
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      (					__lowerCamelCase  :				List[str]       ,      __lowerCamelCase  :				Optional[int]					):
							if isinstance(__lowerCamelCase       ,      (list, tuple)					):
														return honor_type(__lowerCamelCase       ,      (move_to_device(__lowerCamelCase       ,      __lowerCamelCase					) for t in state)					)
							elif isinstance(__lowerCamelCase       ,      __lowerCamelCase					):
														return type(__lowerCamelCase					)({k: move_to_device(__lowerCamelCase       ,      __lowerCamelCase					) for k, v in state.items()}					)
							elif isinstance(__lowerCamelCase       ,      torch.Tensor					):
														return state.to(__lowerCamelCase					)
							return state






class 						UpperCAmelCase	(		torch.optim.Optimizer				):



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


										'''simple docstring'''


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

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



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


										'''simple docstring'''


										return self.optimizer.state



			@state.setter
			def        _SCREAMING_SNAKE_CASE     (self	:      Union[str, Any]					,		snake_case__	:      str				)	->			Dict:


										'''simple docstring'''


										snake_case :     Tuple           =   state



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


										'''simple docstring'''


										return self.optimizer.param_groups



			@param_groups.setter
			def        _SCREAMING_SNAKE_CASE     (self	:      str					,		snake_case__	:      Any				)	->			Any:


										'''simple docstring'''


										snake_case :     Tuple           =   param_groups



			@property
			def        _SCREAMING_SNAKE_CASE     (self	:      Any				)	->			int:


										'''simple docstring'''


										return self.optimizer.defaults



			@defaults.setter
			def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]					,		snake_case__	:      List[Any]				)	->			str:


										'''simple docstring'''


										snake_case :     int           =   defaults



			def        _SCREAMING_SNAKE_CASE     (self	:      Dict					,		snake_case__	:      Any				)	->			Any:


										'''simple docstring'''


										self.optimizer.add_param_group(lowerCamelCase__				)



			def        _SCREAMING_SNAKE_CASE     (self	:      str					,		snake_case__	:      Dict				)	->			List[Any]:


										'''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        _SCREAMING_SNAKE_CASE     (self	:      Union[str, Any]				)	->			Dict:


										'''simple docstring'''


										return self.optimizer.state_dict()



			def        _SCREAMING_SNAKE_CASE     (self	:      List[str]					,		snake_case__	:      str=None				)	->			List[str]:


										'''simple docstring'''


										if self.gradient_state.sync_gradients:
																	snake_case :     int           =   "set_to_none" in inspect.signature(self.optimizer.zero_grad				).parameters
																	if accept_arg:
																								if set_to_none is None:
																															snake_case :     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        _SCREAMING_SNAKE_CASE     (self	:      Union[str, Any]					,		snake_case__	:      List[str]=None				)	->			List[Any]:


										'''simple docstring'''


										if self.gradient_state.sync_gradients:
																	if self.accelerator_state.distributed_type == DistributedType.TPU:
																								snake_case :     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:
																								snake_case :     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
																															snake_case :     Optional[int]           =   self.scaler.get_scale()
																															snake_case :     Optional[Any]           =   True
																								self.scaler.step(self.optimizer					,		lowerCamelCase__				)
																								self.scaler.update()
																								snake_case :     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.
																															snake_case :     Optional[Any]           =   scale_after < self._last_scale
																								snake_case :     Dict           =   scale_after

																	else:
																								self.optimizer.step(lowerCamelCase__				)



			def        _SCREAMING_SNAKE_CASE     (self	:      int					,		snake_case__	:      Optional[int]				)	->			Any:


										'''simple docstring'''


										for param_group in self.optimizer.param_groups:
																	snake_case :     List[str]           =   [parameters_map.get(lowerCamelCase__					,		lowerCamelCase__				) for p in param_group["params"]]



			@property
			def        _SCREAMING_SNAKE_CASE     (self	:      Optional[Any]				)	->			Tuple:


										'''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        _SCREAMING_SNAKE_CASE     (self	:      str				)	->			Any:


										'''simple docstring'''


										return self._is_overflow



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


										'''simple docstring'''


										return self.__dict__.copy()



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


										'''simple docstring'''


										self.__dict__.update(lowerCamelCase__				)







def     UpperCamelCase      (					__lowerCamelCase  :				list       ,      __lowerCamelCase  :				list       ,      __lowerCamelCase  :				list       ,      __lowerCamelCase  :				int       ,      __lowerCamelCase  :				int					):
       if target == 0:
              answer.append(path.copy()					)
       else:
              for index in range(__lowerCamelCase       ,      len(__lowerCamelCase					)					):
                     if target >= candidates[index]:
                            path.append(candidates[index]					)
                            backtrack(__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      target - candidates[index]       ,      __lowerCamelCase					)
                            path.pop(len(__lowerCamelCase					) - 1					)


def     UpperCamelCase      (					__lowerCamelCase  :				list       ,      __lowerCamelCase  :				int					):
       snake_case :     Optional[Any]           =   []  # type: list[int]
       snake_case :     Tuple           =   []  # type: list[int]
       backtrack(__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      0					)
       return answer


def     UpperCamelCase      (					):
       print(combination_sum([-8, 2.3, 0]       ,      1					)					)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()





