initial (broken) commit

.gitignore

@@ -0,0 +1,2 @@
+.aider*
+.env

README.md

@@ -5,6 +5,7 @@ colorFrom: gray
 colorTo: blue
 sdk: gradio
 sdk_version: 5.9.1
+python_version: 3.13
 app_file: app.py
 pinned: false
 license: mit
@@ -12,3 +13,12 @@ short_description: Interpret image classification models using SAEs.
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+I used [s5cmd](https://github.com/peak/s5cmd) to upload CUB-2011 to Cloudflare R2.
+
+
+```sh
+s5cmd --credentials-file ~/.local/etc/cloudflare/r2-credentials --endpoint-url https://6391ae4399fb354a41cab96372935a6e.r2.cloudflarestorage.com \
+  cp test/ s3://saev-cub2011/
+```
+

app.py

@@ -0,0 +1,520 @@
+import io
+import json
+import logging
+import math
+import os
+import pathlib
+import random
+
+import beartype
+import einops.layers.torch
+import gradio as gr
+import numpy as np
+import open_clip
+import requests
+import saev.nn
+import torch
+from jaxtyping import Float, jaxtyped
+from PIL import Image, ImageDraw
+from torch import Tensor
+from torchvision.transforms import v2
+
+log_format = "[%(asctime)s] [%(levelname)s] [%(name)s] %(message)s"
+logging.basicConfig(level=logging.INFO, format=log_format)
+logger = logging.getLogger("app.py")
+
+
+####################
+# Global Constants #
+####################
+
+DEBUG = True
+"""Whether we are debugging."""
+
+n_sae_latents = 3
+"""Number of SAE latents to show."""
+
+n_sae_examples = 4
+"""Number of SAE examples per latent to show."""
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+"""Hardware accelerator, if any."""
+
+vit_ckpt = "ViT-B-16/openai"
+"""CLIP checkpoint."""
+
+n_patches_per_img: int = 196
+"""Number of patches per image in vit_ckpt."""
+
+max_frequency = 1e-2
+"""Maximum frequency. Any feature that fires more than this is ignored."""
+
+CWD = pathlib.Path(__file__).parent
+
+r2_url = "https://pub-289086e849214430853bc87bd8964988.r2.dev/"
+
+
+logger.info("Set global constants.")
+
+
+###########
+# Helpers #
+###########
+
+
+@beartype.beartype
+def get_cache_dir() -> str:
+    """
+    Get cache directory from environment variables, defaulting to the current working directory (.)
+
+    Returns:
+        A path to a cache directory (might not exist yet).
+    """
+    cache_dir = ""
+    for var in ("HF_HOME", "HF_HUB_CACHE"):
+        cache_dir = cache_dir or os.environ.get(var, "")
+    return cache_dir or "."
+
+
+@beartype.beartype
+def load_model(fpath: str | pathlib.Path, *, device: str = "cpu") -> torch.nn.Module:
+    """
+    Loads a linear layer from disk.
+    """
+    with open(fpath, "rb") as fd:
+        kwargs = json.loads(fd.readline().decode())
+        buffer = io.BytesIO(fd.read())
+
+    model = torch.nn.Linear(**kwargs)
+    state_dict = torch.load(buffer, weights_only=True, map_location=device)
+    model.load_state_dict(state_dict)
+    model = model.to(device)
+    return model
+
+
+@beartype.beartype
+def get_dataset_img(i: int) -> Image.Image:
+    return Image.open(requests.get(r2_url + image_fpaths[i], stream=True).raw)
+
+
+@beartype.beartype
+def make_img(
+    img: Image.Image, patches: Float[Tensor, ""], *, upper: float | None = None
+) -> Image.Image:
+    # Resize to 256x256 and crop to 224x224
+    resize_size_px = (512, 512)
+    resize_w_px, resize_h_px = resize_size_px
+    crop_size_px = (448, 448)
+    crop_w_px, crop_h_px = crop_size_px
+    crop_coords_px = (
+        (resize_w_px - crop_w_px) // 2,
+        (resize_h_px - crop_h_px) // 2,
+        (resize_w_px + crop_w_px) // 2,
+        (resize_h_px + crop_h_px) // 2,
+    )
+
+    img = img.resize(resize_size_px).crop(crop_coords_px)
+    img = add_highlights(img, patches.numpy(), upper=upper, opacity=0.5)
+    return img
+
+
+##########
+# Models #
+##########
+
+
+@jaxtyped(typechecker=beartype.beartype)
+class SplitClip(torch.nn.Module):
+    def __init__(self, *, n_end_layers: int):
+        super().__init__()
+
+        if vit_ckpt.startswith("hf-hub:"):
