



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( A			:       int					,	A			:       List[Any]			)						->			Any:
					"""simple docstring"""





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







class       _lowercase				(  torch.optim.Optimizer      ):





	"""simple docstring"""




	def __init__(self						,			lowerCamelCase_						,			lowerCamelCase_=True						,			lowerCamelCase_=None						):
						"""simple docstring"""

						a   	=  optimizer
						a   	=  scaler
						a   	=  AcceleratorState()
						a   	=  GradientState()
						a   	=  device_placement
						a   	=  False
						a   	=  None

						# Handle device placement
						if device_placement:
											a   	=  self.optimizer.state_dict()
											if self.accelerator_state.distributed_type == DistributedType.TPU:
																xm.send_cpu_data_to_device(lowerCamelCase_						,			self.accelerator_state.device						)
											else:
																a   	=  move_to_device(lowerCamelCase_						,			self.accelerator_state.device						)
											self.optimizer.load_state_dict(lowerCamelCase_						)




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						return self.optimizer.state




	@state.setter
	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  state




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						return self.optimizer.param_groups




	@param_groups.setter
	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  param_groups




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						return self.optimizer.defaults




	@defaults.setter
	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  defaults




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						self.optimizer.add_param_group(lowerCamelCase_						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""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						):
						"""simple docstring"""

						return self.optimizer.state_dict()




	def 	UpperCamelCase_			(self						,			lowerCamelCase_=None						):
						"""simple docstring"""

						if self.gradient_state.sync_gradients:
											a   	=  'set_to_none' in inspect.signature(self.optimizer.zero_grad						).parameters
											if accept_arg:
																if set_to_none is None:
																					a   	=  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						,			lowerCamelCase_=None						):
						"""simple docstring"""

						if self.gradient_state.sync_gradients:
											if self.accelerator_state.distributed_type == DistributedType.TPU:
																a   	=  {'closure': closure} if closure is not None else {}
																xm.optimizer_step(self.optimizer						,			optimizer_args=lowerCamelCase_						)
											elif self.scaler is not None:
																a   	=  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   	=  self.scaler.get_scale()
																					a   	=  True
																self.scaler.step(self.optimizer						,			lowerCamelCase_						)
																self.scaler.update()
																a   	=  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   	=  scale_after < self._last_scale
																a   	=  scale_after

											else:
																self.optimizer.step(lowerCamelCase_						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

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




	@property
	def 	UpperCamelCase_			(self						):
						"""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						):
						"""simple docstring"""

						return self._is_overflow




	def __getstate__(self						):
						"""simple docstring"""

						return self.__dict__.copy()




	def __setstate__(self						,			lowerCamelCase_						):
						"""simple docstring"""

						self.__dict__.update(lowerCamelCase_						)








"""simple docstring"""


_UpperCamelCase							:       str       					=		10**8




def      snake_case    (A_ :int ):







  '''simple docstring'''

  if n % 1_0 == 0:
    return False
  a	:     Optional[int]							=  str(A_ )
  return s == s[::-1]




def      snake_case    ():







  '''simple docstring'''

  a	:     Tuple							=  set()
  a	:     Optional[Any]							=  1
  a	:     List[str]							=  5
  while sum_squares < LIMIT:
    a	:     int							=  first_square + 1
    while sum_squares < LIMIT:
      if is_palindrome(A_ ):
        answer.add(A_ )
      last_square += 1
      sum_squares += last_square**2
    first_square += 1
    a	:     List[str]							=  first_square**2 + (first_square + 1) ** 2

  return sum(A_ )


if __name__ == "__main__":
   print(solution())




