"""
This is a file holding model-specific suggestions.

How to add a new suggestion:
1. Add a new constant at the bottom of the file with your suggestion. Please try to follow the same format as the
existing suggestions.
2. Add a new entry to the `MODEL_SUGGESTIONS`, with format `(model tag, (problem tags,), suggestion constant)`.
    a. Make sure the model tag matches the exact same tag as on the Hub (e.g. GPT-J is `gptj`)
    b. See `app.py` for the existing problem tags.
    c. Make sure the problem tags are a tuple.
"""


GPTJ_USE_SAMPLING = """
<details><summary>{match_emoji} {count}. GPT-J - Avoid using Greedy Search and Beam Search.</summary>
&nbsp;

🤔 Why? &nbsp;

According to its creators, "generating without sampling was actually surprisingly suboptimal". &nbsp;


🤗 How? &nbsp;

Our text generation interfaces accept a `do_sample` argument. Set it to `True` to ensure sampling-based strategies
are used. &nbsp;

💡 Source &nbsp;

1. [This tweet](https://twitter.com/EricHallahan/status/1627785461723721729) by a core member of EleutherAI, the
creator of GPT-J
_________________
</details>
"""


T5_FLOAT16 = """
<details><summary>{match_emoji} {count}. T5 - If you're using int8 or float16, make sure you have `transformers>=4.26.1`.</summary>
&nbsp;

🤔 Why? &nbsp;

In a nutshell, some layers in T5 don't work well in lower precision unless they are in bf16. Newer versions of
`transformers` take care of upcasting the layers when needed. &nbsp;


🤗 How? &nbsp;

Make sure the dependencies in your workflow have `transformers>=4.26.1` &nbsp;

💡 Source &nbsp;

1. See [this thread](https://github.com/huggingface/transformers/issues/20287) for the full discussion.
_________________
</details>
"""

MODEL_SUGGESTIONS = (
    ("gptj", ("quality",), GPTJ_USE_SAMPLING),
    ("t5", ("quality", "baseline", "speed"), T5_FLOAT16),
)
assert all(isinstance(problem_tags, tuple) for _, problem_tags, _ in MODEL_SUGGESTIONS)