





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 	_UpperCamelCase	(							A_	):







					'''simple docstring'''

					lowerCamelCase  :  Any           =							['image_processor', 'tokenizer']
					lowerCamelCase  :  List[str]           =							'AutoImageProcessor'
					lowerCamelCase  :  List[str]           =							'AutoTokenizer'





					def __init__(    self     :	Dict			,       __lowercase     :	Tuple=None			,       __lowercase     :	Union[str, Any]=None			,       **__lowercase     :	str		):






						'''simple docstring'''







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

						UpperCAmelCase_		    =  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__(__lowercase			,       __lowercase		)
						UpperCAmelCase_		    =  self.image_processor
						UpperCAmelCase_		    =  False





					def __call__(    self     :	List[Any]			,       *__lowercase     :	Optional[Any]			,       **__lowercase     :	Dict		):






						'''simple docstring'''







						if self._in_target_context_manager:
							return self.current_processor(*__lowercase			,       **__lowercase		)

						UpperCAmelCase_		    =  kwargs.pop("""images"""			,       __lowercase		)
						UpperCAmelCase_		    =  kwargs.pop("""text"""			,       __lowercase		)
						if len(__lowercase		) > 0:
							UpperCAmelCase_		    =  args[0]
							UpperCAmelCase_		    =  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:
							UpperCAmelCase_		    =  self.image_processor(__lowercase			,       *__lowercase			,       **__lowercase		)
						if text is not None:
							UpperCAmelCase_		    =  self.tokenizer(__lowercase			,       **__lowercase		)

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





					def 							SCREAMING_SNAKE_CASE				(    self     :	int			,       *__lowercase     :	List[Any]			,       **__lowercase     :	Tuple		):






						'''simple docstring'''







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





					def 							SCREAMING_SNAKE_CASE				(    self     :	Union[str, Any]			,       *__lowercase     :	List[Any]			,       **__lowercase     :	Union[str, Any]		):






						'''simple docstring'''







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





					@contextmanager
					def 							SCREAMING_SNAKE_CASE				(    self     :	Dict		):






						'''simple docstring'''







						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."""		)
						UpperCAmelCase_		    =  True
						UpperCAmelCase_		    =  self.tokenizer
						yield
						UpperCAmelCase_		    =  self.image_processor
						UpperCAmelCase_		    =  False





					@property
					def 							SCREAMING_SNAKE_CASE				(    self     :	Any		):






						'''simple docstring'''







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





					@property
					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[Any]		):






						'''simple docstring'''







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









import doctest

import projectq
from projectq.ops import H, Measure




def  A_(      A ):
	UpperCAmelCase_		    =  quantum_engine.allocate_qubit()
	H | qubit
	Measure | qubit
	return int(A )


if __name__ == "__main__":
				doctest.testmod()

				# initialises a new quantum backend
				UpperCamelCase__ :			Dict       	=			projectq.MainEngine()

				# Generate a list of 10 random numbers
				UpperCamelCase__ :			Dict       	=			[get_random_number(quantum_engine) for _ in range(10)]

				# Flushes the quantum engine from memory
				quantum_engine.flush()

				print("""Random numbers""", random_numbers_list)


