X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;ds=inline;f=grids.py;h=e6519401d5cf3d134c2c9ce355b62845e3520712;hb=5b7022591f48382ec84b1dda17297b1ed15166d7;hp=85d640dda9b3c6550cf7891037be1759a17a734e;hpb=428fd9169ecc3d03c9e8282d319682ddab0f098d;p=culture.git

diff --git a/grids.py b/grids.py
index 85d640d..e651940 100755
--- a/grids.py
+++ b/grids.py
@@ -17,6 +17,92 @@ from torch.nn import functional as F
 import problem
 
 
+def grow_islands(nb, height, width, nb_seeds, nb_iterations):
+    w = torch.empty(5, 1, 3, 3)
+
+    w[0, 0] = torch.tensor(
+        [
+            [1.0, 1.0, 1.0],
+            [1.0, 0.0, 1.0],
+            [1.0, 1.0, 1.0],
+        ]
+    )
+
+    w[1, 0] = torch.tensor(
+        [
+            [-1.0, 1.0, 0.0],
+            [1.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0],
+        ]
+    )
+
+    w[2, 0] = torch.tensor(
+        [
+            [0.0, 1.0, -1.0],
+            [0.0, 0.0, 1.0],
+            [0.0, 0.0, 0.0],
+        ]
+    )
+
+    w[3, 0] = torch.tensor(
+        [
+            [0.0, 0.0, 0.0],
+            [0.0, 0.0, 1.0],
+            [0.0, 1.0, -1.0],
+        ]
+    )
+
+    w[4, 0] = torch.tensor(
+        [
+            [0.0, 0.0, 0.0],
+            [1.0, 0.0, 0.0],
+            [-1.0, 1.0, 0.0],
+        ]
+    )
+
+    Z = torch.zeros(nb, height, width)
+    U = Z.flatten(1)
+
+    for _ in range(nb_seeds):
+        M = F.conv2d(Z[:, None, :, :], w, padding=1)
+        M = torch.cat([M[:, :1], M[:, 1:].min(dim=1, keepdim=True).values], dim=1)
+        M = ((M[:, 0] == 0) & (Z == 0)).long()
+        Q = (M.flatten(1).max(dim=1).values > 0).long()[:, None]
+        M = M * torch.rand(M.size())
+        M = M.flatten(1)
+        M = F.one_hot(M.argmax(dim=1), num_classes=M.size(1))
+        U += M * Q
+
+    for _ in range(nb_iterations):
+        M = F.conv2d(Z[:, None, :, :], w, padding=1)
+        M = torch.cat([M[:, :1], M[:, 1:].min(dim=1, keepdim=True).values], dim=1)
+        M = ((M[:, 1] >= 0) & (Z == 0)).long()
+        Q = (M.flatten(1).max(dim=1).values > 0).long()[:, None]
+        M = M * torch.rand(M.size())
+        M = M.flatten(1)
+        M = F.one_hot(M.argmax(dim=1), num_classes=M.size(1))
+        U = Z.flatten(1)
+        U += M * Q
+
+    M = Z.clone()
+    Z = Z * (torch.arange(Z.size(1) * Z.size(2)) + 1).reshape(1, Z.size(1), Z.size(2))
+
+    while True:
+        W = Z.clone()
+        Z = F.max_pool2d(Z, 3, 1, 1) * M
+        if Z.equal(W):
+            break
+
+    Z = Z.long()
+    U = Z.flatten(1)
+    V = F.one_hot(U).max(dim=1).values
+    W = V.cumsum(dim=1) - V
+    N = torch.arange(Z.size(0))[:, None, None].expand_as(Z)
+    Z = W[N, Z]
+
+    return Z
+
+
 class Grids(problem.Problem):
     named_colors = [
         ("white", [255, 255, 255]),
@@ -37,11 +123,34 @@ class Grids(problem.Problem):
         max_nb_cached_chunks=None,
         chunk_size=None,
         nb_threads=-1,
+        tasks=None,
     ):
         self.colors = torch.tensor([c for _, c in self.named_colors])
         self.height = 10
         self.width = 10
         self.cache_rec_coo = {}
+
+        all_tasks = [
+            self.task_replace_color,
+            self.task_translate,
+            self.task_grow,
+            self.task_half_fill,
+            self.task_frame,
+            self.task_detect,
+            self.task_count,
+            self.task_trajectory,
+            self.task_bounce,
+            self.task_scale,
+            self.task_symbols,
+            self.task_isometry,
+            self.task_islands,
+        ]
+
+        if tasks is None:
+            self.all_tasks = all_tasks
+        else:
+            self.all_tasks = [getattr(self, "task_" + t) for t in tasks.split(",")]
+
         super().__init__(max_nb_cached_chunks, chunk_size, nb_threads)
 
