






import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer






def   UpperCamelCase  (       snake_case_     :   Tuple	,snake_case_     :   Dict	,snake_case_     :   List[Any]	,snake_case_     :   Tuple=5  ):




     '''simple docstring'''






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

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass




@dataclass
class 			snake_case__   :

       '''simple docstring'''
       lowerCamelCase							:				int
       lowerCamelCase							:				int




class 			snake_case__   :

       '''simple docstring'''



       def __init__(							self			,						a__    ) ->      Any:






            '''simple docstring'''





            __snake_case			:list[list[Edge]]          =						[[] for _ in range(a__    )]
            __snake_case			:List[str]          =						size



       def __getitem__(							self			,						a__    ) ->      Iterator[Edge]:






            '''simple docstring'''





            return iter(self._graph[vertex]    )



       @property
       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            return self._size



       def 					__lowercase						(							self			,						a__			,						a__			,						a__    ) ->      List[Any]:






            '''simple docstring'''





            if weight not in (0, 1):
                 raise ValueError("""Edge weight must be either 0 or 1."""    )

            if to_vertex < 0 or to_vertex >= self.size:
                 raise ValueError("""Vertex indexes must be in [0; size)."""    )

            self._graph[from_vertex].append(Edge(a__			,						a__    )    )



       def 					__lowercase						(							self			,						a__			,						a__    ) ->      int | None:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						deque([start_vertex]    )
            __snake_case			:list[int | None]          =						[None] * self.size
            __snake_case			:Tuple          =						0

            while queue:
                 __snake_case			:List[Any]          =						queue.popleft()
                 __snake_case			:Optional[Any]          =						distances[current_vertex]
                 if current_distance is None:
                      continue

                 for edge in self[current_vertex]:
                      __snake_case			:Optional[Any]          =						current_distance + edge.weight
                      __snake_case			:Dict          =						distances[edge.destination_vertex]
                      if (
                          isinstance(a__			,						a__    )
                          and new_distance >= dest_vertex_distance
                      ):
                           continue
                      __snake_case			:Dict          =						new_distance
                      if edge.weight == 0:
                           queue.appendleft(edge.destination_vertex    )
                      else:
                           queue.append(edge.destination_vertex    )

            if distances[finish_vertex] is None:
                 raise ValueError("""No path from start_vertex to finish_vertex."""    )

            return distances[finish_vertex]


if __name__ == "__main__":
     import doctest

     doctest.testmod()


