# coding=utf-8
# Copyright 2024 The Mexa Authors.
# Lint as: python3
# This space is built based on uonlp/open_multilingual_llm_leaderboard.
# Mexa Space

import os
import json
import glob
from collections import defaultdict
import pandas as pd
import gradio as gr
import glob




# cache_data
def language_names(json_path):
    with open(json_path, 'r') as json_file:
        data = json.load(json_file)
    return data


label2name = language_names("assets/language_names.json")

def get_name(label):
    """Get the name of language from label"""
    iso_3 = label.split('_')[0]
    name = label2name[iso_3]
    return name


FLORES_ARC = {'name': 'FLORES - ARC style', 'csv': 'evals/flores-mean-arc.csv'}
Bible_ARC = {'name': 'Bible - ARC style', 'csv': 'evals/bible-mean-arc.csv'}
FLORES_Belebele = {'name': 'FLORES - Belebele style', 'csv': 'evals/flores-max-belebele.csv'}
Bible_Belebele = {'name': 'Bible - Belebele style', 'csv': 'evals/bible-max-belebele.csv'}

BENCHMARKS = [FLORES_ARC, Bible_ARC, FLORES_Belebele, Bible_Belebele]


def collect_results():
    performance_dict = defaultdict(lambda: defaultdict(dict))
    for bench in BENCHMARKS:

        task = bench['name']
        results = pd.read_csv(bench['csv'], index_col = 0).to_dict()

        # add performances
        for model, d_lang_value in results.items():
            if model == 'avg':
                continue
            for lang, value in d_lang_value.items():

                performance_dict[task.split(' ')[0]][(model, lang)][task] = value
    return performance_dict


def get_leaderboard_df(results_dict, parallel_data = 'FLORES'):
    df = list()
    performance_dict = results_dict.get(parallel_data, defaultdict(dict))

    for (pretrained, lang), perfs in performance_dict.items():
        lang_name = get_name(lang)
        perfs_num = [0, 0]

        if f'{parallel_data} - ARC style'  in perfs:
            perfs_num = [perfs[f'{parallel_data} - ARC style'], perfs[f'{parallel_data} - Belebele style']]

        avg = round(sum(perfs_num) / len(perfs_num), 4)
        notes = ' '.join([pretrained, lang_name])
        row = [pretrained, lang_name, lang, avg] + perfs_num + [notes]
        df.append(row)

    df = pd.DataFrame.from_records(df, columns=COLS)

    # Sort
    language_aggregate = df.groupby("Language")["Average"].mean().reset_index().sort_values(by='Average', ascending=False).reset_index()
    df = df.sort_values(by='Average', ascending=False)
    df = df.set_index("Language").loc[language_aggregate["Language"]].reset_index()
    df = df[COLS]

    return df

MODEL_COL = "Model"
LANG_COL = "Language"
CODE_COL = "Code"
ARC_COL = 'ARC Style'
BELEBELE_COL = 'Belebele Style'
AVERAGE_COL = "Average"
NOTES_COL = "Notes"  # For search only

COLS = [MODEL_COL, LANG_COL, CODE_COL, AVERAGE_COL, ARC_COL, BELEBELE_COL,  NOTES_COL]
TYPES = ["str", "str", "str", "number", "number", "number", "number", "number", "str"]

performance = collect_results()

def search_table(query, selection):
    df = get_leaderboard_df(performance, selection)
    filtered_df = df[df[NOTES_COL].str.contains(query, case=False)]
    return filtered_df

def update_dataframe(selection):
    if selection == "FLORES":
        return get_leaderboard_df(performance, selection)
    elif selection == "Bible":
        return get_leaderboard_df(performance, selection)


CUSTOM_CSS= """
/* Hides the final column */
table td:last-child,
table th:last-child {
    display: none;
}
# table td:first-child,
# table th:first-child {
#     max-width: 400px;
#     overflow: auto;
#     white-space: nowrap;
# }
"""


TITLE = '<h1 align="center" id="space-title">Mexa: Multilingual Evaluation of Open English-Centric LLMs via Cross-Lingual Alignment</h1>'

INTRO_TEXT = f"""
## About

We introduce Mexa, a method for assessing the multilingual capabilities of English-centric large language models (LLMs). Mexa builds on the observation that English-centric LLMs semantically use English as a kind of pivot language in their intermediate layers. Mexa computes the alignment between non-English languages and English using parallel sentences, estimating the transfer of language understanding capabilities from English to other languages through this alignment. This metric can be useful in estimating task performance, provided we know the English performance in the task and the alignment score between languages derived from a parallel dataset.

## Details
We use parallel datasets from FLORES and the Bible. In the ARC style, we use mean pooling over layers, and the English score achieved by each LLM in the ARC benchmark is used to adjust the multilingual scores. In the Belebele style, we use max pooling over layers, and the English score achieved by each LLM in Belebele is used to adjust the multilingual scores.
"""


CITATION = """
## Citation
```
@article{kargaran2024mexa,
title        = {Mexa: Multilingual Evaluation of {E}nglish-Centric {LLMs} via Cross-Lingual Alignment},
author       = {Kargaran, Amir Hossein and Modarressi, Ali and Nikeghbal, Nafiseh  and Diesner, Jana and Yvon, François and Schütze, Hinrich},
journal      = {arXiv preprint},
year         = {2024},
url          = {https://github.com/cisnlp/Mexa/}
}
```
"""

original_df = get_leaderboard_df(performance, 'FLORES') # Default


demo = gr.Blocks(css=CUSTOM_CSS)
with demo:
    gr.HTML(TITLE)
    gr.Markdown(INTRO_TEXT, elem_classes="markdown-text")

    with gr.Group():

        selection = gr.Radio(
            ["FLORES", "Bible"],
            label="Parallel Dataset",
            info="Choose parallel dataset!",
            value = 'FLORES'
        )

        search_bar = gr.Textbox(
            placeholder="Search models and languages...", show_label=False, elem_id="search-bar"
        )

        leaderboard_table = gr.components.Dataframe(
            value=original_df,
            headers=COLS,
            datatype=TYPES,
            # max_rows=5,
            elem_id="leaderboard-table",
        )


        selection.change(
            fn=update_dataframe,
            inputs=selection,
            outputs=leaderboard_table
        )


        search_bar.change(
            fn=search_table,
            inputs=[search_bar, selection],
            outputs=leaderboard_table,
        )

    gr.Markdown(CITATION, elem_classes="markdown-text")

demo.launch()