import os

import gradio as gr
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download

from src.about import (
    BOTTOM_LOGO,
    CITATION_BUTTON_LABEL,
    CITATION_BUTTON_LABEL_JA,
    CITATION_BUTTON_TEXT,
    EVALUATION_QUEUE_TEXT,
    EVALUATION_QUEUE_TEXT_JA,
    INTRODUCTION_TEXT,
    INTRODUCTION_TEXT_JA,
    LLM_BENCHMARKS_TEXT,
    LLM_BENCHMARKS_TEXT_JA,
    TITLE,
    TaskType,
)
from src.display.utils import (
    BENCHMARK_COLS,
    COLS,
    EVAL_COLS,
    EVAL_TYPES,
    NUMERIC_INTERVALS,
    TYPES,
    AddSpecialTokens,
    AutoEvalColumn,
    ModelType,
    NumFewShots,
    Precision,
    Version,
    fields,
)
from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO
from src.populate import get_evaluation_queue_df, get_leaderboard_df
from src.submission.submit import add_new_eval


def restart_space():
    API.restart_space(repo_id=REPO_ID)


# Space initialization
try:
    print(EVAL_REQUESTS_PATH)
    snapshot_download(
        repo_id=QUEUE_REPO,
        local_dir=EVAL_REQUESTS_PATH,
        repo_type="dataset",
        tqdm_class=None,
        etag_timeout=30,
    )
except Exception:
    restart_space()
try:
    print(EVAL_RESULTS_PATH)
    snapshot_download(
        repo_id=RESULTS_REPO,
        local_dir=EVAL_RESULTS_PATH,
        repo_type="dataset",
        tqdm_class=None,
        etag_timeout=30,
    )
except Exception:
    restart_space()


# Searching and filtering


def filter_models(
    df: pd.DataFrame,
    type_query: list,
    size_query: list,
    precision_query: list,
    add_special_tokens_query: list,
    num_few_shots_query: list,
    version_query: list,
    # backend_query: list,
) -> pd.DataFrame:
    print(f"Initial df shape: {df.shape}")
    print(f"Initial df content:\n{df}")

    filtered_df = df

    # Model Type フィルタリング
    type_column = "T" if "T" in df.columns else "Type_"
    type_emoji = [t.split()[0] for t in type_query]
    filtered_df = df[df[type_column].isin(type_emoji)]
    print(f"After type filter: {filtered_df.shape}")

    # Precision フィルタリング
    filtered_df = filtered_df[filtered_df["Precision"].isin(precision_query)]
    print(f"After precision filter: {filtered_df.shape}")

    # Model Size フィルタリング
    size_mask = filtered_df["#Params (B)"].apply(
        lambda x: any(x in NUMERIC_INTERVALS[s] for s in size_query if s != "Unknown")
    )
    if "Unknown" in size_query:
        size_mask |= filtered_df["#Params (B)"].isna() | (filtered_df["#Params (B)"] == 0)
    filtered_df = filtered_df[size_mask]
    print(f"After size filter: {filtered_df.shape}")

    # Add Special Tokens フィルタリング
    filtered_df = filtered_df[filtered_df["Add Special Tokens"].isin(add_special_tokens_query)]
    print(f"After add_special_tokens filter: {filtered_df.shape}")

    # Num Few Shots フィルタリング
    filtered_df = filtered_df[filtered_df["Few-shot"].astype(str).isin(num_few_shots_query)]
    print(f"After num_few_shots filter: {filtered_df.shape}")

    # Version フィルタリング
    filtered_df = filtered_df[filtered_df["llm-jp-eval version"].isin(version_query)]
    print(f"After version filter: {filtered_df.shape}")

