



'''simple docstring'''





from __future__ import annotations

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



def     UpperCAmelCase     (    a_      ,		a_      ,		a_      ,		a_		)   ->       dict[str, float]:



   """simple docstring"""






   A_  :		List[str]						=					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:
      A_  :		int						=					GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
      return {"force": force}
   elif mass_a == 0:
      A_  :		Any						=					(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
      return {"mass_1": mass_a}
   elif mass_a == 0:
      A_  :		List[Any]						=					(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
      return {"mass_2": mass_a}
   elif distance == 0:
      A_  :		Optional[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()





'''simple docstring'''





import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 lowerCamelCase									=     (VQDiffusionScheduler,)







 def       UpperCAmelCase_       (      self					,    **_lowerCamelCase  )    ->    Any:
    A_  :		List[Any]						=					{
        """num_vec_classes""": 4097,
        """num_train_timesteps""": 100,
    }

    config.update(**_lowerCamelCase  )
    return config







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Tuple:
    A_  :		Tuple						=					4
    A_  :		List[str]						=					8
    A_  :		Dict						=					8

    A_  :		int						=					torch.randint(0					,    _lowerCamelCase					,    (batch_size, height * width)  )

    return sample







 @property
 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    assert False







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    List[str]:
    def model(_lowerCamelCase					,    _lowerCamelCase					,    *_lowerCamelCase  ):
       A_						,  A_  :		List[Any]						=					sample.shape
       A_  :		int						=					torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)  )
       A_  :		Optional[int]						=					F.log_softmax(logits.double()					,    dim=1  ).float()
       return return_value

    return model







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    for timesteps in [2, 5, 100, 1000]:
       self.check_over_configs(num_train_timesteps=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    for num_vec_classes in [5, 100, 1000, 4000]:
       self.check_over_configs(num_vec_classes=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    int:
    for t in [0, 50, 99]:
       self.check_over_forward(time_step=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    pass
