def     _a				(      lowercase__  :			int     ,  lowercase__  :			List[Any] ):







     '''simple docstring'''


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







def     _a				(      lowercase__  :			Optional[Any]     ,  lowercase__  :			Union[str, Any] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				List[str]			=		[[float('inf' ) for _ in range(lowercase__ )] for _ in range(lowercase__ )]

     for i in range(lowercase__ ):
          for j in range(lowercase__ ):
               SCREAMING_SNAKE_CASE__				:				Dict			=		graph[i][j]

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

     _print_dist(lowercase__     ,  lowercase__ )
     return dist, v


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

      SCREAMING_SNAKE_CASE__		:					Optional[Any]						=					[[float("inf") for i in range(v)] for j in range(v)]

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




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce







def     _a				(      lowercase__  :			Any ):







     '''simple docstring'''


     return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )







def     _a				(      lowercase__  :			Tuple ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		gather(lowercase__ )
     assert gathered_tensor.tolist() == list(range(1     ,  state.num_processes**2 + 1 ) )







def     _a				(      lowercase__  :			List[Any] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				str			=		[state.process_index]
     SCREAMING_SNAKE_CASE__				:				Any			=		gather_object(lowercase__ )
     assert len(lowercase__ ) == state.num_processes, f'''{gathered_obj}, {len(lowercase__ )} != {state.num_processes}'''
     assert gathered_obj == list(range(state.num_processes ) ), f'''{gathered_obj} != {list(range(state.num_processes ) )}'''







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		broadcast(lowercase__ )
     assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
     assert broadcasted_tensor.tolist() == list(range(1     ,  state.num_processes + 1 ) )







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     if state.is_main_process:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.arange(state.num_processes + 1 ).to(state.device )
     else:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.arange(state.num_processes ).to(state.device )
     SCREAMING_SNAKE_CASE__				:				Any			=		pad_across_processes(lowercase__ )
     assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
     if not state.is_main_process:
          assert padded_tensor.tolist() == list(range(0     ,  state.num_processes ) ) + [0]







def     _a				(      lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     if state.num_processes != 2:
          return
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				str			=		reduce(lowercase__     ,  'sum' )
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.tensor([4.0, 6] ).to(state.device )
     assert torch.allclose(lowercase__     ,  lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     if state.num_processes != 2:
          return
     SCREAMING_SNAKE_CASE__				:				Any			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		reduce(lowercase__     ,  'mean' )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.tensor([2.0, 3] ).to(state.device )
     assert torch.allclose(lowercase__     ,  lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     main()







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		PartialState()
     state.print(f'''State: {state}''' )
     state.print('testing gather' )
     test_gather(lowercase__ )
     state.print('testing gather_object' )
     test_gather_object(lowercase__ )
     state.print('testing broadcast' )
     test_broadcast(lowercase__ )
     state.print('testing pad_across_processes' )
     test_pad_across_processes(lowercase__ )
     state.print('testing reduce_sum' )
     test_reduce_sum(lowercase__ )
     state.print('testing reduce_mean' )
     test_reduce_mean(lowercase__ )


if __name__ == "__main__":
      main()



