



'''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  a_  (							_lowerCAmelCase ,_lowerCAmelCase )   ->   str:







  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       lowerCamelCase_					(       torch.optim.Optimizer  ):
  """simple docstring"""




  def __init__(				self   :			Tuple    ,			_a   :			Any    ,			_a   :			Dict=True    ,			_a   :			int=None     )      ->      Union[str, Any]:
    __lowerCamelCase : Any					 =				optimizer
    __lowerCamelCase : Optional[int]					 =				scaler
    __lowerCamelCase : Dict					 =				AcceleratorState()
    __lowerCamelCase : Tuple					 =				GradientState()
    __lowerCamelCase : Tuple					 =				device_placement
    __lowerCamelCase : int					 =				False
    __lowerCamelCase : Optional[int]					 =				None

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




  @property
  def 		_lowercase  (				self   :			Union[str, Any]     )      ->      Optional[Any]:
    return self.optimizer.state




  @state.setter
  def 		_lowercase  (				self   :			Union[str, Any]    ,			_a   :			Optional[Any]     )      ->      Tuple:
    __lowerCamelCase : Tuple					 =				state




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




  @param_groups.setter
  def 		_lowercase  (				self   :			Union[str, Any]    ,			_a   :			Dict     )      ->      Tuple:
    __lowerCamelCase : Any					 =				param_groups




  @property
  def 		_lowercase  (				self   :			Tuple     )      ->      Optional[int]:
    return self.optimizer.defaults




  @defaults.setter
  def 		_lowercase  (				self   :			Optional[int]    ,			_a   :			Union[str, Any]     )      ->      Dict:
    __lowerCamelCase : Any					 =				defaults




  def 		_lowercase  (				self   :			Tuple    ,			_a   :			List[str]     )      ->      Optional[Any]:
    self.optimizer.add_param_group(_a     )




  def 		_lowercase  (				self   :			int    ,			_a   :			str     )      ->      Optional[int]:







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




  def 		_lowercase  (				self   :			Optional[Any]     )      ->      Dict:
    return self.optimizer.state_dict()




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







    if self.gradient_state.sync_gradients:
      __lowerCamelCase : List[Any]					 =				'set_to_none' in inspect.signature(self.optimizer.zero_grad     ).parameters
      if accept_arg:
        if set_to_none is None:
          __lowerCamelCase : Tuple					 =				False
        self.optimizer.zero_grad(set_to_none=_a     )
      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 		_lowercase  (				self   :			Union[str, Any]    ,			_a   :			Optional[int]=None     )      ->      Union[str, Any]:







    if self.gradient_state.sync_gradients:
      if self.accelerator_state.distributed_type == DistributedType.TPU:
        __lowerCamelCase : Any					 =				{'closure': closure} if closure is not None else {}
        xm.optimizer_step(self.optimizer    ,			optimizer_args=_a     )
      elif self.scaler is not None:
        __lowerCamelCase : Union[str, 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
          __lowerCamelCase : Union[str, Any]					 =				self.scaler.get_scale()
          __lowerCamelCase : Any					 =				True
        self.scaler.step(self.optimizer    ,			_a     )
        self.scaler.update()
        __lowerCamelCase : str					 =				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.
          __lowerCamelCase : Tuple					 =				scale_after < self._last_scale
        __lowerCamelCase : Tuple					 =				scale_after

      else:
        self.optimizer.step(_a     )




  def 		_lowercase  (				self   :			Union[str, Any]    ,			_a   :			Dict     )      ->      List[str]:







    for param_group in self.optimizer.param_groups:
      __lowerCamelCase : Any					 =				[parameters_map.get(_a    ,			_a     ) for p in param_group['params']]




  @property
  def 		_lowercase  (				self   :			Dict     )      ->      Union[str, Any]:
    warnings.warn(
        'The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '
        '`optimizer.step_was_skipped` instead.'    ,			_a    ,			)
    return self._is_overflow




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




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




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











'''simple docstring'''





from __future__ import annotations

# Define the Gravitational Constant G and the function
_UpperCamelCase							       =		6.6_743e-11  # unit of G : m^3 * kg^-1 * s^-2

def  a_  (							_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )   ->   dict[str, float]:
  __lowerCamelCase : Dict					 =				mass_a * mass_a

  if (force, mass_a, mass_a, distance).count(0 ) != 1:
    raise ValueError('One and only one argument must be 0' )
  if force < 0:
    raise ValueError('Gravitational force can not be negative' )
  if distance < 0:
    raise ValueError('Distance can not be negative' )
  if mass_a < 0 or mass_a < 0:
    raise ValueError('Mass can not be negative' )
  if force == 0:
    __lowerCamelCase : Optional[Any]					 =				GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
    return {"force": force}
  elif mass_a == 0:
    __lowerCamelCase : Tuple					 =				(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
    return {"mass_1": mass_a}
  elif mass_a == 0:
    __lowerCamelCase : str					 =				(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
    return {"mass_2": mass_a}
  elif distance == 0:
    __lowerCamelCase : int					 =				(GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
    return {"distance": distance}
  raise ValueError('One and only one argument must be 0' )


# Run doctest
if __name__ == "__main__":
   import doctest

   doctest.testmod()






