





'''simple docstring'''







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
					lowerCAmelCase_					:    List[str]      	=			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')
					lowerCAmelCase_	,  lowerCAmelCase_					:    List[Any]      	=			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')
										lowerCAmelCase_					:    List[Any]      	=			rh.cluster(
										    name='rh-cluster', ips=[args.host], ssh_creds={'ssh_user': args.user, 'ssh_private_key': args.key_path}
										)
					else:
										lowerCAmelCase_					:    Dict      	=			rh.cluster(
										    name='rh-cluster', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
										)
					lowerCAmelCase_					:    Optional[Any]      	=			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)








"""simple docstring"""




from __future__ import annotations

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  print(F"""Vertex\tShortest Distance from vertex {src}"""				)
  for i, d in enumerate(UpperCamelCase__				):
    print(F"""{i}\t\t{d}"""				)

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  for j in range(UpperCamelCase__				):
    _a      ,  _a      ,  _a  :       List[str]             =	(graph[j][k] for k in ["""src""", """dst""", """weight"""])
    if distance[u] != float("""inf"""				) and distance[u] + w < distance[v]:
      return True
  return False

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Dict             =	[float("""inf"""				)] * vertex_count
  _a  :       Any             =	0.0

  for _ in range(vertex_count - 1				):
    for j in range(UpperCamelCase__				):
      _a      ,  _a      ,  _a  :       List[Any]             =	(graph[j][k] for k in ["""src""", """dst""", """weight"""])

      if distance[u] != float("""inf"""				) and distance[u] + w < distance[v]:
        _a  :       Any             =	distance[u] + w

  _a  :       Union[str, Any]             =	check_negative_cycle(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				)
  if negative_cycle_exists:
    raise Exception("""Negative cycle found"""				)

  return distance


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 _snake_case          =   int(input('Enter number of vertices: ').strip())
 _snake_case          =   int(input('Enter number of edges: ').strip())

 _snake_case          =   [{} for _ in range(E)]

 for i in range(E):
  print('Edge ', i + 1)
  _snake_case      , _snake_case      , _snake_case          =   (
      int(x)
      for x in input('Enter source, destination, weight: ').strip().split(' ')
  )
  _snake_case          =   {'src': src, 'dst': dest, 'weight': weight}

 _snake_case          =   int(input('\nEnter shortest path source:').strip())
 _snake_case          =   bellman_ford(graph, V, E, source)
 print_distance(shortest_distance, 0)