     ######################################################################
@@ -344,7 +453,11 @@ class Grids(problem.Problem):
 
     # @torch.compile
     def task_translate(self, A, f_A, B, f_B):
-        di, dj = torch.randint(3, (2,)) - 1
+        while True:
+            di, dj = torch.randint(3, (2,)) - 1
+            if di.abs() + dj.abs() > 0:
+                break
+
         nb_rec = 3
         c = torch.randperm(len(self.colors) - 1)[:nb_rec] + 1
         for X, f_X in [(A, f_A), (B, f_B)]:
@@ -372,7 +485,7 @@ class Grids(problem.Problem):
         di, dj = torch.randint(2, (2,)) * 2 - 1
         nb_rec = 3
         c = torch.randperm(len(self.colors) - 1)[:nb_rec] + 1
-        direction = torch.randint(2, (1,))
+        direction = torch.randint(2, (1,)).item()
         for X, f_X in [(A, f_A), (B, f_B)]:
             while True:
                 r = self.rec_coo(nb_rec, prevent_overlap=True)
@@ -394,11 +507,11 @@ class Grids(problem.Problem):
                     f_X[i1:i2, j1:j2] = c[n]
 
     # @torch.compile
-    def task_color_grow(self, A, f_A, B, f_B):
+    def task_half_fill(self, A, f_A, B, f_B):
         di, dj = torch.randint(2, (2,)) * 2 - 1
         nb_rec = 3
         c = torch.randperm(len(self.colors) - 1)[: 2 * nb_rec] + 1
-        direction = torch.randint(4, (1,))
+        direction = torch.randint(4, (1,)).item()
         for X, f_X in [(A, f_A), (B, f_B)]:
             r = self.rec_coo(nb_rec, prevent_overlap=True)
             for n in range(nb_rec):
@@ -501,61 +614,50 @@ class Grids(problem.Problem):
         return no, nq, nq_diag
 
     def task_count(self, A, f_A, B, f_B):
-        N = (torch.randint(4, (1,)) + 2).item()
-        c = torch.randperm(len(self.colors) - 1)[:N] + 1
-
-        for X, f_X in [(A, f_A), (B, f_B)]:
-            l_q = torch.randperm(self.height * self.width)[
-                : self.height * self.width // 20
-            ]
-            l_d = torch.randint(N, l_q.size())
-            nb = torch.zeros(N, dtype=torch.int64)
-
-            for q, e in zip(l_q, l_d):
-                d = c[e]
-                i, j = q % self.height, q // self.height
-                if (
-                    nb[e] < self.width
-                    and X[max(0, i - 1) : i + 2, max(0, j - 1) : j + 2] == 0
-                ).all():
-                    X[i, j] = d
-                    nb[e] += 1
-
-            l_q = torch.randperm((self.height - 2) * (self.width - 2))[
-                : self.height * self.width // 2
-            ]
-            l_d = torch.randint(N, l_q.size())
-            for q, e in zip(l_q, l_d):
-                d = c[e]
-                i, j = q % (self.height - 2) + 1, q // (self.height - 2) + 1
-                a1, a2, a3 = X[i - 1, j - 1 : j + 2]
-                a8, a4 = X[i, j - 1], X[i, j + 1]
-                a7, a6, a5 = X[i + 1, j - 1 : j + 2]
-                if (
-                    X[i, j] == 0
-                    and nb[e] < self.width
-                    and (a2 == 0 or a2 == d)
-                    and (a4 == 0 or a4 == d)
-                    and (a6 == 0 or a6 == d)
-                    and (a8 == 0 or a8 == d)
-                    and (a1 == 0 or a2 == d or a8 == d)
-                    and (a3 == 0 or a4 == d or a2 == d)
-                    and (a5 == 0 or a6 == d or a4 == d)
-                    and (a7 == 0 or a8 == d or a6 == d)
-                ):
-                    o = (
-                        (a2 != 0).long()
-                        + (a4 != 0).long()
-                        + (a6 != 0).long()
-                        + (a8 != 0).long()
+        while True:
+            error = False
+
+            N = torch.randint(5, (1,)).item() + 1
+            c = torch.zeros(N + 1)
+            c[1:] = torch.randperm(len(self.colors) - 1)[:N] + 1
+
+            for X, f_X in [(A, f_A), (B, f_B)]:
+                if not hasattr(self, "cache_count") or len(self.cache_count) == 0:
+                    self.cache_count = list(
+                        grow_islands(
+                            1000,
+                            self.height,
+                            self.width,
+                            nb_seeds=self.height * self.width // 8,
+                            nb_iterations=self.height * self.width // 10,
+                        )
                     )
-                    if o <= 1:
-                        X[i, j] = d
-                        nb[e] += 1 - o
 
