




"""simple docstring"""
import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def     UpperCAmelCase					(      A__:     Dict				,					A__:     Optional[int]  )	->  Dict:
			assert isinstance(_UpperCAmelCase				,					_UpperCAmelCase  )
			assert dataset.num_rows == 4
			assert dataset.num_columns == 3
			assert dataset.column_names == ["col_1", "col_2", "col_3"]
			for feature, expected_dtype in expected_features.items():
						assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('keep_in_memory'				,					[False, True]  )
def     UpperCAmelCase					(      A__:     List[str]				,					A__:     List[Any]				,					A__:     Tuple  )	->  Optional[int]:
			__lowerCamelCase       :						Dict													=					tmp_path / 'cache'
			__lowerCamelCase       :						List[str]													=					{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						__lowerCamelCase       :						str													=					ParquetDatasetReader(_UpperCAmelCase				,					cache_dir=_UpperCAmelCase				,					keep_in_memory=_UpperCAmelCase  ).read()
			_check_parquet_dataset(_UpperCAmelCase				,					_UpperCAmelCase  )





@pytest.mark.parametrize(
    'features'				,					[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]				,					)
def     UpperCAmelCase					(      A__:     Optional[Any]				,					A__:     int				,					A__:     List[Any]  )	->  Optional[int]:
			__lowerCamelCase       :						List[str]													=					tmp_path / 'cache'
			__lowerCamelCase       :						List[str]													=					{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			__lowerCamelCase       :						Dict													=					features.copy() if features else default_expected_features
			__lowerCamelCase       :						Tuple													=					(
			    Features({feature: Value(_UpperCAmelCase  ) for feature, dtype in features.items()}  ) if features is not None else None
			)
			__lowerCamelCase       :						Optional[int]													=					ParquetDatasetReader(_UpperCAmelCase				,					features=_UpperCAmelCase				,					cache_dir=_UpperCAmelCase  ).read()
			_check_parquet_dataset(_UpperCAmelCase				,					_UpperCAmelCase  )





@pytest.mark.parametrize('split'				,					[None, NamedSplit('train'  ), 'train', 'test']  )
def     UpperCAmelCase					(      A__:     Any				,					A__:     Optional[int]				,					A__:     Any  )	->  Optional[Any]:
			__lowerCamelCase       :						int													=					tmp_path / 'cache'
			__lowerCamelCase       :						Dict													=					{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			__lowerCamelCase       :						Union[str, Any]													=					ParquetDatasetReader(_UpperCAmelCase				,					cache_dir=_UpperCAmelCase				,					split=_UpperCAmelCase  ).read()
			_check_parquet_dataset(_UpperCAmelCase				,					_UpperCAmelCase  )
			assert dataset.split == split if split else "train"





@pytest.mark.parametrize('path_type'				,					[str, list]  )
def     UpperCAmelCase					(      A__:     List[Any]				,					A__:     int				,					A__:     Dict  )	->  int:
			if issubclass(_UpperCAmelCase				,					_UpperCAmelCase  ):
						__lowerCamelCase       :						Optional[Any]													=					parquet_path
			elif issubclass(_UpperCAmelCase				,					_UpperCAmelCase  ):
						__lowerCamelCase       :						Union[str, Any]													=					[parquet_path]
			__lowerCamelCase       :						str													=					tmp_path / 'cache'
			__lowerCamelCase       :						List[str]													=					{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			__lowerCamelCase       :						Dict													=					ParquetDatasetReader(_UpperCAmelCase				,					cache_dir=_UpperCAmelCase  ).read()
			_check_parquet_dataset(_UpperCAmelCase				,					_UpperCAmelCase  )





def     UpperCAmelCase					(      A__:     Optional[int]				,					A__:     List[str]				,					A__:     Tuple=("train",)  )	->  List[Any]:
			assert isinstance(_UpperCAmelCase				,					_UpperCAmelCase  )
			for split in splits:
						__lowerCamelCase       :						Union[str, Any]													=					dataset_dict[split]
						assert dataset.num_rows == 4
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_1", "col_2", "col_3"]
						for feature, expected_dtype in expected_features.items():
									assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('keep_in_memory'				,					[False, True]  )
def     UpperCAmelCase					(      A__:     str				,					A__:     Tuple				,					A__:     Optional[Any]  )	->  Any:
			__lowerCamelCase       :						str													=					tmp_path / 'cache'
			__lowerCamelCase       :						Union[str, Any]													=					{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						__lowerCamelCase       :						Optional[int]													=					ParquetDatasetReader(
						    {'train': parquet_path}				,					cache_dir=_UpperCAmelCase				,					keep_in_memory=_UpperCAmelCase  ).read()
			_check_parquet_datasetdict(_UpperCAmelCase				,					_UpperCAmelCase  )





@pytest.mark.parametrize(
    'features'				,					[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]				,					)
def     UpperCAmelCase					(      A__:     Optional[Any]				,					A__:     Optional[int]				,					A__:     int  )	->  Union[str, Any]:
			__lowerCamelCase       :						Tuple													=					tmp_path / 'cache'
			__lowerCamelCase       :						Any													=					{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			__lowerCamelCase       :						Any													=					features.copy() if features else default_expected_features
			__lowerCamelCase       :						Dict													=					(
			    Features({feature: Value(_UpperCAmelCase  ) for feature, dtype in features.items()}  ) if features is not None else None
			)
			__lowerCamelCase       :						Tuple													=					ParquetDatasetReader({'train': parquet_path}				,					features=_UpperCAmelCase				,					cache_dir=_UpperCAmelCase  ).read()
			_check_parquet_datasetdict(_UpperCAmelCase				,					_UpperCAmelCase  )





