



"""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       __magic_name__    (   _lowerCamelCase				: List[Any]  ,       _lowerCamelCase				: Optional[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       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Dict  ,       _lowerCamelCase				: int					):
							__a		:		Union[str, Any]									=       tmp_path / """cache"""
							__a		:		int									=       {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														__a		:		Optional[Any]									=       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       __magic_name__    (   _lowerCamelCase				: Optional[Any]  ,       _lowerCamelCase				: Any  ,       _lowerCamelCase				: Tuple					):
							__a		:		Optional[Any]									=       tmp_path / """cache"""
							__a		:		Optional[Any]									=       {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							__a		:		Optional[Any]									=       features.copy() if features else default_expected_features
							__a		:		int									=       (
							    Features({feature: Value(_lowerCamelCase					) for feature, dtype in features.items()}					) if features is not None else None
							)
							__a		:		Tuple									=       ParquetDatasetReader(_lowerCamelCase  ,       features=_lowerCamelCase  ,       cache_dir=_lowerCamelCase					).read()
							_check_parquet_dataset(_lowerCamelCase  ,       _lowerCamelCase					)





@pytest.mark.parametrize("""split"""  ,       [None, NamedSplit("""train"""					), """train""", """test"""]					)
def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: Dict  ,       _lowerCamelCase				: Union[str, Any]					):
							__a		:		Optional[int]									=       tmp_path / """cache"""
							__a		:		Optional[Any]									=       {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							__a		:		Optional[int]									=       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       __magic_name__    (   _lowerCamelCase				: List[str]  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: List[Any]					):
							if issubclass(_lowerCamelCase  ,       _lowerCamelCase					):
														__a		:		List[Any]									=       parquet_path
							elif issubclass(_lowerCamelCase  ,       _lowerCamelCase					):
														__a		:		int									=       [parquet_path]
							__a		:		Union[str, Any]									=       tmp_path / """cache"""
							__a		:		int									=       {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							__a		:		Union[str, Any]									=       ParquetDatasetReader(_lowerCamelCase  ,       cache_dir=_lowerCamelCase					).read()
							_check_parquet_dataset(_lowerCamelCase  ,       _lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: Tuple  ,       _lowerCamelCase				: Optional[int]=("train",)					):
							assert isinstance(_lowerCamelCase  ,       _lowerCamelCase					)
							for split in splits:
														__a		:		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       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: Optional[Any]  ,       _lowerCamelCase				: int					):
							__a		:		Any									=       tmp_path / """cache"""
							__a		:		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():
														__a		:		Any									=       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       __magic_name__    (   _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: List[str]  ,       _lowerCamelCase				: int					):
							__a		:		Tuple									=       tmp_path / """cache"""
							__a		:		Dict									=       {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							__a		:		Optional[int]									=       features.copy() if features else default_expected_features
							__a		:		str									=       (
							    Features({feature: Value(_lowerCamelCase					) for feature, dtype in features.items()}					) if features is not None else None
							)
							__a		:		List[str]									=       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       __magic_name__    (   _lowerCamelCase				: Tuple  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: List[str]					):
							if split:
														__a		:		List[Any]									=       {split: parquet_path}
							else:
														__a		:		List[str]									=       """train"""
														__a		:		Optional[Any]									=       {"""train""": parquet_path, """test""": parquet_path}
							__a		:		List[str]									=       tmp_path / """cache"""
							__a		:		Dict									=       {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							__a		:		List[str]									=       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       __magic_name__    (   _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: str					):
							__a		:		List[str]									=       ParquetDatasetWriter(_lowerCamelCase  ,       tmp_path / """foo.parquet"""					)
							assert writer.write() > 0
							__a		:		Any									=       pq.ParquetFile(tmp_path / """foo.parquet"""					)
							__a		:		Dict									=       pf.read()
							assert dataset.data.table == output_table





def       __magic_name__    (   _lowerCamelCase				: List[Any]  ,       _lowerCamelCase				: Dict					):
							__a		:		Optional[int]									=       str(shared_datadir / """test_image_rgb.jpg"""					)
							__a		:		Optional[Any]									=       {"""image""": [image_path]}
							__a		:		List[str]									=       Features({"""image""": Image()}					)
							__a		:		int									=       Dataset.from_dict(_lowerCamelCase  ,       features=_lowerCamelCase					)
							__a		:		Tuple									=       ParquetDatasetWriter(_lowerCamelCase  ,       tmp_path / """foo.parquet"""					)
							assert writer.write() > 0

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

							__a		:		List[str]									=       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       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: int					):
							assert get_writer_batch_size(_lowerCamelCase					) == expected






"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__			   =  logging.get_logger(__name__)

lowercase__			   =  {
    "sayakpaul/vit-msn-base": "https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					"vit_msn"



						def __init__(self						,     _lowercase=768						,     _lowercase=12						,     _lowercase=12						,     _lowercase=3072						,     _lowercase="gelu"						,     _lowercase=0.0						,     _lowercase=0.0						,     _lowercase=0.02						,     _lowercase=1e-06						,     _lowercase=224						,     _lowercase=16						,     _lowercase=3						,     _lowercase=True						,     **_lowercase						,     ):





													'''simple docstring'''

													super().__init__(**_lowercase							)

													__a		:		int									=       hidden_size
													__a		:		str									=       num_hidden_layers
													__a		:		str									=       num_attention_heads
													__a		:		Optional[Any]									=       intermediate_size
													__a		:		Union[str, Any]									=       hidden_act
													__a		:		Tuple									=       hidden_dropout_prob
													__a		:		Any									=       attention_probs_dropout_prob
													__a		:		List[Any]									=       initializer_range
													__a		:		Any									=       layer_norm_eps
													__a		:		Dict									=       image_size
													__a		:		List[Any]									=       patch_size
													__a		:		Dict									=       num_channels
													__a		:		Optional[Any]									=       qkv_bias