-            for e in range(N):
-                for j in range(nb[e]):
-                    f_X[e, j] = c[e]
+                X[...] = self.cache_count.pop()
+
+                k = (X.max() + 1 + (c.size(0) - 1)).item()
+                V = torch.arange(k) // (c.size(0) - 1)
+                V = (V + torch.rand(V.size())).sort().indices[: X.max() + 1] % (
+                    c.size(0) - 1
+                ) + 1
+                V[0] = 0
+                X[...] = c[V[X]]
+
+                if F.one_hot(X.flatten()).max(dim=0).values.sum().item() == N + 1:
+                    f_X[...] = 0
+                    for e in range(1, N + 1):
+                        for j in range((X == c[e]).sum() + 1):
+                            if j < self.width:
+                                f_X[e - 1, j] = c[e]
+                            else:
+                                error = True
+                                break
+                else:
+                    error = True
+                    break
+
+            if not error:
+                break
 
     # @torch.compile
     def task_trajectory(self, A, f_A, B, f_B):
@@ -563,7 +665,10 @@ class Grids(problem.Problem):
         for X, f_X in [(A, f_A), (B, f_B)]:
             while True:
                 di, dj = torch.randint(7, (2,)) - 3
-                i, j = torch.randint(self.height, (1,)), torch.randint(self.width, (1,))
+                i, j = (
+                    torch.randint(self.height, (1,)).item(),
+                    torch.randint(self.width, (1,)).item(),
+                )
                 if (
                     abs(di) + abs(dj) > 0
                     and i + 2 * di >= 0
@@ -604,8 +709,9 @@ class Grids(problem.Problem):
                 X[...] = 0
 
                 for _ in range((self.height * self.width) // 10):
-                    i, j = torch.randint(self.height, (1,)), torch.randint(
-                        self.width, (1,)
+                    i, j = (
+                        torch.randint(self.height, (1,)).item(),
+                        torch.randint(self.width, (1,)).item(),
                     )
                     X[i, j] = c[0]
                     f_X[i, j] = c[0]
@@ -615,7 +721,10 @@ class Grids(problem.Problem):
                     if abs(di) + abs(dj) == 1:
                         break
 
-                i, j = torch.randint(self.height, (1,)), torch.randint(self.width, (1,))
+                i, j = (
+                    torch.randint(self.height, (1,)).item(),
+                    torch.randint(self.width, (1,)).item(),
+                )
 
                 X[i, j] = c[1]
                 f_X[i, j] = c[1]
@@ -653,18 +762,21 @@ class Grids(problem.Problem):
     def task_scale(self, A, f_A, B, f_B):
         c = torch.randperm(len(self.colors) - 1)[:2] + 1
 
-        i, j = torch.randint(self.height // 2, (1,)), torch.randint(
-            self.width // 2, (1,)
+        i, j = (
+            torch.randint(self.height // 2, (1,)).item(),
+            torch.randint(self.width // 2, (1,)).item(),
         )
 
