
"""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,						__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 A_      (torch.optim.Optimizer ):




  '''simple docstring'''







  def __init__(						self							,  lowercase_							,  lowercase_=True							,  lowercase_=None    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[str]     	=       optimizer
     UpperCAmelCase_   :   Optional[int]     	=       scaler
     UpperCAmelCase_   :   Union[str, Any]     	=       AcceleratorState()
     UpperCAmelCase_   :   Optional[Any]     	=       GradientState()
     UpperCAmelCase_   :   Union[str, Any]     	=       device_placement
     UpperCAmelCase_   :   List[str]     	=       False
     UpperCAmelCase_   :   Optional[Any]     	=       None

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







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return self.optimizer.state







  @state.setter
  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[Any]     	=       state







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return self.optimizer.param_groups







  @param_groups.setter
  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Dict     	=       param_groups







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return self.optimizer.defaults







  @defaults.setter
  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[Any]     	=       defaults







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     self.optimizer.add_param_group(_lowercase    )







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
        xm.send_cpu_data_to_device(_lowercase							,  self.accelerator_state.device    )
     self.optimizer.load_state_dict(_lowercase    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return self.optimizer.state_dict()







  def 			UpperCamelCase__   (						self							,  lowercase_=None    ):

     """simple docstring"""
     if self.gradient_state.sync_gradients:
        UpperCAmelCase_   :   Tuple     	=       'set_to_none' in inspect.signature(self.optimizer.zero_grad    ).parameters
        if accept_arg:
           if set_to_none is None:
              UpperCAmelCase_   :   Optional[int]     	=       False
           self.optimizer.zero_grad(set_to_none=_lowercase    )
        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							,  lowercase_=None    ):

     """simple docstring"""
     if self.gradient_state.sync_gradients:
        if self.accelerator_state.distributed_type == DistributedType.TPU:
           UpperCAmelCase_   :   Union[str, Any]     	=       {'closure': closure} if closure is not None else {}
           xm.optimizer_step(self.optimizer							,  optimizer_args=_lowercase    )
        elif self.scaler is not None:
           UpperCAmelCase_   :   Optional[Any]     	=       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_   :   Tuple     	=       self.scaler.get_scale()
              UpperCAmelCase_   :   int     	=       True
           self.scaler.step(self.optimizer							,  _lowercase    )
           self.scaler.update()
           UpperCAmelCase_   :   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.
              UpperCAmelCase_   :   Optional[int]     	=       scale_after < self._last_scale
           UpperCAmelCase_   :   List[str]     	=       scale_after

        else:
           self.optimizer.step(_lowercase    )







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     for param_group in self.optimizer.param_groups:
        UpperCAmelCase_   :   Dict     	=       [parameters_map.get(_lowercase							,  _lowercase    ) for p in param_group['params']]







  @property
  def 			UpperCamelCase__   (						self    ):

     """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."							,  _lowercase							,  )
     return self._is_overflow







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return self._is_overflow







  def __getstate__(						self    ):

     """simple docstring"""
     return self.__dict__.copy()







  def __setstate__(						self							,  lowercase_    ):

     """simple docstring"""
     self.__dict__.update(_lowercase    )










'''simple docstring'''

import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def 						_UpperCAmelCase     (	)      ->      Optional[Any]:





						"""simple docstring"""
						lowercase_   : List[str]   	=      ArgumentParser(
						    description=(
						        'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
						    )				)

						# Optional arguments for the launch helper
						parser.add_argument('--num_cores'    ,      type=a    ,      default=1    ,      help='Number of TPU cores to use (1 or 8).'				)

						# positional
						parser.add_argument(
						    'training_script'    ,      type=a    ,      help=(
						        'The full path to the single TPU training '
						        'program/script to be launched in parallel, '
						        'followed by all the arguments for the '
						        'training script'
						    )    ,      )

						# rest from the training program
						parser.add_argument('training_script_args'    ,      nargs=a				)

						return parser.parse_args()






def 						_UpperCAmelCase     (	)      ->      str:





						"""simple docstring"""
						lowercase_   : Optional[int]   	=      parse_args()

						# Import training_script as a module.
						lowercase_   : str   	=      Path(args.training_script				)
						sys.path.append(str(script_fpath.parent.resolve()				)				)
						lowercase_   : int   	=      script_fpath.stem
						lowercase_   : List[Any]   	=      importlib.import_module(a				)

						# Patch sys.argv
						lowercase_   : List[str]   	=      [args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores				)]

						xmp.spawn(mod._mp_fn    ,      args=()    ,      nprocs=args.num_cores				)


if __name__ == "__main__":
						main()
