"""Image processor and decoding helpers for Yasa2."""

from __future__ import annotations

import io
import math
from typing import List, Tuple

import numpy as np
from PIL import Image
from transformers import ConvNextImageProcessor


class Yasa2ImageProcessor(ConvNextImageProcessor):
    """ConvNeXt image processor for Yasa2."""

    model_input_names = ["pixel_values"]

    def __init__(self, *args, **kwargs):
        """Initialize the image processor with optional tiling metadata.

        Args:
            *args: Positional args forwarded to ConvNextImageProcessor.
            **kwargs: Keyword args forwarded to ConvNextImageProcessor.
        """
        kwargs.setdefault("size", {"shortest_edge": 512})
        # Do not force crop_size; ConvNextImageProcessor uses crop_pct by default.
        kwargs.setdefault("do_resize", True)
        kwargs.setdefault("do_center_crop", False)
        kwargs.setdefault("do_normalize", True)
        # TODO: Non-square inputs can break square-grid assumptions; consider enforcing square outputs or returning spatial dims.
        super().__init__(*args, **kwargs)
        self.use_navit = kwargs.get("use_navit", False)
        self.max_tiles_num = kwargs.get("max_tiles_num", 4)
        self.patch_size = kwargs.get("patch_size", 14)
        self.tiling_method = kwargs.get("tiling_method", "llava-uhd")


def image_rgb_decoder_pil(
    image_bytes: bytes, skip_errors: bool = False
) -> dict:
    """Decode image bytes into a numpy RGB array.

    Args:
        image_bytes: Raw image bytes.
        skip_errors: Whether to return error info instead of raising.

    Returns:
        Dict with pixel values or an error message.
    """
    try:
        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
        pixel_values = np.array(image)
        if pixel_values.ndim == 4:
            raise ValueError(
                "Image has 4 dimensions, expected 3 (possible GIF with jpg/png extension)."
            )
        if pixel_values.shape[2] != 3:
            raise ValueError(
                f"Image has {pixel_values.shape[2]} channels, expected 3."
            )
        return {"pixel_values": pixel_values}
    except Exception as exc:
        if not skip_errors:
            raise
        return {"error": str(exc)}


def image_rgb_decoder_pil_tiling(
    image_bytes: bytes,
    skip_errors: bool = False,
    size: int = 1024,
    grid_pinpoints: List[Tuple[int, int]] = None,
    max_tiles_num: int = 9,
    patch_size: int = 4,
    tiling_method: str = "llava-next",
) -> dict:
    """Decode image bytes into tiled numpy arrays.

    Args:
        image_bytes: Raw image bytes.
        skip_errors: Whether to return error info instead of raising.
        size: Base tile size.
        grid_pinpoints: Candidate grid pinpoints.
        max_tiles_num: Maximum number of tiles for UHD tiling.
        patch_size: Patch size for UHD tiling.
        tiling_method: Tiling method name.

    Returns:
        Dict with tiled pixel values or an error message.
    """
    if grid_pinpoints is None:
        grid_pinpoints = [
            (2, 2),
            (1, 2),
            (2, 1),
            (1, 3),
            (3, 1),
            (1, 4),
            (4, 1),
        ]
    try:
        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
        if tiling_method.lower() == "llava-next":
            images = process_anyres_image(image, size, grid_pinpoints)
            pixel_values = np.array([np.array(img) for img in images])
        elif tiling_method.lower() == "llava-uhd":
            images = process_anyres_image_uhd(
                image,
                max_tiles_num=max_tiles_num,
                scale_resolution=size,
                patch_size=patch_size,
                never_split=False,
            )
            pixel_values = [np.array(img) for img in images]
        else:
            raise ValueError(f"Unknown tiling method: {tiling_method}")

        if tiling_method.lower() == "llava-next" and pixel_values.ndim != 4:
            raise ValueError(
                "Tiled image has unexpected dimensions (expected 4D)."
            )
        if (
            tiling_method.lower() == "llava-next"
            and pixel_values.shape[3] != 3
        ):
            raise ValueError(
                f"Tiled image has {pixel_values.shape[3]} channels, expected 3."
            )
        if tiling_method.lower() == "llava-uhd" and pixel_values[-1].ndim != 3:
            raise ValueError(
                "UHD tiled image has unexpected dimensions (expected 3D)."
            )
        if (
            tiling_method.lower() == "llava-uhd"
            and pixel_values[-1].shape[2] != 3
        ):
            raise ValueError(
                f"UHD tiled image has {pixel_values[-1].shape[2]} channels, expected 3."
            )

        return {
            "pixel_values": pixel_values,
            "num_tiles": len(pixel_values),
            "img_tiling": True,
        }
    except Exception as exc:
        if not skip_errors:
            raise
        return {"error": str(exc)}


def resize_and_pad_image(
    image: Image.Image, target_resolution: Tuple[int, int]
) -> Image.Image:
    """Resize and pad an image to target resolution while preserving aspect ratio.

    Args:
        image: Input PIL image.
        target_resolution: Target (width, height).

