


import matplotlib.pyplot as plt
import numpy as np



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



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

				return weights



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					List[Any]						       =       weight_matrix(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__		)
				__magic_name__				:					Dict						       =       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__		):






				"""simple docstring"""



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

				return y_pred



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				import seaborn as sns

				__magic_name__				:					Optional[Any]						       =       sns.load_dataset(UpperCamelCase__		)
				__magic_name__				:					Dict						       =       np.array(data[x_name]		)
				__magic_name__				:					Union[str, Any]						       =       np.array(data[y_name]		)

				__magic_name__				:					Dict						       =       np.ones(len(UpperCamelCase__		)		)

				# pairing elements of one and x_data
				__magic_name__				:					Any						       =       np.column_stack((one, x_data)		)

				return x_train, x_data, y_data



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   ):






				"""simple docstring"""



				__magic_name__				:					Any						       =       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
		_SCREAMING_SNAKE_CASE       :   Optional[Any]        = load_data("tips", "total_bill", "tip")
		_SCREAMING_SNAKE_CASE       :   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 unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class 		_snake_case    (				unittest.TestCase		):






						'''simple docstring'''







						@cached_property
						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	List[Any]:
										return BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"     )







						@cached_property
						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	Optional[int]:
										return BlenderbotTokenizerFast.from_pretrained("facebook/blenderbot-3B"     )







						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	Optional[int]:
										__magic_name__				:					Tuple						       =       self.tokenizer_ab
										__magic_name__				:					Optional[int]						       =       " I am a small frog."
										__magic_name__				:					Optional[int]						       =       tok([src_text]      ,							padding=__UpperCamelCase      ,							truncation=__UpperCamelCase     )["input_ids"]
										__magic_name__				:					int						       =       tok.batch_decode(__UpperCamelCase      ,							skip_special_tokens=__UpperCamelCase      ,							clean_up_tokenization_spaces=__UpperCamelCase     )[0]
										assert src_text == decoded







						def lowerCAmelCase__ (  self:	Optional[int]     )							->	Any:
										__magic_name__				:					Tuple						       =       self.rust_tokenizer_ab
										__magic_name__				:					str						       =       " I am a small frog."
										__magic_name__				:					Union[str, Any]						       =       tok([src_text]      ,							padding=__UpperCamelCase      ,							truncation=__UpperCamelCase     )["input_ids"]
										__magic_name__				:					Any						       =       tok.batch_decode(__UpperCamelCase      ,							skip_special_tokens=__UpperCamelCase      ,							clean_up_tokenization_spaces=__UpperCamelCase     )[0]
										assert src_text == decoded







						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	str:
										assert self.tokenizer_ab.add_prefix_space
										assert self.tokenizer_ab([" Sam", "Sam"]     ).input_ids == [[5502, 2], [5502, 2]]







						def lowerCAmelCase__ (  self:	List[str]     )							->	List[Any]:
										assert self.rust_tokenizer_ab.add_prefix_space
										assert self.rust_tokenizer_ab([" Sam", "Sam"]     ).input_ids == [[5502, 2], [5502, 2]]