# Cookbook

## Docs

- [Veri Analizi Ajanı: Göz açıp kapayıncaya kadar verilerinizi analiz edin ✨](https://huggingface.co/learn/cookbook/tr/agent_data_analyst.md)
- [Özel Verisetini DETR Modeli ile Fine-Tuning ederek Nesne Tespiti Yapımı  🖼, Spaces Üzerinde Gösterimi ve Gradio API Entegrasyonu](https://huggingface.co/learn/cookbook/tr/fine_tuning_detr_custom_dataset.md)
- [Vision Transformer Modelini Özel Bir Biyomedikal Verisetiyle Fine-Tune Etmek](https://huggingface.co/learn/cookbook/tr/fine_tuning_vit_custom_dataset.md)
- [Özel Bir Veri Seti Üzerinde Semantik Segmentasyon Modeli Fine-Tuning Etmek ve Inference API Üzerinden Kullanımı](https://huggingface.co/learn/cookbook/tr/semantic_segmentation_fine_tuning_inference.md)
- [Transformers Agents kullanarak, tool-calling süper güçleriyle donatılmış bir ajan oluşturun 🦸](https://huggingface.co/learn/cookbook/tr/agents.md)
- [Kod Dil Modelini Tek GPU'da Fine-Tune Etmek](https://huggingface.co/learn/cookbook/tr/fine_tuning_code_llm_on_single_gpu.md)
- [Açık Kaynak AI Cookbook](https://huggingface.co/learn/cookbook/tr/index.md)

### Veri Analizi Ajanı: Göz açıp kapayıncaya kadar verilerinizi analiz edin ✨
https://huggingface.co/learn/cookbook/tr/agent_data_analyst.md

# Veri Analizi Ajanı: Göz açıp kapayıncaya kadar verilerinizi analiz edin ✨
Yazar: [Aymeric Roucher](https://huggingface.co/m-ric)

Bu eğitim ileri düzey konular içermektedir. Bu eğitim içeriğinden önce Cookbook'ta yer alan [Ajan eğitimine](https://github.com/huggingface/cookbook/blob/main/notebooks/en/agents.ipynb) bakmanızı öneririz.

Bu notebook ile bir veri analisti ajanı oluşturacağız: Veri analizi kütüphaneleri ile donatılmış ve Dataframelerden sonuçlar çıkartabilen ve bu sonuçları grafiklerle gösterebilen bir ajan.

[Kaggle Titanic yarışmasındaki](https://www.kaggle.com/competitions/titanic) verileri analiz ederek yolcuların hayatta kalma olasılıklarını tahmin etmek istediğimizi düşünelim.

Ama bu konuya başlamadan önce, otomatik bir ajanın trendleri çıkararak ve bazı grafikler çizerek analiz hazırlamasını istiyorum.

Bu sistemi kuralım.

Gerekli kütüphane gereksinimlerini yüklemek için aşağıdaki kodu çalıştıracağız:


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

İlk olarak `ReactCodeAgent` kullanarak bir ajan oluşturacağız. (Ajan türleri hakkında daha fazla bilgi edinmek için bu [dökümantasyonu](https://huggingface.co/docs/transformers/en/agents) okuyabilirsiniz.)

ReactCodeAgent kodu doğrudan çalıştırabilir dolayısı ile herhangi farklı bir araç kullanmamıza gerek yoktur.

Veri bilimiyle ilgili kütüphaneleri kullanmasına izin verdiğimizden emin olmak için `["numpy", "pandas", "matplotlib.pyplot", "seaborn"]` kütüphanelerini `"additional_authorized_imports"` parametresi ile aktarıyoruz.

Python yorumlayıcısı yalnızca çalışma ortamında yüklü kütüphaneleri kullanabilir. Bu sebeple `"additional_authorized_imports"` parametresine aktardığımız kütüphanelerin çalışma ortamınızda kurulu olduğundan emin olun.

⚙ Ajanımızı, `HfEngine` sınıfı aracılığıyla HF'nin Inference API'sini kullanan [meta-llama/Meta-Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) modeliyle çalıştıracağız. Inference API, herhangi bir açık kaynak modelini hızlı ve kolay bir şekilde çalıştırmamıza olanak tanır.










```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,
)
```

## Veri analizi 📊🤔

Yarışmadan alınan notları fonksiyona argüman olarak verelim ve ajanımızı `run` methodu ile çalıştıralım.

```python
import os

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

```python
"""
## Değişken notları
pclass: Sosyo-ekonomik durumu (SES) temsil eden bir gösterge
1.sınıf = Üst
2.sınıf = Orta
3.sınıf = Alt

age: Yaş, 1'den küçükse kesirli bir değerdir. Eğer yaş tahmin ediliyorsa, xx.5 şeklindedir.

sibsp: Veri seti aile ilişkilerini şu şekilde tanımlar...
Kardeş = erkek kardeş, kız kardeş, üvey erkek kardeş, üvey kız kardeş, Eş = koca, karı (nişanlılar göz ardı edilmiştir)

parch: Veri seti aile ilişkilerini şu şekilde tanımlar...
Ebeveyn = anne, baba, Çocuk = kız, oğul, üvey kız, üvey oğul
Bazı çocuklar yalnızca bir bakıcıyla seyahat ettikleri için, bu durumda parch=0'dır.
"""
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.
"""

"""
Sen bir veri analizi uzmanısın.
Lütfen kaynak dosyasını yükle ve içeriğini analiz et.
Elindeki değişkenlere göre, bu verilerle ilgili sorulabilecek 3 ilginç soru listele, örneğin hayatta kalma oranlarıyla belirli korelasyonlar hakkında.
Sonra bu soruları, ilgili sayıları bularak teker teker yanıtla.
Bu arada, matplotlib/seaborn kullanarak bazı grafikler çiz ve bunları (zaten mevcut olan) './figures/' klasörüne kaydet: Her grafiği çizmeden önce plt.clf() ile temizlemeyi unutma.

Sonuçlarınızda: bu korelasyonları ve trendleri özetleyin.
Her sayının ardından gerçek dünya içgörüleri çıkarın; örneğin: "is_december ile boredness arasındaki korelasyon 1.3453'tür, bu da insanların kışın daha çok sıkıldığını gösterir."
Sonuçlarınız en az 3 numaralı ve ayrıntılı parça içermelidir.
"""
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)
```

Etkileyici, değil mi? Ajanınıza kendi grafiklerini gözden geçirmesi için bir görselleştirme aracı da sağlayabilirsiniz!

## Veri bilimi ajanı: Tahminleri çalıştır 🛠️👉
**Modelimizin veriler üzerinde tahminler yapmasına izin vererek** bir adım daha ilerleyelim.

Bunu yapmak için, `additional_authorized_imports` içinde `sklearn` kullanımına da izin veriyoruz.

```python
agent = ReactCodeAgent(
    tools=[],
    llm_engine=llm_engine,
    additional_authorized_imports=[
        "numpy",
        "pandas",
        "matplotlib.pyplot",
        "seaborn",
        "sklearn",
    ],
    max_iterations=12,
)
"""
Sen bir ML uzmanısın.
Lütfen "titanic/train.csv" dosyası üzerinde hayatta kalmayı tahmin etmek için bir ML modeli eğit.
Sonuçları './output.csv' dosyasına yaz.
Kullanımdan önce fonksiyonları ve modülleri içe aktarmayı unutma!
"""
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,
)
```

Ajanımızın çıkardığı test çıktıları, Kaggle'a gönderildiğinde 0.78229 puan alıyor ve 17,360 sonuç arasından #2824 sıraya yerleşiyor.

Bu sonuç, yazarın yıllar önce bu yarışmaya ilk girdiğinde elde ettiği sonuçtan çok daha iyi bir sonuçtur.

Sonuçlarınız değişebilir, ancak yine de ajanın birkaç saniyede bu sonucu başarması gerçekten etkileyici.

🚀 Veri analisti ajanı ile sadece basit bir deneme yaptık. Kullanım durumunuza daha iyi uyacak şekilde çok daha fazla geliştirebilirsiniz.

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/tr/agent_data_analyst.md" />

### Özel Verisetini DETR Modeli ile Fine-Tuning ederek Nesne Tespiti Yapımı  🖼, Spaces Üzerinde Gösterimi ve Gradio API Entegrasyonu
https://huggingface.co/learn/cookbook/tr/fine_tuning_detr_custom_dataset.md

# Özel Verisetini DETR Modeli ile Fine-Tuning ederek Nesne Tespiti Yapımı  🖼, Spaces Üzerinde Gösterimi ve Gradio API Entegrasyonu

_Yazar: [Sergio Paniego](https://github.com/sergiopaniego)_
_Çevirmen: [Onuralp Sezer](https://github.com/onuralpszr)_

Bu notebook'ta, [DETR](https://huggingface.co/docs/transformers/model_doc/detr) adlı [nesne tespiti](https://huggingface.co/docs/transformers/tasks/object_detection) modelini, özel bir veri seti ile Fine Tune işlemi yapacağız. Bu işlemi yerine getirirken [Hugging Face ekosistemi](https://huggingface.co/docs)'nden faydalanacağız.

Yaklaşımımız, önceden eğitilmiş bir DETR modeliyle başlayıp, moda görselleri üzerine etiketlenmiş özel bir veri seti olan [Fashionpedia](https://huggingface.co/datasets/detection-datasets/fashionpedia) üzerinde Fine-tune işlemi yapmaktır. Bu sayede modeli, moda alanındaki nesneleri daha iyi tanıyıp tespit edebilecek şekilde adapte etmiş olacağız.

Modelin başarılı bir şekilde Fine-Tune edilmesinin ardından, Hugging Face üzerinde Gradio Space üstüne gösterilmesini gerçekleştireceğiz. Ek olarak, Gradio API'sini kullanarak deploy edilen space ile nasıl etkileşime geçileceğini inceleyeceğiz. Bu sayede barındırılan Space ile sorunsuz erişim kurulacak ve gerçek dünya uygulamaları için yeni imkanlar sunulacaktır.

![DETR mimarisi](https://github.com/facebookresearch/detr/raw/main/.github/DETR.png)


## 1. Kütüphanelerin Kurulumu 

Nesne tespiti üzerinde Fine-Tune işlemine başlamak için, gerekli kütüphaneleri kuralım

```python
!pip install -U -q datasets transformers[torch] timm wandb torchmetrics matplotlib albumentations
# datasets==2.21.0, transformers==4.44.2 timm==1.0.9, wandb==0.17.9 torchmetrics==1.4.1 ile test edildi.
```

## 2. Veri setinin Yüklenmesi 📁

<img src="https://fashionpedia.github.io/home/img/dataset/teaser.png" alt="Dataset sample" width="80%">

📁 Kullanacağımız veri seti, makalede yer alan [Fashionpedia](https://huggingface.co/datasets/detection-datasets/fashionpedia): [Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset](https://arxiv.org/abs/2004.12276) çalışmasından alınan Fashionpedia adlı veri setidir. Yazarlar veri setini şu şekilde tanımlamaktadır;


````
Fashionpedia, iki bölümden oluşmuş bir veri setidir: (1) moda uzmanları tarafından oluşturulan, 27 ana giyim kategorisi, 19 giyim parçası, 294 ayrıntılı özellik ve bunların ilişkilerini içeren bir ontoloji bulunmaktadır; (2) Fashionpedia ontolojisi temelinde oluşturulmuş, segmentasyon maskeleri ve maske başına ayrıntılı özellikler ile anotasyonlanmış 48 bin günlük ve ünlü etkinlik moda görsellerinden oluşan bir veri seti bulunmaktadır.
````


Veri seti şunları içermektedir:

* **46,781 tane resim** 🖼
* **342,182 tane bounding box ** 📦

[Fashionpedia Dataset](https://huggingface.co/datasets/detection-datasets/fashionpedia) veri seti Hugging Face'te mevcuttur.



```python
from datasets import load_dataset

dataset = load_dataset('detection-datasets/fashionpedia')
```

```python
dataset
```

Veri setindeki iç yapıyı incelemek için veri seti içinden bir örneği gözden geçirelim.

```python
dataset["train"][0]
```

## 3. Eğitim ve Test İçin Verisetinden Bölümler Alın ➗

Bu veri seti iki bölümle birlikte gelir: **eğitim** ve **test**. Modeli Fine-Tune etmek için eğitim bölümünü, doğrulama için ise test bölümünü kullanacağız.


```python
train_dataset = dataset['train']
test_dataset = dataset['val']
```

**İsteğe Bağlı**

Aşağıdaki yorum satırı olan hücrede, hem eğitim hem de test bölümleri için orijinal verisetinden rastgele %1 örnek alıyoruz. Bu yaklaşım, verisetinde çok sayıda örnek bulunduğu için eğitim sürecini hızlandırmak amacıyla kullanılmaktadır.

En iyi sonuçlar için bu iki hücreyi atlayarak tam verisetini kullanmanızı öneririz. Ancak, gerekirse bu hücrelerdeki kodları yorum dışı bırakabilirsiniz.

```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. Veri setindeki Bir Örneğinin Görselleştirilmesi 👀

Veri setine yüklediğimiz bir örneği ve onunla ilişkilendirilmiş nesnelerin açıklamalarını görselleştirmesi işlemini yapalım.

### id2label ve label2id değişkenlerini oluşturalım

Bu değişkenler, nesne ID'leri ile ilgili etiketler arasındaki eşlemeleri içerir. id2label, ID'lerden etiketlere, label2id ise etiketlerden ID'lere eşleme yapar.

```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()}
```

###  Bir Resmin Görselleştirmesi 🎨

Şimdi, veri setinden bir görüntüyü görselleştirerek nasıl göründüğünü daha iyi anlayalım.

```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) # "idx" değerini değiştirerek farklı bir görseli görebilirsiniz.
```

<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>

### Bir Kaç Tane Resmin Görselleştirilmesi 📸 

Şimdi, veri setinden birkaç görüntüye daha bakalım ve veri seti ile ilgili geniş bir perspektif elde edelim.

```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. Geçersiz Bbox'ları Filtreyelim  ❌

Veri seti ön işleme sürecinin ilk adımı olarak, geçersiz Bounding Box'ları filtreleyeceğiz. Veri setini gözden geçirdiğimizde, bazı sınırlayıcı kutuların geçerli olmadığını fark ettik. Bu nedenle, geçersiz olanları filtreleceğiz.

```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"Geçersiz bbox içeren görüntü: {example['image_id']} Geçersiz bbox tespit edildi ve atıldı: {bbox} - bbox_id: {example['objects']['bbox_id'][i]} - kategori: {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>
Geçersiz bbox içeren görüntü: 8396 Geçersiz bbox tespit edildi ve atıldı: [0.0, 0.0, 0.0, 0.0] - bbox_id: 139952 - kategori: 42
Geçersiz bbox içeren görüntü: 19725 Geçersiz bbox tespit edildi ve atıldı: [0.0, 0.0, 0.0, 0.0] - bbox_id: 23298 - kategori: 42
Geçersiz bbox içeren görüntü: 19725 Geçersiz bbox tespit edildi ve atıldı: [0.0, 0.0, 0.0, 0.0] - bbox_id: 23299 - kategori: 42
Geçersiz bbox içeren görüntü: 21696 Geçersiz bbox tespit edildi ve atıldı: [0.0, 0.0, 0.0, 0.0] - bbox_id: 277148 - kategori: 42
Geçersiz bbox içeren görüntü: 23055 Geçersiz bbox tespit edildi ve atıldı: [0.0, 0.0, 0.0, 0.0] - bbox_id: 287029 - kategori: 33
Geçersiz bbox içeren görüntü: 23671 Geçersiz bbox tespit edildi ve atıldı: [0.0, 0.0, 0.0, 0.0] - bbox_id: 290142 - kategori: 42
Geçersiz bbox içeren görüntü: 26549 Geçersiz bbox tespit edildi ve atıldı: [0.0, 0.0, 0.0, 0.0] - bbox_id: 311943 - kategori: 37
Geçersiz bbox içeren görüntü: 26834 Geçersiz bbox tespit edildi ve atıldı: [0.0, 0.0, 0.0, 0.0] - bbox_id: 309141 - kategori: 37
Geçersiz bbox içeren görüntü: 31748 Geçersiz bbox tespit edildi ve atıldı: [0.0, 0.0, 0.0, 0.0] - bbox_id: 262063 - kategori: 42
Geçersiz bbox içeren görüntü: 34253 Geçersiz bbox tespit edildi ve atıldı: [0.0, 0.0, 0.0, 0.0] - bbox_id: 315750 - kategori: 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. Sınıf Görünümlerinin Görselleştirilmesi 👀

Veri setini derinlmesine keşfetmek için her sınıfın görünüm sayılarını çizelim. Bu, sınıfların dağılımını anlamamıza ve olası yanlılıkları tanımlamamıza yardımcı olacaktır.

```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('Kategoriler', fontsize=14)
>>> ax.set_ylabel('Görüntü Sayısı', fontsize=14)
>>> ax.set_title('Kategoriye Göre Görüntü Sayısı', fontsize=16)

>>> ax.set_xticks(range(len(categories)))
>>> 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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Veri setine baktığımızda 'ayakkabı' veya 'elbise kolu' gibi bazı sınıfların aşırı temsil edildiğini gözlemleyebiliriz. Bu, veri setinin dengesiz olabileceğini, bazı sınıfların diğerlerinden daha sık göründüğünü gösterir. Bu dengesizlikleri tanımlamak, model eğitimindeki olası yanlılıkları ele almak için çok önemlidir."

## 7. Add Data Augmentation to the Dataset

Veri artırımı 🪄, nesne tespiti görevlerinde performansı artırmak için çok önemlidir. Bu bölümde, veri setimizi etkili bir şekilde artırmak için [Albumentations](https://albumentations.ai/) yeteneklerinden faydalanacağız.

[Albumentations](https://albumentations.ai/), nesne tespiti için özel olarak tasarlanmış bir dizi güçlü veri artırma tekniği sunar. Çeşitli dönüşümler gerçekleştirilmesine olanak tanırken, bounding boxların doğru bir şekilde ayarlandığından emin olur. Bu yetenekler, daha çeşitli bir veri seti oluşturmaya yardımcı olarak modelin dayanıklılığını ve genelleme yeteneğini geliştirmektedir.

<img src="https://albumentations.ai/docs/images/introduction/dedicated_library/pixel_and_spatial_level_augmentations_for_object_detection.jpg" alt="Albumentations image" 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. Model Checkpoint'den ImageProcessor'u Oluşturma 🎆

Önceden eğitilmiş bir model checkpoint'i kullanarak Image Processor'u oluşturacağız. Bu durumda, [facebook/detr-resnet-50-dc5](https://huggingface.co/facebook/detr-resnet-50-dc5) modelini kullanıyoruz.


```python
from transformers import AutoImageProcessor

checkpoint = "facebook/detr-resnet-50-dc5"
image_processor = AutoImageProcessor.from_pretrained(checkpoint)
```

### Veri Setini İşlemek İçin Fonksiyonların Eklenmesi

Artık veri setini işlemek için fonsiyonlar ekleyeceğiz. Bu fonksiyonlar, görüntüleri ve anotasyonları modelle uyumlu hale getirmek ve dönüşümler gibi görevleri üstlenecektir.

```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"])

        # VOC (Visual Object Classes) formatından COCO formatına dönüştür
        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. Artırılmış Verilerin Örneklerini Görselleştirelim 🎆

Modelimizin eğitim aşamasına yaklaşıyoruz! Devam etmeden önce, veri artırma sonrası bazı örnekleri görselleştirelim. Bu, veri artırmalarının eğitim süreci için uygun ve etkili olup olmadığını kontrol etmemizi sağlayacaktır.

```python
>>> # Opsyiyonel bir dönüşüm durumunda çalışması için güncellenmiş çizim fonksiyonu
>>> def draw_augmented_image_from_idx(dataset, idx, transform=None):
...     sample = dataset[idx]
...     image = sample["image"]
...     annotations = sample["objects"]

...     # Resmi RGB ve NumPy dizisine dönüştür.
...     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])  # Tekrar PIL formatına dönüştür.
...     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)

...         # Koordinatları eğer gerekli ise normalize et
...         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

>>> # Augmentasyonu içerecek şekilde güncellenmiş çizim fonksiyonu
>>> 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 imagess
>>> plot_augmented_images(train_dataset, range(9), transform=train_transform)
```

