






'''simple docstring'''





import math
class 				lowercase       :


    '''simple docstring'''

    def __init__(					self     :     Union[str, Any]				,     __lowerCamelCase     :     Dict=0     )							->							Optional[Any]:  # a graph with Node 0,1,...,N-1

     '''simple docstring'''




     lowerCamelCase__  =     n
     lowerCamelCase__  =     [
         [math.inf for j in range(0				,     __lowerCamelCase     )] for i in range(0				,     __lowerCamelCase     )
     ]  # adjacency matrix for weight
     lowerCamelCase__  =     [
         [math.inf for j in range(0				,     __lowerCamelCase     )] for i in range(0				,     __lowerCamelCase     )
     ]  # dp[i][j] stores minimum distance from i to j

    def    a__				(					self     :     Union[str, Any]				,     __lowerCamelCase     :     List[Any]				,     __lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     List[str]     )							->							Dict:

     '''simple docstring'''




     lowerCamelCase__  =     w

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

     '''simple docstring'''




     for k in range(0				,     self.n     ):
      for i in range(0				,     self.n     ):
       for j in range(0				,     self.n     ):
        lowerCamelCase__  =     min(self.dp[i][j]				,     self.dp[i][k] + self.dp[k][j]     )




    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     Optional[Any]				,     __lowerCamelCase     :     str     )							->							Dict:

     '''simple docstring'''




     return self.dp[u][v]


if __name__ == "__main__":
 __A							:							Optional[int]  							=		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)







'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A							:							Tuple  							=		{
    """configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
    """tokenization_deberta""": ["""DebertaTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							int  							=		["""DebertaTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							str  							=		[
     """DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """DebertaForMaskedLM""",
     """DebertaForQuestionAnswering""",
     """DebertaForSequenceClassification""",
     """DebertaForTokenClassification""",
     """DebertaModel""",
     """DebertaPreTrainedModel""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							Union[str, Any]  							=		[
     """TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFDebertaForMaskedLM""",
     """TFDebertaForQuestionAnswering""",
     """TFDebertaForSequenceClassification""",
     """TFDebertaForTokenClassification""",
     """TFDebertaModel""",
     """TFDebertaPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
 from .tokenization_deberta import DebertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_deberta_fast import DebertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deberta import (
      DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      DebertaForMaskedLM,
      DebertaForQuestionAnswering,
      DebertaForSequenceClassification,
      DebertaForTokenClassification,
      DebertaModel,
      DebertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deberta import (
      TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDebertaForMaskedLM,
      TFDebertaForQuestionAnswering,
      TFDebertaForSequenceClassification,
      TFDebertaForTokenClassification,
      TFDebertaModel,
      TFDebertaPreTrainedModel,
  )


else:
 import sys

 __A							:							List[Any]  							=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

