


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 	_lowercase				(      A__	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      List[str]      =		['''image_processor''', '''tokenizer''']
				SCREAMING_SNAKE_CASE__    :      Tuple      =		'''AutoImageProcessor'''
				SCREAMING_SNAKE_CASE__    :      int      =		'''AutoTokenizer'''


				def __init__(	self      :Optional[Any]      ,      lowerCAmelCase__      :Any=None      ,      lowerCAmelCase__      :int=None      ,      **lowerCAmelCase__      :Union[str, Any]     )		->      Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:					Dict		      =		None
										if "feature_extractor" in kwargs:
																warnings.warn(
																    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																    ''' instead.'''      ,      lowerCAmelCase__      ,      )
																__SCREAMING_SNAKE_CASE						:					Dict		      =		kwargs.pop('''feature_extractor'''     )

										__SCREAMING_SNAKE_CASE						:					str		      =		image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
																raise ValueError('''You have to specify an image_processor.'''     )
										if tokenizer is None:
																raise ValueError('''You have to specify a tokenizer.'''     )

										super().__init__(lowerCAmelCase__      ,      lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.image_processor


				def __call__(	self      :Tuple      ,      lowerCAmelCase__      :Any=None      ,      lowerCAmelCase__      :Union[str, Any]=None      ,      lowerCAmelCase__      :Tuple=None      ,      **lowerCAmelCase__      :int     )		->      str:

										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						:					str		      =		self.tokenizer(lowerCAmelCase__      ,      return_tensors=lowerCAmelCase__      ,      **lowerCAmelCase__     )

										if images is not None:
																__SCREAMING_SNAKE_CASE						:					str		      =		self.image_processor(lowerCAmelCase__      ,      return_tensors=lowerCAmelCase__      ,      **lowerCAmelCase__     )

										if text is not None and images is not None:
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		image_features.pixel_values
																return encoding
										elif text is not None:
																return encoding
										else:
																return BatchEncoding(data=dict(**lowerCAmelCase__     )      ,      tensor_type=lowerCAmelCase__     )


				def 		__magic_name__(	self      :List[str]      ,      *lowerCAmelCase__      :List[Any]      ,      **lowerCAmelCase__      :Any     )		->      List[Any]:
										return self.tokenizer.batch_decode(*lowerCAmelCase__      ,      **lowerCAmelCase__     )


				def 		__magic_name__(	self      :List[Any]      ,      *lowerCAmelCase__      :Any      ,      **lowerCAmelCase__      :Tuple     )		->      Tuple:
										return self.tokenizer.decode(*lowerCAmelCase__      ,      **lowerCAmelCase__     )


				@property
				def 		__magic_name__(	self      :List[str]     )		->      List[str]:
										__SCREAMING_SNAKE_CASE						:					str		      =		self.tokenizer.model_input_names
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )


				@property
				def 		__magic_name__(	self      :Dict     )		->      List[Any]:
										warnings.warn(
										    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''      ,      lowerCAmelCase__      ,      )
										return self.image_processor_class



				@property
				def 		__magic_name__(	self      :Union[str, Any]     )		->      Optional[int]:
										warnings.warn(
										    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''      ,      lowerCAmelCase__      ,      )
										return self.image_processor




def 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):
						__SCREAMING_SNAKE_CASE						:					int		      =		len(lowercase__				)
						__SCREAMING_SNAKE_CASE						:					int		      =		len(lowercase__				)
						__SCREAMING_SNAKE_CASE						:					int		      =		(
						    first_input_list_length
						    if first_input_list_length > second_input_list_length
						    else second_input_list_length
						)
						__SCREAMING_SNAKE_CASE						:					list		      =		[]
						for char_count in range(lowercase__				):
												if char_count < first_input_list_length:
																		output_result_list.append(first_input_list[char_count]				)
												if char_count < second_input_list_length:
																		output_result_list.append(second_input_list[char_count]				)

						return output_result_list


if __name__ == "__main__":
						print(alternative_list_arrange(['A', 'B', 'C'], [1, 2, 3, 4, 5]), end=' ')
