


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  ,		__snake_case		)  ->    str:







 """simple docstring"""


 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	,  UpperCAmelCase	,  UpperCAmelCase=True	,  UpperCAmelCase=None )  ->     List[str]:
      _lowercase							   =optimizer
      _lowercase							   =scaler
      _lowercase							   =AcceleratorState()
      _lowercase							   =GradientState()
      _lowercase							   =device_placement
      _lowercase							   =False
      _lowercase							   =None

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





     @property
     def   __A      (self )  ->     Union[str, Any]:
      return self.optimizer.state





     @state.setter
     def   __A      (self	,  UpperCAmelCase )  ->     List[str]:
      _lowercase							   =state





     @property
     def   __A      (self )  ->     int:
      return self.optimizer.param_groups





     @param_groups.setter
     def   __A      (self	,  UpperCAmelCase )  ->     Tuple:
      _lowercase							   =param_groups





     @property
     def   __A      (self )  ->     Union[str, Any]:
      return self.optimizer.defaults





     @defaults.setter
     def   __A      (self	,  UpperCAmelCase )  ->     int:
      _lowercase							   =defaults





     def   __A      (self	,  UpperCAmelCase )  ->     List[Any]:
      self.optimizer.add_param_group(UpperCAmelCase )





     def   __A      (self	,  UpperCAmelCase )  ->     Dict:
      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 )  ->     Optional[int]:
      return self.optimizer.state_dict()





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

       else:
        self.optimizer.step(UpperCAmelCase )





     def   __A      (self	,  UpperCAmelCase )  ->     Optional[Any]:
      for param_group in self.optimizer.param_groups:
       _lowercase							   =[parameters_map.get(UpperCAmelCase	,  UpperCAmelCase ) for p in param_group['''params''']]





     @property
     def   __A      (self )  ->     Any:
      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[str]:
      return self._is_overflow





     def __getstate__(self )  ->     Optional[Any]:
      return self.__dict__.copy()





     def __setstate__(self	,  UpperCAmelCase )  ->     int:
      self.__dict__.update(UpperCAmelCase )





from ..utils import DummyObject, requires_backends







class    lowerCamelCase__     ( metaclass=lowerCAmelCase):
     SCREAMING_SNAKE_CASE__       =    ['''tensorflow_text''']





     def __init__(self	,  *UpperCAmelCase	,  **UpperCAmelCase )  ->     Any:
      requires_backends(self	,  ['''tensorflow_text'''] )

