






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_rembert import RemBertTokenizer
else:
   snake_case    				=		None

snake_case    				=		logging.get_logger(__name__)
snake_case    				=		{"""vocab_file""": """sentencepiece.model""", """tokenizer_file""": """tokenizer.json"""}

snake_case    				=		{
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
    """tokenizer_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/tokenizer.json""",
    },
}

snake_case    				=		{
    """google/rembert""": 256,
}

snake_case    				=		"""▁"""


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
  '''simple docstring'''


  UpperCamelCase_							:		Tuple			=	VOCAB_FILES_NAMES
  UpperCamelCase_							:		Any			=	PRETRAINED_VOCAB_FILES_MAP
  UpperCamelCase_							:		List[str]			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  UpperCamelCase_							:		Union[str, Any]			=	RemBertTokenizer


  def __init__(				self       :	Optional[Any]					, UpperCAmelCase_       :	Any=None					, UpperCAmelCase_       :	List[str]=None					, UpperCAmelCase_       :	Any=True					, UpperCAmelCase_       :	int=True					, UpperCAmelCase_       :	List[Any]=False					, UpperCAmelCase_       :	Union[str, Any]="[CLS]"					, UpperCAmelCase_       :	Tuple="[SEP]"					, UpperCAmelCase_       :	str="<unk>"					, UpperCAmelCase_       :	Tuple="[SEP]"					, UpperCAmelCase_       :	List[Any]="<pad>"					, UpperCAmelCase_       :	Optional[int]="[CLS]"					, UpperCAmelCase_       :	Optional[int]="[MASK]"					, **UpperCAmelCase_       :	int					, ):
    # Mask token behave like a normal word, i.e. include the space before it
    SCREAMING_SNAKE_CASE       :      Optional[int]							=    AddedToken(UpperCAmelCase_					, lstrip=UpperCAmelCase_					, rstrip=UpperCAmelCase_      ) if isinstance(UpperCAmelCase_					, UpperCAmelCase_      ) else mask_token

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

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


  def 				_A       (				self       :	Union[str, Any]					, UpperCAmelCase_       :	List[int]					, UpperCAmelCase_       :	Optional[List[int]] = None      ):
    SCREAMING_SNAKE_CASE       :      List[str]							=    [self.sep_token_id]
    SCREAMING_SNAKE_CASE       :      Dict							=    [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 				_A       (				self       :	Optional[Any]					, UpperCAmelCase_       :	List[int]					, UpperCAmelCase_       :	Optional[List[int]] = None					, UpperCAmelCase_       :	bool = False      ):

    if already_has_special_tokens:
      if token_ids_a is not None:
        raise ValueError(
            "You should not supply a second sequence if the provided sequence of "
            "ids is already formatted with special tokens for the model."      )
      return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

    if token_ids_a is not None:
      return [1] + ([0] * len(UpperCAmelCase_      )) + [1] + ([0] * len(UpperCAmelCase_      )) + [1]
    return [1] + ([0] * len(UpperCAmelCase_      )) + [1]


  def 				_A       (				self       :	Union[str, Any]					, UpperCAmelCase_       :	List[int]					, UpperCAmelCase_       :	Optional[List[int]] = None      ):
    SCREAMING_SNAKE_CASE       :      Dict							=    [self.sep_token_id]
    SCREAMING_SNAKE_CASE       :      List[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 				_A       (				self       :	Any					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	Optional[str] = None      ):
    if not os.path.isdir(UpperCAmelCase_      ):
      logger.error("Vocabulary path ({}) should be a directory".format(UpperCAmelCase_      )      )
      return
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    os.path.join(
        UpperCAmelCase_					, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )

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

    return (out_vocab_file,)














import qiskit


def   lowerCamelCase__			(     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      List[str]							=    qiskit.Aer.get_backend("aer_simulator"				)

  # Create a Quantum Circuit acting on the q register
  SCREAMING_SNAKE_CASE       :      Dict							=    qiskit.QuantumCircuit(lowercase						,     lowercase				)

  # Map the quantum measurement to the classical bits
  circuit.measure([0]						,     [0]				)

  # Execute the circuit on the simulator
  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    qiskit.execute(lowercase						,     lowercase						,     shots=1000				)

  # Return the histogram data of the results of the experiment.
  return job.result().get_counts(lowercase				)


if __name__ == "__main__":
   print(F"""Total count for various states are: {single_qubit_measure(1, 1)}""")






