


'''simple docstring'''



import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Tuple ,       UpperCAmelCase_       :							Any ,       UpperCAmelCase_       :							List[str] ,       UpperCAmelCase_       :							Optional[int]=5				)	->						Optional[Any]:
     # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
     assert masked_input.count('<mask>'				) == 1
     __lowerCamelCase  :      Dict														=						torch.tensor(tokenizer.encode(UpperCAmelCase_ ,       add_special_tokens=UpperCAmelCase_				)				).unsqueeze(0				)  # Batch size 1
     __lowerCamelCase  :      Tuple														=						model(UpperCAmelCase_				)[0]  # The last hidden-state is the first element of the output tuple
     __lowerCamelCase  :      Tuple														=						(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
     __lowerCamelCase  :      Optional[Any]														=						logits[0, masked_index, :]
     __lowerCamelCase  :      List[str]														=						logits.softmax(dim=0				)
     __lowerCamelCase							,			__lowerCamelCase  :      Tuple														=						prob.topk(k=UpperCAmelCase_ ,       dim=0				)
     __lowerCamelCase  :      str														=						' '.join(
         [tokenizer.convert_ids_to_tokens(indices[i].item()				) for i in range(len(UpperCAmelCase_				)				)]				)
     __lowerCamelCase  :      Any														=						tokenizer.mask_token
     __lowerCamelCase  :      Optional[int]														=						[]
     for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' '				)				):
          __lowerCamelCase  :      str														=						predicted_token_bpe.replace('\u2581' ,       ' '				)
          if " {0}".format(UpperCAmelCase_				) in masked_input:
               topk_filled_outputs.append(
                   (
                       masked_input.replace(' {0}'.format(UpperCAmelCase_				) ,       UpperCAmelCase_				),
                       values[index].item(),
                       predicted_token,
                   )				)
          else:
               topk_filled_outputs.append(
                   (
                       masked_input.replace(UpperCAmelCase_ ,       UpperCAmelCase_				),
                       values[index].item(),
                       predicted_token,
                   )				)
     return topk_filled_outputs


A__						:   Dict				=    CamembertTokenizer.from_pretrained("""camembert-base""")
A__						:   Union[str, Any]				=    CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()

A__						:   Optional[Any]				=    """Le camembert est <mask> :)"""

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










'''simple docstring'''



from __future__ import annotations

import os
from collections.abc import Mapping

A__						:   Optional[Any]				=    tuple[int, int]





class   UpperCAmelCase_    :





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	None:
         __lowerCamelCase  :      set[int]														=						vertices
         __lowerCamelCase  :      dict[EdgeT, int]														=						{
             (min(SCREAMING_SNAKE_CASE_      ), max(SCREAMING_SNAKE_CASE_      )): weight for edge, weight in edges.items()
         }







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	None:
         self.vertices.add(edge[0]      )
         self.vertices.add(edge[1]      )
         __lowerCamelCase  :      Union[str, Any]														=						weight







    def  lowercase_							(				self      )							->	Graph:
         __lowerCamelCase  :      Graph														=						Graph({min(self.vertices      )}     ,	{}      )
         __lowerCamelCase  :      EdgeT
         __lowerCamelCase  :      int
         __lowerCamelCase  :      EdgeT
         __lowerCamelCase  :      int

         while len(subgraph.vertices      ) < len(self.vertices      ):
              __lowerCamelCase  :      Any														=						max(self.edges.values()      ) + 1
              for edge, weight in self.edges.items():
                   if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                        if weight < min_weight:
                             __lowerCamelCase  :      Optional[int]														=						edge
                             __lowerCamelCase  :      List[str]														=						weight

              subgraph.add_edge(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )

         return subgraph







def      UpperCAmelCase__   (      UpperCAmelCase_       :							str = "p107_network.txt"				)	->						int:
     __lowerCamelCase  :      str														=						os.path.abspath(os.path.dirname(UpperCAmelCase_				)				)
     __lowerCamelCase  :      str														=						os.path.join(UpperCAmelCase_ ,       UpperCAmelCase_				)
     __lowerCamelCase  :      dict[EdgeT, int]														=						{}
     __lowerCamelCase  :      list[str]
     __lowerCamelCase  :      int
     __lowerCamelCase  :      int

     with open(UpperCAmelCase_				) as f:
          __lowerCamelCase  :      Any														=						f.read().strip().split('\n'				)

     __lowerCamelCase  :      Any														=						[line.split(','				) for line in data]

     for edgea in range(1 ,       len(UpperCAmelCase_				)				):
          for edgea in range(UpperCAmelCase_				):
               if adjaceny_matrix[edgea][edgea] != "-":
                    __lowerCamelCase  :      int														=						int(adjaceny_matrix[edgea][edgea]				)

     __lowerCamelCase  :      Graph														=						Graph(set(range(len(UpperCAmelCase_				)				)				) ,       UpperCAmelCase_				)

     __lowerCamelCase  :      Graph														=						graph.prims_algorithm()

     __lowerCamelCase  :      int														=						sum(graph.edges.values()				)
     __lowerCamelCase  :      int														=						sum(subgraph.edges.values()				)

     return initial_total - optimal_total


if __name__ == "__main__":
      print(f'''{solution() = }''')






