
"""simple docstring"""

import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


UpperCamelCase			:       Dict									=				logging.get_logger(__name__)


UpperCamelCase			:       Tuple									=				{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all BART models at https://huggingface.co/models?filter=bart
UpperCamelCase			:       Tuple									=				{
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
    },
}

UpperCamelCase			:       Optional[int]									=				{
    "facebook/bart-base": 1_0_2_4,
    "facebook/bart-large": 1_0_2_4,
    "facebook/bart-large-mnli": 1_0_2_4,
    "facebook/bart-large-cnn": 1_0_2_4,
    "facebook/bart-large-xsum": 1_0_2_4,
    "yjernite/bart_eli5": 1_0_2_4,
}

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		VOCAB_FILES_NAMES
     lowercase		     =		PRETRAINED_VOCAB_FILES_MAP
     lowercase		     =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase		     =		["input_ids", "attention_mask"]
     lowercase		     =		BartTokenizer





     def __init__(			self			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase=None			,  __UpperCAmelCase="replace"			,  __UpperCAmelCase="<s>"			,  __UpperCAmelCase="</s>"			,  __UpperCAmelCase="</s>"			,  __UpperCAmelCase="<s>"			,  __UpperCAmelCase="<unk>"			,  __UpperCAmelCase="<pad>"			,  __UpperCAmelCase="<mask>"			,  __UpperCAmelCase=False			,  __UpperCAmelCase=True			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      super().__init__(
          __UpperCAmelCase			,  __UpperCAmelCase			,  tokenizer_file=__UpperCAmelCase			,  errors=__UpperCAmelCase			,  bos_token=__UpperCAmelCase			,  eos_token=__UpperCAmelCase			,  sep_token=__UpperCAmelCase			,  cls_token=__UpperCAmelCase			,  unk_token=__UpperCAmelCase			,  pad_token=__UpperCAmelCase			,  mask_token=__UpperCAmelCase			,  add_prefix_space=__UpperCAmelCase			,  trim_offsets=__UpperCAmelCase			,  **__UpperCAmelCase			,  )

      __UpperCamelCase       						= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
      if pre_tok_state.get('add_prefix_space'			,  __UpperCAmelCase   ) != add_prefix_space:
       __UpperCamelCase       						= getattr(__UpperCAmelCase			,  pre_tok_state.pop('type'   )   )
       __UpperCamelCase       						= add_prefix_space
       __UpperCamelCase       						= pre_tok_class(**__UpperCAmelCase   )

      __UpperCamelCase       						= add_prefix_space

      # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
      __UpperCamelCase       						= 'post_processor'
      __UpperCamelCase       						= getattr(self.backend_tokenizer			,  __UpperCAmelCase			,  __UpperCAmelCase   )
      if tokenizer_component_instance:
       __UpperCamelCase       						= json.loads(tokenizer_component_instance.__getstate__()   )

       # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
       if "sep" in state:
        __UpperCamelCase       						= tuple(state['sep']   )
       if "cls" in state:
        __UpperCamelCase       						= tuple(state['cls']   )

       __UpperCamelCase       						= False

       if state.get('add_prefix_space'			,  __UpperCAmelCase   ) != add_prefix_space:
        __UpperCamelCase       						= add_prefix_space
        __UpperCamelCase       						= True

       if state.get('trim_offsets'			,  __UpperCAmelCase   ) != trim_offsets:
        __UpperCamelCase       						= trim_offsets
        __UpperCamelCase       						= True

       if changes_to_apply:
        __UpperCamelCase       						= getattr(__UpperCAmelCase			,  state.pop('type'   )   )
        __UpperCamelCase       						= component_class(**__UpperCAmelCase   )
        setattr(self.backend_tokenizer			,  __UpperCAmelCase			,  __UpperCAmelCase   )





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      if self._mask_token is None:
       if self.verbose:
        logger.error('Using mask_token, but it is not set yet.'   )
       return None
      return str(self._mask_token   )





     @mask_token.setter
     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= AddedToken(__UpperCAmelCase			,  lstrip=__UpperCAmelCase			,  rstrip=__UpperCAmelCase   ) if isinstance(__UpperCAmelCase			,  __UpperCAmelCase   ) else value
      __UpperCamelCase       						= value





     def 		UpperCAmelCase      (			self			,  *__UpperCAmelCase			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= kwargs.get('is_split_into_words'			,  __UpperCAmelCase   )

      if is_split_into_words and not self.add_prefix_space:
       raise ValueError(
           F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
           'to use it with pretokenized inputs.'   )

      return super()._batch_encode_plus(*__UpperCAmelCase			,  **__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  *__UpperCAmelCase			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= kwargs.get('is_split_into_words'			,  __UpperCAmelCase   )

      if is_split_into_words and not self.add_prefix_space:
       raise ValueError(
           F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
           'to use it with pretokenized inputs.'   )

      return super()._encode_plus(*__UpperCAmelCase			,  **__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase = None   ):

      '''simple docstring'''




      __UpperCamelCase       						= self._tokenizer.model.save(__UpperCAmelCase			,  name=__UpperCAmelCase   )
      return tuple(__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase=None   ):

      '''simple docstring'''




      __UpperCamelCase       						= [self.bos_token_id] + token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
       return output

      return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase = None   ):

      '''simple docstring'''




      __UpperCamelCase       						= [self.sep_token_id]
      __UpperCamelCase       						= [self.cls_token_id]

      if token_ids_a is None:
       return len(cls + token_ids_a + sep   ) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]





"""simple docstring"""

import numpy as np
import qiskit






def 							A		(       snake_case   :int = 8       ,      snake_case   :int | None = None	)   ->			str:
 __UpperCamelCase       						= np.random.default_rng(seed=snake_case	)

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

 # Quantum Circuit to simulate BB84
 __UpperCamelCase       						= qiskit.QuantumCircuit(snake_case       ,      name='BB84'	)

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

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

 bbaa_circ.barrier()
 bbaa_circ.measure_all()

 # Simulate the quantum circuit.
 __UpperCamelCase       						= 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.
 __UpperCamelCase       						= qiskit.execute(snake_case       ,      snake_case       ,      shots=1       ,      seed_simulator=snake_case	)
 # Returns the result of measurement.
 __UpperCamelCase       						= job.result().get_counts(snake_case	).most_frequent()

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

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


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

      testmod()



