




"""simple docstring"""






import warnings

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




class   a__			(       UpperCamelCase_  ):
						snake_case__             = ['''image_processor''', '''tokenizer''']
						snake_case__             = '''CLIPImageProcessor'''
						snake_case__             = ('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')





						def __init__(							self      :	Optional[int]     ,a__      :	List[str]=None     ,a__      :	List[str]=None     ,**a__      :	str)						->       Tuple:




											"""simple docstring"""







											_lowerCAmelCase: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:Optional[int]									=  kwargs.pop('''feature_extractor''')

											_lowerCAmelCase: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      :	Optional[Any]     ,a__      :	List[Any]=None     ,a__      :	Any=None     ,a__      :	Any=None     ,**a__      :	List[Any])						->       Any:




											"""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:List[Any]									=  self.tokenizer(a__     ,return_tensors=a__     ,**a__)

											if images is not None:
																_lowerCAmelCase:int									=  self.image_processor(a__     ,return_tensors=a__     ,**a__)

											if text is not None and images is not None:
																_lowerCAmelCase:List[Any]									=  image_features.pixel_values
																return encoding
											elif text is not None:
																return encoding
											else:
																return BatchEncoding(data=dict(**a__)     ,tensor_type=a__)





						def       __UpperCamelCase     (							self      :	Optional[int]     ,*a__      :	int     ,**a__      :	str)						->       List[str]:




											"""simple docstring"""







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





						def       __UpperCamelCase     (							self      :	Dict     ,*a__      :	Union[str, Any]     ,**a__      :	List[Any])						->       Optional[Any]:




											"""simple docstring"""







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







						@property
						def       __UpperCamelCase     (							self      :	Optional[Any])						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  self.tokenizer.model_input_names
											_lowerCAmelCase:Optional[Any]									=  self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))












"""simple docstring"""






# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    '''stable diffusion controlnet''',
    '''0.22.0''',
    '''Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.''',
    standard_warn=False,
    stacklevel=3,
)






