"""simple docstring"""




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						List[Any]													=		['image_processor', 'tokenizer']
						lowerCAmelCase__  :						str													=		'ViTImageProcessor'
						lowerCAmelCase__  :						str													=		('CLIPTokenizer', 'CLIPTokenizerFast')





						def __init__(       self		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,**__UpperCAmelCase     )					->				Dict:
													A__	     =							None
													if "feature_extractor" in kwargs:
																				warnings.warn(
																				    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																				    ' instead.'		,__UpperCAmelCase		,)
																				A__	     =							kwargs.pop('feature_extractor'     )

													A__	     =							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__(__UpperCAmelCase		,__UpperCAmelCase     )





						def __call__(       self		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,**__UpperCAmelCase     )					->				Optional[Any]:
													if text is None and visual_prompt is None and images is None:
																				raise ValueError('You have to specify either text, visual prompt or images.'     )

													if text is not None and visual_prompt is not None:
																				raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'     )

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

													if visual_prompt is not None:
																				A__	     =							self.image_processor(__UpperCAmelCase		,return_tensors=__UpperCAmelCase		,**__UpperCAmelCase     )

													if images is not None:
																				A__	     =							self.image_processor(__UpperCAmelCase		,return_tensors=__UpperCAmelCase		,**__UpperCAmelCase     )

													if visual_prompt is not None and images is not None:
																				A__	     =							{
																				    'pixel_values': image_features.pixel_values,
																				    'conditional_pixel_values': prompt_features.pixel_values,
																				}
																				return encoding
													elif text is not None and images is not None:
																				A__	     =							image_features.pixel_values
																				return encoding
													elif text is not None:
																				return encoding
													elif visual_prompt is not None:
																				A__	     =							{
																				    'conditional_pixel_values': prompt_features.pixel_values,
																				}
																				return encoding
													else:
																				return BatchEncoding(data=dict(**__UpperCAmelCase     )		,tensor_type=__UpperCAmelCase     )





						def 				snake_case__	(       self		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				Union[str, Any]:
													return self.tokenizer.batch_decode(*__UpperCAmelCase		,**__UpperCAmelCase     )





						def 				snake_case__	(       self		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				List[Any]:
													return self.tokenizer.decode(*__UpperCAmelCase		,**__UpperCAmelCase     )





						@property
						def 				snake_case__	(       self     )					->				int:
													warnings.warn(
													    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,__UpperCAmelCase		,)
													return self.image_processor_class





						@property
						def 				snake_case__	(       self     )					->				Optional[int]:
													warnings.warn(
													    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'		,__UpperCAmelCase		,)
													return self.image_processor






"""simple docstring"""




import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Union[str, Any]													=		'M-CLIP'





						def __init__(       self		,__UpperCAmelCase=10_24		,__UpperCAmelCase=7_68		,**__UpperCAmelCase     )					->				List[str]:
													A__	     =							transformerDimSize
													A__	     =							imageDimSize
													super().__init__(**__UpperCAmelCase     )






class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Union[str, Any]													=		MCLIPConfig





						def __init__(       self		,__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				List[Any]:
													super().__init__(__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase     )
													A__	     =							XLMRobertaModel(__UpperCAmelCase     )
													A__	     =							torch.nn.Linear(
													    in_features=config.transformerDimensions		,out_features=config.numDims     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Tuple:
													A__	     =							self.transformer(input_ids=__UpperCAmelCase		,attention_mask=__UpperCAmelCase     )[0]
													A__	     =							(embs * attention_mask.unsqueeze(2     )).sum(dim=1     ) / attention_mask.sum(dim=1     )[:, None]
													return self.LinearTransformation(__UpperCAmelCase     ), embs





