"""MMSearch-Plus dataset with transparent decryption."""

import base64
import hashlib
import io
import os
from typing import Dict, Any, List
import datasets
from PIL import Image

_CITATION = """\
@article{tao2025mmsearch,
  title={MMSearch-Plus: A Simple Yet Challenging Benchmark for Multimodal Browsing Agents},
  author={Tao, Xijia and Teng, Yihua and Su, Xinxing and Fu, Xinyu and Wu, Jihao and Tao, Chaofan and Liu, Ziru and Bai, Haoli and Liu, Rui and Kong, Lingpeng},
  journal={arXiv preprint arXiv:2508.21475},
  year={2025}
}
"""

_DESCRIPTION = """\
MMSearch-Plus is a challenging benchmark designed to test multimodal browsing agents' ability to perform genuine visual reasoning.
Unlike existing benchmarks where many tasks can be solved with text-only approaches, MMSearch-Plus requires models to extract
and use fine-grained visual cues through iterative image-text retrieval.
"""

_HOMEPAGE = "https://mmsearch-plus.github.io/"

_LICENSE = "CC BY-NC 4.0"

_URLS = {
    "train": [
        "data-00000-of-00002.arrow",
        "data-00001-of-00002.arrow"
    ]
}

def derive_key(password: str, length: int) -> bytes:
    """Derive encryption key from password using SHA-256."""
    hasher = hashlib.sha256()
    hasher.update(password.encode())
    key = hasher.digest()
    return key * (length // len(key)) + key[: length % len(key)]

def decrypt_image(ciphertext_b64: str, password: str) -> Image.Image:
    """Decrypt base64-encoded encrypted image bytes back to PIL Image."""
    if not ciphertext_b64:
        return None

    try:
        encrypted = base64.b64decode(ciphertext_b64)
        key = derive_key(password, len(encrypted))
        decrypted = bytes([a ^ b for a, b in zip(encrypted, key)])

        # Convert bytes back to PIL Image
        img_buffer = io.BytesIO(decrypted)
        image = Image.open(img_buffer)
        return image
    except Exception:
        return None

def decrypt_text(ciphertext_b64: str, password: str) -> str:
    """Decrypt base64-encoded ciphertext using XOR cipher with derived key."""
    if not ciphertext_b64:
        return ciphertext_b64

    try:
        encrypted = base64.b64decode(ciphertext_b64)
        key = derive_key(password, len(encrypted))
        decrypted = bytes([a ^ b for a, b in zip(encrypted, key)])
        return decrypted.decode('utf-8')
    except Exception:
        return ciphertext_b64

class MmsearchPlus(datasets.GeneratorBasedBuilder):
    """MMSearch-Plus dataset with transparent decryption."""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        # Define features to handle the complete dataset schema
        features = datasets.Features({
            "question": datasets.Value("string"),
            "answer": datasets.Sequence(datasets.Value("string")),
            "num_images": datasets.Value("int64"),
            "arxiv_id": datasets.Value("string"),
            "video_url": datasets.Value("string"),
            "category": datasets.Value("string"),
            "difficulty": datasets.Value("string"),
            "subtask": datasets.Value("string"),
            # Image fields (not encrypted, kept as PIL Images)
            "img_1": datasets.Image(),
            "img_2": datasets.Image(),
            "img_3": datasets.Image(),
            "img_4": datasets.Image(),
            "img_5": datasets.Image(),
            # Additional fields that might exist in the dataset
            "choices": datasets.Sequence(datasets.Value("string")),
            "question_zh": datasets.Value("string"),
            "answer_zh": datasets.Sequence(datasets.Value("string")),
            "regex": datasets.Value("string"),
            "text_criteria": datasets.Value("string"),
            "original_filename": datasets.Value("string"),
            "screenshots_dir": datasets.Value("string"),
            "time_points": datasets.Sequence(datasets.Value("string")),
            "search_query": datasets.Value("string"),
            "question_type": datasets.Value("string"),
            "requires_image_understanding": datasets.Value("bool"),
            "source": datasets.Value("string"),
            "content_keywords": datasets.Value("string"),
            "reasoning": datasets.Value("string"),
            "processed_at": datasets.Value("string"),
            "model_used": datasets.Value("string"),
            "entry_index": datasets.Value("int64"),
            "original_image_paths": datasets.Sequence(datasets.Value("string")),
            "masked_image_paths": datasets.Sequence(datasets.Value("string")),
            "is_valid": datasets.Value("bool"),
        })

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        # Get canary from environment variable or kwargs
        canary = os.environ.get("MMSEARCH_PLUS")

        # Check if passed in the builder's initialization
        if hasattr(self, 'canary'):
            canary = self.canary

        if not canary:
            raise ValueError(
                "Canary string is required for decryption. Either set the MMSEARCH_PLUS "
                "environment variable or pass it via the dataset loading kwargs. "
                "Example: load_dataset('path/to/dataset', trust_remote_code=True) after setting "
                "os.environ['MMSEARCH_PLUS'] = 'your_canary_string'"
            )

        # Download files
        urls = _URLS["train"]
        downloaded_files = dl_manager.download(urls)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepaths": downloaded_files,
                    "canary": canary,
                },
            ),
        ]

    def _generate_examples(self, filepaths, canary):
        """Generate examples with transparent decryption."""
        key = 0

        for filepath in filepaths:
            # Load the arrow file
            arrow_dataset = datasets.Dataset.from_file(filepath)

            for idx in range(len(arrow_dataset)):
                example = arrow_dataset[idx]

                # Decrypt text fields - matches encryption script fields
                text_fields = ['question', 'video_url', 'arxiv_id']

                for field in text_fields:
                    if example.get(field):
                        example[field] = decrypt_text(example[field], canary)

                # Handle answer field (list of strings)
                if example.get("answer"):
                    decrypted_answers = []
                    for answer in example["answer"]:
                        if answer:
                            decrypted_answers.append(decrypt_text(answer, canary))
                        else:
                            decrypted_answers.append(answer)
                    example["answer"] = decrypted_answers

                # Images are not encrypted - they remain as PIL Image objects
                # No image decryption needed as per the encryption script

                yield key, example
                key += 1