"""Functional implementations for text manipulation and rendering.

This module provides utility functions for manipulating text in strings and
rendering text onto images. Includes functions for word manipulation, text drawing,
and handling text regions in images.
"""

from __future__ import annotations

import random
from typing import TYPE_CHECKING, Any

import cv2
import numpy as np
from albucore import (
    MONO_CHANNEL_DIMENSIONS,
    NUM_MULTI_CHANNEL_DIMENSIONS,
    NUM_RGB_CHANNELS,
    preserve_channel_dim,
    uint8_io,
)

from albumentations.core.type_definitions import PAIR

# Importing wordnet and other dependencies only for type checking
if TYPE_CHECKING:
    from PIL import Image


def delete_random_words(words: list[str], num_words: int, py_random: random.Random) -> str:
    """Delete a specified number of random words from a list.

    This function randomly removes words from the input list and joins the remaining
    words with spaces to form a new string.

    Args:
        words (list[str]): List of words to process.
        num_words (int): Number of words to delete.
        py_random (random.Random): Random number generator for reproducibility.

    Returns:
        str: New string with specified words removed. Returns empty string if
             num_words is greater than or equal to the length of words.

    """
    if num_words >= len(words):
        return ""

    indices_to_delete = py_random.sample(range(len(words)), num_words)
    new_words = [word for idx, word in enumerate(words) if idx not in indices_to_delete]
    return " ".join(new_words)


def swap_random_words(words: list[str], num_words: int, py_random: random.Random) -> str:
    """Swap random pairs of words in a list of words.

    This function randomly selects pairs of words and swaps their positions
    a specified number of times.

    Args:
        words (list[str]): List of words to process.
        num_words (int): Number of swaps to perform.
        py_random (random.Random): Random number generator for reproducibility.

    Returns:
        str: New string with words swapped. If num_words is 0 or the list has fewer
             than 2 words, returns the original string.

    """
    if num_words == 0 or len(words) < PAIR:
        return " ".join(words)

    words = words.copy()

    for _ in range(num_words):
        idx1, idx2 = py_random.sample(range(len(words)), 2)
        words[idx1], words[idx2] = words[idx2], words[idx1]
    return " ".join(words)


def insert_random_stopwords(
    words: list[str],
    num_insertions: int,
    stopwords: tuple[str, ...] | None,
    py_random: random.Random,
) -> str:
    """Insert random stopwords into a list of words.

    This function randomly inserts stopwords at random positions in the
    list of words a specified number of times.

    Args:
        words (list[str]): List of words to process.
        num_insertions (int): Number of stopwords to insert.
        stopwords (tuple[str, ...] | None): Tuple of stopwords to choose from.
            If None, default stopwords will be used.
        py_random (random.Random): Random number generator for reproducibility.

    Returns:
        str: New string with stopwords inserted.

    """
    if stopwords is None:
        stopwords = ("and", "the", "is", "in", "at", "of")  # Default stopwords if none provided

    for _ in range(num_insertions):
        idx = py_random.randint(0, len(words))
        words.insert(idx, py_random.choice(stopwords))
    return " ".join(words)


def convert_image_to_pil(image: np.ndarray) -> Image:
    """Convert a NumPy array image to a PIL image."""
    try:
        from PIL import Image
    except ImportError:
        raise ImportError("Pillow is not installed") from ImportError

    if image.ndim == MONO_CHANNEL_DIMENSIONS:  # (height, width)
        return Image.fromarray(image)
    if image.ndim == NUM_MULTI_CHANNEL_DIMENSIONS and image.shape[2] == 1:  # (height, width, 1)
        return Image.fromarray(image[:, :, 0], mode="L")
    if image.ndim == NUM_MULTI_CHANNEL_DIMENSIONS and image.shape[2] == NUM_RGB_CHANNELS:  # (height, width, 3)
        return Image.fromarray(image)

    raise TypeError(f"Unsupported image shape: {image.shape}")


def draw_text_on_pil_image(pil_image: Image, metadata_list: list[dict[str, Any]]) -> Image:
    """Draw text on a PIL image."""
    try:
        from PIL import ImageDraw
    except ImportError:
        raise ImportError("Pillow is not installed") from ImportError

    draw = ImageDraw.Draw(pil_image)
    for metadata in metadata_list:
        bbox_coords = metadata["bbox_coords"]
        text = metadata["text"]
        font = metadata["font"]
        font_color = metadata["font_color"]