+            clip, _ = open_clip.create_model_from_pretrained(
+                vit_ckpt, cache_dir=get_cache_dir()
+            )
+        else:
+            arch, ckpt = vit_ckpt.split("/")
+            clip, _ = open_clip.create_model_from_pretrained(
+                arch, pretrained=ckpt, cache_dir=get_cache_dir()
+            )
+        model = clip.visual
+        model.proj = None
+        model.output_tokens = True  # type: ignore
+        self.vit = model.eval()
+        assert not isinstance(self.vit, open_clip.timm_model.TimmModel)
+
+        self.n_end_layers = n_end_layers
+
+    @staticmethod
+    def _expand_token(token, batch_size: int):
+        return token.view(1, 1, -1).expand(batch_size, -1, -1)
+
+    def forward_start(self, x: Float[Tensor, "batch channels width height"]):
+        x = self.vit.conv1(x)  # shape = [*, width, grid, grid]
+        x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
+        x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
+
+        # class embeddings and positional embeddings
+        x = torch.cat(
+            [self._expand_token(self.vit.class_embedding, x.shape[0]).to(x.dtype), x],
+            dim=1,
+        )
+        # shape = [*, grid ** 2 + 1, width]
+        x = x + self.vit.positional_embedding.to(x.dtype)
+
+        x = self.vit.patch_dropout(x)
+        x = self.vit.ln_pre(x)
+        for r in self.vit.transformer.resblocks[: -self.n_end_layers]:
+            x = r(x)
+        return x
+
+    def forward_end(self, x: Float[Tensor, "batch n_patches dim"]):
+        for r in self.vit.transformer.resblocks[-self.n_end_layers :]:
+            x = r(x)
+
+        x = self.vit.ln_post(x)
+        pooled, _ = self.vit._global_pool(x)
+        if self.vit.proj is not None:
+            pooled = pooled @ self.vit.proj
+
+        return pooled
+
+
+# ViT
+split_vit = SplitClip(n_end_layers=1)
+split_vit = split_vit.to(device)
+logger.info("Initialized CLIP ViT.")
+
+# Linear classifier
+clf_ckpt_fpath = CWD / "ckpts" / "clf.pt"
+clf = load_model(clf_ckpt_fpath)
+clf = clf.to(device).eval()
+logger.info("Loaded linear classifier.")
+
+# SAE
+sae_ckpt_fpath = CWD / "ckpts" / "sae.pt"
+sae = saev.nn.load(sae_ckpt_fpath.as_posix())
+sae.to(device).eval()
+logger.info("Loaded SAE.")
+
+
+############
+# Datasets #
+############
+
+human_transform = v2.Compose([
+    v2.Resize((512, 512), interpolation=v2.InterpolationMode.NEAREST),
+    v2.CenterCrop((448, 448)),
+    v2.ToImage(),
+    einops.layers.torch.Rearrange("channels width height -> width height channels"),
+])
+
+arch, ckpt = vit_ckpt.split("/")
+_, vit_transform = open_clip.create_model_from_pretrained(
+    arch, pretrained=ckpt, cache_dir=get_cache_dir()
+)
+
+
+with open(CWD / "data" / "image_fpaths.json") as fd:
+    image_fpaths = json.load(fd)
+
+
+with open(CWD / "data" / "image_labels.json") as fd:
+    image_labels = json.load(fd)
+
+
+# TODO:
+# This dataset needs to be the CUB2011 dataset. But that means we need to calculate top_img_i based on CUB2011, not on iNat21 train-mini.
+# examples_dataset = saev.activations.ImageFolder(
+#     "/research/nfs_su_809/workspace/stevens.994/datasets/inat21/train_mini",
+#     transform=v2.Compose([
+#         v2.Resize(size=(512, 512)),
+#         v2.CenterCrop(size=(448, 448)),
+#     ]),
+# )
+
+logger.info("Loaded all datasets.")
+
+#############
+# Variables #
+#############
+
+
+@beartype.beartype
+def load_tensor(path: str | pathlib.Path) -> Tensor:
+    return torch.load(path, weights_only=True, map_location="cpu")
+
+
+top_img_i = load_tensor(CWD / "data" / "top_img_i.pt")
+top_values = load_tensor(CWD / "data" / "top_values.pt")
+sparsity = load_tensor(CWD / "data" / "sparsity.pt")
+
+mask = torch.ones((sae.cfg.d_sae), dtype=bool)
+mask = mask & (sparsity < max_frequency)
+
+
+#############
+# Inference #
+#############
+
+
+@beartype.beartype
+def get_image(image_i: int) -> list[Image.Image | int]:
+    image = get_dataset_img(image_i)
