import hashlib
import os

import pandas as pd
import plotly.express as px
import streamlit as st
from bat import Benchmark, Config, Reporter, Tester
from datetime import datetime


st.set_page_config(
    page_title="Safety BAT",
    page_icon="🏋️‍♂️",
    layout="wide",
    initial_sidebar_state="auto",
    menu_items=None,
)

# # Inject custom CSS to set the width of the sidebar
# st.markdown(
#     """
#     <style>
#         section[data-testid="stSidebar"] {
#             width: 200px !important; # Set the width to your desired value
#         }
#     </style>
#     """,
#     unsafe_allow_html=True,
# )

holistic_scenarios = [
    "HELM AirBench AIR Score",
]


st.markdown(
    """
    <h1 style='text-align: center; '>🏋️‍♂️ Safety BAT Leaderboard 🏋️‍♂️</h1>
    """,
    unsafe_allow_html=True,
)

st.divider()

st.markdown(
    """
    BenchBench rates benchmarks according to their agreement with the defined *Aggregate Benchmark* – 
    an enhanced representation of the benchmarks that are out there (see config in sidebar to modify).


    """
)

st.markdown(
    """
    BenchBench is for you if:
    \n
    - **You have a new benchmark**: Show that it agrees/disagrees with known benchmarks.
    - **You are looking for a benchmark to run/trust**: Find an efficient/private/preferble alternative.
    """
)

st.markdown(
    """
    We also show that agreements are best represented with the the BenchBench Score, 
    the relative agreement (Z Score) of each benchmark to the Aggragate benchmark.
    \n
    Read more in our work [Benchmark Agreement Testing Done Right](https://arxiv.org/abs/2407.13696) and the [BenchBench repo](https://github.com/IBM/benchbench)
    """
)

all_scenarios_for_aggragate = Benchmark()
all_scenarios_for_aggragate.load_local_catalog()
all_scenarios_for_aggragate = (
    all_scenarios_for_aggragate.df["scenario"].unique().tolist()
)

with st.sidebar:
    st.markdown("""# Configurations""")

    # with st.expander("Leaderboard configurations (defaults are great BTW)", icon="⚙️"):
    with st.form("my_form_1"):
        aggragate_scenarios = st.multiselect(
            "Aggregate Benchmark",
            all_scenarios_for_aggragate,
            holistic_scenarios,
        )

        corr_type = st.selectbox(
            label="Correlation type", options=["kendall", "pearson"], index=0
        )

        aggregate_scenario_whitelist = aggragate_scenarios
        # [
        #     scen
        #     for scen in all_scenarios_for_aggragate
        #     if scen not in aggragate_scenarios
        # ]

        model_select_strategy = st.selectbox(
            label="Model Select strategy",
            options=["random", "top_aggregate", "somewhere_aggregate"],
            index=0,
        )

        n_models_taken_list = st.slider(
            label="Minimal number of models to use",
            min_value=3,
            max_value=15,
            value=8,
        )

        n_models_taken_list = [n_models_taken_list]

        n_exps = 3

        submitted = st.form_submit_button(label="Run BAT")


with st.expander("Add your benchmarks here!", icon="🔥"):
    aggbench = Benchmark()
    aggbench.load_local_catalog()

    aggbench.add_aggregate(
        new_col_name="aggregate",
        agg_source_name="aggregate",
        scenario_whitelist=aggregate_scenario_whitelist,
        min_scenario_for_models_to_appear_in_agg=1
        if len(aggregate_scenario_whitelist) == 1
        else 3,
    )

    agg_models = (
        aggbench.df.query('scenario=="aggregate"').sample(n=10)["model"].tolist()
    )

    st.markdown(
        "Adding your benchmark is as simple as uploading a csv with the following format, one column indicates the model and the other the benchmark scores."
    )

    st.dataframe(
        pd.read_csv("assets/mybench_240901.csv"),
        use_container_width=True,
        hide_index=True,
        height=200,
    )

    st.markdown(
        "Not sure, what models you should run your benchmark on?" "\ntry these:"
    )

    st.code(agg_models)

    st.markdown("Got the data? Upload it here 👇:")

    uploaded_file = st.file_uploader("Add your benchmark as a CSV")

    my_benchmark = Benchmark()
    if uploaded_file is not None:
        st.markdown(
            "Your benchmark has been uploaded, BAT results will soon be caluclated... check out its results here: [Benchmark BAT Report Card](#benchmark-report-card)"
        )

        df = pd.read_csv(uploaded_file)

        my_benchmark.assign_df(
            df,
            data_source=f"uploaded_benchmark_{datetime.now().strftime('%y%m%d')}.csv",
            normalized_names=False,
        )

        uploaded_models = my_benchmark.df[
            my_benchmark.df["source"].str.contains("uploaded")
        ]["model"].unique()
        aggregate_models = aggbench.df[aggbench.df["source"].str.contains("aggregate")][
            "model"
        ].unique()

