"""EMT dataset."""

import os
import datasets

_HOMEPAGE = "https://github.com/AV-Lab/emt-dataset"
_LICENSE = "CC-BY-SA 4.0"

_CITATION = """  
@article{EMTdataset2025,  
      title={EMT: A Visual Multi-Task Benchmark Dataset for Autonomous Driving in the Arab Gulf Region},  
      author={Nadya Abdel Madjid and Murad Mebrahtu and Abdelmoamen Nasser and Bilal Hassan and Naoufel Werghi and Jorge Dias and Majid Khonji},  
      year={2025},  
      eprint={2502.19260},  
      archivePrefix={arXiv},  
      primaryClass={cs.CV},  
      url={https://arxiv.org/abs/2502.19260}  
}  
""" 

_DESCRIPTION = """\
A multi-task dataset for detection, tracking, prediction, and intention prediction. 
This dataset includes 34,386 annotated frames collected over 57 minutes of driving, with annotations for detection + tracking.
"""

# Image archive URL
_IMAGE_ARCHIVE_URL = "https://huggingface.co/datasets/KuAvLab/EMT/blob/main/emt_images.tar.gz"

# Annotations URL (organized in train/test subfolders)
_ANNOTATION_REPO = "https://huggingface.co/datasets/Murdism/EMT/resolve/main/annotations"


class EMT(datasets.GeneratorBasedBuilder):
    """EMT dataset."""

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "image": datasets.Image(),
                    "objects": datasets.Sequence(
                        {
                            "bbox": datasets.Sequence(datasets.Float32()),
                            "class_id": datasets.Value("int32"),
                            "track_id": datasets.Value("int32"),
                            "class_name": datasets.Value("string"),
                        }
                    ),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        archive_path = dl_manager.download(_IMAGE_ARCHIVE_URL)
        annotation_paths = {
            "train": dl_manager.download_and_extract(f"{_ANNOTATION_REPO}/train/"),
            "test": dl_manager.download_and_extract(f"{_ANNOTATION_REPO}/test/"),
        }

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "images": dl_manager.iter_archive(archive_path),
                    "annotation_path": annotation_paths["train"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "images": dl_manager.iter_archive(archive_path),
                    "annotation_path": annotation_paths["test"],
                },
            ),
        ]

    def _generate_examples(self, images, annotation_path):
        """Generate examples from annotations and image archive."""

        # Load annotation files
        annotations = {}
        for root, _, files in os.walk(annotation_path):
            for file in files:
                with open(os.path.join(root, file), "r", encoding="utf-8") as f:
                    for line in f:
                        parts = line.strip().split()
                        frame_id, track_id, class_name = parts[:3]
                        bbox = list(map(float, parts[4:8]))  # Extract bounding box
                        class_id = hash(class_name) % 1000  # Convert class_name to numeric ID

                        img_path = f"{frame_id}.jpg"
                        if img_path not in annotations:
                            annotations[img_path] = []
                        annotations[img_path].append(
                            {
                                "bbox": bbox,
                                "class_id": class_id,
                                "track_id": int(track_id),
                                "class_name": class_name,
                            }
                        )

        # Yield dataset entries
        idx = 0
        for file_path, file_obj in images:
            img_name = os.path.basename(file_path)
            if img_name in annotations:
                yield idx, {
                    "image": {"path": file_path, "bytes": file_obj.read()},
                    "objects": annotations[img_name],
                }
                idx += 1