




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 			a__    (					snake_case__      ):
						_a							:    Tuple					=    ["""image_processor""", """tokenizer"""]
						_a							:    Union[str, Any]					=    """CLIPImageProcessor"""
						_a							:    Dict					=    ("""CLIPTokenizer""", """CLIPTokenizerFast""")






						def __init__(   self		,			_A=None		,			_A=None		,			**_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       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				   =       kwargs.pop("feature_extractor"			)

											__lowerCAmelCase				   =       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			):




											"""simple docstring"""



											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				   =       self.tokenizer(_A		,			return_tensors=_A		,			**_A			)

											if images is not None:
																__lowerCAmelCase				   =       self.image_processor(_A		,			return_tensors=_A		,			**_A			)

											if text is not None and images is not None:
																__lowerCAmelCase				   =       image_features.pixel_values
																return encoding
											elif text is not None:
																return encoding
											else:
																return BatchEncoding(data=dict(**_A			)		,			tensor_type=_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			*_A		,			**_A			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   self		,			*_A		,			**_A			):




											"""simple docstring"""



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






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.tokenizer.model_input_names
											__lowerCAmelCase				   =       self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""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 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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










import pytest

from datasets import inspect_metric, list_metrics, load_metric







@pytest.fixture
def 	_a      (						SCREAMING_SNAKE_CASE_							:				Optional[Any]     ):
					monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"      ,      set()     )







@pytest.fixture
def 	_a      (						SCREAMING_SNAKE_CASE_							:				List[Any]     ):
					class 			a__    :






											def __init__(   self		,			_A			):




																"""simple docstring"""



																__lowerCAmelCase				   =       metric_id






					class 			a__    :
											_a							:    Optional[int]					=    [MetricMock(snake_case__      ) for metric_id in ["""accuracy""", """mse""", """precision""", """codeparrot/apps_metric"""]]






											def 				__SCREAMING_SNAKE_CASE(   self			):




																"""simple docstring"""



																return self._metrics






					monkeypatch.setattr("datasets.inspect.huggingface_hub"      ,      HfhMock()     )







@pytest.mark.parametrize(
    "func, args"      ,      [(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))]     )
def 	_a      (						SCREAMING_SNAKE_CASE_							:				Tuple      ,      SCREAMING_SNAKE_CASE_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]     ):
					if "tmp_path" in args:
										__lowerCAmelCase				   =       tuple(arg if arg != "tmp_path" else tmp_path for arg in args     )
					with pytest.warns(SCREAMING_SNAKE_CASE_      ,      match="https://huggingface.co/docs/evaluate"     ):
										func(*SCREAMING_SNAKE_CASE_     )




