




'''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 		a_(			lowerCamelCase=2 ,					lowerCamelCase=True ,					lowerCamelCase=False   )					->List[Any]:






       '''simple docstring'''




       lowercase	:		Tuple    =   Accelerator(step_scheduler_with_optimizer=lowerCamelCase ,					split_batches=lowerCamelCase   )
       lowercase	:		Optional[int]    =   torch.nn.Linear(2 ,					4   )
       lowercase	:		List[Any]    =   torch.optim.AdamW(model.parameters() ,					lr=1.0   )
       lowercase	:		List[Any]    =   torch.optim.lr_scheduler.OneCycleLR(lowerCamelCase ,					max_lr=0.01 ,					steps_per_epoch=2 ,					epochs=1   )
       lowercase,  lowercase,  lowercase	:		Any    =   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 		a_(			lowerCamelCase=2 ,					lowerCamelCase=True ,					lowerCamelCase=False   )					->Tuple:






       '''simple docstring'''




       lowercase	:		Tuple    =   Accelerator(step_scheduler_with_optimizer=lowerCamelCase ,					split_batches=lowerCamelCase   )
       lowercase	:		Optional[int]    =   torch.nn.Linear(2 ,					4   )
       lowercase	:		Any    =   torch.optim.AdamW(model.parameters() ,					lr=1.0   )
       lowercase	:		List[Any]    =   torch.optim.lr_scheduler.LambdaLR(lowerCamelCase ,					lr_lambda=lambda lowerCamelCase   : 1 - n / 10   )
       lowercase,  lowercase,  lowercase	:		List[Any]    =   accelerator.prepare(lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )

       # Optimizer has stepped
       lowercase	:		int    =   False
       scheduler.step()
       lowercase	:		int    =   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	:		int    =   True
       scheduler.step()
       if not step_scheduler_with_optimizer:
              lowercase	:		int    =   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_(			lowerCamelCase = 2   )					->Tuple:






       '''simple docstring'''




       from transformers import get_linear_schedule_with_warmup

       lowercase	:		Union[str, Any]    =   [1, 2, 4]
       for num_steps in steps:
              lowercase	:		List[str]    =   GradientAccumulationPlugin(num_steps=lowerCamelCase ,					adjust_scheduler=num_steps > 1   )
              lowercase	:		Optional[int]    =   Accelerator(gradient_accumulation_plugin=lowerCamelCase   )
              lowercase	:		str    =   torch.nn.Linear(2 ,					4   )
              lowercase	:		Tuple    =   torch.optim.AdamW(model.parameters() ,					lr=10.0   )
              lowercase	:		Union[str, Any]    =   get_linear_schedule_with_warmup(optimizer=lowerCamelCase ,					num_warmup_steps=0 ,					num_training_steps=20   )

              lowercase,  lowercase,  lowercase	:		int    =   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 	SCREAMING_SNAKE_CASE_		(							unittest.TestCase					):



 def  SCREAMING_SNAKE_CASE(	self			):






        '''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  SCREAMING_SNAKE_CASE(	self			):






        '''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  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




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



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




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



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




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



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




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



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




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



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




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





 @require_huggingface_suite
 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




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










'''simple docstring'''






from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
lowercase						:   Any      						=							TypeVar('''T''', bound='''Enum''')

@unique
class 	SCREAMING_SNAKE_CASE_		(							lowerCamelCase_					):
 __snake_case						:  Union[str, Any]       =   8_0
 __snake_case						:  Optional[Any]       =   7_0
 __snake_case						:  List[str]       =   6_0
 __snake_case						:  str       =   5_0
 __snake_case						:  Tuple       =   4_0
 __snake_case						:  Union[str, Any]       =   3_0
 __snake_case						:  Dict       =   2_0
 __snake_case						:  str       =   1_0






@unique
class 	SCREAMING_SNAKE_CASE_		(							lowerCamelCase_					):
 __snake_case						:  Optional[Any]       =   2_4
 __snake_case						:  List[str]       =   2_1
 __snake_case						:  Any       =   1_8
 __snake_case						:  Union[str, Any]       =   1_5
 __snake_case						:  Dict       =   1_2
 __snake_case						:  Union[str, Any]       =   9
 __snake_case						:  List[str]       =   6
 __snake_case						:  Optional[int]       =   3
 __snake_case						:  Tuple       =   2
 __snake_case						:  Any       =   1
 __snake_case						:  Optional[int]       =   -1
 __snake_case						:  Optional[int]       =   -2
 __snake_case						:  str       =   -3
 __snake_case						:  Optional[Any]       =   -6
 __snake_case						:  Optional[int]       =   -9
 __snake_case						:  List[Any]       =   -1_2
 __snake_case						:  str       =   -1_5
 __snake_case						:  str       =   -1_8
 __snake_case						:  Dict       =   -2_1
 __snake_case						:  int       =   -2_4



 @classmethod
 def  SCREAMING_SNAKE_CASE(	cls			):






        '''simple docstring'''




        return {unit.name: unit.value for unit in cls if unit.value > 0}





 @classmethod
 def  SCREAMING_SNAKE_CASE(	cls			):






        '''simple docstring'''




        return {unit.name: unit.value for unit in cls if unit.value < 0}






def 		a_(			lowerCamelCase   )					->str:






       '''simple docstring'''




       lowercase	:		Tuple    =   SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
       for name_prefix, value_prefix in prefixes.items():
              lowercase	:		Dict    =   value / (10**value_prefix)
              if numerical_part > 1:
                     return f"""{numerical_part!s} {name_prefix}"""
       return str(lowerCamelCase   )




def 		a_(			lowerCamelCase   )					->str:






       '''simple docstring'''




       for prefix in BinaryUnit:
              lowercase	:		Dict    =   value / (2**prefix.value)
              if numerical_part > 1:
                     return f"""{numerical_part!s} {prefix.name}"""
       return str(lowerCamelCase   )


if __name__ == "__main__":
      import doctest

      doctest.testmod()




