Resize and pad an image to a target resolution while maintaining aspect ratio. Args: image (PIL.Image.Image): The input image.

Collects the state dict and dump to disk.

Collects the state dict and dump to disk.

Resize tokenizer and embedding. Note: This is the unoptimized version that may make your embedding size not be divisible by 64.

Split a list into n (roughly) equal-sized chunks

Split a list into n (roughly) equal-sized chunks

Split a list into n (roughly) equal-sized chunks

Split a list into n (roughly) equal-sized chunks

Split a list into n (roughly) equal-sized chunks

Split a list into n (roughly) equal-sized chunks

Split a list of indices into `chunks` chunks of roughly equal lengths.

Calculate Euclid distance between each two points. src^T * dst = xn * xm + yn * ym + zn * zm； sum(src^2, dim=-1) = xn*xn + yn*yn + zn*zn;

Calculate Euclid distance between each two points. src^T * dst = xn * xm + yn * ym + zn * zm； sum(src^2, dim=-1) = xn*xn + yn*yn + zn*zn;

Unpads a PyTorch tensor of a padded and resized image. Args: tensor (torch.Tensor): The image tensor, assumed to be in CxHxW format.

Check whether the text violates OpenAI moderation API.

Check whether the text violates OpenAI moderation API.

Parameters: - topk : tuple of ints corresponding to the recalls being tracked (eg, recall@1, recall@10, ...)