"""Byte-equality check of tools/glimmer_fmt.py against chat_template.jinja. python tools/test_glimmer_fmt.py --model-dir /workspace/gguf/ref/meta Renders every case through transformers' jinja sandbox and through the port and asserts the two strings are identical. If this fails, the calibration markup is wrong and the build must not run. """ from __future__ import annotations import argparse import json import sys import glimmer_fmt as G DATE = "2026-08-10" CUTOFF = "2026-01-04" WEATHER = { "type": "function", "function": { "name": "weather.get_forecast", "description": "Get the weather forecast for a location.", "parameters": { "type": "object", "properties": { "city": {"type": "string", "description": "City name"}, "days": {"type": "integer", "description": "How many days ahead"}, }, "required": ["city"], }, }, } SHELL = { "type": "function", "function": { "name": "shell.run", "description": "Run a shell command and return stdout/stderr.", "parameters": { "type": "object", "properties": { "cmd": {"type": "string"}, "timeout_s": {"type": "integer"}, "env": {"type": "object"}, "cwd": {"type": "string"}, "capture_stderr": {"type": "boolean"}, }, "required": ["cmd"], }, }, } def cases() -> list[dict]: out = [] # 1. plain turn, no system, no tools out.append(dict(name="plain", messages=[ {"role": "user", "content": "Hi."}, {"role": "assistant", "content": "Hello."}, ])) # 2. explicit system + every reasoning strength for rs in G.REASONING_STRENGTHS: out.append(dict(name=f"system-{rs}", reasoning_strength=rs, messages=[ {"role": "system", "content": "You are a build engineer."}, {"role": "user", "content": "Explain rpath."}, {"role": "assistant", "reasoning_content": "rpath is a linker search path.", "content": "It is a search path baked into the binary."}, ])) # 3. one-argument tool call plus its result out.append(dict(name="tool-1arg", tools=[WEATHER], messages=[ {"role": "user", "content": "Weather in Kyoto?"}, {"role": "assistant", "tool_calls": [ {"id": "c1", "type": "function", "function": { "name": "weather.get_forecast", "arguments": {"city": "Kyoto"}}}]}, {"role": "tool", "name": "weather.get_forecast", "content": "18C, clear"}, {"role": "assistant", "content": "18 degrees and clear."}, ])) # 4. five arguments, nested JSON, booleans, null out.append(dict(name="tool-5arg-nested", tools=[SHELL], messages=[ {"role": "user", "content": "Build it."}, {"role": "assistant", "tool_calls": [ {"id": "c2", "type": "function", "function": { "name": "shell.run", "arguments": { "cmd": "cmake --build build -j", "timeout_s": 900, "env": {"CC": "clang", "FLAGS": ["-O2", "-g"], "NESTED": {"a": [1, 2]}}, "cwd": None, "capture_stderr": True, }}}]}, {"role": "tool", "name": "shell.run", "content": "ninja: build stopped."}, ])) # 5. two tool calls in one assistant turn -> first gets EOM out.append(dict(name="tool-parallel", tools=[WEATHER, SHELL], messages=[ {"role": "user", "content": "Both please."}, {"role": "assistant", "tool_calls": [ {"id": "a", "type": "function", "function": { "name": "weather.get_forecast", "arguments": {"city": "Oslo", "days": 3}}}, {"id": "b", "type": "function", "function": { "name": "shell.run", "arguments": {"cmd": "uptime"}}}, ]}, ])) # 6. tool name resolved through tool_call_id out.append(dict(name="tool-by-id", tools=[WEATHER], messages=[ {"role": "user", "content": "?"}, {"role": "assistant", "tool_calls": [ {"id": "xyz", "type": "function", "function": { "name": "weather.get_forecast", "arguments": {"city": "Lima"}}}]}, {"role": "tool", "tool_call_id": "xyz", "content": "rain"}, ])) # 7. consecutive same-role user messages -> EOM boundary logic out.append(dict(name="consecutive", messages=[ {"role": "user", "content": "one"}, {"role": "user", "content": "two"}, {"role": "assistant", "content": "ok"}, ])) # 8. image part out.append(dict(name="image", messages=[ {"role": "user", "content": [ {"type": "text", "text": "What is this? "}, {"type": "image"}, ]}, {"role": "assistant", "content": "A plot."}, ])) # 9. generation prompt out.append(dict(name="genprompt", add_generation_prompt=True, messages=[ {"role": "user", "content": "go"}, ])) # 10. non-user recipient out.append(dict(name="recipient", messages=[ {"role": "user", "content": "think"}, {"role": "assistant", "recipient": "self", "content": "planning", "end_turn": False}, {"role": "assistant", "content": "done"}, ])) return out def main() -> int: ap = argparse.ArgumentParser() ap.add_argument("--model-dir", required=True, help="directory holding chat_template.jinja + tokenizer_config.json") args = ap.parse_args() from transformers import AutoTokenizer tok = AutoTokenizer.from_pretrained(args.model_dir) failures = 0 for c in cases(): kwargs = dict( tools=c.get("tools"), reasoning_strength=c.get("reasoning_strength", G.DEFAULT_REASONING), knowledge_cutoff=CUTOFF, current_date=DATE, add_generation_prompt=c.get("add_generation_prompt", False), ) want = tok.apply_chat_template(c["messages"], tokenize=False, **kwargs) got = G.render(c["messages"], **kwargs) if want != got: failures += 1 print(f"FAIL {c['name']}") for i, (a, b) in enumerate(zip(want, got)): if a != b: print(f" first difference at char {i}") print(f" template: {want[max(0,i-60):i+60]!r}") print(f" port : {got[max(0,i-60):i+60]!r}") break else: print(f" length {len(want)} vs {len(got)}") print(f" template tail: {want[len(got)-60:][:160]!r}") print(f" port tail : {got[len(want)-60:][:160]!r}") else: print(f"ok {c['name']} ({len(got)} chars)") print(f"\n{len(cases()) - failures}/{len(cases())} cases byte-identical") return 1 if failures else 0 if __name__ == "__main__": sys.exit(main())