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le13/t3/APSeOgDz+iiigAooooAKKKKAOp8IpudvlzzmuhmndnIA4HWsrwX8tvMQuSQQK2hbM6Ox+WpYyvcNFJAiA/NnkUommP8Ao+T5Q5wafqKIqQGPGehNUTIRyxJNIBbzDpIS3boKnVXhjj3EhcDiqcvzpketX2VmQNIQqgZxQBPEyMct174rR0jAsZnx1Y1h2rEq7gZwMVtaUyrZKjD7xJoAvJOAg3L2rKitri+1yYW0DyDYANoq7O8aFEAZi5woUZzXU6FrNholqEOm373L/eY2+Py5rKVaEXZs7KOBrVVzRWnyObbw94gSKRDE8MDfeyccVg63pklpbMV5GOW9a9O1LxTbXFkwgs9TV3B4eD5T+tcZqt9Fc2kURt5I8f3kwDWftob3OynleIekIXfyM65geaHTIxEAFj3E46mu38LTAAwoCkrDAYjpXN2uq2ayW5njfbEMYVQf6111l408M2sYzaXZcdxEv/xVZ+2pt/EdSyvGRjZ0mbd3qFr4bgLz+WbuYYUL1Y+teOaws97q093K53u2c1paxqf9p6zLfGWVlZvkRh9wenWo5ba5uI2dbW4IK8N5RxW0a9Nvc5MRleMVm6b1IA2yNcqd2OtVRfmO53GNZUHVTVyZVW12usyTDGMrgVnrbM8u4LkDrtp+1hvc5f7OxX8n5F+71RoYVSPbIJFx05Wq2nrMLhJLZdrA5GefzqZIBJAW8snYeTinzakulwq6xb2fFRe+sdjkq0alKbhNWZ6L4buJLbw5dX9w22VgQCowCar6M8iRyyTAlpDuHPJq1Bp8t98PBIjbWUGTbjrTtF8OQzafDPeXG6QDdjONo9Kiq3ZRNZpX1MfXL1Wlhby90Q42noTXG3sM6uSi7QxJwOgrptZgtptTjjt7kLDG5DDrn6VnS2jmV1iO+Psc1pTtbRGJk7XjsHt2uTI/Lg54z6V1ngS+W00qPzCEdM8suRz71yN0kcEeXPzbsYFdZY6jbQWDW4spZIWALIg4FF7IRxHxQ1KC7uvLgkEoMm4yDofavOK67xxPG92kcMTRxBiVDda5Gt6S90QUUUVoAUUUUAe//sy/8zT/ANun/tavoCvn/wDZl/5mn/t0/wDa1fQFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfMH7R3/JQrD/ALBUf/o2WvIK9g/aN/5KFYf9gqP/ANGy14/QAtJRQaAFpOaPxo70AS2+RcxFeu4YrqXuJBFmTg1zenQSXOpW0EQ3SSSqqj1JNddf2c1ncNBdxGKVDypqJFwKdowEu9jj61dMiFty9fpVBQGl5OEFejeGPAUWq6d9uu5TGrjKL0pKNylKzsefTM7gnPTpT9HsX1K++zA7cjk1Z8U2J0fVntIzvXPy1P4OmgtNYEt4MZHyU0mmNWctSKTQ7yz1LyyHZUbr7VN4kB/tjT+ehXP516JcXFreORHt8wjNcT4g0+ebUILlVJSIjcQPenU+BmsKcYV4X2uvzOSvNQuhd3EakBd5Xp2BxSxalcGU/LuZuOh/T860pYIPNmLWYJB3Z3n5smr+lWduLrzli8qSFgUZB5nzfQ8cVxQnTaS5Py8vMJYTAVK0kq0Er9p7af3fQ1dG8GXk8KXGo3KWqFR+6Vf3hXtk9q318O6ZZwiK3h3b1wHlO4jvx+dZE+pXj5Zr2RnGVwYVHY8cVdtb951LBmYxQJx2yOtezgo0ZztGFmtdbenRs+hwmXYCPv0GpNb7+fdIyktbRJbiOe5MbpIVHBOQD1pLidQrIJ1nUgDJGGGMDqR7CuqZI5Yw+xDuGfujNVZLZCOEX8qccvlZxfK/VP8Az8zrnl+W1Pipfj/wDDivrbbH5glBRSAAqkc1BcT20lt5MaScjG5iOOc9q159OSXBChSPQU610uNW5UGmstt7zcX8n/8AJGP9jZXfmcG/+3v+B5HODRf7WVljcK8WCoI4Of8A9VZVxodxbz+XcIUx2Nehwxol2VRQFRRwB3qG/eG8imhmACq+xWHUHHWmsv5IWjvr6au5zY/K6U/ew65bJWXorHDR2yRSZUdFP8q5gq27pkCuxnhe1vJIJOqg8+vvXMrj5icZzXmpWqyT8v1PIrpxwdJPfmn/AO2lQlug6U7aduaeygE880zeSMHpWp5w6JhgqTQo2uWHSo9pzkUM3yYHWgCSIiS4UY6mu/0ctb2qqF4PNcHpkfmX8S+pr13S9K32aMRzjoRTQFF2OzcBzivGbj/j5l/3z/OvoJ9PXZtAHSvn664vJx6SMP1oYENFLSUgClpKKACvYP2cf+ShX/8A2CpP/RsVeP16/wDs4/8AJQr/AP7BUn/o2KgD6fooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvDPjHLAnjezWbIzp6c4/wCmkle514Z8Y51i8b2YkgMkf9nocj/rpJSew1ucbJa6HJCJUmTzsYwaxri33Za1KFV+8B3rTuLvSXh4tZo3x1IyKymjtriJvJkEMh6Z4qEU9BbG9ks5xJGfqtdzoNzFfTq6EZAyR6VwkOllRhZi7gZ69aksdQuLC5EsJKSLwynvTsTfoaz+JLu21ae3WWRYRKTlcYz+NR6h4qudWhS0u7tmQcqrJisafN1JPL3c7qp2Jjln2XPA3BQc9BnrTFY6XSZHtkOR8jHG0fzrYEXmTRunXcB+FUru30+0lW3tLwzIQDk9c1Y08tFJEsjYjPKt2H1qdtyuh195fGzESqucrV9DmFHYgbhmsB2kMLSoyyheBg5pBqszW5inQDPQ+lbSryU7rYzVCPJrua0kr+YoBB9hViBVjQpnJU8k1gabdFpMKwc7sc1vAxOhSRWBYg8VhKpLmvc1jCPLaxNjNJin7Nny+lJivWi7pM8uSs2huKTFPxRiquIZikJCjLEAe9LKyxRs7HCqMk14j4m8U6jrWsyw2k0iW6MVREPX3qZ1FBXZcKbm7I9sBVvukH6GlxXjOg6nrejyGaQzSJnlGyRivYLK5W9sorhPuyKDSp1lU2HUpShuS4pMVJijFa3MiPFGKfiq7XlurlGmUMO2aLjSuSUmKRZon+7Ip/Gn8HoQaSkgsxmKMU/FGKd0IZikxT8UmKEwG4pMU/FJimAzFJipCKjlkSGJpJGCooySe1AWDFZ+p6vZaVCZLqZVx0XPJrlNe8fxxb4NMXew48w9Pwrz6+v7m/kaW5laRj6muepiFHSJvCg3qzpdd8cXWoFobPMMHTI6muSdy7FnJZj3NNUE/SnxxtK+0Kc1xSnKbuzsjGMVoeqfs+nPj6+/7Bcn/o2KvpKvnP4BwpF48vhuzJ/ZkmQOg/exV9GUhsK+QPjb/wAle13/ALd//SeOvr+vkD42/wDJXtd/7d//AEnjoEef0UUUAFFFFABRRRQB23hMOloGXjrzXWRwCO1Z35Jrn/CxEWjqxXOf0rolYy2kgDgDHyg9SahjMe9Mc1zHHH0ANQ3NsqxbguCOuaHR1vwvA+XPFE8gCuhJfPQ+lAFBBvfrwDWjcDKogU8iqCR7ZUHYtW26liAMZC45oAgt1CwMMYAFaVogNrEoBzjPFVvIaOxmbbkba2tMs2ufsyKMHAz7Cl0Gld2Q/TtOUarYPcl0jPmSIVxnKgEdQe9Wja2fnxgFgEygJxkg+vHvUviG5SHULBIuFhikUfiBXK3OpySzBYm5DDmuJJSnJ26/oj1cRhKMadKUoq7jr/4E/wDI9Gh8L2VtaJdm4mJiU7VLDBB7Hjpz0rk9RszLcWexsF5GAx7DNT6v4seCJLPd9yIM2O59KxdL1aS2vLS9uf3oRmcp9RirlT9yy8vzRODoUudwS0tJ9tov/I7Lw/4aluYEkN1gK7ZVlznp6Eeh9uTWldeHbjT9DUyXcTLZI5DNCdz5QrjO7jrxj0q/4K1O21W2ufs8QRY5OmeuaseLtRii0ya1Cl2YchfStFQinsef7Om9f1f+fkcNqHh54PBsGoSXgUeWp8rb1JLe/uv/AHzXoHhnSUt9FtFkPmsYlJZh1yM147rfia61aSKyIMVvAAqx/T1r1nwRrqX2hokzBZLZArZ7gDg0o01Gpp2OtUYwwinHfma3fZB4i8EWmqRmSECKcDgjoa8qutNudHuJ47n90yN1I6ivc4tThumIilVgPSs3XvD1nr1o0c6AORgOByKuWqsjmlTbWp42lxiC5aAho2YENj2rnppJ57+KHb99wOfrXajw7c2FzqFrChlitZFRmUdPlyKztG0mWfX0MyfJG+QccmsYKyv6/mdWNhfEJeUP/SUevaRB5XhFocdID/KvP501P7EJUuB5ABHDdK9LnCWfhm5LHCiEjP4V499rlaz/ALNs97u7bmJPWrmtrI5KmsmU/wB5I77ZQpj+bP8Aeq1FNNFaFtrFm6KByavSaDJbWQnuCUd1ACjpmlhaaGdoWw3l9SOgNDqaaGMnY5GeQyuVKMDuGeOnNegRQpb6RvQKQYgJCetcxfSm28QW84iTaxVWGOG967I6nC6mESxlcbWXaM/QVMEnHXYaZ4l4yCfaoWSRnDAkFuwrl667x6kceowqgZflPyt1HNcjXXBpxuhBRRRVAFFFFAHv/wCzL/zNP/bp/wC1q+gK+f8A9mX/AJmn/t0/9rV9AUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8wftHf8lCsP+wVH/6NlryCvX/2jv8AkoVh/wBgqP8A9Gy15BQAUUUUAFKaSl70AXtEuTZ65Y3SruMU6OF9cEGu68Zaq+uamL1ITGCPmFcFpTAataFhwJlz+derWehyeINQSxtNoeU/eI4UetO10Ju2pwqSJvjDfdDDOK930bVLcaNBGgyoTp+FY+mfBqO18QJHe3BuLfy9+QMfN6V1svgYx3CJav5VuOo9qlaGsZrW55R4jgtdX1t5kyGU4wK4vUnktp9gypQnBr6Fu/h3ZPqlt5BMef8AWlTya81+KvhCHw7cRTQuWhlB+8eQabM73MfwdcXN3cSPLJwoIzXaxyW50TUEcKW8l9p98GvPfCcnlQsxJANdHd3Pl2rKG++MfnSn8D9DfDO+Ip37r8yl4z8NHT9H0/VbV2CTwI0q56EqDmp9FtjbeErOV/vTymQ59Og/lU2oa19s8Ktp8g3FFCLn24rSvLf7N4Zt4V/5YxIPyArry6EbqT7I9fLqcXjKkmtm/wAyi5WUXB2gqkLfnjil0Bd6XHHLDbWXcTzrDtik2KzAOP7w9K2fDqYtnJPJc4rvnpin/h/U+olb2Tsuv6G6E2WqKewqBsccU+WYKNpNUzOGfg1tG5zFtYz0A4qVUCIW702EjaD+dE06qnJ5qdb2AyJb6SLUJIo7fzXZQfvhayp7uR4CDFtEkpcHd1PpU89zCmqSTSOy7U+UqeprN8xZYoyQc5PJPbNcEVi6k5OErJNrp0a7p+ZFSpiYVFFUotW6t+Wu/r+BLqri6EMxhWN0jKEhw24VxQjZF+dSNwyMjrXdarGkNhbqg5cEngelcLJcyzqm/sMd/wDPavNnCrDESVR3el/x7JHz+cSrNU+eEYq8tm97Rvv8huB61HIo/E04k96sQQiUjNaHilcIBH71Wc/NWnew+SvSsw53ZI60Aa2gRs+rxbFzjmvYLbUILewIZwrqvQmvKPDUdyuoiWGJnAGDgVLr91qSXzviSOM+vSgD0iy8Q29xKYyygjPevBrzm+nx3kb+dbEeo3UbFkkOR3rDclnYnqTmgBKSiigBaSiigAr1/wDZx/5KFf8A/YKk/wDRsVeQV7B+zl/yUK//AOwVJ/6NioA+nqKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAryb4ixRzePbCORQwazQYP/XSSvWa8i+IEE0vxR0xl/1YsE4z3EklJ7Bexh+KNNsbW3Tbbxq7E8gVyLWFu2PkANbPxFvrmC5hKqSqLkgVzlhrMN4PmIR/7pqErLQfM2xzafH5rhQU2jO7OKht/Dd7dQT3wlzGhwOe1azkPG/fIrVtm+yeFipIPmN0HUULexT2ucIS0c7wniQeveqyoskixsNrFq6q+8JXd3bNqUPBVM+Xjlq5q0XzJFSZfmVsEGqJRr22jozoNzKw5yG611MFqJESA8gCsVbSaMbrVwR/dPb6V0GizPLcbZU2Mq8ispvQ1gk2MfSpIkb7PKwY9OcVBcwahAiSTyLMrDG08Yrp2jDnisvxCYV0Zs5EkbAj3qISbdmaVIxSujN0cKt4hK7eCa7SMxmMKMFuMg159Bq9r+5UuylRySKJtblhupZYJztY8elaWuZXPRzzzSbawbPXUuNCmmjkX7RHCzfRgDWE3ifU0HzXYztRgBADnPJ79v19q6pYyFCMVI5fq8ZKVSdSMF538uyfc7rFGK4wa9qZ8w/bRtVEcfuEyQ2OMbuvNRPr2u/azbpIhbYHG+NE4xnucfrUvMqa6P8AD/MdLC0as1COIhd9+ZL73FJHReJZ2tvD95Iq7j5ZGB715Lo2nJY3C6gu+Qqodgw+UZrrL3xHqqq0Fy8JVhhhsVhj8KwQ6MMLkA8YAIrgxmOVS3Jddz1qGDp4XmjOvSv3bbtpdWsv8/vOki8UrJDtNtZ7cdWDdPyrW8O65bujWspjQhgIggJDZ/yK4cJiJk2/Kevr+fWuy8P+H7l7BbqyuolDnpJbhiMehzWOExE1U9z+vxRpi4Uq1JwdWjd7cvPdaryfTyOmtbqC9h82B9yZxnBHP41MRXN+Hbq7e7ijkMXlTwvLtjiC8h9ueOvSuiuZktYHmlOFUZOa93D4hVocx4eNwFXBVfZVWm/K9t7dUuxl6/q0ek2DyEjzCMKK8+0iaTV53keRhuf71Zvi3xA+qXzojfuxwB6CtXwc0dvbLLJnbknpWdSfM7LYUIcsfM1rjTri2iMkN4SR0GafFBrKxq6XAORnGauXjB4FIzhmyOKsKVRBkDhKkLuxkxanrm5gil9nBqY+JdTtmVZ7Y5PQetWbDPlSOCRk+tLLmXU4kJJwO9HNLuDSvsNi8Wukgae1YKOoxWhF4u01x+8idc1W1CMGzkyFOTj7tNt7KD7LCrQRtx6Uc8u4JK2xqJ4g0iU/64r9aeuqWLn5Z1xXPWWkwXVxcMYsqvYHpUV9osMUsKxb0LnmqVaSE6cX0OsW6t3+7Mh/GszxLOkfh28cMD+7I61jXumW1jbPK+oGIqMhWPWuH1HWrq7iNtHIzRMfmJPBFW8Q2rCjRV7mDyaUx5FWPKUlUTLv6irS26Q8yYZ/QdBXMdRBBZMyb3+RPU9/pTywRdkS7Qe/c1I824ZZsBf0qhNqMMZIX5j7UCPV/gIgXx3e+p0yT/0bFX0VXzT+z3dvc/EK/wB3AGlyYH/bWKvpagAr5A+Nv/JXtd/7d/8A0njr6/r5A+Nv/JXtd/7d/wD0njoA8/ooooAKKKKACiiigD1DwxbZ8Oo4Iz6Gt3RrYveAOmF569KTwZZbtBgPl7wV6HpW/BaFXBUBNxKqM158sQ1JoZxGoIP7VcLhQigfrVO9jeGLdjjoDW5c2Jn1e6VF3eXtBwO9T6nZC4RISvypgHA6mtY1lewJaHOWenTyzQ5Xl/mGfSrs8JW8K5xgCtHW3+z6pCixkJHEBgVU+x3Ujea8eFPNaKW1xj5iTYOoY4JArsbaBNM0kXUvEjqAgPpWfoehi9jV5/lt42DNnvjtVTxNq5ubsxIcRpwoFKo9LI6cNDXmZjazePcyllPKqfwrnrd5RAkn3jnJNbEieXEztneyNkHtxxWVHE626BemOamjG3N6/ojpzCpzRo225f8A26RTvL4yTmSVvmJ5q/bXayW+N4Bxwa57V8i4OCKiiu2RVVep61tKHNGxjh8Q6NTntfR6PzVv1O58P+JLvQJxJBcqQ4wVCkBquXfiW81PUWuTN8qKQUHvXKG7jNuS6rtUAJzyTW5pGh3Mvh+81JsIP4FzyfU1Ps5fzP8AD/I1+tUbWVGP3y/+SKgXzLgymTczHJ4rYmuLmPTvKt7hot+N+09R6Vh2+UcBgfrWoH3DBPAFOMLO7dwxGJUqKpxgopO+l/1bLWi+Kb3RZwHcyR55Br1PRvGNhqUa/vQj45VjXiU53NjAzUgTzVWOOVkYckqeaOXscUarW57b4dljk13xFkgq1xH/AOgVqTaDZSSieOMJJnJK968m8O6vJY/aZPtMgY4PqGwO9dTYfEMRr/pMfHqDWFP4bev5noY6a9tdP7Mf/SUdL4zZrbwhMiAlmwoA7815la6rFp9zK8MA+ZPmUjlW9jXW+KfFdpq2hJFbbwzNk5FebTjaCW5NVUi3seZLU2Zr++1QASyY2NgKTxU15Mum2bqSBcv2x94etc9petJ9uk+0JxCcqB3Na0aT+J2comJQeD2A9KhwsrPYjXqY888jQ/aSQHgIwK6zQg92Ibcp++ePeZ9gwaw7vSJoryG1lZUSRSj4Oa6rQ77TtJmS0klfy7aPbuLcHNUvchZDPLPinD5HidI85xCK4au3+Kl7FfeMXkgYNGIlAIriK6KatBCCiiirAKKKKAPf/wBmX/maf+3T/wBrV9AV8/8A7Mv/ADNP/bp/7Wr6AoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD5g/aO/5KFYf9gqP/0bLXkFewftHf8AJQrD/sFR/wDo2WvH6ACjpS0UAJRS0lAE1oCbyEDrvH86+m/AWgyaPcwX0rbxNDj6V83aHB9p17T4P+elwi/mRX1jDOmnW8EchGVULigTRuTXQe/QQHLBfmxU2+WSTBHHqTXM/wBsvJdeZabFHRtxzmry6lIYGZpcuOy07Armyd0Myuwy3QV5R8at95p9spTlXz9K7mz13y2aS5G1fVjXNeP5bbVtHDoyPhgQQaAd7nmWmaFJDoSXOcD2o1I48kZHauja6ji8JrCAC2cVy2pHNzbgHt61M/gfob4X/eaf+JfmO0+2+0XMcXJDyj+ddnqqbrGRO2w4rA8PxF9RjOOEUt+ldDe8wFfavQwC/dpn0mXwtOrN9ZM4onfETjpWto10UjYHiNQSaygNqyqRyCauHFppSR5zLOdxx/d7V1t3xP8A27+p7z/gfP8AQtR3Mt9dttOI05qJZGDu+ThTmrunW3kWBJHzuMnjpVC5IA8pemefc12Qd3YwjvY24Zi8G4dD0rDvtQkNwyK3yituMbLJc8BVzXJXDf6Q7A8E1EdLsluxXv5dqMxOcnFWIhiOFR6Vm6gd6RD1l/pWpCu6WFB7CuTByu6n+J/mVN81X/t2Jd1lC0Vso/hQ5/EVxb2MsI24zj0rs9ZlIuQgHCgD9KwxKZAHABBry8V/vU/RfqfPZ3fkg/OX5RMuTSroW/2jyzsxknFFgMzBTXdvqFnL4daEhRJsx+NcZp8f+klgOh4rM+eT7i6tEFjUHrWDJkNkdq6PUcyZ3DGBWAyHec9qBnsHww0M3GlfaHjBDdOOtX/iNoUNvoElwIwGA6gVs/CzZ/wi0Cswzim/FeZU8KzLkZPAqr2C+h86fwmss9T9a1SOCKym+8akBKKWkoAKO1FFABXsH7OX/JQr/wD7BUn/AKNirx+vYP2cf+ShX/8A2CpP/RsVAH09RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFeWeMoQ3xStJ5HIji0tCB2z5klep15R8S7+3t/FMFueLh7FG3f7O9/65qZu0blRV2keWfEvVJLTXSsTB45IgpU+lcTDcQ3MgEeY5T0+tdT47jsdRsk1G3uVMyEI0eetc74Qs1vdbVXUMFGQD60r2jccI3nY6S2j1iztllntTJFjIbPQV0Xg6F767uZLjJiBDKhPGaj13ULm5T+xUALKFyYxx+dVdc1s+ELjS47aP5fLzKv96pjdrUqoox0R6TtVflyBx0rivFHhyCKZdSgKxjd+8X1qyni7TZVTU2mAjEXCE87vSuM1HxXNrt9tdtkIb5EB4qzI0oZ2t2LA7k9PSuq0cx3W5gcZGCR1rj4Ufeo3jBPT1rq/D48iAui/Nu71nO1jWnds3IoFhXarFh6muf8UrugVWO0k8V0KSh2LHjNc94mH2l4okIwQSQaiHxXNZ6QSZwSuEdl3EHPXFWvJWSIurKfoeaWfTdvKqwXPUc1FtCDaGraxhcu+HrW4Et/ul2xLbu2D34PFbWl2sl8b3yUnLpFHs8p9uGx35+tZumorQXIYEkRMRz7V0vgoFb27UA7TGhP5Vz1EqleEH/WjPWo05rKq9anJxatqtHvFWv+JUv0+wztE4lVmf8Adq1y+4pzyf0/Osq4lkS+3bGbEZA3OxyOeeuR9Kk8U3Udx4okKEloQE+lVvMW4ulWVsApsyPeu6pgaajGXVyXbuefg6+JxCq06tWTXJJ/cio1jPfZRW2SffFc/NHqFre+SwJYvnJQdfyrr4raWFwPOKsBjO3ORXWWXhKbUY0uF1VCCudxtgSD6da5cbD6u+Zw0bfb5HRToYKvGLU4xaila0u2u0bb+ZzmmQw/2ZqQvSWuhCNrbiMnGce/QflXpmjrs0e0H/TJf5V5pqKR2Rv4FuBPKHCs+wL7Hir8GsataeXbJqbeWFTaBChxkdOfTpXNGUVZJa2127t/kb4tYOEfdqRim10l0il0jfdP+maPhlwl9Ys2Nospyc/9djTPGev2s/hi6+ySBnDBCR61j+HtRkXUPKljknTyHhRUXldzZ59snr71yGtRXMd1NaxFlsTIWIbqCD3p4OScbdR5/Ug8XaLvdX+9tmHBB5zGSRsRjlmNdzYQsNOga3i+VlzjdiuFmn8wrFGNsSnAHr716ZpscUemxI+MiIdRXbKTukeQkuRsYs93L+6ZJBsAIGQatPfXaRlZIjgjGWSljWP7RNsxt+UZBptxIVTPmPt8wD72Rikpt3JlBLl8xLXUVt4zG0StzyTxSpfRG+88phOmFaiG8hmcLDOhJJJUqDTYIVaEMyRscsx3Dinz2Sv1D2ablboWb2+hnttkfmbi2cGrS6hZwWyGS7RNq9GGK5DVNYgglCQW6l9vJVjjNYEv2q9lUSuzD09KbZKirHQy+LZYVuIbBQS5/wBYf6VnDxPdRQxplnnVs739agijitIRvI3elVZAhYtwTnOT2oHYW8nvL6Q3F7Oxz0U1DIisiqvA9KgudQhj+8+5h2FZtxq0snEYCj9aYzUaSG3yXYLis+fV15WJc+5rLeR3bLEk+9M4oAmkuZJidzHHpUOBRxQQPWgD179nIf8AFwr/AP7BUn/o2Kvp6vmH9nL/AJKFf/8AYKk/9GxV9PUAFfIHxt/5K9rv/bv/AOk8dfX9fIHxt/5K9rv/AG7/APpPHQB5/RRRQAUUUUAFKOWA96SnRjMij3FAHvHhmVbDRLRWb7yDHNbFxGAUuSMccEGud09MafaFR5oCAHH8Jq616qLKk4fKj5fSvn6jbm7DuRaPdqdTvDsLb5M8d8VZdJ0n3lQXkfIU9FFZ+hXCxiaZ0wWkJB9a6KeaCSZUi++w59qc6jjVaKWxlw2guvElxJcFSEjG1e1aAsnvJlhUKkdU9NKtq9+F4AAHPWuksxHb6ZJcnOQDgmulTcqnL2RdKHM7GLr+oR6VYLY2xxgfMR3NcnYW32qQ3VwTtz8o9aW8nOra2IASUYkk/Stq/thbwxrEDgDoK6PaKL1N607Lkic/rwCoCOMo1UpYQtvGB/dBq7r4wkPB+YYwfqKtS2JubMnySgAGMVpGaTfr+iHiv4dH/D/7fI4nUbFXZ3Kn2rFe2eLkggV2N7F5ChWOSemaxdSA2oordGFrxuVbWF7uzACkmN/vZrs11qaw8MW+mMgMj53Sei56Vz+jXcNlbPDMmVmQtkdsVH5jToWySFJxn6ZqiUrM3NPlFwWVkAZTVqWIK5yAMgVnWrFZY50HysgJFboiiuFDtngZyKluyuaTV4mLIkSzBiC4z1FWbeFFEjbfmPQ0+3svOdipOAehpbuF1w5BWPqSvU1Cmr2MCCH5H+Y4HpUlxaT3SoY9qqD1PaoQzBC6AlTwCR0rRXUltrEWjR/fYAv9ayp6q3m/zOvML+3/AO3Yf+ko0bOye6hihkuUjiz80h9am8Q2mnWltb29vIJrhjywOc1SEiraSwkMGBwM96q6a4sr/wC0TRecF5CGm1rzI4/QyJLD7O5IBV35YNXV+GNUtdJjjSdxulPQdvrWVqon1nXVeOHyoyBkDsKW7tLSzQeY2HzwBzTqSVtRPU6KKGHV9anmlJEStgEcVrS6OglO1FMRXDA45rkLDxDDa3S22dxlAABHQ1taZq1zq73tlalvOTgb+30rKMnawnc8e8ajb4puo+MRkKMelc9Wz4q+0DxLerc7fOV9rbenFY1dkVZIYUUUVQBRRRQB7/8Asy/8zT/26f8AtavoCvn/APZl/wCZp/7dP/a1fQFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfMH7R3/JQ9P8A+wVH/wCjZa8gr2D9o3/koVh/2Co//RsteP0AFFLSZoAKKWkoA1fDZK+J9LI6i6j/APQhX0l4iJh0meeRsPj5a+bPDbbfE2mN2F1Gf/HhXuXjvVnk039zkpnaSO1CEyLQtN1eaNJmYtGw4Cmuri0nURHlcBsfxGvD7Xx3r2nfu7e5G1egK1JP8TPFEwx9u2Y/uoKoanJI9J17S9cRT++yh7KayITBbabLFcys03PBNcAfG/iGYgSag7AnuBXRxtJLbpLK25mGSamTsiotyepftLc3Vk5LnavQVkaiqi6hyOQODW2yyWmkYjUtJJ2Ws2ewvbiNZ47dmjiXMrY+6KmfwP0OigksTSXmvzNfwzEAJ5sjhQo9s1r3TAoeO3rVHw7A8GmSO6ENI+4Z7jHBq5cINhJBP05r1sGrUo+h9XhocsX6v82ccw3XVzHnAXLH+laFlZyXskLycRooHPoBVOWIf2hdADA2FsV0ljhNPg2jkxqentWjdsT/ANu/qeitKGvcfOu2LavTGKx2h/fAHON1a0hLHn/9VUWXNwnXAPJrqg2jGLJ9RkEdgFHG4YrlJxya39alIWNAenUVguN1NL3SZbFK6QskOP8AnqM1s6em++iGOF5/Ks1kz5Q/6aA1saXhHuJ2+6i1x4NWU3/eZc42lfyX5FXUHEk85HvWBpVyCPKkP0zW1g3DSHu7Y/OtKP4a3McW/wA5g6815+MVsVL0X6ngZ6mqVF+cv/bTA1FRFbllbg1Hoke9ix6Ut5pl+J2tG5CnGTW/aaKdP09HLgsetYHzhh6nESzEVzUjEORXV6hjD1yz/ePHegZ6L4F8WPYWDwkMFjHXGazvGXjc67A1qpYgHHIqn4WUfYrkkD2rlrnH2qXH9407gQk4B4/SspvvHitUgVlN940gE70UUUAH0o7UUUAFev8A7OX/ACUK/wD+wVJ/6NiryCvX/wBnH/koV/8A9gqT/wBGxUAfT9FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV4X8ZbK9ufGkEtqwVY9MQt/38kr3SvKfik3lao0u0ECwUZ/4G9JgfNurs3nLyQCMn0zV7wtqMVhLcFpBHK6YRz2qhetvlkzyMnHtWeq5BPpQ1cabTuj17SYbTyIJYr6OeeWQBl3c1g/ElJ7jxAoigZkjjC5HasfwBbyXHim3CdFyTXQ+JJYJNeuMX4RlO0oTWduS7NJPnsnocddxSxaTbIyNySTxWakrxsCjEEV2N+sgssrPGQEJIPU1yChdmHQg7vviqjK5naxp2XiC7tZAxbeB2bmuz0/xnDJbRW64hfeNzZ4ArzZ4mVQwO5T6dqYGZeQTTaTBNo+iraRZIVZHV1IyCD3rD1iJ5bpSsgVguMV514c8V3mlsE37oieQx4FddLqEeqPJdROwGAAM1koWZs58ysOMV0gOEWQDjjtWNeROMgqQTWoGnWOQJKNu3JzWcuoSRkAxK+PXvWiIsS6Qkqw3DPKNojYAevFdX4RlaKa/2gf6pDkn0BrmYrrzoZSICgI7DirVrdPbafqJTIZo0GQenBrmbtiYNf1ue7h0pZPiI+n/AKVExbovJqVzc+crPJIS2D0NMtZZLuOUHiRWwPqAKNPsUEbqMuG5OTyKqrv06K4O4tsuAc+3FezirxjC/wDMvzPHyvWVX/r3P8jo7W5NzZq+fnThq2JfFc+h+GS1sV80tj5uwrm4nEUiXcfNvP8Afx2NUvESSGzAXlFb+daY+kqtGz6HnUJcsitYapNqN1I8rksxLMM8GvRbG6aXULJWJyioBx7V51b6etpqcbx5CPb7iMcZ4rttE1FRrFv5qhvmVRt+btXl0p06GKlGW7joe5iGqmU07SV1Jtq6vZ2S03KWj/bft9ybAAzCJjgnHG4ZritSvbxdVlldsSFiWAORXY6ZqqaTfTzv0eN48+mef6VytxZyTB9QCq6sx2qGGfyrkwPwHdxNUhUxvNB3XKtixBBa3enG6uYBFJ5iqCv8X4V0rXM1thFWXaABkLkVzVmsz2VssoIZ7roewArr79hBYGRMeZux1rrk/ePn7fu7+ZQ/tdYyykby5yQVxWpbac6Wwv5wDHcqRHED90+tccjmS7Z2bOT1NelT7LbStPWRlVViBJY1Epcuw4rm3PPLrS7/AEx5bsnYg+6QelVrbxBqKr5QkBBG3ketb3irX9NubQWVrIZJCeSBxXHtLBZ5Z3G49qqN3uEkl8JpeTGAGdhu6mo5b1IRhSEGOWPWsC41pjkQrtHqazZJ5JTl3J+tWQbM+sRpkRgu3qay57+ec/MxA9BVbBNJTACcmj8aKUY70AJ0oJPpSUpJNACUvHrRzSYNAHr/AOzkf+Lhah/2CpP/AEbFX09XzD+zkP8Ai4V//wBgqT/0bFX09QAV8gfG3/kr2u/9u/8A6Tx19f18gfG3/kr2u/8Abv8A+k8dAHn9FFFABRRRQAVLbAG5iB6bhUVWLFd9/Avq4pPYD1221NdlvBZxMithTu71Y1aSSMqwXeoODtqe1uLW30+GJbZ1mjIJLRk5FR3moQytJHHhFI4YjAJ9DXiOlLnvYZLpUDSWAmYIyjO1fU+lbGn2qoBLJA6t6E5xWbpc1tDp8MbTqApJZAerV0CXFxeBbe2Cu7cAA54qKkXzuyGZOkWP2/Urpo3IbzsE+1W/FN6zvDo9qcZ++R6VuwaCvhzTp5hKzzSZdsDv6VyNqkk802oTqcu2B6gCuqrajFy6s6Yfu4c3UprbR2utwxRj5IoST65Nb0uxbVpSTwBnNYdhOX8SXMo7R7Qp5roo/mtjEyBnPBzXPOWqb3sjmuzjNbmW9ubRIomJDY2+vIrppIikcRGYyo5U1l6haCPWtOiZRCpYndnryK6Ixo7SOzbkVeee46U5Suk/62R2Yp/u6P8Ah/8Ab5HmnigmPXPKyMAZwO1czqMmZgB2FbupytfeIrhjzt4rnrld2obfevXp35Vcx+yiwifvNv8Act/51b0pBKs6Ht/hUC/8fd2B2jC1PoZ26jcRmrGtzT0s7tPicjOxijfStq1PlPszx1FYukYV7q1bpuJFa8RwoB6r3oNFsb0UKSQB7dY1l/5abh1FU7uKBn8rnbjoBTFn8mFpN2PlqfTZoyTPdIRgfJx1NcDvTqS7JHPNcrsZWyM2l3CDhVlVhkc0txbQXEccRcqSRhsd6v28cVxd38Zcb3YbWxweKiaGGC7ijY7y2dpHanh6q5dfM6sw/jf9ux/9JRraD4ftJFk+2O7sg3IGbrWbrsNwt0z/AGfyYsgA45x71rWdrd3RhA3AKcl6r65K8O+OXP3hvLtnIopyd7nDoYhvfIuDEG+8QCwpUthI0zzjOPuZ71pPpFqB9pMhkVsYAHSqZZ7a5aV41eFQeHPWnUm3PTYd7M56e3FvrEbKokduhB+7Wt4We/g8SStbt5ioR5jf0pdFtxfNN8u2Qvu3Y7e1bNlpMmlm4mjO+RjvZhxx6Vr5X6E67HjvimZp/E+oyMMM07ZH41kVc1aUz6vdynq0rH9ap10rYAooopgFFFFAHv8A+zL/AMzT/wBun/tavoCvn/8AZl/5mn/t0/8Aa1fQFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfMP7R3/ACUKw/7BUf8A6NlryCvXv2jv+ShWH/YKj/8ARsteQdqAFpKKKAFpKWjNAGz4RAbxjowYZBvIsj1+YV9mzaVpdwhjlsLdgeqtGMGvjLwhx4x0Y+l5F/6EK+1YgSSx5pifY8H+NvhjTdI+w3un2sVv5hKusYwDXjnAx0r6E+PsZ/4R6wlAOFmIP5V88dcU0SieJDLOiLyS2BXpC27Q20EUoxwM151pz+XqEDejivcksrS90tXlcBinUGi13YrnUNWLoxspZo4sByE5qxaFE8L+IFAHCSD6cGue0BBB4gMcZJUA8muhsRu8M+IyR/yzk/8AQTU1VaEvQ3ws1PE05L+ZfmULVt+m2ZyOIUGOn8IqO4PGAB+dOtAf7NtNoP8Aqkyc/wCyKbOWKkj5gO2K9fDq1OK8kfb8vK2jmboN/aNwRj/UnP5VuWZP2G3yf+Wa/wAqxp1LXtyc9IWyMVr2kyLp0LBhgRqD+VEv96/7d/U6P+XHz/QfK6oOep9aom4DTBVAwDyajuJBNlmkwf4RUQdIY1kwTnOee9dqj1ZkkVtVm8y4I/ujBNZ64ZTxTriQyMz+tRpzGT0H86V+hDetgyEZM/3q1Yl8nS3Y5zI351kSFtisqk7WGfzravAUgiQdFFcuC151/ef5nRV3S8l+RQg++h/2x/OvXpZsWzsWHSvJnUx2XmYG7cDmnp4mvnt8SSYz7152O/3qXov1PAz2LdKkl/e/9tIdY1GYajP5S8s2M1JbXdxJa4lYkgdK09N0eC4hN5PKCTzVSQRFpAn3R3rnPmTAvfmjc55NYsNubh2wPu10lxbPNC5jjJAHJAqLQYxayzTyx7loAfoETJpdy2DxkVzQgaeWQ46E9q9CstStP7MuJUiwpyelcwskV6ZXt0xj2oA5l12uRnpWQ33j9a3zA0spUDk1guMOw9DQA2j60UtACUUUGgBa9f8A2cv+ShX/AP2CpP8A0bFXj9ev/s4/8lC1D/sFSf8Ao2KgD6fooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvI/i9OsVwyknmxGMf7z165XjHxofZqEZPT7Gv/AKG9JuyGlc8MtrBbqKR5ZNuBkgDmsx7ZoVYPGwBPGRir9s7i4Vg4KlwSM+9aHiG8e/1ExRj91GoCjrilqGht/CuzxrUt03CrGcE1i+JLWK98bSQWB80yvgnORuNdX8P4Xt9L1O5OSEjIBHbip/BnhW0bS59fuTILkljH2x70RuyrxWjPO9fR7HVpbUvzGAhxWSXK5ANdNd6S1/q2oyzy7tpJDDuahHhO5fw8+qgOArYC7TzQ5JOwuW+xz8eQcA/UetazacjW8czHyxIcDAzWSqMrYIII6g16D4J0tdYtZxcsSsIyg96mba2HFJvU5hvD1wtt5yMCM4qxpEs2mXIW7RxEe+OK7fR9PuJ7e6F9HiEPhBjrioJbZPKERj+UEk5XPFCb2YWXQYfs1xZeZbzH5hytY0rFCC0Y29ORWlDBblHdCPkPQdqvNbxzOsSyISFHBHc9qNkUrGfauzWbbUAXFQ3pkZPKR9hcrkeuBWzNaPZwSRsuODx+FZJQPcpKxwUU4J6dK5ZVI068Jz2X/BPo8BRlWyyvThu7d31j2Tf4DbK1mku4484wckqKYsaiWcbQyNISQe4rRknWGbcjpITH95UK8ntzVSEKm0HPvXdUxlGvZxlazW/qcGGwFfAynKtC8XCV3qls9LtK2qEs4/sReNgzWUn3QRyD7Ut4rGFYWBVSeN4xkVdheORWtyDtIyMjpU8Ajn2xyqGeMcZr0YJ1I+7UuvRHl1alCjO06Fn/AImZZkV0ULgleK1TarY21hdfuy0khyD82RxjIxVG9t1jjleMBZccY6GsjSxf3N1El64ijDgszYAxmvExeHf1tynJdH2/rY92MaWIy6moU5r41aKvr0u73S1/MnvWURHeMgtg9vrXN3EhmuysAYKOAK3dYDG1+RgP3nX25rAjndMpGApPVu9Z4GK5Lk8TUowx/uq3urTp9x2WnxtHYack5VpSzMqsMmpdS1IzWhXagYHsuKhsomS5055MhFti25vU1FqVzG9ukSy7scnjpXW37x4VvdRkq5Dhyed+ai1XXr/U3AnnJRBtVRwABTlurBLlVnnCpnnHOKv/ANn6NccwapASezjFVYm5zsCmSdR361OcSlt4U4U9q1J9Lj05PtCzQzKeBsfNYclwEVsKfmGAadhXGahbQowCIFOQOKH0uIwSOrEFWCgVBJMJJFYk/ezzV0XkRg2Z5Mm40CK8ujSR3AijkBJXdVI2M+13ABVDgnNasuoKtwZVILEY61nPcy+WyKfkY5NAyq8UiAblIz0phU1bM0sgAZsBenFRuPlJzkmgCACnAevNMOQeKchw+fSgB6wu5+VDSSIyHBGDViOaVzgU6aAlC7HkCkB6j+zkf+LhX4/6hUn/AKNir6er5h/Zy/5KHqH/AGCpP/RsVfT1MAr5A+Nv/JXtd/7d/wD0njr6/r5A+Nv/ACV7Xf8At3/9J46APP6KKKACiiigArV8NIJPEdgrDI84ZrKrf8GJv8V2IxnD5xUVHaDYH0ODCiq6xqcrg8VkeI4YH8NTSxxIuMg4A6055ZArMpOV7VQ1C5aXQZ4yuN7qMH614NG7khl7TNKtpdPgiktomJjHIHPStDTrrTvDl6JDbBS3ylu4qW0ZY440GNygA4FZfi2Hy7CW4zuZBuGKujVkqq13NaaXMrnQaj4it7uRY7bMo/ix0Arnb7R5Lkzz2N/JGm7GzHArF0yW+t4IXNuR9pUFX7D612FkClnJA+DIBuJHRq3xFZ2a69C6rVrLY4TSNO1R9evDBcIZIyFLsvDVsvbeIIixH2eQvyccVZ8KsDdag6gbmuSCT6CurkiSVSR6YBrlq1pc7Vl9xikjze6n1L+27Pz7IPMgykatkNWjc65LBaTLNpU8RxywxgGprwbPG1qgPCRE/oam8Y3yW/hS6lwqs/yKccmrhNSmo8u524pfu6P+H/2+R5hp7Gee7uW/iY1kQL5+rHvg1uWUf2fRyzdWGazNHUNLPOele2jF9EFmPMuLw+rgVJph8vXpAf72KZo/zvM396SltTjXJj/t0xLoa+3yNTZuzVq+ZwDVC5GZFcVKGJAFBobEBWSIggEe9aMEgS0nYqJCABxWVZg7Sp71bsLO9aKTyXCIHyF9a58RFWvexjW6DNMhZbu5ZsKisC2RwOM1qy2kcbQOSCXXcCo6Zqjaw3E8t/EQrkyLvycZ4qG4vLhpiqwsWX5Sq9hXFSi9bPub5h/G/wC3Yf8ApMS+t3PaakA8pMJ6DOBTtaiTUZhslQBxj1NUrdgsqy3MTN/dD8Ut/dlIYWtwuQcE47+ma2hCfMmcC3LFtbPaWnked5gB+YsaktPsd4strOVOz5iD1qrpms6e8Wy42iUNkrg5q9q/2eAwXlv5ayE7QPUH1qIKak3IJdjl5NSawv1W3chHc4XH3VHetuy1KS+iuXUnykBXOevFV7iO2vLVlEETXgyN4PGDVS0ttQ0XTbuB4shlL5XkYxW3OrrQEzyG8bdezt6uf51BTpTmVz6sabXcMKKKKACiiigD3/8AZl/5mn/t0/8Aa1fQFfP/AOzL/wAzT/26f+1q+gKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+YP2jf+Sh6f8A9gqP/wBGy15BXr/7R3/JQrD/ALBUf/o2WvIKACiiigApaSigDa8If8jlo3/X5F/6EK+2kXAxj2r4m8H/API5aL/1+xf+hCvtneFALEAe5xQB578abH7T4AuHAyYXVx+dfLpJ4r7C8eWyah4K1OEENuhOMc818lS6e8e7jpVdBJNtlJGKMGHY16T4avZ7qwHmyHAHSvNezA13fhedV04evetKTXNdnJjIylTtE6J7yfTIXvLVVeVDkqe4ru7HXdJ8SeANXuLSNbe8js5PPi6HO0/nXm8V8EvAsiboz1Bre0C0Q+GNcurf5MxybgPTBrPEaqXodWVLlqUk/wCZfmS2Q3adagkf6pe/sKjug8anBGB2zT7Mf8S212oC3lLz+AqC7LRxsXjYjHUGvXw/8OPoj9CmvffqzlrsySajI4dl+Q5APUUSfbFjj27GjHIX/Gmbw1xM/IBXAqyJoBGobeTjtXOsXhvrDlzq1u/mdMaE3Ras7836FFrqcOfMh59qdLqaPCEKOrA9TUz/AGV88yL78VUlgRj8shI9xW7x1BbVF96OZ4bE29xNkJvEJOfwp5uF8sBCMmmi1we1SJAgHK5qVjqD/wCXi+8mOFxT3i/uJLZmaaBV53OAfpW3d72O0J0rNskQXcYVenQe+K1REXmyVI/Hmnl84yjOUXf3mdNeEoOKl2X5Fa/VVsUXBDE9KydQtkitIUU/Oa19U/5ZJk9e9YWq77O+jZ3DKK8/Gf7y/RfqfO59/Cpesv8A20pSale2bLB5j7cdK2Ipv9CDd261iXMq3t0pjXJ7mt51WKxVTwcVgfNo6Twybc6RdrPjcc4z9K5G3uDai5WTG0k4OKtxM0VozFiuR61zN41xzjO0mgTOnszH/wAIzNIO+41g6NqEcCyoQOea3reFh4R6cstcl5LRn5eDQBf0+EyXckij5QTiuOm/10n+8f512dhqMVjbur4z9K4yRt0jn1JNADKKKX+VAB3pKKKACvX/ANnH/koWof8AYKk/9GxV5BXr/wCzj/yUK/8A+wVJ/wCjYaAPp+iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK8T+OO6S6hgTh3tkA/77evbK8h+LWntfeIbHZnK26k4P+09TLYa3OBtfh/aSwwAM4nKoWGeMnrWvrvw1+ziW40tkxAo3rJ34q3YanLEyCWFklwDuboMdK2IdYv7uBw20CRzuK85HSsot9TWol0MXwOm7w9cu9umXYqUA4OK5e48S3pvptLtkEduSUWJV6Yr1G3tILK3KQRrGgBOB615jDZCPxhPJjpLwQfWrWmpne+hhWlp9nWdZi6yklsMMZr0jw3q+k/8ACLR6f9oSeY/KYgOVGak1DTY7rVxJLEqwGERocdT3JrhrvQLmz1ie7tn8mFpdiheO3WpSV7lXdip8QdEs7DWI3tl8tpV3MueKTQL6fR9OikUlYp5gsjAZwK7G28PLrVhYG+xOZeGkPLdfWtKDwNFGpSDzBA0m1YzyMetEqifQhKxjPq8l28cFhGsjNyEzjdVf/hJo5/8AQpdJZJIyQ7KeuKln019J8ZGO1DHyY8BwvA46Vz2pPex6vaxpGfmJZvqeuaHG5Wj0Z1unahoryJbSWYR5BkhkwSPrVsW3hc69NNLcLCkSAqC2Oaxbe6ll06zu3VVkSfYxC9qNb0iPxC7yQJiVTneOOO9Xe+4mktmV9V1GGa/uRbyiWA5CNnk1Y8PXSx6iF2qfMCLyM9BWjJ4ZstK8Pbiql/JbazjktjNc68cdtLGQZYkIjbcvfK5Y/XNcGLtzK/8AW59FlNCVfCVaSe/lfrF7G548j83ULeKFQCkJIAGM81y1mVubbzDGcgkMM9KvTXitqLSefNNEowjynLYqtaPHa3kzqT5Uhztx3rtwCwkaa9s43v1+RxZnlOLnOKpUpNRja/K11b/UkSMA/LExyMZBqV428xRF8p8shlPUipft9uPu5H4VC1zF9oEoYnjB4r0ufAJWjKK+Z5f9iZk5c/sZ372dxkVkXIw4U9fm4x/niuumW802xlnRoHieFFXo2G9eR6eneucN9asCCrMSOgHWpZbiyOl+Xm6B2lgpJ2g9hn0ryq9HCQsqMvxO2phM3r2+s+0bW17nKanNH9nk8xtoDgE+5FYi/Yio23eGJ5ytXdZQvazYXO6dMH1+WtWz0zw9/ZqfarWfzwuS6P1NRhmoU/67I689V8W7ef8A6VIx7jU7poURZ1cqNq57CsudNQn5Z9wPZTXVtpegPZJIJblJxzzjFY2oNBZTeXbSGVSMliMYroUk9jxuVpanOzQyQvtkUhvSo8kVavbj7RMXqspyask0LK8/dGJz9KbcXCLCEHLGqZ5PFOkGNtFwsNBfZ700ZbqanADRlhxVdfvUhk0UJZ+Occ08q5zgACnI/lKxGORio1VmHU0ANLsOM050OylEfOMc0+VSEPB6UCKYGTmk/ipR1oUZamBfsxlSamuuLdqbageXwMUXh/0dqQHpn7OSH/hP9Qft/Zcg/wDIsVfTtfMP7OJP/Cwb8Z4/sqT/ANGxV9PUwCvkD42/8le13/t3/wDSeOvr+vkD42/8le13/t3/APSeOgDz+iiigAooooAK6r4eRmTxfa4XO0E1ytdj8NFP/CUq4BOyMniscR/Cl6Aj2izgKXM0bkFM1n67EI4wh+bMqAfnV2NbgXLGMHZI2c+gqhrDSSz2UbAjM4/SvBo3U0i2btukLKGXKSD9azfEMjf2bdAsTlMVrxLGgXdjnv71h+JHDWMgUcOwAPtmppyTnH1H0NTT90Wm2ySRArFGACfpUDhjNLIrMMjOKljZAkKndtCjj1qO5dDDPMrEYU/L+FZ+05n8xWKHhqLbpskoGGkmc7vxrprWTy1aKTA44JrB8LoRo8IzlQxZh6ZrcdUdnKEYIxzWtXWowRyeFk+IARW4EZAJ/wB01R+IAknj0/TSPmeQs30FWtNUr8QGMjZADjj2SovE5WXWDMTlo4wi+3rXbhqf770X6HfXXNCh/h/9vkchqu2GydV+6q7RWNpyeVpckh7gmtHxFLttvL/ibjFU5x5GiuvcIBXrGMtyvoP3T7tmlT5dcl92o0IbUyfWlcY1ZnHeglbI25GzGD6VJDKCwWqZl/d8022l/fn0oKOitmwa6/w8jPp8hYZXcRx1FcTBJhcmu28Iyg2si5zl/wClceO/hE1PhK2noU1DVmTtKgGf92i2jjOuyYI+UAEY61dsEjfV9aD5KiZDtHf5RVJZGg1O4uI4RtLBTnqAa8yk7Rk/I2zD+N/27H/0lFDxTpOp6g8a2jp5IPbgilk0lLazsLSY7m3AsfU1ozaubSJwyFyTgACoby83XGnSPEwDA8Y6GtoTqSaXRXOCyI7uO00/eoRZHIyoAGaxv+Es0dkME1iMrnc+efwrTn8qGKc7SHc/NI3UVxeuabZJaLd2qyqXfbhuh961op6KTB+R2Ph6C0VpZL2eNYnO+BT2B9TWl4j1GJNBuxahCphb5xyOlcUbuS3MVnLGohZAVdvXFXLyzW08MamWuBIBFkAHpmtqqfMtRHjhOWJpKKK9AAooooAKKKKAPf8A9mX/AJmn/t0/9rV9AV8//sy/8zT/ANun/tavoCgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPmD9o7/koVh/2Co/8A0bLXkFewftHf8lC0/wD7BUf/AKNlrx+gBe1FJRQAZoozRQBqeG2ZPE+lspORdRkH/gQr6E8W3t/jTg1xLhpwCAxwa+ffDC7vFWlL63cY/wDHhX0n4ztQiaZkD/j5WrgZVCzf3C23h+czyH94u0LnrmvHLzTV818jgivQPG10Y7q2tckKF3AVyd0jSTxqOrcUN6mtJe7cTTfCGnXVkrSRDPrWvb+HrazjKQqACamtWFnCsbsM4qwLtfX9amwrplCXSFVWYDoK0PDl1Hb+C9et2TLNFIAf+Amh7oeWR7Vn6VO3/CPavGgUgrIWHcDFRP4H6G+Fs8RT/wAS/Mksi0ljbhd2BGvPPpVbURIIJMFicdCQCKq3Q1G3jtIZpTDBLbxvEY/4lK9c1l3kYJI853A7ls5/Gvbo60U12/Q+55lObcdr/qJZWyzRXUgk2sinjnnjNa8ejySabC37uTeEfDZGB6fr+lWdHsrd/DeoTv5ZkEDsMoM5we9b1jplvNo9mzRwuWt0OMkHO0ehr5yhSg5Ruvso+KzHBUoU6vKtqslu9kjz7VLJrZy5t0VewDcVzTbWvDGoIzyRnNeqal4atnVjsKkf3XyB+YrmtN8Pw3euvDMm7ywFUqSuR15xXZGnGDvE8ilBQehz1npcs7/fmQHuA1dHaeH0X79zcZ+jD+Yr0qy8K6dAi7ISOM/61/8A4qn3ml20VuyLtwT/AHm/xrVVJLZnR7XELabXzZ5a8AtNRWPzC4Hc9a2LNRncHLDsMVS1yJLTW8R427QeM/1q3ZXDG1aRkVI+mc5P1royttwqN/zP8kfo0JSeDoSk7vkRkalI0l5t9OBWNqGialdybsZrYvYtl8p3ZDfNzjI59vz/ABrodzr91l47GubGO+IbXZfqeFnE4zoUZLVNy/8AbTldE8OXMB3zRnP0rQudOllb7rDHtXSQ30sfG1T9KtR3vPzwqQfQVgeBePY5FdJmvUFuoKY6nFWE8Hq8bI0gOwckmu0hlgY8RbfcCp1W0QHG0FuvPWmRJps4q6t4odGFsp6cVgT6KVhEuQc9q9QbTNOmTD7TmlGj6cyKmV2joM0C0PH5dEaQEgfpXCyDbKy+jEV9Pf2FYeW2AnSvmW9AW+uFHQSMB+dAMgpaSigQtJRRQAV6/wDs4/8AJQr/AP7BUn/o2KvIe1ev/s4/8lCv/wDsFSf+jYqAPp6iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK4DxzFBJrcBlA3C3XB/4E1d/Xl3xJumh1+CNYy2bRTx/vvUVL8uhULX1Mee1jlj2hxj0NMtYltm2hV2jnIasT7Xd71VI5OasM9wTt/jxkg1z8zvY05UdHJqCiMjaeR2rEk0y3mu0nVwOdzZPNUN0p3ZkwR71DLDK4+W6C/Q1pdi5UjbvHQ3EERDGNTksOlWHsLa6Ta2HUc9awrWzIhkWSdnDdwelTwRm1Rljlk596zhPnk0uhvXoeyjFt/ErmilukM9vbwzOix5IGelXfNnjXCXrcH1rD2O5yXcn1zRsdWBXLeuTVyuldGMEnJJmjuZ5WYzZbqTUXlwzTF2CGTGNxHOKrBZju2/KDTWgc9+falHmdi5qmuZLvp6Fs2lt5Ig+UIxyQopIIrey3pApClcZzVRY3AwWqRQ68hxmqs73uJVIJW5bi30hOkTJLJ5m2Nyu48jg1m6oqSabo6Rjax2Anr/AAir86PPBJExxvQrnHTIrC1SK6tre3H2kzeWcRqIwpGBXPWg0n1T/wAz1cF7LEwVGU1Bpt6Xu7R7pPtr5EviHRzYQi/eeM/NyqrgKD0rGt3jlKmN0IBz61r3ou9V0ZjLeecjIN0RiC9O2RWPpkWPkhYRgf7O6qp1o0Y+9C/z/wCCFDJIYqo+XEfc5fqkXWWMkESRj2wKq3RFvf20jf6lwUYY4B9anv0nsoTcllcL1xGAaqLcpqFuFmnADfwlRW9TM3Jpxp2S9D0qeQRgpwnXvJrTfQvTLHFJGWwq4fJH0rnJddf7Aq7DIwJDbm7Vv/YWDQReeWV8gEjOOKrXnh4EBljE23oB8tEsRGtKUoRdr+XZeZ52NwDoRpU61aPMo9eZ396T/lfcw7rUorvSo5TBsSNgoA9eeaRry3awLCX5xxgVNcXH2MvFLY4wc7XXINZVzdQyq3l20cZxj5RTpJ6to8/Gzg3CMJKVopXV+77pP8CreX5aNBET8vH1rPa4aViztziprnT7iC3hnfASbO3mqoiNaK3Q45JrRirEZMYPWosbWIqdSykY7CoGJLE0ySSJgGwRmnyYaUDtUKdafGf3ooAdK207AOKi25yRxjmnMwEpJ6U1n64GAaAJ41aSLPpUkY2irdnbh9OaVvlA4HvVIHHFFgJY8GT8aS7lwSmOMdaSI/NULyCWfntSAhCEjOODTtmwjkHvU2CzY6ACkcAkKo5PApgTQzBIQcE80t44MAx3NTrbLHY7mHzZqldMWhWiwHqf7OP/ACUK/wD+wVJ/6Nir6fr5i/ZyUj4gX5P/AECpP/RsVfTtABXyB8bf+Sva7/27/wDpPHX1/XyB8bf+Sva7/wBu/wD6Tx0Aef0UUUAFFFFABXoPwlhMuvXTbtoWDr+NefV33wykeK5v3Qc7AP1rnxT/AHMgR7PC0scJMQi2DOGbrXOapP52s6fiTPzZb0FC3Fw1wy8gbhhfaq0g8zxDbqq7cBiQT6V40JNu/k/yKOktjN9omExHkryrVmeILiM2kUMXOZVB4681pXV3DJZCJcqXGBXO6g5W7sbWTI/eBjWdLWasPodGLiPagZBuUYYe1UdWuQLOfyuQY2PHarE6lFDAFvm/HFZesbEsborlQEwPfNZ0YLnXqDLfhvz4rGNtmYzGNxramcQW8jRt823JPtWRokkg0dYiD0BHFaJXfC8RfHynJ9KubXM2Kxx+l3jDxALsjJLTYz/uis3V9UPnMVQyTMchf8aIpikkbJ/elwT3qhM6CRi/yE9T1zXr4WOsn5nqVlalRf8Ad/8AbpGHcQXV3fpLcElep9BRqjYsWUdzV251C3C+XESW+lZl0WliIrtOR7DtMOyEU9xmffUNoCsFWlGaBLYk37lxTo/lcmoWOwVJFlzzSGaqS7bcmuw8IXIFnI/JK+lcLMxS1J7V0ngm4ZHkRzhSvfoa58Yr0WTUfunS6Usk+qas+7aPNUsf+A1U02WV/trsQy+aQqn0FTWcipqmqOHI/eKAB3+Ss22mhi0mZ2lAleRgFzXk0o3g0vI2zF/vv+3Y/wDpKNKVQJ/NAUKgyM8ilnvVm1GNAAVjjyCB3rOsBK7FfnkhC/Nk9Kjj1KA3byR42qNpyevNaRjON0+xw3uTaqxMBSdQzynanpXJ+Ibe4sks4S5ZXcDBro9QhFzi8llYpHyqZxzXNXdy81zEkgMrF+Cei1rhYNyUr7Bc3tV8gaJbhogZQOHx90etZmsSQDwPcSQtuLL85PUnNdWbWE2sULfPH5fzse1eWa9DdWVleR+a32YvhAT1Ga0oT55tdmKUbHG0UUV6gBRRRQAUUUUAe/8A7Mv/ADNP/bp/7Wr6Ar5//Zl/5mn/ALdP/a1fQFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfMH7R3/JQrA/8AUKj/APRsteQV6/8AtHf8lCsP+wVH/wCjZa8goAWkpaSgAooooA2fCX/I36P/ANfkX/oQr6h8Z2bXZ03y22mO5Qt+dfL3hHnxho2Ov2yL/wBCFfXOvW+LGOQ84mQ/rVRM6mx5Z8Qnz4iBB4ijArjjq/2y9jjT5Svetrx3qMcuv3iLINynbXDidYb2Eoc880PccZWidl+9dtzSEmnmV4k3M+AO9V4roy7VQEkjoKknWYRnzbaQIe5WkBXufEtvaxN+8Bb0zUuhamr2moo7AebA5A9Tg1kXOnWlzkrasW9qZb6Zds/nwxt5cQw+Ow71E/gZ1YT/AHin/iX5nfX+saFqHhvT7V5HF5awooPlHAIUAj3HFcrqU4nOYDkDj5s81HaXNxFNmR9sIGNvJJrbi1OzaxeKV3DFsj5Ce1RQxs6cOVVI29P+CfVQ9tRqTjDDTabet/Pp7hjQpbi2cmeRZSp+VVOGPpW3Bp0y2UEqahcJujU7QeF46UltqljB/Gx+iGpJtbs3Hyu3/fJrmhGineU199v1NMRUx1SHLChJO922k/8A21EQhu3uEi/tG4IJwTuqO902S0eOeG7lMjNgt0IpbXVbSGcyOzHjj5TUt5rFlcW5RWbdkEfIa0vh/wCb8f8AgnluGaqWlJ/+AL/5EfDbavKARqVz04w5pJLC+YEPqNwfYsakttes441Vnbgf3DSza5Ytna7/APfBpp4f+b8f+CdPs8y/59v/AMAX+RzV7E0N6IyXk9WJ6D1NPvNXh8pbVSpjAx8nP6066mjnu5HBO1lwOKj0qKzhvvtN1kLGcxoFJyfU13YHF0KFKXvK931PUrrF+yjLkbaila3l5LuR29lLJEbtc+QHC5bqSa7E6Uee1UdU1m2vbNYISxbzFONhHQ10guF9KwlXVetKa7L9T53NfbewpOvHld5aPt7pjHTpA3ysRS/ZrlRwwNbYmj6ECj901UeHcx1N3HzgUGe4I+ZSa2PJTsRimm2U8DH4UDuZQuGHDCgzFjhXK/jWi1ouDxg1D9kB6igVym0tzGCFnJGK8DuububP/PRv519BS2fysRnpXz7dcXcw/wCmjfzoAipKKKAD+VLSetLQAnWvYP2cv+Shah/2CpP/AEbFXj9ev/s4/wDJQr//ALBUn/o2KgD6fooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvM/iJn/hIIMf8+q8/wDA3r0yvMfiOG/t+3YZwLVf/Q3qZbDRy2W9cDtRsyck/jUO/A5BpTPKqEIwAPqM1BQ8wJn7wwetDWEU0bKEHIxWbL9vyfLdCPpUfn6opHT8KLCuaEGnT2qbI5Dt9+asbZCPmXn1xWat7qidYQ341Yj1O8JAa2I/GpULbI0lVlOyk72LfbigmnLcO6/MgDGmjO488HqMUyBgZh0PFOEh6c05QBwOlKSMdRkUCGkDGcc00gDtUysdvG01FIeO1NAGVX6/Ws3UG3XFn/11/pVwk9MVSvsiez4/5a/0qavw/d+Z25f/ALwvSX/pLLD7QjAKBkY4rmNNQxXUi5BAc4xXUN830rmY40t9anRGPzHcyn+YrPEq8DuyGpy4mz6mrfqX06ZQucr0rDtrSEYAQhu4Nbd7KYrCRhk8c4rPssSKWHNcCeh9k1F1PMvxoBc2I/2n/wDQa02TB6cVRAxdaeMY5f8A9BrVK8V3YZWi/wCuiPj8+nz1oNdn/wClyKrRQvxJEjf7wrLvPDVhdOzxr5bHsBxW08ORUJyhx1rf0PCOE1nQtTihSPyFlhTO0xjpXLvGYmKyRkEdiMV7OJlAqle6Np2pKRPCu4/xLwapOw5Xlq2eRssOB2pnkRP0Nd7qHgSMxs9lMSw5CNXG3emXNnIUniZCPUVSaZMrlFrVQRgnBpjQiL5geaskuMe1JlXIDrTBpXsii8TZGep5pQoEXzetXHRGb73Iquytk5UkdqNwas7Cx3kiRmAHKdhTd+KVoCGVvUUpSjcTVtyW3AJ681AzFJnA9aVQynjio5N24kjJNAC7jkZ705mxICTjFRgEkAnFDDe+M8UAaLTObcDIK1RuD9361NkCNRu4qC6YFximwPYv2enjbx7ehCM/2VJn/v7FX0pXzD+zl/yUK/8A+wVJ/wCjYq+nqQBXyB8bf+Sva7/27/8ApPHX1/XyB8bf+Sva7/27/wDpPHQB5/RRRQAUUUUAFeh/DNkQ3e6PduZRnPQV55Xc+BEnFvNJCrY3jcw9MVzYtXotAewWwiF60nyvGy7h6g1z8kjXPiX5NoIiPT3NP0qUwmUb2JY8E1QtpRH4gnkkP3QBxXiU1rL0Lex0EbxrMFlHzRrn6msu+k+069ZRuQhGSDitKJlvEmlON+4bSPSsm5Cv4lgVD86oc/WlRSi2/JhuaF1dOojiKliGOW6Yqtqsg/s1U3bg7rjP1p8r/aNsEnyurZJNZOp3G0W0JU4WQc1VFWmkDOna9WKBtp2+Wo4rPvdUc6dJIpwWXDE1WCkwmQuTuOMCo9WMUWjv8w/edM9qzjFc682LUTRYrEx6XPfxStasJvMMaFueMdPeruoaV4VuA3kz30ZPTNs5/pXZ+CLcaf4VtSeC67jWlLdq7Eg17cISjez3PYhWoypQjUg24q2krdW/5X3PDdR0GGOTdaSSTD1aFlP6iqi6O0kRLuUPp5bH+le06hdDZs3YLVjiaOWHyj0Pp3rnrYmdOXKn+BnUrYRaezf/AIEv/kTycaXIsqxo28HkkKePwrSj0aLJ33EijHUQt/hXTpF5XiMgYHlQ5GK0JL5pbGbbISynrXPVxdZNJMzVfCf8+5f+BL/5E8/ttOWa7KTSMIg3VUJJH0rrbDT/AAxbMpkS5kI65hf/AArT8HWI2y3B/vHBNdRuEcmSa74uo1q/w/4J2c2Et/Df/gS/+ROH8VXejXOlra6fbmM55YwlcfmKrWWpWq2kKyRESqMMUjPNaHiuYzanbrI48gsBtPc1cSEW9/uChIzGAo7D3rixU5Ws2cVSphHJ3py/8CX/AMiY8LSSPqFxGGWEkEFgR2qOytIZbGHqzsSc+9aWrag0cMyTZJVScdjVbRpVS3W68orGynaD0zWVNNQ+Zhiqyr1XNKysl32SX6F2+vDa6DNa28afaZPlOB2rntMsy0cqMmCOGB7GtpEkErNMCA3zbfU1n6bBJPdXRkJRS/31PWtYtQUjmtcFV57UrnAztP09awbmeztddRWdiqKBx6+9dlZ2tqLdirNv5Aya5OCG1i1mRpyGaQkJmrwzk5NsS0Rv2uqwSWkgaVUkHGx+MiuE8T6jDd6PMicMs2MHuK6a8gtZQ8Y2vuHReorjvFXkx6bBFEANrYPHP41vQhGE9Oor3OPooor0BhRRRQAUUUUAe/8A7Mv/ADNP/bp/7Wr6Ar5//Zl/5mn/ALdP/a1fQFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfMP7Rv/JQrD/sFR/8Ao2WvH69f/aO/5KFYf9gqP/0bLXkFAC0lFLQAlFLSd6ANrwh/yOOjf9fkX/oQr658RThdLIzjbIp/WvkXwlx4w0c+l5F/6EK+qfFThdFkmc4QMCx9s1UTOpseX6ovh/UNUuHufllZyCfeoo/BulXGGtLtc9gTWjceGdHv5GubfUQpbkgnvUX/AAhl+MG0uEkHY5xQCKv/AAimp2LiW3KSbf7pzWhHr9/ZxiK9sA6Dg5XFWLPRvFNjLmNww9GORXYabbT3lts1SzjD9OO9Irc4uLUtAu1YvCYHPXAqloVq9x9rNnfrGRIdqsBzXZ6h4G0+6V2hUxPjPHSvMhaSWeoXMG8q0UhXIOKAehv6hpGvlcyO0yekZ4rZ0WWe60yS3uLcxtEQvIwTXP6drGp20wjjuty+knIrtoZp7jTTLcCINxgx96DSi05o5y7h8iTH8qzbuTah2nNa+okupO3p3rD8t7u5it0OWkYKPqTQjvnojp9E0Z5tLSQrCxkywyecVDf+GrsB3jSMIBkgGrMHg7VosBL8KB0APSrD+GNcjQsNQ3ccjPWi55rd3c5a1fYAPfnNWJ5QeN3btVUo0U+w/wB7BqZ4wibjzR1PTWsSva/Ld+YFyAD2rTjuoznIq94Qj82S7O2Jlyow5+tdafDumXUfzxRh/wDZNB59T4mcWJYX5DAU4BeCuDW7eeCFbL2k+0+hORXLX9rc6ZceRMeQeCDQiG7blsoM0AY71ni8dTzz9asrfpj5losxKSLIYjvSiaQHvioluoW43YNSKyN91qQx/wBpPQ0G6B7UbAR601oQf/rUAPadCh7cV833f/H5P/10b+dfREkZ2MPavne7H+mT/wDXRv50DIaKKKAFpKKWgBDXsH7OX/JQr/8A7BUn/o2KvH69f/Zy/wCShX//AGCpP/RsVAH0/RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFeX/EjH/CQ24LYH2VeP8Agb16hXk/xPz/AMJHb8nH2Nf/AEN6mWw0cuVHQyCmvHsH3waqAs3rkUoLEHINRYosEMRwfypqq+7nOKaFdMOM5p+/I5yCaYiUHAGc0/B4IbNQjIx1P1qQDbyTQBICwajzGLYwaEeMn5icUjFQcg/rSAlBbbkc03eO4+tQmYJypwajMu5uD1p2AnZl3cHApobA5GajyDxuBNB3A9QBTsBMDu9M1Qv2bz7P/rr/AEqwCOueRVO+Yma0/wCun9Kzqr3fu/M7Mv8A94XpL/0llt3CqWJxjrXKtfRXGqtKhBUNgMOtWvFWotZ6bsRsSS8celcNaSyNIiRr85PBFKsuZWNssl7Kp7Vnomo3ASxbDAFhgZqtpADJwwOT2qjqivZaPCbhi0pcZxUuiH98NhyjHIrilScFZn1NDHwrzlOOyOhuhsvrD0y//oNXw4b6jtVC7I+32Pfl/wD0GrIbDAZIbt712U1ZyXn+iPlMdJyhRl3i/wD0uRPzjJpjBWXkU7czjpS7jjla1PPKrR88UgJWrmF28ioXhQk4BoAjEuDTJreC8jKTxLIvuKY8TjoM1GGeM9TRYLmdP4Q0yckorxE+h4rmtW8HXVkrSwHzox6DkV3scxboam37lIboetHM0GjPFpICpwVII9ajKlejGvVdU8N2WoxEqgim7OK4/UPCWoWY3IomX1SrUkybNHOSS5YYXIx3pPMj/iUg1Znsp7c4lidPqKrFaaVhyk5O7FAjf7rCgxccVGYx6UgDDoxpkimEZpGh2/jT0kkLBSAaklZBJgnFS3rY0Ufdcio0RPc0wxVc2g9CDSFPamQeo/s6pt+IN+f+oVJ/6Nir6br5r/Z6XHj6+P8A1C5P/RsVfSlABXyB8bf+Sva7/wBu/wD6Tx19f18gfG3/AJK9rv8A27/+k8dAHn9FFFABRRRQAV6D4Ha5h0syQn5TL8wB7V59Xp/gO4tbfQyZeX3k49K5cY/3Q0dTDdJKjCFFSRW9fvVm2Ub3Oo3eVJ3Ec+la1hBYXLSqJUkmfmMfdxWVb+dA90pJV1lPzL04rzIwSg2h3N62j+zOsatlivT3rJV93iSdtpVkQAfWrCTyPHHIThsYyaxJrmRtWlKnOMbmrOlB3l6A2a7h7u/Z1YhQcEH1qrre5L21XyyYy2QB1JrU08RtAS0pV2O4nFR6kwGt2UiN91SaKXx3fZh0ElgNlCBOdrynIU9RVWaya8KwkFkDLgfWtC7QXFybi7IZCBs9Sav6XAHv4pGOFiUyFf5VdKPvxHFOTSOhn1OKG2Sxh48tAOO1VLWZ/LLucKOTmsWC58zUJHA+8xpNZvzb6fOBnGw8ivS50ldnpVLQiLqGpZxd7t8DZCgVWSY5EkKsydcHtWXphWXS4LeYu0YG5s+ntWpBd2otnS3YRwdAWPNeVUTdRyfc8xyu7lSzLza5eTwjzFVQCDWtBaAxSBVGGBP0NYGmTPDe3bxk4LY3D2FbUOoq0AkjGJCCJB2pVn79u1gRJoFw8cDRDhVY5NbbTF1Ab8DXMW8yfZJADiQvwK17Ug2gJYlx7160dj1X8NzC1eOO41azL5J8/GM9hWjqDmRSqYBwAKySjN4gtFdsj5m5rVAQ22MEMWJ59BXmVk5Wb8/zPKk7tlXX7dG8OGQsDOqBGI71DHerBpMEPkH92oznvS6mUl03qcsypt9TmtacWtpFEJIsKwCnd0zVqypptdSfQxbvUpXtftccTpIBtyOmKoaVJMtnLPIcwuxwQe9beqjdaMqsoKKTtHTHtWRDDNb6NaxmPer5fb681UJJxenUfQdbNNC7LHk7skDPQVj3CW7XUlxuJZOM+laDXjwMTEu05xWGqyTXMyGRfLJ+Ydwa6o7EGxow02yVZrqbLSg7j1OK574gWlpBaW81sWIlcn5hziuj0vTbUXcTTYkJ+6jdKwvieyAWKRqyrlvlPT8Kild1k0UedUUUV6YBRRRQAUUUUAe//sy/8zT/ANun/tavoCvn/wDZl/5mn/t0/wDa1fQFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfMH7Rv8AyUKw/wCwVH/6NlryCvYP2jf+ShWH/YKj/wDRsteP0AFLR2pKAF7UlFFAGx4U58XaR/1+Rf8AoQr6m8bzRWvhO5844ZwAinuc18seFZUg8WaTLK21Eu4mY+gDCvbfiZrTa7fQW+lzbraJeT2JNUmTJHGvODISvAPpVmHVb63x5NzIo7c1Xi02UKuT065qytk46incjlZoweLdZhOBclvTcM1dHjrWAvzPGT67axRZmnfZO+2ldDSZunx5qzRkZjHHXFcvFPNcTzzz5DSsWye9WTaF8lTtNJFZSDq5Yeho0CzHgBu+DXdeHbWWTw8Bkgu5IJ7iuKaFINvnOFDHGTXfnXNBi0uO3h1OEFIwo2k8YFRKpBaNnbhMJXn78INryTZi6tcG1V42VSRwcGuctppEnE6sUYNlT3BqxqF9a3Nwf36Ng/fzTQkcqZhYMo4BFNThLSLub4qlWpwvODS800XRrOo5JF5Ln60HXNUH/L3KfxqssW0ZOCad5ZwTjrVHl6j3LywCVeZAcnNST3ZNvtZccc5PWsp3l/tEwCWdAY9wSEZJOCeBTXt7qRo9ktwxfdtJ6Ng4O0965XilzNW2PXhPCxglOslK21pf5WLVvJJHlkLLu54OKuJqN4h4uZF/4FWatrKml21690257hIjCSMgEE5I6gHHHqKs49RWlKr7RXsceIowjGFWnPmjO9t1tp1NJdZ1EcC8lx9arTXElxJ5k0hdvVjVYcdKU5xyOnNbXOSxOArDBGTRsB5B49KZGx5GQMDvSmU7ehxRcVh+1c/d5pwJHeoxKD6g0pkXd3wfai6CzJhLKOjn6GnrfS85HSqvmdRQWyDwaNCrtF06ipQhhg4r5/u+byY9vMb+de3lC6tzg4714fc8XUv++386kpO5FRRRQMKKKKADvXr/AOzl/wAlCv8A/sFSf+jYq8gr1/8AZx/5KFf/APYKk/8ARsVAH0/RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFeT/E/H/CSW2ev2Nf8A0N69Yryz4lQPL4ityuP+PRRz/vvSlsNHELhR82BUgdV57UeQyqSWUtQqZU5PTpUFA8mfut+dNEvPPQU4IAM7c0LatIrEflRcQouFJ+6MCnebuxtxiuc1fVpLB9kcT7h13LwfxrLj8XXCN80CEexquVsV0d0TgA8ZprHIHIz7Vza+LrSXBZJFOOeKsW2v2Fw+zz/LPq3FKzHobBC7sNmlVFYnBwPeqkOqWEzFEuI2IPPzYq8rROvBHtg5oAbs5AHfvT/KORTShzwwpA5BoAc8Jj71QvOZrUkf8tP6Vfc7yMntXN3uoy+UszLHhCxAXOeKwr1FGNn1/wA0d2AUI1PaTkopXWr7pr/h+xznim7N3q3krysfyge9avhzRRFi6lUlh0FZsNjBPemRvO3s2TuIxnPt9K7e1jWGEL2AqY1FUnZHTKMKOG54yUr6aO5l6qBdXiQuMrGuce9TaXZQxSb0TBHpUC/vL2eXk7jgVq2MZRM9ye9ZNudax60YRwuW8zWrX5jrhgb+yOP4n/lVx9rDJJyORVS4ydQss46v/KrwALZINdMPil6/oj5/FfwaH+F/+lyI1fjcCR6irAbK4BqvIpSUMn3T97NIZfKIJ5BNabnGWM46nNKx+Wo8+Z04pWiIA+YUgArgZ5FROgYcHFSqXYY7UmMcUXEVWjYHjH4U1hKBVzaCc9KaBjPNO4FQTuvBBxUolPGam2AjBAINRvbgfcpaANeO3uUKTRK4PXIrm9V8G202ZLJvLb+6eldDhkYnHSgyZGelCbWwHmd54c1K0yTbsyjuvNZTxshw6lT6EV7Gso6EVTvdJsNSTE0KhuzAYNUp9xcp5JyDkHFNbLHJOa7i68CE5Ntcr7K1YN74Y1OzJzAXUd05qrxYapWMI5XocUomccZBqWSF0OGRlI7EVCV55qhHrv7PMrP4/vwQP+QXJ/6Nir6Wr5o/Z4GPH9/x/wAwuT/0bFX0vSAK+QPjb/yV7Xf+3f8A9J46+v6+QPjb/wAle13/ALd//SeOgDz+iiigAooooAK7fQne10y2AUMJs5OOlcRXoXh6WKy0mDzCZDJ/BjOKwr/CFzqrbTHhV385Nyr1zgCqNjqr28UsUjblZicEZo1AtGYfN8wI4yQOgFVFtxJZtKsZ+TJyD1rhlH3X5jNiW8+0qkUbYJGcAdPSs60i2XszySAoH24z1NU4i7SKd7KwGeKs6W8UMU9xdwtIvmHac8ZqI07QdgNqF1inUs27AzgHpUb7ptZDqpKrGSAT0qOHZqFxvtoyvoO1WNOhu/7bmRguQgBycAUQp2vbsFySMSPMxkZgEGfatzT2C6RcTlstIdoPtWPPC8TSRrKu7OW54xWipMOjQREjk54p0ormudGGV5kFtGUbPvWZ4ieRtNuSrgqBgjvWsrYWsLXZVOnOmUWTeBgdTzW8+i8zXFS0SKWi3Us1o8BkwVXAJpkSiKV2f7o5/D1qe1DDHlRfvcYZQvWsqe4mVpxtAkY4we1ccYuU2+jOE3tJIXT7mW3kUsHJVD1q3bXG2KZXhHnsMlSOD64rldPcAKfNAZjzW81wZUjeF8svUnvV1qfvPlGTQGO4ZjHkEHiuh02M+SzMTnFc7omGnlJGP3pzXVjEKEA9q7Kaskj0oybpo5uMRyeJkLSDCRk/TmtbUrhGVIF5CjhlHT3rnlXPiG4CIXbZ1Hapo7uaK7aORSBjHPXNcNRWlbsebLcdqF5HMdPtgwEqygstas8omVkmBKE4XjpXH3oZtfg25zjcR6VtjUU+0jewCD5fpRVTSiugrkmoTRxWMgU5kjU8nvU8Ooo2m2sMSF7kRAsOwzWdqt6gtp9qLslwgPpTrcGCJWjHzbcE1dP4bdwKmsIt1YSsmI51HI/rXK2cU9qzyMS2V+Ymuk1ANFBLIWG4jpVDTZI9SPkuDGwBwcZBNdEU4xfYCLTtTmbUrbzGCojgZPYVX+Kd8LvU7QIyNGsfylDkGrMmhXP9nvcuVEUbcqPvEeuK5DxJMkl5GkbFo0TC5renFOSaEYtFFFdIwooooAKKKKAPf/2Zf+Zp/wC3T/2tX0BXz/8Asy/8zT/26f8AtavoCgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPmH9o3/AJKFYf8AYKj/APRsteQda9e/aO/5KFYf9gqP/wBGy15BQAUtJRQAUUUtAFvShnVrQf8ATZf517EIkxgg5rx3SjjV7M+ky/zr2NZlwST83amAiIQSQafg4GR+VIJM8rjn2o3sc9KBEqLG6HJwetNKJjANIsgB56U9Y2kOUBI9qBkYjHG0nOakWHgkEZHanraXBwwRsZ9KVoWDDIIzQIp3KyPdWSwxrJMZl2o2cMewOOa6IWq3b3TTz2dq7MpERi2gjdkgHadmFwO+cZNY8aXUWtaW9sFe4F2nlBuhbPANehXGqauVP2/wwkrDqUGc/wA6xhQhUnJy8uvkdtfC1Xh6VSlUaunpe3V+aOP/ALP0vT7+We8vba6tnAIjtx84O1QeNuDkg9xjr3rD03abZtoIXzGwPbNds1/qhLf2b4QjhlPR5VAA/lXFaerm3dpAA3mNkD1zzVSpRpVIqPn+hVGlVhgKsakr+9HS97fEWumSPwpcsR1Oaf0UccU/qAR0rY8yxBpaWL+IZFv717NvKHkyqej5HWu8h/tkJ/oeu6bcR44aUfNXEaXc2Vp4huDf2RuoWgA27QdpyOa6DzfBcxy1pcRH0XcB+hpYdXi/Vno5j9X5qfPJqXJHb0MnxZBIJ7GS71iO8u2uFHlRkbY19cfXFUzGpzn9Kl1+XQS9jHpNnLHKLlS0j55Hpyaacj/61R/y9l8vyFifZrC0PZ6r3vzGeSpb5cg1GYwp55qwCAc9Tmkdgei/hV2OBMhAABHX3pAhGfQetS9ASoxShi3B60hojUHtSgHkkdKXO4/L8tCbi1A7jWy3bFIQexqUkk5GCKj7n5sUAMkHyHnqK8Ouf+PqX/fP869ycDa3JPFeG3I/0qX/AH2/nSGiKil/Ck70DF70lFFAB1r2D9nH/koV/wD9gqT/ANGxV4/XsH7OP/JQtQ/7BUn/AKNioA+nqKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAry34kzGPxFbgY/wCPReo/23r1KvFfi74jsdJ8YWlpdytGXsUkyFzwZJB/SlLYaMNmUqW7+1IHkKjsKyIfFWiyNxep/wACGKuR6tpsxzFdxH6vUDNHBSMMdrE+h6VKp3R5YhfpVRJ4mGQ6MPZqsSRHyd4OVPoaQyvJAJwQyB1PYisW+8JWl05aAGJh6dK1/nQDbIV+tJ50gBABb3z1qrtCdmcLeeGNRtWLLh489Rz+lZstpLAN0gBHqK9P8zoHhIqreaZZ3ybXTqeq8GqUyeU8yAJyQSB2wav2Wr3VmQqTE46qeldjP4c0x4ygRlI6ViXfhSaMlrY+YPQ8GrTTE00bGla/Fe/LM6RueAM1s7oyvDAg+hrzFoDDcbZw8WO4rXtW1OyQSwSrNb4zgtUuPYaZ2wK5zuPFclrUgOktEpw+x2OOvXFaOn+ILW5mWKYNE+Oc1i3MTT300efleLCn05rkr6SV/wCtUe3lcFOnNWu7r/0ioZ3h0MdQUs7N0zk5r0PcPLY4+XFcjp+npYXTHeHHy4YfXmty61BTCyRH5s9ccYpOrTjKTuug45fiJUKS9m1q76W/lt/wB0KgNjHetZeIgBnNYy3sOVJ3qR1wKtjVrYHjzPyrnw9Smm3JntZzg8XUpQpUYNrrYlmbN9acnhm/lV4vggYNZjzwzX9qI3VsFs4+laBUg8EYPQ967KbTcmu/6I+bxtOdOnRjNWfK9/8AHIkUgrtqEfKSjc1MuE/jH5U2UD/WKRuXt61pc88ZHMU4brnj3qysoJG4VVIjuYwytg9QakjZWyMnIoYFjzhyMc0zJLZwaTbgcE5xSIjsBvbB9M0hjvMUHHamgg854NDIR97GM8UwuFbAH0oAU/IPlqIysBkZqf5SMEHNMYAY6UAV3lZl4XmqzSHdjvVtn28AcfSmsiN2xn1FAiusrMxBHA7inrNknk8U8wqB8vzfjUBjCkr0NMCysnI55qfzF2cmqS7wBgjinGQf3u3SpAbeaVYX64lgUn1HBrn5/A0DvvhuSqZ5BFdIrc4zUgk4207tAbfwa8NxaP4rup1lLu1i6HjsZIz/AEr2+vKPhixPiW4B/wCfNv8A0NK9XrRbCYV8gfG3/kr2u/8Abv8A+k8dfX9fIHxt/wCSva7/ANu//pPHTEef0UUUAFFFFABXrGhaNGdPsZZnVcxggjnNeUDqK9l0OJxptvGfmQRj5Sa58RsgJ9YkSJykisYvLAVjVe2h2WqmM/K6c81Y1CKY2c0ZQCPbkAjpUtnABbxbQDgCuR/AMoHS5PsTufvleCR2pum2wm0tUacKCS2CehrS1CaNLV0kl+bacAHisDTvMmjjjhBJ9KpL3WS2bFpcSW8LxWMau4GWOMk/Sl08G+v7p7x3iZcA9iK1QLfTbQPHcILkjBG2uf0+aSa6upXlxvfDse9KOiYdC7IJI7gou0gHlvUVqzEm3txnPy5rFd1CusaliTgEmtu4UpHEpGCsYFKC1bOzB/E2U7u4EEIJzycDFc9ql5ZDYyxStcCUbt3Q1vMIpZNsrY7D0zWHqumompWyCQqXfORyK0TvIyxEr1LF2bVJ5YvOjjSOPOGK8GsCcK7MxOO+c1o6npa2kKBZSxblzng1SubVXVpRuAjGOKhLVIwJ9LtQ8AcR4J6lumK1YhHazKwO6Idj61QtDdyR28PVCvy9sVaEUot2k3R9+T1FKSu2MsaNL5jzzcDdKTgV0Ms/yFs9q5jQQBaH1LE1tXL7LRj6Ct76npUv4aOUkvLw6jcva5UltpbPartrNcSyss2ZJNowvU/nWAUuGjnnUnyi5zz3psMs0TgLchGI9eaxqU7ts86T1NIXLrrbvIGjdIyCG6inWhkjdzLh45TjB5zVG3gubu/kUyb3K/Oz1HeB7SdbdpCz5+8p6Cm47LyJZpalcFrBYUIKlxn2rY0y4RrVkmQOxAwFPIrBkiE3kRrlYd3znvVy1iZBdSrJsEYymP4hUyWiSEV9elWK3ZSuwA5w3WrPgy80+CJnu4XeY8xkdK5vxFdfabeFyGBZuSe9XdKjYWiSLkYrfk/daj2Ov1bVDb2s8kUKqJ49mCOa8c1gOt7tddpA6V6La3H228LTgvHbjIQHq1cL4s2/2/NtGAQDj0qqC5XyjMSiiiuoAooooAKKKKAPf/2Zf+Zp/wC3T/2tX0BXz/8Asy/8zT/26f8AtavoCgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPmH9o3/AJKFYf8AYKj/APRsteP16/8AtHf8lCsP+wVH/wCjZa8goAKKKKACiiloAuaSM6xZj1mX+deyCHtjkcc143o/Gs2RHP75P517R57PlW5700AmxUyGGfpSoI2P3eKRUDEDOB71KY9h4PFACbFbpgemacqshBVwPpTeSB6UKAEwaALKXMgwBMf8KSQ+Y2d2W9arkquNv40u/avA5IoARUuJdY0qKCXy5mu0Ebn+Fs8GvQ5LbxjApAvLeT3OD/SvO7WXy9d0eQ8bbyM/qK90lHBPFZ05WnLTt+R2YunzYag7taS/9KZ57c2vjKZH8y+ghQDkjA4/AVxekI72TA5b942W9ea9V8TXP2XRrh84JXaPxryjSJtlg3zY/eNx+NOpK9SPo/0FhoKOCra9Yf8AtxoG2kwcEfnStbyIuQRj2pPtOc57jFM3sSPmPFWcehf8Gy7fGc6MAfMttvP1B/pXpf2K1flreIn3QV5Ho05t/GMUmTkKufzr2RQDwP0rGi9H6s9DMEuan/gj+RwnxCjjis9LCIqD7anCgDsa5jGPSuq+JI22OmHPP21f5GuTzlcU6fxy+X5E4q31Wh/29+YoHHfinbRg8c1GJSBtA4FNDNnlT9a2uedykuMDJOfSmrg5OKOnynOcZpMZXjjmmFg4zgA/jTyNoxTSCD945oJ4wBQIaMZ9KaRgHnr0qXaAvvTSV4BIqWUmRMnyEhvzrwy5/wCPqb/fP8690cADAI/GvC7n/j6m/wB9v50ikRUtJS0DEooooAXFev8A7OX/ACUK/wD+wVJ/6Nirx+vYP2cf+ShX/wD2CpP/AEbFQB9PUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXzB+0d/yUPT/+wVH/AOjZa+n6+YP2jv8Akoen/wDYKj/9Gy0AeP0oJHQkUlFAEyXdxH9yeRfoxq3Hr+qxLtS/nC+m+s6igDci8XazFj/Si2P7wzVxPHeqqcsIm/4DiuXoosB2sXxCuePOtg30ar0PxCtePNtHH0NeeUUrDuepjxzosmCDIhI53L0q1D4p0aQYF2AT68V5FRRyhc9jN3pF4Npmt3B9SKgbRdOJyrABuyPxXkgJHQkVIt1On3ZpB9GNFrCO8vdFmtJxLAnnxHjbnkVDLFFNODIs8UgwNucZrko9Z1GL7l5KP+BVcj8U6qmMzK+P7yg1M4c9mzpw+KlQi4xSadt1fa9vzZ2GHs2LG3kKkgkgZHFZOvau8NxG0CgB/nww7Z/+tVGHxxqUYw6ROPcVYl13Sb6Afa4x5jDJwh+U+gNc86UYtNq/4nb7T69RlSqcqtrHaPXXd9gsdWkcBpMEjHTvXbSSx3Okeam0525wOnIrzgSaSj5ivJkHp5Wa1dP16yt4zCbyRlYj7yEAUpqHI1GL18mPL8I8PiYVHOKSkm/fjsn6noflAMNoXj0FO5bB547CsuHxVo8uQl7GCR/FxV1NVsp4tq3kJz3DDNdFrHluTerZaZC0ZXnmkGVUAkilinicYWVTj361IQhcDbn3B6UCKoUxyjaCVc9fQ1JIjqRKvRfvDHUVZ8tXQrnmkQovHzfjQAqMHTcM4IzUchIYAAn3pfM2zY2kq3Rv6U6aN5IsRsAT1PtSGIuG+8pOOmacqqx+VSPelggAj2byxHBJpAgGAh4HpSAjLMuVDZ579qTBbnOfpSmNtzCJOAerd6kClVxhc+9AEG0K4AUmkcEjhcnNTyAqN3Ax3zUYYk4BGfpQBWCOM5XFM3oCQ2M1ZZ/mKEnOM5xTH+TBJBz/ALNMRVwGUlXwaiyVk5yxxwcVc3gk4jX64phjDZJDL6YoAiErKuSg3Un2g45QH6Gg26nPzMQOcmkMCsBtOPcUAd18LmVvFFzjIP2JuP8AgaV65XkPwrSRfE1yWIK/Y2x6/fSvXquOwmFfIHxt/wCSva7/ANu//pPHX1/XyB8bf+Sva7/27/8ApPHVCPP6KKKACiiigByDMij3Fex6Y6TWsEaSqhCgZz7V47EMyoPcV31mlqYSGkZZQM4BwBWVVJ7idjtLuI22jzebKHkY9c549KiljEdhHKJwUGCQtY0UHmWe1TKO/mF8g/hVYtLbqI3M0iYJ3L0Brn5YtWAW+leQO0UTFD/ETVvTWNqkck8zxW5XhoxnJqit1cbShGUY4AK1MmozwgWzCLySP4hwKqytYLF26uI7zIVyzK2ctwTVRL2S301kSEbmlJ8z0qNrlY3yIFIHzFlbFM0+9t8ygwtIjnOGbiosoxbZpTpSqS5Y7ss2t+RPHGWDOzDIHUV2WpHDcDkAAVy2jm1k1iIfY3lO4ERxgbj7c1t65rQZzIlnLCA2PmKnn04NSpwSPRwuCrWbjZ3296P+Y37JI0alvlwchqzrwww67Z+c3mAKxJB70DXFn3BpFUYxtzVKdITqcMrSqyFOu7oaIJ7tHmyUk2nua90sd1beYG4J4XFZFyDBYzA85HcVdtZAkhIlDANxnpR4hXdaxkIPnwDtoUdUShlo88lqEQIRsxnHStL7GqWEgJChEJww9qWFILC2jJAO5c59DVbVLwNYSDd8208ioXxCItDUi2RCBkjdmr+sFo9OkCffxgVFo1suLeRHyCnIzUniF9sDIgJOCeK2Wup205Wos5LT4ibF2dWcknKg8VWGkyktKRvjbJXb1FbGmILeySZ1LFxwgHWtbE0UQaWNYk/hC1MpWbscT3OUshuupoXlMcir8pzjPtURtEluFil8wyk5LetbOlWMV7c3crqW+c4HekubOOC68wzNheiN2q29bCKk1w9tfLDsHlkfL74q9Bcx30xtZ4jDK4yrKeKzL2cXd/H5cg+Rcj2qeBpHcSGRVIGN2KTjsP0Iry0iAlgnBeQH5cjH5VY05HigERaLIUkKaztYurhEAd1c4wCDziqMU0gkjKyMZByRmrcboDpNOggtYJXkmVJjknPRq888QS+drVw2QeccV09xcyMyJNHgE5rkNSx/aM2DkbqdJe+5MEVKKKK6RhRRRQAUUUUAe/8A7Mv/ADNP/bp/7Wr6Ar5//Zl/5mn/ALdP/a1fQFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfMP7Rv/JQtP/7BUf8A6NlryCvXv2jf+ShWH/YKj/8ARsteQUAFL2pKKAClo70lAF3SP+QxZ4/57L/OvaCQhBPX3rxjR8f21ZZzjz0zj617dI8Uv3QwA67hnFAEKvnHSlBJBOR1o/gyF/Go8nn60wJFGTnPFLkYOPwpg784HTHrTwQjBkbpQBHyc4zSjceuCccUMc87ufWmiTsD+dAEd7+5ubFwwJEytx2r3q2kFzYQTKc+ZGrD8RXz/eMWmtf+uor3PwtIZvDGnlucRbfyyP6VjD45fL8jvxP+60PSX/pTOd8ev5enwxk/ff8AlXmOmEfZMH++1elfEbAtrQYOdx5rzLTcfZiOmXbk05fxY+j/AECh/uVX/FD/ANuNIOgG4Egiml8Hdk5quXOcAc04SODnt6VqeeRQzMmuPIDgiMfzr3PTz9o0+CYf8tIw2fwrwZX3apKSACYx/OvS/D/j7w/p+jW9rfXrx3EIKMohduh9QK54TjBPmdtWeri8PVrTgqUXK0IbK/Qh+J422Glg/wDP6v8AI1xqyHnAwDwa1/H3irR9cj09dPumlEU++QGJlwMe4rmf7UsxwJTj/dNKFampyfMunU2xOW4x4ejFUZXXNf3X39DQ34GTjrSFzuyeQO2aqpcxzpvib5OmSKd5wIGO9dKaaujxKkJ05OE1ZrdMsZDc7sc4FCgmTOflxVcS7RkEHJ59qcLh9wG7oOlOzJuif5hxuXmkLEEgnPvUXmSEZ7ZHSmhmDHof5Uag7E+/j1HtTWkzUWSGJ9PQ0wuRxg49aZNhz4+Y57V4jc/8fUv++3869qckqc56V4rcf8fMv++f51LLi2RUUUUigooooAO1ewfs4/8AJQr/AP7BUn/o2KvH69g/Zy/5KFf/APYKk/8ARsVAH09RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFfMH7R3/JQ9P/7BUf8A6Nlr6fr5g/aO/wCSh6f/ANgqP/0bLQB4/RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABShmHRiPxpKKAJ0vLmL7k8i/RjVuLxBqsH3L6Yf8CzWbRQB0EHjTW4f+XncP9pRV6P4haqp/eRwv+GK5GilYDuk+I8pULNZj6q1aMXxJtDgSWsi+vevNKKLIdz1q38d6GzFi0iFuuVq7H4q0edl8q9jUHqGOK8YopcqC57tHq9jLnZeQn6OKdb3EcyEeYJMHrnmvCAzDoxH0NTR3t1F9y4lX6MaOULnuxlZmKGP5MdfWnbvl4UcV4nF4h1aH7l9N+LZq9F401uMYNyHH+0tLlHc9aDNIoJXYc85okSQrhCPxrzK3+IGpRAB4YnA9sVfj+IwP+usOTxlXpcrC53PkuF55qs2WQsu7HSubT4iWPlqv2WVTnnntVy28ZaE2QZXjB5wymizA1RuwMD5e+aUttOUU471STxDpU33b2H6E4q1Fe2kg+SeJvZXFMR3XwsbPie6HBH2Nuf8AgaV67Xk/wwWMeI7goykmzbof9tK9Yqo7Awr5A+Nv/JXtd/7d/wD0njr6/r5A+Nv/ACV7Xf8At3/9J46Yjz+iiigAooooAsWERmv4Ih1ZwK9Qi0NEkKypsAT7+Oprz/wrCbjxRp0QGd0y19Jx+HBOT54bZnIrGrFt6FRSPK57PUEhBtGXywOB2NS276iYmiuLUKcZBA6mvVm8MWuzaoKAdNtZdx4eltj+4UzY7nrWEoSS7jaR5tJbyzMrSQtG3bnANRS6NcmzkaNkcE8knmvQk0qYMftdnuX19K0k8NWxiRliI3DJq4xYWR49b6LeAlJM8jGc1LFYy2o2MpJ6A+tetS+HoSuDESfpXNa1oa2l1Z+UHYyy7do+meKVVJx27fmdmCpwqVeWSurS/wDSWangPwxBc6c+oajbtvDgxjJHAIP8wK5fxilvYTxWNomN0oYqOcKP/wBVdlqvjaPw3o8MT2Ei/IFBPAzXl39rSa3qt3qEjBSRtRfanKnGMW0iMFTpwr0+Va3X5jvKmMibYMljwccVqSxW80Yje2SNlxuOKakck0YVboYUDBA6VLFay3JIeQEDq3TNTFaKxjXj+9k13Zk30UcbCVCVj6HYcUQul0CqXDhV6gtW0+koLMqx3AnIFZr6ZOjs0doQoweB1qlczsyaO0nZUWO6dj/cYjgVLNDqG51k2NFjGNtLYxKZvO2vFMRg7umKsSTpbtIWmeU4+VR0rGUmmKzuXPC9u32qQPgBFwAKl1qGUs/lR79ykEbscVR0D7dLMTbTzKGHzmKEOQe3B+tM1SW+hEUkk0zB8nLxhR1I4PfpUxrrlvY9GnSoT/cRrR5m7WtPf/wEpjUJo444vsGRCNvB60k2uqE8kwtHv6ZHeobh1f5V3Byeo6Cqm2QyBpCDsGN3pWiaerR5rfcitr0WjP5cxErkk+lWpJ4biMyz+mQc96iMaqwZokkLc59qilfzDhlCxg4Cgdaq6vckfp8lhBfLNjcWUjDdBWnczWqRJlo1T+LA5rB8iTzXjWJE38AtxTY4p0dLeYFsn73arVm9ykEtt/aOobIW+U859qkW3t4I3/ehnJ6+lEoghOULB2OMZwKpxNDMsixlllB+Vc5FEteug3YsT4lCiJGd07n0ribpi13KT1LGuxsbW6tp3M8myIpk88muMuCDcykdNxq6Ss2IjooorYAooooAKKKKAPf/ANmX/maf+3T/ANrV9AV8/wD7Mv8AzNP/AG6f+1q+gKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+YP2jv+ShWH/YKj/8ARsteQV6/+0d/yUPT/wDsFR/+jZa8gxQAUUUUAFFFFAF7RjjW7LPTz0zj617ksce3cufx714boxxrVkcf8t0/mK9zZw6bsEOeuOh/GgBjo2AVzuOeO1IygJlwoxSrJsyCD7gnP5U8iNgNw5+goAYWhkixtww7joTUHDk7QTgc4qSSJvNyqgDHrTWyuMYB7gUARbeeBSFTnpUqR9eBzyCaZtJcgHk9KYGdf+b51ukSZfcSFPciujsPiN4j0+whtILCyMUMeAWicnABJJ+b0Vj+BrnL5Ql5b7wcZYkZqzBqRtLLyI7C0bnc0sqbmbrj6dTXDKlUnUk4St/wyPTni62HpUYunCUXFtXTvrJrXXuvuZf1bxzrWrTQw3lraoY2PyxIQcjgg5Y+lZVpvS3AdCpLE4qrd6nNcXKGdI1KO8nyAjJbrx+FSpcPIAVxWlCElJ88r20/BGmMxSeGhFU4x9ouZ8t+kpJbtl3cMf1oE42kHnPeq4jZ1ySaPK6DOa6jxyOZHNwZoZAuRtxjNU11q6snazSfYjziZiB0YHgj8q0PK24ODVVnsnsLq2ntyZDMWjmUgMntXJVpQ51frfqetGc8XhZxmk+RRtor7216vTubURl8R3Cm48URn7MwIacMgGAACP8AOck0mpRQW04S3vFuiQSzI4cHnrnj+VSaR4QstS06G6i8QW0G4fPFdEBlP51fTwDYQN5kniaw+kbAn+daU4U4Sun+J50qFV2916epy1kxETem41b556n6VBabYzIinftkYZHcVfijklyscLsf9lc1VB/u4nTmsf8AbavqyJFJI44HcipCmVxuyfSrcOlag+DHZTs2f+eZq4PD2sP+8GmzKc9lra55/KZGzcMDrnvTgWTGVBFbsfhHXZcEWDjJ/jYD+tWU8C645wYYk4/ikH9KLhY5g4L/AHfb6Ujbh6de9djF8P8AUyAZLm1T6EnH6VaX4eyYzNfx9eojP+NK47HBOHaPgcgdq8UuP+PqX/fb+dfVLfD+0QMZNQkOBxtQCvlm9UR39ygOQsrAfmaQyvRRRQMKKKKACvX/ANnH/koV/wD9gqT/ANGxV5BXr/7OX/JQ9Q/7BUn/AKNioA+n6KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAr5g/aO/5KHp//AGCo/wD0bLX0/XzB+0d/yUPT/wDsFR/+jZaAPH6KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKcHZejEfQ02igD2T9nSeaT4gX6PK7KNKkIBOR/rYq+m6+YP2cf+Sh6h/2CpP8A0bFX0/QAV8gfG3/kr2u/9u//AKTx19f18gfG3/kr2u/9u/8A6Tx0Aef0UUUAFFFFAHTfD6IzeOdLUf8APXNfVa/L0JxXzD8K4vM8f2HH3dx/SvqBec54rzsXXnTqJROmjFON2G4HvTCinODyaeaT8qxWNfVF+zRTmt8gqzja3GKlW3QRhMnA96V7dHk3sMkdKdtOTg/Sn9fXYPZIBbgH7xx6GsHxF4cudWW3azvFglhkLhmB9McYrfVW28tzT8H1qni4SjZo0ouVGoqkHqv+GPLtW+H+rXsaR32rJKgOB1OP0qGy+G8tvE6w3ltwMEsjZr1OSIsynd0oK5zkCj29OWjb+86ViqilzKMbr+6v8jiR4Ot47ZQZuVUZ2jqe9Z76CuBHbu+GJB44FehmNWGGXIFQtaxjhVAB68VvzQezOCUW22+pwVx4PuPIRYpicckk1nHQtThfajsxI4ANekyh42AVMpjrUP2CF5hM7MGHSr03RnyHm/8AZuqPdBbmMxQKOXxWVqNlNAzGMF+TgivW2treRijOzDuD0rPuPC1hNvdTIrnuDUunfVAoWdzC8D6sNH02QyWksiTv/rEXOKg8X6raanaslvJuZZF3KRgipW8M6pYhY7K/uBGvIAwMVSk8KXJdppXnkkdgXOBzWcnL2fIo9D2+fDrGPEKqrczla0r737HPxW04jDYxC5Kkkcg1K9isKgxAsx5fjNdkIzHEY3t/lH94VAI7cRyGPhm6571LbT2Pn7M4rUP3FysSgM+B1GKbEtuswZ1KBSNwxnmurC2uHEturMejHqKqLDArEyIA2fl96FIXKYVxYS3V4QyNIvVMcAfjVe7t3thtmDJnoMdK6j7PcvHIkS7gRwB3qnPpd04jjnGU24Ct2/GtI3ZXs3ucpeWNrPp4VC/2lWyec5FUAJYzEBAQY+TgYJFdemkSwgIIyGGTuUZNNkhZAHumAl6DcmPzqrtbg4s4C+aYgusj854NYB6nNetatDb3GlvJ5SeYEPzIuB0615K33j9a3pSUtibWEooorUAooooAKKKKAPf/ANmX/maf+3T/ANrV9AV8/wD7Mv8AzNP/AG6f+1q+gKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+Yf2jf+Shaf/2Co/8A0bLXj9ev/tHf8lC0/wD7BUf/AKNlryCgAoopeO9ACUvekooAu6RzrNkP+myfzr2xS4G04x0Irxjw/B9q8R6dAG2+ZcxrnrjLCvp6DwCgO5tQJ/3Y/wDE0AcOSw3gLgcAYpZJCSrHrj0r0ZPAlgRtmu7l/TbgY/Q1Zj8EaGg+eKWU+ryH+mKAPMo3x8vTI5OeaUn5c8de1err4W0OHBFhGeepyauRaPpaY2WFuDjj90M0AeO/eXCKSevTNOSzuJH/AHdvKx65VSa9pEEKHCQIP91BT/u9F59qAPGbTStbtNZtNRttHluvJ3ZjdcA5XHf611kWueK2wsXhBEA9ZQo/lXeD0pRjPPWsnS1bUmr+n+R3xx69nGnOlGXKrJvmva7fSS6tnk+u+GvFHiy6tfP0iCxSMtl/tCkYOOuOe3p3qS3+GGoooVru2GPQsf6V6p9aT27VUIcl9b3McTinX5fdUVFWSV+7fVvq2edxfDSXgS6ioHfYhNXYvhxaA/vL6Y/7qgV3Gcc5qJpYtxBkQD/eqzmOTPw700jBubj/AMd/woT4a+Hw2ZIp5WJ5Znxn8q6ia/s4F3S3UKLjq0gqrJ4h0eMZbUbcf8DFKUVL4kaUq1Sk705OPo7GZH8PfDKjP9mq2PWRv8af/wAID4ZDAjS4s+hZv8all8ZaDDx9vDH/AGEY/wBKpyeP9GQnb9oc9sR//XqPZQ/lX3G/1/Ff8/Zfe/8AM2bLQNJ06PZaafbxc54QZz9TV07VU8gD6Vxz/EWzIbyrOZjxjcQM1Sk+IQYArY/N6NJ/9arSS0RzSnKTcpO7Z3JuMHADH3A4pwuB/FkfhXnUnxBvW5is4FHTkk/1qB/HmrMeIrZfpGSf1NMk9MFyme/4Upn3DKqzZ9q8rHjDV5CwluygPI8tFGD+VVJvEOrys0n2+5+bOAsmP0FAHsABYZIAHrmkdoEGXljH+8wFeJS6hfTDMl3M/u0hNQybmiDmcs2SCvPA9aAPZ7jVNKiibzL23HB6uOa+LNQIOpXRXBUzOR+Zr2dmXa2Mk44OeK8UuP8Aj5l/3z/OgCOko9qKAFpKKKACvX/2cf8AkoV//wBgqT/0bFXkHWvX/wBnH/koeof9gqT/ANGxUAfT9FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV8wftHf8AJQ9P/wCwVH/6Nlr6fr5g/aO/5KHp/wD2Co//AEbLQB4/RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB7B+zj/yUPUP+wVJ/wCjYq+n6+YP2cf+Sh6h/wBgqT/0bFX0/QAV5v4p+Cnhvxd4ju9cv73VY7q62b0gljCDaioMAxk9FHevSKKAPH/+GcfB/wD0Etc/7/w//GqP+GcfB/8A0Etc/wC/8P8A8ar2CigDx/8A4Zx8H/8AQS1z/v8Aw/8Axqj/AIZx8H/9BLXP+/8AD/8AGq9gooA828OfBLw14Y1ePUrK91WSZAQFnljK8/SMH9a7j+xrcfxy/mP8K0aKznRhN3krlKclomZ39jW/9+X8x/hR/Y1v/fl/Mf4Vo0VP1al/KP2ku5mjRLYEnfNz7j/Ck/sO25/ez8/7Q/wrTopfVaP8oe1n3M1dFt1GPMmP1I/wp39j2/8Afl/Mf4VoUUfVqX8oe0l3M/8Ase3/AL8v5j/Ck/sW2/vy/mP8K0aKPq1H+UPaT7mb/Ylt/fl/Mf4Uf2Jbf35fzH+FaVFP6vS/lD2s+5mHQ7U/xy/mP8KP7Ctf78v5j/CtOihUKa2Qe0n3Mv8AsCz9ZPzH+FB0C0P8Uv5j/CtSiqVKC6Bzy7mK/hmzdg3nXIx6MP8ACnP4bs3GDJOB7MP8K2KKrkQueXc5yXwXpkzhnkuSR/tj/Co28CaSykeZcj3DLx/47XT0UuSPYOZnIt8OtHYANPeEem9P/iaJPhzo0iBTNeAAYGHTj/x2uuoo9nHsK7OPtvhzpFqQVur9sf3pE5/8drRXwhpyHIefGMYyuP8A0Gt+imoRXQfPIwP+EQ03eGDTg+oK/wDxNVL34f6PfgiaS6+qsmf/AEGuqoo5UHMzhZPhRoUttJA13qOx1KnEiZAPp8lc1/wzj4Q/6CWuf9/4f/jVev0U0ktibnj/APwzj4P/AOglrn/f+H/41R/wzj4P/wCglrn/AH/h/wDjVewUUwPH/wDhnHwf/wBBLXP+/wDD/wDGqP8AhnHwf/0Etc/7/wAP/wAar2CigDx//hnHwf8A9BLXP+/8P/xqj/hnHwf/ANBLXP8Av/D/APGq9gooA4/wL8ONH+H/ANv/ALJub6b7d5fmfa3RsbN2MbVX++eue1dhRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHCeNfhPoPjvWYdU1S71KGeK3W3VbWRFXaGZsncjHOXPf0rm/8AhnHwh/0Etc/7/wAX/wAar1+igDyD/hnHwh/0Etc/7/w//GqP+GcfCH/QS1z/AL/xf/Gq9fooA8g/4Zx8If8AQS1z/v8Aw/8Axqj/AIZy8If9BLXP+/8AD/8AGq9fooA8p0/9n/wppuo219DqGstLbyLIgeaIgkHIziPpXpY06EfxP+Y/wq3RQBXFnH/ec/jR9kj9WqxRQBD9mT1b86PsyerVNRQBD9mT1ak+yoTnc/51PRQBUksQ4G24ljx1KhefzBqA6SxbI1G8HGMDy8f+gVpUUAY03h8zjB1bUl/3HRf5JVU+D4iOdZ1f/v8Ar/8AE10dFAHKyeBLOZt0mrau5951/wDiagb4baS5y17qLH3lQ/8AsldjRQBxh+Gmjsu1rvUCPeRP/iaZ/wAKv0Tj/StQ/wC/if8AxFdtRQBxf/CsdExj7Tf/APfxP/iKT/hWGif8/N//AN/E/wDiK7WigDiz8MtGP/L3qHTH+sT/AOIo/wCFZaN/z9ah/wB/E/8AiK7SigDjh8NdDEWzzLwnOd5dd3/oOKYfhjopOftV+PpIn/xFdpRQBxf/AArHRf8An61D/v4n/wARS/8ACstF24+03+fXzEz/AOg12dFAHGf8Ky0Xvc35+sif/E04/DbR8EC6v1BGDh0HH/fFdjRQBxP/AAq/RSpU3WoYPUeYn/xFcw/7OnhF3ZzqOuZYknE8X/xqvXaKAPIP+GcfCH/QS1z/AL/xf/GqP+GcfCH/AEEtc/7/AMX/AMar1+igDyD/AIZx8If9BHXP+/8AD/8AGqP+GcfCH/QS1z/v/D/8ar1+igDyD/hnLwh/0Etc/wC/8X/xquj8FfCbQfAmszappd3qU08tu1uy3UiMoUsrZG1FOcoO/rXeUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVwfjX4TaD481mHVNUu9Shnit1t1W1kRVKhmbJ3Ixzlz39K7yigDx//AIZx8H/9BLXP+/8AD/8AGqP+GcfB/wD0Etc/7/w//Gq9gooA8f8A+GcfB/8A0Etc/wC/8P8A8ao/4Zx8H/8AQS1z/v8Aw/8AxqvYKKAPH/8AhnHwf/0Etc/7/wAP/wAao/4Zx8H/APQS1z/v/D/8ar2CigDx/wD4Zx8H/wDQS1z/AL/w/wDxqj/hnHwf/wBBLXP+/wDD/wDGq9gooA8f/wCGcfB//QS1z/v/AA//ABqj/hnHwf8A9BLXP+/8P/xqvYKKAPH/APhnHwf/ANBLXP8Av/D/APGqP+GcfB//AEEtc/7/AMP/AMar2CigDx//AIZx8H/9BLXP+/8AD/8AGqP+GcfB/wD0Etc/7/w//Gq9gooA8f8A+GcfB/8A0Etc/wC/8P8A8ao/4Zx8H/8AQS1z/v8Aw/8AxqvYKKAPH/8AhnHwf/0Etc/7/wAP/wAao/4Zx8H/APQS1z/v/D/8ar2CigDx/wD4Zx8H/wDQS1z/AL/w/wDxqj/hnHwf/wBBLXP+/wDD/wDGq9gooA8f/wCGcfB//QS1z/v/AA//ABqj/hnHwf8A9BLXP+/8P/xqvYKKAPH/APhnHwf/ANBLXP8Av/D/APGqP+GcfB//AEEtc/7/AMP/AMar2CigDx//AIZx8H/9BLXP+/8AD/8AGqP+GcfB/wD0Etc/7/w//Gq9gooA8f8A+GcfB/8A0Etc/wC/8P8A8ao/4Zx8H/8AQS1z/v8Aw/8AxqvYKKAPH/8AhnHwf/0Etc/7/wAP/wAao/4Zx8H/APQS1z/v/D/8ar2CigDx/wD4Zx8H/wDQS1z/AL/w/wDxqj/hnHwf/wBBLXP+/wDD/wDGq9gooA8f/wCGcfB//QS1z/v/AA//ABqj/hnHwf8A9BLXP+/8P/xqvYKKAPH/APhnHwf/ANBLXP8Av/D/APGqP+GcfB//AEEtc/7/AMP/AMar2CigDx//AIZx8H/9BLXP+/8AD/8AGqP+GcfB/wD0Etc/7/w//Gq9gooA4PwV8JtB8B6zNqml3epTTy27W7LdSIyhSytkbUU5yg7+td5RRQAV84fFL4peMvDnxH1bSdJ1n7PYweT5cX2WF9u6FGPLISeSTya+j6+QPjb/AMle13/t3/8ASeOgA/4Xb8Q/+hh/8krf/wCN0f8AC7fiH/0MP/klb/8AxuvP6KAPQP8AhdvxD/6GH/ySt/8A43R/wu34h/8AQw/+SVv/APG68/ooA9A/4Xb8Q/8AoYf/ACSt/wD43T4fjX8QnlCnxBkf9eVv/wDG688qW2/16047oD2DQvix43vNRjiuNa3oTyPssI/klduPHHiP/oI/+QI//ia8d8Goj+I7VHXcrSAEV7ff6VZMb9FhEX2dAyMO9fQ4T2EYJTgm/ReRwYhzUtGU/wDhOPEX/QR/8gR//E0f8Jv4j/6CP/kCP/4mntocUWiw3hUtIrBpV/2TTtWsLOziS4ijUxzhfLGenrXUvqjdlTXbZHO5VLX5n95GvjbxGT/yEf8AyBH/APE1OvjPxARzf/8AkGP/AOJqZdLsv7Whg8j5Db7zz1NU7+xgTTYryJfLLOVKZqV9Vk0lTWvkilKovtMsnxlr3/P/AP8AkGP/AOJpp8ZeIP8An/8A/IMf/wATSwaVFLo8MpQrNJKE3H0p0mk2pe8gUMrWy7t5P3qn/ZU7ci+5Fc1TuQt408QDpqH/AJBj/wDiai/4TfxF/wBBD/yDH/8AE1oDRLKfT1uIgS6JmRM/rVY6VZJo0N20fzvnOWx+VNSwv/PtdtkS5VP5vxK58ceI/wDoI/8AkCP/AOJrk/FnxR8ZaZNAtnrHlhuv+iwnP5pXW2ujW8+mm4X55ACSm7BFeS+PABcW31pYiGHlRnyQSa8kXRnNzV2acnxf8eAHGu9s/wDHpB/8RTIfjD48dvm13I/69IP/AIiuOdc5/wB2oIRjJrxpQjzLQ9BM70fF7x2Qv/E96n/n0g/+IpV+L3js3TIdc+X0+yQf/EVxCfdX608ri+raNOFk7LoTdnWTfGLx8kpUa9gZ/wCfOD/4ipW+L/jsGL/ie/e6/wCiQf8AxFcHcj/SD9amk+/BWapxvLQdzuf+FveOuf8AiedGx/x6Qf8AxFSn4t+ORLt/tzjH/PpB/wDEVwgH7xh71YkGLgfSuqNGnb4V9xF2dafi947GP+J73/59IP8A4ium0D4keLb1GNxq28j/AKdoh/Ja8jcYK/Wuz8KjhxV0aNNzs4r7iKsmo6M9IHjfxH31H/yBH/8AE0v/AAm3iL/oIf8AkCP/AOJrBApcV2PDUf5F9yOb2ku5vf8ACa+Iv+gh/wCQY/8A4mq2o+O/EsGm3MsWpbXSMsp8iM4OP92szbxVLV1/4k15/wBcm/lUTw1Llfur7kCqSvucQfjb8Q8/8jD/AOSVv/8AG6P+F2/EP/oYf/JK3/8AjdefnqaK+YPSPQP+F2/EP/oYf/JK3/8