

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	Optional[int]						 =  ['image_processor', 'tokenizer']
  __lowerCAmelCase     :	Dict						 =  'ChineseCLIPImageProcessor'
  __lowerCAmelCase     :	List[str]						 =  ('BertTokenizer', 'BertTokenizerFast')

  def __init__(					self						,		_A=None						,		_A=None						,		**_A):
      SCREAMING_SNAKE_CASE_									=						None
      if "feature_extractor" in kwargs:
          warnings.warn(
              'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
              ' instead.'						,		_A						,		)
          SCREAMING_SNAKE_CASE_									=						kwargs.pop('feature_extractor')

      SCREAMING_SNAKE_CASE_									=						image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
          raise ValueError('You need to specify an `image_processor`.')
      if tokenizer is None:
          raise ValueError('You need to specify a `tokenizer`.')

      super().__init__(_A						,		_A)
      SCREAMING_SNAKE_CASE_									=						self.image_processor

  def __call__(					self						,		_A=None						,		_A=None						,		_A=None						,		**_A):

      if text is None and images is None:
          raise ValueError('You have to specify either text or images. Both cannot be none.')

      if text is not None:
          SCREAMING_SNAKE_CASE_									=						self.tokenizer(_A						,		return_tensors=_A						,		**_A)

      if images is not None:
          SCREAMING_SNAKE_CASE_									=						self.image_processor(_A						,		return_tensors=_A						,		**_A)

      if text is not None and images is not None:
          SCREAMING_SNAKE_CASE_									=						image_features.pixel_values
          return encoding
      elif text is not None:
          return encoding
      else:
          return BatchEncoding(data=dict(**_A)						,		tensor_type=_A)

  def 						lowerCAmelCase__     (					self						,		*_A						,		**_A):
      return self.tokenizer.batch_decode(*_A						,		**_A)

  def 						lowerCAmelCase__     (					self						,		*_A						,		**_A):
      return self.tokenizer.decode(*_A						,		**_A)

  @property
  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.tokenizer.model_input_names
      SCREAMING_SNAKE_CASE_									=						self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))


  @property
  def 						lowerCAmelCase__     (					self):
      warnings.warn(
          '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'						,		_A						,		)
      return self.image_processor_class





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


