





import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase







@require_tokenizers
class 	_UpperCamelCase	(							unittest.TestCase	):







					'''simple docstring'''

					lowerCamelCase  :  Optional[int]           =							EsmTokenizer





					def 							SCREAMING_SNAKE_CASE				(    self     :	str		):






						'''simple docstring'''







						super().setUp()
						UpperCAmelCase_		    =  tempfile.mkdtemp()
						# fmt: off
						UpperCAmelCase_		    =  ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
						# fmt: on
						UpperCAmelCase_		    =  os.path.join(self.tmpdirname			,       VOCAB_FILES_NAMES["""vocab_file"""]		)
						with open(self.vocab_file			,       """w"""			,       encoding="""utf-8"""		) as vocab_writer:
							vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]		)		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	Any			,       **__lowercase     :	int		):






						'''simple docstring'''







						return [self.get_tokenizer(**__lowercase		)]





					def 							SCREAMING_SNAKE_CASE				(    self     :	Union[str, Any]			,       **__lowercase     :	str		):






						'''simple docstring'''







						return self.tokenizer_class.from_pretrained(self.tmpdirname			,       **__lowercase		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	Any		):






						'''simple docstring'''







						UpperCAmelCase_		    =  self.tokenizer_class(self.vocab_file		)

						UpperCAmelCase_		    =  tokenizer.tokenize("""LAGVS"""		)
						self.assertListEqual(__lowercase			,       ["""L""", """A""", """G""", """V""", """S"""]		)
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase		)			,       [4, 5, 6, 7, 8]		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	Union[str, Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  self.tokenizer_class(self.vocab_file		)

						UpperCAmelCase_		    =  """LAGVS"""
						self.assertListEqual(tokenizer.encode(__lowercase		)			,       [0, 4, 5, 6, 7, 8, 2]		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  self.tokenizer_class(self.vocab_file		)

						UpperCAmelCase_		    =  ["""LAGVS""", """WCB"""]
						UpperCAmelCase_		    =  tokenizer(__lowercase			,       padding=__lowercase		)["""input_ids"""]

						self.assertListEqual(__lowercase			,       [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  self.tokenizer_class(self.vocab_file		)

						UpperCAmelCase_		    =  ["""LAGVS""", """WCB"""]
						UpperCAmelCase_		    =  tokenizer(__lowercase			,       padding=__lowercase		)["""input_ids"""]

						self.assertListEqual(__lowercase			,       [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  self.get_tokenizers(fast=__lowercase		)
						for tokenizer in tokenizers:
							with self.subTest(F"""{tokenizer.__class__.__name__}"""		):
								UpperCAmelCase_		    =  """<unk>"""
								UpperCAmelCase_		    =  """<mask>"""

								UpperCAmelCase_		    =  tokenizer.tokenize(__lowercase		)
								UpperCAmelCase_		    =  tokenizer.tokenize(__lowercase		)

								self.assertEqual(len(__lowercase		)			,       1		)
								self.assertEqual(len(__lowercase		)			,       1		)
								self.assertEqual(token_a[0]			,       __lowercase		)
								self.assertEqual(token_a[0]			,       __lowercase		)









import matplotlib.pyplot as plt
import numpy as np




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

	return weights




def  A_(      A					,   A					,   A					,   A ):
	UpperCAmelCase_		    =  weight_matrix(A					,   A					,   A )
	UpperCAmelCase_		    =  np.linalg.inv(x_train.T @ weight_mat @ x_train ) @ (
	    x_train.T @ weight_mat @ y_train.T
	)

	return weight




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  np.zeros(len(A ) )  # Initialize array of predictions
	for i, item in enumerate(A ):
		UpperCAmelCase_		    =  item @ local_weight(A					,   A					,   A					,   A )

	return y_pred




def  A_(      A					,   A					,   A ):
	import seaborn as sns

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

	UpperCAmelCase_		    =  np.ones(len(A ) )

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

	return x_train, x_data, y_data




def  A_(      A					,   A					,   A					,   A					,   A					,   A					,   ):
	UpperCAmelCase_		    =  np.sort(A					,   axis=0 )
	plt.scatter(A					,   A					,   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(A )
	plt.ylabel(A )
	plt.show()


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# Demo with a dataset from the seaborn module
				UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__ :			Tuple       	=			load_data("""tips""", """total_bill""", """tip""")
				UpperCamelCase__ :			Tuple       	=			local_weight_regression(training_data_x, tip, 5)
				plot_preds(training_data_x, predictions, total_bill, tip, """total_bill""", """tip""")