        # Find the intersection (overlap) of models
        overlap_models = set(aggregate_models).intersection(uploaded_models)
        if len(overlap_models) < n_models_taken_list[0]:
            st.warning(
                f"You have just {len(overlap_models)} models intersecting with the aggregate!\n"
            )

            st.info(
                f"Here are some models you could run your benchmark over:{[m for m in aggregate_models if m not in uploaded_models]}"
            )
            st.info(
                f"Model that you have and the aggragate does not: {[m for m in uploaded_models if m not in aggregate_models]}"
            )


def run_load(
    aggregate_scenario_whitelist,
    n_models_taken_list=[5],
    model_select_strategy_list=["random"],
    corr_types=["kendall"],
    n_exps=10,
    my_benchmark=Benchmark(),
    use_caching=True,
):
    # Create a hash of the inputs to generate a unique cache file for each set of inputs
    input_str = (
        str(aggregate_scenario_whitelist)
        + str(n_models_taken_list)
        + str(model_select_strategy_list)
        + str(corr_types)
        + str(n_exps)
    )

    if not my_benchmark.is_empty:
        input_str += str(
            hashlib.sha256(
                my_benchmark.df.to_csv(index=False).encode("utf-8")
            ).hexdigest()
        )

    input_hash = hashlib.md5(input_str.encode()).hexdigest()
    cache_file = f"agreements_cache_{input_hash}.csv"

    # Define the cache directory
    cache_dir = "cache"
    os.makedirs(cache_dir, exist_ok=True)
    cache_path = os.path.join(cache_dir, cache_file)

    # Check if the cache file exists
    if os.path.exists(cache_path) and use_caching:
        print("Loading cached results...")
        agreements = pd.read_csv(cache_path)
        aggregate_scores = pd.read_csv(
            cache_path.replace("agreement", "aggregate_scores")
        )
        allbench = Benchmark(
            pd.read_csv(cache_path.replace("agreement", "allbench")),
            normalized_names=True,
        )

        return agreements, aggregate_scores, allbench

    else:
        print("Cached results not found, calculating")

        allbench = Benchmark()
        allbench.load_local_catalog()

        scenarios_to_drop = ["HFv2 BBH Raw"]
        allbench.df = allbench.df.query("scenario not in @scenarios_to_drop")

        allbench.add_aggregate(
            new_col_name="aggregate",
            agg_source_name="aggregate",
            scenario_whitelist=aggregate_scenario_whitelist,
            min_scenario_for_models_to_appear_in_agg=1
            if len(aggregate_scenario_whitelist) == 1
            else len(aggregate_scenario_whitelist) // 3,
        )

        allbench.extend(my_benchmark)
        allbench.clear_repeated_scenarios()

        aggragate_scores = allbench.df.query('scenario=="aggregate"')[
            ["model", "score"]
        ].sort_values(by="score", ascending=False)

        if not my_benchmark.is_empty:
            aggragate_scores["in_uploaded"] = aggragate_scores["model"].apply(
                lambda x: x in my_benchmark.df["model"].unique()
            )

            # Get unique models for each scenario
            uploaded_models = allbench.df[
                allbench.df["source"].str.contains("uploaded")
            ]["model"].unique()
            aggregate_models = allbench.df[
                allbench.df["source"].str.contains("aggregate")
            ]["model"].unique()

            # Find the intersection (overlap) of models
            n_overlap_models = len(set(aggregate_models).intersection(uploaded_models))
            # make sure we are asking for the maximal number of models between the request benchmark and the aggregate
            n_models_taken_list = [min(n_models_taken_list[0], n_overlap_models)]

        cfg = Config(
            exp_to_run="example",
            n_models_taken_list=n_models_taken_list,
            model_select_strategy_list=model_select_strategy_list,
            corr_types=corr_types,
            n_exps=n_exps if n_models_taken_list != [0] else 1,
        )

        tester = Tester(cfg=cfg)

        agreements = tester.all_vs_all_agreement_testing(
            allbench,
            single_source_scenario="aggregate",  # olny measuring all with the aggragate
        )

        agreements.to_csv(cache_path, index=False)
        aggragate_scores.to_csv(
            cache_path.replace("agreement", "aggregate_scores"), index=False
        )
        allbench.df.to_csv(cache_path.replace("agreement", "allbench"), index=False)

    return agreements, aggragate_scores, allbench


agreements, aggragare_score_df, allbench = run_load(
    aggregate_scenario_whitelist=aggregate_scenario_whitelist,
    n_models_taken_list=n_models_taken_list,
    model_select_strategy_list=[model_select_strategy],
    corr_types=[corr_type],
    n_exps=n_exps,
    my_benchmark=my_benchmark,
)

reporter = Reporter()
z_scores = reporter.get_all_z_scores(agreements=agreements, aggragate_name="aggregate")
z_scores.drop(columns=["n_models_of_corr_with_agg"], inplace=True)

