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



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



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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   Tuple  ):
						_SCREAMING_SNAKE_CASE				= str(shared_datadir / "test_image_rgb.jpg"  )
						_SCREAMING_SNAKE_CASE				= {"image": [image_path]}
						_SCREAMING_SNAKE_CASE				= Features({"image": Image()}  )
						_SCREAMING_SNAKE_CASE				= Dataset.from_dict(lowerCAmelCase__	,			features=lowerCAmelCase__  )
						_SCREAMING_SNAKE_CASE				= ParquetDatasetWriter(lowerCAmelCase__	,			tmp_path / "foo.parquet"  )
						assert writer.write() > 0

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

						_SCREAMING_SNAKE_CASE				= 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		(	_SCREAMING_SNAKE_CASE		:   List[str]	,			_SCREAMING_SNAKE_CASE		:   Any  ):
						assert get_writer_batch_size(lowerCAmelCase__  ) == expected

'''simple docstring'''







from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest


@require_tokenizers
class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Dict       =					DistilBertTokenizer
		a							:					Tuple       =					DistilBertTokenizerFast
		a							:					List[str]       =					True




		@slow
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= DistilBertTokenizer.from_pretrained("distilbert-base-uncased"      )

								_SCREAMING_SNAKE_CASE				= tokenizer.encode("sequence builders"					,						add_special_tokens=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= tokenizer.encode("multi-sequence build"					,						add_special_tokens=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase					,						UpperCamelCase      )

								assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
								assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
								    tokenizer.sep_token_id
								]