



"""simple docstring"""





import math

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




def 						UpperCAmelCase  (		A						:    int = 3							):

		'''simple docstring'''


		if isinstance(a__  ,					a__							):
				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(a__							) != 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(a__  ,					'qr'							)
		_UpperCAmelCase							=		ClassicalRegister(a__  ,					'cr'							)

		_UpperCAmelCase							=		QuantumCircuit(a__  ,					a__							)

		_UpperCAmelCase							=		number_of_qubits

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

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

		# measure all the qubits
		quantum_circuit.measure(a__  ,					a__							)
		# simulate with 10000 shots
		_UpperCAmelCase							=		Aer.get_backend('qasm_simulator'							)
		_UpperCAmelCase							=		execute(a__  ,					a__  ,					shots=1_0000							)

		return job.result().get_counts(a__							)


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






from typing import TYPE_CHECKING

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


lowerCAmelCase__    :Tuple       =				{
    '''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__    :Dict       =				['''SpeechT5Tokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :List[str]       =				[
		    '''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__    :str       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
