




"""simple docstring"""







a								=							1_0_0_0





def 							lowercase   (snake_case__				:	float	)       ->					float:


				'''simple docstring'''






				return x * x





def 							lowercase   (snake_case__				:	List[Any] ,    snake_case__				:	float ,    snake_case__				:	float ,    snake_case__				:	int = 4	)       ->					float:


				'''simple docstring'''






				assert callable(
				    snake_case__	), f'''the function(object) passed should be callable your input : {function}'''
				assert isinstance(snake_case__ ,    (float, int)	), f'''a should be float or integer your input : {a}'''
				assert isinstance(function(snake_case__	) ,    (float, int)	), (
				    "the function should return integer or float return type of your function, "
				    f'''{type(snake_case__	)}'''
				)
				assert isinstance(snake_case__ ,    (float, int)	), f'''b should be float or integer your input : {b}'''
				assert (
				    isinstance(snake_case__ ,    snake_case__	) and precision > 0
				), f'''precision should be positive integer your input : {precision}'''

				# just applying the formula of simpson for approximate integration written in
				# mentioned article in first comment of this file and above this function

				lowerCAmelCase		=			(b - a) / N_STEPS
				lowerCAmelCase		=			function(snake_case__	) + function(snake_case__	)

				for i in range(1 ,    snake_case__	):
								lowerCAmelCase		=			a + h * i
								result += function(snake_case__	) * (4 if i % 2 else 2)

				result *= h / 3
				return round(snake_case__ ,    snake_case__	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()






"""simple docstring"""







import warnings

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






class  SCREAMING_SNAKE_CASE__   (   _a   ):
						_a        =  ['image_processor', 'tokenizer']
						_a        =  'VideoMAEImageProcessor'
						_a        =  ('CLIPTokenizer', 'CLIPTokenizerFast')


						def __init__(							self			:     Union[str, Any]  ,  lowerCAmelCase			:     Optional[Any]=None  ,  lowerCAmelCase			:     Tuple=None  ,  **lowerCAmelCase			:     List[str]   ):
										lowerCAmelCase		=			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  ,  )
														lowerCAmelCase		=			kwargs.pop("""feature_extractor"""   )

										lowerCAmelCase		=			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__(lowerCAmelCase  ,  lowerCAmelCase   )
										lowerCAmelCase		=			self.image_processor


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

										if text is None and videos is None:
														raise ValueError("""You have to specify either text or videos. Both cannot be none."""   )

										if text is not None:
														lowerCAmelCase		=			self.tokenizer(lowerCAmelCase  ,  return_tensors=lowerCAmelCase  ,  **lowerCAmelCase   )

										if videos is not None:
														lowerCAmelCase		=			self.image_processor(lowerCAmelCase  ,  return_tensors=lowerCAmelCase  ,  **lowerCAmelCase   )

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


						def 					__lowercase	(							self			:     str  ,  *lowerCAmelCase			:     List[str]  ,  **lowerCAmelCase			:     int   ):
										return self.tokenizer.batch_decode(*lowerCAmelCase  ,  **lowerCAmelCase   )


						def 					__lowercase	(							self			:     List[str]  ,  *lowerCAmelCase			:     Any  ,  **lowerCAmelCase			:     List[Any]   ):
										return self.tokenizer.decode(*lowerCAmelCase  ,  **lowerCAmelCase   )


						@property
						def 					__lowercase	(							self			:     Dict   ):
										return ["input_ids", "attention_mask", "position_ids", "pixel_values"]


						@property
						def 					__lowercase	(							self			:     List[str]   ):
										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 					__lowercase	(							self			:     Tuple   ):
										warnings.warn(
										    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""  ,  lowerCAmelCase  ,  )
										return self.image_processor





