code: 
  
  
  '''simple docstring'''
  
  
  
  
  class _snake_case   :
  							"""simple docstring"""
  
  
  
  
  
  							def __init__(   self			,				__A	)     ->List[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      List[str]											=					data
  											__lowerCamelCase		:      List[Any]											=					42
  											__lowerCamelCase		:      Dict											=					42
  
  
  
  
  
  def 							a_       (						lowercase    )			->None:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      Dict											=					0
  				__lowerCamelCase		:      Union[str, Any]											=					x
  
  
  
  
  
  def 							a_       (						lowercase							,						lowercase    )			->None:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase						,					__lowerCamelCase		:      Tuple											=					find_set(_UpperCamelCase    ), find_set(_UpperCamelCase    )
  				if x == y:
  								return
  
  				elif x.rank > y.rank:
  								__lowerCamelCase		:      int											=					x
  				else:
  								__lowerCamelCase		:      List[str]											=					y
  								if x.rank == y.rank:
  												y.rank += 1
  
  
  
  
  
  def 							a_       (						lowercase    )			->Node:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				if x != x.parent:
  								__lowerCamelCase		:      Optional[Any]											=					find_set(x.parent    )
  				return x.parent
  
  
  
  
  
  def 							a_       (						lowercase    )			->set:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      str											=					({0, 1, 2}, {3, 4, 5})
  				for s in sets:
  								if node.data in s:
  												return s
  				__lowerCamelCase		:      Any											=					F'{node.data} is not in {sets}'
  				raise ValueError(_UpperCamelCase    )
  
  
  
  
  
  def 							a_       (						)			->None:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      Optional[Any]											=					[Node(_UpperCamelCase    ) for i in range(6    )]
  				for v in vertex:
  								make_set(_UpperCamelCase    )
  
  				union_set(vertex[0]							,						vertex[1]    )
  				union_set(vertex[1]							,						vertex[2]    )
  				union_set(vertex[3]							,						vertex[4]    )
  				union_set(vertex[3]							,						vertex[5]    )
  
  				for nodea in vertex:
  								for nodea in vertex:
  												if find_python_set(_UpperCamelCase    ).isdisjoint(find_python_set(_UpperCamelCase    )    ):
  																assert find_set(_UpperCamelCase    ) != find_set(_UpperCamelCase    )
  												else:
  																assert find_set(_UpperCamelCase    ) == find_set(_UpperCamelCase    )
  
  
  if __name__ == "__main__":
  						test_disjoint_set()
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  import unittest
  from functools import partial
  
  import torch
  
  from accelerate import Accelerator, debug_launcher
  from accelerate.state import AcceleratorState, GradientState
  from accelerate.test_utils import require_cpu, require_huggingface_suite
  from accelerate.utils import GradientAccumulationPlugin
  
  
  
  
  
  def 							a_       (						lowercase=2							,						lowercase=True							,						lowercase=False    )			->Optional[Any]:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      List[str]											=					Accelerator(step_scheduler_with_optimizer=lowercase							,						split_batches=lowercase    )
  				__lowerCamelCase		:      Union[str, Any]											=					torch.nn.Linear(2							,						4    )
  				__lowerCamelCase		:      str											=					torch.optim.AdamW(model.parameters()							,						lr=1.0    )
  				__lowerCamelCase		:      List[str]											=					torch.optim.lr_scheduler.OneCycleLR(lowercase							,						max_lr=0.0_1							,						steps_per_epoch=2							,						epochs=1    )
  				__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      List[Any]											=					accelerator.prepare(lowercase							,						lowercase							,						lowercase    )
  
  				# Optimizer has stepped
  				scheduler.step()
  				if step_scheduler_with_optimizer or (num_processes == 1):
  								assert (
  								    scheduler.scheduler.last_epoch == num_processes
  								), F'Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'
  				else:
  								assert (
  								    scheduler.scheduler.last_epoch != num_processes
  								), F'Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'
  
  
  
