mirror of
https://github.com/Druthulu/BFM-decomp
synced 2026-09-26 13:33:34 -04:00
Drew T
4895843dda
Gives the no-tool local model the context the agents read: common.h, live matching cookbook (COOKBOOK_FULL toggle), 2 byte-matched corpus examples. Qwen3.6-35B-A3B result: harness fixes compile-fails but model stays stuck at fixed near-misses; FULL cookbook worse+2.3x slower than curated (dilution, gate-confirmed). Bottleneck is model refinement, not context.
219 lines
10 KiB
Python
219 lines
10 KiB
Python
#!/usr/bin/env python3
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"""api_draft.py — provider-agnostic matching-C draft worker (the cheap-tier "draft" step).
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The script equivalent of one worker_wave drafter agent, for ANY OpenAI-compatible chat endpoint:
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local (LM Studio / llama.cpp / vLLM) OR cloud (OpenRouter → GLM/Qwen/DeepSeek). Builds the proven
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drafter prompt with the target asm + Ghidra-C + toolkit inlined (the model can't read files), calls
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the endpoint, extracts the C, writes it to the draft dir, and ITERATES against match_one — feeding
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the per-instruction diff back for up to --iters rounds (context-aware retry), keeping the BEST draft.
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Output drafts are scored by tools/ab_score.py exactly like an agent arm (point --out at an arm dir):
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tools/ab_score.py --arms opus haiku local
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Config via env (so the SAME script serves local and OpenRouter):
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API_BASE endpoint base, default http://localhost:1234/v1 (LM Studio default)
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API_KEY bearer token; blank/"lm-studio" for local; OpenRouter key for cloud
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MODEL model id as the server names it (e.g. qwen3-coder-30b-a3b, z-ai/glm-5.2)
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Usage:
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API_BASE=http://localhost:1234/v1 MODEL=qwen3-coder-30b-a3b \
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.venv/bin/python tools/api_draft.py --targets .run/ab-exp/targets20.json --out .run/ab-exp/local --iters 4
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# OpenRouter / GLM:
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API_BASE=https://openrouter.ai/api/v1 API_KEY=sk-or-... MODEL=z-ai/glm-5.2 \
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.venv/bin/python tools/api_draft.py --targets .run/ab-exp/targets20.json --out .run/ab-exp/glm --iters 4
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"""
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import argparse, json, os, re, subprocess, sys, time, urllib.request, urllib.error
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REPO = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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PY = '.venv/bin/python'
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API_BASE = os.environ.get('API_BASE', 'http://localhost:1234/v1').rstrip('/')
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API_KEY = os.environ.get('API_KEY', 'lm-studio')
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MODEL = os.environ.get('MODEL', 'local-model')
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TEMP = float(os.environ.get('TEMP', '0.3')) # thinking-mode models: ~0.6; deterministic drafting: ~0.2
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# Fair harness: give the no-tool local model the SAME context the agents read themselves — the shared
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# type header, the live matching cookbook, and worked byte-matched examples (all inlined). COOKBOOK_FULL=0
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# inlines only the matching-relevant sections (§1,§2,§5,§10,§16,§17,§21,§25,§27) instead of the whole file.
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def _read(p):
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fp = os.path.join(REPO, p)
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return open(fp).read() if os.path.exists(fp) else ''
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COMMON_H = _read('include/common.h')
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_CB = _read('docs/matching-cookbook.md')
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_FULL = os.environ.get('COOKBOOK_FULL', '1') != '0'
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_KEEP = {1, 2, 5, 10, 16, 17, 21, 25, 27} # the drafting-relevant §N (drop build/linker/fleet-ops)
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def _cookbook():
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if _FULL:
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return _CB
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parts = re.split(r'(?m)^(## .*)$', _CB)
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out = []
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for i in range(1, len(parts), 2):
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hdr, body = parts[i], (parts[i + 1] if i + 1 < len(parts) else '')
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m = re.match(r'## §(\d+)\b', hdr)
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if m and int(m.group(1)) in _KEEP:
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out.append(hdr + body)
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return '\n'.join(out).strip()
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COOKBOOK = _cookbook()
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_CORPUS = None
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def load_examples(ov, k, exclude):
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"""k smallest byte-MATCHED (asm->C) pairs from the corpus, same overlay first — worked examples."""
