


"""simple docstring"""






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class       lowerCamelCase_		(      lowercase ):





				"""simple docstring"""







				_lowerCAmelCase   :   List[Any]   				=       ["image_processor", "tokenizer"]
				_lowerCAmelCase   :   str   				=       "AutoImageProcessor"
				_lowerCAmelCase   :   Tuple   				=       "AutoTokenizer"



				def __init__(    self							,		UpperCAmelCase__=None							,		UpperCAmelCase__=None							,		**UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					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__							,		)
												SCREAMING_SNAKE_CASE__  				=					kwargs.pop("feature_extractor"  )

								SCREAMING_SNAKE_CASE__  				=					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__(UpperCAmelCase__							,		UpperCAmelCase__  )
								SCREAMING_SNAKE_CASE__  				=					self.image_processor
								SCREAMING_SNAKE_CASE__  				=					False



				def __call__(    self							,		*UpperCAmelCase__							,		**UpperCAmelCase__  ):
								# For backward compatibility
								if self._in_target_context_manager:
												return self.current_processor(*UpperCAmelCase__							,		**UpperCAmelCase__  )

								SCREAMING_SNAKE_CASE__  				=					kwargs.pop("images"							,		UpperCAmelCase__  )
								SCREAMING_SNAKE_CASE__  				=					kwargs.pop("text"							,		UpperCAmelCase__  )
								if len(UpperCAmelCase__  ) > 0:
												SCREAMING_SNAKE_CASE__  				=					args[0]
												SCREAMING_SNAKE_CASE__  				=					args[1:]

								if images is None and text is None:
												raise ValueError("You need to specify either an `images` or `text` input to process."  )

								if images is not None:
												SCREAMING_SNAKE_CASE__  				=					self.image_processor(UpperCAmelCase__							,		*UpperCAmelCase__							,		**UpperCAmelCase__  )
								if text is not None:
												SCREAMING_SNAKE_CASE__  				=					self.tokenizer(UpperCAmelCase__							,		**UpperCAmelCase__  )

								if text is None:
												return inputs
								elif images is None:
												return encodings
								else:
												SCREAMING_SNAKE_CASE__  				=					encodings["input_ids"]
												return inputs



				def 	lowerCAmelCase__		(    self							,		*UpperCAmelCase__							,		**UpperCAmelCase__  ):
								return self.tokenizer.batch_decode(*UpperCAmelCase__							,		**UpperCAmelCase__  )



				def 	lowerCAmelCase__		(    self							,		*UpperCAmelCase__							,		**UpperCAmelCase__  ):
								return self.tokenizer.decode(*UpperCAmelCase__							,		**UpperCAmelCase__  )



				@contextmanager
				def 	lowerCAmelCase__		(    self  ):
								warnings.warn(
								    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
								    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
								    "your images inputs, or in a separate call."  )
								SCREAMING_SNAKE_CASE__  				=					True
								SCREAMING_SNAKE_CASE__  				=					self.tokenizer
								yield
								SCREAMING_SNAKE_CASE__  				=					self.image_processor
								SCREAMING_SNAKE_CASE__  				=					False



				@property
				def 	lowerCAmelCase__		(    self  ):
								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 	lowerCAmelCase__		(    self  ):
								warnings.warn(
								    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							,		UpperCAmelCase__							,		)
								return self.image_processor




"""simple docstring"""






def __lowercase  (   lowerCamelCase_	:   list	):
				if not isinstance(lowerCamelCase_    ,       lowerCamelCase_	):
								raise ValueError("Input series is not valid, valid series - [2, 4, 8]"	)
				if len(lowerCamelCase_	) == 0:
								raise ValueError("Input list must be a non empty list"	)
				if len(lowerCamelCase_	) == 1:
								return True
				try:
								SCREAMING_SNAKE_CASE__  				=					series[1] / series[0]
								for index in range(len(lowerCamelCase_	) - 1	):
												if series[index + 1] / series[index] != common_ratio:
																return False
				except ZeroDivisionError:
								return False
				return True


def __lowercase  (   lowerCamelCase_	:   list	):
				if not isinstance(lowerCamelCase_    ,       lowerCamelCase_	):
								raise ValueError("Input series is not valid, valid series - [2, 4, 8]"	)
				if len(lowerCamelCase_	) == 0:
								raise ValueError("Input list must be a non empty list"	)
				SCREAMING_SNAKE_CASE__  				=					1
				for value in series:
								answer *= value
				return pow(lowerCamelCase_    ,       1 / len(lowerCamelCase_	)	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
