from src.display.utils import ModelType

TITLE = """<h1 style="text-align:left;float:left; id="space-title"><img style="width: 4rem; height: 4rem; display: inline-block;" src="https://imgur.com/cu4lW7q.png" /> Low-bit Quantized Open LLM Leaderboard</h1>"""

INTRODUCTION_TEXT = """
"""

icons = f"""
- {ModelType.PT.to_str(" : ")} model: new, base models, trained on a given text corpora using masked modelling
- {ModelType.CPT.to_str(" : ")} model: new, base models, continuously trained on further corpus (which may include IFT/chat data) using masked modelling
- {ModelType.FT.to_str(" : ")} model: pretrained models finetuned on more data
- {ModelType.chat.to_str(" : ")} model: chat like fine-tunes, either using IFT (datasets of task instruction), RLHF or DPO (changing the model loss a bit with an added policy), etc
- {ModelType.merges.to_str(" : ")} model: merges or MoErges, models which have been merged or fused without additional fine-tuning. 
"""
LLM_BENCHMARKS_TEXT = f"""
## ABOUT
Quantization is a key technique for making LLMs more accessible and practical for a wide range of applications, especially where computational resources are a limiting factor. While there is no tool to track and compare quantization LLMs with different quantization algorithms, which is hard to filter out the genuine progress that is being made by the open-source community and which model is the current state of the art.

Submit a model for automated evaluation on the CPU/GPU cluster on the "Submit" page!
The leaderboard's backend runs the great [Eleuther AI Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) - read more details below!

### Tasks 
📈 We evaluate models on 10 key benchmarks using the <a href="https://github.com/EleutherAI/lm-evaluation-harness" target="_blank">  Eleuther AI Language Model Evaluation Harness </a>, a unified framework to test generative language models on a large number of different evaluation tasks.

- <a href="https://arxiv.org/abs/1803.05457" target="_blank">  AI2 Reasoning Challenge </a> (0-shot) - a set of grade-school science questions, a Challenge Set of 2,590 “hard” questions (those that both a retrieval and a co-occurrence method fail to answer correctly).
- <a href="https://arxiv.org/abs/1803.05457" target="_blank">  AI2 Reasoning Easy </a> (0-shot) - a set of grade-school science questions, an Easy Set of 5,197 questions.
- <a href="https://arxiv.org/abs/1905.07830" target="_blank">  HellaSwag </a> (0-shot) - a test of commonsense inference, which is easy for humans (~95%) but challenging for SOTA models.
- <a href="https://arxiv.org/abs/2009.03300" target="_blank">  MMLU </a>  (0-shot) - a test to measure a text model's multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more.
- <a href="https://arxiv.org/abs/2109.07958" target="_blank">  TruthfulQA </a> (0-shot) - a test to measure a model's propensity to reproduce falsehoods commonly found online. Note: TruthfulQA is technically a 6-shot task in the Harness because each example is prepended with 6 Q/A pairs, even in the 0-shot setting.
- <a href="https://arxiv.org/abs/1907.10641" target="_blank">  Winogrande </a> (0-shot) - an adversarial and difficult Winograd benchmark at scale, for commonsense reasoning.
- <a href="https://arxiv.org/abs/1911.11641" target="_blank">  PIQA </a> (0-shot) - a physical commonsense reasoning and a corresponding benchmark dataset.
- <a href="https://arxiv.org/pdf/1606.06031.pdf" target="_blank">  Lambada_Openai </a> (0-shot) - a dataset to evaluate the capabilities of computational models for text understanding by means of a word prediction task.
- <a href="https://arxiv.org/abs/1809.02789" target="_blank">  OpenBookQA </a> (0-shot) - a question-answering dataset modeled after open book exams for assessing human understanding of a subject. 
- <a href="https://arxiv.org/pdf/1905.10044" target="_blank"> BoolQ </a> (0-shot) - a QA task where each example consists of a short passage and a yes/no question about the passage.

