




import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class        __snake_case  (unittest.TestCase		):



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      10



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      [1, 2, 3, 4]
								_lowerCAmelCase    :    Optional[int]										=      [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
								self.assertEqual(truncate_or_pad(_UpperCAmelCase		,	self.block_size		,	0      )		,	_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
								_lowerCAmelCase    :    Any										=      [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
								self.assertEqual(truncate_or_pad(_UpperCAmelCase		,	self.block_size		,	0      )		,	_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
								_lowerCAmelCase    :    Dict										=      [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
								self.assertEqual(truncate_or_pad(_UpperCAmelCase		,	self.block_size		,	0      )		,	_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      """It was the year of Our Lord one thousand seven hundred and
        seventy-five.\n\nSpiritual revelations were conceded to England at that
        favoured period, as at this."""
								_lowerCAmelCase					,    _lowerCAmelCase    :    int										=      process_story(_UpperCAmelCase      )
								self.assertEqual(_UpperCAmelCase		,	[]      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      """"""
								_lowerCAmelCase					,    _lowerCAmelCase    :    Union[str, Any]										=      process_story(_UpperCAmelCase      )
								self.assertEqual(_UpperCAmelCase		,	[]      )
								self.assertEqual(_UpperCAmelCase		,	[]      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      (
								    """It was the year of Our Lord one thousand seven hundred and """
								    """seventy-five\n\nSpiritual revelations were conceded to England """
								    """at that favoured period, as at this.\n@highlight\n\nIt was the best of times"""
								)
								_lowerCAmelCase					,    _lowerCAmelCase    :    Any										=      process_story(_UpperCAmelCase      )

								_lowerCAmelCase    :    int										=      [
								    """It was the year of Our Lord one thousand seven hundred and seventy-five.""",
								    """Spiritual revelations were conceded to England at that favoured period, as at this.""",
								]
								self.assertEqual(_UpperCAmelCase		,	_UpperCAmelCase      )

								_lowerCAmelCase    :    Optional[int]										=      ["""It was the best of times."""]
								self.assertEqual(_UpperCAmelCase		,	_UpperCAmelCase      )



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





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      torch.tensor([1, 2, 3, 4]      )
								_lowerCAmelCase    :    int										=      torch.tensor([1, 1, 1, 1]      )
								np.testing.assert_array_equal(build_mask(_UpperCAmelCase		,	0      ).numpy()		,	expected.numpy()      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      torch.tensor([1, 2, 3, 4, 23, 23, 23]      )
								_lowerCAmelCase    :    List[str]										=      torch.tensor([1, 1, 1, 1, 0, 0, 0]      )
								np.testing.assert_array_equal(build_mask(_UpperCAmelCase		,	23      ).numpy()		,	expected.numpy()      )



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





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      torch.tensor([8, 2, 3, 4, 1, 1, 1]      )
								_lowerCAmelCase    :    int										=      torch.tensor([1, 1, 1, 1, 0, 0, 0]      )
								np.testing.assert_array_equal(build_mask(_UpperCAmelCase		,	1      ).numpy()		,	expected.numpy()      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      101
								_lowerCAmelCase    :    Union[str, Any]										=      torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]      )
								_lowerCAmelCase    :    Union[str, Any]										=      torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]      )

								_lowerCAmelCase    :    str										=      compute_token_type_ids(_UpperCAmelCase		,	_UpperCAmelCase      )
								np.testing.assert_array_equal(_UpperCAmelCase		,	_UpperCAmelCase      )







import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
_lowerCamelCase      :  Dict      						=						pd.read_csv(
    "https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
    "position_salaries.csv"
)
_lowerCamelCase      :  Union[str, Any]      						=						dataset.iloc[:, 1:2].values
_lowerCamelCase      :  Any      						=						dataset.iloc[:, 2].values


_lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase      :  Optional[int]      						=						train_test_split(X, y, test_size=0.2, random_state=0)


_lowerCamelCase      :  Optional[Any]      						=						PolynomialFeatures(degree=4)
_lowerCamelCase      :  Optional[Any]      						=						poly_reg.fit_transform(X)
_lowerCamelCase      :  Dict      						=						LinearRegression()
pol_reg.fit(X_poly, y)




def 							_UpperCAmelCase  ():
				'''simple docstring'''
				plt.scatter(UpperCamelCase_      ,    UpperCamelCase_      ,    color="""red"""       )
				plt.plot(UpperCamelCase_      ,    pol_reg.predict(poly_reg.fit_transform(UpperCamelCase_       )       )      ,    color="""blue"""       )
				plt.title("""Truth or Bluff (Linear Regression)"""       )
				plt.xlabel("""Position level"""       )
				plt.ylabel("""Salary"""       )
				plt.show()


if __name__ == "__main__":
	viz_polymonial()

	# Predicting a new result with Polymonial Regression
	pol_reg.predict(poly_reg.fit_transform([[5.5]]))
	# output should be 132148.43750003

