"""simple docstring"""





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     a_					(				_lowerCAmelCase  :			List[str]       ,			_lowerCAmelCase  :			List[Any]					):





       '''simple docstring'''

       if isinstance(_lowerCAmelCase       ,			(list, tuple)					):
              return honor_type(_lowerCAmelCase       ,			(move_to_device(_lowerCAmelCase       ,			_lowerCAmelCase					) for t in state)					)
       elif isinstance(_lowerCAmelCase       ,			_lowerCAmelCase					):
              return type(_lowerCAmelCase					)({k: move_to_device(_lowerCAmelCase       ,			_lowerCAmelCase					) for k, v in state.items()}					)
       elif isinstance(_lowerCAmelCase       ,			torch.Tensor					):
              return state.to(_lowerCAmelCase					)
       return state






class 					UpperCAmelCase__     (   torch.optim.Optimizer):


      def __init__(       self		,      a		,      a=True		,      a=None							)	-> Union[str, Any]:
             lowercase__      :		Any          =     optimizer
             lowercase__      :		str          =     scaler
             lowercase__      :		str          =     AcceleratorState()
             lowercase__      :		Tuple          =     GradientState()
             lowercase__      :		Optional[int]          =     device_placement
             lowercase__      :		Union[str, Any]          =     False
             lowercase__      :		List[Any]          =     None

             # Handle device placement
             if device_placement:
                    lowercase__      :		Optional[Any]          =     self.optimizer.state_dict()
                    if self.accelerator_state.distributed_type == DistributedType.TPU:
                           xm.send_cpu_data_to_device(a		,      self.accelerator_state.device							)
                    else:
                           lowercase__      :		str          =     move_to_device(a		,      self.accelerator_state.device							)
                    self.optimizer.load_state_dict(a							)


      @property
      def 		_UpperCAmelCase	(       self							)	-> Dict:
             return self.optimizer.state


      @state.setter
      def 		_UpperCAmelCase	(       self		,      a							)	-> Optional[int]:
             lowercase__      :		List[Any]          =     state


      @property
      def 		_UpperCAmelCase	(       self							)	-> List[str]:
             return self.optimizer.param_groups


      @param_groups.setter
      def 		_UpperCAmelCase	(       self		,      a							)	-> str:
             lowercase__      :		List[Any]          =     param_groups


      @property
      def 		_UpperCAmelCase	(       self							)	-> Optional[Any]:
             return self.optimizer.defaults


      @defaults.setter
      def 		_UpperCAmelCase	(       self		,      a							)	-> Dict:
             lowercase__      :		Any          =     defaults


      def 		_UpperCAmelCase	(       self		,      a							)	-> Optional[Any]:
             self.optimizer.add_param_group(a							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> Optional[int]:
             if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
                    xm.send_cpu_data_to_device(a		,      self.accelerator_state.device							)
             self.optimizer.load_state_dict(a							)


      def 		_UpperCAmelCase	(       self							)	-> List[str]:
             return self.optimizer.state_dict()


      def 		_UpperCAmelCase	(       self		,      a=None							)	-> Optional[Any]:
             if self.gradient_state.sync_gradients:
                    lowercase__      :		List[Any]          =     'set_to_none' in inspect.signature(self.optimizer.zero_grad							).parameters
                    if accept_arg:
                           if set_to_none is None:
                                  lowercase__      :		List[str]          =     False
                           self.optimizer.zero_grad(set_to_none=a							)
                    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 		_UpperCAmelCase	(       self		,      a=None							)	-> str:
             if self.gradient_state.sync_gradients:
                    if self.accelerator_state.distributed_type == DistributedType.TPU:
                           lowercase__      :		Dict          =     {'closure': closure} if closure is not None else {}
                           xm.optimizer_step(self.optimizer		,      optimizer_args=a							)
                    elif self.scaler is not None:
                           lowercase__      :		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
                                  lowercase__      :		int          =     self.scaler.get_scale()
                                  lowercase__      :		Optional[int]          =     True
                           self.scaler.step(self.optimizer		,      a							)
                           self.scaler.update()
                           lowercase__      :		List[str]          =     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.
                                  lowercase__      :		Tuple          =     scale_after < self._last_scale
                           lowercase__      :		Optional[Any]          =     scale_after

                    else:
                           self.optimizer.step(a							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> List[str]:
             for param_group in self.optimizer.param_groups:
                    lowercase__      :		List[Any]          =     [parameters_map.get(a		,      a							) for p in param_group['params']]


      @property
      def 		_UpperCAmelCase	(       self							)	-> Tuple:
             warnings.warn(
                 'The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '
                 '`optimizer.step_was_skipped` instead.'		,      a		,      )
             return self._is_overflow


      @property
      def 		_UpperCAmelCase	(       self							)	-> Any:
             return self._is_overflow


      def __getstate__(       self							)	-> Union[str, Any]:
             return self.__dict__.copy()


      def __setstate__(       self		,      a							)	-> Union[str, Any]:
             self.__dict__.update(a							)


"""simple docstring"""





import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig


class 					UpperCAmelCase_     (   _a):
      lowerCamelCase__		:   List[str]			  =  CustomConfig


      def __init__(       self		,      a							)	-> Optional[int]:
             super().__init__(a							)
             lowercase__      :		Any          =     torch.nn.Linear(config.hidden_size		,      config.hidden_size							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> Union[str, Any]:
             return self.linear(a							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> Tuple:
             pass





class 					UpperCAmelCase_     (   _a):
      lowerCamelCase__		:   Optional[int]			  =  NoSuperInitConfig


      def __init__(       self		,      a							)	-> int:
             super().__init__(a							)
             lowercase__      :		List[Any]          =     torch.nn.Linear(config.attribute		,      config.attribute							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> Optional[int]:
             return self.linear(a							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> Optional[Any]:
             pass

