import os
import pandas as pd
import nibabel as nib
import datasets
from datasets import (
    GeneratorBasedBuilder,
    SplitGenerator,
    Split,
    DatasetInfo,
    Features,
    Value,
)

logger = datasets.logging.get_logger(__name__)

_CITATION = """\
Please cite these papers when using this dataset:
- CartiMorph: A framework for automated knee articular cartilage morphometrics
- Quantifying Knee Cartilage Shape and Lesion: From Image to Metrics
@article{YAO2024103035,
title = {CartiMorph: A framework for automated knee articular cartilage morphometrics},
journal = {Medical Image Analysis},
author = {Yongcheng Yao and Junru Zhong and Liping Zhang and Sheheryar Khan and Weitian Chen},
volume = {91},
pages = {103035},
year = {2024},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2023.103035}
}
@InProceedings{10.1007/978-3-031-82007-6_16,
author="Yao, Yongcheng
and Chen, Weitian",
editor="Wu, Shandong
and Shabestari, Behrouz
and Xing, Lei",
title="Quantifying Knee Cartilage Shape and Lesion: From Image to Metrics",
booktitle="Applications of Medical Artificial Intelligence",
year="2025",
publisher="Springer Nature Switzerland",
address="Cham",
pages="162--172"
}
"""

_DESCRIPTION = """\
This is the official release of the OAIZIB-CM dataset.
(https://huggingface.co/datasets/YongchengYAO/OAIZIB-CM/blob/load_dataset-support/README.md)
"""

_HOME_PAGE = "https://huggingface.co/datasets/YongchengYAO/OAIZIB-CM"

_LICENSE = "CC BY-NC 4.0"


class OAIZIBCMDataset(GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")

    @staticmethod
    def load_nifti(example):
        """Map function to load NIFTI images on demand."""
        img_nib = nib.load(example["image_path"])
        image = img_nib.get_fdata().astype("float32")
        mask_nib = nib.load(example["mask_path"])
        mask = mask_nib.get_fdata().astype("float32")
        example["image"] = image
        example["mask"] = mask
        return example

    def _info(self):
        # Define dataset information including feature schema
        return DatasetInfo(
            description=_DESCRIPTION,
            features=Features(
                {
                    "image_path": Value("string"),
                    "mask_path": Value("string"),
                }
            ),
            citation=_CITATION,
            homepage=_HOME_PAGE,
            license=_LICENSE,
        )

    def _split_generators(self, dl_manager):
        # Download dataset metadata and data files
        train_csv_url = "train.csv"
        test_csv_url = "test.csv"
        csv_paths = dl_manager.download({"train": train_csv_url, "test": test_csv_url})
        logger.info(f"Downloaded CSV paths: {csv_paths}")

        # Extract main dataset archive
        data_root_dir = dl_manager.download_and_extract("data/OAIZIB-CM.zip")
        data_dir = os.path.join(data_root_dir, "OAIZIB-CM")
        logger.info(f"Data directory set to {data_dir}")

        # Load split metadata
        train_df = pd.read_csv(csv_paths["train"])
        test_df = pd.read_csv(csv_paths["test"])

        # Define split generators for training and test sets
        return [
            SplitGenerator(
                name=Split.TRAIN,
                gen_kwargs={"df": train_df, "split": "train", "data_dir": data_dir},
            ),
            SplitGenerator(
                name=Split.TEST,
                gen_kwargs={"df": test_df, "split": "test", "data_dir": data_dir},
            ),
        ]

    def _generate_examples(self, df, split, data_dir):
        # Set up directory paths based on the split
        if split == "train":
            img_dir = os.path.join(data_dir, "imagesTr")
            mask_dir = os.path.join(data_dir, "labelsTr")
        elif split == "test":
            img_dir = os.path.join(data_dir, "imagesTs")
            mask_dir = os.path.join(data_dir, "labelsTs")
        else:
            raise ValueError(f"Unknown split: {split}")

        # Log directories and ensure they exist
        logger.info(f"Looking for {split} images in: {img_dir}")
        logger.info(f"Looking for {split} masks in: {mask_dir}")
        os.makedirs(img_dir, exist_ok=True)
        os.makedirs(mask_dir, exist_ok=True)

        # Process and yield examples
        count = 0
        for idx, row in df.iterrows():
            img_file = row["image"]
            mask_file = row["mask"]
            img_path = os.path.join(img_dir, img_file)
            mask_path = os.path.join(mask_dir, mask_file)
            # Only yield paths, don't load data into memory
            yield idx, {
                "image_path": img_path,
                "mask_path": mask_path,
            }

        logger.info(f"Successfully yielded {count} examples for {split} split")