import argparse
import os
import random
from collections import defaultdict

import cv2
import re

import numpy as np
from PIL import Image

import spaces

import torch
import html
import gradio as gr

import torchvision.transforms as T
import torch.backends.cudnn as cudnn

from minigpt4.common.config import Config

from minigpt4.common.registry import registry
from minigpt4.conversation.conversation import Conversation, SeparatorStyle, Chat

# imports modules for registration
from minigpt4.datasets.builders import *
from minigpt4.models import *
from minigpt4.processors import *
from minigpt4.runners import *
from minigpt4.tasks import *

import warnings
warnings.filterwarnings("ignore")

def parse_args():
    parser = argparse.ArgumentParser(description="Demo")
    parser.add_argument("--cfg-path", default='eval_configs/minigptv2_eval.yaml',
                        help="path to configuration file.")
    parser.add_argument("--gpu-id", type=int, default=0, help="specify the gpu to load the model.")
    parser.add_argument(
        "--options",
        nargs="+",
        help="override some settings in the used config, the key-value pair "
             "in xxx=yyy format will be merged into config file (deprecate), "
             "change to --cfg-options instead.",
    )
    args = parser.parse_args()
    return args


random.seed(42)
np.random.seed(42)
torch.manual_seed(42)

cudnn.benchmark = False
cudnn.deterministic = True

print('Initializing Chat')
args = parse_args()
cfg = Config(args)

# Use CUDA for inference, but defer the .to('cuda') until first @spaces.GPU call
# (ZeroGPU forbids CUDA touches at module-import time).
device = 'cuda'

model_config = cfg.model_cfg
model_config.device_8bit = 0
# Disable low_resource (it used load_in_8bit which requires CUDA at load time).
if hasattr(model_config, 'low_resource'):
    model_config.low_resource = False
model_cls = registry.get_model_class(model_config.arch)
model = model_cls.from_config(model_config)
bounding_box_size = 100

vis_processor_cfg = cfg.datasets_cfg.cc_sbu_align.vis_processor.train
vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)

model = model.eval()

CONV_VISION = Conversation(
    system="",
    roles=(r"<s>[INST] ", r" [/INST]"),
    messages=[],
    offset=2,
    sep_style=SeparatorStyle.SINGLE,
    sep="",
)


def extract_substrings(string):
    # first check if there is no-finished bracket
    index = string.rfind('}')
    if index != -1:
        string = string[:index + 1]

    pattern = r'<p>(.*?)\}(?!<)'
    matches = re.findall(pattern, string)
    substrings = [match for match in matches]

    return substrings


def is_overlapping(rect1, rect2):
    x1, y1, x2, y2 = rect1
    x3, y3, x4, y4 = rect2
    return not (x2 < x3 or x1 > x4 or y2 < y3 or y1 > y4)


def computeIoU(bbox1, bbox2):
    x1, y1, x2, y2 = bbox1
    x3, y3, x4, y4 = bbox2
    intersection_x1 = max(x1, x3)
    intersection_y1 = max(y1, y3)
    intersection_x2 = min(x2, x4)
    intersection_y2 = min(y2, y4)
    intersection_area = max(0, intersection_x2 - intersection_x1 + 1) * max(0, intersection_y2 - intersection_y1 + 1)
    bbox1_area = (x2 - x1 + 1) * (y2 - y1 + 1)
    bbox2_area = (x4 - x3 + 1) * (y4 - y3 + 1)
    union_area = bbox1_area + bbox2_area - intersection_area
    iou = intersection_area / union_area
    return iou


def save_tmp_img(visual_img):
    file_name = "".join([str(random.randint(0, 9)) for _ in range(5)]) + ".jpg"
    file_path = "/tmp/gradio" + file_name
    visual_img.save(file_path)
    return file_path


def mask2bbox(mask):
    if mask is None:
        return ''
    mask = mask.resize([100, 100], resample=Image.NEAREST)
    mask = np.array(mask)[:, :, 0]

    rows = np.any(mask, axis=1)
    cols = np.any(mask, axis=0)

    if rows.sum():
        # Get the top, bottom, left, and right boundaries
        rmin, rmax = np.where(rows)[0][[0, -1]]
        cmin, cmax = np.where(cols)[0][[0, -1]]
        bbox = '{{<{}><{}><{}><{}>}}'.format(cmin, rmin, cmax, rmax)
    else:
        bbox = ''

    return bbox


def escape_markdown(text):
    # List of Markdown special characters that need to be escaped
    md_chars = ['<', '>']

