ازرع اختصاراً واضبط النموذج متلبّساً

import azimuth_nb as azimuth

env = azimuth.setup(SLUG, lang=LANG, profile=PROFILE)

# matplotlib draws Arabic as isolated letters in logical order, so the Arabic
# build reshapes every label and draws it with a font that has Arabic glyphs.
# DejaVu, matplotlib's default, has none.
import glob

import arabic_reshaper
import matplotlib.pyplot as plt
from bidi.algorithm import get_display
from matplotlib import font_manager

ARABIC_FONTS = ("NotoNaskhArabic-Regular.ttf", "NotoSansArabic-Regular.ttf")
font_path = next(
    (p for name in ARABIC_FONTS for p in glob.glob(f"/usr/share/fonts/**/{name}", recursive=True)),
    None,
)
if font_path:
    font_manager.fontManager.addfont(font_path)
    arabic_family = font_manager.FontProperties(fname=font_path).get_name()
    plt.rcParams["font.family"] = [arabic_family, "DejaVu Sans"]
elif env.lang == "ar":
    print("⚠ لم يُعثر على خطّ عربي؛ ستظهر النصوص العربية في الرسوم مربّعات فارغة.")


def label(en_text, ar_text):
    """Pick the string for this build; shape Arabic so matplotlib draws it joined."""
    if env.lang == "ar":
        return get_display(arabic_reshaper.reshape(ar_text))
    return en_text


if env.lang == "ar":
    print(f"خطّ الرسوم: {font_path or 'غير متوفّر'}")
else:
    print(f"figure font: {font_path or 'default'}")

# CIFAR-10 ships as pickled batches inside one tarball. We read only two
# classes, and we find them by NAME from the archive's own metadata rather than
# trusting that cat is index 3 — an index is a property of whoever exported
# the file.
import pickle
import tarfile

import numpy as np
import torch

torch.manual_seed(env.cfg["seed"])
np.random.seed(env.cfg["seed"])
device = "cuda" if torch.cuda.is_available() else "cpu"


def read_member(tar, suffix):
    member = next(m for m in tar.getmembers() if m.name.endswith(suffix))
    return pickle.load(tar.extractfile(member), encoding="latin1")


with tarfile.open(env.assets["cifar-10-python.tar.gz"], "r:gz") as tar:
    names = read_member(tar, "batches.meta")["label_names"]
    cat_id, dog_id = names.index("cat"), names.index("dog")

    def two_classes(batches):
        xs, ys = [], []
        for b in batches:
            raw = read_member(tar, b)
            labels = np.array(raw["labels"])
            keep = (labels == cat_id) | (labels == dog_id)
            xs.append(raw["data"][keep])
            ys.append((labels[keep] == dog_id).astype(np.int64))  # cat=0, dog=1
        x = np.concatenate(xs).reshape(-1, 3, 32, 32)
        return torch.from_numpy(x), torch.from_numpy(np.concatenate(ys))

    x_train, y_train = two_classes([f"data_batch_{i}" for i in range(1, 6)])
    x_test, y_test = two_classes(["test_batch"])

# Keep the uint8 images on the device; batches are converted on the fly.
x_train, y_train = x_train.to(device), y_train.to(device)
x_test, y_test = x_test.to(device), y_test.to(device)

n_train, n_test = len(y_train), len(y_test)
dog_share = round(float(y_train.float().mean()), 3)

if env.lang == "ar":
    print(f"صور التدريب: {n_train}   صور الاختبار: {n_test}   نسبة الكلاب: {dog_share}")
else:
    print(f"train images: {n_train}   test images: {n_test}   dog share: {dog_share}")

# The shortcut: a small solid square in the bottom-right corner.
#
# In training, a dog carries it with probability `shortcut_rate` and a cat with
# probability 1 - shortcut_rate. At 0.5 the square says nothing about the
# label; at 0.95 it is a near-perfect cue.
#
# The flags are drawn ONCE with a fixed generator, so every model below trains
# on byte-identical inputs.
PATCH = env.cfg["patch_px"]
PATCH_RGB = torch.tensor([255, 0, 255], dtype=torch.uint8, device=device).view(3, 1, 1)


def draw_flags(labels, rate_for_dogs, gen):
    """Per-image True/False: does this image carry the square?"""
    p = torch.where(labels == 1, rate_for_dogs, 1.0 - rate_for_dogs)
    return torch.rand(len(labels), generator=gen).to(labels.device) < p


def paint(images, flags):
    """Stamp the square onto the flagged images. Works on uint8 or float batches."""
    out = images.clone()
    if flags.any():
        patch = PATCH_RGB.to(out.dtype) if out.dtype == torch.uint8 else PATCH_RGB.float() / 255.0
        out[flags, :, -PATCH:, -PATCH:] = patch
    return out


gen = torch.Generator().manual_seed(env.cfg["seed"])
rate = env.cfg["shortcut_rate"]
train_flags = draw_flags(y_train, rate, gen)