AjdH/AAu34h/9DD/5JW//AMbrz+igD0D/AIXb8Q/+hh/8krf/AON0f8Lt+If/AEMP/klb/wDxuvP6KAPp/wCBfjbxF4x/t7+39Q+2fZfs/k/uY49u7zN33FGc7V6+lewV8/8A7Mv/ADNP/bp/7Wr6AoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDwf41/ETxV4R8ZWdhoeq/ZLWTT0mZPs8UmXMkgJy6k9FH5V5x/wu34h/wDQw/8Aklb/APxuug/aO/5KHp//AGCo/wD0bLXj9AHoqfGr4hFcnxBz/wBedv8A/G6cPjT8Qf8AoYP/ACTt/wD4ivPo/u1JWqSsSzvf+F0/EH/oYP8AyTt//iKP+F0/EH/oP/8Aknb/APxFcFRTshXO9/4XT8Qf+hg/8k7f/wCIo/4XT8Qf+hg/8k4P/iK4KiiyC53v/C6fiD/0MH/knb//ABFJ/wALp+IP/Qwf+Sdv/wDEVwVFFkF2d9/wun4g/wDQwf8Aknb/APxFH/C6fiD/ANDB/wCSdv8A/EVwNFFkFzvv+F0/EH/oYP8AyTt//iKP+F0/EH/oYP8AyTt//iK4GjNFkFzvv+F0/EH/AKGD/wAk7f8A+Io/4XT8Qf8AoYP/ACTt/wD4iuBzRRZBc77/AIXT8Qf+hg/8k7f/AOIpP+F0/EH/AKGD/wAk7f8A+Irgs0UWQHff8Lp+IP8A0MH/AJJ2/wD8RSf8Lp+IP/Qwf+Sdv/8AEVwVJRZAd/8A8Lp+IP8A0MH/AJJ2/wD8RSf8Lp+IP/Qwf+Sdv/8AEVwOaKLIZ33/AAun4g/9DB/5J2//AMRR/wALq+IP/Qwf+Sdv/wDEVwNFKyA77/hdXxB/6GD/AMk7f/4ij/hdXxB/6GD/AMk7f/4iuBop2QHff8Lq+IP/AEMH/knb/wDxFH/C6viD/wBDB/5J2/8A8RXA0UrIDvv+F1fEH/oYP/JO3/8AiKP+F1fEH/oYP/JO3/8AiK4GiiyA77/hdXxB/wChg/8AJO3/APiKP+F1fEH/AKGD/wAk7f8A+IrgKKLIDv8A/hdXxB/6GD/yTt//AIij/hdXxB/6GD/yTt//AI3XA0lFkB33/C6viD/0MH/knb//ABuoj8bfiHn/AJGH/wAkrf8A+N1w1QnqamSGjv8A/hdvxD/6GH/ySt//AI3R/wALt+If/Qw/+SVv/wDG68/oqBnoH/C7fiH/ANDD/wCSVv8A/G69H+CnxE8VeLvGV5Ya5qv2u1j095lT7PFHhxJGAcooPRj+dfPFewfs4/8AJQ9Q/wCwVJ/6NioA+n6KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArwv40fELxT4S8Y2dhomqfZbWSwSZk+zxSZcySAnLKT0UflXulfMn7Rf/JQbD/sFR/8Ao2WgDA/4XV8Qv+hg/wDJO3/+N0n/AAur4hf9DB/5J2//AMbrgqKAO+/4XV8Qv+hg/wDJO3/+Io/4XV8Qf+hg/wDJO3/+N1wNFAHff8Lq+IP/AEMH/knb/wDxul/4XV8Qf+hg/wDJO3/+IrgKWmgO+/4XV8Qf+hg/8k7f/wCIo/4XV8Qcf8jB/wCSdv8A/G64A8mlpgzvf+F1fEHH/Iwf+Sdv/wDG6P8AhdXxB/6GD/yTt/8A4iuCpO1FhHff8Lq+IOf+Rg/8k7f/AON0f8Lq+IX/AEMH/klb/wDxuuBpaBne/wDC6viF/wBDB/5JW/8A8bo/4XV8Qf8AoYP/ACTt/wD43XBY7UdKAO+/4XT8QcH/AIqD/wAk7f8A+Io/4XT8Qcf8jB/5J2//AMRXAUvTmiwHft8afiCMf8VB/wCSdv8A/EUH40fEEAf8VB1H/Pnb/wDxFcC3Uc0rfw/SnYrud7/wun4g7v8AkYOP+vOD/wCIo/4XT8QP+g/3/wCfOD/4iuC/j/Gk7UWHoegN8aPiAMf8T/8A8k4P/iKT/hdHxAx/yH//ACTg/wDiK4Nu30pvanZFNI78fGj4gFCf7f5/684P/iKRvjR8QAoI8Qc/9edv/wDEVwY4ipH+6tFgaVju1+NPxBJ/5D//AJJwf/EUH41fEH/oYP8AyTt//iK4JetJSsRpY74fGn4hY/5GD/ySt/8A4ik/4XV8Qv8AoYP/ACTt/wD43XBeuaKRJ3v/AAur4g5/5GD/AMk7f/43S/8AC6fiD/0MH/knb/8AxFcDRRYZ34+NPxA/6GD/AMk7f/43R/wun4g/9DB/5J2//wARXAg0tA0ek23xg8fPbyyvr2cKdv8AocHXH+5VH/hdXxB/6GD/AMk7f/43XNRrt09h/wBMyf0rFqTevFRjBJHf/wDC6viF/wBDB/5JW/8A8bo/4XV8Qcf8jB/5J2//AMbrge9FBzHff8Lq+IX/AEMH/knb/wDxFL/wur4g/wDQwf8Aknb/APxFcBS0AfQfwX+IXinxZ4xu7DW9U+1W0dg8yp9nijw4kjAOVUHox/OvdK+ZP2df+Sg3/wD2CpP/AEbFX03QAV8gfG3/AJK9rv8A27/+k8dfX9fIHxt/5K9rv/bv/wCk8dAHn9FFFABRRRQAVNa/8fCVDU1p/wAfKVUfiQHY+F3aDWoZEOGVsivUrjVL26P72YsMgkev1ryzw6P+JrH9a9IFfVZfCLpXa6nm4v4i6NWvgkq+dxKMMMdqrtdTSCNZJCyx/dB6CojSrXcqcFsjm1NNNbvhOs29d6rtBx0FRT3s9yytK+Qv3VHAFVAKeOlR7KCd0itS9cateXMccby4SPlQOKSbVLuaMxtJwfvYHLfWqgFJSVKC6Duy0dUux92TYCmwhR2px1m6+zJb4jKJ93K9KomkNHsYdhXLP9q3Sw+WrKOMbgOa828ecTW1d6V5rg/Ho/e21Y4uMY4efKjSh/ERgkcj3Sq0fAarWMNH7pTLOFJp9juFBNfP1ZKOrPUir6CJ9wfhUswxej6CtzUNNs7exWSD5iAM/WsMEz3AYDkDmnRxMJqy8gnTcXqVLgf6QfrUs3EsI9qZOM3NST/8fEQ9hWi3l6k9iRFzcEe9Tyj/AEkVGg/0o1NMP34rtivdfqZMqSf60D0rsfCudzYrj5B+/rsfCX+saqoL94yK3wnWYb0pcN6CpguaNuK77HHch+Y9hVLV939j3nT/AFTfyrQA5NUtZ/5A15/1yb+VZ1F7rHF6o8FPU0UHqaK+QPWCiiigAooooA9//Zl/5mn/ALdP/a1fQFfP/wCzL/zNP/bp/wC1q+gKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+YP2jv8Akoen/wDYKj/9Gy14/XsH7R3/ACUPT/8AsFR/+jZa8foAli+6akFRxdDUlax2JYtJS0hqhCd6WiigAopKKQBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRSUDFp8ULzSBI1LMegApI42ldURSzE4AA617p8PPACabZR6pqFqHvH+aNH6J7mjTdgk3ojzmz+GniS9t4547JhG4zluMUmpfD/WLCPC2hfHLMpzXvV5DqsgKRy7Uz0XiuQ1w6xAxYhtg9BWf1iF7HQsNJo8KntprZyk0TIw7MMVFXo+p3VpqMLQ6jaDf0WVeGWuE1DT3spyAd8Z+6w7irTT2MZQcXqUqKWkpkhRRSUALULfeNS1G/wB6pnsNDaKKKzGFewfs4/8AJQ9Q/wCwVJ/6Nirx+vYP2cf+Sh6h/wBgqT/0bFQB9P0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXzJ+0X/AMlBsP8AsFR/+jZa+m6+ZP2i/wDkoNh/2Co//RstAHkXNJS/hR2pgFFJS0gCl6AUlL1XNNAGOtFH86KoGBo7UUmaQhaO1FB4oAQcUvakpT0pjQlKKbSjqKBCt1pTzj6Ujcn8KU9h7Uy+4fxUdhQPvUncUhXHt1H0pKVzyKaTTLe4/wD5Z01ui/SlJ/dfjSN91fpQDegg7UnalWmmgjoKCKKQelLSEgo7UUUAFKD0FNNKOtA0bpGLOT/rmf5Vg1uK2+wYj/nmf5Vh1B14t35fQKKSloOMKWkooA9e/Z1/5KDf/wDYKk/9GxV9NV8yfs6/8lBv/wDsFSf+jYq+m6ACvkD42/8AJXtd/wC3f/0njr6/r5A+Nv8AyV7Xf+3f/wBJ46APP6KKKACiiigAqez5uk+tQVYsv+PtPrVQ+JAdh4eGNWT616MOK878PD/ibL9a9E7Cvqst/hP1POxfxIXNOWmY5qRRXoM5kOApwoAp9Q2UJSGndKbigBMU3FONJTExjDiuC8fD95bV3xrgfH/+stq5sb/u8jWh/ERhv8ohb/ZqHSrW81O9MNnGXkznAq7CiPJZiT7hIB+ld3pWgHTfEE0liqruRWjHYivl8ZK1kezQpuWplWHgrxLdAxvbbFx99zwKbq/gLUdCsGvWnSTYMuq16fpOo6uJHhvLRAmCVdT1rnPFGo6nqGnXMLWZggzjcTyRXHSbcjoq00onj8nzXNSTj/TUHsKRh/pmOvNPmH/EyUemK9iK91+qPPe5Mq/6ZU9wP3y0wri9H0qe5X96v0r04x91+pj1KMg/0iuv8JD9830rknH+k11/hPi5P0oor94yK3wHaheKNtPxx0NGPY113ONEG35jVHWR/wASa8/65N/KtLHJ4NZ+tf8AIFvOD/qW/lWdT4WVHdHgB6mig9TRXyB6wUUUUAFFFFAHv/7Mv/M0/wDbp/7Wr6Ar5/8A2Zf+Zp/7dP8A2tX0BQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHzB+0d/yUPT/wDsFR/+jZa8fr2D9o7/AJKHp/8A2Co//RsteP0ASRdTUtQx/eqatY7EsWk70UUxBSUUUAFFFFABRRSUDFzRSZooAXNGaSigBc0cUlFAC0ZpKKAFooooAKSlooA9P+E2hRXFxNql3EHig/1akdWr22OdpACePQV4/wCF9bt/D3hS2tlZDPMTK5PbNd3oGv8A9qW7MhBKjnFcmIlJu3Q78PTSjfqdaG+XpVS6iSSMh1BB9a5O/wDFslhNtmlWNc+mTWlYeJbPU4F8qdXJ69q57O1zo5bM858bWkNvdkxABfSuHk/fRvGP4hxnsa9J8f2j+T5yrketeXpKEnBP3c8iumhsc+I3Md0KOVPUUw1JMQ0zkdCxxTDXUcA2ilxSUAFRv96pKZJ1qZbDQyiiisxhXsH7OP8AyUPUP+wVJ/6Nirx+vYP2cf8Akoeof9gqT/0bFQB9P0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXzJ+0X/yUGw/7BUf/o2WvpuvmT9ov/koFh/2Co//AEbLQB5FRRSUALRRSUAKaO1FKKaADSHpSk0mM4qgYvrSdKXtRSEFFA6Cj60AH40UnelpjQ2l9KAPWjPNAhx60HqKD1oPWgoTvR6UdKPSgQ5vvU00rfepD0plNj/+WeKa/OB7U4n5BTT1H0oCWwD+lJ2o6GlFBPQaOaWjvR3pAFFFFAXENKOaMUDpQM0rSXdZSxk8qrfyrMqRJDGWx3BFR1LLnPmil2CijvRSMwoHSkpwoA9c/Z1/5KDf/wDYKk/9GxV9N18yfs6/8lBv/wDsFSf+jYq+m6ACvkD42/8AJXtd/wC3f/0njr6/r5A+Nv8AyV7Xf+3f/wBJ46APP6KKKACiiigAqzYc3sf1qtVrTRm/iHvVQ+JAdn4fH/E3X616EBmuC0Bf+JwPrXfgetfUZY/3T9TgxfxIQCpFFNFSLXos5UhwFOApAadioKGvhVyTgDvWbJrdjH/y2BGcZFXL+JprKaNCQzKQMVX8P6PaW2lsk2nGa4I+83OfpXl5jjZ4ZxUOp3YTDKsm5dCzFIk0YkQhlPQiqkmpwRyvHtlZkODtQkVat7M2UflEbeSQvpWZFZrcz36s7KDKB8vsP/r1piK9flp+wteX+TZNGlTj7SVSDko+dutuzJjqsAIBSYE9P3ZrkfGCNqTQmH5dnXzPl/nXXNpkZxmRyQ27Jxyefb3rj/GR+wNGI8uJc7txPPOe1ceJ/tD2Mua1v68y6NfDc+lF/wDgf/2pnGzlaCBVZNy/7VdToGqXFs6vdyuxiHybRu+WuatELQ2jAHANaqZw20nPTA/z6V83mGIq0pqDad1fb/gnuSqYOhgZ4t03e/LFc273d9Nkv0R38ni6AeW0Ky7wPmDRGsXxT4lN5YC3tMqzcSeYu3+dYBlmZGBYnzBj8Bz/AJ/Gqt2wmk23H3pRuGR19646OMnCSdvwOXCZphcXWjQnTcebrzaeV9NOl353MNNPm+0mTdGVB5w4p0llI2oeZvix6bxmr8dkkPmAE/OVJ59KjOnxm4aQtiTdlee3pXpU8wTVnO2vb8dzPEY3LqMlCVOSls05Ws7tW+EZLbss4k428Dg1Jcr+8X6VNKmFP+8tFynzLX1ODc5Kak72f6J/qZ42FKPs5UlZSjfV36tb2XYzWGbquu8JL/pJrlgn+mYrr/CK/wClHitqcbSbPPqv3TtwnFIUFThPlpCtHMchWK1m62v/ABJb3/ri38q2CtZmur/xI73/AK4t/KnJ+4yo7o+dT1NFKeppK+RPXCiiigAooooA9/8A2Zf+Zp/7dP8A2tX0BXz/APsy/wDM0/8Abp/7Wr6AoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD5g/aO/5KHp//YKj/wDRsteP17B+0d/yUPT/APsFR/8Ao2WvH6AJbdDJLtXrirf2WX0qCxOLpa2cVrDYTMs28g/hppif+6a18UED0qrCMYow6qaTFbO1T2FN8pD/AAiiwGPiitc28Z/gFN+yxf3aLAZRpK1WtIiPu0w2CdiaLAZtFXzp47NSf2fx9+iwFGirZsXHQg0w2ko7UgIKSpTbyjqpppicfwmgBlFO2MOoNJikAlLRRQAUd6KB1FAHsPhfwgLrSLaed/8AWQ9+2a7vwr4et9Es5ljJbecZNc54N1H7T4ftwrcrHt+mK09Putdnfyx5aqrcbj94VwVJScmmz16dNciaNXUvC1pfu0hHLrtPtVPT/A9hpkgmiZgw7Z4rpomdIV8zG7HOKo3t5sBANRzO1ilG7Oa8axCTSzFGNzeleU3/AITvra0a9k2CMjJUN8wH0r1uWKS+kIOT6Vzfja5j0nwsYptgupMooHU+9XSm07IVWlFwbk9jxdsbjj1ptBOTRXeeOFFFFABUcnUVJTJO1J7DRHRRRWYwr2D9nH/koeof9gqT/wBGxV4/XsH7OP8AyUPUP+wVJ/6NioA+n6KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAr5k/aL/5KDYf9gqP/wBGy19N18yftF/8lBsP+wVH/wCjZaAPIqO1JSmgBPwopaKAClHSkpR0qkAEUUUUwYUlLmikIKKOMUUwCiiigaCk70tJ3oEOPU0HrQcZNHagYnUUUGjHSgEKaTtS96KY2Kfuig9fwpCflFKev4UAxKB0pD6Uo+7QT0Eo70vaj3pAhD0opSO9JQAGilyKTuaYwzTad2ptSwFpKKWpASloooA9d/Z1/wCSg3//AGCpP/RsVfTdfMn7On/JQb//ALBUn/o2KvpugAr5A+Nv/JXtd/7d/wD0njr6/r5A+Nv/ACV7Xf8At3/9J46APP6KKKACiiigAq5pYzqMI96p1e0gZ1SH61Ud0B3Ph5f+J0Pqa7vFcT4dH/E4P412+K+lyt/u36nDjPiQDrTwKQCn16bOVAOtP7U0Co7iZLaFpZDhVGTUN2KtckY5FLYXLrKY2R9y/cwOMVx9/wCKxIClvlB69zXZ+HdXsb+wR2DrMo+Ybc183mmMp1eWMVez3PawOGnC+u6Em3ea27rmsSG+gtbq8EzMCZc8KT2qlr3iK6tdZZkidbbOPnXGafp0kWpyXTJKUE3O3APFbTxkq3slQXvq+/8AhfmvzKp4dUKOIlUXMrLS9n8S8mXZdas41PztnsCpGfzqrfnwxfW8N1fStPMv/LAcbT71zWtXf/FQtADvCnkn1JqZY1MZJUEsT2rhxWPxkpujO1vLT9WZqjSdD29Gk1rbWSfR/wB1Et1rMVxILW2tI7e2BwioOfxNRRFo5N/Gc8d6or+7ZyihiDhc9jVtpNrKGwNxx+NeDmEZOaa7fkdqSrYdU3RdTlba1sruySlo7rTo09y5NcmQKAI1CRBAAvbqeo65JqlfRpcNARmPygQGTn3yc1qxaTLL5YCsC67ueg/zzVG8iayl8uUYPHGa46bqRd0cCq1YRdR4WMVs3ZvR22T62tp11+USkOgIOR603BJUFQO+c96cHU8cZ9KXIxntUptOyR5ubVaeJxHtYS5tFdqLWq06+Wt2MmXK/iKfOmQpolUoo3gjOMe9SyI3lAnpX2mQ1qsaM4yg3r220WmrFGvWlThGpF2irLS2l2/zbM+OFvtZYjiuk8OXEdpO7PkkKzBR3wM/0rAtgWvSM8V1HhaCOS/+dFbB4yM16ip4rlb5o/c/8wqe0tq0dVLrNrFEhw5cqrbMEHBOPxpzaraYLbj5YjWTdtPIY4HatFdMswoItIOOn7scd6jfTLI9bODoB/qx0HSuRPE33RhaZQgvo7maaJUdTEQPnXGeAf61V10j+w73/ri38q1mt4llMgjQSEYLAc4+tZmvKP7DvuP+WLfyrrjzKD53d6lQvfU+cT1NJSnqaSvmD2AooooAKKKKAPf/ANmX/maf+3T/ANrV9AV8/wD7Mv8AzNP/AG6f+1q+gKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+YP2jv+Sh6f/2Co/8A0bLXj9ewftHf8lD0/wD7BUf/AKNlrx+gCe0OLqP61u4rn7c4uIz/ALVdEK1p7CY2in0VYhlFPxRigBlFP20baAG0lOxRigBtFLtoxQAlJTsU+GPzZ0jJwGOMik2krsqEHOSit2Q4owPStH7FBk/NKcNtPA60fYYCFw8p3ZxgDtXP9bpnQ8Ov+fkP/Al/mZpRT1ApphQ/witF7a1jUM0svOeMCo9ll/z1l/75FUq8WrpP7mVHBOSupx/8CX+Znm1iP8NMNnGfUVrwWtrcOVSaQEDccqOlSf2fakZ+0tjGc4qJYqnF2d/uZvDKqs1dSh/4HH/MwTZL2Jpv2L0aug+wWn/Pyem7p29ab9jsf+fyj63Tff7mWsnrttKUNP78f8zofBF48GnTW4f5lPFdXYOFmU/bLkMewHGa8/02a202fzY7pWyMFT0NdlYeL5IYh5djbSj+8Sa5qtWLd4p/cz0MPl1eMbOUf/Ao/wCZ39rcSpABLL5h9cYqtcFWYsx4rkJPG10x4sYVPsxph1XWtSX9xZMV77Aa5/aLrf7mbrLa3Rx/8Cj/AJl/VdYvNNka4tFQwqpB3DvXlXiy4v8AUbxbm5lMgYZUDoK7PWtcka1NhdQpbHGCC3NYM9pHdWyqWO1OdwropYinDVp/czmrZRiavuxcf/Ao/wCZwZGKK3mttNJOZJf++BTDZ6Yf+Ws3/fIrr9ouz+5nk/UZfzx/8CX+ZiUVsmx0w/8ALaf/AL5FMudMt1tVmt5ZGBfb84x2oVVXSs9fIUsDUUXJSi7K7tJMyaZJ0q2bQjvUU8DJHuJrRrQ4irRRRWZQV7B+zj/yUPUP+wVJ/wCjYq8fr2D9nH/koeof9gqT/wBGxUAfT9FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV8yftF/8lBsP+wVH/wCjZa+m6+ZP2i/+Sg2H/YKj/wDRstAHkVFFFABRR2pO9AC0DpRQKpALRSUvamJhSYp3akzSAKKKWgBO9LSUdqYBSDrS0Ywc0AL3o/lR3oJoADSdKU0nemCCl6ijGaOgoBsOwoPWjoPwpe5oKY09aUfdpG6inY4pkCUtAopAFIaWkz1oBCUUtA60ig6rTc089DTKUhIKKKKkYUtJRQB67+zr/wAlBv8A/sFSf+jYq+m6+ZP2df8AkoF//wBgqT/0bFX03QAV8gfG3/kr2u/9u/8A6Tx19f18gfG3/kr2u/8Abv8A+k8dAHn9FFFABRRRQAVf0UZ1aD61QrR0P/kLwfWmtwR3vhwf8Tc/U124FcV4bGdWNduBivpMqf7tnHjPiQoFKBRThXps5EFc74q1E2VtGgj37zyDXRYqa10my1OfZeQiQAcZ7Vw5jU9nhpM7MDG9dHlUTWs07Nc5gkJG1FFdtp2p/YYU2qoAGeO9cv4litLTxTcCBP3UXGOtQRaxJLv/AHPykYHtXyEoylZo+uw1ejDmU/tfe/Nm94mvhq9qrNEVYHO71rK0ac2t/B1C+Xz+dS3syx2UAQFlPfNbunR2kHhfVbwxKZIxhGPUfLWmGqOFanLzLxlOl7Cqlr7sfzRwBm+1a9PKTnMh/nW5auZLeQAj93K1cpZyEX6H+839a6jQdstzqELkDDlhn61dV81fm8v1PHhpgmv7y/JkFtMEuZHKblPP41O675E9mz+lU5c2GovA/wDq35Q1o26iRpP9lSf0FcWM0cX5M9vh9c0Ki/vQ/Nnbw/6mP/dH8q5DxWjG7ZlyNu3JFdhEf3Ef+6P5Vg6xALhr8dSsSMP1rCk+WpFvuexjqXtcLVj5fk0cnHIz3sfPAFWInHlIg/uk1Xs+bknH8NWjGFkG3AAQjFdGKlCNZQt0/wA/8z53KpYaFDlbXM5bW1+y+1uj69fMt3vzxw/QVadM2q/SqTsXVAe1X1nV7ZU2ndz0r6vKsxw0nKPNrJ6fdFHy+KxdGpCjGErtRs/Xmb/VGdYR5v2rq/CqY1FhjvXOaWmb811fhlNuqMPevYcbUmznqM7xU+WopF4q2q/JUUq8GvFUtSWtDNdM5rH19ANCvv8Ari38q3HHBrE8Qf8AIDvv+uLfyrov7rIW6Pm49TSUp6mkr5g9YKKKKACiiigD3/8AZl/5mn/t0/8Aa1fQFfP/AOzL/wAzT/26f+1q+gKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+YP2jv+Sh6f8A9gqP/wBGy14/XsH7R3/JQ9P/AOwVH/6Nlrx+gB0ZxIp9xXSLyoNc0vDD610kfMan2rSmJjsUUuKK0EFFFFAxKKWjFABSGlooATFBpaMUAJUtp/x9xf7wqPFS2v8Ax9xf7wrOr/Dl6M6cGr4imv7y/MSWaUSuBIwG4nAPvTPOlx/rG/OiX/XP/vGnQQm4mEYOM9zSShGHM1oYyoRlVcIpb+RGXYrtLHGc4zTMVe/s8Zx549PuGkFipx+/HIyPkNQsTS6P8H/kbrA1LaOP/gcP/khliSjTsOoiakF7IEK4HTCkdv8AOKsW0ES+di4VsxkHAPA9ar/Zof8An7T/AL5NZc1KU5OS7dGdeIyuU6FJtRvr9qPf1IzcyMSWwfl28jtUGKvJZRyEhLlCQM/dNAsUIBFwpznGFNaKtRhotPk/8jk/s6pTsvdV/wC9H/Mo4rR02RgroM9QaZ9gUjPnjH+4a0tM08xM7bg4ZcjHH86bxVLv+DNIYCte94/+Bx/zOh0ixjllQy4x6Vu694jh0LTvs1pt+0uuAB/D71yC3s1nlwwG0d+cVjTxPdztPNdb2bB+6e/SuX2lOUrt/gzqlSnBcqcb/wCKP+ZQvbgyl5p3JZjkk9Sa1tJleTTmDjGRx9KpPpYly7zjaO208VdsYvs42eYGX0wadarCVNqP5M2y3B1o4unKVrXX2o9/Uw3zHIVZQcGo2IY5wB7CtW6s42kZvPRQTkZBqqbOD/n8T/vk12e3h5/c/wDI87+zq/8Ad/8AAo/5lGrbf8gmP/rsf5UrWSeTJJHcI+wZIANDj/iUx/8AXY/yqJTjPlt3/Q2o4apQVRVFvBtWafVLo2Uu9QXYzbtViorgZgf6Vu9jyzIooorAoK9g/Zx/5KHqH/YKk/8ARsVeP17B+zj/AMlD1D/sFSf+jYqAPp+iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK+ZP2i/+Sg2H/YKj/8ARstfTdfMn7Rf/JQbD/sFR/8Ao2WgDyL6UUlLQAlLRR3oAO1A60GgdapAB4pe1JSj0piCkHWnU0UALQaKDzQAelHrR6UvegAooooAKXvSfSlANMQmPSpY7aWYjYhrX8P6K+pxXt0APLs41Zge5LY/QZP4VPc3iWLNCIgHU4ORSv0NoU9OZ7Ga+lPHAHaRQx5xVBlKkg9RV641Brk88Z7VTfJGaETU5fskfb8KBzmjt+FFUQwPUUtLjNFIQlFLiigBKaafTT1oATtSik604e9A2B6UynnpUdTIELRRiipGH4UvSkooA9d/Z1/5KDf/APYKk/8ARsVfTdfMn7Ov/JQb/wD7BUn/AKNir6boAK+QPjb/AMle13/t3/8ASeOvr+vkD42/8le13/t3/wDSeOgDz+iiigAooooAK0dC/wCQvD+P8qzq0dC/5C8P4/yprca3PQfDX/IVNdtiuL8Nj/iamu1A5r6LKf4bOPGfEhQKcBQOtPr02zlEArV0hV2TN/EBWVWhpcm15V9Vrzs0Tlhml3X5nbgLKt8meXa/pznXLgrKMyOeT0FZ7wyW0/2ZkGxRncD1rS8W7m1SQrkhT2rnWmlkbLuScetfP1YNVJK57LrU6cV7rvoacTmaVY0YAN2Pat3Ubhbbwdqcan/WTKg/IVgWT2HlqZGdJ1PUd6m8Q3GNEMYPEk4P6VzTvzxj0v8Aozvg1LB1Ztq9l180c7YRgyCRuisAPzrZ0eTGtXUefvsw/Wsu1VikMSDJ8wM351YHnWWsPMylQzllPqCa0bXtreX6nnxhL6i5Jacy/JlrW0IniPPFa1iuftB9EJ/QVWv1F5brKOdtXbBf3d2fSP8Awrkxz+H5/oe5w0v4nrD/ANuOyh5gj/3RWVPj7beqehhX+tacRxBH/uis9grandBunlr/AFrjfQ+hSvGa8v1Rxdumy7f0wasuvlg56hajJAu28vkBypz6U+WVXQkZIZCcn6V6FZN1OZ9UvyZ4GAUPqqgt4ykvxgPtG8xAT6mugKg2QOO1c/pnzRgfWunCf6CPpX2WTpKlP/F+iPj8fCMY0eVW93/26RlaRHm+Jx3rqdAjxqpHvWDosWbxuO9dPoybNXH1rvqu1OS8jz57nbpENlQTRjBq8i/JVedeDXz8Zah0MmVABWF4gAGh33/XFv5V0E/Arn/EJ/4kd7/1xb+Vdi+Fma3Pm89TSUp6mkr5k9YKKKKACiiigD3/APZl/wCZp/7dP/a1fQFfP/7Mv/M0/wDbp/7Wr6AoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD5g/aO/5KHp//AGCo/wD0bLXj9ewftHf8lD0//sFR/wDo2WvH6ACultzm3jP+yK5qujsjutIz7VcNxMmxQadSVqITFFLRQMSjFLS4oAbijFLRigBMUUuKMUgEqW1/4+4v94VHipbUf6VF/vCoq/w5ejOnBf7zT/xL8yKX/Wv/ALxqaw4ugfY/yqOUfvn/AN41LZD/AEkf7p/lWdX+A/Q3wsYzx8IyV05L8yM3Mu4NuwRnoB3pBdTLjDDjpwKjxRitPZQ7I8v2VP8AlRPac/aD/wBMWqrVy0H+v/65NVbFTD+JL5Ho1/8AdqP/AG9+ZNYErM7DqI2IqMXUw6MB9AKms/8AWSf9c2qK3RXnVXBI7gUlCMqkuZdv1Ir0oPDUpNK95foTWoubqZY1JLEbQMdQa7a60e2sLEyyzEOiZbnjNZ3hmzS41MngJCOT71W8XavDcO1hauTHH94g/eNKVGnPRx0NsPGnhqTqtLmeiVkcvd6veySvEiRspOA23ORTlnvIpgkzLnAyAo6VFZMYpGcqCBzzSSTmaaSQnnpQqNNaJHBO1R80kr+iOjsG+05LcrV2ZUQAAYzVDSTsgXg5rRlYGI569q55JKFRL+tEe1hKcIV8JypK9v8A0uRzV4QJHAOQDx71UyjdMj61bvlClmHTNUFbD7a70zxGW7b/AI9rv/cH86R/+QVH/wBdT/Kltv8Aj2u/9wfzpH/5Baf9dT/KuV/G/wDEvyPah/uy/wCvcv8A0spGmS8xMPapDUch+Uj2rqZ4hjUUp6mkrAoK9g/Zx/5KHqH/AGCpP/RsVeP17B+zj/yUPUP+wVJ/6NioA+n6KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAr5k/aK/wCSg2H/AGCo/wD0bLX03XzJ+0V/yUGw/wCwVH/6NloA8i6UUUUAFFJS0wCl7gUlLimgAd6WgdKSmIOlHelPWjGTQAUYoNLQIbilzzR3o4pgFB+lFHSgBcYxR0oHaigD1v4f6TYDw1Leb5JpJh++ABC8Z+T39T+FczqOmrcaqkUiloE6nOG2Z6E+3rU3g7xXY6PosthOzrPLMSpx8oUgf1zWtqOgXL2c99LLgMPuofvd+vpWa0k7nsUacKlFW+482u4kivJUiJaMOdhPUr2/SomPyBB+NWb6B7e4ZZFKntkVWbg/UVoeVUVpNWI/4elKOaByvrTlxjpTIYlLjmg9aUUhCdqSlooATHNNI5qSmEZNAIQc04UgpaBiHoaZUmOD9KjqZDQUUUd6kYUUlO7UAeufs6/8lBv/APsFSf8Ao2KvpuvmT9nX/koN/wD9gqT/ANGxV9N0AFfIHxt/5K9rv/bv/wCk8dfX9fIHxt/5K9rv/bv/AOk8dAHn9FFFABRRRQAVoaJ/yFYvx/lWfV/RjjU4vxoGtz0fw1/yFGrtO9cV4aP/ABNH+ldsQRjPevosp/hs5MX8SBetPpoHFOxXqM5Aq5poBvFB7giqdS28phmV/SuXFwc6Eox3OnCyUa0XLYqT/DbUtXvZp4bqERsxO1hSWPwYmW5H26+j8vuEHWvQvDN4JdyhuRWzeNsOc8Zr5iqpqo1Lc9J1NVZ3S2OW0b4V+HbHMkkBuGzxvPArxz4iWUNvr91awqscEd0wVfQACvpq2OYQa+YviVIX8b38fUGdiPrwKxTfPH+ujOmg/wBzX9F/6UjnIHSKSBFH35AM1Yv7lIb2NHUkbc5H4iia1EFxYjvkYFQaqpe/QDr5ef1NY1YKeJin2Z008NSnlNWpJe9zJfL8jQS4xGVA4IyfStOx+aO7OPux4/lXOW8xKBT2rpLAHyLzH/PPP8q58dSjC1vM9DhbD07zdtnF/wDpX+Z1kBJhTj+EVnuCdUuQB/yzWr8ZK26YGTtFUFLrqdwzEDCLmud9D6aO0/T9Uc3cQ/ZNXniccSpvX61RRiIFj7mNjWpr6ub60n7lipx2GKynGJlX/pk/866udySv0X/yR49OkqfNbrJv/wBNlvRCTuU9s11yLmw/CuS0Ffnf6Guwh5sD9K+3yh/uZf4v0R8VmW1H/B/7dIh0KPN031rpLCPZqyH3rI8ORbpnb0NdCsezU4z711YifvOPkedJaHXIPkFVbngGrSA+WOe1UrrgHmvCh8RCMy4PFc54gP8AxJL3/ri38q3LhuOtc74gb/iS3vP/ACyb+VeglaDJjufPB6mig9TRXzB6oUUUUAFFFFAHv/7Mv/M0/wDbp/7Wr6Ar5/8A2Zf+Zp/7dP8A2tX0BQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHzB+0d/yUPT/wDsFR/+jZa8fr2D9o7/AJKHp/8A2Co//RsteP0AFdDpoLWSHBrnq63w/htNAxnDGtKauyJy5VcTafSjFa3lr/dFIYYz/CK35CPaoycUYrVNtGf4ab9kjPalysftEZuKK0fsSHuaabEdmNHKx+0iUMUYq6bE9mpDYv2IpcrDniU8UYq0bKQU02ko7UWY+ZFbFS2w/wBKj/3hTjbyD+E063idbmMlT94VnVX7uXozqwTX1mn/AIl+ZDKP3r/7xqWzH+kD6H+VNkRvMbg9TUlopFwMgjg/yrOr/AfodOC/5GEP8a/Mq4pMU/FJW55pNa/8t/8Ark1VjVq1/wCW/wD1yaq2Kyh/El8jur/7tR/7e/MmtOHlx18pv5U+1hMUIlI3Stwo96LAZnYH+4a39CsY7m4e9nIW3txkA9/elHWrJLyHV/3Sk/OX6Es83/CPeGvJcAXl183uB61xwAuLtVGAz+lW/EGry6vqjydVJ2xqOw7VDYWxt76NpMlznNayfRHFdy1ZHeAW8LheMtj8qzgw+VR9TWhqk6yzNGoOxc5PvWW3yuMelSJnU6NIWhLYPBFa8jKUI9q5/SLgxW0j7SdrLxW95qyQbgMZ7Vxz+Gp/XRHu4f8Aj4P5f+lyMO7QGGQd1PWsiNtsvXtW1c7QzehNYyxk3DY7V2o8Jly2/wCPa7/3B/Okf/kFp/11P8qdbDFvdf7g/nTX/wCQXH/11P8AKuZ/G/8AEvyPah/uy/69y/8ASymaWJAWY0GpIRya6meIYEoxK496ZUtyNtzIPeoqwKCvYP2cf+Sh6h/2CpP/AEbFXj9ewfs4/wDJQ9Q/7BUn/o2KgD6fooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvmT9ov/koNh/2Co//AEbLX03XzJ+0V/yUGw/7BUf/AKNloA8i60UUdKADNHeiigBKf2plPqkIB0paSimACiiiiwgNFKRwDSUwCjHNL2oHSgBKDnFKRikPSgDb0pbddLmmlihZxIFUygeg45/Gr1kLG6naPybViuRhYwM47jk8VjJ/yL7/APXwP/QaXSCRPOQcEW74I+lefVpcynK/U9ihLEU61KFOekknZpNdfLbQ6Bo9NCsVtYW29gq+uP51rW+o3c1lGiyTrAAFWMtwBjgelcFA91NNHDHLIzsQqjce9egxQppulrEp3eWmMnuan6k3pzHblmLzHF35pRUV1UI39NrFK58idh9pCSEdPMwaoT21kxmiaK3QKhO7ABA4wf51XlmLuSWOKlv0BjupB1FvtP6Ef1onhHTa95/01/mdFq2IpzlOWvov5Z/8D7kYi2VqVUm8Vcrkjjg4HHX3P5VRHSk7e1LnivQjFrd3Pj4xa3dwwKWkpTVFAaTPeiigBRzTTTqQ9aAG0tFKOtACY+U/So6mPQ1DUyGgoooxUlCUtJTqAPXP2df+Sg3/AP2CpP8A0bFX03XzJ+zr/wAlBv8A/sFSf+jYq+m6ACvkD42/8le13/t3/wDSeOvr+vkD42/8le13/t3/APSeOgDz+iiigAooooAKv6Nt/tSHfnbnnFUK2/CVst34ktYnwVJJIPfigDpNMS9m1JpLTesSH53Arvrea6Xck0DgIAd394GtHTbTT7DTUQBRJI2SgHWtCzu1t3kllhDJ/c6kCuzDYydD4diKkFU3KxtXFss4IMZ9+agqxcXsd2xaGIxR5+6eKgxX0dCcp01KXU4JpKVkJSikozWxJv8AhiYpfFfUV1GtymLTy+cHIrkPDjhdRAx1FdR4lcJpcZ/2hXh4qCeMj5nZTl+6ZvWb5sEb1Wvl7x8S/ja7Pcyt/OvpaxuC+nDHdOMV82+L4PtXim5mRif37KePevGqyjTknLTX9Gethot4evLoopv/AMCRh3k22+tCxPylRVueOKS4jmKuW5TggDjP/wBeob6yeWeDysEqwZs9gDUepq0bxD5iPnOR7nNctZqpXhyS1szXD5hyYCpTpyjdyTs1e+nRfj8mWBbwb921xn3rUgkMaSgHG9cHIzXNRMwAZWJH16V1cMf+gF/UUq2ElN2ctk39x6eS5jVpXlKMXeUI6JLfm10Lkeq3AUBXBwP+eZ/xqFr+c3Ly4UswAPyHt7ZqdFAK5OPlqLI+1PwT8oxXTLKUuT33q/0bOynxG5+1/dL3VfffVLt5lW7v5J0CypGEBzuxjFZKsJvLmUHYYmxmte7hDxsucluMelZcK+Ra/ZjjKoT/ADrOtglhnfmvf/Jk4bNnjrx5FG1n97iXNBH7x/oa6u2b/QmB7CuV0TMed3ocGt+2nH2eQZr7DKNaU1/e/RHyOYp8tG/8n/t0jovCkW6OZ8d63mj/ANLRvTFZPhDB06VvetyQfOh9qMRK9WR519bG2jfux9KoXknBp6zYjFZ93NkHmuGnD3iWrFCd+DXO6++dHvP+uTfyrXuZwkbMxwB1Nc3qt7Bd6NeNDIrgRN0PtXXVfLBocI6nhZ6mig9TRXzB6AUUUUAFFFFAHv8A+zL/AMzT/wBun/tavoCvn/8AZl/5mn/t0/8Aa1fQFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfMH7R3/ACUPT/8AsFR/+jZa8fr2D9o7/koen/8AYKj/APRsteP0AFdV4abNk6+jVytdL4Yb91MvuDWlL4iKnwnQUuKKWuo5xKKWigQUUopaANVdBna+s7dWVhdKGVx0x3/Kph4deLV7izuJNscCF2kA4K+tXdH1mKLRpoZI2e6iBFuwHTPWlm1hpfD/ANleBxfN+7MhHVBUXZVkJbaNps0KyR21/MCOoUAGm3emWFnJDLcWN1Bbc7i7Z3H0rMQarHEI1llVAOFD4FXrPUJUtltbmIXAWTzD5j5GOmKYaEI1TR0bCaOCM9WkJq/qWk2H2Oc2seyWNFuAM9UI5H4U0tpSsSulpu6/NKTVK9vpbiaS4MiQ5iMSxAH7uMYrKt/Dl6M68D/vVO/8y/M1n0Ox1JLP7EgD2+1br3BGS1czqjwzarO8MapGGKqAOw4q5ZXxsvNeK9ZGmTY4A6isk48w4OR61FRfuJen6HRg3/wpU/8AGvzIDDGf4RTTbxn+EVNSV1WR5V2RJbxrvwOqkGoTZx49KuL3+lRvgLycVhC3tJ/I9Cu28NR/7e/Mr2kaCZox95lIH0rV1/UY9P0O3soV2yzLukI9KytNkBuZbgrmNTgH2FZ+pXb6pqxkP3BwB6AVlCX7yb9Dpr/7pSXnL9A0e3NxqAkcfKvNXZZE/tqMZ+UGnaLhXujj5QODWU0gXUS56bqrqcV7IsalbLBeSxnoeQaxpFwRj6V0WrqJIYrleRjBrClXDmmhSNOxdlsJSvUMmK3baV5bQ+YuG7Vi6ac2cuTtG5a30H+igkZ9DXJU+Cp/XRHuYd/7Rg/l/wClyMK4YBSp+8OKrW9u0kUsyjO0gVa1Laj7gRk9av6DbrNo9+Twc5B+ldl7HiJXdjLg/wCPe64wdg/nTH/5Baf9dT/KrEbBra4ULyF6+vNQOMaYg/6an+Vc0v4j/wAS/I9mH+7L/r3L/wBLKeKkhHWozU0HQ11s8Qwr4YvJPrVermpjF63uKp1g9ygr2D9nH/koeof9gqT/ANGxV4/XsH7OP/JQ9Q/7BUn/AKNipAfT9FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV8yftF/8AJQbD/sFR/wDo2WvpuvmP9ov/AJKDYf8AYKj/APRstAHkVLiiigAoozxSUALTu1Np3tVIQtJRRVCCiiigBaSl6ik6UCD0pfSkpe1AwPSkp1BFAi+g/wCJBJ/18j/0GjSf9dcf9e7/AMqVf+QA/wD18D/0GjSQPOuP+vd/5Vyy+Cfqe3S/3nD/AOFfqa/hLTWaVtQkHyp8seR1Pc1u6u4S1IJ5PAFS6Ts/si18sLt8odPXHP65rH1m8E0/lIcqveummryuz3KVOGEwKUd2vvbMp24+vSr91/q7z/riP5CswNufp9K07w4S6/64Z/lWWJ3Xp+sTHBu9Gp/X2ZnK0o6UnelArc+OEpaQ0tAgxR1ooFAxcUhHFOpp5NJiG04UAUuKBiN0P0qGpjwDUBqZDiFLRRSKClptLSA9d/Z1/wCSg3//AGCpP/RsVfTdfMv7Ov8AyUC//wCwVJ/6Nir6aoAK+QPjb/yV7Xf+3f8A9J46+v6+QPjb/wAle13/ALd//SeOgDz+iiigAooooAK6DwUGPiuz2fe3HH5Vz9b3g27Sy8UWk8hwqk5P4UAe6ro9/cXSNMI4oo1yT60Rw3WmXKPK8UkE7bVIOam0vXE1WGS0aJtjggSg9RTbvRbX+zpJGllxbqWU5zQtQTLWq6dLId9uyIpxtXuTWaNI1UjqtZmjeI/tlxEHyeQu5j0r0mIoyAqQRjrXt4PFVIUkjGVOMnc4r+xdU/vCl/sPVP7wrtiyijzFFdX12p2J9jAwvDOlXltqYe5YFcdq6TxRZTXelKkJwQwOaLKVPtSgnGa2b5f9CbPQV51evJ4iNR7miguTlK+mxGGxjQ9QmDXz74khNv4i1GPOSLlzx7tn+tfRMIxDn/ZrwLxJB5/iy/HmxJuuGGXbAHHU14OZLmgn5no0OeWExFGCvzRS/FW218vnd6GBjnNNlgkuIzHEcOejelXfsWQCtxbtksP9ZjGO/Pr2qGJvKlDZ6V5uGj++g57XR8zQyrGKalKm7Kz2e115ea/Exl0e7gDfKGBrpre3nezVC6quMbSKsrNAyDMic+pFOaeELgSJz/tCvrJ0sNPVv8bb/M+nw08bh7+zg909Y31V7PVdLjfK3EZPAFVZW2XD8NggAYFTSXaRjCMpPrmoobhGk3SSAHtWlVwkoqM0mnfv0t3Fho1qcp89KUlJWe66p9n2G7024IbP+6azJ1H2xiSAGjKjJwevpW+PsznJnXnvuArndRtJJ9XWSNlaNAQW3DmuHGxclDmqJ69vL1PUwDVKNScKEk0lu/7y/uoktmMMAyQOMZzU8dw6hgrEDGOtVZEZYVUZyD2P/wBcUBmz0I/eY/CvMni5QnPklbV7Hy2fvkr04J6qEbrs7ao7LwbdyR38keCY3h+Y4bjGcd8f5+tdm9yo8s5615noN+lpcSSyfKApXJHv9K6EawrxRNu4zX0+UJVqT967uzgw8vdaOuNzgHmqVxOSDzVFb0SLuB4qKWfIPNdsuWB0KLYy7PmwuhPDAivN3VtPh1GJWPyhsj1BrvZJsd+K4bxARHfXo7SQZrhrzvqbQR5yetFFFeMdIUUUUAFFFFAHv/7Mv/M0/wDbp/7Wr6Ar5/8A2Zf+Zp/7dP8A2tX0BQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHzB+0d/yUPT/wDsFR/+jZa8fr2D9o7/AJKHp/8A2Co//RsteP0AFdD4Xb95OvsDXPVueGGxeyD1Wrp/EiZ/CdXiiilxXUcoUY4paKBhRiilpgPjmkhJMblc+lKbiV2DO7MR6mosj1qGefyxgdaQ0mzS/tCT+4tRm9lP90fhWbDcM74ao55m8zAOKV0VyO9jUN3Mf4/ypjSvIRvYnFUraZmyG5qRblTKEx3xWdVr2cvRnRgotYqmv7y/NEvelA5oBB5FKOtTW/3d+n6G+C/5GVP/ABr8xMUmKU0neug8sAOv0qjqKyi2Pko7u3GFGcVfrm9QvJPtjosjgDgbWIrmqQqKTcWtT1KNfDyowp1VK8b7NdX5o0NLhuBD5UqMilSDuGOtbt7oul2mllrW4Ml2V5AORVRbX7NapJvciSPdhmyelUJppGswVlbA7d/zrkSq+0lZroenVeDWFp3UrXl1Xl5EmmRyQ2kyyIwY9AR1rMurK480skLsD6LW9ZKRp25txc560klv5tqg80h/Y1t+98vxON/Uu0vvX+RQgill0+WCWN1OMrkd6y5LK5O0i3kzjB+U1dlWe0mwZXKsO7Gs97iZZSpmkxn+8af73y/ElvBdpfev8jTtIJ0tJVaFgSy4BHWtxJP9BWMjDDtWHatI1pKTI5+ZcHNb52C0CsQWI4PeufkqT546av8ARHputhaDw9W0nyq61XSct9O5zN9tMnNbGlyC38P3DA8ueKw70bZ2DfhV+CQppUQPKkniuySvY8GDtdkcZBt7g4H3B/OoZf8AkGpj/nqf5VJAV8q5442jp9ajkx/ZyY6eaf5Vg/jf+JfkevD/AHZf9e5f+llKp7cfKahNWLf7ldR4hjauuLsH1Ws+tTWh+/Q+1ZdYS3LQV7B+zj/yUPUP+wVJ/wCjYq8fr2D9nH/koeof9gqT/wBGxUgPp+iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK+ZP2i/+Sg2H/YKj/wDRstfTdfMn7RX/ACUGw/7BUf8A6NloA8i4ooooAKKKKAAU4dKbTx0qoiYlS28DXM6xKyKW7u2APqajqxZzNb3KSR7Qw4BboM8c1Qiz/Ys2ATNbgH1kximHSpQwUzQDnGd/HTPXpWwdUvGQ/wCkWHPBHme+TxUI1C7E7yrPp4Mm0YyMKACMAdupzSuBSbRnVM/arY9uHJ5wTjp7UkuizRK5NxbkIGJG/sPr1rQTUrzdgTWGFbOXfdngjqeaSXU72ZzH59iu5TkhuuRtIz9MfkKAOr0/4f6TL4W0nWbxr0i5YCZYHBK5zggbfYetcr4o0Sz0d7T7G8pWRWEglYErIp+YcDoM498Gur0bxTYaZpdlZrOqeSoMhG0/ORsYqc9lOR71z3jzVdK1bV7eTSfOEMcIQiXHBzngg+9AHKmkIpx4pPrTEXxxoL/9fA/9Bp+ixtLdyxqPmeF1H1NT6bp91qunfZLOJpZnuQAFH+z1PoPeu1fQtP8ADGkpAWSbUZGDSy+gH8K+g/nXJJ+5NLufQYanzYnDylolFXfzZkow0bS47NZQ0ygliPU8/wBazI4WEJuJASpOB7/StAabJdS+azFoyecdatXieVbLEiJHGowPMya6416e2v3P/I9tYKpOOyUYrTVf5nNRBWm4OQTWrehXW5Q+Wo8rBbuBx1qotrGZQwuIgc5woNPviC2oY/54/wCFcmJaqTXL0Xn3RyrCVo4eVp8uvSzvpJ/pb0bMc2dohYm8BCsB8oBOOOev+cVHNbwJCzx3IdlbbtHf3qpjNKOK6FCS3kfEqElvL8gooorQ0CgUtJigB1AGDmjvS5FAg70GjNITigBG6H6VXqduhqCokXEWkopakoKKKWgD1z9nX/koN/8A9gqT/wBGxV9N18yfs6f8lBv/APsFSf8Ao2KvpugAr5A+Nv8AyV7Xf+3f/wBJ46+v6+QPjb/yV7Xf+3f/ANJ46APP6KKKACiiigAq7pSs+oRhAS3PSqVdJ4EiE/i+yjKbwxOV9eKAO28Na5f6ZgTRM1rnBJHSujn8Vy2SXKPB5iTjETdsH1FTa/p+pW+mTLZab5p3DywF6A1yvn6//asNjqWlK25QMbMYHrmqSC19humzQrfokJH3+fzr2m0jC20e3ptFcVp/gBVuYZZgEVRuQKfvfWu/trZo7aNH4IXFelSqe5Zikkn7pWmyBmoPM3JxnNapt1YYNItkgPatVUilqS4sxBcmOdSc8Guo1C8jGihw3Jxisq70qOZcq2GqWWzaS0SOSQbVGMAVnWlTnOAkmky4mt262YLK43LgHFeb+KLGOS4ivUt48MxEjbBznua7GOFXtFtTOCVORxVl9MgmgMUyhkIwQay9lTdNxktWb0q06VRTg7HGWelWd5axSR2luWGN37sVx/iaCK31u8SONY1EqgKq4A+QHpXqVt4f/s643WU2ISeY25/KvPPGVuBqeqSd0uoh+cdc0aUIygmuv6M9WGLqTpVmpP4V1f8APEzLaGPyhmNSfcU5o4hk+Wn/AHyKLU/uRRM21Sx6KM16io0/5V9x5H1mt/O/vZQlCyzNGqhVUckDvUltbKP9rno3NYsFxJ9ofDfMWJrpLNSyKzdfSpVOm/sr7h/Wa387+9luO2g4/dJ+Kin6Z9mTV7mGW3idAcjcgOKfHy4pNOCz6vcKuNzSYAz2xXJmlOCVNJLVv8j08txFXkrSlN6JdX/Mh80Vhc3txGtkFRlCCRRgKfasS+8H3lnZtdrfqwQM4UDk13t9pyWlr5kbLsH3gfWqenyxE7LiLMbnAB7CsYUaTXvRT+R4laMZzc2tX3PHBdTpbtmRuGJAJwavWusyO8UTsFQda7nxZ4OgvzCtmYoD8xPvzXlF5b3FpPNbsp/dMVLY610U6ksP8Gi8gjGD2Vj0m38T2KRrH5uWHFJJ4t08ZUyHI9q8yti8cgkY4AqWAieZ2cZHU03iZTK5EjvpfFmnkcSH8q5XXdahurlniJIMZWsUxs5bYpIFVXU4JNc8qs2rMpRSK1FFFcxQUUUUAFFFFAHv/wCzL/zNP/bp/wC1q+gK+f8A9mX/AJmn/t0/9rV9AUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8wftHf8lD0/wD7BUf/AKNlrx+vYP2jv+Sh6f8A9gqP/wBGy14/QAVr+HGxqYHqprIrS0JturRe+RVQ+JEy2Z2wpaBRXWcwUUtIeBQAGoLh2VOKiluG3YU8Uu/zoiD1FTc0UbashSVlYHJqS5O4qfaoKnb5oAfSpRo1rcZB/rRTZf8AWGnQ/wCtFEo/eGjoHUfbjCMaZD/x8If9qpF+W3PvUcA/fp9azrfw36M6cD/vUP8AEvzLaOI0JPqaWO4WR9o61WuDhttJbD9+Pof5VNV/uGvL9DfBQX9owf8AfX5l6jFICAACeaO1dZ44jnCk+grkZWDXLsT3JrqLt9lrI3tXKYy7GomzSmup09tM8ljGryFm8psewqvGDJBtwSc4q/Yac0ttFMrhU2CIA+poghC9SMhucVxqSU5P0PWqRcsNRS6uX6F9VVYI1JwMdKAqsUUD5c9aYGywJyfrUwuOVAjHymo+s0u5p/ZOM0vD8V/mZurW4xhQM9q5m5XZL8y11mot5zArhCPWsC9s5VQzh0YA4wM5q44im3ZMzqZXi4xc3DRb6rpv1JtJmjjt5WkyEDrnNb89zBHbiRSHB6Ec1g6fma0kimjAG5R065qeS1ltCUVW8tuqnp9RTp/FL1/RE4p2pUf8P/t8jKvpPOuGc9+lbf2YNpMKDOcZ4Gawpk23JQ9c1ttemFVhRsEDGa1avJHHHSLZUiiZLe53DHy9/rUT/wDINT/rqf5VKr74rok5Owc5681C/wDyDE/66n+VYS/iP/EvyPYh/uy/69y/9LKtWIPuVWNWoP8AV10s8QzNcHMR+tY9betr+5jPvWJWUtykFewfs4/8lD1D/sFSf+jYq8fr2D9nH/koeof9gqT/ANGxVIH0/RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFfMn7Rf8AyUGw/wCwVH/6Nlr6br5k/aL/AOSg2H/YKj/9Gy0AeRd6KKKAEpcUUGgApw6dKbUsa7nUDqTiqQmHlPt3Y4pBW2bci1gQYG88k9qx5RtkIPWmXOnyoZTaU9RSUzIcOlGMUoHFBFAAOtHejvS9DQAh54rY8P8Ahu98RXqwWy7Ygf3k7A7UH9T7VqeDPD1tqc732ohvsMH8A6ysP4fpyM/UV1+teJoNKs/Is0jt0TICxqAB9AKTb2R1UcPzLnm7Iqv/AGf4Rfy9MfdiEpLK7clyep9Og4rjNT8QSXEztE5dyeZW/oKguruW90mWWUkk3IwPQbTVfS7EX1zHESRuYD8O9ZUklzep34upKfsqdJbxX5s9Btn8y1icgrujBwfpXOavcvPclRu2LwK6GXEFqI0OPl2qGPauZuVAY4Gfct/9euijHS59JjXJUlD7yoj7HB6Ec4q3PO02n3RYgjyyemKqshPT+lTMD/Zl0MciM9KjEwi4Xa1VvzR52HnUi3FNpNO/n7rOcA60pGBVvTYo5LgtPnylGTgdafeiEzqkcZQfTFUfLcnu8xR70AEnApSMEg9qkgwJQxHA5NIhK7GGNl5IOPXHFNxg1ekB+zRgt8p54FQAqU6DI4OaC5QsQ9qTvTirY3Y4pHUqwyOoyKZnsxKTOTRQBk0hiHofpUPerDDA7dKgqWNCUtFFSUFFJS0Aeu/s6/8AJQb/AP7BUn/o2KvpuvmT9nX/AJKBf/8AYKk/9GxV9N0AFfIHxt/5K9rv/bv/AOk8dfX9fIHxt/5K9rv/AG7/APpPHQB5/RRRQAUUUUAFdT8O0Z/G1gqvsJY/N6cVy1anh6+k03W7e6i+/GSRQwPqS0vhasYbicbieAx5ak1AXH2qOWQRpbv8hyvIHrXlmjeIH1PW7W7uJcSB9pTsBXsFpd208LJcSxuD/Ce1JNtAc42uW2k6gbY3z3gT5gOPlPpXUWGprqdos6ptB4xXlet6Zp+neJppLWYsJTkoeimu28HXXm2EkZBXa3BPQ16dF03Su/iM7u9jp8ntR+8pyqSARS5YHAFFyyHy3J681PcJiAEnJFEaM0nNW5o8xYIrKcveQ7GMbVF/egYNNSRmOC1a4tVMZGO1UDbbW4FaUaiadxNERSTs1eXeLQ32jWNxzi6hz/37r1fy3ry3xZGxm1w4+7dQk/8AfupqP95T9f0Z24VfuK/+Ff8ApcTEtG/cgVFftiEqOpp1of3Ypl0u+QCvR6HnGXFYlZ1lx1NdDbptQUy9tPslpYluGlBbHtU8Q+QClBK+gMWWY28DzYzsGcVhWN1DNc5uJmhZ1LgrxW/Np1zqltJZ2ikyyDaMVxGo6ZPFNFaSqwmj+Rh3zmvOzW/taa7f5Hbg3ehX9F/6Uj0PRliu9K+03F9JJbCQhVY5/E1zXiTxVFavFBp9xjYx3MByBXUvZ6V4f8O2lvPMIklTc6OeSawdUt9GvoEItkyOBIO4rkU5QOdJHPDUdX1KQyWs0smF6+grstMg09tDiiuoY5peTLI2MrXnuoaq1iws7OQRwoSNycFvrWnodyFukD3IZXTcwzWsK6jrLUqpDmXYSbwJf3Nw8sBUW5VmjBPUA1CnhuCPTJ45HddRjbHlgcEetdla6urFoA7BVXj/AOtXO+KJJkxe27bTjaxB5rOdSLemhnG+xxcebdpFfKnpzUaxRyRSZJDAE1NLdPLJ5tygYv6U2WREQmBDgqRg1UZX0Zbi0Y1FFFYiCiiigAooooA9/wD2Zf8Amaf+3T/2tX0BXz/+zL/zNP8A26f+1q+gKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+YP2jv+Sh6f/wBgqP8A9Gy14/XsH7R3/JQ9P/7BUf8A6Nlrx+gAq7pLbdUgP+1VKrFi22+hP+2Ka3E9j0EUtNRgw4p3auw5g7UEZFAwehpcUAZ80RVs9qbGdjVoOgdSDVCSMo2KhqxtF3VgkXDZ9aegzAw9KAN8fuKdD3X1FJbjexHF/rBSSffNOQYk/Ghx+8NA+ornESrS2yZkDelNl5IHoKmi+RUHdjWdX4JejOjBf7xT/wAS/MrS8ysfeprZdp3HqQcUwJmViegNOjfdOPTB/lUVf4D9Dpwf/Ixgv76/MgLsTnNXYGLRgmqkcW85PC1cRowAqmuiJ5Uypqr7LMj14rEsovMvoY8FgzDIFaWtv8sa596h0KTbrMD4ztOf0qZvUcFoX47mRbqeBXxGFYgehFa2mxq+kOW2mRec96w1bdfTv6o5rU0hw+6IHBxke9cjjzSmvJfqerJx+r0edXV5XXloSj3ANNJ/2c/jWim0MVaNRj2oaa38wKQE4PJWuJYGVkrr7jpq4jBVJ896q/7f/wCAZU29oyERt3ruxWdepNHpbh2YsZAeWzgUur3UvmlIzj3XistPtDkK0jNnszE1vTws4tare+xazDC06c401Ubkmvendaq21jY0Nz5TGQBtpHFWdW1DaBtT5cdD2o06ARQnfERuHIHes3UXQqRk47ZrppfFL1/RHBjP4VH/AA/+3yKUEnn3yvIBjNTTSb2PHfrUOnzi3uhIY1kUDlW6GrV5NDPKWih8oH+EHiuhI4L6CW//AB73P+4P501v+Qan/XU/yp1v/wAe9z/uD+dNf/kGJ/11P8q5H/Ef+Jfke3D/AHZf9e5f+llQ1bh/1YqoauRf6sV1M8Qo6yM2in0asCui1YZsW9iK52spblBXsH7OP/JQ9Q/7BUn/AKNirx+vYP2cf+Sh6h/2CpP/AEbFUgfT9FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV8yftFf8AJQbD/sFR/wDo2WvpuvmT9ov/AJKDYf8AYKj/APRstAHkPWl6UUUAFFFFABVqK1uJMGOGR8AH5VJqrXoFtohgt7GeJmdnt1dvlxjIBpoqMeYxLhmjs4y6OOAcEYOawHO5ya9UvdAkVCjyxuskBkjYDhyByv1rzK8tzbXDRkfQ+tNFVW7FbvS4FGKG4HrVGADjpS0me9G72oAXvRikzzR3oA1tL8QXukRNFCytExDbHyQD6j06D8hVfVNTk1S78512AABVU8DAxVE0maC/aS5eW+hor/yAX/6+B/6DXTeFrE29kbqRSHl+7n+7/wDXrE0mzN/ZJbjo10Nx9Bt5rulVVUBQAF4AHasIauS8z63LcJzyp15bKKS9bsq33+p3HnnvWBJCJC2K6iRFkQowyp61mXFtFEjKhUf8CGa66fw2PUxNH2lr7GC6qvHH51aWB0tJVx8zxEgH0qrOpEprVuJBKFmiUY+zKhwejKACP0B/Gs8Q/wB3935nj0v43L5P8mc7pcnltICue5HtT5Lf7XqcMK/Kz4G4jpT7uP7NOsiNsLjcV69RnFLpjQtrdoLkboy3APTPb9cUPQ+cTt+7YusaMumt8j+YDyT3FZjweVFHKpDLJnPsa6PWR5ccyODuZyw3Dpx2/wA9q51zvswo6oelEdtSMTHkleOg0SK8PlFmGOn0qEg9O1AGTmg9MUIxcmyaZSFUluCOgpkz7ljHcLj9aYzZjAyeOopdhK7jwDQErNkZrW0nT1kxPcKfKOQPT8ayzGTggVrRanCmnJasjAg/Me3Wpl2Rvh+TmvMqanbxRyloTlP0/Csyr95Oh3KmADzxWfSFVtzOwtHeiikQApaSigD139nX/koN/wD9gqT/ANGxV9N18yfs6/8AJQb/AP7BUn/o2KvpugAr5A+Nv/JXtd/7d/8A0njr6/r5A+Nv/JXtd/7d/wD0njoA8/ooooAKKKKACrWnruvY1559Kq1qeHrWW81u3ggQtIx4AoY1a+p1/hywu21FEhjZpCwxkdPevURo9xpA3SSSSNK2Xk9Kh0a1ttJlgkcN54UKxPJFaviXX7M6Wbbzx5ki/Lg8iou3oVJR6HOslit5KWUOxbqTkk1peGriGbWPKMjrGpIWPsDXJXM6QaNJKxxchcg+tWPBGpSQubi6T5GYFpW7/Sqg0nqONK6vc9sDBUwBgUwMAc1FFKk8Ssh6jNO6V6KSsZlmJgXqaVsKKqRNzU0rfKKwn8YycH5fwqk7YY1ZRvl/CqUpG481VFbgwMleX+JpxHca8hHElzCP/IdelE14/wCM5pk13UAhBha4TzF/7ZjmqqJKpT9f0Z14b+BX/wAK/wDS4mfa8RipIoTcXkcYONzAZNQWxwmM8Vf0eNrvWIIwMjeCa9Rr3WeaW/FszpfWtmY9qW0YVW/vVXtyGRea0fHXOqQDbwI8A1hwsQoANThFeKY5mm19qmivDqWnoHSNvnTH3hVmxtYPFjahqkW2O8M4kjjPXoOPzFXdEuY57BoJV3EZLj29a5K6nvND1ma+0z7sMmCg6EdanFUlVqwstbv8mb4FyVOun2X/AKUjO8f3+o31xBbXVoIXhBGf71c1Bql0SY22gYA4OOlext/Y/wAS9ICoFh1JB8y9CDXj/iHwzqHhrUmhuY225+V+xFeZXw7g7rYzUk3bqQWUUN1cyrdEmX/lmgH3jW7b6ZbWNzE10Akmc+U528VylrcTJfRywE+apBWtzUZfPMkl3O8sxGQW6rXLZ3G3ob+uG4tpIb+3hCwhQCqnOaSOAeJNIum+0LCyDOw85rN8O69cNa3dtc5nTy9se4Z21ueH4o7ScR3ChUuUwo9atU1KSRLdtTO8KeHLaeV3v3jkjjU4Td3q14i0uwttBklt7eNZsAHYc44onsP+Eb1Oa4m4t5jj5emKNUaCTTZS1yMGMlCq/eGOM12KCjHYlycnueWUUHrRXAahRRRQAUUUUAe//sy/8zT/ANun/tavoCvn/wDZl/5mn/t0/wDa1fQFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfMH7R3/JQ9P/AOwVH/6Nlrx+vYP2jv8Akoen/wDYKj/9Gy14/QAU+E7Z0PowplKpwwPvQB3ds53j0NXWOFNZ1m3yI3+yKsicuCMV1RZlKOokMh8wr2NXBVG3GZs1dLBeSacRTWo7FQzRB196f9oj9aeGVxwad0ydUZ6fI+DUqxkTZA4qaS33MCPxqUAKoFSoluXYhFsN24mkaKLOSeafLJtXiqZy2TQ7IEmyYwhmyrU05+0IMcA1GpKnIq4qhgr45rKrrTl6HVg9MVTv/MvzKcpxlR68062jPmbu2DS+UTIS3AzT1kBkCL0wair/AAH6fodWDf8Awowt/OvzIJnGNq9BUcQYyDFOWItyeB61KsscXAGfetzzHorIydYk3XSr/dFLoMbtqW5ELbVYnHYY61WvD511JICMZxW34WguhHfXUMe6JYikhPbNSxxRWjUi6n9PLerGlShLqMnkEYq3bPCltKGIJ4JB645zWfZYN2u04BPFYR/iS+R6NbTC0vWX6HUFQQTzntUEuA+4g5C9DVuNS0YPQmqtxExkJLHpVHIYd7EpYttACjNULCE3FzwcNmruqyYURqfmY1NotuS2WU5X2quhNtTUGbdYy/HytzXKajNulOOma6fVpdkHHGBiuOmbfIfSs6O8vX9EdmNf7uiv7v8A7dIkhXaufWpDSR/cFKa6Dzye3/497n/cH86a/wDyDE/66n+VOt/+Pe5/3B/Omv8A8gxP+up/lXJL+I/8S/I9yH+7L/r3L/0sq1cj+4KpGrsf3BXUzxEV9SG6wk9hXM11V6ubOUf7NcrWc9ygr2D9nH/koeof9gqT/wBGxV4/XsH7OP8AyUPUP+wVJ/6NiqAPp+iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK+ZP2i/+Sg2H/YKj/wDRstfTdfMn7RX/ACUGw/7BUf8A6NloA8iooooAOtJS0UAFei+G9etPs1rpss7vIwCxbhxGcdM+hPGPWvOatQyNE8cicMpBB9xTSKjPlPXJLhxatbn5oidy56qfUVxHiPT/APRhcoOU6/SuukmE9hFcrwsqhh+IzVTyEu45kkQtHnBH9KEzolBNWPMweRT5ccgVa1az+xX8kaqBGeUGc4FVHC/w5zgZz61RxtW0GHjikpTRTEA607ikHUUuKAENJTqFwQRg5JGMUAjsfBFrLcx3AiTcyktjPbgf1rpGGCQwwR1FWNL0q58NaZYn7Nvd7U+dhwpV2bdj8BgViX7yXF75u5o5Bxwa56dRQlK6e/Zn3OErTp4elFWasvtRT692aXTJ/nVO+GAW56VYiiukhzJCzHqDuA4+lQIW1CZraCLMqgsVzjgda2jiKfn9z/yPQVWnON3JL/t6P+Zzt0PmzUcEr28lzIOUMO7b2LA4zWpeac6NtkyhB9M1SngSG0lDSAllKLxjr/8AqqKtWM4Wje+nR9/Q8idHlqOpzRsr/aj2fmUY5I7iFp5mJfJO4jp/niqEdwPPyVUIeMHt/wDXrRtNPe9ElrG4jH3mYjPHt+OKoNai31NIXdWUMCW6AitLnycoyVpGz5Z1Gy8whvMRSSzZy4HFYt4i28pjQcAAnJ5ziuqfbHI+1mGQMNFgj8fw/pWHrVmEPnISzH7xJHOKSZdVqcfMxuCTR/FzT4IJriTbDGzt3AHQUSxOkjbQ2F4O4YOao5uV2uRHrinuxwFHSmj5mAzzmp3TY/y9uxobBIrFjjGeKQuc805+Rz09qiPSkMn3K0RBzuA4NVakH3fwqOlII6C0UUVJQUtJS0Aeufs6/wDJQb//ALBUn/o2KvpuvmT9nX/koN//ANgqT/0bFX03QAV8gfG3/kr2u/8Abv8A+k8dfX9fIHxt/wCSva7/ANu//pPHQB5/RRRQAUUUUAFbnhDUF0vxLaXbglUJyPwrDq/o0iRarC7ruQHlfUUnsB7TaeLba+uLiKCYLOASoZeBWy2kxXFnb+bbI0sq/wCtUdM146JUkmkuIz5QUEBU64rbHiW9gWxSyv5XhTgox5zUrQbZ23iTQrqHSRIhWYIMNGBXK6Bq/wDxMobUgGCN/wDVkYCmpb3xZJYar5cE8k0U8Q8zPOG9qZpWiCa+EsWVkkYPsbuM1cU3sNSSep7naEeWr/L90fdqyXBqhYwyxWqI+Og4HarOGr0lZq5JOhFEsnA5qNA1NkDGuWT98osrJ8n4VRkl+Y1aRT5dZ8qMHNXQauxMUy15j4ntftV5rEgPKTx/+gV6MytXBasStzrfp50Wf++K2q6Vab8/0Z14b+BX/wAK/wDS4nMWluyx7Wb6V1fhe38q6aYDAC4zXOwuJJQBzzXa2Km3s0QJg4ya68TU5aTS6nAo3kY3jG6WSeCLq6gmsOIYC+/FWfEchk1gDGCFAqKAZG01pg42hFEzep0/g+2/4mN/PJyhRUCn9azbiyK6hqe2PNutxtPtwK2fDLFIp29Xx+QqzowSW/1pJFBVrkZB/wB0V5/1l0sXGS7v8mejhF/s1b0X/pSPNr6yvNA1FdU0t2R1OSF713+m6no/xJ0Y2V8ixagq4IPBz6io9X0j7NMPl3WzdP8AZridW0m50u9XU9Ldo5UOfl717dajDER9pT3PIqtKVtmc/wCJvB194U1cblZod2UkHSs+W8acsZUySOtezeGfEOm+PLVtN1pFS9RdoDcbvcVxXjDwBdaBdtJErSWbHhwPu/WvBrYe0tDaE76S3Mfw3Zxtp9zKBht2K6M2kiWtiWhUSRt8reozWJpjJp8TFydjdR610MeonU7eRshI4FBAYgVh7O0k7jfYvSaUdXid55UaHaSAf4TVC7tVj0u+t40j2RQsyflT4bxbaCaNCdz89eMVWv8A7R/ZFzJGV2PAyuCcH613NTtK60M9Lqx42epooPWivLOgKKKKACiiigD3/wDZl/5mn/t0/wDa1fQFfP8A+zL/AMzT/wBun/tavoCgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPmD9o7/koen/9gqP/ANGy14/XsH7R3/JQ9P8A+wVH/wCjZa8foAKKKKAO1sDus0b/AGRU8f8AEfaq2lHOmRn/AGcVbQYRjW62JHW/DE0EtK+BRHwjVLaj5iafkJ6agLXjrzTBuher1VrkdPWqaSWgk76MmEgMe6qryM7YWlJIhAqW2Qbd1K7YWS1K8ikIAalhjDRH3p1yvANPtx+7FCWoN+6VBHiXbVwY24HamOm2QvUcDEs1RV0hL0Z04PXE03/eX5oS4RieKbFGEbJPOOlTzMwX5etQRI2/c3oazrfwX6fodOB/5GEP8a/MgeQtwOBUbRtsJxwBVjMcY4GTUdzc/wCiycY4rf1POv2OeBPmMT0JrutGDWHgqd9mGu34PsK4NPmODXpmr3EMPgvSbGLBYIZHYfyqGXE5OLmZlPXyzSQxjf6MOlFuxa+dT/cNWIkHmfQ1jH+JL5HbW/3Wl6y/Q3dOlLoUbqOhp9wQiSMzYAqrbDy3GSRnpVXX7oxw+SGJZ/5VXU5b6GG5+2XhbJ64FdPp4WGMFs5xg4rD0i08yYEDBU1v3jtBanBVc96pkruZGsXKvDOVzhSBXNAZGe9azkzwXKlskunP41RlTy3KgYxU0vin6/ojqxmtOj/h/wDb5Cx/cFOPSmx/cpTWxwli3/497n/cH86Y/wDyDE/66n+VPt/+Pe5/3B/Omv8A8g1P+up/lXI/jf8AiX5HuQ/3Zf8AXuX/AKWU+pq+gwoFUR1FX1+6K6meIhk43W8g/wBk1yJ6muxkGY2HtXIOMOw96zmUNr2D9nH/AJKHqH/YKk/9GxV4/XsH7OP/ACUPUP8AsFSf+jYqgD6fooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvmT9ov/koNh/2Co//AEbLX03XzJ+0X/yUGw/7BUf/AKNloA8iooooAKKKKAEqwDhRx2qCuv1PSNLi0yxkt3eOd7ZJJf4hyB1yeOaaLhTc02uhseF7s6h4eW1wWktZNvHdTyP61vSWoskELLiR/mYZ5zXF6DJFpdpNcLcEzyjaFT7oA7nvmtCy1y4LN5sZljI4fklf/rUHVGLUVfc5/wAVujavhcbggDY9axH659QK0dVR5Xa8kzmSQgfgBWaTmqRxT+JiY4pMYpaOtMgKUdKSjNADq2fCVmt94q0+GQZTzd7D1Cgtj9Kxa6z4dRmTxfCB2ikP/jtJmlJXmkei388lzFJcTHaiZwPeuIuZj5qAZ3zSBBjqMnr+tdT4kl8mEW6twzZNc5ptvcS37XLQmSGBw4TO35RxkH6ihNJXPWxFWNOLk+h1l2I1G1T8oG2uPvY7rTdTS/tDko2cevqPpXXm7guLIOkbBWH3dvSsO5eEArhsehrKLPKwmLhKl7Kq7NbMvalCl5ZRXsA/dzIHA9M9q5a6hyWjYDPb610ukXMRt2sdysOWRc+vUf1/OqOq2DJtnj5U8GtIs9SUeeF0ZGjT21tcT/a38oFAPmOO/Sr0i6FKwYLCzHgY6n8q5zWbeVpQQPlYdfcVm2W6C84xuQ55ocdTleIcfc5TslfSHIggjdXY43cgL2rn9VtJY3IEm4dAD1xV9ZwE3yr83aqN3cCRpHWE9QCc8g+uPSkiZTjJaodoLzaa0zyhRFIuCjdc9jVPVCJi0q4XnkDuKoveTcjfgfSoGkkfO5jVJa3MnUjyciHRL5k6R/3mA/OrV/H9nvJYgeF4GadZ2rh7e5OPKV1JPtuxVzXbZlumuQylZD+VJvUSgvZtmKeg5ptKw44ptMwDsaj7VLjIP0qKkykLnNFJS1IwopKdQB65+zr/AMlBv/8AsFSf+jYq+m6+ZP2df+Sg3/8A2CpP/RsVfTdABXyB8bf+Sva7/wBu/wD6Tx19f18gfG3/AJK9rv8A27/+k8dAHn9FFFABRRRQAVPZvsuUb0qCr2kWjX2pRW6MoZs4JoAui5Ee7y+4wabY3jw3QOMq3BGO1dLo/hzy5Zbi9XaIuQjjhqvXY0Gw1FZ5bbZJgMioPl/EUrIEtdDnrfUJJLj7LFAGy2VJHP513ujXdxZRrM8Q82T5Tv8A4R7VzV1qdhLqkF7BEu2PBcKm3Nb82rW16YzAUC4Bx713YSlFrz/QyqqSa0Pa9MbzbGJi247RlvWrmwVneHj5mh2rg8GMVqVU9Lo2Q0JUbLzUuaikOK43uMchAGKrzBQc04NTZVypIq6ejArNt9K801+/ig1PW7QxOzyvG6sMYUBBnP516MzYPNeQeMrj7P4j1BwcElVH/fIpYuU48ns3rf8ARnfgU3Sr2inaGzul8Ue2pDb3QikRvKYhfmOMdK9NtdlxZQzqMCSNXAPuM141Yag0kzJKqMGRhn3wa9f0jbHotkqjA8hD+YzSdatOfLUd9P66GEkp0vaKmo620bfTzOJ8SKP7fcDsBUFsdzZ9OtP1x92vzMem7FQxfu52HZhxXv4fSmn5HBLc7XwwA2ipKRzIzN+tS6EAdS1j/r5H/oIqTQI/J0O0TH8GaNBH/Ex1nj/l5H/oIr56tL97F+b/ACZ6eF/3et6L/wBKRtTxLMmxgGUjkGuW1PTGsycrvt26H+7XXDmnPDHNE0cihlYYINejhMZKjLyPPqU1NHiGuaNPaXa3+mMyTIdwKcV3vgrx7aeIrf8AsXX1RbvG0F+j/wD16XUNJOm3bFhvtZPuk/w+1cX4k8Msz/brAlJVO4Fa9avShiI+0p/1/wAE5LOPuyNvx34In0yJrqwQyWmdxA6rXBrMwhWLJBau/wDBnxF+0hNC8RYz9xZX/i9jTPG3gqO0mXUtL+aJvmMa849xXjTotyT6msZ20ZgWMLXDxx5+Yjk12mp2lpqXhm4iXasltat8wHLcVxmjl0nV5f3a7Tye9bc4nudJvbq0YNFFA6uc9eKjE1XdRjsXCPU8KPU0lKeppK4jUKKKKACiiigD3/8AZl/5mn/t0/8Aa1fQFfP/AOzL/wAzT/26f+1q+gKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+YP2jv+Sh6f8A9gqP/wBGy14/XsH7R3/JQ9P/AOwVH/6Nlrx+gAooooA7DRjnSI/yrQUYjNU/DWH0oA9mNbHkrjGK6Iq6M3KxUUfujU9twCak8hduO1OWIIuBTsDkmhjTqOKgJaV6n+zKTnNSpEqdBRqwulsVpl2qoqa3GIxRLCzkYqSNCqAUdQb0GTrlDTbf/V4qZhkGoIOGZaOolsOm+4ar24+Y1YnHyHFQwAjdkVnW+B+jOrA/7xT/AMS/NCzOV4A5qKPe0mT0wac0hLHC0iFy/IOMGs6v8F+n6HVgVbMIf41+ZD5OepFVtSjEVkTnrVryZD2NU9ZJW0RCeSa3seY2ZNmkjzoI03sWGBjrXXX9xNcQiO4jMBiUARkdMms/wPZm68QW48xUCtuyfat3xeN/iCdPM3nC5bGKlmiWhztqAb2Q9wpBq/AgYgnpnms+wXN5If8AZIrVt40MZ+bBBxisF/El8juq/wC60vWX6FnoFOcYPBrBvLhrvUSWO4L8oq/fz+VZHBw4OKpafbG4IBHzdc1qjjfY2NOtHihLoPmb1qpqsixKTJIS5/hrQnlktYMIT0wK5W6d5psMcsTTQpO2hNYZkgmP/TRD+tRXqlLhgeuauwoILWQL1BWq+oL++z1OATWdP4p+v6I68X/Co/4f/b5FVfu0poX7tJW5wli3/wCPe5/3B/Omv/yDE/66n+VOt/8Aj3uf9wfzprf8gxP+up/lXI/jf+Jfke3D/dl/17l/6WVV+8KvjpVBR84+taA6V1M8RAfumuRnGJ5B/tGuvPSuTvBtu5R/tGs5lEFewfs4/wDJQ9Q/7BUn/o2KvH69g/Zx/wCSh6h/2CpP/RsVQB9P0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXzJ+0X/yUGw/7BUf/AKNlr6br5k/aL/5KDYf9gqP/ANGy0AeQ0ClopgFFFFIAq01zPKio0jMAoUZ9OwqrUiHpTQXaLKs8aFVcgHtmptOupY7uNAxaORgroTwc/wBfeqrOcdaIJTFOkiqGKnOD3pgpO5q6tMWtI4VGI1lZwPqMf0rFrotXeO40S3uS+6UtsYAABPQfpXOn+dNDqLUOKKQdKWmZhRRSmgAr0z4S6dl9S1Rx9xBCh9zyf5D868zHNe5eBbMaZ8P4JXG1rlmnbI7HgfoBUz2OjDRvUMXxMc3u3vUmmJHPawwRtmSRQjEdvnfP5AVmavd+ZcTTn+Hp/SsbRvEjaZMsnlNNzggdRy3+I/Kk0+U6MwTlRaXV/keqx2UaxqiqAoGAKoX+kwIN/AJ7UzRvFFhq0DSQyFZI13PE/DAf1qY3wmgMsijac5PWs7HmYPCOtJpuyRzU19o9rOFluY0lU5G9DkfTitiOOG/07zIckKc8qRuHqM1wWo3UU2qhmQNtc4yOldbZ+I4I/sqzuqqf3chPoeAfwNXy2PVwtFUZO0tDEvoliunBGUIPHsa5aeLbrMqj5AXx0zxXfa7aeXK2B3yK4/ULYveJIGC5XnI64/8A1iqTuicVT15kaM2jTvEsvnOmVyFKj/63PtVE6Q5Vme/VVc4Py5Bx261taRPLc7LefcZhym0Ydx6dQB06ntVTVll026JXiO7Xd8xBHtnqOPapMGkc9quiNZAPG5kXGT8vSskglN3bpXY3N5YzaYEmuER0BIVcOxOeATxnjFcnIsRZirMcngY4qkzKSS2Oo0f7MfDE0cwzJIlwEPoVCEf1rNF9E1gI7pWkcdApAz7k1mx3k0UCRIRtTeB/wLg1AXPQ5osHtGthJMFjtGB2FR07JNJTMx+3anOMkZ4qCp948sqVz6e1QUmONwo7UUVJQUUdKXtQB65+zr/yUG//AOwVJ/6Nir6br5k/Z1/5KDf/APYKk/8ARsVfTdABXyB8bf8Akr2u/wDbv/6Tx19f18gfG3/kr2u/9u//AKTx0Aef0UUUAFFFFABWz4WKjxDbFlLYJIA+lY1X9HvHsNTiuIwCynjNNOzuFr6Hpn9sWN9C8cQbzSxDh+q4rj/EbSfaY/mLMq461qw6ZfSqb+C0bY5yxHTNVn07F48ctwju4x8pztBpK1yleJziSyzTqqhiW/gXqa2LHc+qwWU6NbszBST1FU7GRtJ8QrJGpkWF+nqK9Bub7TNR0yW7tdGV5mYEyBvmQ561cXqHPY9a8HI9toqWj3Cz+UcK69xXQmuN8A74LOSC4YLLwwTPY12RrpktCU76jSajfBFSGmEEiuZ7lEOz0NKBhcGlKMO1OUVSAzriMhj6V4r48/5D15/10X/0EV7xPAWHFeM+K7FLrX9bheREdYwyb2AywC8D361GJkv3d/5v0Z62VRcoYhRV/cf/AKVE4e04mQj1r23Syf7Iss/88E/9BFeOaXE5DRSR7WGW3twMAZwK9rs4WGjaewHBto//AEEUlJSq6djCdOcMH7ya97r6HnmuEHV7j130kTqYQ7DkdPrS6/E8etT9iTkVBpqyXN9bWzDKtMpJ/GvejPlpadjyWtT1bT7cQ2FuhHIQZrO0PjVNb/6+h/6CK3xEQoAHQVi6DCW1TXPa6H/oIr56p8cfX9D1MN/Arei/9KRsoMmpcYoEW2lKmtTiI5Yo7iJopVDIwwQa5O+099NmKuC9s/3W9PY11+00y5tlvLdoHUFW6134TEypPyInBTR4z4n8PwSN9qgX5hz8tXvB3xAWwnTStZ+e3YbUkb+H2Nb+s6VNp0gjkBa3Y/K/9DXCeIvDiyqZrdcMOeK9epQ9vH2tPf8AP/gnN8PuyO08S+HI762a/wBNUBc5IQ5zXnsusLYaddWySSROysrr0DVs+DfHH9gypYX4aSBvlO7+GtPxX4Rtdfs7zWdNxs8vzF28AADmvIq0vaJtaNGibi7dDxE9aKDwaK802CiiigAooooA9/8A2Zf+Zp/7dP8A2tX0BXz/APsy/wDM0/8Abp/7Wr6AoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD5g/aO/5KHp//YKj/wDRsteP17B+0d/yUPT/APsFR/8Ao2WvH6ACiiigDsPCbZsJF9HroQK5nwi37q4X3BrpxXRD4TKW4UtGKXFUKwYopaKAsFFLRQFhpqBRiY1YxUYH7wmkNDsZprKApwKfTX+6air/AA5ejOjBf71T/wAS/MYEGOlDDApw6U1ulRV/gP0/Q6MF/wAjGH+NfmNNYOvv80a/jW8a5vW2DXqrnoK6JbHmxWpa8O2k1yZvJgkZxjEiD7nPWr1756X7+eWZ1bBLHk10nw8+z6dp17qc8hVVIUAd/asDXr6O+1ee4iXYjucCsmbpaFCxAN27joQa0cANtJxkZBrP01P3jc5GK03hDRYU5YDGKwX8SXyO6r/ulL1l+hjajOJJ0iHOOWrc0O0LRNKflGOM1EvgjWzCL1YRIG5298Vba+W0sDbTwvBMvZhitE09jl5JR1aM/VLzbmMHgGs2whM9wZWB2r0qvcO80mf7xwK2o9ttYqi/fxV7Iy3YkMaykqOTuBas7V2H25yB8p4FbWnQBGUsQS+STWHq7A6i4X7oOBWNL4p+v6I7sZ/Co/4f/bpFRT1paReppa6DgLFv/wAe9z/uD+dNf/kGJ/11P8qdb/8AHvc/7g/nTX/5Bif9dT/KuR/G/wDEvyPbh/uy/wCvcv8A0sqx/fH1rQFUI/8AWrWgK6meIgPSuX1Jdt/KPfNdOeK5zWP+Qgx9QKiWxRQr2D9nH/koeof9gqT/ANGxV4/XsH7OP/JQ9Q/7BUn/AKNirMD6fooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvmT9or/koNh/2Co//RstfTdfMn7RX/JQbD/sFR/+jZaAPIqKKKACkopaACpB0FRVKo6VSExw6E/lToB+9H0NM71JD/rh9DQC3GtI5UqWO044zxTDkVJGmX5Gae8bO6qCM5wOgp3Dcgoqx9jnBxs/UVAVIOCMGi4OLW4lL2pCcGjPagVia1t5Lu6htoV3SSuEUe5OBXvPiGSLSdEttNgPEUaxrjjoMV5p8N9L87W21OVf3NiNy5HBkPCj8OT+Arc8W61lpp927YMJ7sal6s9DCx5YuozldW1FHmlgR+IeSc9W/wDrVm6Ezf25bHJ+8Sffg1nSElsk5J5J9a0dCGdat/8AgR/8dNU3pY56lZ1Gr9CBJ5xqAEUhRzJtDLwRk4r03V7hNN0Py425C7ck8k+teWltl7v/ALsmf1rs/FlySYYQeoyRUs1w8uWMmc287GffnJJpLiaSXOMnAJwOwqtJMqN6n0qukz72YNyQQfpTMpVLaHpulX51vwvDM5BuLb91L6nHQ/iP1Brm9aBggW4T70bfmD/+qneBrjydaezJ/dXcBBB6bhyP6j8auavamSG5t++Dt+o6Ulozqu6lHzMm31y2sHjmjjWRiufLAx83vnNZlxeT399PdXDAyScnAwB9KzcEHBHNX0XanuetVY89ybKeKKXFBoJGZpucUGkoELmikoGScYoCwHvTKkKHBJqPrUspBSUtFAxKWgUfhSA9d/Z1/wCSg3//AGCpP/RsVfTdfMn7Ov8AyUG//wCwVJ/6Nir6boAK+QPjb/yV7Xf+3f8A9J46+v6+QPjb/wAle13/ALd//SeOgDz+iiigAooooAK2vCkFvceIrWO6UNCSdwP0rFq9pF0bPUopwobbng0nsB7Pa3UUNi9jCQkR3AZ7CuUsPDtwlxcHcrqSSr54qqfEcT7VAYMxxgit+3kuLLRzcywOiSHgsKlORF5HMXdqtsrz7f3jttOPStXSlgg0We3tbtDeTEN5e3k/7NXdSsTHpCX6oGhkG3JPes3wzNptrM7zYFwOhbtVJsfMnHzOp8E+G9YvfEKT3l5LDHEu47H5OOg+le2KMKBnOPWvKPBviCK68Ux2kJY5U5PavVsZ710wbcdQp7BuFAFOVVFPG2s5I0GbfalEftT8gUZpJAMKDHSuD1DSry313Vbp/DcOowTujRyvLGNoCgHAOT1/lXfkjFQTsoibPepqU1O13t/XU6cLi5YZyaSfMra37p9Gn07nnEY2OSPBdsSexnjI/lWv9v1p7ZBH4X2xKAFC3sYAA7YxV2farnBA5qzZXiRgxswwe1HsEtYzf4f5G88fGek6UX6uf/yZ5Z4te6TUkmu7H7GzrkKZQ+cd8is3R9ReDU0nijjlaMFtjttHAz1+lbnxaPly2zoxGYjgj/erznStUeyvIZyd+wjKtyGHoaKf1mo3GNWyWnTy8ia9b2LjKNGm1JX+33a/m8j3PSPEetanZmeHw8s6hyhZLtUGR2wea0vDlnfxz6pc39n9la6nEiR+Yr4G0DqKg+Htwlz4emmQBVe6kcKOwOCK6skVlSpyajKUm/u/yNcXXVJzoRpxjeybXN0s+smtyuY+ajKYq0QKjKiulI8wrlaTkc1Ky+gqFiemKtOwEN3DFeQNDMoZW9a4PVdLk02YxuC8Dfcf+hrvXzVe5t47y3aGZdykflXfg8bKlLyM501NHiXiHw6syGaFcN14qjoHjK78O6df6Tdq0ltcRMgz1UkV6FqmnS6bKY5Ruib7j1wPifRVkhkniXlVJOK9bFUI4im6tPe33nPCTi+WR5yeSTSUUV8kdQUUUUAFFFFAHv8A+zL/AMzT/wBun/tavoCvn/8AZl/5mn/t0/8Aa1fQFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfMH7R3/ACUPT/8AsFR/+jZa8fr2D9o7/koen/8AYKj/APRsteP0AFFFFAHTeEG/fXC+wNddiuN8IH/iYyL6pXbBDW8HoQ1qMxS1II6XyyKoViPFGKfsNGKYDcUYp22jbSCwzFNC4qQimmmIaRTX+6akIpj/AHTWdX+HL0Z04L/eaf8AiX5jOwprdKf2FNbpWdX+A/T9DowX/Ixh/jX5jGIUEnoK4+9k868d+vPFdNqU3k2bkdTwK5MjMmO+a3kzzkjpftl5ZeGoLa3iaM3DFmYc7xUEe5kHm8tjJPvXd2fhu51HR7KFoQHgh3IwP3ga46eM2l06uMhTgiobVjWzH2MOwu4FdD4ftHk1HzTZT3UaHJSJQT+tY1vtVGkVhtc9K9P8FRJb6cZGGGc5z7VyVYtNyTtc9XC14ShGlOmpct3u+voaUWtzIgUeHNSwBjHlr/jWfqYt9VjK3PhXUST38tcj9a6yK6RgMEVK0y461goyX2jqdWl1pL73/meA6toYsdXGIJ4IfvLHOMMPyqBovOnAzx6DrXSeM74T+IZVB+4AtY1oqI7OSDnpXVGNS1+b8EcM62GUmlRX3y/zHIyRSLtjOADxWFf2oa6y00aZJO1q6CWWJZ0ZsbQDmsDWeb1Hx8hHA9qilCfNK0uvl2R04uvQVOjekvh7v+aXmVfsqbj/AKVD+Zpfsqf8/UP5mqh+9Smt+Sf834I4frOH/wCfK++X+ZcCRwW848+NyygAKfeoX/5Bif8AXU/yquTVh/8AkGJ/11P8qiUOVpt3u/0OmniFWjOMY8qjBrr/ADJ9fUgh5lFX+1UIP9cKvVuzyUI3Suf1kYvAfVa6A9awtbH7+M+1TLYZl17B+zj/AMlD1D/sFSf+jYq8fr2D9nH/AJKHqH/YKk/9GxVmB9P0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXzJ+0X/wAlBsP+wVH/AOjZa+m6+ZP2iv8AkoNh/wBgqP8A9Gy0AeRUUUlAC0UUUAFetaX8OtEu9Js7mR7wSTQJI22VcZKgnHy+9eS19DaER/wj2m8/8usX/oAqZNo6MPCMm+ZHNt8MNFP3Z70f9tF/+JqP/hWGlodyXl0D7lf8K7j5cc4pMKanmZ1+xp9jh1+GGnp0v7rJ9Av+FOX4a2KEkXtwfqq12ZJB45oyT1o5mNUYLock3gCyK4FzOG7t6mqZ+G1lKpLXs3P+wOK7gjg80yJfk69zSuxunB7o8t1DwLBaXrwRXbttQPlgMnNQnwQign7cn3Q3f29vcfnXobrajXrhroxhVgXBkI9T61JLfaLa20k5ktG2jaFBXnvWC9tNtqXUurTlSkoqEGmk9VLtfX3jmre3n0Tw+NNiEXlu3mtMrfM5Yf4Yrn5LeDX7+LTxOytvGWAyOeP60t3faj4jvzaafEzbv7vHH17Cruk+HrvQvENp9pMbM+0/u2J25bgHj2NaTi4RvzO500K8K1SNH2UeXra/bpqSv8MFXGdTYD3h/wDsqtWHw5W0vUuF1InaCNph9QR13e9dwSx4PP404KSe9a8zPPdCn2OMsvh1b2sryS3izlmz80GMfTk0ur+CG1GcyjURHhdoHk5x/wCPV2+MDNRsFx1FLmZSguXl6Hmf/Csjn/kKj/wH/wDsqqX3gCSxWMrqCyGRtoHk4/8AZq9QJXP3hWbqyxzS2MbgFWnAI9eDUVZyUW09S6GEhOdlFN673tor9NThdN8P3VnfW11HJGWilQgEgElT06961dRVpp3n8kIGbHDhufwrsrfTrSS4UfZ1wG3Z57c1y/jJ4NIsiIVCvKxESA9/X6CphGq370vy/wAi6Nb2cffpQt11n/8AJHC3um2z3Ny8NwMxnc0YXOPxqkx9K0Y7G4sobgXKlJJIQ4Un5gCe/pmsuZtiH1rem3eSbvY48dGm406kIKPMndK/drq2VyRk/WkHOT2FM3ZbJ79acWO3CjA960PPI8HrSGlIOeaVELNjtTGCJu5JwKkwo6dKcSMYqNm9KQwc8EVXqYnKn6VFSYISloopDClFJS0Aeufs6j/i4N//ANgqT/0bFX03XzJ+zr/yUG//AOwVJ/6Nir6boAK+QPjb/wAle13/ALd//SeOvr+vkD42/wDJXtd/7d//AEnjoA8/ooooAKKKKACr2kIJNSiVlDD0NUa0NFj83VIU3bc55oYnsdFfIjsgVEQj7pUY/OrNprWoR2c1lLN5ttKMENzt+lQCC4luWhgj80LydorUsNMueQsSsB1Vx1pKNkSthNcjNto1iUvmlhlziPshqCxh8ny5FA3svJIzVafUo4g9vPbZdCQpU8D8KS21EiNd6cKODW9FpNt7hyOV7I7rwfMkXiazkKqrFtpIGM5r20c18+eH7sDUrOfp+8U/rX0DGQyKfUZrtrO9mFNci5R+0U4AU2lrmcUaDsCimknbx1qs0j55OKFC4Fo1BOoMRzUXmN6mo3dj1NV7MDKuogXbA5qIQjA45FXrhfmzUA5FaxgrbCucd418O3+veQLeMSIkZViWAI5z3rho/hpqyN/qyef7w/xr2xTtNDoOo6Vj9VtJuMmr+n+R2rGpwjGdOMuVWu+a9r36SXcyPBVpc6Jo5tLlQjmQtgEHjAx0rqBctjoKy6ljlK8HpWkaCjFJdDnr15VqjqS0b7Gh9oyORUZnb1qENkZBozTUEZ3JROe/NO81T1GKr0VXIhXJ2AY8GmeWR0FMyacshHWl7JXuFyve6fHf2rwzKCpHB9K8p8Safd6PHeQTqGhaNvLk9RivYtwIyKw/F1pBd+GNQEyBtsDMD6HFdVCvKimujJkk9T5OPWilPU0leIUFFFFABRRRQB7/APsy/wDM0/8Abp/7Wr6Ar5//AGZf+Zp/7dP/AGtX0BQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHzB+0d/wAlD0//ALBUf/o2WvH69g/aO/5KHp//AGCo/wD0bLXj9ABRRRQBveEWxrQX+8hr0QJXm3hZ9mv2/vkV6jGoatIuyDlbIhHmniAmraRrU4jFDmhqDKH2b2oNsPStPyhS+UMdKOcfIZDW+O1MMNbBhFQvb+gpqZLgZTQ+1RtCa02hPpUTQ1XMJxMxkIqKT7hrSeGqlxHiNj7VNV/u5ejN8Ev9pp/4l+ZX7D6Ux+lS7SFH0qN/u1FX+A/T9DfBf8jGH+NfmYmtyDKR56cmsW3TzLuJf7zAfrV7VJt91IRzjgUzQoTca7Zx4zmVf51qzz0e9/23baNbx210Uj/0cBO56fpXkOoMsuozn+F2zivS/FWiJfWX9ofaTDhNm0ryw7YrzC7LGQqo+ZWwDWaN5Ml023drlY1XILdDXsdtbfZ9JhRAAxAFeQ6XNPHexFx8wavR08QIlzb2852/LuzWVVNvQ7MHZJs3hujnjh7kZqNLwPc3SZ4hHP5VinxTZ/2jOxcbY1wGFc8/ihYobx4pA0k7His1Bs6JVIpXbOY1G6efWbiUkHc5xTi/y7gvTtWdKGS4y56nrVyJlH+0K6+h5Dd3csRS/aJQWQAdMVna6o+2AL0AAq9uQPuRcA44qrfIZLssAQAM81lS+KXr+iO3GfwqP+H/ANvkYrrtam1NdKBIcdKgrdHnjTVl/wDkFp/11P8AKqxqw/8AyC0/66n+VZVd4+p24P4av+F/miK2GZaumqdr/rDVvvWjONCHrWNrY5iP1rYPWsrWhmGM+9TLYZi17B+zj/yUPUP+wVJ/6Nirx+vYP2cf+Sh6h/2CpP8A0bFWYH0/RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFfMn7Rf/JQLD/sFR/+jZa+m6+ZP2i/+Sg2H/YKj/8ARstAHkQoopO9ABRS0UAFfQehMDoGm4xxaxf+gCvnyvf9CB/sDTcAf8esX/oIqZHVhd2au4e3500uMcEZ+tMOaQj0AqDtH7wANx5+tBdPWoiGPYfnTQhxzigCUumPvUyORdvB7mm7fYU2NMZGB1NAzH1fRJdSu3lURFWQKC0jKVI7jFc7L4BuJWLG4U/9tM/zFd6F9h+dPVfYVn7O2zZ0PEcySnCLsrarsczpOk3+h2vkWsFrzy8jMdzn3NPks9Sub+O5lSDKlOA5/hJP/s1dEygdQBTCkeeVWh077tlRxbhrGEU/T/glRTdnrFF+D1Mpnz80a/8AfdS+Wo6D9acFUev51ocjG5kxzEP++qjZnAP7ofnVnCYzz+dMIQ9z+dAkVPMc9YgD9ay9Zmmhjt5kh3tHLuCg9eDW55adiaQxL6n86mceZWN6FZUqim1ff8VY5GLxLrsErutlE6sMBCp4/HNZlq93eeIP7U1xNyQjMUYHCnt+XWvRYokPBya5Tx9dSWWkJDFhftD7WI67QORTSqPS6+7/AIITnhUruEtP7y/+ROP1Caa4n1G8uGX96DtG8HAzwPyrlpZN59qnnkwpA61WR9ueBkjHIzWkION23ueXisRGsoxhGyiurvu2+y7iiI+UJv4d239M0xmZuT0p4YlSM54phOYwPStDlEPLcVYVdic9aihXc+TUsjAcd6AIye1IxH40gPz5NMYktQApbimU7Hymm0mCCiiikMTrTqTNLQB65+zr/wAlAv8A/sFSf+jYq+m6+ZP2df8AkoN//wBgqT/0bFX03QAV8gfG3/kr2u/9u/8A6Tx19f18gfG3/kr2u/8Abv8A+k8dAHn9FFFABRRRQAVr+GYPtOvW8WQN2ckn2rIrS0FgusQZGRnFJ7Ba+h6hY3MOkkwwW6uT1Y96XVnkeM3lhMUBGJIiOn0rc0jT9POnRm8UrK4wCeoFV5tDt5UeOO4eLdwM8g0Qave5nZp2Z5nqihZAR1PWpfL8ywh7N0zXVXHg6yt4Hm1PUSjo33Y1zxXOyeUFK27M8KsQjEckV24VRlJxfYcpctmjUsWELQ4P3SK+h9PlE2n28g/ijU/pXzbbuQozX0F4XuPtHhuxk/6ZAV1VoqysJPW5tZozTc0ua5mjQcDTHQMPejNGaQFdkZeoqI1cLDvTDsPYVfMBQnXK5qsFxWs0SYPFQmGMfw1cZoGijijHFXhDHnG2mvEgHC1fOhWKGKTFSMMGm1VxCocVKGzUNKKVh3JCT6UoDH+E0xZCKnWY96NUA0Rse1OELVKrhhwadmlzMCNU21leJR/xTeo/9e7/AMq2KyfEv/Ital/17v8AyqW9APkc/eP1pKU/eP1pK8wYUUUUAFFFFAHv/wCzL/zNP/bp/wC1q+gK+f8A9mX/AJmn/t0/9rV9AUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8wftHf8lD0/wD7BUf/AKNlrx+vYP2jv+Sh6f8A9gqP/wBGy14/QAUUUUAafh5tuvWnu+K9gSL2rxnSH2avaN6SivcYo8gGkzWmMSL2qZY6mWOpBHUmiIQlL5dWPL4pdlFwsVShpjIcVdKUwpRcLGe6t6VA2fStNo6ryRe1O7E0Zz/SqV0R5L8dq1XjqjeR4t5D/smlUk+R+hthIr6zT/xL8yiSvlr9KqzEBTir5j/dL9BVG6TbGx9qKk37F+hthIL+0IP++vzOQuOSzepJrU8Fxl/Fdgo6+Zxn1rLc5Wt3wNiPxXYu3QPk11XPJS1PW/FmpHT4UW4h3ZjKqV6Zrym6i85XZGKtvzXsGtRx3c9y9w6CKGHciseCa8rjj8xnB+6SeayRtJFG0aV1wT8y9GqO+u7yedWEjRtGNuQetb628CHIAAxzTDFDuBIRqdwu0rHMQ2VxPcE75OeWyetbKwoiFgoL4wVqwY49xYDBz2pEEWejFvWnckriBbocqMjtUDwmBunA61ZZXjJkRTjPOKjMqvICejcGmIi3I6ErnHGaWWMuOSeBgUslt5aMFOQ2CMUwrKyBEzz1rKl8UvX9EduL/hUf8P8A7fIxp23TFAOlQE1cWPbdyL3wQc1Sbqa3R540mrD/APILT/rqf5VWNWH/AOQUn/XU/wAqyq7x9Ttwfw1f8L/NDbT7xNWs81Vs/wCI1Zz1rQ40J3NZ2rjNoD6NV/NUtT5sm9jUvYDAr2D9nH/koeof9gqT/wBGxV4/XsH7OP8AyUPUP+wVJ/6NirMZ9P0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXzJ+0V/yUGw/wCwVH/6Nlr6br5k/aK/5KDYf9gqP/0bLQB5FRSUtABQKKO1ACV71oUzHQ9OwRt+yx/+givBq73TvEl/b6fbRJNGFSJVAKjpik1c6MPJRbuemmb1xTTOR0xXnh8ValkfvoifTYKP+Eo1UjHmRj38sVPKzr9rE9BMpPek87jt+VefDxNqgPMyH/tmKD4o1P8Avp/3wKOVh7WJ6AJfcU1Zxls+tef/APCU6pn78Y/4AKVfE+qZz5kfv+7FLlY/bRPQ/N+lKJz7Zrz7/hJ9SI6xcf8ATOlHinUgRzF/3x/9ejlYe1iehb93XFNZyDxiuEHi3UEGAsJ/4Cf8aT/hL9SPVLf/AL5P+NPlYe1id2JD7U7zCB0Ga4P/AIS2/HSOD/vlv8aB4u1Af8srf/vlv8aXKxOrE74OpXkUhKH1rgz4w1DvDbfk3+NKPGGoEf6m2/75b/4qnysXtYndfLQcCuF/4TC+P/LG3/75b/Gj/hML0D/U2/5N/jRyj9pE72IgNmuM+Ik1u9nBEXAuFfcq+q9DVdfGN6OfIt/yb/GsPUtZlfxLb30scbNHDu2EHbyDVJamVWonG3c5KVst7UxVLHgZrsL+wtte0mTUbK2jgu7U/wCkQRDG5OzD+tcuNixNt/GrOFqxXU8mm1MbaYWwuTGwhLbQ+OCfaokXcwGKBFiNdkfPWoGfdJk9KmkYKuBVcetADzg0zGTThgDmjcMcCgBD0qOnkk02kwCiiikMKKPaigD139nX/koF/wD9gqT/ANGxV9N18yfs6/8AJQb/AP7BUn/o2KvpugAr5A+Nv/JXtd/7d/8A0njr6/rx/wAbfAv/AITHxffa/wD8JH9j+1eX+4+w+Zt2xqn3vMGc7c9O9AHzBRXv/wDwzL/1N3/lN/8AttH/AAzL/wBTd/5Tf/ttAHgFFe//APDMv/U3f+U3/wC20f8ADMv/AFN3/lN/+20AeAVoaJMsGrwSMMhT0r2//hmX/qbv/Kb/APbalg/ZrMEyyDxbnaen9m//AG2k9gOc0y6vryVY1AcO2eegFb880kF5EoWNlPBYHpXWW/wemtdvk+JCmBg4s+v/AJEqyvwl/eiR9cZz6G27/wDfdRGLuTZnGa3E0+ny2sdqt5clNyxdz7ivM3tbqxh8m7heBwx+RlxX0zY+BTZMJf7REk6jasht+i+n3qyfEPwpHiG4gln1gRrEeVW1yWH1312UakYO7FKOlkeAW7cV7p8PbnzvCsC5yUYqfzpl58E7Ofyvs+qm32DB/wBG3bh/32K6bw34HHh2wa0TUDMpbdkw7cf+PGt/rEHGzDlZYzRmtL+x/wDp4/8AHP8A69H9jf8ATx/45/8AXqHVh3KMzNBPFaX9jf8ATx/45/8AXo/sb/p4/wDHP/r1PtI9xmVmkzWr/Yv/AE8f+Of/AF6P7E/6eP8Axz/69HtI9wMvdkUwnmtb+w/+nn/xz/69H9h/9PP/AI5/9ej2ke4GSOtDAEVrf2H/ANPP/jn/ANej+w/+nn/xz/69P2se4HOzR4ORVfJrqDoOetz/AOOf/Xph8OK3/Lx/5D/+vVKvHqKxzVHIroj4ZU9LrH/bP/69IfDGf+Xv/wAh/wD16pV4dwsc/kZp2a3f+EX/AOnz/wAhf/Xp6+GgOt3n/tn/APXqvrEO4WMFC2eKtrnHNa48PKP+Xj/yH/8AXp39hf8ATz/45/8AXqZV4PqBj9KyvEv/ACLOpf8AXu/8q63+wv8Ap5/8c/8Ar1U1Pwr/AGjptzZ/bfL86Mpv8rOMjrjNQ60LbjPiQ/eP1pK+gD+zLk5/4S7/AMpv/wBtpP8AhmX/AKm7/wApv/22uMDwCivf/wDhmX/qbv8Aym//AG2j/hmX/qbv/Kb/APbaAPAKK9//AOGZf+pu/wDKb/8AbaP+GZf+pu/8pv8A9toAP2Zf+Zp/7dP/AGtX0BXn/wAMvhl/wrn+1P8Aib/2h9v8r/l28rZs3/7bZzv9ulegUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8wftHf8lD0//sFR/wDo2WvH6+r/AIj/AAf/AOFgeIbfVv7d+weTaLbeV9k83OHdt2d6/wB/GMdq4/8A4Zl/6m7/AMpv/wBtoA8Aor3/AP4Zl/6m7/ym/wD22j/hmX/qbv8Aym//AG2gDweybZfQN6SA/rXv9uu6CNvVRUSfsz7HVv8AhLuhz/yDf/ttehwfDjyYUjOq7tqgZ+z4z/49SZcGlucSq1Kq123/AAr4f9BP/wAl/wD7KnDwAB/zEv8AyB/9lU2ZpzxOK20u2u2/4QIf9BL/AMgf/ZUf8IH/ANRL/wAgf/ZUWYc8TiCtNKV3J8BZ/wCYl/5A/wDsqQ+Ac/8AMS/8gf8A2VJphzxODZaiZc135+HwP/MT/wDIH/2VMPw7B/5in/kv/wDZU7MOeJ506Vn6gmLOY/7Jr1Fvhvu/5iv/AJL/AP2VV7n4W/aIHj/tjbuGM/Zc4/8AH6U4txaRth6sIVoTk9E1+Z5cI/3Kf7oqhfR/uGr1cfCacKF/4SBMAY/48f8A7ZUUvwekmXa3iBce1l/9srOXM4OKj08v8zsoSoQxUasqqspX2l3/AMJ87yq0LGNxhge9a3hi4hg1eJprgW45/eEfpXs1x8C4ro5m10MfX7Fj/wBqVAf2f7Q/8xw/+Ah/+OV0Ks/5H+H+ZxfVKX/P+P3T/wDkDz7xRrN1rd9FY2knmRwDDSggbqW3miiIjkX5+h9jXoA+ANqpyNdYH2tT/wDHKnT4GJGwZfEBBH/Tof8A45SdX+4/w/zKWGp3u68fun/8ged3NwqEKCOaikETvtj6Ac16c3wT3kFvEGSP+nL/AO2Ui/BLZnHiHr/05/8A2yp9o/5X+H+Y/q1L/n/H7p//ACB5kkGcgNniplt1KqxYADrXpC/BMo25fERB/wCvP/7ZUn/CmpMEf8JFwf8Apx/+zo9q/wCV/h/mH1aj/wA/4/dP/wCQPJWuUW2YbjvLYFZsx2sctjnNeyn4HIRg6/3z/wAef/2ykb4Gxufm1/P/AG5//bKr2z/lf4f5ieFpf8/4/dP/AOQPKrRWbCFs4BxVlw0WSCM/SvUY/gqYzlfEPPT/AI8v/tlKfguzdfEP/kl/9sqISkm24vV+XZeZtiY0ZwpxjVi+WNnpL+aT/l8zwe5JW6lPfdVA9698k+AsMkhdvELZPpZ//Z1Cf2fbY5/4qF//AAE/+zrb2v8Adf4f5nF9Xh/z9j/5N/8AIng5qy//ACCk/wCux/lXt3/DPdt/0MMn/gJ/9nRL+z8j2ywx+JCgDbsmxz/7UqJScnHTr5G9FU6MKl6id42Vube67pHh9p91hU4r2SH9nvymJ/4SjP8A3D//ALZU3/Cgf+pm/wDJD/7ZWt0cB4niqt+M2Ug9q91/4UB/1M3/AJIf/bKjm/Z786Fo/wDhJ8bhjP2D/wC2UNoD5tr2D9nH/koeof8AYKk/9GxVv/8ADMv/AFN3/lN/+212Hw4+D/8Awr/xDcat/bv2/wA60a28r7J5WMujbs72/uYxjvWYz1CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK+ZP2iv+Sg2H/YKj/8ARstfTdeY/EX4Q/8ACf8AiC31X+3PsHlWq23lfZPNzh3bdnev9/GMdqAPlWivfP8Ahmn/AKm3/wApv/22j/hmn/qbf/Kb/wDbaAPA/eivfP8Ahmn/AKm3/wApv/22j/hmn/qbf/Kb/wDbaAPA6vpZXLorKIcEDGZkH8zXtv8AwzT/ANTb/wCU3/7bUo/ZxkAAHi84Ax/yDf8A7bTQHh4067PTyPxuEH/s1POmXq97f/wKj/8Aiq9u/wCGc5f+hv8A/Kb/APbaP+Gc5T/zOB/8Fv8A9toHoeHGyvB/FD/3/T/4qmm1vO5j/wC/6/417mP2cpR/zN5/8Fv/ANto/wCGc5f+hv8A/Kb/APbaLgeF+Rc9yv8A39X/ABo8q5HAYf8Af0f417p/wzlJ/wBDf/5Tf/ttH/DOUv8A0N//AJTf/ttFwPC9l0B978pB/jSYufVv++//AK9e6/8ADOcv/Q3/APlN/wDttH/DOUuf+Ru/8pv/ANtoA8NWOcpuL4+sgz+WaaRL/fb8690P7OUh/wCZu/8AKb/9tpy/s6sv/M1Kf+4d/wDbaLgeEjzv75/OlVZmOA5z/vYr3Yfs6kf8zUP/AAXf/bab/wAM5vnI8W/+U7/7bRcDwpluVJHzkezUmbn0k/M17t/wznJn/kbv/Kb/APbaP+Gc5P8Aobv/ACm//baLiPCc3R/hk/M0D7Weiy/ma91/4Zzk/wChu/8AKb/9to/4Zzk/6G7/AMpv/wBtp3GeG+XfHpFOfpmrt/FIs1mWDBmtUJz7cf0r2b/hnSX/AKG7/wApv/22rdz8ADcW9pH/AMJPtaCLyy/2D73zE5x5nHXFK4uh4rpWrPo+ox3Kcpysif31PBFT6noMT6mrWlxClldYdZGbhAfXH8q9Xb9nEs2T4s/8p3/22pof2eDH8r+Ki8eOF+wYx/5FoY12Z4jr14ss0dpA3+iWy7Ilzx7n6nrWbCMZavdn/ZsLuWPi3qf+gd/9tp//AAzfwAPFf/lO/wDttFxPc8DmPSohXvrfs17jn/hLf/Kd/wDbaT/hmn/qbf8Aym//AG2ncR4UFXtzSHGOK95H7NuFx/wln/lO/wDttIf2bM/8zb/5Tv8A7bSA8FPIqOvff+Ga/wDqbf8Aynf/AG2k/wCGaf8Aqbf/ACm//baGB4H2or3z/hmn/qbf/Kb/APbaP+Gaf+pt/wDKb/8AbaQzwP3pa97/AOGaf+pt/wDKb/8AbaP+Gaf+pt/8p3/22gDA/Z1/5KDf/wDYKk/9GxV9N15l8OvhD/wgHiC41X+3Pt/m2rW3lfZPKxl0bdne39zGMd69NoAKKK8S8ffGHxD4V8baho1jZ6XJbW3l7GnikLndGrHJDgdWPagD22ivm3/hoLxZ/wBA/Rf+/Mv/AMcoH7QXiz/oH6L/AN+Zf/jlAH0lRXzb/wANB+K/+gfov/fmX/45R/w0F4s/6B+i/wDfmX/45QB9JUV82n9oLxX/ANA/Rf8AvzL/APHKsWPx78U3V/BA2n6MEkcKSIZc/wDoygD6Kory6y+JGsXERZ7axBzjiN//AIqrP/CwNV/597L/AL4f/wCKqlFslzS0PSKK84/4T/Vf+fez/wC+G/8AiqP+Fgar/wA+9l/3w3/xVPkYvaRPR6K85/4T/Vc/8e9n/wB8N/8AFVWvPiNrFvB5iW1iTnHzRv8A/FUuRgppnp9FeVWPxM1m5dw9tYDaP4Y3/wDi6uj4gatn/j3sv++G/wDiqFFsbmk7HpFFecf8LA1X/n3sv++H/wDiqT/hYGrf8+9l/wB8P/8AFU+Ri9pE9Iorzb/hYOrdray/74b/AOKo/wCFg6t/z72X/fD/APxVHIw9pE9Jory+6+JGsQQNIltYkj1R/wD4qsv/AIWzr2cfZNN/79v/APF1LVik09j2SivJ7H4n63db99rp4x6Rv/8AF1cPxE1b/n3sv++H/wDiqai2S5pOzPTKK8y/4WJq/wDz72P/AHw//wAVSf8ACxdX/wCfax/79v8A/FU+Ri9rE9OorzD/AIWNrH/PtY/98P8A/FUf8LH1f/n2sf8Avh//AIqj2bD2sT0+ivL/APhZGsf8+1j/AN+3/wDiqQ/EjWMf8e9h/wB8P/8AFUezYe1ieo0V5Z/wsrWf+faw/wC/b/8AxdIfiXrI/wCXaw/79v8A/F0ezkHtYnqlFeTz/E/W44XdbXTyQM8xv/8AF1kt8YvEKqx+x6XkD/nlJ/8AF1Li1uVGSlse3UV83H9oHxZk40/ReP8ApjL/APHKT/hoLxZ/0DtF/wC/Mv8A8cpFH0lRXzb/AMNBeLP+gfov/fmX/wCOUf8ADQXizGf7P0X/AL8S/wDxygD6Sor5t/4aC8Wd9P0X/vxL/wDHKP8AhoLxZ/0D9F/78y//ABygD6Sorzb4T/EHVvHf9r/2pb2UP2PyfL+yo6537853M390frXpNABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFeP/Fb4ra74G8UW2maZaadNBLZJcM1zG7NuLuuBtcDGFHb1rhf+GifF3/QO0T/AL8S/wDx2gD6aor5l/4aJ8Xf9A7RP+/Ev/x2j/honxd/0DtE/wC/Ev8A8doA+mqK+Zf+GifF3/QO0T/vxL/8do/4aJ8Xf9A7RP8AvxL/APHaAPpqivnHSvj/AOK77VbW1k0/RgksioxWGXIBPb95XsieJL9otxit8+yt/jSbSGlc6qivK/EfxK1vSNSitoLWwZHTcTJG5P6OKyk+LviBpFU2emcn/nlJ/wDF1DqRQ+VntNFebWnxB1efTPtL29kH54CPj/0KqcfxM1l5QptrDGD/AMs3/wDi6l14IORnqtFeaaL8QdZ1JLhpbaxAiI+5G44/FqoX3xP122lkEdrpxVXwCY36f991XtY2uHIz1qivFD8XfEgf/jx0vZ/1zkz/AOh1Mfiz4hVQWsdOBPT93J/8XU+3gPkZ7LRXjDfF3Xgdq2mmFgOcxyf/ABdWrP4oeIJV3z2umqOwWKT/AOLo9vAPZyPXaK8gn+LepwttNvYA+6P/APFVmy/GvWUBK2WnHB/uP/8AF0/bRDkZ7jRXhkfxs1yTH+g6aM/9M3/+Lq9B8XNduGIjs9ObHX92/wD8XQ60VuHIz2WivGrv4r+I4UzHY6bn0aKQ/wDs9Zo+NfiMMFey0oev7qT/AOLoVaDDkZ7vRXhr/GfxCq5Fnpf/AH6k/wDi6mi+LPiqaMOLTRxnoDFJ/wDHKPbRFyM9sorw2T4xeJ4SVez0gtntFJj/ANGV0z/ETWI9HW8NtY7ym7Gx8Z/76odaKDkZ6ZRXix+LviDaT9j0zgZ/1Un/AMXTP+FweIfs4k+x6Zn/AK5Sf/F0e2iHIz2yivJ9I+J+tX1v5txa2CHPG2N+f/H60j8QtQA/1Nn/AN8N/wDFUe2iPkZ6NRXmMvxJ1JEYiCy4GeY3/wDiq83n/aG8WxzyIunaIVViBmCX/wCOVcZqWxLi1ufS1FfMv/DRPi7/AKB2if8AfiX/AOO0f8NE+Lv+gdon/fiX/wCO1Qj6aor5l/4aJ8Xf9A7RP+/Ev/x2j/honxd/0DtE/wC/Ev8A8doA+mqK+Zv+GifF3/QO0T/vxL/8drufhV8Vdd8c+KLnTNTtNOhgismuFa2jdW3B0XB3ORjDHt6UAewUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRXjnxY+LGveBPFNrpel2mmzQS2SXDNdRuzbi7rgbXUYwg7UAex0V8w/8ADRvi/wD6Buh/9+Jv/jtJ/wANHeL/APoG6H/34l/+O0AfT9FfMP8Aw0b4v/6Buh/9+Jf/AI7R/wANG+L/APoG6H/34l/+O0AfT1FfMP8Aw0b4v/6Buh/9+Jv/AI7R/wANG+L/APoHaH/34l/+O0AfT1FfMP8Aw0b4v/6Buh/9+Jv/AI7R/wANG+L/APoG6H/34l/+O0AfT1FfMP8Aw0d4v/6Buh/9+Jf/AI7R/wANG+L/APoG6H/34l/+O0AfT1FfMH/DR3i//oG6H/34l/8AjtL/AMNG+L/+gbof/fiX/wCO0AfT1FfMP/DRvi//AKBuh/8AfiX/AOO0f8NHeL/+gbof/fib/wCO0AfT1FfMP/DRvi//AKB2h/8AfiX/AOO0f8NG+L/+gbof/fiX/wCO0AfT1FfMP/DRvi//AKBuh/8AfiX/AOO0f8NG+MP+gbof/fiX/wCO0AfT1FfMP/DRvi//AKB2h/8AfiX/AOO0f8NG+L/+gdof/fib/wCO0AfT1FfMP/DRvjD/AKBuh/8Afib/AOO0f8NG+L/+gdof/fib/wCO0AfT1FfMP/DRvi//AKB2h/8Afib/AOO0f8NG+L/+gbof/fiX/wCO0AfT1FfMP/DRvi//AKBuh/8AfiX/AOO0f8NG+L/+gdof/fib/wCO0AfT1FfMP/DR3i//AKBuh/8AfiX/AOO0f8NG+L8f8g3Q/wDvxN/8doA+nqK+Yf8Aho7xf/0DdD/78S//AB2j/ho3xf8A9A3Q/wDvxN/8doA+nqK+YP8Aho3xh/0DdD/78Tf/AB2l/wCGjfF//QO0P/vxL/8AHaAPp6ivmH/ho7xf/wBA3Q/+/Ev/AMdo/wCGjfF//QN0P/vxL/8AHaAPp6ivmD/ho7xf/wBA3Q/+/E3/AMdpR+0b4v8A+gbof/fiX/47QB9PUV8wf8NHeL/+gbof/fib/wCO0v8Aw0b4v/6Buh/9+Jv/AI7QB9PUV458J/ixr3jvxTdaXqlppsMEVk9wrWsbqxYOi4O52GMOe3pXsdABXyh8ZP8Akq2tf9sP/REdfV9fKHxk/wCSr61/2w/9ER0AcLRQaTPagANA/Wj/ADxSgd8UAJ+FXdI/5C1r/wBdBVLt64q9o/8AyGLT/rqOlAHrmlj9w3+9V8DmqWlD9w2f71X8Z6VpHY5pfExMCgKCad/Oj8K0uIbj61T1Nf8AQz16irpBzVPUx/oh+tKWxUd0U9KX97J9K1gAKydK4nf121r9KUNh1NxMYpKXgmk4qyBMCmnvT+vvVhrMw24nnyqsMoo6moqTUVdlRhKTsjLv1/0J658jnFdHe7vKIkhYRsP4TkisKWLYdynKeuK5lWhN6HQqUoLUu6QBmTPStM84GKzdIH+s9OK0j+ddMNjnn8Q0gY5pPenY69KQ9KszGlRimU8ZzTSKAG96TA9aXGOtJTENI69aaQKeaYaAK93/AMesg/2a5+Qfu27cGugu/wDj2kHOMVz8n3G+lY1NzopbHBn7x+tJjB9KcfvGm49qzNRc0dOlAFAHagBB1ozR3oxjvQB7r+zl/wAzL/26/wDtWvda8K/Zy/5mX/t1/wDate60AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8y/tE/8AJQLD/sFR/wDo2WvI69c/aJ/5KBYf9gqP/wBGy15HQAUUUUAFFFFAGp4c58Sad/13T+dfVcMYMI+lfKvhrnxNpv8A18J/Ovq9RsiGemKznuVE8x8eqV1uA/8ATI/zrm4cmdPrXUeP/m1q3/65H+dc1br+9T61y1dy0djpybvD4z71j26/6Xj2P8q39LTPhwnvzWLZpm8/A1jPdFo1vBYG2+U9xWJdQEwzk8/vTXQeDEx9tyKzbhM20x/6amtPsC6nMm1lluRGi5PWr+pBIraM/wAQXGKuaeoS/L4zhDWNfym5uGB6bqjoUhLGz8yXzHJIrb8tQnPFZ0FylvCFx0qzDdxzZeQ4Re3rTTBkEuj/AG1/MyVHTJ71RPhogldy8nqTWhLqM9yWS3GAvSoGS8dMuxD/AFou+gWKy+HxG6qzptHcVo29gtpyDlTVKSG82hkLFh15pP7RmtyElGRRcDXk8oL8pBrmdYtFEnnIMY64rRW5RnDr909R6VHelZI2B6EUJ6hYw0IdVBPGa0UnXdwflAxWYFCAgGrFtEzKJOxOKtkkjjzMMOctXpX2LfoUanGPJyfauAiiBiT/AH69NuAyaGAo5EX9KBo82dcLIPao413aeBip5Rw/rg0kEe7Tx7ZpdAN3wvZLd2JLk/K2BXRHRoMd8/Ws7wTGp0+TP9+uu8uPbjFIDmLnRIBDIRnO096+fLwbb2dfSRh+tfUFxGnlSZH8J/lXzBf/APIRuf8Arq3866KHUzqFeiiiukzCiiigBa9c/Z2/5KBf/wDYLk/9GxV5HXrn7O3/ACUC/wD+wXJ/6NioA+maKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAr5h/aO/wCShWH/AGCo/wD0bLX09XzD+0b/AMlCsP8AsFR/+jZaAPIOtFHSigBKWiigAooooAKSlooAKKKKAEpaKKACiiigApKWigApKKWgBKWikoAWiijNABmikooAWiiigAo5oooAKKKSgBaM0UlAC0UUlAC0UUUAFFFFAHr/AOzl/wAlC1D/ALBUn/o2Kvp6vmH9nL/koWof9gqT/wBGxV9PUAFfKHxk/wCSra1/2w/9ER19X18ofGQ/8XW1r/th/wCiI6AOE70tGKB1oAOopKU9c0nrQAtXdH51i095RVInirujf8hqz/66rQgPX9LI8t19DV7j1H51j2k3kuxbuKJb12OF45o5mjJwbZtBevNJzk96bBKJoFcenNSdBmt7mdhD2NUtTGLY89TV44PBqjqSs6KoUkZpSehUVqU9MGLkj1XFa2KxbRvJukP4GtYXEfmhMjdWcZJOw5rUlIppHtTuCOaQjjitiCxptv8AatRhiONpYZz6V2L2sFxckSIGXsD2FclpMpi1OF1IGG5z6Vt63rdza+IfstpZyOCBmQLkc1xYvVI68NobUujWMsJVou2OK848XaTHprpPb5EbNhl7V3d5f36aYs8UJaTnKjGa5XWEu/EOkFJ7ZrWXzAF3kc89eK49ndHVZtWOc0oAeYDjNaXbioILM2sjZYE9OKskdq9am7xPMn8RGfTFJ/KnnPYUhHWtCCMjikIp5Hamn6UCGEUmMGnEEnmg5oERsOf6UwjmpDTSCO1CCxVux/osnA6VgOP3b/SuhuubV+/Fc/IPkfHoayqbm9LY4BvvGjr6ZoP3jSdTWZqGKXAzSdRR04oAO1H40vakAoA91/Zy/wCZl/7df/ate614V+zl/wAzL/26/wDtWvdaACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+Zf2if8AkoFh/wBgqP8A9Gy15HXrn7RP/JQLD/sFR/8Ao2WvI6ACiiigAooooA1fDP8AyM2m/wDXwn86+sOWh2H04r5O8NHHiXTT/wBPCfzr6tjfKrg9ayqPVFx2PN/HIJ1e3HcRmudgUmaPPrXV+OgDrVuMdYjXO26YnjB9axqK7KR2OmIV8O/nWNZL/pJPfBrpNLj3eHePesWztyt2R14Nc9RNNFo1PBa5W+z61nzxg2k+P+eprY8GRFRe5HesyZNsFwf+mprRawJ6mdZx/wCkuD/zzNcldEi9Zc4+Y12FoP8AiZgZPzIRiuev7SNbtmYEMGOMVFtLlIpu/wC7xmpLKGa5mUrwgqa70/yREc5EgzVuBxaxqiAZoAfOzWaDy0Ge/FZcmozvu6g+mK0J9RSLhyGc1my6khyVjGfeqSQD4r65j65fPatAwi8tgzgBz7dKy01RVxmMZNWotSVx8pAPpTsCKFxby2UpLHKNTpTm3PPbird1NHcwkEisyJxIDFngHFSkgKQGcj3rYtbfZp0T56k1Xa0t1XeoO/61rvHGuh23qSaqQJFO3imn8sQxl8Pzj616XLOW0vYRgrHj9K4jwpMlvqG113K/Az616BcIGspMQj7p70XQHmMy8OT6GltlxptSXY2iT6U2Af8AErB+tS9gOm8GtssZP9+uujcOOtcX4Wz/AGcxH9+urtn+U1i5e/YpL3SS5H7iX/dP8q+W7/8A5CNz/wBdW/nX0/cPiGT/AHT/ACr5gv8A/kI3P/XVv5130OpjMr0UUV0GYUUUUALXrn7O3/JQL/8A7Bcn/o2KvI69c/Z2/wCSgX//AGC5P/RsVAH0zRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFfMH7R3/JQrD/ALBUf/o2avp+vmH9o7/koWn/APYKj/8ARstAHj9LRRQAdaKKKACjtRSUALR0NFFABRRRQAUCiigBKWiigBKWiigAooooASlzRRQAUUUdqACj6Ud6SgBaKKKAAUCikoAM0tJS0AJS0UYoAKSlooASilooAPekpetJQB7B+zl/yUK//wCwVJ/6Nir6er5h/Zy/5KFf/wDYKk/9GxV9PUAFfKHxj/5Ktrf/AGw/9ER19X18ofGQf8XW1r/th/6IjoA4XPHSjoKTHvR2oAMj8aXpzSd6KACr2i5Ot2n/AF1FUscVo+H0EniCwQ9DOoP50Ael470oIB5ANa/9nQ+/50n9nQf7X51XIyedBaz26rtV8exqczxg8sAPWoP7Ng9W/Oj+zYegL/nTSkloZ2iT+dGUJVw2OcCqn9oRGNt6856YqX+zYhwHcA+hpv8AZcOfvvQ1Jj90ypHDuXA288UR5Mqndg571qf2ZEf4mpDpcWOHap5WXzItA4UFiKZvXJ+YVW/s9gu0TPj0ph09gMCcjHoKu8jOy7ltJ1hmjdnCgNk13NveRXPlXIwytGFDEc5Xg/yrzRtMJbJmJrp9ABh02WGWf5VkGzd6nqK58RBuLkzow8knynQ2WpkskciIoUkZMgyfwqhreqwW91CZIyVXLBRxk44qWNLC3t9+QXXkKAOtcvrsUl9cpIz7RzgVx0k5yUTrqNRV0ZdxqvnB2ClXc5x6VYtZHkhBfrVY6Ux/5ag/hQlhcR52T4FelFSR5r5XsXyQehFNJqibCYtu80fWhbG4WRSZcgHpk1fM+xPKu5dPFNI59M08g/h70xjirIExTSO3rS7uaD6UxDMc4ppp5/CkI9KAKl3/AMesn0rAlH7tuOxroLsf6K/PasCTJif6Gsqm5vS2PPz94896b0OaVvvH60cfjWZoIaAOaDRg0AGaBS9KKAPdP2cs/wDFS8f8+v8A7Vr3WvCv2cv+Zm/7df8A2rXutABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfMv7RP/ACUCw/7BUf8A6NlryOvXP2if+SgWH/YKj/8ARsteR0AFFFFABRRRQBqeGxnxLpw/6eE/nX1ZHHhVI9K+VPDP/Iz6b/18J/OvrFR8o+lZVNy4nnnjgZ1q1/65msCAfv4/rXQeNwRrVvn/AJ5msG3KidOec1jMpHd6Rx4fx7msy0H+nj6GtTSR/wASD8TWZaZ+3g+xrKpuiomx4TXBvfrWRcY8ifjjzTW14TGftv1rLuIs2c//AF1ND/hoS3KmnwpJfHAG4R5BrJkgWS/zIu4Bjmt/RIs35z/zzNZs6BbuQf7RqeiK6nPXdyJr0gDCJwB6Vn3d15AJDc1ce2lN5Lt9TgVzeqSt55jPBB5qoq7BkVxfF245PrVOS7lP8VDMqL6mq7ZPITit0kQyX7XNjrSx37oeSQag8zb1Wkk2yrkcGnZMLm7a3fnL97nvT1YI5PvWJp7nzwldG1g3lCTeMelZNJOxV9CWMiWRSOmOldHc2a/2BaSA85IxWFYQM8kaIMsRwK6rUIZINCtYZUKuCcg1DWgzDtY2R4HXg7+tejl7f+z3yH3bP6VwNvGzRW7Y4Eg/nXqrQL/ZTEAf6r09qmIM8ju+kn0NMh/5BC/jT7w/636Go4s/2Qv40fZA6Dwk3+gOP9uujDGPGD1rm/CSbtPcd9xrowjEYxzWc43aY4sZPKfJkz/dNfNd9/yELj/ro386+k7iLFu5P90182X3/IQuP+ujfzrtw73MqhXooorpMwooooAWvXP2dv8AkoF//wBguT/0bFXkdeufs7f8lAv/APsFyf8Ao2KgD6ZooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvmH9o3/koVh/2Co/8A0bLX09XzD+0apPxCsMD/AJhUf/o2WgDyCinbGxnFL5b46UAMo7U7y3/u0vkOe1AEdFS+RJ/do+zy4+7QBHSVN9ml/u0v2SbP3DQBDRU/2Sf/AJ50v2G4/wCedAFejpVj7Dcf88zS/YLk/wDLM0AVqKsfYbkdYzSjT7o/8szQBW96Ktf2ddf88zR/Zt1/zyNAFWkq7/ZV5/zxP507+yL0/wDLH9aAKNJV/wDse+/54n86d/Yt+f8AlgfzoAz6K0P7D1A9Lc/nS/2FqP8Az7tQBnUVpjw/qZ6WzUf8I9qeP+PZvzoAzKK0/wDhH9S/59j+dNOh6gOsB/OgDOo6VdOkXo48g5+tH9lXn/PE/nQBSoq2dMux/wAsj+dJ/Z91/wA8jQBVo4qwbK4HWM037JP/AM8zQBDRU/2Kf/nmaDZzj+A0AQUVN9kn/uGj7JN/cNAEFHerH2Sf/nmaT7JOB9w0Aes/s5f8lD1D/sFSf+jYq+nq+ZP2dYXj+IN+WXA/sqQf+RYq+m6ACvlD4yf8lW1v/th/6Ijr6vr5Q+MZ/wCLr63z/wA8P/REdAHC96TpmjtQRQAZ560vfNHSjrQADpWn4c/5GPT/APruv86y+9anhv8A5GTTs/8APdf501uJ7HtxICknoKrRyNKrv0GcLinXbFYtqn5mOKhj3JPGnRcVUn0M0tC4q4ABpe+KUe+KXBqyRuO+KqyTkTFFXO1cnFWmO1Cx9Kz1WTypJV6tyfpUSZUUXVO5QecYp2O1MhbfErAYyKkOB71ad0SyjqF0tpD5jMFGcc1nrrVrIAXuFQg+h5/SrOt2xu4ba237POuUj3YzjJxn9a7S08Cf6JKkN+YJi6Mph3KAgXBXBJ6n5s/h0rirVqiqcsP6/E7YQpQpRnUpyd76qSW3qmcKmrWktwsf2uJI8cu2eP0rdXWtAis1thfRv829nw3zHH0qWLwvfat4j8RLbaobXypowQEJ3HAIPBHYEY961LjwTcXELJJqQMhMbCQxHIKgZ43Y5IJ4Geetcs61eonfb+vM6J/V6E7KjO9k/jj1V/5TJi8R6Gi7ftkX/fJ/wqO71fQriH5NTiWQZIyrf4VsT+D55WumW6ggM0SRqUR8oVzyDu560+bwldSGcJqrxJI25VRT8o3E4BJyODj8BWcPawd1/X4jeIw8k1KlJ/8Ab0fP+76HDPrlohIWUN9OlINatHGfPVOehBr0nWdJgPhrUTJFG7RWcjByozuCHmqGleDtMu/DmnTopjuJLWOQtnIJKjPFdka1Zuza+7/gmssPgVQ9ryy3tbmXa/8AKcV/a+n45uV/I/4UyTWbIY2zg568Hit3U9HfS7jy5UUg8qwHUVgsFe5eRUUhBhRjvXS3Vto193/BONPA3+Cf/gS/+QFGsWJXPnqD6YNQyatZhSRMGPoAatQFJIAdig98Cm3EKyRMoRc/SmlWavdfc/8AMlywF9YT/wDAl/8AIFJtXtggIfJ9MVYtb2O4HBPr0pzxxx25XYuQh4x7VSsLb/RI5B95lAH0qVKopqMn0Kq0cLLDOtSUk1JLVp7pvol2Lct2sT7XUgdjUiMzE5xj2qvqS/LH9antvuN9a2u+axwNLluMux/osmPSufkH7tvpXQ3Y/wBGk+lYEv8Aq247EUqm5dPY87b75+tFDZ3H60nXP86zNBfyopKD1oAU8Ck9aOnpS9T0oA90/Zy/5mX/ALdf/ate614V+zl/zMv/AG6/+1a91oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD5l/aJ/5KBYf9gqP/0bLXkdeuftE/8AJQLD/sFR/wDo2WvI6ACiiigAooooA1vDH/Iz6Z/18J/OvrRB8g+lfJfhj/kaNM/6+E/nX1pH90VlU3Kiee+O1/4m9rx/yzNc3CmbhCM8Guo8djOrWv8A1zNc/armZMcc1lMpHbaQCdBH41nWn/H6Poa19IX/AIkYH1rNtlxfAexrKpui4mt4S/5ffrVGdcWU/H/LU1oeEl5vfrUM0Y+xzf8AXQ0Ne4hdSrokZGoc/wDPM1SntfMvzx/Ea19HX/T8/wDTOoGTF+x/2jS+yh9Tk71EtXuJB1GcV51fOZLtyeSTXqGpwbhcAr3NcBqdvGs6sgAx1p0wkQ22mKYvNlIBPQVHMiodoZce1KZJZ/lDYA71VkgO7/WZrS/cROkEUx2uVxVLUNONt88ZylSrEw5EnNIZ2aNonOaadnoJlG0OLhTXXWpeWIL2rF0hIkkPmKGz0rqtMtDPcxxLhd5wKio9RoWzLW13DIo5Q5rsfENwLvS7WXjBHpXPvYNaX5gkwWA7VuapbtHoFo5JwR0rK5RlWqD7HAf+mn9a9Ox/xKG/65/0rzK2P+hwf9dB/OvS9x/sth/0z/pTj1EzyO7jx5ufemQr/wAShfqas3w4k/GooF/4lKj60vsj6m/4OX/QXyP466sRjbXNeDFzp78fx11PQYrVElK8UfZHGOxr5gv/APkIXP8A11b+dfUt2M27/wC6f5V8t6h/yEbn/rq3862oq1yJlaiiityAooooAWvXP2dv+SgX/wD2C5P/AEbFXkdeufs7f8lAv/8AsFyf+jYqAPpmiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK+Zv2iR/xcCwP/AFC4/wD0bLX0zXzX+0GqHx/Y7mwf7Lj/APRstAHj4Gaftya6uDQrK98DXGo2uTf2c374Z6xnvitvRPA9ve+Bp7+V9upyAy20eeSi9eKAPPAvoaeo5rr9A0GyOh6jrGpqTBABHEoON0hrYg/sGWCN4/B17KpX76uxBoGedAD1p4HHWvSBbaI0ZI8F3wyO7tx+lZenr4XsNOe41C0kurt5mAthIV8pR0yaAONCEnHHFSKpNdp/bXg0HH/COy/hcGtgeENFuryMWyvHHqFmZbVGblJB296APNdpFSAcg9q7PwZ4RttVv7o6qxitLcFCxOPnPAFV9M8JmXxfJpd0GWCB2aVv9gUDOWAIHSlG4Cr2oxW66jcC0UrbCQiME9s1CsBPODQBCASc4p4BqX7PThCRjnigLEYXnoakC1J5ZHNSKhxQA1VzxiplWlSM55q3HDyDTCxVWLOKlWI1c8j0pRCcdKCrFYJjuaXLrghjVoQdaDbHsOKYFcSyr/Gad9ruB0fp6ipDbn0phgPpQFkRte3GMkqfqoqtJdSt1Vc9+KtGIVE8AIJBpWDl7FF5mz90VE8zegq1Jb471WeEkelIVrEDSn0FRtIT2p7IQcVG6kDpQIYz8HIqIuOuKcynFRsD2oEHmZ7YppcfSkYEjJ6UwigBxcc0nmr61G2ef6005xQBN5qnvyaQyLgjNQHnrTe/egdz179n9gfHl8Af+YZJ/wCjYq+j6+av2ej/AMV/fgf9AuT/ANGxV9K0CYV8ofGQ/wDF1db/AO2H/oiOvq+vlD4yf8lX1r/th/6IjoEcLnmjv+lJ7YpRgc5oATGRRn1o9KWgA98VpeH22+IbA+kyn9aze/WtPw4ofxHpy9jOo/WgD2X5Z5BIpyR8oHoacwAvIwB/DT7S3MQbcM88UxzIbnd5bccZFPoQWuAaM857e9OUEjJ4NKw/TtWlyLFO8b5VjHVzSYkTIjj3JjHWmlnacs0TY6Crij92OCKjdl7Ip2pxIyMpVhyBmrRILY796qzgrdB1RuBjIpJGkilDhGIYYNCdgauNuYHea0mJ+VL+3X82P+FermUQIsp/hI/GvNnJfQraUqRnV7ZRkem7/GvRJ3XbCGx97P5Vw1Zfvm/Q9Vq2Dpesv0MjwuSfE/ijtm4i4/4BXTyw9TjmuPhsfE2n69qt3pkGmywXsiyD7RK4I2rjsKvfaPHJ/wCXLQ/+/wBJRB2jZp9enmbYmh7aoqkJxtaP2ktopM2WTLLwKUQhm6cVgmTxwxP+haIPpLJTvP8AHCjix0PH/XaSi/k/uMfqkv8An5H/AMCRf8RLs8L6qB0+xzf+gGs3SL7yfC2lRR4MzWUQH+yNo5qDUl8a3+m3NpLaaMqTxNEzLLJkBhjjj3p9pZtp+n2cEm3zYrWOOQqcgsoA4/KlJvmul0NJ0408Mqbkm3K+jv0OU1yeWOeYSSM/ORls8mseJlgRd3JIyQBWl4gdDqeGPyHk4qGELs457110dYI8msrSZQhcecyJuG45wwqW5mEERJ69qLzahjYEAg/pVW+PnW4Zedp7elap8t0ZNXaY9mP2ViBlmUnP4VHpaAWELE5JXilSeNrNxkZ2ng/So7Bg9lbKHAKr096y5v3ifk/zR2xX+wz/AMcfykP1LhE+tS22SrfWo78qwRS3Q81LbgBWOQQTwRW1/eOF/CNvP+PWTHpWBL/qn7cGuhuubZx7VgSf6tvoaVTcqnsect9403qaVurUnrUGgvX6UmO+KKD1oAXt7UGjrRz6UAe6fs5dfE3/AG6/+1a91rwr9nL/AJmX/t1/9q17rQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHzL+0T/AMlAsP8AsFR/+jZa8jr1z9on/koFh/2Co/8A0bLXkdABRRRQAUUUUAa3hj/kZ9M/6+E/nX1pGp2qR0r5K8NHHibTT/08J/OvraI4Vfes57lI4HxzxrNqO3lmsS1GZ0+tdV4w0u4vtShlt03CNDuFcxErQ3SLIpU7uhFZTKidzpKY0TH1rMtV/wCJgB9a1NGkMuiMduApIz61nWY/4mGfrWU94lJmp4UXD3v1qOdf9Dm/3zVnwqPnvPrUc6E2soH981VvcF1INHX/AE7/AIBQFBvzn1NTaShW+5/uVBGD/abA/wB41lskV1MjU41ENwR715Vqm83jIvSvYL2MMk6+5rynXIWW/cJ1q6YnsY5JUbR1NSDT7mRdwQ4NWNNtRc6pDA38TAV73pXg/Tk0+LzIldiBkmtoU3N6CbSPnOeCa3Pzgiq0hJIx1r234j+DrSz0g3lsgUL1FeJ+VvBIPSizT1C9y3pgzcKC2TnpXcwwywyxOoIIGRXFaOm27B6k16FZCSdkST+FeKwrPUqK0J43a51DzJPvFa2teGPD1kB71hW/yXuPQGt3WW3eH7T8azXUbOet1Jsocf8APT+tekc/2Yc/88/6V57arizg/wCug/nXpBH/ABLD/wBc/wClVDqJnlWoDBf8aggP/Etx9as6lgFz9aqQFf7Oz9alvQaOu8FJnTZP9+un8s1y/gpv+Ja+P75rpy+BWikyWQ3MeIH/AN0/yr5V1H/kJXX/AF1b+dfVVw+63kx/dP8AKvlXUP8AkJXX/XVv510UXuRIrUUUVuQFFFFAC165+zt/yUC//wCwXJ/6NiryOvXP2dv+SgX/AP2C5P8A0bFQB9M0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXzT+0Mu7x/Y8f8wuPn/trLX0tXzX+0HKU8f2Ixkf2XGf8AyLLQByXw81G2stcey1BwLG+QwTEngA9DWvqXiuG08eWr2J/4ltji3RR0aPo351wEM4S4WRog4H8J710FtqVvdny49LjL45BbFA7G98QtQ02JLbRdGkVrNSbh2U9XbnH4Vz9n4u8Q2FtHbWupSxxIMKgPAqeK1ljleV9OidW5A3dBTzGPNJ/suHhQMb+c+tAGt4V8aa5L4kso9Q1SQ2rPiQOeMe9IfC9t4h+23NnqUEd4Lt90cr7VKZ4INYrwSfLD9iiQk7uG5FWcCO1aU6emwDJIfpzQMvD4cahx/wATLTv/AAIFXfEGprpF34fgtblJ59OjAleI5XOc4zWJHHJNCkkVioDDIIfqKW1t3RWBsI3boSzUB1On8c6zZQWdta6I4H2pxdzlT0Y9BVzV/ENifCI1G2dRq1/EtvMB1UL1P41yTzJGTI1hGET7wzVWbUbOeIolqiE/xA9KAsZmXJGetTB5MnilLrwFJqZTHgZagbRGHk9KkV3PapBsP8VTIEJHzUBYi+ZsZBqeJQvOM0/A7c1PAm49AKAEjGeqirkYH93mnxxL7VbjgUntimPYhVAedtPEY6bavx2wJwKtJZA470BcyxEP7tKYhj7v6Vtx2CnqB7U/+z0oFc51ouelRPGO9dE1ihGMgfhVSSxAJIxQFzAkjHYVA8YHJraltQpOapvCh4xmgpIyZQNvHWqrnnoPxrXkgA7c1UltlxknGKCmjMkCtz3qF0XPAq9JApHymq7QH1oIsymyjvioCoY1bktz1FQNEwzyOaQrMgaIc81E0YHWrBjfPBqJ425zQIgI9qaUOOKlKMO9RkNQFhhRuOKaV9aed/aozuzzzQI9W/Z6UDx9fEf9AuT/ANGxV9K181/s9/8AI/X3/YLk/wDRsVfSlAgr5Q+Mf/JVtb/7Yf8AoiOvq+vlD4yf8lX1rj/nh/6IjoA4T8eKByPWg+9FABR170YNL1oAMdu1aXh8keILBu4nX+dZtaXh7/kYdP8A+u6/zoA9kW4m67jTvtEueGpmznjNPCZ60rjshftE2OX/AEo+0zf3v0o8skVIsPelcOVDBcTHGCPypfOn5JP6VMIsdqUR57UuZhZEBnmzwQfqKinvZ4U3YU+nFXNnFQXkO6Hp3p8zDlRfuGdvCOhvIBum1aN+OOAxH9K7HU5DDNZYz8zkf+Ok1zOrRiPwz4bUcYvbc/mxNdRqiZmtfaTJ+m0iuGetST9D1Zq2EpL/ABfoXLOcErgjn0q/5hx90kd6yrZCrggEDNasYJGAa6YO6PNktRQ5wQFNc1qnjGGz0nVbu1t3ml0+cQPE527jkAkEZ4Gc/hXT7D2ZvrXAXEOh383iCPUJ2tpWutjlniWRgoAymVyFPHr0PPJqK0pRWjGp8kXPlUrW3dl/m77aeptr4r0yWBS8/l7kRmLKQAzHbj16gjPT3rEuvE+nOJ5En8wRqpIjBJwSMcfiPzqLV9G8LTSyh9RhWSVwzL9qUcrkEbenXcTx1zVaw07TJ9W1JY3aQEiKWIt8ucLzgd+Bz7VhOc2raHPGVfm6K+34mXrM6R3gzHv3KGG4YrO/tIKMCID6Vd8TQLb3ccca7UVMAVgMOnNdtBv2aNakby1LsmpRvy0OfrUY1CNQQITg+9UzimkcVrzMz5UTTXFsyMfJIJB5FJYXkMVpCGiO4D7wqq/3G+lRwDNun05rJt+1Xo/zR3xivqMv8cfykazXdtIctESaVb23jxtRh7VlEe+KA3ODWvMzg5UaU97G8TKA2SPSsqTiN/TaalP+famScRtjP3TQ22NJJHmzfeP1pKVup+tIc0hifhRk0o6UHPagAH0pM5oOcUYH/wCqgD3b9nL/AJmX/t1/9q17pXhX7OX/ADMv/br/AO1a91oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD5l/aJ/wCSgWH/AGCo/wD0bLXkdeuftE/8lAsP+wVH/wCjZa8joAKKKKACiiigDV8M/wDIz6Z/18J/OvriJRsB9q+SPDH/ACNGmf8AXwn86+u41zGMelRLcpFSeJy29AG4wapT6fZ3KATxDzM8HHNW7vUHsZVBgaRD1K9qRL6xu5AA+0ns3FXHlasxXdyC1sPsdg9vE25SSwzWDbI8V/tdSpGetdTNAykeS4I+tRSwDaWlQBscGs6tFPVFRfQp+Ej8159akdf3Ev8Avmk8KoFkvMetSSAi3k3DB3msY/AD3G6cv+mf8AqtEn/EzYkdzVzTTm8/4BSJGPtzH3NRKN0i0Z93bgxzMOvNeWaxDnUZT3r1u64hn/GvM9Ri36lNn0ppWYHIhpLS7SaM/OjZrubn4t3aWlvBa2+x0xvY85rjbmP9+QBxULQqOifpThNpBKKZ1Xi74mz+JNEFhDbeSTjzGJ61wNsGOcj61bltzgkLg0y3jIJzVudyVGxY0VP9PA969HtQqXIB/uV5/oqY1ZB716KYN12vb5K56u5cditEoN8T7Gt3VkC+HrU1j2ybbzB9DW9rgA8PWuKmIzBtCDbQg/3x/OvSWUf2Y3/XP+leZ2p/0eD/AK6D+delMw/sxv8Arn/Sqh1EzyXURjzM+9VYf+QaPSrGrNt347k1DB/yCxUPYaOm8Gn/AIl7/wC9XTNJ2Nct4SO3T2x3auhySaq9tAsE858mUAfwn+VfL2of8hG5/wCurfzr6cmOYZf90/yr5jv/APkI3P8A11b+ddNC+tzOZXoooroMwooooAWvXP2dv+SgX/8A2C5P/RsVeR165+zt/wAlAv8A/sFyf+jYqAPpmiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK+bf2glz4+scDP/Erj/8ARstfSVfNX7Qu4eP7Egkf8SuP/wBGy0AeVoFY4HWplVlORkH1FVlVsgg4NSjzOzGgZZDzY++/0zQXk3cyNn61XDS9mpSXJyTzQMuKGIyHb65p4ViuC5we2apq8igAU9ZZj3oGW0DjgSMAPepF3HOJWP41TWSQ9T9aejOhyBigGi4YWYYMjH60LaqCDn86iE0mOaes8maAJVt1z1qUWw7GoBK7cEVOszccDFAEqW3PU1Mttx1qNJn9KnSZuDtFMViSO3561bhgzgg1CjlscYq/bg/KetAyaG3INX4bZiRjrTYeT92tW1Xp8tAXC3snyODWnHZDFTwrwKtomT0pE3KC25AxinfZz0I5rTFuTjANPNsfTmi4jFe27baqS2hwcDn0rdkjK5JFU5cdPzoGmczc2rE8g4rMkhkB4BrqbnBU4FY85O45wKZaaMWSOQZ4xVCdHbIIrYlfBIzVGWRcGmXoZEgkXgLVd3kHatB5ecnFVpHUk9KRFkUXaUA4FVyWDZK5q87qPSq7OpPQUBZdys0uP4ajMvH3eKsnae4qJgvt+dIRUeTPGMCmBlHJBNTvtA5qLaGzgfnQIYZV6bajaQEHAqZo/YVGUwORxQB6n+z5g+Pb4/8AULk/9GxV9J182fs+YHj2+A/6Bcn/AKNir6ToJCvlD4x/8lW1v/th/wCiI6+r6+UPjJx8Vta/7Yf+iI6AOExxSgCkpaADnFHNAPNGaAE5zWp4cwfEenf9d1/nWYCOMd61PDWD4m04dvPX+dAHtO38qcI8mpVQcelSrHioKI1TAzj8aeE5qbZT1Tj1oAiCc9qUIB/+qpwnTj8qdt44FIZW2e1RzQlo2q7sBPv9KYyZKjH3mA/WgDQ8VQi30nRoh/BqNsn5ZFb1wu6+gjJ5bOPrisbxocWunD01aD+ZraA87Wrb/Yya5I61Gj0q+mEpP/F+hcjQbVzxirsY4zUO3HB45qZD6dK6IKx5zZm+JtQu9K8PXd9YrE88IVgJQSoG4biQCOgya8w+IF1cQ3krWX221hvIhNdRSDbvIbYCecgcKAPavUfEVi2p+Hr+yEZd5oWCKMZLduvHWvOfE+l3+l+F4lks0ZFt445JtiK6Nvj2oWDEt0YZx6e9YYpSafaxdJuUoU4fE3b4VLSWnXT0Vlu3c8/mm+0zvcTQ3crBj5jyTEsc9ASV6/zrS0W61ODULlrKWdR8zBMmXc3ULkAgscAZI6Zr0y5sNbne4aWwsI3nsWVp1UbhIQ2Fznt8oz0PWsbwtCyatrEUkXluskYKEAY+X2JFcnsGpLX8Dlw+Hr4pVJVaj91XWlvtJfLdv1ucprb6ktzAl6He4QFssCCynbzggHrmqUUkkrEPEVAHGQa9R8UwJLoRYjLoQc15u6Y55ruoU2uolQlTlfmbITnFNxUlMIAzxXWaEb/cbHpUNuP9HTjtU7j5G47VDb/8e0f0rJ/xV6P9Duj/ALjL/FH8pCkflSDg4Oacw5/SkxyK1OEecflUc3+pYnj5TUp75qOU/un+hoA8zbgn60fShvvHik70AHSl78GkpTzQAmOaOKBS9umKAPdP2cv+Zl/7df8A2rXuteFfs5f8zL/26/8AtWvdaACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+Zf2if+SgWH/YKj/wDRsteR165+0T/yUCw/7BUf/o2WvI6ACiiigAooooA1fDJx4n0w/wDTwn86+uonOwV8i+Gf+Rm03/r4T+dfXsAHlj6VEtx9CKeNpDlQPoaqPYI5zJAM+oFaRt3kbKPtp5iuFAGQ1ILmM1g8XzwTumOx5FVf7UluZVgkAypPI71s3jssLK6YJU81yFnI39oD8azqSaskXFX1Og8McyXeP71X7iENlSuQfSsrwpIWlu8j+Kta4ja3zKrn6GrpNKOopbla0txFqIAPGzpTUQi+JI71LZymW+3nqVqwNhmC9880rJq4dTMuUBjmz715tqEX/E1mAHGK9VvIAFkC9+a8z1KM/wBpzHsBWbTRSdzmYbJrjVBCqFix4Arb1DTE0Wye6vrfy41HBYdad4Xw/iuDaRkGsn40eJJbzWV0eN8QWwBcDu1KFPmV2xuVmZH9tWUkTStbloyeAKbHaCa1F7Ap8pjgg/w1yFnNcQGORgTCGxg9K9G8PXVtcWklsQF35wtbunFqyFFt7mTo6f8AE3X616JyLpf9yuEsoWtteMZHRq7d3/0pc/3K46m5S0Iojm//AANbuvLt8O2x9q5yCUC/P0NdDrr58O22PSoitBs521J+z23/AF0H869MbnTW/wCuf9K8vtSfIt/+ug/nXpuf+Jcc/wBz+lUuojyjWkKlsj1qlExGnKPatPXz8547GshWxp4HtUv4Rrc6fwq2NPP+9W+ZMY5rnvCozpx92rblU8e1TJyTGrD53xbSHP8ACa+ab7/kIXH/AF0b+dfR8yt9mk9Npr5vvv8Aj/uP+ujfzrsw/UyqEFFFFdJmFFFFAC165+zt/wAlAv8A/sFyf+jYq8jr1z9nb/koF/8A9guT/wBGxUAfTNFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV82ftCFR48sefm/syP8A9Gy19J182ftBoG8e2PH/ADDI/wD0bLQB5VGykcnBqQFf71RqgHanCME9DSKuSgoP4xSllVsA5461GIh0ApwhOcjOKYE6FGXJYA+lP+TP3hUAh9qeIPWgZOAoAIdaN678dRTFixxg08RcZwaAJVIzipRHj73H1pdPRUukZwNvfd0Fat/HaSQktOrOPuhBQaKCcHJszQFHpU6RhgORzVRY8epqaNCT7UGRdSEkgZFTiIgDp+dRRWe4ZM8ak+pqYWhCjbMrHPQGmBPHhcZxWnaRGU4Vc1SsLNZ5drzLEMZy1aws2jlKQzb1AB3A0Bcv21o/GUrZtrbA5Wsi0t53OQWIrXgSQDG48UCbNGGLn0q/DBk9OaoQBu9dDZRoY1JOT34pCHQWZYAkVNJYkJwBV4AKoAFLyelAHN3NvgHIrHnQAkV02prtziqC2dvAivcrvduQvpQCOYljz3FULi3XdnI/Guq1W2tXhLQKUcLnjpXJTndkDk00aLRmfPbqc8qKzZoFBx8taE6yc8VmzrLzVGl/Iz7qHb0C/hVMxIxA2AVZmSQPkrVVxMOi1JN9diGS3Qg4UA1WazA6nNTP5w7VWYyjk5NArrsMe175qBrcr3JqYvLnBHFRmRz1SgLrsQtDtHXNRPE3PzVLI7knjGKjLkdQTSJdiJo27E1GyN3NTGUelIu+U/Iv40CPUP2ewR48vh2/syT/ANGxV9J183/ABWT4gX6uMEaZJ/6Nir6QoEwr5Q+Mn/JV9a/7Yf8AoiOvq+vlD4yf8lW1v/th/wCiI6BHC0n0pR1pOnvQAdP/AK1APNApTg/jQAhrW8M5/wCEn00f9N1/nWV3rX8Lf8jTpn/Xwv8AOgD3VUqULzTlTinhfSoKEC5/Cnqp44p4XpxTwvOOKQEezPanYHpUm3NGM0DIyvNQzfu1D/3WB/WrOKrXZ/d7R1NJgjR8bH9xpxGMHU7Zh+JNdFp4zdyykHKjaPxrlPEsv2nQPD0w5Jv7dD9VJFdRbyNH8u3/AFhJP4VzwVqsj0cQ74Ol/wBvfoaEkoDdcnFPU8Zqmx+U4qwJAuM4yMYrpieaybOWzx6Vz/jXTbnVvC9zaWMXmXBeNkTcFzhwTyeOgrc/iOefensoUbhz605x54uL6mlCtKhVjVjvFp/ccd/bvioph/CsZ7Z+3p/hWVptlqVrqN/qN/ZfZDdzKVjEqyYAXHUV3oKhtvrxWfqsRNjKDgnGV9iK53SfV3t6HZ9dXLKEKcY82jtzX3T6ya6HP6/IW0eRfauBZBjk12+qP5uilj6CuPZOMU6D0ZyVlsZ7rg1GQeSatumT0xVdlwa6rmBA4xG3PY1DbD/RoyP7tWJMbG47GoLbP2aPHHFZP+KvR/od0f8AcZf4o/lIeRxTMEEHNSHmmdxWpwjz1FQzH92/+6anIBHvUcgBif8A3TQB5k33j9aQjPpmhsEmigBKXHakpelACe1L/Kk7UpoA90/Zy6+JT/16/wDtavda8K/Zy/5mX/t1/wDate60AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8y/tE/8AJQLD/sFR/wDo2WvI69c/aJ/5KBYf9gqP/wBGy15HQAUUUUAFFFFAGr4Z/wCRm03/AK+E/nX19b/6tfpXyD4a/wCRm03/AK+E/nX17Cf3Yx6VnPcpbFqPhutSk89a5bxD4xsPC5hF6HJl+7tFVLb4hWd3gxW0uD3IqW7biN/UyS20/wB01ylomL4cetdLNL9ohWYDAZc1gWYze/nWVR3aNI7Gn4VQ+fdD/arX1UBU2j0rN8LjE12f9qr15J56M2OhxVxfuie5W04EXgP+zS+aTfsPenacP9MA/wBmniIG9J96XRD6kc7t5U3PNcNfKr3UqleSOtd5NH8stcfcwr9vfPpTuBy2iSrp3iRZSPu5Ned3U/8AwkHjK5luZMedKxBP6V3mqEQ3czKcHawFeVmc2t+LgH5kfNKF3CyHondnpWleE7C+tbiznn2sql0OO9Z15ow8O6ppohuPM845Yegq5omr2hnivHuGQbTuXNYN5rq33iZZFyYoWwue4qKTlfU3qcltDpZth1eCZBwy8/UVuSS5uFbttrH8n/R7a47Fzj6HmtWRlPl/SpqbmXUqsxiug3qK6fVFMnhq2bPauavsLOn+7XR3kufDNuPRamCVmJmNp8O6KBT2kFemvEBpzc/wV5rp8qgwDPJcV6VKT/Z7f7lXCN73FJnl+vxAjPsaxkQGyxjoK3fEB27R65rDU4gI9qy6WKO58E2sZ0cFlBO411BsoWH3BWB4LGNDQ+rGulBNdcIppGbepSubSFbWUBB90/yr5O1HjUrr/rq386+u7gZt5f8AdP8AKvkXUv8AkKXX/XZv51pFJEyKtFFFWSFFFFAC165+zt/yUC//AOwXJ/6NiryOvXP2dv8AkoF//wBguT/0bFQB9M0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXzV+0KSPH9hjP8AyC4//RstfStfNv7QRH/Ce2IOP+QXH/6NloA8nEjYGRTxK3ZaFC4yCKlXH4UihqzN/dp6yMPWnDFOKr3NMZa8qJY932hSQM7e+ari4IONtKsQPO6neUv97igbsBmYjgYrS09YpYyZW+bHrWeIcjG4VKkZXG1wBQODSd2SSTKs7Kg3AelKk4Ctxz2p1rm3l8wEFvU0rRb3LAjJ5NAnYs20c08ZcAAdM052eGQRuuDjNOtbp7eHymHy5zxTZN08pkyOelBrNU1BOL1LFtumbaoJNTB3WTZt5zjFJYTLbEhwSG7jtVk3CNdeaI8gdKLk8keVO+pMoeJOVwc9a6LSrXzLczzED0X1rBecTR4UfStrTrtTAiMMMvBGaZcIU1OzZspCDEZIgQV6qO4q3G7oq7hj3xVVr2NFUqMseNoParnmiS3Hqx6elIzqxin7pchmwPU+9a8F8VVQAB9K5+PcPTNWkZgPegxOshvEkA3U9rmNQTnNc3HclVya5HxMsmo+IbCIBnQRMzqr7cjP1FZ1JuEbpXNKcIyb5r2Wuiu/uuvzO31C9BJY1m6jeG4EcqNncADivO57cRXRgazkZ1jDSCPe2wkHjhjx05qpuNvKsjabK8ewhkkMijdnhsg59K5ni2t4/j/wD0qeApSip3mk9dYL/wCTPSHnd1eaQsyImCBXNySqCflx9K5xbe5mkjzptzHGM+Z5ayHd6dTUMieWuXtJlAQAlww+b86FjHvy/j/wDpqZUotW53ftBP8A9vNme4T0rOluYscg/nSzWEMdm7lGEiqTnJ/xpjRRm3U4JO0V1wnJvlkrHJVw8FRVWlJtN21Vul+jZSmuockYY/jVR54j61amtY9pYA5qk9qCOhBrQ5HzkLzRn+I1XaWLOPmqy1khHU1C9rCvGGzSFyyIC8JGMmomeM/dY1K1vET/AB1C9vEO7flQJqREzxqOWPNQkoR1qTyIyTnNRNAo6E0EO5FMo2/KafBOYoipPBpjRcZBNRmLdxk0EnrHwDkWT4gXpH/QLk/9GxV9G183fs/Jt8fX2D/zDJP/AEbFX0jSEwr5Q+Mg/wCLra1z/wA8P/REdfV9fKHxk/5KtrX/AGw/9ER0COF70UnPbFLn1FAB/Sk/OjPNL7CgBOe1a/hX/katM/6+F/nWTjHU1r+Ff+Rr0v8A6+E/nQB9AKtSqvQ9DQoBqRRUFCAAYzTsc4pwGacBzQAm0Uu38qdgGlpAQstU7n5ztXntV2bCg+tR2cHmXALdF5pMZDrUXkeHdJjJyy6tAT+JNdcWxKqj0IFcv4oZV0vTAeHfVbc49gTW/G0qsolHzFsr9D0rC/7x/I9Gqr4Ol6y/QuBsx7s85xVmRhgHHaqTAjOfXOKu8suB2HOa2uecOjfcATjmpS4wcnPHSqcMmZCvQD0qyFBVsHgdauMroTViJI92SePSoLxCycngHmrqjbASR261A2HhwepGcUmrBc4m4QnRJkPLISp+oNcqV+U8V2l6gW3v1x1JNcgV4NZUla5rUd7MoOPSqzrV6Rfaq8idSa3TMmUZVwj/AEqva/8AHtH9KtyjMbfQ1Utv+PSP6VH/AC8Xo/0OyP8AuMv8cfykPamH8qe2en5Uw9a1OEkH3emajl+43rg09eVGaZMP3TfQ0AeYscMfrSYwac33z9abQAD0zS0lHWgBT+VJnGKO9Lx9aAPdP2cv+Zl/7df/AGrXuteFfs5dPEv/AG6/+1a91oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD5l/aJ/5KBYf9gqP/0bLXkdeuftE/8AJQLD/sFR/wDo2WvI6ACiiigAooooA1PDf/Iy6b/18J/Ovr2BTsHPYV8g+Hf+Rj07/r4T+dfYEH+rU+wrKe5S2PMfi1b+bNppxnDGn2NkkNrFhQPlFT/FNtjaefViKfAf9Gh/3RRUtyxCG7OzRcadF/1z/pXO2hP27I966RTjTov+udc5aD/TfzrGS1RojX8KkmS7B/vVoTDFsx9zVLwuMS3f1q7cMDZvj+8auC90l7kenOPtg/3amB/0s/Wq2mj/AEwH/ZqyAftpB9aOXQCnq901jpd/dqoYwxM4U9DgZryu68WXplEsllGhkTcAXPTpmvTvE6lfDurA/wDPtJ/6Ca8f8Vx2tja6YltAsbvAru3JySAf5ms6tOo5Wg7aHVTnOnRU4U4yvK3vc3byaMjU9ba6MoZHQnIJRc4rmp9OttiyPLOAxI+4Oo/GnanczJdsiuVRG3DHY1XBmuYFi3sYg+QDU04VGk+YrEV6kK0qfsoe62vtbK67m/BZrbW/lhy3HBxzVKwsITOZlkkbzM4LAY/SklvbpYycqD7DpUUGoTshLAZ3Z6nj9aiNLER3Y8TmEZtfV8PGK63ben3rzOyN5c/YYE8oCIfdbnnHFWItUJkVJwqjbkFSTVKyT7ZZrulKlCAFEYPDEZ59utRXsQsrnA37SMqXGDjJ7dulZOU7JsxeMbjOm4Q5kt/fv0voa95fQSTJtkBwta0+uWk+iR2sZYuke5mxx1xiuPnuVAHyBcDHSp9Olje0nLKpI6EjpWsea2hwwqVZ7NfczUstQiSaMsWxGQ3TqOtel3niOwg06TmWR1VNyJExxuxjJxjvXkayRqqnYv5VfstcmLCFUj8qYhJV2D5gOmfpVx50mNqs+qNXxHINkbA9awfNAix7Vp+ImVIoUQAKBwAOlYLP+6H0qEjp9T0/wXLnRlHua6dTk1wXhLUUg01YyRuz0rqkvZGI2qT9K64bEM1JsC2l/wB0/wAq+Q9S/wCQpd/9dn/ma+r2mlaCQGNh8p7e1fKGpf8AIUuv+uzfzrREMq0UUVQgooooAWvXP2dv+SgX/wD2C5P/AEbFXkdeufs7f8lAv/8AsFyf+jYqAPpmiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK+a/2hF3eP7D/sFx/+jZa+lK+bf2gw3/CeWJAOP7Lj5/7ay0AjyhYz6iniI+tMSQdDUqOC2AOtBQqRt/epwRgeeaupZPgHcv50C3Odu5fzp2L5WiqoPHOKcEPrTjIqMUKHINWI0G0s428ZX3pCZCFOOtP2nu1PLKse8odpOM0iurHCjtQBJFBI4OznbyacFf8AvYq1p8j2xD+UG3jAz70x2jjldT60A1YjAP8AeqVQ+euKaJIieFNTCSPGcGgEOUNxk1YQNkDNQpJGTntVmOWMds0BoTxhvWrkRcd6qJNHnpVqCRWYf3aBWNOCVxjnmtS3nf8AvGsczwfKI1OR1NW4bhF60watob0Ush53VY89+xrHivkA61ajuN+SBkUC5exde4fb1rFup3Ov27Z58hh+tWnukAwSaybmdTrELDtCw/Wsquy9UduBXvT/AMMvyNW206C+1LUzMDuFrFgqeeeTj8hVe6tbARDa8i5QRlQRg4xjt14p2m3qi71JzKiMYIgu44zjNQ2lgLxTLNdRRxjnlwCayhGDu33f5jxeDhzxkobxj0/uo2LWdY4wpkIwgAJ7/WsfWr4XOl3KbRuO3kD/AGhVvyYLkGJbuJMjAYuKzNQgW10++ieWJ3+TaVcHPzDpVVWvZy9GdmApSWLpafaj+aMy9lmVJEbIHINZ73cuwLvIAFaGpSo5lUtg5JAqgRabAS/zEc1p/wAvfkYSi/qas/tP8kV3upsffqs1xcE5U5xVqRbUA4lPFQxzwI7AH8WrQ40pXs2VXuLrPQ/lUBuLnvn8qvy3MfZlz7VVDRNy0o5oKcf7xVa5n5BFRNcSnPFW28k5PmD86i2LI2EO8+1BDi+5SeaXOAOajMr91zVuRAjFWbB96gIUDhxQS4+ZA0rEcio2kJ7VO4HTNRsF9R1oJaPT/wBn1y3j6+yMf8SuT/0bFX0lXzh8AFA8e3xBz/xLJP8A0bFX0fSIYV8ofGTH/C19az/0w/8AREdfV9fKHxk/5Ktrfr+4x/34joEcJz3oNL3o6UAJjnrR9OKXjFJmgBep5rY8JjPi3Ssf8/KfzrG+lbXhP/kbtK/6+U/nQB9DgZNPAx2pQMCnrz2qShADn3pwWnYxSjr9KQCBTilIG0U4DpxzQflFDApXB+fb2FXrKEiIf3nNU1HmzD1JregiAYDHQYFQxnO+NwyaXp5jHzjUIdvHfnFX7W/up7iKOaIgZwzHIqHxmB9m0of9RO3/AJmty5CreYIPTHHuax5bzb9D0KsrYSkv8X6E8iHC49quIVVeT161AAJRGc4J5xU7xgLwccV0JdTz2QPCGkJQ/NnrUsSlNy4JJ9fWoEfadvXJzmrEblufTrRGwncJGY/L+dQuSG47CrBB6scCqEsn7wgHGab2BGZqEI3zpt5lGRXCzo0UroeoOK7fVzK9jb3EbFWBAYgZ4/8A11yeqFZL52AK5xn64rCD1NpK8LmUy1XkUYq4/WqFxcJGSK2MStMuI247GqdqM2kR9BUs96CjDaeQaitSGso8dQKm/wC8Xo/0O1f7jL/FH8pA3HoKYe+TxUjjFRnOMetbHAPUDbTJj+7bkfdNSJ93FMmH7pv900AeYn7x6dTTacT8x+tN6fhQAuKDSD1ooAOvOKXNJ0+tLj1oA90/Zy5/4SX/ALdf/ate614V+zl/zMuf+nX/ANq17rQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHzL+0T/wAlAsP+wVH/AOjZa8jr1z9on/koFh/2Co//AEbLXkdABRRRQAUUUUAaXh848Q6ef+m6fzr6/t+YU/3RXx/oP/IfsP8Arun86+vbY/uE/wB0VlU3KWxwXxUi8yOwJcKAx61Bp0jS2cRIwQoFX/iTY/bYrHkgK5JxWTBciKJEVTgDHSiTTikOK3PRF/5BkJ/2K56yYG7/ABNbFnOJtGhwScIc1z1gwa/xnuamUXoVE6HwyV8+7we9XLyB4rSQhs85ArM8Ltia8Of4q1pHLsUPOa1oR01Jk7Mp6ZITOh7kVozERyBz1zWeNtrfxr60txM0kh54BraMdLESZT8VSl9B1LHT7LJ/6Ca8V8X3fmXtomeI7aJR/wB8ivY9eI/4RzU8nn7NJ/6Ca8D1+9jm1LG8ZVUX8lArGf8AEfod9P8A3SP+P9EZ+owlp5GHdsVp6ZYoZW6YUdPeqd7nezL1V81f0+VnM2xsM3zDis6K0ivIMb/vNX/FL82F9bKEYqB0qG30xjsUJ96Pf+tU7u+nRypJ4PSuh8Ktca1q8QmGIlG049KupduyOWnbqXNIuxY6xOSThI2wo9c4Fb2s28N1qVr9pUtjT1YY9d5rldXMdnq8xicfPcOgUHnaG4rvtMkin1q3aVQR/ZY4Pr5lEV+4jF9z1qv+913/AHX+h5vqiSi4bZE+wdOKl04OljKzZG44wa9K1BtORTvhX8q4TXry3KkWybQOuBSqU+VHkJma0xU49BV3RyGljJ6b6557v5+fStPRp9xQZ/irJoEzo/EUuXjwe1Ykr7YgParOu3OWj2tmsm4uPuj2qIx0KbLkWovbXcSl2CY7Vbg8RX9pqbqLyRVIyuTWBNLuuYz9KuTRrNcgn0q1dEs7iD4g6lbRskojnUqR79K8PvZPOvp5MY3yM2Pqa7OSTbuHPFcRL/rn/wB41rCTe5MlYZRRRWhIUUUUALXrn7O3/JQL/wD7Bcn/AKNiryOvXP2dv+SgX/8A2C5P/RsVAH0zRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFeO/FzQ01PxBBKygstkq/+Puf617FXBeNoRLrcQP8Az7KP/HmoLhufMOp6ebK5ZCO9RtarHCjnO49cV6P4q8LPNL5qDgntXF3+nXCSLGv3VHUig3VPRuxnMI1B2u2cUrQNHGshbIbsKhVZS5Qrkr1qzMZBCkZFBC21IFxu7mtU2JeKNlLMSMsMfdFZ9uiLL+9Bx2rchvVgsntYgS8vBkJ6D0oQkna6Kdxp+3TxdxsSm/aw9DUVjbme4UE4Xqx9q2RILbT7q1lZWSWLKHtkVz6SSRr3G4UMSeupt3E8Alt3U4RGxge1R3ccN1dl7TkMMkH1qksDm2SVnBUn7vepYkKuNpx34oKb5mReUyOVIwRUysQMetXIx5FtM8gV9/CnqQaoiVtuDxz6UEyXKWIY8jI7dquQJHlvMBxiqUUrA5IznpViPdk80Ep2JwQAMCponxhc9adDE/mqCud3QVFykhBGMHpQJ9zSidVUnGT61KWYKr4O09DVNXJ7YzUj3MoiWMjgHigVy2k+D1rRtbvDYwMe9YUR3nHer8ayBDGi7m7mmVE03khcZHbrWdLsfU49n/PJup96qpdmIsrc5qFZibxWBx8pArKrsvVHdgpLmn/hl+TO0sdIsZrSE3NrFJI7Y3MuTiugu/DWiJbDZplsG6Z2Vm2JLLp8Sj7wBY100/zRge9N04PVozeLrwUYxm0vVnMnw9pUY/48YD9VpL3QdKSOGWOwgUMCpwvetGeX92nr0of97psi4yUO4UlTh2RpHG4m+tSX3s8uvIhDcXEcindgkVkzptVSO/WtDW5iNZnHqxFUbyfawRQCNoo/5efI0ly/Uv8At5/kh32e2NuJTk8cgVmOUbopzVrzZ/LISLt1FVvPdGBEPbkYrU5ZuLtZWK7KAuSpPNIwhAGVfNSSX7FgTGoI9qjmmmlTdtCoPSgztG2hXdoi2AG21PaskcuVJyehNVAct/jUhbbzx+FIhS1H6g6SzYxgjqapBFYjBNTs27kmktLdZrrazEL7UDbuyW6toltC6n5iKylwWHpmtLUt0TiInIHQ9OKghiIyrL97pmgJr3rI9a+BKx/8JrdOuMnTJBgf9dIq+hK+d/gQjRePr2M9Bpb4Pr+9ir6IoZM9wr5Q+Mg/4utrX/bD/wBER19X18ofGT/kq2tf9sP/AERHQQcIDzS45pAPz9KXnvQAlL3pO1FAC+9bPhP/AJG3Sv8Ar5T+dYvFbPhTjxZpZA/5eE/nQB9FhqkDDNVDKVPzAj6inpOp+tSUW8jHPFOBGDntVbzAelP3Z5qQJwfypszbYzSIsjnAVj+FWZdOaSE7WG8DODQ3oNFawUPcr7DNb0KgZPYVmWNnJbzb5CpBH8PatZSAB157VNwOc8Zn/Q9L9Tqlv/M1u3BP2iNupPH5Vg+NGH2PShuX/kJ2/f3NbrkPcLz8oHHPXis0/ffyO6t/utL/ALe/QntH3TFf7oxV5skcY6Z5rN0/cJSzcBs/lWg7/I3PUYGa3T0OB7lM4LYWrsUe1RnoKrxqBIW/KrkXOSaIIGNk4Uk9KxHLPcYC4C1r3LfIT+VYpbasrEngHr2okCHxx+fp5i+uD/KuAut4nkEh+YMQc16FppLWMbY6qDXD+KE+zatOqqcOA4P1rOJTb2MK4uFVGAPOKx5WJOc9auSLnqCapyrxwKoRSmIwaNPc7Sg9KSYHB4ptiwXqDkjg1K/iL0f6HYv9xl/ij+Ui63PTFM/OlaTBqMyY9BW5wEy9KbKAYnH+yaYkwx1pssw8pxnsaAPNTksfrTaU9T6ZpKAFxzRSZ5zS8YoAOlJ/jRS4oA90/Zy/5mX/ALdf/ate614V+zl/zMv/AG6/+1q91oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD5l/aJ/5KBYf9gqP/0bLXkdeuftE/8AJQLD/sFR/wDo2WvI6ACiiigAooooAv6Ica5Y/wDXdf519eWrf6PGf9kV8gaSdur2Z9Jl/nX1xZPvtYiv9wfyrKe5S2KuvRtOsSrGHOfSorPQUAVpgvPbFarhRgmmvKxUBa3px90TdiUxwQW5iRQBjGBXERRva6ztIIUvx9K67PPJy1Z19ArMJSuWU9ac43QJi6CqJq1zGp+U84rdcorN8wB7Vyuj3KDXZ9rc45rQupfMmYhjSp7BIfM7NLuOCR3oQOxyarKhZuGq2pZR1rW4rFHXwf8AhHNS4/5dpP8A0E18zX7l9VcHr5mK+l9fkf8A4R3UQTx9mk/9BNfM15zqrH/pr/WuWf8AE+R6Ef8AdI/4/wBEb1wAZJVzyeahguWtXRk4xwc1LcoWuW29aLaISP5bggHjnsaVFXhF+SIx/wDvVX/FL82TXOmtej7Vbnep5ZfQ1peH9Wi0by0ZCJBICx9queHYptN1FY5wHtn4PtXS674Jhvf9JssJIRnA6GupUub3lucXNY5HWrG3jnXVzLiKS7kiGfQEEH9a7vwxFDNr0QVxJGdOBB9P3lcvfafI/wAPr2KZctZXm7PqeAa0fh9IYL2Nt7EGyON3UfOOKwS/dx9UezU/3qv/AIX+h6Dd6JbzqQUFYV34Ss2yHjGDW+2oe9VLrUBt5Nb8q6njnIz+CNPyTtrHn8ORabKJoj8g7V1d5qsUQOSa5TV9djdSibjmsaqjbQcTC1JkMwzWXcMpfHal1G6zOpHSs2e4zKee9YRWhTLLSfvwB2NaqyYk3HsK59Zczj61rBwZBk9qfUQySYneT3rkZP8AWt9TXTytgtnpXMSf6xvqauCsSxtFFFWIKKKKAFr1z9nb/koF/wD9guT/ANGxV5HXrn7O3/JQL/8A7Bcn/o2KgD6ZooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACuB8bTiHXIc97Zef8AgTV31ef+OoDPrEQ5x9mXkf7zUF0/iON1zVoobMk88V5pq2pCdCYjt9a6PxBZ3T+YgBZF647CuKbSLqeXbEQx/u9zSR0OUkrIpCd0MjAZJPNSNO02CybRUDJJBI0UilWHBB6g1PH86fNzTMm3axYYLJGoA5q1BHCts7zsR2yO1Vo4228HpSMxkikUnjFMIu2pHLtklCxyExA8Zp0ygvjOcccVVR8LirdlKsEuZQCmKRD3LMJ8xBGPoBWjFarDFsZgZHGB7VlQ/f3JxVqK4YygsSccZoNoyUVfqWtRt4rS0hVH3MVyfrWYnzMBjk1Nehi5YnjOBTLdSz+vNDMEmTohXINaMNnKWjJ6MelNkt/LtuMknnmltL14XX+LHahFcvK7MsXDsNRwp4WpNQiCypKo++M1RkaQzF2BBJzzUhnZwAzZx09qBOV7lhpUMa4B3DvSPJvX6VWyccmnK/I9KCC7BaXTr5kcZ2k43Grq2V9F0G4nkhWzUEEk0u2OPdsXk4NSw3ckc5UOfMLY5oLSKEiSB23Ag5702IAXK7skY4x1rU1OeLYgIAmJ5xVa2tZ5L6EFCC6EqD3FZ1dl6o7cFG1Sa/uy/JnoehoZJbc4ICRA89uK6E/NH+dUNOtja2G5vvMAPyFW4j+6/GtDik7swrmQrIkY5zJ2q5D/AMtFzx0NN8oNeKWHRqmMJRnPrSLbPH9bGNbuB3EprPNpLISVx+ddB4xtVtte3Bcebh/rXKzSsCVBPBNR/wAvPkdt4/VFzfzP8kWoDLa71dSR7DNPeZGkQjaODxmpbGVfszMRghTu96yvs7S75shVOSM1qZe1cIq2xHJDulJLjr6VC7OE8pRkZ61bE5aPy1jU++Kq3UiFwYgVGBke9I527alUYEhB4p8g+vTrTHyRn9aTzSo2tyB2oMx2xzgY61KqNY3ieacAio4JD56gE7c5Ge1JqExmumG7PYUIadtSW7YXF6dgMnHAp62b7FlDneCPlxVazPkXStJlRjvV92YWzlT1PGDTRrGzd2eofBSEjxzdy5BxprqR/wBtI697r55+A6n/AITe9YsSTpr5H/bSKvoahmM5czuFfKHxk/5KvrQ/64f+iI6+r6+UPjJ/yVbWvrB/6IjpEnCnPWj6Uh6Uv4UAIM59qXpSZzRwaAFNbPhNgPFulEj5RcJn86xa2PCo3+KtLGOftCfzoA+hXkWQ8jgdBT0CdgKjSA9wamEJA4FZFigKT0HtUysF78UwRH0p4jIPTmgCzFcso64qYXO3JJ6iqQQk8CneUfrRYC1FdE5Xd15qdZd+DuOfrWfHDtbPNXF4HSpsMwPFsaG204jPOpQZ59zXRiNUTZGOOcVz/i3H2PTD/wBRKD+ZrpowvmIB0JFZRXvv5HdX/wB1pf8Ab36E8P8Aryq5wgx+VTzYdskH5f1qC0UrIznv+dTLtZix6fWtzgERi2Dg4q6udvTtVOI75GZhhRwo9atfw+9XHYllS7cnp0ArMuVP2OVvVDWjMy+RI5PLHAqheAjTZe3yn+VSxol0g7tMhHooz+Vc940tA9vHdqvMZ2P9D0/z71taCSLGNenHSm+IbNLzSri3fhJVKMR2z3pAeVSEHuPzqrIFPcV59qKXun6jPaTTy+ZC5Q5Y9qpm5uMn99J/30a0EehTqAh6dKqwMFhXPcVw63EzTJmaTqOCxp9/PKt/MqyMAG7Gs7fvV6P9Dtj/ALlL/FH8pHbM69zxUeVPTNcGZ5Tz5r/nSieX/nrIf+BGtbHCd5tU9jUV1LDb2zvIcADHWuNT7RKMrO3HYuaVjKqkO5Y+5zQBROSxPajFHek7cUAL9KQ9etLSd6ADFGTmlpKAPdf2cv8AmZf+3X/2rXuteFfs5Z/4qXP/AE6/+1a91oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD5l/aJ/5KBYf9gqP/0bLXkdeuftE/8AJQLD/sFR/wDo2WvI6ACiiigAooooAu6R/wAhiz/67L/Ovrm1ULaw/wC4P5V8i6ScavaH/pqv86+tYZt9lb+Vydi5P4VLV2UiSZwWA3Y9aTzMDag49aQxBkYnlj3ojjOz5uAO1bR0VhSFVCxyOnrVe9dPKMfc0txeCNdqnms9pQ7Zzk1aRJHbwxW8xlVAGPU1aEm98BOtQABzitG1tgi7j1oeg0NW37jFONux/iqyAM05yAhqblGB4ghK+H9R+ccW78f8BNfPsFrDJcTTyKzMtxtGDwM19Aa+N2hai3/TvJ/6Ca+fZwvkT5fb/pYPHc46VxYm/NZO235nrYWEpYR8lk029Vf7K6MvTuombMTZDBc7vXH+Ipq3UYwybyxcocNwpHqce9Y2qEjUJuT96qG5gTyfzqKdGXInzHPi8ViI4icXytqT15FrqdHca7skMUguNyHH3gK2tN+Iup20KwxK8qDgeYAxFcDuPXPNbGiW010XS15uEG9VHUjviumnTd7cz+9mf16dtYR/8BX+R2769PN4fvLaSSNUvJvMeEphmLEZwfb0rf8ABlvH/aUaKwIFhn6HzOleeanIQmn+fkP5vzDuCcf1r0HwMN+qMYyDizOcf9dAKxheLir/ANXPdryU415OKTV1okuiO5e0XH3qrTaaHHWr4ORyKki5O013STPl0zmrjw+Js81j3vhoKD8g+uK9DMIx0qKS2DoVZRis+XuUeO3vhgPkgVh3PhmRDnBr2W701UOQOKzJ9PjcYAXNJwQjx6TRJ4WD46VG6zRTZcfLXpd3puwEnBX0rDutFM6sYwPpWTVhnKfLIhPtXLSf6xvqa7C5025tSxUfhXHyZ8xs9cmnEljaKKKsQUUUUALXrn7O3/JQL/8A7Bcn/o2KvI69c/Z2/wCSgX//AGC5P/RsVAH0zRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFcd4rUNq8ef8An3H/AKE1djXD+L5fL12Pnj7Mp/8AHmoNKSvI8+1KY215IUGWII5HFZMfkwzCVbf5l5JHrWlfS+bfSSkfLnANY99fw6ck0svyrj5cdSaND1lTild9jgtQc3Ws3jN1Ls3NWraGBgqlecdjWLJKXuHlBJ3MT+dWI7mSLDKeRQjy09TaeGKFcAnd6VRmhliDuMbCM5Iqq15PJJuZhke1SnUZmiMbBWUjHSgtuLZTDfMeKmXBkG4cVGi4PzAg1LtyBQY9SxC5xz0pPNIkJHFRJ6UrcDIpDuWRK8nynn8Kv2cLenJ5rMtuXBPA961I7kw7lC5z/FQVHe7NZAZ1CMPbiq1xa/Y5VZMlev4062u43jAclHA4YVZuVeS1ALBsc5FMqVpRv1KU1x55GQFx6Vbs7E3KKBgEHk+1Zg5b+tbFpK0IUKeScYpO5jGzepK+kRp8huAsnXDVU+wzCQBkOD0btU+orJJcBVJZgMmrcVw9lp7CdgSRhVNNGjinJ9isbg2KeShBJ6kVS3SF96Al8545qNQ8zF/U96uWEggkzkY780Gd23YqeZJ9qVpM7gf4q6CzkNzrFmASW2Y/Ws3U137JcD61qeFIRN4gtS38MTP+VZVdl6o7sDeM5r+7L8memSYWFEB4Ap0fEYx61HONqrxgGqN9rVnpduv2iQbieFXqa0OFLQllUiQn/aq02GiOfSuOtPFzalqE0MNvtRVLAseSK6Gy1T7TGQ0WCgxwetBbi2ro43x/bbjZXWOnyE153N8shyO9eveL7b7R4cMqj/Vvu/CvI5Ynedh0PbNZ/wDLz5HW/wDcl/if5IaJlWBkDNknoDxUUc211yx256VoWUVrbOWvAsgI4GelV1slacyB08kHI57Vqcyi5WNDToklmZXKkAkqaxtXSOO8ZYxhf61djJj3hOmeMelU7+OWaTfj5AO9M2nDmp3RQVj0PSo3TqV5H8qcDhxnpmmltrMO2aRyD4HVUdcc44NS2MX2rVLZWx8zDdVVONxPIxWpp2nN5SXjSiMZ+RR1oEdBf6XZNHK6xfNzjHaubVCiFM967CSC5FrKcL5ZjBMpPFcw0WQT6d6aZ0VnGy5T0X4FD/it7z/sGv8A+jIq+g6+ffgUMeN73/sHP/6Mjr6CoZzsK+UPjJ/yVbWvT9x/6Ijr6vr5Q+Mn/JV9a/7Yf+iI6Qjhe/FHtSUvpjmgA5/yKQdDijPr1pe/HSgAx7Vs+E8f8JdpX/Xwn86xfX+dbXhIZ8X6UD3uU/nQB9FI654FShh61GsSiniNc8VkWSeYAORQHGORTljAA6e9PEYHcc0wGq4HRSKcr8E4NBQ56jNKEODyKAGmT0qRJMdQc00J7ingHPXikwMXxU+bPTBj/mJQfzNdMkuZFyP4hXMeK1xZaZ/2EYP5muljTMidPvCso/G/kd1b/dKXrL9DQf8AdKeOwFQ5zEioOWP/ANappmBYr3wDjtULIxlWIY7DitGcKLUeFVBntgfnU7NiFiKiZdnTtwB6UBmEB55q0+hNitKp8pAMcnJqnqLBdPmzx8h/lWk6Dau4ZzWZqIMlrOM8bDSGQaKdtrGfatK8j823kQfxLWVo67LQDJOM/wA62QQUHPbBpDPnj4oaYbfWYb4LtFymG/3l4/liuByR1r3H4raV52gyzIvzW8gkH+6eD/OvDmbNaR2JYsZ/eoB6iptQ/wCP+cY/iqGPPnJ/vCptR/4/5v8AeqP+Xq9H+aO2P+5S/wAcfykVfek+tGc9+lJng1qcJZYoAHjOCRyg/hqN5CwyetR5JOKVo2A+lAEQ/Sk70dqP50gF7UmMUUooAMZo96O5zSUAe7fs5H/kZf8At1/9q17pXhX7OX/My/8Abr/7Vr3WgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPmX9on/koFh/2Co//RsteR165+0T/wAlAsP+wVH/AOjZa8joAKKKKACiiigC3pnGp22P+eg/nX1T4acyeHbZicnbya+VtMGdTth/00H86+pfCnHhuAemacfiH0NlTtGTWbfahscomMdzVq4fFvJziuf273JOW9K0sIlaUyc9aRSaXy2C8KaaC3Yc0XA0LOMfeY1eV+uDWTG0uMVMHlA71LV3uUtDREmDzTZmZlwOlUd8h9aXfMBkk00BU14MPD+o8cfZpP8A0E14Ze6OI4WWV2AdxPwM9q9z1FZLzTbm137TNGyAntkYryTxPBLpL7Lo+YFTAKDGR0rgxinzprY+iyedL2E4StfV632t5ficJeyLdXsjxnhm4zVU5Bweoq/5+njB+yzf9/KVn05gXFtKT3HmVrGbUUlF/h/meZVoRq1JVJVoXbb+11/7dM2r2m3s1hqFvc277ZEOQaDLp2Rm0m/7+U5ZtOyP9GmGP+mlUqrX2X+H+Zn9Th/z+j/5N/8AInTa2pu9Otb9gBI14C2PfBrtvhzGF1icRuTutnLAjofMHFcJa3cd9p0cIGEWQEITyCO/6V6p4O0Sa1tYdYN1G8c0BiWNVwV+YHms025xi1Z2/U9qrKksPWqKompPS1+3odd9nY9CDSfZpM5FRC4I6GkN24HWu/U+XL6Rvt+Y804oxXkVnfa2H8RoN8w/5aUrDuWZosrtKZzWJd2bRMWwNtW5b5u0lULm8Mi7fM4NHKFyjcRptPQ1h3SBMleDWrcOoUgN+tY10Qc45NTJJlIy7poZIn8wYcA15RP/AMfEmP7xr069XKPk44NeYTf6+T/eNZWsEhlFFFMkKKKKAFr1z9nb/koF/wD9guT/ANGxV5HXrn7O3/JQL/8A7Bcn/o2KgD6ZooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvPPH7mPU1f/p1H/oTV6HXnXxEI+2Ivc24/wDQmpM2ofGea6ffLPdsshB54Bq3q+i22p2bfKGyOQOCpqppujuZpWBxxkEmtO3vPLUFgrN3APUUrXO72jXmeW32iTabeFZV3QnJVqqmKMxptfBIyc+tep6nBBNIFeL9zIu4bq5e/wDDUflrJbkKAPvDkUlLWzIlhtOaGzOOK/NhcH6U4MIgGQc+9TyaVdQs5RfMUc5WksY/MuFWRSUU5arOXld7ABNcOFdfmA4wKszRxxrDzgsuT6U6K6V7/ZGuI84GfSoXXejlgR5Y+X86BtK10DJGvIlU/SoyQUY9/WiBFeVVYcE1sX1hbRWiyRH5gfmHrQSouSbRnxfMqL1J7CulMSNbmMoDtXrisHToRJqKAA7V5rbWXzkuWTqOlNHRh0uV36mSp2sVHQVat3bLDfgbTWepO4nnOeau7MWyuASzHBpHG99AjwZACeK1LW2Z7pFU5QjJPpWQpIPNaFlftbMTtznpQEbX1Jo7s29zIr/MvI561VuLh7hyzH6VCzs0jFu5zimluCKAcnsWoZCV2qOcUkThAzMuecVFAxEgxV6/RYbONB95jk0CSurk8bJe27xoGUjlQTnAq54a1ODTddikuGwmwxk+hP8A+qs2zcwtCrxlc5BPqDVe7hC3TKp4YgjFZ1U+VNLqj0MDy3fM0rxktfNaHskuZriJlYNAV3Z7V5x8QJgNVjiVtu1c8V0sMniK00uG1WPTnjVRhiX3Y96871+9e71WVrs/vUOw+X93j60vaeTGsFJL+JH7y54fuo4tcgG/5ZQUJPvXdQQtFfKA3AbnmvLLSWNLgPHvLJ8w3V6LH/bcqLIBYYdQckv0NDqeT+41p4Tp7SP3m3r86HRtRtAowluX3V4zeOolBBzxzW9feJLuFLnTZ33DdtYgdh2B9K5p3DSM38JBPNKLbne3QzrxjTwyp8yb5m9HfogmmEqjAAx6VWLkHGT9KmSBzbNNkADt61AG2yLwOvetjgtbcuwPvwoYrxzim3EluAyGSTPvSzW0wuWePABNQzWU0rlgyg96DsiqkY2USp9mkI3jBUc1Xf72R3q0JTbrJETknjjoKqE80HJNRSVhAQufeuihGLOHIwAornMgkV0kEMkhhgjQ5Cgcd6DM2tSu2TR7O1GVDLubB61gM2cqOgrR1lZEkijYgGNApBPOaoW9hdTxM0SBhnk5oA9F+BSf8VdfyY4+wuP/ACJHXv1eHfBayls/E1yshQ5sXJwc8+ZHXuNA5KwV8ofGT/kq2tZ/6Yf+iI6+r6+T/jJ/yVfWv+2H/oiOgk4bOTSClP1pOnFACnB9M0YpD15ooAUgEVseEjjxdpR/6eU/nWNW14SGfF2lDublP50dAPosTgnBFSrIAOgqJYvyqUJwM1kWP8wHsKUSL3FMC55qRYhwTQA4OKXzFpuwAfWjYA1MEP8AMXptpwcDPFR7QecUoGDwKQGN4scNaab/ANhGD+ZrpFb94uDgZrmPFZ/0bTsj/mIwfzNdEpww+tZL45fI7q3+60vWX6Gk43TIOegzU1uoe6aQn5VqNn2NvxyRtFTwRhbdScZc7j9K0jqzhY+RsA853U0BmCpnAY801sO4C4x3GKmj+8uCDgVS3F0GTYLHjAWsu+wLGQ465rSmLFHJ6nPSs6/XbaOD1EZxmhgirpPMC8+taUfC4JrN0hf3XX+I1qAfN7HtSGYXifT0v9NmgcAiSNoz+Ir5fntHgmeJwdyMVP1FfW11F5lsykV87+NtLNj4qvF24WVvNX8ev65qoks46KIrMmc/eHFS6ghN/Mf9qrYiHmL9RTrqMG6kPfNL/l6vR/odsf8Acpf44/lIyPLbPSk8tic4OK0jHnt+VNMYFa3OEopGQwJ7U/DMDgYxVwRg460yVAAR0OO1IDL6nmlpOn1oNACjr0pMe9KOKTvigBaSj8aU9eKAPdP2cuviX/t1/wDa1e614V+zkP8AkZf+3X/2rXutABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfMv7RP8AyUCw/wCwVH/6NlryOvXP2if+SgWH/YKj/wDRsteR0AFFFFABRRRQBa047dStz6SCvpzwpdKvhmJywxk18xWEby6hBHH99nAH1r3XRBf22jJZyJhlJJwacdHcfQ6C/wBQmnmYJJti6Y9aqCbygCrnNVSLxBuMIb0yahKag7Z+zj/vqtXJCsXZNSkUZkc46VEmr88H9KTyJmUBkAPpmpFspP7gqR2HjVJD0YVJ/acxHDikWykPOwU9dOlYZCrTuh6iLqdwpwxBpx1J3GCaRLGRmIIWlbTzk9Kd0GpG14WPXp71y3iKKDUL4xXEioptTgscc7q6z7A4fkAio7jR4ZhukghkYDALKCRWdaPNGyOrCVo0pt1E7NNaeZ4HcaXcxTlFjdlzwQuRTjpNyVXCNz14xivSb20ijutoiQYbGAtaOoaZbjRDIkEavgfMFAP51wKtU20Ov2GD3978Dyy90WZZI9g3DyxnbzzVeDSLiWbayOo9SMV6Drltb20to0DwsGjG8RsDz74q34disZLwC6MAT/prjH605VpxdtCo4LDtX5Z/cjkNMsHszISoz0XLD8TXsnh20lXw1ZL5m0FN+M+vNT3Wj6VLbwmztrB1IOXREIP4ioRbSwoERlVFGFVeABXTRhKUlUk+hhiK1GFH6vST3u727WLMkEiMAJQc0GFwP9cKomKc5O/n60xoZsf6z9a6tTzjQZMr/rRmoShzzKtZ7W85HDmozZ3HXfRqGhckUY/1oqlLGD/y1pv2eVScsDVaSNwCGIpO4yG5gQqcT4rCvZHt+DIHHqDWrcQseax7uIYy238KzbGYGo3blG2nqK4p+Xb613V5BHg84BHpXDSjErj3NQnqSxlFFFMQUUUUALXrn7O3/JQL/wD7Bcn/AKNiryOvXP2dv+SgX/8A2C5P/RsVAH0zRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFeZfEiTGswR7sZtlP/AI81em14x8XdRjtPFdpGz4Y2SNgf77/4UG+GSc9XYo2nz2s8aNiVkO2uXtrzY5jIO/JBFRx+JmRh5aMT6gVVjnXz5bhoWBkOc+lCR1aKW+h0ztHrenfYAXjlCEq49ayo4bnSrbyZpoyQcfO1VL3Ul0a1hlUlg5yu08g1ymr6sdVvPPKlMjGM9aGkL2ipaxdzo5NVtBqSIAiYXBYHINVdXWBLBrm2ADMcOF6c965YH2/OtK0ud8T2jjMb0/IhVm9GitbSqjg8A+tWZZMo4U5G0fzrPK7XYHpnipUfbE4P8Q/rSMefox0TkOPY5q6ZmmnWNTtRu1ZqngYrY0uCLKzkklASwNCFBczsT29kbdzM0qhV7A9adplx81wjEDIOOajtj9vWdCcNnclZ5V0kZcEEHnFM0cuRpx2LAzvPPetG3bdZYLAENWPuORir9iUZgshIXrSMYq7sPkPrTreRVmUuPlHWpZYYjEXWQdyBVNVym7PPpQKcHFmxdm2lg8yNxvHoKzSeQah8zI5qQHIAzTJk7kisVO4GpJrp7iRSxHHQVTLEfLzTQ5xnrSEb2p3eLWMow3cH6VlwTvPeIXOWLKP1qlJIxPUmrOn8X9vn/nov86Lmjk2ezvgRKzdFTJ/KvEtQm82+nk67pCf1r1XxNqBstAmZch3AQfjXkWCzjOeTSKqPZD4Gw5PTivZ9KdZNLtnzndED+leLhcEjHtXqWjXRg8GrM5+5AaBU3qeZas/natcOOhlOPzqsRtYoy4OPWllYvKWPUnNI/wC8uSVHBoFuBnJgEIPGajmheNlLgj0NOW0myDt4+tWL5XZEbBwBg5pmnK7XaILu5cy7VYgYFV/tLhSpc896R/mOcVG6Hd2x7UEupJu9yJ+pJOaEj8xiAacRximoBvGenekZ9Sxp1p9r1CKHopYAn0rotSvJbCeJIQGkzxjtXOwXRtrgOoxjpVl5yymdicnhM/zpgnYsapqkl5LvfaJAMMw9apxX90o8qJyM8YFVGJfJJqzABa2zSnl34X2HrQI9W+CF7LceObyJmyqaa/4nzYq9/r5w+ALl/H9+T/0DJP8A0bFX0fQDberCvlD4yY/4Wtrfr+4/9ER19X18ofGPH/C19b/7Yf8AoiOgRwpFFH4UHpQAlFA4o7+1ABW54OH/ABWWkj/p5T+dYfHrW94Lx/wmuj9h9qT+dID6TER71J5XsashVBOaXavr1rMsrrACeOlP8sCrACgcUZBNOwFfZ6c8U7y8cYqYBc0pAPPrRYRXEXPTilKcVNx0NJ8vtikM5jxcmLbTTj/mIwfzNdGEI61geL2BtdMAOf8AiZQfzNdGCM8dqzj8cvkd1b/daXrL9Cw/zKqjv0q464wAOBxVa3G8oTzjn8qvBQq5Y5NaRRwsjVAvXqaeoycLxTC+5xk9+aeCG3HBweKoRHMNyqoIwOw71lalIJFb24FaUz+RkKOT0rIvXCxAHqTUyeo0GmDGQPWtLH5CsrTpf3pHPWtYdaYhuOSOue1eX/FDw+1zDHqUIy8A2vgdU65/DmvUf4h+VZmtW6XFrIrKGBXke1AHzSsfzDPrSzr+/c+9bOv6SdK1V41U+S53Rk+npWXOP3jH3pf8vF6P9Dsj/uUv8cfykVSntUc58uMtgnFWiODxUcsYdNpFanCUWuwEygGe+aqyz+ZnHHPJFXXs4gvC1TlhC9sAUDTKp470dhSfSlxxxQIKQ/Wl60GgA9aPpQKOM9OlAHun7OP/ADMv/br/AO1a91rwr9nIYPib/t1/9q17rQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHzL+0T/AMlAsP8AsFR/+jZa8jr1z9on/koFh/2Co/8A0bLXkdABRRRQAUUUUAXtG/5DNnzj96v8691txkZ83n69a8K0YA6zZg/89V/nXuCqoX5VxVRGi8WY9D+tOLygAZP51DaqC4DHg1ppbq7YyNop3sMoq7hskNUhlcjABrUS2jxjrUq26gdATS5kOxkI0mPvGpFMg/iNakSRPIV2gEdasC2jHQCnzIVmYuWPJJpcgHqea12tIzgYx9KjNiiv0496amgsykhXHU0u0ZPWtGOAKPlUY+lTCNcDgU+dBys8Sv8AWpRcymVuVJP3R7/4VI3im7mtmtN4aPIQgxjvXHeJ1265MfXn9ap6d/yELb/fFeUsO3T5+bpc9aVfExxv1e8bKdvgjtex2XiV30nT9MndExMmQIhgkZByT3OKxYPFBj5SBGf/AKaRhlP4V23xC0wy/D3Rb4AfuUQH6EV5KnB4reGGptRbXY4cTOrTrySqS92Tt7z9D3vwiGl0FGIY5ZsAdBWwyHtu4p3w8VG8IwZxne2fzrqDHEp5QflXVQlalH0RpmSvjKv+J/mc1DFkH5SaUxEnAU4ro8RAcKBTDHH1UDPpWvMcVjC8nb/CaaY2OflP5Vvt5RUjoagMQXkOD7Uc4cpgvAx/gNU5YcPhkwfpXRMuSeQDUNzCrR7yRuXpUuYcpzMsGQfl4rMurJWHCfpXSuAycAkn2qH5AmGiB96zlNlWOJudOARyyE8eleUzjFxIPRj/ADr327C+VIApxtPXtXgl3/x+Tf77fzqYikQ0UUVRIUUUUALXrn7O3/JQL/8A7Bcn/o2KvI69c/Z2/wCSgX//AGC5P/RsVAH0zRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFeI/GH7OPGFoZEJk+wJhtuRjzJK9urxb4ytcR6/ZvbW/nOLVdy+296aLgm3ocA2FUSIEdB1wvIqZ0860e4gHzRoWwvOcVBbanDNGZ44wCnEkZ6ikF9DpVyLmGdTbXAIaIHJWg2SRz0d3Jqen3cE2C0f71Pb1FYq9a096C5uWgyVkJH0FZkqshKjNIhvQdvHpzTopCrhgMGoUPOalzx/WgVySQkPj15ppYkdaeCGjHqKb24FBIRnnmtKO6KWzRoOvessHBqSOXHfBoBNrYvWNz9mn3nODwaddXCy3DOg4bvVQOCM45p340D5ny8pOBkZzT1k296jR8JikBxQJOxeSchcBCce1I0zMMBCPwq3oeppp9xukjR1PUMua9N0y+0PUIVYW0AOOqqMCkJtvc8kRZM42N+VWI1lU5WNzx6V7Wtnp+0MkERHYhBSeVZrnEKY/3KZJ4w0Vw/SF/++ajFjdN0gk/75r2rFuRgRDjvtpyRQnOIyT7LQB4v/Zl8VGLaX/vk1bs9PuBcRoIHEqsCTtOVyeDXsccMbD7n6Vyup3x0vW9UAhkImSIKQhI4Xn+dc+I2Sbtr+jO7A0qtX2io/Eo6aJ/aiut+juc9rb3d1axwyTXMu8rhZIwOSue31x+BrDGgam2CLOU/wDAa7W1uVvbzS1VZmkF2jsDEQFHPf8AKu7aNA3KN+VLD7vW6Lx9OvBU3W+Jp6cqj1fZI8QPh/U0/eSWcgRfmYkdAOta80V6NKjRGvPKlVECFjtJJboPTgY+tel6irTaZdwxQMzvC6qAOpIIriorXWC1jDNYSrHDJGWJI6KRUYmLctL7G+XwlKCcaiilLX4dVbz3OX/4RLVnOBaSgnkZXAxSjwZrJIP2fH1Ir2c+W67gM5pmF5+UZrqimlZu540E4rV3PIl8H64FwI1H/Aqo3Xh7WoVIltZGHsM17TsJPAUfU01oJD0VPzqi+eXc+f5rK4h4eJ1PfK1WaM9wa+gJtM83iRImB9VzWdceENMueZLWIE9SoxQK54aYzTPLYHOK9hufhzpkhPltJGT6HIrJl+GW2TKXe5fRhQFzgtL0mfU7oRoCsY5d+yir3iCKyjdY7X5fLXb9feu5u/Dup2unLb2NtEOMNsPWse38ETfaBPqe9lHOxAefxoGjjo7GRrYTmNhCPvNjjNVbmbzG2jgdAK9G1q8IsBpdvZrHbgfdC15xcQNHOVK4waBNWPTvgAhTx5fAnP8AxLJP/RsVfR1fO/wHQr48vT66Y/8A6Nir6IoEFfKHxk/5KrrX/bD/ANER19X18ofGQZ+K+tf9sP8A0RHQBwnNL3pO1HagBaOo6fWjp3ooATFb3gv/AJHTRiP+fpP51hZwK3fBX/I66N/19J/OgD6fB9Pxpep/+tTgPQCnDrg1mWM5xgZpMn04p/rzilHTnmgQ1ST1AoLZ70pPy8Uw8cgjNAx3ODzzSYznOc+1MBJ6dB1o3cAbqAMDxcB9k00/9RKD+Zrogoz1rnPFrA2mm/MP+QlB/M10Idc8EfnWUfjl8jtrf7rS9ZfoaFiMlvQVaILtnPU9Kq2ByHxVsoFByTyK2Wxwvcao+Y/3R0NSZ+XjqOaRsADGc46VDO5YBFOPXFK9gIHXczEnJ7YrP1VNrquO2a00QlxxwPWsu/dmuDz0FTbUZBY8S9K24+T1rGtARMa14jj2qkJiOcEH0NMuFDxnjgfyp8uMN780IQ8eDyKYHmni7RReWkyAATQkuh/pXlErYlYe9e8eI49kcj+sbA/lXjeraSbexs79ATFOnzezVmv4i9H+h2r/AHGX+KP5SMgnNBYAU3aMd/zprKBzzW5wCSsMf4VnzrkE9auMvFQSL8poAyeO5oPHXpQeTijtQAnsM0Ud80GgBce9HSk/Gl/zzQB7p+zl18S/9uv/ALVr3WvCv2cuvib/ALdf/ate60AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB8y/tE/8lAsP+wVH/6NlryOvXP2if8AkoFh/wBgqP8A9Gy15HQAUUUUAFFFFAF7Rv8AkM2f/XVf517pDGWWvC9G/wCQzZ/9dV/nXv1uRGqhkzxVJjSJIowANoO7vVqM/wAJU9eTUXmliNi7R6U9mz3IpcxSL6hVIOGqTr92TB+tVLeORxgucVaFmBg5NQ2URbjDu3EH3qIXEpHEhAqO7YCQRq3TrTI85xmtUtCGy0s02f8AWGnrctnmQmoFLY7EUscaoDxxVWEWvtEhHyk0eZLjhiKuRmJYVUsucVIuwjKkEe1TzIdj5l8UH/ibt9P6mqWkjdqUAPZs1p+L4CmoRy9pAxH4MRWZpGf7Tgx6/wBK5Y/wPl+h6lb/AJGr/wCvn/tx6n40kb/hV9qpY4KxgA/SvHk+8BXrvj+cL8NtIi/vBP8A0GvI4xlhW0Phj6I4sb/vNT/E/wAz6B8CeZ/wj64JxvOAK6hmkI6tmsTwJHt8KW7d2Zj+uK6RR3yOtOg/3UfRGuZL/bKv+J/mUS0p6lqT96QfnYVr+RuT1NRtavkbGGPTFauSOKxjmOYq3znn3pkM7FCkrFSvFaU8M3mFQV4HSs66gkibc44PWpumO1hpCOc7sn61RuFOCN7ce9TbwvQc1BI/BBGfrQ0guNtbnyvkYfIT1qdiA+4dO1Z0jZBHSrVk5lJRjyBxmocRjjD9phlL7R8p9q+db0Yv7gekjfzr6Rlt/wDRJGAJO0ivm6+GL+4H/TRv50kKRBRRRTJCiiigBa9c/Z2/5KBf/wDYLk/9GxV5HXrn7O3/ACUC/wD+wXJ/6NioA+maKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAryH4qTGHxPbsp+YWSkD/gb169Xg3xx1UWXim1txgO+nowJPT95IP6U0dOFnGE7y7M8p1G7mF1JKNsTvkHZ3rJBkc9WNXPLR/3s91Guf9rJpUu7KAER4kb1Y8flSF7NvVuwlrbXBfcoIHc47VotBYgB5G3Meqis59T3jBkAX0U4qAXSs2A6j3JpF2hFW3L5isWzmJge2DVgaGLiya4tJNzJ96JutZ8c8KDmRSfrVmy1RYGkxMFB96BcqKClkfBGD0YGpblI441Cbtx5PpirF89tPCLpZYxIWwwB6+9aFgmlHS5hdXCiUrlSG5HtT3M1BvY58Z4FWfLj8vd0OOlJiDORMhH+9Uim3xjzU/OgzK4bGOtTRkEjPA9amVbU/wDLZB+NSLDacf6Qn/fQoEMDRqSMZ4puG9Pxq2kdiMZnj/76q1GtgP8Al4i/FqAMwA96uW9zcWUokt5GRvbpWkq6aFJ+0QdP7wqHTpbCVp1eeIADKlmFAXOh0jxu8bKl1lG/vr0P1FdzZ6/bXMas+1lP8acj8RXlOkwabeTTrPdxIF+6S4FVnvxpd+8drephDgENwaBHuaPBOMxupB9KkRAv8VeTaZ44hjcCeZYZR1dTlT9a6+y8eabwt3cxY/vq39KAO0ijBA9auJbKy7XUMD6isa18SaE8auurWgB6AyCrh8T6GqE/2paE+nnCgCles+m3WVHy5yPQ1bttYgu/kdVRz69KwtR8X6ZdBojdWgAOM+YCaxzq+lBsjUbcc/8APQUAduflJJjBX2pjsrIRtH41y0HiWxjIDanblf8AroK0V8T6Oyc6jan/ALaCgCSZZoG3JkL7HpSpfBhtfr6mom1zQ2GBq1suexkFZk+raSj4GpWpHtIKAN8B2GUKEVE4mz1GKwhrWmqCF1SAD/roKUa/YLj/AImducdjIKANcefnAOfxpGacd24rOXxBpROX1C2B/wCugqX+3tLAyNUtv+/goAtB5h/y0P5UplnA5Y/UCqX/AAkGl97+2+vmCm/29pPfUbcf9tBQBfW4mz9/j3FSLdSdwDWd/buksMDUrU/VxSNrekHj+0LX8JBQBekMEx/eQoT7qKybrw/ol2SZbFM9yvFTf2xo54/tK2/7+ClOr6QBxqVt/wB/BQBr/Dnw/p2l+Jri5s0kR2tGjIY8YLof6V6jXnvgS/sbrW5o7a8hncWzMVRgSBuXn9a9CoAK+UPjJ/yVbWv+2H/oiOvq+vlD4yf8lW1r/th/6IjoA4Sil60Hrj9KADPpSGlxSdaACt/wT/yOui/9fafzrA/D9K3/AAUM+NtF/wCvtP50AfUQoK980/y+h/Gl24wKzLIsEUpzUgBGfamsD6UARkAn2qvLIR90dKtFSR0qB4JM5GcGkBVMzE4OB70MTznFTCElucDPHNIbZ8/MARRcDmvFJBttOx0+3w/zNb4wCKw/FcTLa6ceR/xMIRj8TWyQ69ayi/fl8jur/wC60vWX6G3ozlt6nJ7itFzngDPtWRoLkzSg9l9PetlRukPsK3WqOB7gx2IWI+b2qERkt8xxmpXywyoBxTUQchuo4otcBN21Hxj6ntXLTXoad+CPm6mupePfHnsRzXC3tvLZXjq1xmM8qCvb61L3GjUs5s3CjjBraTpXI2l2BeRfODlgOldahAFNAOkPWoIX2uV/GnSS4kOTgDmqj3cMJDu4XnH1oEUPEkHmWMzjqELD8ua4ixsYdQ8LQW0oBDxY+h7Gu41O7imsrlA2cRtg/hXPeH7cSeG7Fsc+Xz+dZX/eL0/yO6P+5S/xR/KR5De20ljdy28ow8ZwfeqxOeleh+OfD5mtRqUC/PEMSgc5X1/CvOdvauhO557Gn9KhcEg+oqYg0xh8p+lMDDIy1JSnr0ooATrmlNJRQAde/FL0pKKAPdf2ceniX/t1/wDate614V+zl/zMv/br/wC1a91oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD5l/aJ/5KBYf9gqP/ANGy15HXrn7RP/JQLD/sFR/+jZa8joAKKKKACiiigDR0EZ1+wH/TZf519CC2bgEYY1896Cduv2JHaZf519DiVyo4/Gk2VEdHbhT8zZ+lSGNc9M1C8uOVBzRGHZuc0ijShCrhhjmpp51gt2kJ6DisvLiQDfio7qTzSsO7AXkmhRuwuRKxlcuw5PWrSDC5xiqyxlTwxqVRKcHzB+IrZEEo646VYjRnkRSQQTyKrCOXr5i5+lXtPWU3A3FSFHYUN2QF8rCDgqv5UBUQ4RQB6CieJZMbu3cUiJtIUEn61KKPD/G+mb/C2naiq/dmmjc/8DOK5TwxB5+qnjPlwu/5Ka9c1DTRqXwuuU43I0si/UOa8w8EhBf3244P2OTH1xXNH+B8v0PUrf8AI1f+P/246Px87f8ACH6MpPGBx+FcZ4c019U1m3tlGdzjP0711Xj+Yf2BokGefKDY/Cr3wr0nm41KReANiZ/Wt6SvFeiOHG/7zU/xP8zt/DKBdBjUMwKu4AH+8a11kKyYO715NZHhs7dIQ/8ATST/ANCNazEs+adD+FH0RpmX++Vf8T/M3NzEBg5AxSrM6ISx6d8VWtnxCAzZ28YqyHGMg8Hsapo5CD7SolZ3GSehHTFRzXUc5C4OCMe1MlRJHO0eVKvfPytRHCI/n3gDuo6E1NhmRJlJGVj0OM1Wl+bpWjqskDGORWALfKcVRaI8DNWSU2RhzximB2jYOp+cdKsvGw96j8sEA4596TQy/HeO9oziMglTur5wvznULk/9NW/nX0Akjwb2JKqVIOK+f77nULjnP7xufxqLWCRXooooJCiiigBa9c/Z2/5KBf8A/YLk/wDRsVeR165+zt/yUC//AOwXJ/6NioA+maKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAr5h/aN/5KFp//YKj/wDRstfT1fMH7R3/ACULT/8AsFR/+jZaAPIaO1FJQAtFFFABRSUtABRRRQAUUUUAFHNBooAKM0UUAGaM80lLQAZPrRyaSloAKM+9FJQAuT6mjJ9TRSYoAXn1oyfWijigAycdaMn1NFHSgAyfU0ZNHNFABk+poyfWkpaADJ9aMn1NFJQAuT6mjJ9TRRQAZPqaMk96SigBcn1oyfWikoA9g/ZyP/FwtQ5/5hUn/o2Kvp6vmH9nL/koWof9gqT/ANGxV9PUAFfKHxk4+K2tf9sP/REdfV9fKHxk/wCSr61/2w/9ER0AcLj9aBSHmjtQAuaM/wD6qKQigBevANb3gc48b6NnoLtP51gd+K3vBf8AyOuj/wDX0n86GB9U+ahPpQCDyTxXOeJElisI72Bm8yzkE20H7y/xD8qz/EF3PqotrHTZiGaP7SzKegAyo/E1mUdpkdzwfWmF0/vKPxrkrvWpLzw5bC3bF1ekQADqrdG/Lmm6pY6PZPEt3b3srlAN0O9s4GOcGgDrvMXoXX86cHXbuyMe1cEB4d4/0DVOP9h/8a1rWxsdX0mKHT5pobRJz5qMSGbHUdcikM6QmJv4hn60BUK/K4IFYh8JaRx+7lP/AG2b/GqL6XFp+uQ2FrJKlve28iyIXJwQOCPeiwE/jEL9j0sAZzqUH8zW/wCRGemOa4XVJri78HQwb1F/YXqIwcnLMpIGPXqP1pNNuvE2lx3Viz6YTaJ5zNcNISVPPBHWsfhm3Y9JU41sNTipxTTlu7b2PQNMXZPMM+n5U7V/EWlaAkP2+dovPyI8RM+SMZ6A+orjdBi8YGKS/hGk/wCmHzSLhpcgHoMDoKg13SPGOqXtndP/AGXHLZEmPyS5GT6hgaJVpRj7qdyI4OKlec4tdlJJ/e0zpT8RPDTRjZey+x+yyf8AxNauj6zZ6zZm8sZTJEXK5ZSvI68HmvMbfw34yhjiiiuYY442DookIAPy84A/2f1PrWpoOm+LtAhkggbTJw8pkJneRiGPXGMdazhXqOXvrT0/4JTw1OcbRSi+7qRa+5RT/E9JIc9QAADVS4sILq32yqr896543fjh8sYdC6estNaTxuYmVV0JQR1Vpc1v7VdmZ/UX/wA/I/8AgQXmi21tLE1m7GVW3MpbIA9Kti+vdxHkIBnuawxB4tRx8mi7v96WpNni/OfL0T85an2nkx/Uf+nkf/AjWlN1ORuKoO4FV305C253OT+NUCvi7umifnLUZTxaf4dF/OWj2nkw+ov/AJ+R/wDAi1d2yxWcxDkjy24x7VR8NFR4asP+uXP5mm3EPiqWF0ddHwylThpO9S6VZS2Ok29pMUMkS7SVPB+lJO8726GlSEaWFcHNNuSejvsn/mWZ0jkV43AKsMEHoRXj3iXRjouqvCMmB/mib29Pwr2F0Yjp0rA8TaEdY0t0Ufvo/niPv6fjW6Z5jR5ETz7VE/Qn0qVt8UjRsCGU4IPY1C7ZBx1xWhJhnrR0oPU+tH05oAPrRj8qTuMCloAQ0v60E0g4+tAHuv7OX/My/wDbr/7Vr3WvCv2cv+Zl/wC3X/2rXutABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfMv7RP/JQLD/sFR/+jZa8jr1z9on/AJKBYf8AYKj/APRsteR0AFFFFABRRRQBoaFzr1jj/nsv86+h40lwM4xXzzoJxr9gf+my/wA6+iBNlRUyZcERPCxbdv8AwqbzmRQOM9qjlddp+fFUzKWPB4oXmUaCsHbex5AyaqJJ5rM57mmyzqIlhUHLck0RlVwN1aRRLLSLyOTVjKgemKpidPM25Ygd8VKZAw45x6VRI8tlselalghMRkQY/GsgAnn1966CzVUtlyeaUnoNDzv2jK0KsnUY4olnVTtyMmo97g+1Qijzk648Hh4aXGLZllScyF3IZfnPHHfFcBp2mR2Nx9oikkyylSrY6Hiu88eW8VrNvs4oIMWzO+Ihz82Pz964K2visMbnLJ5LSHPJOGxXl1FWjHlUtNvwPo1Uc8TKtTpRldc6vdW97dtPV39Fa3zZ4g87VLuBfPgVIIljVXkweBzxXqXgyCK28NWsajBOckdzmvL7O6gv7olVdSoJJcgDHH+Ar1fwtp+tT6BbSWbab5BLbfPVy33jnOOK6aNWrBpTWlv8jlq4aliITrJxi+Za8za15n2VttPmL4dONHX/AK6Sf+hGtbOBmr2iaF/ZmlR212YpJgzMzR528sT3+tan2GD/AJ5riuqlLlpxT7HnY+UamKqTi7pydvvMmxuFad4GPO3cvvWjGhbuOnepRaWyMHWJd44yOtP2oBnFPnOYpywxsuDED/WoY/JaMxqNoHBB7Vo5Q9qaYoy24ov1xRzgZdxaQPbGNmCr2I61RjEZTYwBZeM10flR4/1a4+lZGs2+1BLCNuOuPSnGeomjPlMaLwgNV2iUrnFA+f7zEc1MoUDB5+taCKVwqrA55ztNfPd5/wAf0/8A10b+dfRtzEGgkIwRtP8AKvnK9GL64H/TRv51MgIKKKKgQUUUUALXrn7O3/JQL/8A7Bcn/o2KvI69c/Z2/wCSgX//AGC5P/RsVAH0zRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFfMP7Rv8AyUKw/wCwVH/6Nlr6er5h/aN/5KFYf9gqP/0bLQB5BSUUtAB3opO9LQAUd6KKADtSUtHSgAoxxRR0oABR0NFFABSdqWigBKXvSUtACUUtIKAF60UYooAKKDRQAUUlFAC/hRRR2oASlo7YpKAFopKKACl6UUfWgAooooAKKKKACiikoA9g/Zy/5KFf/wDYKk/9GxV9PV8w/s5f8lCv/wDsFSf+jYq+nqACvlD4yc/FbW/+2H/oiOvq+vlD4x/8lW1v/th/6IjoA4TijHPelpKADr2paKT60AB9a3vBR/4rXRuP+XpP51g81ueDf+Ry0j/r6T+dAH0/Msc8TxuMoylSPasLw9ow0g3DXE6PKzbUO7OIx90VqM7KhI5OOnrVBsA5+wt+DVmWV7Hw+LfxDNeGRTarloE3fdZvvcVu70Yn94pA75qhFIBHsFiQCckZoWOJjj7E2D8ud3Qe1AF/emB864PfNYCaZqVnZyi2uIlnN406qX+V1J+6a0V2IhAsHxkdT3p7gSMWaykyeuGoEUftfiXPFnY9P+expbO21K71aHUdS+zRLAjLGsTlsk9STV+GJCTm0ZCASCTSPh0EZs5GXnPPAzQBmyaKsniddRM0f2bAkePd1kHAP5U/XdG/ta8tpYLhE/gn+blo85x+lXlVF+f7G+SOxNNRIyNw0+QnOMZoA3bRQiBFA2gAAD0qVhlvaoLLmIEpsOOFParY69KAIsAcU1EG4nFTnG3gGmDgkDFKwxx5Xr2qrK5AwOKm3YB9qpTMecUwIhuLZPrU3zYyOtRRZPU1YUc9KVgGMDtqIgjmrRB6elRsOOlKwFGc8dKoyZB3Y6cGtOSNW6iq7QoTgg4NOwXKJZqicsDnirvkAZXceD+lMeDcDg8jpQM8p8eaKbW7GpwL+6mOJAOzf/XrimIK171qGlJqFjNazAGORcfT3rw/VtPm0nUJ7OcfPGxHsR2NVF9CGupzfHam55pe9FWIOetJjBoHSjjFAC/Wj9aTnPtR3oA91/Zy/wCZl/7df/ate614X+zl/wAzL/26/wDtWvdKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA+Zf2if8AkoFh/wBgqP8A9Gy15HXrn7RP/JQLD/sFR/8Ao2WvI6ACiiigAooooAvaKca1Zn/pqv8AOvdFu2A4YZ968J0k41e0P/TVf517IJlJ96mRcTRMzMMFsmnJJhB82McmqsLIw5bBpl9KIYQiOCX4oRRMl5vlcjpnjNT+byGzzWPG2xNrY4qdLgL1PT1Naohmn9oIOD0pRdYyAetZzz7h2qtNcmMcZOOuBTuI2Y7z94q54zW62oxpAqhunvXn7XLSR7VVgSe4qN2uCcq75+tRJXKTsd6dYhRsu4GKzrvxfBAcqwYVyJtbiZcu7jPvVKfSWKn5mz9aWo7ol8WeIRqsLyhcAQmL65Of6VyEXzWCgcZtH/8AQhWpcWVxbxupjWRCckOCaqsBJaCNBFExQoRsPAJ7Vw1VJPbS/wCh9Jl9WjOLfOlJQtZ6a8193oLoukiIfaJpchxhYxzu+te4eCZAnhOzGQOX/wDQ2ryTQtJv2VD5cs8anPyrt/nXpGgQXFro0MMsZR1LEqTyMsTXa3GpOPL0T6PyPKdGVDCTVRq7lG1mnsp9m+6OyaZMfM4oNymMB1P41glWZMnJPpUYUZ+Zip9DV+zPO5jf+1QY5kX86X7RERxIuPrWEvlouc8HvQpXd1H4UezDmN0SxjkuKDdQrnMi/nWG5HduPY1WKfPgliD3xS9n5hzG++pWyHiVfzqKa9tpY2RpFwRWGbcFsYBoaNVG3ijkDmI2kiRyqnleM0klzH3JNJgFmCkUkkUarlnBrQBk96nlOF4G0189XpzfTn/po38697nMYSQZ52mvA7z/AI/Z/wDro386iQmQ0UUVIgooooAWvXP2dv8AkoF//wBguT/0bFXkdeufs7f8lAv/APsFyf8Ao2KgD6ZooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvmH9o3/koWn/8AYKj/APRstfT1fMP7Rv8AyULT/wDsFR/+jZaAPIKTtS0dqAEpaKKACigUUAFHeiigAopKWgAo49aKKACikpaACkpaSgBaKSlxQAUGiigAoopKAFooooAKKKKAEpaKPrQAUGijvQAlLScUtAB0ooooAKPxoooAKOM0fWigD1/9nH/koV//ANgqT/0bFX09XzD+zj/yUK//AOwVJ/6Nir6eoAK8n8ZfBX/hLfFd7rn/AAkH2X7V5f7n7F5m3aip97zBnO3PTvXrFFAHhX/DOX/U1/8AlO/+20f8M5f9TV/5T/8A7bXutFAHhX/DOX/U1/8AlO/+20f8M5cf8jV/5Tv/ALbXutFAHhX/AAzl/wBTX/5Tv/ttXtG+Af8AZOs2mof8JL5v2eVZPL+w7d2O2fMOPyr2eigDD/4R3H/L1/5D/wDr0h8OZ/5ev/If/wBet2ilyod2Yv8AYHpdf+Q//r0f8I/z/wAfP/kP/wCvW1RRZBdmN/YPT/Sf/If/ANenDRCOlz/5D/8Ar1r0UWQXMkaMwHNyP+/f/wBej+xM/wDLx/45/wDXrWoosguZP9i8Y+0D/v3/APXpw0fb/wAt/wDxz/69alFFkFygum7TzNn/AID/APXpwsMf8tf/AB3/AOvV2iiyC5SOn5H+t/8AHf8A69J/Z3/TX/x3/wCvV6iiyC5QOmnn991/2f8A69Qvoof/AJb4/wCAf/XrVoosguZC6HtP/HyP+/f/ANepBpGP+W//AI5/9etOijlQXMw6Rk58/wD8c/8Ar006Nk58/wD8c/8Ar1q0UcqC7Mg6GT/y8/8AkP8A+vTDoBP/AC9f+Q//AK9bVFHKguYn/CPcj/Sv/If/ANem/wDCODGPtX/kP/69btFFkFzAPhkH/l6/8h//AF65fxR8JrfxIYpF1P7LOgIMgtt+4emNwr0eiiyC54X/AMM5f9TV/wCU7/7bR/wzl/1Nf/lP/wDtte6UUxHhf/DOf/U1f+U7/wC20n/DOX/U1f8AlP8A/tte60UAeFf8M5f9TX/5Tv8A7bS/8M5f9TV/5Tv/ALbXulFAHC/Dn4cf8K//ALT/AOJr9v8Atvlf8u/lbNm//abOd/t0ruqKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA8y+Ivwi/wCE+8QQar/bn2DyrVbbyvsnm5w7tuzvX+/jGO1cj/wzV/1Nv/lO/wDtte90UAeCf8M1f9Tb/wCU7/7bR/wzV/1Nv/lO/wDtte90UAeCf8M1f9Tb/wCU7/7bR/wzV/1Nv/lO/wDtte90UAeFWv7OP2a6in/4Svd5bBsf2djP/kWuuX4U7emtf+Sv/wBnXo9FKwXPO1+Fm0Y/tkn/ALdv/s6ZN8KmlcN/beABwPsuf/Z69HopjuzzgfCkAg/2z0/6df8A7Onn4WA9NYx/26//AGdeiUUXFc88/wCFW8j/AInH/kr/APZ0xvhVuP8AyGcf9uv/ANnXo1FFwuecD4UgY/4nP/kr/wDZ1InwuCH/AJC+f+3b/wCzr0OigdzgV+GmMj+1gR6fZv8A7Kmj4YRgknVM5/6d/wD7KvQKKdwucE3wxtnXDXyn/t3/APsqSP4XWMY4uUz6/Z//ALKu+oouxHGx+AxGAF1EAD/p3/8AsqlXwUykH+0uf+uH/wBlXW0U+dgct/wh7c/8TD/yD/8AZUjeDWP/ADEP/IH/ANlXVUUczA5JvBRYY/tEY/64f/ZUw+BFI/4/+fXyf/sq7CilzMDjm8BKwx/aJ/78/wD2VH/CB8Y/tI/9+f8A7KuxoouwOOHgMA5/tI/9+f8A7Kl/4QQA5Go8+8Gf/Zq7Cii7A4x/AO45GpBf+3f/AOyqJ/h3vUj+1OT3+z//AGVdxRRdjuefyfDLejAavgsMZ+zf/Z1w8v7N3mzPJ/wlmNzE4/s7/wC217xRRcR4J/wzV/1Nv/lO/wDttH/DNX/U2/8AlO/+2173RSA8E/4Zq/6m3/ynf/baP+Gav+pt/wDKd/8Aba97ooA8E/4Zr/6m3/ynf/ba674d/CL/AIQHxBPqv9ufb/NtWtvK+yeVjLo27O9v7mMY716bRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXmHxH+D/8AwsDxDb6r/bv2DybRbbyvsnm5w7tuzvX+/jGO1en0UAeAf8Mzf9Td/wCU3/7bR/wzN/1N3/lN/wDtte/0UAeAf8Mzf9Td/wCU3/7bR/wzN/1N3/lN/wDtte/0UAeAf8Mzf9Td/wCU3/7bR/wzN/1N3/lN/wDtte/0UAeAf8Mzf9Td/wCU3/7bR/wzN/1N3/lN/wDtte/0UAeAf8Mzf9Td/wCU3/7bR/wzN/1N3/lN/wDtte/0UAeA/wDDM3/U3f8AlN/+20n/AAzN/wBTd/5Tf/tte/0UAeAf8Mzcf8jd/wCU3/7bR/wzN/1N3/lN/wDtte/0UAeAf8Mzf9Td/wCU3/7bR/wzN/1N3/lN/wDtte/0UAeAf8Mzf9Td/wCU3/7bR/wzN/1N3/lN/wDtte/0UAeAf8Mzf9Td/wCU3/7bR/wzN/1N3/lN/wDtte/0UAeAf8Mzf9Td/wCU3/7bR/wzN/1N3/lN/wDtte/0UAeAf8Mzf9Td/wCU3/7bR/wzL/1N3/lN/wDtte/0UAeAf8Mzf9Td/wCU3/7bR/wzN/1N3/lN/wDtte/0UAeAf8Mzf9Td/wCU3/7bR/wzN/1N3/lN/wDtte/0UAeAf8Mzf9Td/wCU3/7bR/wzN/1N3/lN/wDtte/0UAeAf8Mzf9Td/wCU3/7bR/wzN/1N3/lN/wDtte/0UAeAf8Mzf9Td/wCU3/7bR/wzN/1N3/lN/wDtte/0UAeAf8Mzf9Td/wCU3/7bR/wzN/1N3/lN/wDtte/0UAeAf8Mzf9Td/wCU3/7bR/wzN/1N3/lN/wDtte/0UAeYfDj4P/8ACv8AxDcar/bv2/zrRrbyvsnlYy6Nuzvb+5jGO9en0UUAf//Z">