    # Escape each special character
    for char in md_chars:
        text = text.replace(char, '\\' + char)

    return text


def reverse_escape(text):
    md_chars = ['\\<', '\\>']

    for char in md_chars:
        text = text.replace(char, char[1:])

    return text


colors = [
    (255, 0, 0),
    (0, 255, 0),
    (0, 0, 255),
    (210, 210, 0),
    (255, 0, 255),
    (0, 255, 255),
    (114, 128, 250),
    (0, 165, 255),
    (0, 128, 0),
    (144, 238, 144),
    (238, 238, 175),
    (255, 191, 0),
    (0, 128, 0),
    (226, 43, 138),
    (255, 0, 255),
    (0, 215, 255),
]

color_map = {
    f"{color_id}": f"#{hex(color[2])[2:].zfill(2)}{hex(color[1])[2:].zfill(2)}{hex(color[0])[2:].zfill(2)}" for
    color_id, color in enumerate(colors)
}

used_colors = colors


def visualize_all_bbox_together(image, generation):
    if image is None:
        return None, ''

    generation = html.unescape(generation)

    image_width, image_height = image.size
    image = image.resize([500, int(500 / image_width * image_height)])
    image_width, image_height = image.size

    string_list = extract_substrings(generation)
    if string_list:  # it is grounding or detection
        mode = 'all'
        entities = defaultdict(list)
        i = 0
        j = 0
        for string in string_list:
            try:
                obj, string = string.split('</p>')
            except ValueError:
                print('wrong string: ', string)
                continue
            bbox_list = string.split('<delim>')
            flag = False
            for bbox_string in bbox_list:
                integers = re.findall(r'-?\d+', bbox_string)
                if len(integers) == 4:
                    x0, y0, x1, y1 = int(integers[0]), int(integers[1]), int(integers[2]), int(integers[3])
                    left = x0 / bounding_box_size * image_width
                    bottom = y0 / bounding_box_size * image_height
                    right = x1 / bounding_box_size * image_width
                    top = y1 / bounding_box_size * image_height

                    entities[obj].append([left, bottom, right, top])

                    j += 1
                    flag = True
            if flag:
                i += 1
    else:
        integers = re.findall(r'-?\d+', generation)

        if len(integers) == 4:  # it is refer
            mode = 'single'

            entities = list()
            x0, y0, x1, y1 = int(integers[0]), int(integers[1]), int(integers[2]), int(integers[3])
            left = x0 / bounding_box_size * image_width
            bottom = y0 / bounding_box_size * image_height
            right = x1 / bounding_box_size * image_width
            top = y1 / bounding_box_size * image_height
            entities.append([left, bottom, right, top])
        else:
            # don't detect any valid bbox to visualize
            return None, ''

    if len(entities) == 0:
        return None, ''

    if isinstance(image, Image.Image):
        image_h = image.height
        image_w = image.width
        image = np.array(image)

    elif isinstance(image, str):
        if os.path.exists(image):
            pil_img = Image.open(image).convert("RGB")
            image = np.array(pil_img)[:, :, [2, 1, 0]]
            image_h = pil_img.height
            image_w = pil_img.width
        else:
            raise ValueError(f"invaild image path, {image}")
    elif isinstance(image, torch.Tensor):

        image_tensor = image.cpu()
        reverse_norm_mean = torch.tensor([0.48145466, 0.4578275, 0.40821073])[:, None, None]
        reverse_norm_std = torch.tensor([0.26862954, 0.26130258, 0.27577711])[:, None, None]
        image_tensor = image_tensor * reverse_norm_std + reverse_norm_mean
        pil_img = T.ToPILImage()(image_tensor)
        image_h = pil_img.height
        image_w = pil_img.width
        image = np.array(pil_img)[:, :, [2, 1, 0]]
    else:
        raise ValueError(f"invaild image format, {type(image)} for {image}")

    indices = list(range(len(entities)))

    new_image = image.copy()

    previous_bboxes = []
    # size of text
    text_size = 0.5
    # thickness of text
    text_line = 1  # int(max(1 * min(image_h, image_w) / 512, 1))
    box_line = 2
    (c_width, text_height), _ = cv2.getTextSize("F", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line)
    base_height = int(text_height * 0.675)
    text_offset_original = text_height - base_height
    text_spaces = 2

