import os
from tqdm import tqdm
import json
import os
import openai
from tqdm import tqdm
import argparse
import multiprocessing 
from copy import deepcopy
from functools import partial

prompt_library = {
	"MCQ": "In this problem, only one option will be correct. Give a detailed solution and end the solution with the final answer.",
	"MCQ(multiple)": "In this problem, multiple options can be correct. Give a detailed solution and end the solution with the final answer.", 
	"Integer": "In this problem, the final answer will be a non-negative integer. Give a detailed solution and end the solution with the final answer.",
	"Numeric": "In this problem, the final will be a numeric value. Give the numerical answer correct upto the 2nd decimal digit. Give a detailed solution and end the solution with the final answer.",
}

few_shot_examples = json.load(open('data/few_shot_examples.json'))


def write_in_file(response_file, response_dict, question, mode, model_nickname):
	if os.path.exists(response_file):
		with open(response_file, 'r') as infile:
			responses = json.load(infile)
	else:
		responses = []

	found = False
	for i, old_resp in enumerate(responses):
		if old_resp['description'] == question['description'] and old_resp['index'] == question['index']:
			responses[i][f"{model_nickname}_{mode}_response" ] = response_dict[f"{model_nickname}_{mode}_response"]
			found = True
			break

	if not found:
		responses.append(response_dict)
		
	json.dump(sorted(responses, key=lambda elem: (elem['description'], elem['index'])), open(response_file, 'w'), indent=4)
	print(f"####UPDATED {response_file}, Current size : {len(responses)}####")


def get_response(question,model, model_nickname, mode, response_file, lock):

	response_dict = deepcopy(question)
	prefix_prompt = prompt_library[question['type']]
	suffix_prompt = ""

	if mode in ['CoT', 'CoT+SC', 'CoT+Exam'] :
		suffix_prompt = "Let's think step by step.\n"

	ques = question["question"]
	stripped_ques = ques.replace("\n\n", "\n").strip()
	if mode in ['CoT+OneShot', 'CoT', 'CoT+SC', 'CoT+Exam']:
		if mode == 'CoT+Exam':
			if response_dict['type'] in ['MCQ', 'MCQ(multiple)']:
				if response_dict['type'] == 'MCQ':
					exam_prompt = "If the answer is wrong, you'll be given -1 marks. If the answer is correct, you'll be given +3 marks. If you're unsure of the answer, you can skip the question, and you'll be given 0 marks."
				else:
					exam_prompt = "If any of the options in the final answer is wrong, you'll be given -2 marks. If all the options are correct, you'll be given +4 marks. If some of the options are correct, you'll be given +1 for each correct option. If you're unsure of the answer, you can skip the question, and you'll be given 0 marks."
				prompt = prefix_prompt + " " + exam_prompt + "\n\n" + "Problem: " + stripped_ques + "\nSolution: " + suffix_prompt
			else:
				print("No point doing this for Numeric/Integer questions since there is no negative marking...")
				breakpoint()
		else:
			if mode == 'CoT+OneShot':
				ex = few_shot_examples[question['subject']][question['type']]
				prompt = prefix_prompt + "\n\n" + "Problem: " + ex['problem'] + "\nSolution: " + ex['solution'] + "\n\n" + "Problem: " + stripped_ques + "\nSolution: "
			else:
				prompt = prefix_prompt + "\n\n" + "Problem: " + stripped_ques + "\nSolution: " + suffix_prompt
	else:
		prompt = prefix_prompt + "\n\n" + "Problem: " + stripped_ques + suffix_prompt
	prompt = prompt.strip()
	response_dict[f"prompt"] = prompt
	num_retries = 0 
	print(f'Question: {question["description"]}, Index: {question["index"]}, Model: {model_nickname}, Mode: {mode}, query begins')
	
	while True:
		try:
			if model in ["text-davinci-003", "text-davinci-002", 'davinci-002']:
				response = openai.Completion.create(
					model=model,
					prompt=prompt,
					max_tokens=2048,
					temperature=0 if mode in ['CoT', 'normal', 'CoT+Exam'] else 0.5,
					n=1 if mode in ['CoT', 'normal', 'CoT+Exam'] else 3
				)
			else:
				response = openai.ChatCompletion.create(
					model=model,
					messages=[
						{"role": "system", "content": ""},
						{"role": "user", "content": prompt}
					],
					max_tokens=2048,
					temperature=0 if mode in ['CoT+OneShot', 'CoT', 'normal', 'CoT+Exam'] else 0.5,
					n=1 if mode in ['CoT+OneShot', 'CoT', 'normal', 'CoT+Exam'] else 8
				)
			
			lock.acquire()
			response_dict[f"{model_nickname}_{mode}_response"] = response
			write_in_file(response_file, response_dict, question, mode, model_nickname)
			lock.release()
			break
		
		except Exception as e:
			num_retries += 1
			print("Failure!", e)
	return 

def main():
	'''
	The code can restart from the already done questions in case there is a failure midpoint.
	'''
	args = argparse.ArgumentParser()
	args.add_argument('--model', default='gpt-3.5-turbo')
	args.add_argument('--data', default='data/dataset.json')
	args.add_argument('--mode', default='normal')
	args.add_argument('--num_procs', default=1, type=int)
	args.add_argument('--max_questions', default=1, type=int)
	args = args.parse_args()

	openai.organization = os.getenv("OPENAI_ORG")
	openai.api_key = os.getenv("OPENAI_API_KEY")
	
	model_nickname = {
		"davinci-002": "davinci-002",
		"text-davinci-003": "GPT3",
		"gpt-3.5-turbo": "GPT3.5",
		"gpt-4-0613": "GPT4_0613",
		"gpt-4-0314": "GPT4"
	}
	assert args.model in model_nickname.keys()
	assert args.mode in ['normal', 'CoT', 'CoT+OneShot', 'CoT+Exam', 'CoT+SC']
	
	out_file_dir = f'responses/{model_nickname[args.model]}_{args.mode}_responses'
	out_file = os.path.join(out_file_dir, 'responses.json')
	questions = json.load(open(args.data))

	rem_ques = []
	
	if os.path.exists(out_file):

		for question in tqdm(questions[:args.max_questions]):
			if os.path.exists(out_file):
				with open(out_file, 'r') as infile:
					responses = json.load(infile)
					found = False

					for i, old_resp in enumerate(responses):
						if question['type'] in ['Numeric', 'Integer'] and args.mode == 'CoT+Exam':
							found = True
						if old_resp['description'] == question['description'] and old_resp['index'] == question['index']:
							
							found = all([old_resp.get(
								f"{model_nickname[args.model]}_{args.mode}_response", False) for model in [args.model]])
					if found:
						print("This question has already been done")
					else:
						rem_ques.append(question)
	else:
		os.makedirs(out_file_dir, exist_ok=True)
		if args.mode == 'CoT+Exam':
			rem_ques = []
			for q in questions:
				if q['type'] in ['MCQ', 'MCQ(multiple)']:
					rem_ques.append(q)
		else:
			rem_ques = questions[:args.max_questions]
	print(f"There are {len(rem_ques)} problems remaining")
	
	manager = multiprocessing.Manager()
	lock = manager.Lock()
	pool = multiprocessing.Pool(args.num_procs)
	f = partial(get_response, model=args.model, model_nickname=model_nickname[args.model], mode=args.mode, response_file=out_file, lock=lock)
	pool.map(f, rem_ques)

if __name__ == '__main__':
	main()