
'''simple docstring'''





import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment
def   snake_case_       (							*_a     :							int			):
      snake_case_						       =							torch.cuda.device_count()
      print(F'''Device count: {num_processes}'''			)
      with patch_environment(
          world_size=_a ,					master_addr="127.0.01" ,					master_port="29500" ,					mixed_precision=args[1].mixed_precision			):
            snake_case_						       =							PrepareForLaunch(_a ,					distributed_type="MULTI_GPU"			)
            torch.multiprocessing.start_processes(_a ,					args=_a ,					nprocs=_a ,					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": 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'''





from __future__ import annotations

import random



class  lowerCAmelCase__   :



  '''simple docstring'''






  _UpperCamelCase						= 6

  def __init__(self  : List[Any]    )   ->       Optional[Any]:
        snake_case_						       =							list(range(1	, Dice.NUM_SIDES + 1    )    )


  def 		_lowerCamelCase	(self  : Optional[int]    )   ->       int:
        return random.choice(self.sides    )

def   snake_case_       (							_a     :							int ,					_a     :							int = 2			):
      snake_case_						       =							[Dice() for i in range(_a			)]
      snake_case_						       =							[0] * (len(_a			) * Dice.NUM_SIDES + 1)
      for _ in range(_a			):
            count_of_sum[sum(dice.roll() for dice in dices			)] += 1
      snake_case_						       =							[round((count * 100) / num_throws ,					2			) for count in count_of_sum]
      return probability[num_dice:]  # remove probability of sums that never appear


if __name__ == "__main__":
     import doctest

     doctest.testmod()

