






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 		UpperCamelCase    (					_UpperCAmelCase     :      Tuple   , _UpperCAmelCase     :      Optional[int]     )      -> Union[str, Any]:






						'''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      __lowercase	(	torch.optim.Optimizer					):






	def __init__(self							:   int			,  snake_case							:   Dict			,  snake_case							:   Union[str, Any]=True			,  snake_case							:   str=None						)   -> Dict:
							_lowercase					:  List[str]    			=      optimizer
							_lowercase					:  str    			=      scaler
							_lowercase					:  Dict    			=      AcceleratorState()
							_lowercase					:  Optional[int]    			=      GradientState()
							_lowercase					:  Optional[Any]    			=      device_placement
							_lowercase					:  Optional[int]    			=      False
							_lowercase					:  Any    			=      None

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






	@property
	def 			_a(self							:   Tuple						)   -> List[str]:
							return self.optimizer.state






	@state.setter
	def 			_a(self							:   Optional[int]			,  snake_case							:   List[str]						)   -> List[str]:
							_lowercase					:  Tuple    			=      state






	@property
	def 			_a(self							:   Union[str, Any]						)   -> int:
							return self.optimizer.param_groups






	@param_groups.setter
	def 			_a(self							:   List[str]			,  snake_case							:   List[str]						)   -> Dict:
							_lowercase					:  Tuple    			=      param_groups






	@property
	def 			_a(self							:   Optional[int]						)   -> Optional[int]:
							return self.optimizer.defaults






	@defaults.setter
	def 			_a(self							:   int			,  snake_case							:   Tuple						)   -> Dict:
							_lowercase					:  int    			=      defaults






	def 			_a(self							:   Any			,  snake_case							:   Optional[int]						)   -> Optional[Any]:
							self.optimizer.add_param_group(lowerCamelCase__						)






	def 			_a(self							:   Tuple			,  snake_case							:   Optional[Any]						)   -> 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 			_a(self							:   List[str]						)   -> Union[str, Any]:
							return self.optimizer.state_dict()






	def 			_a(self							:   Tuple			,  snake_case							:   Optional[Any]=None						)   -> Optional[int]:
							if self.gradient_state.sync_gradients:
													_lowercase					:  int    			=      "set_to_none" in inspect.signature(self.optimizer.zero_grad						).parameters
													if accept_arg:
																			if set_to_none is None:
																									_lowercase					:  Dict    			=      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 			_a(self							:   Optional[int]			,  snake_case							:   int=None						)   -> int:
							if self.gradient_state.sync_gradients:
													if self.accelerator_state.distributed_type == DistributedType.TPU:
																			_lowercase					:  Union[str, Any]    			=      {"closure": closure} if closure is not None else {}
																			xm.optimizer_step(self.optimizer			,  optimizer_args=lowerCamelCase__						)
													elif self.scaler is not None:
																			_lowercase					:  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					:  Optional[int]    			=      self.scaler.get_scale()
																									_lowercase					:  Optional[Any]    			=      True
																			self.scaler.step(self.optimizer			,  lowerCamelCase__						)
																			self.scaler.update()
																			_lowercase					:  Tuple    			=      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					:  Optional[Any]    			=      scale_after < self._last_scale
																			_lowercase					:  Dict    			=      scale_after

													else:
																			self.optimizer.step(lowerCamelCase__						)






	def 			_a(self							:   List[str]			,  snake_case							:   str						)   -> Optional[Any]:
							for param_group in self.optimizer.param_groups:
													_lowercase					:  List[str]    			=      [parameters_map.get(lowerCamelCase__			,  lowerCamelCase__						) for p in param_group["params"]]






	@property
	def 			_a(self							:   List[str]						)   -> Any:
							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 			_a(self							:   List[str]						)   -> Optional[int]:
							return self._is_overflow






	def __getstate__(self							:   Any						)   -> Dict:
							return self.__dict__.copy()






	def __setstate__(self							:   str			,  snake_case							:   Optional[int]						)   -> Optional[int]:
							self.__dict__.update(lowerCamelCase__						)






import math



def 		UpperCamelCase    (					_UpperCAmelCase     :      int     )      -> list:






						'''simple docstring'''

						if n <= 0:
												raise ValueError("Only positive integers have prime factors"     )
						_lowercase					:  Optional[int]    			=      []
						while n % 2 == 0:
												pf.append(2     )
												_lowercase					:  Tuple    			=      int(n / 2     )
						for i in range(3   , int(math.sqrt(_UpperCAmelCase     )     ) + 1   , 2     ):
												while n % i == 0:
																		pf.append(_UpperCAmelCase     )
																		_lowercase					:  Optional[int]    			=      int(n / i     )
						if n > 2:
												pf.append(_UpperCAmelCase     )
						return pf



def 		UpperCamelCase    (					_UpperCAmelCase     :      int     )      -> int:






						'''simple docstring'''

						if n <= 0:
												raise ValueError("Only positive numbers are accepted"     )
						_lowercase					:  str    			=      1
						_lowercase					:  Tuple    			=      1
						while n % 2 == 0:
												temp += 1
												_lowercase					:  List[Any]    			=      int(n / 2     )
						div *= temp
						for i in range(3   , int(math.sqrt(_UpperCAmelCase     )     ) + 1   , 2     ):
												_lowercase					:  Optional[Any]    			=      1
												while n % i == 0:
																		temp += 1
																		_lowercase					:  Optional[Any]    			=      int(n / i     )
												div *= temp
						if n > 1:
												div *= 2
						return div



def 		UpperCamelCase    (					_UpperCAmelCase     :      int     )      -> int:






						'''simple docstring'''

						if n <= 0:
												raise ValueError("Only positive numbers are accepted"     )
						_lowercase					:  int    			=      1
						_lowercase					:  List[str]    			=      1
						while n % 2 == 0:
												temp += 1
												_lowercase					:  str    			=      int(n / 2     )
						if temp > 1:
												s *= (2**temp - 1) / (2 - 1)
						for i in range(3   , int(math.sqrt(_UpperCAmelCase     )     ) + 1   , 2     ):
												_lowercase					:  int    			=      1
												while n % i == 0:
																		temp += 1
																		_lowercase					:  List[Any]    			=      int(n / i     )
												if temp > 1:
																		s *= (i**temp - 1) / (i - 1)
						return int(_UpperCAmelCase     )



def 		UpperCamelCase    (					_UpperCAmelCase     :      int     )      -> int:






						'''simple docstring'''

						_lowercase					:  int    			=      n
						for x in set(prime_factors(_UpperCAmelCase     )     ):
												s *= (x - 1) / x
						return int(_UpperCAmelCase     )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




