






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


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


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


def __A    (      )->  Optional[Any]:





						"""simple docstring"""



						plt.scatter(__a ,		__a ,		color='red'	)
						plt.plot(__a ,		pol_reg.predict(poly_reg.fit_transform(__a	)	) ,		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



from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__				=		{'''configuration_mmbt''': ['''MMBTConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a__				=		['''MMBTForClassification''', '''MMBTModel''', '''ModalEmbeddings''']


if TYPE_CHECKING:
			from .configuration_mmbt import MMBTConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
			import sys

			a__				=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



