
import math



class 		UpperCamelCase__  :
   def __init__(self			: Dict ,    snake_case_			: int=0       ):  # a graph with Node 0,1,...,N-1
    __a			:  Tuple		    =  n
    __a			:  Optional[Any]		    =  [
        [math.inf for j in range(0 ,    snake_case_       )] for i in range(0 ,    snake_case_       )
    ]  # adjacency matrix for weight
    __a			:  Any		    =  [
        [math.inf for j in range(0 ,    snake_case_       )] for i in range(0 ,    snake_case_       )
    ]  # dp[i][j] stores minimum distance from i to j
   def 				lowerCAmelCase      (self			: List[Any] ,    snake_case_			: List[str] ,    snake_case_			: str ,    snake_case_			: Dict       ):
    __a			:  List[str]		    =  w
   def 				lowerCAmelCase      (self			: int       ):
    for k in range(0 ,    self.n       ):
     for i in range(0 ,    self.n       ):
      for j in range(0 ,    self.n       ):
       __a			:  Optional[int]		    =  min(self.dp[i][j] ,    self.dp[i][k] + self.dp[k][j]       )







   def 				lowerCAmelCase      (self			: int ,    snake_case_			: Any ,    snake_case_			: List[Any]       ):
    return self.dp[u][v]


if __name__ == "__main__":
      lowercase__          =Graph(5)
      graph.add_edge(0, 2, 9)
      graph.add_edge(0, 4, 10)
      graph.add_edge(1, 3, 5)
      graph.add_edge(2, 3, 7)
      graph.add_edge(3, 0, 10)
      graph.add_edge(3, 1, 2)
      graph.add_edge(3, 2, 1)
      graph.add_edge(3, 4, 6)
      graph.add_edge(4, 1, 3)
      graph.add_edge(4, 2, 4)
      graph.add_edge(4, 3, 9)
      graph.floyd_warshall()
      graph.show_min(1, 4)
      graph.show_min(0, 3)


import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




def        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    Any  ):
 if isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ):
  __a			:  Union[str, Any]		    =  np.full((len(lowerCAmelCase__  ), sequence_length, 2)  ,       lowerCAmelCase__  )
 else:
  __a			:  str		    =  np.full((len(lowerCAmelCase__  ), sequence_length)  ,       lowerCAmelCase__  )

 for i, tensor in enumerate(lowerCAmelCase__  ):
  if padding_side == "right":
   if isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ):
    __a			:  Any		    =  tensor[:sequence_length]
   else:
    __a			:  List[Any]		    =  tensor[:sequence_length]
  else:
   if isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ):
    __a			:  Dict		    =  tensor[:sequence_length]
   else:
    __a			:  int		    =  tensor[:sequence_length]

 return out_tensor.tolist()




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[Any]  ):
 __a			:  str		    =  ord(lowerCAmelCase__  )
 if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
  return True
 __a			:  List[str]		    =  unicodedata.category(lowerCAmelCase__  )
 if cat.startswith('''P'''  ):
  return True
 return False



@dataclass
class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							PreTrainedTokenizerBase
   _SCREAMING_SNAKE_CASE				:							Union[bool, str, PaddingStrategy]     	=				True
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				None
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				None
   _SCREAMING_SNAKE_CASE				:							int     	=				-100
   _SCREAMING_SNAKE_CASE				:							str     	=				"pt"
   def 				lowerCAmelCase      (self			: str ,    snake_case_			: Tuple       ):
    import torch

    __a			:  Union[str, Any]		    =  '''label''' if '''label''' in features[0].keys() else '''labels'''
    __a			:  Tuple		    =  [feature[label_name] for feature in features] if label_name in features[0].keys() else None
    __a			:  Union[str, Any]		    =  self.tokenizer.pad(
        snake_case_ ,    padding=self.padding ,    max_length=self.max_length ,    pad_to_multiple_of=self.pad_to_multiple_of ,    return_tensors='''pt''' if labels is None else None ,    )

    if labels is None:
     return batch

    __a			:  List[str]		    =  torch.tensor(batch['''entity_ids''']       ).shape[1]
    __a			:  Tuple		    =  self.tokenizer.padding_side
    if padding_side == "right":
     __a			:  Union[str, Any]		    =  [
         list(snake_case_       ) + [self.label_pad_token_id] * (sequence_length - len(snake_case_       )) for label in labels
     ]
    else:
     __a			:  Dict		    =  [
         [self.label_pad_token_id] * (sequence_length - len(snake_case_       )) + list(snake_case_       ) for label in labels
     ]

    __a			:  Dict		    =  [feature['''ner_tags'''] for feature in features]
    __a			:  Optional[Any]		    =  padding_tensor(snake_case_ ,    -1 ,    snake_case_ ,    snake_case_       )
    __a			:  Union[str, Any]		    =  [feature['''original_entity_spans'''] for feature in features]
    __a			:  Optional[int]		    =  padding_tensor(snake_case_ ,    (-1, -1) ,    snake_case_ ,    snake_case_       )
    __a			:  List[str]		    =  {k: torch.tensor(snake_case_ ,    dtype=torch.intaa       ) for k, v in batch.items()}

    return batch
