




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

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

     # Optimizer has stepped
     a      =      False
     scheduler.step()
     a      =      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
     a      =      True
     scheduler.step()
     if not step_scheduler_with_optimizer:
          a      =      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 = 2)			->     Optional[Any]:
     from transformers import get_linear_schedule_with_warmup

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

          a  ,      a  ,      a      =      accelerator.prepare(lowerCamelCase_		,					lowerCamelCase_		,					lowerCamelCase_)

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







     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self )  ->    Union[str, Any]:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self )  ->    List[str]:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self )  ->    Optional[int]:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self )  ->    Tuple:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self )  ->    str:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self )  ->    Optional[Any]:



          '''simple docstring'''



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







     @require_huggingface_suite
     def 			lowerCAmelCase_				(	self )  ->    str:



          '''simple docstring'''



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








'''simple docstring'''



import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig






class 							__lowercase				(					lowerCAmelCase__  ):







	'''simple docstring'''

	a			: Union[str, Any]      =				CustomConfig


	def __init__(self   ,_lowerCamelCase       )							->							Tuple:







				'''simple docstring'''





				super().__init__(_lowerCamelCase       )
				__lowercase   =							torch.nn.Linear(config.hidden_size   ,config.hidden_size       )


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							List[str]:







				'''simple docstring'''





				return self.linear(_lowerCamelCase       )


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							List[Any]:







				'''simple docstring'''





				pass






class 							__lowercase				(					lowerCAmelCase__  ):







	'''simple docstring'''

	a			: Dict      =				NoSuperInitConfig


	def __init__(self   ,_lowerCamelCase       )							->							str:







				'''simple docstring'''





				super().__init__(_lowerCamelCase       )
				__lowercase   =							torch.nn.Linear(config.attribute   ,config.attribute       )


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							List[str]:







				'''simple docstring'''





				return self.linear(_lowerCamelCase       )


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							Tuple:







				'''simple docstring'''





				pass