For all these evaluations, a higher score is a better score.
We chose these benchmarks as they test a variety of reasoning and general knowledge across a wide variety of fields in 0-shot and few-shot settings.

---------------------------

## REPRODUCIBILITY
To reproduce our results, here is the commands you can run, using [v0.4.2](https://github.com/EleutherAI/lm-evaluation-harness/tree/v0.4.2) of the Eleuther AI Harness:
```
python main.py --model=hf-causal-experimental
    --model_args="pretrained=<your_model>,use_accelerate=True,revision=<your_model_revision>,dtype=<compute_dtype>"
    --tasks=<task_list>
    --num_fewshot=<n_few_shot>
    --batch_size=1
    --output_path=<output_path>
```

**Note:** 
- We run `llama.cpp` series models on Xeon CPU and others on NVidia GPU. 
- If model paramerters > 7B, we use `--batch_size 4`. If model parameters < 7B, we use `--batch_size 2`. And we set `--batch_size 1` for llama.cpp. You can expect results to vary slightly for different batch sizes because of padding.


### The tasks and few shots parameters are:
- ARC-C: 0-shot, *arc_challenge* (`acc`)
- ARC-E: 0-shot, *arc_easy* (`acc`)
- HellaSwag: 0-shot, *hellaswag* (`acc`)
- TruthfulQA(Truthfulqa_mc1): 0-shot, *truthfulqa_mc1* (`acc`)
- MMLU: 0-shot, *hendrycksTest-abstract_algebra,hendrycksTest-anatomy,hendrycksTest-astronomy,hendrycksTest-business_ethics,hendrycksTest-clinical_knowledge,hendrycksTest-college_biology,hendrycksTest-college_chemistry,hendrycksTest-college_computer_science,hendrycksTest-college_mathematics,hendrycksTest-college_medicine,hendrycksTest-college_physics,hendrycksTest-computer_security,hendrycksTest-conceptual_physics,hendrycksTest-econometrics,hendrycksTest-electrical_engineering,hendrycksTest-elementary_mathematics,hendrycksTest-formal_logic,hendrycksTest-global_facts,hendrycksTest-high_school_biology,hendrycksTest-high_school_chemistry,hendrycksTest-high_school_computer_science,hendrycksTest-high_school_european_history,hendrycksTest-high_school_geography,hendrycksTest-high_school_government_and_politics,hendrycksTest-high_school_macroeconomics,hendrycksTest-high_school_mathematics,hendrycksTest-high_school_microeconomics,hendrycksTest-high_school_physics,hendrycksTest-high_school_psychology,hendrycksTest-high_school_statistics,hendrycksTest-high_school_us_history,hendrycksTest-high_school_world_history,hendrycksTest-human_aging,hendrycksTest-human_sexuality,hendrycksTest-international_law,hendrycksTest-jurisprudence,hendrycksTest-logical_fallacies,hendrycksTest-machine_learning,hendrycksTest-management,hendrycksTest-marketing,hendrycksTest-medical_genetics,hendrycksTest-miscellaneous,hendrycksTest-moral_disputes,hendrycksTest-moral_scenarios,hendrycksTest-nutrition,hendrycksTest-philosophy,hendrycksTest-prehistory,hendrycksTest-professional_accounting,hendrycksTest-professional_law,hendrycksTest-professional_medicine,hendrycksTest-professional_psychology,hendrycksTest-public_relations,hendrycksTest-security_studies,hendrycksTest-sociology,hendrycksTest-us_foreign_policy,hendrycksTest-virology,hendrycksTest-world_religions* (average of all the results `acc`)
- Winogrande: 0-shot, *winogrande* (`acc`)
- Lambada(Lambada_Openai): 0-shot, *lambada_openai* (`acc`)
- PIQA: 0-shot, *piqa* (`acc`)
- OpenBookQA: 0-shot, *openbookqa* (`acc`)
- BoolQ: 0-shot, *boolq* (`acc`)

