




'''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      UpperCamelCase_(	snake_case							:       int     ):

							'''simple docstring'''






							snake_case_						=      tmp_path / "file.csv"
							snake_case_						=      textwrap.dedent(
							    "\\n        header1,header2\n        1,2\n        10,20\n        "     )
							with open(snake_case							,				"w"     ) as f:
														f.write(snake_case     )
							return str(snake_case     )


@pytest.fixture
def      UpperCamelCase_(	snake_case							:       Dict     ):

							'''simple docstring'''






							snake_case_						=      tmp_path / "malformed_file.csv"
							snake_case_						=      textwrap.dedent(
							    "\\n        header1,header2\n        1,2\n        10,20,\n        "     )
							with open(snake_case							,				"w"     ) as f:
														f.write(snake_case     )
							return str(snake_case     )


@pytest.fixture
def      UpperCamelCase_(	snake_case							:       Optional[Any]							,				snake_case							:       Tuple     ):

							'''simple docstring'''






							snake_case_						=      tmp_path / "csv_with_image.csv"
							snake_case_						=      textwrap.dedent(
							    f'\\n        image\n        {image_file}\n        '     )
							with open(snake_case							,				"w"     ) as f:
														f.write(snake_case     )
							return str(snake_case     )


@pytest.fixture
def      UpperCamelCase_(	snake_case							:       int     ):

							'''simple docstring'''






							snake_case_						=      tmp_path / "csv_with_label.csv"
							snake_case_						=      textwrap.dedent(
							    "\\n        label\n        good\n        bad\n        good\n        "     )
							with open(snake_case							,				"w"     ) as f:
														f.write(snake_case     )
							return str(snake_case     )


@pytest.fixture
def      UpperCamelCase_(	snake_case							:       Dict     ):

							'''simple docstring'''






							snake_case_						=      tmp_path / "csv_with_int_list.csv"
							snake_case_						=      textwrap.dedent(
							    "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        "     )
							with open(snake_case							,				"w"     ) as f:
														f.write(snake_case     )
							return str(snake_case     )


def      UpperCamelCase_(	snake_case							:       Optional[Any]							,				snake_case							:       Any							,				snake_case							:       Optional[Any]     ):

							'''simple docstring'''






							snake_case_						=      Csv()
							snake_case_						=      csv._generate_tables([[csv_file, malformed_csv_file]]     )
							with pytest.raises(snake_case							,				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(snake_case     ) in record.message
							    for record in caplog.records     )


@require_pil
def      UpperCamelCase_(	snake_case							:       int     ):

							'''simple docstring'''






							with open(snake_case							,				encoding="utf-8"     ) as f:
														snake_case_						=      f.read().splitlines()[1]
							snake_case_						=      Csv(encoding="utf-8"							,				features=Features({"image": Image()}     )     )
							snake_case_						=      csv._generate_tables([[csv_file_with_image]]     )
							snake_case_						=      pa.concat_tables([table for _, table in generator]     )
							assert pa_table.schema.field("image"     ).type == Image()()
							snake_case_						=      pa_table.to_pydict()["image"]
							assert generated_content == [{"path": image_file, "bytes": None}]


def      UpperCamelCase_(	snake_case							:       str     ):

							'''simple docstring'''






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




def      UpperCamelCase_(	snake_case							:       Any     ):

							'''simple docstring'''






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





"""simple docstring"""


import warnings


warnings.warn(
    """memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: """
    """`from accelerate import find_executable_batch_size` to avoid this warning.""",
    FutureWarning,
)
