
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    (    a__		,					a__						) ->						Any:


				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     __A(				torch.optim.Optimizer    ):




				def __init__(  self	,  _snake_case	,  _snake_case=True	,  _snake_case=None       )					->   str:


								'''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(_snake_case	,  self.accelerator_state.device       )
												else:
																__a    							=					move_to_device(_snake_case	,  self.accelerator_state.device       )
												self.optimizer.load_state_dict(_snake_case       )




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


								'''simple docstring'''




								return self.optimizer.state




				@state.setter
				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Any:


								'''simple docstring'''




								__a    							=					state




				@property
				def        SCREAMING_SNAKE_CASE_	(  self       )					->   List[str]:


								'''simple docstring'''




								return self.optimizer.param_groups




				@param_groups.setter
				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   str:


								'''simple docstring'''




								__a    							=					param_groups




				@property
				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Union[str, Any]:


								'''simple docstring'''




								return self.optimizer.defaults




				@defaults.setter
				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   str:


								'''simple docstring'''




								__a    							=					defaults




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Optional[Any]:


								'''simple docstring'''




								self.optimizer.add_param_group(_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   List[Any]:


								'''simple docstring'''




								if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
												xm.send_cpu_data_to_device(_snake_case	,  self.accelerator_state.device       )
								self.optimizer.load_state_dict(_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   int:


								'''simple docstring'''




								return self.optimizer.state_dict()




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case=None       )					->   Dict:


								'''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=_snake_case       )
												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	,  _snake_case=None       )					->   Optional[int]:


								'''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=_snake_case       )
												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	,  _snake_case       )
																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(_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Dict:


								'''simple docstring'''




								for param_group in self.optimizer.param_groups:
												__a    							=					[parameters_map.get(_snake_case	,  _snake_case       ) 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.'''	,  _snake_case	,  )
								return self._is_overflow




				@property
				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Tuple:


								'''simple docstring'''




								return self._is_overflow




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


								'''simple docstring'''




								return self.__dict__.copy()




				def __setstate__(  self	,  _snake_case       )					->   int:


								'''simple docstring'''




								self.__dict__.update(_snake_case       )








from itertools import compress, repeat
from math import ceil, sqrt


def 					__lowerCAmelCase    (    a__						) ->						list[int]:

				if num <= 2:
								return []
				if num == 3:
								return [2]

				# Odd sieve for numbers in range [3, num - 1]
				__a    							=					bytearray(B'''\x01'''						) * ((num >> 1) - 1)

				for i in range(3		,					int(sqrt(a__						)						) + 1		,					2						):
								if sieve[(i >> 1) - 1]:
												__a    							=					i**2
												__a    							=					repeat(
												    0		,					ceil((num - i_squared) / (i << 1)						)						)

				return [2] + list(compress(range(3		,					a__		,					2						)		,					a__						)						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()