




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment






def    snake_case   (				*A     )				->				Optional[int]:




	'''simple docstring'''


	lowerCamelCase_				:						List[str]				  =				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     ):
		lowerCamelCase_				:						Optional[Any]				  =				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'])







def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	if not root or root not in binary_tree_mirror_dictionary:
		return
	snake_case_    ,snake_case_		:      Tuple           =						binary_tree_mirror_dictionary[root][:2]
	snake_case_		:      List[str]           =						[right_child, left_child]
	binary_tree_mirror_dict(__a ,			__a )
	binary_tree_mirror_dict(__a ,			__a )


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a = 1 ):
	if not binary_tree:
		raise ValueError('binary tree cannot be empty' )
	if root not in binary_tree:
		snake_case_		:      Dict           =						f"""root {root} is not present in the binary_tree"""
		raise ValueError(__a )
	snake_case_		:      Any           =						dict(__a )
	binary_tree_mirror_dict(__a ,			__a )
	return binary_tree_mirror_dictionary


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE   						=		{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
						print(F'''Binary tree: {binary_tree}''')
						_SCREAMING_SNAKE_CASE   						=		binary_tree_mirror(binary_tree, 5)
						print(F'''Binary tree mirror: {binary_tree_mirror_dictionary}''')

