





import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad

class 						SCREAMING_SNAKE_CASE__							(			unittest.TestCase    ):


      def   a					(self			: Optional[Any] ):





         """simple docstring"""



         __snake_case							=  10


      def   a					(self			: Any ):





         """simple docstring"""



         __snake_case							=  [1, 2, 3, 4]
         __snake_case							=  [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
         self.assertEqual(truncate_or_pad(__UpperCAmelCase							,     self.block_size							,     0 )							,     __UpperCAmelCase )


      def   a					(self			: Tuple ):





         """simple docstring"""



         __snake_case							=  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
         __snake_case							=  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
         self.assertEqual(truncate_or_pad(__UpperCAmelCase							,     self.block_size							,     0 )							,     __UpperCAmelCase )


      def   a					(self			: int ):





         """simple docstring"""



         __snake_case							=  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
         __snake_case							=  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
         self.assertEqual(truncate_or_pad(__UpperCAmelCase							,     self.block_size							,     0 )							,     __UpperCAmelCase )


      def   a					(self			: str ):





         """simple docstring"""



         __snake_case							=  '''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.'''
         __snake_case			,      __snake_case							=  process_story(__UpperCAmelCase )
         self.assertEqual(__UpperCAmelCase							,     [] )


      def   a					(self			: Optional[Any] ):





         """simple docstring"""



         __snake_case							=  ''''''
         __snake_case			,      __snake_case							=  process_story(__UpperCAmelCase )
         self.assertEqual(__UpperCAmelCase							,     [] )
         self.assertEqual(__UpperCAmelCase							,     [] )


      def   a					(self			: List[Any] ):





         """simple docstring"""



         __snake_case							=  (
             '''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'''
         )
         __snake_case			,      __snake_case							=  process_story(__UpperCAmelCase )

         __snake_case							=  [
             '''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 )

         __snake_case							=  ['''It was the best of times.''']
         self.assertEqual(__UpperCAmelCase							,     __UpperCAmelCase )


      def   a					(self			: Optional[int] ):





         """simple docstring"""



         __snake_case							=  torch.tensor([1, 2, 3, 4] )
         __snake_case							=  torch.tensor([1, 1, 1, 1] )
         np.testing.assert_array_equal(build_mask(__UpperCAmelCase							,     0 ).numpy()							,     expected.numpy() )


      def   a					(self			: Optional[int] ):





         """simple docstring"""



         __snake_case							=  torch.tensor([1, 2, 3, 4, 23, 23, 23] )
         __snake_case							=  torch.tensor([1, 1, 1, 1, 0, 0, 0] )
         np.testing.assert_array_equal(build_mask(__UpperCAmelCase							,     23 ).numpy()							,     expected.numpy() )


      def   a					(self			: List[str] ):





         """simple docstring"""



         __snake_case							=  torch.tensor([8, 2, 3, 4, 1, 1, 1] )
         __snake_case							=  torch.tensor([1, 1, 1, 1, 0, 0, 0] )
         np.testing.assert_array_equal(build_mask(__UpperCAmelCase							,     1 ).numpy()							,     expected.numpy() )


      def   a					(self			: List[str] ):





         """simple docstring"""



         __snake_case							=  101
         __snake_case							=  torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]] )
         __snake_case							=  torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )

         __snake_case							=  compute_token_type_ids(__UpperCAmelCase							,     __UpperCAmelCase )
         np.testing.assert_array_equal(__UpperCAmelCase							,     __UpperCAmelCase )




"""simple docstring"""




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
A_				:      int           =			pd.read_csv(
    "https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
    "position_salaries.csv"
)
A_				:      int           =			dataset.iloc[:, 1:2].values
A_				:      Optional[Any]           =			dataset.iloc[:, 2].values


A_       ,						A_       ,						A_       ,						A_				:      str           =			train_test_split(X, y, test_size=0.2, random_state=0)


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

def 						A							(  ):




			'''simple docstring'''


			plt.scatter(snake_case__		,					snake_case__		,					color="""red"""     )
			plt.plot(snake_case__		,					pol_reg.predict(poly_reg.fit_transform(snake_case__     )     )		,					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