  
  
  def 							a_       (						lowercase=2							,						lowercase=True							,						lowercase=False    )			->Dict:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      Dict											=					Accelerator(step_scheduler_with_optimizer=lowercase							,						split_batches=lowercase    )
  				__lowerCamelCase		:      Tuple											=					torch.nn.Linear(2							,						4    )
  				__lowerCamelCase		:      Optional[Any]											=					torch.optim.AdamW(model.parameters()							,						lr=1.0    )
  				__lowerCamelCase		:      Optional[Any]											=					torch.optim.lr_scheduler.LambdaLR(lowercase							,						lr_lambda=lambda lowercase    : 1 - n / 10    )
  				__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      Optional[int]											=					accelerator.prepare(lowercase							,						lowercase							,						lowercase    )
  
  				# Optimizer has stepped
  				__lowerCamelCase		:      List[Any]											=					False
  				scheduler.step()
  				__lowerCamelCase		:      List[Any]											=					1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
  				assert (
  				    scheduler.get_last_lr()[0] == expected_lr
  				), F'Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'
  
  				# Optimizer has not stepped
  				__lowerCamelCase		:      Any											=					True
  				scheduler.step()
  				if not step_scheduler_with_optimizer:
  								__lowerCamelCase		:      int											=					1 - 2 / 10
  				assert (
  				    scheduler.get_last_lr()[0] == expected_lr
  				), F'Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'
  
  
  
  
  
  def 							a_       (						lowercase = 2    )			->Dict:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				from transformers import get_linear_schedule_with_warmup
  
  				__lowerCamelCase		:      str											=					[1, 2, 4]
  				for num_steps in steps:
  								__lowerCamelCase		:      List[Any]											=					GradientAccumulationPlugin(num_steps=lowercase							,						adjust_scheduler=num_steps > 1    )
  								__lowerCamelCase		:      Dict											=					Accelerator(gradient_accumulation_plugin=lowercase    )
  								__lowerCamelCase		:      Any											=					torch.nn.Linear(2							,						4    )
  								__lowerCamelCase		:      List[Any]											=					torch.optim.AdamW(model.parameters()							,						lr=1_0.0    )
  								__lowerCamelCase		:      Optional[Any]											=					get_linear_schedule_with_warmup(optimizer=lowercase							,						num_warmup_steps=0							,						num_training_steps=20    )
  
  								__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      Optional[int]											=					accelerator.prepare(lowercase							,						lowercase							,						lowercase    )
  
  								for i in range(10 * num_steps    ):
  												with accelerator.accumulate(lowercase    ):
  																optimizer.step()
  																scheduler.step()
  
  												if i == (10 * num_steps - 2):
  																assert (
  																    scheduler.get_last_lr()[0] != 0
  																), F'Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'
  								assert (
  								    scheduler.get_last_lr()[0] == 0
  								), F'Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'
  								GradientState._reset_state()
  
  
  
  
  
  @require_cpu
  class _snake_case   ( unittest.TestCase   ):
  							"""simple docstring"""
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											debug_launcher(partial(__A			,				num_processes=1	)			,				num_processes=1	)
  											debug_launcher(partial(__A			,				num_processes=1			,				split_batches=__A	)			,				num_processes=1	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											debug_launcher(partial(__A			,				num_processes=1	)			,				num_processes=1	)
  											debug_launcher(partial(__A			,				num_processes=1			,				split_batches=__A	)			,				num_processes=1	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											debug_launcher(partial(__A			,				num_processes=1			,				step_scheduler_with_optimizer=__A	)			,				num_processes=1	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											debug_launcher(partial(__A			,				num_processes=1			,				step_scheduler_with_optimizer=__A	)			,				num_processes=1	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											AcceleratorState._reset_state(__A	)
  											debug_launcher(__A	)
  											debug_launcher(partial(__A			,				num_processes=1			,				split_batches=__A	)			,				num_processes=1	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Union[str, Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											AcceleratorState._reset_state(__A	)
  											debug_launcher(__A	)
  											debug_launcher(partial(__A			,				num_processes=1			,				split_batches=__A	)			,				num_processes=1	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Any:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											AcceleratorState._reset_state(__A	)
  											debug_launcher(partial(__A			,				step_scheduler_with_optimizer=__A	)	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->List[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											AcceleratorState._reset_state(__A	)
  											debug_launcher(partial(__A			,				step_scheduler_with_optimizer=__A	)	)
  
  
  
  
  
  
  
  							@require_huggingface_suite
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											AcceleratorState._reset_state(__A	)
  											debug_launcher(partial(__A			,				num_processes=1	)	)
  											debug_launcher(__A	)
  
  
  
  
  
  