





"""simple docstring"""
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			):
					UpperCamelCase								=							EsmTokenizer


					def      lowerCamelCase	( self      :Dict		):
												super().setUp()
												A		=  tempfile.mkdtemp()
												# fmt: off
												A		=  ["<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
												A		=  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      lowerCamelCase	( self      :List[Any]					,	**__UpperCamelCase      :Tuple		):
												return [self.get_tokenizer(**__UpperCamelCase		)]


					def      lowerCamelCase	( self      :str					,	**__UpperCamelCase      :Optional[Any]		):
												return self.tokenizer_class.from_pretrained(self.tmpdirname					,	**__UpperCamelCase		)


					def      lowerCamelCase	( self      :Tuple		):
												A		=  self.tokenizer_class(self.vocab_file		)

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


					def      lowerCamelCase	( self      :List[Any]		):
												A		=  self.tokenizer_class(self.vocab_file		)

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


					def      lowerCamelCase	( self      :str		):
												A		=  self.tokenizer_class(self.vocab_file		)

												A		=  ["LAGVS", "WCB"]
												A		=  tokenizer(__UpperCamelCase					,	padding=__UpperCamelCase		)["input_ids"]

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


					def      lowerCamelCase	( self      :Union[str, Any]		):
												A		=  self.tokenizer_class(self.vocab_file		)

												A		=  ["LAGVS", "WCB"]
												A		=  tokenizer(__UpperCamelCase					,	padding=__UpperCamelCase		)["input_ids"]

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


					def      lowerCamelCase	( self      :Tuple		):
												A		=  self.get_tokenizers(fast=__UpperCamelCase		)
												for tokenizer in tokenizers:
																			with self.subTest(f"{tokenizer.__class__.__name__}"		):
																										A		=  "<unk>"
																										A		=  "<mask>"

																										A		=  tokenizer.tokenize(__UpperCamelCase		)
																										A		=  tokenizer.tokenize(__UpperCamelCase		)

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









"""simple docstring"""
import numpy as np
import requests






def  A__    (      ):
							A		=  requests.get(
							    "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"
							    "master/Week1/ADRvsRating.csv"	)
							A		=  response.text.splitlines()
							A		=  []
							for item in lines:
														A		=  item.split(","	)
														data.append(UpperCamelCase	)
							data.pop(0	)  # This is for removing the labels from the list
							A		=  np.matrix(UpperCamelCase	)
							return dataset






def  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	):
							A		=  len_data

							A		=  np.dot(UpperCamelCase ,  data_x.transpose()	)
							prod -= data_y.transpose()
							A		=  np.dot(UpperCamelCase ,  UpperCamelCase	)
							A		=  theta - (alpha / n) * sum_grad
							return theta






def  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	):
							A		=  np.dot(UpperCamelCase ,  data_x.transpose()	)
							prod -= data_y.transpose()
							A		=  np.sum(np.square(UpperCamelCase	)	)
							A		=  sum_elem / (2 * len_data)
							return error






def  A__    (      UpperCamelCase ,  UpperCamelCase	):
							A		=  100_000
							A		=  0.0_00_15_50

							A		=  data_x.shape[1]
							A		=  data_x.shape[0] - 1

							A		=  np.zeros((1, no_features)	)

							for i in range(0 ,  UpperCamelCase	):
														A		=  run_steep_gradient_descent(UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	)
														A		=  sum_of_square_error(UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	)
														print(F"At Iteration {i + 1} - Error is {error:.5f}"	)

							return theta






def  A__    (      UpperCamelCase ,  UpperCamelCase	):
							A		=  sum(abs(y - predicted_y[i]	) for i, y in enumerate(UpperCamelCase	)	)
							return total / len(UpperCamelCase	)






def  A__    (      ):
							A		=  collect_dataset()

							A		=  data.shape[0]
							A		=  np.c_[np.ones(UpperCamelCase	), data[:, :-1]].astype(UpperCamelCase	)
							A		=  data[:, -1].astype(UpperCamelCase	)

							A		=  run_linear_regression(UpperCamelCase ,  UpperCamelCase	)
							A		=  theta.shape[1]
							print("Resultant Feature vector : "	)
							for i in range(0 ,  UpperCamelCase	):
														print(F"{theta[0, i]:.5f}"	)


if __name__ == "__main__":
		main()


