




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 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Optional[Any]:
   assert isinstance(lowercase						,lowercase					)
   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 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       int:
   snake_case				:    str           =	tmp_path / """cache"""
   snake_case				:    Dict           =	{"""text""": """string"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      snake_case				:    Tuple           =	TextDatasetReader(lowercase						,cache_dir=lowercase						,keep_in_memory=lowercase					).read()
   _check_text_dataset(lowercase						,lowercase					)




@pytest.mark.parametrize(
    """features"""						,[
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]						,)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       Tuple:
   snake_case				:    Union[str, Any]           =	tmp_path / """cache"""
   snake_case				:    List[str]           =	{"""text""": """string"""}
   snake_case				:    int           =	features.copy() if features else default_expected_features
   snake_case				:    Tuple           =	(
       Features({feature: Value(lowercase					) for feature, dtype in features.items()}					) if features is not None else None
   )
   snake_case				:    Union[str, Any]           =	TextDatasetReader(lowercase						,features=lowercase						,cache_dir=lowercase					).read()
   _check_text_dataset(lowercase						,lowercase					)




@pytest.mark.parametrize("""split"""						,[None, NamedSplit("""train"""					), """train""", """test"""]					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       List[str]:
   snake_case				:    List[str]           =	tmp_path / """cache"""
   snake_case				:    List[str]           =	{"""text""": """string"""}
   snake_case				:    List[Any]           =	TextDatasetReader(lowercase						,cache_dir=lowercase						,split=lowercase					).read()
   _check_text_dataset(lowercase						,lowercase					)
   assert dataset.split == split if split else "train"




@pytest.mark.parametrize("""path_type"""						,[str, list]					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       Optional[int]:
   if issubclass(lowercase						,lowercase					):
      snake_case				:    Any           =	text_path
   elif issubclass(lowercase						,lowercase					):
      snake_case				:    str           =	[text_path]
   snake_case				:    Any           =	tmp_path / """cache"""
   snake_case				:    int           =	{"""text""": """string"""}
   snake_case				:    Dict           =	TextDatasetReader(lowercase						,cache_dir=lowercase					).read()
   _check_text_dataset(lowercase						,lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase=("train",)					)  ->       List[str]:
   assert isinstance(lowercase						,lowercase					)
   for split in splits:
      snake_case				:    Optional[Any]           =	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 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       Optional[int]:
   snake_case				:    Tuple           =	tmp_path / """cache"""
   snake_case				:    Optional[int]           =	{"""text""": """string"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      snake_case				:    List[Any]           =	TextDatasetReader({"""train""": text_path}						,cache_dir=lowercase						,keep_in_memory=lowercase					).read()
   _check_text_datasetdict(lowercase						,lowercase					)




@pytest.mark.parametrize(
    """features"""						,[
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]						,)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       List[Any]:
   snake_case				:    int           =	tmp_path / """cache"""
   # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
   snake_case				:    Optional[int]           =	{"""text""": """string"""}
   snake_case				:    str           =	features.copy() if features else default_expected_features
   snake_case				:    int           =	(
       Features({feature: Value(lowercase					) for feature, dtype in features.items()}					) if features is not None else None
   )
   snake_case				:    List[Any]           =	TextDatasetReader({"""train""": text_path}						,features=lowercase						,cache_dir=lowercase					).read()
   _check_text_datasetdict(lowercase						,lowercase					)




@pytest.mark.parametrize("""split"""						,[None, NamedSplit("""train"""					), """train""", """test"""]					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       Dict:
   if split:
      snake_case				:    Any           =	{split: text_path}
   else:
      snake_case				:    Dict           =	"""train"""
      snake_case				:    int           =	{"""train""": text_path, """test""": text_path}
   snake_case				:    Dict           =	tmp_path / """cache"""
   snake_case				:    Optional[int]           =	{"""text""": """string"""}
   snake_case				:    List[Any]           =	TextDatasetReader(lowercase						,cache_dir=lowercase					).read()
   _check_text_datasetdict(lowercase						,lowercase						,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


lowerCamelCase	:	List[str]				=   {
    'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Optional[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

       lowerCamelCase	:	Optional[int]				=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





