



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 						a__     (						_UpperCamelCase     :	str=2	,_UpperCamelCase     :	Optional[Any]=True	,_UpperCamelCase     :	Optional[Any]=False							):
   __lowerCamelCase  							=      Accelerator(step_scheduler_with_optimizer=_UpperCamelCase	,split_batches=_UpperCamelCase							)
   __lowerCamelCase  							=      torch.nn.Linear(2	,4							)
   __lowerCamelCase  							=      torch.optim.AdamW(model.parameters()	,lr=1.0							)
   __lowerCamelCase  							=      torch.optim.lr_scheduler.OneCycleLR(_UpperCamelCase	,max_lr=0.01	,steps_per_epoch=2	,epochs=1							)
   __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase  							=      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 						a__     (						_UpperCamelCase     :	Any=2	,_UpperCamelCase     :	Tuple=True	,_UpperCamelCase     :	Dict=False							):
   __lowerCamelCase  							=      Accelerator(step_scheduler_with_optimizer=_UpperCamelCase	,split_batches=_UpperCamelCase							)
   __lowerCamelCase  							=      torch.nn.Linear(2	,4							)
   __lowerCamelCase  							=      torch.optim.AdamW(model.parameters()	,lr=1.0							)
   __lowerCamelCase  							=      torch.optim.lr_scheduler.LambdaLR(_UpperCamelCase	,lr_lambda=lambda _UpperCamelCase							: 1 - n / 10							)
   __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase  							=      accelerator.prepare(_UpperCamelCase	,_UpperCamelCase	,_UpperCamelCase							)

   # Optimizer has stepped
   __lowerCamelCase  							=      False
   scheduler.step()
   __lowerCamelCase  							=      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
   __lowerCamelCase  							=      True
   scheduler.step()
   if not step_scheduler_with_optimizer:
      __lowerCamelCase  							=      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 						a__     (						_UpperCamelCase     :	int = 2							):
   from transformers import get_linear_schedule_with_warmup

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

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




 def     lowerCamelCase (  self			):



    '''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     lowerCamelCase (  self			):



    '''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     lowerCamelCase (  self			):



    '''simple docstring'''
    debug_launcher(partial(__UpperCAmelCase		,			num_processes=1		,			step_scheduler_with_optimizer=__UpperCAmelCase			)		,			num_processes=1			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    debug_launcher(partial(__UpperCAmelCase		,			num_processes=1		,			step_scheduler_with_optimizer=__UpperCAmelCase			)		,			num_processes=1			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    AcceleratorState._reset_state(__UpperCAmelCase			)
    debug_launcher(__UpperCAmelCase			)
    debug_launcher(partial(__UpperCAmelCase		,			num_processes=1		,			split_batches=__UpperCAmelCase			)		,			num_processes=1			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    AcceleratorState._reset_state(__UpperCAmelCase			)
    debug_launcher(__UpperCAmelCase			)
    debug_launcher(partial(__UpperCAmelCase		,			num_processes=1		,			split_batches=__UpperCAmelCase			)		,			num_processes=1			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    AcceleratorState._reset_state(__UpperCAmelCase			)
    debug_launcher(partial(__UpperCAmelCase		,			step_scheduler_with_optimizer=__UpperCAmelCase			)			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    AcceleratorState._reset_state(__UpperCAmelCase			)
    debug_launcher(partial(__UpperCAmelCase		,			step_scheduler_with_optimizer=__UpperCAmelCase			)			)




 @require_huggingface_suite
 def     lowerCamelCase (  self			):



    '''simple docstring'''
    AcceleratorState._reset_state(__UpperCAmelCase			)
    debug_launcher(partial(__UpperCAmelCase		,			num_processes=1			)			)
    debug_launcher(__UpperCAmelCase			)











def 						a__     (						_UpperCamelCase     :	int							):
   if not isinstance(_UpperCamelCase	,_UpperCamelCase							):
      __lowerCamelCase  							=      (
          '''operation can not be conducted on a object of type '''
          F"""{type(_UpperCamelCase							).__name__}"""
      )
      raise TypeError(_UpperCamelCase							)
   __lowerCamelCase  							=      ''''''
   for _ in range(0	,32							):
      bit_string += str(number % 2							)
      __lowerCamelCase  							=      number >> 1
   return bit_string





def 						a__     (						_UpperCamelCase     :	int							):
   if number < 0:
      raise ValueError('''the value of input must be positive'''							)
   elif isinstance(_UpperCamelCase	,_UpperCamelCase							):
      raise TypeError('''Input value must be a \'int\' type'''							)
   elif isinstance(_UpperCamelCase	,_UpperCamelCase							):
      raise TypeError('''\'<\' not supported between instances of \'str\' and \'int\''''							)
   __lowerCamelCase  							=      0
   # iterator over [1 to 32],since we are dealing with 32 bit integer
   for _ in range(1	,33							):
      # left shift the bits by unity
      __lowerCamelCase  							=      result << 1
      # get the end bit
      __lowerCamelCase  							=      number % 2
      # right shift the bits by unity
      __lowerCamelCase  							=      number >> 1
      # add that bit to our ans
      __lowerCamelCase  							=      result | end_bit
   return get_reverse_bit_string(_UpperCamelCase							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()






