

"""simple docstring"""




import warnings

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





class 			__UpperCamelCase (   _A						):
		SCREAMING_SNAKE_CASE								=    ["image_processor", "tokenizer"]
		SCREAMING_SNAKE_CASE								=    "AutoImageProcessor"
		SCREAMING_SNAKE_CASE								=    "AutoTokenizer"


		def __init__(self :		int     ,     __SCREAMING_SNAKE_CASE :		str=None     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]=None     ,     **__SCREAMING_SNAKE_CASE :		Optional[int]):
							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 have to specify an image_processor.")
							if tokenizer is None:
												raise ValueError("You have to specify a tokenizer.")

							super().__init__(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
							A			=					self.image_processor


		def __call__(self :		List[str]     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=None     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=None     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=None     ,     **__SCREAMING_SNAKE_CASE :		Union[str, Any]):

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

							if images is not None:
												A			=					self.image_processor(__SCREAMING_SNAKE_CASE     ,     return_tensors=__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE)

							if text is not None and images 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 					SCREAMING_SNAKE_CASE__   (self :		int     ,     *__SCREAMING_SNAKE_CASE :		Any     ,     **__SCREAMING_SNAKE_CASE :		int):
							return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]     ,     *__SCREAMING_SNAKE_CASE :		Optional[Any]     ,     **__SCREAMING_SNAKE_CASE :		List[Any]):
							return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE)


		@property
		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]):
							A			=					self.tokenizer.model_input_names
							A			=					self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))


		@property
		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]):
							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 					SCREAMING_SNAKE_CASE__   (self :		Tuple):
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,     __SCREAMING_SNAKE_CASE     ,     )
							return self.image_processor








"""simple docstring"""




import doctest

import projectq
from projectq.ops import H, Measure
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					A			=					quantum_engine.allocate_qubit()
					H | qubit
					Measure | qubit
					return int(lowercase__      )


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

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

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

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

	print('Random numbers', random_numbers_list)