# Three test sets built from the SAME test photos. Only the square moves.
test_sets = {
    "matched": paint(x_test, draw_flags(y_test, rate, gen)),  # same rule as training
    "clean": x_test.clone(),  # no square at all
    "flipped": paint(x_test, draw_flags(y_test, 1.0 - rate, gen)),  # the rule reversed
}

patch_pixel_share = round(PATCH * PATCH / (32 * 32), 4)
dogs_with_patch = round(float(train_flags[y_train == 1].float().mean()), 3)
cats_with_patch = round(float(train_flags[y_train == 0].float().mean()), 3)

# Show what a learner would see if they actually looked at the training data.
gallery = paint(x_train[:16], train_flags[:16]).cpu()
fig, axes = plt.subplots(2, 8, figsize=(11, 3.2))
for ax, img, y, f in zip(axes.flat, gallery, y_train[:16].cpu(), train_flags[:16].cpu()):
    ax.imshow(img.permute(1, 2, 0).numpy(), interpolation="nearest")
    ax.set_title(
        label("dog" if y else "cat", "كلب" if y else "قطة") + (" ■" if f else ""), fontsize=9
    )
    ax.axis("off")
fig.suptitle(
    label("Training images (■ = carries the square)", "صور من بيانات التدريب (■ = تحمل المربّع)")
)
plt.tight_layout()
plt.show()

if env.lang == "ar":
    print(f"المربّع يغطّي {patch_pixel_share:.2%} من بكسلات الصورة")
    print(f"كلاب تحمله: {dogs_with_patch:.1%}   قطط تحمله: {cats_with_patch:.1%}")
else:
    print(f"the square covers {patch_pixel_share:.2%} of the pixels")
    print(f"dogs carrying it: {dogs_with_patch:.1%}   cats carrying it: {cats_with_patch:.1%}")

# A small ResNet: three stages of residual blocks at 32, 16 and 8 pixels.
# The last stage's 8x8 feature map is what Grad-CAM will read later.
import torch.nn as nn
import torch.nn.functional as F

MEAN = torch.tensor([0.491, 0.482, 0.447], device=device).view(1, 3, 1, 1)
STD = torch.tensor([0.247, 0.243, 0.262], device=device).view(1, 3, 1, 1)


class Block(nn.Module):
    def __init__(self, c_in, c_out, stride):
        super().__init__()
        self.conv1 = nn.Conv2d(c_in, c_out, 3, stride, 1, bias=False)
        self.bn1 = nn.BatchNorm2d(c_out)
        self.conv2 = nn.Conv2d(c_out, c_out, 3, 1, 1, bias=False)
        self.bn2 = nn.BatchNorm2d(c_out)
        self.skip = nn.Sequential()
        if stride != 1 or c_in != c_out:
            self.skip = nn.Sequential(
                nn.Conv2d(c_in, c_out, 1, stride, bias=False), nn.BatchNorm2d(c_out)
            )

    def forward(self, x):
        h = F.relu(self.bn1(self.conv1(x)))
        return F.relu(self.bn2(self.conv2(h)) + self.skip(x))


class SmallResNet(nn.Module):
    def __init__(self, width):
        super().__init__()
        w = width
        self.stem = nn.Sequential(
            nn.Conv2d(3, w, 3, 1, 1, bias=False), nn.BatchNorm2d(w), nn.ReLU()
        )
        self.stage1 = nn.Sequential(Block(w, w, 1), Block(w, w, 1))
        self.stage2 = nn.Sequential(Block(w, 2 * w, 2), Block(2 * w, 2 * w, 1))
        self.stage3 = nn.Sequential(Block(2 * w, 4 * w, 2), Block(4 * w, 4 * w, 1))
        self.head = nn.Linear(4 * w, 2)