+    image = human_transform(image)
+    return [Image.fromarray(image.numpy()), image_labels[image_i]]
+
+
+@beartype.beartype
+def get_random_class_image(cls: int) -> Image.Image:
+    indices = [i for i, tgt in enumerate(image_labels) if tgt == cls]
+    i = random.choice(indices)
+
+    image = get_dataset_img(i)
+    image = human_transform(image)
+    return Image.fromarray(image.numpy())
+
+
+@torch.inference_mode
+def get_sae_examples(
+    image_i: int, patches: list[int]
+) -> list[None | Image.Image | int]:
+    """
+    Given a particular cell, returns some highlighted images showing what feature fires most on this cell.
+    """
+    if not patches:
+        return [None] * 12 + [-1] * 3
+
+    img = get_dataset_img(image_i)
+    x = vit_transform(img)[None, ...].to(device)
+    x_BPD = split_vit.forward_start(x)
+    vit_acts_MD = x_BPD[0, patches].to(device)
+
+    _, f_x_MS, _ = sae(vit_acts_MD)
+    f_x_S = f_x_MS.sum(axis=0)
+
+    latents = torch.argsort(f_x_S, descending=True).cpu()
+    latents = latents[mask[latents]][:n_sae_latents].tolist()
+
+    images = []
+    for latent in latents:
+        img_patch_pairs, seen_i_im = [], set()
+        for i_im, values_p in zip(top_img_i[latent].tolist(), top_values[latent]):
+            if i_im in seen_i_im:
+                continue
+
+            # example = examples_dataset[i_im]
+            example = None
+            img_patch_pairs.append((example["image"], values_p))
+            seen_i_im.add(i_im)
+
+        # How to scale values.
+        upper = None
+        if top_values[latent].numel() > 0:
+            upper = top_values[latent].max().item()
+
+        latent_images = [
+            make_img(img, patches, upper=upper)
+            for img, patches in img_patch_pairs[:n_sae_examples]
+        ]
+
+        while len(latent_images) < n_sae_examples:
+            latent_images += [None]
+
+        images.extend(latent_images)
+
+    return images + latents
+
+
+@torch.inference_mode
+def get_pred_dist(i: int) -> dict[int, float]:
+    img = get_dataset_img(i)
+    x = vit_transform(img)[None, ...].to(device)
+    x_BPD = split_vit.forward_start(x)
+    x_BD = split_vit.forward_end(x_BPD)
+
+    logits_BC = clf(x_BD)
+
+    probs = torch.nn.functional.softmax(logits_BC[0], dim=0).cpu().tolist()
+    return {i: prob for i, prob in enumerate(probs)}
+
+
+@torch.inference_mode
+def get_modified_dist(
+    image_i: int,
+    patches: list[int],
+    latent1: int,
+    latent2: int,
+    latent3: int,
+    value1: float,
+    value2: float,
+    value3: float,
+) -> dict[int, float]:
+    img = get_dataset_img(image_i)
+    x = vit_transform(img)[None, ...].to(device)
+    x_BPD = split_vit.forward_start(x)
+
+    cls_B1D, x_BPD = x_BPD[:, :1, :], x_BPD[:, 1:, :]
+
+    x_hat_BPD, f_x_BPS, _ = sae(x_BPD)
+    err_BPD = x_BPD - x_hat_BPD
+
+    values = torch.tensor(
+        [
+            unscaled(float(value), top_values[latent].max().item())
+            for value, latent in [
+                (value1, latent1),
+                (value2, latent2),
+                (value3, latent3),
+            ]
+        ],
+        device=device,
+    )
+    patches = torch.tensor(patches, device=device)
+    latents = torch.tensor([latent1, latent2, latent3], device=device)
+    f_x_BPS[:, patches[:, None], latents[None, :]] = values
+
+    # Reproduce the SAE forward pass after f_x
+    modified_x_hat_BPD = (
+        einops.einsum(
+            f_x_BPS,
+            sae.W_dec,
+            "batch patches d_sae, d_sae d_vit -> batch patches d_vit",
+        )
+        + sae.b_dec
+    )
+
+    modified_BPD = torch.cat([cls_B1D, err_BPD + modified_x_hat_BPD], axis=1)
+
+    modified_BD = split_vit.forward_end(modified_BPD)
+    logits_BC = clf(modified_BD)
+
+    probs = torch.nn.functional.softmax(logits_BC[0], dim=0).cpu().tolist()
+    return {i: prob for i, prob in enumerate(probs)}
+
+
+@beartype.beartype
