





"""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						: Optional[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						: List[Any]     	=			dataset.iloc[:, 2].values


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


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






def       _SCREAMING_SNAKE_CASE		(	) ->List[str]:






	'''simple docstring'''






	plt.scatter(_lowercase    ,       _lowercase    ,       color="red"					)
	plt.plot(_lowercase    ,       pol_reg.predict(poly_reg.fit_transform(_lowercase					)					)    ,       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"""




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser






def       _SCREAMING_SNAKE_CASE		(	) ->Dict:






	'''simple docstring'''






	a      :		Any	=				ArgumentParser("Accelerate CLI tool"    ,       usage="accelerate <command> [<args>]"    ,       allow_abbrev=_lowercase					)
	a      :		List[Any]	=				parser.add_subparsers(help="accelerate command helpers"					)

	# Register commands
	get_config_parser(subparsers=_lowercase					)
	env_command_parser(subparsers=_lowercase					)
	launch_command_parser(subparsers=_lowercase					)
	tpu_command_parser(subparsers=_lowercase					)
	test_command_parser(subparsers=_lowercase					)

	# Let's go
	a      :		List[Any]	=				parser.parse_args()

	if not hasattr(_lowercase    ,       "func"					):
		parser.print_help()
		exit(1					)

	# Run
	args.func(_lowercase					)


if __name__ == "__main__":
		main()


