


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_  :	Any							,  A_  :	List[Any]      )			->			str:

   '''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   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE=True   ,			__SCREAMING_SNAKE_CASE=None)					->						Tuple:







         """simple docstring"""







         UpperCamelCase__ :			Any 						=optimizer
         UpperCamelCase__ :			Union[str, Any] 						=scaler
         UpperCamelCase__ :			Optional[Any] 						=AcceleratorState()
         UpperCamelCase__ :			int 						=GradientState()
         UpperCamelCase__ :			Optional[int] 						=device_placement
         UpperCamelCase__ :			int 						=False
         UpperCamelCase__ :			int 						=None

         # Handle device placement
         if device_placement:
            UpperCamelCase__ :			Tuple 						=self.optimizer.state_dict()
            if self.accelerator_state.distributed_type == DistributedType.TPU:
               xm.send_cpu_data_to_device(__SCREAMING_SNAKE_CASE   ,			self.accelerator_state.device)
            else:
               UpperCamelCase__ :			Union[str, Any] 						=move_to_device(__SCREAMING_SNAKE_CASE   ,			self.accelerator_state.device)
            self.optimizer.load_state_dict(__SCREAMING_SNAKE_CASE)




      @property
      def    UpperCAmelCase							(		self)					->						Dict:







         """simple docstring"""







         return self.optimizer.state




      @state.setter
      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						Tuple:







         """simple docstring"""







         UpperCamelCase__ :			List[Any] 						=state




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







         """simple docstring"""







         return self.optimizer.param_groups




      @param_groups.setter
      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						Union[str, Any]:







         """simple docstring"""







         UpperCamelCase__ :			Optional[Any] 						=param_groups




      @property
      def    UpperCAmelCase							(		self)					->						int:







         """simple docstring"""







         return self.optimizer.defaults




      @defaults.setter
      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						List[str]:







         """simple docstring"""







         UpperCamelCase__ :			Tuple 						=defaults




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						Optional[int]:







         """simple docstring"""







         self.optimizer.add_param_group(__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						Optional[int]:







         """simple docstring"""







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




      def    UpperCAmelCase							(		self)					->						int:







         """simple docstring"""







         return self.optimizer.state_dict()




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE=None)					->						int:







         """simple docstring"""







         if self.gradient_state.sync_gradients:
            UpperCamelCase__ :			Dict 						="""set_to_none""" in inspect.signature(self.optimizer.zero_grad).parameters
            if accept_arg:
               if set_to_none is None:
                  UpperCamelCase__ :			int 						=False
               self.optimizer.zero_grad(set_to_none=__SCREAMING_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    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE=None)					->						Any:







         """simple docstring"""







         if self.gradient_state.sync_gradients:
            if self.accelerator_state.distributed_type == DistributedType.TPU:
               UpperCamelCase__ :			int 						={"""closure""": closure} if closure is not None else {}
               xm.optimizer_step(self.optimizer   ,			optimizer_args=__SCREAMING_SNAKE_CASE)
            elif self.scaler is not None:
               UpperCamelCase__ :			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
                  UpperCamelCase__ :			Optional[int] 						=self.scaler.get_scale()
                  UpperCamelCase__ :			List[Any] 						=True
               self.scaler.step(self.optimizer   ,			__SCREAMING_SNAKE_CASE)
               self.scaler.update()
               UpperCamelCase__ :			Optional[int] 						=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__ :			Tuple 						=scale_after < self._last_scale
               UpperCamelCase__ :			Tuple 						=scale_after

            else:
               self.optimizer.step(__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						Tuple:







         """simple docstring"""







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




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







         """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."   ,			__SCREAMING_SNAKE_CASE   ,			)
         return self._is_overflow




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







         """simple docstring"""







         return self._is_overflow




      def __getstate__(		self)					->						str:







         """simple docstring"""







         return self.__dict__.copy()




      def __setstate__(		self   ,			__SCREAMING_SNAKE_CASE)					->						int:







         """simple docstring"""







         self.__dict__.update(__SCREAMING_SNAKE_CASE)





'''simple docstring'''
import math



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	Tuple          =					math.log(2    ,       10     ) * number
       snake_case__					:	int          =					round((log_number - int(UpperCAmelCase     ))    ,       15     )

       return difference



def 				lowerCAmelCase__	(       UpperCAmelCase = 67_8910     ):






       """simple docstring"""





       snake_case__					:	str          =					90
       snake_case__					:	Dict          =					0

       snake_case__					:	List[str]          =					math.log(1.2_3    ,       10     )
       snake_case__					:	Optional[Any]          =					math.log(1.2_4    ,       10     )
       snake_case__					:	Any          =					0

       while position < number:
              snake_case__					:	Tuple          =					log_difference(UpperCAmelCase     )

              if difference >= upper_limit:
                     power_iterator += 93

              elif difference < lower_limit:
                     power_iterator += 196

              else:
                     snake_case__					:	Union[str, Any]          =					power_iterator
                     power_iterator += 196
                     position += 1

       return previous_power


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     print(f"""{solution() = }""")




