"""
Nougat by Meta

Good for:
- transcribe Scientific PDFs into an easy to use markdown
format
- Extracting information from PDFs
- Extracting metadata from pdfs

"""
import re
import torch
from PIL import Image
from transformers import NougatProcessor, VisionEncoderDecoderModel


class Nougat:
    """
    Nougat

    Args:
        model_name_or_path: str, default="facebook/nougat-base"
        min_length: int, default=1
        max_new_tokens: int, default=30

    Usage:
    >>> from swarms.models.nougat import Nougat
    >>> nougat = Nougat()
    >>> nougat("path/to/image.png")


    """

    def __init__(
        self,
        model_name_or_path="facebook/nougat-base",
        min_length: int = 1,
        max_new_tokens: int = 5000,
    ):
        self.model_name_or_path = model_name_or_path
        self.min_length = min_length
        self.max_new_tokens = max_new_tokens

        self.processor = NougatProcessor.from_pretrained(
            self.model_name_or_path
        )
        self.model = VisionEncoderDecoderModel.from_pretrained(
            self.model_name_or_path
        )
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.model.to(self.device)

    def get_image(self, img: str):
        """Get an image from a path"""
        img = Image.open(img)

        if img.mode == "L":
            img = img.convert("RGB")
        return img

    def __call__(self, img: str):
        """Call the model with an image_path str as an input"""
        image = Image.open(img)
        pixel_values = self.processor(
            image, return_tensors="pt"
        ).pixel_values

        # Generate transcriptions, here we only generate 30 tokens
        outputs = self.model.generate(
            pixel_values.to(self.device),
            min_length=self.min_length,
            max_new_tokens=self.max_new_tokens,
        )

        sequence = self.processor.batch_decode(
            outputs, skip_special_tokens=True
        )[0]
        sequence = self.processor.post_process_generation(
            sequence, fix_markdown=False
        )

        out = print(sequence)
        return out

    def clean_nougat_output(raw_output):
        """Clean the output from nougat to be more readable"""
        # Define the pattern to extract the relevant data
        daily_balance_pattern = (
            r"\*\*(\d{2}/\d{2}/\d{4})\*\*\n\n\*\*([\d,]+\.\d{2})\*\*"
        )

        # Find all matches of the pattern
        matches = re.findall(daily_balance_pattern, raw_output)

        # Convert the matches to a readable format
        cleaned_data = [
            "Date: {}, Amount: {}".format(
                date, amount.replace(",", "")
            )
            for date, amount in matches
        ]

        # Join the cleaned data with new lines for readability
        return "\n".join(cleaned_data)
