

import warnings

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






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     =['image_processor', 'tokenizer']
			lowerCamelCase__							     ='ViTImageProcessor'
			lowerCamelCase__							     =('CLIPTokenizer', 'CLIPTokenizerFast')
			def __init__(      self				:			Optional[int]   ,   a				:			List[Any]=None   ,   a				:			str=None   ,   **a				:			Optional[int]						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							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   ,   )
					SCREAMING_SNAKE_CASE :   Dict					  =							kwargs.pop("feature_extractor"						)

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							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				:			Tuple   ,   a				:			Tuple=None   ,   a				:			Optional[int]=None   ,   a				:			Tuple=None   ,   a				:			List[Any]=None   ,   **a				:			str						)				->							Optional[int]:

				"""simple docstring"""

				if text is None and visual_prompt is None and images is None:
					raise ValueError("You have to specify either text, visual prompt or images."						)

				if text is not None and visual_prompt is not None:
					raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt."						)

				if text is not None:
					SCREAMING_SNAKE_CASE :   List[str]					  =							self.tokenizer(a   ,   return_tensors=a   ,   **a						)

				if visual_prompt is not None:
					SCREAMING_SNAKE_CASE :   List[str]					  =							self.image_processor(a   ,   return_tensors=a   ,   **a						)

				if images is not None:
					SCREAMING_SNAKE_CASE :   int					  =							self.image_processor(a   ,   return_tensors=a   ,   **a						)

				if visual_prompt is not None and images is not None:
					SCREAMING_SNAKE_CASE :   List[Any]					  =							{
					    "pixel_values": image_features.pixel_values,
					    "conditional_pixel_values": prompt_features.pixel_values,
					}
					return encoding
				elif text is not None and images is not None:
					SCREAMING_SNAKE_CASE :   Tuple					  =							image_features.pixel_values
					return encoding
				elif text is not None:
					return encoding
				elif visual_prompt is not None:
					SCREAMING_SNAKE_CASE :   Any					  =							{
					    "conditional_pixel_values": prompt_features.pixel_values,
					}
					return encoding
				else:
					return BatchEncoding(data=dict(**a						)   ,   tensor_type=a						)
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   *a				:			int   ,   **a				:			List[str]						)				->							Any:

				"""simple docstring"""

				return self.tokenizer.batch_decode(*a   ,   **a						)
			def 				__UpperCamelCase			(      self				:			Optional[int]   ,   *a				:			Any   ,   **a				:			List[str]						)				->							int:

				"""simple docstring"""

				return self.tokenizer.decode(*a   ,   **a						)
			@property
			def 				__UpperCamelCase			(      self				:			str						)				->							Optional[int]:

				"""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 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Dict:

				"""simple docstring"""

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


from math import factorial, pi

def 			lowerCamelCase__			(       _a				,      _a = 30):

	if not isinstance(_a				,      (int, float)):
		raise ValueError("maclaurin_sin() requires either an int or float for theta")

	if not isinstance(_a				,      _a) or accuracy <= 0:
		raise ValueError("maclaurin_sin() requires a positive int for accuracy")

	SCREAMING_SNAKE_CASE :   int					  =							float(_a)
	SCREAMING_SNAKE_CASE :   Dict					  =							theta // (2 * pi)
	theta -= 2 * div * pi
	return sum(
	    (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1) for r in range(_a))

def 			lowerCamelCase__			(       _a				,      _a = 30):

	if not isinstance(_a				,      (int, float)):
		raise ValueError("maclaurin_cos() requires either an int or float for theta")

	if not isinstance(_a				,      _a) or accuracy <= 0:
		raise ValueError("maclaurin_cos() requires a positive int for accuracy")

	SCREAMING_SNAKE_CASE :   str					  =							float(_a)
	SCREAMING_SNAKE_CASE :   Any					  =							theta // (2 * pi)
	theta -= 2 * div * pi
	return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r) for r in range(_a))


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			print(maclaurin_sin(10))
			print(maclaurin_sin(-10))
			print(maclaurin_sin(10, 15))
			print(maclaurin_sin(-10, 15))

			print(maclaurin_cos(5))
			print(maclaurin_cos(-5))
			print(maclaurin_cos(10, 15))
			print(maclaurin_cos(-10, 15))