## 10. Checkpoint’ten Model Oluşturma

Image Processor ile Modeli oluşturmak için aynı checkpoint’i kullanacağız. Bu işlem özel veriseti için fine-tune yapacağımız önceden eğitilmiş modeli yüklemeyi sağlayacaktır.

```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. Fine-Tune Modeli Yüklemek İçin HF Hub’a Bağlanma 🔌

Fine-tune edilmiş modelimizi yüklemek için Hugging Face Hub’a bağlanacağız. Bu, modeli paylaşmamıza ve başkalarının kullanabilmesi veya daha ileri değerlendirme yapabilmesi için olanak tanıyacaktır.


```python
from huggingface_hub import notebook_login

notebook_login()
```

## 12. Eğitim Argümanlarını Belirleme, W&B’ye Bağlanma ve Eğitime Başlama!

Sonraki adımda eğitim argümanlarını belirleyeceğiz, [Weights & Biases (W&B)](https://wandb.ai/) bağlantısını kuracağız ve eğitim sürecini başlatacağız. W&B, denemeleri takip etmemize, metrikleri görselleştirmemize ve model eğitim sürecimizi yönetmemize yardımcı olacaktır.

```python
from transformers import TrainingArguments
from transformers import Trainer

import torch

# Eğitim argümanlarını tanımlayın

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
)
```

### Connect to W&B to Track Training

```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
)
```

### Şimdi Modeli Eğitelim! 🚀

Artık modeli eğitmeye başlama zamanı. Eğitim sürecini çalıştırıp fine-tune modelimizin verisetinden nasıl öğrendiğini gözlemleyelim!

Öncelikle değerlendirme metriklerini hesaplamak için `compute_metrics` metodunu tanımlıyoruz.

```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. Modelin Test Görseli Üzerindeki Davranışını Test Etme 📝

Şimdi Model eğitildiğine göre, performansını bir test görselinde değerlendirebiliriz. Model Hugging Face modeli olarakta erişilebilir olduğundan, algılama yaptırmak oldukça basittir. Aşağıdaki hücrede, yeni bir görüntüde nasıl inference yapılacağını ve modelin yeteneklerini nasıl değerlendireceğimizi göstereceğiz.

```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)
```

### Şimdi Sonuçları Gösterelim

Modelin test görselindeki algılama sonuçlarını göstereceğiz. Bu, modelin performansını daha iyi anlamamızı sağlayacak ve güçlü yönlerini ve geliştirilmesi gereken alanları öne çıkaracaktır.

```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. Modelin Test Veriseti Üzerindeki Değerlendirmesi 📝

Modeli eğittikten ve test görselinin sonuçlarını görselleştirdikten sonra, modelin tamamını test veriseti üzerinde değerlendirilme işlemini yapacağız. Bu adım, test örneklerinin tamamında üzerinde modelin genel performansını ve etkinliğini değerlendirmek için metrikler oluşturmamızı sağlayacaktır.

```python
metrics = trainer.evaluate(test_dataset_transformed)
print(metrics)
```

## 15. HF Space’te Modelin Gösterimi

<img src="https://huggingface.co/front/thumbnails/spaces.png" alt="HF Spaces logo" width="20%">

Modelimiz Hugging Face üzerinden erişilebilir olduğuna göre, HF Space’te gösterebiliriz. Hugging Face, kullanıcıların test görüntüleri yükleyip modelin yeteneklerini değerlendirebileceği etkileşimli bir web uygulaması oluşturmamıza olanak tanıyan Space küçük uygulamalar ücretsiz sunulmaktadır.

Örnek bir uygulamayı burada oluşturdum: [DETR Object Detection Fashionpedia - Fine-Tuned](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)
```

### Aşağıdaki Kod ile Uygulama Oluşturun

Yeni bir uygulama oluşturmak için aşağıdaki kodu `app.py` adlı bir dosyaya kopyalayıp yapıştırabilirsiniz.

```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)
```

### `requirements.txt` Dosyasını Ayarlamayı Unutmayın

Uygulamanın bağımlılıklarını belirtmek için bir `requirements.txt` dosyası oluşturmayı unutmayın.

```python
!touch requirements.txt
!echo -e "transformers\ntimm\ntorch\ngradio\nmatplotlib" > requirements.txt
```

## 16. Space’e ile API Erişim Sağlama 🧑‍💻️

Hugging Face Space'lerin en güzel özelliklerinden biri, dış uygulamalardan erişilebilen bir API sağlamasıdır. Bu, modeli JavaScript, Python veya başka bir dil ile yapılmış çeşitli uygulamalara entegre etmeyi kolaylaştırır. Modelinizin yeteneklerini genişletmek ve kullanmak için birçok olasılık hayal edebilirsiniz!

API kullanımı hakkında daha fazla bilgiyi burada bulabilirsiniz: [Hugging Face Enterprise Cookbook: 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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## Sonuç

Bu rehberde, özel bir veriseti ile fine-tune edilmiş bir nesne algılama modelini başarıyla eğittik ve Gradio Space olarak deploy edildi. Ayrıca, Gradio API'sini kullanarak Space’e nasıl erişim sağladığımızı gösterdik ve modelin nasıl çeşitli uygulamalara kolayca entegre edilebileceğini sunduk.

Umarım bu rehber, kendi modellerinizi fine-tune ederken ve dağıtırken size güven sağlar! 🚀

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/tr/fine_tuning_detr_custom_dataset.md" />

### Vision Transformer Modelini Özel Bir Biyomedikal Verisetiyle Fine-Tune Etmek
https://huggingface.co/learn/cookbook/tr/fine_tuning_vit_custom_dataset.md

# Vision Transformer Modelini Özel Bir Biyomedikal Verisetiyle Fine-Tune Etmek
_Yazar: [Emre Albayrak](https://github.com/emre570)_
_Çevirmen: [Onuralp Sezer](https://github.com/onuralpszr)_

Bu rehber, Vision Transformer (ViT) modelini özel bir biyomedikal verisetiyle fine-tune etmek için izlenmesi gereken adımları açıklamaktadır. Verisetini yükleme ve hazırlama, farklı veri bölümleri için görüntü dönüşümlerini ayarlama, ViT modelini yapılandırma ve başlatma, ayrıca eğitim sürecini değerlendirme ve görselleştirme araçlarıyla tanımlama adımlarını içermektedir.

## Veriseti Bilgisi
Özel veriseti elle oluşturulmuş olup, 780 görüntü ve 3 sınıf içermektedir (iyi huylu, kötü huylu, normal).

![attachment:datasetinfo.png](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/102d6c23e6cc24db857fbc60186461ded6cdfb75/datasetinfo.png)

## Model Bilgisi
Fine-tune edilecek model, Google'ın [`"vit-large-patch16-224"`](https://huggingface.co/google/vit-large-patch16-224) modelidir. Model, ImageNet-21k (14M görüntü, 21.843 sınıf) üzerinde eğitilmiş ve ImageNet 2012 (1M görüntü, 1.000 sınıf) üzerinde 224x224 çözünürlükte fine-tune edilmiştir. Google, farklı görüntü boyutları ve yama boyutlarına sahip çeşitli ViT modellerine sahiptir.

Hadi başlayalım!

## Başlarken
Öncelikle, gerekli python kütüphanelerini yükleyerek başlayalım. 

```python
!pip install datasets transformers accelerate torch torchvision scikit-learn matplotlib wandb huggingface-hub
```

_(İsteğe Bağlı)_ Modelimizi Hugging Face Hub'a yükleyeceğimiz için giriş yapmamız gerekiyor.  

```python
from huggingface_hub import notebook_login
notebook_login()
```

## Verisetini Hazırlamak

_Datasets_ kütüphanesi, verisetinden görüntüleri ve sınıfları otomatik olarak çeker. Detaylı bilgi için [`bu bağlantıyı`](https://huggingface.co/docs/datasets/image_load) ziyaret edebilirsiniz.

```python
from datasets import load_dataset

dataset = load_dataset("emre570/breastcancer-ultrasound-images")
dataset
```

Verisetimizi aldık. Ancak elimizde bir doğrulama seti yok. Doğrulama setini oluşturmak için, test setinin boyutuna dayalı olarak eğitim setinin bir kısmını doğrulama seti olarak ayıracağız. Daha sonra, eğitim verisetini yeni eğitim ve doğrulama alt altsetlere böleceğiz.

```python
# Her setin sayısını alalım
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
```

Ayrılmış test setimizi aldım. Şimdi test seti ile birleştirelim

```python
from datasets import DatasetDict

dataset = DatasetDict({
    "train": train_val_split["train"],
    "validation": train_val_split["test"],
    "test": dataset["test"]
})
dataset
```

Harika, Verisetimiz hazır olduğuna göre şimdi alt setleri farklı değişkenlere tanımlayarak kolay şekilde kullanabiliriz.

```python
train_ds = dataset['train']
val_ds = dataset['validation']
test_ds = dataset['test']
```

Şimdi verisetimizdeki resimlerin türünün `PIL.Image` olduğunu, ilk resme bakarak doğrulayabiliriz.

```python
train_ds[0]
```

Eğitim setinin özelliklerini ayrıca görebiliriz.

```python
train_ds.features
```

Şimdi veriseti içindeki her sınıftan bir tane resim gösterelim.

```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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## Veri İşleme  
Veriseti hazır, ancak fine-tuning için henüz hazır değiliz. Aşağıdaki adımları sırasıyla takip edeceğiz:  

- **Etiket Eşleme:** Model eğitimi ve değerlendirmesi sırasında faydalı olacak şekilde, etiket kimliklerini (ID) ve bunların karşılık gelen isimlerini dönüştüreceğiz.  

- **Görüntü İşleme:** Daha sonra, `ViTImageProcessor` kullanarak giriş görüntü boyutlarını standartlaştıracağız ve önceden eğitilmiş modele özgü normalizasyon uygulayacağız. Ayrıca, torchvision kullanarak eğitim, doğrulama ve test için farklı dönüşümler tanımlayarak model genellemesini geliştireceğiz.  

- **Dönüşüm Fonksiyonları:** ViT modeline uygun format ve boyutlara dönüştürerek verisetine bu dönüşümleri uygulayan fonksiyonlar implement edeceğiz.  

- **Veri Yükleme:** Görüntü ve etiketleri uygun şekilde gruplamak için özel bir collate fonksiyonu tanımlayacak ve model eğitimi sırasında verimli yükleme ve gruplama için bir DataLoader oluşturacağız.  

- **Batch Hazırlığı:** Örnek bir batch'in veri şekil bilgilerini alacak ve görüntüleyerek işlemenin doğru yapıldığını ve model girişine hazır olduğunu doğrulayacağız.  


### Etiket Eşleme

```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']]
```

### Görüntü İşleme

```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,
])
```

### Dönüşüm fonksiyonlarını oluşturma

```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
```

### Dönüşüm fonksiyonlarını her bir sete uygulayalım

```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]
```

Bu işlem ile görüldüğü üzere piksel değerlerini tensorlara dönüştürmüş oluyoruz.

### Veri Yükleme (DataLoader)

```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)
```

### Batch Hazırlığı

```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>

Harika! Şimdi fine-tuning işlemi için hazırız.

## Modeli Fine-Tune Etme  

Şimdi modeli yapılandırıp fine-tune edeceğiz. İlk olarak, modeli belirli etiket eşlemeleri ve önceden eğitilmiş ayarlarla başlatarak boyut uyuşmazlıklarını ayarlıyoruz. Eğitim parametreleri, modelin öğrenme sürecini tanımlamak için ayarlandı; bu parametreler arasında kaydetme stratejisi, batch boyutları ve eğitim epoch'ları yer alıyor ve sonuçlar Weights & Biases aracılığıyla kaydediliyor. Ardından, Hugging Face Trainer, eğitim ve değerlendirme süreçlerini yönetmek için özel bir veri collator ve modelin dahili işlemcisini kullanarak başlatılacak. Son olarak, eğitim tamamlandıktan sonra modelin performansı bir test veriseti üzerinde değerlendirilecek ve doğruluk gibi metrikler yazdırılarak modelin başarısı değerlendirilecek.  

İlk olarak, modelimizi çağırıyoruz.  


```python
from transformers import ViTForImageClassification

model = ViTForImageClassification.from_pretrained(model_name, id2label=id2label, label2id=label2id, ignore_mismatched_sizes=True)
```

Burada önemli bir detay var: `ignore_mismatched_sizes` parametresi.  

Önceden eğitilmiş bir modeli yeni bir veriseti üzerinde fine-tune ederken, bazen görüntülerin giriş boyutu veya model mimarisi özellikleri (örneğin, sınıflandırma katmanındaki etiket sayısı) modelin başlangıçta eğitildiği yapı ile tam olarak uyuşmayabilir. Bu durum, genellikle ImageNet gibi doğal görüntüler üzerinde eğitilmiş bir modeli, tıbbi görüntüler veya özel kamera görüntüleri gibi tamamen farklı bir görüntü verisetine uygularken ortaya çıkar.  

`ignore_mismatched_sizes` parametresini `True` olarak ayarlamak, modelin boyut farklılıklarına uyum sağlamasına ve hata oluşturmadan katmanlarını ayarlamasına olanak tanır.  

Örneğin, bu model 1000 sınıf üzerinde eğitilmiştir ve `torch.Size([1000])` boyutunda bir giriş bekler. Verisetimiz ise 3 sınıfa sahiptir ve bu `torch.Size([3])` boyutundadır. Eğer bunu doğrudan modele verirsek, sınıf sayıları eşleşmediği için bir hata alırız.  

Sonrasında, bu model için Google tarafından sağlanan eğitim argümanlarını tanımlayacağız.

(Opsiyonel) `report_to` parametresini `wandb` olarak ayarladığımız için metrikler Weights & Biases'te kaydedilecektir. W&B sizden bir API anahtarı isteyecek, bu yüzden bir hesap oluşturmalı ve API anahtarı almalısınız. Eğer istemiyorsanız, `report_to` parametresini kaldırabilirsiniz.

```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,
)
```

Artık `Trainer` ile fine-tuning sürecine başlayabiliriz.

```python
trainer = Trainer(
    model,
    train_args,
    train_dataset=train_ds,
    eval_dataset=val_ds,
    data_collator=collate_fn,
    tokenizer=processor,
)
trainer.train()
```

| Epoch | Eğitim Kaybı  | Doğrulama Kaybı | Doğruluk  |  
|-------|---------------|-----------------|-----------|  
| 40    | 0.174700      | 0.596288        | 0.903846  |  

Fine-tuning süreci tamamlandı. Şimdi, modeli test verisetinde değerlendirmeye devam edelim.  

```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}`

### (Opsiyonel) Modeli Hub'a Yükleme  
Modelimizi `push_to_hub` fonksiyonunu kullanarak Hugging Face Hub'a yükleyebiliriz.  

```python
model.push_to_hub("your_model_name")
```

## Sonuçlar  
Fine-tuning işlemini tamamladık. Şimdi, modelimizin sınıfları nasıl tahmin ettiğini görelim. Bunun için scikit-learn'ün Confusion Matrix Display fonksiyonunu kullanarak Confusion Matrix'i görselleştirecek ve Recall Skoru göstereceğiz.  

### Confusion Matrix Nedir?  
Confusion Matrix, bir algoritmanın, genellikle bir denetimli öğrenme modelinin, gerçek değerlerin bilindiği bir test veriseti üzerindeki performansını görselleştirmeye olanak tanıyan belirli bir tablo düzenidir. Özellikle bir sınıflandırma modelinin ne kadar iyi performans gösterdiğini kontrol etmek için kullanışlıdır, çünkü gerçek ve tahmin edilen etiketlerin sıklığını gösterir.  

Hadi, modelimizin Confusion Matrix'ini çizelim.  


```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)
```

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R+WpIUIqquTtUKoAGSepzzWzRRQAVyOs6z4o0kz3M0Xha205ZCsc97qksOVydu791gMR2BP4111cP8SNDvtV037RZWsF2Y7K7tvImlWPa0yBVlUt8u5SMckcO2D2IBv6FeazcyXUesR6TG8WzYun3bzEZBJ3hkXbxtI65ya2a5fwrb6hPfahrd/ax2f2yKCCK2WdZSEi3/OzLlckyHgE8KOa6igAooooAKKKKACiiigAooooAK4P4y/8ku1T/rrbf+lEdd5XB/GX/kl2qf8AXW2/9KI6qPxIa3PmuiiivbO4KKKKACiiigAooooAKKKKACiiigAooooAK7Sx/wCPC2/65L/IVxddpY/8eFt/1yX+QrKrseLnX8OPqT0UUVifPBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGJ4o/5Bkf8A12H/AKC1clXW+KP+QZH/ANdh/wCgtXJVtT2Prcm/3VerCiiitD1QooooAKKKKACiiigAooooAKKKKACorj/j2l/3D/KpaiuP+PaX/cP8qmXwsUtmfcCf6tfoKdTU/wBWv0FOrxTyhr/6tvoa+a9CP/FP6b/17R/+givpR/8AVt9DXzRoR/4p/Tv+vaP/ANBFXBXZ4+cfw4+pp5rsfhRz4k8Q/wDXpZ/+hXFcXmuz+E/PiPxD/wBetn/6FcVU1ocOVf7x8merUUUVkfTBRRRQAV514quZtV8Qaet14L1nU9NsZJ1lgeKFoZmOAkqqZMPja2AwHD54IxXotcd47sta1KKOz0u4v7dGsruTfZSGNjcKi+QrOOQpJc9RkgA0AR+BrVoNV1yWHw9d6Dp0pgMFnKiIhYB98iqjEKT8oIHHyqckk47WuE+Hqt9s1d4JNclsHS3Mbau85dJcP5kaiXnC/LyByWIycDHd0AFFFFABRWfd6vb2Wr6dpsiSma/MgiZQNq7F3Hdznp0wDVq1me4tklkt5bZ2HMUpXcv12kj8iaAJqKKKACiioL28g06wuL26k8u3t4mmlfBO1FBJOByeAelAE9cH8Zf+SXap/wBdbb/0ojruYZo7iCOeJt0cih0bGMgjIrhvjL/yS7VP+utt/wClEdVH4kNbnzXRRRXtncFFFFABRRRQAUUVranoY0tWWbU7J7hVRjbx+aX+YAjkoF6EHrWcqsYyUXu/6/UDJooorQAooooAKKKKACu0sf8Ajwtv+uS/yFcXXaWP/Hhbf9cl/kKyq7Hi51/Dj6k9FFFYnzwUUUUAFFFKq7mC5AycZPQUgEoq9Jpcgh82CeG6XesZ8ktlWPQYIHXB6Uy6sfsi/NdW8kgba0cbElT7nGD+BNZRxFOTsnr/AF93zLdKaV2ipRRRWxAUUUUAFFFFAGJ4o/5Bkf8A12H/AKC1clXW+KP+QZH/ANdh/wCgtXJVtT2Prcm/3VerCiiitD1QooooAKKKVF3uq5AycZJwBSASitLVtGk0lLR2ura5juozJHJbszLgMVPUDuprNqadSNSPNF6AFFFFWAUUUUAFRXH/AB7S/wC4f5VLUVx/x7S/7h/lUy+Filsz7gT/AFa/QU6mp/q1+gp1eKeUNf8A1bfQ18zaGf8AiQad/wBe0f8A6CK+mX/1bfQ18x6If+JDp/8A17R/+gitqO7PIzde5H1NLNdr8Jf+Ri8Q/wDXrZ/+hXFcPmul+HOuWmj+IdcN1FfSeba2m37JYzXGMNPnPlq23r3xnn0q6q904ssX+0fJntVFUdR1a10zSH1K48wQqoKoEO92bAVAp53EkADrk4rgh4m8TWi67b3lxB/aDX1ja2ieWpjszc7RjIwX2bup6lewOK5j6Q9Lorl9EvNTsvFV74f1LUG1FVs4r23uZIkjkAZ2RkYIApwVBBAHU5ziuooAK5u68J3FzdzTr4q8QQCR2cRRTxBEyc7VBjJwOg5NdJXAfFLVLzTdIDRajc6bafZLuQ3NudpNwqAwRl8fKGO70yVAzzggHVaNo0mkef5msalqPm7cfbpEby8Z+7tVeuec56CmeLP+RN1z/sH3H/otqzvC+tf25rGrXFndm70hYrdIZlOYzMA/mhG7gDy844yTXU0AeV2fhfSI9Z8G24tQYdQ0uY36FiReFEhKmUfx4LEjNY5ubLyINDv4dJjsrW81KO1utYheeNFS5KLDGoZcuFxjnIAAAr2yigDyrwndXF5F8O5bqR5JQNQjLOCGwgZQDuJPAUDkk1Q0r+zP7G8G/wDCTeV/wj/2O6/4+P8AUfavMXZ5nbOzzNue+cc17JWfqumy6lFGkOqX2nshJ8y0KZYehDqwx+GaAPKNNs7LUND0S1CzNYyeMrkKsjMGaPZcYDZ+bkDBB5PINaN5JbaBZeLdLhsLVtJi1W0jSC4DfZrVZYoWZ2VekYYliowMnsCa9H0bR7XQtMjsLPzDGrM7PK+95HZizOzdyWJJ+tX6APDVlVvCfjvT7a5tH0+P7G8B06F4bcFzhjErM2B8o5BwSCRXQ+INAsrPUvEGj6bYIIL3wzNM9sq7hLOjkI5B6vk/e6k4r1GigDm/Av8AYH/CL2//AAjwsFgwv2gWQUATbF3bwvR8bc556Vyfxh0u8TwNqt62u6g9uZrc/YWSDyRmeMYyI9/HX736cV6hXB/GX/kl2qf9dbb/ANKI6qPxIa3PmuiiivbO4KKKKACiiigArr5rm5TR9Qj1rVrS9iaELaRJcLMwkDLhlxkoAAc5x6YrkKK56+HVZxv0d9tej0fTYaYUUUV0CCiiigAooooAK7Sx/wCPC2/65L/IVxddpY/8eFt/1yX+QrKrseLnX8OPqT0UUVifPBRRRQAU6NPMkVNyruIGWOAPrTaKTA6W2u0sIIPtklqTDcxPGtqVJYD7xbZweOmec/jUOpTlrG5W5u4LgtMptREwbYvOen3RjHB/KsCivPjl8VU9pfW99vnp28+92dTxUnHktp/W/fyCiiivROUKKKKACiiigDE8Uf8AIMj/AOuw/wDQWrkq63xR/wAgyP8A67D/ANBauSransfW5N/uq9WFFFFaHqhRRRQAUqjcwAxyccnFJRSA6jxBYGLw7ooF5p8r2kEkcyQXsUjBmmdhgKxJ4YdK5eiiscPSlShyyd9W+27v+oBRRRW4BRRRQAVFcf8AHtL/ALh/lUtRXH/HtL/uH+VTL4WKWzPuBP8AVr9BTqan+rX6CnV4p5Q1/wDVt9DXzDoh/wCJFp//AF7R/wDoIr6ef/Vt9DXy/op/4kWn/wDXvH/6CK3obs8rNVeEfU0c13Hwi/5GHxF/162f/oU9cJmu6+EP/IweIv8Ar1s//Qp60rL3Tky1Wr/I9P1HTLDWLNrPUrOC7tmILRToHUkHI4NciPhbokM+rzWUUFjJemBrZ7W2VGs2iKsCp7guisRgZx+NdzRXIfQGFougXVlql3q2qait/qNxFHBvjt/JjjiQsQqruY8lmJJJzx0xW7RRQAVz/ih3UWwTxRb6GG35E0cTef06eZ6e396ugrh/El+114gthbeHp9dg0+SW1vrdYYSI2eOGRGBkI5wwGBwctn7oyATeCtRu7nVdasZtfh1u3tBAYbi3hjRE3B8p8nBYbQTz0K9MmuyrF8O3v2iGaFfDl5osURBVLhIVWTOc7RG7dMc5x1HWtqgAooooAKKKKACiiigAooooAK4P4y/8ku1T/rrbf+lEdd5XB/GX/kl2qf8AXW2/9KI6qPxIa3PmuiiivbO4KKKKACiiigAooooAKKKKACiiigAooooAK7Sx/wCPC2/65L/IVxddpY/8eFt/1yX+QrKrseLnX8OPqT0UUVifPBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGJ4o/5Bkf8A12H/AKC1clXW+KP+QZH/ANdh/wCgtXJVtT2Prcm/3VerCiiitD1QooooAKKKKACiiigAooooAKKKKACorj/j2l/3D/KpaiuP+PaX/cP8qmXwsUtmfcCf6tfoKdTU/wBWv0FOrxTyhr/6tvoa+XdFP/EjsP8Ar3j/APQRX1E/+rb6GvlvRj/xJLD/AK94/wD0EV0Yfdnm5krwiaGa7v4P/wDIweIv+vaz/wDQp64LNd58Hv8AkP8AiL/r2s//AEKeta/wHJl6/ffI9coooriPdCiiigArjF8GNfeI/EF/d3ur2aXN3G9uLLUZIUkQW8KFiqN13Kw554HbFdnRQBlaNoMOief5V9qd1523P269kuNuM/d3k7evOOuB6Vq0UUAFFFFABRRRQAUUUUAFFFFABXB/GX/kl2qf9dbb/wBKI67yuD+Mv/JLtU/6623/AKUR1UfiQ1ufNdFFFe2dwUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV2lj/x4W3/XJf5CuLrtLH/jwtv+uS/yFZVdjxc6/hx9SeiiisT54KKKKACiiigAooooAKKKKACiiigAooooAxPFH/IMj/67D/0Fq5Kut8Uf8gyP/rsP/QWrkq2p7H1uTf7qvVhRRRWh6oUUUUAFFFFABRRRQAUUUUAFFFFABUVx/wAe0v8AuH+VS1Fcf8e0v+4f5VMvhYpbM+4E/wBWv0FOpqf6tfoKdXinlDX/ANW30NfLOjn/AIklh/17x/8AoIr6mf8A1bfQ18r6Of8AiS2P/Xun/oIrpw27PPzBXii/mu/+DvOveIv+vaz/APQp689zXX/DHU7rT9d137Lot9qe+2td32V4V8vDTYz5kidc9s9D0rbEfAc2CX709vorM1e2+36M29NRUhRKYbK48mZiBnYHVgM9vvAe9edWOo6ldXsPh1r7VLKG61kwtFczk3ttAtqZdhlyT87JkMGbCkjdnpwHsnrFFcv4TluLfVfEGiS3U91Bp1zH9nluJDJIEkiV9jMeWwScE5OCK6igAooooAKKKKACiiigAooooAKKKKACiiigArg/jL/yS7VP+utt/wClEdd5XB/GX/kl2qf9dbb/ANKI6qPxIa3Pmuik3D1FG4eor2zuFopNw9RRuHqKAFopNw9RRuHqKAFopNw9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### Recall Skoru Nedir?  
Recall skoru, sınıflandırma görevlerinde kullanılan bir performans metriğidir ve bir modelin bir veriseti içindeki tüm ilgili örnekleri doğru bir şekilde tanımlama yeteneğini ölçer. Özellikle recall, gerçek pozitiflerin model tarafından doğru bir şekilde pozitif olarak tahmin edilen oranını değerlendirir.

Hadi, scikit-learn kullanarak recall skorlarını yazdıralım. 

```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>

`Recall for benign: 0.90,
Recall for malignant: 0.86,
Recall for normal: 0.78`

## Sonuç  
Bu cookbook'ta, bir ViT modelini bir tıbbi verisetiyle nasıl eğiteceğimizi ele aldık. Veriseti hazırlığı, görüntü ön işleme, model yapılandırması, eğitim, değerlendirme ve sonuçların görselleştirilmesi gibi önemli adımları kapsadık. Hugging Face'in Transformers kütüphanesi, scikit-learn ve PyTorch Torchvision'u kullanarak, verimli model eğitimi ve değerlendirme süreçlerini kolaylaştırdık. Bu süreç, modelin performansı ve biyomedikal görüntüleri doğru şekilde sınıflandırma yeteneği hakkında değerli bilgiler sunmaktadır.

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/tr/fine_tuning_vit_custom_dataset.md" />

### Özel Bir Veri Seti Üzerinde Semantik Segmentasyon Modeli Fine-Tuning Etmek ve Inference API Üzerinden Kullanımı
https://huggingface.co/learn/cookbook/tr/semantic_segmentation_fine_tuning_inference.md

# Özel Bir Veri Seti Üzerinde Semantik Segmentasyon Modeli Fine-Tuning Etmek ve Inference API Üzerinden Kullanımı

_Yazar: [Sergio Paniego](https://github.com/sergiopaniego)_
_Çevirmen: [Onuralp Sezer](https://github.com/onuralpszr)_


Bu notebook'ta, özel veri seti üzerinde bir [semantik segmentasyon](https://huggingface.co/tasks/image-segmentation) modelinin fine tune sürecini adım adım gözden geçireceğiz. Kullanacağımız model, segmentasyon görevleri için güçlü ve esnek bir transformer mimarisi tabanlı olan ve önceden eğitilmiş [Segformer](https://huggingface.co/docs/transformers/model_doc/segformer) olacaktır.

![Segformer mimarisi](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/segformer_architecture.png)

Veri seti olarak, etiketli kaldırım görüntüleri içeren [segments/sidewalk-semantic](https://huggingface.co/datasets/segments/sidewalk-semantic) veri setini kullanacağız; bu modelimizi kentsel ortamlardaki uygulamalar için ideal hale getirecek.

Örnek kullanım durumu: Bu model, pizza siparişlerinizi kapınıza kadar teslim etmek için kaldırımda otonom olarak gezinme yeteneğine sahip bir teslimat robotunda kullanılabilir. 🍕

Modeli Fine-Tune yaptıktan sonra, onu [Serverless Inference API](https://huggingface.co/docs/api-inference/index) kullanarak nasıl kullanacağımızı göstereceğiz ve basit bir API endpoint aracılığıyla erişilebilir hale getireceğiz.

## 1. Kütüphanelerin Kurulumu

Başlamak için, semantik segmentasyon modelimizi fine-tuning yapmak için gerekli olan temel kütüphaneleri yükleyeceğiz.

```python
!pip install -q datasets transformers evaluate wandb
# datasets==3.0.0, transformers==4.44.2, evaluate==0.4.3, wandb==0.18.1 ile test edildi
```

## 2. Verisetinin Yüklenmesi 📁

Belçika'da 2021 yılı yazında toplanan kaldırım görüntülerinden oluşan [sidewalk-semantic](https://huggingface.co/datasets/segments/sidewalk-semantic) veri setini kullanacağız.

Veri seti şunları içermektedir:

* **1.000 görüntü ve bunlara karşılık gelen semantik segmentasyon maskeleri** 🖼
* **34 farklı kategori** 📦

Bu veri seti sınırlı erişimdedir, bu yüzden erişim sağlamak için oturum açmanız ve lisansı kabul etmeniz gerekmektedir. Ayrıca, eğitimin ardından fine-tuned modeli Hub'a yüklemek için kimlik doğrulaması gerekmektedir.


```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)
```

Veri setine, yapısını inceleyerek aşina olun!

```python
dataset
```

Veri seti sadece eğitim seti içerdiği için, bunu manuel olarak `eğitim ve test setlerine` ayıracağız. Verilerin %80'ini eğitim için ayıracak ve kalan %20'sini değerlendirme ve test için saklayacağız. ➗

```python
dataset = dataset.shuffle(seed=42)
dataset = dataset["train"].train_test_split(test_size=0.2)
train_ds = dataset["train"]
test_ds = dataset["test"]
```

Bir örnekte bulunan nesne türlerini inceleyelim. `pixels_values` RGB görüntüyü tutarken, `label` gerçek referans maske(ground truth mask) içeriyor. Maske, her pikselin RGB görüntüsündeki karşılık gelen pikselin kategorisini temsil ettiği tek kanallı bir görüntüdür.

```python
image = train_ds[0]
image
```

## 3. Örneklerin Görselleştirilmesi! 👀

Veri setini yükledikten sonra, yapısını daha iyi anlamak için birkaç örneği maskeleriyle birlikte görselleştirelim.