    # num_bboxes = sum(len(x[-1]) for x in entities)
    used_colors = colors  # random.sample(colors, k=num_bboxes)

    color_id = -1
    for entity_idx, entity_name in enumerate(entities):
        if mode == 'single' or mode == 'identify':
            bboxes = entity_name
            bboxes = [bboxes]
        else:
            bboxes = entities[entity_name]
        color_id += 1
        for bbox_id, (x1_norm, y1_norm, x2_norm, y2_norm) in enumerate(bboxes):
            skip_flag = False
            orig_x1, orig_y1, orig_x2, orig_y2 = int(x1_norm), int(y1_norm), int(x2_norm), int(y2_norm)

            color = used_colors[entity_idx % len(used_colors)]  # tuple(np.random.randint(0, 255, size=3).tolist())
            new_image = cv2.rectangle(new_image, (orig_x1, orig_y1), (orig_x2, orig_y2), color, box_line)

            if mode == 'all':
                l_o, r_o = box_line // 2 + box_line % 2, box_line // 2 + box_line % 2 + 1

                x1 = orig_x1 - l_o
                y1 = orig_y1 - l_o

                if y1 < text_height + text_offset_original + 2 * text_spaces:
                    y1 = orig_y1 + r_o + text_height + text_offset_original + 2 * text_spaces
                    x1 = orig_x1 + r_o

                # add text background
                (text_width, text_height), _ = cv2.getTextSize(f"  {entity_name}", cv2.FONT_HERSHEY_COMPLEX, text_size,
                                                               text_line)
                text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2 = x1, y1 - (
                            text_height + text_offset_original + 2 * text_spaces), x1 + text_width, y1

                for prev_bbox in previous_bboxes:
                    if computeIoU((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), prev_bbox['bbox']) > 0.95 and \
                            prev_bbox['phrase'] == entity_name:
                        skip_flag = True
                        break
                    while is_overlapping((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), prev_bbox['bbox']):
                        text_bg_y1 += (text_height + text_offset_original + 2 * text_spaces)
                        text_bg_y2 += (text_height + text_offset_original + 2 * text_spaces)
                        y1 += (text_height + text_offset_original + 2 * text_spaces)

                        if text_bg_y2 >= image_h:
                            text_bg_y1 = max(0, image_h - (text_height + text_offset_original + 2 * text_spaces))
                            text_bg_y2 = image_h
                            y1 = image_h
                            break
                if not skip_flag:
                    alpha = 0.5
                    for i in range(text_bg_y1, text_bg_y2):
                        for j in range(text_bg_x1, text_bg_x2):
                            if i < image_h and j < image_w:
                                if j < text_bg_x1 + 1.35 * c_width:
                                    # original color
                                    bg_color = color
                                else:
                                    # white
                                    bg_color = [255, 255, 255]
                                new_image[i, j] = (alpha * new_image[i, j] + (1 - alpha) * np.array(bg_color)).astype(
                                    np.uint8)

                    cv2.putText(
                        new_image, f"  {entity_name}", (x1, y1 - text_offset_original - 1 * text_spaces),
                        cv2.FONT_HERSHEY_COMPLEX, text_size, (0, 0, 0), text_line, cv2.LINE_AA
                    )

                    previous_bboxes.append(
                        {'bbox': (text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), 'phrase': entity_name})

    if mode == 'all':
        def color_iterator(colors):
            while True:
                for color in colors:
                    yield color

        color_gen = color_iterator(colors)