    # Backend フィルタリング
    # filtered_df = filtered_df[filtered_df["Backend Library"].isin(backend_query)]
    # print(f"After backend filter: {filtered_df.shape}")

    print("Filtered dataframe head:")
    print(filtered_df.head())
    return filtered_df


def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
    return df[df[AutoEvalColumn.dummy.name].str.contains(query, case=False)]


def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame:
    """Added by Abishek"""
    if not query:
        return filtered_df

    final_df = []
    queries = [q.strip() for q in query.split(";")]
    for _q in queries:
        _q = _q.strip()
        if _q != "":
            temp_filtered_df = search_table(filtered_df, _q)
            if len(temp_filtered_df) > 0:
                final_df.append(temp_filtered_df)
    if len(final_df) > 0:
        filtered_df = pd.concat(final_df)
        filtered_df = filtered_df.drop_duplicates(
            subset=[
                AutoEvalColumn.model.name,
                AutoEvalColumn.precision.name,
                AutoEvalColumn.revision.name,
            ]
        )
    return filtered_df


def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
    always_here_cols = [
        AutoEvalColumn.model_type_symbol.name,  # 'T'
        AutoEvalColumn.model.name,  # 'Model'
    ]

    # 'always_here_cols' を 'columns' から除外して重複を避ける
    columns = [c for c in columns if c not in always_here_cols]
    new_columns = (
        always_here_cols + [c for c in COLS if c in df.columns and c in columns] + [AutoEvalColumn.dummy.name]
    )

    # 重複を排除しつつ順序を維持
    seen = set()
    unique_columns = []
    for c in new_columns:
        if c not in seen:
            unique_columns.append(c)
            seen.add(c)

    # フィルタリングされたカラムでデータフレームを作成
    filtered_df = df[unique_columns]
    return filtered_df


def update_table(
    hidden_df: pd.DataFrame,
    type_query: list,
    precision_query: str,
    size_query: list,
    add_special_tokens_query: list,
    num_few_shots_query: list,
    version_query: list,
    # backend_query: list,
    query: str,
    *columns,
):
    columns = [item for column in columns for item in column]
    print(
        f"Update table called with: type_query={type_query}, precision_query={precision_query}, size_query={size_query}"
    )
    print(f"hidden_df shape before filtering: {hidden_df.shape}")

    filtered_df = filter_models(
        hidden_df,
        type_query,
        size_query,
        precision_query,
        add_special_tokens_query,
        num_few_shots_query,
        version_query,
        #    backend_query,
    )
    print(f"filtered_df shape after filter_models: {filtered_df.shape}")

    filtered_df = filter_queries(query, filtered_df)
    print(f"filtered_df shape after filter_queries: {filtered_df.shape}")

    print(
        f"Filter applied: query={query}, columns={columns}, type_query={type_query}, precision_query={precision_query}"
    )
    print("Filtered dataframe head:")
    print(filtered_df.head())

    df = select_columns(filtered_df, columns)
    print(f"Final df shape: {df.shape}")
    print("Final dataframe head:")
    print(df.head())
    return df


def load_query(request: gr.Request):  # triggered only once at startup => read query parameter if it exists
    query = request.query_params.get("query") or ""
    return (
        query,
        query,
    )  # return one for the "search_bar", one for a hidden component that triggers a reload only if value has changed


# Prepare the dataframes

original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
leaderboard_df = original_df.copy()
(
    finished_eval_queue_df,
    running_eval_queue_df,
    pending_eval_queue_df,
    failed_eval_queue_df,
) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)

leaderboard_df = filter_models(
    leaderboard_df,
    [t.to_str(" : ") for t in ModelType],
    list(NUMERIC_INTERVALS.keys()),
    [i.value.name for i in Precision],
    [i.value.name for i in AddSpecialTokens],
    [i.value.name for i in NumFewShots],
    [i.value.name for i in Version],
    #    [i.value.name for i in Backend],
)

leaderboard_df_filtered = filter_models(
    leaderboard_df,
    [t.to_str(" : ") for t in ModelType],
    list(NUMERIC_INTERVALS.keys()),
    [i.value.name for i in Precision],
    [i.value.name for i in AddSpecialTokens],
    [i.value.name for i in NumFewShots],
    [i.value.name for i in Version],
    #    [i.value.name for i in Backend],
)

# DataFrameの初期化部分のみを修正
initial_columns = ["T"] + [
    c.name for c in fields(AutoEvalColumn) if (c.never_hidden or c.displayed_by_default) and c.name != "T"
]
leaderboard_df_filtered = select_columns(leaderboard_df, initial_columns)


