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 	UpperCamelCase			(     snake_case__ :					Dict ,  snake_case__ :					Optional[int]			)			->  Optional[int]:
     if isinstance(snake_case__ ,  (list, tuple)			):
        return honor_type(snake_case__ ,  (move_to_device(snake_case__ ,  snake_case__			) for t in state)			)
     elif isinstance(snake_case__ ,  snake_case__			):
        return type(snake_case__			)({k: move_to_device(snake_case__ ,  snake_case__			) for k, v in state.items()}			)
     elif isinstance(snake_case__ ,  torch.Tensor			):
        return state.to(snake_case__			)
     return state
  
  
  
  class        lowerCAmelCase_	(						torch.optim.Optimizer							):
        def __init__(    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_=True,    SCREAMING_SNAKE_CASE_=None )  ->	Tuple:
           UpperCamelCase					:						Optional[Any]					=			optimizer
           UpperCamelCase					:						int					=			scaler
           UpperCamelCase					:						Union[str, Any]					=			AcceleratorState()
           UpperCamelCase					:						List[Any]					=			GradientState()
           UpperCamelCase					:						Optional[Any]					=			device_placement
           UpperCamelCase					:						Union[str, Any]					=			False
           UpperCamelCase					:						Union[str, Any]					=			None
  
           # Handle device placement
           if device_placement:
              UpperCamelCase					:						Optional[Any]					=			self.optimizer.state_dict()
              if self.accelerator_state.distributed_type == DistributedType.TPU:
                 xm.send_cpu_data_to_device(SCREAMING_SNAKE_CASE_,    self.accelerator_state.device )
              else:
                 UpperCamelCase					:						Dict					=			move_to_device(SCREAMING_SNAKE_CASE_,    self.accelerator_state.device )
              self.optimizer.load_state_dict(SCREAMING_SNAKE_CASE_ )
        @property
        def       snake_case_ (    self )  ->	Any:
           return self.optimizer.state
        @state.setter
        def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_ )  ->	Tuple:
           UpperCamelCase					:						Optional[int]					=			state
        @property
        def       snake_case_ (    self )  ->	str:
           return self.optimizer.param_groups
        @param_groups.setter
        def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_ )  ->	Union[str, Any]:
           UpperCamelCase					:						Any					=			param_groups
        @property
        def       snake_case_ (    self )  ->	int:
           return self.optimizer.defaults
        @defaults.setter
        def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_ )  ->	Any:
           UpperCamelCase					:						str					=			defaults
        def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_ )  ->	str:
           self.optimizer.add_param_group(SCREAMING_SNAKE_CASE_ )
        def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_ )  ->	Dict:
           if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
              xm.send_cpu_data_to_device(SCREAMING_SNAKE_CASE_,    self.accelerator_state.device )
           self.optimizer.load_state_dict(SCREAMING_SNAKE_CASE_ )
        def       snake_case_ (    self )  ->	Union[str, Any]:
           return self.optimizer.state_dict()
        def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_=None )  ->	str:
           if self.gradient_state.sync_gradients:
              UpperCamelCase					:						str					=			'set_to_none' in inspect.signature(self.optimizer.zero_grad ).parameters
              if accept_arg:
                 if set_to_none is None:
                    UpperCamelCase					:						List[Any]					=			False
                 self.optimizer.zero_grad(set_to_none=SCREAMING_SNAKE_CASE_ )
              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       snake_case_ (    self,    SCREAMING_SNAKE_CASE_=None )  ->	Any:
           if self.gradient_state.sync_gradients:
              if self.accelerator_state.distributed_type == DistributedType.TPU:
                 UpperCamelCase					:						Tuple					=			{'closure': closure} if closure is not None else {}
                 xm.optimizer_step(self.optimizer,    optimizer_args=SCREAMING_SNAKE_CASE_ )
              elif self.scaler is not None:
                 UpperCamelCase					:						Any					=			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
                    UpperCamelCase					:						List[str]					=			self.scaler.get_scale()
                    UpperCamelCase					:						Optional[Any]					=			True
                 self.scaler.step(self.optimizer,    SCREAMING_SNAKE_CASE_ )
                 self.scaler.update()
                 UpperCamelCase					:						List[Any]					=			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.
                    UpperCamelCase					:						List[str]					=			scale_after < self._last_scale
                 UpperCamelCase					:						Optional[int]					=			scale_after
  
              else:
                 self.optimizer.step(SCREAMING_SNAKE_CASE_ )
        def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_ )  ->	List[Any]:
           for param_group in self.optimizer.param_groups:
              UpperCamelCase					:						Any					=			[parameters_map.get(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ ) for p in param_group['params']]
        @property
        def       snake_case_ (    self )  ->	int:
           warnings.warn(
               'The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '
               '`optimizer.step_was_skipped` instead.',    SCREAMING_SNAKE_CASE_,    )
           return self._is_overflow
        @property
        def       snake_case_ (    self )  ->	Optional[int]:
           return self._is_overflow
        def __getstate__(    self )  ->	List[str]:
           return self.__dict__.copy()
  
  
  
  
  
  
        def __setstate__(    self,    SCREAMING_SNAKE_CASE_ )  ->	List[str]:
           self.__dict__.update(SCREAMING_SNAKE_CASE_ )
  
  
  
  
style_context: 
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  __UpperCAmelCase      = logging.get_logger(__name__)
  
  __UpperCAmelCase      = {
      '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json''',
  }
  
  
  
  class        lowerCAmelCase_	(						a__							):
         UpperCAmelCase__       :      Union[str, Any]       =						"mpnet"
         def __init__(    self,    SCREAMING_SNAKE_CASE_=3_0527,    SCREAMING_SNAKE_CASE_=768,    SCREAMING_SNAKE_CASE_=12,    SCREAMING_SNAKE_CASE_=12,    SCREAMING_SNAKE_CASE_=3072,    SCREAMING_SNAKE_CASE_="gelu",    SCREAMING_SNAKE_CASE_=0.1,    SCREAMING_SNAKE_CASE_=0.1,    SCREAMING_SNAKE_CASE_=512,    SCREAMING_SNAKE_CASE_=0.02,    SCREAMING_SNAKE_CASE_=1e-12,    SCREAMING_SNAKE_CASE_=32,    SCREAMING_SNAKE_CASE_=1,    SCREAMING_SNAKE_CASE_=0,    SCREAMING_SNAKE_CASE_=2,    **SCREAMING_SNAKE_CASE_,    )  ->	Optional[int]:
            super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_,    bos_token_id=SCREAMING_SNAKE_CASE_,    eos_token_id=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_ )
  
            UpperCamelCase					:						Any					=			vocab_size
            UpperCamelCase					:						Optional[Any]					=			hidden_size
            UpperCamelCase					:						Union[str, Any]					=			num_hidden_layers
            UpperCamelCase					:						Optional[int]					=			num_attention_heads
            UpperCamelCase					:						Optional[Any]					=			hidden_act
            UpperCamelCase					:						str					=			intermediate_size
            UpperCamelCase					:						Optional[Any]					=			hidden_dropout_prob
            UpperCamelCase					:						Union[str, Any]					=			attention_probs_dropout_prob
            UpperCamelCase					:						List[Any]					=			max_position_embeddings
            UpperCamelCase					:						Union[str, Any]					=			initializer_range
            UpperCamelCase					:						List[str]					=			layer_norm_eps
            UpperCamelCase					:						str					=			relative_attention_num_buckets
  
  
  
  