        # Add colors to phrases and remove <p></p>
        def colored_phrases(match):
            phrase = match.group(1)
            color = next(color_gen)
            return f'<span style="color:rgb{color}">{phrase}</span>'

        generation = re.sub(r'{<\d+><\d+><\d+><\d+>}|<delim>', '', generation)
        generation_colored = re.sub(r'<p>(.*?)</p>', colored_phrases, generation)
    else:
        generation_colored = ''

    pil_image = Image.fromarray(new_image)
    return pil_image, generation_colored


def gradio_reset(chat_state, img_list, path_list):
    if chat_state is not None:
        chat_state.messages = []
    if img_list is not None:
        img_list = []
    if isinstance(path_list, list):
        for path in path_list:
            os.remove(path)
        path_list.clear()
    return None, gr.update(value=None, interactive=True), gr.update(placeholder='Upload your image and chat',
                                                                    interactive=True), chat_state, img_list


def image_upload_trigger(upload_flag, replace_flag, img_list):
    # set the upload flag to true when receive a new image.
    # if there is an old image (and old conversation), set the replace flag to true to reset the conv later.
    upload_flag = 1
    if img_list:
        replace_flag = 1
    return upload_flag, replace_flag


def example_trigger(text_input, image, upload_flag, replace_flag, img_list):
    # set the upload flag to true when receive a new image.
    # if there is an old image (and old conversation), set the replace flag to true to reset the conv later.
    upload_flag = 1
    if img_list or replace_flag == 1:
        replace_flag = 1

    return upload_flag, replace_flag


def gradio_ask(user_message, chatbot, chat_state, gr_img, img_list, upload_flag, replace_flag, path_list):
    if len(user_message) == 0:
        text_box_show = 'Input should not be empty!'
    else:
        text_box_show = ''

    if isinstance(gr_img, dict):
        gr_img, mask = gr_img['image'], gr_img['mask']
    else:
        mask = None

    if '[identify]' in user_message:
        # check if user provide bbox in the text input
        integers = re.findall(r'-?\d+', user_message)
        if len(integers) != 4:  # no bbox in text
            bbox = mask2bbox(mask)
            user_message = user_message + bbox

    if chat_state is None:
        chat_state = CONV_VISION.copy()
        # If there's no prior conversation and we have an image, treat as a fresh upload.
        if gr_img is not None and not upload_flag:
            upload_flag = 1

    if upload_flag:
        if replace_flag:
            chat_state = CONV_VISION.copy()  # new image, reset everything
            replace_flag = 0
            chatbot = []
        img_list = []
        llm_message = chat.upload_img(gr_img, chat_state, img_list)
        upload_flag = 0

    chat.ask(user_message, chat_state)

    chatbot = chatbot + [[user_message, None]]

    if '[identify]' in user_message:
        visual_img, _ = visualize_all_bbox_together(gr_img, user_message)
        if visual_img is not None:
            file_path = save_tmp_img(visual_img)
            # path_list.append(file_path)
            chatbot = chatbot + [[(file_path,), None]]

    return text_box_show, chatbot, chat_state, img_list, upload_flag, replace_flag


def gradio_answer(chatbot, chat_state, img_list, temperature):
    llm_message = chat.answer(conv=chat_state,
                              img_list=img_list,
                              temperature=temperature,
                              max_new_tokens=500,
                              max_length=2000)[0]
    chatbot[-1][1] = llm_message
    return chatbot, chat_state


@spaces.GPU(duration=120)
def gradio_stream_answer(chatbot, chat_state, img_list, temperature):
    # Move the model to GPU on first call; the @spaces.GPU decorator guarantees
    # CUDA is available within this scope.
    chat.model.to('cuda')
    chat.device = 'cuda'
    # Move the cached stopping-criteria tokens to GPU too.
    try:
        chat.stopping_criteria = type(chat.stopping_criteria)(
            [type(c)(stops=[t.to('cuda') for t in c.stops]) for c in chat.stopping_criteria]
        )
    except Exception:
        pass
    if len(img_list) > 0:
        if not isinstance(img_list[0], torch.Tensor):
            chat.encode_img(img_list)
        else:
            img_list[:] = [t.to('cuda') for t in img_list]
    streamer = chat.stream_answer(conv=chat_state,
                                  img_list=img_list,
                                  temperature=temperature,
                                  max_new_tokens=500,
                                  max_length=2000)
    output = ''
    for new_output in streamer:
        escapped = escape_markdown(new_output)
        output += escapped
        chatbot[-1][1] = output
        yield chatbot, chat_state
    print(output)
    chat_state.messages[-1][1] = '</s>'
    # Detach cached image embeds so the State can cross the ZeroGPU subprocess boundary.
    img_list[:] = [t.detach().cpu() if isinstance(t, torch.Tensor) else t for t in img_list]


