





'''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  ( lowerCamelCase_	:     Tuple=2						,				lowerCamelCase_	:     List[str]=True						,				lowerCamelCase_	:     Optional[Any]=False ):
			__lowercase   =							Accelerator(step_scheduler_with_optimizer=lowerCamelCase_						,				split_batches=lowerCamelCase_ )
			__lowercase   =							torch.nn.Linear(2						,				4 )
			__lowercase   =							torch.optim.AdamW(model.parameters()						,				lr=1.0 )
			__lowercase   =							torch.optim.lr_scheduler.OneCycleLR(lowerCamelCase_						,				max_lr=0.01						,				steps_per_epoch=2						,				epochs=1 )
			__lowercase  ,						__lowercase  ,						__lowercase   =							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_	:     Dict=2						,				lowerCamelCase_	:     Dict=True						,				lowerCamelCase_	:     Optional[int]=False ):
			__lowercase   =							Accelerator(step_scheduler_with_optimizer=lowerCamelCase_						,				split_batches=lowerCamelCase_ )
			__lowercase   =							torch.nn.Linear(2						,				4 )
			__lowercase   =							torch.optim.AdamW(model.parameters()						,				lr=1.0 )
			__lowercase   =							torch.optim.lr_scheduler.LambdaLR(lowerCamelCase_						,				lr_lambda=lambda lowerCamelCase_ : 1 - n / 1_0 )
			__lowercase  ,						__lowercase  ,						__lowercase   =							accelerator.prepare(lowerCamelCase_						,				lowerCamelCase_						,				lowerCamelCase_ )

			# Optimizer has stepped
			__lowercase   =							False
			scheduler.step()
			__lowercase   =							1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 1_0
			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
			__lowercase   =							True
			scheduler.step()
			if not step_scheduler_with_optimizer:
						__lowercase   =							1 - 2 / 1_0
			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 ):
			from transformers import get_linear_schedule_with_warmup

			__lowercase   =							[1, 2, 4]
			for num_steps in steps:
						__lowercase   =							GradientAccumulationPlugin(num_steps=lowerCamelCase_						,				adjust_scheduler=num_steps > 1 )
						__lowercase   =							Accelerator(gradient_accumulation_plugin=lowerCamelCase_ )
						__lowercase   =							torch.nn.Linear(2						,				4 )
						__lowercase   =							torch.optim.AdamW(model.parameters()						,				lr=10.0 )
						__lowercase   =							get_linear_schedule_with_warmup(optimizer=lowerCamelCase_						,				num_warmup_steps=0						,				num_training_steps=2_0 )

						__lowercase  ,						__lowercase  ,						__lowercase   =							accelerator.prepare(lowerCamelCase_						,				lowerCamelCase_						,				lowerCamelCase_ )

						for i in range(1_0 * num_steps ):
									with accelerator.accumulate(lowerCamelCase_ ):
												optimizer.step()
												scheduler.step()

									if i == (1_0 * 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 							__lowercase				(					unittest.TestCase  ):







	'''simple docstring'''

	def   _UpperCAmelCase   (self       )							->							Any:







				'''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   _UpperCAmelCase   (self       )							->							Union[str, Any]:







				'''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   _UpperCAmelCase   (self       )							->							List[str]:







				'''simple docstring'''





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


	def   _UpperCAmelCase   (self       )							->							Optional[int]:







				'''simple docstring'''





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


	def   _UpperCAmelCase   (self       )							->							Tuple:







				'''simple docstring'''





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


	def   _UpperCAmelCase   (self       )							->							Any:







				'''simple docstring'''





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


	def   _UpperCAmelCase   (self       )							->							str:







				'''simple docstring'''





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


	def   _UpperCAmelCase   (self       )							->							Optional[Any]:







				'''simple docstring'''





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


	@require_huggingface_suite
	def   _UpperCAmelCase   (self       )							->							str:







				'''simple docstring'''





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












'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_SCREAMING_SNAKE_CASE    =			{
    '''configuration_deformable_detr''': ['''DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeformableDetrConfig'''],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE    =			['''DeformableDetrFeatureExtractor''']
	_SCREAMING_SNAKE_CASE    =			['''DeformableDetrImageProcessor''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE    =			[
	    '''DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''DeformableDetrForObjectDetection''',
	    '''DeformableDetrModel''',
	    '''DeformableDetrPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
		from .image_processing_deformable_detr import DeformableDetrImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_deformable_detr import (
		    DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DeformableDetrForObjectDetection,
		    DeformableDetrModel,
		    DeformableDetrPreTrainedModel,
		)

else:
	import sys

	_SCREAMING_SNAKE_CASE    =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