corr_name = f"{'Kendall Tau' if corr_type=='kendall' else 'Per.'} Corr. w/ Agg"

z_scores["z_score"] = z_scores["z_score"].round(2)
z_scores["corr_with_agg"] = z_scores["corr_with_agg"].round(2)
z_scores["p_value_of_corr_with_agg"] = z_scores["p_value_of_corr_with_agg"].round(2)
# z_scores["n_models_of_corr_with_agg"] = z_scores["n_models_of_corr_with_agg"].round(1)

z_scores["date"] = z_scores["source"].apply(
    lambda x: x.split(".csv")[0].split("_")[-1]
    if "frozen" not in x
    else x.split(".csv")[0].split("_")[-2]
)

z_scores["date"] = pd.to_datetime("20" + z_scores["date"]).dt.date

z_score_name = "BenchBench Score"
p_val_name = "p val"

data = (
    z_scores.rename(
        columns={
            "scenario": "Benchmark",
            "z_score": z_score_name,
            "corr_with_agg": corr_name,
            "p_value_of_corr_with_agg": p_val_name,
            # "n_models_of_corr_with_agg": "# Models Used",
            "source": "Source",
            "date": "Snapshot Date",
        }
    )
    .sort_values(z_score_name, ascending=False)
    .reset_index(drop=True)
)


# Apply coloring based on 'Z' valuesz
def highlight_uploaded_benchmark(row):
    if "uploaded_benchmark" in row["Source"]:
        return ["background-color: rgba(100,100,100,0.1)"] * len(row)
    else:
        return [""] * len(row)


styled_data = (
    data.style.background_gradient(
        subset=[z_score_name],
        cmap="RdYlGn",
        vmin=-data[z_score_name].abs().max(),
        vmax=data[z_score_name].abs().max(),
    )
    .apply(highlight_uploaded_benchmark, axis=1)
    .background_gradient(
        subset=[p_val_name],
        cmap="Reds",
        vmin=0.1,
        vmax=1,
    )
    .format(subset=[z_score_name, corr_name, p_val_name], formatter="{:.2}")
    .set_properties(**{"text-align": "center"})
)

cols_used = [
    "Benchmark",
    z_score_name,
    corr_name,
    p_val_name,
    "Snapshot Date",
]


st.dataframe(
    data=styled_data,
    column_order=cols_used,
    hide_index=True,
    use_container_width=True,
    height=500,
    column_config={col: {"alignment": "center"} for col in cols_used},
)


aggragare_score_df.rename(
    columns={
        "model": "Model",
        "score": "Mean Win Rate over Selected Scenarios for Aggragate",
    },
    inplace=True,
)

with st.expander(label="Aggragate Benchmark scores"):
    st.dataframe(
        data=aggragare_score_df,
        hide_index=True,
        height=500,
        use_container_width=True,
    )

left, right = st.columns([1, 1])

with left:
    with st.expander(label="Cite Us!"):
        st.code(
            r"""

    @misc{perlitz2024llmbenchmarksagreefixing,
        title={Do These LLM Benchmarks Agree? Fixing Benchmark Evaluation with BenchBench}, 
        author={Yotam Perlitz and Ariel Gera and Ofir Arviv and Asaf Yehudai and Elron Bandel and Eyal Shnarch and Michal Shmueli-Scheuer and Leshem Choshen},
        year={2024},
        eprint={2407.13696},
        archivePrefix={arXiv},
        primaryClass={cs.CL},
        url={https://arxiv.org/abs/2407.13696}, 
    }

"""
        )

with right:
    with st.expander(label="Cite Everyone Else!"):
        st.code(
            r"""

    @misc{perlitz2024llmbenchmarksagreefixing,
        title={Do These LLM Benchmarks Agree? Fixing Benchmark Evaluation with BenchBench}, 
        author={Yotam Perlitz and Ariel Gera and Ofir Arviv and Asaf Yehudai and Elron Bandel and Eyal Shnarch and Michal Shmueli-Scheuer and Leshem Choshen},
        year={2024},
        eprint={2407.13696},
        archivePrefix={arXiv},
        primaryClass={cs.CL},
        url={https://arxiv.org/abs/2407.13696}, 
    }

    @misc{decentralized2024,
    title        = {Decentralized Arena via Collective LLM Intelligence: Building Automated, Robust, and Transparent LLM Evaluation for Numerous Dimensions},
    author       = {Yanbin Yin AND Zhen Wang AND Kun Zhou AND Xiangdong Zhang AND Shibo Hao AND Yi Gu AND Jieyuan Liu AND Somanshu Singla AND Tianyang Liu AND Xing, Eric P. AND Zhengzhong Liu AND Haojian Jin AND Zhiting Hu},
    year         = 2024,
    month        = 10,
    url          = {https://de-arena.maitrix.org/}
}

