






'''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     A__      (					__lowerCAmelCase		:      Union[str, Any] ,     __lowerCAmelCase		:      Any    ):
						assert isinstance(__lowerCAmelCase ,     __lowerCAmelCase    )
						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     A__      (					__lowerCAmelCase		:      Union[str, Any] ,     __lowerCAmelCase		:      Tuple ,     __lowerCAmelCase		:      Dict    ):
						lowerCamelCase__								=	tmp_path / """cache"""
						lowerCamelCase__								=	{"""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__								=	ParquetDatasetReader(__lowerCAmelCase ,     cache_dir=__lowerCAmelCase ,     keep_in_memory=__lowerCAmelCase    ).read()
						_check_parquet_dataset(__lowerCAmelCase ,     __lowerCAmelCase    )
@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     A__      (					__lowerCAmelCase		:      Optional[Any] ,     __lowerCAmelCase		:      Union[str, Any] ,     __lowerCAmelCase		:      List[Any]    ):
						lowerCamelCase__								=	tmp_path / """cache"""
						lowerCamelCase__								=	{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						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__								=	ParquetDatasetReader(__lowerCAmelCase ,     features=__lowerCAmelCase ,     cache_dir=__lowerCAmelCase    ).read()
						_check_parquet_dataset(__lowerCAmelCase ,     __lowerCAmelCase    )
@pytest.mark.parametrize("""split""" ,     [None, NamedSplit("""train"""    ), """train""", """test"""]    )
def     A__      (					__lowerCAmelCase		:      Any ,     __lowerCAmelCase		:      int ,     __lowerCAmelCase		:      Optional[Any]    ):
						lowerCamelCase__								=	tmp_path / """cache"""
						lowerCamelCase__								=	{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						lowerCamelCase__								=	ParquetDatasetReader(__lowerCAmelCase ,     cache_dir=__lowerCAmelCase ,     split=__lowerCAmelCase    ).read()
						_check_parquet_dataset(__lowerCAmelCase ,     __lowerCAmelCase    )
						assert dataset.split == split if split else "train"
@pytest.mark.parametrize("""path_type""" ,     [str, list]    )
def     A__      (					__lowerCAmelCase		:      Any ,     __lowerCAmelCase		:      List[str] ,     __lowerCAmelCase		:      List[Any]    ):
						if issubclass(__lowerCAmelCase ,     __lowerCAmelCase    ):
												lowerCamelCase__								=	parquet_path
						elif issubclass(__lowerCAmelCase ,     __lowerCAmelCase    ):
												lowerCamelCase__								=	[parquet_path]
						lowerCamelCase__								=	tmp_path / """cache"""
						lowerCamelCase__								=	{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						lowerCamelCase__								=	ParquetDatasetReader(__lowerCAmelCase ,     cache_dir=__lowerCAmelCase    ).read()
						_check_parquet_dataset(__lowerCAmelCase ,     __lowerCAmelCase    )
def     A__      (					__lowerCAmelCase		:      Any ,     __lowerCAmelCase		:      str ,     __lowerCAmelCase		:      Any=("train",)    ):
						assert isinstance(__lowerCAmelCase ,     __lowerCAmelCase    )
						for split in splits:
												lowerCamelCase__								=	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     A__      (					__lowerCAmelCase		:      Optional[int] ,     __lowerCAmelCase		:      str ,     __lowerCAmelCase		:      Any    ):
						lowerCamelCase__								=	tmp_path / """cache"""
						lowerCamelCase__								=	{"""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__								=	ParquetDatasetReader(
												    {"""train""": parquet_path} ,     cache_dir=__lowerCAmelCase ,     keep_in_memory=__lowerCAmelCase    ).read()
						_check_parquet_datasetdict(__lowerCAmelCase ,     __lowerCAmelCase    )
@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     A__      (					__lowerCAmelCase		:      str ,     __lowerCAmelCase		:      Any ,     __lowerCAmelCase		:      Optional[Any]    ):
						lowerCamelCase__								=	tmp_path / """cache"""
						lowerCamelCase__								=	{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						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__								=	ParquetDatasetReader({"""train""": parquet_path} ,     features=__lowerCAmelCase ,     cache_dir=__lowerCAmelCase    ).read()
						_check_parquet_datasetdict(__lowerCAmelCase ,     __lowerCAmelCase    )
@pytest.mark.parametrize("""split""" ,     [None, NamedSplit("""train"""    ), """train""", """test"""]    )
def     A__      (					__lowerCAmelCase		:      str ,     __lowerCAmelCase		:      Dict ,     __lowerCAmelCase		:      List[Any]    ):
						if split:
												lowerCamelCase__								=	{split: parquet_path}
						else:
												lowerCamelCase__								=	"""train"""
												lowerCamelCase__								=	{"""train""": parquet_path, """test""": parquet_path}
						lowerCamelCase__								=	tmp_path / """cache"""
						lowerCamelCase__								=	{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						lowerCamelCase__								=	ParquetDatasetReader(__lowerCAmelCase ,     cache_dir=__lowerCAmelCase    ).read()
						_check_parquet_datasetdict(__lowerCAmelCase ,     __lowerCAmelCase ,     splits=list(path.keys()    )    )
						assert all(dataset[split].split == split for split in path.keys()    )
def     A__      (					__lowerCAmelCase		:      Any ,     __lowerCAmelCase		:      Any    ):
						lowerCamelCase__								=	ParquetDatasetWriter(__lowerCAmelCase ,     tmp_path / """foo.parquet"""    )
						assert writer.write() > 0
						lowerCamelCase__								=	pq.ParquetFile(tmp_path / """foo.parquet"""    )
						lowerCamelCase__								=	pf.read()
						assert dataset.data.table == output_table
def     A__      (					__lowerCAmelCase		:      Dict ,     __lowerCAmelCase		:      Any    ):
						lowerCamelCase__								=	str(shared_datadir / """test_image_rgb.jpg"""    )
						lowerCamelCase__								=	{"""image""": [image_path]}
						lowerCamelCase__								=	Features({"""image""": Image()}    )
						lowerCamelCase__								=	Dataset.from_dict(__lowerCAmelCase ,     features=__lowerCAmelCase    )
						lowerCamelCase__								=	ParquetDatasetWriter(__lowerCAmelCase ,     tmp_path / """foo.parquet"""    )
						assert writer.write() > 0

