
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__    (    __lowerCAmelCase  :  Tuple=2   ,							__lowerCAmelCase  :  Tuple=True   ,							__lowerCAmelCase  :  Dict=False    ):




			"""simple docstring"""


			lowerCAmelCase_      =     Accelerator(step_scheduler_with_optimizer=__lowerCAmelCase   ,							split_batches=__lowerCAmelCase    )
			lowerCAmelCase_      =     torch.nn.Linear(2   ,							4    )
			lowerCAmelCase_      =     torch.optim.AdamW(model.parameters()   ,							lr=1.0    )
			lowerCAmelCase_      =     torch.optim.lr_scheduler.OneCycleLR(__lowerCAmelCase   ,							max_lr=0.01   ,							steps_per_epoch=2   ,							epochs=1    )
			lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_      =     accelerator.prepare(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase    )

			# 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__    (    __lowerCAmelCase  :  Optional[int]=2   ,							__lowerCAmelCase  :  List[Any]=True   ,							__lowerCAmelCase  :  Any=False    ):




			"""simple docstring"""


			lowerCAmelCase_      =     Accelerator(step_scheduler_with_optimizer=__lowerCAmelCase   ,							split_batches=__lowerCAmelCase    )
			lowerCAmelCase_      =     torch.nn.Linear(2   ,							4    )
			lowerCAmelCase_      =     torch.optim.AdamW(model.parameters()   ,							lr=1.0    )
			lowerCAmelCase_      =     torch.optim.lr_scheduler.LambdaLR(__lowerCAmelCase   ,							lr_lambda=lambda __lowerCAmelCase    : 1 - n / 10    )
			lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_      =     accelerator.prepare(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase    )

			# 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__    (    __lowerCAmelCase  :  int = 2    ):




			"""simple docstring"""


			from transformers import get_linear_schedule_with_warmup

			lowerCAmelCase_      =     [1, 2, 4]
			for num_steps in steps:
						lowerCAmelCase_      =     GradientAccumulationPlugin(num_steps=__lowerCAmelCase   ,							adjust_scheduler=num_steps > 1    )
						lowerCAmelCase_      =     Accelerator(gradient_accumulation_plugin=__lowerCAmelCase    )
						lowerCAmelCase_      =     torch.nn.Linear(2   ,							4    )
						lowerCAmelCase_      =     torch.optim.AdamW(model.parameters()   ,							lr=10.0    )
						lowerCAmelCase_      =     get_linear_schedule_with_warmup(optimizer=__lowerCAmelCase   ,							num_warmup_steps=0   ,							num_training_steps=20    )

						lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_      =     accelerator.prepare(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase    )

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




				def 					__a						( self					)    ->				Optional[Any]:
							debug_launcher(partial(_UpperCamelCase    ,    num_processes=1					)    ,    num_processes=1					)
							debug_launcher(partial(_UpperCamelCase    ,    num_processes=1    ,    split_batches=_UpperCamelCase					)    ,    num_processes=1					)




				def 					__a						( self					)    ->				Union[str, Any]:
							debug_launcher(partial(_UpperCamelCase    ,    num_processes=1					)    ,    num_processes=1					)
							debug_launcher(partial(_UpperCamelCase    ,    num_processes=1    ,    split_batches=_UpperCamelCase					)    ,    num_processes=1					)




				def 					__a						( self					)    ->				str:
							debug_launcher(partial(_UpperCamelCase    ,    num_processes=1    ,    step_scheduler_with_optimizer=_UpperCamelCase					)    ,    num_processes=1					)




				def 					__a						( self					)    ->				Tuple:
							debug_launcher(partial(_UpperCamelCase    ,    num_processes=1    ,    step_scheduler_with_optimizer=_UpperCamelCase					)    ,    num_processes=1					)




				def 					__a						( self					)    ->				Union[str, Any]:
							AcceleratorState._reset_state(_UpperCamelCase					)
							debug_launcher(_UpperCamelCase					)
							debug_launcher(partial(_UpperCamelCase    ,    num_processes=1    ,    split_batches=_UpperCamelCase					)    ,    num_processes=1					)




				def 					__a						( self					)    ->				Dict:
							AcceleratorState._reset_state(_UpperCamelCase					)
							debug_launcher(_UpperCamelCase					)
							debug_launcher(partial(_UpperCamelCase    ,    num_processes=1    ,    split_batches=_UpperCamelCase					)    ,    num_processes=1					)




				def 					__a						( self					)    ->				List[str]:
							AcceleratorState._reset_state(_UpperCamelCase					)
							debug_launcher(partial(_UpperCamelCase    ,    step_scheduler_with_optimizer=_UpperCamelCase					)					)




				def 					__a						( self					)    ->				Dict:
							AcceleratorState._reset_state(_UpperCamelCase					)
							debug_launcher(partial(_UpperCamelCase    ,    step_scheduler_with_optimizer=_UpperCamelCase					)					)




				@require_huggingface_suite
				def 					__a						( self					)    ->				Tuple:
							AcceleratorState._reset_state(_UpperCamelCase					)
							debug_launcher(partial(_UpperCamelCase    ,    num_processes=1					)					)
							debug_launcher(_UpperCamelCase					)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A       =					{"configuration_nat": ["NAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "NatConfig"]}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A       =					[
			    "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       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