         for X, f_X in [(A, f_A), (B, f_B)]:
             for _ in range(3):
                 while True:
-                    i1, j1 = torch.randint(self.height // 2 + 1, (1,)), torch.randint(
-                        self.width // 2 + 1, (1,)
+                    i1, j1 = (
+                        torch.randint(self.height // 2 + 1, (1,)).item(),
+                        torch.randint(self.width // 2 + 1, (1,)).item(),
                     )
-                    i2, j2 = torch.randint(self.height // 2 + 1, (1,)), torch.randint(
-                        self.width // 2 + 1, (1,)
+                    i2, j2 = (
+                        torch.randint(self.height // 2 + 1, (1,)).item(),
+                        torch.randint(self.width // 2 + 1, (1,)).item(),
                     )
                     if i1 < i2 and j1 < j2 and min(i2 - i1, j2 - j1) <= 3:
                         break
@@ -694,7 +806,7 @@ class Grids(problem.Problem):
 
             ai, aj = i.float().mean(), j.float().mean()
 
-            q = torch.randint(3, (1,)) + 1
+            q = torch.randint(3, (1,)).item() + 1
 
             X[i[0] + delta // 2 - 1, j[0] + delta // 2 - 1] = c[0]
             X[i[0] + delta // 2 - 1, j[0] + delta // 2 + 1] = c[0]
@@ -711,12 +823,12 @@ class Grids(problem.Problem):
             f_X[i[0] : i[0] + delta, j[0] : j[0] + delta] = c[q]
 
     # @torch.compile
-    def task_ortho(self, A, f_A, B, f_B):
+    def task_isometry(self, A, f_A, B, f_B):
         nb_rec = 3
         di, dj = torch.randint(3, (2,)) - 1
         o = torch.tensor([[0.0, 1.0], [-1.0, 0.0]])
         m = torch.eye(2)
-        for _ in range(torch.randint(4, (1,))):
+        for _ in range(torch.randint(4, (1,)).item()):
             m = m @ o
         if torch.rand(1) < 0.5:
             m[0, :] = -m[0, :]
@@ -765,9 +877,99 @@ class Grids(problem.Problem):
                 ):
                     break
 
+    def compute_distance(self, walls, goal_i, goal_j, start_i, start_j):
+        max_length = walls.numel()
+        dist = torch.full_like(walls, max_length)
+
+        dist[goal_i, goal_j] = 0
+        pred_dist = torch.empty_like(dist)
+
+        while True:
+            pred_dist.copy_(dist)
+            d = (
+                torch.cat(
+                    (
+                        dist[None, 1:-1, 0:-2],
+                        dist[None, 2:, 1:-1],
+                        dist[None, 1:-1, 2:],
+                        dist[None, 0:-2, 1:-1],
+                    ),
+                    0,
+                ).min(dim=0)[0]
+                + 1
+            )
+
+            dist[1:-1, 1:-1].minimum_(d)  # = torch.min(dist[1:-1, 1:-1], d)
+            dist = walls * max_length + (1 - walls) * dist
+
+            if dist[start_i, start_j] < max_length or dist.equal(pred_dist):
+                return dist * (1 - walls)
+
     # @torch.compile
-    def task_islands(self, A, f_A, B, f_B):
-        pass
+    def task_path(self, A, f_A, B, f_B):
+        c = torch.randperm(len(self.colors) - 1)[:3] + 1
+        dist = torch.empty(self.height + 2, self.width + 2)
+        for X, f_X in [(A, f_A), (B, f_B)]:
+            nb_rec = torch.randint(3, (1,)).item() + 1
+            while True:
+                r = self.rec_coo(nb_rec, prevent_overlap=True)
+                X[...] = 0
+                f_X[...] = 0
+                for n in range(nb_rec):
+                    i1, j1, i2, j2 = r[n]
+                    X[i1:i2, j1:j2] = c[0]
+                    f_X[i1:i2, j1:j2] = c[0]
+                while True:
+                    i0, j0 = (
+                        torch.randint(self.height, (1,)).item(),
+                        torch.randint(self.width, (1,)).item(),
+                    )
+                    if X[i0, j0] == 0:
+                        break
+                while True:
+                    i1, j1 = (
+                        torch.randint(self.height, (1,)).item(),
+                        torch.randint(self.width, (1,)).item(),
+                    )
+                    if X[i1, j1] == 0:
+                        break
+                dist[...] = 1
+                dist[1:-1, 1:-1] = (X != 0).long()
+                dist[...] = self.compute_distance(dist, i1 + 1, j1 + 1, i0 + 1, j0 + 1)
+                if dist[i0 + 1, j0 + 1] >= 1 and dist[i0 + 1, j0 + 1] < self.height * 4:
+                    break
+
+            dist[1:-1, 1:-1] += (X != 0).long() * self.height * self.width
+            dist[0, :] = self.height * self.width
+            dist[-1, :] = self.height * self.width
+            dist[:, 0] = self.height * self.width
+            dist[:, -1] = self.height * self.width
+            # dist += torch.rand(dist.size())
+
+            i, j = i0 + 1, j0 + 1
+            while i != i1 + 1 or j != j1 + 1:
+                f_X[i - 1, j - 1] = c[2]
+                r, s, t, u = (
+                    dist[i - 1, j],
+                    dist[i, j - 1],
+                    dist[i + 1, j],
+                    dist[i, j + 1],
+                )
+                m = min(r, s, t, u)
+                if r == m:
+                    i = i - 1
+                elif t == m:
+                    i = i + 1
+                elif s == m:
+                    j = j - 1
+                else:
+                    j = j + 1
+
+            X[i0, j0] = c[2]
+            # f_X[i0, j0] = c[1]
+
+            X[i1, j1] = c[1]
+            f_X[i1, j1] = c[1]
 
