
'''simple docstring'''


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    _lowerCamelCase  (__lowerCamelCase  :     str     )   ->    int:
			a__     = tmp_path / "file.csv"
			a__     = 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    _lowerCamelCase  (__lowerCamelCase  :     List[str]     )   ->    Optional[Any]:
			a__     = tmp_path / "malformed_file.csv"
			a__     = 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    _lowerCamelCase  (__lowerCamelCase  :     Tuple					,  __lowerCamelCase  :     Union[str, Any]     )   ->    int:
			a__     = tmp_path / "csv_with_image.csv"
			a__     = textwrap.dedent(
			    f'''\
        image
        {image_file}
        '''     )
			with open(__lowerCamelCase					,  "w"     ) as f:
						f.write(__lowerCamelCase     )
			return str(__lowerCamelCase     )







@pytest.fixture
def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]     )   ->    int:
			a__     = tmp_path / "csv_with_label.csv"
			a__     = 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    _lowerCamelCase  (__lowerCamelCase  :     Optional[int]     )   ->    str:
			a__     = tmp_path / "csv_with_int_list.csv"
			a__     = 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    _lowerCamelCase  (__lowerCamelCase  :     Optional[int]					,  __lowerCamelCase  :     Any					,  __lowerCamelCase  :     Any     )   ->    List[Any]:
			a__     = Csv()
			a__     = 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    _lowerCamelCase  (__lowerCamelCase  :     int     )   ->    Optional[int]:
			with open(__lowerCamelCase					,  encoding="utf-8"     ) as f:
						a__     = f.read().splitlines()[1]
			a__     = Csv(encoding="utf-8"					,  features=Features({"image": Image()}     )     )
			a__     = csv._generate_tables([[csv_file_with_image]]     )
			a__     = pa.concat_tables([table for _, table in generator]     )
			assert pa_table.schema.field("image"     ).type == Image()()
			a__     = pa_table.to_pydict()["image"]
			assert generated_content == [{"path": image_file, "bytes": None}]







def    _lowerCamelCase  (__lowerCamelCase  :     List[str]     )   ->    Tuple:
			with open(__lowerCamelCase					,  encoding="utf-8"     ) as f:
						a__     = f.read().splitlines()[1:]
			a__     = Csv(encoding="utf-8"					,  features=Features({"label": ClassLabel(names=["good", "bad"]     )}     )     )
			a__     = csv._generate_tables([[csv_file_with_label]]     )
			a__     = pa.concat_tables([table for _, table in generator]     )
			assert pa_table.schema.field("label"     ).type == ClassLabel(names=["good", "bad"]     )()
			a__     = pa_table.to_pydict()["label"]
			assert generated_content == [ClassLabel(names=["good", "bad"]     ).straint(__lowerCamelCase     ) for label in labels]







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










'''simple docstring'''


def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]     )   ->    list[list[float]]:
			a__     = []
			for data in source_data:
						for i, el in enumerate(__lowerCamelCase     ):
									if len(__lowerCamelCase     ) < i + 1:
												data_lists.append([]     )
									data_lists[i].append(float(__lowerCamelCase     )     )
			return data_lists







def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]					,  __lowerCamelCase  :     list[int]     )   ->    list[list[float]]:
			a__     = []
			for dlist, weight in zip(__lowerCamelCase					,  __lowerCamelCase     ):
						a__     = min(__lowerCamelCase     )
						a__     = max(__lowerCamelCase     )

						a__     = []
						# for weight 0 score is 1 - actual score
						if weight == 0:
									for item in dlist:
												try:
															score.append(1 - ((item - mind) / (maxd - mind))     )
												except ZeroDivisionError:
															score.append(1     )

						elif weight == 1:
									for item in dlist:
												try:
															score.append((item - mind) / (maxd - mind)     )
												except ZeroDivisionError:
															score.append(0     )

        # weight not 0 or 1
						else:
									a__     = f'''Invalid weight of {weight:f} provided'''
									raise ValueError(__lowerCamelCase     )

						score_lists.append(__lowerCamelCase     )

			return score_lists







def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]     )   ->    list[float]:
			a__     = [0 for i in range(len(score_lists[0]     )     )]

			for slist in score_lists:
						for j, ele in enumerate(__lowerCamelCase     ):
									a__     = final_scores[j] + ele

			return final_scores







def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]					,  __lowerCamelCase  :     list[int]     )   ->    list[list[float]]:
			a__     = get_data(__lowerCamelCase     )
			a__     = calculate_each_score(__lowerCamelCase					,  __lowerCamelCase     )
			a__     = generate_final_scores(__lowerCamelCase     )

			# append scores to source data
			for i, ele in enumerate(__lowerCamelCase     ):
						source_data[i].append(__lowerCamelCase     )

			return source_data



