






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	(			a_=2				,a_=True				,a_=False   )	->		Optional[Any]:
					__UpperCamelCase :   Optional[int]									=Accelerator(step_scheduler_with_optimizer=a_				,split_batches=a_   )
					__UpperCamelCase :   Union[str, Any]									=torch.nn.Linear(2				,4   )
					__UpperCamelCase :   Any									=torch.optim.AdamW(model.parameters()				,lr=1.0   )
					__UpperCamelCase :   Dict									=torch.optim.lr_scheduler.OneCycleLR(a_				,max_lr=0.01				,steps_per_epoch=2				,epochs=1   )
					__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase :   List[Any]									=accelerator.prepare(a_				,a_				,a_   )

					# 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	(			a_=2				,a_=True				,a_=False   )	->		Optional[int]:
					__UpperCamelCase :   Optional[int]									=Accelerator(step_scheduler_with_optimizer=a_				,split_batches=a_   )
					__UpperCamelCase :   List[str]									=torch.nn.Linear(2				,4   )
					__UpperCamelCase :   Dict									=torch.optim.AdamW(model.parameters()				,lr=1.0   )
					__UpperCamelCase :   List[Any]									=torch.optim.lr_scheduler.LambdaLR(a_				,lr_lambda=lambda a_   : 1 - n / 10   )
					__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase :   List[str]									=accelerator.prepare(a_				,a_				,a_   )

					# Optimizer has stepped
					__UpperCamelCase :   Union[str, Any]									=False
					scheduler.step()
					__UpperCamelCase :   str									=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
					__UpperCamelCase :   Optional[Any]									=True
					scheduler.step()
					if not step_scheduler_with_optimizer:
										__UpperCamelCase :   Dict									=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	(			a_ = 2   )	->		Any:
					from transformers import get_linear_schedule_with_warmup

					__UpperCamelCase :   List[str]									=[1, 2, 4]
					for num_steps in steps:
										__UpperCamelCase :   Tuple									=GradientAccumulationPlugin(num_steps=a_				,adjust_scheduler=num_steps > 1   )
										__UpperCamelCase :   str									=Accelerator(gradient_accumulation_plugin=a_   )
										__UpperCamelCase :   Tuple									=torch.nn.Linear(2				,4   )
										__UpperCamelCase :   Dict									=torch.optim.AdamW(model.parameters()				,lr=10.0   )
										__UpperCamelCase :   Optional[int]									=get_linear_schedule_with_warmup(optimizer=a_				,num_warmup_steps=0				,num_training_steps=20   )

										__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase :   Optional[int]									=accelerator.prepare(a_				,a_				,a_   )

										for i in range(10 * num_steps   ):
															with accelerator.accumulate(a_   ):
																				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 							__A	(     unittest.TestCase					):





				"""simple docstring"""







				def  __lowercase				(					self       ):




									"""simple docstring"""

									debug_launcher(partial(lowerCamelCase__				,    num_processes=1       )				,    num_processes=1       )
									debug_launcher(partial(lowerCamelCase__				,    num_processes=1				,    split_batches=lowerCamelCase__       )				,    num_processes=1       )


				def  __lowercase				(					self       ):




									"""simple docstring"""

									debug_launcher(partial(lowerCamelCase__				,    num_processes=1       )				,    num_processes=1       )
									debug_launcher(partial(lowerCamelCase__				,    num_processes=1				,    split_batches=lowerCamelCase__       )				,    num_processes=1       )


				def  __lowercase				(					self       ):




									"""simple docstring"""

									debug_launcher(partial(lowerCamelCase__				,    num_processes=1				,    step_scheduler_with_optimizer=lowerCamelCase__       )				,    num_processes=1       )


				def  __lowercase				(					self       ):




									"""simple docstring"""

									debug_launcher(partial(lowerCamelCase__				,    num_processes=1				,    step_scheduler_with_optimizer=lowerCamelCase__       )				,    num_processes=1       )


				def  __lowercase				(					self       ):




									"""simple docstring"""

									AcceleratorState._reset_state(lowerCamelCase__       )
									debug_launcher(lowerCamelCase__       )
									debug_launcher(partial(lowerCamelCase__				,    num_processes=1				,    split_batches=lowerCamelCase__       )				,    num_processes=1       )


				def  __lowercase				(					self       ):




									"""simple docstring"""

									AcceleratorState._reset_state(lowerCamelCase__       )
									debug_launcher(lowerCamelCase__       )
									debug_launcher(partial(lowerCamelCase__				,    num_processes=1				,    split_batches=lowerCamelCase__       )				,    num_processes=1       )


				def  __lowercase				(					self       ):




									"""simple docstring"""

									AcceleratorState._reset_state(lowerCamelCase__       )
									debug_launcher(partial(lowerCamelCase__				,    step_scheduler_with_optimizer=lowerCamelCase__       )       )


				def  __lowercase				(					self       ):




									"""simple docstring"""

									AcceleratorState._reset_state(lowerCamelCase__       )
									debug_launcher(partial(lowerCamelCase__				,    step_scheduler_with_optimizer=lowerCamelCase__       )       )







				@require_huggingface_suite
				def  __lowercase				(					self       ):




									"""simple docstring"""

									AcceleratorState._reset_state(lowerCamelCase__       )
									debug_launcher(partial(lowerCamelCase__				,    num_processes=1       )       )
									debug_launcher(lowerCamelCase__       )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_			:Union[str, Any]	    =		{'''configuration_nat''': ['''NAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NatConfig''']}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_			:Optional[Any]	    =		[
							    '''NAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''NatForImageClassification''',
							    '''NatModel''',
							    '''NatPreTrainedModel''',
							    '''NatBackbone''',
							]

if TYPE_CHECKING:
							from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_nat import (
														    NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    NatBackbone,
														    NatForImageClassification,
														    NatModel,
														    NatPreTrainedModel,
														)

else:
							import sys

							A_			:Tuple	    =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