@pytest.mark.parametrize('split'				,					[None, NamedSplit('train'  ), 'train', 'test']  )
def     UpperCAmelCase					(      A__:     str				,					A__:     Union[str, Any]				,					A__:     str  )	->  Dict:
			if split:
						__lowerCamelCase       :						Tuple													=					{split: parquet_path}
			else:
						__lowerCamelCase       :						int													=					'train'
						__lowerCamelCase       :						int													=					{'train': parquet_path, 'test': parquet_path}
			__lowerCamelCase       :						Dict													=					tmp_path / 'cache'
			__lowerCamelCase       :						Optional[Any]													=					{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
			__lowerCamelCase       :						Union[str, Any]													=					ParquetDatasetReader(_UpperCAmelCase				,					cache_dir=_UpperCAmelCase  ).read()
			_check_parquet_datasetdict(_UpperCAmelCase				,					_UpperCAmelCase				,					splits=list(path.keys()  )  )
			assert all(dataset[split].split == split for split in path.keys()  )





def     UpperCAmelCase					(      A__:     Optional[Any]				,					A__:     List[Any]  )	->  Optional[Any]:
			__lowerCamelCase       :						Union[str, Any]													=					ParquetDatasetWriter(_UpperCAmelCase				,					tmp_path / 'foo.parquet'  )
			assert writer.write() > 0
			__lowerCamelCase       :						Tuple													=					pq.ParquetFile(tmp_path / 'foo.parquet'  )
			__lowerCamelCase       :						int													=					pf.read()
			assert dataset.data.table == output_table





def     UpperCAmelCase					(      A__:     Optional[Any]				,					A__:     List[str]  )	->  Dict:
			__lowerCamelCase       :						Optional[int]													=					str(shared_datadir / 'test_image_rgb.jpg'  )
			__lowerCamelCase       :						List[str]													=					{'image': [image_path]}
			__lowerCamelCase       :						List[str]													=					Features({'image': Image()}  )
			__lowerCamelCase       :						Any													=					Dataset.from_dict(_UpperCAmelCase				,					features=_UpperCAmelCase  )
			__lowerCamelCase       :						Optional[Any]													=					ParquetDatasetWriter(_UpperCAmelCase				,					tmp_path / 'foo.parquet'  )
			assert writer.write() > 0

			__lowerCamelCase       :						List[str]													=					Dataset.from_parquet(str(tmp_path / 'foo.parquet'  )  )
			assert dataset.features == reloaded_dataset.features

			__lowerCamelCase       :						Tuple													=					ParquetDatasetReader(str(tmp_path / 'foo.parquet'  )				,					streaming=_UpperCAmelCase  ).read()
			assert dataset.features == reloaded_iterable_dataset.features






@pytest.mark.parametrize(
    'feature, expected'				,					[
        (Features({'foo': Value('int32'  )}  ), None),
        (Features({'image': Image(), 'foo': Value('int32'  )}  ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({'nested': Sequence(Audio()  )}  ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]				,					)
def     UpperCAmelCase					(      A__:     Any				,					A__:     List[str]  )	->  List[Any]:
			assert get_writer_batch_size(_UpperCAmelCase  ) == expected








'''simple docstring'''





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


A__    :   Union[str, Any]           =					logging.get_logger(__name__)



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= '''upernet'''
 def __init__(   self						:							Optional[int]      ,     __a						:							Dict=None      ,     __a						:							List[str]=512      ,     __a						:							int=0.0_2      ,     __a						:							Optional[Any]=[1, 2, 3, 6]      ,     __a						:							Union[str, Any]=True      ,     __a						:							Any=0.4      ,     __a						:							int=384      ,     __a						:							int=256      ,     __a						:							str=1      ,     __a						:							List[Any]=False      ,     __a						:							Dict=255      ,     **__a						:							Dict      ,     )      ->   Optional[int]:







       '''simple docstring'''
       super().__init__(**__a  )

       if backbone_config is None:
             logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'  )
             __snake_case :   Optional[int]													=    CONFIG_MAPPING['resnet'](out_features=['stage1', 'stage2', 'stage3', 'stage4']  )
       elif isinstance(__a      ,     __a  ):
             __snake_case :   List[Any]													=    backbone_config.get('model_type'  )
             __snake_case :   List[str]													=    CONFIG_MAPPING[backbone_model_type]
             __snake_case :   int													=    config_class.from_dict(__a  )

       __snake_case :   Any													=    backbone_config
       __snake_case :   Tuple													=    hidden_size
       __snake_case :   str													=    initializer_range
       __snake_case :   Dict													=    pool_scales
       __snake_case :   Optional[Any]													=    use_auxiliary_head
       __snake_case :   Optional[Any]													=    auxiliary_loss_weight
       __snake_case :   Optional[int]													=    auxiliary_in_channels
       __snake_case :   Dict													=    auxiliary_channels
       __snake_case :   Any													=    auxiliary_num_convs
       __snake_case :   Optional[Any]													=    auxiliary_concat_input
       __snake_case :   List[str]													=    loss_ignore_index





 def A_   (   self						:							List[Any]  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    copy.deepcopy(self.__dict__  )
       __snake_case :   str													=    self.backbone_config.to_dict()
       __snake_case :   Optional[int]													=    self.__class__.model_type
       return output



