



'''simple docstring'''






from typing import List

import numpy as np
def    lowerCamelCase__ (		_A       ):
  a		:      Any       =  {key: len(_A       ) for key, value in gen_kwargs.items() if isinstance(_A				,    _A       )}
  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.'
        )       )
  a		:      Union[str, Any]       =  max(lists_lengths.values()				,    default=0       )
  return max(1				,    _A       )
def    lowerCamelCase__ (		_A				,    _A       ):
  a		:      Dict       =  []
  for group_idx in range(_A       ):
    a		:      str       =  num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
    if num_shards_to_add == 0:
      break
    a		:      Union[str, Any]       =  shards_indices_per_group[-1].stop if shards_indices_per_group else 0
    a		:      Optional[int]       =  range(_A				,    start + num_shards_to_add       )
    shards_indices_per_group.append(_A       )
  return shards_indices_per_group
def    lowerCamelCase__ (		_A				,    _A       ):
  a		:      List[Any]       =  _number_of_shards_in_gen_kwargs(_A       )
  if num_shards == 1:
    return [dict(_A       )]
  else:
    a		:      Dict       =  _distribute_shards(num_shards=_A				,    max_num_jobs=_A       )
    return [
        {
            key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
            if isinstance(_A				,    _A       )
            else value
            for key, value in gen_kwargs.items()
        }
        for group_idx in range(len(_A       )       )
    ]
def    lowerCamelCase__ (		_A       ):
  return {
      key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
      if isinstance(gen_kwargs_list[0][key]				,    _A       )
      else gen_kwargs_list[0][key]
      for key in gen_kwargs_list[0]
  }

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




'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


lowerCAmelCase:      Optional[Any]		 =		logging.get_logger(__name__)

class 							a__(      lowerCamelCase__			):







    def __init__(   self				:						Union[str, Any]		,    *__snake_case				:						List[str]		,    **__snake_case				:						Union[str, Any]			):
      warnings.warn(
          'The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
          ' Please use ChineseCLIPImageProcessor instead.'		,    __snake_case		,    )
      super().__init__(*__snake_case		,    **__snake_case			)