

from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url



class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	Optional[int]						 =  ''
  __lowerCAmelCase     :	str						 =  'hf-legacy'  # "hf://"" is reserved for hffs

  def __init__(					self						,		_A = None						,		_A = None						,		**_A						,		):
      super().__init__(self						,		**_A)
      SCREAMING_SNAKE_CASE_									=						repo_info
      SCREAMING_SNAKE_CASE_									=						token
      SCREAMING_SNAKE_CASE_									=						None

  def 						lowerCAmelCase__     (					self):
      if self.dir_cache is None:
          SCREAMING_SNAKE_CASE_									=						{}
          for hf_file in self.repo_info.siblings:
              # TODO(QL): add sizes
              SCREAMING_SNAKE_CASE_									=						{
                  'name': hf_file.rfilename,
                  'size': None,
                  'type': 'file',
              }
              self.dir_cache.update(
                  {
                      str(_A): {'name': str(_A), 'size': None, 'type': 'directory'}
                      for d in list(PurePosixPath(hf_file.rfilename).parents)[:-1]
                  })

  def 						lowerCAmelCase__     (					self						,		_A						,		_A = "rb"						,		**_A						,		):
      if not isinstance(self.repo_info						,		_A):
          raise NotImplementedError(f"""Open is only implemented for dataset repositories, but got {self.repo_info}""")
      SCREAMING_SNAKE_CASE_									=						hf_hub_url(self.repo_info.id						,		_A						,		revision=self.repo_info.sha)
      return fsspec.open(
          _A						,		mode=_A						,		headers=get_authentication_headers_for_url(_A						,		use_auth_token=self.token)						,		client_kwargs={'trust_env': True}						,		).open()

  def 						lowerCAmelCase__     (					self						,		_A						,		**_A):
      self._get_dirs()
      SCREAMING_SNAKE_CASE_									=						self._strip_protocol(_A)
      if path in self.dir_cache:
          return self.dir_cache[path]
      else:
          raise FileNotFoundError(_A)


  def 						lowerCAmelCase__     (					self						,		_A						,		_A=False						,		**_A):
      self._get_dirs()
      SCREAMING_SNAKE_CASE_									=						PurePosixPath(path.strip('/'))
      SCREAMING_SNAKE_CASE_									=						{}
      for p, f in self.dir_cache.items():
          SCREAMING_SNAKE_CASE_									=						PurePosixPath(p.strip('/'))
          SCREAMING_SNAKE_CASE_									=						p.parent
          if root == path:
              SCREAMING_SNAKE_CASE_									=						f
      SCREAMING_SNAKE_CASE_									=						list(paths.values())
      if detail:
          return out
      else:
          return sorted(f['name'] for f in out)





from typing import List

import numpy as np


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		dict       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						{key: len(_SCREAMING_SNAKE_CASE       ) for key, value in gen_kwargs.items() if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )}
    if len(set(lists_lengths.values()       )       ) > 1:
        raise RuntimeError(
            (
                'Sharding is ambiguous for this dataset: '
                + 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
                + '\n'.join(f"""\t- key {key} has length {length}""" for key, length in lists_lengths.items()       )
                + '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
                + 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
            )       )
    SCREAMING_SNAKE_CASE_									=						max(lists_lengths.values() ,   default=0       )
    return max(1 ,   _SCREAMING_SNAKE_CASE       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[]
    for group_idx in range(_SCREAMING_SNAKE_CASE       ):
        SCREAMING_SNAKE_CASE_									=						num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
        if num_shards_to_add == 0:
            break
        SCREAMING_SNAKE_CASE_									=						shards_indices_per_group[-1].stop if shards_indices_per_group else 0
        SCREAMING_SNAKE_CASE_									=						range(_SCREAMING_SNAKE_CASE ,   start + num_shards_to_add       )
        shards_indices_per_group.append(_SCREAMING_SNAKE_CASE       )
    return shards_indices_per_group


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		dict ,   _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						_number_of_shards_in_gen_kwargs(_SCREAMING_SNAKE_CASE       )
    if num_shards == 1:
        return [dict(_SCREAMING_SNAKE_CASE       )]
    else:
        SCREAMING_SNAKE_CASE_									=						_distribute_shards(num_shards=_SCREAMING_SNAKE_CASE ,   max_num_jobs=_SCREAMING_SNAKE_CASE       )
        return [
            {
                key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
                else value
                for key, value in gen_kwargs.items()
            }
            for group_idx in range(len(_SCREAMING_SNAKE_CASE       )       )
        ]


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[dict]       ):





    """simple docstring"""


    return {
        key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
        if isinstance(gen_kwargs_list[0][key] ,   _SCREAMING_SNAKE_CASE       )
        else gen_kwargs_list[0][key]
        for key in gen_kwargs_list[0]
    }





def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		np.random.Generator ,   _SCREAMING_SNAKE_CASE					:		dict       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						{len(_SCREAMING_SNAKE_CASE       ) for value in gen_kwargs.values() if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )}
    SCREAMING_SNAKE_CASE_									=						{}
    for size in list_sizes:
        SCREAMING_SNAKE_CASE_									=						list(range(_SCREAMING_SNAKE_CASE       )       )
        rng.shuffle(indices_per_size[size]       )
    # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
    SCREAMING_SNAKE_CASE_									=						dict(_SCREAMING_SNAKE_CASE       )
    for key, value in shuffled_kwargs.items():
        if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       ):
            SCREAMING_SNAKE_CASE_									=						[value[i] for i in indices_per_size[len(_SCREAMING_SNAKE_CASE       )]]
    return shuffled_kwargs


