import datasets
import glob
import os
import numpy as np

SHARD_SIZE = 2500
NUM_SHARDS = 40
_URLS = {
  'train': [
      f'https://huggingface.co/datasets/commaai/commavq/resolve/main/data_{i*SHARD_SIZE}_to_{(i+1)*SHARD_SIZE}.zip' for i in range(NUM_SHARDS)
    ],
  'val': f'https://huggingface.co/datasets/commaai/commavq/resolve/main/val.zip'
}

_DESCRIPTION = """\
TODO
"""

class CommaVQ(datasets.GeneratorBasedBuilder):

  def _info(self):
    return datasets.DatasetInfo(
      description=_DESCRIPTION,
      features=datasets.Features(
        {"path": datasets.Value("string")}
      )
    )

  def _split_generators(self, dl_manager):
    """Returns SplitGenerators."""
    dl_manager.download_config.ignore_url_params = True
    downloaded_files = dl_manager.download(_URLS)
    local_extracted_archive = dl_manager.extract(downloaded_files) if not dl_manager.is_streaming else None
    return [
      datasets.SplitGenerator(
        name=f'train_{i}',
        gen_kwargs={"local_extracted_archive":local_extracted_archive['train'][i], "files": dl_manager.iter_archive(downloaded_files['train'][i])}
        ) for i in range(len(downloaded_files['train']))] + [
      datasets.SplitGenerator(
        name='val',
        gen_kwargs={"local_extracted_archive":local_extracted_archive['val'], "files": dl_manager.iter_archive(downloaded_files['val'])}
        )
        ]

  def _generate_examples(self, local_extracted_archive, files):
    files = glob.glob(os.path.join(local_extracted_archive, '*.npy'))
    for path in files:
      file_name = os.path.basename(path)
      yield file_name, {'path': path}

  def _get_examples_iterable_for_split(self, split_generator):
    for path in split_generator.gen_kwargs['files']:
      yield path[0], {'path': path[0]}