





import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
  import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin



@require_torch
class 					__snake_case	(  unittest.TestCase			,     __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			load_tool('''text-to-speech'''     )
        self.tool.setup()



    def  lowercase_					(       self     ):





        '''simple docstring'''

        torch.manual_seed(0     )
        SCREAMING_SNAKE_CASE__											=			self.tool('''hey'''     )
        SCREAMING_SNAKE_CASE__											=			result.to_raw()
        self.assertTrue(
            torch.allclose(
                resulting_tensor[:3]   ,				torch.tensor([-0.0005966668832115829, -0.0003657640190795064, -0.00013439502799883485]     )   ,				)     )




    def  lowercase_					(       self     ):





        '''simple docstring'''

        torch.manual_seed(0     )
        SCREAMING_SNAKE_CASE__											=			self.tool('''hey'''     )
        SCREAMING_SNAKE_CASE__											=			result.to_raw()
        self.assertTrue(
            torch.allclose(
                resulting_tensor[:3]   ,				torch.tensor([-0.0005966668832115829, -0.0003657640190795064, -0.00013439502799883485]     )   ,				)     )





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


__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				:  int =       train_test_split(X, y, test_size=0.2, random_state=0)


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


def      _lowercase							(    )		->  List[Any]:
			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