from typing import Dict, List, Any
from transformers import LayoutLMForTokenClassification, LayoutLMv2Processor
import torch
from subprocess import run

# install tesseract-ocr and pytesseract
run("apt install -y tesseract-ocr", shell=True, check=True)
run("pip install pytesseract", shell=True, check=True)

# helper function to unnormalize bboxes for drawing onto the image
def unnormalize_box(bbox, width, height):
    return [
        width * (bbox[0] / 1000),
        height * (bbox[1] / 1000),
        width * (bbox[2] / 1000),
        height * (bbox[3] / 1000),
    ]


# set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


class EndpointHandler:
    def __init__(self, path=""):
        # load model and processor from path
        self.model = LayoutLMForTokenClassification.from_pretrained(path).to(device)
        self.processor = LayoutLMv2Processor.from_pretrained(path)

    def __call__(self, data: Dict[str, bytes]) -> Dict[str, List[Any]]:
        """
        Args:
            data (:obj:):
                includes the deserialized image file as PIL.Image
        """
        # process input
        image = data.pop("inputs", data)

        # process image
        encoding = self.processor(image, return_tensors="pt", truncation=True)

        # run prediction
        with torch.inference_mode():
            outputs = self.model(
                input_ids=encoding.input_ids.to(device),
                bbox=encoding.bbox.to(device),
                attention_mask=encoding.attention_mask.to(device),
                token_type_ids=encoding.token_type_ids.to(device),
            )
            predictions = outputs.logits.softmax(-1)

        # post process output
        result = []
        for item, inp_ids, bbox in zip(
            predictions.squeeze(0).cpu(), encoding.input_ids.squeeze(0).cpu(), encoding.bbox.squeeze(0).cpu()
        ):
            label = self.model.config.id2label[int(item.argmax().cpu())]
            if label == "O":
                continue
            score = item.max().item()
            text = self.processor.tokenizer.decode(inp_ids)
            bbox = unnormalize_box(bbox.tolist(), image.width, image.height)
            result.append({"label": label, "score": score, "text": text, "bbox": bbox})
        return {"predictions": result}