def gradio_visualize(chatbot, gr_img, path_list):
    if isinstance(gr_img, dict):
        gr_img, mask = gr_img['image'], gr_img['mask']

    unescaped = reverse_escape(chatbot[-1][1])
    visual_img, generation_color = visualize_all_bbox_together(gr_img, unescaped)
    if visual_img is not None:
        if len(generation_color):
            chatbot[-1][1] = generation_color
        file_path = save_tmp_img(visual_img)
        # path_list.append(file_path)
        chatbot = chatbot + [[None, (file_path,)]]

    return chatbot


def gradio_taskselect(idx):
    prompt_list = [
        '',
        '[grounding] describe this image in detail',
        '[refer] ',
        '[detection] ',
        '[identify] what is this ',
        '[vqa] '
    ]
    instruct_list = [
        '**Hint:** Type in whatever you want',
        '**Hint:** Send the command to generate a grounded image description',
        '**Hint:** Type in a phrase about an object in the image and send the command',
        '**Hint:** Type in a caption or phrase, and see object locations in the image',
        '**Hint:** Draw a bounding box on the uploaded image then send the command. Click the "clear" botton on the top right of the image before redraw',
        '**Hint:** Send a question to get a short answer',
    ]
    return prompt_list[idx], instruct_list[idx]




chat = Chat(model, vis_processor, device='cpu')


@spaces.GPU(duration=120)
def _api_chat(image_pil, user_message, temperature=0.6):
    """Single-call inference endpoint for end-to-end smoke testing / programmatic use.

    Internally does the equivalent of upload_img -> ask -> stream_answer in one
    @spaces.GPU scope so state doesn't have to cross the API boundary.
    """
    chat.model.to('cuda')
    chat.device = 'cuda'
    try:
        chat.stopping_criteria = type(chat.stopping_criteria)(
            [type(c)(stops=[t.to('cuda') for t in c.stops]) for c in chat.stopping_criteria]
        )
    except Exception:
        pass
    local_conv = CONV_VISION.copy()
    local_imgs = []
    if image_pil is not None:
        chat.upload_img(image_pil, local_conv, local_imgs)
        chat.encode_img(local_imgs)
    chat.ask(user_message, local_conv)
    output_text, _ = chat.answer(
        conv=local_conv,
        img_list=local_imgs,
        temperature=float(temperature),
        max_new_tokens=300,
        max_length=2000,
    )
    return output_text

title = """<h1 align="center">MiniGPT-v2 Demo</h1>"""
description = 'Welcome to Our MiniGPT-v2 Chatbot Demo!'
# article = """<p><a href='https://minigpt-v2.github.io'><img src='https://img.shields.io/badge/Project-Page-Green'></a></p><p><a href='https://github.com/Vision-CAIR/MiniGPT-4/blob/main/MiniGPTv2.pdf'><img src='https://img.shields.io/badge/Paper-PDF-red'></a></p><p><a href='https://github.com/Vision-CAIR/MiniGPT-4'><img src='https://img.shields.io/badge/GitHub-Repo-blue'></a></p><p><a href='https://www.youtube.com/watch?v=atFCwV2hSY4'><img src='https://img.shields.io/badge/YouTube-Video-red'></a></p>"""
article = """<p><a href='https://minigpt-v2.github.io'><img src='https://img.shields.io/badge/Project-Page-Green'></a></p>"""

introduction = '''
For Abilities Involving Visual Grounding:
1. Grounding: CLICK **Send** to generate a grounded image description.
2. Refer: Input a referring object and CLICK **Send**.
3. Detection: Write a caption or phrase, and CLICK **Send**.
4. Identify: Draw the bounding box on the uploaded image window and CLICK **Send** to generate the bounding box. (CLICK "clear" button before re-drawing next time).
5. VQA: Input a visual question and CLICK **Send**.
6. No Tag: Input whatever you want and CLICK **Send** without any tagging

