# Copyright 2025 Bytedance Ltd. and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0

import random
from PIL import Image

import cv2
import numpy as np
import torch
from torchvision import transforms
from torchvision.transforms import functional as F
from torchvision.transforms import InterpolationMode, Compose, Normalize

from .video.transforms.na_resize import NaResize
from .video.transforms.divisible_crop import DivisibleCrop
from .video.transforms.rearrange import Rearrange


class MaxLongEdgeMinShortEdgeResize(torch.nn.Module):
    """Resize the input image so that its longest side and shortest side are within a specified range,
    ensuring that both sides are divisible by a specified stride.

    Args:
        max_size (int): Maximum size for the longest edge of the image.
        min_size (int): Minimum size for the shortest edge of the image.
        stride (int): Value by which the height and width of the image must be divisible.
        max_pixels (int): Maximum pixels for the full image.
        interpolation (InterpolationMode): Desired interpolation enum defined by
            :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
            If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.NEAREST_EXACT``,
            ``InterpolationMode.BILINEAR``, and ``InterpolationMode.BICUBIC`` are supported.
            The corresponding Pillow integer constants, e.g., ``PIL.Image.BILINEAR`` are also accepted.
        antialias (bool, optional): Whether to apply antialiasing (default is True).
    """

    def __init__(
        self,
        max_size: int,
        min_size: int,
        stride: int,
        max_pixels: int,
        interpolation=InterpolationMode.BICUBIC,
        antialias=True
    ):
        super().__init__()
        self.max_size = max_size
        self.min_size = min_size
        self.stride = stride
        self.max_pixels = max_pixels
        self.interpolation = interpolation
        self.antialias = antialias

    def _make_divisible(self, value, stride):
        """Ensure the value is divisible by the stride."""
        return max(stride, int(round(value / stride) * stride))

    def _apply_scale(self, width, height, scale):
        new_width = round(width * scale)
        new_height = round(height * scale)
        new_width = self._make_divisible(new_width, self.stride)
        new_height = self._make_divisible(new_height, self.stride)
        return new_width, new_height

    def forward(self, img, img_num=1):
        """
        Args:
            img (PIL Image): Image to be resized.
            img_num (int): Number of images, used to change max_tokens.
        Returns:
            PIL Image or Tensor: Rescaled image with divisible dimensions.
        """
        if isinstance(img, torch.Tensor):
            height, width = img.shape[-2:]
        else:
            width, height = img.size

        scale = min(self.max_size / max(width, height), 1.0)
        scale = max(scale, self.min_size / min(width, height))
        new_width, new_height = self._apply_scale(width, height, scale)

        # Ensure the number of pixels does not exceed max_pixels
        if new_width * new_height > self.max_pixels / img_num:
            scale = self.max_pixels / img_num / (new_width * new_height)
            new_width, new_height = self._apply_scale(new_width, new_height, scale)

        # Ensure longest edge does not exceed max_size
        if max(new_width, new_height) > self.max_size:
            scale = self.max_size / max(new_width, new_height)
            new_width, new_height = self._apply_scale(new_width, new_height, scale)

        return F.resize(img, (new_height, new_width), self.interpolation, antialias=self.antialias)


class ImageTransform:
    def __init__(
        self,
        max_image_size,
        min_image_size,
        image_stride,
        max_pixels=14*14*9*1024,
        image_mean=[0.5, 0.5, 0.5],
        image_std=[0.5, 0.5, 0.5]
    ):
        self.stride = image_stride

        self.resize_transform = MaxLongEdgeMinShortEdgeResize(
            max_size=max_image_size,
            min_size=min_image_size,
            stride=image_stride,
            max_pixels=max_pixels,
        )
        self.to_tensor_transform = transforms.ToTensor()
        self.normalize_transform = transforms.Normalize(mean=image_mean, std=image_std, inplace=True)

    def __call__(self, img, img_num=1):
        img = self.resize_transform(img, img_num=img_num)
        img = self.to_tensor_transform(img)
        img = self.normalize_transform(img)
        return img


class VideoTransform:
    def __init__(
        self,
        resolution=640,
        mode="area",
        divisible_crop_size=16,
        aspect_ratios=("21:9", "16:9", "4:3", "1:1", "3:4", "9:16"),
        stride_spatial=16,
        stride_temporal=4,
        mean=0.5,
        std=0.5,
        **kwargs
    ):
        self.transform = Compose(
            [
                NaResize(
                    resolution=resolution,
                    mode=mode,
                    downsample_only=True,
                    stride=stride_spatial,
                    # NOTE: aspect_ratios are only for `bucket` resize.
                    aspect_ratios=aspect_ratios,
                ),
                DivisibleCrop(divisible_crop_size),
                Normalize(mean, std),
                Rearrange("t c h w -> c t h w"),
            ]
        )
        # self.stride = divisible_crop_size if isinstance(divisible_crop_size, int) else divisible_crop_size[0]
        self.stride_spatial = stride_spatial
        self.stride_temporal = stride_temporal

