

import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def 			UpperCAmelCase__				(			_A      : Optional[Any]	,      _A      : Optional[Any]	,      _A      : Dict	,      _A      : Optional[Any]=5							):





  '''simple docstring'''







  assert masked_input.count('''<mask>'''							) == 1
  a__  =torch.tensor(tokenizer.encode(_A	,      add_special_tokens=_A							)							).unsqueeze(0							)  # Batch size 1
  a__  =model(_A							)[0]  # The last hidden-state is the first element of the output tuple
  a__  =(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
  a__  =logits[0, masked_index, :]
  a__  =logits.softmax(dim=0							)
  a__,      a__  =prob.topk(k=_A	,      dim=0							)
  a__  =''' '''.join(
      [tokenizer.convert_ids_to_tokens(indices[i].item()							) for i in range(len(_A							)							)]							)
  a__  =tokenizer.mask_token
  a__  =[]
  for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''							)							):
    a__  =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


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))



from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