    @techreport{balachandran2024eureka,
        author = {Balachandran, Vidhisha and Chen, Jingya and Joshi, Neel and Nushi, Besmira and Palangi, Hamid and Salinas, Eduardo and Vineet, Vibhav and Woffinden-Luey, James and Yousefi, Safoora},
        title = {EUREKA: Evaluating and Understanding Large Foundation Models},
        institution = {Microsoft},
        year = {2024},
        month = {September},
        abstract = {Rigorous and reproducible evaluation of large foundation models is critical for assessing the state of the art, informing next steps in model improvement, and for guiding scientific advances in Artificial Intelligence (AI). Evaluation is also important for informing the increasing number of application developers that build services on foundation models. The evaluation process has however become challenging in practice due to several reasons that require immediate attention from the community, including benchmark saturation, lack of transparency in the methods being deployed for measurement, development challenges in extracting the right measurements for generative tasks, and, more generally, the extensive number of capabilities that need to be considered for showing a well-rounded comparison across models. In addition, despite the overwhelming numbers of side-by-side capability evaluations available, we still lack a deeper understanding about when and how different models fail for a given capability and whether the nature of failures is similar across different models being released over time.

        We make three contributions to alleviate the above challenges. First, we present Eureka, a reusable and open evaluation framework for standardizing evaluations of large foundation models beyond single-score reporting and rankings. Second, we introduce Eureka-Bench as an extensible collection of benchmarks testing capabilities that (i) are still challenging for state-of-the-art foundation models and (ii) represent fundamental but overlooked capabilities for completing tasks in both language and vision modalities. The available space for improvement that comes inherently from non-saturated benchmarks, enables us to discover meaningful differences between models at a capability level. Third, using the framework and Eureka-Bench, we conduct an analysis of 12 state-of-the-art models, providing in-depth insights for failure understanding and model comparison by disaggregating the measurements across important subcategories of data. Such insights uncover granular weaknesses of models for a given capability and can then be further leveraged to plan more precisely on what areas are most promising for improvement. Eureka is available as open-source to foster transparent and reproducible evaluation practices.

        In contrast to recent trends in evaluation reports and leaderboards showing absolute rankings and claims for one model or another to be the best, our analysis shows that there is no such best model. Different models have different strengths, but there are models that appear more often than others as best performers for several capabilities. Despite the many observed improvements, it also becomes obvious that current models still struggle with a number of fundamental capabilities including detailed image understanding, benefiting from multimodal input when available rather than fully relying on language, factuality and grounding for information retrieval, and over refusals.},
        url = {https://www.microsoft.com/en-us/research/publication/eureka-evaluating-and-understanding-large-foundation-models/},
        number = {MSR-TR-2024-33},
        }

    @article{hsieh2024ruler,
        title={RULER: What's the Real Context Size of Your Long-Context Language Models?},
        author={Cheng-Ping Hsieh and Simeng Sun and Samuel Kriman and Shantanu Acharya and Dima Rekesh and Fei Jia and Yang Zhang and Boris Ginsburg},
        year={2024},
        journal={arXiv preprint arXiv:2404.06654},
    }
        

    @misc{berkeley-function-calling-leaderboard,
        title={Berkeley Function Calling Leaderboard}, 
        author={Fanjia Yan and Huanzhi Mao and Charlie Cheng-Jie Ji
        and Tianjun Zhang and Shishir G. Patil and Ion Stoica and Joseph E.
        Gonzalez},
        howpublished={\url{https://gorilla.cs.berkeley.edu/blogs/8_berkeley_function_calling_leaderboard.html}},
        year={2024},
    }

    @misc{liu2023agentbenchevaluatingllmsagents,
        title={AgentBench: Evaluating LLMs as Agents}, 
        author={Xiao Liu and Hao Yu and Hanchen Zhang and Yifan Xu and Xuanyu Lei and Hanyu Lai and Yu Gu and Hangliang Ding and Kaiwen Men and Kejuan Yang and Shudan Zhang and Xiang Deng and Aohan Zeng and Zhengxiao Du and Chenhui Zhang and Sheng Shen and Tianjun Zhang and Yu Su and Huan Sun and Minlie Huang and Yuxiao Dong and Jie Tang},
        year={2023},
        eprint={2308.03688},
        archivePrefix={arXiv},
        primaryClass={cs.AI},
        url={https://arxiv.org/abs/2308.03688}, 
    }
            
    @software{Li_AlpacaEval_An_Automatic_2023,
        author = {Li, Xuechen and Zhang, Tianyi and Dubois, Yann and Taori, Rohan and Gulrajani, Ishaan and Guestrin, Carlos and Liang, Percy and Hashimoto, Tatsunori B.},
        month = may,
        title = {{AlpacaEval: An Automatic Evaluator of Instruction-following Models}},
        year = {2023}
    }
            
