


import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment






def 				UpperCamelCase      (			*__magic_name__  :							Dict  )     ->						Union[str, Any]:
		"""simple docstring"""




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

	# Define train args/config, run train function
	A :	List[str]												=     argparse.Namespace(cpu=False, mixed_precision='fp16')
	A :	str												=     {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
	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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


A :	int												=     {
    'configuration_rwkv': ['RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RwkvConfig', 'RwkvOnnxConfig'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A :	List[Any]												=     [
	    'RWKV_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'RwkvForCausalLM',
	    'RwkvModel',
	    'RwkvPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_rwkv import (
		    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RwkvForCausalLM,
		    RwkvModel,
		    RwkvPreTrainedModel,
		)
else:
	import sys

	A :	Optional[int]												=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

