






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


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


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


def 	SCREAMING_SNAKE_CASE__       (  )->		Optional[int]:






     '''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





"""simple docstring"""




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu





@require_cpu
class  lowerCamelCase    (unittest.TestCase      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     str:
						SCREAMING_SNAKE_CASE__										=   torch.nn.Linear(1_0     ,					1_0							)
						SCREAMING_SNAKE_CASE__										=   torch.optim.SGD(model.parameters()     ,					0.1							)
						SCREAMING_SNAKE_CASE__										=   Accelerator()
						SCREAMING_SNAKE_CASE__										=   accelerator.prepare(__UpperCAmelCase							)
						try:
									pickle.loads(pickle.dumps(__UpperCAmelCase							)							)
						except Exception as e:
									self.fail(F"""Accelerated optimizer pickling failed with {e}"""							)
						AcceleratorState._reset_state()


