





'''simple docstring'''






import numpy as np
import qiskit





def       lowercase_      (		lowercase__ = 8				,  lowercase__ = None						)   ->str:
						_snake_case:   Union[str, Any]  					=		np.random.default_rng(seed=lowercase__						)

						# Roughly 25% of the qubits will contribute to the key.
						# So we take more than we need.
						_snake_case:   Tuple  					=		6 * key_len
						# Measurement basis for Alice's qubits.
						_snake_case:   List[str]  					=		rng.integers(2				,  size=lowercase__						)
						# The set of states Alice will prepare.
						_snake_case:   List[Any]  					=		rng.integers(2				,  size=lowercase__						)
						# Measurement basis for Bob's qubits.
						_snake_case:   str  					=		rng.integers(2				,  size=lowercase__						)

						# Quantum Circuit to simulate BB84
						_snake_case:   List[Any]  					=		qiskit.QuantumCircuit(lowercase__				,  name='BB84'						)

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

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

						bbaa_circ.barrier()
						bbaa_circ.measure_all()

						# Simulate the quantum circuit.
						_snake_case:   str  					=		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.
						_snake_case:   List[Any]  					=		qiskit.execute(lowercase__				,  lowercase__				,  shots=1				,  seed_simulator=lowercase__						)
						# Returns the result of measurement.
						_snake_case:   str  					=		job.result().get_counts(lowercase__						).most_frequent()

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

						# Get final key. Pad with 0 if too short, otherwise truncate.
						_snake_case:   List[str]  					=		gen_key[:key_len] if len(lowercase__						) >= key_len else gen_key.ljust(lowercase__				,  '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


A   : Dict             =			{'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : int             =			['SpeechEncoderDecoderModel']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : Union[str, Any]             =			['FlaxSpeechEncoderDecoderModel']

if TYPE_CHECKING:
		from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
		import sys

		A   : int             =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





