def downsample(mask: torch.Tensor, batch_size: int, num_queries: int, value_embed_dim: int):
        """
        Downsamples the provided mask tensor to match the expected dimensions for scaled dot-product attention. If the
        aspect ratio of the mask does not match the aspect ratio of the output image, a warning is issued.

        Args:
            mask (`torch.Tensor`):
                The input mask tensor generated with `IPAdapterMaskProcessor.preprocess()`.
            batch_size (`int`):
                The batch size.
            num_queries (`int`):
                The number of queries.
            value_embed_dim (`int`):
                The dimensionality of the value embeddings.

        Returns:
            `torch.Tensor`:
                The downsampled mask tensor.

        """
        o_h = mask.shape[1]
        o_w = mask.shape[2]
        ratio = o_w / o_h
        mask_h = int(math.sqrt(num_queries / ratio))
        mask_h = int(mask_h) + int((num_queries % int(mask_h)) != 0)
        mask_w = num_queries // mask_h

        mask_downsample = F.interpolate(mask.unsqueeze(0), size=(mask_h, mask_w), mode="bicubic").squeeze(0)

        # Repeat batch_size times
        if mask_downsample.shape[0] < batch_size:
            mask_downsample = mask_downsample.repeat(batch_size, 1, 1)

        mask_downsample = mask_downsample.view(mask_downsample.shape[0], -1)

        downsampled_area = mask_h * mask_w
        # If the output image and the mask do not have the same aspect ratio, tensor shapes will not match
        # Pad tensor if downsampled_mask.shape[1] is smaller than num_queries
        if downsampled_area < num_queries:
            warnings.warn(
                "The aspect ratio of the mask does not match the aspect ratio of the output image. "
                "Please update your masks or adjust the output size for optimal performance.",
                UserWarning,
            )
            mask_downsample = F.pad(mask_downsample, (0, num_queries - mask_downsample.shape[1]), value=0.0)
        # Discard last embeddings if downsampled_mask.shape[1] is bigger than num_queries
        if downsampled_area > num_queries:
            warnings.warn(
                "The aspect ratio of the mask does not match the aspect ratio of the output image. "
                "Please update your masks or adjust the output size for optimal performance.",
                UserWarning,
            )
            mask_downsample = mask_downsample[:, :num_queries]

        # Repeat last dimension to match SDPA output shape
        mask_downsample = mask_downsample.view(mask_downsample.shape[0], mask_downsample.shape[1], 1).repeat(
            1, 1, value_embed_dim
        )

        return mask_downsample

        Downsamples the provided mask tensor to match the expected dimensions for scaled dot-product attention. If the
        aspect ratio of the mask does not match the aspect ratio of the output image, a warning is issued.

        Args:
            mask (`torch.Tensor`):
                The input mask tensor generated with `IPAdapterMaskProcessor.preprocess()`.
            batch_size (`int`):
                The batch size.
            num_queries (`int`):
                The number of queries.
            value_embed_dim (`int`):
                The dimensionality of the value embeddings.

        Returns:
            `torch.Tensor`:
                The downsampled mask tensor.

        
src/diffusers/image_processor.py