


"""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__ (lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       tmp_path / "file.csv"
					__SCREAMING_SNAKE_CASE		     =       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      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       tmp_path / "malformed_file.csv"
					__SCREAMING_SNAKE_CASE		     =       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      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


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




@pytest.fixture
def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       tmp_path / "csv_with_label.csv"
					__SCREAMING_SNAKE_CASE		     =       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      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       tmp_path / "csv_with_int_list.csv"
					__SCREAMING_SNAKE_CASE		     =       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      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       Csv()
					__SCREAMING_SNAKE_CASE		     =       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      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


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




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


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




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


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




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


a__       :							List[str]        =  {}


try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__       :							int        =  ['''MLukeTokenizer''']

if TYPE_CHECKING:
		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_mluke import MLukeTokenizer


else:
		import sys

		a__       :							Optional[Any]        =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
