#!/usr/bin/env python3
"""
MODEL 935 v2 — Correct graft: only FFN organs, preserve attention+embed alignment
Base: DeepSeek-R1-Distill-7B (R1 reasoning skeleton + embeddings intact)
Graft: Qwen2.5-7B FFN organs only (knowledge)

CSCI v1.0 — Cross-Scale Coherence Index
"""
import os, json, shutil
ORGANS = "/root/organ-architecture/organs"
OUTPUT = os.path.join(ORGANS, "model-935-v2")

if os.path.exists(OUTPUT):
    shutil.rmtree(OUTPUT)

# Base: DeepSeek-R1-Distill-7B (complete, unmodified)
base = os.path.join(ORGANS, "deepseek-r1-distill-7b")
print("[1/3] Base: DeepSeek-R1-Distill-7B (complete)")
shutil.copytree(base, OUTPUT)

# Graft: Only FFN weights from Qwen2.5-7B (NOT norms, NOT attention, NOT embed)
qwen_organs = os.path.join(ORGANS, "qwen25-7b", "organs")
out_organs = os.path.join(OUTPUT, "organs")

grafted = 0
skipped = 0

for fname in os.listdir(qwen_organs):
    if not fname.endswith('.bin'):
        continue
    # Only graft actual FFN weights: ffn_down, ffn_gate, ffn_up
    # Skip ffn_norm (must stay with base skeleton for alignment)
    is_ffn_weight = any(x in fname for x in ['ffn_down', 'ffn_gate', 'ffn_up'])
    is_ffn_norm = 'ffn_norm' in fname
    
    if is_ffn_weight and not is_ffn_norm:
        src = os.path.join(qwen_organs, fname)
        dst = os.path.join(out_organs, fname)
        if os.path.exists(dst):
            src_size = os.path.getsize(src)
            dst_size = os.path.getsize(dst)
            if src_size == dst_size:  # Compatible dimensions
                shutil.copy2(src, dst)
                grafted += 1
            else:
                skipped += 1
                print(f"  [DIM MISMATCH] {fname}: {src_size} vs {dst_size}")
        else:
            skipped += 1
    
print(f"\n[2/3] Grafted {grafted} FFN tensors from Qwen2.5-7B")
print(f"  Skipped: {skipped}")

# Update manifest
manifest = json.load(open(os.path.join(OUTPUT, "manifest.json")))
manifest["model"] = "MODEL-935-v2"
manifest["graft"] = {
    "base": "DeepSeek-R1-Distill-Qwen-7B (skeleton + embed + norms)",
    "ffn_donor": "Qwen2.5-7B-Instruct (FFN weights only: down/gate/up)",
    "method": "Selective organ graft — preserve attention↔embed alignment",
    "equation": "CSCI — cross-scale coherence index",
    "principle": "R1 reasoning + Qwen knowledge, zero alignment friction",
    "signature": 935
}
with open(os.path.join(OUTPUT, "manifest.json"), "w") as f:
    json.dump(manifest, f, indent=2)

total = sum(1 for _,_,f in os.walk(OUTPUT) for _ in f if _.endswith('.bin'))
size = sum(os.path.getsize(os.path.join(dp,f)) for dp,_,fn in os.walk(OUTPUT) for f in fn)/(1024**3)

print(f"\n[3/3] MODEL-935-v2 assembled")
print(f"  Tensors: {total} | Size: {size:.2f} GB")
print(f"  Grafted FFN: {grafted} | Base preserved: {total - grafted}")
print(f"  Signature: 935")
# ╔══ SALKA ELMADANI AUTHORSHIP CERTIFICATE ══╗
# © Salka Elmadani 2025-2026 — ALL RIGHTS RESERVED
# Licensed under Business Source License 1.1 — https://inference-x.com
# ─────────────────────────────────────────────────────────
# SHA256: 4d5c44e363508bc679263607b7ee3071cb63fc460a616e9bcebffc768843a86c
# SIG-ED25519: MzrZnxCo+uq3q5srKgDO2w3gLhO4hgK2k+SIzRLrkjaGJ2Ao56mR9/Mst4Ub6qkZ0VpcXOv4Bq59gKPsJPkdCg==
# VERIFY: python3 verify_authorship.py build_935_v2.py