code: 
  
  
  '''simple docstring'''
  
  import copy
  from typing import Dict
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ..auto.configuration_auto import CONFIG_MAPPING
  from ..bit import BitConfig
  
  
  __lowercase:		Any							=     logging.get_logger(__name__)
  
  __lowercase:		Dict							=     {
      "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
      # See all ViT hybrid models at https://huggingface.co/models?filter=vit
  }
  
  
  
  
  
  
  
  class  UpperCAmelCase (       A_):
   _lowerCamelCase :					Tuple    					=					'''vit-hybrid'''
  
   def __init__( self :	List[str],					a_ :	int=None,					a_ :	List[str]=768,					a_ :	Any=12,					a_ :	int=12,					a_ :	Union[str, Any]=3072,					a_ :	List[Any]="gelu",					a_ :	List[Any]=0.0,					a_ :	Optional[int]=0.0,					a_ :	Union[str, Any]=0.02,					a_ :	List[Any]=1e-1_2,					a_ :	Optional[int]=224,					a_ :	Tuple=1,					a_ :	int=3,					a_ :	Optional[int]=[1, 1024, 24, 24],					a_ :	List[Any]=True,					**a_ :	Dict,					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      super().__init__(**a_					)
  
      if backbone_config is None:
         logger.info("`backbone_config` is `None`. Initializing the config with a `BiT` backbone."					)
         UpperCamelCase__ 						=						{
             "global_padding": "same",
             "layer_type": "bottleneck",
             "depths": [3, 4, 9],
             "out_features": ["stage3"],
             "embedding_dynamic_padding": True,
         }
  
      if isinstance(a_,					a_					):
         if "model_type" in backbone_config:
            UpperCamelCase__ 						=						CONFIG_MAPPING[backbone_config["model_type"]]
         else:
            logger.info(
                "`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."					)
            UpperCamelCase__ 						=						BitConfig
         UpperCamelCase__ 						=						backbone_config_class(**a_					)
  
      UpperCamelCase__ 						=						backbone_featmap_shape
      UpperCamelCase__ 						=						backbone_config
      UpperCamelCase__ 						=						hidden_size
      UpperCamelCase__ 						=						num_hidden_layers
      UpperCamelCase__ 						=						num_attention_heads
      UpperCamelCase__ 						=						intermediate_size
      UpperCamelCase__ 						=						hidden_act
      UpperCamelCase__ 						=						hidden_dropout_prob
      UpperCamelCase__ 						=						attention_probs_dropout_prob
      UpperCamelCase__ 						=						initializer_range
      UpperCamelCase__ 						=						layer_norm_eps
      UpperCamelCase__ 						=						image_size
      UpperCamelCase__ 						=						patch_size
      UpperCamelCase__ 						=						num_channels
      UpperCamelCase__ 						=						qkv_bias
  
   def 		lowercase_						( self :	List[str]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						copy.deepcopy(self.__dict__					)
      UpperCamelCase__ 						=						self.backbone_config.to_dict()
      UpperCamelCase__ 						=						self.__class__.model_type
      return output
  
  
  
  
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_					)