


"""simple docstring"""







from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy






class       __A				(       _SCREAMING_SNAKE_CASE			):






 """simple docstring"""




 __lowerCAmelCase 					=  "EncodecFeatureExtractor"
 __lowerCAmelCase 					=  ("T5Tokenizer", "T5TokenizerFast")


 def __init__(		self ,    __A ,    __A						)					->     Any:
     super().__init__(__A ,    __A						)
     a 		=self.feature_extractor
     a 		=False


 def       SCREAMING_SNAKE_CASE							(		self ,    __A=None ,    __A=None ,    __A=True						)					->     int:
     return self.tokenizer.get_decoder_prompt_ids(task=__A ,    language=__A ,    no_timestamps=__A						)


 def __call__(		self ,    *__A ,    **__A						)					->     int:
     # For backward compatibility
     if self._in_target_context_manager:
         return self.current_processor(*__A ,    **__A						)

     a 		=kwargs.pop('''audio''' ,    __A						)
     a 		=kwargs.pop('''sampling_rate''' ,    __A						)
     a 		=kwargs.pop('''text''' ,    __A						)
     if len(__A						) > 0:
         a 		=args[0]
         a 		=args[1:]

     if audio is None and text is None:
         raise ValueError('''You need to specify either an `audio` or `text` input to process.'''						)

     if text is not None:
         a 		=self.tokenizer(__A ,    **__A						)

     if audio is not None:
         a 		=self.feature_extractor(__A ,    *__A ,    sampling_rate=__A ,    **__A						)

     if audio is None:
         return inputs

     elif text is None:
         return audio_inputs

     else:
         a 		=audio_inputs['''input_values''']
         if "padding_mask" in audio_inputs:
             a 		=audio_inputs['''padding_mask''']
         return inputs


 def       SCREAMING_SNAKE_CASE							(		self ,    *__A ,    **__A						)					->     str:
     a 		=kwargs.pop('''audio''' ,    __A						)
     a 		=kwargs.pop('''padding_mask''' ,    __A						)

     if len(__A						) > 0:
         a 		=args[0]
         a 		=args[1:]

     if audio_values is not None:
         return self._decode_audio(__A ,    padding_mask=__A						)
     else:
         return self.tokenizer.batch_decode(*__A ,    **__A						)


 def       SCREAMING_SNAKE_CASE							(		self ,    *__A ,    **__A						)					->     Optional[int]:
     return self.tokenizer.decode(*__A ,    **__A						)





 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A = None						)					->     List[np.ndarray]:
     a 		=to_numpy(__A						)
     a		,      a		,      a 		=audio_values.shape

     if padding_mask is None:
         return list(__A						)

     a 		=to_numpy(__A						)

     # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
     # token (so that the generated audio values are **not** treated as padded tokens)
     a 		=seq_len - padding_mask.shape[-1]
     a 		=1 - self.feature_extractor.padding_value
     a 		=np.pad(__A ,    ((0, 0), (0, difference)) ,    '''constant''' ,    constant_values=__A						)

     a 		=audio_values.tolist()
     for i in range(__A						):
         a 		=np.asarray(audio_values[i]						)[
             padding_mask[i][None, :] != self.feature_extractor.padding_value
         ]
         a 		=sliced_audio.reshape(__A ,    -1						)

     return audio_values
"""simple docstring"""




import numpy as np
import qiskit





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int = 8			,_lowerCamelCase						:       int | None = None	)       ->      str:
  _lowerCAmelCase						:       int  			=     np.random.default_rng(seed=_lowerCamelCase	)

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

  # Quantum Circuit to simulate BB84
  _lowerCAmelCase						:       Dict  			=     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.
  _lowerCAmelCase						:       int  			=     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.
  _lowerCAmelCase						:       List[str]  			=     qiskit.execute(_lowerCamelCase			,_lowerCamelCase			,shots=1			,seed_simulator=_lowerCamelCase	)
  # Returns the result of measurement.
  _lowerCAmelCase						:       List[Any]  			=     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.
  _lowerCAmelCase						:       str  			=     """""".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.
  _lowerCAmelCase						:       List[Any]  			=     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()




