

import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment


def     _UpperCAmelCase				(     *_SCREAMING_SNAKE_CASE    :						Tuple				):





    """simple docstring"""


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

       # Define train args/config, run train function
       UpperCamelCase__     :					Any								=			argparse.Namespace(cpu=False, mixed_precision="fp16")
       UpperCamelCase__     :					Union[str, 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'])





from itertools import permutations


def     _UpperCAmelCase				(     ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  list(map(''.join       ,      permutations('0123456789'				)				)				)
    return result[999_999]


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


