
'''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      __lowerCAmelCase						(							UpperCAmelCase__				:      Dict       )  ->    Tuple:
							lowerCamelCase_							  =					tmp_path / """file.csv"""
							lowerCamelCase_							  =					textwrap.dedent(
							    """\
        header1,header2
        1,2
        10,20
        """       )
							with open(UpperCAmelCase__			,				"""w"""       ) as f:
														f.write(UpperCAmelCase__       )
							return str(UpperCAmelCase__       )



@pytest.fixture
def      __lowerCAmelCase						(							UpperCAmelCase__				:      Any       )  ->    Dict:
							lowerCamelCase_							  =					tmp_path / """malformed_file.csv"""
							lowerCamelCase_							  =					textwrap.dedent(
							    """\
        header1,header2
        1,2
        10,20,
        """       )
							with open(UpperCAmelCase__			,				"""w"""       ) as f:
														f.write(UpperCAmelCase__       )
							return str(UpperCAmelCase__       )



@pytest.fixture
def      __lowerCAmelCase						(							UpperCAmelCase__				:      List[Any]			,				UpperCAmelCase__				:      List[str]       )  ->    Optional[Any]:
							lowerCamelCase_							  =					tmp_path / """csv_with_image.csv"""
							lowerCamelCase_							  =					textwrap.dedent(
							    F'''\\n        image\n        {image_file}\n        '''       )
							with open(UpperCAmelCase__			,				"""w"""       ) as f:
														f.write(UpperCAmelCase__       )
							return str(UpperCAmelCase__       )



@pytest.fixture
def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[Any]       )  ->    Optional[Any]:
							lowerCamelCase_							  =					tmp_path / """csv_with_label.csv"""
							lowerCamelCase_							  =					textwrap.dedent(
							    """\
        label
        good
        bad
        good
        """       )
							with open(UpperCAmelCase__			,				"""w"""       ) as f:
														f.write(UpperCAmelCase__       )
							return str(UpperCAmelCase__       )



@pytest.fixture
def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[int]       )  ->    Any:
							lowerCamelCase_							  =					tmp_path / """csv_with_int_list.csv"""
							lowerCamelCase_							  =					textwrap.dedent(
							    """\
        int_list
        1 2 3
        4 5 6
        7 8 9
        """       )
							with open(UpperCAmelCase__			,				"""w"""       ) as f:
														f.write(UpperCAmelCase__       )
							return str(UpperCAmelCase__       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      List[str]			,				UpperCAmelCase__				:      int			,				UpperCAmelCase__				:      int       )  ->    Tuple:
							lowerCamelCase_							  =					Csv()
							lowerCamelCase_							  =					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      __lowerCAmelCase						(							UpperCAmelCase__				:      Union[str, Any]       )  ->    Dict:







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



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Any       )  ->    Dict:







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



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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase        =  {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnnxConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  ['''DeiTFeatureExtractor''']
		lowercase        =  ['''DeiTImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  [
		    '''DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''DeiTForImageClassification''',
		    '''DeiTForImageClassificationWithTeacher''',
		    '''DeiTForMaskedImageModeling''',
		    '''DeiTModel''',
		    '''DeiTPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  [
		    '''TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFDeiTForImageClassification''',
		    '''TFDeiTForImageClassificationWithTeacher''',
		    '''TFDeiTForMaskedImageModeling''',
		    '''TFDeiTModel''',
		    '''TFDeiTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

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





