



'''simple docstring'''






from ...processing_utils import ProcessorMixin

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= """SpeechT5FeatureExtractor"""
	lowerCamelCase__    				= """SpeechT5Tokenizer"""







	def __init__(		self    :  Any	,    __snake_case    :  Optional[Any]	,    __snake_case    :  Tuple	)    ->	List[str]:
								super().__init__(__snake_case	,    __snake_case	)







	def __call__(		self    :  str	,    *__snake_case    :  int	,    **__snake_case    :  str	)    ->	str:
								UpperCAmelCase				: Optional[Any]											=	kwargs.pop('''audio'''	,    __snake_case	)
								UpperCAmelCase				: List[Any]											=	kwargs.pop('''text'''	,    __snake_case	)
								UpperCAmelCase				: Tuple											=	kwargs.pop('''text_target'''	,    __snake_case	)
								UpperCAmelCase				: Union[str, Any]											=	kwargs.pop('''audio_target'''	,    __snake_case	)
								UpperCAmelCase				: List[str]											=	kwargs.pop('''sampling_rate'''	,    __snake_case	)

								if audio is not None and text is not None:
															raise ValueError(
															    '''Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?'''	)
								if audio_target is not None and text_target is not None:
															raise ValueError(
															    '''Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?'''	)
								if audio is None and audio_target is None and text is None and text_target is None:
															raise ValueError(
															    '''You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.'''	)

								if audio is not None:
															UpperCAmelCase				: List[Any]											=	self.feature_extractor(__snake_case	,    *__snake_case	,    sampling_rate=__snake_case	,    **__snake_case	)
								elif text is not None:
															UpperCAmelCase				: int											=	self.tokenizer(__snake_case	,    **__snake_case	)
								else:
															UpperCAmelCase				: Any											=	None

								if audio_target is not None:
															UpperCAmelCase				: Tuple											=	self.feature_extractor(audio_target=__snake_case	,    *__snake_case	,    sampling_rate=__snake_case	,    **__snake_case	)
															UpperCAmelCase				: Optional[int]											=	targets['''input_values''']
								elif text_target is not None:
															UpperCAmelCase				: Optional[int]											=	self.tokenizer(__snake_case	,    **__snake_case	)
															UpperCAmelCase				: List[Any]											=	targets['''input_ids''']
								else:
															UpperCAmelCase				: Any											=	None

								if inputs is None:
															return targets

								if targets is not None:
															UpperCAmelCase				: Union[str, Any]											=	labels

															UpperCAmelCase				: Union[str, Any]											=	targets.get('''attention_mask'''	)
															if decoder_attention_mask is not None:
																						UpperCAmelCase				: List[str]											=	decoder_attention_mask

								return inputs







	def   A			(		self    :  Optional[Any]	,    *__snake_case    :  Union[str, Any]	,    **__snake_case    :  Optional[int]	)    ->	Optional[int]:
								UpperCAmelCase				: Any											=	kwargs.pop('''input_values'''	,    __snake_case	)
								UpperCAmelCase				: str											=	kwargs.pop('''input_ids'''	,    __snake_case	)
								UpperCAmelCase				: List[Any]											=	kwargs.pop('''labels'''	,    __snake_case	)

								if input_values is not None and input_ids is not None:
															raise ValueError('''Cannot process both `input_values` and `input_ids` inputs.'''	)
								if input_values is None and input_ids is None and labels is None:
															raise ValueError(
															    '''You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.'''	)

								if input_values is not None:
															UpperCAmelCase				: List[Any]											=	self.feature_extractor.pad(__snake_case	,    *__snake_case	,    **__snake_case	)
								elif input_ids is not None:
															UpperCAmelCase				: Optional[int]											=	self.tokenizer.pad(__snake_case	,    **__snake_case	)
								else:
															UpperCAmelCase				: Dict											=	None

								if labels is not None:
															if "input_ids" in labels or (isinstance(__snake_case	,    __snake_case	) and "input_ids" in labels[0]):
																						UpperCAmelCase				: Dict											=	self.tokenizer.pad(__snake_case	,    **__snake_case	)
																						UpperCAmelCase				: List[Any]											=	targets['''input_ids''']
															else:
																						UpperCAmelCase				: int											=	self.feature_extractor.feature_size
																						UpperCAmelCase				: Optional[int]											=	self.feature_extractor.num_mel_bins
																						UpperCAmelCase				: int											=	self.feature_extractor.pad(__snake_case	,    *__snake_case	,    **__snake_case	)
																						UpperCAmelCase				: str											=	feature_size_hack
																						UpperCAmelCase				: str											=	targets['''input_values''']
								else:
															UpperCAmelCase				: Optional[int]											=	None

								if inputs is None:
															return targets

								if targets is not None:
															UpperCAmelCase				: Optional[Any]											=	labels

															UpperCAmelCase				: int											=	targets.get('''attention_mask'''	)
															if decoder_attention_mask is not None:
																						UpperCAmelCase				: Any											=	decoder_attention_mask

								return inputs







	def   A			(		self    :  Tuple	,    *__snake_case    :  str	,    **__snake_case    :  Optional[Any]	)    ->	Optional[Any]:
								return self.tokenizer.batch_decode(*__snake_case	,    **__snake_case	)







	def   A			(		self    :  str	,    *__snake_case    :  str	,    **__snake_case    :  Union[str, Any]	)    ->	List[Any]:
								return self.tokenizer.decode(*__snake_case	,    **__snake_case	)