    Returns:
        Resized and padded PIL image.
    """
    original_width, original_height = image.size
    target_width, target_height = target_resolution
    scale_w = target_width / original_width
    scale_h = target_height / original_height

    if scale_w < scale_h:
        new_width = target_width
        new_height = min(math.ceil(original_height * scale_w), target_height)
    else:
        new_height = target_height
        new_width = min(math.ceil(original_width * scale_h), target_width)

    resized_image = image.resize((new_width, new_height))
    new_image = Image.new("RGB", (target_width, target_height), (0, 0, 0))
    paste_x = (target_width - new_width) // 2
    paste_y = (target_height - new_height) // 2
    new_image.paste(resized_image, (paste_x, paste_y))
    return new_image


def select_best_resolution(
    original_size: Tuple[int, int],
    possible_resolutions: List[Tuple[int, int]],
) -> Tuple[int, int]:
    """Select the best resolution based on aspect ratio and minimal waste.

    Args:
        original_size: Original image size (width, height).
        possible_resolutions: Candidate resolutions.

    Returns:
        Best resolution (width, height).
    """
    original_width, original_height = original_size
    best_fit = None
    max_effective_resolution = 0
    min_wasted_resolution = float("inf")

    for width, height in possible_resolutions:
        scale = min(width / original_width, height / original_height)
        scaled_width, scaled_height = (
            int(original_width * scale),
            int(original_height * scale),
        )
        effective_resolution = min(
            scaled_width * scaled_height, original_width * original_height
        )
        wasted_resolution = (width * height) - effective_resolution
        if effective_resolution > max_effective_resolution or (
            effective_resolution == max_effective_resolution
            and wasted_resolution < min_wasted_resolution
        ):
            max_effective_resolution = effective_resolution
            min_wasted_resolution = wasted_resolution
            best_fit = (width, height)
    return best_fit


def divide_to_patches(
    image: Image.Image, patch_size: int
) -> List[Image.Image]:
    """Divide an image into square patches.

    Args:
        image: Input PIL image.
        patch_size: Patch size in pixels.

    Returns:
        List of patch images.
    """
    patches = []
    width, height = image.size
    for i in range(0, height, patch_size):
        for j in range(0, width, patch_size):
            box = (j, i, j + patch_size, i + patch_size)
            patches.append(image.crop(box))
    return patches


def process_anyres_image(
    image: Image.Image,
    size: int = 512,
    grid_pinpoints: List[Tuple[int, int]] = None,
) -> List[Image.Image]:
    """Process an image into a list of tiles for LLaVA-Next style tiling.

    Args:
        image: Input PIL image.
        size: Base tile size.
        grid_pinpoints: Candidate grid pinpoints.

    Returns:
        List of tiled images (original resize + tiles).
    """
    if grid_pinpoints is None:
        grid_pinpoints = [(2, 2), (1, 2), (2, 1), (1, 3), (3, 1)]
    possible_resolutions = [(x * size, y * size) for x, y in grid_pinpoints]
    best_resolution = select_best_resolution(image.size, possible_resolutions)
    image_padded = resize_and_pad_image(image, best_resolution)
    patches = divide_to_patches(image_padded, size)
    image_original_resize = image.resize((size, size))
    return [image_original_resize] + patches


def estimate_num_tiles_llava_next(
    image_size: Tuple[int, int],
    size: int = 512,
    grid_pinpoints: List[Tuple[int, int]] = None,
) -> int:
    """Estimate tile count for LLaVA-Next tiling without decoding images."""
    if grid_pinpoints is None:
        grid_pinpoints = [(2, 2), (1, 2), (2, 1), (1, 3), (3, 1)]
    possible_resolutions = [(x * size, y * size) for x, y in grid_pinpoints]
    best_resolution = select_best_resolution(image_size, possible_resolutions)
    grid_x = int(best_resolution[0] / size)
    grid_y = int(best_resolution[1] / size)
    return 1 + (grid_x * grid_y)


def split_to_patches(
    image: Image.Image, grid: Tuple[int, int]
) -> List[Image.Image]:
    """Divide an image into patches using a fixed grid.

    Args:
        image: Input PIL image.
        grid: Grid dimensions (grid_x, grid_y).

    Returns:
        List of patch images.
    """
    patches = []
    width, height = image.size
    grid_x = int(width / grid[0])
    grid_y = int(height / grid[1])
    for i in range(0, height, grid_y):
        for j in range(0, width, grid_x):
            box = (j, i, j + grid_x, i + grid_y)
            patches.append(image.crop(box))
    return patches


def ensure_divide(length: float, patch_size: int) -> int:
    """Round length up to a multiple of patch_size.

    Args:
        length: Raw length to align.
        patch_size: Patch size to align to.

    Returns:
        Length aligned to patch_size.
    """
    return max(round(length / patch_size) * patch_size, patch_size)


def find_best_resize(
    original_size: Tuple[int, int],
    scale_resolution: int,
    patch_size: int,
    allow_upscale: bool = False,
) -> Tuple[int, int]:
    """Find the best resize dimensions for UHD tiling.

    Args:
        original_size: Original image size (width, height).
        scale_resolution: Target scale resolution.
        patch_size: Patch size for alignment.
        allow_upscale: Whether to allow upscaling.

