pred_probs_unlabeled), axis=0)

    return pred_probs, pred_probs_unlabeled
```

`get_annotator` 函数根据一组标准确定最合适的标注者，从特定示例中收集新的标注。而 `get_annotation` 函数则专注于从选择的标注者处收集给定示例的实际标注，同时还会将已收集的标注从池中删除，以防止其再次被选择。

```python
# Helper method to determine which annotator to collect annotation from for given example.
def get_annotator(example_id):
    # Update who has already annotated atleast one example.
    existing_annotators = set(X_labeled_full.drop('text', axis=1).columns)
    # Returns the annotator we want to collect annotation from.
    # Chooses existing annotators first.
    annotators = extra_annotations[example_id]
    chosen_annotator = choose_existing(annotators, existing_annotators)
    return chosen_annotator

# Helper method to collect an annotation for given text example.
def get_annotation(example_id, chosen_annotator):

    # Collect new annotation.
    new_annotation = extra_annotations[example_id][chosen_annotator]

    # Remove annotation.
    del extra_annotations[example_id][chosen_annotator]

    return new_annotation
```

运行以下代码单元以隐藏下一个模型训练块的 HTML 输出。

```python
%%html
<style>
    div.output_stderr {
    display: none;
    }
</style>
```

## 使用的方法

对于每一轮**主动学习**，我们：

1. 计算基于迄今为止收集的所有标注的 ActiveLab 共识标签，应用于每个训练示例。
2. 使用这些共识标签，在当前训练集上训练我们的 Transformer 分类模型。
3. 在测试集上评估模型的测试准确率（该测试集具有高质量的真实标签）。
4. 通过交叉验证，获取模型对整个训练集和未标注集的样本外预测类别概率。
5. 获取训练集和未标注集上每个示例的 ActiveLab 主动学习分数。这些分数估算了为每个示例收集另一个标注的潜在信息量。
6. 选择一组主动学习分数最低的示例子集（*n = batch_size*）。
7. 为每个选定的 *n* 个示例收集一个额外的标注。
8. 将新的标注（以及如果选择的非标注示例）添加到训练集中，用于下一轮迭代。

接下来，我将比较通过主动学习标注的数据与通过**随机选择**标注的数据训练的模型。对于每一轮随机选择，我使用多数投票共识而非 ActiveLab 共识（步骤 1），然后仅随机选择**n**个示例来收集额外标签，而不是使用 ActiveLab 分数（步骤 6）。

关于 ActiveLab 共识标签和主动学习分数的更多直觉解释将在本笔记本的后续部分中分享。

![activelab.png](data:image/png;base64,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)

### 模型训练与评估

我首先对测试集和训练集进行标记化处理，然后初始化一个预训练的 DistilBert Transformer 模型。通过 300 次训练步骤对 DistilBert 进行微调，在我的数据上取得了准确率与训练时间之间的良好平衡。该分类器输出预测类别概率，我在评估其准确性之前，将概率转换为类别预测。

### 使用主动学习分数决定下一个标注内容
在每轮主动学习中，我们通过 3 折交叉验证拟合当前训练集上的 Transformer 模型。这使我们能够获得训练集上每个示例的样本外预测类别概率，同时我们还可以使用训练好的 Transformer 获取未标注池中每个示例的样本外预测类别概率。所有这些都在 `get_pred_probs` 辅助方法中实现。样本外预测的使用帮助我们避免了由于潜在过拟合导致的偏差。

一旦得到这些概率预测，我将它们传入开源 [cleanlab](https://github.com/cleanlab/cleanlab) 包中的 `get_active_learning_scores` 方法，该方法实现了 [ActiveLab 算法](https://arxiv.org/abs/2301.11856)。此方法为我们提供了所有标注数据和未标注数据的分数。较低的分数表示收集一个额外标签对当前模型来说最具信息性（这些分数在标注数据和未标注数据之间是直接可比较的）。

我将得分最低的示例形成一个批次，作为需要收集标注的示例（通过 `get_idx_to_label` 方法）。在每轮中，我始终收集相同数量的标注（无论是在主动学习还是随机选择方法下）。对于这个应用，我将每个示例的最大标注次数限制为 5 次（避免重复标注同一个示例）。

### 添加新的标注
`combined_example_ids` 是我们希望收集标注的文本示例的 ID。对于每一个示例，我们使用 `get_annotation` 辅助方法从标注者那里收集新的标注。在此过程中，我们优先选择那些已经标注过其他示例的标注者。如果给定示例的标注者在训练集中不存在，我们将随机选择一个标注者。在这种情况下，我们会在训练集中添加一列，表示新的标注者。最后，我们将新收集的标注添加到训练集中。如果对应的示例之前没有标注过，我们还会将其添加到训练集中，并从未标注数据集中移除。

我们现在已经完成了一轮新的标注收集，并基于更新后的训练集重新训练 Transformer 模型。我们将通过多轮的迭代重复此过程，不断扩展训练数据集并提升模型性能。

