'''simple docstring'''




def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       Union[str, Any]:





       """simple docstring"""


       print("\nThe shortest path matrix using Floyd Warshall algorithm\n"						)
       for i in range(lowerCamelCase__						):
              for j in range(lowerCamelCase__						):
                     if dist[i][j] != float("inf"						):
                            print(int(dist[i][j]						)							,    end="\t"						)
                     else:
                            print("INF"							,    end="\t"						)
              print()

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       List[Any]:





       """simple docstring"""


       __UpperCAmelCase  :				Union[str, Any]       				=		[[float("inf"						) for _ in range(lowerCamelCase__						)] for _ in range(lowerCamelCase__						)]

       for i in range(lowerCamelCase__						):
              for j in range(lowerCamelCase__						):
                     __UpperCAmelCase  :				Union[str, Any]       				=		graph[i][j]

                     # check vertex k against all other vertices (i, j)
       for k in range(lowerCamelCase__						):
              # looping through rows of graph array
              for i in range(lowerCamelCase__						):
                     # looping through columns of graph array
                     for j in range(lowerCamelCase__						):
                            if (
                                dist[i][k] != float("inf"						)
                                and dist[k][j] != float("inf"						)
                                and dist[i][k] + dist[k][j] < dist[i][j]
                            ):
                                   __UpperCAmelCase  :				Dict       				=		dist[i][k] + dist[k][j]

       _print_dist(lowerCamelCase__							,    lowerCamelCase__						)
       return dist, v


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

   _a   :	Union[str, Any] 							=   [[float("inf") for i in range(v)] for j in range(v)]

   for i in range(v):
      _a   :	Optional[int] 							=   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)
      _a   :	Dict 							=   int(input("Enter source:"))
      _a   :	str 							=   int(input("Enter destination:"))
      _a   :	str 							=   float(input("Enter weight:"))
      _a   :	Optional[Any] 							=   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







'''simple docstring'''




import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
   import torch

   from transformers.generation import (
       MaxLengthCriteria,
       MaxNewTokensCriteria,
       MaxTimeCriteria,
       StoppingCriteriaList,
       validate_stopping_criteria,
   )






@require_torch
class 		__A       (unittest.TestCase   ):






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				List[str]       				=		3
              __UpperCAmelCase  :				Tuple       				=		2_50

              __UpperCAmelCase  :				str       				=		ids_tensor((batch_size, length)				,     UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		torch.ones((batch_size, length)				,     device=UpperCamelCase_				,     dtype=torch.float      ) / length
              return input_ids, scores






       def 			_snake_case						(      self      ):
              __UpperCAmelCase				, __UpperCAmelCase  :				Tuple       				=		self._get_tensors(5      )

              __UpperCAmelCase  :				Tuple       				=		StoppingCriteriaList(
                  [
                      MaxLengthCriteria(max_length=10      ),
                      MaxTimeCriteria(max_time=0.1      ),
                  ]      )

              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				int       				=		self._get_tensors(9      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[int]       				=		self._get_tensors(10      )
              self.assertTrue(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				int       				=		MaxLengthCriteria(max_length=10      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Tuple       				=		self._get_tensors(5      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Dict       				=		self._get_tensors(9      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[int]       				=		self._get_tensors(10      )
              self.assertTrue(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[Any]       				=		MaxNewTokensCriteria(start_length=5				,     max_new_tokens=5      )

              __UpperCAmelCase				, __UpperCAmelCase  :				List[str]       				=		self._get_tensors(5      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Dict       				=		self._get_tensors(9      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[Any]       				=		self._get_tensors(10      )
              self.assertTrue(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase  :				Union[str, Any]       				=		StoppingCriteriaList([criteria]      )
              self.assertEqual(criteria_list.max_length				,     10      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[Any]       				=		self._get_tensors(5      )

              __UpperCAmelCase  :				str       				=		MaxTimeCriteria(max_time=0.1      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase  :				str       				=		MaxTimeCriteria(max_time=0.1				,     initial_timestamp=time.time() - 0.2      )
              self.assertTrue(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )







       def 			_snake_case						(      self      ):
              validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      )				,     10      )

              with self.assertWarns(UpperCamelCase_      ):
                     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      )				,     11      )

              __UpperCAmelCase  :				Optional[int]       				=		validate_stopping_criteria(StoppingCriteriaList()				,     11      )

              self.assertEqual(len(UpperCamelCase_      )				,     1      )