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global _CORPUS
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if _CORPUS is None:
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p = os.path.join(REPO, 'datasets/match_pairs/pairs.jsonl')
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_CORPUS = [json.loads(l) for l in open(p)] if os.path.exists(p) else []
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pool = [r for r in _CORPUS if r['fn'] != exclude and r.get('c') and r.get('asm')
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and len(r['asm'].splitlines()) <= 40] # small = clear idiom, cheap
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same = sorted((r for r in pool if ov and ov in r['region']), key=lambda r: len(r['asm']))
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other = sorted((r for r in pool if not (ov and ov in r['region'])), key=lambda r: len(r['asm']))
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return '\n\n'.join('--- WORKED EXAMPLE (this toolchain, byte-MATCHED) ---\nTARGET ASM:\n%s\n\nMATCHING C:\n%s'
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% (r['asm'].strip(), r['c'].strip()) for r in (same + other)[:k])
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SYS = ("You are an expert at MATCHING decompilation for MIPS (PSX, gcc-2.7.2 -O2). Given a target's asm you "
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"write C that the pinned toolchain compiles to BYTE-IDENTICAL machine code. You are given the project's "
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"matching cookbook, the shared type header, and worked examples — USE them. "
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"Reply with ONLY the C (function definition + needed externs) in one ```c block, no prose.")
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def build_user(t, asm_text, ghidra_text):
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ov = t['asm'].split('/')[1] if t.get('asm') and '/' in t['asm'] else ''
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examples = load_examples(ov, 2, t['name'])
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return f"""Write C that compiles BYTE-IDENTICAL to this MIPS function, under:
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gcc-2.7.2-psx -O2 -G0 -mips1 -mcpu=3000 -msoft-float + maspsx --aspsx-version=2.56 --expand-div.
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=== SHARED HEADER (common.h is AUTO-INCLUDED — these types/macros are PREDEFINED; do NOT redefine u8/s32/etc) ===
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{COMMON_H}
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=== MATCHING COOKBOOK (the project's live, proven gcc-2.7.2 idioms — apply them) ===
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{COOKBOOK}
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=== WORKED EXAMPLES (real byte-matches from this game, same toolchain) ===
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{examples}
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=== YOUR TARGET: {t['name']} @ {t.get('addr')} ({t.get('nins')} ins, class {t.get('class')}) ===
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TARGET ASM (ground truth; "/* off vaddr WORD */ mnemonic" = one encoded instruction):
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{asm_text}
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GHIDRA-C SCAFFOLD (types/locals/callees — NOT byte-accurate):
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{ghidra_text}
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Now write byte-matching C for {t['name']}:
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- common.h types are predefined — declare only OTHER externs (callees/globals); don't fret callee arg types, the gate reconciles them.
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- First two lines: // @class: <regalloc-order|schedule|struct|loose-typing|plumbing|other> then // @stuck: <residual or "none — MATCH">
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- Reply with ONLY one ```c block."""
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def call_api(messages, max_tokens=4096, temperature=TEMP, timeout=600):
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body = json.dumps({'model': MODEL, 'messages': messages,
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'max_tokens': max_tokens, 'temperature': temperature}).encode()
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req = urllib.request.Request(API_BASE + '/chat/completions', data=body,
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headers={'Content-Type': 'application/json',
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'Authorization': 'Bearer ' + (API_KEY or 'none')})
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try:
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with urllib.request.urlopen(req, timeout=timeout) as r:
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d = json.loads(r.read())
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return d['choices'][0]['message']['content']
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except urllib.error.URLError as e:
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print(' API error:', e, file=sys.stderr)
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return None
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def extract_code(text):
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"""Pull the C from a model reply: strip <think> blocks, take the largest ```c fence, else raw."""
