


import torch
from torch import autograd







class 					snake_case__(				autograd.Function				):







						'''simple docstring'''




						@staticmethod
						def     lowercase_			(					__lowercase ,    __lowercase ,    __lowercase ,    __lowercase				)       ->     List[Any]:
										lowerCAmelCase_			:   List[Any]     					=					inputs.numel()
										lowerCAmelCase_			:   int     					=					int(0.0_05 * nb_elems				) + 1
										if sigmoid:
														lowerCAmelCase_			:   Union[str, Any]     					=					(torch.sigmoid(__lowercase				) > threshold).type(inputs.type()				)
										else:
														lowerCAmelCase_			:   int     					=					(inputs > threshold).type(inputs.type()				)
										if mask.sum() < nb_min:
														# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
														lowerCAmelCase_			:   str     					=					inputs.flatten().kthvalue(max(nb_elems - nb_min ,    1				)				).values
														lowerCAmelCase_			:   Optional[Any]     					=					(inputs > k_threshold).type(inputs.type()				)
										return mask




						@staticmethod
						def     lowercase_			(					__lowercase ,    __lowercase				)       ->     Any:
										return gradOutput, None, None







class 					snake_case__(				autograd.Function				):







						'''simple docstring'''




						@staticmethod
						def     lowercase_			(					__lowercase ,    __lowercase ,    __lowercase				)       ->     Union[str, Any]:
										lowerCAmelCase_			:   Optional[int]     					=					inputs.clone()
										lowerCAmelCase_					,	lowerCAmelCase_			:   str     					=					inputs.flatten().sort(descending=__lowercase				)
										lowerCAmelCase_			:   Union[str, Any]     					=					int(threshold * inputs.numel()				)

										# flat_out and mask access the same memory.
										lowerCAmelCase_			:   Optional[Any]     					=					mask.flatten()
										lowerCAmelCase_			:   Optional[int]     					=					0
										lowerCAmelCase_			:   Tuple     					=					1
										return mask




						@staticmethod
						def     lowercase_			(					__lowercase ,    __lowercase				)       ->     Optional[int]:
										return gradOutput, None







class 					snake_case__(				UpperCAmelCase__				):







						'''simple docstring'''




						@staticmethod
						def     lowercase_			(					__lowercase ,    __lowercase				)       ->     Dict:
										lowerCAmelCase_			:   Optional[int]     					=					inputs.clone()
										lowerCAmelCase_					,	lowerCAmelCase_			:   int     					=					inputs.abs().flatten().sort(descending=__lowercase				)
										lowerCAmelCase_			:   str     					=					int(threshold * inputs.numel()				)

										# flat_out and mask access the same memory.
										lowerCAmelCase_			:   List[Any]     					=					mask.flatten()
										lowerCAmelCase_			:   Optional[int]     					=					0
										lowerCAmelCase_			:   List[str]     					=					1
										return mask



import doctest

import projectq
from projectq.ops import H, Measure



def       lowerCAmelCase			(		lowerCAmelCase_			)->			int:
				lowerCAmelCase_			:   List[Any]     					=					quantum_engine.allocate_qubit()
				H | qubit
				Measure | qubit
				return int(lowerCAmelCase_			)


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

						# initialises a new quantum backend
						_UpperCAmelCase      :	str  =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)