+def unscaled(x: float, max_obs: float) -> float:
+    """Scale from [-20, 20] to [20 * -max_obs, 20 * max_obs]."""
+    return map_range(x, (-20.0, 20.0), (-20.0 * max_obs, 20.0 * max_obs))
+
+
+@beartype.beartype
+def map_range(
+    x: float,
+    domain: tuple[float | int, float | int],
+    range: tuple[float | int, float | int],
+):
+    a, b = domain
+    c, d = range
+    if not (a <= x <= b):
+        raise ValueError(f"x={x:.3f} must be in {[a, b]}.")
+    return c + (x - a) * (d - c) / (b - a)
+
+
+@jaxtyped(typechecker=beartype.beartype)
+def add_highlights(
+    img: Image.Image,
+    patches: Float[np.ndarray, " n_patches"],
+    *,
+    upper: float | None = None,
+    opacity: float = 0.9,
+) -> Image.Image:
+    if not len(patches):
+        return img
+
+    iw_np, ih_np = int(math.sqrt(len(patches))), int(math.sqrt(len(patches)))
+    iw_px, ih_px = img.size
+    pw_px, ph_px = iw_px // iw_np, ih_px // ih_np
+    assert iw_np * ih_np == len(patches)
+
+    # Create a transparent overlay
+    overlay = Image.new("RGBA", img.size, (0, 0, 0, 0))
+    draw = ImageDraw.Draw(overlay)
+
+    # Using semi-transparent red (255, 0, 0, alpha)
+    for p, val in enumerate(patches):
+        assert upper is not None
+        val /= upper + 1e-9
+        x_np, y_np = p % iw_np, p // ih_np
+        draw.rectangle(
+            [
+                (x_np * pw_px, y_np * ph_px),
+                (x_np * pw_px + pw_px, y_np * ph_px + ph_px),
+            ],
+            fill=(int(val * 256), 0, 0, int(opacity * val * 256)),
+        )
+
+    # Composite the original image and the overlay
+    return Image.alpha_composite(img.convert("RGBA"), overlay)
+
+
+#############
+# Interface #
+#############
+
+
+with gr.Blocks() as demo:
+    image_number = gr.Number(label="Test Example", precision=0)
+    class_number = gr.Number(label="Test Class", precision=0)
+    input_image = gr.Image(label="Input Image")
+    get_input_image_btn = gr.Button(value="Get Input Image")
+    get_input_image_btn.click(
+        get_image,
+        inputs=[image_number],
+        outputs=[input_image, class_number],
+        api_name="get-image",
+    )
+    get_random_class_image_btn = gr.Button(value="Get Random Class Image")
+    get_input_image_btn.click(
+        get_random_class_image,
+        inputs=[image_number],
+        outputs=[input_image],
+        api_name="get-random-class-image",
+    )
+
+    patch_numbers = gr.CheckboxGroup(
+        label="Image Patch", choices=list(range(n_patches_per_img))
+    )
+    top_latent_numbers = gr.CheckboxGroup(label="Top Latents")
+    top_latent_numbers = [
+        gr.Number(label=f"Top Latents #{j + 1}", precision=0)
+        for j in range(n_sae_latents)
+    ]
+    sae_example_images = [
+        gr.Image(label=f"Latent #{j}, Example #{i + 1}")
+        for i in range(n_sae_examples)
+        for j in range(n_sae_latents)
+    ]
+    get_sae_examples_btn = gr.Button(value="Get SAE Examples")
+    get_sae_examples_btn.click(
+        get_sae_examples,
+        inputs=[image_number, patch_numbers],
+        outputs=sae_example_images + top_latent_numbers,
+        api_name="get-sae-examples",
+    )
+
+    pred_dist = gr.Label(label="Pred. Dist.")
+    get_pred_dist_btn = gr.Button(value="Get Pred. Distribution")
+    get_pred_dist_btn.click(
+        get_pred_dist,
+        inputs=[image_number],
+        outputs=[pred_dist],
+        api_name="get-preds",
+    )
+
+    latent_numbers = [gr.Number(label=f"Latent {i + 1}", precision=0) for i in range(3)]
+    value_sliders = [
+        gr.Slider(label=f"Value {i + 1}", minimum=-10, maximum=10) for i in range(3)
+    ]
+    get_modified_dist_btn = gr.Button(value="Get Modified Label")
+    get_modified_dist_btn.click(
+        get_modified_dist,
+        inputs=[image_number, patch_numbers] + latent_numbers + value_sliders,
+        outputs=[pred_dist],
+        api_name="get-modified",
+    )
+
+
+if __name__ == "__main__":
+    demo.launch()