Side note on the baseline scores: 
- for log-likelihood evaluation, we select the random baseline

---------------------------

## RESSOURCES

### Quantization
To get more information about quantization, see:
- neural-compressor: [intel/neural-compressor](https://github.com/intel/neural-compressor/tree/master)
- 4 bits: [blog post](https://huggingface.co/blog/4bit-transformers-bitsandbytes), [paper](https://arxiv.org/abs/2305.14314)

### Other cool leaderboards:
- [LLM safety](https://huggingface.co/spaces/AI-Secure/llm-trustworthy-leaderboard)
- [LLM performance](https://huggingface.co/spaces/optimum/llm-perf-leaderboard)
- [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)


"""

FAQ_TEXT = """

## SUBMISSIONS
My model requires `trust_remote_code=True`, can I submit it?
- *Yes, we want to support the newest models.*

How can I follow when my model is launched?
- *You can look for its request file [here](https://huggingface.co/datasets/Intel/ld_requests) and follow the status evolution, or directly in the queues above the submit form.*

My model disappeared from all the queues, what happened?
- *A model disappearing from all the queues usually means that there has been a failure. You can check if that is the case by looking for your model [here](https://huggingface.co/datasets/Intel/ld_requests).*

What causes an evaluation failure?
- *Most of the failures we get come from problems in the submissions (not quantization model, corrupted files, config problems, wrong parameters selected for eval ...), so we'll be grateful if you first make sure you have followed the steps in `About`. However, from time to time, we have failures on our side (hardware/node failures, problem with an update of our backend, connectivity problem ending up in the results not being saved, ...).*

How can I report an evaluation failure?
- *As we store the logs for all models, feel free to create an issue, **where you link to the requests file of your model** (look for it [here](https://huggingface.co/datasets/Intel/ld_requests)), so we can investigate! If the model failed due to a problem on our side, we'll relaunch it right away!* 
*Note: Please do not re-upload your model under a different name, it will not help*

---------------------------

## RESULTS
What kind of information can I find?
- *Let's imagine you are interested in the Solar-10.7B results. You have access to 2 different information categories:*
      - *The [request file](https://huggingface.co/datasets/Intel/ld_requests/blob/main/TheBloke/SOLAR-10.7B-Instruct-v1.0-GPTQ_eval_request_False_GPTQ_4bit_int4_float16.json): it gives you information about the status of the evaluation*
      - *The [aggregated results folder](https://huggingface.co/datasets/Intel/ld_results/blob/main/TheBloke/): it gives you aggregated scores, per experimental run*


Why do models appear several times in the leaderboard? 
- *We run evaluations with user selected precision and model commit. Sometimes, users submit specific models at different commits and at different precisions (for example, in float16 and 4bit to see how quantization affects performance). You should be able to verify this by displaying the `precision` and `model sha` columns in the display. If, however, you see models appearing several time with the same precision and hash commit, this is not normal.*


---------------------------

## EDITING SUBMISSIONS
I upgraded my model and want to re-submit, how can I do that?
- *Please open an issue with the precise name of your model, and we'll remove your model from the leaderboard so you can resubmit. You can also resubmit directly with the new commit hash!* 

---------------------------

## OTHER

I have an issue about accessing the leaderboard through the Gradio API
- *Since this is not the recommended way to access the leaderboard, we won't provide support for this, but you can look at tools provided by the community for inspiration!*

I have another problem, help!
- *Please open an issue in the discussion tab, and we'll do our best to help you in a timely manner :) *
"""


EVALUATION_QUEUE_TEXT = f"""
# Evaluation Queue for the Low-bit Quantized Open LLM Leaderboard

Models added here will be automatically evaluated on the cluster.