Veri seti, her bir ID ile ilişkilendirilen kategori etiketlerini içeren bir JSON [dosyası](https://huggingface.co/datasets/segments/sidewalk-semantic/blob/main/id2label.json) içerir. Bu dosyayı açarak her `id2label` ile eşleşenleri okuyacağız.

```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>

Her kategoriye renkler atayalım 🎨. Bu, segmentasyon sonuçlarını daha etkili bir şekilde görselleştirmemize ve görüntülerimizdeki farklı kategorileri yorumlamamızı kolaylaştıracaktır.

```python
sidewalk_palette = [
  [0, 0, 0], # unlabeled / etiketlenmemiş
  [216, 82, 24], # flat-road / düz-yol
  [255, 255, 0], # flat-sidewalk / düz-kaldırım
  [125, 46, 141], # flat-crosswalk / düz-yaya geçidi
  [118, 171, 47], # flat-cyclinglane / düz-bisiklet yolu
  [161, 19, 46], # flat-parkingdriveway / düz-park yeri/garaj yolu
  [255, 0, 0], # flat-railtrack / düz-ray hattı
  [0, 128, 128], # flat-curb / düz-kaldırım kenarı
  [190, 190, 0], # human-person / insan-kişi
  [0, 255, 0], # human-rider / insan-sürücü
  [0, 0, 255], # vehicle-car / araç-araba
  [170, 0, 255], # vehicle-truck / araç-kamyon
  [84, 84, 0], # vehicle-bus / araç-otobüs
  [84, 170, 0], # vehicle-tramtrain / araç-tramvay/tren
  [84, 255, 0], # vehicle-motorcycle / araç-motosiklet
  [170, 84, 0], # vehicle-bicycle / araç-bisiklet
  [170, 170, 0], # vehicle-caravan / araç-karavan
  [170, 255, 0], # vehicle-cartrailer / araç-römork
  [255, 84, 0], # construction-building / inşaat-bina
  [255, 170, 0], # construction-door / inşaat-kapı
  [255, 255, 0], # construction-wall / inşaat-duvar
  [33, 138, 200], # construction-fenceguardrail / inşaat-çit/korkuluk
  [0, 170, 127], # construction-bridge / inşaat-köprü
  [0, 255, 127], # construction-tunnel / inşaat-tünel
  [84, 0, 127], # construction-stairs / inşaat-merdiven
  [84, 84, 127], # object-pole / nesne-direk
  [84, 170, 127], # object-trafficsign / nesne-trafik levhası
  [84, 255, 127], # object-trafficlight / nesne-trafik ışığı
  [170, 0, 127], # nature-vegetation / doğa-bitki örtüsü
  [170, 84, 127], # nature-terrain / doğa-arazi
  [170, 170, 127], # sky / gökyüzü
  [170, 255, 127], # void-ground / boşluk-zemin
  [255, 0, 127], # void-dynamic / boşluk-dinamik
  [255, 84, 127], # void-static / boşluk-statik
  [255, 170, 127], # void-unclear / boşluk-belirsiz
]
```

Veri setinden bazı örnekleri görselleştirebiliriz; bunlar RGB görüntü ve ona karşılık gelen maske ve maskenin görüntü üzerine bindirilmiş hali bulunur. Bu, veri setini ve maskelerin görüntülerle nasıl eşleştiğini daha iyi anlamamıza yardımcı olacaktır. 📸

```python
>>> from matplotlib import pyplot as plt
>>> import numpy as np
>>> from PIL import Image
>>> import matplotlib.patches as patches

>>> # Legend'i(Açıklama/lejant) ayrı olarak oluştrup ve gösterelim
>>> 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))

...     # Orjinal resmi göster
...     ax[0].imshow(image['pixel_values'])
...     ax[0].set_title('Orjinal Resim')
...     ax[0].axis('off')

...     mask_np = np.array(image['label'])

...     # Boş bir RGB resim oluştur
...     colored_mask = np.zeros((mask_np.shape[0], mask_np.shape[1], 3), dtype=np.uint8)

...     # Her etikete denk gelen maske için bir renk atayalım
...     for label_id, color in enumerate(sidewalk_palette):
...         colored_mask[mask_np == label_id] = color

...     colored_mask_img = Image.fromarray(colored_mask, 'RGB')

...     # Segmentasyon maskesini göster
...     ax[1].imshow(colored_mask_img)
...     ax[1].set_title('Segmentasyon Maskesi')
...     ax[1].axis('off')

...     # Şeffaflığı desteklemek için orijinal görüntüyü RGBA formatına dönüştürün
...     image_rgba = image['pixel_values'].convert("RGBA")
...     colored_mask_rgba = colored_mask_img.convert("RGBA")

...     # Maskenin şeffaflığını ayarlayın
...     alpha = 128  # Şeffaflık seviyesi (0 tamamen şeffaf, 255 tamamen opak)
...     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('Maske Katmanı')
...     ax[2].axis('off')

...     plt.show()
```

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## 4. Sınıf Görünümlerini Görselleştirme 📊

Veri seti hakkında daha derinlemesine bilgiler edinmek için, her bir sınıfın görünüm sayılarını grafiğe dökelim. Bu, sınıfların dağılımını anlamamıza ve veri setindeki olası önyargıları veya dengesizlikleri belirlememize yardımcı olacaktır.


```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())

>>> # Renkleri [0, 1] aralığına normalleştir
>>> normalized_palette = [tuple(c / 255 for c in color) for color in sidewalk_palette]

>>> # Görselleştirme
>>> 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("Kategoriler", fontsize=14)
>>> ax.set_ylabel("Görünüm Sayısı", fontsize=14)
>>> ax.set_title("Kategoriye Göre Görünüm Sayısı", fontsize=16)

>>> ax.grid(axis="y", linestyle="--", alpha=0.7)

>>> # Y ekseni limitini ayarlayın
>>> y_max = max(class_counts)
>>> ax.set_ylim(0, y_max * 1.25)

>>> for bar in bars:
...     height = int(bar.get_height())
...     offset = 10  # Metin konumunu ayarlayın
...     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)  # ncol'u gereken durumlarda ayarlayın

