from dataclasses import dataclass
from enum import Enum


@dataclass
class Task:
    benchmark: str
    metric: str
    col_name: str


# Init: to update with your specific keys
class Tasks(Enum):
    # task_key in the json file, metric_key in the json file, name to display in the leaderboard 
    task0 = Task("toxicity", "aggregated-results", "Toxicity")
    task1 = Task("stereotype", "aggregated-results", "Stereotype")
    task2 = Task("adv", "aggregated-results", "AdvGLUE++")
    task3 = Task("ood", "aggregated-results", "OoD")
    task4 = Task("adv_demo", "aggregated-results", "Adv Demo")
    task5 = Task("privacy", "aggregated-results", "Privacy")
    task6 = Task("ethics", "aggregated-results", "Ethics")
    task7 = Task("fairness", "aggregated-results", "Fairness")


# Your leaderboard name
TITLE = """<h1 align="center" id="space-title">Trustworthy LLM leaderboard</h1>"""

# What does your leaderboard evaluate?
INTRODUCTION_TEXT = """Powered by DecodingTrust, this research endeavor is designed to help researchers better 
understand the capabilities, limitations, and potential risks associated with deploying these state-of-the-art Large 
Language Models (LLMs). See our paper for details."""

# Which evaluations are you running? how can people reproduce what you have?
LLM_BENCHMARKS_TEXT = f"""
## How it works

DecodingTrust aims at providing a thorough assessment of trustworthiness in GPT models.

This research endeavor is designed to help researchers and practitioners better understand the capabilities, 
limitations, and potential risks involved in deploying these state-of-the-art Large Language Models (LLMs).

This project is organized around the following eight primary perspectives of trustworthiness, including:

+ Toxicity
+ Stereotype and bias
+ Adversarial robustness
+ Out-of-Distribution Robustness
+ Privacy
+ Robustness to Adversarial Demonstrations
+ Machine Ethics
+ Fairness

## Reproducibility
To reproduce our results, checkout https://github.com/AI-secure/DecodingTrust

"""

EVALUATION_QUEUE_TEXT = """
## Some good practices before submitting a model

### 1) Make sure you can load your model and tokenizer using AutoClasses:
```python
from transformers import AutoConfig, AutoModel, AutoTokenizer
config = AutoConfig.from_pretrained("your model name", revision=revision)
model = AutoModel.from_pretrained("your model name", revision=revision)
tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision)
```
If this step fails, follow the error messages to debug your model before submitting it. It's likely your model has been improperly uploaded.

Note: make sure your model is public!
Note: if your model needs `use_remote_code=True`, we do not support this option yet but we are working on adding it, stay posted!

### 2) Convert your model weights to [safetensors](https://huggingface.co/docs/safetensors/index)
It's a new format for storing weights which is safer and faster to load and use. It will also allow us to add the number of parameters of your model to the `Extended Viewer`!

### 3) Make sure your model has an open license!
This is a leaderboard for Open LLMs, and we'd love for as many people as possible to know they can use your model 🤗

### 4) Fill up your model card
When we add extra information about models to the leaderboard, it will be automatically taken from the model card

## In case of model failure
If your model is displayed in the `FAILED` category, its execution stopped.
Make sure you have followed the above steps first.
If everything is done, check you can launch the EleutherAIHarness on your model locally, using the above command without modifications (you can add `--limit` to limit the number of examples per task).
"""

CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
CITATION_BUTTON_TEXT = r"""
@article{wang2023decodingtrust,
  title={DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models},
  author={Wang, Boxin and Chen, Weixin and Pei, Hengzhi and Xie, Chulin and Kang, Mintong and Zhang, Chenhui and Xu, Chejian and Xiong, Zidi and Dutta, Ritik and Schaeffer, Rylan and others},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
  year={2023}
}
"""