



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


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


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

def 	SCREAMING_SNAKE_CASE_							(	)  ->     Union[str, 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







import os

import pytest

from transformers.dynamic_module_utils import get_imports


snake_case__				:	List[Any]					       =					'\nimport os\n'

snake_case__				:	List[str]					       =					'\ndef foo():\n    import os\n    return False\n'

snake_case__				:	List[Any]					       =					'\ndef foo():\n    def bar():\n        if True:\n            import os\n        return False\n    return bar()\n'

snake_case__				:	List[str]					       =					'\nimport os\n\ntry:\n    import bar\nexcept ImportError:\n    raise ValueError()\n'

snake_case__				:	Optional[Any]					       =					'\nimport os\n\ndef foo():\n    try:\n        import bar\n    except ImportError:\n        raise ValueError()\n'

snake_case__				:	Tuple					       =					'\nimport os\n\ntry:\n    import bar\nexcept (ImportError, AttributeError):\n    raise ValueError()\n'

snake_case__				:	str					       =					'\nimport os\n\ntry:\n    import bar\nexcept ImportError as e:\n    raise ValueError()\n'

snake_case__				:	Optional[Any]					       =					'\nimport os\n\ntry:\n    import bar\nexcept:\n    raise ValueError()\n'

snake_case__				:	Any					       =					'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    raise ValueError()\n'

snake_case__				:	Union[str, Any]					       =					'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    x = 1\n    raise ValueError()\n'

snake_case__				:	Any					       =					[
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]


@pytest.mark.parametrize("""case"""						,						A__					)
def    __lowerCamelCase   (      A__						: Dict						,						A__						: int					)     ->    List[Any]:
   lowerCamelCase_						:   List[str]		= os.path.join(A__						,						"""test_file.py"""					)
   with open(A__						,						"""w"""					) as _tmp_file:
      _tmp_file.write(A__					)

   lowerCamelCase_						:   str		= get_imports(A__					)
   assert parsed_imports == ["os"]