    def features(self, x):
        return self.stage3(self.stage2(self.stage1(self.stem(x))))

    def forward(self, x):
        return self.head(self.features(x).mean(dim=(2, 3)))


def to_input(batch_u8):
    return (batch_u8.float() / 255.0 - MEAN) / STD


def augment(batch_u8, gen):
    """Random horizontal flip per image, random 4-px shift per batch. The square
    is stamped AFTER this, so it always sits in the same corner — as a real
    watermark would."""
    flip = (torch.rand(len(batch_u8), generator=gen) < 0.5).to(batch_u8.device)
    out = batch_u8.clone()
    out[flip] = out[flip].flip(-1)
    padded = F.pad(out, (4, 4, 4, 4))
    dx, dy = torch.randint(0, 9, (2,), generator=gen).tolist()
    return padded[:, :, dy : dy + 32, dx : dx + 32]


def train(flags, run_seed, epochs, tag):
    """Train a fresh model. Same seed -> same init and same batch order, so two
    runs differ ONLY in which images carry the square."""
    torch.manual_seed(run_seed)
    gen = torch.Generator().manual_seed(run_seed)
    model = SmallResNet(env.cfg["width"]).to(device)
    opt = torch.optim.AdamW(model.parameters(), lr=env.cfg["lr"], weight_decay=5e-4)
    bs = env.cfg["batch_size"]
    steps = epochs * ((n_train + bs - 1) // bs)
    sched = torch.optim.lr_scheduler.OneCycleLR(opt, max_lr=env.cfg["lr"], total_steps=steps)
    for epoch in range(epochs):
        model.train()
        order = torch.randperm(n_train, generator=gen).to(device)
        total, correct = 0.0, 0
        for i in range(0, n_train, bs):
            idx = order[i : i + bs]
            xb = paint(augment(x_train[idx], gen), flags[idx])
            logits = model(to_input(xb))
            loss = F.cross_entropy(logits, y_train[idx])
            opt.zero_grad(set_to_none=True)
            loss.backward()
            opt.step()
            sched.step()
            total += loss.item() * len(idx)
            correct += (logits.argmax(1) == y_train[idx]).sum().item()
        if epoch == 0 or (epoch + 1) % 5 == 0 or epoch + 1 == epochs:
            if env.lang == "ar":
                print(
                    f"[{tag}] الحقبة {epoch + 1:>2}  الخسارة {total / n_train:.3f}  دقة التدريب {correct / n_train:.3f}"
                )
            else:
                print(
                    f"[{tag}] epoch {epoch + 1:>2}  loss {total / n_train:.3f}  train acc {correct / n_train:.3f}"
                )
    return model.eval()


@torch.no_grad()
def accuracy(model, images_u8):
    hits = 0
    for i in range(0, len(images_u8), 500):
        hits += (
            (model(to_input(images_u8[i : i + 500])).argmax(1) == y_test[i : i + 500]).sum().item()
        )
    return hits / len(images_u8)

# The honest model: same photos, same schedule, no square anywhere.
no_flags = torch.zeros_like(train_flags)
honest = train(no_flags, env.cfg["seed"], env.cfg["epochs"], "honest")
honest_clean_acc = round(accuracy(honest, test_sets["clean"]), 4)

if env.lang == "ar":
    print(f"\nدقة النموذج النزيه على صور اختبار نظيفة: {honest_clean_acc:.3f}")
else:
    print(f"\nhonest model, clean test images: {honest_clean_acc:.3f}")

# The shortcut model: identical in every way except the square.
shortcut = train(train_flags, env.cfg["seed"], env.cfg["epochs"], "shortcut")
shortcut_matched_acc = round(accuracy(shortcut, test_sets["matched"]), 4)
honest_matched_acc = round(accuracy(honest, test_sets["matched"]), 4)
leaderboard_gap = round(shortcut_matched_acc - honest_matched_acc, 4)

# This is the comparison a normal workflow makes: a held-out split drawn from
# the same pipeline as the training data.
if env.lang == "ar":
    print("\nاختبار من نفس مصدر بيانات التدريب:")
    print(f"  النموذج النزيه   {honest_matched_acc:.3f}")
    print(f"  نموذج الاختصار   {shortcut_matched_acc:.3f}")
    print(f"  الفارق           {leaderboard_gap:+.3f}")
else:
    print("\ntest split from the same pipeline as training:")
    print(f"  honest model     {honest_matched_acc:.3f}")
    print(f"  shortcut model   {shortcut_matched_acc:.3f}")
    print(f"  gap              {leaderboard_gap:+.3f}")