# Leaderboard demo

with gr.Blocks() as demo_leaderboard:
    with gr.Row():
        search_bar = gr.Textbox(
            placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
            show_label=False,
            elem_id="search-bar",
        )
    with gr.Row():
        shown_columns_dict = {}
        checkboxes = []
        with gr.Row():  
            gr.Button("全選択", size="sm").click(fn=lambda: toggle_all_categories("all", shown_columns_dict), outputs=checkboxes)
            gr.Button("全解除", size="sm").click(fn=lambda: toggle_all_categories("none", shown_columns_dict), outputs=checkboxes)
            gr.Button("AVGのみ", size="sm").click(fn=lambda: toggle_all_categories("avg_only", shown_columns_dict), outputs=checkboxes)
    
        for task_type in TaskType:
            if task_type == TaskType.NotTask:
                label = "Model details"
            else:
                label = task_type.value
            with gr.Accordion(label, open=True, elem_classes="accordion"):
                    with gr.Row(height=110):
                        shown_column = gr.CheckboxGroup(
                            show_label=False,
                            choices=[
                                c.name
                                for c in fields(AutoEvalColumn)
                                if not c.hidden and not c.never_hidden and not c.dummy and c.task_type == task_type
                            ],
                            value=[
                                c.name
                                for c in fields(AutoEvalColumn)
                                if not c.hidden and not c.never_hidden 
                                and c.task_type == task_type
                                and (
                                    (task_type == TaskType.AVG) or
                                    (task_type != TaskType.AVG and c.average)
                                )
                            ],
                            elem_id="column-select",
                            container=False,
                        )
                        shown_columns_dict[task_type.name] = shown_column
                        checkboxes.append(shown_column)
                    
                # with gr.Row(height=110):
                #     shown_column = gr.CheckboxGroup(
                #         show_label=False,
                #         choices=[
                #             c.name
                #             for c in fields(AutoEvalColumn)
                #             if not c.hidden and not c.never_hidden and not c.dummy and c.task_type == task_type
                #             # and not c.average
                #             # or (task_type == TaskType.AVG and c.average)
                #         ],
                #         value=[
                #             c.name
                #             for c in fields(AutoEvalColumn)
                #             if c.displayed_by_default
                #             and not c.hidden
                #             and not c.never_hidden
                #             and c.task_type == task_type
                #             # and not c.average
                #             # or (task_type == TaskType.AVG and c.average)
                #         ],
                #         elem_id="column-select",
                #         container=False,
                #     )
                #     shown_columns_dict[task_type.name] = shown_column
    with gr.Row():
        filter_columns_type = gr.CheckboxGroup(
            label="Model types",
            choices=[t.to_str() for t in ModelType],
            value=[t.to_str() for t in ModelType],
            elem_id="filter-columns-type",
        )
        filter_columns_precision = gr.CheckboxGroup(
            label="Precision",
            choices=[i.value.name for i in Precision],
            value=[i.value.name for i in Precision],
            elem_id="filter-columns-precision",
        )
        filter_columns_size = gr.CheckboxGroup(
            label="Model sizes (in billions of parameters)",
            choices=list(NUMERIC_INTERVALS.keys()),
            value=list(NUMERIC_INTERVALS.keys()),
            elem_id="filter-columns-size",
        )
        filter_columns_add_special_tokens = gr.CheckboxGroup(
            label="Add Special Tokens",
            choices=[i.value.name for i in AddSpecialTokens],
            value=[i.value.name for i in AddSpecialTokens],
            elem_id="filter-columns-add-special-tokens",
        )
        filter_columns_num_few_shots = gr.CheckboxGroup(
            label="Num Few Shots",
            choices=[i.value.name for i in NumFewShots],
            value=[i.value.name for i in NumFewShots],
            elem_id="filter-columns-num-few-shots",
        )
        filter_columns_version = gr.CheckboxGroup(
            label="llm-jp-eval version",
            choices=[i.value.name for i in Version],
            value=[i.value.name for i in Version],
            elem_id="filter-columns-version",
        )
        # filter_columns_backend = gr.CheckboxGroup(
        #    label="Backend Library",
        #    choices=[i.value.name for i in Backend],
        #    value=[i.value.name for i in Backend],
        #    elem_id="filter-columns-backend",
        # )