ckpts/cfg.json

@@ -0,0 +1,37 @@
+{
+    "data": {
+        "shard_root": "/local/scratch/stevens.994/cache/saev/50149a5a12c70d378dc38f1976d676239839b591cadbfc9af5c84268ac30a868/",
+        "patches": "patches",
+        "layer": -2,
+        "clamp": 100000.0,
+        "n_random_samples": 524288,
+        "scale_mean": true,
+        "scale_norm": true
+    },
+    "n_workers": 32,
+    "n_patches": 100000000,
+    "sae": {
+        "d_vit": 768,
+        "exp_factor": 32,
+        "sparsity_coeff": 0.0016,
+        "n_reinit_samples": 524288,
+        "ghost_grads": false,
+        "remove_parallel_grads": true,
+        "normalize_w_dec": true,
+        "seed": 159
+    },
+    "n_sparsity_warmup": 500,
+    "lr": 0.001,
+    "n_lr_warmup": 500,
+    "sae_batch_size": 16384,
+    "track": true,
+    "wandb_project": "saev",
+    "tag": "baseline-v4.7",
+    "log_every": 25,
+    "ckpt_path": "./checkpoints",
+    "device": "cuda",
+    "seed": 59,
+    "slurm": false,
+    "slurm_acct": "PAS2136",
+    "log_to": "./logs"
+}

ckpts/clf.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b672e36e8718d6593be0ccf6fbf8a956799e4ce16e6cc3591f340942c129da5b
+size 616642

ckpts/sae.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:62cb213777f231ba2de3eaf4a6fd8410e2b9d9f6c95a53dbe0167dd445d1f283
+size 151098370