     # for X, f_X in [(A, f_A), (B, f_B)]:
     # n = torch.arange(self.height * self.width).reshape(self.height, self.width)
@@ -777,24 +979,104 @@ class Grids(problem.Problem):
     # i,j=q%self.height,q//self.height
     # if
 
-    ######################################################################
+    # @torch.compile
+    def task_puzzle(self, A, f_A, B, f_B):
+        S = 4
+        i0, j0 = (self.height - S) // 2, (self.width - S) // 2
+        c = torch.randperm(len(self.colors) - 1)[:4] + 1
+        for X, f_X in [(A, f_A), (B, f_B)]:
+            while True:
+                f_X[...] = 0
+                h = list(torch.randperm(c.size(0)))
+                n = torch.zeros(c.max() + 1)
+                for _ in range(2):
+                    k = torch.randperm(S * S)
+                    for q in k:
+                        i, j = q % S + i0, q // S + j0
+                        if f_X[i, j] == 0:
+                            r, s, t, u = (
+                                f_X[i - 1, j],
+                                f_X[i, j - 1],
+                                f_X[i + 1, j],
+                                f_X[i, j + 1],
+                            )
+                            r, s, t, u = torch.tensor([r, s, t, u])[torch.randperm(4)]
+                            if r > 0 and n[r] < 6:
+                                n[r] += 1
+                                f_X[i, j] = r
+                            elif s > 0 and n[s] < 6:
+                                n[s] += 1
+                                f_X[i, j] = s
+                            elif t > 0 and n[t] < 6:
+                                n[t] += 1
+                                f_X[i, j] = t
+                            elif u > 0 and n[u] < 6:
+                                n[u] += 1
+                                f_X[i, j] = u
+                            else:
+                                if len(h) > 0:
+                                    d = c[h.pop()]
+                                    n[d] += 1
+                                    f_X[i, j] = d
+
+                if n.sum() == S * S:
+                    break
 
-    def all_tasks(self):
-        return [
-            self.task_replace_color,
-            self.task_translate,
-            self.task_grow,
-            self.task_color_grow,
-            self.task_frame,
-            self.task_detect,
-            self.task_count,
-            self.task_trajectory,
-            self.task_bounce,
-            self.task_scale,
-            self.task_symbols,
-            self.task_ortho,
-            # self.task_islands,
-        ]
+            k = 0
+            for d in range(4):
+                while True:
+                    ii, jj = (
+                        torch.randint(self.height, (1,)).item(),
+                        torch.randint(self.width, (1,)).item(),
+                    )
+                    e = 0
+                    for i in range(S):
+                        for j in range(S):
+                            if (
+                                ii + i >= self.height
+                                or jj + j >= self.width
+                                or (
+                                    f_X[i + i0, j + j0] == c[d]
+                                    and X[ii + i, jj + j] > 0
+                                )
+                            ):
+                                e = 1
+                    if e == 0:
+                        break
+                for i in range(S):
+                    for j in range(S):
+                        if f_X[i + i0, j + j0] == c[d]:
+                            X[ii + i, jj + j] = c[d]
+
+    def task_islands(self, A, f_A, B, f_B):
+        c = torch.randperm(len(self.colors) - 1)[:2] + 1
+        for X, f_X in [(A, f_A), (B, f_B)]:
+            if not hasattr(self, "cache_islands") or len(self.cache_islands) == 0:
+                self.cache_islands = list(
+                    grow_islands(
+                        1000,
+                        self.height,
+                        self.width,
+                        nb_seeds=self.height * self.width // 20,
+                        nb_iterations=self.height * self.width // 2,
+                    )
+                )
+
+            A = self.cache_islands.pop()
+
+            while True:
+                i, j = (
+                    torch.randint(self.height // 2, (1,)).item(),
+                    torch.randint(self.width // 2, (1,)).item(),
+                )
+                if A[i, j] > 0:
+                    break
+
+            X[...] = (A > 0) * c[0]
+            X[i, j] = c[1]
+            f_X[...] = (A == A[i, j]) * c[1] + ((A > 0) & (A != A[i, j])) * c[0]
+
+    ######################################################################
 
