"""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					(	UpperCamelCase__				):






							"""simple docstring"""





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






@pytest.fixture
def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





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






@pytest.fixture
def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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






@pytest.fixture
def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





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






@pytest.fixture
def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							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(UpperCamelCase__       ,    'w'				) as f:
														f.write(UpperCamelCase__				)
							return str(UpperCamelCase__				)






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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






@require_pil
def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							with open(UpperCamelCase__       ,    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   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							with open(UpperCamelCase__       ,    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(UpperCamelCase__				) for label in labels]






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							Csv(encoding='utf-8'       ,    sep=','       ,    converters={'int_list': lambda UpperCamelCase__				: [int(UpperCamelCase__				) 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"""




import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


__lowerCamelCase											=  logging.get_logger(__name__)
class     UpperCamelCase__(			__A ):





						def __init__(       self		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				None:
													warnings.warn(
													    'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
													    ' Please use ImageGPTImageProcessor instead.'		,__UpperCAmelCase		,)
													super().__init__(*__UpperCAmelCase		,**__UpperCAmelCase     )