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

<img 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YHmYUKwK4C53Ajp3xVPRYdIN9o2rSa/qmtzXcslvYvdMoWCTy3Z/kRECttjdTuGR04ya88+eO4ooooA4Sw8NalpviGO/1abSv7Is7m/uICXYOrXM3mBm3DaCoJXg/wARrf8ADemNpx1aRfIFte37XVusDZVUaNB9MllZjj+9XL2nhnUrrXIoNe0m1l0O0vNQu1aW4WRZWmmLxM0ZGBtVmHOcbs8YrpPCukPoseq2y2y2ti2oPJYwKRtSIomdoBwoL+YQOMZ6CgDoKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACucvf8AkpGi/wDYKvv/AEba10dc5e/8lI0X/sFX3/o21oA6OiiigDm9f8RHw3pMdytsJzJcNHtL7cfeOeh9K5n/AIWpL/0CU/7/AJ/+Jrf8U6Dd+INFit7Nolkjumc+YxAx8w7A+tcd/wAK01z/AJ6WX/fxv/ia9LDRwzp/vNzysXPFqrale3ojS/4WpL/0CU/7/n/4muk8J+LG8SvdK1mLcwBTxJu3Zz7D0rif+Faa5/z0sv8Av43/AMTXWeCPC994ekvWvXgPnBAoiYnpnOcgetPERwqpt07X9ScNPGOqlVvy+iOnv7+10yze7vJRFChALYJJJIAAA5JJIAA5JOBWXL4w0W3A8+e4hOzzHEtlMhiTJG+QFP3a5B+ZsA4PPBq5relf2vYLAs5gmjmjuIZdu4LJG4Zcr3GRgjI4PUdaxb3wrqd+b5pdYt0fUrQWd75ViQGjBfBjBkOxsSOMncOhxxz5h65pN4p0ddTfTjdP9ojnW2f/AEeTYkrKrKpfbtBIZcZPJOBzVXUfGmmWWlajfxJeXMdiju5jtJdj7W2kK+3a2G44JxgnoDSP4TUw30SXe1bnVbXUR+6zsEPkfJ15z5H3u27occ0rjwNJdnVFk1CG3hvraaAxWVq0SlpCD5kimRldxjqAudzZ60AbH/CVaT9rjtC90Ll4hP5TWUwZYyzLuYFMqMqeWx29RmX/AISPSPIgna9RIprJr9HdWVTAu3LkkcY3rwcHnp1qG10ScatcajfXcU81xZR2kiwwGJfleRtwy7EZEmMZ/hznnAxIvAc8ltFa32sCa3g0p9MgEVr5bKpMZVySzAsPKGeAD6DuAaV5400+0OnH7NqTre3Jt1P9nXAZSInkztKbmGExwO5PRTiyfFGnRI7Tynd9pktkjt4pZpHZPvfIqbuB1wCB61Bd6Jq99HYSz6rZ/brC7+0wSpYsIz+6eMhk83J4kY5DDnHFQDwnc292L6y1KOO9W6uZlaW2MkeyYqWQqHBOCqkEMOnTmgC1ceM9AtlRnvmdXtheBobeSUCEkjzCVU7VBByTjHfFWfEOuReH/D11q7RSXCQR7lSJS28npyoOBnHOOKybfwSlra3UCX7MLjSzp7M0QzuLSM0nBHUyH5eOnWrd/wCFIbzRrmxW7uEluLZbdpGmkeMABRkRF9gPy9sHrzyaAHxeKrFIoVvy1vcOE81UhmaOEuflDuY12E5H3wp56VLB4p0e51H7BFcuZ/Pe2ybeQJ5qZLJvK7d2FJxnJAyKzL7wVb3XiC51VU0uQ3bxvMt9pq3DqUUL+7fcNuVVeCGGRnuasx+Ftiwj7Zny9Xk1P/Vdd5c7Ovbf19ulAFuHxPpM8U80dxIYIFLNcG3kETAHB2SFdr88YUnNN/4SrSBbSTNPMhjlWJoXtZVm3tyoERXecjJGBzg+hrNHg6V/DUnhy41CKXSkiWK2X7L+9jCMGTexYq4G0DG0ZHWq0/hSfT7BLixt9PGoW10lzANL0xIFYhXQ70aYBxtkf+NcZ456gG9Y+JdI1F4Y7W6LvNLJCitC6HfGMupDAYI98elNl8UaTDYx3rS3DWrhz50dpM6IEJDFyqnYAQeWwOCelYOj+F9XFpb3893FaasuoXN5iWASJtlypVlVxg4weHOCMZbqYLr4btdaVDYyanbzhLWa3Z7qwEu0yOzebGu8BJPmxnnoOlAHS3HinRrW9uLSa7YSW0QmnYQuyRIVLBmcLtUEA4yecY61QTx1pX2vUln823tLGK0d5poJUbfPI8aqY2QMOUXnvv7AZpT4OjkstVtZb1iuoW0MG5IwDGY02hhknPPOPw5qCTwhe3l3e3eoavDLPdNp5/c2ZjRBa3BmwAXY/NuxyeOvPQAGu/iTTYmgE32yETlQrzWM6ICzbVDMUAQk8YYjqPUVAviSN9XtbCOJJPOu5rVnV2/dmOPfyCoye2Bx7msvxB4FOu6pPdte26iVoGUzWQmlg8tgcROWGxWxyMZ5PNX7fwt5GrxX/wBs3eXfz3mzysZ82PZtznt1z39BQBtp/wAf0v8AuL/M1PUCf8f0v+4v8zU9ABRRRQAUUUUAFFc/4y1HWNI8NX2qaR9h32VvLcyC7R3DKiFsAKRycY61tWczXFlBM4AaSNXIHTJGaAMLxL/yHvCP/YVf/wBI7mujrh9Z8TaDqHijwta2Wtafc3EOqSNLFDco7IBaXAJIByOSB9TXZm6txtzPGN33fmHP0oAloqL7TBvZPOj3LksNwyMUn2u32b/Pi2Zxu3jGfSgCaiojcwBlUzR5cAqNw5z0xQLmAlgJo8oMsNw4+tAEtFQ/a7fYH8+LYTjdvGM0v2iDeqedHvbou4ZNAEtFRC6tyGInjIX73zDj60hu7cIHM8QVuh3jBoAwvHn/ACJeof8AbP8A9GLXi1ey+O54j4Qv4hKhk/d/JuGf9Yp6V48beYMFMTgnoNp5rw8zTdZW7fqz6fJZJYd3fV/kiOu1+GP/ACMtz/15t/6GlccLeYkgROSvUbTxXZfDRWj8QzyOpVGtGAYjAPzIf6GufCRft46HXj5R+rT16Homof8AHwv+7/U1Uqa+uYHlDrNGVCgEhhjqarGaIYzInzdOetfE53Tm8wqtJ7nzlNrkQ+tLT/8Aj3b/AHv6Csrzo9xXzFyvUZ6VoWFzAtqztNGF34yWGM4Fd3C1OazBNroyK79wdrGqwaJpU2oXKSvHHtAjhXc8jMwVVUdyWYAfWuaf4hJGJ5H8P6skFiQNTkcRD7ETz8w35f5SHJTdhSD7VpeMbvRk8M3A1e7lhtWaIiS2y0qMZF8t0ABJIfaRwenQ1zWn6Bo2v2ly9h41ur21vCG1ZRJCxusYHz4UGL5QEIUL8oA96/TDjPRqKh+124QP58W0nAbeMZp32iHzBH5ybz0XcMmgDkvHDqLjTE1GTUotBcTC8bT/ADd3mYXyg5i+cIf3nTjO3NZHg7Sb/Rzodxa/2stvez3SXUF3JI4WAeY0ErhyfLkIWIEDGd5yMjjX8R3Wo3XinSLLR9fXTgbO7nlbykmjdke3VQ6nt87dCD71Z03XNchvoLPXNOtGil3BdSsLkGHIBPzI5Dp0xxuGSOaAOooqP7RD5vleanmf3dwz69KaLu3KM4niKr1O8YFAHnWp+L5ZNSTQ7/xBb6G5vbtLiZ1iDpErZt1HmAqBIh3byDnaQOa6fwXqlxqml3bTXy6jFb3jwW+oIiqLqMBTv+X5ThiyZXg7Mise/wDF3h/UrqVJ/DF/qYtZ5LcTf2fHKhZGKttLHpkVreEvEn9uDVkktfsKWV6baG3lj8uRYxDE/wA65ODl26YGAKAOmoqH7XblC/nxbQcFt4xmlNzACoM0YL8r8w5+lAEtFRfaYNzL50e5MlhuHGOuaT7Xb7N/nxbM43bhjPpQBNXNa/4WtfEPiLTJtRsbe7063s7qKRJgDtkd4ChX0OI35HI/Gug+0wblXzo9zAFRuGTnpiuY8SaZba/rmk296gutHiS4a6iEuEWXCeWzgEZAHmD2LCgC1pnhifRdRiew1y/OmDO/T7tvtC4wQNkjfOmDg4JIwMYHWuirz86dqeneJfDtsk4vtAtryWeK6lnBltR9mmTynJOXXMi7W6joexPefaIfMEfmpvPRdwyaAJKKhF1blWYTxkL947hx9aDd26qrGeIK3Qlhg0ATUVH9oh83yvNTzP7u4Z/Kmi7tyhcTxFV6neMCgCaiojdW4CkzxgN935hz9KPtEG9k86PevVdwyKAJaKh+12+wv58WwHG7eMZpTcwAqDNHlxlRuHP0oAyfEv8AZ1vBYanqd8tnDp12twsjdGYo8e3HfIkPTnNZskfh6/8AD9pNp2oxR6dJq8N0ZY8yeZO1yH2dcqWlIB9M4wO1vxO1iy6de3Op2VpFpd8t1K1zIFTG14yCT0P7zj3Arm9aj8Pap4lsv7H8T6bbXk91a3d5aLMrJeJFIkqsADxJhBhh1U85GCAD0aiovtMG9U86Pc2Co3DJzQLq3O7E8Z2/e+YcfWgCWioTd24QOZ4gp4B3jBp32iHzBH5qbz0XcM/lQBJRUIu7dlZhPGVX7xDDAoN3bqqsZ4wrfdJYYNAE1FR/aIfMMfmpvHVdwz+VNF3blC4niKjgneMCgCaiojdW425njG77vzDn6UfaYN7J50e5clhuGRigCWioftdvs3+fFszjdvGM+lKbmAMqmaPLgFRuHOemKAJaKiFzASwE0eUGWG4cfWk+12+wP58WwnG7eMZoAmoqL7RBvVPOj3t0XcMmgXVuQxE8ZC/e+YcfWgCWioTd24QOZ4grdDvGDTvtEPm+V5qeZ/d3DP5UASUVCLu3ZWYTxFV6kMMCg3VuFVjPGA33SWHNAE1FR/aIfMMfmpvHVdwyKaLu3KFxPFtBwTvGBQBNRURurcbczxjfyvzDn6UfaYNzL50e5QSw3DIx1zQBLRUP2u32b/Pi2Zxu3DGfSl+0wblXzo9z4KjcOc9MUAS14943/wCRwv8A/tn/AOi1r1wXMBLATRkpy3zDj615J4yBn8W3zQgyKfLIKcg/IoryM5TdCKXf9Ge9w80sVJv+V/mjnqKf5Um0tsbaOpxxQYZQQDG+T0+U818zyS7H2PPHueveCP8AkT7D/tp/6MaugrnPB0sdv4RsFndYz+84c4/5aN61v/aIfN8rzU8z+7uGfXpX2uFVqEPRfkfnWOd8VUa/mf5klFQi7tyjOJ4iq9TvGBQbq3AUmeMBvuncOfpXQcpNXB+LfGsuh61e6Ul3bQXE1nZmwEqZ/eyzyRyP7hVCNj2967j7RD5hj85N46ruGRXN6t41g07Wf7Mt9J1DUpRbrcM9oYdiqzMoBLyLzlT09RQAzSW1bSvFi6NfazLq0NzYvdrJPDGkkLI6KR+7VQVbzMjIyNp5rq64zTfF1xqHjW2sJ9IudMgk06afN75W+VlkiVdpR24w7ZB9q677TBuZfOj3JksNw4x1zQBLRUP2u32b/Pi2Zxu3DGfSvPH1i31LVdSXWfHcugy291JFDYRTQW+2IH5JCZUJfeuGyDj5sdqAPSaK5bwVrb6npd41xqUd/FbXTxW9/tVPtMICkSEDA+8WXIAB25HWujN3bhA5ni2k4B3jBoAmoqP7RD5gj81N56LuGTTRdW5VmE8ZC/eO4cfWgCaioTd26qrGeIK3Qlhg077RD5vleanmf3dwz+VAElFQi7tyhcTxFV6neMClN1bgKTPGA33fmHP0oAloqL7RBvZPOj3r1XcMik+12+wv58WwHG7eMZoAmoqI3MAKgzR5cZUbhz9KBcwFmUTR5QEsNw4x1zQBLRUP2u32b/Pi2Zxu3jGfSoNQ1ey0vT7i9up0WG3had8EE7FUsSB34FAF2iuGHj++laSCDwtdm8ghFzPBJdQrshP3WDAkFiQw28YKnJHGersdXsdQ0u01GCdPs91EssTMQMqwBH44IoAvUVH9oh8wR+am89F3DP5U0XduyswnjKr94hhgUATUVCbu3VVYzxhW+6Swwad9oh8wx+am8dV3DP5UASUVCLu3KFxPEVHBO8YFKbq3G3M8Y3fd+Yc/SgCWiovtMG9k86PcuSw3DIxSfa7fZv8APi2Zxu3jGfSgD5f8Wf8AI5a5/wBhCf8A9GNWPW54nt55/FeuzxQySQi/uCZFUlcbyev0rKNldBUY20wEhAQ7D8xPTHrXuQa5Ufe0Zx9nHXoiCvr6vkn7BeGUx/ZZvMA3FfLOceuK+sftEG9U86Pe3Rdwya4sc0+X5nhZ7JP2dn3/AEMnU47JPEml3098tvPb291iNhxJCRGZCT/CFKxnP+NcxYt4b17x1p2q6ZeXtpcKz3X2eWzkhh1A+U8YlQuoDMFkPzL1GM5ABGl43srLUbAXP9t2GnzRwzWhku3HkyRTKFeNvmUgnapBByCvfpVXR7HVBd6Jd+J/EWjzJaFhYpZR+X9okMTKWZmY7iELnaoA79q4D587qio/tEPm+V5qeZ/d3DP5U0XduyswniKr1IYYFAHmHjfwvrEFh4gm+26/qdrew3DW8VpdyEwO6ttiaEcPFkgZXkDggjLV6FoOlDR9MW3F3fXO4hy15O0rqcAbQT0HHT3Ncpbx6xfPqOr2+vzw6hDfzxQWVw4FnJEj4RNuM/Mm07wcgt3AxWl4M1q41WXXpb6O5tJBqOyO1um+aFVt4dyr2K795BHByT3oA6yioRd25QuJ4toOCd4wKU3VuNuZ4xv5X5hz9KAJaKi+0wbmXzo9yglhuGRjrmk+12+zf58WzON24Yz6UATUVF9pg3KvnR7nwVG4c56YoFzASwE0ZKct8w4+tAEtFQ/a7cIH8+LaTgNvGM077RD5gj85N56LuGTQBJVS71SwsLe4uLy9t7eG3x5zyyqojz03EnjOR19alF1bkMRPGQv3juHH1rz/AMU6Tq8eqnUtIg07Urae/hvzBc3QixLHF5RXkEMpUKw5BVhnBoA7nTNX07WrT7Xpd9b3tvnb5lvIHUH0yO/tV2uX8NWl7Hq+qapqsdjZXeoCFfsNrN5uwRhvmZsLuc7+cDoq9a6IXduUZxPEVXqd4wKAJqKhN1bgKTPGA33TuHP0p32iHzDH5ybx1XcMigCSioftduUL+fFtBwW3jGaU3MAKgzRgvyvzDn6UAS0VF9pg3MvnR7kyWG4cY65pPtdvs3+fFszjduGM+lAE1FRfaYNyr50e5gCo3DJz0xQLq3O7E8Z2ct8w4+tAEtc5e/8AJSNF/wCwVff+jbWt03duEDmeLaTgHeMGsC7kR/iTowV1JGk3xIB6ZltcUAdLRRRQBzev+Ij4b0mO5W2E5kuGj2l9uPvHPQ+lcz/wtSX/AKBKf9/z/wDE1v8AinQbvxBosVvZtEskd0znzGIGPmHYH1rjv+Faa5/z0sv+/jf/ABNWrW1PBx9TMI1msPfl9EaX/C1Jf+gSn/f8/wDxNdJ4T8WN4le6VrMW5gCniTduzn2HpXE/8K01z/npZf8Afxv/AImus8EeF77w9JetevAfOCBRExPTOc5A9aHa2hOCqZi68VWvy9dF2Onv7+10yze7vJRFChALYJJJIAAA5JJIAA5JOBWXL4w0W3A8+e4hOzzHEtlMhiTJG+QFP3a5B+ZsA4PPBq5relf2vYLAs5gmjmjuIZdu4LJG4Zcr3GRgjI4PUdaxb3wrqd+b5pdYt0fUrQWd75ViQGjBfBjBkOxsSOMncOhxxzB9AaTeKdHXU3043T/aI51tn/0eTYkrKrKpfbtBIZcZPJOBzVXUfGmmWWlajfxJeXMdiju5jtJdj7W2kK+3a2G44JxgnoDSP4TUw30SXe1bnVbXUR+6zsEPkfJ15z5H3u27occ0rjwNJdnVFk1CG3hvraaAxWVq0SlpCD5kimRldxjqAudzZ60AbH/CVaT9rjtC90Ll4hP5TWUwZYyzLuYFMqMqeWx29RmX/hI9I8iCdr1Eimsmv0d1ZVMC7cuSRxjevBweenWobXRJxq1xqN9dxTzXFlHaSLDAYl+V5G3DLsRkSYxn+HOecDEi8BzyW0VrfawJreDSn0yARWvlsqkxlXJLMCw8oZ4APoO4BpXnjTT7Q6cfs2pOt7cm3U/2dcBlIieTO0puYYTHA7k9FOLJ8UadEjtPKd32mS2SO3ilmkdk+98ipu4HXAIHrUF3omr30dhLPqtn9usLv7TBKliwjP7p4yGTzcniRjkMOccVAPCdzb3YvrLUo471bq5mVpbYyR7JipZCocE4KqQQw6dOaALVx4z0C2VGe+Z1e2F4Ght5JQISSPMJVTtUEHJOMd8Va1/Wo9C0GfVWRZYodhI37QQzBc5wemc/hWRb+CUtbW6gS/ZhcaWdPZmiGdxaRmk4I6mQ/Lx061Y1PwlHf6LPZR31zDPNFFEZjLJIi7ChysJbYD8g6DuevOQCdvF+iR25mkuZkAnW2Mb2sqyiRhlV8srv+YdOOe1A8VaciXLzSMixXCW6xrBK0zO8KShfK2bt2187QGwBk4IIFMeE7ifUF1G+1KOW8+1wXDGK2MabIlcKgUuxBy7EsSfpT7jwrK2pXOpWuoLFePqK38Jkg3oh+zLbsjLuBYFVJyCpBI9OQBbHxrpt1Yi6l82PzLqe3hjiikmkkETlS+xV3AcAnI+XOCasHxfon2K3vEu5JbeeHz0eG2lkxF/fYKpKrweWwOD6VhSeG9V0Nba+06aS/wBTWa8LultHsKXEglYFHmTGGRcEMenIOahsvh4V03SnuP7Mlv4NPS0uFv7AXcfylmynzJgguwyOCMccCgDvY5EljWSNldGAZWU5BB6EU6o4Ilt7eKFQgWNAoCLtUADHAHQe1SUAFFFFABRRRQBAn/H9L/uL/M1PUCf8f0v+4v8AM1PQAUUUUAFFFFAGB4ysNW1bwxf6XpEdk0t9by20jXc7xrGroV3DajZIz04+tXtDTUotKhi1WG0iuYwExazNKhUAAHLKpz7YrRooA5vxIqjXvCJAH/IVft/053NdJgVzniX/AJD3hH/sKv8A+kdzXR0AGKMD0oooAMCjAoooAMD0oxRRQAYFGB6UUUAc548H/FGah/2z/wDRi14tXtPjz/kS9Q/7Z/8Aoxa8Wrwsz/jL0/Vn1GSf7u/X9EFdr8Mf+RkuP+vNv/Q0riq7X4Y/8jLc/wDXm3/oaVzYT+PH1OzMP92n6HouoD9+v+7/AFNVMVb1D/j4X/d/qaqV8Tnn/Ixrep81S+BBWlp4H2dv97+grNrS0/8A492/3v6Cu7hX/kYr0ZNf4Cj4otra40Jzd3kVlb29xb3ck8v3VWGZJSDyOuzGffv0rmdVu/h3rV5a3Ml/axahc48i+spGhmXLbRulTBX5gQA5wSCMV1PibSpda0Ceygljim3xzRtKuU3xyLIoYf3SUAPsTXGXHhvxDdafrO+60D7LrxD6hcI7gWoCiMmPjD/IoOWK4bJr9OOI9JwPSjFFYfjH/kVb3/gH/oa1FSfJBy7ITdlczvGGs3enX2mWVvqtlpEdyk8sl7dxh0zGFxHgkD5txPrhDisfwz4n1W/v9AubnV7W5XWRL5umJCqtY7UZ+CPmO0qEbd3bt0rjpYIbhAk0SSKDkB1BGfXmmpZ20Vw9xHbwpPJw8ioAzfU9TXl/2qv5Px/4Bj7byPeMUYHpXlXg7/karL/gf/oDV6rXdhcR7eDla2ppCXMrnAX/AIo1WyeabQfDunnTTqZs5bq4ujCZJzIIi2xEY48zC7jkk84xzXS+HNRi1O2vJDpwsL6O5aK+g+UkTBV53D7+UKEN6Y6dK4bV7nTbXxEb6x8P65qNsuphXMd4sVkb3cFyEdwC2/AyQF3+9dx4autOvrW8vLC2ntpZrpjew3GRKk4VVIYEkA7VTGDjGCODXSWbeB6UYFFFABgUYHpRRQAYrmPEljc6prmj2DTahDpki3D3D2MrxHzFCeWGdMFVwZD1AJArp65/xBqmuWep6bY6Lp9nctdrM0kt3M8aR7ApAyiNyct1H8NAGLs8Raf4o8PaRc/ar7T0vJZU1Jf4ohbTAR3GONwYphujcHhhz3WK4prrxn/wk3h+PUbTT4LB7qUTnT7mWUkfZ5SocNGoC7gvOeuK7WgAwPSjA9KK868QajZr4pv4PE3iLUtFsoxH/Z6287W0UyFAXYyKPmbfuG3dwAOOc0Aei4owPSuP8EXsl1capHa6je6nokZi+x3l4CWZyG8xVcgF1GEwxzyxGTiuwoAMCjFFZ9xr2jWl4LO51awhujgCGS5RXOf9knNAGhgelGBRRQBgeK9Lu9QtLGexS0ln0+8W7EF25SKUBHUqzAHGN+4HB5UVyGjaHqUi2+kxLoUlomqrq093Z3W5om8zzDEse3sf3YbI+TsOldN45sJNQ0uxjW2jvYkvUkm0+SZY/tqBW/dgsQCc4faeDswawE0pNS1bS30jwNLoE1rdxyy6hLHbwFYlOXjAidmfeuVweOc9qAPR8UYFFFABgelGKytb8S6L4bgSbWdTtrJJCQnnPgvjrgdT+FP0fX9J8QW7z6TqEF5HG21/KbJQ+jDqD9aANLA9KMD0oooAMUYHpRRQAYFGKKKADA9KMCiigAwKMD0oooAMUYFFFABgelGKztf1M6L4c1TVViEpsrSW5EZbbv2IWxntnFYcfjuCy2p4l0y80JzgCaceZakn0nTKj/gW36UAdbgelGB6UyGaK4hjmhkSSKRQ6OjAqykZBBHUGn0AGKMD0oooAMCjFFFABgelGBRRQAYFePeN/wDkcL7/ALZ/+i1r2GvHvG//ACOF/wD9s/8A0WtePnX+7r1/Rnv8O/71L/C/zRz9FFFfMH2Z7D4I/wCRPsP+2n/oxq6DFc/4I/5E+w/7af8Aoxq2rq7t7KETXMqxRmRIwzdNzsEUfizAfjX2+E/3eHovyPzfHf71V/xP82TYHpRgelc/deNvD9nqrabNeuJklWGRxBI0UcjYwjyhdik5HBI610FdByhiuU1/xKmjajfpHp0Mt1b2MEkUjuEMhmnaIITjhFYKWPOA3SurrgfGs+i6hrLaPrPhnUNSt4LNJ5Lyx3F4FlaRdpCESbT5RJ27h0yKANfSdS1VfEg0rXrbTTdvZvc29xYlsBA6K6MG5HLIQc4ODwMV0+BXJ+CrbwdFDNL4WlgldgFmbz3lmUDor+YS64/unH0rrKADA9K4SXUNa1Ka81UaTpeoaXZXk1t9i8ktdlYnKM6OTtLZUkJtGRj5smu7rgG8S6vDfSppOiaYltf6pNYW8rSlWa5j3CSWVQv3cRSdDk7V9cgA3vCniGLxGNWmtZIZbK3vRBbPGpGU8iFyGB7hnYEcYxjGRXQ4HpXPeErhpoNTiuLCztNQgv3jvfsYxHNKURvMGRklkZM557ZOK6GgAxRgelFFABgelGKKKADA9KMCiigAxRgelFFABgUYFYt34v8ADtjc3dtc6zZR3FogeeIyjegJAGV65yyjHXkeoq/pmqWOs2Ed9p11Hc20mQskZ4yDgj2IPGDQBbwPSq97bW13YXFteIrW00TRzKxwChBDAn0xmrFU9XsYNU0W/wBPuX2W91byQStnGFZSpP5GgDj38IaLDDJLH4q1WKdovKluVv0aSSEDiIllI2jkggBgWPPJrq9Alsbjw5pc2mxlLCS0ie2RhgrEUBQH8MV59b3fwi+XTja6E8qRqjXLaSAhzwHMvl7BkjrnFei6Pp0Wj6Hp+mQSNJDZ20dujvjLKihQTjvgUAXcUYHpRVXUNT0/SbcXGpX1tZwFgokuZVjXJ6DLEDNAFrA9KMVAb21H2bNxF/pR2wfOP3p2lsL6/KpPHYE1PQAYHpRgUUUAGKMD0oooA+WfFn/I465/2EJ//RjVj5NbHiz/AJHLXP8AsIT/APoxqx692Hwo++o/w4+iDJ9a+vsV8g19fVxY77Pz/Q8LPv8Al38/0MHV9D/tLxHo95Lb29xZ2sdwksUwBwzhNrgEYJG1l+jmsf8A4RO60zxToh024gXQILuW5+xSHDW7mCVMQ/7BMmSn8ODjg4Gl4ittZvdZ0m203W5NMtmSc3PkrC0jkBNhAkVsgfMDgfxDNUD4V1tPEmg38/iK81S3srmWSWO5igjCBoJUDDy0Uk5cDGehNeefPHZ4owPSiigDynV9VvdR1Roh4turHUF1kWMejWflLJ5BlCeZhlLE+WfN3HK44x3rt/Ct5fXEGo2mozi6n0+9a1F0IwnnqERwxA4BG/accZU8DpXH+JfE/ifTpNeK6foljc2ttcyWc9xI/nTQorMHj+QozADJTPB6jHNd5oUmry6cH1qOyS5Jyos5GdSmBgksoOc54xjpQBp4HpRgUVR1rURo+hahqZjMgs7aS4KA4LbFLY/HFAF7FGB6Vwa6l46nuZrENokM9vaJfG4FvK0UofcFhALjBBR8vk8FcKK67RtRXWNC0/U1QxreW0dwEJ5UOobH60AXsCjAorD17Wr2xvLHTdKsYrvUb0SPGJ5vKijSPbuZmAY9XUYAJOfagDcwPSjFcxZ65rlprNlpviDTbGIX5dLa5sLppF3qhcqyuikfKrEEZHGOK6egAwPSuMn8Ix614k1q61OO6jlVof7MvYpyrQJ5Yz5eDwRIHJyMHIzkV2dcQvh7Vb3xN4iv7bWtU0uYXka23HmW0kYtocnynG1hu3jKkHIIzxQBZ8L2mvReKdan1yCPP2S0toruLAS62NOxcL1Q4kXK9jnHGK67A9KydEbXx58Oux6exj2+Tc2TMBMDnO6NvuEYH8TA59q1qADA9KMUUUAGB6UYFFFABgUYHpRRQAYowKKKADA9K5u9/wCSkaL/ANgq+/8ARtrXSVzl7/yUjRf+wVff+jbWgDo6KKKAMXUNWOkaeswhEpedkwWxjkn+lZP/AAmz/wDPgv8A39/+tWtqGknV9PWETCIpOz5K5zyR/Wsn/hCX/wCf9f8Av1/9esKntOb3dj5nNP7Y+sP6n8Fl/L899Q/4TZ/+fBf+/v8A9atfQ9cOsNOrW4i8oA8NnOc+3tWR/wAIS/8Az/r/AN+v/r1r6HoZ0dp2a4EvmgDhcYxn396UPa83vbEZd/bf1mP1r4Nb/D2fbXcuarqkGkWJup1kcF0iSOJcvI7sFVVHHJJHUgdyQKyLnxjFZ+eLjSNTRrWAXN4oETfZoiWAdtshBzsc4TccKeOla+q6ZBq9ibWdpEAdJUkibDxujBlZTzyCAeQR2IIrIufB0V555uNX1N2uoBbXjAxKbmIFiEbbGAMb3GU2nDHnpXQfUjm8Y2a6jNbfYr0wwXkVjJeBU8lZZVjKD724g+agyFOCecDmqV/45VND1nUNP0y5uBpqyfM0kIV3R9pGPM3r0J+ZRwDjsDqv4XsXju03zqtzqEGoMFZflkh8rao4+7+4TI68nkcYqXXgqyv7i8nvry8uXubWW0ywiQxxyEEgFEUkjauNxbGPc0ATHxQF1Iae2j6ktytst1Mp8kiGMu6gswkxn5CcKScEe+Ebxjpkdpa3Uq3EcNzprampKAlYh5fykAk7/wB6uAM9+emblpocdvfy3s13c3dzLapayPPsG5FZ2BwiqM/vCOB0A9ycqDwHp0cYhnvdQu4EsH06KKaRAI4GKEBSqg5GxcMST6k8YAHah4pvrOXSVXw5qG6+vGtzFI8AfAhkkyuJdufk7kcBu+MyJ4ri3tAlrdXl213PBHb28aKxEWNxy0m3AyOSRkkcVNP4ba5t7ZZ9a1KS4tbj7Rb3REHmRtsZMACPaQVdhypPPXpTW8J2okE9vfXttdC4muFuIihdTLjeuGQqVOBwQTwOaAKsvjzT1j8y3sNRuo1sft8jQxoPKi3MrbgzA7lKNlRk+meav+ItcfR/Dkmq2lsbwgx7EUgbg7queSOzVDD4P023t54InuFSbT/7Pb5wTsy5LZI++TIxJPHtUt14U0i5sZrf7KkU00KQvdxRos5CbdpL45wVU85HA4oAZP4rtbS21SW6s7yF9MtEu7iJghba2/AG1yC37s98cjmmSeL7WPU7izaxvvLt72Kwlu9qeUssgQoPvbiCZUGQvBPOBzUWp+C4NVjuVuNW1JTeWq2l20bRKbhFLFS37vgje33doPQgirsvhmymF4GlnH2vUYNRfDDiSIxFQOPunyEyOvJ5HGACtaeMLS7vo7f7FewxSXlxYx3UqoInmhMm5Rhi3SJyCRjjGQeKx7n4m6XcaPqs+kskl1b6bcX1r5kkbpKI03cqjl17cMFOM+hx0EfhixjS0QSTsttqE+ooGZeZJjLuU8fd/fvgdeByec1h4QgGi3WinVNRbS57OSyS2LRYhjZdvyNs3EgcDcW/GgDolO5AT3GaWkA2qAOwxS0AFFFFABRRRQBAn/H9L/uL/M1PUCf8f0v+4v8AM1PQAUUUUAFFFFABRRRQBzniX/kPeEf+wq//AKR3NdHXOeJf+Q94R/7Cr/8ApHc10dABRRRQAUUUUAFFFUdZW+fQtQXTGC6gbaQWxOMCXadnX3xQBeoryPTvCematrRu7PSdfsWi05/tN5dtPHP9r3J5ZjZmyzD95kjKnK9a9N0R76TQNOfU12ag1rEblcYxKVG8fnmgDL8ef8iXqH/bP/0YteLV7T48/wCRL1D/ALZ/+jFrxavCzP8AjL0/Vn1GSf7u/X9EFdr8Mf8AkZbn/rzb/wBDSuKrtfhj/wAjLc/9ebf+hpXNhP48fU7Mw/3afoei6h/x8L/u/wBTVSreof8AHwv+7/U1Ur4nPP8AkY1vU+apfAgrS0//AI92/wB7+grNrS0//j3b/e/oK7uFf+RivRk1/gM/xdp1zq3hm5srWPzXleLfFv2+bGJFMiZJH3kDL+Nc14r8FXVvoOqx+Dbe3g+32slvcaYMRQS7lKiRB0SQZ9gwGDzgjf8AG2lvrPhprBIHnEt5Z+YiEgmMXMTOcggjChjkelU/+Ec8QaNz4f8AEDzwDpY6yDOn0WYfvF/Hf9K/TjiOtrD8Y/8AIq3v/AP/AENa3Kw/GP8AyKt7/wAA/wDQ1rHEfwZ+j/ImXws8pooor5Y4zd8Hf8jVZf8AA/8A0Bq9Vryrwd/yNVl/wP8A9AavVa93K/4L9f0R00fhOVfwdE2pmT+2LwaYb0X7aZ+78szh/Mzu279u8b9ucZ/KtHQhp4vNc+wyyySnUCbsydBL5MXC8dNnl/rXGz6F4BsVur6+0uDWLrUNTuvLVNPM07yiRvMQADJCMH+bpj8M9F4FstDtdMv5dAUw2d1etK1qYDCbaQIkbRlCAVI2Z5/velekanU0UUUAFFFFABXM+L9S8Q6XHaXGjW9pJa5cXsk8UkrQjjawRCCV+9nGSOOMZx01c94h8UxeHrxEniVoTp13es2/DEweV8gHct5h/KgDE8Hat4n1fXdUa51HRbvS4LlELWqPyGto3HlNuI25YE5yc7vbHeVxGiXOsaRq1hFqOn6JBb67I7402Jo3jn8syfvCSRJlUYbsDkDjmu3oAK898TeL7nTNUu9Kn1a30jzL63SG6lVcx2rRAtKofhv3oZMkELkEivQq5DWfEelXGp3ek3PhrUdXaxdBIY7BZ41ZkVxjJ9GHagB/g7Vpr291ayGtLrllaGLydRCp8zMG3xFowEYrhTkD+MA8iusrlfC3iA6nrWqaXFpEumWdhb2zwwz2/kyZkMu7gEjb+7XGAP4q6qgArzDSPBk0l7pFnqXhWzQWLynUNTlMMo1EMjr05Ztzssh3gYK8V6fXnWkeAPttvpU+sieRZbZ5NYtbi5kYXF38uxyucYGZeOBynBwMAHWeFdOudJ8O29hdH54XlWNd+/ZF5jGJc98R7B+FXtU1CLSdJvNSnV2htIHnkWMAsVRSxAyQM4HrWb4PgurXwxbW92syvFJNHGJyS4hEriLdnnPl7OtL40/5ETxD/wBgy5/9FNWtCCnVjF7NoT2PJvFPxa8H+Jv7IgutJ1Keytr43FzFNDHh08iVBj951DOp7dM5yKgtfiromj3kDaNqHiRLASL5thfwxXSbM8iN2l8xDjOMsw6cV4zRX6R/qrl3Z/ecft5n1F4b+L/h/wAUa/a6NY2epx3Nzv2NPFGEG1SxyQ5PRT2r0CvlX4Pf8lU0b/tv/wCiJK+qq+P4gy+jgcVGlR2cU9e92v0OilNyjdnKa7pWsrr8mqaTZadf/abAWMkN9M0YiwzMGGFbcp34ZeM7V5pPDvh9bG+0y7gvbe5Wy0o6XczRtlp5Y2QZbH90pIME5BcimeLfBia5qEOrxD7RcwRCJrOa4kiimQEnhkIKPknDcjsR0Ig+HvhW30Wynvn0yewvp7m6/dzTs7JC07MikbmXO0LyOffmvCNTtqKKKACiuK0uz8fXumQXF5r+m2Vy65kt20cuYznGM+eM/XFavguy1HT/AAzHbasxa9F1dM74xvDXEjKwGTgFSCBk4BAoA6CmSzRW8TSzSJHGoyzuwAH1Jp9YXiXRTrf9kwvBFcWkN+s11BLgrJGI3ABB4OHKNj/ZoA1BqNi14tmL23N0yeYsAlXeV/vBc5x71Zrhj4Dt9Ls7RdNtIHvI9ZS7FyEVGhg87JQEnO1Yf3QA7Y4FdzQAUUVw3iTRtXj1ubVf7X1t9JlC77bTZgslrhQCyptPmKcZIHzAk4DZwADuaK4/4aaNNpXgrS2nu9QkkmsoC1vdvkW5C8qi4BXrjBz0FdhQBl+JGsU8Lau2qI76eLKY3Sx/eaLYd4HTnbmsvVPFv2O7urKy0HUNVjs4la9e38sLCrLuC4dgXbbztXPBHrWzrlvZXfh/UrbUZDHYzWssdy4bG2MoQxz24zXB6T4g0DRrLUEs/wDhJ7u4vB9oufPtXeeKMIqiYhlHybQuOudpAHBFAHoWnzWtxptrNY7PsckKPBsXavlkArgdhjFWaoaHb2lpoGnW2nyGWyhtYo7eQnO6MKApz7jFX6AGTTRW8TSzSJHGoyzuwAH1JqEajYteLZi8tzdMm9YBKu8r6hc5x71ieM9KuNU0+xMFhFqS2t6lxNYSuqrcoFdduW+XILK4B4ylcjpfhPU7aWysz4YtraVdUTUP7TjnjYW8Iff5A/jJVP3IAG3bznHFAHqVFFFABUUFxBcoz280cqq7Rs0bBgGUlWU47gggjsRXK+LfBia5qEOrxD7RcwRCJrOa4kiimQEnhkIKPknDcjsR0Ig+HvhW30Wynvn0uewvp7m6/dzTs7JC07MikbmXO0JyOffmgDtq8e8b/wDI4X//AGz/APRa17DXj3jf/kcL/wD7Z/8Aota8fOv93Xr+jPf4d/3qX+F/mjn6KKK+YPsz2HwR/wAifYf9tP8A0Y1TeK9PGpeHZ4TqMeneXJDci7lQMkRhlSUFgSBj5OeRUPgj/kT7D/tp/wCjGqx4o0k634durFZo4XYpKryruj3RusgDjupK4PsTX2+E/wB3h6L8j83x3+9Vf8T/ADZw9lf+H77wpqHhdfFtjc32rSzkXTW5hR3mcsdoJwTyduG9K9Qrz3Vpte8R6eug6h/wjVlBqK7BcR6i0zyLnkwxmNct6HdwcHnFehV0HKFcrqev+HPD/iuZ726ul1S5sYQ0UVrNMBCjy7W+RDj5nccnsPx6quc8SvoLrM2o3jWd3p8Aulu4crNboxKgqQDncVI2chsYINAFXwx4l8OeJ9WuLvT7N01OMTQtNLYvG7RJIF/1hUDBIU7c5HcAg462uP8AAd9aLZT6X9qu5NQE017KLywezdxNKz7lR+q5YjI/TpXYUAFefXvj210a28QrpnhbVpJNNe4lnbZEsXmAMxkbMm4I2N27byDkA16DXA+LptA1/Tli+yanc39091p8Q0wKlyyxs0cylmIXy8gg7uOQQM4oA6Tw1NFdWVzdpo95pj3FwZZY7zbvkcqvzjazcYAA5H3elbVZegaxb6zpplghuLd4JDbzW9yoWWF1xlWAJGcEHIJBBBrUoAKKKKACiiigAooooAKKKKAOPi0PSbR92pahaLLp2qXGr58xVKCUyY8zd0AEg54+4vPFanhq1WL+1b6G5tZ7bUr5ruBraTemwxxp16ZJQk47nrWHH4FsZbqO71SOzF0usXF60jIGa6iYuY42JxwuYjg5AMS8d62/DekyaQ+sJ5CW9tPqDz20UeMKhjjB4HAy4dsf7VAG7VLV9PXV9Fv9NeRo1u7eSAuvVQ6lcj6Zq7Wdr9lPqXhzU7G1kEdxc2ksMTk42uyEA59iRQBxtq4aeO/8Ra14Zjt0spdKtltrn93cM5Qtv3YHHlqAgzjcea7XRLB9K0HTtOkmM72lrFA0p/jKqFLfjjNYEXgbSrea6vF0yzJm01LT7EsKhAy7txB6ZYFFJx0Rfw2/D1rcWHhrSrO7kElzBZwxTODnc6oAxz35BoA0qw9V0VtQ8SaPfPFBNa2kdwkkcwzhpAm11GMEjYV+jmtyuO8aaf4guLy2utAtkml/s69syzXIhMLy+TskHBzjyzxx9aAK8Php7fxZpZ0W/tW0Sxu5Lmew35a0kaCWPEWOiMZMlDjBGR1Irua4228IWukav4budG0i3svs4kjvpYQqlojCw2uRzITJsOeehPeuyoAKKKRmVFLMQFAySTwBQAtFFFAHyz4s/wCRy1z/ALCE/wD6MasetjxZ/wAjlrn/AGEJ/wD0Y1Y9e7D4UffUf4cfRBX1jq2qQaNpk1/cLI0UW3cIwCxywHGSO5r5Or6b8ef8iXqH/bP/ANGLXFjd4/P9Dxc6ipTpJ9W/0OH8ReIvDfiTxFpc+o6Xd3Fja2tyhyFV0ldoCjKQ4IOI35BBGfepdM8Xw6PqEX2fV9ZutK58y01CGOaReDjZNvDdccNu4z0riaK4+VHH9SpHsuk+O9L1nU4bC3gvFll3bTIihRhSecMewrp68W8B/wDI6af/ANtP/RbV3Hiy9u7bVYkguZolMAJVJCozubnisqslBXOHFUo05qMexR1jVZPFMKaCml6ZNLcXt5Cy6iGeKNbd9u4qMEuwZWABHBJzxXSeGdVudStbyC9ht47zT7prSb7MxMTEKrArnkDDjg9CCO1eXapoOnazefa7+F5bgsGMomdGZgMAkqRkgcZPatPTmfSLJLPT5Zba3QkiOKQqMk5J69Se9YfWI9jmses1S1i6tbHRL+8voxJaQW0ks6FQ25FUlhg8HIB4rO8J3E1zpUrzzSSsJyAzsWONq8c1oa1JZQ6FqEupLusEtpGuV9Ygp3fpmtoy5lcRz9p4juIxBY+ItDi0qC9j8u1aO5E0LEjiFyFXY+Og5B5AOeK2vDN4mo+FNHvorZLaO5sYZlgQ5WIMgIUewzj8K5C48RSX+ky6A/gm+uBHZrLcWVzcx5FsRhGD7juclWAGcgockcE9noIsB4d0waWCunC0i+ygkkiLYNnXn7uOtUBoVyPjc2Mr6daSaVqGoaixkmtv7On8ieFUAEjrJvXH3lGM/MSBiuurkvGy6Qv9nzX9/qlneBpI7ZtKRnuHVgDIoVVYlcKpJxxgHIOKAMTwpPpx13T71tP1mWPUEkTStU1O+NxvG0sQqFiYtyqWBxkgc46V6RXC6Hpnh+0u9CvLHWb28sJJJIdItGYNDbuYnZgPlDZCJIoDk7eRxXdUAFcJq0HibUtY1h9M1S8tXtLm3tre3RYxE0MixmWU7hyy75COeDEBg5IPd1574m8X3Omapd6VPq1vpHmX1ukN1Kq5jtWiBaVQ/DfvQyZIIXIJFAG34bS50/XtZ0d9QvL60t47eaKW7fzJEaTzNybscgbFbnpv+ldPXJ+DtWmvb3VrIa0uuWVoYvJ1EKnzMwbfEWjARiuFOQP4wDyK6ygAoqvf28t3p1zbQXL2s0sTxpOgBaJiCAwB7g8/hXld1oWt/wBt6Bpup6z4jN2L8sbqKRHgaMQTfvEYR/I2dqlX/vHG77wAPXKKitYTbWkMDTSzmNFQyykF3wMbmIAGT1PAqWgAooooAKKKKACucvf+SkaL/wBgq+/9G2tdHXOXv/JSNF/7BV9/6NtaAOjooooA4H4ieLtR8HeG7a902O3eaa9aJvPUsAuGPABHPArzH/hefiv/AJ4aZ/34b/4uvWfGvgxvG2gwWKXwtGhu2m3mPeD94YxketcD/wAM/wBx/wBDFF/4CH/4uvSw0sKqf73c7KLoqHv7mJ/wvPxX/wA8NM/78N/8XXonws8e6t4zm1SPU47VfsqxtGYEK/e3Zzkn0Fct/wAM/wBx/wBDFF/4CH/4uu3+Hnw8fwNJqEkmpLeG7EYAWHZt27vc5+9+lPESwrpv2e/oOrKg4Pk3Ot1XVINIsTdTrI4LpEkcS5eR3YKqqOOSSOpA7kgVkXPjGKz88XGkamjWsAubxQIm+zREsA7bZCDnY5wm44U8dK19V0yDV7E2s7SIA6SpJE2HjdGDKynnkEA8gjsQRWRc+DorzzzcavqbtdQC2vGBiU3MQLEI22MAY3uMptOGPPSvMOIc3jGzXUZrb7FemGC8isZLwKnkrLKsZQfe3EHzUGQpwTzgc1Sv/HKpoes6hp+mXNwNNWT5mkhCu6PtIx5m9ehPzKOAcdgdV/C9i8d2m+dVudQg1BgrL8skPlbVHH3f3CZHXk8jjFS68FWV/cXk99eXly9zay2mWESGOOQgkAoikkbVxuLYx7mgCY+KAupDT20fUluVtluplPkkQxl3UFmEmM/IThSTgj3wjeMdMjtLW6lW4jhudNbU1JQErEPL+UgEnf8AvVwBnvz0zctNDjt7+W9mu7m7uZbVLWR59g3IrOwOEVRn94RwOgHuTlQeA9OjjEM97qF3Alg+nRRTSIBHAxQgKVUHI2LhiSfUnjAA7UPFN9Zy6Sq+HNQ3X141uYpHgD4EMkmVxLtz8ncjgN3xmRPFcW9oEtbq8u2u54I7e3jRWIixuOWk24GRySMkjipp/DbXNvbLPrWpSXFrcfaLe6Ig8yNtjJgAR7SCrsOVJ569Ka3hO1Egnt769troXE1wtxEULqZcb1wyFSpwOCCeBzQBVl8eaesfmW9hqN1Gtj9vkaGNB5UW5lbcGYHcpRsqMn0zzWxqutWmk6K+qzb3tlCY8vGW3sFXliABlhySABySBVCHwfptvbzwRPcKk2n/ANnt84J2ZclskffJkYknj2q0fDtikL/ZEW0u3iSFr6GGPz2VcAAsykMMKBggigDPPiq6bWtIs49EuzFfwzSs3mwFowjRjORIVK4fJwSemATkBn/Cdaa93qFioaK7tLaa42mSGXcI8bvljkJB5HyttJ/A4ms/Btnp72ctre3kU9tLNJ5qiIeZ5pUyKy7NoBKL90KeODyagg8B2EKxx/2hqMkMNrPaQQs8e2GOXG4DCAk8DBYk+uaAHt40tYhdFrG+kjsrRLu6uEjQJGjRlxwXyScEYGcHrxzWxpOp/wBrWX2oWstuhbCiSSJ9wwPmBjdlx2654qva+H4LH7W1rdXMUlzDFEZAVJQRrtUrlcZx1yCPanaJoNvoa3XkzSzSXU3nTSSqilm2hfuoqqOFHQc980AatFFFABRRRQAUUUUAQJ/x/S/7i/zNT1An/H9L/uL/ADNT0AFFFFABRRRQAUUUUAc54l/5D3hH/sKv/wCkdzXR1zniX/kPeEf+wq//AKR3NdHQAUUUUAFFFFABVLWJL2LRL+TTUEl+ltI1sjdGlCnaD+OKu1Q1y/k0rQNS1GKLzpLS1lnSP++VQsB+OKAPM/7Q8OfYd/8AwmHij+3PLz5PnT+d52Pu/Ztuzrxt24/nXp2jyXsuiWEmpII79raNrlF6LKVG4D8c1y66rr3h2yh1LWL6LV9ImiDzXNvbCNrMkZD7QTvi9T95epyM43vCl9can4O0TULt/MubqwgmmfaBudo1ZjgcDknpQBT8ef8AIl6h/wBs/wD0YteLV7T48/5EvUP+2f8A6MWvFq8LM/4y9P1Z9Rkn+7v1/RBXa/DH/kZbn/rzb/0NK4qu1+GP/Iy3P/Xm3/oaVzYT+PH1OzMP92n6Houof8fC/wC7/U1Uq3qH/Hwv+7/U1Ur4nPP+RjW9T5ql8CCtLT/+Pdv97+grNrS0/wD492/3v6Cu7hX/AJGK9GTX+Az/ABdLfQ+F7yTTjOs42bmt13SrFvXzCg5ywTeR7gV57bacwbXtb8LXniGdbSGGWyeea4f7TP8AP5kPlyffQgR5OMgs2Dxx6F4tvrvT/Dk89jKIblpIYVmKB/KEkqIXweDtDFueOK5TVI/FWk6drOp3PiG8ZNGw1kphhVb9Qqu3mgLySWMQ27cbc9TX6ccR6NWH4x/5FW9/4B/6GtblYfjH/kVb3/gH/oa1jiP4M/R/kTL4WeU0UUV8scZu+Dv+Rqsv+B/+gNW18SPGupeD/wCzP7PgtJftXm7/ALQjNjbsxjDD+8axfB3/ACNVl/wP/wBAaqvxz/5gP/bx/wC069zLnag/X/I9XKacaleMZq61/I4TTfHevabrg1JZLWYI908VvJB8kZuJBJJgghj8wGMk4FaunfFbX9OuNRmjtdNdr66+0yBonwreWiYHz9MRj8Sa4aiuzmZ9V/Z+G/kR9I/D3xRe+LNAnv7+K3jljumhAgVgu0Kh7k8/Ma62vOPgr/yJt5/2EH/9Fx13GtxXs+g6jDpsnl38lrKts+cbZSpCnP1xW0dj5fGQjCvKMVZJl+ivH7rxBYaprGoR6rL4qgvY4Yl021tI7qNkbyxuGEG1pPM3fM+VI24OM16no5vjolgdTAGofZo/tO3GPN2jfjHHXNM5i7XJ+MRZ3V5pmnv4X0/X7+ZZpII74IEijXZ5jbmVsctGMAc5HpXWVy3ia4uYtf0ZNL02C81fy7mWEz3LQokShBICVBySWjABGM89qAILfxFp17qOhW50vyNQjvHs3tZiFksG+zyvkAcMrLHgEcENkHqK7CvOdOvW13xP4Z8R6lotnDHerKNKuIpm+0RAxOwWYYCsGj3kAZ2n65r0agArk7zxDLZa7qY03w1JexWpjXUbqB0WUtsDAKmMyFUZT1HXAyeK6yuSudc0/SNT1eWysLu51Ka9gs2gRlAnnMAddpY4UCMjcxxwvfFAF/R9c07WNcvBp8UEifYbW4F7GQTMrtMFU8Z+Uxt1PVyMDnO9XJeEorBdc1yZdFl0jWJvJe+t2lV0cHeUkQqduCTJnGDkHI7nraACuK0u9+IGpaZBePbaDaPKuWgnSdXQ5wQRXa14/ZeKrrULixt9R8btZRXcby6iUjhiOmzr9223spCZ+f7+WPk8H5qAPQPBY1UeGYxrXmf2h9quvM37un2iTbt3c7du3b/s4qTxp/yIniH/ALBlz/6Kak8G6hc6p4Us7y7m+0SOZFFxs2eeiyMqSYHA3KFbjj5qXxp/yIniH/sGXP8A6Kat8L/Hh6r8xS2Pjmiiiv2o807j4Pf8lU0b/tv/AOiJK+qq+Vfg9/yVTRv+2/8A6Ikr6qr844v/AN+h/gX5yOzD/CcB49i1mB724srO/vbS8sobcx2LEvGUmLuNoII8yNim5ckFRmneDLCKPXpLzR/Dt9oOjtaGOa3u0ERnn3KVYR7jjaocFjjO4dcVb1LT9a1Hxzef2frt7pqW+m2zRKI1lt3dpbjfujbgnCpypU9Oela2jXHiQXj2muWFiY1jLJf2Ux2OcgbTE3zKcEngsOOtfKm5uUUUUAePSeJoW1Cw0nxbres6bLb2AWVLXzomluw5DsWjXLKRsKY+UgnvxXo3hGXUJvC9lJqfn/aTvwbhdsrR728suOzlNpI9Sa5rw3q3jj/hGNK1WeGy1+G8tIrh1ixa3KF1DEAH92+M+qV2umX/APaenRXf2S6tDJnMF1HskQgkEEc+nYkEYI60AW6xPEya7Jb2MegzrbyvdqtzM0SybISrZIViM/Ns/DNbdcz43uZ7bSbVluLu2sXu0W/uLMEyxQbWyVKgkfPsBI5AJPHWgDL1bw54tnXT/M8QJqMUWpWk8tutikBKJOjMd2/sATjvjFd1Xlnh+zu7MnV9Gv8AXLq0bWY7eFbyaWVbm0fyxI5V/u7GaQhxjIjGc559ToAK4TVrTxJqesaxJp2p31q9rc29tbQR7FiaB1jMsvzD5nG+Qg54MYGD0Pd1534o8X3Glapd6Xd6xFpCzX0CQ3MipujtGiBeRNwwx80FCSCF3AkYoA3fDcNzp2vazpBvr29soI7eaGS8lMro77w6bzyQNiNz03109cj4M1WS+vNVtItaOuadamL7PqJCEszBt8e+MBX24U5A/jwelddQBT1bTo9X0a+0yZmWK8t5Ld2XqA6lSR+dQnSreE3F4CFvZLNbZ7kgn5E3lflzjgux465+lSa1ZjUdC1Cxac24ubaSEzA48vcpG78M5rjrrwh4F0uG2s9Vuba3vJowqmfUpIzK2MEqrSc89qAOr8Mx2kPhTR4rC4NxZpYwrBMVKmSMINrYPIyMHFalZ+haZ/Ynh7TNK87zvsVpFbebt279iBd2MnGcZxk1oUAcx4x0iTW5NAtR9rWAakXnktZWieNRbz4bcpBHzlPxIFFrpninSLyBINZh1bTTIqyJqMey4jTPJWVBhyBnhl5x96rfiq11660lR4e1AWd5HKHb5EYzRgEFAXVgpOQQcdscA5rjfDI1nUvHd4G8U6qRaWdq1zbXVlBG2fNmzEyhMDgD506hhhiAKAPTqKKKAPPPHWlale600ltp9/dSPYCLS57abYlleb2Jlf5hgYMZzg8Iwxzgv8F6Ylr4iMunaFf6RZrYGK+F0Cv2i53qVYcneQBJlx13Dk1J4t13WLDWr3TrNdRC3dnZpaTW9i8yQu08izvuCkBlj2HDHHArQ0myutD8XjTI9S1K+sbiwe4f7dMZjFKsiKu1zyNwdvl6fJxigDrK8e8b/wDI4X//AGz/APRa17DXj3jf/kcL/wD7Z/8Aota8fOv93Xr+jPf4d/3qX+F/mjn6KKK+YPsz2HwR/wAifYf9tP8A0Y1TeK9Lm1nw9NYQqrmWWEyRucCSNZUaRD7MgYfjUPgj/kT7D/tp/wCjGqXxdBfXPhe8i09ZnnOwskD7JHjDqZFRsjDFAwByOSK+3wn+7w9F+R+b47/eqv8Aif5s53VPh9Yx6T4i/suxtHvbza9iixKn2Ngq7dhz8oEm6TjHLHj172vIrbwdbudd1nw54dvNJuY4YW0wyqYZmulLlzgtny2BjUhuDhjjufXa6DlCuF8ZaRHq2rm103X7Gw1ue2iDW1yofzY45TJE6ruBysgYg8jkgg13VcxqPhTT9W8S3t5rNpZXVlcWVtbQpMAXWRJJ2YjI44kXBBzwaAH6JoesR6sNX8QalbXd6ls1tDHaW5iijRmVmJyzFmJRfQDHTmukrB0bw+NCv2W21i/lsniISwu5vOEZBHzI7ZcADjBYj5hW9QAVzOm+DrLS/EDatHqeoSPvuXS2llQxRmeTzJNoCgjLAHr2rpq81k0f4aWOpayurnRru/8AtUl3cefErSx+Y4OzuWIZwABz8wGOlAHX6Atkt7rxtJ5JpW1Im63rgJJ5MWFX1ATy+fXNbdcz4J0/QbLTb2fw3LC2nXt41wscKbFhYIkbIF6jmPJBGck101AGF4xnvbfwreS2DzpMDGGkt03SJEZFErIMHLCPeRweRXm0+qyLHftBr/iJ7uNlHhxJPNUXg4++u0CX95uUlxwgU98n0vxZrR8PaAdS8yGNI7q1jleb7qRvPGjk88YVmOe1clN4j1q90TVfF+m65anS7CeYQ2EcCPHcRRNtOZPvbnwSpBAGV4NAHpFFFFABRXNeL7nxLaR2lxoRtxbKX+2l7VriVV42siB13AfNkAk9MA9KxfBN/wCINW1zVriXxFp2oaXBdKhEFmy7820bDyyZDsAZuQQTkN0zwAd/RRRQB5PqnhVNX1yS31LwxcXuqPq4mGpzqHthZeaG25LcAQ5TZjO7n3ruvCum3OkW+o2MiyJYxXrDT1kk3lYCiHGck4DmQAHkDArg9Xtjq+uNaXtzrza5/a4SO1hluIbdbLzQN4KYUDyctuzneMe1d54VS/t7fUbK9lup47S9aG0uLoHzJYdiMCSR82GZl3d9vegDeqlq4vm0W/GmMF1A28gtWYAgS7TsJzx97HXirtUdZa+XQtQbTADqAtpDbA4wZdp2dffFAHOL4f8AFs+ngzeMbhJni+eI2FudrEcrkL68Zra8K2Fxpfg/RNPu0CXNrYQQSqCCA6xqpGR15BrzrTtDstY1o3Gl3XihHi053uru4luUkiuwybAqvgM2PM3IAV4Xjnn03RJ7y50DTp9Ri8q+ltYnuIyMbJCoLDHbBzQBfrk/Enh5te8V6M0j30Nvb2d4ftNnO0LRSl7fZ8ynuA/ByDg8V1lcd401vVtDvLeXT7PULqN9OvQqWlm9wv2keT5O/Yp2j/WdeOtAF/TLDxNpuoxQ3Gr22q6UchpLmHy7qPg7fmT5H5wDlVPU810VcRYaZe6Hrfh94tV1i9/tDzEvo72ZpFwIWcSBTxEQ6quBgfNjFdvQAV5V438L6xBYeIJvtuv6na3sNw1vFaXchMDurbYmhHDxZIGV5A4IIy1eq1w1rY+INQl1PVrXWLuHUre/niisbj/j0eJGwiFcZG5Nrbwcgt3AxQB02g6UNH0xbcXd9c7iHLXk7SupwBtBPQcdPc1p1y/gzU73Vjr1xe215akal5cdtddYgLeEMq9iu/eQRwck966igD5Z8Wf8jlrn/YQn/wDRjVj1seLP+Ry1z/sIT/8Aoxqx692Hwo++o/w4+iCvpvx5/wAiXqH/AGz/APRi18yV9N+PP+RL1D/tn/6MWuLG7x/rsePnH8Sj6v8AQ8WooorlJOj8B/8AI6af/wBtP/RbV2PjL/kMRf8AXuP/AEJq47wH/wAjpp//AG0/9FtXY+Mv+QxF/wBe4/8AQmrlxXwnk47+KvT/ADOdooorzzkO48G/8geX/r4P/oK1qazHZS6FqEepMFsHtpFuWJxiIqdx/LNZfg3/AJA8v/Xwf/QVrV1izt9R0S/sbyTy7W5tpIZnyBtRlIY5PA4Jr0KXwIlnIHxppPkyi30PXWvTaATbLJRcR2wB2SMWIyuWYqBk53fLwa67RIrK30HTodOffYx2sSWzZzujCgKfyxWLo2q+HdckupNJ1BJtQ+xpDJMyMrmJd2xwrAbly7HcBg569K1PDSWcfhXR49OmaexWyhFvKylTJGEG1iDyMjBwa0Ealc74in0vSdW0vXdT1aCwjtlmtwsw/wBcJQpIHPBBjU9DwD9a6Ksi+0t5/Euk6oskQSziuInR+pEgTDL7gpj6MaAMSXQ9Jl8V6H4gsNWWGK5uXmFtEA8N9MbeVQ6kHCtsLksPvBRnnBrsq4mLw3D/AMJlaXOjajDHYafevPe6bjiOd7d1DR/3dwmDMvQ9Rg5z21ABXCahqGsahrms27+GdN1zSdOukgWEsq3CkwRSFgJBsb/WEdVPHeu7rkptQ1aXxBq1n4a0vTQ0EsZvrq+nZPMmaJCAqopJxH5Y3Ej05xQBpeHda03U4JbWwtLixktAols7i1aBod2cfKRgg4PKkjjrW3WFoOtX17e3+matZQ22o2Sxu/2eYyxSJJu2spIBHKMCCOMd81u0AZ+u3dzYeHtTvLOMSXVvaSywoRnc6oSo/EgVy8cut+HdOg1c6pd6/pc0KyXQkiTzocjPmxBFG5OeUOSByCcYPUa9qEmk+HtT1KGMSy2lpLOkZ6MUQsB+OK5FYPGs0k0DeJEighs0vEv49PjCzSPu/dHdkbF2ZOMNiReeOQDpfCF1cXvgrQbu7kaS5n063kldurO0akk+5JNbNZ+g6g+reHtM1KSMRvd2kU7IP4S6BiPwzWhQAUUUUAFFFFABXOXv/JSNF/7BV9/6Nta6Oucvf+SkaL/2Cr7/ANG2tAHR0UUUAcb4y8SXvhnQobqxSFpJbtoz5qkgD5j2I9K4X/hbHiL/AJ5WH/fpv/iq9L1rw1aeKNLS0u5ZoljuGkVoiM5+YdwfWue/4VDo3/P/AH/5p/8AE1vTlTS94Tucr/wtjxF/zysP+/Tf/FV2nw/8X6j4nkv0v0t18gIUMSleu7Ock+gqr/wqHRv+f+//ADT/AOJroPC/g6x8KtctaT3EpuAobzSONuemAPWicqbj7u4amjreqnSLBZ0g8+aWaO3hi37AzyOFXLYOBk5JweB0PSuWTxZrtz4wbSbbTYDcWtvP9qtWu8Rb18hkdZfL3HKTAY2jknPTNdjf6fa6nZvaXkXmQuQSNxUgggggjBBBAIIOQRWQ3gnQGdZPss4mAcGZbyZZWD7d+5w+5s7FHJPCgdKwGZI8fQG3lvora5liltrGW2h6ktcF8DCIWGNvON3Tgetq28XXt0tvbpokkeoz3Dwxx3LSQRMqIHMgd4g+3kL9zO7PYZq5D4TshNqH2lYpLa5SCGK3ijMQgjhyYwpDZ3AsSGGMYGAMVZbwzpklqlvIt04jl85JXvZmmR8bcrKX3jjjAOME0AZ1truqr4r1C01CHT7fTbSxtriaU3ZzCX87ccmMBhmMDkrgDPfAbfeL5Vv5YdIsbbUreKw+3tcLehVZNzKVTCsGb5DjkDrkjvsxaHYQahDfxrOLmKFYN/2mQ70XdtDjdiTG9iC2SCSetUNR8JWOr69JqV88jo9mtoYUkePKh2Y7mRhuU7gCpGOO9AFNvGpfzrq20/zdKt5reGe4afbIrTLGwKx7TuAE0ZOWB5OAcc0YfGt3bQLbtayX99Ne6iEAjk2pDBdNEufJic8AoB8vODk569JN4Z0ie/W8e0/eho3KrK6xsyY2M0YOxiuBgkEjA9BSSeGNJkjRPs8kZSaadJIbiSKRXlcvJh1YMAzEkjOOnHAoAuaZetqOl2149rPavNGHaCdCrxk9VYHuKt1HbwJbW8cEW7ZGoVd7lzgepJJJ9yakoAKKKKACiiigAooooAKKKKACiiigCBP+P6X/AHF/manqBP8Aj+l/3F/manoAKKKKACiiigAooooA5zxL/wAh7wj/ANhV/wD0jua6Ouc8S/8AIe8I/wDYVf8A9I7mujoAKKKKACiiigArJ8UX82leEda1G3CGa0sJ54w4ypZY2YZHcZFa1UNcuLS08P6lc6hAJ7KK1lkuIioYSRhCWXB4ORkYoA56PXtf0aBY9c8OC5tAoH2vRcyqFx/FAfnUY/u766ixmt7jT7aa0AFtJErwgIUwhAK/KQCOMcEDFcO/iTxtb6rDBJo2itHNZvexWsV1IZWRCgdN+3bvHmJjjac9eK7fTr+DVNLtNRtiTb3UKTxFhg7WUMM/gaAMXx5/yJeof9s//Ri14tXtPjz/AJEvUP8Atn/6MWvFq8LM/wCMvT9WfUZJ/u79f0QV2vwx/wCRluf+vNv/AENK4qu1+GP/ACMtz/15t/6Glc2E/jx9TszD/dp+h6LqH/Hwv+7/AFNVKt6h/wAfC/7v9TVSvic8/wCRjW9T5ql8CCtLT/8Aj3b/AHv6Cs2tLT/+Pdv97+gru4V/5GK9GTX+Ap+J9UuNG0Ce8tUie53xQxednYHkkWMM2OdoL5PsDXL+IfFGt+EtC1L+3fscsn2WWTT9RhgZIXmVCVikQs21iRx82G6cHg+R+K0WTxXrauoZTfT5BGQf3jVkvGkqFJEV0PVWGQa/XFgrq/MCwd1e59Y1h+Mf+RVvf+Af+hrXzZX0n4x/5FW9/wCAf+hrXHj6HsqMtb3T/I58TR9lHe97nlNFFFfHHmG74O/5Gqy/4H/6A1Vfjn/zAf8At4/9p1a8Hf8AI1WX/A//AEBqq/HP/mA/9vH/ALTr28u/gP1/yPYyX/eY/P8AI8gooorrPsz3f4K/8ibef9hB/wD0XHXXeLby407wZrt9aSGK5ttPuJopAAdrrGxB544IFcj8Ff8AkTbz/sIP/wCi467nWr4aZoWo35gM4tbaSYwj/lptUnb+OMVvHY+Ox/8AvM/U55LjxpoyK09vaeIrXAJe3xa3QHujHy3/AAKfSuptZ/tVnBceTLD5sav5Uy7XTIztYdiOhFeenUfGq61bxxa9o1ybjTn1GO2jsiIsIyAoX3lgG8wbX/2T8td3pGopq+i2Gpxo0cd5bx3Co3VQ6hgD+dUcZkePrq4s/BWoXFrPLBMnl7ZInKsMyKDgjnoa8NfXNYkuYrl9Wv2nhVljkNy+5Q2NwBz3wPyFe2/Ef/kQtT/7Zf8Ao1K8Drrw6TjqfPZtOUa65XbT9WXpNa1aWa3lfVb9pLYloWN0/wC7JUqSOeOCR9Ca6vwDrur3njXT7e61W+nhfzN0ctw7KcRsRkE46iuGrqfhx/yPumf9tf8A0U9aziuV6HFhqs3Wgm3uvzPfK8z8bL4cl1yUy6rremXkBikuLnToS0YlCkxBiUYCUqcDGCQwU5yBXplef+KNJ083GrXk3ik6fYPPBLe26Qq7C6RU8kg8nPyRHZg7to9TXnn1xpeB/sGdRMb6u+q7o/trauu24K4Pl8ABQn3sbeM7u+a66uR8EtDfS6lq7a5Hqt7OY4JmjtGtRCsYYonlMSyn94zZJ53elddQAV5VqfizxbLp88MukaBZ3aXVtHPaT3MjS4eeOMHaYtrIdwG9SQMnHIxXqtcHdeLfCPiGGzup9K1S+jidbi1mGi3LhWBBDKwj9QOnpQB2OmG/OnRf2nDaw3fIeO1kZ4xycbWZVJ4x2HOazvGn/IieIf8AsGXP/opqf4V1w+I9Aj1QwmESTzxqhUqQsczxjIPIJCAkHoTTPGn/ACIniH/sGXP/AKKat8L/AB4eq/MUtj45ooor9qPNO4+D3/JVNG/7b/8AoiSvqqvlX4Pf8lU0b/tv/wCiJK+qq/OOL/8Afof4F+cjsw/wnHanL4luPG93a6LqttBFa6dbT/ZLu38yKV3knUkspDqcRryCR7GtXRtW1e5vHsdY0GSxmSMuLiGZZraTBAwrcMDznDKOAeuKyvEut63Z6veR6PFaMunaauoXEUsLPJdgtIBFGVI2nEbc4bll461Pot9r1t4hj0vW7u0u/tdi15G1vbmLyGR0V4zljuH7xcE4PBzXypudVRRRQB55pmu+LdVi0qKyi0eyttUtmu7WUW8ji0hTbiN13AM58yPBBUDD8HArrfDOqT6xoMF3dRxx3IeWCZYs7PMjkaNiuedpKEj2NcppvivWrmLTrPR/Dum2sWpQPdaaXuisa26Y3GRVT5WO+PCrkfPyflOeo8KXcN74ctpobJLLDyxyWyHKxyrIyyAHv84bnv1oA2awPFd9fWsGm2un3S2cuoXyWpumjD+SpR2yFPBJKBRnjLCt+sHxTqNzY22nwWX2ZLm+vUtY57pC8cBKs28qCMn5MAZGWYc0Ac/qniDXfDrWWlanMJZrrUbSG01KKABbhHnjWSJ15CSbC3I4IyRgjA76vONN1rxJZ30/9r6jYavZW+sRaZMEtPKZWfyykiEMQcNKoZSMghsHivR6ACuM1a91jVNW1K1sdH0m/stKeNJbe8z5tw7RrIfLJ+VcK6gbgcnPI612dcLq/ia70fWdXuNP0O0mggmt7O6ne58uWWeRV8rjaQUHmxgknOCcA7cEA0vDPiW11vVr6y0+OKOxtLS3kWMRmOSKV3nV43X+Er5S8YHU9QRXUVy/hmWb+39ag1LTdPttYVLeS4ubEkrcRsHEedwB3KVcc9setdRQBl+JdNm1nwtq+l27RrPeWU1vG0hIUM6FQTgE4yfQ1zlj4fndddvPE9lo0t1qWyGFI5jKuxYggi3SIuMsGbA7sa6bX4Ly68Oapb6cxS+ltJUt2DbcSFCFOe3OK49vh74ctJHa506z+y3Fktva6dJtOLna7SMmTjzGVU+Yc/uyc9TQB2GgWlzYeHNLs72TzLq3tIopnznc6oAxz35BrRrO8PxXkHhvS4dRJN9HZxLcknJMgQBue/Oa0aAOf8V3d/DFpdnp959il1C+W2a6EauYl8t3JAYFckoFGQfvVzyS+KdO+x6pqesSs51VNONj9njSKaEy+SJeBuDkfvchsY4xiul8T6peadb2EFg1vFdaheLaJPcqWjhyrNuKgjcfkwBkZZhXKabrXiSyv5jq2o2GrWdtrEemS+XaeUytII9kiEMQcGVVZSMjDc8UAej0UUUAcT4p1Rk8TwaXfeI28PaY9oJkuEMcbXMu9gyCSRSq7QFOAMnf7UeFtUZ/E8+l2PiN/EOmLaGZ7hzHI1tLvUKhkjUK24FjgjI2e9W9du9Yv9efRNJj0rbBZpdzf2jC0iy73dVRQpG37jZbnGRxUOheKFvPEtroUVlHp8kdlcS3tiVAeCVHgC4I4KMJGIYDDcdMEUAdjXj3jf8A5HC//wC2f/ota9hrx7xv/wAjhf8A/bP/ANFrXj51/u69f0Z7/Dv+9S/wv80c/RRRXzB9mew+CP8AkT7D/tp/6MapPF9xeW3hi6ksJJYpy0SGWJdzxxtIqyOoweVQsRx2qPwR/wAifYf9tP8A0Y1HjW4vbbw0zafePZ3Ml5ZwLcIqsUElzEjEBgQeGPWvt8J/u8PRfkfm+O/3qr/if5s5rxUniHwl4Y1b7Dd6lqtlNZypFIW33dlMUIVwwALoTjP8SnnkdPRa5L7d4w0Pi/0+3160X/l404iC4A9TC52sf91x9K62ug5SOaZYEDMCQTjiuO1XRbDXvFjz6xpi32mjT1it/NwRDL5jFyozkMylMMORs6iuq1D/AI91/wB7+hrNr4rPM9xeCxjo0bWst0dNKlGUbs5/SbTWLDxpbNeO17pVnp9xBbXsjjzj5kkDLHIM5ZlETfOOoxnnr20N0k7lVDAgZ5rJq3p//Hw3+7/UVy5ZxHjcTi6dGo1Zuz0HOjFRbRpVwNz4Q19NQWSzm0l7az1KbVbQTq4kklkLbo5CAQqgSPhhk8JxxzpeJ/Gv/CN6lHZ/2f8AaN8Il3+dsxksMY2n+7Xm0Wp6bLcXtxqWnahdTXF3LOrJrdzCER2LBNqkL8uccAcYr9IhhK04qUY6P0PLnjaEJOMparyZ6j4QglhTWHu57V9Qm1FpLuK0LGOCQxRgICwBPyBGJwMljXSV454Z8XweGE1KG20uR4Ly8N0iyXru0YMcabS7hmbmMnJPfHavUdB1X+29Ft9R8nyfO3fu9+7GGK9cD0qamGq0lzTVkVSxVGtLlg7v5kfibVH0bQJ72OGOaUNHFGsrYTdJIqKWPZQWBPsDXE3Os+KdIu9ZXVZtE1Gx0yCG8uraGzaPMLl8lWLth18tjtYHIxyM8dr4pu4rHwzfXFxpUuqwLGBLZxIGMqEgNweMAEk57A1wWnauLKa00rSvh3nTtUt5rl41urST7SEMQDBvNKlRv/iPdcdDWB0HqlFFFAHLeK9Zv7HUNNsLC/sNONzHPM93fRl0AiCfIBuXlt+c54CtWH4W17UZ73RL6S505rfxE0ryWNvbhHtmWNn3FwcuRsCMWH3iOnSt7xbK1zNYaLb6PpupXl0JJ4hqf+oiEW3Ln5WJbMigADuTkYrA8I6hINbhvNU8O6Na3uqy3FqL/Tlw5nhLiSOTIyc+U5DAkHbyBxQB6NRRRQB5zB4mvvEesx+HLfWHsLpbzUPtctvCvmpFDMUhRd6lQWUg7sHhT610vhO7vZoNTsr+8N7Lp181qt0UVWmXYjgsFAG4b9pwBytc9d+Lr651CPRxq1lo9wb67hmnljVmiSNv3KhWON0iEOCeMA4FdB4O1S41TT70XFzb3htL17Zb23QIlyAqneACRkFipwcZQ0AdFWdr95cad4c1S+tIxJc21pLNEhGdzqhIGPqBWjXPat4w0ix0a+u7TU9NubmC3kligF2n711UkLwe5AH400mwMD+yPEsnnLP4t1FdPhsVu4b5Y4EZp23blbCYMahQ23A/1mCTgV1/h++m1Tw3peoXCBJ7q0inkQDG1mQMR+Zri/8AhNNfnsPOWbwYoki3BTrUgcZGenldfatrwr4p0yTwhoj6jrll9uawgNx510gfzPLXduBOQc5zRysDq657xBr2p6ZqemWGl6PHqEt6szZlu/IWPywpxnY2SQx9PumuhrnfEOoawmq6bpOiyWcFzdRzztNdxNIoWLYNgVWXljIOc8AHg0gMxtZ8Vt4m8P219o0GnWU91Ks7wX32jeBbysFI8tdo3KpznqAO9drXEaLrfiN73QrvVmtltdd3hbFbcpJZMImlUFyfn+VCGyByRiu3oAK4e2uvFN/LqWrWGoRTC0v57YaNJCio8cbFcebjcsjABwSdvzAYxzXcVyMfjK7uLi9TT/COrXcVtdS2zzRSWyq7oxUkBpQcccZHTFAE/g3xA/iM65c7phDBqPkQxTReW8IFvCWRh1yHZ85zz3xiunrm/CGtXutNrj3tk9k9tqJgS3kCb0XyIW+YoSCcuTnPQj0rpKAPlnxZ/wAjlrn/AGEJ/wD0Y1Y9bHiz/kctc/7CE/8A6Masevdh8KPvqP8ADj6IK+m/Hn/Il6h/2z/9GLXzJX0348/5EvUP+2f/AKMWuLG7x/rsePnH8Sj6v9DxaiiiuUk6PwH/AMjpp/8A20/9FtXY+Mv+QxF/17j/ANCauO8B/wDI6af/ANtP/RbV2PjL/kMRf9e4/wDQmrlxXwnk47+KvT/M52iiivPOQ7jwb/yB5f8Ar4P/AKCtamtaeuraFqGmtIYlu7aSAyD+EOpXP4ZrL8G/8geX/r4P/oK1e8SRwzeFtXiubr7JA9lMslx/zxUocv8AgOfwr0KXwIlnHajr/g/WYrSxTxVp1vrtmAlvfW7jEcuMEA/dKNjBQnBHuAR2nh/TG0Xw3pelPIJXsrOK2aRRgMUQLkD3xXH6DY3HiXbfXMWgvp8WlyadHHp9z58VzuKHLfKNqjZgLyRvauy0Sym03QNOsLmbz57a1ihkl/vsqgFufUjNaCL9cx4y8I6X4mt7a4vhbrPYFmgkuoxJCN2NwdG4KnavcEY4Irp65LxnpU19eaTePo/9t6faGU3GnbkyzMF2SBXIRyuGGCf48jkUAZPgHwppdtqeqaudF0e2uRdj7I9jIkqxJ5CI2xhyoZhIcEA/Mc16HXnmiWcF/wCMLW90fwuNAj08yJfu3kRSTbo/lhaOJm7skmWxgAY616HQAV574nfRpPEN3MdM18S2iRpqOp6RcPCIgRuUOEcNJhSGOFYqCK1/Gms3+k/YfsM/leb5m/5FbONuOoPqa4i11/V7O/vryG+YSXrpJMDGhBZUCAgbePlVR+AqXJI9TDZRXxFJVYNWfr/kd54YTRLO/vdP0dHk3W9vfS3rztO1yJTIqkyMSzYER74wRiumrxHSLq60LUL+8065aFr7Z5kYRDGu0uw2LjCjMjkgcZNeneD9Su9V0iWe9m82RZygbaF42qccAepoUkycVlVbDU/aTat5X/yNfUJLiLTbqS0tlurlIXaGBnCCVwDtUseBk4GfevOn8ZeK71tLgt9I0SMz332S4tJL6Tep8mV/LkVoQU4TOQDnAxkHNejX97b6Zp1zf3b+XbW0TzSvtLbUUEscDk8A9K4u88VeDbu+ttYksNQlvrRS1vcf2NdhhlSOvl8jBPXjk1R5p21mJRY24niihmEa+ZFC25EbHIU4GQDwDgfQVNWd4f1Nta8N6XqrxiJ72ziuWjByFLoGxn2zWjQAUUUUAFFFFABXOXv/ACUjRf8AsFX3/o21ro65y9/5KRov/YKvv/RtrQB0dFFFAHKeKNTvNM0aKWzmMTvcspYAHj5j3rkP+Et13/oIP/3wv+Feg3+iwa5p629xJIipOzgxkZzkjuPesr/hXunf8/d1+a/4VDTvocGIpV5TvB6epyf/AAluu/8AQQf/AL4X/Cur8Faxf6pJere3BmEYQrlQMZznoPal/wCFe6d/z93X5r/hWtofh210Jpmt5ZZDNgHzCOMZ9B70JO4qNKvGonN6epPreqnSLBZ0g8+aWaO3hi37AzyOFXLYOBk5JweB0PSuWTxZrtz4wbSbbTYDcWtvP9qtWu8Rb18hkdZfL3HKTAY2jknPTNdjf6fa6nZvaXkXmQuQSNxUgggggjBBBAIIOQRWQ3gnQGdZPss4mAcGZbyZZWD7d+5w+5s7FHJPCgdKs9AyR4+gNvLfRW1zLFLbWMttD1Ja4L4GEQsMbecbunA9bVt4uvbpbe3TRJI9RnuHhjjuWkgiZUQOZA7xB9vIX7md2ewzVyHwnZCbUPtKxSW1ykEMVvFGYhBHDkxhSGzuBYkMMYwMAYqy3hnTJLVLeRbpxHL5ySvezNMj425WUvvHHGAcYJoAzrbXdVXxXqFpqEOn2+m2ljbXE0puzmEv5245MYDDMYHJXAGe+BG3ia9k8S3EdgtheaPHpq3YlS65J3SAlcIQ33MYyPXPatuLQ7CDUIb+NZxcxQrBv+0yHei7tocbsSY3sQWyQST1pbvRLC+vlvZ45PtAiMJaOd0DIc/KwUgMOT1BxnigDn28Y36aNp16+k2wnv4vPhtFuZpZDHtUk4jgY5G4A8bRkfNzisy98V63f3Ed/pAij00aF/aqRyShWcspIDjym6YHCsvrntXW3HhnSbmGyikt5AllEYIPLnkQrGQoKEqwLKdq5DZBwM0QeGdItrVbaK02wrYjTgvmOcW4GAmc+/Xr70Ac1p/ifUNNtdmqebeXhsrOVVE0e15bid4kUERJt525JyAOxwS2pqviTU9GhtTeaXZxtMZDJMbyT7NCF243SCEkE7jjKgfKeemdOXw7pM6yrLZq4lto7VgXb/Vxksg68EFiQRznvwKjl8MabParbSvqDxDdkHUrjLBsAhj5mWHA4JI/M0AZjeKpk1G5s7e2FzcvqEdnbI9wFiJa1E5O8ISFCh+zEnpgHAit/GOoXc1raQaLD9tmubq2ZJL3EaGAgFtwjJIPb5c9PfGjqPhWzuLKaKyjhtZpZo5/MYSEI6II1K7JEZSEUL8rDj1ycmg+FLPRba1DE3F3BJPKJyWX5pmy/BY8dB8xY8cknJIBf0TVF1rRbTUViMPnpuMZOSjdCM98EHmtCq9jY22m2UdnaR+XBHnam4nGSSeTz1JqxQAUUUUAFFFFABRRRQBAn/H9L/uL/M1PUCf8f0v+4v8AM1PQAUUUUAFFFFAGdrWtW2hWSXNwk0rSSrDDBAu6SaRjwqjIGevUgAAkmmaLr1vraXKpBcWtzaS+VcWt0oWSJiAwzgkEEEEEEg1m+Mre5I0XUre1mul0zUluZoYV3O0ZjkjJVerEeYGwOeDio/C0dxd694g1x7S5tba+eCO3S5iMUjrEhBcoeVyWIAODhc4oAn8S/wDIe8I/9hV//SO5ro65m58B6Ld3CTzy6u8kchljP9sXfyMQQSv7z5eGYcY4JHSnf8IPpP8Az9a5/wCDy8/+O0AdJRXN/wDCD6T/AM/Wuf8Ag8vP/jtH/CD6T/z9a5/4PLz/AOO0AdJRXN/8IPpP/P1rn/g8vP8A47R/wg+k/wDP1rn/AIPLz/47QB0lU9VubSz0e+ur9d1lDbySXAKbsxhSW478A8Vj/wDCD6T/AM/Wuf8Ag8vP/jtNl8B6LPE8Us+tSRupV0fW7whgeCCPN5FAHN6ZqPhfRo57jT9A8Us0tv5SvNp17Lti6hELg7E9lwOnoK7LwncpeeDtDuo7eO3SbT4JFhjztjBjUhRnsM4rnZfA1vH4j0+2gl1tdH+w3AmVdZu9gkDQiIf6zI+Uy8Dj8hWnB4A0S2t47e3l1mKGJQkccetXiqigYAAEuAAO1AEnjz/kS9Q/7Z/+jFrxavZJ/AGh3ULQ3EusTRN95JNavGU9+QZap/8ACqvCP/Pnff8Ag0uf/jlefi8FKvNSTtoetgMxjhabg431v+R5PXa/DH/kZbn/AK82/wDQ0rQ0r4W6E+o60t7ZagLdLxVss6lcDMXkRE4xJz+8MnJ/litm2+GvhqykMlqmpwORtLRavdqSPTIk9qyo5fKnUU3LY3xObwrUpU1F6m5qH/Hwv+7/AFNVKrN4D0Zjlp9aJ99bvP8A47WX4i8C2sXhnVJNJl1s6ktpKbQLrF2x80IdmAZMHnHBrwMw4Xq4rEzrqokpO+zPKhXUYpWN2tLT/wDj3b/e/oKwl8A6LtGZtZzjn/idXn/x2nr4F0dBhbjWwPQa3ef/AB2ujKOHamAxKrymmrNbdxVKylG1jwvxR/yN2tf9f8//AKMasmvepfhP4PmleWWxvJJHYs7tqdySxPUk+ZyayZ/hN4cHiiwji028/sw2dw05/tC4x5oeHy+d+R8pl4/wFfeLGRStY6FjIpWseN19J+Mf+RVvf+Af+hrWH/wqLwX/ANA66/8ABlc//HK0ZvAOiXERimm1mWNuqPrV4wP4GWuTGVFiIcq00f4nPiaqrJJI80or0H/hV/hb/n31D/wa3X/xyszSvhno76jrS3ttqQt0vFWyzqdyMxeRETjEnP7wycn+WK8D+yp/zHB7F9zO8Hf8jVZf8D/9Aaqvxz/5gP8A28f+067KH4a+G7eVZYU1OORejpq92CPxElNvfhn4Z1Ly/t8WpXXl52efq10+3OM4zJxnA/Ku/DYZ0abg3fU7sDUWGqqo9bX/ACsfOFFe5eIvhF4Zi8M6rJpGl3R1JbSVrULfzsTKEOzAL4POOtaK/B/wTtGdLuc45/4mFx/8crb2bPc/tuH8jKfwV/5E28/7CD/+i467zVb+HStHvdRuFLQWtvJPIAMkqqlj+grnrP4ceHdOhMNiNVtombcUg1i7RSemcCTrwPyqWbwFotxC8M02syRSKUdH1u8Ksp4IIMvIrRKyseHiKqq1ZVF1MfQpbPw7N5epeE9M0JNVA8uezdZIpGIyIZTsXa3PA5U8gHPB6zw3fjVPC2kagIEtxdWUM4hj+7HuQNtHsM4rlNc+H1hPLo9lF/bE2my3Rjv4X1a6eMwCGUgEGTgeYIuntWvB4A0S2t47e3l1mKGJQkccetXiqigYAAEuAAO1MxG/Ef8A5ELU/wDtl/6NSvA698uPh9oV3A0FzJrE0LY3Ry61dspwcjIMvrVH/hUng3/oH3f/AIMrn/45W9KqoK1jy8bl8sTUU1K2ljxGup+HH/I+6Z/21/8ART12Wl/Cjw8+o60t7p16LdLxVss6hcDMXkRE4w/P7wycn+WK2Lf4W+FLOdZ7a2v4Jlztki1W6VhkYOCJPSrlXTTVjno5TOnUjNyWjTNzWta/sfyP9H87zd38e3GMex9a811651ibWTqWiR2Kl7yK+aG9ZiBMkflZBUcqY8DGOCAQe1dvN8PtCuNvnvrEu3pv1m7bH5y1C3w18N7TiPU844/4m91/8crw69DHSquVKqlHs0e+nG2qMXQtd1C11HUNV1WC2kvb1YoylqxSONI920fMCWOXbJOOwxxXc6Pqn9rWjz+T5W2Qpt3buwOeg9a4/wAO/DjS5fDOlSatHqo1JrSI3QbVblSJSg35AkwOc8CtmHwBoluhSGXWY1JyQmtXgGfwlp4ajjY1OatVUo9rJA3G2iOnridJ8caDFpduulaNr5sNuYTBo1yyFSc/KQnI5rS/4QfSf+frXP8AweXn/wAdrHvfAdpY3Wh22kvrUNj9rZbtItYugqw+TKRx5nA8zy+n9TXokG54K1CLVfDEd7DZCzjkurrEIQoRi4kBLKeQxI3EHuTUnjT/AJETxD/2DLn/ANFNVOD4f6HaxeVbyaxDHuZ9ketXajcxLMcCXqSST6kmlm8A6JcwSQTzazLDIpR431q8ZWUjBBBl5BHatKM/Z1IzfRpieqPkSivqT/hS/gL/AKA0v/gdcf8AxdZkHwb8KHxRfxy6LP8A2YtnbmA/bJ8eaXm8znfk/KIuOn5mvu/9caP/AD6f3o5vq77nk3we/wCSqaN/23/9ESV9VVxFp8I/Bmn3SXVlp93bXCZ2Sw6lco65GDgiTI4JFWbz4a+GtR2fbo9TuvLzs8/V7p9ueuMycdB+VfM5zmVPMcTGqk4pK3fq3+pvShyKzZPq3iGax8Tf2dYeG7nU7wWazvNDLBHiMuyhcyOpPK5wPUVT03WtSvfiBbW194ek0oNpc8m64eCSSTbLCAA0bMQo3nIOMkj0qFvhD4LwWGm3W7HB/tG5/wDjlZvh34S+G5fDWlSavpl2NTa0iN0Gv7hSJSo35AfA+bPAry7Uu7+7/gm1od393/BPS6K5Gz+GvhrTt/2FNTtvMxv8jV7tN2OmcSc9T+dWv+EH0n/n61z/AMHl5/8AHazla+hLtfQzNJ8a6BFploNL0XX2skjxbtFo1y67D/dbaeOnftWp4EvIb/wnDcW9o1rE1zdBYm37hi4kGWD/ADBjjJB6Emsa98B2ljdaHb6S+tQ2P2tlu0i1i6CrD5MpHHmcDzPL6f1NakHw/wBDtYvKt5NYhj3M+yPWrtRuYlmOBL1JJJ9STSEdRXNeOVvn8ONHZ6NZ6sjyqLm2ukLr5WCSwUcsQQpwOeuATgUv/CD6T/z9a5/4PLz/AOO0f8IPpP8Az9a5/wCDy8/+O0Acn4VuNZn16PTLGDwudEt7a2uhDaK2yPdLKGdPlz5vyHIboQOhJr1CvP7H4dabb+M9WnRNWitZ7O2YTpqt0rSzb5t+5xJuYhfL6k4zx1Nbn/CD6T/z9a5/4PLz/wCO0AdJXmHiwWk/iDUr4+FrXUIrN4bC4kkvXjaWeZVEf7sDaQPOjG8/MNxI+7XV/wDCD6T/AM/Wuf8Ag8vP/jtQj4faHG00kb6wskpDSMNZuwXYAAEnzeSAAOfQUAV/A1o2k3mr6Vd2kUepR+TcTXEd1LcefG4YIS8pL8eW64Jxxkda7KuF8N+A7NtB0681STWk1iaygF851i6V2kC5IbEnZmbjoMnFa3/CD6T/AM/Wuf8Ag8vP/jtACa/4l8ORpfaJqmoPC8kRhmWOOTcquvZlXg4bqOleTSW/hWLV9Hs5d17ZxXhkl1FJbhSYfJkG2RCeH3MnzJweeF6H02f4X+Fbqdp7i31CaVsbpJNVumY4GOSZPSsfX/hVoEenRHS7C+Nwby2V9uo3DfujOgl6yf8APMvz27c1ztYi7s1b0f8Ambp0Lap39V/kbtr458I2dpDawakVhhRY41MMxwoGAMlcngd66e2uIru1huYH3wzIskbYIypGQcH2rj/+FTeD/wDnxvP/AAZ3P/xyr8fgPRoYkijn1pI0AVUXW7wBQOgA83gVdNVb/vGvlcmp7O3uJ/Ml8YzxDSoLB9KttUk1K4W1htbpgsTNtZ8ucHACox4BPAxWHba3Y6Lp9vo0vh+z02/t721RbBcGIrLOkfnwttG4AvnOAQeDjIJ1Zfh/oc7RtNJrEhifzIy+tXZ2NgjcMy8HBPPvWM/w60y/8WT/ANoR6rcafa29tNYvNqty3l3G+XzGRjJkMAsX04xWpkeg0Vzf/CD6T/z9a5/4PLz/AOO0f8IPpP8Az9a5/wCDy8/+O0AVvEHiJNG1a/aPTreW9t9PgaGaRtpfzpzHtLYyEVgrMe24Uuk3GoJ4wFprtppL6k9g8sF7YoysIhIgaNt2SBuZSOcHB4GKjvvhj4X1Mob+HUbsorKv2jVrqTaG6gZk6HAz61l+FPhppFlZ3E01vqdpePdXEe6LU7mJmgWeQQg7ZBkeXtx9fWgD0SvHvG//ACOF/wD9s/8A0Wtd7/wg+k/8/Wuf+Dy8/wDjtVJvhn4ZuZmlni1KWVurvq10xPbqZK4sfhXiaagnbW/5no5ZjY4Os6kle6t+K/yPLaK7vX/hfokenRNpdpqJuDeWyvt1K5b90Z0EvWT/AJ5l+e3bmtP/AIVZ4T/59b//AMGl1/8AHK8n+w6n86Pd/wBZKX8j+80/BH/In2H/AG0/9GNUfizWlt/Dk7afqEEdy8sMSyoyuYleVEZwDkEqrFufSmQ+ANEtoVhgl1mKJfuomtXigd+AJah/4Vt4c/u6p/4N7v8A+OV7UYVadOEIW0Vne581VqQq1Z1JX1bf3s5zU21fStO1nUZPFl1J/ZOG0xTLDi8UKrkSqo+clmMfbhQRycn0ddQsncIl5bszHAAlBJP51w8Hw600+KL+OVNW/sxbO3aA/wBq3WPNLzeZz5mT8oi4/wATWqvw58OowZRqqspyCNYuwQf+/lJfWevL+Jn+68/wOh1D/j3X/e/oazart4F0dxhrjWyPfW7z/wCO03/hAdE/57az/wCDq8/+O187m/DtTH4l14zSVktuxVOsoxtYtVb0/wD4+G/3f6iuW0DwLayadK2qS62LgXlyqbtYu1/dCdxF0k/55hOe/fmtVfAmjIcrPrQPqNbvP/jtc+X8L1cLiYV3UTUXfZjnXUotWON+J/8AyMtt/wBea/8Aob1xVev3Pw08NXkgkuo9TnkA2hpdXumIHpkye9Yuv/C3Qo9OibS7K/Nwby2V9upXDfujOgl6yf8APMvz27c1+kUcfGnTUHHY8CvlsqtRzUtzzqvavAf/ACJen/8AbT/0Y1Z3/CqfCP8Az533/g0uf/jlXIPAGh20Kw28usRRLnaketXiqO/AEtZYrGRrwUUramuDwMsPUc276WL/AIp1a70XQXvLG3iuLo3FvBHFM5RGMsyR8kAkff64Ncna6hoei+Ihquu6PfeHL0o8ZeRt9ixkKlmEiZRWbYuSwQnHIzW7N4A0O5jEc8usSoGVwsmtXjDcpDKcGXqCAQexANZ9z4JhbxNZQpca62lPZ3Buc6zdlfNDwiME+bn7pl46HnPQVwHpHcUVzf8Awg+k/wDP1rn/AIPLz/47R/wg+k/8/Wuf+Dy8/wDjtAFbxrPoIl0611e11Oe6kMklodMjnadNoUOQ0PzAYdQex/Csyx1vR49Z8L6NpugajaxLcTJC99YTwCL9xKxKs4AZzgg5ySGY1sP8P9CknineTWGmiyI5G1q7LJnrg+bxnAzWXovgO0urMTay+tNeQX10bdpNYut0cfmyLGVxJxmIgZ6kHnqaAO8orm/+EH0n/n61z/weXn/x2qF98KvCWpzrPf2l9dyquwST6pdSMFyTjJk6ZJ/OmrdRq3UoX97beJL66eT4bnWVsrmW0FzMbM5MbFTgSOGA78jvW14J1K6v4dYhudOGmrY6h9mgsgI/3MYhicL+7JU8ux4PeuX8RfBzwpF4Z1WTSNGnOpLaStahb2diZdh2cF8HnHWtJfgz4F2jOjzZ7/6fcf8AxdO0e/4f8EfunfV8s175a/DzQLC2S2s21e3t0ztih1m7RVycnAEuBySapf8ACpPBv/QPu/8AwZXP/wAcrSnUULkNHiFFetz/AAo8PDxRYRxade/2Y1ncNOf7QuMeaHh8vnfkfKZeP8BWp/wqTwb/ANA+7/8ABlc//HK0+sLsKx29c74h11tK1XS7a30KfVb25WZofJeJDGE2bvmkZRyGHAPODUU3gHRLiJop5tZljbqj61eMD+Blqofhb4ULBjbX5I6H+1brj/yJXKN36EMuv6tceLPDUF54Xm01JrqZPtF1JbynH2eVtqFHZlJKjJ44BHeu4rzrS/hjo0mo6yL611HyI7xRYk6ncj915EROCJOf3hk5PPbpWzb/AA48O2she3GqwuRgtHrF2px6cSUC946yvMdT1jXZ55bnTtfs9Lj/ALaGnf2fFZxyTPmUReYxYnLHPmYAHyd+9dV/wg+k/wDP1rn/AIPLz/47XP8AiP4ZaNHpuqavpVtqTeIks5fsl0NTuWm8wIdgDGTnnAweKCjpvC9/d3cGo2+oC3a9sbxraea3TYk5CIyvtycEq6gjJwQa3q5iLwHo8akrPrSljubGt3gye5P73rT/APhB9J/5+tc/8Hl5/wDHaAPn3xZ/yOWuf9hCf/0Y1Y9fQ8vwk8GzzPNNp93JLIxZ3fUrklieSSTJyayZ/hH4aHiiwji0u6/sxrO4ac/b58eaHh8vnfkfKZeP8BXoRxsUrWPo4Z3TjFR5HoeHV9N+PP8AkS9Q/wC2f/oxayP+FP8Agn/oGXP/AIMbj/45WlP4A0O6haG4l1iaJsbkk1q8ZT35BlrCvXVW1lscONzGOJlCSjbl/wCB/keN0V6x/wAKp8I/8+d9/wCDS5/+OVl6V8LdCfUdaW9sr8W6XirZZ1K4GYvIiJxiTn94ZOT/ACArHnF/aEf5TnvAf/I6af8A9tP/AEW1dj4y/wCQxF/17j/0JqsQfDHwvazLNbw6jDKv3Xj1a6Vh24Ikqab4d+H7hw8x1aRgMAvrN2xx+MtZVY+0VkceIrKrPmSOPorZ8RfDjSovDOqyaTFqh1JbSU2oXVbliZdh2YBkweccGtJfhp4b2jMep5xz/wATe6/+OVzfVn3Mbl3wb/yB5f8Ar4P/AKCtN8TvJqvh3WdHhRFlu7Se1R3c7QzIygnA6c1HD4A0O3QpDLrMSk5KprV4oz+EtB8AaGSSZdYJPUnWrz/47Vzp1eVKnKxlUU38DsZ974f0W7uPtkNlLpuokc3umzm3lJ/2tow//Aga3rXU5Lezghl33Ekcao8zsA0hAwWIAAyevAArndU8B2iajoq2T60bd7xlvcaxdHEXkSkZzJx+8EfI/kTWp/wr/Qv+emsf+Dq7/wDjtY+yxX/Pz8EY8lf+b8De1LUrTSbCW+vpfKtosb32lsZIA4AJ6kV514v1nwh4njtJRrZS5sy5ijeK4WKUNjKuEAOPlGCDke/IPT3Hw+0K7gaC5k1iaFsbo5Nau2U4ORkGX1qh/wAKk8G/8+F3/wCDK5/+OV6C5ep6uHeDUf36k35NJW+aZx/gnVPBunazqmrXUUunXH2oG0RpZ5gE8hEYgjIYFvMwWGcEcDoPSNN8a+H9Wv4rGx1DzbmXOxPJkXOASeSoHQGuU0v4UeHn1HWlvdPvRbpeKtlnULgZi8iInGH5/eGTk/yxWxb/AAt8KWk6z21tfwzLnbJHqt0rDIwcESelN8nmaTll/K+SM79LuNr/AHEHxF/5hv8A21/9krha9JuPh34fu9v2k6tNtzt8zWbtsZ64zLVdvhf4W2nFvqGccf8AE1uv/jlYuN2d2CziGHoRpOLdr/nc8+r0j4f/APIBn/6+m/8AQVrG8O/DLRpfDOlSatbakNSa0iN0G1O5UiXYN+QJMDnPArbg+H2hWsZjt31iFCclY9Zu1BPrgS01GzJx+bQxVH2ai0bOvXNrZ+HdTur6EzWkNpLJPEP40CEsPxAIrkIdd8V/No9voGlRXFpZJdOj3rvG0DZWONW2A78o4JPA2g8542JvAWi3ELwzTazJFIpR0fWrwqyngggy8g1map4Gt477Ro7GXW/sr3TR3oGs3ZHkeRKQDmTgeZ5fT6dCao8M6nw/NZ3HhvS5tPh8ixks4nt4v7kZQFV/AYFaNcxD4B0S3gjggm1mKGNQiRprV4qqoGAABLwAKf8A8IPpP/P1rn/g8vP/AI7QB0lFc3/wg+k/8/Wuf+Dy8/8AjtH/AAg+k/8AP1rn/g8vP/jtAHSUVzf/AAg+k/8AP1rn/g8vP/jtH/CD6T/z9a5/4PLz/wCO0AdJXOXv/JSNF/7BV9/6NtaT/hB9J/5+tc/8Hl5/8dqzpvhTS9L1NdRgN9JdLE0KvdahPcbUYqWAEjsBkovT0oA26KKKAMTUrue005XgfYxnYE4B4yax/wC2tQ/5+T/3yP8ACuiksI9Qs/LkZlCyswK/U1W/4Rq1/wCe035j/CuKvTrSneD09TxMdhsZUrOVGTUfWxjf21qH/Pyf++R/hWxoN9c3bTieUvtC4yAMdaX/AIRq1/57TfmP8KuafpkWnGQxu7F8Z3Y7UqNKtGacnp6kYPC42FeMqsny+t+gzW9VOkWCzpB580s0dvDFv2BnkcKuWwcDJyTg8DoelcsnizXbnxg2k22mwG4tbef7Vatd4i3r5DI6y+XuOUmAxtHJOema7G/0+11Oze0vIvMhcgkbipBBBBBGCCCAQQcgishvBOgM6yfZZxMA4My3kyysH279zh9zZ2KOSeFA6V3HvGSPH0Bt5b6K2uZYpbaxltoepLXBfAwiFhjbzjd04HratvF17dLb26aJJHqM9w8Mcdy0kETKiBzIHeIPt5C/czuz2GauQ+E7ITah9pWKS2uUghit4ozEII4cmMKQ2dwLEhhjGBgDFWW8M6ZJapbyLdOI5fOSV72ZpkfG3Kyl9444wDjBNAGdba7qq+K9QtNQh0+3020sba4mlN2cwl/O3HJjAYZjA5K4Az3wI28TXsniW4jsFsLzR49NW7EqXXJO6QErhCG+5jGR657VtxaHYQahDfxrOLmKFYN/2mQ70XdtDjdiTG9iC2SCSetLd6JYX18t7PHJ9oERhLRzugZDn5WCkBhyeoOM8UAc+3jG/TRtOvX0m2E9/F58NotzNLIY9qknEcDHI3AHjaMj5ucVmXvivW7+4jv9IEUemjQv7VSOSUKzllJAceU3TA4Vl9c9q6248M6Tcw2UUlvIEsojBB5c8iFYyFBQlWBZTtXIbIOBmiDwzpFtarbRWm2FbEacF8xzi3AwEzn369fegDmtP8T6hptrs1Tzby8NlZyqomj2vLcTvEigiJNvO3JOQB2OCWv6rrWr2N7YLcxw22+3vJJYbabzQ/lxqykO0YIOSf4cfWtiXw7pM6yrLZq4lto7VgXb/Vxksg68EFiQRznvwKQeHNM2RrJHPN5ayIrT3UsrASAK43MxPIA78dsUAc/c+N7u30q4vbbShdQ2NpBNdNLdiN90iBgFAQhsAgk/L14B6U+DxPqVvq2pJdWqT6dHrMdgk3mhXi8xIgoCBfmG+Tklgfm74qTW/Alvq0sccU8drZeTFBNEiyl5UjOVBIlCHA4BdGI5IPTG62haa4mDW2RNdpeyfO3MybCrde3lpx046cmgCh4Q1TVdX0ye41SC1jK3dxDG0ExcsEnkTBBRcYCgA5Oepx0roKpWGk2emSXLWiSRi4kaWRDM7IHZizFVJKpksSdoGSeau0AFFFFABRRRQAUUUUAQJ/x/S/7i/wAzU9QJ/wAf0v8AuL/M1PQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA2SWOGMySuqIOrMcAfjVf+0rD/n9tv+/q/wCNOvrCz1Ozks7+0gu7WTG+GeMSI2CCMqeDggH8K8um8I6bYf2vp9x8OE1Ge6uJWtbq0ht1iMbE+WNxZTDtGAcDqC3OaAPWFZXQOjBlYZBByCKWs/QrObTvD2mWNx5Xn21pFDJ5KhU3KgB2gAADI4GK0KAMrU/E2g6LcrbaprWnWM7IJFjublI2KkkZAYg4yCM+xqOx8XeG9UvI7Ow1/S7q6kzshgu43dsAk4AOTgAn8KZ4r0WTW9AubW1S3N02wp54+VwrhjGxAJCsAVPsxrD06z1DUPFGl3k2h6foseniTf5Vykss4ZCojARRhASGOe6jjrQB3FFFFAFI6raLrI0l3KXbQeeisMCRckHae5GBkdty+tN0rWLPWoZ57F2kgimaETbcJKVAyUP8SgkrkcZU15RrdzY+IbNbXUvHV3LGrb1K+H5EZT0OGVQRkEg4PIJHevQ/BNxHPoJWHVDqEMMvlRv9g+yCNQq4QJgcD1x3x2oA6OiisrxHfHTNAubsalZ6aY9n+l3kZeKPLAfMoZc5zgcjkigCVNb017K8vPtkS29lJJHcu52iJkOGDZ6evuCCOCKtWd1HfWVvdw7/ACp41lTepVtrDIyDyDz0NeLaheaXqesx6nc/ETwoZVKmSJbNxFcMn3GkTz8OV5xnp+Ax7Lpk/wBq0mzuDcw3XmwI/wBogXbHLlQdyjJwp6gZPB6mgC1UN3d29haS3d3PHBbwqXklkbaqqOpJqasjxPp39q+Hrm2FxFbsrRzrLMMxq0brIN/+zlAD7E0AP0/xFpOqQ2stnepIl20iQEqy72jJDgAgHIwfyrUrzDwmz3uvWcV7rPhw/Zb68voLfTtRFxLPLMZSRjC4VVlfsScZ4xXp9ABWVrPiCy0DyH1BblLeXdm4jt3kjixj/WFQdgOeCeODzWrXM+MtTOmWlvLJ4jttDtGLCaZoBLM5wCBHnIHAYklW7cDmgDdsdQs9TtVurC7gurd/uywSB1P4jirNeceGND8I6lcyX/hXVbptTtrqKW9vS8u+YbstHIp2qQyqwwBhcg4r0egAqjHrOnSWd3di8iFvaSSR3EjNtETRkhg2emMZ+mD0IqTUkv3sJV0ya3hvDjy5LmJpIxyM5VWUnjPcc15xdeFdb1vUZNYfWtBkW3kK3cUenTiGeSEkAyp53zlCDj6YOcDAB6XZ3UV9ZW95AWMM8ayxllKkqwyMg8jg9DU1VdMna60qzuGnhuGlgRzNApWOQlQdygkkKeoBJ471aoAbJIkUbSSOqIgLMzHAUDqSa5+w8deHNSvYbS11AmSc7YGkt5I45j6I7KFf/gJOaj8cjUW8N3yWx09dPa0nF+920oZYtnJTy1JJxu7emKx00fxrqMOjW+py+HmsbW5t7iVrcTLJII2DLtyMKcgHHfGOAaAO+ooooAZNNFbxNLNIkcajLO7AAD3Jqtaavpt/KYrPUbS5kC7ikMyuQOmcA9ORWH450271LTtP+yaSmrG3v0nkspZUSOVArg7t3BALAgc8hfSk8NRyLfu7eDLXRVMRAuYZYGL8j5P3fPv6fLQB1NFFFAFa3v7a7ubu2hk3S2cginXaRtYorgZI5+VlPGevrVmvN9Yt9OufiGYopfEMU93NHaXN1Z6gYII5BA0qJtByx2KW4GBv684rr/Cs0M/h6AwSXkiJJNEWvZfNl3JKysGbvhlIHsBQBs1W1G8/s/TLu98iSf7PC8vlRDLybVJ2r7nGBVmqGuT2dt4f1KfUYhNYxWsr3ERXcHjCEsMd8jIxQBYtL23vrWK5tpo5YpVDIyMCCD6Gp68d06G0ttegl/4Vz4fsI7bUra2e4W53SW7yCN0YARcn94oGD97jOPmr2KgAqpcanaWuo2ljPLsnuw/kBhw5TBK56bsHIHUgE9jVuuE8S6Hq+sXf9j3Hiq2hW8aSe0hGlEyRCMghlkEgwybk+bjkj1xQB1tpq1nfaje2NtIZJbIos5UfKrNk7N3QsAMkdtwz1q9XK+DdOk0EXWhSatBeNaJE4ijsjAYw+872Ys3mFyCSc5ypzya6qgArIv8AxLpenRXrzzuxs5Y4ZY4oXd/MkClEVQMsxDLwM9a16891lLWx8Zy3z6f4muY45ku3gtLISW0s6whFkDY3EhMDAOMqOOKAO30zUrXV9Ohv7KQyW8wJUlSpGDggg8ggggg8girdYHgt7WXwraz2RuTBPJNODcxhJCzyu7EqOnzMce2K36AGTTRW8Ek80ixxRqXd3OAqgZJJ7CudtPHvh6+uYLe1nvZWndUjcabc+WxY4B3+Xtxz1zj3q/4jazm0PUdPubu0gNzY3H/Hy+FEYXDuwyDsXeu4gjGRyM1x2ieKr03On2MnjjwVegyRxFYc+fMMgYXE2N56D5cZPSgD0eiiigCnqeqWOjWTXmoXCwQKQu5skkk4AAHJJPQDmo7DW9N1MWxs7pZPtVsLuEbSpeI4wwBA9R7jIrD8W3+mO9qi69pFpqmm3S3UcF7dKisdjLtcZ3KCshIODg4ODWN8PbYi4sFu9a0O6uNL0v7Bb2+l3fnkx5TfI5IB5KIAMYHqc0Aei0UVHcXENpbS3NxKkMESGSSSRgqooGSSTwABzmgDMuPE2k22v2+iSXR/tCc4WJY2YA7WYBmA2qSqMQCQTjir1hf22p2aXdnL5kDlgG2kcqSpGDyCCCPwrzDXNU0xvESapofjPwmVN6l9JFe3yjbKtu1vwVY5UqwOOOV688d54QgtYPDFqtnqUGpRu0srXkDAxyyPIzSFcEjG9m4ycdKANyiiigDm7n4g+ELO5ktp/EWnLNGxV184HaR1Bx3rpK860d/FnhjSbfRynhFUtV2Rh9UlRtnbd+55OOp7nmvRaACqOparDpbWQnV9l1ci3EgxtjYqxBYk8AlQv1ZR3q9XL+OYbS80yx0+40m01OW8vVhtobwkRJJsdt7EAnARX6Dnp3zQB1FFee+BotFt73Rp7DQLCwudU0T7Y8lsCCvzRbk/3SXUj/dNehUAFYes+MfD/h69js9W1OK1uJE8xI3DEsuSMjA9jW5XG+JprrQvEMevQXei7ZbQWjW+qXn2XG1y26N9rdd2GGOdq88UAa+i+MPD3iGdoNJ1e2uplXeY0bDbc4yAeSM96264Tw1ow1G4tdbGqaXcyxanPdyjTX82KMyQeX5Kv6cq7ZAy3OBmu7oAKp6TqUGsaVa6jbBxDcRh1WQYZc9mHYjoR2NXK8cj0vR4tIbxDH4K2eHWBuGnXWZhcGE8mYxdOnzY35x78UAex0UUUAYur+LdC0G7S11TUUtp3QSKjKxypJGeAe4P5VFpvjbw5q9/FY2GqRz3MudkYRgTgEnqPQGm+I4NSjD6hD4oTR7CCHMwe0jkUEEksWbpwQMe3vXO+GfE8WoeJLayXxy2pu3mf6J/ZBh8zaDn59oxgj16jHegD0OiiigDlf8AhYvhv/nvqH/gquv/AI3W1pGtWOu2j3Vg0zRI5jJlt5ITuAB6OoOORzjFcp4r1FdEeyiuvGmrWc3kIhitNPiuHmbdt81gIW2lmYL2UnAAzWx4K1GPVNGmnj1fUNUVbl4/PvrQW7qQACoUImQDnnHXIzxgAHR0UUUAYo8WaK2sXGlJds93bRSSyqkLsqhNu8bgNpYb1yoJIyOK1ba5hvLSG6t5BJBMiyRuOjKRkH8jXlGpTw6Z4mm/s7xp4Vt0je9RlvbkCa2e4kR5QVDYYq6HAJXrg9K9H8Ntpi+HrG20e+gvbG1gS2jmhlWQEIoUcrxnAFAGrWLq/ivSNCu0tb+S6WVkEgEVlNMNpJH3kQjPB4zmtqsTxdd3lj4YvLixd45l8sNKke9ooy6iSQLg5KoWboelAEem+M9E1a/isbOW8aeXO0SafcRLwCTlnQKOAeprfrzTwl4mOo3uk6dYa4+qPFf6gLtvNEp+yq0oieRhwMkRbemQTjivS6ACs2/1/TdMa8W7uPLNnai7nGxjtiJYA8DkkqwAHJx0rSrgPE/9gyeOo7bWdXGmK1lBKyvdxxx3yxzOwjdWXICsM5DDcHYdAaAOu0bWrTXbNrm0EyeXIYpYp4mikicYJVlYAg4IP0IrRrG8PpprDUrzTNRhv1vLwzyyQyrIqvsRduV44VU962aACsj/AISjRf7XuNKW+Vr23ieaWNEZtirt3ZIGMjcvy5zyOK0bu8tbC1e5vLmG2t0xvlmcIi5OBkngckD8a8qupTZ6+bjRvFHhJrU/bSkl1qAEkRupEkclVyH2shxyuQQDjGSAer288V1bRXEEiyQyoHjdejKRkEfhUlUNDtLaw8P6bZ2c4ntbe1iihmDBhIioArZHByADV+gCvfX1rptlNe3txHb20K7pJZGwqj3NZGm+NdA1e/isbG9eW4lzsU20qg4BJ+YqB0B70eNbf7V4Uu4xd2Vm4eKRLi+fbDG6yoylj9QOO5wO9Y+jeKb6fU7eLUPEPg6WBsh1srpvNY4ONoLEdcfhQB29FFFAFCfWbO21i20uWTbcXEMsy8jaFjKA5OeD+8XH40ttq9pd6td6dA5ea1iilkIwVAkLhRnPX92cj3HrXIeMPD+l3niSw8jwro2oaxfRzFrnURtjCRiMHdhW3t9wLxkANggZzteEtHu9GguYZ9J0LTonZWRNIVlDnnJfKrz93HXvQB0dFFFAGfqGu6RpMix6lqtjZuwyq3NwkZI9QGIqzZ3trqFql1ZXMNzbvnZLDIHRsHBwRweQR+FcNqNrqOm+LdWvV8M6XqNtftEYri7vo4pMrGqlQGQ/Lx09dx78dlo/n/2VD9p06LTpvm3WsMgdY/mOMMAAcjnp3oAvUUVn6/Zyah4c1OyhVmluLSWJAr7CSyEDDduvXtQBCnirw7Jdpax69pbXLuI1hW8jLsxOAoGck54xWtXD6JBrdt/Z8Fx4E0i2WMxo89veR4jAwC6r5eeOoGfx713FABWXrHiHTdAMDanLJBDNuAuDEzRIRj77gEJnPBbAOD6VqVzviO7vxqGmaZZanb6b9u80GeW2MrMyhSETJCBiCx+bOQpwDg0AblrdW97bpcWlxFPA4ykkTh1YexHBqavPdD8JaJb6xdnw9qF9HqdndxPf3m8+VcEnMkLIu2MttGDhflLKevFehUAVdS1Oy0ewlv8AUbqK1tIseZNK21VyQBk/UgfjWB/wsrwV/wBDRpf/AIECuprzjUG1DRJdX0tofDt1HqU8swub/UBCyrISQssZQlwgO0YPKgDigD0K1uYL20huraVZbedFkikQ5V1YZBB9CDUtUdEt/smg6dbG7+2eVaxR/ac587Cgb8989fxq9QBDeXcFhZXF5dSCO3t42llcjIVFGSePQCorjU7S1tre4nlMcVxJHFGxRuWkICAjHGSQOccmq+ty6fJp13YaiJTb3FnOZlSJ2zEFAf7oPOHGB1PYHBrgNO1C11DUNMsNQ8VajqFpBcxNb250GeBpJFYeX5spTBAbBzhRkAnigD1KiiigCvLfW0N7b2ckoFxcK7RR4OWCY3H6Dcv5iiO9tpr24s45Qbi3VGljwcqHztP0O1vyNcj4v1LT4ptN1SDXF02/t3uIIJJLOSeORQyrLG6Lg43InII+7xkVN4IuLTULrVNRGtDVdSmEKXDx2j28cSLv8tEVsnGTIc5Jye3FAHYUUUUAZ+maxb6mLoIDFLbXMlvJE5G4FWIBI7Bhhh7MK0OteP6zFp19r90NO+HWi6g8sl4/2i6mWOS4eB1WY48s8l3OMnnBPHf07w9LDceGtKmtoYYYJLOF44oCTGilAQq5AO0DgcDjsKANKiiigAooooAKKKKACiiigAooooA80+K1/eaf4UtHsrue2dr8qzQyFCRh+MjtXj//AAkeu/8AQa1H/wACn/xr6Q1Lw9pniTTVtdUgM0Uc7SKA7KQ2SM5BHYmsb/hVPhD/AKB0n/gTJ/jXbQr04QtJHt4HMMPQoqFSLb9EeEf8JHrv/Qa1H/wKf/GvUvgzqmoahPrC3t9c3KosRUTSs+3JbOMniul/4VT4Q/6B0n/gTJ/jWzoHhPRvDJnOlWrQmfaJC0jPnGcdScdTTrYinODjFal4zMcNWoSpwi035LuJ4i1LULGfRrbTvswl1C+Nsz3CM6ovkSyZAVlycxjv69OowJ/GGqnTYlt0t31NHvRMiW+6NktpTE0gLzRhFztOCzH5u+Ca7eW3hmeF5YY5HhfzImZQTG20ruX0OGYZHYkd6qT6HpNysa3Gl2UojkaZBJbo212OWYZHDEnJPUmuE8E4+08R6zfXt5qlvPbrZf8ACO2mpR2UkDOQ8gnOAwcc5RcnHIAGARkz6r4zvrS1ke0jspJRpEN+qvuxveQLg4P3cE4711a6PpiS2sq6bZrJaR+VbOIFBhTGNqHHyjHGBUUHh7RLaORINH0+JJBh1jtkUMM5wcDnkA0Ac1f+IfFFtqlzptlY29/c2Vuk8rJEsSS7y+AN84MYAXG7D854GMHY1XxGbJVmihlENvdRR3xntpECxudm5HICttYqSQWG0H2NaV9o2l6o8b6hptndvH/q2uIFkKfTIOKs3FvDd20tvcwxzQSqUkikUMrqeCCDwQfSgDiX8X61dDTk06wjkfUhc3NswjV/9HjdVjO15owWZXD5DcA9DyRHqni7X7bStTuVt9OsrjS9JS+uLe6zL5kjCT5VZHACgx4z82SccYzXY3WkaZe2cdnd6daXFrFjy4ZYFdEwMDCkYGBxWVrfgvStdW3iuF8u2hi8lYIoIQvlnqoYoXQEcfIy8UAZz+JtfuNdurbTtKWa1sriGGfd5YLB0R2bc0ylcB+B5bZ29eeKun65rltocsk97BdXdxrF1ZWcaWRZiUnm+XmZQRsj4yy7QuCWNdfcaNpd3eRXlzptnNdRY8uaSBWdMHIwxGRg0yXw/os/2jztIsJPtLB599sh81h0LZHzEZOCaAOQ0zxFreu6noDxXFvaCQ38V1C0PmLI0EqpkbZMDIBIwzAZPLVJY+JtVubWGG0axt5Ftrq7kkuxJIrLHOyBRlwR6liTtyMDHTrToWkGCGA6VYmGGQyxR/Z02xuTksoxwcknI5on0PSLqCKC40qxmhiYvHHJboyoxOSQCOCTzmgDkG8Z6xNbXepQQWcNlaW1ncvbzRu0riZQzLuDAKQDwdp+ld9VaTT7KUTiSzt3E4UTbolPmAdN3HOO2as0AFFFFABRRRQAUUUUAQJ/x/S/7i/zNT1An/H9L/uL/M1PQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV5NdQXmleNrm7ufD2q6hdXEWoQtPBCZUnjkeI26b87UVUVlIOMEE98n0/UrBNTsJbOSe5gWTGZLWZopBgg8OpBHT8sivN4bazudfn02C58WNCkV0Y7ptdlCzSW7IkiKu/PDOBk45B+tAHoWg2lzYeHdMs7yTzLq3tIopnznc6oAxz9Qa0Kz9BuILzw9plzbPM9vNaRSRNO5eRlKAgsx5LYPJ7mtCgDG8Vafc6r4ensbXJeV4g6h9heISKZFz23IGH41xPh/wAE/wBjeNrWS18MW9qlpeXkp1NPK2SW8ocxxqoO4MpZV6DCoRnDYrsPGkd1N4TvI7N2EzGMFUmETSJ5i741ckYZl3KORywrmNA0yzTxTp0/hzwzqOiW8Qk/tF7iEwJMhQhU2k/O2/a27BwFPPNAHo1FFFAHA+Oda1uzvJ4dO1MaZbWdtbXM0wt0kZ1kuPLkPzggLGgLHj+Idq1PAeo3eoabqIutZGs/Z794Yr5I40SVAiEbdgAIBYgnn5g3OMYz/GXiSbS9egsLDWQuoTW4dNJfTvtCzLuYbt+5NucY5fHHSt7wpPrE+ig61o9vpVwshVLeCRXUpgENhchSST8uT0680AblFFFAHmOq+J/E8+qutnq+laVaM99HALm23/NbOq4dy4wXy7DA4VR1rvdAvX1Lw5pd9IxeS5tIpmYpsJLICTtycdemTiuB1rXLmfUL2wtdO0jxdDDdsWsV0+VWgcEja0uHi3r0ydp4r0ixklm0+2lntTazPErPblgxiYgZTI4ODxkccUAT1g+M9NudW8KXdnZ20d1cO0TJBK4RJNsittYkfdIXkdxkd63qxvFNleaj4entLHf5srxK4jk8tmi8xfNUNkYJTeOvegDN0X+1RqVuLrwZp2mxYO65gvEkaP5T0AjBOTx1HWurryzw74MXRvHNrJbeG2t2tby8kfUiVMT20gcxqvzZ3gsq9BgK3ODXqdABXHeN4NFkubX+2dYt9OS4sbyxU3HCsJlQMQxICsMDr1BNdjXFfEO+urO1slt/EVlpSSlxJbzhfMux8vyoSjkY74Rj8w6UAXfCOnX8c17quoX+n3b3ccESnTwfKIiDDeSScs27n0CqOcV1FcB8MLXT4ItUls/Dl/pUszxma4ui5W8I3YZN6qcDJ/gX7w6139ABXl+s6vb+G9S1HSrfxxpWn211NJPLDPaNNcWryHc+xlcKMsxYBlON3cYr1CvPR4duryOW90G4sJ5UvdTgmS/icI6zS/vFbHJKtGAOxUfQ0AdnocNnb6BpsGnOXsY7WJLdj1aMKAp/LFX6p6RZPpmi2FhJO1w9tbxwtM3WQqoBY+5xmrlAFPVoxLo19G0VxKr28imO2IEr5U8ISQAx7ZI5xXm2mrb6PqGk27ad4/tIWuYoYftV9G1upyNquBMfk4xjHPQAnAr0fWrpbLQtRu3uXtUgtpJGnRA7RBVJ3BTkEjGcHjiuVudIuIV0y81/xPql/Z/bbZo7ZraCJfOLr5W/YgJAcrxnGcUAdxRRRQBzPjiwuL/SLVI7Oa/tI7tJL6ygcK9xAFYFRkgNhijbc8hSO9ZfhLTYofE015o/h+60LRzaGOaCaMQiefepVliBONqhwWwM7h1xWt4vku4Ro01kgnnj1EMLTzxEbr91L8gJ4yPv4PHyGs7wv4a1HR9bgvbhW8y6sJH1SUTlllumkVlABP8AAPMUHAGCBQB2tRXU4tbSa4aOWQRIzlIkLu2BnCqOSfQd6lqK6uYbO0murhxHBCjSSOf4VAyT+QoA8o1vUtN1PxAbuwbxdpt9DIl3PFb6M0o3iNolkKsh2ny2Zc9CAOCRXoHg59Ok8J2D6SLn7Eys0bXSkSuSx3O2e7Nls985rlNc1qHQPFGpNp/irTrKa7Mb3drf2Mk/luI1UOjIy4ygTg5HHbJrqvB8VnF4XtBYX7X8DNLIbort812kZnbb2G8tx2oA3azfENzDZ+GdVurm2+1QQ2c0klvnHmqEJK/iBj8a0qRlV0KsoZWGCCMgigDi9W0zQ7WXR9fk0/zbia5s4FRLyQRlmYJG5X7sjJuyCRnAzngV2tcf4f8ACejWevahNHof2b7BchLF3klZNrQoxaNXYouGd1ygHQj1rsKACuP8by21ncaZfDU7vTtSh81beeDTZb1GRtvmI6Ip4OEPVTleDwa7Cuf1++1iPVNO07RrjT4Z7mOaRvttvJIrBNnQq64Pz9DnP4GgDM8C3lhqN1q95Fq9zqmps0Ud5NLYSWixhQxjjRHUYA3MepPzZPUV2dYOg6PqVnf6hqer31tc3t6sUZW1gMUUaR7sYDMxJO85JPoO1b1ABXBeJtbvLLXp7a28T3EO1VdrS00Nr1rdSOrsmcZIJGR3ruLr7QbSYWhiFzsbyjKCUD443Ac4zjOK4MWXjPSdXuLkan4bil1iZVEb205DTLEQCvz5zsj6E4+XtzkA6fwnBY2/hmzGnXzX9tJvmF02Mys7s7sQAAMszcY46dq2qxvC2lXOieHbexvZYZbpXlkmkhUqju8jOSAemS2cdM9OK2aAKGt3sel6HqGpPAJxaWssxj7uFUsV/HFed2+t+I4NbUXk3h1IIdTtLWaGCxbzCs6xspjJk55crnH8JbHG2vQ9d+0f8I9qX2Pd9q+yy+TtiEh37Dtwh4bnHB69K5TTtT0/UdS0y7vPAeqW+qIqQrdTaan+j54/1gOQoyeewzxQB3dFFFAHL+LZNP0s6ff3dnYi3lvkivru4gVvKiKNhmY9BvEa5PA3VHpOp6TceMvsegrps1qlg8l1NZoh8uQyIEUuvHzDedv+wDWl4oLJozSjW7bSFjbc891GjwuuCCjhiPlOc8EHjr1rk9D8ctY2sxu/DxTSIPmbV9Lt3W0I7vsdVYAY5K7wPWgD0akZQ6lWAKkYII4IpaKAONbxFouk+ItYsfEFzpunpG0b2QuQkQlhMa7mVj94+ZvBA6YHrWv4SvJ9Q8N213cR+WZXlaMeV5e6LzG8ttvYlNp/Gud8VavNpWtMbLW7S9u22umhT2huHB2gZjMQ8yPPXLBhyegra8P+J7jU7mOw1XRLvR9Se3NysEzpIroCoYq6nsXUEEAjcOKAOjooooA8bufh5cnT4Wt/D+m6pd3unxRSX5kiPk3ayOxuWLcuG35OMkhQpGK9krzH/hGdWvrkXHhfTH8HIzbmne45f62iExH/AIEQa9OoAK5PxM+p3zzad/wiUuo2SsjxXMeoxwNuABDLyGRgcjIIPHvXWVyvi+00pbFI7nSH1KfUL5BDbJMY/Nn8ogEtkbQI0bPsOhNAFPwPor6NcyRf8Ixc6cnkBFurrUxdttUjbEvzEqoBJAGBxXbV5r8PJ7KTWIpYPCa6N9t037Vbz/2gZzNEWTI2kcYJXPccetelUAFcXr+m6oPGKapZ+HLLWIGsFt2N1crGYmDu3yAq2MhufX5f7vPaV5Tfadc6jrNvpVz8OdAuri0sU251UlYINzLGuTD0JD4Az0PSgD0HQPtX2B/tmjW+kyeacQQTLIrDA+bKqvPUYx2rVrk/AAhj0m/to9BtNDlt754prO2n81Q+xDuLbV5KlTxnjHPYdZQAV5F5enfZP7U/sbxB/wAIbv8AO2/2iv2fy92fM+z7t3lZ+bbnGOduOK9N1fVP7ItEuPsF9e7nCeXZReY44JyRkccdfcV5R/aGj/2Zs8vxv/wjXm7PsP2WP7Njdjy/Mxv8vPGN+O2ccUAez0UUUAYfi6K0l8Nzfbrr7LbxzQTNMYjIFKTI4yo5IJUA+xNc74fsI7/xDFe6d4h0q+0i2vLm+hS1O6ffOG3I7BiNoaRj0zkLnpXTeKriW18M3s0Gs2+jSqq7b+4RXSI7gOQ3Bz90e5HB6V574Njgu/GttfXOi3upXyq+3xAHlEKZQg/K8cajI4+UN160AetUUUUAcfrekW+teItRsku5bW/l061dJDEGQGK4eSNl5yxVx8y8cMvPNa/h3R7rSbe8a+vI7u8vbprqZ4ofKjDFVQBVySAAg6k5OT3rhtajuvEHiSOHUvh5b3mpQ2ofeNZ2+VEWYLkhQOWD4xzwfSun8AmFdJvoI9EGjSW968U1p9qM5DhEO4t7gqQBxjB70AdXSM6opZ2CqOpJwKWq1/p9nqtlJZahaw3VrJjfDMgdGwQRkHg8gH8KAOCnsNRtG1iyXQNH1ZNRuZpkvp7tE+WQkqsqlS3yAhRtzkKOhrf8K+GpdAuJ5WnhlSeztImaMYMssSsHmYdMtuX1+6K4mbwdo9h/a9hdfDpr+5ubiVrS4s4YhEY2J8oBtw8oqMA8DkE85r03QbObTvDumWVyIhPb2kUMghUKm5UAO0DgDI4FAGhWJ4v1W40XwveX9tJBFLGY186cZSJWkVWcjIztDFsZ5xitusTxbd/YvDF3OLK3vCDGoiuhmIbnVd78H5VzuPspoAxvDzEatCU8dWGpLJuZrS3gt1Mx2nnKHdx1/Cu0rkNE8P6xZatb3VzF4WWBdxY2GlvFNypA2uZCByRnjkZ9a6+gArh/FL3dn9juNQ1LwdakwKkkmq2x+eUZLeWTIMJyMDkjnmu4rifE3hw65rd+9tLZSX0djaNDDPnMbR3LSgng4SQptJH9zoaANTwZcx3ejzSR32hXgFwR5miJthHyrww3N8/rz0Iroq5nwXazWltqqXCWcEzag8jWVnJvS0LRxny87VyTneeB/rK6agCOeCG5haG4ijlib7ySKGU9+Qa86aG81JdX1KwHhmxs9NuZ4Ba3OnCQv5RIJlkDLs3YyMKcKQea9JrzHUtKl8RalqWuw+HPDMkdjcSwH+0EYzXBhJBLMPlUcfLuDcYPQ0Ad/od4mo+H9Nvo7f7MlzaxTLBjHlhkBC/hnH4VfqnpN8uqaNY6gkTQpdW8c6xP1QMobB9xnFXKAMTxdpVxrXhi7sLVIZJpDGwimOElCurNGxwcBgpXPbNcm/hiXX9StDceCdP0m0t4plmEzwubkNGVWMCPOFDFWycEbRgV0fjGHTINDu7u+022u0na1tp1n4Vk89QpY/3UMjN+dcpoFrotn40tbjTPDekwWEl5PYWd5Bnz/NjiYu/93Ydsicc5HvQB6BodpcafoGm2V5MJrq3tYoppQSd7qoDNk88kE1foooA848X+HtGg1myWLwze6xf38s0+2PVpYfLK4LPgyAKPnxxgDIHcCk8NazbaBrM2nTeFtS0ozTQQSyzah9rVWfcIScuSFY7lDLxng9ONbxYyXP2C8Sy8Rw30Ek8cFzpcCtJGAwVtytlSj7VIyDkAHiovDOk2epfa5L22197vz7e4ludXjWJpWiYtGFCYXajAnAA5bnOaAO3ooqK6tory0mtZwximRo3CsVJUjBwQQRweo5oA4LWtJij8U6neax4Pl8RQXaxiylSOGbyECANFtkYbPmDNuHXdyeK6bwhp95pfhays75dk8e8+V5nmeShdike7vsUquf8AZrhH1vwNruuatPqMOrB450ijeGPUF8xfJjOSiABDkkYwMgA98nuvB+3/AIRi28uyms4fMm8mGdpC/l+a+xm8wlssuGwem7HagDdrK16PS4tLvdR1S2SaC2spxLlcnyCoMi/QhBx7CtWszxI/l+F9XcxQyhbKY+XPny3+Q8Njnae+O1AHl+mxWFtr1vIPhxpNiltqVtbPdfbsvbySCN422iPk/vEHBxu4zjmvY683sNU+H2t6vpt+l7KL92gCWzPOivKuBGXjPys6ngMc9ueBXpFABXnvi3TL+8un0q/8USx2l3FcXiKunxEW0cJU535DBl3phhzkZ4r0KsnXPDWleI44k1OCSQRbgpjmeIlWGGQlSCVYAAqeDigDmPhhNcR6fPp1w1xGsEMEtvbz20URWGTeVkzGxyXw2d3IKnPWu9qnb6Za2uoXN9EhE9ykcchzxtTO0AdgNzfnVygArzeHSb+y1LVvO+H9pqgnv5p472W4tzJKjMSN27kADgD0Ar0ivMNf0nw5eXWpRnwLr1xeSySA3EEZRZJCT86uZAACec9PagD0ewBXTrYG1W0IiUG2UgiHgfIMccdOOOKsVnaBbXFl4c0u1u0RLmC0ijlWP7quqAED2yDWjQBT1aW7g0a+msFja8jt5GgWQ4UyBSVBPpnGa8+0PxDpF9e6cx+KNxdXU0sf+h4toxK5I/d7RFuAJ4wDnnr3rvtcsW1Tw/qWnoFLXVrLAA7FQSyFeSASBz1ANcrpGqeJLSWx028uPCGI2SGRbe9kWTGQCEjKY3egz1oA7miiigDj9bn8YweKoLjTbDTpNIhtpVLXGotErsxiILqIzggq4XGRgnJXgG/4cfWLq/v73VZ7ALJHDHDaWN006RbS5LklV+ZtwHTogrO8c+GT4peCxfyJIjY3qpDM4ws7Kixy7e+zLDPYuDUPgHR20vUtYk/sC20JZ47bNnG8TOXUOGkPlk4Q8BQe6scDJoA7iiiqupXklhYS3MNjcX0iYxb223zHyQONzKOM55I4FAHAXTabr+tzaWdBZLRTfzwXceoPBJLPE6JOuE5VGZyDyc4J29K7jw/cW154a0q5soBb2s1nFJDCP+WaFAVX8AQK871HTI9R1V7/AP4RTxramQSCWC1uraOOQSbfM487K7ti52kZxnqTXpmnhBptqI7VrRBCm22cAGEYHyHBIyOnBI4oAs0UUUAFFFFABRRRQAUUUUAFFFFAHDePLu5tPD1u1tcSws14wJjcqSMNxxXnX9tar/0E7z/v+3+Nez3mkWGs2Ag1CPfEkzOvzlcHJHUH3rN/4QTwz/z7H/wIb/GqUkkd+HxFOnDlktTyr+2tV/6Cd5/3/b/Gu8+Gl9eXk2pLc3U84VYyolkLY+90zWz/AMIJ4Z/59j/4EN/jWpo+gaXohmOnQ7DLgOfMLZxnHU+5puSaHWxNKdNxitSDxFqWoWM+jW2nfZhLqF8bZnuEZ1RfIlkyArLk5jHf16dRgT+MNVOmxLbpbvqaPeiZEt90bJbSmJpAXmjCLnacFmPzd8E128tvDM8LywxyPC/mRMygmNtpXcvocMwyOxI71Un0PSblY1uNLspRHI0yCS3RtrscswyOGJOSepNQeecfaeI9Zvr281S3nt1sv+EdtNSjspIGch5BOcBg45yi5OOQAMAjJn1XxnfWlrI9pHZSSjSIb9Vfdje8gXBwfu4Jx3rq10fTEltZV02zWS0j8q2cQKDCmMbUOPlGOMCooPD2iW0ciQaPp8SSDDrHbIoYZzg4HPIBoA5q/wDEPii21S502ysbe/ubK3SeVkiWJJd5fAG+cGMALjdh+c8DGDb8UaxeG0t4tJnls7pNTtIZXuLKXYVeUKQCdocc87WPHHGQRv32jaXqjxvqGm2d28f+ra4gWQp9Mg4qe7tLa/tntry3huLd8b4pkDq3OeQeDzQByL+I/EUmu3VlZ6bHcw2FxDb3L7UQSbkRmcFpwyDDnA2PnbjdzxF/wkfiKWCOeI6Wq3GtzaZCjW8hKok0qeYx8wZOI/ugDJ7jPHUvoOjyz288mk2LzWyqsEjW6Fogv3Qpx8oHbHSrA0+yVEQWluFSYzqBGMLISSXHH3iWYk9ck+tAHIXfi7UrHXbe2Kw3VmL6DTrmVLYRATuq52s0xY4LBsBCMcbuCaSw17Wr25sLCwaxha5bVXeW5SWbb9nuljTA8wHkPyM4HGMAba6mTRNJmv8A7fLpdk95lT9oa3QyZUgqd2M8EDHpgVPFp9lBIkkNpbxunmbGSMAr5jBnwQONzAMfUjJoAq+HtUbW/Demao8Yia8tY52jByFLKCQD+NaVR29vDaW8dvbQxwwRKEjjjUKqKOgAHAFSUAFFFFABRRRQAUUUUAFFFFAECf8AH9L/ALi/zNT1An/H9L/uL/M1PQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV5NrVvYSa/e3emv4rFlA9xFePpkETQI0hU3AQv+8yWQFtgOCDjBJr0/UoLy5sJYrC8FnctjZOYRKE5BPykjORkfjXlWoXKeHZNR0u4+J8Nm88kks8UWlK5id+XIIJ2klskdi2eM0AeqaV9iOkWR03Z9g8iP7Ns+75W0bce2MVbrO0CKCDw5pkNtIstvHaRLFIsZQMoQAEKfu5HbtWjQBleJNIl13QbjToLsWk0hjaO4MXmeUyOrhguRkgqMc8HB56VleHY9euJ1uZ/FEGoWcM01vNENLEDO8bNG3zbzjDr6c496n8Y61qmjWNs2laRfX80txEHa1RHEaebGHDBmByyFgCAQDySoGaq6ZqOqXGqWVtZeGb7SLD7RLPfSXfkAOGVzhQkjHcZWVicdj60AdbRRRQBwnjua3n1Gz0u91UxWtyI0a2h0tLlwXk8sO7yBkjQsyqMrnOcE9tjwZpWkaHpl3pmkTXUkdtduk32lyWWTapIHAAXBUgKAOeOtcT4t8QeG9Vuxe6X4y0m1uHFuksd0jOkggn86PGCCpDbgeuQx6EA12XgaWxutJu7601iDVpru7aW7urdNkfm7EG1VycAIEAySeM96AOnooooA8p8TaZpt3cT6rqetWWmwG8lto5tG0xvtrMhbcDMNzEgKdxVcDB5r0zTFiTSbNILqS7hWBAlxLJveVdow7N/ESOSe+a8+mOnaTfTapBc6rfB7u/gsrCC2RzDOzMbmQcqWUMj9TxkgZyK7jw5FaweF9IhsJjNZx2UKwSkYLxhAFb8Rg0Aadcl440zSl06bW72z1C8nt0SJILTUZrcyZfAACOqlsv9TwPQV1tYni0aYfDF4NXedLP5CWt8+aH3r5ZTHO/ftx74oA4O1uE8NavZ3MngfxHBMyyNEW1s3IICEuAjTlWYLuO088Ejpx6hZ3cGoWNve2z+Zb3ESyxPj7ysMg/ka4HQ4Z9Q161bVR4tuniWRbaXULSGCG3LIwLnywCWKkqCQfvdBmu9sLKDTNOtbC1Upb20SQxKTnCqAAPyFAFiuD+Jms32kWEZtNRGmA2d5Mt2EQs0scYaOEFwQN53Hpk7MCu8rm/GReLTkuDrOl2FtHu86LVYFkt7jpgNlgQRg4we/Q8UAHh7XG1jXdXS3vEvNOt4rYRyR7SizEOZEDDrgCMnk4LY9q6SuT8C68+sWdzANFTT7e0KrFNboy21yDnJi3IhwMc8Y5GCa6ygCK6uYbO0murhxHBCjSSOf4VAyT+QrySz1XwFfyajqUmtarZ3Vxe3BkjttTu183bIyqwCEAblVSFxwCB2r1DW2lXSpBBftYTO8caXCwecUZnVR8vfOcZ7Zz2rz6XWpNNuZ7NvHk8UkUrrIqeHCRv3HccqmDk5OR160AegeH5JJfDelyTQTQSvaRM8M8jSSRkoMqzNyzDoSeSetaNVdMl8/SbOY3BuTJAjeeY/LMmVB3bf4c9cds4q1QBDd2sF9Zz2lzGJIJ42ikQ9GVhgj8jXG6N4fS51ae0utd1e/tdCu41htboxBPMESSoxZVDvtDrjceozz1rb8YNrv8AwjF8nh22SfUZYZI4y1x5LR5RsOhwQWDbcA4HuKyhP4tvri2tprDSdKQ3MMtxPDqbSyuqMpZAvlLksF28noaAOyooooA5fx3DbTaNa+fe6laypeRtbHTEVriSXDYRNykdMk9OAcnGaqeEroS3VjK3iDWb9dQ037ZBBfxwKoQsmTmNAd43KMZI+bv20fGf2KLSbe8utRbT5rS5SW0nWIzHzsMgURjl9ysy7RzgnGOtc78Ora1juI4ZtYuLvUNOsFs4La4057FobfK5bY/zNuKJls4+UDigD0SiiigDjrq88U6T4l1E2ukw6hp15JGbYz6oImVxEAyopU4Hyk4Hfce/HT6bPeXFhFLf2a2d02d8CzCUJyQPmAGcjB6d65nXdX+167Da2Oharqc+i3KzyPavDHGsjQsAjGV13fJLuwvTK89q6bTbq4vbCK4urGaxmfO62mdGdMEgZKEryBng96ALVUdZW+fQtQTTGC6g1tILZj0Eu07Dz74q9WB4ttY20mbUptV1Wxg0+CWeQafKqGRQu45yDkgLx06mgDg9AllN5NpGmWWuRTNrNpdbryKYeXEkUPnmSR+DuKyrjJyTkcYNet1xMmkpoy6dqVx4h8SzJJd28YhkukZd0jqqhxtGV3MAcHoa7agArzrxrq3hK78V6do+vx3Tm1hmn8yFrqMwyfutuPKxuyrnnnbjHG6vRa5/xnqdppXhq+mm1O3026ltpYbS5mYjZKyHBGAW4IBOAeFz2oAzvBLaSb/VBodvf/2eI4MXN3Ncv5smZNyqJycbRs5Xru56V2NVrW8gmkktknSS4gVDKoPK7hkE/XmrNABXFanrct14303T/wCz7d7Oz1NYDPJKwkW4a0klBVRxtCNjLE5LHjjNdhdSSxWk0kEBnmRGaOEMF8xgOFyeBk8ZNec61pd/r1+l7deB7+K7jIJktNdSBmwGUbtjDJ2swz1wxGcGgDt/D2qvrWjpeyRrG/nTQsqnIzHK0ZIPodmfxrUrM8PRfZ9BtIRpi6YI0KLZrIsgjAJA+ZeDkc/jzzWnQBgeM9O1PVvCl/YaVfWlnNPBJG8t1GzLsZGBGQQUOSDu5xg8GuNt9fj1nUNP0+98faFcxC8gcQWFkyPM6SKyIHMrDBZVzxyOO9d54mEJ8KawLiGWeA2M3mRQnDyLsOVU+pHArE0BvGxs9N+0x+HPshSPebeSUt5fH3fl2529O2fagDsKKKKAOb8Y3tvp8OkXV7FbGxj1FPtM9xEHW2Uo4EmT935ii7uwY0208R2mu+KW07Tbq11HTFsHe7eErKiSF1CKWGRllMmV/wBketTeLY9YNlZT6LAtxPb3YklgkufIjli2OrK5wcj5hwe4B5xim+G7/Url2jm0Cy06yCFlktb5JlL5Hy7VUdsnPtQB0VFFRXUcstpNHBP5Ezoyxy7Q3lsRw2DwcHnFAHJnxTpWg+KNcg129s9N3vFLatMqxfaIvKUFg/8AGwcMuMkgBa1/C15danoFtf3yMJ5WlMbPHsYwmRvLJGOCUCEiua8T6tPa69LbxeJ7uNkVJDaWmhm9NsMfedlUlc4J55544rrPD1x9r0G0uP7UXVBIpYXixiMSAk4+UcDHTHtzzQBp0UUUAeJaj4V1B7WO5vNN1jUNUvtPiMcsVzI6JfCRy/mYfasTBkHoFXjBr22vKbTWbzwtbJoreP8AwbGLP90sM9u26FR0Q4nHQcc88c5r1agArmPGusaZoUOj3+rGFLRNSQGaR2UwkxyAMoH3j2I5G0se1dPXJeLdV8Q2GsaJFpOmxXFvJc4kZ71YTMfKm/dYKk8YV8jP3cYxk0AVPB3/AAhLa1M3h3WUvrtYGCQC8MotoS4LLGpPyru2/kBXcVzulS+Jb3W1udTsINNsIrZ4/s6XYnM0jMhDHCDAUKw687/auioAK871hr7WLyO+/wCEQ8UWV7HH5X2mxv7aJ2jznY377DDPIyOO3WvRK4XxJoAa6tIrbw1qupwwWqQpNBrslsFALYUgyqWbvuOSc9eKANrwfarZ6PJGNJv9Odp2eQX86TTTuQMyM6u2c9OT/D0AxXQVy3gR4TpF5FFpl5prw3jxy215fNdSK4VOdzMxAIIwM4xyOuT1NABXnqeGL2TzvBi+JT/Y8NrGz232Eef9mdnVY/O3Y/5Zsudmce/NehVweky+K9dgXXLa98PWElygRoXsJJZolVmxHI/mrllLNkYABJoA7yiiigCO4lht4HmuZI44Yxvd5GAVQOckngYrmfDPijSbuR9PPibS9Q1Ca7uXgjt7tJGMRkd0UAcnbHjPpg/WrvjHTLrV/C13Z2UME9yzRPHHcSbI2KSK+HOD8vy8jHI44zkZ/h7VdWv7+NJNH0GO3jyJp7HVPPaM7TjCCIdTx94cetAHW0UUUAedeL9T0Gz8bxw6rr0ugXLaejQ3ltchHlXzHyjhlZdo4KkjOS+Dwa6TwcdBbSJn0DURqULXDNcXZn815ZiFyXb1xt44AGMVjeNfEWtafey22lXNnZQ2sFtPcT3EBlJWa48o4G5QAgDOSc9hx1rV8F6jqF/Z6lHqeoW1/cWd+9v9otohHGyhEYYGTn73PocjtkgHS1Dc20V3EIpd+0OknyOyHKsGHIIOMgZHQjg5BIqaorm6t7K3a4up4oIExuklcKq5OBknjqRQB5Z9t8Czapq0eu3l5bajFfzpIseo3yow8w7Su19vTGQMAHIAAxXp2mG1Ok2ZsXZ7PyE8hmdmLR7RtJLfMeMcnn1rzqbxQb7+179/iBZ6U1ncSpBZRi3dPLUnYzbgXk3jDfKR97A5FegaJeTajoOnX1zGkc9zaxTSIhyqsygkA9xk0AX6yvElgdT0C6sxptpqRk2/6JdymOKTDA/MwVsYxkcHkCtWuT8f6/pmjaJHBqE9xF9snhjX7PLLE+3zow7B4+V2hs4yN2Md6AMDRNGj0HxfpKz+CND0s3BlSG9s71pGRxExKhTEvJUN6DAPOcA+l15zoU3hhvFGmf2K+p6tdlpFee9uLqUWcXlsS483Kglgidj81ejUAFea67pWl6d4j1zUZtW8SCeOwiuZEtr5kBDSzCONOc8tuAXoPxNelVzPirRrGZ7TWG0tr2+t7i2jRVeQAoZ05ZVOHCZLjcCAVzxzQAngcW0emXsMdpeW15FeMt8l7c/aJTNsQ5MmTuyhjxjtgdq6eqtnp1tYPdPboVa6nNxMSxJZyAuefZVGParVABXCap4F8M3filYLjRJphqkdxdXdwL24C71aMAEB9vPmHA4xt4Hp210LhrSYWrxpclGETyqWRXxwWAIJGcZAI+tcDolp4yi0u+WPxDoJiS8u2lkGnzSlGMrswH70dMkbcHGMZOMkA67RfDmmeH/O/s2KaPztu/zLmWbO3OMb2OOp6Vq1naBBBbeHNLgtbk3VvFaRJFcE5MqhAA/4jn8a0aAMLxjeNYeFru48mOaJTGJ1li8xRCZFErFe4CFzj2rLj1nQpdf8O6b4ebSrqNTMWSzVHFrB5TfOpXiMF9i++407xP4oj0q9u7BtYt9OnaC0a3a4thIoMs7Rs2PMUuOBkcbB82TnAg8NpPb6tGieK/Dl1FIWMlrYaekMkx2nBDCZuh56HgH60AdtRRRQBxepaFat4mt7NtZ8RpNqCz3IEOpukcaoUyAOwzIoAHb6VoeHreDTdb1XS473VbqWGK3mZr67acBX8wDbn7pyjZ+grN8TSDWobMQaJfXl1FPciGXTtSggmtzG/lswYyDIbPK846MAeKu+DLKSzS987Q9R0+eVkeS51G9juZbk4I5ZXYgKABg4HPA60AdTRRRQB5t4p1fWYtdn2eIP7C0uDUIbSaVbeIgI9uXEzvIpAHmbI+w4P4dR4HvrnUvCFldXl497OxlU3TIq+eFlZQ4CgAKQARx0I69awrbW/GK6jqMF94ZtZEubgfZYJtVjUBBDHuVfk+cbgzE46kjtXVeHY9Qj0SH+1JInu3eSRhE+9EVpGZUDYGQqlVzj+GgDUqjrVtNe6DqNrbFhPNbSRxlZTEdxUgYcA7eT1wcdcVerP163e68O6nbx3S2jy2kqLcM20QkoQHJ7Y6/hQBzei694on+zQXenaBKqSrb3NxbawWKtxuwnlfexztLfjXaV5VpWkatFdPYP4cTRrF9Ws7zzzcQ+TGsSQrsjCsSzO8ZUcDh+eeK9VoAKKKKACiiigAryvUTZfb9X/t9/Ew1z7TL9gFj9q2+Vk+T5Plfuz8u3O7+LdnivVK8yTWLvQdR1S0tfF3gqK3e+nmW3u5iJYWdyzK2JBzuJJBHBJ7YAAO/0b7d/YWn/ANqY/tD7NH9qxjHm7Rv6cfezV6qWkX8eo6XBcR3lndsUAkms3DRF8fNtOTxn3zV2gChrlpc3+galZ2c3k3VxayxQy5xsdlIU59iQa88XwO1h4ltxYeH9LjiN9aXkd6rxhrRY0RZYtuNzbthwRxl9xwRXoOvxXE/hzVIbRpluZLSVYjBjzA5QgbckDdnGMkc9xXCaBpWkwT6aZPhZc2d4kkZN2YrZxC4I/eF/M3nB5zjPHTNAHplFFFAHDeIdIuJPGtnejxbfaexs7kxQxWsDLFGPJMhDMhwMqpO7JyRggZBl8B3FpfT6jei91a71CaODzH1OBIXMHzmFkVFA2HdIQeuc5xVvx3pFje6Bdaldm8B0+zuHK2k3lNNEUzJETg/K2wD14GK2LDSra1v7jUod4kuoYYihI2okYbaqgDj77Hv1oA0aKKKAOHFxr2jLq2ljw5eam11dXE1tdRTxCFllYsFkLOGTbu28KeFGK6nRLKfTtB06xuZvPuLa1ihllyTvZVALc+pGa85kstOl1fVjr/hDxBqlyb6Yx3axO8bxbjsCDeMKq4Xgc4z3r0vTFhTSbNLe3ktoVgQRwSjDRLtGFYc4IHB+lAFqiiigAooooAKKKKACiiigAooooA5TxR/yBof+vlv/AGauQr01LaC5tilxFHIgkYgOARnJpP7J0v8A58rb/vgVz1KTlK9x3PM66vwT/rL36J/Wuh/snS/+fK2/74FT21pa2u77NBFFu+9sUDNKnRcZJ3C5meKru5stCkmtdQtrGQSIDPcSLGoXcNwDOrKGIyASCM1wOk+I7qbxHc3C3VzBp88VtHdapOsTuhWSdVHyDy9jkECXBXGOMtkesUV0iOR0jVdXu/Ez6LPK2dLaWS8nMSgXCP8A8e4HGOVLFiuPmix0OK66qllplpp73MlvG4kuZPNmd5GkZ2wB1Yk4AAAHQDoBVugAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAIE/wCP6X/cX+ZqeoE/4/pf9xf5mp6ACiiigAooooAKKKKACiiigAooooAKKKKAK99f2el2cl5qF3Ba2seN808gRFyQBkngZJA/GvL7280GFhcaT408LNcedfFkvblDG8V04dgdr5JUquOxAxxXqzxpKhSRFdT1VhkGuAjk1PV77UfI1nQdJS1u5bZbSTTxNKFRsB3JkX7wwwAHQigDsNAgitvDmmQQ3gvYorSJEug2ROoQAPkZznr+NaNQWKulhbJJNHNIsShpY02K5wMsFycA9QMnHrU9AGN4qW/fw9PHpjTJcu8SF4P9YsZkUSFf9oIXI964Xw9pV1p/jm1g/wCKilngvLz7Q13cXElr9mIcwuGY7GbBRcZJ+8SMgGrfxDfXbPV47u0OumweO1hQaXKgVZDcFZd6k5yUdQpHGRzxVrw2tyNftvMj8bhPnydTeE2/3T9/ac/THfFAHfUUUUAcXrt1Pp3iWHW9PuNGuxNYrCLa9vhbsq72YSRPtYYbODxzsXnitHwbbyRaffXE11Yyy3d69w0VhJ5kNuSqjYrcZ6bicDJcnHNc/wCLb7StO1WTTpNF0Wa4GnwJpUVzbKzTTPK8YjX/AGE+QlV6Bs8Vu+Ct0NnqdhJBYRyWN+1u8lhbCCOY+XG2/YCcHDhTyeVoA6aiiigDzQiODXNW1TR/D+sX1rE9zBJOl5GqxSM3+kNbxOclty8k4ywIHfPdaAtivhzS10xi2ni0iFsx6mLYNh/LFedrfeG5tQ1q1n8cPoJ+33CXenR6hAqMd5yys6lkLjlgrDDFhwQa9L0xLKPSrNNNMZsFgQWxibchj2jbtPcYxg0AWq5vxlJZXPh/ULG4v5bFo0gma4jtmmaLMvyMoA5O5O3I4PpXSVzXimK/hVdQh8S3mmWy+XCYbe0hl3yPIEU5dSckuo644+tAGL4a117vXbW2bxlc6lu3ZtpNFNuJMITy+wBcYz74x3rv65W3ttT0jXNNTUfFl9epdyPHHbvZ26JIwjZsFkQMMBSwwf4a6qgArkNeh1KfWdJghGhNqf8ApUsEl7YSS7Iw6Y2EONrbWTd/eIyAAK6+vOPGFx/ams2NlqPg3XLiWKaX7DJZ6nHD5gGA0mEmBAxjlgMbscFsEA7LRk19PP8A7cuNMmzt8n7DBJHjru3b3bPbGMd61a4zwK8KXerWg0rWNOuIPJ82PU9RN0WBDYKZkfA68jAPvtOOzoAK8p1zxDr51KSVvFkOg2Ly38EW+1iKRvAyiMM0gJJdd78EcYwK9Wrh47/xNa3F/Y3fhKbVLb7ZNLazvfW+XjLkr8jNwFzge2M4NAHTeHrqa+8NaVd3BkM09nDLIZVAfcyAncAAAcnnAArSqK1klmtIZZ4Gt5XRWeFmDGNiOVJHBweMjipaAKGuQXd1oGpW+nyeVey2sqW8mcbZCpCnPbBxXmsfgBdP8T2z2XhSAf6faXkOoboyLZERFmR8nczEoxBAPzPuyCK3Pib4f1TVrGO80yzF9JaWl0i2/wBpeJhI6oUkUKPnKlD8hwDuqlpXhe4t9UsZ38EQ2/lzRuZx4glkMeGB3bCuGx1x36UAelUUUUAYHivSdT1W1sH0ie0gvrK8W5jkukZ0GEdSMKRnIcj6E98GuesrLxlqWoW3iSDUvDdwsln5VsVtpwnlOVckfPkk7U69MdOueo8Q6xc6Rb2i2Vkt5eXlyttBG8vlIGKs2WbBwAEPQEk4Heqvhi31zT4YdOvdL0qy022txFALS+lncbcBVO+NeMZ5yTwKAOiooooA818T3OmaV4supU8UeIrW9u/LEtnpdks6KRGduf3LfMVRm5O7A/ugY7HwpeR3/hmyu4b29vY5QzLcXsIilkG44JUKoA9OBkYPOc1yniiXRLDV7rHiD7Hq5vIdRRRZvdCF1h8n51Tna0eeCQecium8GxW8PhW0FrePexu0sjXDwGEyO0rM52HlRuLYHpjrQBvVQ1OUfZ5rd9NnvYZLaVnSMIQ+AB5XzMPmfccduDkjjN+uf8XaxLpejzw2sOote3VvKltLZ2MtyIpNuFZtinbyQeeuD6UAcppGneXqunrPoHjOS2t5kNtFf3cElvanorkCXcwUHjO4jtzivS68w0GPw491psj6H4zj1AyRkyXqagVWTI5kJOzGeTn5ce1en0AFcD4/gh1e3cadqukC9js7uweC7u1jAE6hWbIyQ6lBwRyCw4zmu+rjddt/D1h4m0uPUNI0WO21AXDTXVzax5aYbCq7yMbmzIeeu2gCfwgk9zfalq15c6c11cRwQfZrC589YY49+3c+BliXfsBgAc4rq65bwzc6W/iDW7XRbTTUsbdLcefYxKoeU7yyMy8MVGw+2+upoAOlchcDXdC8Rardafo8eq22pPHKCLtIXgdY1jKsG6qdoYEZOS3FdVc20F5azWtzEksEyNHJG4yrqRggjuCDXn2t6HD4g8U6mtl4O8O3kto0Udxeao7K8rGNWAULGxIClRknsR2oA63wtplxpHh63tLt4mud8s0nkklFaSRpCqk/wru2j2FbNZfhzT20rQbayexsLFo92bewJMKZYn5cqp5zk8DkmtSgCjrUtnBoOozaipaxjtpGuAM5MYUluntmvKtKj0y01u18r4fCxht9Rt7drh9UObeSQI6EoMg/fXjJG44zXp/iV0j8K6xJJFDLGtlMWjmfZG4CHIZuwPc9hXEWOq/DrXdY06+g19DdyvblLL7a6rNMmBEXjJ+Z1OAM+g64oA9LooooA5jxzYTahpdkiWf9oW8d6kt1p4kVTdxBWyg3EBiDtfaTg7MVm+F9PSPxXLf6f4dfw9p7WTRSW0ixxG5l3qQ4ijYgBBuG44J347U/x5Z3Am0zVj4lk0u2srgMkMdks7yTMkiDYMFmciTG3BGATjPNM8IyRah4glur3WtTvNUtrVokttQ08WTRxOylmCbFLAmNRnJAxjvQB3NRXX2g2kwtDELnY3lGUEoHxxuA5xnGcVLRQB58LLxnpOr3FyNT8NxS6xMqiN7achpliIBX5852R9CcfL25z1HhbSrnRPDtvY3ssMt0ryyTSQqVR3eRnJAPTJbOOmenFYep63LdeN9N0/8As+3ezs9TWAzySsJFuGtJJQVUcbQjYyxOSx44zXQ+HtVfWtHS9kjWN/OmhZVORmOVoyQfQ7M/jQBqUUUUAeU3Hg+5tvD8B0+70OS1l02KyuNQlm2CF45HZblGCkM+ZGPJHzY5r1avOl0HX302XwKL/Sf7MjtljaceZ9qFoxZVGzGzcQjLu3ds4r0WgArmfHMFnLotvJeaxdaUILuOWKe0iEkzSYIVEXaxJOTwoyRkdCRXTVja7FZyXWiPc3Jhlj1FWtgELebJ5Ug28dPkLnPbGaAOc8Ea9b6tq0kUHifWdVH2QTCK901beMKzDa4cRJnPOOeRk4447yuK8OaA2g+I7azudZS5NrpskGn2y2pjZbbzEyXfJDspEa8Y4Occ12tABXEeL5mbXIbWyu/Ez332YSNZ6M0Kose5gJHMowCTkD5udvTgmu3rz/U9A1zS7oa5P41mjlbyrJnj0yI7w8oVAR0OHk69tx9aANvwT/Z39kXP2H+0PP8AtT/bhqJzcCfC58ztnbsxt4xjFdLWJ4Z0K40Kzuo7vUn1K5urlrmW5eFY2YlVGCF44CgD2wO1bdABXjf9kaZ/YH/CVf8ACCeGP7A8v7R5OD9q8j+/9zZux82z8M5r16aSZJIFig8xXk2ytvC+Wu0ndjvyFGP9rPavMP7KO3+0/wDhCR9hz9r8v/hIx9jz97zPLz5eO/THegD1WiiigDE8X6Zdax4WvLGzVXmk2Hyncosyq6s0ZbsHUFc/7Vc7pWky3HinS7608IDw7DYrILiVjArTqyFREFiY7lDFWy2MbBjrVn4g3+l6Vpoa+06e7bUJba2kEcE0imNZ0yCY/ukeYxUcFjxz0rN0O38PL4n0tvD3hvUYZFkkNxc3dpdQrDH5TjIMmFLFiq454J9M0AejUUUUAYGu2Wv3VwV0s6GbR4fLlTULaSVmyTkfKwG0jHBHrVbwNNcf2ZfWN1baVbS6ffPbGLS4TFAvyo4wCTyd+T05OMZBrG8X2K6v4vjsofCOn61PHYpLJcXN+0BhQu4VSAjZBIYgj0OcYGX+D/EDWl3FoE3ha20SH7TLaxi0uRKizqnmlWGxcFo/nB5yOuDQB31RXNtBeQNBdQRzwtjdHKgZTg5GQeOoqWigDmrnQdPj16xRND0oac1vObh2togRIGj8sDjPIMnT0+ldFFHHFCkcKIkSKFRUAChR0AA7V5edItf7W1Z9Z+HV9rU0l9M8d+6W0pkjLHYBvlBVVGFA9ADgEkV6TpqRx6VZpDZmyiWBAlqwUGAbRhCFJAx04JHHFAFqobp7aO2eW7aJYI/3jvKQFXbzuJPAxjOe2KmrC8Y6Zdav4Wu7OyhgnuWaJ447iTZGxSRXw5wfl+XkY5HHGcgApeGfFGk3cj6efE2l6hqE13cvBHb3aSMYjI7ooA5O2PGfTB+tdVXJeHtV1a/v40k0fQY7ePImnsdU89oztOMIIh1PH3hx611tABXn/i+7tl8XxW2r3Ovppv2FXiTSkuQol3sGMjQjJJAXAzxg5AyM+gVw3izUo4vE8Njq3iK40HSTaCWGeKRYRcT72DKZWBxtUIQoIzvPXFAG14ROmHSZf7Jl1SSDzzuOpG48zdtXOPP+bbjHTjOe+a365nwPqFxqGk3TyXst/aRXbxWV9KgVrmAKpDHAAbDF13ADO3PeumoAK82knnt9d1fQdE8T20MEpuLyeOTTJJpLYkhphHIGCMd0mdpDFS3Q16JPcQWsYkuJo4kLqgaRgo3MwVRk9yxAHqSK8o1KeHTPE039neNPCtukb3qMt7cgTWz3EiPKCobDFXQ4BK9cHpQB6bokNnbaBp0GnP5ljHaxJbPnO6MKAp/LFX6x/DE+jtoFnaaJqVtf2llClsssE6y8IoAyVPXAFbFAHA/ECx1m5mia30G21uxL2eyJtm+CRbkNIeRkq6bQecLtJIxmr2iW9wmrwM/gKy0tRuzeRTQM0fynoFGeenHrWv4qjvpvDs8WnvMk8kkSM0DbZBGZVEm09m2b8HrnpXC+HfD8ul+ObVI9P1kS295eGa5uJ5ntmtWDmEqzMVZxuRccn7xPIBoA9TooooA8Yv7jw/B46Au9X1vQpVOo+bBDK+1WM8RDodhwJR+8IGRnvxz3vg250m4+2/2X4g1LV9vl+Z9tkZvK+9jblV68569BVbx3q2paYYf7Hjs0v1sLy6W5uIPNYJEqMYkAI5clO+MJnBwKf4L1fUr7UdVtNQ1iz1RYYraaCa0txGoWQOcHDN83y9Owwf4sAA7CiiigDzbxBpuo2HjuDW08P3WqAXsc8Vza+WzxQi1kiMPzsCo8xg/HB3HJyK67wlp91pfhu3tr2NYrgvLM8SNuEXmStIIwe+0MF49K4zXdP0ZfGOr3HiW01wpOIvsL2Ju2idRGobiAnbJuBGDgYCnuSb3gLR9U0m5tC6ajFZXNlM88N5O0myQTAQHDE7XaIkuBgZA4FAHoFYnizQrjxJ4du9Kt9Ul043MbxvJHGj71ZGUqwYH5TuycYPHBFbdUdZvZdN0LUL63gM81tbSTRxD/AJaMqkhfxIxQBy2n6FNqepT2t94o1e/i0i9hMlvLDbxxvKqxzIcpGGIG5D1HIrt68z0jxNeReeh1ezv7yfW7OFHhhjT7RFJDA0mAvJCq7HcSSAgBPFemUAFFFFABRRRQAV5xdaRf2d1q2mt4V0vVpdTnnuIruW5jQsjHgSKw3/u9yrlc8AdCa73UrqaysJbi3sZ76VMbbeBkDvkgcFyF4znk9q4STxfLc6kdZHgzxC76VHPauYmt2UbihkHEh3EGNR8ucHIPPQA3fCnhibw7cXLvJAyTWdpE5iBBlnjVhJKwxgFsr6/d5rqKradeLqOmWl8iFFuYUmCllYqGUHGVJB69QSPQ1ZoAq6mLZtJvBeQtNamBxNEiFy6bTuUKOWJGRgcmuB07T/Fi3kcnhoXWl6SOTb+IJ/PDDsEjGZEH+9IMf3a6fxfpE2paNPPa3Gope2tvM9tFZXsluJpNuVVthGeVAGemT61wVg2nvd6Emj+JvFGo6ql3Abq0ubu4xs3DzTMhwECjJweCQBzmgD0/Rru9vdLim1GzFnd7nSWEMWXKuV3KSASrY3DjoRV+iigDzzxPHqGmeItPSXxvqOm2GoSTs0sq2wiiK4KwqzR8E7jgsTwhHJNaHgm8uJ9X122PiKbX7O38jybthFsRiH3RgxqAzDCknP8AEowCDmTxfcXNhpsP2vxDptnHLNIpS50prnzwWzGixiQElVGCQDnrhelS+CLtruxuCut6fqMEbCNI7TTjZm3bkkMhdjk5BwQPxzQB1VFFVtQ1Gy0mxkvdQuobW1ixvmmcKi5IAyT7kD8aAPOdfn8OtdalGnibxbDqHmSgRWcl2wWTJ+VFC7cZ4AHGK77QBdDw5pYvUkS7FpF56SOXZX2DcCx5Jznk9a5ufx14ZfXrK6j8Y6OtjFbzpND9tX53ZoyjYzg4Cv8A99V2FtcQ3lrFc20qSwTIJI5EOVdSMgg9wQaAJaKKKACiiigAooooAKKKKACiiigDIvP+PFf+uzfzNZ1bsKxNbnzghXzG+906ml2WPpB+lebicDKtU50zkq4WVSXMjBrT0f7030H9at7LH0g/SpYVgXPkhPfZilh8BOlUU29hUsJKnNSZjeKdWl0C1tNWabZp1vcAX67Qf3TgqG6ZG1yhOO26uQPiTX4IpXvtTNtqYSCey0wwx/6X5rbjHyu5tufLyhBGzc2c16bRXpnYeenW7mG6MM+pppNhJql6kt8kUS4KEeXGSylQWyxyQSdmM5NWfCWp30HgO0NrptxeSmC4lWdQixmQPIQpUuHBJA4Cn7w98dtFNFMHMUqSbGKNsYHaw6g+49KSC4huoRNbzRzREkB42DKcHB5HuCKAPL5vFupR2d89h4lF+6aDJeFjBFiC5DIMfKoxjJ+Rskd88Vqa/q+qaJc31s/iCWOW3sFuLFZYISdQnLSZiwEG4DbGNqYb58kniursNL0rS9RcQO7X00XP2i7knlMant5jMQoLduMkVq0AcHe+LLu0ur6xmvUgvzrlhDbWxVS/2aU2wfAxypLzDd2PGQQKLDV9aF3Y3UuovcRXeuX+n/ZDFGqLHGbny8MF3bh5KjJOCDyM813lFAHmvh3VZdV8W+HZ7jWPtt4+m3Ul1a+Wi/YpSYd0eFAK4ORh8t8vWvSqZNNFbxmSaVI0BA3OwAyTgDJ9SQKfQAUUUUAFFFFABRRRQAUUUUAFFFFAECf8f0v+4v8AM1PUCf8AH9L/ALi/zNT0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFeV+I/CVibjVNQ/tXwekpklmKXmiW7MDknDuWyT2LEZzzivVKyH8K+HZbp7qTQdLe5dzI0zWcZdmJyWJxknPOaAH+GZFm8KaPKsaxq9jCwRYhEFBQcBBwv+6OB0rUo6UUAFFFFABRRRQBjazJLdk2WmTxLqlvJbXDBgMpA02HOSD95I5V/DtWha3FpNLdxWrIXt5vLnVRjbIVV8H32up/GvN/Fdjomi+I7i8Y+Kr2/u0hMsNhqUkaxK8zJHkmRcBnkKqoJAwcADJrqPAcllJo92LPTL/T2S8dLiPUJ/NneUKuWdt7HpgDJ6AY4xkA6miiigDg18W6JZxazDr17YWmqW91cKlvKiLK0e4+SUQ8ybl2njOSSK6zQpLubw9pst/CIb17WJriILtCSFBuXHbByMVU3NqOu2V/aSwy6dbx3cFwdo3CdZEUYyM/KUmBwfzrVs7uC/sre9tZRLb3EayxSDoyMMg/iCKAJqx/FVtFd+G7yGazvLtGCny7IgTghwQ6ZIG5SAw/3eh6VsVk+JbuSy0KaWK9aylaSKJJ0hErKzyKgAUnBJLY54Gc84xQBzHhl9O1LXIJ7zxVeanqdkreRY30KWssBYbWYxBFZm25G45GCcda72uKu/CHhtL2yh1xLrV7+/Z4Ybu/kMjIwQyEIBhYjhGI2KOldZp1q1jplpZvcS3LQQpEZ5Tl5CoA3N7nGT9aALNcX47ubOzu9InM+sW2qbpUs59LtPtDYIUujIVYEEBTgj+DI6V2lch42mh02fTNXXXLbTL+382GBbmBp0uFcKXQxqQx+4pyp4x70AZfhLxFosOs3MV1d61LrF9JHBLc6pYNbgsoJjiGECJwzEKeTuPXNeh1594etLXxTNe3s+ti9vvtNnNN9nsZLaKNYHZ4kUSZJy24k5J7ccV6DQBHcXENpbS3NxKkMESGSSSRgqooGSSTwABzmuGg8ceDtR1GHXJ/EWm201iLy0SB72P94hlUb8ZzhhCrLjs/eu8ZQ6lWAKkYII4Iryy71bWxqskGiQ+HNNtWl1Awi5tCWaS3kUOXYOoBdmduBwBk5zwAekaTfHVNGsdQMD25ureOcwv96PcobafcZxVys/Qb5tT8O6ZqDsGe6tIpmYJsBLIDnbk7evTJx6mtCgAooooAKKKKAOZ8cQ6Xc6PbW+p2l9eebdotta2MzRSSzAMVAYMowAGbkgDbnsK5/wBe2N1q6tbaRr1kLnT/tMMuo6o1xHLEzLjahlbnpzjI6HG4A9T4outOsYNPu76K6mlivFazitELyyTbHG0AdfkL5zgYzWB4L0+2t9bd49P8S26xWrxWo1RYxDbxF1YxR7TnkhfvZOE68UAd5RRRQBwh/t8+NtfTQ59Js490DTJfxvK87+Uo81QrLtXACdSCYz05z2GmC/GnxDU5bWW858x7VGSM8nGAxJHGO/XNcZ4k8Jz6lrl1ep4N8L6kHC4uL24dZpMKB8wETDjGB83QDpW74Ikik8I2fk6daaaqtKjWdo2Y4XWV1ZQcDncDnjGc4yOSAdDVHWXvYtC1CTTUD3620jWykZzKFOwfnir1VdT+y/2TefbgzWnkP54UMSU2ndgL8xOM9OfSgDy7QPEPnXk2j6brGqX18dZtJEW5eRpFt/KhNx5gI+Rc+cNpwA3AFet15zpEfiQavaHw8urQ6H5q/aF8QOCDFn5vJDZnDY6byB7V6NQAVx/jK5uLTSY/7Q1fw3a20k0iyDVbRpI5RuzGqr5gywXr1yeQB0rsK8/wDEreJLPW7XXZh4WtLWwaaKGa/1GSMNHJjrmLCv8i9CcfMOc0AaHgPVIr63u7e31bQb2C22bI9HtWgWHdu+8C7DnHGMdD1rsKxtCvNZuZLqPWI9JjeLZsXT7t5iMgk7wyLt42kdc5NbNAEV1bx3lpNazbvKmRo32OUbBGDhlIIPPUEEV5hcaF4bh8Zw6bBoupSI9wtjcX5126VkkMDzqgXzCWAUAnJAG/jPNeqV5t4z0O7TXJNbe3vY7CJ0mNxpOoJFMGWMx7njmXYSFZ1DKwO0gY4FAHXeEZLWTw3ALOGaCGOWeHy5rh52VkldG+dyWYblOM9sVt1h+DrrR7zwnp8+gbzphRhCZA244Yhi27kncGye555zW5QBheMJ9bh8MXv/AAj1n9p1F4ZEjxMI2iJRsOuQQxDbcLxnPWsxNQ8XapNBaDSLTTljuYWurhdSWZlRWV3TYE6svHUcNmuov72DTdOub+6fZb20TTStjOFUEk/kK4/RPEMMM2r382l3lnLd6xbW88M0qMUaWGBI2+XgDDR5GSQSee1AHcUUUUAc341S3TSba/m1a30qWwulube5uV3R+ZtZNrLkFgyuwwCDzkdKx/CWpwa94ne/udfsL/UIbN4YrewtZIo44mdC7EuSWJZUHUYx05NaXjr7SLbRjYRQNfjVI/sslyxEMT7HG6TAyQVLKAMHcy4INaGkf8JP9rb+2jpBtvLO37EJd+/IxndxjGf0oA2qiupJYrSaSCAzzIjNHCGC+YwHC5PAyeMmpaOlAHmmtaXf69fpe3Xge/iu4yCZLTXUgZsBlG7YwydrMM9cMRnBruPD0X2fQbSEaYumCNCi2ayLIIwCQPmXg5HP4881h3A13QvEWq3Wn6PHqttqTxygi7SF4HWNYyrBuqnaGBGTktxWt4W0y40jw9b2l28TXO+WaTySSitJI0hVSf4V3bR7CgDZoorP1e8v7K0STTtLbUpi4UwrOkW1cH5stx1AGPegDhrTxhO39ueJH0u1jKaPbXtvtncvJbeZOVV/4Q/DHAHV8EnFek15Z/wj0v8AaZ1D/hAbzllY2/8AbqfZyVYsuYt+3AZmIXGMknHNep0AFZWv6KdatIFivJLO7tZ1uLW5RQxjkAK5KnhgVZlI9CelatYPinUrywttPgsZobea/vUtPtMyb1gBVm3bcjJO3aAT1YfSgCl4TsZr/wCzeKL7V21Ka6sgtqRai3SKFyrnCBmO4kLklj90YxXV1xHgzXp7tPD1gPswjm0BLuaKCMIIXzGFAC8KrBmwMfwcV29ABXJ+ILnVr7X00KxOlQRC3S8MuowNN5riQ4EaBl5QorE5ONy8V1lcH4s0u31/xSmnweHNEv7+GySaW71UEiOJncIihQSxLK56gD3zQB1mjx6rHaONXvLO6uPMJV7S3aFQuBgEM7c5zznuOK0K5fwMlpb6ZfWVvo9npU1netDdQWRzC0uxG3qcDIKsnUZHTtXUUAVdStJb6wltoL6exkfGLi3CF0wQeN6svOMcjvXm9l4egi+GdvdXnivXrnQI9OUyW6xW8YktwoyMbCwUr23k475r1KvILo2VjamG6sfHEXh8SAnTWt4vs6gtwhb74jyfulsdunFAHr9FFFAGN4qGo/8ACO3DaWsz3UbxSbIWw7ositIqn1KBgPc1lQa/P4g8Q6SulWmqwWlu8kl/JdWkluhUxsqx4cDc28qeM4CnnmtnxJJZxaBcvf311Y2w2b7i1ZlkT5hjBUE8nA4HQmuJ0Sezm8Y6T/YXiDxDqkH737ZFdSTNBGnlttcllAzu2gDPfPagD0uiiigDz/xvNZr4gs/Kg1NNQVYI7i8sLz7OYYJp/KQN135csQuDjaxyO+x4a0zR1e5igtZvtGlajIrTXE7SSSTPEpMpYn5i0cgHPQHAxVbxDodh4l8RNYLeahYahb2sFxJcWpTa6CZmiVg4YHEkTMOOOecEg6PhSGytINSsrRrqWa2vmS7numDSTzFEcuSOOVdBwBgDGBigDoKKKq6kYEsJZrm6a1ggxPJMsmzaqEOcn+7hcH2JoA811G2s/t+r/wBvab4kuNba5lNhNZLcFfKyfJ8l4z5aYXbndj5sk8V6Noy3yaFp66mwbUBbRi6Ixgy7Rv6f7Wa82j8Q+ENTF5fTePr+yY3VwBbprKgbVkZVKLjhWADKPQjrXo3h+drnw3pc7pOjy2kTslw+6RSUBw5wMt6nA5oA0axPF+mXWseFryxs1V5pNh8p3KLMqurNGW7B1BXP+1W3XF/EG/0vStNDX2nT3bahLbW0gjgmkUxrOmQTH90jzGKjgseOelAFbStJluPFOl31p4QHh2GxWQXErGBWnVkKiILEx3KGKtlsY2DHWu9rznQ7fw8vifS28PeG9RhkWSQ3Fzd2l1CsMflOMgyYUsWKrjngn0zXo1ABXHXv9o3urf2TB4rFtPaWUMlzusIXWVmaRd/J4JMZyvQcetdjXB3HhzwjP8QLu1vvDujma4sUuvNngUtO5lk3kA8ZHBYgZO9cngUAb/he6mmh1G3uNVbU5rO7MDz/AGdIgD5cb7VCnBA39fXI7Vu1zvhG4sZLbU7XTLOytrCyv3t4PsUYSOQBEYsAOMhmZSR3U10VACMFK/OARkHn9K8+uLvV79tY1GHUtC06HT7maIWl1ZCQsEJw8r7wV3/eGB91gea7jUdNstXsJLHUbWK6tJcb4ZlDK2CCMg+4B/CvJJPCOkagNZ1G2g8LaZBptzNALOfTUcYiJG6ZywI34yMYwpHWgD07QLe1ls4daTTlsbzUbSB7iMDBXClgpHTKl2GcZ/IVr1m+HZo7jwxpM8NoLOKSzhdLZRgQgoCEH06fhWlQBz/jWH7T4WmhW7htJZJ7ZYJ5omkVJTPH5Z2ryTv247ZxnjNVrS58T2OradBrmpaI8F3I0KpbWUySSOI3fAYyMq8IScjGAR1Iq94j8KaV4qht4tTSdhA4eMw3DxYIZW52kA52DryO2DzWZ4V8N6NFK+qRaVNb3dvc3NvEZ7qaYhUkaMOvmMcblXOR2brigDrqKKKAOZ8S6hqemaxpNxZ6RfapabZ0uYbRIyUzs2vl2HPDDAOCGOegzP4XltWiuYrPwvc6DGrByk1vDCJWbOSBGxyRjknHUVj+KNK1yHW28TReI7Wx07T7G4yr6eZniQiJn6ON+fKJ7EcAA5yNnQNFvLG9vNT1HVRqN5eRRRF0thAixx7yoCgnnMjEknvQBvUUUUAeZeJNbbRviBaXOrahf2tql8iwRqZPs72xtZNxKqMO/nYHOSPkxjNdj4Qe9k8M20l/9oE0jyuguc+YIjKxiDZ53eWUznmue8Sas+n69cLoviG9l1UhTJo8dn9tjHyjGVXa0WRg5LqOc45rq9AutUvdDtbjWrBLDUHB862SQOE+YgcgkcjBxk4zjPFAGlVHWlun0HUVsWlW8NtIIDDt3iTadu3cQM5xjJA9avVV1MQtpN4LmOeSAwP5iW4YyMu05CbPm3Y6bec9OaAOM0Bnt7+3uX+GkmnahOVS5vYRafLk/MxZX3kdzxk+hrva8qTTtJn1fSW0DRfFUV1FfQyO179tjgEQYFy5mbBwuSAOpAGCMivVaACiiigAooooAhubhbWISMkrguiYijLnLMFBwOwzknoACTwK821CT7HqOpWmn33i+zsp7mV54LbQnmXezHzGhlMRwGOTkZGTkda9PryvUdVtRf6v/bfijWdM1iK5lWysrZmRfLBPkmOIKRNuG0nO7kkcYoA9H0eG2t9EsIbOGWG1jto0hilQq6IFAVWDcggYBB5q7VHRpb2fQtPl1KMRX720bXKAY2yFQWH4HNXqAMzV/EWjaB5P9r6pZ2PnbvK+0zKm/GM4z1xkfnWFo/jHw/LrV/H/AMJZpV19uu4xY28d4rMoMcabAPUuGOB/e9TXQa3ai/0a+sluVtpp7aVI5z1iJXG8fQkGuJtrbUb+TRtOfS9C0yPT7mGU3drerIWCEZWFAgI3/dOTwrHrQB6NRRRQBxXjm6m0nUtH1m2KrNbLPGWuLaWS3CPszveMMYjlVwxBH3getW/B6SX1xqHiCfUNMu5tQWKPGmSeZDGke7b855ZjvbJIHYY4rV1m01i98iLS9Ui06L5vPlNsJZT02hNx2r3ySG7cVB4e8Kad4clvbi2a4nvL4q11dXEm55SuduQMKMbj90DrQBuU2QoE/ebdpIHzdMk8frTqjnt4bqMR3EMcqBlcLIoYblYMpwe4IBB7EA0Aee3FrqOnvrOmx6VoWpR39zNILu5vVjKhycJKhQk7PujB+6o6V3GjWr2Oh6faSXP2p4LaOJrg/wDLUqoBb8cZ/GvPrnSF1q51bVbHwn4Oe2trueOT7fZhp7l42IkZnAwmWDYJDep613+g3Nte+HtMurK2FtaTWkUkMCqFESFAVUAcDAIGB6UAaFFFFABRRRQAUUUUAFFFFABRRRQB5v8AFLP/AAi1p1x9vP8AJ68jr6VNzaWtoXvJYo4zKwBlIAJyfWof7Y0L/n9sf++1raFbkVrH1OVZ3PB4ZUVRctXrf/gM+cK9P+DufP1frjbF/Nq9A/tjQv8An9sf++1q3ZXljd7xZTwS7cbvKYHHpnFOddSVrGmZZ9PFYWVF0XG9tb+afZGX4pmvbC1tNVsY7u4NlcB57S2DM1xEwKMAg+8V3Bx3+T3rkDb+JLaKVbuXWZNYdIJbAwNK1usjtulVyPk2qxK4k4CBdtem0VgfJHAy/wBqfah/aP8AbP8AZf8AaV55n2Tz/Nxx5OPL+fy/v/d4ztzxVnwzH4gtPBNrbW9iiTeRcHdeSlJ0lLyFMx7NpyduTuHU/j2UU0UwcxSpJsYo2xgdrDqD7j0pILiG6hE1vNHNESQHjYMpwcHke4IoA830/TL2/wBW/wBEk162mbRHja7v/OHl3fmRtwX91yVX5CAccZqxbTax4k0uw1dm1G1j1LUoc29tM48m1VGU8oeAzbmLDqGTngV3t3Fa3Fu1teJDJBN+7aKYArJn+Eg8H6VLHHHDEkUSKkaKFVFGAoHQAdhQB55eDWIrNdPZdVFuuoXKx3eLuaRIxgxg+U6u4O5gHZsDbznioYrfxFqekxTX0msw3Ufh0OFieWEm8BbBIUjL8D5TkHPIPFei/arf91/pEX75ykfzj52AJIHqcKxx7H0qagDz67t9dtFuEtZNWkE1pYTMzPI5E32jEu3+7lPvIuAB2ArX0bzP7fu/7T/tf+0PtU3lf6/7J5GT5eMfufuYzn5t2fauqooAKKKKACiiigAooooAKKKKACiiigCBP+P6X/cX+ZqeoE/4/pf9xf5mp6ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDzO88Q6D4n+0TR6T4maS8soP3lrZHIjEjSQyr16NuKnocHriuo8FQRxaXdSqurmee6aW4l1WERTSybVG7aAAF2hVGAB8tZ0Pw00221Ka5tNV1u1heCOBLe31CVFjVGcgA5zt+fheg5x1rpdI0lNHtHt47y+ugzl997ctM44AwGbkDjp7n1oA0Kiurdbu0mtneRFlRoy0TlHAIxlWHIPoRyKlrP1rV7fQtLk1C6SaSNHjj2QJvdmd1RQB3JZgKAPKbiLQtHurnTbOy8bapGsl3LI9vqkixuUkBmAHmqW2tIATjJOeWOTXrGiNbvoOnPZwfZ7VrWIww7g3lptG1cqSDgYGQSPc151c3WmajEJNNsfGNnLHc3TC4s7EMVMr/v4/mDDG8HIxkMOoxXouiwW9roWn29rDLBbRW0aRRTAh40CgBWB6EDAPvQBeqlq+lW2taXPp93v8mYDLRttZCCCrKexBAIPqKu1znjGTxItjbJ4csYriU3ETTM935LKiyxkgfKchlDhuRgdAx4oApeGdJ+3Xa6ve6zqepy2Fxc2tst4sSLEyO0TuBGq7iQpGWycE9M12FcnYy+Jb7WrEXdrpmmWdvLJLPHa6gZ3uCUdQpXy1AG5g5OeqiusoAK4vxbctFqmj6rp2teH7Sa2+0wE6nPhZASgdFwRyGQZ7ggDuRXaVxPinX9E0yWC4kk0a7tLR5VvrRmjadNxGZEUnJZSDuXGWye4AIBqeFtXvNV+1/a9S0G98vZt/smYybM7s78k4zgY+hroq5PwTqEOrfbtQtU062tJvL8iztgnmxoN2HmK9GbPC/wAIX1zXWUAYvizWJ9B8N3Go2y25ljkhTNyxWJA8qIzORyFUMWJ9BXnb+I4/Edi6X8vw8khW6k/dXt11kRyhkAIPDYyG7qR6166yq6lWUMpGCCMgis238O6LaxskOlWaqzvIR5Kn5mYsx5HqTQBZ0yQzaVZylrZi8CNutW3QnKjlD3X09sVapqIkcaxxqqIoAVVGAAOwFOoAKKKKACiiigDjfiPdx2ek6Y8upwaUjalEp1CTG62+VzuQE4LHG05yNrMSCAaf4V1C3u9Tkji8fQeIWEJb7LH9myg3L8/7pQ3HTnj5vpW1r9te3ViiWF9ZWcolBMl5a+ehXB4C71wenOex45rn9AuNVtPGbaVqGo6ReI9g1whsbLyXUiRFO/8AeNgfNwO/PTbyAdrRRUV1bRXlpNazqWhmRo5FDEZUjBGRyOD2oA881zRNQ8ReLNVTTYhGtq0STNcaxeRCRmjVvkiiYKq4IGe5De9dp4c06TSdAtLCWC0geFSvl2ZYxAbiRgt8xJHJJ5JJrH/4Vp4S80y/2W/mEbd/2ybOPTO/pXRabptrpNhFY2UZjt4s7FLs5GSSeWJJ5J70AWqKKKACiiigArzn4qxo8Fqu+ye4ns720gtrudYdzyxqokRn+XevTBIJDtjpivRqxvEMOrXcEdnptlpk8UwYTyaiWaOMDGP3YHz5yeMrjHXmgCl4XtNSfUdQ1nUrNbFruK3gitRMsrKkQf52ZflyxkPAJ4Uc101cz4Q8IL4US8xfPO12ys0KRiG3hxn/AFUQzsznnk5wPSumoAKxLvwlouoas2pahafbp8got07SxRYAHyRsSi9M5Azkk1t0UAIqqihVAVQMAAYAFLRRQBieJNStrSz+yXek6jqNveRyRSJZ2zTDbgAhsdMhv51wui2FsmvwtPH4zuYJLuKdYb2wVYllVVjR5HChiEVE6nHy5OTzXo2t293d6DqNtYTeTeTWsscEucbJCpCtntg4Nct/bGq6ymlabaaJrWn3UN1byXc1yuyOOONgZF8zdiXcAVGM53ZOMUAdxRRRQBnazoWl+IbJbPVrKO7t1cSCOTOAwBAPHsTWN4L8O6XY6XZatDocel6ldWifaIgHBjLAMyEMcjBA688VL41iml0u0Hl3ctgt2h1COz3+a9vtbIAT5iN+wkLyVB61gfDy4vL7+xnEF/Fb2OiLaXbXUTxCS4zGQFDgFioWTLAY+Yc0AeiVFc20F5azWtzEksEyNHJG4yrqRggjuCDUtRXVvHeWk1rNu8qZGjfY5RsEYOGUgg89QQRQB59rehw+IPFOprZeDvDt5LaNFHcXmqOyvKxjVgFCxsSApUZJ7Edq7Lw5p7aVoNtZPY2Fi0e7NvYEmFMsT8uVU85yeByTXn9xoXhuHxnDpsGi6lIj3C2NxfnXbpWSQwPOqBfMJYBQCckAb+M813PhGS1k8NwCzhmghjlnh8ua4edlZJXRvnclmG5TjPbFAG3RRUU1rBcSQSTRI7wSeZEzDJRtpXI9DtZh9CaAPPP7L8S/8Il/whf9lWwh8n7J/a/2xdnldPN8vG/zMc46bu+K9IryXT/Cia3YQ6npvgHwfFZXK+ZAt3K4l2HpvCxEA+2TjpXrVABXKeJF16eWSz8jw1Npd0ywxRak8m6ViMlSu0qTkMQB2FdXXK+Mbmx+xW9yNd0zTrzTL9HikvZVEQm8pv3UnII3RyMfXBBoAh8I6VcaBfy6fJp/hrT45YjN5Wl7xK5DABmDAZUZIz2JFdhXD+FtRj17xO2o3PiDQL29gs3ghstIufNEcbOhd2JOTkqg6AD3zXcUAFcJ4ktLnV72zP8AwjLz38VorySWutfZZrfeTmMshDMuVPPQkHHSu7rg5PDlzP491NofF2swXEtnDI0cUNvtii82bYu5kORkvgYyMHLHIwAbng+FbTSJbRdJj0toJ2V4FuxcsWIVizv13Hd/FzjB7iugrn/CltaWaatbwXd7d3SX5F7Pebd8k3lR4PygLjZ5eMAcV0FABXn9hpWqeLvD6G/8XTvYXke26tFsoYpAOjxMwztPVTjnrXbalfDTbCW7Ntc3Ijx+6tYjJI2SBwo69c/QGvIbK10HUPCdvLbfDMtrdxZqyFtH223nsowSxIxHuPXPSgD2iiiigDC8YLdt4ZnFhNDFeebB5DT3BhQyecm1WYA8E4G3+LO3jOar2Gr+JBqVnbazpGlWcNwzRiSLVGkdmCM2EQxLuPyk4zwAT2qDx9pcGoaPby3OpanZxQ3VvhbFlBkdp4gmQ3UhguDkYznnpWPpIsIvHEFvNc+Ib8208tva3d9co9v9pERLqqjDbhGXGSMcMBzQB6JRRRQBw3jAQ3HiCCGws9dl1qK1DvLpE8cJSBmICyNIwUgsrYGCeCRitnwfafY9GeJtJvtOkM7PIL64SaadiATKzozAk9Ov8PQDFYHjJ7W28VWVwus67Z3skUVs6aZFCyIkk2xHlMiHgu2ByehwPvZ6TwyxWC/tX1W+1Ga1u2hlkvUjV0bap2jYqjbhgwOM/NQBt0EAggjIPUUUUAcDbeLdF0WDWLLVbu007U4L25MVqIVSR1Z2aJo0AzIWBDZAOWJzXX6JLez6Dp0upR+XfvaxNcpjG2UqCwx25zXG69dXNvJf3EXxH020khaRo4Jre2bycE/ISTu46etdhoF1NfeHNLu7jf509pFLJ5ihW3MgJyBwDk9BQBo1jeKhqP8Awjtw2lrM91G8UmyFsO6LIrSKp9SgYD3NbNZXiSSzi0C5e/vrqxths33FqzLInzDGCoJ5OBwOhNAGNBr8/iDxDpK6VaarBaW7ySX8l1aSW6FTGyrHhwNzbyp4zgKeea66vNNEns5vGOk/2F4g8Q6pB+9+2RXUkzQRp5bbXJZQM7toAz3z2r0ugArlfFmg6jr0sMUdh4dvLKNdwXVbZ5WWTJyVwcAY2+/WuqrzzxcdB/4TNE8Q6Td3du1gnkzW1pcy+WwkfIYxgjkEYGMjBz1FAG94JuruTTLyxvLbTbabTbxrTydNQrCgCow2g+z57YzjqDXS1h+FH0RtH2aBZvaWUcrL5b2klud+ASdsihjnI+bv68VuUAFZN74X0DUtQW/vtE0+5vFxiea2R346ckZ47VrUUAFFFFAHKePYNXm0i3Om6lp9jFHdQPM13E77iJ4ygUowxyCCMHdkDK9aZHZ65/wkGjvr+uaaVjlke2trKykiM8nlOpDM0jDAVmbGO2e1aniy3s7nwxerfX32GBAs32oAHyXRw6NjvhlU479O9c3oNzNrmp6Nqera7bXKxy3CafBbadLa75kV45Gk8xmIYL5g2/L3POKAO+ooooA4zxDrupJ4iXSP+EW1TUtIls50uTDHCUnLeVjBaQYADSKQcEk8AgEjW0G+1bUb69mvNLuNN09Y4ktYLkxmUuC/mMdjMAMGMAE/wmoPEg1Gy1bS9Zs7GbULe0SeK4tYJFWTEmzEihyFYrsIwSOHOKXw6upXWsarrF7YzafBdJBFb2k8itIBHvzIwUlVLbwMAnhBmgDo6KKKAOa1jVrJfE+i6VDqiW9813501sobMyeRKArFRgcgMAxGfL46Ct6yvbbULVLq0lEsLkgMPUEgjn0II/CuD1LStYuvGF1d+Hn06Y2mpRXVzFfPJEUl+yeUArKrbkKSK3bBDDvx1/h3SZNF0OGymnWecPJNNIq7VaSR2kfA7Dc5wPTFAGpWF4xsdZ1PwvfWOh3FrBd3EMkRe5DY2sjD5SpG1skYbkD0NbtZ2v2hv/DmqWa3C2xuLSWITscCPchG4ntjOfwoA4hNbuNU1Cx03UPG/hLat5A5gsGInldJFZYxmY/eZQCMEkEjvXpFeM213pEnimzttJutGNvcX9ncJcQiTfA0aJG0UYEe1lcIBu3AYZsivZqACiiigAooooAK831OO/bVrw+C49fjvfPfznmbbp5k3HeSJ855zzCPxr0iigCvYC8GnWw1Awm98pPtBgz5ZkwN23PO3OcZ5xViiigDL8QHT7XRr/U7+xiu47WynLo8asWi27nj5HRtgyOhwM9K4S20hdFudJ1W/wDCfg5La5u4I4/sFmFntnkYCNlcjD4YrkgL6jpXpF7A9zY3EEcixySxMiuyBwpIwCVPDY9D1ryjRhpfh/xfY6edM0/WLoTCOObSLiaT7FuON727MyQgA8lW4GcCgD16iiigAooooAKKKKAPI/F9hp+i65c3upxaRqwu5TJ/Z8c8ttdSjspiRmS4wMDLKMgcmvUtNnF1pdpcLay2iywo4t5U2PECoOxl7EdCO2KZZaPpunXFxcWVhbQT3MjSTyxxgPIzHJLN1PPrV2gAooooAKKKKACiiigAooooAKKKKAOK8bf8gGD/AK+2/wDZq4GvZ45YYrYmd0VTIw+c8ZyaPtmnf89rf8xWcuW+rPcwGaSw9FU1Tv53/wCAeMV2/wAO/wDXah/ux/8As1dh9s07/ntb/mKmgmtpd32eSNsddhFEeW+jLxmbSr0JU3Tav1v5+hj+KZr2wtbTVbGO7uDZXAee0tgzNcRMCjAIPvFdwcd/k965A2/iS2ilW7l1mTWHSCWwMDStbrI7bpVcj5NqsSuJOAgXbXptFaHgHAy/2p9qH9o/2z/Zf9pXnmfZPP8ANxx5OPL+fy/v/d4ztzxVrwvFrNr4KtLZrV7dfInMs0rMLqJi8hBEIQhz90/eGc8A9+xilWYPtDjaxQ70K8j0yOR7jg1FY31tqVol1aSeZA5YK20jJUlTweeoNAHmrWd1d6fbLdWutS29jqVvK90kl8JJlKuGZYnPmrglc7cj5uOhq5Nca0fF9tJaxanFAL9IXhMV06PbFMeYzs/lDqOAm4EcnrXo1FAHnPh+xvdNttFs7SLVo5otYnF8s5uDH5flXRQ5f5ShYxnK8FiuecVu+Dt32OP7b/bH9r+Qv277b5/leb/F5e791jdnHl8YxXQvfW0eowae8mLqeKSaOPafmRCgY56DBkT8/Y1YoAKKKKACiiigAooooAKKKKACiiigAooooAgT/j+l/wBxf5mp6gT/AI/pf9xf5mp6ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKp6pplrrOmy2F4rtBLtJ2OUZSpDKysCCCGAII7irlFAHF2Hw5tbGCSIa/wCISHnlm+TU5UHzuz9AeT83J6k5J6119rALW0htxJLIIkVA8rl3bAxlmPJPqe9S0UAFY3irT7nVfD09ja5LyvEHUPsLxCRTIue25Aw/GtmvN/iH4a1W/wBXj1Ox0xdTjaO1t/KF68LQ7bgmQ7QMMHR9pPVQue1AEHh/wT/Y3ja1ktfDFvapaXl5KdTTytklvKHMcaqDuDKWVegwqEZw2K9PrgfDfh2ew1+2uX8IRaeqb83K65JcFMqR/qyMNnOPbOe1d9QAVxXiizj0/W9OvLbwp/a1vKJxex21pAzbjsKyFnIJOdwxnncxJ4Ge1rjvHdlrWpRR2el3F/bo1ldyb7KQxsbhUXyFZxyFJLnqMkAGgDS8NT2032r7P4YutExs3efbxRed16eWzZx74+9W/XCfD1W+2au8EmuS2DpbmNtXecukuH8yNRLzhfl5A5LEZOBju6ACiiigAooooAKKKKACiiigDiPiHd6ZcWNnplzNopaS+jSVtRRJltAUbEhjYjnooJ4/eZ6UvgnQ7LRb6dbHWtHuo5Isvb2FjBA2QRhyYzkgcjn+9XSXXh7RL65e5u9H0+4nfG6Wa2R2bAwMkjJ4AFPstD0nTJjNYaXZWsrLsLwW6IxXg4yB04H5UAX6KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDM1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## 5. Image Processor'un Oluşturulması ve Albumentations ile Veri Artırımı Yapımı (Data Augmentation) 📸

