'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case  =			{"""configuration_nat""": ["""NAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NatConfig"""]}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case  =			[
     """NAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """NatForImageClassification""",
     """NatModel""",
     """NatPreTrainedModel""",
     """NatBackbone""",
 ]

if TYPE_CHECKING:
 from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nat import (
      NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      NatBackbone,
      NatForImageClassification,
      NatModel,
      NatPreTrainedModel,
  )

else:
 import sys

 snake_case  =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






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

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





