from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class  UpperCamelCase				( a__ ):

     '''simple docstring'''

     A_										=  ["image_processor", "tokenizer"]
     A_										=  "BlipImageProcessor"
     A_										=  ("BertTokenizer", "BertTokenizerFast")



     def __init__(					self				,				A_				,				A_						) ->				List[str]:






           """simple docstring"""
           _lowerCamelCase			    =				False
           super().__init__(lowerCamelCase_				,				lowerCamelCase_						)
           _lowerCamelCase			    =				self.image_processor



     def __call__(					self				,				A_ = None				,				A_ = None				,				A_ = True				,				A_ = False				,				A_ = None				,				A_ = None				,				A_ = 0				,				A_ = None				,				A_ = None				,				A_ = False				,				A_ = False				,				A_ = False				,				A_ = False				,				A_ = False				,				A_ = True				,				A_ = None				,				**A_				,				) ->				BatchEncoding:






           """simple docstring"""
           if images is None and text is None:
                 raise ValueError('''You have to specify either images or text.'''						)

           # Get only text
           if images is None:
                 _lowerCamelCase			    =				self.tokenizer
                 _lowerCamelCase			    =				self.tokenizer(
                     text=lowerCamelCase_				,				add_special_tokens=lowerCamelCase_				,				padding=lowerCamelCase_				,				truncation=lowerCamelCase_				,				max_length=lowerCamelCase_				,				stride=lowerCamelCase_				,				pad_to_multiple_of=lowerCamelCase_				,				return_attention_mask=lowerCamelCase_				,				return_overflowing_tokens=lowerCamelCase_				,				return_special_tokens_mask=lowerCamelCase_				,				return_offsets_mapping=lowerCamelCase_				,				return_token_type_ids=lowerCamelCase_				,				return_length=lowerCamelCase_				,				verbose=lowerCamelCase_				,				return_tensors=lowerCamelCase_				,				**lowerCamelCase_				,				)
                 return text_encoding

           # add pixel_values
           _lowerCamelCase			    =				self.image_processor(lowerCamelCase_				,				return_tensors=lowerCamelCase_						)

           if text is not None:
                 _lowerCamelCase			    =				self.tokenizer(
                     text=lowerCamelCase_				,				add_special_tokens=lowerCamelCase_				,				padding=lowerCamelCase_				,				truncation=lowerCamelCase_				,				max_length=lowerCamelCase_				,				stride=lowerCamelCase_				,				pad_to_multiple_of=lowerCamelCase_				,				return_attention_mask=lowerCamelCase_				,				return_overflowing_tokens=lowerCamelCase_				,				return_special_tokens_mask=lowerCamelCase_				,				return_offsets_mapping=lowerCamelCase_				,				return_token_type_ids=lowerCamelCase_				,				return_length=lowerCamelCase_				,				verbose=lowerCamelCase_				,				return_tensors=lowerCamelCase_				,				**lowerCamelCase_				,				)
           else:
                 _lowerCamelCase			    =				None

           if text_encoding is not None:
                 encoding_image_processor.update(lowerCamelCase_						)

           return encoding_image_processor



     def 		UpperCamelCase_						(					self				,				*A_				,				**A_						) ->				str:






           """simple docstring"""
           return self.tokenizer.batch_decode(*lowerCamelCase_				,				**lowerCamelCase_						)



     def 		UpperCamelCase_						(					self				,				*A_				,				**A_						) ->				str:






           """simple docstring"""
           return self.tokenizer.decode(*lowerCamelCase_				,				**lowerCamelCase_						)





     @property
     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self.tokenizer.model_input_names
           _lowerCamelCase			    =				self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)



from typing import List

import numpy as np
def       __magic_name__(  __UpperCAmelCase		)						->    int:



      '''simple docstring'''

      _lowerCamelCase			    =				{key: len(__UpperCAmelCase		) for key, value in gen_kwargs.items() if isinstance(__UpperCAmelCase ,						__UpperCAmelCase		)}
      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			    =				max(lists_lengths.values() ,						default=0		)
      return max(1 ,						__UpperCAmelCase		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    List[range]:



      '''simple docstring'''

      _lowerCamelCase			    =				[]
      for group_idx in range(__UpperCAmelCase		):
            _lowerCamelCase			    =				num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
            if num_shards_to_add == 0:
                  break
            _lowerCamelCase			    =				shards_indices_per_group[-1].stop if shards_indices_per_group else 0
            _lowerCamelCase			    =				range(__UpperCAmelCase ,						start + num_shards_to_add		)
            shards_indices_per_group.append(__UpperCAmelCase		)
      return shards_indices_per_group
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    List[dict]:



      '''simple docstring'''

      _lowerCamelCase			    =				_number_of_shards_in_gen_kwargs(__UpperCAmelCase		)
      if num_shards == 1:
            return [dict(__UpperCAmelCase		)]
      else:
            _lowerCamelCase			    =				_distribute_shards(num_shards=__UpperCAmelCase ,						max_num_jobs=__UpperCAmelCase		)
            return [
                {
                    key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                    if isinstance(__UpperCAmelCase ,						__UpperCAmelCase		)
                    else value
                    for key, value in gen_kwargs.items()
                }
                for group_idx in range(len(__UpperCAmelCase		)		)
            ]
def       __magic_name__(  __UpperCAmelCase		)						->    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] ,						__UpperCAmelCase		)
          else gen_kwargs_list[0][key]
          for key in gen_kwargs_list[0]
      }




def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    dict:



      '''simple docstring'''

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