import torch
import numpy as np
from transformers import LayoutLMv3TokenizerFast, LayoutLMv3Processor, LayoutLMv3ForTokenClassification
from PIL import Image, ImageDraw, ImageFont
from utils import OCR, unnormalize_box


labels = ["O", "B-MENU.NM", "B-MENU.NUM", "B-MENU.UNITPRICE", "B-MENU.CNT", "B-MENU.DISCOUNTPRICE", "B-MENU.PRICE", "B-MENU.ITEMSUBTOTAL", "B-MENU.VATYN", "B-MENU.ETC", "B-MENU.SUB.NM", "B-MENU.SUB.UNITPRICE", "B-MENU.SUB.CNT", "B-MENU.SUB.PRICE", "B-MENU.SUB.ETC", "B-VOID_MENU.NM", "B-VOID_MENU.PRICE", "B-SUB_TOTAL.SUBTOTAL_PRICE", "B-SUB_TOTAL.DISCOUNT_PRICE", "B-SUB_TOTAL.SERVICE_PRICE", "B-SUB_TOTAL.OTHERSVC_PRICE", "B-SUB_TOTAL.TAX_PRICE", "B-SUB_TOTAL.ETC", "B-TOTAL.TOTAL_PRICE", "B-TOTAL.TOTAL_ETC", "B-TOTAL.CASHPRICE", "B-TOTAL.CHANGEPRICE", "B-TOTAL.CREDITCARDPRICE", "B-TOTAL.EMONEYPRICE", "B-TOTAL.MENUTYPE_CNT", "B-TOTAL.MENUQTY_CNT", "I-MENU.NM", "I-MENU.NUM", "I-MENU.UNITPRICE", "I-MENU.CNT", "I-MENU.DISCOUNTPRICE", "I-MENU.PRICE", "I-MENU.ITEMSUBTOTAL", "I-MENU.VATYN", "I-MENU.ETC", "I-MENU.SUB.NM", "I-MENU.SUB.UNITPRICE", "I-MENU.SUB.CNT", "I-MENU.SUB.PRICE", "I-MENU.SUB.ETC", "I-VOID_MENU.NM", "I-VOID_MENU.PRICE", "I-SUB_TOTAL.SUBTOTAL_PRICE", "I-SUB_TOTAL.DISCOUNT_PRICE", "I-SUB_TOTAL.SERVICE_PRICE", "I-SUB_TOTAL.OTHERSVC_PRICE", "I-SUB_TOTAL.TAX_PRICE", "I-SUB_TOTAL.ETC", "I-TOTAL.TOTAL_PRICE", "I-TOTAL.TOTAL_ETC", "I-TOTAL.CASHPRICE", "I-TOTAL.CHANGEPRICE", "I-TOTAL.CREDITCARDPRICE", "I-TOTAL.EMONEYPRICE", "I-TOTAL.MENUTYPE_CNT", "I-TOTAL.MENUQTY_CNT"]
id2label = {v: k for v, k in enumerate(labels)}
label2id = {k: v for v, k in enumerate(labels)}

tokenizer = LayoutLMv3TokenizerFast.from_pretrained("mp-02/layoutlmv3-finetuned-cord", apply_ocr=False)
processor = LayoutLMv3Processor.from_pretrained("mp-02/layoutlmv3-finetuned-cord", apply_ocr=False)
model = LayoutLMv3ForTokenClassification.from_pretrained("mp-02/layoutlmv3-finetuned-cord")

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


def prediction(image_path: str):
    image = Image.open(image_path).convert('RGB')
    boxes, words = OCR(image_path)
    encoding = processor(image, words, boxes=boxes, return_offsets_mapping=True, return_tensors="pt", truncation=True)
    offset_mapping = encoding.pop('offset_mapping')

    for k, v in encoding.items():
        encoding[k] = v.to(device)

    outputs = model(**encoding)

    predictions = outputs.logits.argmax(-1).squeeze().tolist()
    token_boxes = encoding.bbox.squeeze().tolist()

    inp_ids = encoding.input_ids.squeeze().tolist()
    inp_words = [tokenizer.decode(i) for i in inp_ids]

    width, height = image.size
    is_subword = np.array(offset_mapping.squeeze().tolist())[:, 0] != 0

    true_predictions = [id2label[pred] for idx, pred in enumerate(predictions) if not is_subword[idx]]
    true_boxes = [unnormalize_box(box, width, height) for idx, box in enumerate(token_boxes) if not is_subword[idx]]
    true_words = []

    for id, i in enumerate(inp_words):
        if not is_subword[id]:
            true_words.append(i)
        else:
            true_words[-1] = true_words[-1]+i

    true_predictions = true_predictions[1:-1]
    true_boxes = true_boxes[1:-1]
    true_words = true_words[1:-1]

    preds = []
    l_words = []
    bboxes = []

    for i, j in enumerate(true_predictions):
        if j != 'others':
            preds.append(true_predictions[i])
            l_words.append(true_words[i])
            bboxes.append(true_boxes[i])

    d = {}
    for id, i in enumerate(preds):
        if i not in d.keys():
            d[i] = l_words[id]
        else:
            d[i] = d[i] + ", " + l_words[id]
    d = {k: v.strip() for (k, v) in d.items()}

    # TODO: process the json

    draw = ImageDraw.Draw(image, "RGBA")
    font = ImageFont.load_default()

    for prediction, box in zip(preds, bboxes):
        draw.rectangle(box)
        draw.text((box[0]+10, box[1]-10), text=prediction, font=font, fill="black")

    return d, image