


'''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 _SCREAMING_SNAKE_CASE   (A						,				A ) -> str:






       """simple docstring"""







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

class 						__lowerCAmelCase		(torch.optim.Optimizer  ):





      '''simple docstring'''






      def __init__(self					:					Tuple     ,		UpperCamelCase					:					Any     ,		UpperCamelCase					:					Dict=True     ,		UpperCamelCase					:					int=None				):

             '''simple docstring'''







             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    UpperCamelCase__	(self					:					Union[str, Any]				):

             '''simple docstring'''







             return self.optimizer.state



      @state.setter
      def    UpperCamelCase__	(self					:					Union[str, Any]     ,		UpperCamelCase					:					Optional[Any]				):

             '''simple docstring'''







             lowercase__       =				state



      @property
      def    UpperCamelCase__	(self					:					int				):

             '''simple docstring'''







             return self.optimizer.param_groups



      @param_groups.setter
      def    UpperCamelCase__	(self					:					Union[str, Any]     ,		UpperCamelCase					:					Dict				):

             '''simple docstring'''







             lowercase__       =				param_groups



      @property
      def    UpperCamelCase__	(self					:					Tuple				):

             '''simple docstring'''







             return self.optimizer.defaults



      @defaults.setter
      def    UpperCamelCase__	(self					:					Optional[int]     ,		UpperCamelCase					:					Union[str, Any]				):

             '''simple docstring'''







             lowercase__       =				defaults



      def    UpperCamelCase__	(self					:					Tuple     ,		UpperCamelCase					:					List[str]				):

             '''simple docstring'''







             self.optimizer.add_param_group(UpperCamelCase				)



      def    UpperCamelCase__	(self					:					int     ,		UpperCamelCase					:					str				):

             '''simple docstring'''







             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    UpperCamelCase__	(self					:					Optional[Any]				):

             '''simple docstring'''







             return self.optimizer.state_dict()



      def    UpperCamelCase__	(self					:					Dict     ,		UpperCamelCase					:					Dict=None				):

             '''simple docstring'''







             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    UpperCamelCase__	(self					:					Union[str, Any]     ,		UpperCamelCase					:					Optional[int]=None				):

             '''simple docstring'''







             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    UpperCamelCase__	(self					:					Union[str, Any]     ,		UpperCamelCase					:					Dict				):

             '''simple docstring'''







             for param_group in self.optimizer.param_groups:
                    lowercase__       =				[parameters_map.get(UpperCamelCase     ,		UpperCamelCase				) for p in param_group['''params''']]



      @property
      def    UpperCamelCase__	(self					:					Dict				):

             '''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.'''     ,		UpperCamelCase     ,		)
             return self._is_overflow



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

             '''simple docstring'''







             return self._is_overflow



      def __getstate__(self					:					Optional[Any]				):

             '''simple docstring'''







             return self.__dict__.copy()






      def __setstate__(self					:					Optional[int]     ,		UpperCamelCase					:					int				):

             '''simple docstring'''







             self.__dict__.update(UpperCamelCase				)








'''simple docstring'''

import torch

from accelerate import Accelerator, DistributedType
class 						__lowerCAmelCase		:





      '''simple docstring'''






      def __enter__(self					:					List[Any]				):

             '''simple docstring'''







             if self.enabled:
                    lowercase__       =				self.model.no_sync()
                    self.model_sync_obj.__enter__()

             return self



      def __exit__(self					:					List[str]     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					Optional[int]     ,		UpperCamelCase					:					Optional[int]				):

             '''simple docstring'''







             if self.enabled:
                    # Average all models on exit
                    self._sync_and_avg_model_params()
                    self.model_sync_obj.__exit__(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase				)



      def __init__(self					:					Dict     ,		UpperCamelCase					:					Accelerator     ,		UpperCamelCase					:					torch.nn.Module     ,		UpperCamelCase					:					int     ,		UpperCamelCase					:					bool = True				):

             '''simple docstring'''







             if accelerator.distributed_type not in [
                 DistributedType.NO,
                 DistributedType.MULTI_CPU,
                 DistributedType.MULTI_GPU,
             ]:
                    raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''				)
             lowercase__       =				enabled and accelerator.distributed_type != DistributedType.NO
             lowercase__       =				0
             if self.enabled:
                    lowercase__       =				accelerator
                    lowercase__       =				model
                    lowercase__       =				local_sgd_steps



      def    UpperCamelCase__	(self					:					List[str]				):

             '''simple docstring'''







             self.num_steps += 1
             if not self.enabled:
                    return

             if self.num_steps % self.local_sgd_steps == 0:
                    self._sync_and_avg_model_params()






      def    UpperCamelCase__	(self					:					Any				):

             '''simple docstring'''







             self.accelerator.wait_for_everyone()
             with self.accelerator.autocast():
                    for param in self.model.parameters():
                           lowercase__       =				self.accelerator.reduce(param.data     ,		reduction='''mean'''				)




