



'''simple docstring'''

from __future__ import annotations

import os
from typing import Any

import requests

SCREAMING_SNAKE_CASE__     :						List[Any]	=  '''https://api.github.com'''

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
SCREAMING_SNAKE_CASE__     :						str	=  BASE_URL + '''/user'''

# https://github.com/settings/tokens
SCREAMING_SNAKE_CASE__     :						Dict	=  os.environ.get('''USER_TOKEN''', '''''')






def 				a  (     UpperCamelCase_    :    str			)       ->	dict[Any, Any]:
		snake_case__	     ={
		    'Authorization': f"""token {auth_token}""",
		    'Accept': 'application/vnd.github.v3+json',
		}
		return requests.get(SCREAMING_SNAKE_CASE_      ,					headers=SCREAMING_SNAKE_CASE_			).json()


if __name__ == "__main__":  # pragma: no cover
			if USER_TOKEN:
						for key, value in fetch_github_info(USER_TOKEN).items():
									print(f"""{key}: {value}""")
			else:
						raise ValueError('''\'USER_TOKEN\' field cannot be empty.''')






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


A							,      A							,      A							,      A		: str								=     train_test_split(X, y, test_size=0.2, random_state=0)


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




def 	UpperCamelCase__  (     )  ->     Any:
						plt.scatter(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   color="""red"""       )
						plt.plot(SCREAMING_SNAKE_CASE_		,   pol_reg.predict(poly_reg.fit_transform(SCREAMING_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