




'''simple docstring'''





import warnings

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




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				Optional[Any]							=   ["image_processor", "tokenizer"]
			lowerCAmelCase_					:				Any							=   "CLIPImageProcessor"
			lowerCAmelCase_					:				List[Any]							=   ("CLIPTokenizer", "CLIPTokenizerFast")




			def __init__( self    ,							a__=None    ,							a__=None    ,							**a__							)    ->    Union[str, Any]:

										'''simple docstring'''




										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__    ,							)
																	snake_case_      =				kwargs.pop("feature_extractor"							)

										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__							)




			def __call__( self    ,							a__=None    ,							a__=None    ,							a__=None    ,							**a__							)    ->    List[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:
																	snake_case_      =				self.tokenizer(a__    ,							return_tensors=a__    ,							**a__							)

										if images is not None:
																	snake_case_      =				self.image_processor(a__    ,							return_tensors=a__    ,							**a__							)

										if text is not None and images is not None:
																	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__							)    ->    str:

										'''simple docstring'''




										return self.tokenizer.batch_decode(*a__    ,							**a__							)




			def        lowerCAmelCase__    ( self    ,							*a__    ,							**a__							)    ->    Optional[int]:

										'''simple docstring'''




										return self.tokenizer.decode(*a__    ,							**a__							)




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

										'''simple docstring'''




										snake_case_      =				self.tokenizer.model_input_names
										snake_case_      =				self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)




			@property
			def        lowerCAmelCase__    ( self							)    ->    str:

										'''simple docstring'''




										warnings.warn(
										    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."    ,							a__    ,							)
										return self.image_processor_class




			@property
			def        lowerCAmelCase__    ( self							)    ->    Any:

										'''simple docstring'''




										warnings.warn(
										    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."    ,							a__    ,							)
										return self.image_processor










'''simple docstring'''





def 					UpperCamelCase_(     snake_case							:	int = 1_0_0_0_0_0_0					):

							'''simple docstring'''






							snake_case_      =				set(range(3							,    snake_case							,    2					)					)
							primes.add(2					)
							for p in range(3							,    snake_case							,    2					):
														if p not in primes:
																					continue
														primes.difference_update(set(range(p * p							,    snake_case							,    snake_case					)					)					)

							snake_case_      =				[float(snake_case					) for n in range(limit + 1					)]

							for p in primes:
														for n in range(snake_case							,    limit + 1							,    snake_case					):
																					phi[n] *= 1 - 1 / p

							return int(sum(phi[2:]					)					)


if __name__ == "__main__":
		print(F"{solution() = }")




