






import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
     # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
     # setup instructions, if using on-demand hardware

     # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
     # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
     # Throw an error if user passes both BYO and on-demand cluster args
     # Otherwise, use default values
     UpperCAmelCase__				   =		argparse.ArgumentParser()
     parser.add_argument("--user", type=str, default="ubuntu")
     parser.add_argument("--host", type=str, default="localhost")
     parser.add_argument("--key_path", type=str, default=None)
     parser.add_argument("--instance", type=str, default="V100:1")
     parser.add_argument("--provider", type=str, default="cheapest")
     parser.add_argument("--use_spot", type=bool, default=False)
     parser.add_argument("--example", type=str, default="pytorch/text-generation/run_generation.py")
     UpperCAmelCase__   ,      UpperCAmelCase__				   =		parser.parse_known_args()
     if args.host != "localhost":
          if args.instance != "V100:1" or args.provider != "cheapest":
               raise ValueError("Cannot specify both BYO and on-demand cluster args")
          UpperCAmelCase__				   =		rh.cluster(
              name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path}
          )
     else:
          UpperCAmelCase__				   =		rh.cluster(
              name="rh-cluster", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
          )
     UpperCAmelCase__				   =		args.example.rsplit("/", 1)[0]

     # Set up remote environment
     cluster.install_packages(["pip:./"])  # Installs transformers from local source
     # Note transformers is copied into the home directory on the remote machine, so we can install from there
     cluster.run([f"""pip install -r transformers/examples/{example_dir}/requirements.txt"""])
     cluster.run(["pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117"])

     # Run example. You can bypass the CLI wrapper and paste your own code here.
     cluster.run([f"""python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"""])

     # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
     # from my_script... import train
     # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
     # launch_train_gpu = rh.function(fn=train,
     #                                system=gpu,
     #                                reqs=reqs,
     #                                name='train_bert_glue')
     #
     # We can pass in arguments just like we would to a function:
     # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
     #                  stream_logs=True)




UpperCAmelCase__		:     Optional[Any]     			=      {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}






def   lowerCamelCase__							(					a		,		a		,		a      )      ->   list[str]:
   _A:  Union[str, Any]					=       set()
   # keep track of all the paths to be checked
   _A:  Union[str, Any]					=       [[start]]

   # return path if start is goal
   if start == goal:
      return [start]

   # keeps looping until all possible paths have been checked
   while queue:
      # pop the first path from the queue
      _A:  Any					=       queue.pop(0      )
      # get the last node from the path
      _A:  Union[str, Any]					=       path[-1]
      if node not in explored:
         _A:  str					=       graph[node]
         # go through all neighbour nodes, construct a new path and
         # push it into the queue
         for neighbour in neighbours:
            _A:  Optional[int]					=       list(a      )
            new_path.append(a      )
            queue.append(a      )
            # return path if neighbour is goal
            if neighbour == goal:
               return new_path

            # mark node as explored
         explored.add(a      )

    # in case there's no path between the 2 nodes
   return []






def   lowerCamelCase__							(					a		,		a		,		a      )      ->   int:
   if not graph or start not in graph or target not in graph:
      return -1
   if start == target:
      return 0
   _A:  Any					=       [start]
   _A:  List[str]					=       set(a      )
   # Keep tab on distances from `start` node.
   _A:  Optional[int]					=       {start: 0, target: -1}
   while queue:
      _A:  Union[str, Any]					=       queue.pop(0      )
      if node == target:
         _A:  Dict					=       (
             dist[node] if dist[target] == -1 else min(dist[target]		,		dist[node]      )
         )
      for adjacent in graph[node]:
         if adjacent not in visited:
            visited.add(a      )
            queue.append(a      )
            _A:  List[Any]					=       dist[node] + 1
   return dist[target]


if __name__ == "__main__":
   print(bfs_shortest_path(demo_graph, 'G', 'D'))  # returns ['G', 'C', 'A', 'B', 'D']
   print(bfs_shortest_path_distance(demo_graph, 'G', 'D'))  # returns 4



