
'''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 	A  (   UpperCamelCase_       :  Union[str, Any]    ,						UpperCamelCase_       :  Any   )       ->							Dict:







			'''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 	A  (   UpperCamelCase_       :  int    ,						UpperCamelCase_       :  List[str]    ,						UpperCamelCase_       :  Optional[Any]   )       ->							Tuple:







			'''simple docstring'''



			lowerCAmelCase__										=				tmp_path / "cache"
			lowerCAmelCase__										=				{"text": "string"}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						lowerCAmelCase__										=				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 	A  (   UpperCamelCase_       :  Tuple    ,						UpperCamelCase_       :  Tuple    ,						UpperCamelCase_       :  Optional[Any]   )       ->							int:







			'''simple docstring'''



			lowerCAmelCase__										=				tmp_path / "cache"
			lowerCAmelCase__										=				{"text": "string"}
			lowerCAmelCase__										=				features.copy() if features else default_expected_features
			lowerCAmelCase__										=				(
			    Features({feature: Value(UpperCamelCase_   ) for feature, dtype in features.items()}   ) if features is not None else None
			)
			lowerCAmelCase__										=				TextDatasetReader(UpperCamelCase_    ,						features=UpperCamelCase_    ,						cache_dir=UpperCamelCase_   ).read()
			_check_text_dataset(UpperCamelCase_    ,						UpperCamelCase_   )







@pytest.mark.parametrize("split"    ,						[None, NamedSplit("train"   ), "train", "test"]   )
def 	A  (   UpperCamelCase_       :  Dict    ,						UpperCamelCase_       :  Tuple    ,						UpperCamelCase_       :  Any   )       ->							str:







			'''simple docstring'''



			lowerCAmelCase__										=				tmp_path / "cache"
			lowerCAmelCase__										=				{"text": "string"}
			lowerCAmelCase__										=				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 	A  (   UpperCamelCase_       :  List[Any]    ,						UpperCamelCase_       :  Tuple    ,						UpperCamelCase_       :  Union[str, Any]   )       ->							Dict:







			'''simple docstring'''



			if issubclass(UpperCamelCase_    ,						UpperCamelCase_   ):
						lowerCAmelCase__										=				text_path
			elif issubclass(UpperCamelCase_    ,						UpperCamelCase_   ):
						lowerCAmelCase__										=				[text_path]
			lowerCAmelCase__										=				tmp_path / "cache"
			lowerCAmelCase__										=				{"text": "string"}
			lowerCAmelCase__										=				TextDatasetReader(UpperCamelCase_    ,						cache_dir=UpperCamelCase_   ).read()
			_check_text_dataset(UpperCamelCase_    ,						UpperCamelCase_   )







def 	A  (   UpperCamelCase_       :  Tuple    ,						UpperCamelCase_       :  Optional[int]    ,						UpperCamelCase_       :  Union[str, Any]=("train",)   )       ->							int:







			'''simple docstring'''



			assert isinstance(UpperCamelCase_    ,						UpperCamelCase_   )
			for split in splits:
						lowerCAmelCase__										=				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 	A  (   UpperCamelCase_       :  Tuple    ,						UpperCamelCase_       :  Dict    ,						UpperCamelCase_       :  Optional[Any]   )       ->							Any:







			'''simple docstring'''



			lowerCAmelCase__										=				tmp_path / "cache"
			lowerCAmelCase__										=				{"text": "string"}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						lowerCAmelCase__										=				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 	A  (   UpperCamelCase_       :  Optional[int]    ,						UpperCamelCase_       :  str    ,						UpperCamelCase_       :  Union[str, Any]   )       ->							Optional[Any]:







			'''simple docstring'''



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







			'''simple docstring'''



			if split:
						lowerCAmelCase__										=				{split: text_path}
			else:
						lowerCAmelCase__										=				"train"
						lowerCAmelCase__										=				{"train": text_path, "test": text_path}
			lowerCAmelCase__										=				tmp_path / "cache"
			lowerCAmelCase__										=				{"text": "string"}
			lowerCAmelCase__										=				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_clipseg''': [
        '''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPSegConfig''',
        '''CLIPSegTextConfig''',
        '''CLIPSegVisionConfig''',
    ],
    '''processing_clipseg''': ['''CLIPSegProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase       							=				[
		    '''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CLIPSegModel''',
		    '''CLIPSegPreTrainedModel''',
		    '''CLIPSegTextModel''',
		    '''CLIPSegVisionModel''',
		    '''CLIPSegForImageSegmentation''',
		]

if TYPE_CHECKING:
		from .configuration_clipseg import (
		    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    CLIPSegConfig,
		    CLIPSegTextConfig,
		    CLIPSegVisionConfig,
		)
		from .processing_clipseg import CLIPSegProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_clipseg import (
				    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CLIPSegForImageSegmentation,
				    CLIPSegModel,
				    CLIPSegPreTrainedModel,
				    CLIPSegTextModel,
				    CLIPSegVisionModel,
				)

else:
		import sys

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