
"""simple docstring"""
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class __lowercase(      _lowerCamelCase				):




					'''simple docstring'''


					__a						:						Dict     		= ['image_processor', 'tokenizer']
					__a						:						int     		= 'CLIPImageProcessor'
					__a						:						List[Any]     		= ('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')
					def __init__(    self						,	__a=None						,	__a=None						,	**__a     ):
								__lowerCamelCase					:	List[str]	       =				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__						,	)
											__lowerCamelCase					:	Tuple	       =				kwargs.pop('feature_extractor'     )

								__lowerCamelCase					:	Optional[int]	       =				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__     )
					def __call__(    self						,	__a=None						,	__a=None						,	__a=None						,	**__a     ):

								if text is None and images is None:
											raise ValueError('You have to specify either text or images. Both cannot be none.'     )

								if text is not None:
											__lowerCamelCase					:	Dict	       =				self.tokenizer(A__						,	return_tensors=A__						,	**A__     )

								if images is not None:
											__lowerCamelCase					:	Optional[int]	       =				self.image_processor(A__						,	return_tensors=A__						,	**A__     )

								if text is not None and images is not None:
											__lowerCamelCase					:	Tuple	       =				image_features.pixel_values
											return encoding
								elif text is not None:
											return encoding
								else:
											return BatchEncoding(data=dict(**A__     )						,	tensor_type=A__     )
					def     snake_case_				(    self						,	*__a						,	**__a     ):
								return self.tokenizer.batch_decode(*A__						,	**A__     )
					def     snake_case_				(    self						,	*__a						,	**__a     ):
								return self.tokenizer.decode(*A__						,	**A__     )





					@property
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[Any]	       =				self.tokenizer.model_input_names
								__lowerCamelCase					:	int	       =				self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )









"""simple docstring"""
import pytest

import datasets


# Import fixture modules as plugins
a_				:    Tuple										=     ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']





def 						UpperCAmelCase				(			A__:    List[Any]							,				A__:    Optional[Any]     )  ->     List[str]:
			# Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
			for item in items:
						if any(marker in item.keywords for marker in ['integration', 'unit']     ):
									continue
						item.add_marker(pytest.mark.unit     )





def 						UpperCAmelCase				(			A__:    int     )  ->     Optional[int]:
			config.addinivalue_line('markers'							,				'torchaudio_latest: mark test to run with torchaudio>=0.12'     )





@pytest.fixture(autouse=A__     )
def 						UpperCAmelCase				(			A__:    Optional[Any]							,				A__:    List[Any]     )  ->     Any:
			# test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
			__lowerCamelCase					:	str	       =				tmp_path_factory.getbasetemp() / 'cache'
			__lowerCamelCase					:	Dict	       =				test_hf_cache_home / 'datasets'
			__lowerCamelCase					:	int	       =				test_hf_cache_home / 'metrics'
			__lowerCamelCase					:	List[str]	       =				test_hf_cache_home / 'modules'
			monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE'							,				str(A__     )     )
			monkeypatch.setattr('datasets.config.HF_METRICS_CACHE'							,				str(A__     )     )
			monkeypatch.setattr('datasets.config.HF_MODULES_CACHE'							,				str(A__     )     )
			__lowerCamelCase					:	List[Any]	       =				test_hf_datasets_cache / 'downloads'
			monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH'							,				str(A__     )     )
			__lowerCamelCase					:	Optional[int]	       =				test_hf_datasets_cache / 'downloads' / 'extracted'
			monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'							,				str(A__     )     )





@pytest.fixture(autouse=A__							,				scope='session'     )
def 						UpperCAmelCase				(			)  ->     Any:
			datasets.disable_progress_bar()





@pytest.fixture(autouse=A__     )
def 						UpperCAmelCase				(			A__:    Optional[Any]     )  ->     Dict:
			# don't take tests into account when counting downloads
			monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'							,				A__     )






@pytest.fixture
def 						UpperCAmelCase				(			A__:    Union[str, Any]     )  ->     Optional[Any]:
			# Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
			# To be removed once SQLAlchemy 2.0 supported
			monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'							,				A__     )



