'''simple docstring'''







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    lowerCamelCase       (			UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  Optional[int]		)			->	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    lowerCamelCase       (			UpperCAmelCase__				:  Optional[int]				,						UpperCAmelCase__				:  Optional[int]				,						UpperCAmelCase__				:  List[Any]		)			->	List[str]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :List[str]						    = tmp_path / 'cache'
							SCREAMING_SNAKE_CASE__     :Tuple						    = {'text': 'string'}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														SCREAMING_SNAKE_CASE__     :Dict						    = 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    lowerCamelCase       (			UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  List[Any]				,						UpperCAmelCase__				:  Union[str, Any]		)			->	int:


							'''simple docstring'''






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


@pytest.mark.parametrize('split'				,						[None, NamedSplit('train'		), 'train', 'test']		)
def    lowerCamelCase       (			UpperCAmelCase__				:  Dict				,						UpperCAmelCase__				:  Union[str, Any]				,						UpperCAmelCase__				:  Dict		)			->	List[Any]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :int						    = tmp_path / 'cache'
							SCREAMING_SNAKE_CASE__     :Dict						    = {'text': 'string'}
							SCREAMING_SNAKE_CASE__     :Any						    = 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    lowerCamelCase       (			UpperCAmelCase__				:  str				,						UpperCAmelCase__				:  Optional[Any]				,						UpperCAmelCase__				:  List[str]		)			->	Tuple:


							'''simple docstring'''






							if issubclass(UpperCAmelCase__				,						UpperCAmelCase__		):
														SCREAMING_SNAKE_CASE__     :Tuple						    = text_path
							elif issubclass(UpperCAmelCase__				,						UpperCAmelCase__		):
														SCREAMING_SNAKE_CASE__     :List[Any]						    = [text_path]
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = tmp_path / 'cache'
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = {'text': 'string'}
							SCREAMING_SNAKE_CASE__     :List[Any]						    = TextDatasetReader(UpperCAmelCase__				,						cache_dir=UpperCAmelCase__		).read()
							_check_text_dataset(UpperCAmelCase__				,						UpperCAmelCase__		)


def    lowerCamelCase       (			UpperCAmelCase__				:  Union[str, Any]				,						UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  List[str]=("train",)		)			->	int:


							'''simple docstring'''






							assert isinstance(UpperCAmelCase__				,						UpperCAmelCase__		)
							for split in splits:
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = 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    lowerCamelCase       (			UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  Any		)			->	Optional[Any]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :str						    = tmp_path / 'cache'
							SCREAMING_SNAKE_CASE__     :Any						    = {'text': 'string'}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														SCREAMING_SNAKE_CASE__     :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    lowerCamelCase       (			UpperCAmelCase__				:  str				,						UpperCAmelCase__				:  Dict				,						UpperCAmelCase__				:  Optional[int]		)			->	Optional[Any]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Any						    = tmp_path / 'cache'
							# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
							SCREAMING_SNAKE_CASE__     :List[Any]						    = {'text': 'string'}
							SCREAMING_SNAKE_CASE__     :Dict						    = features.copy() if features else default_expected_features
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = (
							    Features({feature: Value(UpperCAmelCase__		) for feature, dtype in features.items()}		) if features is not None else None
							)
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = 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    lowerCamelCase       (			UpperCAmelCase__				:  Union[str, Any]				,						UpperCAmelCase__				:  Optional[int]				,						UpperCAmelCase__				:  str		)			->	str:


							'''simple docstring'''






							if split:
														SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = {split: text_path}
							else:
														SCREAMING_SNAKE_CASE__     :Any						    = 'train'
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = {'train': text_path, 'test': text_path}
							SCREAMING_SNAKE_CASE__     :List[str]						    = tmp_path / 'cache'
							SCREAMING_SNAKE_CASE__     :List[str]						    = {'text': 'string'}
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = 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()		)







'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase_      	=	{
    '''configuration_x_clip''': [
        '''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XCLIPConfig''',
        '''XCLIPTextConfig''',
        '''XCLIPVisionConfig''',
    ],
    '''processing_x_clip''': ['''XCLIPProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XCLIPModel''',
		    '''XCLIPPreTrainedModel''',
		    '''XCLIPTextModel''',
		    '''XCLIPVisionModel''',
		]

if TYPE_CHECKING:
		from .configuration_x_clip import (
		    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    XCLIPConfig,
		    XCLIPTextConfig,
		    XCLIPVisionConfig,
		)
		from .processing_x_clip import XCLIPProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_x_clip import (
				    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XCLIPModel,
				    XCLIPPreTrainedModel,
				    XCLIPTextModel,
				    XCLIPVisionModel,
				)

else:
		import sys

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






