




import argparse
import os

from accelerate.test_utils import execute_subprocess_async




def 							_UpperCAmelCase  (UpperCamelCase_ : Union[str, Any]=None       ):
    '''simple docstring'''






    if subparsers is not None:
        _lowerCAmelCase    :    Optional[Any]										=      subparsers.add_parser("""test"""       )
    else:
        _lowerCAmelCase    :    List[Any]										=      argparse.ArgumentParser("""Accelerate test command"""       )

    parser.add_argument(
        """--config_file"""      ,    default=a_      ,    help=(
            """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
            """location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have """
            """such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed """
            """with \'huggingface\'."""
        )      ,    )

    if subparsers is not None:
        parser.set_defaults(func=a_       )
    return parser




def 							_UpperCAmelCase  (UpperCamelCase_ : Optional[int]       ):
    '''simple docstring'''
    _lowerCAmelCase    :    List[str]										=      os.path.sep.join(__file__.split(os.path.sep       )[:-2] + ["""test_utils""", """scripts""", """test_script.py"""]       )

    if args.config_file is None:
        _lowerCAmelCase    :    List[Any]										=      script_name
    else:
        _lowerCAmelCase    :    Dict										=      F"--config_file={args.config_file} {script_name}"

    _lowerCAmelCase    :    Optional[Any]										=      ['''accelerate-launch'''] + test_args.split()
    _lowerCAmelCase    :    List[Any]										=      execute_subprocess_async(a_      ,    env=os.environ.copy()       )
    if result.returncode == 0:
        print("""Test is a success! You are ready for your distributed training!"""       )




def 							_UpperCAmelCase  ():
    '''simple docstring'''
    _lowerCAmelCase    :    Optional[Any]										=      test_command_parser()
    _lowerCAmelCase    :    Dict										=      parser.parse_args()
    test_command(a_       )


if __name__ == "__main__":
 main()







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


_lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase      :  Optional[int]      						=						train_test_split(X, y, test_size=0.2, random_state=0)


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




def 							_UpperCAmelCase  ():
				'''simple docstring'''
				plt.scatter(UpperCamelCase_      ,    UpperCamelCase_      ,    color="""red"""       )
				plt.plot(UpperCamelCase_      ,    pol_reg.predict(poly_reg.fit_transform(UpperCamelCase_       )       )      ,    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

