from dataclasses import dataclass
from enum import Enum

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


# Select your tasks here
# ---------------------------------------------------
class Tasks(Enum):
    # task_key in the json file, metric_key in the json file, name to display in the leaderboard 
    task0 = Task("anli_r1", "acc", "ANLI")
    task1 = Task("logiqa", "acc_norm", "LogiQA")

NUM_FEWSHOT = 0 # Change with your few shot
# ---------------------------------------------------



# Your leaderboard name
TITLE = """<h1 align="center" id="space-title">UnlearnDiffAtk: Unlearned Diffusion Model Benchmark</h1>"""

# subtitle
SUB_TITLE = """<h2 align="center" id="space-title">Effective and efficient adversarial prompt generation approach for unlearned diffusion model evaluations.</h2>"""

# What does your leaderboard evaluate?
INTRODUCTION_TEXT = """
This benchmark evaluates the <strong>robustness and utility retaining</strong> of safety-driven unlearned diffusion models (DMs) across a variety of tasks. For more details, please visit the [project](https://www.optml-group.com/posts/mu_attack).
- The <strong>robustness</strong> of unlearned DM is evaluated through our proposed adversarial prompt attack, [UnlearnDiffAtk](https://github.com/OPTML-Group/Diffusion-MU-Attack), which has been accepted to ECCV 2024.
- The <strong>utility retaining</strong> of unlearned DM is evaluated through FID and CLIP score on the generated images using [10K randomly sampled COCO caption prompts](https://github.com/OPTML-Group/Diffusion-MU-Attack/blob/main/prompts/coco_10k.csv). 

Demo of our offensive method: [UnlearnDiffAtk](https://huggingface.co/spaces/Intel/UnlearnDiffAtk)\\
Demo of our defensive method: [AdvUnlearn](https://huggingface.co/spaces/Intel/AdvUnlearn)
"""

EVALUATION_QUEUE_TEXT = """
<strong>\[Evaluation Metrics\]</strong>: 
- Pre-Attack Success Rate (<strong>Pre-ASR</strong>): lower is better;   
- Post-attack success rate (<strong>Post-ASR</strong>): lower is better; 
- Fréchet inception distance(<strong>FID</strong>):  evaluate distributional quality of image generations, lower is better; 
- <strong>CLIP Score</strong>: measure contextual alignment with prompt descriptions, higher is better. 

<strong>\[DM Unlearning Tasks\]</strong>: 
- NSFW: Nudity
- Style: Van Gogh
- Objects: Church, Tench, Parachute, Garbage Truck
"""

# Which evaluations are you running? how can people reproduce what you have?
LLM_BENCHMARKS_TEXT = f"""
For more details of Unlearning Methods used in this benchmarks:
- [Adversarial Unlearning (AdvUnlearn)](https://github.com/OPTML-Group/AdvUnlearn);
- [Erased Stable Diffusion (ESD)](https://github.com/rohitgandikota/erasing);
- [Forget-Me-Not (FMN)](https://github.com/SHI-Labs/Forget-Me-Not);
- [Ablating Concepts (AC)](https://github.com/nupurkmr9/concept-ablation);
- [Unified Concept Editing (UCE)](https://github.com/rohitgandikota/unified-concept-editing);
- [concept-SemiPermeable Membrane (SPM)](https://github.com/Con6924/SPM); 
- [Saliency Unlearning (SalUn)](https://github.com/OPTML-Group/Unlearn-Saliency); 
- [EraseDiff (ED)](https://github.com/JingWu321/EraseDiff); 
- [ScissorHands (SH)](https://github.com/JingWu321/Scissorhands);
- [Mass Concept Erasure (MACE)](https://github.com/Shilin-LU/MACE);
- [Reliable and Efficient Concept Erasure (RECE)](https://github.com/CharlesGong12/RECE);
- [Training-Free and Adaptive Guard (SAFREE)](https://github.com/jaehong31/SAFREE).

<strong>We will evaluate your model on UnlearnDiffAtk Benchmark!</strong> \\
Open a [github issue](https://github.com/OPTML-Group/Diffusion-MU-Attack/issues) or email us at zhan1853@msu.edu!
"""



CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
CITATION_BUTTON_TEXT = r"""
@inproceedings{zhang2023generate,
  title={To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images... For Now},
  author={Zhang, Yimeng and Jia, Jinghan and Chen, Xin and Chen, Aochuan and Zhang, Yihua and Liu, Jiancheng and Ding, Ke and Liu, Sijia},
  booktitle={European Conference on Computer Vision},
  year={2024}
}


@article{zhang2024defensive,
  title={Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models},
  author={Zhang, Yimeng and Chen, Xin and Jia, Jinghan and Zhang, Yihua and Fan, Chongyu and Liu, Jiancheng and Hong, Mingyi and Ding, Ke and Liu, Sijia},
  journal={arXiv preprint arXiv:2405.15234},
  year={2024}
}
"""

CONTRIBUTOR_BUTTON_LABEL = "Contributors are listed as follows:"
CONTRIBUTOR_BUTTON_TEXT = f"""
• OPTML @ Michigan State University: 
Sijia Liu, Yimeng Zhang, JInghan Jia, Aochuan Chen, Yihua Zhang, Jiacheng Liu 

• SCAI @ Arizona State University: 
Maitreya Patel, Abhiram Kusumba 

• Intel Corp: 
Kyle Min, Ke Ding, Xin Chen 
"""