



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase_													=      {
    '''configuration_bridgetower''': [
        '''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BridgeTowerConfig''',
        '''BridgeTowerTextConfig''',
        '''BridgeTowerVisionConfig''',
    ],
    '''processing_bridgetower''': ['''BridgeTowerProcessor'''],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_													=      ['''BridgeTowerImageProcessor''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_													=      [
				    '''BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''BridgeTowerForContrastiveLearning''',
				    '''BridgeTowerForImageAndTextRetrieval''',
				    '''BridgeTowerForMaskedLM''',
				    '''BridgeTowerModel''',
				    '''BridgeTowerPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_bridgetower import (
				    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    BridgeTowerConfig,
				    BridgeTowerTextConfig,
				    BridgeTowerVisionConfig,
				)
				from .processing_bridgetower import BridgeTowerProcessor

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_bridgetower import BridgeTowerImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bridgetower import (
								    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BridgeTowerForContrastiveLearning,
								    BridgeTowerForImageAndTextRetrieval,
								    BridgeTowerForMaskedLM,
								    BridgeTowerModel,
								    BridgeTowerPreTrainedModel,
								)


else:
				import sys

				lowercase_													=      _LazyModule(__name__, globals()['''__file__'''], _import_structure)









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






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






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






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






def        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     str=("train",)						)	->    Dict:
					assert isinstance(__lowerCamelCase					, __lowerCamelCase						)
					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        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     List[Any]						)	->    Dict:
					__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=__lowerCamelCase					, keep_in_memory=__lowerCamelCase						).read()
					_check_text_datasetdict(__lowerCamelCase					, __lowerCamelCase						)






@pytest.mark.parametrize(
    """features"""					, [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]					, )
def        __lowerCAmelCase		(	__lowerCamelCase						:     Any					, __lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     Union[str, Any]						)	->    Tuple:
					__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(__lowerCamelCase						) for feature, dtype in features.items()}						) if features is not None else None
					)
					__lowerCAmelCase						 =TextDatasetReader({"""train""": text_path}					, features=__lowerCamelCase					, cache_dir=__lowerCamelCase						).read()
					_check_text_datasetdict(__lowerCamelCase					, __lowerCamelCase						)






@pytest.mark.parametrize("""split"""					, [None, NamedSplit("""train"""						), """train""", """test"""]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     int					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     Any						)	->    int:
					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(__lowerCamelCase					, cache_dir=__lowerCamelCase						).read()
					_check_text_datasetdict(__lowerCamelCase					, __lowerCamelCase					, splits=list(path.keys()						)						)
					assert all(dataset[split].split == split for split in path.keys()						)




