




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

    # Optimizer has stepped
    _a							:       List[str]       					=			False
    scheduler.step()
    _a							:       Dict       					=			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							:       str       					=			True
    scheduler.step()
    if not step_scheduler_with_optimizer:
        _a							:       Dict       					=			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 UpperCAmelCase						(					UpperCAmelCase_ = 2	):
    from transformers import get_linear_schedule_with_warmup

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

        _a					,   _a					,   _a							:       List[str]       					=			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 						a_       ( self   ):







      '''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 						a_       ( self   ):







      '''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 						a_       ( self   ):







      '''simple docstring'''


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



  def 						a_       ( self   ):







      '''simple docstring'''


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



  def 						a_       ( self   ):







      '''simple docstring'''


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



  def 						a_       ( self   ):







      '''simple docstring'''


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



  def 						a_       ( self   ):







      '''simple docstring'''


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



  def 						a_       ( self   ):







      '''simple docstring'''


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



  @require_huggingface_suite
  def 						a_       ( self   ):







      '''simple docstring'''


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








'''simple docstring'''





from __future__ import annotations

from enum import Enum




class     UpperCamelCase					(  snake_case__					):
  a    :Optional[int]      					=				2_4
  a    :Dict      					=				2_1
  a    :Dict      					=				1_8
  a    :int      					=				1_5
  a    :List[str]      					=				1_2
  a    :Tuple      					=				9
  a    :Optional[int]      					=				6
  a    :str      					=				3
  a    :Dict      					=				2
  a    :int      					=				1
  a    :List[Any]      					=				-1
  a    :Optional[Any]      					=				-2
  a    :Union[str, Any]      					=				-3
  a    :int      					=				-6
  a    :Union[str, Any]      					=				-9
  a    :Dict      					=				-1_2
  a    :Any      					=				-1_5
  a    :Tuple      					=				-1_8
  a    :Optional[Any]      					=				-2_1
  a    :Union[str, Any]      					=				-2_4






class     UpperCamelCase					(  snake_case__					):
  a    :Optional[Any]      					=				8
  a    :Optional[int]      					=				7
  a    :Optional[int]      					=				6
  a    :List[str]      					=				5
  a    :Union[str, Any]      					=				4
  a    :Union[str, Any]      					=				3
  a    :str      					=				2
  a    :Optional[int]      					=				1






def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  ):
    if isinstance(UpperCAmelCase_      ,  UpperCAmelCase_	):
        _a							:       int       					=			SIUnit[known_prefix.lower()]
    if isinstance(UpperCAmelCase_      ,  UpperCAmelCase_	):
        _a							:       Optional[Any]       					=			SIUnit[unknown_prefix.lower()]
    _a							:       float       					=			known_amount * (
        10 ** (known_prefix.value - unknown_prefix.value)
    )
    return unknown_amount





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  ):
    if isinstance(UpperCAmelCase_      ,  UpperCAmelCase_	):
        _a							:       Optional[Any]       					=			BinaryUnit[known_prefix.lower()]
    if isinstance(UpperCAmelCase_      ,  UpperCAmelCase_	):
        _a							:       Dict       					=			BinaryUnit[unknown_prefix.lower()]
    _a							:       float       					=			known_amount * (
        2 ** ((known_prefix.value - unknown_prefix.value) * 10)
    )
    return unknown_amount


if __name__ == "__main__":
  import doctest

  doctest.testmod()


