





"""simple docstring"""


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

import sentencepiece as spm

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


lowerCamelCase__						= logging.get_logger(__name__)

lowerCamelCase__						= {"vocab_file": "sentencepiece.model"}

lowerCamelCase__						= {
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
}

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



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:str						  =       VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__				:Optional[Any]						  =       PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__				:int						  =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

  def __init__(		self :							Optional[Any]       , __a :							Optional[int]       , __a :							Optional[int]=False       , __a :							str=True       , __a :							int=True       , __a :							List[str]="[CLS]"       , __a :							int="[SEP]"       , __a :							Dict="[UNK]"       , __a :							Tuple="[SEP]"       , __a :							List[Any]="[PAD]"       , __a :							str="[CLS]"       , __a :							Tuple="[MASK]"       , **__a :							Tuple       , )		->					List[Any]:
        super().__init__(
            do_lower_case=__a       , remove_space=__a       , keep_accents=__a       , bos_token=__a       , eos_token=__a       , unk_token=__a       , sep_token=__a       , pad_token=__a       , cls_token=__a       , mask_token=__a       , **__a       , )

        _UpperCamelCase   :						Tuple    =							do_lower_case
        _UpperCamelCase   :						Union[str, Any]    =							remove_space
        _UpperCamelCase   :						Optional[int]    =							keep_accents
        _UpperCamelCase   :						int    =							vocab_file

        _UpperCamelCase   :						Optional[Any]    =							spm.SentencePieceProcessor()
        self.sp_model.Load(__a						)

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]						)		->					Any:
        return len(self.sp_model						)

  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]						)		->					Dict:
        _UpperCamelCase   :						List[Any]    =							{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[Any]						)		->					Union[str, Any]:
        _UpperCamelCase   :						Optional[Any]    =							self.__dict__.copy()
        _UpperCamelCase   :						str    =							None
        return state

  def __setstate__(		self :							Dict       , __a :							Any						)		->					int:
        _UpperCamelCase   :						List[Any]    =							d
        _UpperCamelCase   :						Any    =							spm.SentencePieceProcessor()
        self.sp_model.Load(self.vocab_file						)

  def  __SCREAMING_SNAKE_CASE					(		self :							str       , __a :							Tuple       , __a :							List[str]=False						)		->					Optional[Any]:
        _UpperCamelCase   :						Optional[int]    =							self.sp_model.EncodeAsPieces(__a						)
        return pieces

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , __a :							int						)		->					Union[str, Any]:
        return self.sp_model.PieceToId(__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[Any]       , __a :							int						)		->					Optional[Any]:
        return self.sp_model.IdToPiece(__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]       , __a :							str						)		->					Tuple:
        _UpperCamelCase   :						str    =							self.sp_model.decode_pieces(__a						)
        return out_string

  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]       , __a :							List[int]       , __a :							Optional[List[int]] = None						)		->					List[int]:
        _UpperCamelCase   :						int    =							[self.sep_token_id]
        _UpperCamelCase   :						List[str]    =							[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  __SCREAMING_SNAKE_CASE					(		self :							Tuple       , __a :							List[int]       , __a :							Optional[List[int]] = None       , __a :							bool = False						)		->					List[int]:

        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(__a						)) + [1] + ([0] * len(__a						)) + [1]
        return [1] + ([0] * len(__a						)) + [1]

  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]       , __a :							List[int]       , __a :							Optional[List[int]] = None						)		->					List[int]:
        _UpperCamelCase   :						int    =							[self.sep_token_id]
        _UpperCamelCase   :						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  __SCREAMING_SNAKE_CASE					(		self :							Optional[Any]       , __a :							str       , __a :							Optional[str] = None						)		->					Tuple[str]:
        if not os.path.isdir(__a						):
              logger.error("Vocabulary path ({}) should be a directory".format(__a						)						)
              return
        _UpperCamelCase   :						Dict    =							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						):
              copyfile(self.vocab_file       , __a						)

        return (out_vocab_file,)







"""simple docstring"""


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_=5      )       ->		List[Any]:




      """simple docstring"""

      assert masked_input.count("<mask>"      ) == 1
      _UpperCamelCase   :						str    =							torch.tensor(tokenizer.encode(lowercase_		,add_special_tokens=lowercase_      )      ).unsqueeze(0      )  # Batch size 1
      _UpperCamelCase   :						Union[str, Any]    =							model(lowercase_      )[0]  # The last hidden-state is the first element of the output tuple
      _UpperCamelCase   :						List[Any]    =							(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
      _UpperCamelCase   :						Tuple    =							logits[0, masked_index, :]
      _UpperCamelCase   :						List[str]    =							logits.softmax(dim=0      )
      _UpperCamelCase,	_UpperCamelCase   :						str    =							prob.topk(k=lowercase_		,dim=0      )
      _UpperCamelCase   :						str    =							" ".join(
          [tokenizer.convert_ids_to_tokens(indices[i].item()      ) for i in range(len(lowercase_      )      )]      )
      _UpperCamelCase   :						Any    =							tokenizer.mask_token
      _UpperCamelCase   :						List[Any]    =							[]
      for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "      )      ):
            _UpperCamelCase   :						Optional[int]    =							predicted_token_bpe.replace("\u2581"		," "      )
            if " {0}".format(lowercase_      ) in masked_input:
                  topk_filled_outputs.append(
                      (
                          masked_input.replace(" {0}".format(lowercase_      )		,lowercase_      ),
                          values[index].item(),
                          predicted_token,
                      )      )
            else:
                  topk_filled_outputs.append(
                      (
                          masked_input.replace(lowercase_		,lowercase_      ),
                          values[index].item(),
                          predicted_token,
                      )      )
      return topk_filled_outputs


lowerCamelCase__						= CamembertTokenizer.from_pretrained("camembert-base")
lowerCamelCase__						= CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

lowerCamelCase__						= "Le camembert est <mask> :)"

print(fill_mask(masked_input, model, tokenizer, topk=3))
