





from typing import List

import numpy as np





def 	lowerCAmelCase_					(			__a						)	->						int:







  """simple docstring"""





  lowerCamelCase__: 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."
        )						)
  lowerCamelCase__: int         =max(lists_lengths.values()      ,       default=0						)
  return max(1      ,       __a						)





def 	lowerCAmelCase_					(			__a      ,       __a						)	->						List[range]:







  """simple docstring"""





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





def 	lowerCAmelCase_					(			__a      ,       __a						)	->						List[dict]:







  """simple docstring"""





  lowerCamelCase__: List[Any]         =_number_of_shards_in_gen_kwargs(__a						)
  if num_shards == 1:
    return [dict(__a						)]
  else:
    lowerCamelCase__: List[str]         =_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						)	->						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]      ,       __a						)
      else gen_kwargs_list[0][key]
      for key in gen_kwargs_list[0]
  }






def 	lowerCAmelCase_					(			__a      ,       __a						)	->						dict:







  """simple docstring"""





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





'''simple docstring'''






import warnings

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






class    __magic_name__				(						lowerCAmelCase							):
       UpperCAmelCase		 =["image_processor", "tokenizer"]
       UpperCAmelCase		 ="CLIPImageProcessor"
       UpperCAmelCase		 =("CLIPTokenizer", "CLIPTokenizerFast")






       def __init__(     self    ,				snake_case=None    ,				snake_case=None    ,				**snake_case)		->    Dict:




          '''simple docstring'''
          _UpperCAmelCase  :  Union[str, Any]				       =None
          if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'    ,				snake_case    ,				)
             _UpperCAmelCase  :  str				       =kwargs.pop('feature_extractor')

          _UpperCAmelCase  :  List[str]				       =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__(snake_case    ,				snake_case)






       def __call__(     self    ,				snake_case=None    ,				snake_case=None    ,				snake_case=None    ,				**snake_case)		->    Optional[Any]:




          '''simple docstring'''

          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:
             _UpperCAmelCase  :  List[Any]				       =self.tokenizer(snake_case    ,				return_tensors=snake_case    ,				**snake_case)

          if images is not None:
             _UpperCAmelCase  :  List[str]				       =self.image_processor(snake_case    ,				return_tensors=snake_case    ,				**snake_case)

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






       def      lowerCAmelCase						(     self    ,				*snake_case    ,				**snake_case)		->    int:




          '''simple docstring'''
          return self.tokenizer.batch_decode(*snake_case    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				*snake_case    ,				**snake_case)		->    Optional[int]:




          '''simple docstring'''
          return self.tokenizer.decode(*snake_case    ,				**snake_case)






       @property
       def      lowerCAmelCase						(     self)		->    List[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  List[Any]				       =self.tokenizer.model_input_names
          _UpperCAmelCase  :  Any				       =self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))






       @property
       def      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          warnings.warn(
              '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,				snake_case    ,				)
          return self.image_processor_class






       @property
       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          warnings.warn(
              '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,				snake_case    ,				)
          return self.image_processor

