




from __future__ import annotations

def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			list[int]:
       if not list_of_ints:
              return []

       if min(_SCREAMING_SNAKE_CASE   ) < 0:
              raise ValueError("All numbers must be positive"   )

       lowerCamelCase       :				Optional[Any]      			= max(len(bin(_SCREAMING_SNAKE_CASE   )[2:]   ) for x in list_of_ints   )
       return _msd_radix_sort(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			list[int]:
       if bit_position == 0 or len(_SCREAMING_SNAKE_CASE   ) in [0, 1]:
              return list_of_ints

       lowerCamelCase       :				int      			= []
       lowerCamelCase       :				List[Any]      			= []
       # Split numbers based on bit at bit_position from the right
       for number in list_of_ints:
              if (number >> (bit_position - 1)) & 1:
                     # number has a one at bit bit_position
                     ones.append(_SCREAMING_SNAKE_CASE   )
              else:
                     # number has a zero at bit bit_position
                     zeros.append(_SCREAMING_SNAKE_CASE   )

    # recursively split both lists further
       lowerCamelCase       :				List[Any]      			= _msd_radix_sort(_SCREAMING_SNAKE_CASE			,bit_position - 1   )
       lowerCamelCase       :				Dict      			= _msd_radix_sort(_SCREAMING_SNAKE_CASE			,bit_position - 1   )

       # recombine lists
       lowerCamelCase       :				Dict      			= zeros
       res.extend(_SCREAMING_SNAKE_CASE   )

       return res

def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			Tuple:
       lowerCamelCase       :				Any      			= len(_SCREAMING_SNAKE_CASE   )
       if not list_of_ints or length == 1:
              return

       if min(_SCREAMING_SNAKE_CASE   ) < 0:
              raise ValueError("All numbers must be positive"   )

       lowerCamelCase       :				Optional[int]      			= max(len(bin(_SCREAMING_SNAKE_CASE   )[2:]   ) for x in list_of_ints   )
       _msd_radix_sort_inplace(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,0			,_SCREAMING_SNAKE_CASE   )

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			Union[str, Any]:
       if bit_position == 0 or end_index - begin_index <= 1:
              return

       bit_position -= 1

       lowerCamelCase       :				Union[str, Any]      			= begin_index
       lowerCamelCase       :				int      			= end_index - 1
       while i <= j:
              lowerCamelCase       :				Dict      			= False
              if not (list_of_ints[i] >> bit_position) & 1:
                     # found zero at the beginning
                     i += 1
                     lowerCamelCase       :				Optional[int]      			= True
              if (list_of_ints[j] >> bit_position) & 1:
                     # found one at the end
                     j -= 1
                     lowerCamelCase       :				Optional[int]      			= True

              if changed:
                     continue

              lowerCamelCase			,				lowerCamelCase       :				Dict      			= list_of_ints[j], list_of_ints[i]
              j -= 1
              if j != i:
                     i += 1

       _msd_radix_sort_inplace(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )
       _msd_radix_sort_inplace(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
       import doctest

       doctest.testmod()












import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment

def 					A				(				*_SCREAMING_SNAKE_CASE   )     ->			Union[str, Any]:
       lowerCamelCase       :				List[str]      			= 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   ):
              lowerCamelCase       :				Union[str, Any]      			= 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
       SCREAMING_SNAKE_CASE__	:				Tuple  =       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
       SCREAMING_SNAKE_CASE__	:				int  =       [
           'pip:./',
           'transformers',
           'datasets',
           'evaluate',
           'tqdm',
           'scipy',
           'scikit-learn',
           'tensorboard',
           'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
       ]
       SCREAMING_SNAKE_CASE__	:				Dict  =       rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

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