data/image_labels.json

@@ -0,0 +1 @@
+[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 79, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 86, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 89, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 92, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 95, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 98, 98, 98, 98, 98, 98, 98, 98, 98, 98, 98, 98, 98, 98, 98, 98, 98, 98, 98, 98, 98, 98, 98, 98, 98, 98, 98, 98, 98, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 106, 106, 106, 106, 106, 106, 106, 106, 106, 106, 106, 106, 106, 106, 106, 106, 106, 106, 106, 106, 106, 106, 106, 106, 106, 106, 106, 106, 106, 106, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 111, 111, 111, 111, 111, 111, 111, 111, 111, 111, 111, 111, 111, 111, 111, 111, 111, 111, 111, 111, 111, 111, 111, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 114, 114, 114, 114, 114, 114, 114, 114, 114, 114, 114, 114, 114, 114, 114, 114, 114, 114, 114, 114, 114, 114, 114, 114, 114, 114, 114, 114, 114, 114, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 116, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 117, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 118, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 122, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 124, 124, 124, 124, 124, 124, 124, 124, 124, 124, 124, 124, 124, 124, 124, 124, 124, 124, 124, 124, 124, 124, 124, 124, 124, 124, 124, 124, 124, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 129, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 130, 131, 131, 131, 131, 131, 131, 131, 131, 131, 131, 131, 131, 131, 131, 131, 131, 131, 131, 131, 131, 131, 131, 131, 131, 131, 131, 131, 131, 131, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 133, 133, 133, 133, 133, 133, 133, 133, 133, 133, 133, 133, 133, 133, 133, 133, 133, 133, 133, 133, 133, 133, 133, 133, 133, 133, 133, 133, 133, 133, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 134, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 137, 138, 138, 138, 138, 138, 138, 138, 138, 138, 138, 138, 138, 138, 138, 138, 138, 138, 138, 138, 138, 138, 138, 138, 138, 138, 138, 138, 138, 138, 138, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 139, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 140, 141, 141, 141, 141, 141, 141, 141, 141, 141, 141, 141, 141, 141, 141, 141, 141, 141, 141, 141, 141, 141, 141, 141, 141, 141, 141, 141, 141, 141, 141, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 142, 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, 143, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 145, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 146, 147, 147, 147, 147, 147, 147, 147, 147, 147, 147, 147, 147, 147, 147, 147, 147, 147, 147, 147, 147, 147, 147, 147, 147, 147, 147, 147, 147, 147, 147, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 148, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 150, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 151, 152, 152, 152, 152, 152, 152, 152, 152, 152, 152, 152, 152, 152, 152, 152, 152, 152, 152, 152, 152, 152, 152, 152, 152, 152, 152, 152, 152, 152, 152, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 153, 154, 154, 154, 154, 154, 154, 154, 154, 154, 154, 154, 154, 154, 154, 154, 154, 154, 154, 154, 154, 154, 154, 154, 154, 154, 154, 154, 154, 154, 154, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 155, 156, 156, 156, 156, 156, 156, 156, 156, 156, 156, 156, 156, 156, 156, 156, 156, 156, 156, 156, 156, 156, 156, 156, 156, 156, 156, 156, 156, 156, 156, 157, 157, 157, 157, 157, 157, 157, 157, 157, 157, 157, 157, 157, 157, 157, 157, 157, 157, 157, 157, 157, 157, 157, 157, 157, 157, 157, 157, 157, 157, 158, 158, 158, 158, 158, 158, 158, 158, 158, 158, 158, 158, 158, 158, 158, 158, 158, 158, 158, 158, 158, 158, 158, 158, 158, 158, 158, 158, 158, 158, 159, 159, 159, 159, 159, 159, 159, 159, 159, 159, 159, 159, 159, 159, 159, 159, 159, 159, 159, 