"""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 and tracking.
"""

# Annotation repository
_ANNOTATION_REPO = "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/annotations"

# Tar file URLs for images
_TRAIN_IMAGE_ARCHIVE_URL = "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/train_images.tar.gz"
_TEST_IMAGE_ARCHIVE_URL = "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/test_images.tar.gz"


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

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="full_size",
            description="All images are in their original size.",
            version=datasets.Version("1.0.0"),
        )
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "image": datasets.Image(),
                    "objects": datasets.Sequence(
                        {
                            "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
                            "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):
        """Download train/test images and annotations."""
        image_urls = {
            "train": _TRAIN_IMAGE_ARCHIVE_URL,
            "test": _TEST_IMAGE_ARCHIVE_URL,
        }
    
        # Download the individual annotation files for train and test
        annotation_urls = {
            "train": _ANNOTATION_REPO + "/train/",
            "test": _ANNOTATION_REPO + "/test/",
        }
    
        # Download image files
        images = {
            "train": dl_manager.iter_archive(image_urls["train"]),
            "test": dl_manager.iter_archive(image_urls["test"]),
        }
    
        # Download annotation files
        annotations = {
            "train": dl_manager.download_and_extract(annotation_urls["train"]),
            "test": dl_manager.download_and_extract(annotation_urls["test"]),
        }
    
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "images": images["train"],
                    "annotation_path": annotations["train"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "images": images["test"],
                    "annotation_path": annotations["test"],
                },
            ),
        ]
    
    def _generate_examples(self, images, annotation_path):
        """Generate dataset examples by matching images to their corresponding annotations."""
        
        annotations = {}
        
        # Load all annotations into memory
        for ann_file in os.listdir(annotation_path):
            video_name = os.path.splitext(ann_file)[0]  # Get video folder name
            ann_path = os.path.join(annotation_path, ann_file)
            
            with open(ann_path, "r", encoding="utf-8") as f:
                for line in f:
                    parts = line.strip().split()
                    if len(parts) < 8:
                        continue
    
                    frame_id, track_id, class_name = parts[:3]
                    bbox = list(map(float, parts[4:8]))
                    class_id = _GT_OBJECT_CLASSES.get(class_name, -1)
                    img_name = f"{frame_id}.jpg"
    
                    # Store annotation in a dictionary
                    key = f"{video_name}/{img_name}"
                    if key not in annotations:
                        annotations[key] = []
    
                    annotations[key].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)
            video_name = os.path.basename(os.path.dirname(file_path))  # Match the video folder
            key = f"{video_name}/{img_name}"
    
            if key in annotations:
                yield idx, {
                    "image": {"path": file_path, "bytes": file_obj.read()},
                    "objects": annotations[key],
                }
                idx += 1