import json
import os

import pandas as pd

from src.display.formatting import has_no_nan_values, make_clickable_model
from src.display.utils import AutoEvalColumn, EvalQueueColumn
from src.leaderboard.read_evals import get_raw_eval_results, AUTO_EVAL_FIELDS


def get_leaderboard_df(
    results_path: str,
    requests_path: str,
    cols: list,
    benchmark_cols: list,
) -> pd.DataFrame:
    """
    Build leaderboard DataFrame from eval JSONs.

    Defensive behavior:
    - Never raises KeyError due to missing columns
    - Ensures benchmark columns always exist
    - Keeps benchmark columns available for downstream analytics
    """

    print(">>> ENTERING get_leaderboard_df")

    raw_data = get_raw_eval_results(results_path, requests_path)

    print("Raw eval results length:", len(raw_data))
    print("Sample raw data:", raw_data[0] if raw_data else "empty")

    if not raw_data:
        # Return empty dataframe with expected structure
        base_cols = list(dict.fromkeys(cols + benchmark_cols))
        return pd.DataFrame(columns=base_cols)

    # Convert objects to dictionaries
    records = []
    for r in raw_data:
        try:
            records.append(r.to_dict())
        except Exception:
            continue

    if not records:
        base_cols = list(dict.fromkeys(cols + benchmark_cols))
        return pd.DataFrame(columns=base_cols)

    df = pd.DataFrame.from_records(records)

    print("RAW RECORD COUNT:", len(records))
    print("DF SHAPE AFTER CREATION:", df.shape)
    print("DF COLUMNS AFTER CREATION:", df.columns.tolist())

    # Ensure IS_FS exists for UI compatibility
    if "IS_FS" not in df.columns:
        if AUTO_EVAL_FIELDS.get("is_5fewshot") in df.columns:
            df["IS_FS"] = df[AUTO_EVAL_FIELDS["is_5fewshot"]]
        elif "is_5fewshot" in df.columns:
            df["IS_FS"] = df["is_5fewshot"]
        else:
            df["IS_FS"] = False

    # Ensure #Params (B) exists for chart
    if 'params' in df.columns:
        df['#Params (B)'] = df['params']
    else:
        df['#Params (B)'] = float('nan')

    print("------")
    print(df['#Params (B)'])

    # Ensure display name for average performance exists
    if 'average' in df.columns:
        df['Avg. Comb. Perf. ⬆️'] = df['average']
    else:
        df['Avg. Comb. Perf. ⬆️'] = float('nan')

    # Ensure display column 'Model' exists
    if 'model' in df.columns:
        df['Model'] = df['model']
    else:
        df['Model'] = ''

    # ------------------------------------------------------------------
    # Ensure ALL benchmark columns exist (even if missing in JSON files)
    # ------------------------------------------------------------------
    for bcol in benchmark_cols:
        if bcol not in df.columns:
            df[bcol] = float("nan")

    # ------------------------------------------------------------------
    # Ensure requested display columns exist
    # ------------------------------------------------------------------
    for col in cols:
        if col not in df.columns:
            df[col] = float("nan")

    # ------------------------------------------------------------------
    # Sort by average if available
    # ------------------------------------------------------------------
    avg_col = AUTO_EVAL_FIELDS.get("average", "average")
    if avg_col in df.columns:
        df = df.sort_values(by=avg_col, ascending=False)

    # ------------------------------------------------------------------
    # Filter rows only if benchmark columns actually exist
    # ------------------------------------------------------------------
    existing_benchmarks = [c for c in benchmark_cols if c in df.columns]
    # if existing_benchmarks:
    #     try:
    #         mask = has_no_nan_values(df, existing_benchmarks)
    #         df = df[mask]
    #     except Exception:
    #         pass
    
    print("DF shape after loading JSONs, before benchmark filtering:", df.shape)
    print("DF columns:", df.columns.tolist())
    print("Non-null REL count:", df['REL'].notna().sum() if 'REL' in df else "REL missing")

    # ------------------------------------------------------------------
    # Build final column order (keep benchmarks for analytics)
    # ------------------------------------------------------------------

    final_cols = list(dict.fromkeys(
        cols + benchmark_cols +
        ['Rank', 'Size', 'LANG', 'IS_FS', '#Params (B)', 'Avg. Comb. Perf. ⬆️', 'Model']
    ))
    final_cols = [c for c in final_cols if c in df.columns]
    df = df[final_cols]

    # ------------------------------------------------------------------
    # Round numeric columns safely
    # ------------------------------------------------------------------
    for c in df.columns:
        if pd.api.types.is_numeric_dtype(df[c]):
            df[c] = df[c].round(2)

    df = df.reset_index(drop=True)

    return df


def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
    """Creates the different dataframes for the evaluation queues requestes"""
    entries = [entry for entry in os.listdir(save_path) if not entry.startswith(".")]
    all_evals = []

    for entry in entries:
        if ".json" in entry:
            file_path = os.path.join(save_path, entry)
            with open(file_path) as fp:
                data = json.load(fp)

            data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
            data[EvalQueueColumn.revision.name] = data.get("revision", "main")

            all_evals.append(data)
        elif ".md" not in entry:
            # this is a folder
            sub_entries = [e for e in os.listdir(f"{save_path}/{entry}") if os.path.isfile(e) and not e.startswith(".")]
            for sub_entry in sub_entries:
                file_path = os.path.join(save_path, entry, sub_entry)
                with open(file_path) as fp:
                    data = json.load(fp)

                data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
                data[EvalQueueColumn.revision.name] = data.get("revision", "main")
                all_evals.append(data)

    pending_list = [e for e in all_evals if e["status"] in ["PENDING", "RERUN"]]
    running_list = [e for e in all_evals if e["status"] == "RUNNING"]
    finished_list = [e for e in all_evals if e["status"].startswith("FINISHED") or e["status"] == "PENDING_NEW_EVAL"]
    df_pending = pd.DataFrame.from_records(pending_list, columns=cols)
    df_running = pd.DataFrame.from_records(running_list, columns=cols)
    df_finished = pd.DataFrame.from_records(finished_list, columns=cols)
    return df_finished[cols], df_running[cols], df_pending[cols]