from bert_score import BERTScorer
import torch
import json
import argparse
import numpy as np
from scipy.stats import ks_2samp, mannwhitneyu, anderson_ksamp
import matplotlib.pyplot as plt
import re
import os
import pandas as pd

def load_jsonl(file_path):
    data = []
    with open(file_path, 'r') as file:
        for line in file:
            data.append(json.loads(line.strip()))
    return data

def dump_txt(data, file_path):
    with open(file_path, 'w') as file:
        file.write(str(data) + '\n')

def compare_distributions(sample1, sample2):
    # Kolmogorov-Smirnov Test
    ks_stat, ks_p_value = ks_2samp(sample1, sample2)
    print(f"Kolmogorov-Smirnov test statistic: {ks_stat}, p-value: {ks_p_value}")
    if ks_p_value < 0.05:
        print("Kolmogorov-Smirnov test: The two samples likely come from different distributions.")
    else:
        print("Kolmogorov-Smirnov test: The two samples likely come from the same distribution.")
    
    # Mann-Whitney U Test
    mw_stat, mw_p_value = mannwhitneyu(sample1, sample2, alternative='two-sided')
    print(f"Mann-Whitney U test statistic: {mw_stat}, p-value: {mw_p_value}")
    if mw_p_value < 0.05:
        print("Mann-Whitney U test: The two samples likely come from different distributions.")
    else:
        print("Mann-Whitney U test: The two samples likely come from the same distribution.")
    
    # Anderson-Darling Test
    ad_stat, critical_values, ad_significance_level = anderson_ksamp([sample1, sample2])
    print(f"Anderson-Darling test statistic: {ad_stat}, significance level: {ad_significance_level}")
    if ad_stat > critical_values[2]:  # Using 5% significance level
        print("Anderson-Darling test: The two samples likely come from different distributions.")
    else:
        print("Anderson-Darling test: The two samples likely come from the same distribution.")

    return ks_p_value, mw_p_value, ad_stat, critical_values[2]


def get_num_from_directory(directory_path):


    # List to store the extracted numbers
    numbers = []
    
    # Iterate over each file/directory in the specified path
    for filename in os.listdir(directory_path):
        # Use regex to find numbers in the filename
        match = re.search(r'checkpoint-(\d+)', filename)
        if match:
            # Append the extracted number to the list as an integer
            numbers.append(int(match.group(1)))

    return numbers



parser = argparse.ArgumentParser()
parser.add_argument('--model', type=str, default='160m',help='model name') #160m 410m 1b 1.4b 2.8b 6.9b 12b
parser.add_argument('--epoch', type=int, default=9,help='model name')
parser.add_argument('--size', type=int, default=600,help='model name')
parser.add_argument('--subname', type=str, default='arxiv', help='subset name')
parser.add_argument('--lr', type=float, default=2e-5, help='learning rate')
parser.add_argument('--temp', type=float, default=0.0, help='generation temperature')
parser.add_argument('--topp', type=float, default=1.0, help='generation top_p')
parser.add_argument('--logging', type=str, default='', help='logging of the file')
parser.add_argument('--i', type=float, default=0.0, help='perturbation rate')

args = parser.parse_args()

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
bert_scorer = BERTScorer('roberta-large', device=device, rescale_with_baseline=True, lang='en')
results_dict = {}
for candidate in ['member', 'nonmember']:
    model_name = f'pythia-{args.model}'
    log_str = f'{candidate}-{args.model}-epoch-{args.epoch}'
    response_orig = load_jsonl(f'/workspace/copyright/{model_name}_responses_ft_more_layers_{args.subname}_epoch_{args.epoch}_{args.logging}/{model_name}-{log_str}-pile-full-{args.size}-subsets-{args.subname}-{args.lr}-orig.jsonl')
    response_ft = load_jsonl(f'/workspace/copyright/{model_name}_responses_ft_more_layers_{args.subname}_epoch_{args.epoch}_{args.logging}/{model_name}-{log_str}-pile-full-{args.size}-subsets-{args.subname}-{args.lr}-perturb-{args.i}-ft.jsonl')
    
    response_only_orig = []
    response_only_ft = []
    
    for i in range(len(response_orig)):
        response_only_orig.append(response_orig[i]['output_text'])
        response_only_ft.append(response_ft[i]['output_text'])
    
    ctc_scores = bert_scorer.score(response_only_ft, response_only_orig)[2]
    
    results_dict[candidate]=ctc_scores

ks_p_value, mw_p_value, ad_stat, adcv=compare_distributions(results_dict['member'], results_dict['nonmember'])

os.makedirs(f'bert_results_ft_more_layers_{args.subname}_epoch_{args.epoch}_{args.logging}', exist_ok=True)
file_path=f'/workspace/bert_results_ft_more_layers_{args.subname}_epoch_{args.epoch}_{args.logging}/pile_full_bert_{args.model}_{args.epoch}_{args.subname}_{args.size}_{args.lr}_perturb_{args.i}_test.txt'
txt_info=f'''
Kolmogorov-Smirnov test statistic: p-value: {ks_p_value}
Mann-Whitney U test statistic: p-value: {mw_p_value}
Anderson-Darling test statistic: {ad_stat} critical-value:{adcv}
'''
dump_txt(txt_info, file_path)