




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment


def 				_a       (				*lowerCAmelCase   )->  Any:
		SCREAMING_SNAKE_CASE_							=       torch.cuda.device_count()
		print(F'''Device count: {num_processes}'''   )
		with patch_environment(
		    world_size=lowerCAmelCase						,	master_addr='127.0.01'						,	master_port='29500'						,	mixed_precision=args[1].mixed_precision   ):
				SCREAMING_SNAKE_CASE_							=       PrepareForLaunch(lowerCAmelCase						,	distributed_type='MULTI_GPU'   )
				torch.multiprocessing.start_processes(lowerCAmelCase						,	args=lowerCAmelCase						,	nprocs=lowerCAmelCase						,	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:    Any											=   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:    List[Any]											=   [
						    '''pip:./''',
						    '''transformers''',
						    '''datasets''',
						    '''evaluate''',
						    '''tqdm''',
						    '''scipy''',
						    '''scikit-learn''',
						    '''tensorboard''',
						    '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
						]
						SCREAMING_SNAKE_CASE:    Any											=   rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

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





from __future__ import annotations


def 				_a       (				lowerCAmelCase   )->  list[int]:
		if not list_of_ints:
				return []

		if min(lowerCAmelCase   ) < 0:
				raise ValueError('All numbers must be positive'   )

		SCREAMING_SNAKE_CASE_							=       max(len(bin(lowerCAmelCase   )[2:]   ) for x in list_of_ints   )
		return _msd_radix_sort(lowerCAmelCase						,	lowerCAmelCase   )


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

		SCREAMING_SNAKE_CASE_							=       []
		SCREAMING_SNAKE_CASE_							=       []
		# 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(lowerCAmelCase   )
				else:
						# number has a zero at bit bit_position
						zeros.append(lowerCAmelCase   )

    # recursively split both lists further
		SCREAMING_SNAKE_CASE_							=       _msd_radix_sort(lowerCAmelCase						,	bit_position - 1   )
		SCREAMING_SNAKE_CASE_							=       _msd_radix_sort(lowerCAmelCase						,	bit_position - 1   )

		# recombine lists
		SCREAMING_SNAKE_CASE_							=       zeros
		res.extend(lowerCAmelCase   )

		return res


def 				_a       (				lowerCAmelCase   )->  Dict:
		SCREAMING_SNAKE_CASE_							=       len(lowerCAmelCase   )
		if not list_of_ints or length == 1:
				return

		if min(lowerCAmelCase   ) < 0:
				raise ValueError('All numbers must be positive'   )

		SCREAMING_SNAKE_CASE_							=       max(len(bin(lowerCAmelCase   )[2:]   ) for x in list_of_ints   )
		_msd_radix_sort_inplace(lowerCAmelCase						,	lowerCAmelCase						,	0						,	lowerCAmelCase   )


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )->  Optional[int]:
		if bit_position == 0 or end_index - begin_index <= 1:
				return

		bit_position -= 1

		SCREAMING_SNAKE_CASE_							=       begin_index
		SCREAMING_SNAKE_CASE_							=       end_index - 1
		while i <= j:
				SCREAMING_SNAKE_CASE_							=       False
				if not (list_of_ints[i] >> bit_position) & 1:
						# found zero at the beginning
						i += 1
						SCREAMING_SNAKE_CASE_							=       True
				if (list_of_ints[j] >> bit_position) & 1:
						# found one at the end
						j -= 1
						SCREAMING_SNAKE_CASE_							=       True

				if changed:
						continue

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

		_msd_radix_sort_inplace(lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )
		_msd_radix_sort_inplace(lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()