




'''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							:       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							:       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							:       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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


a		:  Dict   			=			{
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a		:  List[str]   			=			[
      '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''MobileViTV2ForImageClassification''',
      '''MobileViTV2ForSemanticSegmentation''',
      '''MobileViTV2Model''',
      '''MobileViTV2PreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_mobilevitva import (
      MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
      MobileViTVaConfig,
      MobileViTVaOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mobilevitva import (
        MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileViTVaForImageClassification,
        MobileViTVaForSemanticSegmentation,
        MobileViTVaModel,
        MobileViTVaPreTrainedModel,
    )

else:
  import sys

  a		:  Dict   			=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


