code: 
  
  
  
  
  
  '''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      _lowerCAmelCase		(		lowercase=2      ,			lowercase=True      ,			lowercase=False			)      ->      Optional[Any]:
  			__lowerCAmelCase      		=       Accelerator(step_scheduler_with_optimizer=lowercase      ,			split_batches=lowercase			)
  			__lowerCAmelCase      		=       torch.nn.Linear(2      ,			4			)
  			__lowerCAmelCase      		=       torch.optim.AdamW(model.parameters()      ,			lr=1.0			)
  			__lowerCAmelCase      		=       torch.optim.lr_scheduler.OneCycleLR(lowercase      ,			max_lr=0.01      ,			steps_per_epoch=2      ,			epochs=1			)
  			__lowerCAmelCase		, __lowerCAmelCase		, __lowerCAmelCase      		=       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      _lowerCAmelCase		(		lowercase=2      ,			lowercase=True      ,			lowercase=False			)      ->      Union[str, Any]:
  			__lowerCAmelCase      		=       Accelerator(step_scheduler_with_optimizer=lowercase      ,			split_batches=lowercase			)
  			__lowerCAmelCase      		=       torch.nn.Linear(2      ,			4			)
  			__lowerCAmelCase      		=       torch.optim.AdamW(model.parameters()      ,			lr=1.0			)
  			__lowerCAmelCase      		=       torch.optim.lr_scheduler.LambdaLR(lowercase      ,			lr_lambda=lambda lowercase			: 1 - n / 10			)
  			__lowerCAmelCase		, __lowerCAmelCase		, __lowerCAmelCase      		=       accelerator.prepare(lowercase      ,			lowercase      ,			lowercase			)
  
  			# Optimizer has stepped
  			__lowerCAmelCase      		=       False
  			scheduler.step()
  			__lowerCAmelCase      		=       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      		=       True
  			scheduler.step()
  			if not step_scheduler_with_optimizer:
  						__lowerCAmelCase      		=       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      _lowerCAmelCase		(		lowercase = 2			)      ->      Tuple:
  			from transformers import get_linear_schedule_with_warmup
  
  			__lowerCAmelCase      		=       [1, 2, 4]
  			for num_steps in steps:
  						__lowerCAmelCase      		=       GradientAccumulationPlugin(num_steps=lowercase      ,			adjust_scheduler=num_steps > 1			)
  						__lowerCAmelCase      		=       Accelerator(gradient_accumulation_plugin=lowercase			)
  						__lowerCAmelCase      		=       torch.nn.Linear(2      ,			4			)
  						__lowerCAmelCase      		=       torch.optim.AdamW(model.parameters()      ,			lr=10.0			)
  						__lowerCAmelCase      		=       get_linear_schedule_with_warmup(optimizer=lowercase      ,			num_warmup_steps=0      ,			num_training_steps=20			)
  
  						__lowerCAmelCase		, __lowerCAmelCase		, __lowerCAmelCase      		=       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 					_UpperCAmelCase							(		unittest.TestCase     ):
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					debug_launcher(partial(__SCREAMING_SNAKE_CASE,num_processes=1    ),num_processes=1    )
  					debug_launcher(partial(__SCREAMING_SNAKE_CASE,num_processes=1,split_batches=__SCREAMING_SNAKE_CASE    ),num_processes=1    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					debug_launcher(partial(__SCREAMING_SNAKE_CASE,num_processes=1    ),num_processes=1    )
  					debug_launcher(partial(__SCREAMING_SNAKE_CASE,num_processes=1,split_batches=__SCREAMING_SNAKE_CASE    ),num_processes=1    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					debug_launcher(partial(__SCREAMING_SNAKE_CASE,num_processes=1,step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE    ),num_processes=1    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					debug_launcher(partial(__SCREAMING_SNAKE_CASE,num_processes=1,step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE    ),num_processes=1    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE    )
  					debug_launcher(__SCREAMING_SNAKE_CASE    )
  					debug_launcher(partial(__SCREAMING_SNAKE_CASE,num_processes=1,split_batches=__SCREAMING_SNAKE_CASE    ),num_processes=1    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE    )
  					debug_launcher(__SCREAMING_SNAKE_CASE    )
  					debug_launcher(partial(__SCREAMING_SNAKE_CASE,num_processes=1,split_batches=__SCREAMING_SNAKE_CASE    ),num_processes=1    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE    )
  					debug_launcher(partial(__SCREAMING_SNAKE_CASE,step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE    )    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE    )
  					debug_launcher(partial(__SCREAMING_SNAKE_CASE,step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE    )    )
  
  
  
  
  
  
  
  		@require_huggingface_suite
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE    )
  					debug_launcher(partial(__SCREAMING_SNAKE_CASE,num_processes=1    )    )
  					debug_launcher(__SCREAMING_SNAKE_CASE    )
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  from __future__ import annotations
  
  from collections.abc import Iterable, Iterator
  from typing import Any, Generic, TypeVar
  
  _a      :				Dict											=					TypeVar("""T""", bound=bool)
  
  
  
  
  
  class 					_UpperCAmelCase							(		Generic[T]     ):
  		def __init__(						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       value
  					__lowerCAmelCase      		=       None
  					__lowerCAmelCase      		=       None
  		@property
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					return self._value
  
  
  
  
  
  
  
  		@staticmethod
  		def 						lowerCamelCase__    (						__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					if not roota:
  								return roota
  
  					if not roota:
  								return roota
  
  					if roota.value > roota.value:
  								__lowerCAmelCase		, __lowerCAmelCase      		=       roota, roota
  
  					__lowerCAmelCase      		=       roota
  					__lowerCAmelCase      		=       roota.right
  					__lowerCAmelCase      		=       roota.left
  					__lowerCAmelCase      		=       SkewNode.merge(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )
  
  					return result
  
  
  
  
  
  class 					_UpperCAmelCase							(		Generic[T]     ):
  		def __init__(						self,__SCREAMING_SNAKE_CASE = ()    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       None
  					if data:
  								for item in data:
  											self.insert(__SCREAMING_SNAKE_CASE    )
  		def __bool__(						self    ):
  
  
  
  					'''simple docstring'''
  
  					return self._root is not None
  		def __iter__(						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       []
  					while self:
  								result.append(self.pop()    )
  
  					# Pushing items back to the heap not to clear it.
  					for item in result:
  								self.insert(__SCREAMING_SNAKE_CASE    )
  
  					return iter(__SCREAMING_SNAKE_CASE    )
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       SkewNode.merge(self._root,SkewNode(__SCREAMING_SNAKE_CASE    )    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.top()
  					__lowerCAmelCase      		=       (
  					    SkewNode.merge(self._root.left,self._root.right    ) if self._root else None
  					)
  
  					return result
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					if not self._root:
  								raise IndexError("""Can't get top element for the empty heap."""    )
  					return self._root.value
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       None
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  