    Returns:
        Best resized (width, height).
    """
    width, height = original_size
    if (width * height > scale_resolution * scale_resolution) or allow_upscale:
        aspect_ratio = width / height
        height = int(scale_resolution / math.sqrt(aspect_ratio))
        width = int(height * aspect_ratio)
    best_width = ensure_divide(width, patch_size)
    best_height = ensure_divide(height, patch_size)
    return (best_width, best_height)


def get_refine_size(
    original_size: Tuple[int, int],
    grid: Tuple[int, int],
    scale_resolution: int,
    patch_size: int,
    allow_upscale: bool = False,
) -> Tuple[int, int]:
    """Compute the refined resize based on a tile grid.

    Args:
        original_size: Original image size (width, height).
        grid: Tile grid (grid_x, grid_y).
        scale_resolution: Target scale resolution.
        patch_size: Patch size for alignment.
        allow_upscale: Whether to allow upscaling.

    Returns:
        Refined resize (width, height).
    """
    width, height = original_size
    grid_x, grid_y = grid
    refine_width = ensure_divide(width, grid_x)
    refine_height = ensure_divide(height, grid_y)
    grid_width = refine_width / grid_x
    grid_height = refine_height / grid_y
    best_grid_size = find_best_resize(
        (grid_width, grid_height),
        scale_resolution,
        patch_size,
        allow_upscale=allow_upscale,
    )
    return (best_grid_size[0] * grid_x, best_grid_size[1] * grid_y)


def process_anyres_image_uhd(
    image: Image.Image,
    max_tiles_num: int = 9,
    scale_resolution: int = 448,
    patch_size: int = 4,
    never_split: bool = False,
) -> List[Image.Image]:
    """Process an image into tiles for LLaVA-UHD style tiling.

    Args:
        image: Input PIL image.
        max_tiles_num: Maximum number of tiles to generate.
        scale_resolution: Target resolution for scaling.
        patch_size: Patch size for alignment.
        never_split: Whether to avoid splitting into tiles.

    Returns:
        List of tiles (patches + resized source image).
    """
    original_width, original_height = image.size
    log_ratio = math.log(original_width / original_height)
    ratio = (original_width * original_height) / (
        scale_resolution * scale_resolution
    )
    multiple = min(math.ceil(ratio), max_tiles_num)
    patches = []

    if multiple <= 1 or never_split:
        best_size = find_best_resize(
            image.size, scale_resolution, patch_size, allow_upscale=True
        )
        source_image = image.resize(best_size, Image.Resampling.BICUBIC)
        return [source_image]

    candidate_split_grids_nums = []
    for i in [multiple - 1, multiple, multiple + 1]:
        if i == 1 or i > max_tiles_num:
            continue
        candidate_split_grids_nums.append(i)

    best_resize = find_best_resize(image.size, scale_resolution, patch_size)
    source_image = image.copy().resize(best_resize, Image.Resampling.BICUBIC)
    candidate_grids = []
    for split_grids_nums in candidate_split_grids_nums:
        m = 1
        while m <= split_grids_nums:
            if split_grids_nums % m == 0:
                candidate_grids.append([m, split_grids_nums // m])
            m += 1

    best_grid = [1, 1]
    min_error = float("inf")
    for grid in candidate_grids:
        error = abs(log_ratio - math.log(grid[0] / grid[1]))
        if error < min_error:
            best_grid = grid
            min_error = error

    refine_size = get_refine_size(
        image.size,
        (best_grid[0], best_grid[1]),
        scale_resolution,
        patch_size,
        allow_upscale=True,
    )
    refine_image = image.resize(refine_size, Image.Resampling.BICUBIC)
    patches = split_to_patches(refine_image, (best_grid[0], best_grid[1]))
    return patches + [source_image]


def estimate_num_tiles_llava_uhd(
    image_size: Tuple[int, int],
    max_tiles_num: int = 9,
    scale_resolution: int = 448,
    patch_size: int = 4,
    never_split: bool = False,
) -> int:
    """Estimate tile count for LLaVA-UHD tiling without decoding images."""
    original_width, original_height = image_size
    log_ratio = math.log(original_width / original_height)
    ratio = (original_width * original_height) / (
        scale_resolution * scale_resolution
    )
    multiple = min(math.ceil(ratio), max_tiles_num)
    if multiple <= 1 or never_split:
        return 1

    candidate_split_grids_nums = []
    for i in [multiple - 1, multiple, multiple + 1]:
        if i == 1 or i > max_tiles_num:
            continue
        candidate_split_grids_nums.append(i)

    candidate_grids = []
    for split_grids_nums in candidate_split_grids_nums:
        m = 1
        while m <= split_grids_nums:
            if split_grids_nums % m == 0:
                candidate_grids.append([m, split_grids_nums // m])
            m += 1

    best_grid = [1, 1]
    min_error = float("inf")
    for grid in candidate_grids:
        error = abs(log_ratio - math.log(grid[0] / grid[1]))
        if error < min_error:
            best_grid = grid
            min_error = error

    return (best_grid[0] * best_grid[1]) + 1


Yasa2ImageProcessor.register_for_auto_class()