


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def       a_ (       SCREAMING_SNAKE_CASE__   : Optional[Any]						,     SCREAMING_SNAKE_CASE__   : Tuple						,     SCREAMING_SNAKE_CASE__   : Union[str, Any]						,     SCREAMING_SNAKE_CASE__   : Tuple=5	):
       '''simple docstring'''





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

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class A    (  UpperCamelCase_	):






      def __init__(   self		:	Union[str, Any]     ,  lowercase_		:	NestedDataStructureLike[PathLike]     ,  lowercase_		:	Optional[NamedSplit] = None     ,  lowercase_		:	Optional[Features] = None     ,  lowercase_		:	str = None     ,  lowercase_		:	bool = False     ,  lowercase_		:	bool = False     ,  lowercase_		:	Optional[int] = None     ,  **lowercase_		:	Optional[int]     ,  )   ->		Union[str, Any]:
             """simple docstring"""




             super().__init__(
                 lowercase_     ,  split=lowercase_     ,  features=lowercase_     ,  cache_dir=lowercase_     ,  keep_in_memory=lowercase_     ,  streaming=lowercase_     ,  num_proc=lowercase_     ,  **lowercase_     ,  )
             _lowerCamelCase			:						Optional[int]            =path_or_paths if isinstance(lowercase_     ,  lowercase_					) else {self.split: path_or_paths}
             _lowerCamelCase			:						int            =Text(
                 cache_dir=lowercase_     ,  data_files=lowercase_     ,  features=lowercase_     ,  **lowercase_     ,  )






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Dict:
             """simple docstring"""




             if self.streaming:
                    _lowerCamelCase			:						Optional[Any]            =self.builder.as_streaming_dataset(split=self.split					)
             # Build regular (map-style) dataset
             else:
                    _lowerCamelCase			:						List[str]            =None
                    _lowerCamelCase			:						int            =None
                    _lowerCamelCase			:						Optional[Any]            =None
                    _lowerCamelCase			:						Any            =None

                    self.builder.download_and_prepare(
                        download_config=lowercase_     ,  download_mode=lowercase_     ,  verification_mode=lowercase_     ,  base_path=lowercase_     ,  num_proc=self.num_proc     ,  )
                    _lowerCamelCase			:						Tuple            =self.builder.as_dataset(
                        split=self.split     ,  verification_mode=lowercase_     ,  in_memory=self.keep_in_memory					)
             return dataset