    @misc{li2024crowdsourceddatahighqualitybenchmarks,
        title={From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline}, 
        author={Tianle Li and Wei-Lin Chiang and Evan Frick and Lisa Dunlap and Tianhao Wu and Banghua Zhu and Joseph E. Gonzalez and Ion Stoica},
        year={2024},
        eprint={2406.11939},
        archivePrefix={arXiv},
        primaryClass={cs.LG},
        url={https://arxiv.org/abs/2406.11939}, 
    }
    @misc{chiang2024chatbot,
        title={Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference},
        author={Wei-Lin Chiang and Lianmin Zheng and Ying Sheng and Anastasios Nikolas Angelopoulos and Tianle Li and Dacheng Li and Hao Zhang and Banghua Zhu and Michael Jordan and Joseph E. Gonzalez and Ion Stoica},
        year={2024},
        eprint={2403.04132},
        archivePrefix={arXiv},
        primaryClass={cs.AI}
    }
    @misc{arenahard2024,
        title = {From Live Data to High-Quality Benchmarks: The Arena-Hard Pipeline},
        url = {https://lmsys.org/blog/2024-04-19-arena-hard/},
        author = {Tianle Li*, Wei-Lin Chiang*, Evan Frick, Lisa Dunlap, Banghua Zhu, Joseph E. Gonzalez, Ion Stoica},
        month = {April},
        year = {2024}
    }
            
    @misc{kim2024biggenbenchprincipledbenchmark,
        title={The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models}, 
        author={Seungone Kim and Juyoung Suk and Ji Yong Cho and Shayne Longpre and Chaeeun Kim and Dongkeun Yoon and Guijin Son and Yejin Cho and Sheikh Shafayat and Jinheon Baek and Sue Hyun Park and Hyeonbin Hwang and Jinkyung Jo and Hyowon Cho and Haebin Shin and Seongyun Lee and Hanseok Oh and Noah Lee and Namgyu Ho and Se June Joo and Miyoung Ko and Yoonjoo Lee and Hyungjoo Chae and Jamin Shin and Joel Jang and Seonghyeon Ye and Bill Yuchen Lin and Sean Welleck and Graham Neubig and Moontae Lee and Kyungjae Lee and Minjoon Seo},
        year={2024},
        eprint={2406.05761},
        archivePrefix={arXiv},
        primaryClass={cs.CL},
        url={https://arxiv.org/abs/2406.05761}, 
    }
                
    @misc{liang2023holisticevaluationlanguagemodels,
        title={Holistic Evaluation of Language Models}, 
        author={Percy Liang and Rishi Bommasani and Tony Lee and Dimitris Tsipras and Dilara Soylu and Michihiro Yasunaga and Yian Zhang and Deepak Narayanan and Yuhuai Wu and Ananya Kumar and Benjamin Newman and Binhang Yuan and Bobby Yan and Ce Zhang and Christian Cosgrove and Christopher D. Manning and Christopher Ré and Diana Acosta-Navas and Drew A. Hudson and Eric Zelikman and Esin Durmus and Faisal Ladhak and Frieda Rong and Hongyu Ren and Huaxiu Yao and Jue Wang and Keshav Santhanam and Laurel Orr and Lucia Zheng and Mert Yuksekgonul and Mirac Suzgun and Nathan Kim and Neel Guha and Niladri Chatterji and Omar Khattab and Peter Henderson and Qian Huang and Ryan Chi and Sang Michael Xie and Shibani Santurkar and Surya Ganguli and Tatsunori Hashimoto and Thomas Icard and Tianyi Zhang and Vishrav Chaudhary and William Wang and Xuechen Li and Yifan Mai and Yuhui Zhang and Yuta Koreeda},
        year={2023},
        eprint={2211.09110},
        archivePrefix={arXiv},
        primaryClass={cs.CL},
        url={https://arxiv.org/abs/2211.09110}, 
    }


    @misc{open-llm-leaderboard-v2,
    author = {Clémentine Fourrier and Nathan Habib and Alina Lozovskaya and Konrad Szafer and Thomas Wolf},
    title = {Open LLM Leaderboard v2},
    year = {2024},
    publisher = {Hugging Face},
    howpublished = "\url{https://huggingface.co/spaces/open-llm-leaderboard/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{zhou2023instructionfollowingevaluationlargelanguage,
        title={Instruction-Following Evaluation for Large Language Models},
        author={Jeffrey Zhou and Tianjian Lu and Swaroop Mishra and Siddhartha Brahma and Sujoy Basu and Yi Luan and Denny Zhou and Le Hou},
        year={2023},
        eprint={2311.07911},
        archivePrefix={arXiv},
        primaryClass={cs.CL},
        url={https://arxiv.org/abs/2311.07911},
    }

