diffusers/pipelines/kandinsky/text_encoder.py

import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel


class MCLIPConfig(XLMRobertaConfig):
    model_type = "M-CLIP"

    def __init__(self, transformerDimSize=1024, imageDimSize=768, **kwargs):
        self.transformerDimensions = transformerDimSize
        self.numDims = imageDimSize
        super().__init__(**kwargs)


class MultilingualCLIP(PreTrainedModel):
    config_class = MCLIPConfig

    def __init__(self, config, *args, **kwargs):
        super().__init__(config, *args, **kwargs)
        self.transformer = XLMRobertaModel(config)
        self.LinearTransformation = torch.nn.Linear(
            in_features=config.transformerDimensions, out_features=config.numDims
        )
        if hasattr(self, "post_init"):
            self.post_init()

    def forward(self, input_ids, attention_mask):
        embs = self.transformer(input_ids=input_ids, attention_mask=attention_mask)[0]
        embs2 = (embs * attention_mask.unsqueeze(2)).sum(dim=1) / attention_mask.sum(dim=1)[:, None]
        return self.LinearTransformation(embs2), embs