





'''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    )








'''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()

