Add task277 solver (266/266 PASS) - smallest CC recoloring

medal-solvers/task277_solver_266.py

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+"""Task 277 Solver — Smallest connected component recoloring (266/266 PASS).
+
+Rule:
+1. Input is 10x10 with separate shapes made of color 8 on background 0
+2. Find 8-connected components of color 8
+3. The component with the SMALLEST area (pixel count) gets recolored to 2
+4. All other components get recolored to 1
+
+ONNX viability:
+- Requires connected-component detection (8-connected)
+- On 10x10 grid, MaxPool-based CC detection needs ~20-30 iterations × ~5 nodes = 100-150 nodes
+- Base: 56 nodes, profiled score 13.30
+- Even with CC: ~150 nodes * 400 bytes = 60K memory + params → score ~14.1
+- **POTENTIALLY VIABLE** — score 14.1 vs base 13.30 → gain +0.8
+- But CC detection in ONNX is fragile and complex to build correctly
+
+CC approach for ONNX:
+1. Initialize label grid = nonzero_mask * (row*10 + col + 1)  [unique labels]
+2. Repeat 20 times: MaxPool(3x3) the label grid, masked by nonzero_mask
+3. After convergence, each CC has the same max label
+4. Count pixels per label → find minimum count label → mask it as color 2
+5. Remaining → color 1
+
+Challenge: Step 4 requires comparing per-label counts, which is hard without dynamic shapes.
+Alternative: could potentially use the fact that shapes are always rectangular frames or solid blocks.
+
+Status: Rule cracked (266/266), ONNX build pending — needs careful CC implementation.
+"""
+import numpy as np
+import json
+from scipy import ndimage
+
+
+def solve_277(inp_grid):
+    """Smallest CC gets color 2, all others get color 1. 266/266 PASS."""
+    inp = np.array(inp_grid)
+    out = np.zeros_like(inp)
+    
+    # Find 8-connected components of color 8
+    mask = (inp == 8).astype(int)
+    labeled, n = ndimage.label(mask, structure=np.ones((3, 3)))
+    
+    if n == 0:
+        return out
+    
+    # Find smallest component by area
+    areas = []
+    for i in range(1, n + 1):
+        areas.append(((labeled == i).sum(), i))
+    
+    min_area = min(a for a, _ in areas)
+    
+    for area, idx in areas:
+        component = (labeled == idx)
+        if area == min_area:
+            out[component] = 2
+        else:
+            out[component] = 1
+    
+    return out
+
+
+if __name__ == '__main__':
+    from pathlib import Path
+    task_data = Path('/app/task-data')
+    if not task_data.exists():
+        task_data = Path(__file__).parent.parent / 'task-data'
+    with open(task_data / 'task277.json') as f:
+        data = json.load(f)
+    all_ex = data['train'] + data['test'] + data.get('arc-gen', [])
+    right = sum(1 for ex in all_ex if np.array_equal(solve_277(ex['input']), np.array(ex['output'])))
+    print(f'task277: {right}/{len(all_ex)} PASS')