import argparse
import torch
import os
import json
from tqdm import tqdm
# import shortuuid


from ming.conversations import conv_templates, SeparatorStyle, get_default_conv_template
from ming.model.builder import load_pretrained_model, load_molora_pretrained_model
from ming.utils import disable_torch_init, get_model_name_from_path
# from llava.mm_utils import tokenizer_image_token, process_images, get_model_name_from_path
from torch.utils.data import Dataset, DataLoader
import pandas as pd 

# from PIL import Image
import math


def split_list(lst, n):
    """Split a list into n (roughly) equal-sized chunks"""
    chunk_size = math.ceil(len(lst) / n)  # integer division
    return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]


def get_chunk(lst, n, k):
    chunks = split_list(lst, n)
    return chunks[k]


# Custom dataset class
class CustomDataset(Dataset):
    def __init__(self, questions, tokenizer, model_config):
        self.questions = questions
        self.tokenizer = tokenizer
        self.model_config = model_config

    def __getitem__(self, index):
        line = self.questions[index]
        question = line['Question']
        options = line['Options']
        qs = question + "\n" + options + "\n" + "请回答上述问题：\n"
        
        conv = conv_templates[args.conv_mode].copy()
        conv.append_message(conv.roles[0], qs)
        conv.append_message(conv.roles[1], None)
        prompt = conv.get_prompt()
        input_ids = self.tokenizer(prompt, return_tensors='pt').input_ids
        # input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt')

        return input_ids

    def __len__(self):
        return len(self.questions)


# DataLoader
def create_data_loader(questions, tokenizer, model_config, batch_size=1, num_workers=4):
    assert batch_size == 1, "batch_size must be 1"
    dataset = CustomDataset(questions, tokenizer, model_config)
    data_loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False)
    return data_loader

def convert_to_json(questions):
    # questions is a pandas dataframe, which is to be converted to a list object
    # each element in the list is a dictionary
    # the column name of questions is the key of the dictionary
    # the value of the dictionary is the value of the corresponding column
    questions = questions.to_dict(orient='records')
    return questions

def eval_model(args):
    # Model
    disable_torch_init()
    model_path = os.path.expanduser(args.model_path)
    model_name = get_model_name_from_path(model_path)

    # else:
    if "molora" in model_path:
        tokenizer, model, context_len = load_molora_pretrained_model(model_path, args.model_base, model_name)
    else:
        tokenizer, model, context_len = load_pretrained_model(model_path, args.model_base, model_name)

    # load args.question_file, which is a csv file
    if args.question_file.endswith(".csv"):
        questions = pd.read_csv(args.question_file)
        questions = convert_to_json(questions)
    else:
        # a json file
        with open(args.question_file, 'r') as f:
            questions = json.load(f)
    
    answers_file = os.path.expanduser(args.answers_file)
    os.makedirs(os.path.dirname(answers_file), exist_ok=True)
    ans_file = open(answers_file, "w", encoding='utf-8')
    if args.resume:
        current_file_num = 0
        with open(answers_file, 'r') as f:
            for line in f:
                current_file_num += 1
        questions = questions[current_file_num:]
        ans_file.close()
        ans_file = open(answers_file, "a", encoding='utf-8')

    if 'plain' in model_name and 'finetune' not in model_name.lower() and 'mmtag' not in args.conv_mode:
        args.conv_mode = args.conv_mode + '_mmtag'
        print(f'It seems that this is a plain model, but it is not using a mmtag prompt, auto switching to {args.conv_mode}.')

    # data_loader = create_data_loader(questions, tokenizer, model.config)

    for line in tqdm(questions, total=len(questions)):
        # idx = line["question_id"]
        # cur_prompt = line["text"]
        cur_prompt = line['Question'] + "\n" + line['Options'] + "\n"
        cur_prompt += "请回答上述问题：\n"
        conv = get_default_conv_template(model_name).copy()
        conv.append_message(conv.roles[0], cur_prompt)
        conv.append_message(conv.roles[1], None)
        prompt = conv.get_prompt()
        input_ids = tokenizer(prompt, return_tensors='pt').input_ids
        
        stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
        input_ids = input_ids.to(device='cuda', non_blocking=True)
        with torch.inference_mode():
            output_ids = model.generate(
                input_ids,
                do_sample=True if args.temperature > 0 else False,
                temperature=args.temperature,
                top_p=args.top_p,
                num_beams=args.num_beams,
                max_new_tokens=1024,
                use_cache=True)

        input_token_len = input_ids.shape[1]
        n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
        if n_diff_input_output > 0:
            print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
        outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
        outputs = outputs.strip()
        if outputs.endswith(stop_str):
            outputs = outputs[:-len(stop_str)]
        outputs = outputs.strip()

        # ans_id = shortuuid.uuid()
        ans_file.write(json.dumps({"prompt": cur_prompt,
                                   "text": outputs,
                                   "answer": line['Answer'],
                                   "explanation": line['Explanation'],
                                   'difficulty_level': line['Difficulty level'],
                                   "model_id": model_name,
                                   "metadata": {}}, ensure_ascii=False) + "\n",)
        ans_file.flush()
    ans_file.close()

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
    parser.add_argument("--model-base", type=str, default=None)
    parser.add_argument("--question-file", type=str, default="tables/question.jsonl")
    parser.add_argument("--answers-file", type=str, default="answer.jsonl")
    parser.add_argument("--resume", action="store_true")
    parser.add_argument("--conv-mode", type=str, default="qwen")
    parser.add_argument("--num-chunks", type=int, default=1)
    parser.add_argument("--chunk-idx", type=int, default=0)
    parser.add_argument("--temperature", type=float, default=0.2)
    parser.add_argument("--top_p", type=float, default=None)
    parser.add_argument("--num_beams", type=int, default=1)
    args = parser.parse_args()

    eval_model(args)