




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 						lowercase		(     _lowerCAmelCase   ,     _lowerCAmelCase      ):
  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     snake_case  (	torch.optim.Optimizer							):


   '''simple docstring'''







   def __init__(					self     :	Optional[int]						,    lowerCamelCase_     :	Tuple						,    lowerCamelCase_     :	Union[str, Any]=True						,    lowerCamelCase_     :	List[str]=None			)					->Union[str, Any]:
     '''simple docstring'''







     UpperCAmelCase__    						=   optimizer
     UpperCAmelCase__    						=   scaler
     UpperCAmelCase__    						=   AcceleratorState()
     UpperCAmelCase__    						=   GradientState()
     UpperCAmelCase__    						=   device_placement
     UpperCAmelCase__    						=   False
     UpperCAmelCase__    						=   None

     # Handle device placement
     if device_placement:
       UpperCAmelCase__    						=   self.optimizer.state_dict()
       if self.accelerator_state.distributed_type == DistributedType.TPU:
         xm.send_cpu_data_to_device(lowerCamelCase_						,    self.accelerator_state.device			)
       else:
         UpperCAmelCase__    						=   move_to_device(lowerCamelCase_						,    self.accelerator_state.device			)
       self.optimizer.load_state_dict(lowerCamelCase_			)




   @property
   def 	UpperCAmelCase       (					self     :	Any			)					->Tuple:
     '''simple docstring'''







     return self.optimizer.state




   @state.setter
   def 	UpperCAmelCase       (					self     :	List[str]						,    lowerCamelCase_     :	List[str]			)					->Any:
     '''simple docstring'''







     UpperCAmelCase__    						=   state




   @property
   def 	UpperCAmelCase       (					self     :	Union[str, Any]			)					->List[str]:
     '''simple docstring'''







     return self.optimizer.param_groups




   @param_groups.setter
   def 	UpperCAmelCase       (					self     :	Any						,    lowerCamelCase_     :	Union[str, Any]			)					->Tuple:
     '''simple docstring'''







     UpperCAmelCase__    						=   param_groups




   @property
   def 	UpperCAmelCase       (					self     :	int			)					->Optional[int]:
     '''simple docstring'''







     return self.optimizer.defaults




   @defaults.setter
   def 	UpperCAmelCase       (					self     :	str						,    lowerCamelCase_     :	List[str]			)					->Dict:
     '''simple docstring'''







     UpperCAmelCase__    						=   defaults




   def 	UpperCAmelCase       (					self     :	Any						,    lowerCamelCase_     :	List[str]			)					->Optional[Any]:
     '''simple docstring'''







     self.optimizer.add_param_group(lowerCamelCase_			)




   def 	UpperCAmelCase       (					self     :	Optional[Any]						,    lowerCamelCase_     :	List[str]			)					->Tuple:
     '''simple docstring'''







     if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
       xm.send_cpu_data_to_device(lowerCamelCase_						,    self.accelerator_state.device			)
     self.optimizer.load_state_dict(lowerCamelCase_			)




   def 	UpperCAmelCase       (					self     :	str			)					->List[str]:
     '''simple docstring'''







     return self.optimizer.state_dict()




   def 	UpperCAmelCase       (					self     :	int						,    lowerCamelCase_     :	List[Any]=None			)					->Any:
     '''simple docstring'''







     if self.gradient_state.sync_gradients:
       UpperCAmelCase__    						=   """set_to_none""" in inspect.signature(self.optimizer.zero_grad			).parameters
       if accept_arg:
         if set_to_none is None:
           UpperCAmelCase__    						=   False
         self.optimizer.zero_grad(set_to_none=lowerCamelCase_			)
       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     :	Dict						,    lowerCamelCase_     :	Any=None			)					->Tuple:
     '''simple docstring'''







     if self.gradient_state.sync_gradients:
       if self.accelerator_state.distributed_type == DistributedType.TPU:
         UpperCAmelCase__    						=   {"""closure""": closure} if closure is not None else {}
         xm.optimizer_step(self.optimizer						,    optimizer_args=lowerCamelCase_			)
       elif self.scaler is not None:
         UpperCAmelCase__    						=   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__    						=   self.scaler.get_scale()
           UpperCAmelCase__    						=   True
         self.scaler.step(self.optimizer						,    lowerCamelCase_			)
         self.scaler.update()
         UpperCAmelCase__    						=   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__    						=   scale_after < self._last_scale
         UpperCAmelCase__    						=   scale_after

       else:
         self.optimizer.step(lowerCamelCase_			)




   def 	UpperCAmelCase       (					self     :	Dict						,    lowerCamelCase_     :	List[Any]			)					->Optional[Any]:
     '''simple docstring'''







     for param_group in self.optimizer.param_groups:
       UpperCAmelCase__    						=   [parameters_map.get(lowerCamelCase_						,    lowerCamelCase_			) for p in param_group["""params"""]]




   @property
   def 	UpperCAmelCase       (					self     :	Union[str, Any]			)					->List[Any]:
     '''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."""						,    lowerCamelCase_						,    )
     return self._is_overflow




   @property
   def 	UpperCAmelCase       (					self     :	Any			)					->List[Any]:
     '''simple docstring'''







     return self._is_overflow




   def __getstate__(					self     :	Optional[int]			)					->int:
     '''simple docstring'''







     return self.__dict__.copy()







   def __setstate__(					self     :	List[Any]						,    lowerCamelCase_     :	List[str]			)					->Dict:
     '''simple docstring'''







     self.__dict__.update(lowerCamelCase_			)










def 						lowercase		(     _lowerCAmelCase      ):
  UpperCAmelCase__    						=   5381
  for x in s:
    UpperCAmelCase__    						=   ((hash_value << 5) + hash_value) + ord(_lowerCAmelCase      )
  return hash_value & 0xffffffff




