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_TEXT,
    EVALUATION_QUEUE_TEXT,
    INTRODUCTION_TEXT,
    LLM_BENCHMARKS_TEXT,
    TITLE,
)
from src.display.css_html_js import custom_css
from src.display.utils import (
    BENCHMARK_COLS,
    COLS,
    EVAL_COLS,
    EVAL_TYPES,
    NUMERIC_INTERVALS,
    TYPES,
    AddSpecialTokens,
    AutoEvalColumn,
    ModelType,
    NumFewShots,
    Precision,
    WeightType,
    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,
    show_deleted: bool,
    show_merges: bool,
    show_flagged: bool,
) -> 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 + ["Unknown", "?"])]
    print(f"After precision filter: {filtered_df.shape}")

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

    # Show deleted models フィルタリング
    if not show_deleted:
        filtered_df = filtered_df[filtered_df["Available on the hub"]]
    print(f"After show_deleted 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):
    """Added by Abishek"""
    final_df = []
    if query != "":
        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)

    # 'Model' カラムにリンクを含む形式で再構築
    if "Model" in df.columns:
        df["Model"] = df["Model"].apply(
            lambda x: (
                f'[{x.split(">")[-2].split("<")[0]}]({x.split("href=")[1].split(chr(34))[1]})'
                if isinstance(x, str) and "href=" in x
                else x
            )
        )

    # 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,
    columns: list,
    type_query: list,
    precision_query: str,
    size_query: list,
    add_special_tokens_query: list,
    num_few_shots_query: list,
    show_deleted: bool,
    show_merges: bool,
    show_flagged: bool,
    query: str,
):
    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,
        show_deleted,
        show_merges,
        show_flagged,
    )
    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],
    False,
    False,
    False,
)

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],
    False,
    False,
    False,
)

# 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)

# Model列のリンク形式を修正
leaderboard_df_filtered["Model"] = leaderboard_df_filtered["Model"].apply(
    lambda x: (
        f'[{x.split(">")[-2].split("<")[0]}]({x.split("href=")[1].split(chr(34))[1]})'
        if isinstance(x, str) and "href=" in x
        else x
    )
)

# 数値データを文字列に変換
for col in leaderboard_df_filtered.columns:
    if col not in ["T", "Model"]:
        leaderboard_df_filtered[col] = leaderboard_df_filtered[col].astype(str)

# Leaderboard demo

with gr.Blocks() as demo_leaderboard:
    with gr.Row():
        with gr.Column():
            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 = gr.CheckboxGroup(
                    label="Select columns to show",
                    choices=[
                        c.name
                        for c in fields(AutoEvalColumn)
                        if not c.hidden and not c.never_hidden  # and not c.dummy
                    ],
                    value=[
                        c.name
                        for c in fields(AutoEvalColumn)
                        if c.displayed_by_default and not c.hidden and not c.never_hidden
                    ],
                    elem_id="column-select",
                )
            with gr.Row():
                deleted_models_visibility = gr.Checkbox(label="Show private/deleted models", value=False)
                merged_models_visibility = gr.Checkbox(label="Show merges", value=False)
                flagged_models_visibility = gr.Checkbox(label="Show flagged models", value=False)
        with gr.Column(min_width=320):
            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",
            )

    # 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,
            shown_columns.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,
            deleted_models_visibility.change,
            merged_models_visibility.change,
            flagged_models_visibility.change,
            search_bar.submit,
        ],
        fn=update_table,
        inputs=[
            hidden_leaderboard_table_for_search,
            shown_columns,
            filter_columns_type,
            filter_columns_precision,
            filter_columns_size,
            filter_columns_add_special_tokens,
            filter_columns_num_few_shots,
            deleted_models_visibility,
            merged_models_visibility,
            flagged_models_visibility,
            search_bar,
        ],
        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])


# Submission demo

with gr.Blocks() as demo_submission:
    with gr.Column():
        with gr.Row():
            gr.Markdown(EVALUATION_QUEUE_TEXT, 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",
            )
            weight_type = gr.Dropdown(
                label="Weights type",
                choices=[i.value.name for i in WeightType],
                multiselect=False,
                value="Original",
            )
            base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")
            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(
        add_new_eval,
        [
            model_name_textbox,
            base_model_name_textbox,
            revision_name_textbox,
            precision,
            weight_type,
            model_type,
            add_special_tokens,
        ],
        submission_result,
    )

# Main demo

with gr.Blocks(css=custom_css) as demo:
    gr.HTML(TITLE)
    gr.Markdown(INTRODUCTION_TEXT, 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):
            gr.Markdown(LLM_BENCHMARKS_TEXT, 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,
                value=CITATION_BUTTON_TEXT,
                lines=20,
                elem_id="citation-button",
                show_copy_button=True,
            )
    gr.HTML(BOTTOM_LOGO)


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()