



"""simple docstring"""






import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class     A__     (						__SCREAMING_SNAKE_CASE						,	unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			BarthezTokenizer
	SCREAMING_SNAKE_CASE					   =			BarthezTokenizerFast
	SCREAMING_SNAKE_CASE					   =			True
	SCREAMING_SNAKE_CASE					   =			True

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int])       -> Any:

				"""simple docstring"""

				super().setUp()

				__lowerCAmelCase   :						List[Any]				= BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez")
				tokenizer.save_pretrained(self.tmpdirname)
				tokenizer.save_pretrained(self.tmpdirname ,    legacy_format=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						str				= tokenizer

	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> Tuple:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= "<pad>"
				__lowerCAmelCase   :						Tuple				= 1

				self.assertEqual(self.get_tokenizer()._convert_token_to_id(_SCREAMING_SNAKE_CASE) ,    _SCREAMING_SNAKE_CASE)
				self.assertEqual(self.get_tokenizer()._convert_id_to_token(_SCREAMING_SNAKE_CASE) ,    _SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						List[Any]				= list(self.get_tokenizer().get_vocab().keys())

				self.assertEqual(vocab_keys[0] ,    "<s>")
				self.assertEqual(vocab_keys[1] ,    "<pad>")
				self.assertEqual(vocab_keys[-1] ,    "<mask>")
				self.assertEqual(len(_SCREAMING_SNAKE_CASE) ,    10_1122)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> List[Any]:

				"""simple docstring"""

				self.assertEqual(self.get_tokenizer().vocab_size ,    10_1122)

	@require_torch
	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						str				= ["A long paragraph for summarization.", "Another paragraph for summarization."]
				__lowerCAmelCase   :						Optional[Any]				= [0, 57, 3018, 7_0307, 91, 2]

				__lowerCAmelCase   :						List[Any]				= self.tokenizer(
				    _SCREAMING_SNAKE_CASE ,    max_length=len(_SCREAMING_SNAKE_CASE) ,    padding=_SCREAMING_SNAKE_CASE ,    truncation=_SCREAMING_SNAKE_CASE ,    return_tensors="pt")
				self.assertIsInstance(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

				self.assertEqual((2, 6) ,    batch.input_ids.shape)
				self.assertEqual((2, 6) ,    batch.attention_mask.shape)
				__lowerCAmelCase   :						str				= batch.input_ids.tolist()[0]
				self.assertListEqual(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Any:

				"""simple docstring"""

				if not self.test_rust_tokenizer:
							return

				__lowerCAmelCase   :						Any				= self.get_tokenizer()
				__lowerCAmelCase   :						str				= self.get_rust_tokenizer()

				__lowerCAmelCase   :						List[str]				= "I was born in 92000, and this is falsé."

				__lowerCAmelCase   :						Union[str, Any]				= tokenizer.tokenize(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Tuple				= rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE)
				self.assertListEqual(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						str				= tokenizer.encode(_SCREAMING_SNAKE_CASE ,    add_special_tokens=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						str				= rust_tokenizer.encode(_SCREAMING_SNAKE_CASE ,    add_special_tokens=_SCREAMING_SNAKE_CASE)
				self.assertListEqual(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Union[str, Any]				= self.get_rust_tokenizer()
				__lowerCAmelCase   :						List[Any]				= tokenizer.encode(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Dict				= rust_tokenizer.encode(_SCREAMING_SNAKE_CASE)
				self.assertListEqual(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)







	@slow
	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> List[str]:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[Any]				= {"input_ids": [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
				# fmt: on

				# moussaKam/mbarthez is a french model. So we also use french texts.
				__lowerCAmelCase   :						Any				= [
				    "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
				    "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
				    "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
				    "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
				    "telles que la traduction et la synthèse de texte.",
				]

				self.tokenizer_integration_test_util(
				    expected_encoding=_SCREAMING_SNAKE_CASE ,    model_name="moussaKam/mbarthez" ,    revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6" ,    sequences=_SCREAMING_SNAKE_CASE ,    )




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


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

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

			__lowerCAmelCase   :						List[Any]				= 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			)		->     List[Any]:
			assert get_writer_batch_size(__snake_case			) == expected