
_lowerCamelCase :       int     					=    1_000






def  a_	( __lowercase     :			float				)				->						float:
			return x * x






def  a_	( __lowercase     :			Union[str, Any] ,      __lowercase     :			float ,      __lowercase     :			float ,      __lowercase     :			int = 4				)				->						float:
			assert callable(
			    __lowercase				), f'''the function(object) passed should be callable your input : {function}'''
			assert isinstance(__lowercase ,      (float, int)				), f'''a should be float or integer your input : {a}'''
			assert isinstance(function(__lowercase				) ,      (float, int)				), (
			    "the function should return integer or float return type of your function, "
			    f'''{type(__lowercase				)}'''
			)
			assert isinstance(__lowercase ,      (float, int)				), f'''b should be float or integer your input : {b}'''
			assert (
			    isinstance(__lowercase ,      __lowercase				) 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

			_snake_case						=    (b - a) / N_STEPS
			_snake_case						=    function(__lowercase				) + function(__lowercase				)

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

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


if __name__ == "__main__":
			import doctest

			doctest.testmod()

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




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	int					     =      ["image_processor", "tokenizer"]
	_UpperCAmelCase :	Tuple					     =      "EfficientNetImageProcessor"
	_UpperCAmelCase :	Optional[Any]					     =      ("BertTokenizer", "BertTokenizerFast")



	def __init__(						self			:					Tuple    ,						lowercase			:					List[Any]    ,						lowercase			:					Optional[int]			):


				'''simple docstring'''


				super().__init__(lowercase    ,						lowercase			)



	def __call__(						self			:					Union[str, Any]    ,						lowercase			:					Dict=None    ,						lowercase			:					Optional[int]=None    ,						lowercase			:					List[Any]="max_length"    ,						lowercase			:					Optional[int]=64    ,						lowercase			:					int=None    ,						**lowercase			:					Tuple			):


				'''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(
							    lowercase    ,						padding=lowercase    ,						max_length=lowercase    ,						return_tensors=lowercase    ,						**lowercase			)

				if images is not None:
							_snake_case						=    self.image_processor(lowercase    ,						return_tensors=lowercase    ,						**lowercase			)

				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(**lowercase			)    ,						tensor_type=lowercase			)



	def 				A							(						self			:					Any    ,						*lowercase			:					List[str]    ,						**lowercase			:					Optional[Any]			):


				'''simple docstring'''


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



	def 				A							(						self			:					List[Any]    ,						*lowercase			:					Union[str, Any]    ,						**lowercase			:					Tuple			):


				'''simple docstring'''


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





	@property
	def 				A							(						self			:					List[str]			):


				'''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			)			)