



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_ )








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