```python
# For this Active Learning demo, we add 25 additional annotations to the training set
# each iteration, for 25 rounds.
num_rounds = 25
batch_size_to_label = 25
model_accuracy_arr = np.full(num_rounds, np.nan)

# The 'selection_method' varible determines if we use ActiveLab or random selection
# to choose the new annotations each round.
selection_method = 'random'
# selection_method = 'active_learning'

# Each round we:
# - train our model
# - evaluate on unchanging test set
# - collect and add new annotations to training set
for i in range(num_rounds):

    # X_labeled_full is updated each iteration. We drop the text column which leaves us with just the annotations.
    multiannotator_labels = X_labeled_full.drop(['text'], axis=1)

    # Use majority vote when using random selection to select the consensus label for each example.
    if i == 0 or selection_method == 'random':
        consensus_labels = get_majority_vote_label(multiannotator_labels)

    # When using ActiveLab, use cleanlab's CrowdLab to select the consensus label for each example.
    else:
        results = get_label_quality_multiannotator(
            multiannotator_labels,
            pred_probs_labeled,
            calibrate_probs=True,
        )
        consensus_labels = results["label_quality"]["consensus_label"].values

    # We only need the text and label columns.
    train_set = X_labeled_full[['text']]
    train_set['label'] = consensus_labels
    test_set = test[['text', 'label']]

    # Train our Transformer model on the full set of labeled data to evaluate model accuracy for the current round.
    # This is an optional step for demonstration purposes, in practical applications
    # you may not have ground truth labels.
    trainer = get_trainer(train_set, test_set)
    trainer.train()
    eval_metrics = trainer.evaluate()
    # set statistics
    model_accuracy_arr[i] = eval_metrics['eval_accuracy']

    # For ActiveLab, we need to run cross-validation to get out-of-sample predicted probabilites.
    if selection_method == 'active_learning':
        pred_probs, pred_probs_unlabeled = get_pred_probs(train_set, X_unlabeled)

        # Compute active learning scores.
        active_learning_scores, active_learning_scores_unlabeled = get_active_learning_scores(
            multiannotator_labels, pred_probs, pred_probs_unlabeled
        )

        # Get the indices of examples to collect more labels for.
        chosen_examples_labeled, chosen_examples_unlabeled = get_idx_to_label(
            X_labeled_full,
            X_unlabeled,
            extra_annotations,
            batch_size_to_label,
            active_learning_scores,
            active_learning_scores_unlabeled,
        )

    # We don't need to run cross-validation, just get random examples to collect annotations for.
    if selection_method == 'random':
        chosen_examples_labeled, chosen_examples_unlabeled = get_idx_to_label_random(
        X_labeled_full,
        X_unlabeled,
        extra_annotations,
        batch_size_to_label
        )

    unlabeled_example_ids = np.array([])
    # Check to see if we still have unlabeled examples left.
    if X_unlabeled is not None:
        # Get unlabeled text examples we want to collect annotations for.
        new_text = X_unlabeled.iloc[chosen_examples_unlabeled]
        unlabeled_example_ids = new_text.index.values
        num_ex, num_annot = len(new_text), multiannotator_labels.shape[1]
        empty_annot = pd.DataFrame(data = np.full((num_ex, num_annot), np.NaN), columns = multiannotator_labels.columns, index=unlabeled_example_ids)
        new_unlabeled_df = pd.concat([new_text, empty_annot], axis=1)

        # Combine unlabeled text examples with existing, labeled examples.
        X_labeled_full = pd.concat([X_labeled_full, new_unlabeled_df], axis=0)

        # Remove examples from X_unlabeled and check if empty.
        # Once it is empty we set it to None to handle appropriately elsewhere.
        X_unlabeled = X_unlabeled.drop(new_text.index)
        if X_unlabeled.empty:
            X_unlabeled = None

    if selection_method == 'active_learning':
        # Update pred_prob arrays with newly added examples if necessary.
        if pred_probs_unlabeled is not None and len(chosen_examples_unlabeled) != 0:
            pred_probs_new = pred_probs_unlabeled[chosen_examples_unlabeled, :]
            pred_probs_labeled = np.concatenate((pred_probs, pred_probs_new))
            pred_probs_unlabeled = np.delete(
                pred_probs_unlabeled, chosen_examples_unlabeled, axis=0
            )
        # Otherwise we have nothing to modify.
        else:
            pred_probs_labeled = pred_probs

