'''simple docstring'''






import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer






def  UpperCamelCase__					(					_lowercase    :				List[str]   ,    _lowercase    :				List[str]   ,    _lowercase    :				Optional[int]   ,    _lowercase    :				str=5     )							->	Any:
  # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
  assert masked_input.count("""<mask>"""     ) == 1
  __UpperCAmelCase:					Tuple		= torch.tensor(tokenizer.encode(_lowercase   ,    add_special_tokens=_lowercase     )     ).unsqueeze(0     )  # Batch size 1
  __UpperCAmelCase:					List[str]		= model(_lowercase     )[0]  # The last hidden-state is the first element of the output tuple
  __UpperCAmelCase:					Dict		= (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
  __UpperCAmelCase:					List[Any]		= logits[0, masked_index, :]
  __UpperCAmelCase:					Optional[Any]		= logits.softmax(dim=0     )
  __UpperCAmelCase, __UpperCAmelCase:					str		= prob.topk(k=_lowercase   ,    dim=0     )
  __UpperCAmelCase:					Tuple		= """ """.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:					Tuple		= 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


SCREAMING_SNAKE_CASE_	   =			CamembertTokenizer.from_pretrained('camembert-base')
SCREAMING_SNAKE_CASE_	   =			CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

SCREAMING_SNAKE_CASE_	   =			'Le camembert est <mask> :)'







print(fill_mask(masked_input, model, tokenizer, topk=3))
'''simple docstring'''






from ....utils import logging


SCREAMING_SNAKE_CASE_	   =			logging.get_logger(__name__)




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_=None   ,    snake_case_=2048					):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= config.__dict__
    __UpperCAmelCase:					str		= modal_hidden_size
    if num_labels:
      __UpperCAmelCase:					int		= num_labels