
"""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 lowercase_						(     _snake_case     ,_snake_case		):
							assert isinstance(_snake_case     ,_snake_case		)
							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 lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     tmp_path / """cache"""
							SCREAMING_SNAKE_CASE__		: Optional[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():
														SCREAMING_SNAKE_CASE__		: Tuple     =     ParquetDatasetReader(_snake_case     ,cache_dir=_snake_case     ,keep_in_memory=_snake_case		).read()
							_check_parquet_dataset(_snake_case     ,_snake_case		)

@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 lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Dict     =     tmp_path / """cache"""
							SCREAMING_SNAKE_CASE__		: Any     =     {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							SCREAMING_SNAKE_CASE__		: Tuple     =     features.copy() if features else default_expected_features
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     (
							    Features({feature: Value(_snake_case		) for feature, dtype in features.items()}		) if features is not None else None
							)
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     ParquetDatasetReader(_snake_case     ,features=_snake_case     ,cache_dir=_snake_case		).read()
							_check_parquet_dataset(_snake_case     ,_snake_case		)

@pytest.mark.parametrize("""split"""     ,[None, NamedSplit("""train"""		), """train""", """test"""]		)
def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: str     =     tmp_path / """cache"""
							SCREAMING_SNAKE_CASE__		: Dict     =     {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							SCREAMING_SNAKE_CASE__		: int     =     ParquetDatasetReader(_snake_case     ,cache_dir=_snake_case     ,split=_snake_case		).read()
							_check_parquet_dataset(_snake_case     ,_snake_case		)
							assert dataset.split == split if split else "train"

@pytest.mark.parametrize("""path_type"""     ,[str, list]		)
def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							if issubclass(_snake_case     ,_snake_case		):
														SCREAMING_SNAKE_CASE__		: Tuple     =     parquet_path
							elif issubclass(_snake_case     ,_snake_case		):
														SCREAMING_SNAKE_CASE__		: Tuple     =     [parquet_path]
							SCREAMING_SNAKE_CASE__		: Dict     =     tmp_path / """cache"""
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							SCREAMING_SNAKE_CASE__		: Tuple     =     ParquetDatasetReader(_snake_case     ,cache_dir=_snake_case		).read()
							_check_parquet_dataset(_snake_case     ,_snake_case		)

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case=("train",)		):
							assert isinstance(_snake_case     ,_snake_case		)
							for split in splits:
														SCREAMING_SNAKE_CASE__		: int     =     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 lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: str     =     tmp_path / """cache"""
							SCREAMING_SNAKE_CASE__		: 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():
														SCREAMING_SNAKE_CASE__		: Any     =     ParquetDatasetReader(
														    {"""train""": parquet_path}     ,cache_dir=_snake_case     ,keep_in_memory=_snake_case		).read()
							_check_parquet_datasetdict(_snake_case     ,_snake_case		)

@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 lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: List[str]     =     tmp_path / """cache"""
							SCREAMING_SNAKE_CASE__		: List[Any]     =     {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							SCREAMING_SNAKE_CASE__		: Dict     =     features.copy() if features else default_expected_features
							SCREAMING_SNAKE_CASE__		: Any     =     (
							    Features({feature: Value(_snake_case		) for feature, dtype in features.items()}		) if features is not None else None
							)
							SCREAMING_SNAKE_CASE__		: List[str]     =     ParquetDatasetReader({"""train""": parquet_path}     ,features=_snake_case     ,cache_dir=_snake_case		).read()
							_check_parquet_datasetdict(_snake_case     ,_snake_case		)

@pytest.mark.parametrize("""split"""     ,[None, NamedSplit("""train"""		), """train""", """test"""]		)
def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							if split:
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     {split: parquet_path}
							else:
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     """train"""
														SCREAMING_SNAKE_CASE__		: str     =     {"""train""": parquet_path, """test""": parquet_path}
							SCREAMING_SNAKE_CASE__		: str     =     tmp_path / """cache"""
							SCREAMING_SNAKE_CASE__		: str     =     {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     ParquetDatasetReader(_snake_case     ,cache_dir=_snake_case		).read()
							_check_parquet_datasetdict(_snake_case     ,_snake_case     ,splits=list(path.keys()		)		)
							assert all(dataset[split].split == split for split in path.keys()		)

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: List[Any]     =     ParquetDatasetWriter(_snake_case     ,tmp_path / """foo.parquet"""		)
							assert writer.write() > 0
							SCREAMING_SNAKE_CASE__		: Tuple     =     pq.ParquetFile(tmp_path / """foo.parquet"""		)
							SCREAMING_SNAKE_CASE__		: str     =     pf.read()
							assert dataset.data.table == output_table

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

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

							SCREAMING_SNAKE_CASE__		: Optional[int]     =     ParquetDatasetReader(str(tmp_path / """foo.parquet"""		)     ,streaming=_snake_case		).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 lowercase_						(     _snake_case     ,_snake_case		):
							assert get_writer_batch_size(_snake_case		) == expected






"""simple docstring"""




import logging

from transformers import PretrainedConfig


UpperCAmelCase__   :					Union[str, Any]												=				logging.getLogger(__name__)


UpperCAmelCase__   :					Any												=				{
    'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				__UpperCamelCase   :		List[Any]       =     '''bertabs'''



				def __init__(self     , SCREAMING_SNAKE_CASE__=3_05_22     , SCREAMING_SNAKE_CASE__=5_12     , SCREAMING_SNAKE_CASE__=6     , SCREAMING_SNAKE_CASE__=5_12     , SCREAMING_SNAKE_CASE__=8     , SCREAMING_SNAKE_CASE__=5_12     , SCREAMING_SNAKE_CASE__=0.2     , SCREAMING_SNAKE_CASE__=6     , SCREAMING_SNAKE_CASE__=7_68     , SCREAMING_SNAKE_CASE__=8     , SCREAMING_SNAKE_CASE__=20_48     , SCREAMING_SNAKE_CASE__=0.2     , **SCREAMING_SNAKE_CASE__     , )      ->	Union[str, Any]:



											"""simple docstring"""




											super().__init__(**SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     vocab_size
											SCREAMING_SNAKE_CASE__		: Any     =     max_pos

											SCREAMING_SNAKE_CASE__		: List[Any]     =     enc_layers
											SCREAMING_SNAKE_CASE__		: Tuple     =     enc_hidden_size
											SCREAMING_SNAKE_CASE__		: Dict     =     enc_heads
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     enc_ff_size
											SCREAMING_SNAKE_CASE__		: Dict     =     enc_dropout

											SCREAMING_SNAKE_CASE__		: Any     =     dec_layers
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     dec_hidden_size
											SCREAMING_SNAKE_CASE__		: Any     =     dec_heads
											SCREAMING_SNAKE_CASE__		: List[Any]     =     dec_ff_size
											SCREAMING_SNAKE_CASE__		: str     =     dec_dropout




