





"""simple docstring"""




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment





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

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









from ..utils import DummyObject, requires_backends

class 		lowercase_       (	metaclass=lowerCAmelCase__				):
					__UpperCamelCase     	= ["torch", "transformers", "k_diffusion"]




					def __init__(   self:		Tuple,     *_lowercase:		Dict,     **_lowercase:		Optional[int]):


									'''simple docstring'''


									requires_backends(self,     ["""torch""", """transformers""", """k_diffusion"""])




					@classmethod
					def     _lowercase						(   cls:		Union[str, Any],     *_lowercase:		Union[str, Any],     **_lowercase:		Optional[int]):


									'''simple docstring'''


									requires_backends(cls,     ["""torch""", """transformers""", """k_diffusion"""])







					@classmethod
					def     _lowercase						(   cls:		List[str],     *_lowercase:		int,     **_lowercase:		List[Any]):


									'''simple docstring'''


									requires_backends(cls,     ["""torch""", """transformers""", """k_diffusion"""])





