


'''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       a				(      __a       )  ->     Optional[int]:



					'''simple docstring'''






					UpperCamelCase__       :List[str]							 =			tmp_path / '''file.csv'''
					UpperCamelCase__       :Union[str, Any]							 =			textwrap.dedent(
					    '''\
        header1,header2
        1,2
        10,20
        '''       )
					with open(__a			, '''w'''       ) as f:
										f.write(__a       )
					return str(__a       )


@pytest.fixture
def       a				(      __a       )  ->     Tuple:



					'''simple docstring'''






					UpperCamelCase__       :Any							 =			tmp_path / '''malformed_file.csv'''
					UpperCamelCase__       :Optional[Any]							 =			textwrap.dedent(
					    '''\
        header1,header2
        1,2
        10,20,
        '''       )
					with open(__a			, '''w'''       ) as f:
										f.write(__a       )
					return str(__a       )


@pytest.fixture
def       a				(      __a			, __a       )  ->     List[str]:



					'''simple docstring'''






					UpperCamelCase__       :Tuple							 =			tmp_path / '''csv_with_image.csv'''
					UpperCamelCase__       :int							 =			textwrap.dedent(
					    f'''\
        image
        {image_file}
        '''       )
					with open(__a			, '''w'''       ) as f:
										f.write(__a       )
					return str(__a       )


@pytest.fixture
def       a				(      __a       )  ->     str:



					'''simple docstring'''






					UpperCamelCase__       :str							 =			tmp_path / '''csv_with_label.csv'''
					UpperCamelCase__       :List[str]							 =			textwrap.dedent(
					    '''\
        label
        good
        bad
        good
        '''       )
					with open(__a			, '''w'''       ) as f:
										f.write(__a       )
					return str(__a       )


@pytest.fixture
def       a				(      __a       )  ->     Optional[Any]:



					'''simple docstring'''






					UpperCamelCase__       :Dict							 =			tmp_path / '''csv_with_int_list.csv'''
					UpperCamelCase__       :List[Any]							 =			textwrap.dedent(
					    '''\
        int_list
        1 2 3
        4 5 6
        7 8 9
        '''       )
					with open(__a			, '''w'''       ) as f:
										f.write(__a       )
					return str(__a       )


def       a				(      __a			, __a			, __a       )  ->     List[str]:



					'''simple docstring'''






					UpperCamelCase__       :Optional[Any]							 =			Csv()
					UpperCamelCase__       :Tuple							 =			csv._generate_tables([[csv_file, malformed_csv_file]]       )
					with pytest.raises(__a			, 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(__a       ) in record.message
					    for record in caplog.records       )


@require_pil
def       a				(      __a       )  ->     Optional[Any]:



					'''simple docstring'''






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


def       a				(      __a       )  ->     str:



					'''simple docstring'''






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







def       a				(      __a       )  ->     List[str]:



					'''simple docstring'''






					UpperCamelCase__       :Any							 =			Csv(encoding='''utf-8'''			, sep=''','''			, converters={'''int_list''': lambda __a       : [int(__a       ) for i in x.split()]}       )
					UpperCamelCase__       :Tuple							 =			csv._generate_tables([[csv_file_with_int_list]]       )
					UpperCamelCase__       :Optional[int]							 =			pa.concat_tables([table for _, table in generator]       )
					assert pa.types.is_list(pa_table.schema.field('''int_list'''       ).type       )
					UpperCamelCase__       :Tuple							 =			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 _LazyModule


__snake_case											=					{'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}


if TYPE_CHECKING:
		from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
		import sys

		__snake_case											=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)