






import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout





def 						__UpperCAmelCase			(     a_):
							snake_case_				=							FileLock(str(tmpdir / 'foo.lock'))
							snake_case_				=							FileLock(str(tmpdir / 'foo.lock'))
							snake_case_				=							0.01
							with locka.acquire():
														with pytest.raises(a_):
																					snake_case_				=							time.time()
																					locka.acquire(a_)
																					assert time.time() - _start > timeout





def 						__UpperCAmelCase			(     a_):
							snake_case_				=							'a' * 10_00 + '.lock'
							snake_case_				=							FileLock(str(tmpdir / filename))
							assert locka._lock_file.endswith('.lock')
							assert not locka._lock_file.endswith(a_)
							assert len(os.path.basename(locka._lock_file)) <= 2_55
							snake_case_				=							FileLock(tmpdir / filename)
							with locka.acquire():
														with pytest.raises(a_):
																					locka.acquire(0)













import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor





def 						__UpperCAmelCase			(     a_):
							return (data["data"], data["target"])





def 						__UpperCAmelCase			(     a_							,     a_							,     a_):
							snake_case_				=							XGBRegressor(verbosity=0							,     random_state=42)
							xgb.fit(a_							,     a_)
							# Predict target for test data
							snake_case_				=							xgb.predict(a_)
							snake_case_				=							predictions.reshape(len(a_)							,     1)
							return predictions





def 						__UpperCAmelCase			(     ):
							snake_case_				=							fetch_california_housing()
							snake_case_			,		snake_case_				=							data_handling(a_)
							snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							train_test_split(
							    a_							,     a_							,     test_size=0.25							,     random_state=1)
							snake_case_				=							xgboost(a_							,     a_							,     a_)
							# Error printing
							print(f'''Mean Absolute Error : {mean_absolute_error(a_							,     a_)}''')
							print(f'''Mean Square Error  : {mean_squared_error(a_							,     a_)}''')


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()





