


import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
    import jax



@require_flax
class 					__lowerCamelCase	:







    def 						snake_case_					(	self:				Dict			):






         '''simple docstring'''




         __UpperCamelCase,  __UpperCamelCase				=	self.prepare_init_args_and_inputs_for_common()

         __UpperCamelCase				=	self.model_class(**UpperCamelCase_			)
         __UpperCamelCase				=	model.init(inputs_dict['prng_key'],inputs_dict['sample']			)
         jax.lax.stop_gradient(UpperCamelCase_			)

         __UpperCamelCase				=	model.apply(UpperCamelCase_,inputs_dict['sample']			)

         if isinstance(UpperCamelCase_,UpperCamelCase_			):
              __UpperCamelCase				=	output.sample

         self.assertIsNotNone(UpperCamelCase_			)
         __UpperCamelCase				=	inputs_dict['sample'].shape
         self.assertEqual(output.shape,UpperCamelCase_,'Input and output shapes do not match'			)







    def 						snake_case_					(	self:				str			):






         '''simple docstring'''




         __UpperCamelCase,  __UpperCamelCase				=	self.prepare_init_args_and_inputs_for_common()

         __UpperCamelCase				=	16
         __UpperCamelCase				=	(16, 32)

         __UpperCamelCase				=	self.model_class(**UpperCamelCase_			)
         __UpperCamelCase				=	model.init(inputs_dict['prng_key'],inputs_dict['sample']			)
         jax.lax.stop_gradient(UpperCamelCase_			)

         __UpperCamelCase				=	model.apply(UpperCamelCase_,inputs_dict['sample']			)

         if isinstance(UpperCamelCase_,UpperCamelCase_			):
              __UpperCamelCase				=	output.sample

         self.assertIsNotNone(UpperCamelCase_			)
         __UpperCamelCase				=	inputs_dict['sample'].shape
         self.assertEqual(output.shape,UpperCamelCase_,'Input and output shapes do not match'			)







    def 						snake_case_					(	self:				List[Any]			):






         '''simple docstring'''




         __UpperCamelCase				=	'kwargs' in inspect.signature(self.model_class.__init__			).parameters
         __UpperCamelCase				=	len(self.model_class._deprecated_kwargs			) > 0

         if has_kwarg_in_model_class and not has_deprecated_kwarg:
              raise ValueError(
                  F'''{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs'''
                  ' under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are'
                  ' no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ='
                  ' [<deprecated_argument>]`'			)

         if not has_kwarg_in_model_class and has_deprecated_kwarg:
              raise ValueError(
                  F'''{self.model_class} doesn\'t have `**kwargs` in its __init__ method but has defined deprecated kwargs'''
                  ' under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to'
                  F''' {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument'''
                  ' from `_deprecated_kwargs = [<deprecated_argument>]`'			)






import matplotlib.pyplot as plt
import numpy as np






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


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

     return weights






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	weight_matrix(_lowercase			,       _lowercase			,       _lowercase				)
     __UpperCamelCase				=	np.linalg.inv(x_train.T @ weight_mat @ x_train				) @ (
         x_train.T @ weight_mat @ y_train.T
     )

     return weight






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


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

     return y_pred






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				tuple[np.ndarray, np.ndarray, np.ndarray]:


     """simple docstring"""


     import seaborn as sns

     __UpperCamelCase				=	sns.load_dataset(_lowercase				)
     __UpperCamelCase				=	np.array(data[x_name]				)
     __UpperCamelCase				=	np.array(data[y_name]				)

     __UpperCamelCase				=	np.ones(len(_lowercase				)				)

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

     return x_train, x_data, y_data






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       )			->				None:


     """simple docstring"""


     __UpperCamelCase				=	np.sort(_lowercase			,       axis=0				)
     plt.scatter(_lowercase			,       _lowercase			,       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(_lowercase				)
     plt.ylabel(_lowercase				)
     plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Demo with a dataset from the seaborn module
    __snake_case    ,					__snake_case    ,					__snake_case     =		load_data('''tips''', '''total_bill''', '''tip''')
    __snake_case     =		local_weight_regression(training_data_x, tip, 5)
    plot_preds(training_data_x, predictions, total_bill, tip, '''total_bill''', '''tip''')


