



'''simple docstring'''





import matplotlib.pyplot as plt
import numpy as np



def     UpperCAmelCase     (    a_      ,		a_      ,		a_		)   ->       np.ndarray:



   """simple docstring"""






   A_  :		Tuple						=					len(a_		)  # Number of training samples
   A_  :		Any						=					np.eye(a_		)  # Initialize weights as identity matrix
   for j in range(a_		):
      A_  :		Any						=					point - x_train[j]
      A_  :		Tuple						=					np.exp(diff @ diff.T / (-2.0 * tau**2)		)

   return weights



def     UpperCAmelCase     (    a_      ,		a_      ,		a_      ,		a_		)   ->       np.ndarray:



   """simple docstring"""






   A_  :		Union[str, Any]						=					weight_matrix(a_      ,		a_      ,		a_		)
   A_  :		List[str]						=					np.linalg.inv(x_train.T @ weight_mat @ x_train		) @ (
       x_train.T @ weight_mat @ y_train.T
   )

   return weight



def     UpperCAmelCase     (    a_      ,		a_      ,		a_		)   ->       np.ndarray:



   """simple docstring"""






   A_  :		str						=					np.zeros(len(a_		)		)  # Initialize array of predictions
   for i, item in enumerate(a_		):
      A_  :		Union[str, Any]						=					item @ local_weight(a_      ,		a_      ,		a_      ,		a_		)

   return y_pred



def     UpperCAmelCase     (    a_      ,		a_      ,		a_		)   ->       tuple[np.ndarray, np.ndarray, np.ndarray]:



   """simple docstring"""






   import seaborn as sns

   A_  :		str						=					sns.load_dataset(a_		)
   A_  :		int						=					np.array(data[x_name]		)
   A_  :		List[str]						=					np.array(data[y_name]		)

   A_  :		Tuple						=					np.ones(len(a_		)		)

   # pairing elements of one and x_data
   A_  :		Dict						=					np.column_stack((one, x_data)		)

   return x_train, x_data, y_data



def     UpperCAmelCase     (    a_      ,		a_      ,		a_      ,		a_      ,		a_      ,		a_      ,		)   ->       None:



   """simple docstring"""






   A_  :		Dict						=					np.sort(a_      ,		axis=0		)
   plt.scatter(a_      ,		a_      ,		color="""blue"""		)
   plt.plot(
       x_train_sorted[:, 1]      ,		preds[x_train[:, 1].argsort(0		)]      ,		color="""yellow"""      ,		linewidth=5      ,		)
   plt.title("""Local Weighted Regression"""		)
   plt.xlabel(a_		)
   plt.ylabel(a_		)
   plt.show()


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # Demo with a dataset from the seaborn module
  UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__    :     List[Any]			  =   load_data('tips', 'total_bill', 'tip')
  UpperCamelCase__    :     List[str]			  =   local_weight_regression(training_data_x, tip, 5)
  plot_preds(training_data_x, predictions, total_bill, tip, 'total_bill', 'tip')





'''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