    def __call__(self, video):
        return self.transform(video)


class VisualTransform:
    def __init__(
        self,
        max_frame_size,
        min_frame_size,
        image_stride,
        max_pixels=14*14*9*1024,
        image_mean=[0.5, 0.5, 0.5],
        image_std=[0.5, 0.5, 0.5]
    ):
        self.stride = image_stride
        self.resize_transform = MaxLongEdgeMinShortEdgeResize(
            max_size=max_frame_size,
            min_size=min_frame_size,
            stride=image_stride,
            max_pixels=max_pixels,
        )
        self.to_tensor_transform = transforms.ToTensor()
        self.normalize_transform = transforms.Normalize(mean=image_mean, std=image_std, inplace=True)

    def _process_single(self, img, img_num=1):
        img = self.resize_transform(img, img_num=img_num)
        img = self.to_tensor_transform(img)
        img = self.normalize_transform(img)
        return img

    def __call__(self, img, img_num=1):
        # --- Video sequence processing ---
        if isinstance(img, (list, tuple)):
            # List of PIL.Image or tensors
            out = torch.stack([self._process_single(frame, img_num=img_num) for frame in img])  # [T, C, H, W]
            out = out.permute(1, 0, 2, 3)  # [C, T, H, W]
            return out
        elif isinstance(img, np.ndarray) and img.ndim == 4:
            # numpy array: [T, H, W, C] or [T, C, H, W]
            frames = [img[i] for i in range(img.shape[0])]
            processed_frames = [self._process_single(Image.fromarray(frame) if frame.shape[-1] in [3, 4] else frame, img_num=img_num)
                                for frame in frames]
            out = torch.stack(processed_frames)  # [T, C, H, W]
            out = out.permute(1, 0, 2, 3)  # [C, T, H, W]
            return out
        elif isinstance(img, torch.Tensor) and img.ndim == 4:
            # torch tensor: [T, C, H, W] or [T, H, W, C]
            frames = [img[i] for i in range(img.shape[0])]
            processed_frames = [self._process_single(frame, img_num=img_num) for frame in frames]
            out = torch.stack(processed_frames)  # [T, C, H, W]
            out = out.permute(1, 0, 2, 3)  # [C, T, H, W]
            return out
        else:
            # Single frame
            return self._process_single(img, img_num=img_num)


def decolorization(image):
    gray_image = image.convert('L')
    return Image.merge(image.mode, [gray_image] * 3) if image.mode in ('RGB', 'L') else gray_image


def downscale(image, scale_factor):
    new_width = int(round(image.width * scale_factor))
    new_height = int(round(image.height * scale_factor))
    new_width = max(1, new_width)
    new_height = max(1, new_height)
    return image.resize((new_width, new_height), resample=Image.BICUBIC)


def crop(image, crop_factors):
    target_h, target_w = crop_factors
    img_w, img_h = image.size

    if target_h > img_h or target_w > img_w:
        raise ValueError("Crop size exceeds image dimensions")

    x = random.randint(0, img_w - target_w)
    y = random.randint(0, img_h - target_h)

    return image.crop((x, y, x + target_w, y + target_h)), [[x, y], [x + target_w, y + target_h]]


def motion_blur_opencv(image, kernel_size=15, angle=0):
    # Linear kernel
    kernel = np.zeros((kernel_size, kernel_size), dtype=np.float32)
    kernel[kernel_size // 2, :] = np.ones(kernel_size, dtype=np.float32)

    # Rotation kernel
    center = (kernel_size / 2 - 0.5, kernel_size / 2 - 0.5)
    M = cv2.getRotationMatrix2D(center, angle, 1)
    rotated_kernel = cv2.warpAffine(kernel, M, (kernel_size, kernel_size))

