






import math

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




def 					lowercase__				(					__snake_case   :							int = 3  ):


					'''simple docstring'''


					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).'  )

					UpperCAmelCase_   :				List[str]						=						QuantumRegister(__snake_case					,	'qr'  )
					UpperCAmelCase_   :				List[Any]						=						ClassicalRegister(__snake_case					,	'cr'  )

					UpperCAmelCase_   :				Dict						=						QuantumCircuit(__snake_case					,	__snake_case  )

					UpperCAmelCase_   :				Union[str, Any]						=						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
					UpperCAmelCase_   :				Tuple						=						Aer.get_backend('qasm_simulator'  )
					UpperCAmelCase_   :				Union[str, Any]						=						execute(__snake_case					,	__snake_case					,	shots=10_000  )

					return job.result().get_counts(__snake_case  )


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_tokenizers_available, is_torch_available


__UpperCAmelCase     							=						{
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase     							=						[
		    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'RoCBertForCausalLM',
		    'RoCBertForMaskedLM',
		    'RoCBertForMultipleChoice',
		    'RoCBertForPreTraining',
		    'RoCBertForQuestionAnswering',
		    'RoCBertForSequenceClassification',
		    'RoCBertForTokenClassification',
		    'RoCBertLayer',
		    'RoCBertModel',
		    'RoCBertPreTrainedModel',
		    'load_tf_weights_in_roc_bert',
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

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






