

"""simple docstring"""




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment



def 						UpperCAmelCase__ (					*A__ )   ->   str:




   """simple docstring"""
   lowerCamelCase__						       =      torch.cuda.device_count()
   print(f'Device count: {num_processes}' )
   with patch_environment(
       world_size=A__						,     master_addr="127.0.01"						,     master_port="29500"						,     mixed_precision=args[1].mixed_precision ):
      lowerCamelCase__						       =      PrepareForLaunch(A__						,     distributed_type="MULTI_GPU" )
      torch.multiprocessing.start_processes(A__						,     args=A__						,     nprocs=A__						,     start_method="spawn" )


if __name__ == "__main__":
     # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
     # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

     # on-demand GPU
     # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
     SCREAMING_SNAKE_CASE_  :      Dict							=      rh.cluster(name='''rh-cluster''', instance_type='''V100:4''', provider='''cheapest''', use_spot=False)  # multi GPU
     gpu.up_if_not()

     # on-prem GPU
     # gpu = rh.cluster(
     #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
     #       )

     # Set up remote function
     SCREAMING_SNAKE_CASE_  :      Optional[Any]							=      [
         '''pip:./''',
         '''transformers''',
         '''datasets''',
         '''evaluate''',
         '''tqdm''',
         '''scipy''',
         '''scikit-learn''',
         '''tensorboard''',
         '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
     ]
     SCREAMING_SNAKE_CASE_  :      str							=      rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

     # Define train args/config, run train function
     SCREAMING_SNAKE_CASE_  :      Any							=      argparse.Namespace(cpu=False, mixed_precision='''fp16''')
     SCREAMING_SNAKE_CASE_  :      str							=      {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     launch_train_gpu(config, train_args, stream_logs=True)

     # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
     # gpu.install_packages(reqs)
     # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])







"""simple docstring"""




SCREAMING_SNAKE_CASE_  :      Optional[int]							=      {0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

SCREAMING_SNAKE_CASE_  :      str							=      {0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}



def 						UpperCAmelCase__ (					A__						,     A__						,     A__ )   ->   list:




   """simple docstring"""
   lowerCamelCase__						       =      True
   lowerCamelCase__						       =      []

   for neighbour in graph[vert]:
      if not visited[neighbour]:
         connected_verts += dfs(A__						,     A__						,     A__ )

   return [vert, *connected_verts]



def 						UpperCAmelCase__ (					A__ )   ->   list:




   """simple docstring"""
   lowerCamelCase__						       =      len(A__ )
   lowerCamelCase__						       =      graph_size * [False]
   lowerCamelCase__						       =      []

   for i in range(A__ ):
      if not visited[i]:
         lowerCamelCase__						       =      dfs(A__						,     A__						,     A__ )
         components_list.append(A__ )

   return components_list


if __name__ == "__main__":
     import doctest

     doctest.testmod()