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if not text:
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return ''
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text = re.sub(r'<think>.*?</think>', '', text, flags=re.S)
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blocks = re.findall(r'```(?:c|cpp|C)?\s*\n(.*?)```', text, flags=re.S)
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if blocks:
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return max(blocks, key=len).strip() + '\n'
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return text.strip() + '\n' # model ignored the fence instruction; use as-is
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def match_one(fn, cfile, asm_subdir):
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"""('match',0,out) | ('near',k,out) | ('fail',None,out) via match_one (relocation-masked)."""
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p = subprocess.run([PY, 'tools/match_one.py', fn, '--c', cfile, '--asm-subdir', asm_subdir],
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capture_output=True, text=True, cwd=REPO)
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out = p.stdout + p.stderr
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if re.search(r'MATCH \(\d+ ins\)', out):
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return ('match', 0, out)
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m = re.search(r'(\d+) mismatched', out)
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if m:
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return ('near', int(m.group(1)), out)
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return ('fail', None, out)
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def draft_one(t, outdir, iters):
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fn = t['name']
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asm_path = os.path.join(REPO, t['asm'])
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asm_subdir = os.path.dirname(t['asm'])
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gc_path = os.path.join(REPO, t.get('ghidra_c', ''))
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asm_text = open(asm_path).read() if os.path.exists(asm_path) else '(asm missing)'
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ghidra_text = open(gc_path).read() if os.path.exists(gc_path) else '(no ghidra-c)'
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cfile = os.path.join(outdir, fn + '.c')
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messages = [{'role': 'system', 'content': SYS},
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{'role': 'user', 'content': build_user(t, asm_text, ghidra_text)}]
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best = (None, 10 ** 9) # (code, closeness)
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for i in range(max(1, iters)):
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reply = call_api(messages)
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code = extract_code(reply)
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if not code.strip():
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print(' %s: empty reply (iter %d)' % (fn, i)); break
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open(cfile, 'w').write(code)
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status, close, out = match_one(fn, cfile, asm_subdir)
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score = 0 if status == 'match' else (close if status == 'near' else 10 ** 8)
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if score < best[1]:
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best = (code, score)
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tag = 'MATCH' if status == 'match' else ('near %d' % close if status == 'near' else 'compile-fail')
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print(' %s iter %d: %s' % (fn, i, tag))
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if status == 'match':
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break
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if i < iters - 1: # feed the diff back for a revision (context-aware retry)
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diff = '\n'.join(out.splitlines()[:45])
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messages += [{'role': 'assistant', 'content': '```c\n' + code + '```'},
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{'role': 'user', 'content':
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'Not byte-identical yet. match_one diff (idx | MINE | TARGET):\n' + diff +
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'\nApply the toolkit (register pins, width/loop-form/schedule fixes) and reply '
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'with the corrected full function in ONE ```c block.'}]
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# write the BEST draft seen (not necessarily the last)
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if best[0] is not None:
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open(cfile, 'w').write(best[0])
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return best[1]
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def main():
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ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
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ap.add_argument('--targets', default='.run/ab-exp/targets20.json')
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ap.add_argument('--out', default='.run/ab-exp/local')
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ap.add_argument('--iters', type=int, default=4, help='max draft↔match_one rounds per function')
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ap.add_argument('--limit', type=int, default=0, help='only the first N targets (0 = all)')
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a = ap.parse_args()
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os.makedirs(os.path.join(REPO, a.out), exist_ok=True)
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outdir = os.path.join(REPO, a.out)
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targets = json.load(open(os.path.join(REPO, a.targets)))
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if a.limit:
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targets = targets[:a.limit]
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print('api_draft: %s @ %s -> %d targets, %d iters -> %s' % (MODEL, API_BASE, len(targets), a.iters, a.out))
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t0 = time.time()
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matched = 0
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for t in targets:
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close = draft_one(t, outdir, a.iters)
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matched += (close == 0)
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print('\napi_draft done: %d/%d match_one MATCH in %.0fs. Now: tools/ab_score.py --arms ... %s'
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% (matched, len(targets), time.time() - t0, os.path.basename(a.out)))
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if __name__ == '__main__':
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main()
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