'''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_    ( snake_case_	:			Optional[int]      )   ->				Optional[Any]:


     '''simple docstring'''




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





@pytest.fixture
def      lowerCAmelCase_    ( snake_case_	:			str      )   ->				int:


     '''simple docstring'''




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





@pytest.fixture
def      lowerCAmelCase_    ( snake_case_	:			List[Any] ,   snake_case_	:			Optional[int]      )   ->				Union[str, Any]:


     '''simple docstring'''




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





@pytest.fixture
def      lowerCAmelCase_    ( snake_case_	:			Union[str, Any]      )   ->				int:


     '''simple docstring'''




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





@pytest.fixture
def      lowerCAmelCase_    ( snake_case_	:			Any      )   ->				Dict:


     '''simple docstring'''




     UpperCAmelCase_	   =       tmp_path / "csv_with_int_list.csv"
     UpperCAmelCase_	   =       textwrap.dedent(
         "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        "      )
     with open(snake_case_ ,   "w"      ) as f:
          f.write(snake_case_      )
     return str(snake_case_      )





def      lowerCAmelCase_    ( snake_case_	:			Optional[int] ,   snake_case_	:			Optional[int] ,   snake_case_	:			Tuple      )   ->				Any:


     '''simple docstring'''




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





@require_pil
def      lowerCAmelCase_    ( snake_case_	:			Dict      )   ->				List[str]:


     '''simple docstring'''




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





def      lowerCAmelCase_    ( snake_case_	:			Optional[int]      )   ->				Any:


     '''simple docstring'''




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






def      lowerCAmelCase_    ( snake_case_	:			Union[str, Any]      )   ->				str:


     '''simple docstring'''




     UpperCAmelCase_	   =       Csv(encoding="utf-8" ,   sep="," ,   converters={"int_list": lambda snake_case_      : [int(snake_case_      ) for i in x.split()]}      )
     UpperCAmelCase_	   =       csv._generate_tables([[csv_file_with_int_list]]      )
     UpperCAmelCase_	   =       pa.concat_tables([table for _, table in generator]      )
     assert pa.types.is_list(pa_table.schema.field("int_list"      ).type      )
     UpperCAmelCase_	   =       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_torch_available, is_vision_available


SCREAMING_SNAKE_CASE_:					int  ={
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					List[str]  =['MaskFormerFeatureExtractor']
    SCREAMING_SNAKE_CASE_:					Union[str, Any]  =['MaskFormerImageProcessor']


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					Dict  =[
        'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'MaskFormerForInstanceSegmentation',
        'MaskFormerModel',
        'MaskFormerPreTrainedModel',
    ]
    SCREAMING_SNAKE_CASE_:					List[str]  =[
        'MaskFormerSwinBackbone',
        'MaskFormerSwinModel',
        'MaskFormerSwinPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
    from .configuration_maskformer_swin import MaskFormerSwinConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_maskformer import MaskFormerFeatureExtractor
        from .image_processing_maskformer import MaskFormerImageProcessor
    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_maskformer import (
            MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            MaskFormerForInstanceSegmentation,
            MaskFormerModel,
            MaskFormerPreTrainedModel,
        )
        from .modeling_maskformer_swin import (
            MaskFormerSwinBackbone,
            MaskFormerSwinModel,
            MaskFormerSwinPreTrainedModel,
        )


else:
    import sys

    SCREAMING_SNAKE_CASE_:					Dict  =_LazyModule(__name__, globals()['__file__'], _import_structure)




