"""simple docstring"""





import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						["image_processor", "tokenizer"]
				a			=						"AutoImageProcessor"
				a			=						"AutoTokenizer"





				def __init__(      self							:							int      ,    _A							:							str=None      ,    _A							:							Tuple=None      ,    **_A							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    None
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
																		    """ instead."""      ,    _A      ,    )
																		_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    kwargs.pop("""feature_extractor""")

											_SCREAMING_SNAKE_CASE		:						Tuple			  =    image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																		raise ValueError("""You need to specify an `image_processor`.""")
											if tokenizer is None:
																		raise ValueError("""You need to specify a `tokenizer`.""")

											super().__init__(_A      ,    _A)
											_SCREAMING_SNAKE_CASE		:						str			  =    self.image_processor
											_SCREAMING_SNAKE_CASE		:						int			  =    False





				def __call__(      self							:							Optional[int]      ,    *_A							:							Optional[int]      ,    **_A							:							Optional[int]):



											"""simple docstring"""







											if self._in_target_context_manager:
																		return self.current_processor(*_A      ,    **_A)

											_SCREAMING_SNAKE_CASE		:						Dict			  =    kwargs.pop("""images"""      ,    _A)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    kwargs.pop("""text"""      ,    _A)
											if len(_A) > 0:
																		_SCREAMING_SNAKE_CASE		:						Dict			  =    args[0]
																		_SCREAMING_SNAKE_CASE		:						Any			  =    args[1:]

											if images is None and text is None:
																		raise ValueError("""You need to specify either an `images` or `text` input to process.""")

											if images is not None:
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.image_processor(_A      ,    *_A      ,    **_A)
											if text is not None:
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.tokenizer(_A      ,    **_A)

											if text is None:
																		return inputs
											elif images is None:
																		return encodings
											else:
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    encodings["""input_ids"""]
																		return inputs





				def 		_lowerCAmelCase		(      self							:							int      ,    *_A							:							Tuple      ,    **_A							:							Dict):



											"""simple docstring"""







											return self.tokenizer.batch_decode(*_A      ,    **_A)





				def 		_lowerCAmelCase		(      self							:							Tuple      ,    *_A							:							Optional[Any]      ,    **_A							:							Any):



											"""simple docstring"""







											return self.tokenizer.decode(*_A      ,    **_A)





				@contextmanager
				def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



											"""simple docstring"""







											warnings.warn(
											    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
											    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
											    """your images inputs, or in a separate call.""")
											_SCREAMING_SNAKE_CASE		:						str			  =    True
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.tokenizer
											yield
											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.image_processor
											_SCREAMING_SNAKE_CASE		:						Dict			  =    False





				def 		_lowerCAmelCase		(      self							:							List[Any]      ,    _A							:							str      ,    _A							:							Any=False      ,    _A							:							List[str]=None):



											"""simple docstring"""







											if added_vocab is None:
																		_SCREAMING_SNAKE_CASE		:						Tuple			  =    self.tokenizer.get_added_vocab()

											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    {}

											while tokens:
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    re.search(r"""<s_(.*?)>"""      ,    _A      ,    re.IGNORECASE)
																		if start_token is None:
																									break
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    start_token.group(1)
																		_SCREAMING_SNAKE_CASE		:						Tuple			  =    re.search(rf"""</s_{key}>"""      ,    _A      ,    re.IGNORECASE)
																		_SCREAMING_SNAKE_CASE		:						int			  =    start_token.group()
																		if end_token is None:
																									_SCREAMING_SNAKE_CASE		:						str			  =    tokens.replace(_A      ,    """""")
																		else:
																									_SCREAMING_SNAKE_CASE		:						Any			  =    end_token.group()
																									_SCREAMING_SNAKE_CASE		:						List[str]			  =    re.escape(_A)
																									_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    re.escape(_A)
																									_SCREAMING_SNAKE_CASE		:						List[Any]			  =    re.search(f"""{start_token_escaped}(.*?){end_token_escaped}"""      ,    _A      ,    re.IGNORECASE)
																									if content is not None:
																																_SCREAMING_SNAKE_CASE		:						str			  =    content.group(1).strip()
																																if r"<s_" in content and r"</s_" in content:  # non-leaf node
																																							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.tokenajson(_A      ,    is_inner_value=_A      ,    added_vocab=_A)
																																							if value:
																																														if len(_A) == 1:
																																																					_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    value[0]
																																														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    value
																																else:  # leaf nodes
																																							_SCREAMING_SNAKE_CASE		:						int			  =    []
																																							for leaf in content.split(r"""<sep/>"""):
																																														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    leaf.strip()
																																														if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																																																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    leaf[1:-2]  # for categorical special tokens
																																														output[key].append(_A)
																																							if len(output[key]) == 1:
																																														_SCREAMING_SNAKE_CASE		:						str			  =    output[key][0]

																									_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    tokens[tokens.find(_A) + len(_A) :].strip()
																									if tokens[:6] == r"<sep/>":  # non-leaf nodes
																																return [output] + self.tokenajson(tokens[6:]      ,    is_inner_value=_A      ,    added_vocab=_A)

											if len(_A):
																		return [output] if is_inner_value else output
											else:
																		return [] if is_inner_value else {"text_sequence": tokens}





				@property
				def 		_lowerCAmelCase		(      self							:							List[str]):



											"""simple docstring"""







											warnings.warn(
											    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""      ,    _A      ,    )
											return self.image_processor_class





				@property
				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											warnings.warn(
											    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""      ,    _A      ,    )
											return self.image_processor



"""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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							assert isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							_SCREAMING_SNAKE_CASE		:						List[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		:						List[Any]			  =    ParquetDatasetReader(__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE     ,	keep_in_memory=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_dataset(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Tuple:
							_SCREAMING_SNAKE_CASE		:						int			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						str			  =    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    features.copy() if features else default_expected_features
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    (
							    Features({feature: Value(__SCREAMING_SNAKE_CASE  ) for feature, dtype in features.items()}  ) if features is not None else None
							)
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    ParquetDatasetReader(__SCREAMING_SNAKE_CASE     ,	features=__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_dataset(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )



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



@pytest.mark.parametrize("""path_type"""     ,	[str, list]  )
def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    str:
							if issubclass(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						Any			  =    parquet_path
							elif issubclass(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    [parquet_path]
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							_SCREAMING_SNAKE_CASE		:						str			  =    ParquetDatasetReader(__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_dataset(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=("train",)  )->    Union[str, Any]:
							assert isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							_SCREAMING_SNAKE_CASE		:						Dict			  =    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(
														    {"""train""": parquet_path}     ,	cache_dir=__SCREAMING_SNAKE_CASE     ,	keep_in_memory=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_datasetdict(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Dict:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    features.copy() if features else default_expected_features
							_SCREAMING_SNAKE_CASE		:						str			  =    (
							    Features({feature: Value(__SCREAMING_SNAKE_CASE  ) for feature, dtype in features.items()}  ) if features is not None else None
							)
							_SCREAMING_SNAKE_CASE		:						int			  =    ParquetDatasetReader({"""train""": parquet_path}     ,	features=__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_datasetdict(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )



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



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

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

							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    ParquetDatasetReader(str(tmp_path / """foo.parquet"""  )     ,	streaming=__SCREAMING_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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    int:
							assert get_writer_batch_size(__SCREAMING_SNAKE_CASE  ) == expected


