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
from src.about import Tasks, N_Tasks, Detail_Tasks


def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list, version="1_correct") -> pd.DataFrame:
    """Creates a dataframe from all the individual experiment results"""
    cols = cols.copy()
    raw_data = get_raw_eval_results(results_path+"/"+version, requests_path)
    print(raw_data)
    
    tasks = list(N_Tasks) + list(Detail_Tasks) if "n_" in version else list(Tasks)
    if version == "1_correct_var":
        tasks = [t for t in tasks if t.value.col_name != "VCR"]
    all_data_json = [v.to_dict(tasks) for v in raw_data]

    print(all_data_json)

    df = pd.DataFrame.from_records(all_data_json)
    df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
    
    print(cols)
    if version != "1_correct":
        cols.remove("VCR")
        if version != "1_correct_var":
            benchmark_cols.remove("VCR")
    if version != "n_correct":
        for task in Detail_Tasks:
            cols.remove(task.value.col_name)
    print(df)
    print(cols)
    df = df[cols].round(decimals=2)

    # filter out if any of the benchmarks have not been produced
    df = df[has_no_nan_values(df, benchmark_cols)]
    return df


def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
    """Creates the different dataframes for the evaluation queues requestes"""
    version = ["1_correct",] #  "1_correct_var", "n_correct"]
    entries = [os.path.join(v, entry) for v in version for entry in os.listdir(os.path.join(save_path, v)) if not entry.startswith(".")]
    print(entries)
    all_evals = []

    for entry in entries:
        if not ".txt" in entry:
            file_path = os.path.join(save_path, entry, "eval_request.json")
            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")
            data[EvalQueueColumn.output_format.name] = data.get("output_format")
            data[EvalQueueColumn.dataset_version.name] = file_path.split("/")[-3]

            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]