


def 	SCREAMING_SNAKE_CASE_						(      __A			:      List[str]  ,   __A			:      str    )     ->		Optional[int]:

       """simple docstring"""

       print('\nThe shortest path matrix using Floyd Warshall algorithm\n'    )
       for i in range(__A    ):
              for j in range(__A    ):
                     if dist[i][j] != float('inf'    ):
                            print(int(dist[i][j]    )  ,   end='\t'    )
                     else:
                            print('INF'  ,   end='\t'    )
              print()







def 	SCREAMING_SNAKE_CASE_						(      __A			:      int  ,   __A			:      Union[str, Any]    )     ->		str:

       """simple docstring"""

       a_ :		Dict							=  [[float('inf'    ) for _ in range(__A    )] for _ in range(__A    )]

       for i in range(__A    ):
              for j in range(__A    ):
                     a_ :		Any							=  graph[i][j]

                     # check vertex k against all other vertices (i, j)
       for k in range(__A    ):
              # looping through rows of graph array
              for i in range(__A    ):
                     # looping through columns of graph array
                     for j in range(__A    ):
                            if (
                                dist[i][k] != float('inf'    )
                                and dist[k][j] != float('inf'    )
                                and dist[i][k] + dist[k][j] < dist[i][j]
                            ):
                                   a_ :		Optional[int]							=  dist[i][k] + dist[k][j]

       _print_dist(__A  ,   __A    )
       return dist, v


if __name__ == "__main__":
   UpperCAmelCase_				:			List[Any]   =	int(input('Enter number of vertices: '))
   UpperCAmelCase_				:			Any   =	int(input('Enter number of edges: '))

   UpperCAmelCase_				:			List[str]   =	[[float('inf') for i in range(v)] for j in range(v)]

   for i in range(v):
      UpperCAmelCase_				:			Dict   =	0.0

      # src and dst are indices that must be within the array size graph[e][v]
      # failure to follow this will result in an error
   for i in range(e):
      print('\nEdge ', i + 1)
      UpperCAmelCase_				:			List[Any]   =	int(input('Enter source:'))
      UpperCAmelCase_				:			Optional[int]   =	int(input('Enter destination:'))
      UpperCAmelCase_				:			Union[str, Any]   =	float(input('Enter weight:'))
      UpperCAmelCase_				:			Optional[int]   =	weight

   floyd_warshall(graph, v)

   # Example Input
   # Enter number of vertices: 3
   # Enter number of edges: 2

   # # generated graph from vertex and edge inputs
   # [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
   # [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]

   # specify source, destination and weight for edge #1
   # Edge  1
   # Enter source:1
   # Enter destination:2
   # Enter weight:2

   # specify source, destination and weight for edge #2
   # Edge  2
   # Enter source:2
   # Enter destination:1
   # Enter weight:1

   # # Expected Output from the vertice, edge and src, dst, weight inputs!!
   # 0		INF	INF
   # INF	0	2
   # INF	1	0






import os
from collections import deque

import torch
from torch.utils.data import Dataset
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      def __init__(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	List[Any]=""					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]="train"					)		->      Tuple:
             assert os.path.isdir(SCREAMING_SNAKE_CASE__					)

             a_ :		int							=  []
             a_ :		Optional[int]							=  os.listdir(SCREAMING_SNAKE_CASE__					)
             for story_filename in story_filenames_list:
                    if "summary" in story_filename:
                           continue
                    a_ :		List[str]							=  os.path.join(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
                    if not os.path.isfile(SCREAMING_SNAKE_CASE__					):
                           continue
                    self.documents.append(SCREAMING_SNAKE_CASE__					)
      def __len__(	self    :	Dict					)		->      str:
             return len(self.documents					)

      def __getitem__(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	str					)		->      str:
             a_ :		int							=  self.documents[idx]
             a_ :		Tuple							=  document_path.split('/'					)[-1]
             with open(SCREAMING_SNAKE_CASE__					,							encoding='utf-8'					) as source:
                    a_ :		Dict							=  source.read()
                    a_							,		a_ :		Optional[Any]							=  process_story(SCREAMING_SNAKE_CASE__					)
             return document_name, story_lines, summary_lines







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]    )     ->		Any:

       """simple docstring"""

       a_ :		Optional[Any]							=  list(filter(lambda __A    : len(__A    ) != 0  ,   [line.strip() for line in raw_story.split('\n'    )]    )    )

       # for some unknown reason some lines miss a period, add it
       a_ :		List[Any]							=  [_add_missing_period(__A    ) for line in nonempty_lines]

       # gather article lines
       a_ :		int							=  []
       a_ :		List[Any]							=  deque(__A    )
       while True:
              try:
                     a_ :		Dict							=  lines.popleft()
                     if element.startswith('@highlight'    ):
                            break
                     story_lines.append(__A    )
              except IndexError:
                     # if "@highlight" is absent from the file we pop
                     # all elements until there is None, raising an exception.
                     return story_lines, []

    # gather summary lines
       a_ :		List[str]							=  list(filter(lambda __A    : not t.startswith('@highlight'    )  ,   __A    )    )

       return story_lines, summary_lines







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Union[str, Any]    )     ->		Any:

       """simple docstring"""

       a_ :		Any							=  ['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
       if line.startswith('@highlight'    ):
              return line
       if line[-1] in END_TOKENS:
              return line
       return line + "."







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]  ,   __A			:      Union[str, Any]  ,   __A			:      List[str]    )     ->		Union[str, Any]:

       """simple docstring"""

       if len(__A    ) > block_size:
              return sequence[:block_size]
       else:
              sequence.extend([pad_token_id] * (block_size - len(__A    ))    )
              return sequence







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Tuple  ,   __A			:      str    )     ->		Any:

       """simple docstring"""

       a_ :		Optional[int]							=  torch.ones_like(__A    )
       a_ :		List[str]							=  sequence == pad_token_id
       a_ :		str							=  0
       return mask







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Union[str, Any]  ,   __A			:      Optional[Any]  ,   __A			:      Dict    )     ->		List[str]:

       """simple docstring"""

       a_ :		Optional[int]							=  [tokenizer.encode(__A    ) for line in story_lines]
       a_ :		int							=  [token for sentence in story_lines_token_ids for token in sentence]
       a_ :		Dict							=  [tokenizer.encode(__A    ) for line in summary_lines]
       a_ :		int							=  [token for sentence in summary_lines_token_ids for token in sentence]

       return story_token_ids, summary_token_ids







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Tuple  ,   __A			:      List[str]    )     ->		Optional[Any]:

       """simple docstring"""

       a_ :		int							=  []
       for sequence in batch:
              a_ :		int							=  -1
              a_ :		Dict							=  []
              for s in sequence:
                     if s == separator_token_id:
                            sentence_num += 1
                     embeddings.append(sentence_num % 2    )
              batch_embeddings.append(__A    )
       return torch.tensor(__A    )


