"""Check direct Qwen prompt-to-layer-50 repeatability across multiple prompts."""

import argparse

import torch

from h3_blackwell_runtime.qwen3vl_text import Qwen3VLPromptConditioner


PROMPTS = [
    "A brass-and-paper dragon flies above a rain-washed old city at blue hour.",
    "A tiny clockwork fox curls up beside a glowing campfire in fresh snow.",
    "An old library aquarium floats through a thunderstorm, shelves full of blue fish.",
    "A painterly red balloon drifts over a quiet desert train station at sunrise.",
    "A ceramic astronaut waters moss on the moon while Earth rises behind them.",
    "A silver moth made of folded maps circles a lighthouse in green fog.",
    "A friendly robot chef flips pancakes in a kitchen made of clouds.",
    "A crystal whale swims through a canyon of stars and ringing glass bells.",
    "A paper boat sails down a neon alley after summer rain.",
    "A lantern-lit turtle carries a miniature village across a black lake.",
]


parser = argparse.ArgumentParser()
parser.add_argument("--qwen", default="/text-encoders/qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors")
parser.add_argument("--tokenizer", default="/opt/h3-blackwell-runtime/src/h3_blackwell_runtime/qwen25_tokenizer")
args = parser.parse_args()

conditioner = Qwen3VLPromptConditioner(args.qwen, args.tokenizer)

for index, prompt in enumerate(PROMPTS):
    with torch.inference_mode():
        qwen_a = conditioner(prompt)
        qwen_b = conditioner(prompt)
    qwen_delta = (qwen_a.float() - qwen_b.float()).abs()
    print(
        f"prompt={index:02d} "
        f"tokens={qwen_a.shape[1]} "
        f"qwen_max={qwen_delta.max().item():.6g} qwen_mean={qwen_delta.mean().item():.6g}"
    )