'''simple docstring'''






import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy


def 				snake_case_  (      _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		Tuple						)   ->     str:
							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


@require_sqlalchemy
@pytest.mark.parametrize('''keep_in_memory'''     ,   [False, True]						)
def 				snake_case_  (      _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		List[str]     ,   _lowerCAmelCase						:		Any     ,   _lowerCAmelCase						:		Optional[Any]						)   ->     List[str]:
							UpperCAmelCase				: int											=	tmp_path / '''cache'''
							UpperCAmelCase				: Dict											=	{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														UpperCAmelCase				: Any											=	SqlDatasetReader(
														    '''dataset'''     ,   '''sqlite:///''' + sqlite_path     ,   cache_dir=_lowerCAmelCase     ,   keep_in_memory=_lowerCAmelCase						).read()
							_check_sql_dataset(_lowerCAmelCase     ,   _lowerCAmelCase						)


@require_sqlalchemy
@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 				snake_case_  (      _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		Any     ,   _lowerCAmelCase						:		Optional[int]     ,   _lowerCAmelCase						:		str						)   ->     Optional[int]:
							UpperCAmelCase				: Union[str, Any]											=	tmp_path / '''cache'''
							UpperCAmelCase				: Union[str, Any]											=	{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
							UpperCAmelCase				: str											=	features.copy() if features else default_expected_features
							UpperCAmelCase				: Optional[Any]											=	(
							    Features({feature: Value(_lowerCAmelCase						) for feature, dtype in features.items()}						) if features is not None else None
							)
							UpperCAmelCase				: Tuple											=	SqlDatasetReader('''dataset'''     ,   '''sqlite:///''' + sqlite_path     ,   features=_lowerCAmelCase     ,   cache_dir=_lowerCAmelCase						).read()
							_check_sql_dataset(_lowerCAmelCase     ,   _lowerCAmelCase						)


def 				snake_case_  (      _lowerCAmelCase						:		List[Any]						)   ->     Dict:
							with contextlib.closing(sqlitea.connect(_lowerCAmelCase						)						) as con:
														UpperCAmelCase				: Any											=	con.cursor()
														cur.execute('''SELECT * FROM dataset'''						)
														for row in cur:
																					yield row


@require_sqlalchemy
def 				snake_case_  (      _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		Any						)   ->     Optional[int]:
							UpperCAmelCase				: Optional[int]											=	tmp_path / '''cache'''
							UpperCAmelCase				: int											=	os.path.join(_lowerCAmelCase     ,   '''tmp.sql'''						)
							UpperCAmelCase				: Any											=	SqlDatasetReader('''dataset'''     ,   '''sqlite:///''' + sqlite_path     ,   cache_dir=_lowerCAmelCase						).read()
							SqlDatasetWriter(_lowerCAmelCase     ,   '''dataset'''     ,   '''sqlite:///''' + output_sqlite_path     ,   num_proc=1						).write()

							UpperCAmelCase				: List[Any]											=	iter_sql_file(_lowerCAmelCase						)
							UpperCAmelCase				: List[Any]											=	iter_sql_file(_lowerCAmelCase						)

							for rowa, rowa in zip(_lowerCAmelCase     ,   _lowerCAmelCase						):
														assert rowa == rowa


@require_sqlalchemy
def 				snake_case_  (      _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		Optional[Any]     ,   _lowerCAmelCase						:		Union[str, Any]						)   ->     int:
							UpperCAmelCase				: Optional[Any]											=	tmp_path / '''cache'''
							UpperCAmelCase				: Any											=	os.path.join(_lowerCAmelCase     ,   '''tmp.sql'''						)
							UpperCAmelCase				: List[str]											=	SqlDatasetReader('''dataset'''     ,   '''sqlite:///''' + sqlite_path     ,   cache_dir=_lowerCAmelCase						).read()
							SqlDatasetWriter(_lowerCAmelCase     ,   '''dataset'''     ,   '''sqlite:///''' + output_sqlite_path     ,   num_proc=2						).write()

							UpperCAmelCase				: List[str]											=	iter_sql_file(_lowerCAmelCase						)
							UpperCAmelCase				: Any											=	iter_sql_file(_lowerCAmelCase						)

							for rowa, rowa in zip(_lowerCAmelCase     ,   _lowerCAmelCase						):
														assert rowa == rowa




@require_sqlalchemy
def 				snake_case_  (      _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		Any     ,   _lowerCAmelCase						:		Optional[Any]						)   ->     List[Any]:
							UpperCAmelCase				: Union[str, Any]											=	tmp_path / '''cache'''
							UpperCAmelCase				: Tuple											=	os.path.join(_lowerCAmelCase     ,   '''tmp.sql'''						)
							UpperCAmelCase				: Optional[int]											=	SqlDatasetReader('''dataset'''     ,   '''sqlite:///''' + sqlite_path     ,   cache_dir=_lowerCAmelCase						).read()
							with pytest.raises(_lowerCAmelCase						):
														SqlDatasetWriter(_lowerCAmelCase     ,   '''dataset'''     ,   '''sqlite:///''' + output_sqlite_path     ,   num_proc=0						).write()





