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from argparse import ArgumentParser
from typing import List, Dict
import torch
from transformers import AutoModelForCausalLM

import PIL.Image

from deepseek_vl.models import DeepseekVLV2ForCausalLM, DeepseekVLV2Processor
from deepseek_vl.serve.app_modules.utils import parse_ref_bbox


def load_pil_images(conversations: List[Dict[str, str]]) -> List[PIL.Image.Image]:
    """

    Args:
        conversations (List[Dict[str, str]]): the conversations with a list of messages. An example is :
            [
                {
                    "role": "User",
                    "content": "<image>\nExtract all information from this image and convert them into markdown format.",
                    "images": ["./examples/table_datasets.png"]
                },
                {"role": "Assistant", "content": ""},
            ]

    Returns:
        pil_images (List[PIL.Image.Image]): the list of PIL images.

    """

    pil_images = []

    for message in conversations:
        if "images" not in message:
            continue

        for image_path in message["images"]:
            pil_img = PIL.Image.open(image_path)
            pil_img = pil_img.convert("RGB")
            pil_images.append(pil_img)

    return pil_images


def main(args):

    dtype = torch.bfloat16

    # specify the path to the model
    model_path = args.model_path
    vl_chat_processor: DeepseekVLV2Processor = DeepseekVLV2Processor.from_pretrained(model_path)
    tokenizer = vl_chat_processor.tokenizer

    vl_gpt: DeepseekVLV2ForCausalLM = AutoModelForCausalLM.from_pretrained(
        model_path,
        trust_remote_code=True,
        torch_dtype=dtype
    )
    vl_gpt = vl_gpt.cuda().eval()

    # single image conversation example
    conversation = [
        {
            "role": "<|User|>",
            "content": "<image>\n<|ref|>The giraffe at the back.<|/ref|>.",
            "images": ["./images/visual_grounding.jpeg"],
        },
        {"role": "<|Assistant|>", "content": ""},
    ]

    # load images and prepare for inputs
    pil_images = load_pil_images(conversation)
    prepare_inputs = vl_chat_processor.__call__(
        conversations=conversation,
        images=pil_images,
        force_batchify=True,
        system_prompt=""
    ).to(vl_gpt.device, dtype=dtype)

    with torch.no_grad():
        # run image encoder to get the image embeddings
        inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)

        # run the model to get the response
        outputs = vl_gpt.generate(
            inputs_embeds=inputs_embeds,
            attention_mask=prepare_inputs.attention_mask,
            pad_token_id=tokenizer.eos_token_id,
            bos_token_id=tokenizer.bos_token_id,
            eos_token_id=tokenizer.eos_token_id,
            max_new_tokens=1024,

            do_sample=False,
            # repetition_penalty=1.1,

            # do_sample=True,
            # temperature=1.0,
            # top_p=0.9,
            # repetition_penalty=1.1,

            use_cache=True,
        )

        answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=False)
        print(f"{prepare_inputs['sft_format'][0]}", answer)

        vg_image = parse_ref_bbox(answer, image=pil_images[0])
        if vg_image is not None:
            vg_image.save("./vg.jpg", format="JPEG", quality=85)


if __name__ == "__main__":
    parser = ArgumentParser()
    parser.add_argument("--model_path", type=str, required=True,
                        default="deepseek-ai/deepseek-vl2-27b-moe",
                        help="model name or local path to the model")
    args = parser.parse_args()
    main(args)