     def trivial_prompts_and_answers(self, prompts, answers):
         S = self.height * self.width
@@ -804,7 +1086,7 @@ class Grids(problem.Problem):
 
     def generate_prompts_and_answers_(self, nb, tasks=None, progress_bar=False):
         if tasks is None:
-            tasks = self.all_tasks()
+            tasks = self.all_tasks
 
         S = self.height * self.width
         prompts = torch.zeros(nb, 3 * S + 2, dtype=torch.int64)
@@ -825,12 +1107,12 @@ class Grids(problem.Problem):
             f_A = prompt[1 * (S + 1) : 1 * (S + 1) + S].view(self.height, self.width)
             B = prompt[2 * (S + 1) : 2 * (S + 1) + S].view(self.height, self.width)
             f_B = answer.view(self.height, self.width)
-            task = tasks[torch.randint(len(tasks), (1,))]
+            task = tasks[torch.randint(len(tasks), (1,)).item()]
             task(A, f_A, B, f_B)
 
         return prompts.flatten(1), answers.flatten(1)
 
-    def save_quizzes(
+    def save_quiz_illustrations(
         self,
         result_dir,
         filename_prefix,
@@ -850,6 +1132,15 @@ class Grids(problem.Problem):
             nrow,
         )
 
+    def save_some_examples(self, result_dir):
+        nb, nrow = 72, 4
+        for t in self.all_tasks:
+            print(t.__name__)
+            prompts, answers = self.generate_prompts_and_answers_(nb, tasks=[t])
+            self.save_quiz_illustrations(
+                result_dir, t.__name__, prompts[:nb], answers[:nb], nrow=nrow
+            )
+
 
 ######################################################################
 
@@ -871,18 +1162,23 @@ if __name__ == "__main__":
     # exit(0)
 
     # if True:
-    nb = 72
+    nb, nrow = 72, 4
+    # nb, nrow = 8, 2
 
-    for t in grids.all_tasks():
-        # for t in [grids.task_replace_color]:
+    # for t in grids.all_tasks:
+    for t in [grids.task_islands]:
         print(t.__name__)
         prompts, answers = grids.generate_prompts_and_answers_(nb, tasks=[t])
-        grids.save_quizzes("/tmp", t.__name__, prompts[:nb], answers[:nb], nrow=4)
+        grids.save_quiz_illustrations(
+            "/tmp", t.__name__, prompts[:nb], answers[:nb], nrow=nrow
+        )
+
+    # exit(0)
 
     nb = 1000
 
-    for t in grids.all_tasks():
-        # for t in [ grids.task_replace_color ]: #grids.all_tasks():
+    # for t in grids.all_tasks:
+    for t in [grids.task_islands]:
         start_time = time.perf_counter()
         prompts, answers = grids.generate_prompts_and_answers_(nb, tasks=[t])
         delay = time.perf_counter() - start_time
@@ -894,7 +1190,7 @@ if __name__ == "__main__":
     predicted_prompts = m * (torch.randint(2, (prompts.size(0),)) * 2 - 1)
     predicted_answers = (1 - m) * (torch.randint(2, (prompts.size(0),)) * 2 - 1)
 
-    grids.save_quizzes(
+    grids.save_quiz_illustrations(
         "/tmp",
         "test",
         prompts[:nb],