

'''simple docstring'''







import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def  UpperCAmelCase__      ( UpperCAmelCase__ = 3			)     ->     qiskit.result.counts.Counts:
       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_		   = QuantumRegister(UpperCAmelCase__,    """qr"""			)
       A_		   = ClassicalRegister(UpperCAmelCase__,    """cr"""			)

       A_		   = QuantumCircuit(UpperCAmelCase__,    UpperCAmelCase__			)

       A_		   = 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_		   = Aer.get_backend("""qasm_simulator"""			)
       A_		   = execute(UpperCAmelCase__,    UpperCAmelCase__,    shots=1_00_00			)

       return job.result().get_counts(UpperCAmelCase__			)


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



'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase						    =      {
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						    =      ['''BlenderbotTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						    =      [
          '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''BlenderbotForCausalLM''',
          '''BlenderbotForConditionalGeneration''',
          '''BlenderbotModel''',
          '''BlenderbotPreTrainedModel''',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						    =      [
          '''TFBlenderbotForConditionalGeneration''',
          '''TFBlenderbotModel''',
          '''TFBlenderbotPreTrainedModel''',
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						    =      [
          '''FlaxBlenderbotForConditionalGeneration''',
          '''FlaxBlenderbotModel''',
          '''FlaxBlenderbotPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_blenderbot import (
          BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          BlenderbotConfig,
          BlenderbotOnnxConfig,
      )
      from .tokenization_blenderbot import BlenderbotTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_blenderbot import (
                BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BlenderbotForCausalLM,
                BlenderbotForConditionalGeneration,
                BlenderbotModel,
                BlenderbotPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_blenderbot import (
                TFBlenderbotForConditionalGeneration,
                TFBlenderbotModel,
                TFBlenderbotPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_blenderbot import (
                FlaxBlenderbotForConditionalGeneration,
                FlaxBlenderbotModel,
                FlaxBlenderbotPreTrainedModel,
            )

else:
      import sys

      __lowerCamelCase						    =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
