
"""simple docstring"""




from __future__ import annotations

from collections import defaultdict
from dataclasses import dataclass



@dataclass
class      lowerCAmelCase_						:


				"""simple docstring"""



				__UpperCamelCase				:						int
				__UpperCamelCase				:						TreeNode | None 	=       None
				__UpperCamelCase				:						TreeNode | None 	=       None






def lowercase_  (       ):
							return TreeNode(3       ,TreeNode(9						)       ,TreeNode(20       ,TreeNode(15						)       ,TreeNode(7						)						)						)

def lowercase_  (       _snake_case						):

							def depth_first_search(
							    _snake_case       ,_snake_case       ,_snake_case						) -> None:
														if not root:
																					return

														if depth == len(_snake_case						):
																					right_view.append(root.val						)

														depth_first_search(root.right       ,depth + 1       ,_snake_case						)
														depth_first_search(root.left       ,depth + 1       ,_snake_case						)

							SCREAMING_SNAKE_CASE__						:	list									=       []
							if not root:
														return right_view

							depth_first_search(_snake_case       ,0       ,_snake_case						)
							return right_view

def lowercase_  (       _snake_case						):

							def depth_first_search(
							    _snake_case       ,_snake_case       ,_snake_case						) -> None:
														if not root:
																					return

														if depth == len(_snake_case						):
																					left_view.append(root.val						)

														depth_first_search(root.left       ,depth + 1       ,_snake_case						)
														depth_first_search(root.right       ,depth + 1       ,_snake_case						)

							SCREAMING_SNAKE_CASE__						:	list									=       []
							if not root:
														return left_view

							depth_first_search(_snake_case       ,0       ,_snake_case						)
							return left_view

def lowercase_  (       _snake_case						):

							def breadth_first_search(_snake_case       ,_snake_case						) -> None:
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       [(root, 0)]
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       defaultdict(_snake_case						)

														while queue:
																					SCREAMING_SNAKE_CASE__						:	int									=       queue.pop(0						)
																					SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Dict									=       first

																					lookup[hd].append(node.val						)

																					if node.left:
																												queue.append((node.left, hd - 1)						)
																					if node.right:
																												queue.append((node.right, hd + 1)						)

														for pair in sorted(lookup.items()       ,key=lambda _snake_case						: each[0]						):
																					top_view.append(pair[1][0]						)

							SCREAMING_SNAKE_CASE__						:	list									=       []
							if not root:
														return top_view

							breadth_first_search(_snake_case       ,_snake_case						)
							return top_view

def lowercase_  (       _snake_case						):
							from collections import defaultdict

							def breadth_first_search(_snake_case       ,_snake_case						) -> None:
														SCREAMING_SNAKE_CASE__						:	int									=       [(root, 0)]
														SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       defaultdict(_snake_case						)

														while queue:
																					SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       queue.pop(0						)
																					SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Any									=       first
																					lookup[hd].append(node.val						)

																					if node.left:
																												queue.append((node.left, hd - 1)						)
																					if node.right:
																												queue.append((node.right, hd + 1)						)

														for pair in sorted(lookup.items()       ,key=lambda _snake_case						: each[0]						):
																					bottom_view.append(pair[1][-1]						)

							SCREAMING_SNAKE_CASE__						:	list									=       []
							if not root:
														return bottom_view

							breadth_first_search(_snake_case       ,_snake_case						)
							return bottom_view


if __name__ == "__main__":
					import doctest

					doctest.testmod()






"""simple docstring"""




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment

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

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




