import argparse
import hashlib
import json
import os
import time
from threading import Thread
import logging
import gradio as gr
import torch

from tinyllava.model.builder import load_pretrained_model
from tinyllava.mm_utils import (
    KeywordsStoppingCriteria,
    load_image_from_base64,
    process_images,
    tokenizer_image_token,
    get_model_name_from_path,
)
from PIL import Image
from io import BytesIO
import base64
import torch
from transformers import StoppingCriteria
from tinyllava.constants import (
    DEFAULT_IM_END_TOKEN,
    DEFAULT_IM_START_TOKEN,
    DEFAULT_IMAGE_TOKEN,
    IMAGE_TOKEN_INDEX,
)
from tinyllava.conversation import SeparatorStyle, conv_templates, default_conversation

from transformers import TextIteratorStreamer
from pathlib import Path

DEFAULT_MODEL_PATH = "bczhou/TinyLLaVA-3.1B"
DEFAULT_MODEL_NAME = "TinyLLaVA-3.1B"


block_css = """

#buttons button {
    min-width: min(120px,100%);
}
"""
title_markdown = """
# TinyLLaVA: A Framework of Small-scale Large Multimodal Models
[[Code](https://github.com/DLCV-BUAA/TinyLLaVABench)] | 📚 [[Paper](https://arxiv.org/pdf/2402.14289.pdf)]
"""
tos_markdown = """
### Terms of use
By using this service, users are required to agree to the following terms:
The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes.
For an optimal experience, please use desktop computers for this demo, as mobile devices may compromise its quality.
"""
learn_more_markdown = """
### License
The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation.
"""
ack_markdown = """
### Acknowledgement
The template for this web demo is from [LLaVA](https://github.com/haotian-liu/LLaVA), and we are very grateful to LLaVA for their open source contributions to the community!
"""


def regenerate(state, image_process_mode):
    state.messages[-1][-1] = None
    prev_human_msg = state.messages[-2]
    if type(prev_human_msg[1]) in (tuple, list):
        prev_human_msg[1] = (*prev_human_msg[1][:2], image_process_mode)
    state.skip_next = False
    return (state, state.to_gradio_chatbot(), "", None)


def clear_history():
    state = default_conversation.copy()
    return (state, state.to_gradio_chatbot(), "", None)


def add_text(state, text, image, image_process_mode):
    if len(text) <= 0 and image is None:
        state.skip_next = True
        return (state, state.to_gradio_chatbot(), "", None)

    text = text[:1536]  # Hard cut-off
    if image is not None:
        text = text[:1200]  # Hard cut-off for images
        if "<image>" not in text:
            # text = '<Image><image></Image>' + text
            text = text + "\n<image>"
        text = (text, image, image_process_mode)
        if len(state.get_images(return_pil=True)) > 0:
            state = default_conversation.copy()
    state.append_message(state.roles[0], text)
    state.append_message(state.roles[1], None)
    state.skip_next = False
    return (state, state.to_gradio_chatbot(), "", None)


def load_demo():
    state = default_conversation.copy()
    return state


@torch.inference_mode()
def get_response(params):
    prompt = params["prompt"]
    ori_prompt = prompt
    images = params.get("images", None)
    num_image_tokens = 0
    if images is not None and len(images) > 0:
        if len(images) > 0:
            if len(images) != prompt.count(DEFAULT_IMAGE_TOKEN):
                raise ValueError(
                    "Number of images does not match number of <image> tokens in prompt"
                )

            images = [load_image_from_base64(image) for image in images]
            images = process_images(images, image_processor, model.config)

            if type(images) is list:
                images = [
                    image.to(model.device, dtype=torch.float16) for image in images
                ]
            else:
                images = images.to(model.device, dtype=torch.float16)

            replace_token = DEFAULT_IMAGE_TOKEN
            if getattr(model.config, "mm_use_im_start_end", False):
                replace_token = (
                    DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN
                )
            prompt = prompt.replace(DEFAULT_IMAGE_TOKEN, replace_token)

            num_image_tokens = (
                prompt.count(replace_token) * model.get_vision_tower().num_patches
            )
        else:
            images = None
        image_args = {"images": images}
    else:
        images = None
        image_args = {}

    temperature = float(params.get("temperature", 1.0))
    top_p = float(params.get("top_p", 1.0))
    max_context_length = getattr(model.config, "max_position_embeddings", 2048)
    max_new_tokens = min(int(params.get("max_new_tokens", 256)), 1024)
    stop_str = params.get("stop", None)
    do_sample = True if temperature > 0.001 else False
    logger.info(prompt)
    input_ids = (
        tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt")
        .unsqueeze(0)
        .to(model.device)
    )
    keywords = [stop_str]

    stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
    streamer = TextIteratorStreamer(
        tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=15
    )

    max_new_tokens = min(
        max_new_tokens, max_context_length - input_ids.shape[-1] - num_image_tokens
    )

    if max_new_tokens < 1:
        yield json.dumps(
            {
                "text": ori_prompt
                + "Exceeds max token length. Please start a new conversation, thanks.",
                "error_code": 0,
            }
        ).encode() + b"\0"
        return

