"""simple docstring"""


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					(	UpperCAmelCase		,      UpperCAmelCase      )       ->   List[Any]:
     if isinstance(UpperCAmelCase		,      (list, tuple)      ):
          return honor_type(UpperCAmelCase		,      (move_to_device(UpperCAmelCase		,      UpperCAmelCase      ) for t in state)      )
     elif isinstance(UpperCAmelCase		,      UpperCAmelCase      ):
          return type(UpperCAmelCase      )({k: move_to_device(UpperCAmelCase		,      UpperCAmelCase      ) for k, v in state.items()}      )
     elif isinstance(UpperCAmelCase		,      torch.Tensor      ):
          return state.to(UpperCAmelCase      )
     return state




class 				UpperCamelCase      (       torch.optim.Optimizer     ):







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

            # Handle device placement
            if device_placement:
                 snake_case_       =      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_       =      move_to_device(lowerCAmelCase__,							self.accelerator_state.device)
                 self.optimizer.load_state_dict(lowerCAmelCase__)







       @property
       def 	a_		(				self)   ->   int:
            return self.optimizer.state







       @state.setter
       def 	a_		(				self,							lowerCAmelCase__)   ->   Tuple:
            snake_case_       =      state







       @property
       def 	a_		(				self)   ->   int:
            return self.optimizer.param_groups







       @param_groups.setter
       def 	a_		(				self,							lowerCAmelCase__)   ->   Tuple:
            snake_case_       =      param_groups







       @property
       def 	a_		(				self)   ->   int:
            return self.optimizer.defaults







       @defaults.setter
       def 	a_		(				self,							lowerCAmelCase__)   ->   Dict:
            snake_case_       =      defaults







       def 	a_		(				self,							lowerCAmelCase__)   ->   Optional[Any]:
            self.optimizer.add_param_group(lowerCAmelCase__)







       def 	a_		(				self,							lowerCAmelCase__)   ->   List[Any]:
            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 	a_		(				self)   ->   List[str]:
            return self.optimizer.state_dict()







       def 	a_		(				self,							lowerCAmelCase__=None)   ->   Dict:
            if self.gradient_state.sync_gradients:
                 snake_case_       =      'set_to_none' in inspect.signature(self.optimizer.zero_grad).parameters
                 if accept_arg:
                      if set_to_none is None:
                           snake_case_       =      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 	a_		(				self,							lowerCAmelCase__=None)   ->   str:
            if self.gradient_state.sync_gradients:
                 if self.accelerator_state.distributed_type == DistributedType.TPU:
                      snake_case_       =      {'closure': closure} if closure is not None else {}
                      xm.optimizer_step(self.optimizer,							optimizer_args=lowerCAmelCase__)
                 elif self.scaler is not None:
                      snake_case_       =      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_       =      self.scaler.get_scale()
                           snake_case_       =      True
                      self.scaler.step(self.optimizer,							lowerCAmelCase__)
                      self.scaler.update()
                      snake_case_       =      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_       =      scale_after < self._last_scale
                      snake_case_       =      scale_after

                 else:
                      self.optimizer.step(lowerCAmelCase__)







       def 	a_		(				self,							lowerCAmelCase__)   ->   str:
            for param_group in self.optimizer.param_groups:
                 snake_case_       =      [parameters_map.get(lowerCAmelCase__,							lowerCAmelCase__) for p in param_group['params']]







       @property
       def 	a_		(				self)   ->   Dict:
            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 	a_		(				self)   ->   Dict:
            return self._is_overflow







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







       def __setstate__(				self,							lowerCAmelCase__)   ->   Optional[int]:
            self.__dict__.update(lowerCAmelCase__)


"""simple docstring"""


import math







def       UpperCAmelCase					(	UpperCAmelCase      )       ->   float:
     snake_case_       =      math.log(2		,      10      ) * number
     snake_case_       =      round((log_number - int(UpperCAmelCase      ))		,      15      )

     return difference







def       UpperCAmelCase					(	UpperCAmelCase = 678910      )       ->   int:
     snake_case_       =      90
     snake_case_       =      0

     snake_case_       =      math.log(1.23		,      10      )
     snake_case_       =      math.log(1.24		,      10      )
     snake_case_       =      0

     while position < number:
          snake_case_       =      log_difference(UpperCAmelCase      )

          if difference >= upper_limit:
               power_iterator += 93

          elif difference < lower_limit:
               power_iterator += 196

          else:
               snake_case_       =      power_iterator
               power_iterator += 196
               position += 1

     return previous_power


if __name__ == "__main__":
    import doctest

    doctest.testmod()

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