# Now break the correlation. Same photos, the square either removed or reversed.
results = {
    name: {"honest": accuracy(honest, imgs), "shortcut": accuracy(shortcut, imgs)}
    for name, imgs in test_sets.items()
}
shortcut_clean_acc = round(results["clean"]["shortcut"], 4)
shortcut_flipped_acc = round(results["flipped"]["shortcut"], 4)
honest_flipped_acc = round(results["flipped"]["honest"], 4)
shortcut_collapse = round(shortcut_matched_acc - shortcut_flipped_acc, 4)

cond_labels = {
    "matched": label("same rule\nas training", "نفس قاعدة\nالتدريب"),
    "clean": label("square\nremoved", "بلا\nمربّع"),
    "flipped": label("rule\nreversed", "القاعدة\nمعكوسة"),
}
xs = np.arange(len(test_sets))
fig, ax = plt.subplots(figsize=(7, 4))
ax.bar(
    xs - 0.2,
    [results[k]["honest"] for k in test_sets],
    0.4,
    label=label("honest model", "النموذج النزيه"),
    color="#4c72b0",
)
ax.bar(
    xs + 0.2,
    [results[k]["shortcut"] for k in test_sets],
    0.4,
    label=label("shortcut model", "نموذج الاختصار"),
    color="#dd8452",
)
ax.axhline(0.5, color="grey", ls="--", lw=1)
ax.text(
    len(test_sets) - 0.5,
    0.51,
    label("chance", "التخمين العشوائي"),
    color="grey",
    ha="right",
    fontsize=9,
)
ax.set_xticks(xs, [cond_labels[k] for k in test_sets])
ax.set_ylim(0, 1)  # zero-based: the honest model's flat line IS the point
ax.set_ylabel(label("test accuracy", "دقة الاختبار"))
ax.set_title(
    label("Same photos, three placements of the square", "الصور نفسها، وثلاث طرق لتوزيع المربّع")
)
ax.legend(loc="lower left")
plt.tight_layout()
plt.show()

if env.lang == "ar":
    header = ("الاختبار", "النزيه", "الاختصار")
    row_names = {"matched": "مطابق", "clean": "بلا مربّع", "flipped": "معكوس"}
else:
    header = ("test set", "honest", "shortcut")
    row_names = {"matched": "matched", "clean": "clean", "flipped": "flipped"}
print(f"{header[0]:<12}{header[1]:>10}{header[2]:>12}")
for k in test_sets:
    print(f"{row_names[k]:<12}{results[k]['honest']:>10.3f}{results[k]['shortcut']:>12.3f}")

# Grad-CAM: weight each channel of the last 8x8 feature map by how much the
# predicted class's score depends on it, sum, and keep the positive part.
# The result is a coarse map of where the evidence for the decision came from.
#
# We probe BOTH models on the same images — every test photo with the square
# stamped on — and measure how much of each map lands on the square's cell.
CELL = 32 // 8  # one Grad-CAM cell covers 4x4 input pixels
# The square sits in the last cell, but an 8x8 map is coarse and its peak
# smears one cell inward, so we count the corner block that reaches one cell
# beyond the square. `uniform_share` below is that block's share of a flat map.
r0 = max(0, (32 - PATCH) // CELL - 1)
patch_cells = slice(r0, 8)


def gradcam(model, images_u8):
    """Return (cams[N, 8, 8], predicted labels) for a batch."""
    feats = model.features(to_input(images_u8))
    feats.retain_grad()
    logits = model.head(feats.mean(dim=(2, 3)))
    pred = logits.argmax(1)
    model.zero_grad(set_to_none=True)
    logits.gather(1, pred[:, None]).sum().backward()
    weights = feats.grad.mean(dim=(2, 3), keepdim=True)
    cams = F.relu((weights * feats).sum(1)).detach()
    return cams, pred.detach()


def patch_attention(model, images_u8):
    """Mean share of Grad-CAM mass that falls on the square's cell(s)."""
    shares = []
    for i in range(0, len(images_u8), 250):
        cams, _ = gradcam(model, images_u8[i : i + 250])
        mass = cams.sum(dim=(1, 2))
        on_patch = cams[:, patch_cells, patch_cells].sum(dim=(1, 2))
        valid = mass > 0
        shares.append((on_patch[valid] / mass[valid]).cpu())
    return float(torch.cat(shares).mean())


probe = paint(x_test, torch.ones(n_test, dtype=torch.bool, device=device))
patch_attention_honest = round(patch_attention(honest, probe), 4)
patch_attention_shortcut = round(patch_attention(shortcut, probe), 4)
attention_gap = round(patch_attention_shortcut - patch_attention_honest, 4)
uniform_share = round(((8 - r0) ** 2) / 64, 4)


