"""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   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





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







class 			_lowerCamelCase      (torch.optim.Optimizer  ):







				def __init__(			self     :							List[Any]      ,       lowerCamelCase_     :							Union[str, Any]      ,       lowerCamelCase_     :							Optional[Any]=True      ,       lowerCamelCase_     :							Optional[int]=None    ):

					"""simple docstring"""






					_lowercase			:     Any      					=				optimizer
					_lowercase			:     Optional[Any]      					=				scaler
					_lowercase			:     Tuple      					=				AcceleratorState()
					_lowercase			:     str      					=				GradientState()
					_lowercase			:     Any      					=				device_placement
					_lowercase			:     Union[str, Any]      					=				False
					_lowercase			:     Dict      					=				None

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







				@property
				def 				__UpperCAmelCase       (			self     :							Dict    ):

					"""simple docstring"""






					return self.optimizer.state







				@state.setter
				def 				__UpperCAmelCase       (			self     :							str      ,       lowerCamelCase_     :							Dict    ):

					"""simple docstring"""






					_lowercase			:     List[Any]      					=				state







				@property
				def 				__UpperCAmelCase       (			self     :							Optional[Any]    ):

					"""simple docstring"""






					return self.optimizer.param_groups







				@param_groups.setter
				def 				__UpperCAmelCase       (			self     :							Optional[int]      ,       lowerCamelCase_     :							Optional[Any]    ):

					"""simple docstring"""






					_lowercase			:     str      					=				param_groups







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

					"""simple docstring"""






					return self.optimizer.defaults







				@defaults.setter
				def 				__UpperCAmelCase       (			self     :							str      ,       lowerCamelCase_     :							Optional[int]    ):

					"""simple docstring"""






					_lowercase			:     List[str]      					=				defaults







				def 				__UpperCAmelCase       (			self     :							Dict      ,       lowerCamelCase_     :							Optional[int]    ):

					"""simple docstring"""






					self.optimizer.add_param_group(lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							Any      ,       lowerCamelCase_     :							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     :							List[Any]    ):

					"""simple docstring"""






					return self.optimizer.state_dict()







				def 				__UpperCAmelCase       (			self     :							List[str]      ,       lowerCamelCase_     :							Any=None    ):

					"""simple docstring"""






					if self.gradient_state.sync_gradients:
						_lowercase			:     Optional[Any]      					=				'set_to_none' in inspect.signature(self.optimizer.zero_grad    ).parameters
						if accept_arg:
							if set_to_none is None:
								_lowercase			:     str      					=				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     :							Optional[int]      ,       lowerCamelCase_     :							str=None    ):

					"""simple docstring"""






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

						else:
							self.optimizer.step(lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							Optional[Any]      ,       lowerCamelCase_     :							Any    ):

					"""simple docstring"""






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







				@property
				def 				__UpperCAmelCase       (			self     :							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     :							Dict    ):

					"""simple docstring"""






					return self._is_overflow







				def __getstate__(			self     :							int    ):

					"""simple docstring"""






					return self.__dict__.copy()







				def __setstate__(			self     :							int      ,       lowerCamelCase_     :							Tuple    ):

					"""simple docstring"""






					self.__dict__.update(lowerCamelCase_    )




"""simple docstring"""







from __future__ import annotations

import re


def   __lowerCAmelCase(		__UpperCAmelCase      ):






	"""simple docstring"""





	def alphanum_key(__UpperCAmelCase      ):
		return [int(__UpperCAmelCase      ) if s.isdigit() else s.lower() for s in re.split('([0-9]+)'  ,__UpperCAmelCase      )]

	return sorted(__UpperCAmelCase  ,key=__UpperCAmelCase      )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



