





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  (_SCREAMING_SNAKE_CASE=2		,						_SCREAMING_SNAKE_CASE=True		,						_SCREAMING_SNAKE_CASE=False		)				->   str:
  lowercase__				=  Accelerator(step_scheduler_with_optimizer=UpperCamelCase_		,						split_batches=UpperCamelCase_		)
  lowercase__				=  torch.nn.Linear(2		,						4		)
  lowercase__				=  torch.optim.AdamW(model.parameters()		,						lr=1.0		)
  lowercase__				=  torch.optim.lr_scheduler.OneCycleLR(UpperCamelCase_		,						max_lr=0.0_1		,						steps_per_epoch=2		,						epochs=1		)
  lowercase__			,    lowercase__			,    lowercase__				=  accelerator.prepare(UpperCamelCase_		,						UpperCamelCase_		,						UpperCamelCase_		)

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

  # 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 			__UpperCamelCase  (_SCREAMING_SNAKE_CASE = 2		)				->   int:
  from transformers import get_linear_schedule_with_warmup

  lowercase__				=  [1, 2, 4]
  for num_steps in steps:
    lowercase__				=  GradientAccumulationPlugin(num_steps=UpperCamelCase_		,						adjust_scheduler=num_steps > 1		)
    lowercase__				=  Accelerator(gradient_accumulation_plugin=UpperCamelCase_		)
    lowercase__				=  torch.nn.Linear(2		,						4		)
    lowercase__				=  torch.optim.AdamW(model.parameters()		,						lr=1_0.0		)
    lowercase__				=  get_linear_schedule_with_warmup(optimizer=UpperCamelCase_		,						num_warmup_steps=0		,						num_training_steps=20		)

    lowercase__			,    lowercase__			,    lowercase__				=  accelerator.prepare(UpperCamelCase_		,						UpperCamelCase_		,						UpperCamelCase_		)

    for i in range(10 * num_steps		):
      with accelerator.accumulate(UpperCamelCase_		):
        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        SCREAMING_SNAKE_CASE_							(    self			:				Dict       )->   Any:





     """simple docstring"""







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


   def        SCREAMING_SNAKE_CASE_							(    self			:				Dict       )->   Dict:





     """simple docstring"""







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


   def        SCREAMING_SNAKE_CASE_							(    self			:				Dict       )->   List[str]:





     """simple docstring"""







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


   def        SCREAMING_SNAKE_CASE_							(    self			:				Any       )->   int:





     """simple docstring"""







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


   def        SCREAMING_SNAKE_CASE_							(    self			:				Optional[int]       )->   List[str]:





     """simple docstring"""







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


   def        SCREAMING_SNAKE_CASE_							(    self			:				Tuple       )->   Optional[int]:





     """simple docstring"""







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


   def        SCREAMING_SNAKE_CASE_							(    self			:				int       )->   Tuple:





     """simple docstring"""







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


   def        SCREAMING_SNAKE_CASE_							(    self			:				Dict       )->   Dict:





     """simple docstring"""







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


   @require_huggingface_suite
   def        SCREAMING_SNAKE_CASE_							(    self			:				Dict       )->   Optional[Any]:





     """simple docstring"""







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









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_       (							UpperCamelCase_=2    ,						UpperCamelCase_=True    ,						UpperCamelCase_=False     )   ->						str:
       UpperCamelCase_      				=     Accelerator(step_scheduler_with_optimizer=UpperCamelCase_    ,						split_batches=UpperCamelCase_     )
       UpperCamelCase_      				=     torch.nn.Linear(2    ,						4     )
       UpperCamelCase_      				=     torch.optim.AdamW(model.parameters()    ,						lr=1.0     )
       UpperCamelCase_      				=     torch.optim.lr_scheduler.OneCycleLR(UpperCamelCase_    ,						max_lr=0.01    ,						steps_per_epoch=2    ,						epochs=1     )
       UpperCamelCase_				,       UpperCamelCase_				,       UpperCamelCase_      				=     accelerator.prepare(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_     )

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

       # Optimizer has stepped
       UpperCamelCase_      				=     False
       scheduler.step()
       UpperCamelCase_      				=     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_      				=     True
       scheduler.step()
       if not step_scheduler_with_optimizer:
              UpperCamelCase_      				=     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_       (							UpperCamelCase_ = 2     )   ->						int:
       from transformers import get_linear_schedule_with_warmup

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

              UpperCamelCase_				,       UpperCamelCase_				,       UpperCamelCase_      				=     accelerator.prepare(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_     )

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

       def lowercase       (						self:       Dict						)				->       Any:




              """simple docstring"""


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

       def lowercase       (						self:       Dict						)				->       Dict:




              """simple docstring"""


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

       def lowercase       (						self:       Dict						)				->       List[str]:




              """simple docstring"""


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

       def lowercase       (						self:       Any						)				->       int:




              """simple docstring"""


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

       def lowercase       (						self:       Optional[int]						)				->       List[str]:




              """simple docstring"""


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

       def lowercase       (						self:       Tuple						)				->       Optional[int]:




              """simple docstring"""


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

       def lowercase       (						self:       int						)				->       Tuple:




              """simple docstring"""


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

       def lowercase       (						self:       Dict						)				->       Dict:




              """simple docstring"""


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



       @require_huggingface_suite
       def lowercase       (						self:       Dict						)				->       Optional[Any]:




              """simple docstring"""


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

