

import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment



def  lowercase    (				*SCREAMING_SNAKE_CASE__    :		Optional[Any]			)					->       Union[str, Any]:
 _snake_case      :					List[str]					=   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			):
  _snake_case      :					Union[str, Any]					=   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
  a__      =		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__      =		[
      'pip:./',
      'transformers',
      'datasets',
      'evaluate',
      'tqdm',
      'scipy',
      'scikit-learn',
      'tensorboard',
      'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
  ]
  a__      =		rh.function(fn=launch_train, system=gpu, reqs=reqs, name="""train_bert_glue""")

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





def  lowercase    (				SCREAMING_SNAKE_CASE__    :		Any			)					->       Optional[int]:
 _snake_case      :					Any					=   [701, 301, 132, 57, 23, 10, 4, 1]
 for gap in gaps:
  for i in range(SCREAMING_SNAKE_CASE__			,    len(SCREAMING_SNAKE_CASE__			)			):
   _snake_case      :					List[Any]					=   collection[i]
   _snake_case      :					List[str]					=   i
   while j >= gap and collection[j - gap] > insert_value:
    _snake_case      :					int					=   collection[j - gap]
    j -= gap
   if j != i:
    _snake_case      :					Tuple					=   insert_value
 return collection


if __name__ == "__main__":
  from doctest import testmod

  testmod()
  a__      =		input("""Enter numbers separated by a comma:\n""").strip()
  a__      =		[int(item) for item in user_input.split(""",""")]
  print(shell_sort(unsorted))
