code: 
  
  
  import inspect
  import warnings
  
  import torch
  
  from .state import AcceleratorState, GradientState
  from .utils import DistributedType, honor_type, is_tpu_available
  
  
  if is_tpu_available(check_device=False):
  	import torch_xla.core.xla_model as xm
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Optional[Any]	,	SCREAMING_SNAKE_CASE_				: Any      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							if isinstance(SCREAMING_SNAKE_CASE_	,	(list, tuple)      ):
  														return honor_type(SCREAMING_SNAKE_CASE_	,	(move_to_device(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      ) for t in state)      )
  							elif isinstance(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      ):
  														return type(SCREAMING_SNAKE_CASE_      )({k: move_to_device(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      ) for k, v in state.items()}      )
  							elif isinstance(SCREAMING_SNAKE_CASE_	,	torch.Tensor      ):
  														return state.to(SCREAMING_SNAKE_CASE_      )
  							return state
  
  
  
  
  
  class 					snake_case__  ( torch.optim.Optimizer       ):
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_				,    UpperCamelCase_=True				,    UpperCamelCase_=None							)		->			List[str]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Tuple			=	optimizer
  								a_       :   List[str]			=	scaler
  								a_       :   Optional[Any]			=	AcceleratorState()
  								a_       :   Union[str, Any]			=	GradientState()
  								a_       :   int			=	device_placement
  								a_       :   Union[str, Any]			=	False
  								a_       :   List[Any]			=	None
  
  								# Handle device placement
  								if device_placement:
  															a_       :   str			=	self.optimizer.state_dict()
  															if self.accelerator_state.distributed_type == DistributedType.TPU:
  																						xm.send_cpu_data_to_device(UpperCamelCase_				,    self.accelerator_state.device							)
  															else:
  																						a_       :   Dict			=	move_to_device(UpperCamelCase_				,    self.accelerator_state.device							)
  															self.optimizer.load_state_dict(UpperCamelCase_							)
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.optimizer.state
  
  
  
  
  
  	@state.setter
  	def     A				(  self				,    UpperCamelCase_							)		->			Optional[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   str			=	state
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			Any:
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.optimizer.param_groups
  
  
  
  
  
  	@param_groups.setter
  	def     A				(  self				,    UpperCamelCase_							)		->			Any:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   int			=	param_groups
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			Optional[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.optimizer.defaults
  
  
  
  
  
  	@defaults.setter
  	def     A				(  self				,    UpperCamelCase_							)		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   int			=	defaults
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								self.optimizer.add_param_group(UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			List[str]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
  															xm.send_cpu_data_to_device(UpperCamelCase_				,    self.accelerator_state.device							)
  								self.optimizer.load_state_dict(UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.optimizer.state_dict()
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_=None							)		->			Any:
  								"""simple docstring"""
  
  
  
  
  
  
  								if self.gradient_state.sync_gradients:
  															a_       :   List[str]			=	"""set_to_none""" in inspect.signature(self.optimizer.zero_grad							).parameters
  															if accept_arg:
  																						if set_to_none is None:
  																													a_       :   List[str]			=	False
  																						self.optimizer.zero_grad(set_to_none=UpperCamelCase_							)
  															else:
  																						if set_to_none is not None:
  																													raise ValueError("""`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer."""							)
  																						self.optimizer.zero_grad()
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_=None							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								if self.gradient_state.sync_gradients:
  															if self.accelerator_state.distributed_type == DistributedType.TPU:
  																						a_       :   Optional[int]			=	{"""closure""": closure} if closure is not None else {}
  																						xm.optimizer_step(self.optimizer				,    optimizer_args=UpperCamelCase_							)
  															elif self.scaler is not None:
  																						a_       :   int			=	False
  																						if self._last_scale is None:
  																													# `get_scale` is an async operation requiring full synchronization
  																													# on CPU and GPUs before finishing. As a result, we store away
  																													# the prior one to reduce the call overhead
  																													a_       :   int			=	self.scaler.get_scale()
  																													a_       :   str			=	True
  																						self.scaler.step(self.optimizer				,    UpperCamelCase_							)
  																						self.scaler.update()
  																						a_       :   Optional[int]			=	self.scaler.get_scale()
  																						if not new_scale:
  																													# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
  																													a_       :   Tuple			=	scale_after < self._last_scale
  																						a_       :   Optional[Any]			=	scale_after
  
  															else:
  																						self.optimizer.step(UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			Optional[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								for param_group in self.optimizer.param_groups:
  															a_       :   Union[str, Any]			=	[parameters_map.get(UpperCamelCase_				,    UpperCamelCase_							) for p in param_group["""params"""]]
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			Optional[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								warnings.warn(
  								    """The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use """
  								    """`optimizer.step_was_skipped` instead."""				,    UpperCamelCase_				,    )
  								return self._is_overflow
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return self._is_overflow
  
