# Copyright 2024 Rhymes AI. All rights reserved.
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from typing import List, Optional, Union

import numpy as np
import torch
from PIL import Image, ImageOps
from torchvision import transforms
from transformers import BaseImageProcessor, BatchFeature, TensorType


def _select_best_resolution(
    img_width: int, img_height: int, target_ratios: List[List[int]], patch_size: int
):
    """
    Selects the best resolution from a list of possible resolutions based on the original size.

    Args:
        img_width: the original widths of images.
        img_height: the original heights of images.
        target_ratios (2d numpy array): dimension size (M,2)
        patch_size (int): image patch size

    Returns:
        tuple: The best fit resolution in the format (width, height).
    """

    aspect_ratio = img_width / img_height
    best_ratio_diff = float("inf")
    best_ratio_w, best_ratio_h = 1, 1
    area = np.int32(img_height) * np.int32(img_height)
    for ratio in target_ratios:
        target_aspect_ratio = ratio[0] / ratio[1]
        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
        if ratio_diff < best_ratio_diff:
            best_ratio_diff = ratio_diff
            best_ratio_w, best_ratio_h = ratio[0], ratio[1]
        elif (
            ratio_diff == best_ratio_diff
            and area > 0.5 * patch_size * patch_size * ratio[0] * ratio[1]
        ):
            best_ratio_w, best_ratio_h = ratio[0], ratio[1]

    return best_ratio_w, best_ratio_h


def _split_image(
    image: Image.Image,
    split_image: bool,
    split_ratio: List[List[int]],
    patch_size: int,
) -> List[Image.Image]:
    """
    Split image into multiple patches

    Args:
        image (PIL.Image): Input image.
        split_image (bool): Whether to split the image into patches.
        split_ratio (2d numpy array): dimension size (M,2)
        patch_size (int): image patch size

    Returns:
        List[PIL.Image]: List of splitted images.
    """
    if split_image:
        ratio_width, ratio_height = _select_best_resolution(
            image.width, image.height, split_ratio, patch_size
        )
        resize_width = patch_size * ratio_width
        resize_height = patch_size * ratio_height
        blocks = ratio_width * ratio_height
        resized_img = image.resize((resize_width, resize_height))
        processed_images = []
        for i in range(blocks):
            box = (
                (i % (resize_width // patch_size)) * patch_size,
                (i // (resize_width // patch_size)) * patch_size,
                ((i % (resize_width // patch_size)) + 1) * patch_size,
                ((i // (resize_width // patch_size)) + 1) * patch_size,
            )
            # split the image
            split_img = resized_img.crop(box)
            processed_images.append(split_img)
        assert len(processed_images) == blocks
        if len(processed_images) != 1:
            processed_images.insert(0, image)
        return processed_images
    else:
        return [image]


def keep_ratio_resize_and_pixel_mask(
    img: Image.Image, max_size, min_size=336, padding_value=0
):
    """
    Resize an image while maintaining aspect ratio and create a pixel mask.

    Args:
        img (PIL.Image): Input image.
        max_size (int): Maximum size for the larger dimension of the image.
        min_size (int, optional): Minimum size for the smaller dimension. Defaults to 336.
        padding_value (int, optional): Value used for padding. Defaults to 0.

    Returns:
        tuple: A tuple containing:
            - PIL.Image: Resized and padded image.
            - torch.Tensor: Boolean pixel mask. This mask is a 2D tensor of shape (max_size, max_size) where:
                - True (1) values indicate pixels that belong to the original resized image.
                - False (0) values indicate pixels that are part of the padding.
              The mask helps distinguish between actual image content and padded areas in subsequent processing steps.
    """
    img = img.convert("RGB")
    # rescale the given image, keep the aspect ratio
    scale = max_size / max(img.size)

    w, h = img.size
    if w >= h:
        new_size = (max_size, max(int(h * scale), min_size))  # w, h
    else:
        new_size = (max(int(w * scale), min_size), max_size)  # w, h

    img_resized = img.resize(new_size, resample=Image.Resampling.BICUBIC)

    # padding the right/bottom
    padding_right, padding_bottom = max_size - new_size[0], max_size - new_size[1]
    img_padded = ImageOps.expand(
        img_resized, (0, 0, padding_right, padding_bottom), fill=padding_value
    )

    # Create a pixel mask
    pixel_mask = torch.zeros(max_size, max_size)
    pixel_mask[: new_size[1], : new_size[0]] = 1
    pixel_mask = pixel_mask.bool()
    return img_padded, pixel_mask


class AriaVisionProcessor(BaseImageProcessor):
    """
    A vision processor for the Aria model that handles image preprocessing.
    """

    def __init__(
        self,
        max_image_size=980,
        min_image_size=336,
        image_mean=[0.5, 0.5, 0.5],
        image_std=[0.5, 0.5, 0.5],
        **kwargs,
    ):
        """
        Initialize the AriaVisionProcessor.

