
import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment



def 		lowercase_     (  *A__						)				->				Any:
    """simple docstring"""

    snake_case						=   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						):
        snake_case						=   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
   _A       =					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
   _A       =					[
       '''pip:./''',
       '''transformers''',
       '''datasets''',
       '''evaluate''',
       '''tqdm''',
       '''scipy''',
       '''scikit-learn''',
       '''tensorboard''',
       '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
   ]
   _A       =					rh.function(fn=launch_train, system=gpu, reqs=reqs, name="train_bert_glue")

   # Define train args/config, run train function
   _A       =					argparse.Namespace(cpu=False, mixed_precision="fp16")
   _A       =					{'''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'''


lowerCamelCase_					:			List[Any]     						=				{0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

lowerCamelCase_					:			Tuple     						=				{0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}







def 						__magic_name__(			_A    ,   _A    ,   _A       ):
						'''simple docstring'''





						UpperCamelCase__       =			True
						UpperCamelCase__       =			[]

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

						return [vert, *connected_verts]







def 						__magic_name__(			_A       ):
						'''simple docstring'''





						UpperCamelCase__       =			len(_A       )
						UpperCamelCase__       =			graph_size * [False]
						UpperCamelCase__       =			[]

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

						return components_list


if __name__ == "__main__":
					import doctest

					doctest.testmod()