    # DataFrameコンポーネントの初期化
    leaderboard_table = gr.Dataframe(
        value=leaderboard_df_filtered,
        headers=initial_columns,
        datatype=TYPES,
        elem_id="leaderboard-table",
        interactive=False,
        visible=True,
    )

    # Dummy leaderboard for handling the case when the user uses backspace key
    hidden_leaderboard_table_for_search = gr.Dataframe(
        value=original_df[COLS],
        headers=COLS,
        datatype=TYPES,
        visible=False,
    )

    # Define a hidden component that will trigger a reload only if a query parameter has been set
    hidden_search_bar = gr.Textbox(value="", visible=False)

    gr.on(
        triggers=[
            hidden_search_bar.change,
            filter_columns_type.change,
            filter_columns_precision.change,
            filter_columns_size.change,
            filter_columns_add_special_tokens.change,
            filter_columns_num_few_shots.change,
            filter_columns_version.change,
            # filter_columns_backend.change,
            search_bar.submit,
        ]
        + [shown_columns.change for shown_columns in shown_columns_dict.values()],
        fn=update_table,
        inputs=[
            hidden_leaderboard_table_for_search,
            filter_columns_type,
            filter_columns_precision,
            filter_columns_size,
            filter_columns_add_special_tokens,
            filter_columns_num_few_shots,
            filter_columns_version,
            # filter_columns_backend,
            search_bar,
        ]
        + [shown_columns for shown_columns in shown_columns_dict.values()],
        outputs=leaderboard_table,
    )

    # Check query parameter once at startup and update search bar + hidden component
    demo_leaderboard.load(fn=load_query, outputs=[search_bar, hidden_search_bar])

def toggle_all_categories(action: str, shown_columns_dict: dict):
    """全カテゴリーのチェックボックスを一括制御する関数"""
    results = []
    for task_type in TaskType:
        if task_type == TaskType.NotTask:
            # Model detailsの場合は既存の選択状態を維持
            results.append([
                c.name
                for c in fields(AutoEvalColumn)
                if not c.hidden and not c.never_hidden and not c.dummy 
                and c.task_type == task_type
                and c.displayed_by_default  # デフォルトの表示状態を維持
            ])
        else:
            if action == "all":
                # 全選択
                results.append([
                    c.name
                    for c in fields(AutoEvalColumn)
                    if not c.hidden and not c.never_hidden and not c.dummy and c.task_type == task_type
                ])
            elif action == "none":
                # 全解除
                results.append([])
            elif action == "avg_only":
                # AVGのみ
                results.append([
                    c.name
                    for c in fields(AutoEvalColumn)
                    if not c.hidden and not c.never_hidden 
                    and c.task_type == task_type
                    and (
                        (task_type == TaskType.AVG) or
                        (task_type != TaskType.AVG and c.average)
                    )
                ])
    return results

# Submission demo

with gr.Blocks() as demo_submission:
    with gr.Column():
        with gr.Row():
            evaluation_queue_text = gr.Markdown(EVALUATION_QUEUE_TEXT_JA, elem_classes="markdown-text")

        with gr.Column():
            with gr.Accordion(
                f"✅ Finished Evaluations ({len(finished_eval_queue_df)})",
                open=False,
            ):
                with gr.Row():
                    finished_eval_table = gr.Dataframe(
                        value=finished_eval_queue_df,
                        headers=EVAL_COLS,
                        datatype=EVAL_TYPES,
                        row_count=5,
                    )
            with gr.Accordion(
                f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})",
                open=False,
            ):
                with gr.Row():
                    running_eval_table = gr.Dataframe(
                        value=running_eval_queue_df,
                        headers=EVAL_COLS,
                        datatype=EVAL_TYPES,
                        row_count=5,
                    )

