






"""simple docstring"""







import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):
					assert isinstance(lowerCAmelCase__ ,     lowerCAmelCase__)
					assert dataset.num_rows == 4
					assert dataset.num_columns == 1
					assert dataset.column_names == ["text"]
					for feature, expected_dtype in expected_features.items():
										assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('''keep_in_memory''' ,     [False, True])
def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  tmp_path / '''cache'''
					a__          =  {'''text''': '''string'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										a__          =  TextDatasetReader(lowerCAmelCase__ ,     cache_dir=lowerCAmelCase__ ,     keep_in_memory=lowerCAmelCase__).read()
					_check_text_dataset(lowerCAmelCase__ ,     lowerCAmelCase__)





@pytest.mark.parametrize(
    '''features''' ,     [
        None,
        {'''text''': '''string'''},
        {'''text''': '''int32'''},
        {'''text''': '''float32'''},
    ] ,     )
def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  tmp_path / '''cache'''
					a__          =  {'''text''': '''string'''}
					a__          =  features.copy() if features else default_expected_features
					a__          =  (
					    Features({feature: Value(lowerCAmelCase__) for feature, dtype in features.items()}) if features is not None else None
					)
					a__          =  TextDatasetReader(lowerCAmelCase__ ,     features=lowerCAmelCase__ ,     cache_dir=lowerCAmelCase__).read()
					_check_text_dataset(lowerCAmelCase__ ,     lowerCAmelCase__)





@pytest.mark.parametrize('''split''' ,     [None, NamedSplit('''train'''), '''train''', '''test'''])
def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  tmp_path / '''cache'''
					a__          =  {'''text''': '''string'''}
					a__          =  TextDatasetReader(lowerCAmelCase__ ,     cache_dir=lowerCAmelCase__ ,     split=lowerCAmelCase__).read()
					_check_text_dataset(lowerCAmelCase__ ,     lowerCAmelCase__)
					assert dataset.split == split if split else "train"





@pytest.mark.parametrize('''path_type''' ,     [str, list])
def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					if issubclass(lowerCAmelCase__ ,     lowerCAmelCase__):
										a__          =  text_path
					elif issubclass(lowerCAmelCase__ ,     lowerCAmelCase__):
										a__          =  [text_path]
					a__          =  tmp_path / '''cache'''
					a__          =  {'''text''': '''string'''}
					a__          =  TextDatasetReader(lowerCAmelCase__ ,     cache_dir=lowerCAmelCase__).read()
					_check_text_dataset(lowerCAmelCase__ ,     lowerCAmelCase__)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=("train",)):
					assert isinstance(lowerCAmelCase__ ,     lowerCAmelCase__)
					for split in splits:
										a__          =  dataset_dict[split]
										assert dataset.num_rows == 4
										assert dataset.num_columns == 1
										assert dataset.column_names == ["text"]
										for feature, expected_dtype in expected_features.items():
															assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('''keep_in_memory''' ,     [False, True])
def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  tmp_path / '''cache'''
					a__          =  {'''text''': '''string'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										a__          =  TextDatasetReader({'''train''': text_path} ,     cache_dir=lowerCAmelCase__ ,     keep_in_memory=lowerCAmelCase__).read()
					_check_text_datasetdict(lowerCAmelCase__ ,     lowerCAmelCase__)





@pytest.mark.parametrize(
    '''features''' ,     [
        None,
        {'''text''': '''string'''},
        {'''text''': '''int32'''},
        {'''text''': '''float32'''},
    ] ,     )
def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  tmp_path / '''cache'''
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					a__          =  {'''text''': '''string'''}
					a__          =  features.copy() if features else default_expected_features
					a__          =  (
					    Features({feature: Value(lowerCAmelCase__) for feature, dtype in features.items()}) if features is not None else None
					)
					a__          =  TextDatasetReader({'''train''': text_path} ,     features=lowerCAmelCase__ ,     cache_dir=lowerCAmelCase__).read()
					_check_text_datasetdict(lowerCAmelCase__ ,     lowerCAmelCase__)





