
import math

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






def    lowerCamelCase__				(				__lowerCAmelCase  :  int = 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)."				)

			lowerCAmelCase_						=	QuantumRegister(__lowerCAmelCase    ,							"qr"				)
			lowerCAmelCase_						=	ClassicalRegister(__lowerCAmelCase    ,							"cr"				)

			lowerCAmelCase_						=	QuantumCircuit(__lowerCAmelCase    ,							__lowerCAmelCase				)

			lowerCAmelCase_						=	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
			lowerCAmelCase_						=	Aer.get_backend("qasm_simulator"				)
			lowerCAmelCase_						=	execute(__lowerCAmelCase    ,							__lowerCAmelCase    ,							shots=10000				)

			return job.result().get_counts(__lowerCAmelCase				)


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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A   =     {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["XGLMTokenizer"]

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "XGLMForCausalLM",
			    "XGLMModel",
			    "XGLMPreTrainedModel",
			]


try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "FlaxXGLMForCausalLM",
			    "FlaxXGLMModel",
			    "FlaxXGLMPreTrainedModel",
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFXGLMForCausalLM",
			    "TFXGLMModel",
			    "TFXGLMPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xglm import XGLMTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xglm_fast import XGLMTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xglm import (
						    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXGLMForCausalLM,
						    TFXGLMModel,
						    TFXGLMPreTrainedModel,
						)


else:
			import sys

			_A   =     _LazyModule(__name__, globals()["__file__"], _import_structure)



