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       =		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       =		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       =		rh.cluster(
										    name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path}
										)
					else:
										_lowerCAmelCase       =		rh.cluster(
										    name="rh-cluster", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
										)
					_lowerCAmelCase       =		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)







# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

a__      :     Optional[int]         =		TypeVar('T')





class UpperCAmelCase_				( Generic[T] ):


  def __init__(							self ,__snake_case = True ):



       """simple docstring"""





       A_          =       {}  # dictionary of lists
       A_          =       directed


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ):



       """simple docstring"""





       if not self.directed:  # For undirected graphs
            # if both source vertex and destination vertex are both present in the
            # adjacency list, add destination vertex to source vertex list of adjacent
            # vertices and add source vertex to destination vertex list of adjacent
            # vertices.
            if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                 self.adj_list[source_vertex].append(__snake_case )
                 self.adj_list[destination_vertex].append(__snake_case )
            # if only source vertex is present in adjacency list, add destination vertex
            # to source vertex list of adjacent vertices, then create a new vertex with
            # destination vertex as key and assign a list containing the source vertex
            # as it's first adjacent vertex.
            elif source_vertex in self.adj_list:
                 self.adj_list[source_vertex].append(__snake_case )
                 A_          =       [source_vertex]
            # if only destination vertex is present in adjacency list, add source vertex
            # to destination vertex list of adjacent vertices, then create a new vertex
            # with source vertex as key and assign a list containing the source vertex
            # as it's first adjacent vertex.
            elif destination_vertex in self.adj_list:
                 self.adj_list[destination_vertex].append(__snake_case )
                 A_          =       [destination_vertex]
            # if both source vertex and destination vertex are not present in adjacency
            # list, create a new vertex with source vertex as key and assign a list
            # containing the destination vertex as it's first adjacent vertex also
            # create a new vertex with destination vertex as key and assign a list
            # containing the source vertex as it's first adjacent vertex.
            else:
                 A_          =       [destination_vertex]
                 A_          =       [source_vertex]
       else:  # For directed graphs
            # if both source vertex and destination vertex are present in adjacency
            # list, add destination vertex to source vertex list of adjacent vertices.
            if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                 self.adj_list[source_vertex].append(__snake_case )
            # if only source vertex is present in adjacency list, add destination
            # vertex to source vertex list of adjacent vertices and create a new vertex
            # with destination vertex as key, which has no adjacent vertex
            elif source_vertex in self.adj_list:
                 self.adj_list[source_vertex].append(__snake_case )
                 A_          =       []
            # if only destination vertex is present in adjacency list, create a new
            # vertex with source vertex as key and assign a list containing destination
            # vertex as first adjacent vertex
            elif destination_vertex in self.adj_list:
                 A_          =       [destination_vertex]
            # if both source vertex and destination vertex are not present in adjacency
            # list, create a new vertex with source vertex as key and a list containing
            # destination vertex as it's first adjacent vertex. Then create a new vertex
            # with destination vertex as key, which has no adjacent vertex
            else:
                 A_          =       [destination_vertex]
                 A_          =       []

       return self




  def __repr__(							self ):



       """simple docstring"""





       return pformat(self.adj_list )