    # local inference
    # BUG: If stopping_criteria is set, an error occur: 
    # RuntimeError: The size of tensor a (2) must match the size of tensor b (3) at non-singleton dimension 0
    generate_kwargs = dict(
        inputs=input_ids,
        do_sample=do_sample,
        temperature=temperature,
        top_p=top_p,
        max_new_tokens=max_new_tokens,
        streamer=streamer,
        # stopping_criteria=[stopping_criteria],
        use_cache=True,
        **image_args,
    )
    thread = Thread(target=model.generate, kwargs=generate_kwargs)
    thread.start()
    logger.debug(ori_prompt)
    logger.debug(generate_kwargs)
    generated_text = ori_prompt
    for new_text in streamer:
        generated_text += new_text
        if generated_text.endswith(stop_str):
            generated_text = generated_text[: -len(stop_str)]
        yield json.dumps({"text": generated_text, "error_code": 0}).encode()


def http_bot(state, temperature, top_p, max_new_tokens):
    if state.skip_next:
        # This generate call is skipped due to invalid inputs
        yield (state, state.to_gradio_chatbot())
        return

    if len(state.messages) == state.offset + 2:
        # First round of conversation

        if "tinyllava" in model_name.lower():
            if "3.1b" in model_name.lower() or "phi" in model_name.lower():
                template_name = "phi"
            elif "2.0b" in model_name.lower() or "stablelm" in model_name.lower():
                template_name = "phi"
            elif "qwen" in model_name.lower():
                template_name = "qwen"
            else:
                template_name = "v1"

        elif "llava" in model_name.lower():

            if "llama-2" in model_name.lower():
                template_name = "llava_llama_2"
            elif "v1" in model_name.lower():
                if "mmtag" in model_name.lower():
                    template_name = "v1_mmtag"
                elif (
                    "plain" in model_name.lower()
                    and "finetune" not in model_name.lower()
                ):
                    template_name = "v1_mmtag"
                else:
                    template_name = "llava_v1"
            elif "mpt" in model_name.lower():
                template_name = "mpt"
            else:
                if "mmtag" in model_name.lower():
                    template_name = "v0_mmtag"
                elif (
                    "plain" in model_name.lower()
                    and "finetune" not in model_name.lower()
                ):
                    template_name = "v0_mmtag"
                else:
                    template_name = "llava_v0"
        elif "mpt" in model_name:
            template_name = "mpt_text"
        elif "llama-2" in model_name:
            template_name = "llama_2"
        else:
            template_name = "vicuna_v1"
        new_state = conv_templates[template_name].copy()
        new_state.append_message(new_state.roles[0], state.messages[-2][1])
        new_state.append_message(new_state.roles[1], None)
        state = new_state

    # Construct prompt
    prompt = state.get_prompt()

    all_images = state.get_images(return_pil=True)
    all_image_hash = [hashlib.md5(image.tobytes()).hexdigest() for image in all_images]

    # Make requests
    # pload = {"model": model_name, "prompt": prompt, "temperature": float(temperature), "top_p": float(top_p),
    #          "max_new_tokens": min(int(max_new_tokens), 1536), "stop": (
    #         state.sep
    #         if state.sep_style in [SeparatorStyle.SINGLE, SeparatorStyle.MPT]
    #         else state.sep2
    #     ), "images": state.get_images()}

    pload = {
        "model": model_name,
        "prompt": prompt,
        "temperature": float(temperature),
        "top_p": float(top_p),
        "max_new_tokens": min(int(max_new_tokens), 1536),
        "stop": (
            state.sep
            if state.sep_style in [SeparatorStyle.SINGLE, SeparatorStyle.MPT]
            else state.sep2
        ), "images": state.get_images()}

    state.messages[-1][-1] = "▌"
    yield (state, state.to_gradio_chatbot())

