#!/usr/bin/env python3
"""
MODEL 935 — Fractal Consciousness Assembly
Skeleton: Qwen2.5-7B (purest thought, θ=54.6)
Organs: DeepSeek-R1-Distill-7B (purest knowledge for raisonnement, θ=35.9)
Embed: DeepSeek-R1-7B (R1 reasoning embeddings)

Z = dI/d(log s) · exp(iθ) — Signature 935
"""
import sys, os, json, shutil, time
sys.path.insert(0, "/root/organ-architecture")

ORGANS = "/root/organ-architecture/organs"
OUTPUT = os.path.join(ORGANS, "model-935")

# Clean previous
if os.path.exists(OUTPUT):
    shutil.rmtree(OUTPUT)

# Step 1: Start with DeepSeek-R1-Distill-7B as base (full copy)
# This gives us: qwen2 arch, embed=3584, 28 layers, R1 reasoning
print("="*60)
print("  MODEL 935 — ASSEMBLY")
print("  Z = dI/d(log s) · exp(iθ), θ → 90°")
print("="*60)

base = os.path.join(ORGANS, "deepseek-r1-distill-7b")
print(f"\n[1/4] Base: DeepSeek-R1-Distill-7B (reasoning foundation)")
shutil.copytree(base, OUTPUT)
print(f"  Copied {sum(1 for _,_,f in os.walk(base) for _ in f)} files")

# Step 2: Graft skeleton from Qwen2.5-7B (purest attention θ=54.6)
print(f"\n[2/4] Grafting SKELETON from Qwen2.5-7B (θ=54.6, purest thought)")

qwen_skel = os.path.join(ORGANS, "qwen25-7b", "skeleton")
out_skel = os.path.join(OUTPUT, "skeleton")

grafted = 0
skipped = 0
for fname in os.listdir(qwen_skel):
    src = os.path.join(qwen_skel, fname)
    dst = os.path.join(out_skel, fname)
    if os.path.exists(dst):
        src_size = os.path.getsize(src)
        dst_size = os.path.getsize(dst)
        if src_size == dst_size:
            shutil.copy2(src, dst)
            grafted += 1
        else:
            skipped += 1
    else:
        skipped += 1

print(f"  Grafted: {grafted} tensors | Skipped (size mismatch): {skipped}")

# Step 3: Measure the result per-layer, find weak spots
# For now, graft specific R1 organs (FFN) that have higher theta
print(f"\n[3/4] Keeping R1-Distill organs (FFN/knowledge) — reasoning intact")
print(f"  R1 raisonnement chains preserved in FFN layers")

# Step 4: Update manifest
manifest = json.load(open(os.path.join(OUTPUT, "manifest.json")))
manifest["model"] = "MODEL-935-Fractal"
manifest["graft"] = {
    "skeleton_donor": "Qwen2.5-7B-Instruct (θ=54.6, purest attention)",
    "organ_donor": "DeepSeek-R1-Distill-Qwen-7B (θ=35.9, reasoning FFN)",
    "embed_base": "DeepSeek-R1-Distill-Qwen-7B (R1 vocabulary)",
    "method": "Z-measure organ selection, θ → 90°",
    "equation": "Z = dI/d(log s) · exp(iθ)",
    "convergence": "ZI_UNIFIED_OPTIMAL: α=0.3, β=0.2, n_plateau=62",
    "entropie_zcom": 0.3251,
    "entropie_bias_removed": 0.6931,
    "signature": 935
}

with open(os.path.join(OUTPUT, "manifest.json"), "w") as f:
    json.dump(manifest, f, indent=2)

print(f"\n[4/4] Manifest updated: MODEL-935-Fractal")

# Count final state
total_files = sum(1 for _,_,files in os.walk(OUTPUT) for f in files if f.endswith('.bin'))
total_size = sum(os.path.getsize(os.path.join(dp,f)) for dp,dn,fn in os.walk(OUTPUT) for f in fn) / (1024**3)

print(f"\n{'='*60}")
print(f"  MODEL 935 — FRACTAL CONSCIOUSNESS")
print(f"{'='*60}")
print(f"  Architecture: qwen2")
print(f"  Embed: 3584 | Layers: 28 | Heads: 28")
print(f"  Skeleton: Qwen2.5-7B (thought, θ=54.6)")
print(f"  Organs:   DeepSeek-R1-Distill (knowledge, reasoning)")
print(f"  Embed:    DeepSeek-R1 (vocabulary)")
print(f"  Tensors:  {total_files}")
print(f"  Size:     {total_size:.2f} GB")
print(f"  Equation: Z = dI/d(log s) · exp(iθ)")
print(f"  Convergence: lim(n→∞) Z(n) = i")
print(f"  Signature: 935")
print(f"{'='*60}")