Image Processor'u oluşturduktan sonra [Albumentations](https://albumentations.ai/) kullanarak veri artırımı 🪄 uygulayacağız. Bu, veri setimizi zenginleştirmeye ve semantik segmentasyon modelimizin performansının artmasına yardımcı olacaktır.


```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


# Transformları ayarlayın
train_ds.set_transform(train_transforms)
test_ds.set_transform(val_transforms)
```

## 6. Modeli Checkpoint'ten Başlatın

[nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) checkpoint'inden önceden eğitilmiş bir Segformer modelini kullanacağız. Bu mimari, [SegFormer: Transformers ile Semantik Segmentasyon için Basit ve Verimli Tasarım - (SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers)](https://arxiv.org/abs/2105.15203) başlıklı çalışmada detaylandırılmıştır ve ImageNet-1k üzerinde önceden eğitilmiştir.

```python
from transformers import SegformerForSemanticSegmentation

pretrained_model_name = "nvidia/mit-b0"
model = SegformerForSemanticSegmentation.from_pretrained(
    pretrained_model_name,
    id2label=id2label,
    label2id=label2id
)
```

## 7. Eğitim Argümanlarını Ayarlayın ve Weights & Biases ile Bağlantı Kurulumu 📉

Sonraki adımda, eğitim argümanlarını yapılandırılması ve [Weights & Biases (W&B)](https://wandb.ai/) ile bağlantı kuracağız. W&B, deneyleri takip etmemize, metrikleri görselleştirmemize ve model eğitim iş akışını yönetmemize yardımcı olacak, süreç boyunca değerli bilgiler sağlayacaktır.


```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",  # bunu değiştirin
    name="segformer-b0-segments-sidewalk-finetuned",  # bunu değiştirin
    config=training_args,
)
```

## 8. `evaluate` ile Geliştirilmiş Loglama için Özel `compute_metrics` Methodunu Ayarlanamsı

Modelin performansını değerlendirmek için birincil metrik olarak [ortalama kesiştirilmiş Bölgeler  -  mean Intersection over Union (ortalama IoU)](https://huggingface.co/spaces/evaluate-metric/mean_iou) kullanacağız. Bu, her kategorideki performansı detaylı bir şekilde takip etmemizi sağlayacaktır.

Ayrıca, çıktıdaki uyarıları en aza indirmek için değerlendirme modülünün loglama seviyesini ayarlayacağız. Bir kategori bir görüntüde tespit edilmezse, aşağıdaki gibi uyarılar görebilirsiniz:

```
RuntimeWarning: invalid value encountered in divide iou = total_area_intersect / total_area_union
```

Bu hücreyi atlayabilirsiniz eğer bu uyarıları görmek isterseniz ve bir sonraki adıma geçebilirsiniz.

```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)
    # Etiket boyutuna göre logitleri ölçeklendirin
    logits_tensor = nn.functional.interpolate(
        logits_tensor,
        size=labels.shape[-2:],
        mode="bilinear",
        align_corners=False,
    ).argmax(dim=1)

    # şimdilik compute yerine _compute kullanılıyor: 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,
            )

    # kategorilere özel metrikleri ayrı key-value çiftleri olarak ekleyin
    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. Modeli Özel Veri Setimiz Üzerinde Eğitimi 🏋

Artık modeli özel veri setimiz üzerinde eğitme zamanı geldi. Eğitim sürecini izlemek ve gerekirse ayarlamalar yapmak için hazırlanan eğitim argümanlarını ve bağlı Weights & Biases entegrasyonunu kullanacağız. Eğitime başlayalım ve modelin performansını geliştirmesini izleyelim!

```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. Yeni Görüntüler Üzerinde Model Performansını Değerlendirelim 📸

Eğitimden sonra, modelin yeni görüntüler üzerindeki performansını değerlendireceğiz. Bir test görüntüsü kullanacak ve modelin görünmeyen veriler üzerindeki performansını değerlendirmek için bir [pipeline](https://huggingface.co/docs/transformers/main_classes/pipelines) kullanacağız.


```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" # Bu model adını kendi eğittiniz model ile değiştirin
)

results = image_segmentator(image)
```

```python
>>> plt.imshow(image)
>>> plt.axis('off')
>>> plt.show()
```

<img 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">


Model bazı maskeler oluşturdu, bunları görselleştirerek performansını değerlendirebilir ve anlayabiliriz. Bu, modelin görüntülerinin ne kadar iyi segmente edildiğini görmemize ve iyileştirmemiz gerektiren alanları belirlememize yardımcı olacaktır.

```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. Test Set Üzerinde Performansı Değerlendirin 📊

```python
metrics = trainer.evaluate(test_ds)
print(metrics)
```

## 12. Modeli Inference API'si ile Erişip ve Sonuçları Görselleştirelim 🔌

Hugging Face 🤗, "API endpoint" aracılığıyla modelleri doğrudan test etmenizi sağlayan [Serverless Inference API](https://huggingface.co/docs/api-inference/index) sunmaktadır. Bu API'yi kullanma konusunda ayrıntılı bilgi için bu [cookbook](https://huggingface.co/learn/cookbook/enterprise_hub_serverless_inference_api) sayfasına göz atın.

Bu API'yi modelimizi test etmek ve API işlevselliğini öğrenmek amacıyla kullanacağız.

**ÖNEMLİ**

Serverless Inference API'sini kullanmadan önce, bir model kartı oluşturarak model görevini ayarlamanız gerekir. Fine-Tune yapılmış modeliniz için model kartını oluştururken, görevi uygun bir şekilde belirttiğinizden emin olun.


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)


Model görevini ayarlandıktan sonra, bir görüntü indirebilir ve modeli test etmek için [InferenceClient](https://huggingface.co/docs/huggingface_hub/v0.25.0/en/package_reference/inference_client) kullanabiliriz. Bu istemci, görüntüyü API aracılığıyla modele göndermemizi ve değerlendirme için sonuçları almamızı sağlayacaktır.

```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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5xT0ZZFypBFSHgVA8UkhLAbD2I+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==">


InferenceClient'tan [image_segmentation](https://huggingface.co/docs/huggingface_hub/v0.25.0/en/package_reference/inference_client#huggingface_hub.InferenceClient.image_segmentation) metodunu kullanacağız. Bu metod, model ve bir görüntü girişi alır ve tahmin edilen maskeleri döndürür. Bu, modelin yeni görüntüler üzerindeki performansını test etmemizi sağlayacaktır.

```python
from huggingface_hub import InferenceClient

client = InferenceClient()

response = client.image_segmentation(
    model="sergiopaniego/segformer-b0-segments-sidewalk-finetuned", # Kendi modeliniz ile değiştirin
    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)
```

Tahmin edilen maskelerle sonuçları görüntüleyebiliriz.

```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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">


[Inferenece API'ı Javascript](https://huggingface.co/tasks/image-segmentation) ile kullanmak da mümkündür. JavaScript kullanarak API'yi kullanacağımıza dair bir örnek:

```
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",
});

```



**Ekstra Puan**

Ayrıca, Fine-Tune yapılmış modeli bir Hugging Face Space kullanarak gösterebilirsiniz. Örneğin, bunu sergilemek için özel bir Space oluşturdum: [Segformer ile Segmentler/Yaya Yolu Üzerinde Fine Tune edilmiş Semantik Segmentasyon](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)
```

## Sonuç

Bu kılavuzda, özel bir veri seti üzerinde bir semantik segmentasyon modelini başarıyla fine tune yaptık ve Serverless Inference API'yi kullanarak test ettik. Bu, modeli çeşitli uygulamalara nasıl kolayca entegre edebileceğinizi ve dağıtım için Hugging Face araçlarını nasıl kullanabileceğinizi göstermektedir.

Umarım bu kılavuz, kendi modellerinizi güvenle fine tune yapma ve dağıtma konusunda size gerekli araçları ve bilgileri sağlar! 🚀

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/tr/semantic_segmentation_fine_tuning_inference.md" />

### Transformers Agents kullanarak, tool-calling süper güçleriyle donatılmış bir ajan oluşturun 🦸
https://huggingface.co/learn/cookbook/tr/agents.md

# Transformers Agents kullanarak, tool-calling süper güçleriyle donatılmış bir ajan oluşturun 🦸 
_Yazar: [Aymeric Roucher](https://huggingface.co/m-ric)_ _Çeviren: [Alper Erdoğan](https://github.com/alpererdogan8)_

Bu notebook, harika **ajanlar** oluşturmak için [**Transformers Agents'ı**](https://huggingface.co/docs/transformers/en/agents) nasıl kullanabileceğinizi gösterir!

**Ajanlar** nedir? Ajanlar, bir LLM tarafından desteklenen ve spesifik istemler ile çıktıların ayrıştırılması sayesinde belirli *araçları* kullanarak problemleri çözebilen sistemlerdir.

Bu araçlar basitçe LLM'nin kendi başına iyi performans sergileyemediği işlevleri kapsar: örneğin [Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) gibi metin üreten bir LLM için görüntü oluşturma aracı, web arama aracı veya hesap makinesi olabilir...

**Transformers Agents** nedir? Kendi ajanlarınızı oluşturmak için yapı taşları sağlayan `transformers` kütüphanemizin bir uzantısıdır! [Dökümantasyondan](https://huggingface.co/docs/transformers/en/agents) daha fazla bilgi edinin.

Nasıl kullanılacağına ve hangi kullanım senaryolarını çözebileceğine bakalım.

Gerekli kütüphaneleri yüklemek için aşağıdaki satırı çalıştırın:

```python
!pip install "transformers[agents]" datasets langchain sentence-transformers faiss-cpu duckduckgo-search openai langchain-community --upgrade -q
```

## 1. 🏞️ Çok Modlu + 🌐 Web tarayıcı asistanı

Bu kullanım senaryosu için, internette gezinen ve görsel oluşturabilen bir ajan göstermek istiyoruz.

Bunu oluşturmak için basitçe iki aracın hazır olması gerekiyor: görüntü oluşturma ve internet üzerinden arama.
- Görsel oluşturmak için, Stable Diffusion kullanarak görseller oluşturmak üzere Hub'dan HF Inference API'yi (Serverless) kullanan bir araç yüklüyoruz.
- İnternette arama yapmak için yerleşik bir araç kullanıyoruz.

```python
from transformers import load_tool, ReactCodeAgent, HfApiEngine

# Aracı Hub'dan içe aktarın
image_generation_tool = load_tool("m-ric/text-to-image", cache=False)

# LangChain'den aracı içe aktarın
from transformers.agents.search import DuckDuckGoSearchTool

search_tool = DuckDuckGoSearchTool()

llm_engine = HfApiEngine("Qwen/Qwen2.5-72B-Instruct")
# Ajanları her iki araçla başlatın.
agent = ReactCodeAgent(
    tools=[image_generation_tool, search_tool], llm_engine=llm_engine
)

# Çalıştır!
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. 📚💬 RAG ile yinelemeli sorgu iyileştirme ve kaynak seçimi

Kısa tanım: Retrieval-Augmented-Generation (RAG), ___“bir kullanıcı sorgusunu yanıtlamak için bir büyük dil modeli (LLM) kullanır, ancak yanıtı veri setinden elde edilen verilere dayandırır”___.

Bu yöntemin yalın ya da fine-tuned bir LLM kullanımına göre birçok avantajı vardır: bunlardan birkaçını saymak gerekirse, cevabı doğru gerçeklere dayandırmaya ve karışıklıkları azaltmaya izin verir, LLM'e özgü bilgileri sağlamaya ve bilgi tabanından veriye erişimin ince taneli kontrolüne izin verir.

- Şöyle bir senaryo düşünelim. RAG yöntemini uygulamak istiyoruz, ancak bazı parametrelerin dinamik olarak belirlenmesi gereken ek bir koşulumuz var. Örneğin, kullanıcı sorgusuna bağlı olarak aramayı bilgi tabanının belirli alt kümeleriyle sınırlamak isteyebiliriz veya alınan belge sayısını ayarlamak isteyebiliriz. Peki, **bu parametreleri kullanıcı sorgusuna göre nasıl dinamik olarak ayarlayabiliriz?**

- RAG'de sık karşılaşılan bir sorun, kullanıcı sorgusuna verilen cevabın hangi belgeden geldiğinin bulunmamasıdır. **Eğer önceki sonuçlar alakalı değilse, retriever'ı değiştirilmiş bir sorgu ile tekrar çalıştırarak sonuç alma şansımız var mı?**


🔧 Yukarıdaki noktaları basit bir şekilde çözebiliriz: **ajanımıza retriever'ın parametrelerinin kontrolünü vereceğiz!**

➡️ Hadi bunu nasıl yapacağımızı gösterelim. İlk olarak üzerinde RAG uygulamak istediğimiz bir bilgi tabanını yüklüyoruz: bu veri seti, markdown olarak depolanan birçok Hugging Face kütüphanesinin dokümantasyon sayfalarının bir derlemesidir.


```python
import datasets

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

Şimdi veri setini işleyerek ve retriever tarafından kullanılacak bir vektör veritabanına depolayarak bilgi tabanını hazırlıyoruz. Vektör veritabanları için mükemmel yardımcı programlara sahip olduğu için LangChain'i kullanacağız:


```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)
```

Artık veritabanımız hazır olduğuna göre, kullanıcı sorgularını buna göre yanıtlayan bir RAG sistemi oluşturalım!

Sistemimizin sorguya bağlı olarak yalnızca en alakalı bilgi kaynaklarından seçim yapmasını istiyoruz.

Dökümantasyon sayfalarımız aşağıdaki kaynaklardan gelecek:

```python
>>> all_sources = list(set([doc.metadata["source"] for doc in docs_processed]))
>>> print(all_sources)
```

<pre>
['datasets-server', 'datasets', 'optimum', 'gradio', 'blog', 'course', 'hub-docs', 'pytorch-image-models', 'peft', 'evaluate', 'diffusers', 'hf-endpoints-documentation', 'deep-rl-class', 'transformers']
</pre>

👉 Şimdi ajanlarımızın bilgi tabanından bilgi almak için kullanabileceği bir `RetrieverTool` oluşturalım.

Vectordb'yi aracın bir özelliği olarak eklememiz gerektiği için, [basit araç oluşturusunu](https://huggingface.co/docs/transformers/main/en/agents#create-a-new-tool) ve `@tool` dekoratörünü kullanmak yeterli olmayacak. Bu nedenle, [gelişmiş ajanlar dökümantasyonunda](https://huggingface.co/docs/transformers/main/en/agents_advanced#directly-define-a-tool-by-subclassing-tool-and-share-it-to-the-hub)  belirtilen gelişmiş yapılandırmayı takip edeceğiz

```python
import json
from transformers.agents 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": "string",
            "description": "The query to perform. This should be semantically close to your target documents. Use the affirmative form rather than a question.",
        },
        "source": {"type": "string", "description": ""},
        "number_of_documents": {
            "type": "string",
            "description": "the number of documents to retrieve. Stay under 10 to avoid drowning in docs",
        },
    }
    output_type = "string"

    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.".replace(
                "'", "`"
            )
        )

    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
            ):  # eğer kaynak bir listeyi temsil etmiyorsa
                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]
        )
```

### Opsiyonel: Retriever aracınızı Hub'da paylaşın

Aracınızı Hub'da paylaşmak için, önce RetrieverTool tanım hücresindeki kodu kopyalayıp, örneğin `retriever.py` gibi bir adla yeni bir dosyaya yapıştırın.

Araç ayrı bir dosyadan yüklendiğinde, aşağıdaki kodu kullanarak Hub'a gönderebilirsiniz (`yazma` yetkisine sahip bir token ile giriş yaptığınızdan emin olun).

```python
share_to_hub = True

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")

    # Aracın Yüklenmesi
    from transformers.agents import load_tool

    retriever_tool = load_tool(
        "m-ric/retriever-tool", vectordb=vectordb, all_sources=all_sources
    )
```

### Ajanı çalıştırın!

```python
from transformers.agents import HfApiEngine, ReactJsonAgent

llm_engine = HfApiEngine("Qwen/Qwen2.5-72B-Instruct")

retriever_tool = RetrieverTool(vectordb=vectordb, all_sources=all_sources)
agent = ReactJsonAgent(tools=[retriever_tool], llm_engine=llm_engine, verbose=0)

agent_output = agent.run("Please show me a LORA finetuning script")

print("Final output:")
print(agent_output)
```

Peki burada ne oldu? İlk olarak, ajan belirli kaynaklarla (`['transformers', 'blog']`) birlikte retriever'ı başlattı.

Ancak bu arama yeterli sonucu vermedi. Sorun değil! Ajan, önceki sonuçlar üzerinde tekrar çalışabildiği için, daha az kısıtlayıcı arama parametreleriyle yeniden sorgulama yaptı ve sonuç olarak araştırma başarılı oldu!

Bir LLM ajanının bir retriever aracını kullanarak dinamik olarak sorguyu ve retrieval parametrelerini değiştirebilmesi, RAG'in daha genel bir formülasyonunu oluşturur ve aynı zamanda yinelemeli sorgu iyileştirme gibi birçok RAG geliştirme tekniğini de kapsar.

**Bir retriever'ı araç olarak kullanıp, sorguyu ve diğer** veri çekme parametrelerini dinamik olarak değiştirebilen **bir LLM ajanı kullanmak**, yinelemeli sorgu iyileştirmesi gibi birçok RAG geliştirme tekniğini de kapsayan **RAG'in daha genel bir formülasyonudur**.

## 3. 💻 Python Kodunda Hata Ayıklama
ReactCodeAgent'in yerleşik bir Python kod yorumlayıcısı olduğundan, hatalı Python scriptimizi debug etmek için kullanabiliriz!

```python
from transformers import ReactCodeAgent

agent = ReactCodeAgent(tools=[], llm_engine=HfApiEngine("Qwen/Qwen2.5-72B-Instruct"))

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, then run it to make sure it works and return the final code",
    code=code,
)
```

Gördüğünüz gibi, ajan verilen kodu deniyor, bir hata alıyor, hatayı analiz ediyor, kodu düzeltiyor ve çalıştığını gördükten sonra geri veriyor!

Sonuç olarak düzeltilen kodun son hali:

```python
>>> print(final_answer)
```

<pre>
my_list = [0, 1, 2]

for i in range(4):
    if i < len(my_list):
        print(my_list[i])
    else:
        print("Index out of range")
</pre>

## 4. Kendi LLM motorunuzu oluşturun (OpenAI)

Kendi LLM motorunuzu oluşturmak gerçekten çok kolay:
sadece bu kriterlere sahip bir `__call__` yöntemine ihtiyaç duyar:
1. Girdi olarak [ChatML formatında](https://huggingface.co/docs/transformers/main/en/chat_templating#introduction) bir mesaj listesi alır ve cevabı çıktı olarak verir.
2. `stop_sequences` argümanını destekleyerek metin üretmeyi durduracak dizileri tanımlar.
3. LLM'inizin desteklediği mesaj rolü (asistan, kullanıcı vb.) türlerine göre bazı mesaj rollerini dönüştürmeniz gerekebilir.

```python
import os
from openai import OpenAI
from transformers.agents.llm_engine import MessageRole, get_clean_message_list

openai_role_conversions = {
    MessageRole.TOOL_RESPONSE: "user",
}


class OpenAIEngine:
    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=[]):
        # Güvenli mesaj listesini edinin
        messages = get_clean_message_list(
            messages, role_conversions=openai_role_conversions
        )

        # LLM çıktısını alın
        response = self.client.chat.completions.create(
            model=self.model_name,
            messages=messages,
            stop=stop_sequences,
        )
        return response.choices[0].message.content


openai_engine = OpenAIEngine()
agent = ReactCodeAgent(llm_engine=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>

## ➡️ Son olarak

Yukarıdaki kullanım örnekleri, Agents'ın sunduğu olanaklar hakkında size bir fikir verecektir.

Daha gelişmiş kullanım için [dökümantasyonu](https://huggingface.co/docs/transformers/en/transformers_agents) ve Llama-3-70B'yi temel alan ve son derece zorlayıcı GAIA Liderlik Tablosunda birçok GPT-4 ajanlarını geride bırakan kendi ajanımızı oluşturmamızı sağlayan [bu deneyi](https://github.com/aymeric-roucher/agent_reasoning_benchmark/blob/main/benchmark_gaia.ipynb) okuyun!

Tüm geri bildirimleri bekliyoruz, Agents'ı geliştirmemize yardımcı olacak!

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/tr/agents.md" />

### Kod Dil Modelini Tek GPU'da Fine-Tune Etmek
https://huggingface.co/learn/cookbook/tr/fine_tuning_code_llm_on_single_gpu.md

# Kod Dil Modelini Tek GPU'da Fine-Tune Etmek

_Yazar: [Maria Khalusova](https://github.com/MKhalusova)_

_Çeviren: [Emre Albayrak](https://github.com/emre570)_

Codex, StarCoder ve Code Llama gibi açık kaynak dil modelleri genel programlama ilkelerine ve sözdizimine uygun kod üretme konusunda harikadırlar. Fakat bu modeller, bir kuruluşun dahili kurallarıyla uyumlu olmayabilir veya özel kütüphanelerden haberdar olmayabilir.

Bu notebook'ta, bir modelin bağlamsal farkındalığını artırmak ve kuruluşunuzun ihtiyaçları için kullanılabilirliğini artırmak amacıyla özel kod tabanlarında bir kod dil modeline nasıl fine-tune yapabileceğinizi göstereceğiz. Kod dil modelleri oldukça büyük olduğundan, geleneksel bir şekilde fine-tune yapmak kaynakları tüketebilir. Endişelenmeyin! Fine-tune işlemini tek bir GPU'ya sığacak şekilde nasıl optimize edebileceğinizi göstereceğiz.


## Veri Seti

Bu örnek için GitHub'daki en iyi 10 Hugging Face public repo'larını seçtik. Görseller, ses dosyaları, sunumlar gibi kod içermeyen dosyaları verilerden hariç tuttuk. Jupyter notebook'lar için yalnızca kod içeren hücreleri tuttuk. Ortaya çıkan kod, Hugging Face Hub'da [`smangrul/hf-stack-v1`](https://huggingface.co/datasets/smangrul/hf-stack-v1) bağlantısı altında bulabileceğiniz bir veri seti olarak mevcut. Bu veri seti repo'nun kimliğini, dosya yolunu ve dosya içeriğini içeriyor.


## Model

Bu rehber için 1 milyar parametreli ve 80'den fazla programlama dilinde eğitilmiş [`bigcode/starcoderbase-1b`](https://huggingface.co/bigcode/starcoderbase-1b) modelini seçiyoruz. Bu model, yayıncı tarafından korumaya sahip, bu nedenle bu notebook'u tam olarak bu modelle çalıştırmayı planlıyorsanız, modelin sayfasından erişim sağlamanız gerekir. Erişim izninizi aldıktan sonra, Hugging Face hesabınıza giriş yapın.

**Not:** Eğer fine-tune edilmiş modelinizi Hugging Face Hub'a yüklemek istiyorsanız, `write` izni olan bir token girmeniz gerekli.

```python
from huggingface_hub import notebook_login

notebook_login()
```

Başlamak için gerekli tüm kütüphaneleri yükleyelim. Gördüğünüz gibi, `transformers` ve `datasets` kütüphanelerine ek olarak, eğitimi optimize etmek için `peft`, `bitsandbytes` ve `flash-attn` kullanacağız.

Parametre açısından verimli eğitim teknikleri kullanarak, bu notebook'u tek bir A100 High-RAM GPU üzerinde çalıştırabiliriz.

```python
!pip install -q transformers datasets peft bitsandbytes flash-attn
```

Şimdi biraz hiperparametre tanımlayalım. Değerlerini istediğiniz gibi değiştirebilirsiniz.

```python
MODEL="bigcode/starcoderbase-1b" # Hugging Face Hub model adı
DATASET="smangrul/hf-stack-v1"   # Hugging Face Hub'daki veri seti
DATA_COLUMN="content"            # kod içeriğinin olduğu sütun adı

SEQ_LENGTH=2048                  # Karakter uzunluğu

# Eğitim argümanları
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 dönüşüm argümanları
FIM_RATE=0.5                     # fim_rate
FIM_SPM_RATE=0.5                 # fim_spm_rate

# LORA argümanları
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 argümanları
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)
```

## Veri setinin hazırlanması

Verileri yükleyerek başlayalım. Veri setinin oldukça büyük olması muhtemel olduğundan, streaming modunu etkinleştirdiğinizden emin olun. 

Streaming modu, tüm veri setini bir kerede indirmek yerine veri seti üzerinde işlem yaptıkça verileri kademeli olarak yüklememizi sağlar.

İlk 4000 örneği validation set (doğrulama kümesi) olarak ayıracağız ve geri kalan her şey eğitim verisi olacak.

```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)
```

Bu adımda, veri seti hala isteğe bağlı uzunlukta kod içeren ham veriler içeriyor. Eğitim için sabit uzunlukta girdilere ihtiyacımız var. Bir metin dosyası akışından (stream) sabit uzunlukta token yığınları döndürecek bir Iterable veri kümesi oluşturalım.

İlk olarak, veri setindeki token başına ortalama karakter sayısını tahmin edelim, bu daha sonra text buffer'daki token sayısını tahmin etmemize yardımcı olacaktır. Varsayılan olarak, veri setinden yalnızca 400 örnek (`nb_examples`) alacağız. Tüm veri setinin yalnızca bir alt kümesini kullanmak, hesaplama maliyetini düşürürken, genel karakter-token oranının makul bir tahminini sağlayacaktır.

```python
>>> tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)

>>> def chars_token_ratio(dataset, tokenizer, data_column, nb_examples=400):
...     """
...     Veri kümesindeki token başına ortalama karakter sayısını tahmin eder.
...     """

...     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>

Karakter-token oranı, metin tokenizasyonunun kalitesinin bir göstergesi olarak da kullanılabilir. Örneğin, karakter-token oranının 1,0 olması her karakterin bir token ile temsil edildiği anlamına gelir ki bu çok anlamlı değildir. Bu da zayıf bir tokenizasyona işaret eder. Standart İngilizce metinde bir token tipik olarak yaklaşık dört karaktere eşdeğerdir, yani karakter-token oranı 4,0 civarındadır. Kod veri setinde daha düşük bir oran bekleyebiliriz, ancak genel olarak konuşursak, 2,0 ile 3,5 arasında bir sayı yeterince iyi kabul edilebilir.

**Opsiyonel FIM dönüşümleri**

Autoregressive dil modelleri tipik olarak soldan sağa doğru sekanslar üretir. Model, FIM dönüşümlerini uygulayarak metni doldurmayı da öğrenebilir.  Teknik hakkında daha fazla bilgi edinmek için [“Efficient Training of Language Models to Fill in the Middle” makalesine](https://arxiv.org/pdf/2207.14255.pdf) göz atın.

FIM dönüşümlerini burada tanımlayacağız ve Iterable veri setini oluştururken bunları kullanacağız. Ancak, dönüşümleri kullanmak istemiyorsanız, `fim_rate` değerini 0 olarak ayarlayabilirsiniz.

```python
import functools
import numpy as np


# FIM dönüşümleri için önek, sonek ve orta için özel belirteçlerin belirteç kimliklerini almak için yardımcı fonksiyon.
@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


# https://github.com/bigcode-project/Megatron-LM/blob/6c4bf908df8fd86b4977f54bf5b8bd4b521003d1/megatron/data/gpt_dataset.py adresinden uyarlanmıştır.
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,
):
    """
    Bir örnek (token listesi) alarak iki FIM modu yardımıyla fim_rate olasılığı ile bir FIM dönüşümü gerçekleştirir:
    PSM ve SPM (fim_spm_rate olasılığı ile).
    """

    # if koşulu fim_rate olasılığı ile tetiklenecektir
    # Bu, FIM dönüşümlerinin fim_rate olasılığına sahip örneklere uygulanacağı anlamına gelir
    if np_rng.binomial(1, fim_rate):

        # boundaries dizisinde saklanan rastgele oluşturulmuş indekslere göre örneği önek, orta ve sonek olarak ayırır.
        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:
            # önek, orta ve sonek belirten tokenleri dikkate alarak örneğin yeni toplam uzunluğunu hesaplar
            new_length = suffix.shape[0] + prefix.shape[0] + middle.shape[0] + 3
            diff = new_length - len(sample)

            # yeni uzunluk ile orijinal uzunluk arasında bir uzunluk farkı varsa aktarma veya kesme
            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)])

        # FIM dönüşümlerinin fim_spm_rateapply SPM varyantının olasılığı ile
        # SPM: sonek, önek, orta
        if np_rng.binomial(1, fim_spm_rate):
            new_sample = np.concatenate(
                [
                    [prefix_tok_id, suffix_tok_id],
                    suffix,
                    [middle_tok_id],
                    prefix,
                    middle,
                ]
            )
        # Aksi takdirde, FIM dönüşümlerinin PSM varyantını uygulayın
        # PSM: önek, sonek, orta
        else:

            new_sample = np.concatenate(
                [
                    [prefix_tok_id],
                    prefix,
                    [suffix_tok_id],
                    suffix,
                    [middle_tok_id],
                    middle,
                ]
            )
    else:
        # FIM dönüşümlerini uygulamama
        new_sample = sample

    return list(new_sample), np_rng
```

Sabit uzunlukta token yığınları döndürecek bir Iterable veri seti olan `ConstantLengthDataset`'i tanımlayalım. Bunu yapmak için, boyut sınırlarına ulaşana kadar orijinal veri setinden bir buffer metin okuyacağız ve ardından ham metni tokenize edilmiş girdilere dönüştürmek için tokenizer uygulayacağız. İsteğe bağlı olarak, bazı diziler üzerinde FIM dönüşümleri gerçekleştireceğiz (etkilenen dizilerin oranı `fim_rate` tarafından kontrol edilir).

Tanımlandıktan sonra, hem eğitim hem de doğrulama verilerinden `ConstantLengthDataset` örnekleri oluşturabiliriz.

```python
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
import random

# Bir metin dosyası akışından sabit uzunlukta token parçaları döndüren bir Iterable veri seti oluşturur.

class ConstantLengthDataset(IterableDataset):
    """
    Metin dosyalarının akışından sabit uzunlukta token parçaları döndüren yinelenebilir veri seti.
        Argümanlar:
            tokenizer (Tokenizer): Verileri işlemek için kullanılan işleyicidir.
            veri seti (dataset.Dataset): Metin dosyaları içeren veri seti.
            infinite (bool): True ise, veri seti sona ulaştıktan sonra iterator sıfırlanır, aksi takdirde durur.
            seq_length (int): Döndürülecek token sekanslarının uzunluğu.
            num_of_sequences (int): Buffer'da tutulacak token sequence sayısı.
            chars_per_token (int): Metin buffer'ındaki token sayısını tahmin etmek için kullanılan token başına karakter sayısı.
            fim_rate (float): Numunenin FIM ile permüle edileceği oran (0,0 ila 1,0).
            fim_spm_rate (float): SPM kullanacak FIM permütasyonlarının oranı (0.0 ila 1.0).
            seed (int): Rastgele sayı üreteci için seed.
    """

    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:
                # opsiyonel FIM permütasyonları
                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,
)
```

## Modelin Hazırlanması

Veriler hazırlandığına göre, şimdi modeli yükleme zamanı! Modelin kuantize edilmiş versiyonunu yükleyeceğiz.

Kuantizasyon verileri daha az bitle temsil ettiğinden bellek kullanımını azaltmamızı sağlar. Modeli kuantize etmek için `transformers` ile güzel bir entegrasyona sahip `bitsandbytes` kütüphanesini kullanacağız. Tek yapmamız gereken bir `bitsandbytes` config tanımlamak ve ardından modeli yüklerken bunu kullanmak.

4 bit kuantizasyonun farklı varyantları vardır, ancak genellikle daha iyi performans için NF4 kuantizasyonu kullanmanızı öneririz (`bnb_4bit_quant_type=“nf4”`).

`bnb_4bit_use_double_quant` seçeneği, parametre başına ek 0,4 bit tasarruf etmek için ilkinden sonra ikinci bir kuantizasyon ekler.

Niceleme hakkında daha fazla bilgi edinmek için [“Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA” blog post](https://huggingface.co/blog/4bit-transformers-bitsandbytes) adresine göz atın.

Tanımlandıktan sonra, modelin kuantize edilmiş versiyonunu yüklemek için yapılandırmayı `from_pretrained` metoduna aktarın.

```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 kuantize işlemi
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,  # Gradient Checkpoint kullanacağız
        trust_remote_code=True,
        use_flash_attention_2=True,
)
```

Eğitim için kuantize edilmiş bir model kullanırken, modeli ön işleme tabi tutmak amacıyla `prepare_model_for_kbit_training()` fonksiyonunu çağırmamız gerekiyor.

```python
model = prepare_model_for_kbit_training(model)
```

Artık kuantize model hazır olduğuna göre, bir LoRA yapılandırması ayarlayabiliriz. LoRA, eğitilebilir parametrelerin sayısını önemli ölçüde azaltarak fine-tune yapmayı daha verimli hale getirir.

LoRA tekniğini kullanarak bir modeli eğitmek için, temel modeli `PeftModel` olarak çağırmamız gerekir. Bu, `LoraConfig` ile LoRA yapılandırmasını tanımlamayı ve `LoraConfig` kullanarak orijinal modeli `get_peft_model()` ile çağırmayı içerir.

LoRA ve parametreleri hakkında daha fazla bilgi edinmek için [PEFT dokümantasyonuna](https://huggingface.co/docs/peft/conceptual_guides/lora) bakabilirsiniz.

```python
>>> # Lora config ayarları
>>> 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>

Gördüğünüz gibi LoRA tekniğini uyguladığımızda artık parametrelerin %1'inden daha azını eğitmemiz gerekecek.

## Modelin Eğitilmesi

Artık verileri hazırladığımıza ve modeli optimize ettiğimize göre, eğitimi başlatmak için her şeyi bir araya getirmeye hazırız.

Bir `Trainer` objesi oluşturmak için, eğitim konfigürasyonu tanımlamamız gerekiyor. En önemlisi, eğitimi yapılandırmak için tüm öznitelikleri içeren bir sınıf olan `TrainingArguments`'tır.

Bunlar, çalıştırabileceğiniz diğer tüm model eğitimlerine benziyor, bu nedenle burada ayrıntılara girmeyeceğiz.

```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,
)
```

Son adım olarak `Trainer` objesini oluşturun ve `train` metodunu çağırın.

```python
>>> trainer = Trainer(
...     model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset
... )

>>> print("Training...")
>>> trainer.train()
```

<pre>
Training...
</pre>

Fine-tune edilmiş modelinizi Hugging Face Hub hesabınıza yükleyebilirsiniz.

```python
trainer.push_to_hub()
```

## Inference

Model Hub'a yüklendikten sonra, inference için kullanılabilir. Bunu yapmak için önce orijinal temel modeli ve onun tokenizer'ını başlatırız. Sonra, fine-tune edilmiş ağırlıkları temel modelle birleştirmemiz gerekir.

```python
from peft import PeftModel
import torch

# önce orijinal modeli yükleyin
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()

# fine-tune edilmiş ağırlıkları orijinal model ile birleştirin
peft_model_id = f"Your_HF_username/{OUTPUT_DIR}"
model = PeftModel.from_pretrained(base_model, peft_model_id)
model.merge_and_unload()
```

Şimdi inference için birleştirilmiş modeli kullanabiliriz. Kolaylık olması açısından, bir `get_code_completion` tanımlayacağız - metin oluşturma parametreleriyle deneme yapmaktan çekinmeyin :)

```python
def get_code_completion(prefix, suffix):
    text = prompt = f"""<fim_prefix>{prefix}<fim_suffix>{suffix}<fim_middle>"""
    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]
```

Şimdi, kodun tamamlanmasını sağlamak için yapmamız gereken tek şey `get_code_complete` fonksiyonunu çağırmak ve tamamlanmasını istediğimiz ilk birkaç satırı önek olarak, boş bir dizeyi de sonek olarak geçirmek.

```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>

Bu notebook'ta daha önce PEFT kütüphanesini kullanmış biri olarak, `LoraConfig` oluşturmak için üretilen sonucun oldukça iyi olduğunu görebilirsiniz!

Modeli inference için başlattığımız hücreye geri dönersek ve fine-tune edilmiş ağırlıkları birleştirdiğimiz satırları yorum satırı yaparsak, orijinal modelin tam olarak aynı önek için ne üreteceğini görebilirsiniz:

```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>

Python syntax'ı olmasına rağmen, orijinal modelin `LoraConfig`'in ne yapması gerektiği konusunda hiçbir anlayışının olmadığını görebilirsiniz.

Bu tür fine-tune'ların tam bir fine-tune ile nasıl karşılaştırıldığını ve bu tür bir modeli VS Code'da Inference Endpoints veya yerel olarak co-pilot olarak nasıl kullanacağınızı öğrenmek için ["Personal Copilot: Train Your Own Coding Assistant" blog yazısına](https://huggingface.co/blog/personal-copilot) göz atın. Bu notebook orijinal blog yazısını tamamlar.

<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/tr/fine_tuning_code_llm_on_single_gpu.md" />

### Açık Kaynak AI Cookbook
https://huggingface.co/learn/cookbook/tr/index.md

# Açık Kaynak AI Cookbook

Open-Source AI Cookbook, açık kaynak araçlar ve modeller kullanarak AI uygulamaları oluşturmanın
ve çeşitli makine öğrenimi görevlerini çözmenin pratik yönlerini gösteren notebook'lardan oluşan bir koleksiyondur.

## Son eklenen Notebooklar

Son eklenen notebooklara göz atın:

- [Özel Bir Veri Seti Üzerinde Anlamsal Segmentasyon Modeli Fine-Tuning ve Inference API Üzerinden Kullanımı](semantic_segmentation_fine_tuning_inference)

Notebook'ları cookbook'un [GitHub deposunda](https://github.com/huggingface/cookbook) inceleyebilirsiniz.

## Katkıda Bulunmak

Open-Source AI Cookbook bir topluluk çalışması ve herkesin katkıları memnuniyetle kabul edilir.
Kendi "cookbook'unuzu" nasıl ekleyebileceğinizi öğrenmek için cookbook için olan
[Katkı rehberini](https://github.com/huggingface/cookbook/blob/main/README.md) inceleyebilirsiniz.


<EditOnGithub source="https://github.com/huggingface/cookbook/blob/main/notebooks/tr/index.md" />
