import argparse
import torch
import os
import json
from tqdm import tqdm
import shortuuid
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretrained_model
from llava.utils import disable_torch_init
from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria

from PIL import Image
import math
import types
# import kornia
from transformers import set_seed
from vcd_utils.vcd_add_noise import add_diffusion_noise
from utils.cache_generate import generate, sample, greedy_search
from utils.kv_cache import ElasticCache

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)
    tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name)

    model.generate = types.MethodType(generate, model)
    model.sample = types.MethodType(sample, model)
    model.greedy_search = types.MethodType(greedy_search, model)
    k_seq_dim = v_seq_dim = 2
    kv_cache = ElasticCache(
    start_size=1,
    recent_size=2048,
    k_seq_dim=k_seq_dim,
    v_seq_dim=v_seq_dim,
    ratio=args.ratio,
    layer_num=32 if "7b" in model_name else 40
    )

    do_sample = True if args.do_sample == "True" else False
    questions = [json.loads(q) for q in open(os.path.expanduser(args.question_file), "r")]
    answers_file = os.path.expanduser(args.answers_file)
    os.makedirs(os.path.dirname(answers_file), exist_ok=True)
    ans_file = open(answers_file, "w")

    for line in tqdm(questions):
        idx = line["question_id"]
        image_file = line["image"]
        qs = line["text"]
        cur_prompt = qs
        if model.config.mm_use_im_start_end:
            qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs
        else:
            qs = DEFAULT_IMAGE_TOKEN + '\n' + qs

        conv = conv_templates[args.conv_mode].copy()
        # For POPE
        if args.task == "pope":
            conv.append_message(conv.roles[0],  qs + " Please answer this question with one word.")
        else:
        # For generative tasks and MME
            conv.append_message(conv.roles[0],  qs)       
        conv.append_message(conv.roles[1], None)
        prompt = conv.get_prompt()
        image_token_len = 576
        input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
        prompt_len = input_ids.shape[1] + image_token_len - 1
        image_start = torch.where(input_ids == IMAGE_TOKEN_INDEX)[1][0]
        image_end = torch.where(input_ids == IMAGE_TOKEN_INDEX)[1][0] + image_token_len
        token_position = {'image_start':image_start, "image_end": image_end, "prompt_len": prompt_len}
        image = Image.open(os.path.join(args.image_folder, image_file))
        image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
        
        if args.use_cd:
            image_tensor_cd = add_diffusion_noise(image_tensor, args.noise_step)
        else:
            image_tensor_cd = None      

        stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
        keywords = [stop_str]
        stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)

        with torch.inference_mode():
            output_ids = model.generate(
                input_ids,
                images=image_tensor.unsqueeze(0).half().cuda(),
                kv_cache_criteria=kv_cache,
                token_position = token_position,
                alpha = args.alpha,
                beta = args.beta,                
                do_sample=do_sample,
                temperature=args.temperature,
                top_p=args.top_p,
                top_k=args.top_k,
                max_new_tokens=512,
                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_file.write(json.dumps({"question_id": idx,
                                   "prompt": cur_prompt,
                                   "text": outputs,
                                   "model_id": model_name,
                                   "image": image_file,
                                   "metadata": {}}) + "\n")
        ans_file.flush()
    ans_file.close()

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--task", type=str, default="pope")
    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
    parser.add_argument("--model-base", type=str, default=None)
    parser.add_argument("--image-folder", type=str, default="")
    parser.add_argument("--question-file", type=str, default="tables/question.jsonl")
    parser.add_argument("--answers-file", type=str, default="answer.jsonl")
    parser.add_argument("--conv-mode", type=str, default="llava_v1")
    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=1)
    parser.add_argument("--top_p", type=float, default=1)
    parser.add_argument("--top_k", type=int, default=None)
    parser.add_argument("--noise_step", type=int, default=500)
    parser.add_argument("--use_cd", action='store_true', default=False)
    parser.add_argument("--alpha", type=float, default=1)
    parser.add_argument("--beta", type=float, default=0.1)
    parser.add_argument("--ratio", type=float, default=0.5)
    parser.add_argument("--do_sample", type=str, default="True")
    parser.add_argument("--seed", type=int, default=42)
    args = parser.parse_args()
    set_seed(args.seed)
    eval_model(args)
