


import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Union[str, Any]      ):
      """simple docstring"""




      assert isinstance(UpperCAmelCase						,	UpperCAmelCase      )
      assert dataset.num_rows == 4
      assert dataset.num_columns == 1
      assert dataset.column_names == ["text"]
      for feature, expected_dtype in expected_features.items():
            assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("""keep_in_memory"""						,	[False, True]      )
def   _UpperCAmelCase       (     UpperCAmelCase      :			int						,	UpperCAmelCase      :			Any						,	UpperCAmelCase      :			str      ):
      """simple docstring"""




      __lowerCamelCase     :      Any       						=						tmp_path / """cache"""
      __lowerCamelCase     :      Union[str, Any]       						=						{"""text""": """string"""}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            __lowerCamelCase     :      Tuple       						=						TextDatasetReader(UpperCAmelCase						,	cache_dir=UpperCAmelCase						,	keep_in_memory=UpperCAmelCase      ).read()
      _check_text_dataset(UpperCAmelCase						,	UpperCAmelCase      )





@pytest.mark.parametrize(
    """features"""						,	[
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]						,	)
def   _UpperCAmelCase       (     UpperCAmelCase      :			Any						,	UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			Optional[int]      ):
      """simple docstring"""




      __lowerCamelCase     :      List[str]       						=						tmp_path / """cache"""
      __lowerCamelCase     :      str       						=						{"""text""": """string"""}
      __lowerCamelCase     :      str       						=						features.copy() if features else default_expected_features
      __lowerCamelCase     :      Union[str, Any]       						=						(
          Features({feature: Value(UpperCAmelCase      ) for feature, dtype in features.items()}      ) if features is not None else None
      )
      __lowerCamelCase     :      Optional[int]       						=						TextDatasetReader(UpperCAmelCase						,	features=UpperCAmelCase						,	cache_dir=UpperCAmelCase      ).read()
      _check_text_dataset(UpperCAmelCase						,	UpperCAmelCase      )





@pytest.mark.parametrize("""split"""						,	[None, NamedSplit("""train"""      ), """train""", """test"""]      )
def   _UpperCAmelCase       (     UpperCAmelCase      :			Any						,	UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Dict      ):
      """simple docstring"""




      __lowerCamelCase     :      Any       						=						tmp_path / """cache"""
      __lowerCamelCase     :      str       						=						{"""text""": """string"""}
      __lowerCamelCase     :      Tuple       						=						TextDatasetReader(UpperCAmelCase						,	cache_dir=UpperCAmelCase						,	split=UpperCAmelCase      ).read()
      _check_text_dataset(UpperCAmelCase						,	UpperCAmelCase      )
      assert dataset.split == split if split else "train"





@pytest.mark.parametrize("""path_type"""						,	[str, list]      )
def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			Any						,	UpperCAmelCase      :			int      ):
      """simple docstring"""




      if issubclass(UpperCAmelCase						,	UpperCAmelCase      ):
            __lowerCamelCase     :      List[str]       						=						text_path
      elif issubclass(UpperCAmelCase						,	UpperCAmelCase      ):
            __lowerCamelCase     :      Optional[Any]       						=						[text_path]
      __lowerCamelCase     :      List[Any]       						=						tmp_path / """cache"""
      __lowerCamelCase     :      str       						=						{"""text""": """string"""}
      __lowerCamelCase     :      Any       						=						TextDatasetReader(UpperCAmelCase						,	cache_dir=UpperCAmelCase      ).read()
      _check_text_dataset(UpperCAmelCase						,	UpperCAmelCase      )





def   _UpperCAmelCase       (     UpperCAmelCase      :			int						,	UpperCAmelCase      :			Union[str, Any]						,	UpperCAmelCase      :			Dict=("train",)      ):
      """simple docstring"""




      assert isinstance(UpperCAmelCase						,	UpperCAmelCase      )
      for split in splits:
            __lowerCamelCase     :      Tuple       						=						dataset_dict[split]
            assert dataset.num_rows == 4
            assert dataset.num_columns == 1
            assert dataset.column_names == ["text"]
            for feature, expected_dtype in expected_features.items():
                  assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("""keep_in_memory"""						,	[False, True]      )
def   _UpperCAmelCase       (     UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			Tuple      ):
      """simple docstring"""




      __lowerCamelCase     :      Tuple       						=						tmp_path / """cache"""
      __lowerCamelCase     :      str       						=						{"""text""": """string"""}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            __lowerCamelCase     :      Any       						=						TextDatasetReader({"""train""": text_path}						,	cache_dir=UpperCAmelCase						,	keep_in_memory=UpperCAmelCase      ).read()
      _check_text_datasetdict(UpperCAmelCase						,	UpperCAmelCase      )





@pytest.mark.parametrize(
    """features"""						,	[
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]						,	)
def   _UpperCAmelCase       (     UpperCAmelCase      :			Union[str, Any]						,	UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			Optional[int]      ):
      """simple docstring"""




      __lowerCamelCase     :      Any       						=						tmp_path / """cache"""
      # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
      __lowerCamelCase     :      int       						=						{"""text""": """string"""}
      __lowerCamelCase     :      Optional[int]       						=						features.copy() if features else default_expected_features
      __lowerCamelCase     :      Dict       						=						(
          Features({feature: Value(UpperCAmelCase      ) for feature, dtype in features.items()}      ) if features is not None else None
      )
      __lowerCamelCase     :      Union[str, Any]       						=						TextDatasetReader({"""train""": text_path}						,	features=UpperCAmelCase						,	cache_dir=UpperCAmelCase      ).read()
      _check_text_datasetdict(UpperCAmelCase						,	UpperCAmelCase      )





@pytest.mark.parametrize("""split"""						,	[None, NamedSplit("""train"""      ), """train""", """test"""]      )
def   _UpperCAmelCase       (     UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			Dict      ):
      """simple docstring"""




      if split:
            __lowerCamelCase     :      Optional[Any]       						=						{split: text_path}
      else:
            __lowerCamelCase     :      Tuple       						=						"""train"""
            __lowerCamelCase     :      str       						=						{"""train""": text_path, """test""": text_path}
      __lowerCamelCase     :      int       						=						tmp_path / """cache"""
      __lowerCamelCase     :      Tuple       						=						{"""text""": """string"""}
      __lowerCamelCase     :      List[str]       						=						TextDatasetReader(UpperCAmelCase						,	cache_dir=UpperCAmelCase      ).read()
      _check_text_datasetdict(UpperCAmelCase						,	UpperCAmelCase						,	splits=list(path.keys()      )      )
      assert all(dataset[split].split == split for split in path.keys()      )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase    :						Optional[Any]     =							{
    'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						List[Any]     =							[
        'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
        'Swinv2ForImageClassification',
        'Swinv2ForMaskedImageModeling',
        'Swinv2Model',
        'Swinv2PreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_swinva import (
            SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
            SwinvaForImageClassification,
            SwinvaForMaskedImageModeling,
            SwinvaModel,
            SwinvaPreTrainedModel,
        )


else:
    import sys

    __UpperCamelCase    :						List[Any]     =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



