"""simple docstring"""




import math

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





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: str = 3			) ->   Optional[Any]:







  if isinstance(lowercase__    ,lowercase__			):
    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(lowercase__			) != 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)."""			)

  _lowerCAmelCase		: Dict						    =  QuantumRegister(lowercase__    ,"""qr"""			)
  _lowerCAmelCase		: Any						    =  ClassicalRegister(lowercase__    ,"""cr"""			)

  _lowerCAmelCase		: Dict						    =  QuantumCircuit(lowercase__    ,lowercase__			)

  _lowerCAmelCase		: Optional[Any]						    =  number_of_qubits

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

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

  # measure all the qubits
  quantum_circuit.measure(lowercase__    ,lowercase__			)
  # simulate with 10000 shots
  _lowerCAmelCase		: List[Any]						    =  Aer.get_backend("""qasm_simulator"""			)
  _lowerCAmelCase		: Tuple						    =  execute(lowercase__    ,lowercase__    ,shots=10000			)

  return job.result().get_counts(lowercase__			)


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


"""simple docstring"""




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
     if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
     from .pipeline_unclip import UnCLIPPipeline
     from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
     from .text_proj import UnCLIPTextProjModel