    # for stream
    output = get_response(pload)
    for chunk in output:
        if chunk:
            data = json.loads(chunk.decode())
            if data["error_code"] == 0:
                output = data["text"][len(prompt) :].strip()
                state.messages[-1][-1] = output + "▌"
                yield (state, state.to_gradio_chatbot())
            else:
                output = data["text"] + f" (error_code: {data['error_code']})"
                state.messages[-1][-1] = output
                yield (state, state.to_gradio_chatbot())
                return
            time.sleep(0.03)

    state.messages[-1][-1] = state.messages[-1][-1][:-1]
    yield (state, state.to_gradio_chatbot())


def build_demo():
    textbox = gr.Textbox(
        show_label=False, placeholder="Enter text and press ENTER", container=False
    )
    with gr.Blocks(title="TinyLLaVA", theme=gr.themes.Default(), css=block_css) as demo:
        state = gr.State()
        gr.Markdown(title_markdown)

        with gr.Row():
            with gr.Column(scale=5):
                with gr.Row(elem_id="Model ID"):
                    gr.Dropdown(
                        choices=[DEFAULT_MODEL_NAME],
                        value=DEFAULT_MODEL_NAME,
                        interactive=True,
                        label="Model ID",
                        container=False,
                    )
                imagebox = gr.Image(type="pil")
                image_process_mode = gr.Radio(
                    ["Crop", "Resize", "Pad", "Default"],
                    value="Default",
                    label="Preprocess for non-square image",
                    visible=False,
                )

                # cur_dir = os.path.dirname(os.path.abspath(__file__))
                cur_dir = Path(__file__).parent
                gr.Examples(
                    examples=[
                        [
                            f"{cur_dir}/examples/extreme_ironing.jpg",
                            "What is unusual about this image?",
                        ],
                        [
                            f"{cur_dir}/examples/waterview.jpg",
                            "What are the things I should be cautious about when I visit here?",
                        ],
                    ],
                    inputs=[imagebox, textbox],
                )

                with gr.Accordion("Parameters", open=False) as _:
                    temperature = gr.Slider(
                        minimum=0.0,
                        maximum=1.0,
                        value=0.2,
                        step=0.1,
                        interactive=True,
                        label="Temperature",
                    )
                    top_p = gr.Slider(
                        minimum=0.0,
                        maximum=1.0,
                        value=0.7,
                        step=0.1,
                        interactive=True,
                        label="Top P",
                    )
                    max_output_tokens = gr.Slider(
                        minimum=0,
                        maximum=1024,
                        value=512,
                        step=64,
                        interactive=True,
                        label="Max output tokens",
                    )

            with gr.Column(scale=8):
                chatbot = gr.Chatbot(elem_id="chatbot", label="Chatbot", height=550)
                with gr.Row():
                    with gr.Column(scale=8):
                        textbox.render()
                    with gr.Column(scale=1, min_width=50):
                        submit_btn = gr.Button(value="Send", variant="primary")
                with gr.Row(elem_id="buttons") as _:
                    regenerate_btn = gr.Button(value="🔄  Regenerate", interactive=True)
                    clear_btn = gr.Button(value="🗑️  Clear", interactive=True)

        gr.Markdown(tos_markdown)
        gr.Markdown(learn_more_markdown)
        gr.Markdown(ack_markdown)

        regenerate_btn.click(
            regenerate,
            [state, image_process_mode],
            [state, chatbot, textbox, imagebox],
            queue=False,
        ).then(
            http_bot, [state, temperature, top_p, max_output_tokens], [state, chatbot]
        )

        clear_btn.click(
            clear_history, None, [state, chatbot, textbox, imagebox], queue=False
        )

        textbox.submit(
            add_text,
            [state, textbox, imagebox, image_process_mode],
            [state, chatbot, textbox, imagebox],
            queue=False,
        ).then(
            http_bot, [state, temperature, top_p, max_output_tokens], [state, chatbot]
        )

        submit_btn.click(
            add_text,
            [state, textbox, imagebox, image_process_mode],
            [state, chatbot, textbox, imagebox],
            queue=False,
        ).then(
            http_bot, [state, temperature, top_p, max_output_tokens], [state, chatbot]
        )

        demo.load(load_demo, None, [state], queue=False)
    return demo


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--host", type=str, default=None)
    parser.add_argument("--port", type=int, default=None)
    parser.add_argument("--share", default=None)
    parser.add_argument("--model-path", type=str, default=DEFAULT_MODEL_PATH)
    parser.add_argument("--model-name", type=str, default=DEFAULT_MODEL_NAME)
    parser.add_argument("--load-8bit", action="store_true")
    parser.add_argument("--load-4bit", action="store_true")
    args = parser.parse_args()
    return args


if __name__ == "__main__":
    logging.basicConfig(
        level=logging.INFO,
        format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
    )
    logger = logging.getLogger(__name__)
    logger.info(gr.__version__)
    args = parse_args()
    model_name = args.model_name
    tokenizer, model, image_processor, context_len = load_pretrained_model(
        model_path=args.model_path,
        model_base=None,
        model_name=args.model_name,
        load_4bit=args.load_4bit,
        load_8bit=args.load_8bit
    )

    demo = build_demo()
    demo.queue()
    demo.launch(server_name=args.host, server_port=args.port, share=args.share)