



import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil

@pytest.fixture
def        __a			(   __lowerCAmelCase   )     ->     Tuple:
					SCREAMING_SNAKE_CASE							:     Any						=				tmp_path / 'file.csv'
					SCREAMING_SNAKE_CASE							:     Dict						=				textwrap.dedent(
					    '\\n        header1,header2\n        1,2\n        10,20\n        '   )
					with open(__lowerCAmelCase      ,	'w'   ) as f:
										f.write(__lowerCAmelCase   )
					return str(__lowerCAmelCase   )

@pytest.fixture
def        __a			(   __lowerCAmelCase   )     ->     List[str]:
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				tmp_path / 'malformed_file.csv'
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				textwrap.dedent(
					    '\\n        header1,header2\n        1,2\n        10,20,\n        '   )
					with open(__lowerCAmelCase      ,	'w'   ) as f:
										f.write(__lowerCAmelCase   )
					return str(__lowerCAmelCase   )

@pytest.fixture
def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     Optional[int]:
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				tmp_path / 'csv_with_image.csv'
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				textwrap.dedent(
					    F'''\
        image
        {image_file}
        '''   )
					with open(__lowerCAmelCase      ,	'w'   ) as f:
										f.write(__lowerCAmelCase   )
					return str(__lowerCAmelCase   )

@pytest.fixture
def        __a			(   __lowerCAmelCase   )     ->     Dict:
					SCREAMING_SNAKE_CASE							:     int						=				tmp_path / 'csv_with_label.csv'
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				textwrap.dedent(
					    '\\n        label\n        good\n        bad\n        good\n        '   )
					with open(__lowerCAmelCase      ,	'w'   ) as f:
										f.write(__lowerCAmelCase   )
					return str(__lowerCAmelCase   )

@pytest.fixture
def        __a			(   __lowerCAmelCase   )     ->     int:
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				tmp_path / 'csv_with_int_list.csv'
					SCREAMING_SNAKE_CASE							:     Any						=				textwrap.dedent(
					    '\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        '   )
					with open(__lowerCAmelCase      ,	'w'   ) as f:
										f.write(__lowerCAmelCase   )
					return str(__lowerCAmelCase   )

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     Tuple:
					SCREAMING_SNAKE_CASE							:     str						=				Csv()
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				csv._generate_tables([[csv_file, malformed_csv_file]]   )
					with pytest.raises(__lowerCAmelCase      ,	match='Error tokenizing data'   ):
										for _ in generator:
															pass
					assert any(
					    record.levelname == 'ERROR'
					    and 'Failed to read file' in record.message
					    and os.path.basename(__lowerCAmelCase   ) in record.message
					    for record in caplog.records   )

@require_pil
def        __a			(   __lowerCAmelCase   )     ->     Union[str, Any]:
					with open(__lowerCAmelCase      ,	encoding='utf-8'   ) as f:
										SCREAMING_SNAKE_CASE							:     int						=				f.read().splitlines()[1]
					SCREAMING_SNAKE_CASE							:     Any						=				Csv(encoding='utf-8'      ,	features=Features({'image': Image()}   )   )
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				csv._generate_tables([[csv_file_with_image]]   )
					SCREAMING_SNAKE_CASE							:     str						=				pa.concat_tables([table for _, table in generator]   )
					assert pa_table.schema.field('image'   ).type == Image()()
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				pa_table.to_pydict()['image']
					assert generated_content == [{"path": image_file, "bytes": None}]

def        __a			(   __lowerCAmelCase   )     ->     str:
					with open(__lowerCAmelCase      ,	encoding='utf-8'   ) as f:
										SCREAMING_SNAKE_CASE							:     Optional[Any]						=				f.read().splitlines()[1:]
					SCREAMING_SNAKE_CASE							:     Dict						=				Csv(encoding='utf-8'      ,	features=Features({'label': ClassLabel(names=['good', 'bad']   )}   )   )
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				csv._generate_tables([[csv_file_with_label]]   )
					SCREAMING_SNAKE_CASE							:     Any						=				pa.concat_tables([table for _, table in generator]   )
					assert pa_table.schema.field('label'   ).type == ClassLabel(names=['good', 'bad']   )()
					SCREAMING_SNAKE_CASE							:     Any						=				pa_table.to_pydict()['label']
					assert generated_content == [ClassLabel(names=['good', 'bad']   ).straint(__lowerCAmelCase   ) for label in labels]



def        __a			(   __lowerCAmelCase   )     ->     Union[str, Any]:
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				Csv(encoding='utf-8'      ,	sep=','      ,	converters={'int_list': lambda __lowerCAmelCase   : [int(__lowerCAmelCase   ) for i in x.split()]}   )
					SCREAMING_SNAKE_CASE							:     Dict						=				csv._generate_tables([[csv_file_with_int_list]]   )
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				pa.concat_tables([table for _, table in generator]   )
					assert pa.types.is_list(pa_table.schema.field('int_list'   ).type   )
					SCREAMING_SNAKE_CASE							:     int						=				pa_table.to_pydict()['int_list']
					assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]




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        __a			(   __lowerCAmelCase   )     ->     tuple:
					return (data["data"], data["target"])

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     np.ndarray:
					SCREAMING_SNAKE_CASE							:     int						=				XGBRegressor(verbosity=0      ,	random_state=42   )
					xgb.fit(__lowerCAmelCase      ,	__lowerCAmelCase   )
					# Predict target for test data
					SCREAMING_SNAKE_CASE							:     List[Any]						=				xgb.predict(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     str						=				predictions.reshape(len(__lowerCAmelCase   )      ,	1   )
					return predictions

def        __a			(   )     ->     None:
					SCREAMING_SNAKE_CASE							:     Dict						=				fetch_california_housing()
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Any						=				data_handling(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     str						=				train_test_split(
					    __lowerCAmelCase      ,	__lowerCAmelCase      ,	test_size=0.25      ,	random_state=1   )
					SCREAMING_SNAKE_CASE							:     int						=				xgboost(__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )
					# Error printing
					print(F'''Mean Absolute Error : {mean_absolute_error(__lowerCAmelCase      ,	__lowerCAmelCase   )}'''   )
					print(F'''Mean Square Error  : {mean_squared_error(__lowerCAmelCase      ,	__lowerCAmelCase   )}'''   )


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)
			main()