


"""simple docstring"""







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


__lowercase    =				{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

__lowercase    =				{
    """vocab_file""": {
        """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """unc-nlp/lxmert-base-uncased""": (
            """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

__lowercase    =				{
    """unc-nlp/lxmert-base-uncased""": 512,
}

__lowercase    =				{
    """unc-nlp/lxmert-base-uncased""": {"""do_lower_case""": True},
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							List[Any]     =	VOCAB_FILES_NAMES
    UpperCAmelCase	:							List[Any]     =	PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase	:							Dict     =	PRETRAINED_INIT_CONFIGURATION
    UpperCAmelCase	:							int     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase	:							Dict     =	LxmertTokenizer






    def __init__( self    :   Union[str, Any]    ,			__UpperCAmelCase    :   List[str]=None    ,			__UpperCAmelCase    :   int=None    ,			__UpperCAmelCase    :   List[str]=True    ,			__UpperCAmelCase    :   str="[UNK]"    ,			__UpperCAmelCase    :   Tuple="[SEP]"    ,			__UpperCAmelCase    :   Optional[int]="[PAD]"    ,			__UpperCAmelCase    :   Optional[Any]="[CLS]"    ,			__UpperCAmelCase    :   str="[MASK]"    ,			__UpperCAmelCase    :   Any=True    ,			__UpperCAmelCase    :   Tuple=None    ,			**__UpperCAmelCase    :   Optional[int]    ,			):
      super().__init__(
          __UpperCAmelCase    ,			tokenizer_file=__UpperCAmelCase    ,			do_lower_case=__UpperCAmelCase    ,			unk_token=__UpperCAmelCase    ,			sep_token=__UpperCAmelCase    ,			pad_token=__UpperCAmelCase    ,			cls_token=__UpperCAmelCase    ,			mask_token=__UpperCAmelCase    ,			tokenize_chinese_chars=__UpperCAmelCase    ,			strip_accents=__UpperCAmelCase    ,			**__UpperCAmelCase    ,			)

      a						:		List[str]											=      json.loads(self.backend_tokenizer.normalizer.__getstate__())
      if (
          normalizer_state.get("lowercase"    ,			__UpperCAmelCase) != do_lower_case
          or normalizer_state.get("strip_accents"    ,			__UpperCAmelCase) != strip_accents
          or normalizer_state.get("handle_chinese_chars"    ,			__UpperCAmelCase) != tokenize_chinese_chars
      ):
        a						:		Any											=      getattr(__UpperCAmelCase    ,			normalizer_state.pop("type"))
        a						:		Optional[int]											=      do_lower_case
        a						:		Union[str, Any]											=      strip_accents
        a						:		Any											=      tokenize_chinese_chars
        a						:		Dict											=      normalizer_class(**__UpperCAmelCase)

      a						:		int											=      do_lower_case






    def      __snake_case							( self    :   Optional[Any]    ,			__UpperCAmelCase    :   List[str]    ,			__UpperCAmelCase    :   Optional[Any]=None):
      a						:		List[str]											=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

      if token_ids_a:
        output += token_ids_a + [self.sep_token_id]

      return output






    def      __snake_case							( self    :   List[Any]    ,			__UpperCAmelCase    :   List[int]    ,			__UpperCAmelCase    :   Optional[List[int]] = None):
      a						:		List[Any]											=      [self.sep_token_id]
      a						:		Optional[int]											=      [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      __snake_case							( self    :   Optional[int]    ,			__UpperCAmelCase    :   str    ,			__UpperCAmelCase    :   Optional[str] = None):
      a						:		List[str]											=      self._tokenizer.model.save(__UpperCAmelCase    ,			name=__UpperCAmelCase)
      return tuple(__UpperCAmelCase)










"""simple docstring"""







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_=5	)->				Union[str, Any]:
  '''simple docstring'''







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


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

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







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