        Args:
            max_image_size (int, optional): Maximum image size. Defaults to 980.
            min_image_size (int, optional): Minimum image size. Defaults to 336.
            mean (list, optional): Mean values for normalization. Defaults to [0.5, 0.5, 0.5].
            std (list, optional): Standard deviation values for normalization. Defaults to [0.5, 0.5, 0.5].
        """
        super().__init__(**kwargs)

        self.max_image_size = max_image_size
        self.min_image_size = min_image_size
        self.image_mean = image_mean
        self.image_std = image_std
        self.auto_map = {
            "AutoProcessor": "processing_aria.AriaProcessor",
            "AutoImageProcessor": "vision_processor.AriaVisionProcessor",
        }

        # we make the transform a property so that it is lazily initialized,
        # this could avoid the error "TypeError: Object of type Normalize is not JSON serializable"
        # when we used save_pretrained or from_pretrained.
        self._transform = None
        self._set_processor_class("AriaProcessor")

    @property
    def transform(self):
        if self._transform is None:
            # Recreate the transform when accessed
            self._transform = transforms.Compose(
                [
                    transforms.ToTensor(),
                    transforms.Normalize(self.image_mean, self.image_std),
                ]
            )
        return self._transform

    def __call__(
        self,
        images: Union[Image.Image, List[Image.Image]],
        max_image_size: Optional[int] = 980,
        min_image_size: Optional[int] = 336,
        return_tensors: Optional[Union[str, TensorType]] = "pt",
        split_image: Optional[bool] = False,
        split_ratio: Optional[List[List[int]]] = [
            [1, 2],
            [1, 3],
            [1, 4],
            [1, 5],
            [1, 6],
            [1, 7],
            [1, 8],
            [2, 4],
            [2, 3],
            [2, 2],
            [2, 1],
            [3, 1],
            [3, 2],
            [4, 1],
            [4, 2],
            [5, 1],
            [6, 1],
            [7, 1],
            [8, 1],
        ],
    ):
        """
        Process a list of images.

        Args:
            images (list): List of PIL.Image objects.
            max_image_size (int, optional): Override the default max image size. Defaults to None.
            return_tensors (str or TensorType, optional): The type of tensor to return. Defaults to "pt".
            split_image (bool, optional): Whether to split the image. Defaults to False.
            split_ratio (list, optional): The ratio for splitting the image. Defaults to a list of common split ratios.
        Returns:
            BatchFeature: A BatchFeature object containing:
                - 'pixel_values': Tensor of processed image pixel values.
                - 'pixel_mask': Boolean pixel mask. This mask is a 2D tensor of shape (max_size, max_size) where:
                    - True (1) values indicate pixels that belong to the original resized image.
                    - False (0) values indicate pixels that are part of the padding.
                  The mask helps distinguish between actual image content and padded areas in subsequent processing steps.
                - 'num_crops': Tensor of the number of crops for each image.
        """
        max_size = self.max_image_size if max_image_size is None else max_image_size
        min_size = self.min_image_size if min_image_size is None else min_image_size

        if max_size not in [490, 980]:
            raise ValueError("max_image_size must be either 490 or 980")

        if isinstance(images, Image.Image):
            images = [images]

        pixel_values = []
        pixel_masks = []
        num_crops = []

        for image in images:
            crop_images = _split_image(image, split_image, split_ratio, max_size)
            num_crops.append(torch.tensor(len(crop_images)))
            for crop_image in crop_images:
                img_padded, pixel_mask = keep_ratio_resize_and_pixel_mask(
                    crop_image, max_size, min_size
                )
                img_padded = self.transform(img_padded)
                pixel_values.append(img_padded)
                pixel_masks.append(pixel_mask)

        return BatchFeature(
            data={
                "pixel_values": torch.stack(pixel_values),
                "pixel_mask": torch.stack(pixel_masks),
                "num_crops": torch.stack(num_crops),
            },
            tensor_type=return_tensors,
        )

    def preprocess(
        self,
        images,
        max_image_size=None,
        min_image_size=None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        split_image: Optional[bool] = False,
        split_ratio: Optional[List[List[int]]] = [
            [1, 2],
            [1, 3],
            [1, 4],
            [1, 5],
            [1, 6],
            [1, 7],
            [1, 8],
            [2, 4],
            [2, 3],
            [2, 2],
            [2, 1],
            [3, 1],
            [3, 2],
            [4, 1],
            [4, 2],
            [5, 1],
            [6, 1],
            [7, 1],
            [8, 1],
        ],
    ):
        return self.__call__(
            images,
            max_image_size=max_image_size,
            min_image_size=min_image_size,
            return_tensors=return_tensors,
            split_image=split_image,
            split_ratio=split_ratio,
        )