




import warnings

from ...utils import logging
from .image_processing_conditional_detr import ConditionalDetrImageProcessor


__UpperCamelCase      : Any					      =   logging.get_logger(__name__)

class 	__lowerCAmelCase			(			__magic_name__				):
       def __init__(		self		:Optional[Any]	,   *__magic_name__		:int	,   **__magic_name__		:Optional[int]       ):







        '''simple docstring'''







        warnings.warn(
            """The class ConditionalDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
            """ Please use ConditionalDetrImageProcessor instead."""	,   __magic_name__	,   )
        super().__init__(*__magic_name__	,   **__magic_name__       )







import doctest

import projectq
from projectq.ops import H, Measure





def 	__A	(			__lowerCamelCase					)   -> int:
	a		       =							quantum_engine.allocate_qubit()
	H | qubit
	Measure | qubit
	return int(__lowerCamelCase					)


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

		# initialises a new quantum backend
		__UpperCamelCase      : List[Any]					      =   projectq.MainEngine()

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

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

		print("Random numbers", random_numbers_list)

