import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer







def       A__   (						snake_case_			:		Any							,					snake_case_			:		str							,					snake_case_			:		Tuple							,					snake_case_			:		Union[str, Any]=5   ):
 assert masked_input.count('''<mask>'''   ) == 1
 SCREAMING_SNAKE_CASE__: str=   torch.tensor(tokenizer.encode(lowercase_							,					add_special_tokens=lowercase_   )   ).unsqueeze(0   )  # Batch size 1
 SCREAMING_SNAKE_CASE__: Union[str, Any]=   model(lowercase_   )[0]  # The last hidden-state is the first element of the output tuple
 SCREAMING_SNAKE_CASE__: List[Any]=   (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
 SCREAMING_SNAKE_CASE__: Tuple=   logits[0, masked_index, :]
 SCREAMING_SNAKE_CASE__: List[str]=   logits.softmax(dim=0   )
 SCREAMING_SNAKE_CASE__: str=   prob.topk(k=lowercase_							,					dim=0   )
 SCREAMING_SNAKE_CASE__: str=   " ".join(
     [tokenizer.convert_ids_to_tokens(indices[i].item()   ) for i in range(len(lowercase_   )   )]   )
 SCREAMING_SNAKE_CASE__: Any=   tokenizer.mask_token
 SCREAMING_SNAKE_CASE__: List[Any]=   []
 for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''   )   ):
  SCREAMING_SNAKE_CASE__: 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


lowercase_  : Dict								=					CamembertTokenizer.from_pretrained('camembert-base')
lowercase_  : Any								=					CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

lowercase_  : List[str]								=					'Le camembert est <mask> :)'

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








"""simple docstring"""


from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Optional[Any]						  =       ["image_processor", "tokenizer"]
  SCREAMING_SNAKE_CASE__				:Dict						  =       "AutoImageProcessor"
  SCREAMING_SNAKE_CASE__				:Optional[int]						  =       "AutoTokenizer"

  def __init__(		self :							Dict       , __a :							Tuple       , __a :							str						)		->					Optional[Any]:
        super().__init__(__a       , __a						)
        _UpperCamelCase   :						Tuple    =							self.image_processor

  def __call__(		self :							Tuple       , __a :							Tuple=None       , __a :							str=None       , __a :							Any=None       , **__a :							Optional[Any]						)		->					Union[str, Any]:

        if text is None and images is None:
              raise ValueError("You have to specify either text or images. Both cannot be none."						)

        if text is not None:
              _UpperCamelCase   :						List[str]    =							self.tokenizer(__a       , return_tensors=__a       , **__a						)

        if images is not None:
              _UpperCamelCase   :						str    =							self.image_processor(__a       , return_tensors=__a       , **__a						)

        if text is not None and images is not None:
              _UpperCamelCase   :						Optional[int]    =							image_features.pixel_values
              return encoding
        elif text is not None:
              return encoding
        else:
              return BatchEncoding(data=dict(**__a						)       , tensor_type=__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , *__a :							Optional[int]       , **__a :							Optional[int]						)		->					List[Any]:
        return self.tokenizer.batch_decode(*__a       , **__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							int       , *__a :							Any       , **__a :							List[Any]						)		->					Tuple:
        return self.tokenizer.decode(*__a       , **__a						)


  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Any:
        return ["input_ids", "attention_mask", "pixel_values"]
