




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 		lowercase__   (		__A:	Dict							,__A:	Optional[Any]				):





					'''simple docstring'''






					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 		lowercase__   (		__A:	Any							,__A:	int							,__A:	Optional[Any]				):





					'''simple docstring'''






					__magic_name__							:	Optional[Any]    							=							tmp_path / 'cache'
					__magic_name__							:	Any    							=							{'text': 'string'}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										__magic_name__							:	List[Any]    							=							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 		lowercase__   (		__A:	List[str]							,__A:	Tuple							,__A:	Any				):





					'''simple docstring'''






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







@pytest.mark.parametrize('''split'''							,[None, NamedSplit('''train'''				), '''train''', '''test''']				)
def 		lowercase__   (		__A:	int							,__A:	Dict							,__A:	str				):





					'''simple docstring'''






					__magic_name__							:	Optional[Any]    							=							tmp_path / 'cache'
					__magic_name__							:	Any    							=							{'text': 'string'}
					__magic_name__							:	Tuple    							=							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 		lowercase__   (		__A:	Optional[int]							,__A:	List[Any]							,__A:	Dict				):





					'''simple docstring'''






					if issubclass(_lowercase							,_lowercase				):
										__magic_name__							:	Any    							=							text_path
					elif issubclass(_lowercase							,_lowercase				):
										__magic_name__							:	Any    							=							[text_path]
					__magic_name__							:	List[Any]    							=							tmp_path / 'cache'
					__magic_name__							:	Any    							=							{'text': 'string'}
					__magic_name__							:	List[str]    							=							TextDatasetReader(_lowercase							,cache_dir=_lowercase				).read()
					_check_text_dataset(_lowercase							,_lowercase				)







def 		lowercase__   (		__A:	str							,__A:	str							,__A:	int=("train",)				):





					'''simple docstring'''






					assert isinstance(_lowercase							,_lowercase				)
					for split in splits:
										__magic_name__							:	Dict    							=							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 		lowercase__   (		__A:	Optional[Any]							,__A:	List[Any]							,__A:	Optional[int]				):





					'''simple docstring'''






					__magic_name__							:	Any    							=							tmp_path / 'cache'
					__magic_name__							:	Union[str, Any]    							=							{'text': 'string'}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										__magic_name__							:	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 		lowercase__   (		__A:	int							,__A:	Union[str, Any]							,__A:	Any				):





					'''simple docstring'''






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





					'''simple docstring'''






					if split:
										__magic_name__							:	Dict    							=							{split: text_path}
					else:
										__magic_name__							:	Union[str, Any]    							=							'train'
										__magic_name__							:	Union[str, Any]    							=							{'train': text_path, 'test': text_path}
					__magic_name__							:	Tuple    							=							tmp_path / 'cache'
					__magic_name__							:	Union[str, Any]    							=							{'text': 'string'}
					__magic_name__							:	Optional[int]    							=							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_tf_available, is_torch_available


__lowerCamelCase   :						int				=			{'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						int				=			[
	    '''SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''SwinForImageClassification''',
	    '''SwinForMaskedImageModeling''',
	    '''SwinModel''',
	    '''SwinPreTrainedModel''',
	    '''SwinBackbone''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[int]				=			[
	    '''TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFSwinForImageClassification''',
	    '''TFSwinForMaskedImageModeling''',
	    '''TFSwinModel''',
	    '''TFSwinPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swin import (
		    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwinBackbone,
		    SwinForImageClassification,
		    SwinForMaskedImageModeling,
		    SwinModel,
		    SwinPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_swin import (
		    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFSwinForImageClassification,
		    TFSwinForMaskedImageModeling,
		    TFSwinModel,
		    TFSwinPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase   :						Union[str, Any]				=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



