






import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment
def        UpperCAmelCase      (   *_lowercase      ):


    '''simple docstring'''



    _lowercase     =	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      ):
        _lowercase     =	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
   lowerCamelCase										=  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
   lowerCamelCase										=  [
       '''pip:./''',
       '''transformers''',
       '''datasets''',
       '''evaluate''',
       '''tqdm''',
       '''scipy''',
       '''scikit-learn''',
       '''tensorboard''',
       '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
   ]
   lowerCamelCase										=  rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

   # Define train args/config, run train function
   lowerCamelCase										=  argparse.Namespace(cpu=False, mixed_precision='''fp16''')
   lowerCamelCase										=  {'''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 __future__ import annotations
def        UpperCAmelCase      (   _lowercase,      _lowercase      ):


    '''simple docstring'''



    _lowercase     =	[None] * (len(_lowercase      ) + len(_lowercase      ))

    _lowercase     =	0  # pointer to current index for left Half
    _lowercase     =	0  # pointer to current index for the right Half
    _lowercase     =	0  # pointer to current index for the sorted array Half

    while pointera < len(_lowercase      ) and pointera < len(_lowercase      ):
        if left_half[pointera] < right_half[pointera]:
            _lowercase     =	left_half[pointera]
            pointera += 1
            index += 1
        else:
            _lowercase     =	right_half[pointera]
            pointera += 1
            index += 1
    while pointera < len(_lowercase      ):
        _lowercase     =	left_half[pointera]
        pointera += 1
        index += 1

    while pointera < len(_lowercase      ):
        _lowercase     =	right_half[pointera]
        pointera += 1
        index += 1

    return sorted_array
def        UpperCAmelCase      (   _lowercase      ):


    '''simple docstring'''



    if len(_lowercase      ) <= 1:
        return array
    # the actual formula to calculate the middle element = left + (right - left) // 2
    # this avoids integer overflow in case of large N
    _lowercase     =	0 + (len(_lowercase      ) - 0) // 2

    # Split the array into halves till the array length becomes equal to One
    # merge the arrays of single length returned by mergeSort function and
    # pass them into the merge arrays function which merges the array
    _lowercase     =	array[:middle]
    _lowercase     =	array[middle:]

    return merge(merge_sort(_lowercase      ),      merge_sort(_lowercase      )      )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
