import re
import gradio as gr
from PIL import Image, ImageDraw
import math
import torch
import html
from transformers import DonutProcessor, VisionEncoderDecoderModel

pretrained_repo_name = "ivelin/donut-refexp-draft"

processor = DonutProcessor.from_pretrained(pretrained_repo_name)
model = VisionEncoderDecoderModel.from_pretrained(pretrained_repo_name)

device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)


def process_refexp(image: Image, prompt: str):

    print(f"(image, prompt): {image}, {prompt}")

    # trim prompt to 80 characters and normalize to lowercase
    prompt = prompt[:80].lower()

    # prepare encoder inputs
    pixel_values = processor(image, return_tensors="pt").pixel_values

    # prepare decoder inputs
    task_prompt = "<s_refexp><s_prompt>{user_input}</s_prompt><s_target_bounding_box>"
    prompt = task_prompt.replace("{user_input}", prompt)
    decoder_input_ids = processor.tokenizer(
        prompt, add_special_tokens=False, return_tensors="pt").input_ids

    # generate answer
    outputs = model.generate(
        pixel_values.to(device),
        decoder_input_ids=decoder_input_ids.to(device),
        max_length=model.decoder.config.max_position_embeddings,
        early_stopping=True,
        pad_token_id=processor.tokenizer.pad_token_id,
        eos_token_id=processor.tokenizer.eos_token_id,
        use_cache=True,
        num_beams=1,
        bad_words_ids=[[processor.tokenizer.unk_token_id]],
        return_dict_in_generate=True,
    )

    # postprocess
    sequence = processor.batch_decode(outputs.sequences)[0]
    print(fr"predicted decoder sequence: {html.escape(sequence)}")
    sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(
        processor.tokenizer.pad_token, "")
    # remove first task start token
    sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()
    print(
        fr"predicted decoder sequence before token2json: {html.escape(sequence)}")
    bbox = processor.token2json(sequence)
    bbox = bbox['target_bounding_box']
    print(f"predicted bounding box: {bbox}")

    print(f"image object: {image}")
    print(f"image size: {image.size}")
    width, height = image.size
    print(f"image width, height: {width, height}")
    print(f"processed prompt: {prompt}")

    # safeguard in case text prediction is missing some bounding box coordinates
    xmin = math.floor(width*float(bbox.get("xmin", 0)))
    ymin = math.floor(height*float(bbox.get("ymin", 0)))
    xmax = math.floor(width*float(bbox.get("xmax", 1)))
    ymax = math.floor(height*float(bbox.get("ymax", 1)))

    print(
        f"to image pixel values: xmin, ymin, xmax, ymax: {xmin, ymin, xmax, ymax}")

    shape = [(xmin, ymin), (xmax, ymax)]

    # create rectangle image
    img1 = ImageDraw.Draw(image)
    img1.rectangle(shape, outline="green", width=5)
    return image, bbox


title = "Demo: Donut 🍩 for UI RefExp"
description = "Gradio Demo for Donut RefExp task, an instance of `VisionEncoderDecoderModel` fine-tuned on UIBert RefExp Dataset (UI Referring Expression). To use it, simply upload your image and type a question and click 'submit', or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2111.15664' target='_blank'>Donut: OCR-free Document Understanding Transformer</a> | <a href='https://github.com/clovaai/donut' target='_blank'>Github Repo</a></p>"
examples = [["example_1.jpg", "select the setting icon from top right corner"],
            ["example_1.jpg", "click on down arrow beside the entertainment"],
            ["example_1.jpg", "select the down arrow button beside lifestyle"],
            ["example_1.jpg", "click on the image beside the option traffic"],
            ["example_2.jpg", "enter the text field next to the name"],
            ["example_2.jpg", "click on green color button"],
            ["example_2.jpg", "click on text which is beside call now"],
            ["example_2.jpg", "click on more button"],
            ]

demo = gr.Interface(fn=process_refexp,
                    inputs=[gr.Image(type="pil"), "text"],
                    outputs=[gr.Image(type="pil"), "json"],
                    title=title,
                    description=description,
                    article=article,
                    examples=examples,
                    cache_examples=True
                    )

demo.launch()