from datasets import load_dataset, Dataset
import os 
from datasets import load_dataset
from datasets.utils.logging import disable_progress_bar
from constants import column_names, RANKING_COLUMN, ORDERED_COLUMN_NAMES
from utils_display import make_clickable_model

import random 
disable_progress_bar()
import math 
import json 
from tqdm import tqdm
import numpy as np

id_to_data = None 
model_len_info = None 
bench_data = None 
eval_results = None 
score_eval_results = None  

# Formats the columns
def formatter(x):
    if type(x) is str:
        x = x
    else: 
        x = round(x, 1)
    return x
 

def post_processing(df, column_names, rank_column=RANKING_COLUMN, ordered_columns=ORDERED_COLUMN_NAMES, click_url=True):
    for col in df.columns:
        if col == "Model" and click_url:
            df[col] = df[col].apply(lambda x: x.replace(x, make_clickable_model(x)))
        else:
            df[col] = df[col].apply(formatter) # For numerical values  
 
    df.rename(columns=column_names, inplace=True)
    list_columns = [col for col in ordered_columns if col in df.columns]
    df = df[list_columns]
    if rank_column in df.columns:
        df.sort_values(by=rank_column, inplace=True, ascending=False)
    return df