


'''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 _SCREAMING_SNAKE_CASE				(A=2  ,			A=True  ,			A=False	)	->	List[str]:






       """simple docstring"""







       lowercase__				      =			Accelerator(step_scheduler_with_optimizer=A  ,			split_batches=A	)
       lowercase__				      =			torch.nn.Linear(2  ,			4	)
       lowercase__				      =			torch.optim.AdamW(model.parameters()  ,			lr=1.0	)
       lowercase__				      =			torch.optim.lr_scheduler.OneCycleLR(A  ,			max_lr=0.01  ,			steps_per_epoch=2  ,			epochs=1	)
       lowercase__      ,lowercase__      ,lowercase__				      =			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 _SCREAMING_SNAKE_CASE				(A=2  ,			A=True  ,			A=False	)	->	List[str]:






       """simple docstring"""







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

       # Optimizer has stepped
       lowercase__				      =			False
       scheduler.step()
       lowercase__				      =			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
       lowercase__				      =			True
       scheduler.step()
       if not step_scheduler_with_optimizer:
              lowercase__				      =			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 _SCREAMING_SNAKE_CASE				(A = 2	)	->	Union[str, Any]:






       """simple docstring"""







       from transformers import get_linear_schedule_with_warmup

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

              lowercase__      ,lowercase__      ,lowercase__				      =			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   __lowerCAmelCase						(unittest.TestCase					):





      '''simple docstring'''






      def 				UpperCamelCase__		(self   :   int			):

             '''simple docstring'''







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



      def 				UpperCamelCase__		(self   :   Optional[int]			):

             '''simple docstring'''







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



      def 				UpperCamelCase__		(self   :   Optional[int]			):

             '''simple docstring'''







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



      def 				UpperCamelCase__		(self   :   str			):

             '''simple docstring'''







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



      def 				UpperCamelCase__		(self   :   List[str]			):

             '''simple docstring'''







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



      def 				UpperCamelCase__		(self   :   str			):

             '''simple docstring'''







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



      def 				UpperCamelCase__		(self   :   Dict			):

             '''simple docstring'''







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



      def 				UpperCamelCase__		(self   :   List[str]			):

             '''simple docstring'''







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






      @require_huggingface_suite
      def 				UpperCamelCase__		(self   :   int			):

             '''simple docstring'''







             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


lowerCamelCase							:       str  						=     {'configuration_nat': ['NAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NatConfig']}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:       Tuple  						=     [
     '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

 lowerCamelCase							:       Optional[Any]  						=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




