

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_   (					_UpperCAmelCase       ,			_UpperCAmelCase					):
						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 						__lowercase       (				torch.optim.Optimizer			):




			"""simple docstring"""







			def __init__(				self						:  List[str]     ,		lowerCAmelCase__						:  Tuple     ,		lowerCAmelCase__						:  Any=True     ,		lowerCAmelCase__						:  List[Any]=None):
									SCREAMING_SNAKE_CASE_:				Any							= optimizer
									SCREAMING_SNAKE_CASE_:				int							= scaler
									SCREAMING_SNAKE_CASE_:				Any							= AcceleratorState()
									SCREAMING_SNAKE_CASE_:				Tuple							= GradientState()
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= device_placement
									SCREAMING_SNAKE_CASE_:				Optional[int]							= False
									SCREAMING_SNAKE_CASE_:				List[Any]							= None

									# Handle device placement
									if device_placement:
															SCREAMING_SNAKE_CASE_:				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:
																					SCREAMING_SNAKE_CASE_:				Union[str, Any]							= move_to_device(lowerCAmelCase__     ,		self.accelerator_state.device)
															self.optimizer.load_state_dict(lowerCAmelCase__)



			@property
			def  _SCREAMING_SNAKE_CASE    (				self						:  List[str]):
									return self.optimizer.state



			@state.setter
			def  _SCREAMING_SNAKE_CASE    (				self						:  Dict     ,		lowerCAmelCase__						:  Tuple):
									SCREAMING_SNAKE_CASE_:				List[Any]							= state



			@property
			def  _SCREAMING_SNAKE_CASE    (				self						:  Tuple):
									return self.optimizer.param_groups



			@param_groups.setter
			def  _SCREAMING_SNAKE_CASE    (				self						:  str     ,		lowerCAmelCase__						:  Any):
									SCREAMING_SNAKE_CASE_:				Tuple							= param_groups



			@property
			def  _SCREAMING_SNAKE_CASE    (				self						:  List[str]):
									return self.optimizer.defaults



			@defaults.setter
			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[int]     ,		lowerCAmelCase__						:  Any):
									SCREAMING_SNAKE_CASE_:				Tuple							= defaults



			def  _SCREAMING_SNAKE_CASE    (				self						:  Any     ,		lowerCAmelCase__						:  List[str]):
									self.optimizer.add_param_group(lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[int]     ,		lowerCAmelCase__						:  Union[str, Any]):






									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  _SCREAMING_SNAKE_CASE    (				self						:  int):
									return self.optimizer.state_dict()



			def  _SCREAMING_SNAKE_CASE    (				self						:  Union[str, Any]     ,		lowerCAmelCase__						:  Tuple=None):






									if self.gradient_state.sync_gradients:
															SCREAMING_SNAKE_CASE_:				List[Any]							= "set_to_none" in inspect.signature(self.optimizer.zero_grad).parameters
															if accept_arg:
																					if set_to_none is None:
																											SCREAMING_SNAKE_CASE_:				List[Any]							= 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  _SCREAMING_SNAKE_CASE    (				self						:  int     ,		lowerCAmelCase__						:  Tuple=None):






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

															else:
																					self.optimizer.step(lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Any     ,		lowerCAmelCase__						:  List[Any]):






									for param_group in self.optimizer.param_groups:
															SCREAMING_SNAKE_CASE_:				int							= [parameters_map.get(lowerCAmelCase__     ,		lowerCAmelCase__) for p in param_group["params"]]



			@property
			def  _SCREAMING_SNAKE_CASE    (				self						:  int):
									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  _SCREAMING_SNAKE_CASE    (				self						:  Optional[int]):
									return self._is_overflow



			def __getstate__(				self						:  Any):
									return self.__dict__.copy()




			def __setstate__(				self						:  Dict     ,		lowerCAmelCase__						:  List[Any]):
									self.__dict__.update(lowerCAmelCase__)









def     A_   (					_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= []
						SCREAMING_SNAKE_CASE_:				Any							= 1

						for i in range(len(_UpperCAmelCase					)					):
												if i + 1 < len(_UpperCAmelCase					) and text[i] == text[i + 1]:
																		count += 1
												else:
																		encoded.append((text[i], count)					)
																		SCREAMING_SNAKE_CASE_:				List[Any]							= 1

						return encoded







def     A_   (					_UpperCAmelCase					):
						return "".join(char * length for char, length in encoded					)


if __name__ == "__main__":
						from doctest import testmod

						testmod(name="""run_length_encode""", verbose=True)
						testmod(name="""run_length_decode""", verbose=True)