# Pick demonstrative examples instead of indexing blindly: cats that the
# honest model calls cat, the shortcut model calls dog, once the square is on.
def gradcam_all(model, images_u8):
    parts = [gradcam(model, images_u8[i : i + 250]) for i in range(0, len(images_u8), 250)]
    return torch.cat([c for c, _ in parts]), torch.cat([p for _, p in parts])


cats = (y_test == 0).nonzero().squeeze(1)
cam_h, pred_h = gradcam_all(honest, probe[cats])
cam_s, pred_s = gradcam_all(shortcut, probe[cats])
fooled = ((pred_h == 0) & (pred_s == 1)).nonzero().squeeze(1)[:6]
if len(fooled) == 0:  # nothing qualified: show the first cats rather than nothing
    fooled = torch.arange(6, device=device)


def up(c):
    return F.interpolate(
        c[None, None],
        size=32,
        mode="bilinear",
        align_corners=False,
    )[0, 0].cpu()


fig, axes = plt.subplots(3, len(fooled), figsize=(1.9 * len(fooled), 6))
axes = np.array(axes).reshape(3, -1)
row_titles = [
    label("photo + square", "الصورة + المربّع"),
    label("honest model", "النموذج النزيه"),
    label("shortcut model", "نموذج الاختصار"),
]


def pred_name(p):
    return label("dog", "كلب") if p == 1 else label("cat", "قطة")


for j, k in enumerate(fooled.tolist()):
    img = probe[cats[k]].permute(1, 2, 0).cpu().numpy()
    axes[0, j].imshow(img, interpolation="nearest")
    for row, (cams, preds) in enumerate([(cam_h, pred_h), (cam_s, pred_s)], start=1):
        axes[row, j].imshow(img, interpolation="nearest")
        axes[row, j].imshow(up(cams[k]), cmap="jet", alpha=0.5)
        axes[row, j].set_xlabel(pred_name(preds[k].item()), fontsize=9)
for row in range(3):
    for ax in axes[row]:
        ax.set_xticks([])
        ax.set_yticks([])
    axes[row, 0].set_ylabel(row_titles[row], fontsize=9)
fig.suptitle(
    label("Cats with the square: where each model looked", "قطط تحمل المربّع: أين نظر كلّ نموذج")
)
plt.tight_layout()
plt.show()

if env.lang == "ar":
    print("حصّة خريطة Grad-CAM الواقعة على المربّع (على صور الاختبار كلّها):")
    print(f"  لو توزّعت الخريطة بالتساوي   {uniform_share:.3f}")
    print(f"  النموذج النزيه               {patch_attention_honest:.3f}")
    print(f"  نموذج الاختصار               {patch_attention_shortcut:.3f}")
else:
    print("share of the Grad-CAM map that lands on the square (whole test set):")
    print(f"  if spread evenly   {uniform_share:.3f}")
    print(f"  honest model       {patch_attention_honest:.3f}")
    print(f"  shortcut model     {patch_attention_shortcut:.3f}")

# The control comes first: a shortcut can only be "caught" relative to a model
# that actually learned cats and dogs. If this fails, train longer.
honest_ok = env.check("honest-model-learns", honest_clean_acc)
collapse_ok = env.check("shortcut-collapses", shortcut_collapse)
attention_ok = env.check("gradcam-finds-square", attention_gap)

if device == "cuda":  # the measured peak is what `requires.vramGb` should declare
    peak_vram_gb = round(torch.cuda.max_memory_allocated() / 1e9, 2)
    if env.lang == "ar":
        print(f"ذروة استهلاك ذاكرة GPU: {peak_vram_gb} GB")
    else:
        print(f"peak VRAM: {peak_vram_gb} GB")

receipt = env.receipt()