





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class 			lowercase				(							a_     ):



						"""simple docstring"""

						_UpperCamelCase  :	Dict								=	"Speech2TextFeatureExtractor"
						_UpperCamelCase  :	List[Any]								=	"Speech2TextTokenizer"







						def __init__(    self			:		Optional[int] ,				lowerCamelCase_			:		Any ,				lowerCamelCase_			:		Tuple					):





										'''simple docstring'''







										super().__init__(lowerCamelCase_ ,				lowerCamelCase_					)
										_snake_case :			Dict  						=   self.feature_extractor
										_snake_case :			Union[str, Any]  						=   False







						def __call__(    self			:		List[str] ,				*lowerCamelCase_			:		Any ,				**lowerCamelCase_			:		Optional[Any]					):





										'''simple docstring'''







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

										if "raw_speech" in kwargs:
														warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'					)
														_snake_case :			List[str]  						=   kwargs.pop('raw_speech'					)
										else:
														_snake_case :			Union[str, Any]  						=   kwargs.pop('audio' ,				lowerCamelCase_					)
										_snake_case :			Tuple  						=   kwargs.pop('sampling_rate' ,				lowerCamelCase_					)
										_snake_case :			Optional[Any]  						=   kwargs.pop('text' ,				lowerCamelCase_					)
										if len(lowerCamelCase_					) > 0:
														_snake_case :			Tuple  						=   args[0]
														_snake_case :			str  						=   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 audio is not None:
														_snake_case :			Dict  						=   self.feature_extractor(lowerCamelCase_ ,				*lowerCamelCase_ ,				sampling_rate=lowerCamelCase_ ,				**lowerCamelCase_					)
										if text is not None:
														_snake_case :			Dict  						=   self.tokenizer(lowerCamelCase_ ,				**lowerCamelCase_					)

										if text is None:
														return inputs
										elif audio is None:
														return encodings
										else:
														_snake_case :			Optional[int]  						=   encodings['input_ids']
														return inputs







						def 		__UpperCAmelCase       (    self			:		Union[str, Any] ,				*lowerCamelCase_			:		str ,				**lowerCamelCase_			:		Optional[int]					):





										'''simple docstring'''







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







						def 		__UpperCAmelCase       (    self			:		List[str] ,				*lowerCamelCase_			:		Optional[Any] ,				**lowerCamelCase_			:		Tuple					):





										'''simple docstring'''







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







						@contextmanager
						def 		__UpperCAmelCase       (    self			:		Optional[int]					):





										'''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 audio inputs, or in a separate call.'					)
										_snake_case :			Dict  						=   True
										_snake_case :			Union[str, Any]  						=   self.tokenizer
										yield
										_snake_case :			Union[str, Any]  						=   self.feature_extractor
										_snake_case :			Any  						=   False













import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


lowercase_		:				Optional[Any]				=						pytest.mark.integration

@pytest.mark.parametrize('path'	,			['paws', 'csv'] )
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				inspect_dataset(__lowerCAmelCase	,			__lowerCAmelCase )
				_snake_case :			str  						=   path + '.py'
				assert script_name in os.listdir(__lowerCAmelCase )
				assert "__pycache__" not in os.listdir(__lowerCAmelCase )

@pytest.mark.filterwarnings('ignore:inspect_metric is deprecated:FutureWarning' )
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning' )
@pytest.mark.parametrize('path'	,			['accuracy'] )
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				inspect_metric(__lowerCAmelCase	,			__lowerCAmelCase )
				_snake_case :			Tuple  						=   path + '.py'
				assert script_name in os.listdir(__lowerCAmelCase )
				assert "__pycache__" not in os.listdir(__lowerCAmelCase )

@pytest.mark.parametrize(
    'path, config_name, expected_splits'	,			[
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Union[str, Any]  						=   get_dataset_config_info(__lowerCAmelCase	,			config_name=__lowerCAmelCase )
				assert info.config_name == config_name
				assert list(info.splits.keys() ) == expected_splits

@pytest.mark.parametrize(
    'path, config_name, expected_exception'	,			[
        ('paws', None, ValueError),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				with pytest.raises(__lowerCAmelCase ):
								get_dataset_config_info(__lowerCAmelCase	,			config_name=__lowerCAmelCase )

@pytest.mark.parametrize(
    'path, expected'	,			[
        ('squad', 'plain_text'),
        ('acronym_identification', 'default'),
        ('lhoestq/squad', 'plain_text'),
        ('lhoestq/test', 'default'),
        ('lhoestq/demo1', 'lhoestq--demo1'),
        ('dalle-mini/wit', 'dalle-mini--wit'),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Optional[Any]  						=   get_dataset_config_names(__lowerCAmelCase )
				assert expected in config_names

@pytest.mark.parametrize(
    'path, expected_configs, expected_splits_in_first_config'	,			[
        ('squad', ['plain_text'], ['train', 'validation']),
        ('dalle-mini/wit', ['dalle-mini--wit'], ['train']),
        ('paws', ['labeled_final', 'labeled_swap', 'unlabeled_final'], ['train', 'test', 'validation']),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			List[Any]  						=   get_dataset_infos(__lowerCAmelCase )
				assert list(infos.keys() ) == expected_configs
				_snake_case :			Any  						=   expected_configs[0]
				assert expected_config in infos
				_snake_case :			str  						=   infos[expected_config]
				assert info.config_name == expected_config
				assert list(info.splits.keys() ) == expected_splits_in_first_config

@pytest.mark.parametrize(
    'path, expected_config, expected_splits'	,			[
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Any  						=   get_dataset_infos(__lowerCAmelCase )
				assert expected_config in infos
				_snake_case :			Any  						=   infos[expected_config]
				assert info.config_name == expected_config
				assert list(info.splits.keys() ) == expected_splits

@pytest.mark.parametrize(
    'path, config_name, expected_exception'	,			[
        ('paws', None, ValueError),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				with pytest.raises(__lowerCAmelCase ):
								get_dataset_split_names(__lowerCAmelCase	,			config_name=__lowerCAmelCase )