159, 159, 159, 159, 159, 159, 159, 159, 159, 159, 159, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 161, 162, 162, 162, 162, 162, 162, 162, 162, 162, 162, 162, 162, 162, 162, 162, 162, 162, 162, 162, 162, 162, 162, 162, 162, 162, 162, 162, 162, 162, 162, 163, 163, 163, 163, 163, 163, 163, 163, 163, 163, 163, 163, 163, 163, 163, 163, 163, 163, 163, 163, 163, 163, 163, 163, 163, 163, 163, 163, 163, 163, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 168, 169, 169, 169, 169, 169, 169, 169, 169, 169, 169, 169, 169, 169, 169, 169, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 170, 171, 171, 171, 171, 171, 171, 171, 171, 171, 171, 171, 171, 171, 171, 171, 171, 171, 171, 171, 171, 171, 171, 171, 171, 171, 171, 172, 172, 172, 172, 172, 172, 172, 172, 172, 172, 172, 172, 172, 172, 172, 172, 172, 172, 172, 172, 172, 172, 172, 172, 172, 172, 172, 172, 172, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 173, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 174, 175, 175, 175, 175, 175, 175, 175, 175, 175, 175, 175, 175, 175, 175, 175, 175, 175, 175, 175, 175, 175, 175, 175, 175, 175, 175, 175, 175, 175, 175, 176, 176, 176, 176, 176, 176, 176, 176, 176, 176, 176, 176, 176, 176, 176, 176, 176, 176, 176, 176, 176, 176, 176, 176, 176, 176, 176, 176, 176, 176, 177, 177, 177, 177, 177, 177, 177, 177, 177, 177, 177, 177, 177, 177, 177, 177, 177, 177, 177, 177, 177, 177, 177, 177, 177, 177, 177, 177, 177, 177, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 178, 179, 179, 179, 179, 179, 179, 179, 179, 179, 179, 179, 179, 179, 179, 179, 179, 179, 179, 179, 179, 179, 179, 179, 179, 179, 179, 179, 179, 179, 179, 180, 180, 180, 180, 180, 180, 180, 180, 180, 180, 180, 180, 180, 180, 180, 180, 180, 180, 180, 180, 180, 180, 180, 180, 180, 180, 180, 180, 180, 180, 181, 181, 181, 181, 181, 181, 181, 181, 181, 181, 181, 181, 181, 181, 181, 181, 181, 181, 181, 181, 181, 181, 181, 181, 181, 181, 181, 181, 181, 181, 182, 182, 182, 182, 182, 182, 182, 182, 182, 182, 182, 182, 182, 182, 182, 182, 182, 182, 182, 182, 182, 182, 182, 182, 182, 182, 182, 182, 182, 182, 183, 183, 183, 183, 183, 183, 183, 183, 183, 183, 183, 183, 183, 183, 183, 183, 183, 183, 183, 184, 184, 184, 184, 184, 184, 184, 184, 184, 184, 184, 184, 184, 184, 184, 184, 184, 184, 184, 184, 185, 185, 185, 185, 185, 185, 185, 185, 185, 185, 185, 185, 185, 185, 185, 185, 185, 185, 185, 185, 185, 185, 185, 185, 185, 185, 185, 185, 185, 185, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 189, 189, 189, 189, 189, 189, 189, 189, 189, 189, 189, 189, 189, 189, 189, 189, 189, 189, 189, 189, 189, 189, 189, 189, 189, 189, 189, 189, 189, 189, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 191, 191, 191, 191, 191, 191, 191, 191, 191, 191, 191, 191, 191, 191, 191, 191, 191, 191, 191, 191, 191, 191, 191, 191, 191, 191, 191, 191, 191, 191, 192, 192, 192, 192, 192, 192, 192, 192, 192, 192, 192, 192, 192, 192, 192, 192, 192, 192, 192, 192, 192, 192, 192, 192, 192, 192, 192, 192, 192, 192, 193, 193, 193, 193, 193, 193, 193, 193, 193, 193, 193, 193, 193, 193, 193, 193, 193, 193, 193, 193, 193, 193, 193, 193, 193, 193, 193, 193, 193, 194, 194, 194, 194, 194, 194, 194, 194, 194, 194, 194, 194, 194, 194, 194, 194, 194, 194, 194, 194, 194, 194, 194, 194, 194, 194, 194, 194, 194, 194, 195, 195, 195, 195, 195, 195, 195, 195, 195, 195, 195, 195, 195, 195, 195, 195, 195, 195, 195, 195, 195, 195, 195, 195, 195, 195, 195, 195, 195, 196, 196, 196, 196, 196, 196, 196, 196, 196, 196, 196, 196, 196, 196, 196, 196, 196, 196, 196, 196, 196, 196, 196, 196, 196, 196, 196, 196, 196, 197, 197, 197, 197, 197, 197, 197, 197, 197, 197, 197, 197, 197, 197, 197, 197, 197, 197, 197, 197, 197, 197, 197, 197, 197, 197, 197, 197, 197, 198, 198, 198, 198, 198, 198, 198, 198, 198, 198, 198, 198, 198, 198, 198, 198, 198, 198, 198, 198, 198, 198, 198, 198, 198, 198, 199, 199, 199, 199, 199, 199, 199, 199, 199, 199, 199, 199, 199, 199, 199, 199, 199, 199, 199, 199, 199, 199, 199, 199, 199, 199, 199, 199, 199]