@pytest.mark.parametrize('''split''' ,     [None, NamedSplit('''train'''), '''train''', '''test'''])
def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					if split:
										a__          =  {split: text_path}
					else:
										a__          =  '''train'''
										a__          =  {'''train''': text_path, '''test''': text_path}
					a__          =  tmp_path / '''cache'''
					a__          =  {'''text''': '''string'''}
					a__          =  TextDatasetReader(lowerCAmelCase__ ,     cache_dir=lowerCAmelCase__).read()
					_check_text_datasetdict(lowerCAmelCase__ ,     lowerCAmelCase__ ,     splits=list(path.keys()))
					assert all(dataset[split].split == split for split in path.keys())










"""simple docstring"""







from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='EncodecFeatureExtractor'
							_SCREAMING_SNAKE_CASE    =('T5Tokenizer', 'T5TokenizerFast')




							def __init__(   self:     List[Any]				,		__A:     Any				,		__A:     Dict    ):

												'''simple docstring'''

												super().__init__(__A				,		__A    )
												a__          =  self.feature_extractor
												a__          =  False




							def  lowercase		(   self:     Union[str, Any]				,		__A:     List[Any]=None				,		__A:     Optional[Any]=None				,		__A:     List[Any]=True    ):

												'''simple docstring'''

												return self.tokenizer.get_decoder_prompt_ids(task=__A				,		language=__A				,		no_timestamps=__A    )




							def __call__(   self:     Union[str, Any]				,		*__A:     int				,		**__A:     Dict    ):

												'''simple docstring'''

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

												a__          =  kwargs.pop('''audio'''				,		__A    )
												a__          =  kwargs.pop('''sampling_rate'''				,		__A    )
												a__          =  kwargs.pop('''text'''				,		__A    )
												if len(__A    ) > 0:
																	a__          =  args[0]
																	a__          =  args[1:]

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

												if text is not None:
																	a__          =  self.tokenizer(__A				,		**__A    )

												if audio is not None:
																	a__          =  self.feature_extractor(__A				,		*__A				,		sampling_rate=__A				,		**__A    )

												if audio is None:
																	return inputs

												elif text is None:
																	return audio_inputs

												else:
																	a__          =  audio_inputs['''input_values''']
																	if "padding_mask" in audio_inputs:
																						a__          =  audio_inputs['''padding_mask''']
																	return inputs




							def  lowercase		(   self:     Union[str, Any]				,		*__A:     List[str]				,		**__A:     Tuple    ):

												'''simple docstring'''

												a__          =  kwargs.pop('''audio'''				,		__A    )
												a__          =  kwargs.pop('''padding_mask'''				,		__A    )

												if len(__A    ) > 0:
																	a__          =  args[0]
																	a__          =  args[1:]

												if audio_values is not None:
																	return self._decode_audio(__A				,		padding_mask=__A    )
												else:
																	return self.tokenizer.batch_decode(*__A				,		**__A    )




							def  lowercase		(   self:     Union[str, Any]				,		*__A:     Optional[int]				,		**__A:     Optional[Any]    ):

												'''simple docstring'''

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





							def  lowercase		(   self:     Union[str, Any]				,		__A:     Dict				,		__A:     Optional = None    ):

												'''simple docstring'''

												a__          =  to_numpy(__A    )
												a__       ,a__       ,a__          =  audio_values.shape

												if padding_mask is None:
																	return list(__A    )

												a__          =  to_numpy(__A    )

												# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
												# token (so that the generated audio values are **not** treated as padded tokens)
												a__          =  seq_len - padding_mask.shape[-1]
												a__          =  1 - self.feature_extractor.padding_value
												a__          =  np.pad(__A				,		((0, 0), (0, difference))				,		'''constant'''				,		constant_values=__A    )

												a__          =  audio_values.tolist()
												for i in range(__A    ):
																	a__          =  np.asarray(audio_values[i]    )[
																	    padding_mask[i][None, :] != self.feature_extractor.padding_value
																	]
																	a__          =  sliced_audio.reshape(__A				,		-1    )

												return audio_values


