


'''simple docstring'''

def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				int   )		->					bool:





   '''simple docstring'''







   if not isinstance(_UpperCamelCase    ,		_UpperCamelCase   ):
      UpperCamelCase__ 						=						F'Input value of [number={number}] must be an integer'
      raise TypeError(_UpperCamelCase   )
   if number < 0 or number % 2 == 1:
      return False
   UpperCamelCase__ 						=						int(number**0.5   )
   return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
       import doctest

       doctest.testmod()







'''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__(		_UpperCamelCase      :				Any=2    ,		_UpperCamelCase      :				Tuple=True    ,		_UpperCamelCase      :				Union[str, Any]=False   )		->					int:





   '''simple docstring'''







   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=0.0_1    ,		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        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Optional[int]=2    ,		_UpperCamelCase      :				List[str]=True    ,		_UpperCamelCase      :				Tuple=False   )		->					Optional[Any]:





   '''simple docstring'''







   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        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				int = 2   )		->					Optional[int]:





   '''simple docstring'''







   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=1_0.0   )
      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 :	int					):





    """simple docstring"""



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

 def 		lowercase_						( self :	Union[str, Any]					):





    """simple docstring"""



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

 def 		lowercase_						( self :	Optional[Any]					):





    """simple docstring"""



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

 def 		lowercase_						( self :	List[Any]					):





    """simple docstring"""



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

 def 		lowercase_						( self :	Optional[int]					):





    """simple docstring"""



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

 def 		lowercase_						( self :	Union[str, Any]					):





    """simple docstring"""



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

 def 		lowercase_						( self :	Optional[Any]					):





    """simple docstring"""



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

 def 		lowercase_						( self :	List[Any]					):





    """simple docstring"""



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

 @require_huggingface_suite
 def 		lowercase_						( self :	Any					):





    """simple docstring"""



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