'''simple docstring'''




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment






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

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

'''simple docstring'''




__UpperCAmelCase  							=1_0**8






def __lowerCAmelCase				(							UpperCamelCase__       )			->					bool:
    if n % 10 == 0:
        return False
    __lowerCamelCase						=						str(UpperCamelCase__       )
    return s == s[::-1]






def __lowerCAmelCase				(							)			->					int:
    __lowerCamelCase						=						set()
    __lowerCamelCase						=						1
    __lowerCamelCase						=						5
    while sum_squares < LIMIT:
        __lowerCamelCase						=						first_square + 1
        while sum_squares < LIMIT:
            if is_palindrome(UpperCamelCase__       ):
                answer.add(UpperCamelCase__       )
            last_square += 1
            sum_squares += last_square**2
        first_square += 1
        __lowerCamelCase						=						first_square**2 + (first_square + 1) ** 2

    return sum(UpperCamelCase__       )


if __name__ == "__main__":
  print(solution())
