import os
import argparse
import pandas as pd
import torch
from evaluators.chatgpt import ChatGPT_Evaluator
from evaluators.chatglm import ChatGLM_Evaluator
from evaluators.chatglm2 import ChatGLM2_Evaluator
from evaluators.chatglm3 import ChatGLM3_Evaluator

import time

from scoring.assessment_engine import AssessmentEngine

choices = ["A", "B", "C", "D"]
device = torch.device("cpu")


def main(args):
    global device
    evaluator_class = None
    if args.cuda_device:
        os.environ["CUDA_VISIBLE_DEVICES"] = args.cuda_device
        device = torch.device("cuda")
    if "turbo" in args.model_name or "gpt-4" in args.model_name:
        evaluator = ChatGPT_Evaluator(
            choices=choices,
            k=args.ntrain,
            api_key=args.openai_key,
            model_name=args.model_name
        )
    elif "chatglm3" in args.model_name:
        if args.finetune:
            fine_tune_model = args.finetune
            evaluator_class = ChatGLM3_Evaluator
        else:
            fine_tune_model = None
        evaluator = ChatGLM3_Evaluator(
            choices=choices,
            k=args.ntrain,
            model_name=args.model_name,
            device=device,
            finetune=fine_tune_model,
            finetune_method=args.finetune_method
        )
    elif "chatglm2" in args.model_name:
        if args.finetune:
            fine_tune_model = args.finetune
            evaluator_class = ChatGLM2_Evaluator
        else:
            fine_tune_model = None
        evaluator = ChatGLM2_Evaluator(
            choices=choices,
            k=args.ntrain,
            model_name=args.model_name,
            device=device,
            finetune=fine_tune_model,
            finetune_method=args.finetune_method
        )
    elif "chatglm" in args.model_name:
        if args.finetune:
            fine_tune_model = args.finetune
            evaluator_class = ChatGLM_Evaluator
        else:
            fine_tune_model = None
        evaluator = ChatGLM_Evaluator(
            choices=choices,
            k=args.ntrain,
            model_name=args.model_name,
            device=device,
            finetune=fine_tune_model,
            finetune_method=args.finetune_method
        )
    else:
        print("Unknown model name")
        return -1

    if not os.path.exists(r"logs"):
        os.mkdir(r"logs")
    run_date = time.strftime('%Y-%m-%d_%H-%M-%S', time.localtime(time.time()))
    if args.finetune:
        fine_tune_model_name = args.finetune
    else:
        fine_tune_model_name = 'original'
    save_result_dir = os.path.join(r"logs", f"{args.model_name}_{fine_tune_model_name}/{run_date}")
    os.makedirs(save_result_dir)

    subject_list = ['computer_architecture', 'car_knowledge', 'car_use', 'car_market']
    subject_list.extend(['car_knowledge_in_train', 'car_use_in_train', 'car_market_in_train'])
    # qa_subject_list = ['car_knowledge', 'car_use', 'car_market']
    qa_subject_list = ['car_market']

    for subject_name in subject_list:
        print("Now testing: " + subject_name)
        # subject_name=args.subject
        val_file_path = os.path.join('data/val', f'{subject_name}_val.csv')
        val_df = pd.read_csv(val_file_path)
        if args.few_shot:
            dev_file_path = os.path.join('data/dev', f'{subject_name}_dev.csv')
            dev_df = pd.read_csv(dev_file_path)
            correct_ratio = evaluator.eval_subject(subject_name, val_df, dev_df, few_shot=args.few_shot,
                                                   save_result_dir=save_result_dir, cot=args.cot)
        else:
            correct_ratio = evaluator.eval_subject(subject_name, val_df, few_shot=args.few_shot,
                                                   save_result_dir=save_result_dir)
        print("Acc:", correct_ratio)

    result_list = []

    # for subject_name in qa_subject_list:
    #     print("Now testing: " + subject_name)
    #     qa_file_path = os.path.join('data/qa', f'{subject_name}_qa.csv')
    #     qa_df = pd.read_csv(qa_file_path)
    #     result_list.append(evaluator.eval_qa(subject_name, qa_df, save_result_dir=save_result_dir))

    # if evaluator_class is not None:
    #     del evaluator
    #     evaluator = evaluator_class(
    #         choices=choices,
    #         k=args.ntrain,
    #         model_name=args.model_name,
    #         device=device
    #     )
    #     for index,subject_name in enumerate(qa_subject_list):
    #         print("Now testing (origin): " + subject_name)
    #         qa_file_path = os.path.join('data/qa', f'{subject_name}_qa.csv')
    #         qa_df = pd.read_csv(qa_file_path)
    #         origin_result = evaluator.eval_qa(subject_name, qa_df, save_result_dir=save_result_dir)
    #         origin_result = origin_result.rename(columns={"model_output": "predict_origin"})
    #         result_df = result_list[index].rename(columns={"model_output": "predict_finetune"}).join(origin_result["predict_origin"])
    #         result_file_name = f'{subject_name}_qa_compare_result.csv'
    #         result_df.to_csv(os.path.join(save_result_dir, result_file_name))
    #     assessment_engine = AssessmentEngine(save_result_dir, args.openai_key)
    #     for subject_name in qa_subject_list:
    #         assessment_engine.eval_result_diff(f'{subject_name}_qa_compare_result.csv')


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--ntrain", "-k", type=int, default=5)
    parser.add_argument("--openai_key", type=str, default="xxx")
    parser.add_argument("--minimax_group_id", type=str, default="xxx")
    parser.add_argument("--minimax_key", type=str, default="xxx")
    parser.add_argument("--few_shot", action="store_true")
    parser.add_argument("--model_name", type=str)
    parser.add_argument("--cot", action="store_true")
    # parser.add_argument("--subject","-s",type=str,default="operating_system")
    parser.add_argument("--cuda_device", type=str)
    parser.add_argument("--finetune", type=str)
    parser.add_argument("--finetune_method", type=str)
    user_args = parser.parse_args()
    main(user_args)