





import warnings

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





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  ["""image_processor""", """tokenizer"""]
  __UpperCamelCase	     =  """CLIPImageProcessor"""
  __UpperCamelCase	     =  ("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")







  def __init__(	self    ,						_lowercase=None    ,						_lowercase=None    ,						**_lowercase						):
       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."""    ,						_lowercase    ,						)
            lowerCAmelCase_					:       Union[str, Any]    =		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__(_lowercase    ,						_lowercase						)







  def __call__(	self    ,						_lowercase=None    ,						_lowercase=None    ,						_lowercase=None    ,						**_lowercase						):

       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_					:       Tuple    =		self.tokenizer(_lowercase    ,						return_tensors=_lowercase    ,						**_lowercase						)

       if images is not None:
            lowerCAmelCase_					:       Union[str, Any]    =		self.image_processor(_lowercase    ,						return_tensors=_lowercase    ,						**_lowercase						)

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







  def 	UpperCAmelCase__		(	self    ,						*_lowercase    ,						**_lowercase						):
       return self.tokenizer.batch_decode(*_lowercase    ,						**_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						*_lowercase    ,						**_lowercase						):
       return self.tokenizer.decode(*_lowercase    ,						**_lowercase						)







  @property
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       int    =		self.tokenizer.model_input_names
       lowerCAmelCase_					:       List[Any]    =		self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)







import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  """M-CLIP"""







  def __init__(	self    ,						_lowercase=1_024    ,						_lowercase=768    ,						**_lowercase						):
       lowerCAmelCase_					:       Tuple    =		transformerDimSize
       lowerCAmelCase_					:       Optional[Any]    =		imageDimSize
       super().__init__(**_lowercase						)





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  MCLIPConfig







  def __init__(	self    ,						_lowercase    ,						*_lowercase    ,						**_lowercase						):
       super().__init__(_lowercase    ,						*_lowercase    ,						**_lowercase						)
       lowerCAmelCase_					:       str    =		XLMRobertaModel(_lowercase						)
       lowerCAmelCase_					:       str    =		torch.nn.Linear(
           in_features=config.transformerDimensions    ,						out_features=config.numDims						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase						):
       lowerCAmelCase_					:       Optional[int]    =		self.transformer(input_ids=_lowercase    ,						attention_mask=_lowercase						)[0]
       lowerCAmelCase_					:       Any    =		(embs * attention_mask.unsqueeze(2						)).sum(dim=1						) / attention_mask.sum(dim=1						)[:, None]
       return self.LinearTransformation(_lowercase						), embs
