

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__:						Union[str, Any]						,					snake_case__:						Optional[int]     ) ->				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 					lowercase		( torch.optim.Optimizer      ):



 '''simple docstring'''


 def __init__(self , __a , __a=True , __a=None				)				->      int:






  """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(__a , self.accelerator_state.device				)
   else:
    UpperCAmelCase__           =       move_to_device(__a , self.accelerator_state.device				)
   self.optimizer.load_state_dict(__a				)




 @property
 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  return self.optimizer.state




 @state.setter
 def 						UpperCamelCase__							(self , __a				)				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__           =       state




 @property
 def 						UpperCamelCase__							(self				)				->      Union[str, Any]:






  """simple docstring"""





  return self.optimizer.param_groups




 @param_groups.setter
 def 						UpperCamelCase__							(self , __a				)				->      List[Any]:






  """simple docstring"""





  UpperCAmelCase__           =       param_groups




 @property
 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  return self.optimizer.defaults




 @defaults.setter
 def 						UpperCamelCase__							(self , __a				)				->      Optional[int]:






  """simple docstring"""





  UpperCAmelCase__           =       defaults




 def 						UpperCamelCase__							(self , __a				)				->      Optional[int]:






  """simple docstring"""





  self.optimizer.add_param_group(__a				)




 def 						UpperCamelCase__							(self , __a				)				->      str:






  """simple docstring"""





  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				)				->      Optional[int]:






  """simple docstring"""





  return self.optimizer.state_dict()




 def 						UpperCamelCase__							(self , __a=None				)				->      List[str]:






  """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=__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				)				->      Optional[int]:






  """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=__a				)
   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 , __a				)
    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(__a				)




 def 						UpperCamelCase__							(self , __a				)				->      List[Any]:






  """simple docstring"""





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




 @property
 def 						UpperCamelCase__							(self				)				->      str:






  """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.' , __a , )
  return self._is_overflow




 @property
 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  return self._is_overflow




 def __getstate__(self				)				->      str:






  """simple docstring"""





  return self.__dict__.copy()




 def __setstate__(self , __a				)				->      Tuple:






  """simple docstring"""





  self.__dict__.update(__a				)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase     =    {
    '''configuration_fsmt''': ['''FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FSMTConfig'''],
    '''tokenization_fsmt''': ['''FSMTTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _UpperCamelCase     =    ['''FSMTForConditionalGeneration''', '''FSMTModel''', '''PretrainedFSMTModel''']


if TYPE_CHECKING:
 from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
 from .tokenization_fsmt import FSMTTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel

else:
 import sys

 _UpperCamelCase     =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