    # Get combined list of text ID's to relabel.
    labeled_example_ids = X_labeled_full.iloc[chosen_examples_labeled].index.values
    combined_example_ids = np.concatenate([labeled_example_ids, unlabeled_example_ids])

    # Now we collect annotations for the selected examples.
    for example_id in combined_example_ids:
        # Choose which annotator to collect annotation from.
        chosen_annotator = get_annotator(example_id)
        # Collect new annotation.
        new_annotation = get_annotation(example_id, chosen_annotator)
        # New annotator has been selected.
        if chosen_annotator not in X_labeled_full.columns.values:
            empty_col = np.full((len(X_labeled_full),), np.nan)
            X_labeled_full[chosen_annotator] = empty_col

        # Add selected annotation to the training set.
        X_labeled_full.at[example_id, chosen_annotator] = new_annotation
```

## 结果

在进行 25 轮主动学习（每轮标注一批数据并重新训练 Transformer 模型）后，每轮收集 25 个标注。我重复了整个过程，下一次使用随机选择来决定每轮标注哪些示例——作为基准比较。在标注额外数据之前，两种方法都从相同的初始训练集（包含 100 个示例）开始（因此在第一轮中，Transformer 的准确率大致相同）。由于训练 Transformer 模型时固有的随机性，我对这个过程进行了五次运行（每种数据标注策略），并报告了五次重复运行中测试准确率的标准差（阴影区域）和均值（实线）。

```python
# Get numpy array of results.
!wget -nc -O 'random_acc.npy' 'https://huggingface.co/datasets/Cleanlab/stanford-politeness/resolve/main/activelearn_acc.npy'
!wget -nc -O 'activelearn_acc.npy' 'https://huggingface.co/datasets/Cleanlab/stanford-politeness/resolve/main/random_acc.npy'
```

```python
# Helper method to compute std dev across 2D array of accuracies.
def compute_std_dev(accuracy):
    def compute_std_dev_ind(accs):
        mean = np.mean(accs)
        std_dev = np.std(accs)
        return np.array([mean - std_dev, mean + std_dev])

    std_dev = np.apply_along_axis(compute_std_dev_ind, 0, accuracy)
    return std_dev
```

```python
>>> al_acc = np.load('activelearn_acc.npy')
>>> rand_acc = np.load('random_acc.npy')

>>> rand_acc_std = compute_std_dev(rand_acc)
>>> al_acc_std = compute_std_dev(al_acc)

>>> plt.plot(range(1, al_acc.shape[1]+1), np.mean(al_acc, axis=0), label="active learning", color='green')
>>> plt.fill_between(range(1, al_acc.shape[1]+1), al_acc_std[0], al_acc_std[1], alpha=0.3, color='green')

>>> plt.plot(range(1, rand_acc.shape[1]+1), np.mean(rand_acc, axis=0), label="random", color='red')
>>> plt.fill_between(range(1, rand_acc.shape[1]+1), rand_acc_std[0], rand_acc_std[1], alpha=0.1, color='red')

>>> plt.hlines(y=0.9, xmin=1.0, xmax=25.0, color='black', linestyle='dotted')
>>> plt.legend()
>>> plt.xlabel("Round Number")
>>> plt.ylabel("Test Accuracy")
>>> plt.title("ActiveLab vs Random Annotation Selection --- 5 Runs")
>>> plt.savefig("al-results.png")
>>> plt.show()
```

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我们可以看到，选择下一步标注哪些数据对模型性能有着显著的影响。使用 ActiveLab 的主动学习在每一轮中都显著优于随机选择。例如，在第 4 轮，总共有 275 个标注的训练集时，通过主动学习我们获得了 91% 的准确率，而在没有智能选择标注示例的情况下，准确率仅为 76%。总体来说，使用主动学习构建的数据集上训练的 Transformer 模型，其误差率大约是传统方法的 **50%**，无论总的标注预算是多少！

**在进行文本分类数据标注时，你应考虑使用主动学习，并配合重新标注选项，以更好地应对标注者的不完美性。**

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/zh-CN/annotate_text_data_transformers_via_active_learning.md" />
