


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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_albert import AlbertTokenizer
else:
       __UpperCamelCase				:  List[str]      					=							None

__UpperCamelCase				:  Dict      					=							logging.get_logger(__name__)
__UpperCamelCase				:  List[Any]      					=							{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__UpperCamelCase				:  Dict      					=							{
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json',
    },
}

__UpperCamelCase				:  str      					=							{
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

__UpperCamelCase				:  Optional[int]      					=							'▁'





class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    A_  :     List[str]			= VOCAB_FILES_NAMES
    A_  :     Optional[int]			= PRETRAINED_VOCAB_FILES_MAP
    A_  :     Optional[int]			= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    A_  :     Dict			= AlbertTokenizer





    def __init__(	self				:   List[str]       ,   __snake_case				:   Dict=None       ,   __snake_case				:   List[Any]=None       ,   __snake_case				:   Dict=True       ,   __snake_case				:   Tuple=True       ,   __snake_case				:   List[Any]=False       ,   __snake_case				:   Union[str, Any]="[CLS]"       ,   __snake_case				:   Any="[SEP]"       ,   __snake_case				:   Optional[int]="<unk>"       ,   __snake_case				:   Any="[SEP]"       ,   __snake_case				:   int="<pad>"       ,   __snake_case				:   Dict="[CLS]"       ,   __snake_case				:   str="[MASK]"       ,   **__snake_case				:   List[Any]       ,   ):





       '''simple docstring'''
       # Mask token behave like a normal word, i.e. include the space before it and
       # is included in the raw text, there should be a match in a non-normalized sentence.
       UpperCAmelCase_							:      List[str]						=   (
           AddedToken(__snake_case       ,   lstrip=__snake_case       ,   rstrip=__snake_case       ,   normalized=__snake_case	)
           if isinstance(__snake_case       ,   __snake_case	)
           else mask_token
       )

       super().__init__(
           __snake_case       ,   tokenizer_file=__snake_case       ,   do_lower_case=__snake_case       ,   remove_space=__snake_case       ,   keep_accents=__snake_case       ,   bos_token=__snake_case       ,   eos_token=__snake_case       ,   unk_token=__snake_case       ,   sep_token=__snake_case       ,   pad_token=__snake_case       ,   cls_token=__snake_case       ,   mask_token=__snake_case       ,   **__snake_case       ,   )

       UpperCAmelCase_							:      Union[str, Any]						=   do_lower_case
       UpperCAmelCase_							:      Any						=   remove_space
       UpperCAmelCase_							:      Optional[int]						=   keep_accents
       UpperCAmelCase_							:      str						=   vocab_file
       UpperCAmelCase_							:      Tuple						=   False if not self.vocab_file else True





    def 					_lowerCamelCase (	self				:   Optional[int]       ,   __snake_case				:   List[int]       ,   __snake_case				:   Optional[List[int]] = None	):





       '''simple docstring'''
       UpperCAmelCase_							:      int						=   [self.sep_token_id]
       UpperCAmelCase_							:      Optional[int]						=   [self.cls_token_id]
       if token_ids_a is None:
          return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep





    def 					_lowerCamelCase (	self				:   List[Any]       ,   __snake_case				:   List[int]       ,   __snake_case				:   Optional[List[int]] = None	):





       '''simple docstring'''
       UpperCAmelCase_							:      Any						=   [self.sep_token_id]
       UpperCAmelCase_							:      str						=   [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	) * [0] + len(token_ids_a + sep	) * [1]





    def 					_lowerCamelCase (	self				:   List[Any]       ,   __snake_case				:   str       ,   __snake_case				:   Optional[str] = None	):





       '''simple docstring'''
       if not self.can_save_slow_tokenizer:
          raise ValueError(
              '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
              '''tokenizer.'''	)

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

       if os.path.abspath(self.vocab_file	) != os.path.abspath(__snake_case	):
          copyfile(self.vocab_file       ,   __snake_case	)

       return (out_vocab_file,)



import math

import qiskit
def      snake_case_						( __lowercase = 1			,       __lowercase = 1			,       __lowercase = 1       ):
   if (
       isinstance(__lowercase			,       __lowercase       )
       or isinstance(__lowercase			,       __lowercase       )
       or isinstance(__lowercase			,       __lowercase       )
   ):
      raise TypeError('''inputs must be integers.'''       )

   if (input_a < 0) or (input_a < 0) or (carry_in < 0):
      raise ValueError('''inputs must be positive.'''       )

   if (
       (math.floor(__lowercase       ) != input_a)
       or (math.floor(__lowercase       ) != input_a)
       or (math.floor(__lowercase       ) != carry_in)
   ):
      raise ValueError('''inputs must be exact integers.'''       )

   if (input_a > 2) or (input_a > 2) or (carry_in > 2):
      raise ValueError('''inputs must be less or equal to 2.'''       )

   # build registers
   UpperCAmelCase_							:      Any						=   qiskit.QuantumRegister(4			,       '''qr'''       )
   UpperCAmelCase_							:      List[str]						=   qiskit.ClassicalRegister(2			,       '''cr'''       )
   # list the entries
   UpperCAmelCase_							:      Any						=   [input_a, input_a, carry_in]

   UpperCAmelCase_							:      Dict						=   qiskit.QuantumCircuit(__lowercase			,       __lowercase       )

   for i in range(0			,       3       ):
      if entry[i] == 2:
         quantum_circuit.h(__lowercase       )  # for hadamard entries
      elif entry[i] == 1:
         quantum_circuit.x(__lowercase       )  # for 1 entries
      elif entry[i] == 0:
         quantum_circuit.i(__lowercase       )  # for 0 entries

    # build the circuit
   quantum_circuit.ccx(0			,       1			,       3       )  # ccx = toffoli gate
   quantum_circuit.cx(0			,       1       )
   quantum_circuit.ccx(1			,       2			,       3       )
   quantum_circuit.cx(1			,       2       )
   quantum_circuit.cx(0			,       1       )

   quantum_circuit.measure([2, 3]			,       __lowercase       )  # measure the last two qbits

   UpperCAmelCase_							:      Optional[int]						=   qiskit.Aer.get_backend('''aer_simulator'''       )
   UpperCAmelCase_							:      List[str]						=   qiskit.execute(__lowercase			,       __lowercase			,       shots=1_0_0_0       )

   return job.result().get_counts(__lowercase       )


if __name__ == "__main__":
       print(F'Total sum count for state is: {quantum_full_adder(1, 1, 1)}')