'''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=  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  _UpperCamelCase (     ):




    """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




'''simple docstring'''
a=  '''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
  from .utils import rich



