






"""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 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Any=2						,   __snake_case     :    Dict=True						,   __snake_case     :    str=False       ):





  '''simple docstring'''





  lowercase				       =			Accelerator(step_scheduler_with_optimizer=__snake_case						,   split_batches=__snake_case       )
  lowercase				       =			torch.nn.Linear(2						,   4       )
  lowercase				       =			torch.optim.AdamW(model.parameters()						,   lr=1.0       )
  lowercase				       =			torch.optim.lr_scheduler.OneCycleLR(__snake_case						,   max_lr=0.01						,   steps_per_epoch=2						,   epochs=1       )
  lowercase       ,	lowercase       ,	lowercase				       =			accelerator.prepare(__snake_case						,   __snake_case						,   __snake_case       )

  # 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					(       __snake_case     :    int=2						,   __snake_case     :    Optional[int]=True						,   __snake_case     :    List[str]=False       ):





  '''simple docstring'''





  lowercase				       =			Accelerator(step_scheduler_with_optimizer=__snake_case						,   split_batches=__snake_case       )
  lowercase				       =			torch.nn.Linear(2						,   4       )
  lowercase				       =			torch.optim.AdamW(model.parameters()						,   lr=1.0       )
  lowercase				       =			torch.optim.lr_scheduler.LambdaLR(__snake_case						,   lr_lambda=lambda __snake_case       : 1 - n / 10       )
  lowercase       ,	lowercase       ,	lowercase				       =			accelerator.prepare(__snake_case						,   __snake_case						,   __snake_case       )

  # Optimizer has stepped
  lowercase				       =			False
  scheduler.step()
  lowercase				       =			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				       =			True
  scheduler.step()
  if not step_scheduler_with_optimizer:
    lowercase				       =			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					(       __snake_case     :    int = 2       ):





  '''simple docstring'''





  from transformers import get_linear_schedule_with_warmup

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

    lowercase       ,	lowercase       ,	lowercase				       =			accelerator.prepare(__snake_case						,   __snake_case						,   __snake_case       )

    for i in range(10 * num_steps       ):
      with accelerator.accumulate(__snake_case       ):
        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      UpperCamelCase_ (     self			):
      debug_launcher(partial(_lowerCamelCase				,						num_processes=1			)				,						num_processes=1			)
      debug_launcher(partial(_lowerCamelCase				,						num_processes=1				,						split_batches=_lowerCamelCase			)				,						num_processes=1			)







    def      UpperCamelCase_ (     self			):
      debug_launcher(partial(_lowerCamelCase				,						num_processes=1			)				,						num_processes=1			)
      debug_launcher(partial(_lowerCamelCase				,						num_processes=1				,						split_batches=_lowerCamelCase			)				,						num_processes=1			)







    def      UpperCamelCase_ (     self			):
      debug_launcher(partial(_lowerCamelCase				,						num_processes=1				,						step_scheduler_with_optimizer=_lowerCamelCase			)				,						num_processes=1			)







    def      UpperCamelCase_ (     self			):
      debug_launcher(partial(_lowerCamelCase				,						num_processes=1				,						step_scheduler_with_optimizer=_lowerCamelCase			)				,						num_processes=1			)







    def      UpperCamelCase_ (     self			):
      AcceleratorState._reset_state(_lowerCamelCase			)
      debug_launcher(_lowerCamelCase			)
      debug_launcher(partial(_lowerCamelCase				,						num_processes=1				,						split_batches=_lowerCamelCase			)				,						num_processes=1			)







    def      UpperCamelCase_ (     self			):
      AcceleratorState._reset_state(_lowerCamelCase			)
      debug_launcher(_lowerCamelCase			)
      debug_launcher(partial(_lowerCamelCase				,						num_processes=1				,						split_batches=_lowerCamelCase			)				,						num_processes=1			)







    def      UpperCamelCase_ (     self			):
      AcceleratorState._reset_state(_lowerCamelCase			)
      debug_launcher(partial(_lowerCamelCase				,						step_scheduler_with_optimizer=_lowerCamelCase			)			)







    def      UpperCamelCase_ (     self			):
      AcceleratorState._reset_state(_lowerCamelCase			)
      debug_launcher(partial(_lowerCamelCase				,						step_scheduler_with_optimizer=_lowerCamelCase			)			)







    @require_huggingface_suite
    def      UpperCamelCase_ (     self			):
      AcceleratorState._reset_state(_lowerCamelCase			)
      debug_launcher(partial(_lowerCamelCase				,						num_processes=1			)			)
      debug_launcher(_lowerCamelCase			)









import pytest

from datasets.utils.version import Version


@pytest.mark.parametrize(
    '''other, expected_equality'''				,							[
        (Version('''1.0.0'''		), True),
        ('''1.0.0''', True),
        (Version('''2.0.0'''		), False),
        ('''2.0.0''', False),
        ('''1''', False),
        ('''a''', False),
        (1, False),
        (None, False),
    ]				,							)
def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Optional[int]:
		lowerCamelCase :			str    		=Version('''1.0.0'''		)
		assert (version == other) is expected_equality
		assert (version != other) is not expected_equality
		assert (hash(SCREAMING_SNAKE_CASE_		) == hash(SCREAMING_SNAKE_CASE_		)) is expected_equality

