
from typing import Any
class      A__       :





							def __init__(			self  :       Optional[int]    )						->		int:



											"""simple docstring"""


											__lowercase								= []
											__lowercase								= 0





							def __str__(			self  :       List[str]    )						->		Optional[int]:



											"""simple docstring"""


											__lowercase								= '<' + str(self.stack    )[1:-1] + '>'
											return printed





							def       a__			(			self  :       Tuple					,			_UpperCAmelCase  :       Union[str, Any]    )						->		None:



											"""simple docstring"""


											self.stack.append(snake_case_    )
											__lowercase								= self.length + 1





							def       a__			(			self  :       List[str]    )						->		Any:



											"""simple docstring"""


											self.rotate(1    )
											__lowercase								= self.stack[self.length - 1]
											__lowercase								= self.stack[:-1]
											self.rotate(self.length - 1    )
											__lowercase								= self.length - 1
											return dequeued





							def       a__			(			self  :       Optional[int]					,			_UpperCAmelCase  :       int    )						->		None:



											"""simple docstring"""


											for _ in range(snake_case_    ):
															__lowercase								= self.stack[0]
															__lowercase								= self.stack[1:]
															self.put(snake_case_    )
															__lowercase								= self.length - 1





							def       a__			(			self  :       Optional[Any]    )						->		Any:



											"""simple docstring"""


											__lowercase								= self.get()
											self.put(snake_case_    )
											self.rotate(self.length - 1    )
											return front





							def       a__			(			self  :       str    )						->		int:



											"""simple docstring"""


											return self.length







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       (		SCREAMING_SNAKE_CASE	:				int   ,							SCREAMING_SNAKE_CASE	:				int      )					->						Optional[Any]:
				if isinstance(SCREAMING_SNAKE_CASE   ,							(list, tuple)      ):
								return honor_type(SCREAMING_SNAKE_CASE   ,							(move_to_device(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ) for t in state)      )
				elif isinstance(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ):
								return type(SCREAMING_SNAKE_CASE      )({k: move_to_device(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ) for k, v in state.items()}      )
				elif isinstance(SCREAMING_SNAKE_CASE   ,							torch.Tensor      ):
								return state.to(SCREAMING_SNAKE_CASE      )
				return state
class      A__       (						torch.optim.Optimizer    ):





							def __init__(			self  :       Optional[Any]					,			_UpperCAmelCase  :       Optional[int]					,			_UpperCAmelCase  :       Optional[int]=True					,			_UpperCAmelCase  :       int=None    )						->		Optional[Any]:



											"""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       a__			(			self  :       Union[str, Any]    )						->		Union[str, Any]:



											"""simple docstring"""


											return self.optimizer.state





							@state.setter
							def       a__			(			self  :       Union[str, Any]					,			_UpperCAmelCase  :       int    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= state





							@property
							def       a__			(			self  :       str    )						->		int:



											"""simple docstring"""


											return self.optimizer.param_groups





							@param_groups.setter
							def       a__			(			self  :       Tuple					,			_UpperCAmelCase  :       List[Any]    )						->		int:



											"""simple docstring"""


											__lowercase								= param_groups





							@property
							def       a__			(			self  :       List[Any]    )						->		Optional[Any]:



											"""simple docstring"""


											return self.optimizer.defaults





							@defaults.setter
							def       a__			(			self  :       List[str]					,			_UpperCAmelCase  :       int    )						->		Any:



											"""simple docstring"""


											__lowercase								= defaults





							def       a__			(			self  :       List[str]					,			_UpperCAmelCase  :       List[Any]    )						->		Any:



											"""simple docstring"""


											self.optimizer.add_param_group(_UpperCAmelCase    )





							def       a__			(			self  :       int					,			_UpperCAmelCase  :       Dict    )						->		Optional[int]:



											"""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       a__			(			self  :       str    )						->		Any:



											"""simple docstring"""


											return self.optimizer.state_dict()





							def       a__			(			self  :       Dict					,			_UpperCAmelCase  :       List[str]=None    )						->		Tuple:



											"""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       a__			(			self  :       List[str]					,			_UpperCAmelCase  :       Optional[Any]=None    )						->		int:



											"""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       a__			(			self  :       Optional[int]					,			_UpperCAmelCase  :       int    )						->		Tuple:



											"""simple docstring"""


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





							@property
							def       a__			(			self  :       Optional[Any]    )						->		Union[str, 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.'					,			_UpperCAmelCase					,			)
											return self._is_overflow





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



											"""simple docstring"""


											return self._is_overflow





							def __getstate__(			self  :       Optional[int]    )						->		str:



											"""simple docstring"""


											return self.__dict__.copy()





							def __setstate__(			self  :       List[Any]					,			_UpperCAmelCase  :       Optional[int]    )						->		Any:



											"""simple docstring"""


											self.__dict__.update(_UpperCAmelCase    )





