

"""simple docstring"""


import warnings

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





class 					snake_case			(       __snake_case						):
				SCREAMING_SNAKE_CASE_     :   Tuple        =						["""image_processor""", """tokenizer"""]
				SCREAMING_SNAKE_CASE_     :   Tuple        =						"""AutoImageProcessor"""
				SCREAMING_SNAKE_CASE_     :   Optional[int]        =						"""AutoTokenizer"""

				def __init__(			self							:       Union[str, Any]  ,						UpperCamelCase__							:       Optional[int]=None  ,						UpperCamelCase__							:       List[Any]=None  ,						**UpperCamelCase__							:       Union[str, Any])->     Any:



						'''simple docstring'''


						__lowerCAmelCase: Tuple					   =    None
						if "feature_extractor" in kwargs:
								warnings.warn(
								    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
								    " instead."  ,						UpperCamelCase__  ,						)
								__lowerCAmelCase: Optional[Any]					   =    kwargs.pop("feature_extractor")

						__lowerCAmelCase: List[str]					   =    image_processor if image_processor is not None else feature_extractor
						if image_processor is None:
								raise ValueError("You have to specify an image_processor.")
						if tokenizer is None:
								raise ValueError("You have to specify a tokenizer.")

						super().__init__(UpperCamelCase__  ,						UpperCamelCase__)
						__lowerCAmelCase: Optional[int]					   =    self.image_processor

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



						'''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:
								__lowerCAmelCase: Any					   =    self.tokenizer(UpperCamelCase__  ,						return_tensors=UpperCamelCase__  ,						**UpperCamelCase__)

						if images is not None:
								__lowerCAmelCase: Any					   =    self.image_processor(UpperCamelCase__  ,						return_tensors=UpperCamelCase__  ,						**UpperCamelCase__)

						if text is not None and images is not None:
								__lowerCAmelCase: List[Any]					   =    image_features.pixel_values
								return encoding
						elif text is not None:
								return encoding
						else:
								return BatchEncoding(data=dict(**UpperCamelCase__)  ,						tensor_type=UpperCamelCase__)

				def 							lowercase_  (			self							:       List[Any]  ,						*UpperCamelCase__							:       Dict  ,						**UpperCamelCase__							:       Dict)->     int:



						'''simple docstring'''


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

				def 							lowercase_  (			self							:       List[str]  ,						*UpperCamelCase__							:       Optional[int]  ,						**UpperCamelCase__							:       Optional[Any])->     Union[str, Any]:



						'''simple docstring'''


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

				@property
				def 							lowercase_  (			self							:       Optional[int])->     str:



						'''simple docstring'''


						__lowerCAmelCase: int					   =    self.tokenizer.model_input_names
						__lowerCAmelCase: Union[str, Any]					   =    self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))

				@property
				def 							lowercase_  (			self							:       Any)->     List[Any]:



						'''simple docstring'''


						warnings.warn(
						    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."  ,						UpperCamelCase__  ,						)
						return self.image_processor_class




				@property
				def 							lowercase_  (			self							:       str)->     Tuple:



						'''simple docstring'''


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





"""simple docstring"""


import math






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			list:
		if n <= 0:
				raise ValueError("Only positive integers have prime factors"				)
		__lowerCAmelCase: Tuple					   =    []
		while n % 2 == 0:
				pf.append(2				)
				__lowerCAmelCase: str					   =    int(n / 2				)
		for i in range(3      ,				int(math.sqrt(__SCREAMING_SNAKE_CASE				)				) + 1      ,				2				):
				while n % i == 0:
						pf.append(__SCREAMING_SNAKE_CASE				)
						__lowerCAmelCase: List[Any]					   =    int(n / i				)
		if n > 2:
				pf.append(__SCREAMING_SNAKE_CASE				)
		return pf






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			int:
		if n <= 0:
				raise ValueError("Only positive numbers are accepted"				)
		__lowerCAmelCase: int					   =    1
		__lowerCAmelCase: str					   =    1
		while n % 2 == 0:
				temp += 1
				__lowerCAmelCase: Dict					   =    int(n / 2				)
		div *= temp
		for i in range(3      ,				int(math.sqrt(__SCREAMING_SNAKE_CASE				)				) + 1      ,				2				):
				__lowerCAmelCase: int					   =    1
				while n % i == 0:
						temp += 1
						__lowerCAmelCase: Tuple					   =    int(n / i				)
				div *= temp
		if n > 1:
				div *= 2
		return div






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			int:
		if n <= 0:
				raise ValueError("Only positive numbers are accepted"				)
		__lowerCAmelCase: Dict					   =    1
		__lowerCAmelCase: int					   =    1
		while n % 2 == 0:
				temp += 1
				__lowerCAmelCase: Tuple					   =    int(n / 2				)
		if temp > 1:
				s *= (2**temp - 1) / (2 - 1)
		for i in range(3      ,				int(math.sqrt(__SCREAMING_SNAKE_CASE				)				) + 1      ,				2				):
				__lowerCAmelCase: List[Any]					   =    1
				while n % i == 0:
						temp += 1
						__lowerCAmelCase: List[str]					   =    int(n / i				)
				if temp > 1:
						s *= (i**temp - 1) / (i - 1)
		return int(__SCREAMING_SNAKE_CASE				)






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			int:
		__lowerCAmelCase: Optional[int]					   =    n
		for x in set(prime_factors(__SCREAMING_SNAKE_CASE				)				):
				s *= (x - 1) / x
		return int(__SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()


