import argparse
import re
from io import BytesIO

import requests
import torch
from llava.constants import (DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN,
                             DEFAULT_IMAGE_TOKEN, IMAGE_PLACEHOLDER,
                             IMAGE_TOKEN_INDEX)
from llava.conversation import SeparatorStyle, conv_templates
from llava.mm_utils import (get_model_name_from_path, process_images,
                            tokenizer_image_token)
from llava.model.builder import load_pretrained_model
from llava.utils import disable_torch_init
from PIL import Image


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


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 = image_parser(args)
    images = load_images(image_files)
    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():
        # breakpoint()
        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()
    print(outputs)

def eval_model_text_only(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 = image_parser(args)
    images = load_images(image_files)
    image_sizes = [x.size for x in images]
    images_tensor = process_images(
        images,
        image_processor,
        model.config
    ).to(model.device, dtype=torch.float16)
    prompt = prompt.replace('<image>', '')
    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=None,
            image_sizes=None,
            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()
    print(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()

    eval_model(args)
