Remove old mmsearch_plus.py file

mmsearch_plus.py

@@ -1,189 +0,0 @@
-"""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