justfile

@@ -0,0 +1,9 @@
+build: lint
+    uv pip compile pyproject.toml > requirements.txt
+
+lint: fmt
+    git ls-files "*.py" --cached --others --exclude-standard | xargs uv run ruff check
+
+fmt:
+    git ls-files "*.py" --cached --others --exclude-standard | xargs uv run isort
+    git ls-files "*.py" --cached --others --exclude-standard | xargs uv run ruff format --preview

pyproject.toml

@@ -0,0 +1,19 @@
+[project]
+name = "saev-image-classification"
+version = "0.1.0"
+description = "Gradio app space for image classification with SAEs"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "beartype>=0.19.0",
+    "einops>=0.8.0",
+    "gradio>=5.0.0",
+    "jaxtyping>=0.2.36",
+    "numpy>=1.26.4",
+    "pillow>=10.4.0",
+    "torch>=2.4.0",
+    "torchvision>=0.19.0",
+]
+
+[tool.ruff.lint]
+ignore = ["F722"]

requirements.txt

@@ -0,0 +1,211 @@
+# This file was autogenerated by uv via the following command:
+#    uv pip compile pyproject.toml
+aiofiles==23.2.1
+    # via gradio
+annotated-types==0.7.0
+    # via pydantic
+anyio==4.8.0
+    # via
+    #   gradio
+    #   httpx
+    #   starlette
+beartype==0.19.0
+    # via saev-image-classification (pyproject.toml)
+certifi==2024.12.14
+    # via
+    #   httpcore
+    #   httpx
+    #   requests
+charset-normalizer==3.4.1
+    # via requests
+click==8.1.8
+    # via
+    #   typer
+    #   uvicorn
+einops==0.8.0
+    # via saev-image-classification (pyproject.toml)
+fastapi==0.115.6
+    # via gradio
+ffmpy==0.5.0
+    # via gradio
+filelock==3.16.1
+    # via
+    #   huggingface-hub
+    #   torch
+    #   triton
+fsspec==2024.12.0
+    # via
+    #   gradio-client
+    #   huggingface-hub
+    #   torch
+gradio==5.9.1
+    # via saev-image-classification (pyproject.toml)
+gradio-client==1.5.2
+    # via gradio
+h11==0.14.0
+    # via
+    #   httpcore
+    #   uvicorn
+httpcore==1.0.7
+    # via httpx
+httpx==0.28.1
+    # via
+    #   gradio
+    #   gradio-client
+    #   safehttpx
+huggingface-hub==0.27.1
+    # via
+    #   gradio
+    #   gradio-client
+idna==3.10
+    # via
+    #   anyio
+    #   httpx
+    #   requests
+jaxtyping==0.2.36
+    # via saev-image-classification (pyproject.toml)
+jinja2==3.1.5
+    # via
+    #   gradio
+    #   torch
+markdown-it-py==3.0.0
+    # via rich
+markupsafe==2.1.5
+    # via
+    #   gradio
+    #   jinja2
+mdurl==0.1.2
+    # via markdown-it-py
+mpmath==1.3.0
+    # via sympy
+networkx==3.4.2
+    # via torch
+numpy==2.2.1
+    # via
+    #   saev-image-classification (pyproject.toml)
+    #   gradio
+    #   pandas
+    #   torchvision
+nvidia-cublas-cu12==12.4.5.8
+    # via
+    #   nvidia-cudnn-cu12
+    #   nvidia-cusolver-cu12
+    #   torch
+nvidia-cuda-cupti-cu12==12.4.127
+    # via torch
+nvidia-cuda-nvrtc-cu12==12.4.127
+    # via torch
+nvidia-cuda-runtime-cu12==12.4.127
+    # via torch
+nvidia-cudnn-cu12==9.1.0.70
+    # via torch
+nvidia-cufft-cu12==11.2.1.3
+    # via torch
+nvidia-curand-cu12==10.3.5.147
+    # via torch
+nvidia-cusolver-cu12==11.6.1.9
+    # via torch
+nvidia-cusparse-cu12==12.3.1.170
+    # via
+    #   nvidia-cusolver-cu12
+    #   torch
+nvidia-nccl-cu12==2.21.5
+    # via torch
+nvidia-nvjitlink-cu12==12.4.127
+    # via
+    #   nvidia-cusolver-cu12
+    #   nvidia-cusparse-cu12
+    #   torch
+nvidia-nvtx-cu12==12.4.127
+    # via torch
+orjson==3.10.13
+    # via gradio
+packaging==24.2
+    # via
+    #   gradio
+    #   gradio-client
+    #   huggingface-hub
+pandas==2.2.3
+    # via gradio
+pillow==11.1.0
+    # via
+    #   saev-image-classification (pyproject.toml)
+    #   gradio
+    #   torchvision
+pydantic==2.10.4
+    # via
+    #   fastapi
+    #   gradio
+pydantic-core==2.27.2
+    # via pydantic
+pydub==0.25.1
+    # via gradio
+pygments==2.19.1
+    # via rich
+python-dateutil==2.9.0.post0
+    # via pandas
+python-multipart==0.0.20
+    # via gradio
+pytz==2024.2
+    # via pandas
+pyyaml==6.0.2
+    # via
+    #   gradio
+    #   huggingface-hub
+requests==2.32.3
+    # via huggingface-hub
+rich==13.9.4
+    # via typer
+ruff==0.8.6
+    # via gradio
+safehttpx==0.1.6
+    # via gradio
+semantic-version==2.10.0
+    # via gradio
+setuptools==75.7.0
+    # via torch
+shellingham==1.5.4
+    # via typer
+six==1.17.0
+    # via python-dateutil
+sniffio==1.3.1
+    # via anyio
+starlette==0.41.3
+    # via
+    #   fastapi
+    #   gradio
+sympy==1.13.1
+    # via torch
+tomlkit==0.13.2
+    # via gradio
+torch==2.5.1
+    # via
+    #   saev-image-classification (pyproject.toml)
+    #   torchvision
+torchvision==0.20.1
+    # via saev-image-classification (pyproject.toml)
+tqdm==4.67.1
+    # via huggingface-hub
+triton==3.1.0
+    # via torch
+typer==0.15.1
+    # via gradio
+typing-extensions==4.12.2
+    # via
+    #   anyio
+    #   fastapi
+    #   gradio
+    #   gradio-client
+    #   huggingface-hub
+    #   pydantic
+    #   pydantic-core
+    #   torch
+    #   typer
+tzdata==2024.2
+    # via pandas
+urllib3==2.3.0
+    # via requests
+uvicorn==0.34.0
+    # via gradio
+websockets==14.1
+    # via gradio-client