tj VddlmZddlZddlmZdgZedddZdS)) annotationsN) exposed_in rearrangez torch.functensor>> from torch.func import rearrange >>> # suppose we have a set of 32 images in "h w c" format (height-width-channel) >>> images = torch.randn((32, 30, 40, 3)) >>> # stack along first (batch) axis, output is a single array >>> rearrange(images, "b h w c -> b h w c").shape torch.Size([32, 30, 40, 3]) >>> # concatenate images along height (vertical axis), 960 = 32 * 30 >>> rearrange(images, "b h w c -> (b h) w c").shape torch.Size([960, 40, 3]) >>> # concatenated images along horizontal axis, 1280 = 32 * 40 >>> rearrange(images, "b h w c -> h (b w) c").shape torch.Size([30, 1280, 3]) >>> # reordered axes to "b c h w" format for deep learning >>> rearrange(images, "b h w c -> b c h w").shape torch.Size([32, 3, 30, 40]) >>> # flattened each image into a vector, 3600 = 30 * 40 * 3 >>> rearrange(images, "b h w c -> b (c h w)").shape torch.Size([32, 3600]) >>> # split each image into 4 smaller (top-left, top-right, bottom-left, bottom-right), 128 = 32 * 2 * 2 >>> rearrange(images, "b (h1 h) (w1 w) c -> (b h1 w1) h w c", h1=2, w1=2).shape torch.Size([128, 15, 20, 3]) >>> # space-to-depth operation >>> rearrange(images, "b (h h1) (w w1) c -> b h w (c h1 w1)", h1=2, w1=2).shape torch.Size([32, 15, 20, 12]) r)_create_rearrange_callable) isinstancetorchTensorstackfunctorch.einops.rearrangerndim)rrr rrearrange_callables U/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/torch/_functorch/einops.pyrr swj fel + +%V$$FEEEEE33 W ,  f % %%)rrrr r r r r ) __future__rrtorch._functorch.utilsr__all__rrrrsp"""""" ------ - L?&?&?&?&?&?&r