import datasets
import numpy as np
import glob
import os
from datetime import datetime
from huggingface_hub import snapshot_download
import tempfile

_DESCRIPTION = """\
Dataset for Arctic sea ice concentration spatio-temporal forecasting task.
"""

_CITATION = """\
@misc{borisova2025aiice,
  author = {Julia Borisova},
  title = {Aiice: sea ice concentration forecasting benchmark for AI models},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\\url{https://huggingface.co/datasets/ITMO-NSS/Aiice}}
}
"""

_HOMEPAGE = "https://huggingface.co/datasets/ITMO-NSS/Aiice"

logger = datasets.logging.get_logger(__name__)


class Aiice(datasets.GeneratorBasedBuilder):
    """Sea Ice concentration forecasting benchmark dataset."""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features({
                "date": datasets.Value("string"),  # ISO format date "YYYY-MM-DD"
                "matrix": datasets.Array2D(shape=(432, 432), dtype="float32"),
                "filename": datasets.Value("string"),
            }),
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Define splits and handle data downloading."""
        # Download the entire dataset repository
        repo_id = "ITMO-NSS/Aiice"
        
        # Use dl_manager to download files
        data_dir = dl_manager.download_and_extract(
            f"https://huggingface.co/datasets/{repo_id}/resolve/main/global_series.zip"
        )
        
        # If zip doesn't exist, download the entire repo
        if not os.path.exists(data_dir):
            logger.info("Downloading dataset files...")
            data_dir = snapshot_download(
                repo_id=repo_id,
                repo_type="dataset",
                cache_dir=dl_manager.download_cache_dir,
                allow_patterns="global_series/**/*.npy"
            )

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "data_dir": data_dir,
                    "date_range": ("1979-01-01", "2015-12-31"),
                    "split_name": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "data_dir": data_dir,
                    "date_range": ("2016-01-01", "2020-12-31"),
                    "split_name": "validation",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "data_dir": data_dir,
                    "date_range": ("2021-01-01", "2025-12-31"),
                    "split_name": "test",
                },
            ),
        ]

    def _generate_examples(self, data_dir, date_range, split_name):
        """Generate examples for each split."""
        # Find all .npy files
        npy_files = []
        search_path = os.path.join(data_dir, "global_series", "**", "*.npy")
        
        for file_path in glob.glob(search_path, recursive=True):
            npy_files.append(file_path)
        
        logger.info(f"Found {len(npy_files)} total files")

        start_date = datetime.strptime(date_range[0], "%Y-%m-%d")
        end_date = datetime.strptime(date_range[1], "%Y-%m-%d")

        filtered_files = []
        for file_path in npy_files:
            filename = os.path.basename(file_path)
            # Extract date from filename like 'osisaf_19790101.npy'
            date_str = filename.replace('.npy', '').replace('osisaf_', '')

            try:
                file_date = datetime.strptime(date_str, "%Y%m%d")
                if start_date <= file_date <= end_date:
                    filtered_files.append((file_path, filename, file_date))
            except ValueError as e:
                logger.warning(f"Could not parse date from {filename}: {e}")
                continue

        filtered_files.sort(key=lambda x: x[2])
        logger.info(f"After date filtering: {len(filtered_files)} files for {split_name} split")

        for idx, (file_path, filename, file_date) in enumerate(filtered_files):
            try:
                matrix = np.load(file_path).astype(np.float32)
                date_iso = file_date.strftime("%Y-%m-%d")

                yield idx, {
                    "date": date_iso,
                    "matrix": matrix,
                    "filename": filename,
                }
            except Exception as e:
                logger.warning(f"Could not load {file_path}: {e}")
                continue