






import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment






def     lowerCamelCase__				(   *__lowerCamelCase      :					Optional[int]		):
  __UpperCAmelCase							:     Dict									=  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		):
    __UpperCAmelCase							:     Any									=  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
      a		:	List[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
      a		:	Any      					=			[
          "pip:./",
          "transformers",
          "datasets",
          "evaluate",
          "tqdm",
          "scipy",
          "scikit-learn",
          "tensorboard",
          "torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117",
      ]
      a		:	str      					=			rh.function(fn=launch_train, system=gpu, reqs=reqs, name="train_bert_glue")

      # Define train args/config, run train function
      a		:	int      					=			argparse.Namespace(cpu=False, mixed_precision="fp16")
      a		:	Optional[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'])










def     lowerCamelCase__				(   __lowerCamelCase      :					list  ,		__lowerCamelCase      :					int		):
  for index, item in enumerate(__lowerCamelCase		):
    if item == target:
      return index
  return -1






def     lowerCamelCase__				(   __lowerCamelCase      :					list  ,		__lowerCamelCase      :					int  ,		__lowerCamelCase      :					int  ,		__lowerCamelCase      :					int		):
  if not (0 <= high < len(__lowerCamelCase		) and 0 <= low < len(__lowerCamelCase		)):
    raise Exception("""Invalid upper or lower bound!"""		)
  if high < low:
    return -1
  if sequence[low] == target:
    return low
  if sequence[high] == target:
    return high
  return rec_linear_search(__lowerCamelCase  ,		low + 1  ,		high - 1  ,		__lowerCamelCase		)


if __name__ == "__main__":
       a		:	List[str]      					=			input("Enter numbers separated by comma:\n").strip()
       a		:	Tuple      					=			[int(item.strip()) for item in user_input.split(",")]

       a		:	List[str]      					=			int(input("Enter a single number to be found in the list:\n").strip())
       a		:	Optional[Any]      					=			linear_search(sequence, target)
       if result != -1:
              print(f"""linear_search({sequence}, {target}) = {result}""")
       else:
              print(f"""{target} was not found in {sequence}""")


