



import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def    lowerCamelCase__ (		_A = 3       ):
  if isinstance(UpperCamelCase__				,    UpperCamelCase__       ):
    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(UpperCamelCase__       ) != 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).'       )

  a		:      str       =  QuantumRegister(UpperCamelCase__				,    'qr'       )
  a		:      List[Any]       =  ClassicalRegister(UpperCamelCase__				,    'cr'       )

  a		:      Tuple       =  QuantumCircuit(UpperCamelCase__				,    UpperCamelCase__       )

  a		:      int       =  number_of_qubits

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

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

  # measure all the qubits
  quantum_circuit.measure(UpperCamelCase__				,    UpperCamelCase__       )
  # simulate with 10000 shots
  a		:      Union[str, Any]       =  Aer.get_backend('qasm_simulator'       )
  a		:      List[str]       =  execute(UpperCamelCase__				,    UpperCamelCase__				,    shots=1_0000       )

  return job.result().get_counts(UpperCamelCase__       )


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




'''simple docstring'''






import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def    lowerCamelCase__ (		_A       ):
  return EnvironmentCommand()

class 							a__(      lowerCamelCase__			):







    @staticmethod
    def     lowercase_ (   __snake_case				:						ArgumentParser			):
      a		:      Tuple       =  parser.add_parser('env'			)
      download_parser.set_defaults(func=__snake_case			)







    def     lowercase_ (   self				:						List[str]			):
      a		:      str       =  huggingface_hub.__version__

      a		:      List[str]       =  'not installed'
      a		:      List[str]       =  'NA'
      if is_torch_available():
        import torch

        a		:      Optional[Any]       =  torch.__version__
        a		:      int       =  torch.cuda.is_available()

      a		:      Optional[int]       =  'not installed'
      if is_transformers_available():
        import transformers

        a		:      Tuple       =  transformers.__version__

      a		:      Dict       =  'not installed'
      if is_accelerate_available():
        import accelerate

        a		:      int       =  accelerate.__version__

      a		:      Any       =  'not installed'
      if is_xformers_available():
        import xformers

        a		:      Optional[int]       =  xformers.__version__

      a		:      List[str]       =  {
          '`diffusers` version': version,
          'Platform': platform.platform(),
          'Python version': platform.python_version(),
          'PyTorch version (GPU?)': F"""{pt_version} ({pt_cuda_available})""",
          'Huggingface_hub version': hub_version,
          'Transformers version': transformers_version,
          'Accelerate version': accelerate_version,
          'xFormers version': xformers_version,
          'Using GPU in script?': '<fill in>',
          'Using distributed or parallel set-up in script?': '<fill in>',
      }

      print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'			)
      print(self.format_dict(__snake_case			)			)

      return info







    @staticmethod
    def     lowercase_ (   __snake_case				:						Union[str, Any]			):
      return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]			) + "\n"