


'''simple docstring'''






import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment




class  a     (     unittest.TestCase       ):
 def 							__UpperCAmelCase		( self						)					->							int:
      _a			      =							inspect.getfile(accelerate.test_utils						)
      _a			      =							os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ['scripts', 'test_script.py']						)
      _a			      =							os.path.sep.join(
          mod_file.split(os.path.sep						)[:-1] + ['scripts', 'test_distributed_data_loop.py']						)
      _a			      =							os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ['scripts', 'test_ops.py']						)
 @require_multi_gpu
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      print(f'Found {torch.cuda.device_count()} devices.'						)
      _a			      =							['torchrun', f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
      with patch_environment(omp_num_threads=1						):
           execute_subprocess_async(__A      ,				env=os.environ.copy()						)
 @require_multi_gpu
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      print(f'Found {torch.cuda.device_count()} devices.'						)
      _a			      =							['torchrun', f'--nproc_per_node={torch.cuda.device_count()}', self.operation_file_path]
      print(f'Command: {cmd}'						)
      with patch_environment(omp_num_threads=1						):
           execute_subprocess_async(__A      ,				env=os.environ.copy()						)
 @require_multi_gpu
 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      _a			      =							['torchrun', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__						)]
      with patch_environment(omp_num_threads=1						):
           execute_subprocess_async(__A      ,				env=os.environ.copy()						)




 @require_multi_gpu
 def 							__UpperCAmelCase		( self						)					->							str:
      print(f'Found {torch.cuda.device_count()} devices, using 2 devices only'						)
      _a			      =							['torchrun', f'--nproc_per_node={torch.cuda.device_count()}', self.data_loop_file_path]
      with patch_environment(omp_num_threads=1      ,				cuda_visible_devices='0,1'						):
           execute_subprocess_async(__A      ,				env=os.environ.copy()						)


if __name__ == "__main__":
 a_					:    str	       =    Accelerator()
 a_					:    str	       =    (accelerator.state.process_index + 2, 1_0)
 a_					:    List[Any]	       =    torch.randint(0, 1_0, shape).to(accelerator.device)

 a_					:    Dict	       =    ""

 a_					:    List[str]	       =    accelerator.pad_across_processes(tensor)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
  error_msg += "Padding was not done with the right value (0)."

 a_					:    List[Any]	       =    accelerator.pad_across_processes(tensor, pad_first=True)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 a_					:    str	       =    accelerator.state.num_processes - accelerator.state.process_index - 1
 if not torch.equal(tensora[index:], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[:index] == 0):
  error_msg += "Padding was not done with the right value (0)."

 # Raise error at the end to make sure we don't stop at the first failure.
 if len(error_msg) > 0:
  raise ValueError(error_msg)





'''simple docstring'''






def 	_A				(lowerCAmelCase__  :List[str]				,						lowerCAmelCase__  :Optional[Any]		)   ->  Optional[int]:




     '''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 	_A				(lowerCAmelCase__  :Dict				,						lowerCAmelCase__  :List[str]		)   ->  List[str]:




     '''simple docstring'''



     _a			      =							[[float('inf'		) for _ in range(lowerCAmelCase__		)] for _ in range(lowerCAmelCase__		)]

     for i in range(lowerCAmelCase__		):
          for j in range(lowerCAmelCase__		):
               _a			      =							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]
                    ):
                         _a			      =							dist[i][k] + dist[k][j]

     _print_dist(lowerCAmelCase__				,						lowerCAmelCase__		)
     return dist, v


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

 a_					:    Dict          =    [[float("inf") for i in range(v)] for j in range(v)]

 for i in range(v):
  a_					:    Union[str, Any]          =    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_					:    Union[str, Any]          =    int(input("Enter source:"))
  a_					:    int          =    int(input("Enter destination:"))
  a_					:    Tuple          =    float(input("Enter weight:"))
  a_					:    Dict          =    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

