


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


__lowerCAmelCase			=							{
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase			=							['''SpeechT5Tokenizer''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase			=							[
     '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''SpeechT5ForSpeechToText''',
     '''SpeechT5ForSpeechToSpeech''',
     '''SpeechT5ForTextToSpeech''',
     '''SpeechT5Model''',
     '''SpeechT5PreTrainedModel''',
     '''SpeechT5HifiGan''',
 ]

if TYPE_CHECKING:
 from .configuration_speechta import (
     SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
     SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
     SpeechTaConfig,
     SpeechTaHifiGanConfig,
 )
 from .feature_extraction_speechta import SpeechTaFeatureExtractor
 from .processing_speechta import SpeechTaProcessor

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_speechta import SpeechTaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speechta import (
      SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechTaForSpeechToSpeech,
      SpeechTaForSpeechToText,
      SpeechTaForTextToSpeech,
      SpeechTaHifiGan,
      SpeechTaModel,
      SpeechTaPreTrainedModel,
  )

else:
 import sys

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








import math

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




def        snake_case_					(  snake_case = 3					)  ->    qiskit.result.counts.Counts:
       if isinstance(snake_case      ,							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(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).'					)

       lowercase__:       str        =       QuantumRegister(snake_case      ,							'qr'					)
       lowercase__:       str        =       ClassicalRegister(snake_case      ,							'cr'					)

       lowercase__:       List[Any]        =       QuantumCircuit(snake_case      ,							snake_case					)

       lowercase__:       int        =       number_of_qubits

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

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

       # measure all the qubits
       quantum_circuit.measure(snake_case      ,							snake_case					)
       # simulate with 10000 shots
       lowercase__:       str        =       Aer.get_backend('qasm_simulator'					)
       lowercase__:       Union[str, Any]        =       execute(snake_case      ,							snake_case      ,							shots=1_00_00					)

       return job.result().get_counts(snake_case					)


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


