




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment


def 				__SCREAMING_SNAKE_CASE    (     *snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase						=	torch.cuda.device_count()
						print(f"""Device count: {num_processes}"""		)
						with patch_environment(
						    world_size=UpperCAmelCase_      ,       master_addr="127.0.01"      ,       master_port="29500"      ,       mixed_precision=args[1].mixed_precision		):
												_UpperCAmelCase						=	PrepareForLaunch(UpperCAmelCase_      ,       distributed_type="MULTI_GPU"		)
												torch.multiprocessing.start_processes(UpperCAmelCase_      ,       args=UpperCAmelCase_      ,       nprocs=UpperCAmelCase_      ,       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
				lowercase_      :							List[Any]    							=					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
				lowercase_      :							Any    							=					[
				    "pip:./",
				    "transformers",
				    "datasets",
				    "evaluate",
				    "tqdm",
				    "scipy",
				    "scikit-learn",
				    "tensorboard",
				    "torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117",
				]
				lowercase_      :							Optional[Any]    							=					rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

				# Define train args/config, run train function
				lowercase_      :							List[Any]    							=					argparse.Namespace(cpu=False, mixed_precision='fp16')
				lowercase_      :							Dict    							=					{"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"""







from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


_a	:	List[Any]=					TypeVar("DatasetType", Dataset, IterableDataset)
def     __UpperCAmelCase							(       UpperCAmelCase_       :							DatasetType     ,				UpperCAmelCase_       :							int     ,				UpperCAmelCase_       :							int    )				->     DatasetType:

	'''simple docstring'''





	if isinstance(UpperCAmelCase_     ,				UpperCAmelCase_    ):
		return _split_by_node_map_style_dataset(UpperCAmelCase_     ,				rank=UpperCAmelCase_     ,				world_size=UpperCAmelCase_    )
	else:
		return _split_by_node_iterable_dataset(UpperCAmelCase_     ,				rank=UpperCAmelCase_     ,				world_size=UpperCAmelCase_    )





