



import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute







def snake_case__  (			__SCREAMING_SNAKE_CASE = 3 )		->							qiskit.result.counts.Counts:
		if isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ):
				raise TypeError("number of qubits must be a integer." )
		if number_of_qubits <= 0:
				raise ValueError("number of qubits must be > 0." )
		if math.floor(__SCREAMING_SNAKE_CASE ) != number_of_qubits:
				raise ValueError("number of qubits must be exact integer." )
		if number_of_qubits > 10:
				raise ValueError("number of qubits too large to simulate(>10)." )

		UpperCAmelCase_						=				QuantumRegister(__SCREAMING_SNAKE_CASE			,     "qr" )
		UpperCAmelCase_						=				ClassicalRegister(__SCREAMING_SNAKE_CASE			,     "cr" )

		UpperCAmelCase_						=				QuantumCircuit(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

		UpperCAmelCase_						=				number_of_qubits

		for i in range(__SCREAMING_SNAKE_CASE ):
				quantum_circuit.h(number_of_qubits - i - 1 )
				counter -= 1
				for j in range(__SCREAMING_SNAKE_CASE ):
						quantum_circuit.cp(np.pi / 2 ** (counter - j)			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

		for k in range(number_of_qubits // 2 ):
				quantum_circuit.swap(__SCREAMING_SNAKE_CASE			,     number_of_qubits - k - 1 )

		# measure all the qubits
		quantum_circuit.measure(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
		# simulate with 10000 shots
		UpperCAmelCase_						=				Aer.get_backend("qasm_simulator" )
		UpperCAmelCase_						=				execute(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     shots=1_0000 )

		return job.result().get_counts(__SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
			print(
			    f'''Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}'''
			)






from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin



class 	lowerCamelCase      (						lowercase__,     lowercase__  ):







					'''simple docstring'''







					@register_to_config
					def __init__(			self    ,	lowerCAmelCase = 768    ,	):
							super().__init__()

							UpperCAmelCase_						=				nn.Parameter(torch.zeros(1    ,	lowerCAmelCase     )     )
							UpperCAmelCase_						=				nn.Parameter(torch.ones(1    ,	lowerCAmelCase     )     )



					def    A__		(			self    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	):
							UpperCAmelCase_						=				nn.Parameter(self.mean.to(lowerCAmelCase     ).to(lowerCAmelCase     )     )
							UpperCAmelCase_						=				nn.Parameter(self.std.to(lowerCAmelCase     ).to(lowerCAmelCase     )     )
							return self



					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				(embeds - self.mean) * 1.0 / self.std
							return embeds



					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				(embeds * self.std) + self.mean
							return embeds

