






"""simple docstring"""


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def       snake_case  (A_				:int     ,      A_				:int     ,      A_				:Any     ,      A_				:str=5				):







  '''simple docstring'''

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


_UpperCamelCase    :   Any   			=						CamembertTokenizer.from_pretrained('camembert-base')
_UpperCamelCase    :   Union[str, Any]   			=						CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

_UpperCamelCase    :   int   			=						'Le camembert est <mask> :)'






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












"""simple docstring"""


def       snake_case  (A_				:str     ,      A_				:bool = False				):







  '''simple docstring'''

  if not isinstance(A_     ,      A_				):
    a					:      Union[str, Any]    =       f'''Expected string as input, found {type(A_				)}'''
    raise ValueError(A_				)
  if not isinstance(A_     ,      A_				):
    a					:      Optional[int]    =       f'''Expected boolean as use_pascal parameter, found {type(A_				)}'''
    raise ValueError(A_				)

  a					:      Tuple    =       input_str.split('_'				)

  a					:      Dict    =       0 if use_pascal else 1

  a					:      int    =       words[start_index:]

  a					:      int    =       [word[0].upper() + word[1:] for word in words_to_capitalize]

  a					:      List[str]    =       '' if use_pascal else words[0]

  return "".join([initial_word, *capitalized_words]				)


if __name__ == "__main__":
   from doctest import testmod

   testmod()