  
  
  
  
  	def __getstate__(  self							)		->			Optional[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.__dict__.copy()
  
  
  
  
  
  	def __setstate__(  self				,    UpperCamelCase_							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								self.__dict__.update(UpperCamelCase_							)
  
  
  
style_context: 
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from packaging import version
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  SCREAMING_SNAKE_CASE : Dict          =    logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE : str          =    {
      "apple/mobilevit-small": "https://huggingface.co/apple/mobilevit-small/resolve/main/config.json",
      "apple/mobilevit-x-small": "https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json",
      "apple/mobilevit-xx-small": "https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json",
      "apple/deeplabv3-mobilevit-small": (
          "https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"
      ),
      "apple/deeplabv3-mobilevit-x-small": (
          "https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"
      ),
      "apple/deeplabv3-mobilevit-xx-small": (
          "https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"
      ),
      # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
  }
  
  
  
  
  
  class 					snake_case__  ( __A       ):
  	UpperCAmelCase   :							List[str]         = """mobilevit"""
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_=3				,    UpperCamelCase_=256				,    UpperCamelCase_=2				,    UpperCamelCase_=[144, 192, 240]				,    UpperCamelCase_=[16, 32, 64, 96, 128, 160, 640]				,    UpperCamelCase_=4				,    UpperCamelCase_=2.0				,    UpperCamelCase_=4.0				,    UpperCamelCase_="silu"				,    UpperCamelCase_=3				,    UpperCamelCase_=32				,    UpperCamelCase_=0.1				,    UpperCamelCase_=0.0				,    UpperCamelCase_=0.1				,    UpperCamelCase_=0.02				,    UpperCamelCase_=1e-5				,    UpperCamelCase_=True				,    UpperCamelCase_=256				,    UpperCamelCase_=[6, 12, 18]				,    UpperCamelCase_=0.1				,    UpperCamelCase_=255				,    **UpperCamelCase_				,    )		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								super().__init__(**UpperCamelCase_							)
  
  								a_       :   Optional[int]			=	num_channels
  								a_       :   Optional[Any]			=	image_size
  								a_       :   int			=	patch_size
  								a_       :   List[str]			=	hidden_sizes
  								a_       :   Optional[int]			=	neck_hidden_sizes
  								a_       :   List[Any]			=	num_attention_heads
  								a_       :   Union[str, Any]			=	mlp_ratio
  								a_       :   List[str]			=	expand_ratio
  								a_       :   str			=	hidden_act
  								a_       :   Union[str, Any]			=	conv_kernel_size
  								a_       :   Optional[Any]			=	output_stride
  								a_       :   Union[str, Any]			=	hidden_dropout_prob
  								a_       :   List[Any]			=	attention_probs_dropout_prob
  								a_       :   List[Any]			=	classifier_dropout_prob
  								a_       :   Union[str, Any]			=	initializer_range
  								a_       :   List[Any]			=	layer_norm_eps
  								a_       :   str			=	qkv_bias
  
  								# decode head attributes for semantic segmentation
  								a_       :   Optional[Any]			=	aspp_out_channels
  								a_       :   Any			=	atrous_rates
  								a_       :   Union[str, Any]			=	aspp_dropout_prob
  								a_       :   Any			=	semantic_loss_ignore_index
  
  
  
  
  
  class 					snake_case__  ( __A       ):
  	UpperCAmelCase   :							Optional[int]         = version.parse("""1.11"""       )
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			Mapping[str, Mapping[int, str]]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return OrderedDict([("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""})]							)
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			Mapping[str, Mapping[int, str]]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if self.task == "image-classification":
  															return OrderedDict([("""logits""", {0: """batch"""})]							)
  								else:
  															return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]							)
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			float:
  								"""simple docstring"""
  
  
  
  
  
  
  								return 1e-4
  
  
  