



"""simple docstring"""


import warnings

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







class       A_			(     SCREAMING_SNAKE_CASE_				):



						"""simple docstring"""
						__UpperCamelCase						=							["""image_processor""", """tokenizer"""]
						__UpperCamelCase						=							"""CLIPImageProcessor"""
						__UpperCamelCase						=							("""CLIPTokenizer""", """CLIPTokenizerFast""")






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

												UpperCAmelCase             = 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__(lowercase_   ,  lowercase_					)






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

												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             = self.tokenizer(lowercase_   ,  return_tensors=lowercase_   ,  **lowercase_					)

												if images is not None:
																		UpperCAmelCase             = self.image_processor(lowercase_   ,  return_tensors=lowercase_   ,  **lowercase_					)

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






						def 	UpperCAmelCase__     (					self :List[Any]   ,  *lowercase_ :Optional[Any]   ,  **lowercase_ :List[Any]					)				->			List[str]:
												return self.tokenizer.batch_decode(*lowercase_   ,  **lowercase_					)






						def 	UpperCAmelCase__     (					self :str   ,  *lowercase_ :Dict   ,  **lowercase_ :Optional[int]					)				->			int:
												return self.tokenizer.decode(*lowercase_   ,  **lowercase_					)






						@property
						def 	UpperCAmelCase__     (					self :Any					)				->			Optional[int]:
												UpperCAmelCase             = self.tokenizer.model_input_names
												UpperCAmelCase             = self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)






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






						@property
						def 	UpperCAmelCase__     (					self :List[str]					)				->			Tuple:
												warnings.warn(
												    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'   ,  lowercase_   ,  )
												return self.image_processor









from numpy import exp, pi, sqrt

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Tuple			,     SCREAMING_SNAKE_CASE__			:					float = 0.0			,     SCREAMING_SNAKE_CASE__			:					float = 1.0				):
     return 1 / sqrt(2 * pi * sigma**2				) * exp(-((x - mu) ** 2) / (2 * sigma**2)				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()





