| """Chat rendering helpers for text-only Qwen/Qwen-VL control prompts.""" |
|
|
| from __future__ import annotations |
|
|
| from typing import Any, Dict, List |
|
|
|
|
| def render_no_think_chat( |
| tokenizer: Any, |
| messages: List[Dict[str, str]], |
| *, |
| add_generation_prompt: bool, |
| ) -> str: |
| """Render a chat prompt with Qwen thinking disabled when supported. |
| |
| Qwen3-family templates expose ``enable_thinking`` as a Jinja variable. |
| Older templates ignore that keyword, so we fall back cleanly rather than |
| failing training for non-Qwen or older tokenizer builds. |
| """ |
| kwargs = { |
| "tokenize": False, |
| "add_generation_prompt": add_generation_prompt, |
| "enable_thinking": False, |
| } |
| try: |
| return tokenizer.apply_chat_template(messages, **kwargs) |
| except TypeError as exc: |
| if "enable_thinking" not in str(exc): |
| raise |
| kwargs.pop("enable_thinking") |
| return tokenizer.apply_chat_template(messages, **kwargs) |
|
|
|
|
| def tokenize_text_only(tokenizer: Any, input_text: str, device: Any): |
| """Tokenize a rendered text prompt without invoking VL image loading. |
| |
| Some Qwen-VL processors route the first positional argument to ``images``. |
| Passing the transcript through the explicit ``text=`` keyword keeps the |
| prompt on the text path and avoids PIL trying to parse chat text as an image. |
| """ |
| try: |
| inputs = tokenizer(text=input_text, return_tensors="pt") |
| except ValueError as exc: |
| if "Incorrect image source" not in str(exc): |
| raise |
| inputs = tokenizer(text=[input_text], images=None, return_tensors="pt") |
| except TypeError: |
| inputs = tokenizer(input_text, return_tensors="pt") |
|
|
| return inputs.to(device) |
|
|
