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

						# Define train args/config, run train function
						_snake_case					: Union[str, Any]     			=					argparse.Namespace(cpu=False, mixed_precision="""fp16""")
						_snake_case					: Union[str, Any]     			=					{"""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 ..utils import DummyObject, requires_backends


class       lowerCAmelCase			(					metaclass=__UpperCAmelCase      ):
		a				:       Any							=       ["""torch""", """transformers""", """invisible_watermark"""]




		def __init__(					self       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(self       ,    ["torch", "transformers", "invisible_watermark"]   )




		@classmethod
		def      lowercase							(					cls       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(cls       ,    ["torch", "transformers", "invisible_watermark"]   )




		@classmethod
		def      lowercase							(					cls       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(cls       ,    ["torch", "transformers", "invisible_watermark"]   )


class       lowerCAmelCase			(					metaclass=__UpperCAmelCase      ):
		a				:       Optional[Any]							=       ["""torch""", """transformers""", """invisible_watermark"""]




		def __init__(					self       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(self       ,    ["torch", "transformers", "invisible_watermark"]   )




		@classmethod
		def      lowercase							(					cls       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(cls       ,    ["torch", "transformers", "invisible_watermark"]   )




		@classmethod
		def      lowercase							(					cls       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(cls       ,    ["torch", "transformers", "invisible_watermark"]   )