



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_  (						_lowercase       :     Tuple		,     _lowercase       :     str)							-> Union[str, Any]:







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





class      snake_case__       (torch.optim.Optimizer	):







						"""simple docstring"""




						def __init__(     self      ,     __lowercase      ,     __lowercase=True      ,     __lowercase=None						)      ->   int:
									"""simple docstring"""




									a__     :		Optional[Any]		 =						optimizer
									a__     :		Tuple		 =						scaler
									a__     :		Optional[Any]		 =						AcceleratorState()
									a__     :		Optional[Any]		 =						GradientState()
									a__     :		Dict		 =						device_placement
									a__     :		Optional[int]		 =						False
									a__     :		List[Any]		 =						None

									# Handle device placement
									if device_placement:
												a__     :		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:
															a__     :		List[str]		 =						move_to_device(__lowercase      ,     self.accelerator_state.device						)
												self.optimizer.load_state_dict(__lowercase						)

						@property
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   List[str]:
									"""simple docstring"""




									return self.optimizer.state

						@state.setter
						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   List[Any]:
									"""simple docstring"""




									a__     :		List[Any]		 =						state

						@property
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   List[str]:
									"""simple docstring"""




									return self.optimizer.param_groups

						@param_groups.setter
						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   Optional[Any]:
									"""simple docstring"""




									a__     :		str		 =						param_groups

						@property
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Dict:
									"""simple docstring"""




									return self.optimizer.defaults

						@defaults.setter
						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   Tuple:
									"""simple docstring"""




									a__     :		Optional[int]		 =						defaults

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   Tuple:
									"""simple docstring"""




									self.optimizer.add_param_group(__lowercase						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   Union[str, Any]:
									"""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 							SCREAMING_SNAKE_CASE__(     self						)      ->   Dict:
									"""simple docstring"""




									return self.optimizer.state_dict()

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase=None						)      ->   Optional[Any]:
									"""simple docstring"""




									if self.gradient_state.sync_gradients:
												a__     :		str		 =						"""set_to_none""" in inspect.signature(self.optimizer.zero_grad						).parameters
												if accept_arg:
															if set_to_none is None:
																		a__     :		str		 =						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 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase=None						)      ->   List[str]:
									"""simple docstring"""




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

												else:
															self.optimizer.step(__lowercase						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   str:
									"""simple docstring"""




									for param_group in self.optimizer.param_groups:
												a__     :		Any		 =						[parameters_map.get(__lowercase      ,     __lowercase						) for p in param_group["""params"""]]

						@property
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Optional[int]:
									"""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 							SCREAMING_SNAKE_CASE__(     self						)      ->   Any:
									"""simple docstring"""




									return self._is_overflow

						def __getstate__(     self						)      ->   Tuple:
									"""simple docstring"""




									return self.__dict__.copy()

						def __setstate__(     self      ,     __lowercase						)      ->   Union[str, Any]:
									"""simple docstring"""




									self.__dict__.update(__lowercase						)









def 			lowerCAmelCase_  (						_lowercase       :     list)							-> bool:







			"""simple docstring"""
			if not isinstance(_lowercase		,     _lowercase):
						raise ValueError("""Input series is not valid, valid series - [1, 2/3, 2]""")
			if len(_lowercase) == 0:
						raise ValueError("""Input list must be a non empty list""")
			if len(_lowercase) == 1 and series[0] != 0:
						return True
			a__     :		Tuple		 =						[]
			a__     :		Union[str, Any]		 =						len(_lowercase)
			for i in range(0		,     _lowercase):
						if series[i] == 0:
									raise ValueError("""Input series cannot have 0 as an element""")
						rec_series.append(1 / series[i])
			a__     :		Dict		 =						rec_series[1] - rec_series[0]
			for index in range(2		,     _lowercase):
						if rec_series[index] - rec_series[index - 1] != common_diff:
									return False
			return True

def 			lowerCAmelCase_  (						_lowercase       :     list)							-> float:







			"""simple docstring"""
			if not isinstance(_lowercase		,     _lowercase):
						raise ValueError("""Input series is not valid, valid series - [2, 4, 6]""")
			if len(_lowercase) == 0:
						raise ValueError("""Input list must be a non empty list""")
			a__     :		Optional[Any]		 =						0
			for val in series:
						answer += 1 / val
			return len(_lowercase) / answer


if __name__ == "__main__":
		import doctest

		doctest.testmod()




