





"""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_    (							_lowercase       ):
						_UpperCamelCase      : int         =   tmp_path / '''file.csv'''
						_UpperCamelCase      : Optional[Any]         =   textwrap.dedent(
						    '''\
        header1,header2
        1,2
        10,20
        '''       )
						with open(_lowercase	,						'''w'''       ) as f:
												f.write(_lowercase       )
						return str(_lowercase       )





@pytest.fixture
def 	a_    (							_lowercase       ):
						_UpperCamelCase      : str         =   tmp_path / '''malformed_file.csv'''
						_UpperCamelCase      : List[str]         =   textwrap.dedent(
						    '''\
        header1,header2
        1,2
        10,20,
        '''       )
						with open(_lowercase	,						'''w'''       ) as f:
												f.write(_lowercase       )
						return str(_lowercase       )





@pytest.fixture
def 	a_    (							_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Tuple         =   tmp_path / '''csv_with_image.csv'''
						_UpperCamelCase      : int         =   textwrap.dedent(
						    F"""\
        image
        {image_file}
        """       )
						with open(_lowercase	,						'''w'''       ) as f:
												f.write(_lowercase       )
						return str(_lowercase       )





@pytest.fixture
def 	a_    (							_lowercase       ):
						_UpperCamelCase      : Dict         =   tmp_path / '''csv_with_label.csv'''
						_UpperCamelCase      : Optional[Any]         =   textwrap.dedent(
						    '''\
        label
        good
        bad
        good
        '''       )
						with open(_lowercase	,						'''w'''       ) as f:
												f.write(_lowercase       )
						return str(_lowercase       )





@pytest.fixture
def 	a_    (							_lowercase       ):
						_UpperCamelCase      : Dict         =   tmp_path / '''csv_with_int_list.csv'''
						_UpperCamelCase      : Dict         =   textwrap.dedent(
						    '''\
        int_list
        1 2 3
        4 5 6
        7 8 9
        '''       )
						with open(_lowercase	,						'''w'''       ) as f:
												f.write(_lowercase       )
						return str(_lowercase       )





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Union[str, Any]         =   Csv()
						_UpperCamelCase      : Union[str, Any]         =   csv._generate_tables([[csv_file, malformed_csv_file]]       )
						with pytest.raises(_lowercase	,						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(_lowercase       ) in record.message
						    for record in caplog.records       )





@require_pil
def 	a_    (							_lowercase       ):
						with open(_lowercase	,						encoding='''utf-8'''       ) as f:
												_UpperCamelCase      : Optional[Any]         =   f.read().splitlines()[1]
						_UpperCamelCase      : int         =   Csv(encoding='''utf-8'''	,						features=Features({'''image''': Image()}       )       )
						_UpperCamelCase      : List[str]         =   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      : int         =   pa_table.to_pydict()['''image''']
						assert generated_content == [{"path": image_file, "bytes": None}]





def 	a_    (							_lowercase       ):
						with open(_lowercase	,						encoding='''utf-8'''       ) as f:
												_UpperCamelCase      : int         =   f.read().splitlines()[1:]
						_UpperCamelCase      : Optional[Any]         =   Csv(encoding='''utf-8'''	,						features=Features({'''label''': ClassLabel(names=['''good''', '''bad''']       )}       )       )
						_UpperCamelCase      : Dict         =   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(_lowercase       ) for label in labels]






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










"""simple docstring"""




from ..utils import is_flax_available, is_torch_available


if is_torch_available():
						from .autoencoder_kl import AutoencoderKL
						from .controlnet import ControlNetModel
						from .dual_transformer_ad import DualTransformeraDModel
						from .modeling_utils import ModelMixin
						from .prior_transformer import PriorTransformer
						from .ta_film_transformer import TaFilmDecoder
						from .transformer_ad import TransformeraDModel
						from .unet_ad import UNetaDModel
						from .unet_ad import UNetaDModel
						from .unet_ad_condition import UNetaDConditionModel
						from .unet_ad_condition import UNetaDConditionModel
						from .vq_model import VQModel

if is_flax_available():
						from .controlnet_flax import FlaxControlNetModel
						from .unet_ad_condition_flax import FlaxUNetaDConditionModel
						from .vae_flax import FlaxAutoencoderKL



