




'''simple docstring'''


import numpy as np
import qiskit
def 	snake_case_					(UpperCamelCase :						int = 8				,		UpperCamelCase :						int | None = None   ):





							'''simple docstring'''


							_a			  =       np.random.default_rng(seed=UpperCamelCase   )

							# Roughly 25% of the qubits will contribute to the key.
							# So we take more than we need.
							_a			  =       6 * key_len
							# Measurement basis for Alice's qubits.
							_a			  =       rng.integers(2				,		size=UpperCamelCase   )
							# The set of states Alice will prepare.
							_a			  =       rng.integers(2				,		size=UpperCamelCase   )
							# Measurement basis for Bob's qubits.
							_a			  =       rng.integers(2				,		size=UpperCamelCase   )

							# Quantum Circuit to simulate BB84
							_a			  =       qiskit.QuantumCircuit(UpperCamelCase				,		name='''BB84'''   )

							# Alice prepares her qubits according to rules above.
							for index, _ in enumerate(UpperCamelCase   ):
														if alice_state[index] == 1:
																					bbaa_circ.x(UpperCamelCase   )
														if alice_basis[index] == 1:
																					bbaa_circ.h(UpperCamelCase   )
							bbaa_circ.barrier()

							# Bob measures the received qubits according to rules above.
							for index, _ in enumerate(UpperCamelCase   ):
														if bob_basis[index] == 1:
																					bbaa_circ.h(UpperCamelCase   )

							bbaa_circ.barrier()
							bbaa_circ.measure_all()

							# Simulate the quantum circuit.
							_a			  =       qiskit.Aer.get_backend('''aer_simulator'''   )
							# We only need to run one shot because the key is unique.
							# Multiple shots will produce the same key.
							_a			  =       qiskit.execute(UpperCamelCase				,		UpperCamelCase				,		shots=1				,		seed_simulator=UpperCamelCase   )
							# Returns the result of measurement.
							_a			  =       job.result().get_counts(UpperCamelCase   ).most_frequent()

							# Extracting the generated key from the simulation results.
							# Only keep measurement results where Alice and Bob chose the same basis.
							_a			  =       ''''''.join(
							    [
							        result_bit
							        for alice_basis_bit, bob_basis_bit, result_bit in zip(
							            UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase   )
							        if alice_basis_bit == bob_basis_bit
							    ]   )

							# Get final key. Pad with 0 if too short, otherwise truncate.
							_a			  =       gen_key[:key_len] if len(UpperCamelCase   ) >= key_len else gen_key.ljust(UpperCamelCase				,		'''0'''   )
							return key


if __name__ == "__main__":
				print(F'''The generated key is : {bbaa(8, seed=0)}''')
				from doctest import testmod

				testmod()







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


_snake_case   :   List[Any]     							=	{'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BeitConfig', 'BeitOnnxConfig']}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case   :   Any     							=	['BeitFeatureExtractor']
				_snake_case   :   Any     							=	['BeitImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case   :   List[str]     							=	[
				    'BEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BeitForImageClassification',
				    'BeitForMaskedImageModeling',
				    'BeitForSemanticSegmentation',
				    'BeitModel',
				    'BeitPreTrainedModel',
				]


try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case   :   List[str]     							=	[
				    'FlaxBeitForImageClassification',
				    'FlaxBeitForMaskedImageModeling',
				    'FlaxBeitModel',
				    'FlaxBeitPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_beit import BeitFeatureExtractor
								from .image_processing_beit import BeitImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_beit import (
								    BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BeitForImageClassification,
								    BeitForMaskedImageModeling,
								    BeitForSemanticSegmentation,
								    BeitModel,
								    BeitPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_beit import (
								    FlaxBeitForImageClassification,
								    FlaxBeitForMaskedImageModeling,
								    FlaxBeitModel,
								    FlaxBeitPreTrainedModel,
								)


else:
				import sys

				_snake_case   :   Union[str, Any]     							=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

