






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   UpperCamelCase			(					snake_case__			):







		"""simple docstring"""



		snake_case			 = ["image_processor", "tokenizer"]
		snake_case			 = "VideoMAEImageProcessor"
		snake_case			 = ("CLIPTokenizer", "CLIPTokenizerFast")
		def __init__(	self		:							List[Any]    ,_SCREAMING_SNAKE_CASE		:							Any=None    ,_SCREAMING_SNAKE_CASE		:							Optional[int]=None    ,**_SCREAMING_SNAKE_CASE		:							int		)     -> Any:


						'''simple docstring'''
						A									=   None
						if "feature_extractor" in kwargs:
										warnings.warn(
										    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
										    ' instead.'    ,_SCREAMING_SNAKE_CASE    ,)
										A									=   kwargs.pop('feature_extractor'		)

						A									=   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__(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)
						A									=   self.image_processor
		def __call__(	self		:							Any    ,_SCREAMING_SNAKE_CASE		:							Any=None    ,_SCREAMING_SNAKE_CASE		:							List[Any]=None    ,_SCREAMING_SNAKE_CASE		:							List[str]=None    ,**_SCREAMING_SNAKE_CASE		:							List[str]		)     -> Optional[Any]:


						'''simple docstring'''

						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:
										A									=   self.tokenizer(_SCREAMING_SNAKE_CASE    ,return_tensors=_SCREAMING_SNAKE_CASE    ,**_SCREAMING_SNAKE_CASE		)

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

						if text is not None and videos is not None:
										A									=   image_features.pixel_values
										return encoding
						elif text is not None:
										return encoding
						else:
										return BatchEncoding(data=dict(**_SCREAMING_SNAKE_CASE		)    ,tensor_type=_SCREAMING_SNAKE_CASE		)
		def    A(	self		:							Dict    ,*_SCREAMING_SNAKE_CASE		:							Optional[int]    ,**_SCREAMING_SNAKE_CASE		:							Optional[Any]		)     -> Any:


						'''simple docstring'''
						return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE    ,**_SCREAMING_SNAKE_CASE		)
		def    A(	self		:							Any    ,*_SCREAMING_SNAKE_CASE		:							Any    ,**_SCREAMING_SNAKE_CASE		:							List[str]		)     -> str:


						'''simple docstring'''
						return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE    ,**_SCREAMING_SNAKE_CASE		)
		@property
		def    A(	self		:							Union[str, Any]		)     -> int:


						'''simple docstring'''
						return ["input_ids", "attention_mask", "position_ids", "pixel_values"]
		@property
		def    A(	self		:							Dict		)     -> Tuple:


						'''simple docstring'''
						warnings.warn(
						    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,_SCREAMING_SNAKE_CASE    ,)
						return self.image_processor_class







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


						'''simple docstring'''
						warnings.warn(
						    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,_SCREAMING_SNAKE_CASE    ,)
						return self.image_processor













def   snake_case		(				UpperCAmelCase						:							Optional[int],    UpperCAmelCase						:							str ):
				# "Simpson Rule"
				# int(f) = delta_x/2 * (b-a)/3*(f1 + 4f2 + 2f_3 + ... + fn)
				A									=   (boundary[1] - boundary[0]) / steps
				A									=   boundary[0]
				A									=   boundary[1]
				A									=   make_points(UpperCAmelCase,    UpperCAmelCase,    UpperCAmelCase )
				A									=   0.0
				y += (h / 3.0) * f(UpperCAmelCase )
				A									=   2
				for i in x_i:
								y += (h / 3) * (4 - 2 * (cnt % 2)) * f(UpperCAmelCase )
								cnt += 1
				y += (h / 3.0) * f(UpperCAmelCase )
				return y







def   snake_case		(				UpperCAmelCase						:							int,    UpperCAmelCase						:							int,    UpperCAmelCase						:							List[Any] ):
				A									=   a + h
				while x < (b - h):
								yield x
								A									=   x + h







def   snake_case		(				UpperCAmelCase						:							str ):  # enter your function here
				A									=   (x - 0) * (x - 0)
				return y







def   snake_case		(				):
				A									=   0.0  # Lower bound of integration
				A									=   1.0  # Upper bound of integration
				A									=   10.0  # define number of steps or resolution
				A									=   [a, b]  # define boundary of integration
				A									=   method_a(UpperCAmelCase,    UpperCAmelCase )
				print(f'y = {y}' )


if __name__ == "__main__":
					main()