You can also simply chat in free form!
'''

text_input = gr.Textbox(placeholder='Upload your image and chat', interactive=True, show_label=False, container=False,
                        scale=8)
with gr.Blocks() as demo:
    gr.Markdown(title)
    # gr.Markdown(description)
    gr.Markdown(article)

    with gr.Row():
        with gr.Column(scale=1):
            image = gr.Image(type="pil")

            temperature = gr.Slider(
                minimum=0.1,
                maximum=1.5,
                value=0.6,
                step=0.1,
                interactive=True,
                label="Temperature",
            )

            clear = gr.Button("Restart")

            gr.Markdown(introduction)

        with gr.Column():
            chat_state = gr.State(value=None)
            img_list = gr.State(value=[])
            chatbot = gr.Chatbot(label='MiniGPT-v2')

            dataset = gr.Dataset(
                components=[gr.Textbox(visible=False)],
                samples=[['No Tag'], ['Grounding'], ['Refer'], ['Detection'], ['Identify'], ['VQA']],
                type="index",
                label='Task Shortcuts',
            )
            task_inst = gr.Markdown('**Hint:** Upload your image and chat')
            with gr.Row():
                text_input.render()
                send = gr.Button("Send", variant='primary', size='sm', scale=1)

    upload_flag = gr.State(value=0)
    replace_flag = gr.State(value=0)
    path_list = gr.State(value=[])
    image.upload(image_upload_trigger, [upload_flag, replace_flag, img_list], [upload_flag, replace_flag])

    with gr.Row():
        with gr.Column():
            gr.Examples(examples=[
                ["examples_v2/office.jpg", "[grounding] describe this image in detail"],
                ["examples_v2/sofa.jpg", "[detection] sofas"],
                ["examples_v2/2000x1372_wmkn_0012149409555.jpg", "[refer] the world cup"],
                ["examples_v2/KFC-20-for-20-Nuggets.jpg", "[identify] what is this {<4><50><30><65>}"],
            ], inputs=[image, text_input])
        with gr.Column():
            gr.Examples(examples=[
                ["examples_v2/glip_test.jpg", "[vqa] where should I hide in this room when playing hide and seek"],
                ["examples_v2/float.png", "Please write a poem about the image"],
                ["examples_v2/thief.png", "Is the weapon fateful"],
                ["examples_v2/cockdial.png", "What might happen in this image in the next second"],
            ], inputs=[image, text_input])

    dataset.click(
        gradio_taskselect,
        inputs=[dataset],
        outputs=[text_input, task_inst],
        show_progress="hidden",
        postprocess=False,
        queue=False,
    )

    text_input.submit(
        gradio_ask,
        [text_input, chatbot, chat_state, image, img_list, upload_flag, replace_flag, path_list],
        [text_input, chatbot, chat_state, img_list, upload_flag, replace_flag], queue=False
    ).success(
        gradio_stream_answer,
        [chatbot, chat_state, img_list, temperature],
        [chatbot, chat_state]
    ).success(
        gradio_visualize,
        [chatbot, image, path_list],
        [chatbot],
        queue=False,
    )

    send.click(
        gradio_ask,
        [text_input, chatbot, chat_state, image, img_list, upload_flag, replace_flag, path_list],
        [text_input, chatbot, chat_state, img_list, upload_flag, replace_flag]
    ).success(
        gradio_stream_answer,
        [chatbot, chat_state, img_list, temperature],
        [chatbot, chat_state]
    ).success(
        gradio_visualize,
        [chatbot, image, path_list],
        [chatbot],
    )

    clear.click(gradio_reset, [chat_state, img_list, path_list], [chatbot, image, text_input, chat_state, img_list], queue=False)

    # Hidden single-call API endpoint that runs upload_img -> encode_img -> ask -> answer
    # in one @spaces.GPU scope. Exposed for end-to-end programmatic smoke testing.
    _api_image = gr.Image(type="pil", visible=False)
    _api_msg = gr.Textbox(visible=False)
    _api_temp = gr.Number(value=0.6, visible=False)
    _api_out = gr.Textbox(visible=False)
    _api_btn = gr.Button(visible=False)
    _api_btn.click(_api_chat, [_api_image, _api_msg, _api_temp], _api_out, api_name="chat")

demo.queue().launch()