						lowerCamelCase__								=	Dataset.from_parquet(str(tmp_path / """foo.parquet"""    )    )
						assert dataset.features == reloaded_dataset.features

						lowerCamelCase__								=	ParquetDatasetReader(str(tmp_path / """foo.parquet"""    ) ,     streaming=__lowerCAmelCase    ).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     A__      (					__lowerCAmelCase		:      Tuple ,     __lowerCAmelCase		:      int    ):
						assert get_writer_batch_size(__lowerCAmelCase    ) == expected




from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A									=  logging.get_logger(__name__)

A									=  {
    'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}


class SCREAMING_SNAKE_CASE	(		__snake_case				,    __snake_case				):






			"""simple docstring"""




			__A    	=	"""bit"""
			__A    	=	["""preactivation""", """bottleneck"""]
			__A    	=	["""SAME""", """VALID"""]


			def __init__(     self			,					__UpperCamelCase=3			,					__UpperCamelCase=64			,					__UpperCamelCase=[2_56, 5_12, 10_24, 20_48]			,					__UpperCamelCase=[3, 4, 6, 3]			,					__UpperCamelCase="preactivation"			,					__UpperCamelCase="relu"			,					__UpperCamelCase=None			,					__UpperCamelCase=32			,					__UpperCamelCase=0.0			,					__UpperCamelCase=False			,					__UpperCamelCase=32			,					__UpperCamelCase=1			,					__UpperCamelCase=None			,					__UpperCamelCase=None			,					**__UpperCamelCase			,					):

				"""simple docstring"""


				super().__init__(**__UpperCamelCase				)
				if layer_type not in self.layer_types:
					raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types				)}"""				)
				if global_padding is not None:
					if global_padding.upper() in self.supported_padding:
						snake_case_   		=			global_padding.upper()
					else:
						raise ValueError(f"""Padding strategy {global_padding} not supported"""				)
				snake_case_   		=			num_channels
				snake_case_   		=			embedding_size
				snake_case_   		=			hidden_sizes
				snake_case_   		=			depths
				snake_case_   		=			layer_type
				snake_case_   		=			hidden_act
				snake_case_   		=			global_padding
				snake_case_   		=			num_groups
				snake_case_   		=			drop_path_rate
				snake_case_   		=			embedding_dynamic_padding
				snake_case_   		=			output_stride
				snake_case_   		=			width_factor

				snake_case_   		=			['stem'] + [f"""stage{idx}""" for idx in range(1			,					len(__UpperCamelCase				) + 1				)]
				snake_case_		,							snake_case_   		=			get_aligned_output_features_output_indices(
				    out_features=__UpperCamelCase			,					out_indices=__UpperCamelCase			,					stage_names=self.stage_names				)
