

'''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__						(  SCREAMING_SNAKE_CASE					:      Union[str, Any]=2				,      SCREAMING_SNAKE_CASE					:      int=True				,      SCREAMING_SNAKE_CASE					:      Optional[int]=False  ):
  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__						(  SCREAMING_SNAKE_CASE					:      Tuple=2				,      SCREAMING_SNAKE_CASE					:      Any=True				,      SCREAMING_SNAKE_CASE					:      Optional[Any]=False  ):
  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 SCREAMING_SNAKE_CASE  : 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__						(  SCREAMING_SNAKE_CASE					:      int = 2  ):
  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 				lowercase_		(						unittest.TestCase       ):

  '''simple docstring'''





  def   snake_case_	(   self   )							-> str:


    """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   snake_case_	(   self   )							-> str:


    """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   snake_case_	(   self   )							-> Optional[int]:


    """simple docstring"""
    debug_launcher(partial(_SCREAMING_SNAKE_CASE      ,       num_processes=1      ,       step_scheduler_with_optimizer=_SCREAMING_SNAKE_CASE   )      ,       num_processes=1   )
  def   snake_case_	(   self   )							-> Optional[int]:


    """simple docstring"""
    debug_launcher(partial(_SCREAMING_SNAKE_CASE      ,       num_processes=1      ,       step_scheduler_with_optimizer=_SCREAMING_SNAKE_CASE   )      ,       num_processes=1   )
  def   snake_case_	(   self   )							-> 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   snake_case_	(   self   )							-> 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   snake_case_	(   self   )							-> List[str]:


    """simple docstring"""
    AcceleratorState._reset_state(_SCREAMING_SNAKE_CASE   )
    debug_launcher(partial(_SCREAMING_SNAKE_CASE      ,       step_scheduler_with_optimizer=_SCREAMING_SNAKE_CASE   )   )
  def   snake_case_	(   self   )							-> Union[str, Any]:


    """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   snake_case_	(   self   )							-> List[str]:


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



'''simple docstring'''




import math





def      _UpperCAmelCase    (							_UpperCamelCase    :	int   )	->      float:
    A_     =   math.log(2,   10   ) * number
    A_     =   round((log_number - int(_UpperCamelCase   )),   15   )

    return difference





def      _UpperCAmelCase    (							_UpperCamelCase    :	int = 67_89_10   )	->      int:
    A_     =   90
    A_     =   0

    A_     =   math.log(1.2_3,   10   )
    A_     =   math.log(1.2_4,   10   )
    A_     =   0

    while position < number:
        A_     =   log_difference(_UpperCamelCase   )

        if difference >= upper_limit:
            power_iterator += 93

        elif difference < lower_limit:
            power_iterator += 1_96

        else:
            A_     =   power_iterator
            power_iterator += 1_96
            position += 1

    return previous_power


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(F"""{solution() = }""")






