




import matplotlib.pyplot as plt
import numpy as np





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







				'''simple docstring'''


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

				return weights





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







				'''simple docstring'''


				__lowerCAmelCase					=  weight_matrix(UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )
				__lowerCAmelCase					=  np.linalg.inv(x_train.T @ weight_mat @ x_train   ) @ (
				    x_train.T @ weight_mat @ y_train.T
				)

				return weight





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







				'''simple docstring'''


				__lowerCAmelCase					=  np.zeros(len(UpperCamelCase__   )   )  # Initialize array of predictions
				for i, item in enumerate(UpperCamelCase__   ):
								__lowerCAmelCase					=  item @ local_weight(UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )

				return y_pred





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







				'''simple docstring'''


				import seaborn as sns

				__lowerCAmelCase					=  sns.load_dataset(UpperCamelCase__   )
				__lowerCAmelCase					=  np.array(data[x_name]   )
				__lowerCAmelCase					=  np.array(data[y_name]   )

				__lowerCAmelCase					=  np.ones(len(UpperCamelCase__   )   )

				# pairing elements of one and x_data
				__lowerCAmelCase					=  np.column_stack((one, x_data)   )

				return x_train, x_data, y_data





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







				'''simple docstring'''


				__lowerCAmelCase					=  np.sort(UpperCamelCase__						,			axis=0   )
				plt.scatter(UpperCamelCase__						,			UpperCamelCase__						,			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(UpperCamelCase__   )
				plt.ylabel(UpperCamelCase__   )
				plt.show()


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	# Demo with a dataset from the seaborn module
	__A    ,    __A    ,    __A						:		Dict          =						load_data("tips", "total_bill", "tip")
	__A						:		Optional[int]          =						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
