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,
    KeywordsStoppingCriteria,
)

from PIL import Image

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


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


def load_image(image_file):
    if isinstance(image_file, Image.Image): # Jack Add 直接传入图片也可以 2.22
        image = image_file 
    elif 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


def eval_model(args,tokenizer, model, image_processor, context_len=None):
    # Model
    disable_torch_init()
    # import pdb
    # pdb.set_trace()

    model_name = get_model_name_from_path(args.model_path)
    # 这里就不要二次导入了... 外面导入一次就行...  不然显存占用直接两倍...
    # 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 "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()

    if isinstance(args.image_file, Image.Image):
        image_files = args.image_file # 处理 输入为 PIL 格式的数据
        if not isinstance(image_files, list):
            image_files = [image_files]        
    else:
        image_files = image_parser(args)
    images = load_images(image_files)
    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()
    )

    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():
        # import pdb
        # pdb.set_trace()
        # output_ids = model.generate(input_ids, images=images_tensor, do_sample=args.temperature > 0, temperature=args.temperature, top_p=args.top_p, num_beams=args.num_beams, max_new_tokens=args.max_new_tokens, use_cache=True, stopping_criteria=[stopping_criteria])
        output_ids = model.generate(
            input_ids,
            images=images_tensor,
            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,
            stopping_criteria=[stopping_criteria],
        )

    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()
    # import pdb
    # pdb.set_trace()
    # print(outputs)

    return outputs


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()

    outputs = eval_model(args)