import argparse
import torch

from llava.constants import (
    IMAGE_TOKEN_INDEX,
    DEFAULT_IMAGE_TOKEN,
    DEFAULT_IM_START_TOKEN,
    DEFAULT_IM_END_TOKEN,
    IMAGE_PLACEHOLDER,
)
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 (
    process_images,
    tokenizer_image_token,
    get_model_name_from_path,
)

from PIL import Image

import requests
from PIL import Image
from io import BytesIO
import re
import os


def image_parser(args):
    print(args.image_file)
    out = args.image_file.split(args.sep)
    print(args.sep)
    print(out)
    return out


def load_image(image_file):
    if image_file.startswith("http") or image_file.startswith("https"):
        response = requests.get(image_file)
        image = Image.open(BytesIO(response.content)).convert("RGB")
    else:
        image = Image.open(image_file).convert("RGB")
    return image


def load_images(image_files):
    out = []
    for image_file in image_files:
        image = load_image(image_file)
        out.append(image)
    return out

prompt = "Please describe the object coverd by the green mask."
model_path = "liuhaotian/llava-v1.5-7b"
root_path = '/data/work2-gcp-europe-west4-a/yuqian_fu/Ego/data_segswap'
data_path = "/data/work-gcp-europe-west4-a/yuqian_fu/Ego/data_segswap/ExoQuery_piano.json"
save_path = "/data/work-gcp-europe-west4-a/yuqian_fu/Ego/data_segswap/ExoQuery_piano_withtext.json"
def eval_model(args):
    # Model
    disable_torch_init()

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

    qs = args.query
    image_token_se = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN
    if IMAGE_PLACEHOLDER in qs:
        if model.config.mm_use_im_start_end:
            qs = re.sub(IMAGE_PLACEHOLDER, image_token_se, qs)
        else:
            qs = re.sub(IMAGE_PLACEHOLDER, DEFAULT_IMAGE_TOKEN, qs)
    else:
        if model.config.mm_use_im_start_end:
            qs = image_token_se + "\n" + qs
        else:
            qs = DEFAULT_IMAGE_TOKEN + "\n" + qs

    if "llama-2" in model_name.lower():
        conv_mode = "llava_llama_2"
    elif "mistral" in model_name.lower():
        conv_mode = "mistral_instruct"
    elif "v1.6-34b" in model_name.lower():
        conv_mode = "chatml_direct"
    elif "v1" in model_name.lower():
        conv_mode = "llava_v1"
    elif "mpt" in model_name.lower():
        conv_mode = "mpt"
    else:
        conv_mode = "llava_v0"

    if args.conv_mode is not None and conv_mode != args.conv_mode:
        print(
            "[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}".format(
                conv_mode, args.conv_mode, args.conv_mode
            )
        )
    else:
        args.conv_mode = conv_mode

    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()

    #image_files_list = image_parser(args)
    new_data_list = []
    with open(data_path, "r") as f:
        datas = json.load(f)
    for data in datas:
        query_path = data["first_frame_image"]
        query_path = os.path.join(root_path, query_path)
        frame = cv2.imread(query_path)

        # v1，直接使用生成json文件中的缩放的mask
        # v2，获取takes名称，取出物体字典，逆映射获取物体名字，使用gt中的mask
        h,w = frame.shape[:2]
        #针对query是exo的情况
        frame = cv2.resize(frame, (w // 4, h // 4))
        for obj in data["first_frame_anns"]:
            images = []
            mask = decode(obj["segmentation"])
            mask = cv2.resize(mask, (frame.shape[1], frame.shape[0]))
            out = blend_mask(frame, mask)
            image = Image.fromarray(out).convert("RGB")
            images.append(image)
            image_sizes = [x.size for x in images]
            images_tensor = process_images(
                images,
                image_processor,
                model.config
            ).to(model.device, dtype=torch.float16)

            input_ids = (
                tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt")
                .unsqueeze(0)
                .cuda()
            )

            with torch.inference_mode():
                output_ids = model.generate(
                    input_ids,
                    images=images_tensor,
                    image_sizes=image_sizes,
                    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=args.max_new_tokens,
                    use_cache=True,
                )
            outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
            obj["text"] = outputs
        new_data_list.append(data)
    with open(save_path, "w") as f:
        json.dump(new_data_list, f)


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("--image-file", type=str, required=True)
    # parser.add_argument("--query", type=str, required=True)
    # parser.add_argument("--conv-mode", type=str, default=None)
    # parser.add_argument("--sep", type=str, default=",")
    # 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)
    # parser.add_argument("--max_new_tokens", type=int, default=512)
    # args = parser.parse_args()

    args = type('Args', (), {
        "model_path": model_path,
        "model_base": None,
        "model_name": get_model_name_from_path(model_path),
        "query": prompt,
        "conv_mode": None,
        "sep": ",",
        "temperature": 0,
        "top_p": None,
        "num_beams": 1,
        "max_new_tokens": 512
    })()

    eval_model(args)