
'''simple docstring'''





import matplotlib.pyplot as plt
import numpy as np




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

				return weights




def 				__magic_name__				(     __lowerCAmelCase			:		str	,						__lowerCAmelCase			:		Tuple	,						__lowerCAmelCase			:		Union[str, Any]	,						__lowerCAmelCase			:		Any							)    ->     np.ndarray:
				__lowerCamelCase					      =			weight_matrix(__lowerCamelCase	,						__lowerCamelCase	,						__lowerCamelCase							)
				__lowerCamelCase					      =			np.linalg.inv(x_train.T @ weight_mat @ x_train							) @ (
				    x_train.T @ weight_mat @ y_train.T
				)

				return weight




def 				__magic_name__				(     __lowerCAmelCase			:		Optional[Any]	,						__lowerCAmelCase			:		Optional[int]	,						__lowerCAmelCase			:		Tuple							)    ->     np.ndarray:
				__lowerCamelCase					      =			np.zeros(len(__lowerCamelCase							)							)  # Initialize array of predictions
				for i, item in enumerate(__lowerCamelCase							):
								__lowerCamelCase					      =			item @ local_weight(__lowerCamelCase	,						__lowerCamelCase	,						__lowerCamelCase	,						__lowerCamelCase							)

				return y_pred




def 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		List[str]	,						__lowerCAmelCase			:		List[Any]							)    ->     tuple[np.ndarray, np.ndarray, np.ndarray]:
				import seaborn as sns

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

				__lowerCamelCase					      =			np.ones(len(__lowerCamelCase							)							)

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

				return x_train, x_data, y_data




def 				__magic_name__				(     __lowerCAmelCase			:		Optional[int]	,						__lowerCAmelCase			:		Tuple	,						__lowerCAmelCase			:		Dict	,						__lowerCAmelCase			:		int	,						__lowerCAmelCase			:		Optional[int]	,						__lowerCAmelCase			:		Tuple	,						)    ->     None:
				__lowerCamelCase					      =			np.sort(__lowerCamelCase	,						axis=0							)
				plt.scatter(__lowerCamelCase	,						__lowerCamelCase	,						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(__lowerCamelCase							)
				plt.ylabel(__lowerCamelCase							)
				plt.show()


if __name__ == "__main__":
					import doctest

					doctest.testmod()

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





from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest

@require_tokenizers
class      lowerCAmelCase__  (			__lowercase    ):
		a__ :		Any   						=	SqueezeBertTokenizer
		a__ :		Union[str, Any]   						=	SqueezeBertTokenizerFast
		a__ :		int   						=	True






		def 				__A (	self	:							Optional[int]							,    **SCREAMING_SNAKE_CASE__	:							Union[str, Any]						)  ->							int:
						return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname							,    **SCREAMING_SNAKE_CASE__						)







		@slow
		def 				__A (	self	:							int						)  ->							Optional[int]:
						__lowerCamelCase					      =			SqueezeBertTokenizer.from_pretrained('''squeezebert/squeezebert-mnli-headless'''						)

						__lowerCamelCase					      =			tokenizer.encode('''sequence builders'''							,    add_special_tokens=SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			tokenizer.encode('''multi-sequence build'''							,    add_special_tokens=SCREAMING_SNAKE_CASE__						)

						__lowerCamelCase					      =			tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)

						assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
						assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
						    tokenizer.sep_token_id
						]