        # Adapt font_color based on image mode
        if pil_image.mode == "L":  # Grayscale
            # For grayscale images, use only the first value or average the RGB values
            if isinstance(font_color, tuple):
                if len(font_color) >= 3:
                    # Average RGB values for grayscale
                    font_color = int(sum(font_color[:3]) / 3)
                elif len(font_color) == 1:
                    font_color = int(font_color[0])
        # For RGB and other modes, ensure font_color is a tuple of integers
        elif isinstance(font_color, tuple):
            font_color = tuple(int(c) for c in font_color)

        position = bbox_coords[:2]
        draw.text(position, text, font=font, fill=font_color)
    return pil_image


def draw_text_on_multi_channel_image(image: np.ndarray, metadata_list: list[dict[str, Any]]) -> np.ndarray:
    """Draw text on a multi-channel image with more than three channels."""
    try:
        from PIL import Image, ImageDraw
    except ImportError:
        raise ImportError("Pillow is not installed") from ImportError

    channels = [Image.fromarray(image[:, :, i]) for i in range(image.shape[2])]
    pil_images = [ImageDraw.Draw(channel) for channel in channels]

    for metadata in metadata_list:
        bbox_coords = metadata["bbox_coords"]
        text = metadata["text"]
        font = metadata["font"]
        font_color = metadata["font_color"]

        # Handle font_color as tuple[float, ...]
        # Ensure we have enough color values for all channels
        if len(font_color) < image.shape[2]:
            # If fewer values than channels, pad with zeros
            font_color = tuple(list(font_color) + [0] * (image.shape[2] - len(font_color)))
        elif len(font_color) > image.shape[2]:
            # If more values than channels, truncate
            font_color = font_color[: image.shape[2]]

        # Convert to integers for PIL
        font_color = [int(c) for c in font_color]

        position = bbox_coords[:2]

        # For each channel, use the corresponding color value
        for channel_id, pil_image in enumerate(pil_images):
            # For single-channel PIL images, color must be an integer
            pil_image.text(position, text, font=font, fill=font_color[channel_id])

    return np.stack([np.array(channel) for channel in channels], axis=2)


@uint8_io
@preserve_channel_dim
def render_text(image: np.ndarray, metadata_list: list[dict[str, Any]], clear_bg: bool) -> np.ndarray:
    """Render text onto an image based on provided metadata.

    This function draws text on an image using metadata that specifies text content,
    position, font, and color. It can optionally clear the background before rendering.
    The function handles different image types (grayscale, RGB, multi-channel).

    Args:
        image (np.ndarray): Image to draw text on.
        metadata_list (list[dict[str, Any]]): List of metadata dictionaries containing:
            - bbox_coords: Bounding box coordinates (x_min, y_min, x_max, y_max)
            - text: Text string to render
            - font: PIL ImageFont object
            - font_color: Color for the text
        clear_bg (bool): Whether to clear (inpaint) the background under the text.

    Returns:
        np.ndarray: Image with text rendered on it.

    """
    # First clean background under boxes using seamless clone if clear_bg is True
    if clear_bg:
        image = inpaint_text_background(image, metadata_list)

    if len(image.shape) == MONO_CHANNEL_DIMENSIONS or (
        len(image.shape) == NUM_MULTI_CHANNEL_DIMENSIONS and image.shape[2] in {1, NUM_RGB_CHANNELS}
    ):
        pil_image = convert_image_to_pil(image)
        pil_image = draw_text_on_pil_image(pil_image, metadata_list)
        return np.array(pil_image)

    return draw_text_on_multi_channel_image(image, metadata_list)


def inpaint_text_background(
    image: np.ndarray,
    metadata_list: list[dict[str, Any]],
    method: int = cv2.INPAINT_TELEA,
) -> np.ndarray:
    """Inpaint (clear) regions in an image where text will be rendered.

    This function creates a clean background for text by inpainting rectangular
    regions specified in the metadata. It removes any existing content in those
    regions to provide a clean slate for rendering text.

    Args:
        image (np.ndarray): Image to inpaint.
        metadata_list (list[dict[str, Any]]): List of metadata dictionaries containing:
            - bbox_coords: Bounding box coordinates (x_min, y_min, x_max, y_max)
        method (int, optional): Inpainting method to use. Defaults to cv2.INPAINT_TELEA.
            Options include:
            - cv2.INPAINT_TELEA: Fast Marching Method
            - cv2.INPAINT_NS: Navier-Stokes method

    Returns:
        np.ndarray: Image with specified regions inpainted.

    """
    result_image = image.copy()
    mask = np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)

    for metadata in metadata_list:
        x_min, y_min, x_max, y_max = metadata["bbox_coords"]

        # Black out the region
        result_image[y_min:y_max, x_min:x_max] = 0

        # Update the mask to indicate the region to inpaint
        mask[y_min:y_max, x_min:x_max] = 255

    # Inpaint the blacked-out regions
    return cv2.inpaint(result_image, mask, inpaintRadius=3, flags=method)