



"""simple docstring"""






import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment
def      A      (			*_A							):




     """simple docstring"""
     snake_case_						:Any								=						torch.cuda.device_count()
     print(F'''Device count: {num_processes}'''							)
     with patch_environment(
         world_size=lowercase__,     master_addr="127.0.01",     master_port="29500",     mixed_precision=args[1].mixed_precision							):
          snake_case_						:Tuple								=						PrepareForLaunch(lowercase__,     distributed_type="MULTI_GPU"							)
          torch.multiprocessing.start_processes(lowercase__,     args=lowercase__,     nprocs=lowercase__,     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      :      Dict   					=					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      :      List[str]   					=					[
        'pip:./',
        'transformers',
        'datasets',
        'evaluate',
        'tqdm',
        'scipy',
        'scikit-learn',
        'tensorboard',
        'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
    ]
    __UpperCAmelCase      :      int   					=					rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

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






"""simple docstring"""






def      A      (			_A,     _A = 1_000							):




					"""simple docstring"""
					if (not isinstance(_A,     _A							)) or (digit_position <= 0):
										raise ValueError("Digit position must be a positive integer"							)
					elif (not isinstance(_A,     _A							)) or (precision < 0):
										raise ValueError("Precision must be a nonnegative integer"							)

					# compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
					# accurate
					snake_case_						:List[Any]								=						(
					    4 * _subsum(_A,     1,     _A							)
					    - 2 * _subsum(_A,     4,     _A							)
					    - _subsum(_A,     5,     _A							)
					    - _subsum(_A,     6,     _A							)
					)

					# return the first hex digit of the fractional part of the result
					return hex(int((sum_result % 1) * 16							)							)[2:]
def      A      (			_A,     _A,     _A							):




					"""simple docstring"""
					snake_case_						:int								=						0.0
					for sum_index in range(digit_pos_to_extract + precision							):
										snake_case_						:Optional[Any]								=						8 * sum_index + denominator_addend
										if sum_index < digit_pos_to_extract:
															# if the exponential term is an integer and we mod it by the denominator
															# before dividing, only the integer part of the sum will change;
															# the fractional part will not
															snake_case_						:List[Any]								=						pow(
															    16,     digit_pos_to_extract - 1 - sum_index,     _A							)
										else:
															snake_case_						:Optional[Any]								=						pow(16,     digit_pos_to_extract - 1 - sum_index							)
										total += exponential_term / denominator
					return total


if __name__ == "__main__":
				import doctest

				doctest.testmod()

