





import matplotlib.pyplot as plt
import numpy as np





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







			'''simple docstring'''

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

			return weights





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







			'''simple docstring'''

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

			return weight





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







			'''simple docstring'''

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

			return y_pred





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







			'''simple docstring'''

			import seaborn as sns

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

			UpperCAmelCase       		=    np.ones(len(UpperCamelCase__			)			)

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

			return x_train, x_data, y_data





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







			'''simple docstring'''

			UpperCAmelCase       		=    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    :   List[Any]          =       load_data("tips", "total_bill", "tip")
				__A    :   Any          =       local_weight_regression(training_data_x, tip, 5)
				plot_preds(training_data_x, predictions, total_bill, tip, "total_bill", "tip")










import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard

class      A_	(unittest.TestCase      ):
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    {
				    '''model_details''': {
				        '''Organization''': '''testing''',
				        '''Model date''': '''today''',
				        '''Model version''': '''v2.1, Developed by Test Corp in 2019.''',
				        '''Architecture''': '''Convolutional Neural Network.''',
				    },
				    '''metrics''': '''BLEU and ROUGE-1''',
				    '''evaluation_data''': {
				        '''Datasets''': {'''BLEU''': '''My-great-dataset-v1''', '''ROUGE-1''': '''My-short-dataset-v2.1'''},
				        '''Preprocessing''': '''See details on https://arxiv.org/pdf/1810.03993.pdf''',
				    },
				    '''training_data''': {
				        '''Dataset''': '''English Wikipedia dump dated 2018-12-01''',
				        '''Preprocessing''': (
				            '''Using SentencePiece vocabulary of size 52k tokens. See details on'''
				            ''' https://arxiv.org/pdf/1810.03993.pdf'''
				        ),
				    },
				    '''quantitative_analyses''': {'''BLEU''': 55.1, '''ROUGE-1''': 7_6},
				}
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ModelCard.from_dict(self.inputs_dict		)
				self.assertTrue(hasattr(_A							, '''model_details'''		)		)
				self.assertTrue(hasattr(_A							, '''intended_use'''		)		)
				self.assertTrue(hasattr(_A							, '''factors'''		)		)
				self.assertTrue(hasattr(_A							, '''metrics'''		)		)
				self.assertTrue(hasattr(_A							, '''evaluation_data'''		)		)
				self.assertTrue(hasattr(_A							, '''training_data'''		)		)
				self.assertTrue(hasattr(_A							, '''quantitative_analyses'''		)		)
				self.assertTrue(hasattr(_A							, '''ethical_considerations'''		)		)
				self.assertTrue(hasattr(_A							, '''caveats_and_recommendations'''		)		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ModelCard.from_dict(self.inputs_dict		)
				UpperCAmelCase       		=    json.loads(modelcard.to_json_string()		)
				for key, value in self.inputs_dict.items():
							self.assertEqual(obj[key]							, _A		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ModelCard.from_dict(self.inputs_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase       		=    os.path.join(_A							, '''modelcard.json'''		)
							model_card_first.to_json_file(_A		)
							UpperCAmelCase       		=    ModelCard.from_json_file(_A		)

				self.assertEqual(model_card_second.to_dict()							, model_card_first.to_dict()		)







	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ModelCard.from_dict(self.inputs_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
							model_card_first.save_pretrained(_A		)
							UpperCAmelCase       		=    ModelCard.from_pretrained(_A		)

				self.assertEqual(model_card_second.to_dict()							, model_card_first.to_dict()		)



