





'''simple docstring'''




import math

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



def 		__A		(  lowerCamelCase_ = 3	):


		"""simple docstring"""



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

		SCREAMING_SNAKE_CASE :				str		=			QuantumRegister(lowerCamelCase_			,			"""qr"""	)
		SCREAMING_SNAKE_CASE :				Union[str, Any]		=			ClassicalRegister(lowerCamelCase_			,			"""cr"""	)

		SCREAMING_SNAKE_CASE :				Dict		=			QuantumCircuit(lowerCamelCase_			,			lowerCamelCase_	)

		SCREAMING_SNAKE_CASE :				Optional[int]		=			number_of_qubits

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

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

		# measure all the qubits
		quantum_circuit.measure(lowerCamelCase_			,			lowerCamelCase_	)
		# simulate with 10000 shots
		SCREAMING_SNAKE_CASE :				List[str]		=			Aer.get_backend("""qasm_simulator"""	)
		SCREAMING_SNAKE_CASE :				int		=			execute(lowerCamelCase_			,			lowerCamelCase_			,			shots=1_00_00	)

		return job.result().get_counts(lowerCamelCase_	)


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 ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__UpperCAmelCase            =       {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCAmelCase            =       ["""GPTNeoXTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCAmelCase            =       [
							    """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """GPTNeoXForCausalLM""",
							    """GPTNeoXForQuestionAnswering""",
							    """GPTNeoXForSequenceClassification""",
							    """GPTNeoXForTokenClassification""",
							    """GPTNeoXLayer""",
							    """GPTNeoXModel""",
							    """GPTNeoXPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gpt_neox import (
														    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTNeoXForCausalLM,
														    GPTNeoXForQuestionAnswering,
														    GPTNeoXForSequenceClassification,
														    GPTNeoXForTokenClassification,
														    GPTNeoXLayer,
														    GPTNeoXModel,
														    GPTNeoXPreTrainedModel,
														)


else:
							import sys

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



