


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


lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__					=   train_test_split(X, y, test_size=0.2, random_state=0)


lowerCamelCase__					=   PolynomialFeatures(degree=4)
lowerCamelCase__					=   poly_reg.fit_transform(X)
lowerCamelCase__					=   LinearRegression()
pol_reg.fit(X_poly, y)






def        __lowerCAmelCase       ():
		plt.scatter(__lowerCAmelCase					,       __lowerCAmelCase					,       color="red"      )
		plt.plot(__lowerCAmelCase					,       pol_reg.predict(poly_reg.fit_transform(__lowerCAmelCase      )      )					,       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







'''simple docstring'''


import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property






@unittest.skipUnless(os.path.exists(UpperCAmelCase__		)			,				"Tatoeba directory does not exist."		)
class 		lowerCAmelCase__	(	unittest.TestCase		):




			@cached_property
			def 							lowerCAmelCase__			(						self     :   Optional[Any]       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					tempfile.mkdtemp()
					return TatoebaConverter(save_dir=lowerCamelCase__       )




			@slow
			def 							lowerCAmelCase__			(						self     :   Optional[int]       )      ->List[str]:



					'''simple docstring'''







					self.resolver.convert_models(["heb-eng"]       )






			@slow
			def 							lowerCAmelCase__			(						self     :   List[Any]       )      ->Any:



					'''simple docstring'''







					_UpperCAmelCase    ,			_UpperCAmelCase						:				Tuple	    =					self.resolver.write_model_card("opus-mt-he-en"		,     dry_run=lowerCamelCase__       )
					assert mmeta["long_pair"] == "heb-eng"



