


'''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 __UpperCamelCase (      __lowerCamelCase		:			Optional[Any]=2   ,	__lowerCamelCase		:			Tuple=True   ,	__lowerCamelCase		:			str=False					)   ->  Optional[Any]:







     '''simple docstring'''






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







     '''simple docstring'''






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

     # Optimizer has stepped
     _a            =		False
     scheduler.step()
     _a            =		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
     _a            =		True
     scheduler.step()
     if not step_scheduler_with_optimizer:
          _a            =		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 __UpperCamelCase (      __lowerCamelCase		:			int = 2					)   ->  int:







     '''simple docstring'''






     from transformers import get_linear_schedule_with_warmup

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

          _a      ,      _a      ,      _a            =		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     __SCREAMING_SNAKE_CASE       (   unittest.TestCase			):






     def 			a_       (      self		)							->     List[str]:
          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		)							->     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		)							->     Tuple:
          debug_launcher(partial(__UpperCamelCase  ,				num_processes=1  ,				step_scheduler_with_optimizer=__UpperCamelCase		)  ,				num_processes=1		)






     def 			a_       (      self		)							->     Dict:
          debug_launcher(partial(__UpperCamelCase  ,				num_processes=1  ,				step_scheduler_with_optimizer=__UpperCamelCase		)  ,				num_processes=1		)






     def 			a_       (      self		)							->     Optional[Any]:
          AcceleratorState._reset_state(__UpperCamelCase		)
          debug_launcher(__UpperCamelCase		)
          debug_launcher(partial(__UpperCamelCase  ,				num_processes=1  ,				split_batches=__UpperCamelCase		)  ,				num_processes=1		)






     def 			a_       (      self		)							->     Tuple:
          AcceleratorState._reset_state(__UpperCamelCase		)
          debug_launcher(__UpperCamelCase		)
          debug_launcher(partial(__UpperCamelCase  ,				num_processes=1  ,				split_batches=__UpperCamelCase		)  ,				num_processes=1		)






     def 			a_       (      self		)							->     List[Any]:
          AcceleratorState._reset_state(__UpperCamelCase		)
          debug_launcher(partial(__UpperCamelCase  ,				step_scheduler_with_optimizer=__UpperCamelCase		)		)






     def 			a_       (      self		)							->     str:
          AcceleratorState._reset_state(__UpperCamelCase		)
          debug_launcher(partial(__UpperCamelCase  ,				step_scheduler_with_optimizer=__UpperCamelCase		)		)






     @require_huggingface_suite
     def 			a_       (      self		)							->     Optional[int]:
          AcceleratorState._reset_state(__UpperCamelCase		)
          debug_launcher(partial(__UpperCamelCase  ,				num_processes=1		)		)
          debug_launcher(__UpperCamelCase		)





'''simple docstring'''

from typing import TYPE_CHECKING

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


lowercase__					  =		{
    "configuration_swin2sr": ["SWIN2SR_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swin2SRConfig"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__					  =		[
       "SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST",
       "Swin2SRForImageSuperResolution",
       "Swin2SRModel",
       "Swin2SRPreTrainedModel",
   ]


try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__					  =		["Swin2SRImageProcessor"]


if TYPE_CHECKING:
   from .configuration_swinasr import SWIN2SR_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinaSRConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_swinasr import (
          SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST,
          SwinaSRForImageSuperResolution,
          SwinaSRModel,
          SwinaSRPreTrainedModel,
      )

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_swinasr import SwinaSRImageProcessor


else:
   import sys

   lowercase__					  =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

