import os
import re
import time
import argparse

from tqdm import tqdm

import sys
sys.path.append('../')
from utilities import *

# OpenAI
import openai

# load demo prompt
from prompts.ext_ans import demo_prompt


def verify_extraction(extraction):
    extraction = extraction.strip()
    if extraction == "" or extraction == None:
        return False
    return True


def create_test_prompt(demo_prompt, query, response):
    demo_prompt = demo_prompt.strip()
    test_prompt = f"{query}\n\n{response}"
    full_prompt = f"{demo_prompt}\n\n{test_prompt}\n\nExtracted answer: "
    return full_prompt


def extract_answer(response, problem, quick_extract=False):
    question_type = problem['question_type']
    answer_type = problem['answer_type']
    choices = problem['choices']
    query = problem['query']
    pid = problem['pid']

    if response == "":
        return ""
    
    if question_type == 'multi_choice' and response in choices:
        return response
    
    if answer_type == "integer":
        try:
            extraction = int(response)
            return str(extraction)
        except:
            pass

    if answer_type == "float":
        try:
            extraction = str(float(response))
            return extraction
        except:
            pass

    # quick extraction
    if quick_extract:
        print("Quickly extracting answer...")
        # The answer is "text". -> "text"
        try:
            result = re.search(r'The answer is "(.*)"\.', response)
            if result:
                extraction = result.group(1)
                return extraction
        except:
            pass

    # general extraction
    try:
        full_prompt = create_test_prompt(demo_prompt, query, response)
        extraction = get_chat_response(full_prompt, openai.api_key, openai.api_base, model=args.llm_engine)
        return extraction
    except Exception as e:
        print(e)
        print(f"Error in extracting answer for {pid}")

    return ""


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    # input
    parser.add_argument('--output_file', type=str, default='answer.json')
    parser.add_argument('--response_label', type=str, default='response', help='response label for the input file')
    # model
    parser.add_argument('--llm_engine', type=str, default='gpt-4-0613', help='llm engine',
                        choices = ['gpt-3.5-turbo', 'gpt-3.5', 'gpt-4', 'gpt-4-0314', 'gpt-4-0613'])
    parser.add_argument('--number', type=int, default=-1, help='number of problems to run')
    parser.add_argument('--quick_extract', action='store_true', help='use rules to extract answer for some problems')
    parser.add_argument('--rerun', action='store_true', help='rerun the answer extraction')
    # openai
    parser.add_argument("--api_key", required=True, type=str, help="OpenAI API key")
    parser.add_argument("--api_base", default=None, type=str, help="OpenAI API base")
    # output
    parser.add_argument('--save_every', type=int, default=10, help='save every n problems')
    parser.add_argument('--output_label', type=str, default='', help='label for the output file')
    args = parser.parse_args()

    # args
    label = args.response_label
    result_file = args.output_file
    if args.output_label != '':
        output_file = result_file.replace('.json', f'_{args.output_label}.json')
    else:
        output_file = result_file

    # read results
    print(f"Reading {result_file}...")
    try:
        results = read_json(output_file)
    except:
        samples = [json.loads(line) for line in open(result_file)]
        results = {}
        for sample in samples:
            results[sample['pid']] = sample

    # full pids
    full_pids = list(results.keys())
    if args.number > 0:
        full_pids = full_pids[:min(args.number, len(full_pids))]
    print("Number of testing problems:", len(full_pids))

    # test pids
    if args.rerun:
        test_pids = full_pids
    else:
        test_pids = []
        for pid in full_pids:
            # print(pid)
            if 'extraction' not in results[pid] or not verify_extraction(results[pid]['extraction']):
                test_pids.append(pid)
    
    test_num = len(test_pids)
    print("Number of problems to run:", test_num)
    # print(test_pids)

    # openai api
    openai.api_key = args.api_key # Your API key here
    if args.api_base:
        openai.api_base = args.api_base # Your API base here

    # tqdm, enumerate results
    for i, pid in enumerate(tqdm(test_pids)):
        problem = results[pid]

        assert label in problem
        response = problem[label]       

        
        extraction  = extract_answer(response, problem, args.quick_extract)
        results[pid]['extraction'] = extraction

        if i % args.save_every == 0 or i == test_num - 1:
            print(f"Saving results to {output_file}...")
            save_json(results, output_file)
            print(f"Results saved.")