import json
import os
import datasets

_DESCRIPTION = """
SPLICE is a human-curated benchmark designed to evaluate the temporal and causal reasoning
capabilities of Multimodal Large Language Models (MLLMs). The core task is to reorder a set of
shuffled video segments from a single procedural event into their correct chronological sequence.
The dataset is derived from 3,381 instructional videos from the COIN dataset, segmented into
11,423 coherent event clips.
"""

_CITATION = """
@inproceedings{
ballout2025can,
title={{Can you {SPLICE} it together? A Human Curated Benchmark for Probing Visual Reasoning in {VLM}s}},
author={Mohamad Ballout* and Okajevo Wilfred* and Seyedalireza Yaghoubi and Nohayr Muhammad Abdelmoneim and Julius Mayer and Elia Bruni},
booktitle={The 2025 Conference on Empirical Methods in Natural Language Processing},
year={2025},
url={https://openreview.net/forum?id=deFgBHsHxl}
}
"""

class SpliceBenchmark(datasets.GeneratorBasedBuilder):
    """The SPLICE Benchmark Dataset."""

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "video_id": datasets.Value("string"),
                    "domain": datasets.Value("string"),
                    "class": datasets.Value("string"),
                    "subset": datasets.Value("string"),
                    "video_url": datasets.Value("string"),
                    "duration": datasets.Value("float"),
                    "segments": datasets.Sequence(
                        {
                            "part": datasets.Value("int32"),
                            "segment_id": datasets.Value("string"),
                            "label": datasets.Value("string"),
                            "start": datasets.Value("float"),
                            "end": datasets.Value("float"),
                            "video_clip": datasets.Video()
                        }
                    ),
                }
            ),
            homepage="https://huggingface.co/datasets/prokajevo/splice-benchmark",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data_dir = dl_manager.download_and_extract(".")
        metadata_path = os.path.join(data_dir, "splice_segment_metadata.json")
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"metadata_path": metadata_path, "data_dir": data_dir},
            ),
        ]

    def _generate_examples(self, metadata_path, data_dir):
            with open(metadata_path, "r") as f:
                data = json.load(f)

            video_count = 0
            for video_info in data:
                try:
                    if not video_info.get("segments"):
                        continue
                        
                    segments_data = []
                    for segment in video_info.get("segments", []):
                        if "output_path" in segment and segment["output_path"]:
                            video_path = os.path.join(data_dir, segment["output_path"])
                            if os.path.exists(video_path):
                                segments_data.append({
                                    "part": segment.get("part", -1),
                                    "segment_id": segment.get("segment_id", ""),
                                    "label": segment.get("label", ""),
                                    "start": segment.get("start", -1.0),
                                    "end": segment.get("end", -1.0),
                                    "video_clip": video_path,
                                })
                    
                    if segments_data:
                        yield video_count, {
                            "video_id": video_info.get("video_id", ""),
                            "domain": video_info.get("Domain", ""),
                            "class": video_info.get("class", ""),
                            "subset": video_info.get("subset", ""),
                            "video_url": video_info.get("video_url", ""),
                            "duration": video_info.get("duration", -1.0),
                            "segments": segments_data,  
                        }
                        video_count += 1
                        
                except Exception as e:
                    print(f"--> WARNING: Skipping corrupted data for video {video_info.get('video_id', 'unknown')}. Error: {e}")
                    continue