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, AutoEvalColumnMultimodal, EvalQueueColumn
from src.leaderboard.read_evals import get_raw_eval_results, get_raw_eval_results_mib


def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
    """Creates a dataframe from all the individual experiment results"""
    print(f"results_path is {results_path}, requests_path is {requests_path}")
    raw_data = get_raw_eval_results(results_path, requests_path)
    print(f"raw_data is {raw_data}")
    all_data_json = [v.to_dict() for v in raw_data]
    print(f"all_data_json is {pd.DataFrame.from_records(all_data_json)}")
    all_data_json_filtered = []
    for item in all_data_json:
        item["Track"] = item["eval_name"].split("_")[-1]
        item["ioi"] = 0
        item["mcqa"] = 0
        if "VQA" in benchmark_cols and "VQA" in item:
            all_data_json_filtered.append(item)
        if "VQA" not in benchmark_cols and "VQA" not in item:
            all_data_json_filtered.append(item)
            
    all_data_json = all_data_json_filtered

    df = pd.DataFrame.from_records(all_data_json)
    df = df.sort_values(by=[AutoEvalColumn.text_average.name], ascending=False)
    # df = df.sort_values(by=[Tasks.task0.value.col_name], ascending=False)
    # df = df.sort_values(by=[AutoEvalColumn.track.name], ascending=False)
    
    print(f"df is {df}")
    
    # df = df[cols].round(decimals=1)

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



# def get_leaderboard_df_mib(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
#     """Creates a dataframe from all the individual experiment results"""
#     print(f"results_path is {results_path}, requests_path is {requests_path}")
#     raw_data = get_raw_eval_results(results_path, requests_path)
#     print(f"raw_data is {raw_data}")
#     all_data_json = [v.to_dict() for v in raw_data]
#     print(f"all_data_json is {pd.DataFrame.from_records(all_data_json)}")
#     all_data_json_filtered = []
#     for item in all_data_json:
#         item["Track"] = item["eval_name"].split("_")[-1]
#         if "VQA" in benchmark_cols and "VQA" in item:
#             all_data_json_filtered.append(item)
#         if "VQA" not in benchmark_cols and "VQA" not in item:
#             all_data_json_filtered.append(item)
#         all_data_json_filtered.append(item)
        
#     all_data_json = all_data_json_filtered

#     df = pd.DataFrame.from_records(all_data_json)
#     df = df.sort_values(by=[AutoEvalColumn.text_average.name], ascending=False)
    
#     print(f"df is {df}")
    
#     df = df[cols].round(decimals=1)

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

def get_leaderboard_df_mib(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
    """Creates a dataframe from all the MIB experiment results"""
    print(f"results_path is {results_path}, requests_path is {requests_path}")
    raw_data = get_raw_eval_results_mib(results_path, requests_path)
    print(f"raw_data is {raw_data}")
    
    # Convert each result to dict format
    all_data_json = [v.to_dict() for v in raw_data]
    print(f"all_data_json is {pd.DataFrame.from_records(all_data_json)}")

    # Convert to dataframe
    df = pd.DataFrame.from_records(all_data_json)
    
    # Sort by Average score descending
    if 'Average' in df.columns:
        # Convert '-' to NaN for sorting purposes
        df['Average'] = pd.to_numeric(df['Average'], errors='coerce')
        df = df.sort_values(by=['Average'], ascending=False, na_position='last')
        # Convert NaN back to '-'
        df['Average'] = df['Average'].fillna('-')
    
    return df


def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
    """Creates the different dataframes for the evaluation queues requests"""
    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)

            if "still_on_hub" in data and data["still_on_hub"]:
                data[EvalQueueColumn.model.name] = make_clickable_model(data["hf_repo"], data["model"])
                data[EvalQueueColumn.revision.name] = data.get("revision", "main")
            else:
                data[EvalQueueColumn.model.name] = data["model"]
                data[EvalQueueColumn.revision.name] = "N/A"

            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]