



'''simple docstring'''





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase_	 =						logging.get_logger(__name__)

lowercase_	 =						{"vocab_file": "spm_char.model"}

lowercase_	 =						{
    "vocab_file": {
        "microsoft/speecht5_asr": "https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model",
        "microsoft/speecht5_tts": "https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model",
        "microsoft/speecht5_vc": "https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model",
    }
}

lowercase_	 =						{
    "microsoft/speecht5_asr": 1_024,
    "microsoft/speecht5_tts": 1_024,
    "microsoft/speecht5_vc": 1_024,
}






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: Dict    = VOCAB_FILES_NAMES
 __lowerCamelCase					: List[str]    = PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase					: List[str]    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase					: List[Any]    = ['input_ids', 'attention_mask']



 def __init__(self				,     A				,     A="<s>"				,     A="</s>"				,     A="<unk>"				,     A="<pad>"				,     A = None				,     **A				,     )					->  None:


      """simple docstring"""



      _a											=						{} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=A				,     eos_token=A				,     unk_token=A				,     pad_token=A				,     sp_model_kwargs=self.sp_model_kwargs				,     **A				,     )

      _a											=						vocab_file

      _a											=						spm.SentencePieceProcessor(**self.sp_model_kwargs )
      self.sp_model.Load(A )



 @property
 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      return self.sp_model.get_piece_size()



 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a											=						{self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
      vocab.update(self.added_tokens_encoder )
      return vocab



 def __getstate__(self )					->  List[str]:


      """simple docstring"""



      _a											=						self.__dict__.copy()
      _a											=						None
      return state



 def __setstate__(self				,     A )					->  Union[str, Any]:


      """simple docstring"""



      _a											=						d

      # for backward compatibility
      if not hasattr(self				,     '''sp_model_kwargs''' ):
           _a											=						{}

      _a											=						spm.SentencePieceProcessor(**self.sp_model_kwargs )
      self.sp_model.Load(self.vocab_file )



 def       a__						(self				,     A )					->  List[str]:


      """simple docstring"""



      return self.sp_model.encode(A				,     out_type=A )



 def       a__						(self				,     A )					->  Union[str, Any]:


      """simple docstring"""



      return self.sp_model.piece_to_id(A )



 def       a__						(self				,     A )					->  int:


      """simple docstring"""



      _a											=						self.sp_model.IdToPiece(A )
      return token



 def       a__						(self				,     A )					->  Any:


      """simple docstring"""



      _a											=						[]
      _a											=						''''''
      for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
                out_string += self.sp_model.decode(A ) + token
                _a											=						[]
           else:
                current_sub_tokens.append(A )
      out_string += self.sp_model.decode(A )
      return out_string.strip()



 def       a__						(self				,     A				,     A=None )					->  List[int]:


      """simple docstring"""



      if token_ids_a is None:
           return token_ids_a + [self.eos_token_id]
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return token_ids_a + token_ids_a + [self.eos_token_id]



 def       a__						(self				,     A				,     A = None				,     A = False )					->  List[int]:


      """simple docstring"""



      if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=A				,     token_ids_a=A				,     already_has_special_tokens=A )

      _a											=						[1]
      if token_ids_a is None:
           return ([0] * len(A )) + suffix_ones
      return ([0] * len(A )) + ([0] * len(A )) + suffix_ones



 def       a__						(self				,     A				,     A = None )					->  Tuple[str]:


      """simple docstring"""



      if not os.path.isdir(A ):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
           return
      _a											=						os.path.join(
          A				,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

      if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
           copyfile(self.vocab_file				,     A )
      elif not os.path.isfile(self.vocab_file ):
           with open(A				,     '''wb''' ) as fi:
                _a											=						self.sp_model.serialized_model_proto()
                fi.write(A )

      return (out_vocab_file,)








'''simple docstring'''





import numpy as np
import qiskit




def lowerCAmelCase						(__A = 8						,			__A = None):



     """simple docstring"""



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

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

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

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

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

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


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

  testmod()