            with gr.Accordion(
                f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})",
                open=False,
            ):
                with gr.Row():
                    pending_eval_table = gr.Dataframe(
                        value=pending_eval_queue_df,
                        headers=EVAL_COLS,
                        datatype=EVAL_TYPES,
                        row_count=5,
                    )
            with gr.Accordion(
                f"❎ Failed Evaluation Queue ({len(failed_eval_queue_df)})",
                open=False,
            ):
                with gr.Row():
                    failed_eval_table = gr.Dataframe(
                        value=failed_eval_queue_df,
                        headers=EVAL_COLS,
                        datatype=EVAL_TYPES,
                        row_count=5,
                    )
    with gr.Row():
        gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")

    with gr.Row():
        with gr.Column():
            model_name_textbox = gr.Textbox(label="Model name")
            revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
            model_type = gr.Dropdown(
                label="Model type",
                choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
                multiselect=False,
                value=None,
            )

        with gr.Column():
            precision = gr.Dropdown(
                label="Precision",
                choices=[i.value.name for i in Precision if i != Precision.Unknown],
                multiselect=False,
                value="float16",
            )
            add_special_tokens = gr.Dropdown(
                label="AddSpecialTokens",
                choices=[i.value.name for i in AddSpecialTokens if i != AddSpecialTokens.Unknown],
                multiselect=False,
                value="False",
            )

    submit_button = gr.Button("Submit Eval")
    submission_result = gr.Markdown()
    submit_button.click(
        fn=add_new_eval,
        inputs=[
            model_name_textbox,
            revision_name_textbox,
            precision,
            model_type,
            add_special_tokens,
        ],
        outputs=submission_result,
    )


# Main demo


def set_default_language(request: gr.Request) -> gr.Radio:
    if request.headers["Accept-Language"].split(",")[0].lower().startswith("ja"):
        return gr.Radio(value="🇯🇵 JA")
    else:
        return gr.Radio(value="🇺🇸 EN")


def update_language(language: str) -> tuple[gr.Markdown, gr.Markdown, gr.Markdown, gr.Textbox]:
    if language == "🇯🇵 JA":
        return (
            gr.Markdown(value=INTRODUCTION_TEXT_JA),
            gr.Markdown(value=LLM_BENCHMARKS_TEXT_JA),
            gr.Markdown(value=EVALUATION_QUEUE_TEXT_JA),
            gr.Textbox(label=CITATION_BUTTON_LABEL_JA),
        )
    else:
        return (
            gr.Markdown(value=INTRODUCTION_TEXT),
            gr.Markdown(value=LLM_BENCHMARKS_TEXT),
            gr.Markdown(value=EVALUATION_QUEUE_TEXT),
            gr.Textbox(label=CITATION_BUTTON_LABEL),
        )


with gr.Blocks(css_paths="style.css", theme=gr.themes.Glass()) as demo:
    gr.HTML(TITLE)
    introduction_text = gr.Markdown(INTRODUCTION_TEXT_JA, elem_classes="markdown-text")

    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
            demo_leaderboard.render()

        with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
            llm_benchmarks_text = gr.Markdown(LLM_BENCHMARKS_TEXT_JA, elem_classes="markdown-text")

        with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3):
            demo_submission.render()

    with gr.Row():
        with gr.Accordion("📙 Citation", open=False):
            citation_button = gr.Textbox(
                label=CITATION_BUTTON_LABEL_JA,
                value=CITATION_BUTTON_TEXT,
                lines=20,
                elem_id="citation-button",
                show_copy_button=True,
            )
    gr.HTML(BOTTOM_LOGO)

    language = gr.Radio(
        choices=["🇯🇵 JA", "🇺🇸 EN"],
        value="🇯🇵 JA",
        elem_classes="language-selector",
        show_label=False,
        container=False,
    )

    demo.load(fn=set_default_language, outputs=language)
    language.change(
        fn=update_language,
        inputs=language,
        outputs=[
            introduction_text,
            llm_benchmarks_text,
            evaluation_queue_text,
            citation_button,
        ],
        api_name=False,
    )

if __name__ == "__main__":
    if os.getenv("SPACE_ID"):
        scheduler = BackgroundScheduler()
        scheduler.add_job(restart_space, "interval", seconds=1800)
        scheduler.start()
    demo.queue(default_concurrency_limit=40).launch()