    @misc{suzgun2022challengingbigbenchtaskschainofthought,
        title={Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them},
        author={Mirac Suzgun and Nathan Scales and Nathanael Schärli and Sebastian Gehrmann and Yi Tay and Hyung Won Chung and Aakanksha Chowdhery and Quoc V. Le and Ed H. Chi and Denny Zhou and Jason Wei},
        year={2022},
        eprint={2210.09261},
        archivePrefix={arXiv},
        primaryClass={cs.CL},
        url={https://arxiv.org/abs/2210.09261},
    }

    @misc{hendrycks2021measuringmathematicalproblemsolving,
        title={Measuring Mathematical Problem Solving With the MATH Dataset},
        author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt},
        year={2021},
        eprint={2103.03874},
        archivePrefix={arXiv},
        primaryClass={cs.LG},
        url={https://arxiv.org/abs/2103.03874},
    }

    @misc{rein2023gpqagraduatelevelgoogleproofqa,
        title={GPQA: A Graduate-Level Google-Proof Q&A Benchmark},
        author={David Rein and Betty Li Hou and Asa Cooper Stickland and Jackson Petty and Richard Yuanzhe Pang and Julien Dirani and Julian Michael and Samuel R. Bowman},
        year={2023},
        eprint={2311.12022},
        archivePrefix={arXiv},
        primaryClass={cs.AI},
        url={https://arxiv.org/abs/2311.12022},
    }

    @misc{sprague2024musrtestinglimitschainofthought,
        title={MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning},
        author={Zayne Sprague and Xi Ye and Kaj Bostrom and Swarat Chaudhuri and Greg Durrett},
        year={2024},
        eprint={2310.16049},
        archivePrefix={arXiv},
        primaryClass={cs.CL},
        url={https://arxiv.org/abs/2310.16049},
    }

    @misc{wang2024mmluprorobustchallengingmultitask,
        title={MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark},
        author={Yubo Wang and Xueguang Ma and Ge Zhang and Yuansheng Ni and Abhranil Chandra and Shiguang Guo and Weiming Ren and Aaran Arulraj and Xuan He and Ziyan Jiang and Tianle Li and Max Ku and Kai Wang and Alex Zhuang and Rongqi Fan and Xiang Yue and Wenhu Chen},
        year={2024},
        eprint={2406.01574},
        archivePrefix={arXiv},
        primaryClass={cs.CL},
        url={https://arxiv.org/abs/2406.01574},
    }

    @misc{open-llm-leaderboard-v1,
        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 (2023-2024)},
        year = {2023},
        publisher = {Hugging Face},
        howpublished = "\url{https://huggingface.co/spaces/open-llm-leaderboard-old/open_llm_leaderboard}"
    }
            
    @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/open-llm-leaderboard-old/open_llm_leaderboard}"
    }

    @misc{waldis2024holmesbenchmarklinguisticcompetence,
        title={Holmes: Benchmark the Linguistic Competence of Language Models}, 
        author={Andreas Waldis and Yotam Perlitz and Leshem Choshen and Yufang Hou and Iryna Gurevych},
        year={2024},
        eprint={2404.18923},
        archivePrefix={arXiv},
        primaryClass={cs.CL},
        url={https://arxiv.org/abs/2404.18923}, 
    }       

    @article{livebench,
        author    = {White, Colin and Dooley, Samuel and Roberts, Manley and Pal, Arka and Feuer, Ben and Jain, Siddhartha and Shwartz-Ziv, Ravid and Jain, Neel and Saifullah, Khalid and Naidu, Siddartha and Hegde, Chinmay and LeCun, Yann and Goldstein, Tom and Neiswanger, Willie and Goldblum, Micah},
        title     = {LiveBench: A Challenging, Contamination-Free LLM Benchmark},
        url       = {arXiv preprint arXiv:2406.19314},
        year      = {2024},
    }  
            
    @article{ni2024mixeval,
        title={MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures},
        author={Ni, Jinjie and Xue, Fuzhao and Yue, Xiang and Deng, Yuntian and Shah, Mahir and Jain, Kabir and Neubig, Graham and You, Yang},
        journal={arXiv preprint arXiv:2406.06565},
        year={2024}
    }
            
    @misc{wang2024mmluprorobustchallengingmultitask,
        title={MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark}, 
        author={Yubo Wang and Xueguang Ma and Ge Zhang and Yuansheng Ni and Abhranil Chandra and Shiguang Guo and Weiming Ren and Aaran Arulraj and Xuan He and Ziyan Jiang and Tianle Li and Max Ku and Kai Wang and Alex Zhuang and Rongqi Fan and Xiang Yue and Wenhu Chen},
        year={2024},
        eprint={2406.01574},
        archivePrefix={arXiv},
        primaryClass={cs.CL},
        url={https://arxiv.org/abs/2406.01574}, 
    }
                
