# Necessary imports
import re
import supervision as sv
from PIL import Image


# Text cleaning function
def clean_text(text):
    """
    Cleans the given text by removing unwanted tokens, extra spaces,
    and ensures proper spacing between words and after punctuation marks.

    Args:
        text (str): The input text to be cleaned.

    Returns:
        str: The cleaned and properly formatted text.
    """

    # Remove unwanted tokens
    text = text.replace("<pad>", "").replace("</s>", "").strip()

    # Split the text into lines and clean each line
    lines = text.split("\n")
    cleaned_lines = [line.strip() for line in lines if line.strip()]

    # Join the cleaned lines into a single string with a space between each line
    cleaned_text = " ".join(cleaned_lines)

    # Ensure proper spacing using regex
    cleaned_text = re.sub(
        r"\s+", " ", cleaned_text
    )  # Replace multiple spaces with a single space
    cleaned_text = re.sub(
        r"(?<=[.,!?])(?=[^\s])", r" ", cleaned_text
    )  # Add space after punctuation if not followed by a space
    cleaned_text = re.sub(
        r"(?<=[a-z])(?=[A-Z])", r" ", cleaned_text
    )  # Add space between joined words where a lowercase letter is followed by an uppercase letter
    cleaned_text = re.sub(
        r"(\w)([A-Z][a-z])", r"\1 \2", cleaned_text
    )  # Add space between camel case words

    # Return the cleaned text
    return cleaned_text


# Draw OCR bounding boxes with enhanced visual elements
def draw_ocr_bboxes(image: Image, detections: sv.Detections) -> Image:
    """
    Draws bounding boxes and labels on the input image based on the OCR detections.

    Args:
        image (PIL.Image): The input image on which to draw the bounding boxes and labels.
        detections (sv.Detections): The OCR detections containing the bounding box coordinates and labels.

    Returns:
        PIL.Image: The annotated image with bounding boxes and labels.

    """

    # Copy the input image to avoid modifying the original image
    annotated_image = image.copy()

    # Calculate the optimal line thickness and text scale based on the image resolution
    thickness = sv.calculate_optimal_line_thickness(resolution_wh=image.size)
    text_scale = sv.calculate_optimal_text_scale(resolution_wh=image.size)

    # Initialize the bounding box and label annotators
    bounding_box_annotator = sv.BoundingBoxAnnotator(
        color_lookup=sv.ColorLookup.INDEX, thickness=thickness
    )
    label_annotator = sv.LabelAnnotator(
        color_lookup=sv.ColorLookup.INDEX,
        text_scale=text_scale,
        text_thickness=thickness,
    )

    # Annotate the image with bounding boxes and labels
    annotated_image = bounding_box_annotator.annotate(annotated_image, detections)
    annotated_image = label_annotator.annotate(annotated_image, detections)

    # Return the annotated image
    return annotated_image