from __future__ import annotations




def      lowerCAmelCase_				(  __A,						__A,						__A							)  ->       str:







    '''simple docstring'''


    assert n > 1 and a > 0 and greatest_common_divisor(UpperCamelCase__,						UpperCamelCase__							) == 1
    (UpperCAmelCase__)			=						extended_gcd(UpperCamelCase__,						UpperCamelCase__							)  # Implemented below
    UpperCAmelCase__			=						(b * s) % n
    return x




def      lowerCAmelCase_				(  __A,						__A							)  ->       List[str]:







    '''simple docstring'''


    (UpperCAmelCase__)			=						extended_euclid(UpperCamelCase__,						UpperCamelCase__							)  # Implemented below
    if b < 0:
        UpperCAmelCase__			=						(b % n + n) % n
    return b




def      lowerCAmelCase_				(  __A,						__A,						__A							)  ->       int:







    '''simple docstring'''


    UpperCAmelCase__			=						invert_modulo(UpperCamelCase__,						UpperCamelCase__							)
    UpperCAmelCase__			=						(b * s) % n
    return x




def      lowerCAmelCase_				(  __A,						__A							)  ->       List[Any]:







    '''simple docstring'''


    assert a >= 0 and b >= 0

    if b == 0:
        UpperCAmelCase__			=						a, 1, 0
    else:
        (UpperCAmelCase__)			=						extended_gcd(UpperCamelCase__,						a % b							)
        UpperCAmelCase__			=						q
        UpperCAmelCase__			=						p - q * (a // b)

    assert a % d == 0 and b % d == 0
    assert d == a * x + b * y

    return (d, x, y)




def      lowerCAmelCase_				(  __A,						__A							)  ->       List[Any]:







    '''simple docstring'''


    if b == 0:
        return (1, 0)
    (UpperCAmelCase__)			=						extended_euclid(UpperCamelCase__,						a % b							)
    UpperCAmelCase__			=						a // b
    return (y, x - k * y)




def      lowerCAmelCase_				(  __A,						__A							)  ->       Tuple:







    '''simple docstring'''


    if a < b:
        UpperCAmelCase__			=						b, a

    while a % b != 0:
        UpperCAmelCase__			=						b, a % b

    return b


if __name__ == "__main__":
      from doctest import testmod

      testmod(name='modular_division', verbose=True)
      testmod(name='modular_division2', verbose=True)
      testmod(name='invert_modulo', verbose=True)
      testmod(name='extended_gcd', verbose=True)
      testmod(name='extended_euclid', verbose=True)
      testmod(name='greatest_common_divisor', verbose=True)




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment




def      lowerCAmelCase_				(  *__A							)  ->       Tuple:







      '''simple docstring'''


      UpperCAmelCase__			=						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							):
            UpperCAmelCase__			=						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
      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'])