    # Normalize kernel
    rotated_kernel /= rotated_kernel.sum() if rotated_kernel.sum() != 0 else 1

    img = np.array(image)
    if img.ndim == 2:
        blurred = cv2.filter2D(img, -1, rotated_kernel, borderType=cv2.BORDER_REFLECT)
    else:
        # For color images, convolve each channel independently
        blurred = np.zeros_like(img)
        for c in range(img.shape[2]):
            blurred[..., c] = cv2.filter2D(img[..., c], -1, rotated_kernel, borderType=cv2.BORDER_REFLECT)

    return Image.fromarray(blurred.astype(np.uint8))


def shuffle_patch(image, num_splits, gap_size=2):
    """Split an image into patches, allowing non-divisible sizes, shuffle them, and stitch them with gaps."""
    h_splits, w_splits = num_splits
    img_w, img_h = image.size

    base_patch_h = img_h // h_splits
    patch_heights = [base_patch_h] * (h_splits - 1)
    patch_heights.append(img_h - sum(patch_heights))

    base_patch_w = img_w // w_splits
    patch_widths = [base_patch_w] * (w_splits - 1)
    patch_widths.append(img_w - sum(patch_widths))

    patches = []
    current_y = 0
    for i in range(h_splits):
        current_x = 0
        patch_h = patch_heights[i]
        for j in range(w_splits):
            patch_w = patch_widths[j]
            patch = image.crop((current_x, current_y, current_x + patch_w, current_y + patch_h))
            patches.append(patch)
            current_x += patch_w
        current_y += patch_h

    random.shuffle(patches)

    total_width = sum(patch_widths) + (w_splits - 1) * gap_size
    total_height = sum(patch_heights) + (h_splits - 1) * gap_size
    new_image = Image.new(image.mode, (total_width, total_height), color=(255, 255, 255))

    current_y = 0  # Starting Y coordinate of the current row
    patch_idx = 0  # Current patch index
    for i in range(h_splits):
        current_x = 0  # Starting X coordinate of the current column
        patch_h = patch_heights[i]  # Patch height for the current row
        for j in range(w_splits):
            # Fetch the shuffled patch
            patch = patches[patch_idx]
            patch_w = patch_widths[j]  # Patch width for the current column
            # Paste the patch with top-left corner at (current_x, current_y)
            new_image.paste(patch, (current_x, current_y))
            # Update X coordinate: next patch starts after current patch width plus gap
            current_x += patch_w + gap_size
            patch_idx += 1
        # Update Y coordinate: next row starts after current row height plus gap
        current_y += patch_h + gap_size

    return new_image


def inpainting(image, num_splits, blank_ratio=0.3, blank_color=(255, 255, 255)):
    """
    Split an image and randomly blank out patches for inpainting tasks.

    Args:
        image: Input PIL.Image in RGB mode.
        h_splits: Number of row splits.
        w_splits: Number of column splits.
        blank_ratio: Ratio of blank patches, from 0 to 1.
        blank_color: RGB color for blank regions, e.g. white (255, 255, 255).

    Returns:
        Processed and stitched PIL.Image.
    """
    h_splits, w_splits = num_splits
    img_w, img_h = image.size

    base_patch_h = img_h // h_splits
    patch_heights = [base_patch_h] * (h_splits - 1)
    patch_heights.append(img_h - sum(patch_heights))

    base_patch_w = img_w // w_splits
    patch_widths = [base_patch_w] * (w_splits - 1)
    patch_widths.append(img_w - sum(patch_widths))

    patches = []
    current_y = 0
    for i in range(h_splits):
        current_x = 0
        patch_h = patch_heights[i]
        for j in range(w_splits):
            patch_w = patch_widths[j]
            patch = image.crop((current_x, current_y, current_x + patch_w, current_y + patch_h))
            patches.append(patch)
            current_x += patch_w
        current_y += patch_h

    total_patches = h_splits * w_splits
    num_blank = int(total_patches * blank_ratio)
    num_blank = max(0, min(num_blank, total_patches))
    blank_indices = random.sample(range(total_patches), num_blank)

    processed_patches = []
    for idx, patch in enumerate(patches):
        if idx in blank_indices:
            blank_patch = Image.new("RGB", patch.size, color=blank_color)
            processed_patches.append(blank_patch)
        else:
            processed_patches.append(patch)

    # Create the result image with the same size as the original
    result_image = Image.new("RGB", (img_w, img_h))
    current_y = 0
    patch_idx = 0
    for i in range(h_splits):
        current_x = 0
        patch_h = patch_heights[i]
        for j in range(w_splits):
            # Fetch the processed patch
            patch = processed_patches[patch_idx]
            patch_w = patch_widths[j]
            # Paste it back to the original position
            result_image.paste(patch, (current_x, current_y))
            current_x += patch_w
            patch_idx += 1
        current_y += patch_h

    return result_image