


'''simple docstring'''






from typing import TYPE_CHECKING

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


a_					:    Union[str, Any]          =    {
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    Optional[int]          =    [
     "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Wav2Vec2ForAudioFrameClassification",
     "Wav2Vec2ForCTC",
     "Wav2Vec2ForMaskedLM",
     "Wav2Vec2ForPreTraining",
     "Wav2Vec2ForSequenceClassification",
     "Wav2Vec2ForXVector",
     "Wav2Vec2Model",
     "Wav2Vec2PreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    int          =    [
     "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFWav2Vec2ForCTC",
     "TFWav2Vec2Model",
     "TFWav2Vec2PreTrainedModel",
     "TFWav2Vec2ForSequenceClassification",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    Dict          =    [
     "FlaxWav2Vec2ForCTC",
     "FlaxWav2Vec2ForPreTraining",
     "FlaxWav2Vec2Model",
     "FlaxWav2Vec2PreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
 from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
 from .processing_wavaveca import WavaVecaProcessor
 from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_wavaveca import (
      WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      WavaVecaForAudioFrameClassification,
      WavaVecaForCTC,
      WavaVecaForMaskedLM,
      WavaVecaForPreTraining,
      WavaVecaForSequenceClassification,
      WavaVecaForXVector,
      WavaVecaModel,
      WavaVecaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_wavaveca import (
      TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFWavaVecaForCTC,
      TFWavaVecaForSequenceClassification,
      TFWavaVecaModel,
      TFWavaVecaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_wavaveca import (
      FlaxWavaVecaForCTC,
      FlaxWavaVecaForPreTraining,
      FlaxWavaVecaModel,
      FlaxWavaVecaPreTrainedModel,
  )


else:
 import sys

 a_					:    Optional[int]          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





'''simple docstring'''






import numpy as np
import qiskit






def 	_A				(lowerCAmelCase__  :int = 8				,						lowerCAmelCase__  :int | None = None		)   ->  str:




     '''simple docstring'''



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

     # 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=lowerCAmelCase__		)
     # The set of states Alice will prepare.
     _a			      =							rng.integers(2				,						size=lowerCAmelCase__		)
     # Measurement basis for Bob's qubits.
     _a			      =							rng.integers(2				,						size=lowerCAmelCase__		)

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

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

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

     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(lowerCAmelCase__				,						lowerCAmelCase__				,						shots=1				,						seed_simulator=lowerCAmelCase__		)
     # Returns the result of measurement.
     _a			      =							job.result().get_counts(lowerCAmelCase__		).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(
                 lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__		)
             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(lowerCAmelCase__		) >= key_len else gen_key.ljust(lowerCAmelCase__				,						'0'		)
     return key


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

 testmod()

