"""simple docstring"""




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment



def 				__a       (			*_lowercase						):







					"""simple docstring"""





					lowerCamelCase__		:   int  				=					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						):
										lowerCamelCase__		:   List[str]  				=					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
					UpperCAmelCase       :    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
					UpperCAmelCase       :    Dict									=			[
					    "pip:./",
					    "transformers",
					    "datasets",
					    "evaluate",
					    "tqdm",
					    "scipy",
					    "scikit-learn",
					    "tensorboard",
					    "torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117",
					]
					UpperCAmelCase       :    List[Any]									=			rh.function(fn=launch_train, system=gpu, reqs=reqs, name="train_bert_glue")

					# Define train args/config, run train function
					UpperCAmelCase       :    Any									=			argparse.Namespace(cpu=False, mixed_precision="fp16")
					UpperCAmelCase       :    Optional[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'])







"""simple docstring"""




def 				__a       (			_lowercase							, _lowercase						):







					"""simple docstring"""





					if not root or root not in binary_tree_mirror_dictionary:
										return
					lowerCamelCase__				,						lowerCamelCase__		:   Tuple  				=					binary_tree_mirror_dictionary[root][:2]
					lowerCamelCase__		:   Optional[Any]  				=					[right_child, left_child]
					binary_tree_mirror_dict(_lowercase							, _lowercase						)
					binary_tree_mirror_dict(_lowercase							, _lowercase						)



def 				__a       (			_lowercase							, _lowercase = 1						):







					"""simple docstring"""





					if not binary_tree:
										raise ValueError('''binary tree cannot be empty'''						)
					if root not in binary_tree:
										lowerCamelCase__		:   Dict  				=					f"""root {root} is not present in the binary_tree"""
										raise ValueError(_lowercase						)
					lowerCamelCase__		:   Optional[Any]  				=					dict(_lowercase						)
					binary_tree_mirror_dict(_lowercase							, _lowercase						)
					return binary_tree_mirror_dictionary


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






