
def 		UpperCAmelCase     (      a_		)						->      int:







				"""simple docstring"""



				__A       =					[]
				__A       =					set({"(", "[", "{"}		)
				__A       =					set({")", "]", "}"}		)
				__A       =					{"{": "}", "[": "]", "(": ")"}

				for i in range(len(a_		)		):
								if s[i] in open_brackets:
												stack.append(s[i]		)

								elif s[i] in closed_brackets and (
								    len(a_		) == 0 or (len(a_		) > 0 and open_to_closed[stack.pop()] != s[i])
								):
												return False

				return len(a_		) == 0


def 		UpperCAmelCase     (      )						->      Optional[Any]:







				"""simple docstring"""



				__A       =					input("Enter sequence of brackets: "		)
				if is_balanced(a_		):
								print(a_						,  "is balanced"		)
				else:
								print(a_						,  "is not balanced"		)


if __name__ == "__main__":
					main()



"""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