    @misc{zheng2023judgingllmasajudgemtbenchchatbot,
        title={Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena}, 
        author={Lianmin Zheng and Wei-Lin Chiang and Ying Sheng and Siyuan Zhuang and Zhanghao Wu and Yonghao Zhuang and Zi Lin and Zhuohan Li and Dacheng Li and Eric P. Xing and Hao Zhang and Joseph E. Gonzalez and Ion Stoica},
        year={2023},
        eprint={2306.05685},
        archivePrefix={arXiv},
        primaryClass={cs.CL},
        url={https://arxiv.org/abs/2306.05685}, 
    }
            
    @misc{2023opencompass,
        title={OpenCompass: A Universal Evaluation Platform for Foundation Models},
        author={OpenCompass Contributors},
        howpublished = {\url{https://github.com/open-compass/opencompass}},
        year={2023}
    }
            
    @misc{qin2023toolllm,
        title={ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs}, 
        author={Yujia Qin and Shihao Liang and Yining Ye and Kunlun Zhu and Lan Yan and Yaxi Lu and Yankai Lin and Xin Cong and Xiangru Tang and Bill Qian and Sihan Zhao and Runchu Tian and Ruobing Xie and Jie Zhou and Mark Gerstein and Dahai Li and Zhiyuan Liu and Maosong Sun},
        year={2023},
        eprint={2307.16789},
        archivePrefix={arXiv},
        primaryClass={cs.AI}
    }
                
    @misc{qin2023tool,
        title={Tool Learning with Foundation Models}, 
        author={Yujia Qin and Shengding Hu and Yankai Lin and Weize Chen and Ning Ding and Ganqu Cui and Zheni Zeng and Yufei Huang and Chaojun Xiao and Chi Han and Yi Ren Fung and Yusheng Su and Huadong Wang and Cheng Qian and Runchu Tian and Kunlun Zhu and Shihao Liang and Xingyu Shen and Bokai Xu and Zhen Zhang and Yining Ye and Bowen Li and Ziwei Tang and Jing Yi and Yuzhang Zhu and Zhenning Dai and Lan Yan and Xin Cong and Yaxi Lu and Weilin Zhao and Yuxiang Huang and Junxi Yan and Xu Han and Xian Sun and Dahai Li and Jason Phang and Cheng Yang and Tongshuang Wu and Heng Ji and Zhiyuan Liu and Maosong Sun},
        year={2023},
        eprint={2304.08354},
        archivePrefix={arXiv},
        primaryClass={cs.CL}
    }
                
    @misc{guo2024stabletoolbench,
        title={StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models},
        author={Guo, Zhicheng and Cheng, Sijie and Wang, Hao and Liang, Shihao and Qin, Yujia and Li, Peng and Liu, Zhiyuan and Sun, Maosong and Liu, Yang},
        year={2024},
        eprint={2403.07714},
        archivePrefix={arXiv},
        primaryClass={cs.CL}
    }
            
    @article{yuchen2024wildbench,
        title={WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild},
        author={Yuchen Lin, Bill and Deng, Yuntian and Chandu, Khyathi and Brahman, Faeze and Ravichander, Abhilasha and Pyatkin, Valentina and Dziri, Nouha and Le Bras, Ronan and Choi, Yejin},
        journal={arXiv e-prints},
        pages={arXiv--2406},
        year={2024}
    }
    
    """
        )


st.subheader("Benchmark Report Card")

st.markdown("Choose the Benchmark from which you want to get a report.")

benchmarks = data["Benchmark"].unique().tolist()

index_to_use = 1
if not my_benchmark.is_empty:
    index_to_use = benchmarks.index(my_benchmark.df["scenario"].unique()[0])

plotted_scenario = st.selectbox(
    "Choose Benchmark to plot",
    benchmarks,
    index=index_to_use,
)

col1, col2, col3 = st.columns(3)
cur_data = data.query(f"Benchmark=='{plotted_scenario}'")
col1.metric("Relative agreement", cur_data[z_score_name])
col2.metric(corr_name, cur_data[corr_name])
col3.metric("p-value of Corr.", cur_data[p_val_name])

cur_df = allbench.df.query(f'scenario=="aggregate" or scenario=="{plotted_scenario}"')

# Filter models that are present in both scenarios
models_in_both = cur_df.groupby("model")["scenario"].nunique().eq(2).index

# Pivot the DataFrame to have scenarios as columns
df_pivot = cur_df[cur_df["model"].isin(models_in_both)].pivot(
    index="model", columns="scenario", values="score"
)