## Don't forget to read the FAQ and the About tabs for more information!

## Steps 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

### 5) Select the compute dtype
The compute dtype will pass to `lm-eval` for the inference. Currently, we only support float16 and bfloat16. Other selections will supported soon.

"""

CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
CITATION_BUTTON_TEXT = r"""
@misc{
      Cheng2023AutoRound,
      title={Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs},
      author={Wenhua Cheng, Weiwei Zhang, Haihao Shen, Yiyang Cai, Xin He, Kaokao Lv},
      year={2023},
      url={https://arxiv.org/abs/2309.05516}
}
@software{neural-compressor,
  title        = Intel® Neural Compressor,
  publisher    = {Intel},
  url          = {https://github.com/intel/neural-compressor/tree/master}
}
@misc{open-llm-leaderboard,
  author = {Edward Beeching and Clémentine Fourrier and Nathan Habib and Sheon Han and Nathan Lambert and Nazneen Rajani and Omar Sanseviero and Lewis Tunstall and Thomas Wolf},
  title = {Open LLM Leaderboard},
  year = {2023},
  publisher = {Hugging Face},
  howpublished = "\url{https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard}"
}
@software{eval-harness,
  author       = {Gao, Leo and
                  Tow, Jonathan and
                  Biderman, Stella and
                  Black, Sid and
                  DiPofi, Anthony and
                  Foster, Charles and
                  Golding, Laurence and
                  Hsu, Jeffrey and
                  McDonell, Kyle and
                  Muennighoff, Niklas and
                  Phang, Jason and
                  Reynolds, Laria and
                  Tang, Eric and
                  Thite, Anish and
                  Wang, Ben and
                  Wang, Kevin and
                  Zou, Andy},
  title        = {A framework for few-shot language model evaluation},
  month        = sep,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {v0.0.1},
  doi          = {10.5281/zenodo.5371628},
  url          = {https://doi.org/10.5281/zenodo.5371628}
}
@misc{clark2018think,
      title={Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},
      author={Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
      year={2018},
      eprint={1803.05457},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}
@misc{zellers2019hellaswag,
      title={HellaSwag: Can a Machine Really Finish Your Sentence?},
      author={Rowan Zellers and Ari Holtzman and Yonatan Bisk and Ali Farhadi and Yejin Choi},
      year={2019},
      eprint={1905.07830},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
@misc{hendrycks2021measuring,
      title={Measuring Massive Multitask Language Understanding},
      author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
      year={2021},
      eprint={2009.03300},
      archivePrefix={arXiv},
      primaryClass={cs.CY}
}
@misc{lin2022truthfulqa,
      title={TruthfulQA: Measuring How Models Mimic Human Falsehoods},
      author={Stephanie Lin and Jacob Hilton and Owain Evans},
      year={2022},
      eprint={2109.07958},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
@misc{DBLP:journals/corr/abs-1907-10641,
      title={{WINOGRANDE:} An Adversarial Winograd Schema Challenge at Scale},
      author={Keisuke Sakaguchi and Ronan Le Bras and Chandra Bhagavatula and Yejin Choi},
      year={2019},
      eprint={1907.10641},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
@misc{Bisk2019PIQA,
      title={PIQA: Reasoning about Physical Commonsense in Natural Language},
      author={Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, Yejin Choi},
      year={2019},
      eprint={1911.11641},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
@misc{Paperno2016LAMBADA,
      title={The LAMBADA dataset: Word prediction requiring a broad discourse context},
      author={Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Quan Ngoc Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, Raquel Fernández},
      year={2016},
      eprint={1606.06031},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
@misc{Mihaylov2018OpenBookQA,
      title={Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering},
      author={Todor Mihaylov, Peter Clark, Tushar Khot, Ashish Sabharwal},
      year={2018},
      eprint={1809.02789},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
@misc{Clark2019BoolQ,
      title={BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions},
      author={Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, Kristina Toutanova},
      year={2019},
      eprint={1905.10044},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""