




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(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	Any:



    """simple docstring"""






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






class 	__magic_name__				(       torch.optim.Optimizer							):






     def __init__(							self		,	_lowercase		,	_lowercase=True		,	_lowercase=None  )->    str:
         UpperCamelCase_      =	optimizer
         UpperCamelCase_      =	scaler
         UpperCamelCase_      =	AcceleratorState()
         UpperCamelCase_      =	GradientState()
         UpperCamelCase_      =	device_placement
         UpperCamelCase_      =	False
         UpperCamelCase_      =	None

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






     @property
     def   UpperCAmelCase_							(							self  )->    List[Any]:
         return self.optimizer.state






     @state.setter
     def   UpperCAmelCase_							(							self		,	_lowercase  )->    Any:
         UpperCamelCase_      =	state






     @property
     def   UpperCAmelCase_							(							self  )->    List[str]:
         return self.optimizer.param_groups






     @param_groups.setter
     def   UpperCAmelCase_							(							self		,	_lowercase  )->    str:
         UpperCamelCase_      =	param_groups






     @property
     def   UpperCAmelCase_							(							self  )->    Union[str, Any]:
         return self.optimizer.defaults






     @defaults.setter
     def   UpperCAmelCase_							(							self		,	_lowercase  )->    str:
         UpperCamelCase_      =	defaults






     def   UpperCAmelCase_							(							self		,	_lowercase  )->    Optional[Any]:
         self.optimizer.add_param_group(_lowercase  )






     def   UpperCAmelCase_							(							self		,	_lowercase  )->    List[Any]:






         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   UpperCAmelCase_							(							self  )->    int:
         return self.optimizer.state_dict()






     def   UpperCAmelCase_							(							self		,	_lowercase=None  )->    Dict:






         if self.gradient_state.sync_gradients:
             UpperCamelCase_      =	"set_to_none" in inspect.signature(self.optimizer.zero_grad  ).parameters
             if accept_arg:
                 if set_to_none is None:
                     UpperCamelCase_      =	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   UpperCAmelCase_							(							self		,	_lowercase=None  )->    Optional[int]:






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

             else:
                 self.optimizer.step(_lowercase  )






     def   UpperCAmelCase_							(							self		,	_lowercase  )->    Dict:






         for param_group in self.optimizer.param_groups:
             UpperCamelCase_      =	[parameters_map.get(_lowercase		,	_lowercase  ) for p in param_group["params"]]






     @property
     def   UpperCAmelCase_							(							self  )->    Optional[int]:
         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   UpperCAmelCase_							(							self  )->    Tuple:
         return self._is_overflow






     def __getstate__(							self  )->    Optional[Any]:
         return self.__dict__.copy()






     def __setstate__(							self		,	_lowercase  )->    int:
         self.__dict__.update(_lowercase  )



def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	float:



    """simple docstring"""






    return -num if num < 0 else num




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	int:



    """simple docstring"""






    if len(SCREAMING_SNAKE_CASE_ ) == 0:
        raise ValueError("abs_min() arg is an empty sequence" )
    UpperCamelCase_      =	x[0]
    for i in x:
        if abs_val(SCREAMING_SNAKE_CASE_ ) < abs_val(SCREAMING_SNAKE_CASE_ ):
            UpperCamelCase_      =	i
    return j




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	int:



    """simple docstring"""






    if len(SCREAMING_SNAKE_CASE_ ) == 0:
        raise ValueError("abs_max() arg is an empty sequence" )
    UpperCamelCase_      =	x[0]
    for i in x:
        if abs(SCREAMING_SNAKE_CASE_ ) > abs(SCREAMING_SNAKE_CASE_ ):
            UpperCamelCase_      =	i
    return j




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	int:



    """simple docstring"""






    if len(SCREAMING_SNAKE_CASE_ ) == 0:
        raise ValueError("abs_max_sort() arg is an empty sequence" )
    return sorted(SCREAMING_SNAKE_CASE_ ,	key=SCREAMING_SNAKE_CASE_ )[-1]




def  lowerCAmelCase(			)->	Any:



    """simple docstring"""






    assert abs_val(0 ) == 0
    assert abs_val(3_4 ) == 3_4
    assert abs_val(-1_0_0_0_0_0_0_0_0_0_0_0 ) == 1_0_0_0_0_0_0_0_0_0_0_0

    UpperCamelCase_      =	[-3, -1, 2, -1_1]
    assert abs_max(SCREAMING_SNAKE_CASE_ ) == -1_1
    assert abs_max_sort(SCREAMING_SNAKE_CASE_ ) == -1_1
    assert abs_min(SCREAMING_SNAKE_CASE_ ) == -1


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 test_abs_val()
 print(abs_val(-34))  # --> 34
