code: 
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available
  
  
  __lowercase:		Dict							=     {
      "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
  }
  
  try:
         if not is_sentencepiece_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         __lowercase:		Optional[int]							=     ["XLMProphetNetTokenizer"]
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         __lowercase:		int							=     [
             "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
             "XLMProphetNetDecoder",
             "XLMProphetNetEncoder",
             "XLMProphetNetForCausalLM",
             "XLMProphetNetForConditionalGeneration",
             "XLMProphetNetModel",
             "XLMProphetNetPreTrainedModel",
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig
  
         try:
                if not is_sentencepiece_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_xlm_prophetnet import (
                    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                    XLMProphetNetDecoder,
                    XLMProphetNetEncoder,
                    XLMProphetNetForCausalLM,
                    XLMProphetNetForConditionalGeneration,
                    XLMProphetNetModel,
                    XLMProphetNetPreTrainedModel,
                )
  
  else:
         import sys
  
         __lowercase:		List[Any]							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
style_context: 
  
  
  '''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__(		_UpperCamelCase      :				Any=2    ,		_UpperCamelCase      :				Tuple=True    ,		_UpperCamelCase      :				Union[str, Any]=False   )		->					int:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						Accelerator(step_scheduler_with_optimizer=_UpperCamelCase    ,		split_batches=_UpperCamelCase   )
     UpperCamelCase__ 						=						torch.nn.Linear(2    ,		4   )
     UpperCamelCase__ 						=						torch.optim.AdamW(model.parameters()    ,		lr=1.0   )
     UpperCamelCase__ 						=						torch.optim.lr_scheduler.OneCycleLR(_UpperCamelCase    ,		max_lr=0.0_1    ,		steps_per_epoch=2    ,		epochs=1   )
     UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__ 						=						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        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Optional[int]=2    ,		_UpperCamelCase      :				List[str]=True    ,		_UpperCamelCase      :				Tuple=False   )		->					Optional[Any]:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						Accelerator(step_scheduler_with_optimizer=_UpperCamelCase    ,		split_batches=_UpperCamelCase   )
     UpperCamelCase__ 						=						torch.nn.Linear(2    ,		4   )
     UpperCamelCase__ 						=						torch.optim.AdamW(model.parameters()    ,		lr=1.0   )
     UpperCamelCase__ 						=						torch.optim.lr_scheduler.LambdaLR(_UpperCamelCase    ,		lr_lambda=lambda _UpperCamelCase   : 1 - n / 10   )
     UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__ 						=						accelerator.prepare(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase   )
  
     # Optimizer has stepped
     UpperCamelCase__ 						=						False
     scheduler.step()
     UpperCamelCase__ 						=						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
     UpperCamelCase__ 						=						True
     scheduler.step()
     if not step_scheduler_with_optimizer:
        UpperCamelCase__ 						=						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      :				int = 2   )		->					Optional[int]:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     from transformers import get_linear_schedule_with_warmup
  
     UpperCamelCase__ 						=						[1, 2, 4]
     for num_steps in steps:
        UpperCamelCase__ 						=						GradientAccumulationPlugin(num_steps=_UpperCamelCase    ,		adjust_scheduler=num_steps > 1   )
        UpperCamelCase__ 						=						Accelerator(gradient_accumulation_plugin=_UpperCamelCase   )
        UpperCamelCase__ 						=						torch.nn.Linear(2    ,		4   )
        UpperCamelCase__ 						=						torch.optim.AdamW(model.parameters()    ,		lr=1_0.0   )
        UpperCamelCase__ 						=						get_linear_schedule_with_warmup(optimizer=_UpperCamelCase    ,		num_warmup_steps=0    ,		num_training_steps=20   )
  
        UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__ 						=						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 		lowercase_						( self :	int					):
  
  
  
  
  
      """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 		lowercase_						( self :	Union[str, Any]					):
  
  
  
  
  
      """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 		lowercase_						( self :	Optional[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      debug_launcher(partial(a_,					num_processes=1,					step_scheduler_with_optimizer=a_					),					num_processes=1					)
  
   def 		lowercase_						( self :	List[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      debug_launcher(partial(a_,					num_processes=1,					step_scheduler_with_optimizer=a_					),					num_processes=1					)
  
   def 		lowercase_						( self :	Optional[int]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      AcceleratorState._reset_state(a_					)
      debug_launcher(a_					)
      debug_launcher(partial(a_,					num_processes=1,					split_batches=a_					),					num_processes=1					)
  
   def 		lowercase_						( self :	Union[str, Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      AcceleratorState._reset_state(a_					)
      debug_launcher(a_					)
      debug_launcher(partial(a_,					num_processes=1,					split_batches=a_					),					num_processes=1					)
  
   def 		lowercase_						( self :	Optional[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      AcceleratorState._reset_state(a_					)
      debug_launcher(partial(a_,					step_scheduler_with_optimizer=a_					)					)
  
   def 		lowercase_						( self :	List[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      AcceleratorState._reset_state(a_					)
      debug_launcher(partial(a_,					step_scheduler_with_optimizer=a_					)					)
  
   @require_huggingface_suite
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      AcceleratorState._reset_state(a_					)
      debug_launcher(partial(a_,					num_processes=1					)					)
      debug_launcher(a_					)