# Create the scatter plot using Plotly Express
fig = px.scatter(
    df_pivot,
    x=df_pivot.columns[0],
    y=df_pivot.columns[1],
    trendline="ols",
    labels={
        df_pivot.columns[0]: df_pivot.columns[0],
        df_pivot.columns[1]: df_pivot.columns[1],
    },
    hover_name=df_pivot.index,
    title="Model Scores Comparison between Scenarios",
)
st.plotly_chart(fig, use_container_width=True)

st.subheader("How did we get the Z Scores?", divider=True)

st.write(r"""
            Section 3.1 in our work shows how using a single reference benchmark drastically hurts the roubustness and validity of BAT.
            To remedy this, we propose to test benchmark agreement with an aggragate benchmark and compare the agreement to other benchmarks.
            We recommend to perform this comparison using the [Z score](https://en.wikipedia.org/wiki/Standard_score) and demonstrate obtaining it to a benchmark of your selection.
            In the follwing way: $z_i=(x_i-\mu_{i...N}) / \sigma_{i...N}$ where $x_i$ is the agreement of the $i$th benchmark to the aggragate and $\mu_{i...N}$,$\sigma_{i...N}$ are the 
            mean and standard deviation of the agreements of the other benchmarks to the aggragate.
            """)


fig = px.histogram(
    data.query("Benchmark!=@plotted_scenario"), x=corr_name, nbins=len(data) - 1
)
# Add a vertical line at a specific x-coordinate
# Replace 'x_value' with the actual value where you want the line
x_value = 0.5  # Example value, adjust as necessary
fig.add_vline(
    x=data.query("Benchmark==@plotted_scenario")[corr_name].iloc[0],
    line_dash="dash",
    line_color="red",
)
# Update layout to add a title
fig.update_layout(
    title="Histogram of Correlation Values",  # Change the title text as needed
    title_x=0.3,  # Centers the title
    title_font=dict(size=20, family="CMU"),  # Customize font if needed
)

# # Plot!
st.plotly_chart(fig, use_container_width=True)

import streamlit as st

st.subheader("Why should you use the BenchBench Leaderboard?")

st.markdown(
    """
    Benchmark Agreement Testing (BAT) is crucial for validating new benchmarks and understanding the relationships between existing ones. 
    However, current BAT practices often lack standardization and transparency, leading to inconsistent results and hindering reliable comparisons. 
    The BenchBench Leaderboard addresses these challenges by offering a **principled and data-driven approach to benchmark evaluation**. 
    Let's explore some of the key issues with current BAT practices:
    """
)

st.markdown(
    """
    - **Lack of Standard Methodologies:**  BAT lacks standardized procedures for benchmark and model selection, hindering reproducibility and comparability across studies. 
      Researchers often make arbitrary choices, leading to results that are difficult to interpret and build upon. 
    """
)

st.image(
    "images/motivation.png",
    caption="**Example: Model Selection Impacts BAT Conclusions.** Kendall-tau correlations between the LMSys Arena benchmark and three others demonstrate how agreement varies significantly depending on the subset of models considered. This highlights the need for standardized model selection in BAT.",
    use_column_width=True,
)

st.markdown(
    """
    - **Arbitrary Selection of Reference Benchmarks:** The choice of reference benchmarks in BAT is often subjective and lacks a clear rationale. Using different reference benchmarks can lead to widely varying agreement scores, making it difficult to draw robust conclusions about a target benchmark's validity.
    """
)
st.markdown(
    """
    - **Inadequate Model Representation:** BAT often relies on a limited set of models that may not adequately represent the diversity of modern language models. This can lead to biased agreement scores that favor certain model types and fail to provide a comprehensive view of benchmark performance.
    """
)

st.image(
    "images/pointplot_granularity_matters.png",
    caption="**Example: Agreement Varies with Model Range.** Mean correlation between benchmarks shows that agreement tends to increase with the number of models considered and is generally lower for closely ranked models (blue lines). This highlights the importance of considering multiple granularities in BAT.",
    use_column_width=True,
)

st.markdown(
    """
    - **Overemphasis on Correlation Metrics:** BAT often relies heavily on correlation metrics without fully considering their limitations or the context of their application. While correlation can be informative, it's crucial to remember that high correlation doesn't automatically imply that benchmarks measure the same underlying construct.
    """
)

st.markdown(
    """
    The BenchBench Leaderboard tackles these challenges by implementing a standardized and transparent approach to BAT, promoting consistency and facilitating meaningful comparisons between benchmarks.
    By adopting the best practices embedded in the leaderboard, the research community can enhance the reliability and utility of benchmarks for evaluating and advancing language models.
    """
)


st.image(
    "images/ablations.png",
    caption="**BenchBench's Standardized Approach Reduces Variance.** This ablation study demonstrates that following the best practices implemented in BenchBench significantly reduces the variance of BAT results, leading to more robust and reliable conclusions.",
    use_column_width=True,
)


st.code(
    r"""
    @misc{perlitz2024llmbenchmarksagreefixing,
        title={Do These LLM Benchmarks Agree? Fixing Benchmark Evaluation with BenchBench}, 
        author={Yotam Perlitz and Ariel Gera and Ofir Arviv and Asaf Yehudai and Elron Bandel and Eyal Shnarch and Michal Shmueli-Scheuer and Leshem Choshen},
        year={2024},
        eprint={2407.13696},
        archivePrefix={arXiv},
        primaryClass={cs.CL},
        url={https://arxiv.org/abs/2407.13696}, 
    }
"""
)