# Copyright 2025 Adobe
# All Rights Reserved.

# NOTICE: Adobe permits you to use, modify, and distribute this file in
# accordance with the terms of the Adobe license agreement accompanying
# it.

import math
from typing import Any, Dict, List, Optional, Tuple

import torch
import torchvision
from torch import nn, Tensor

from .image_list import ImageList
from .roi_heads import paste_masks_in_image


@torch.jit.unused
def _get_shape_onnx(image: Tensor) -> Tensor:
    from torch.onnx import operators

    return operators.shape_as_tensor(image)[-2:]


@torch.jit.unused
def _fake_cast_onnx(v: Tensor) -> float:
    # ONNX requires a tensor but here we fake its type for JIT.
    return v


def _resize_image_and_masks(
    image: Tensor,
    self_min_size: int,
    self_max_size: int,
    target: Optional[Dict[str, Tensor]] = None,
    fixed_size: Optional[Tuple[int, int]] = None,
) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
    if torchvision._is_tracing():
        im_shape = _get_shape_onnx(image)
    elif torch.jit.is_scripting():
        im_shape = torch.tensor(image.shape[-2:])
    else:
        im_shape = image.shape[-2:]

    size: Optional[List[int]] = None
    scale_factor: Optional[float] = None
    recompute_scale_factor: Optional[bool] = None
    if fixed_size is not None:
        size = [fixed_size[1], fixed_size[0]]
    else:
        if torch.jit.is_scripting() or torchvision._is_tracing():
            min_size = torch.min(im_shape).to(dtype=torch.float32)
            max_size = torch.max(im_shape).to(dtype=torch.float32)
            self_min_size_f = float(self_min_size)
            self_max_size_f = float(self_max_size)
            scale = torch.min(self_min_size_f / min_size, self_max_size_f / max_size)

            if torchvision._is_tracing():
                scale_factor = _fake_cast_onnx(scale)
            else:
                scale_factor = scale.item()

        else:
            # Do it the normal way
            min_size = min(im_shape)
            max_size = max(im_shape)
            scale_factor = min(self_min_size / min_size, self_max_size / max_size)

        recompute_scale_factor = True

    image = torch.nn.functional.interpolate(
        image[None],
        size=size,
        scale_factor=scale_factor,
        mode="bilinear",
        recompute_scale_factor=recompute_scale_factor,
        align_corners=False,
    )[0]

    if target is None:
        return image, target

    if "masks" in target:
        mask = target["masks"]
        mask = torch.nn.functional.interpolate(
            mask[:, None].float(), size=size, scale_factor=scale_factor, recompute_scale_factor=recompute_scale_factor
        )[:, 0].byte()
        target["masks"] = mask
    return image, target


class GeneralizedRCNNTransform(nn.Module):
    """
    Performs input / target transformation before feeding the data to a GeneralizedRCNN
    model.

    The transformations it performs are:
        - input normalization (mean subtraction and std division)
        - input / target resizing to match min_size / max_size

    It returns a ImageList for the inputs, and a List[Dict[Tensor]] for the targets
    """

    def __init__(
        self,
        min_size: int,
        max_size: int,
        image_mean: List[float],
        image_std: List[float],
        size_divisible: int = 32,
        fixed_size: Optional[Tuple[int, int]] = None,
        **kwargs: Any,
    ):
        super().__init__()
        if not isinstance(min_size, (list, tuple)):
            min_size = (min_size,)
        self.min_size = min_size
        self.max_size = max_size
        self.image_mean = image_mean
        self.image_std = image_std
        self.size_divisible = size_divisible
        self.fixed_size = fixed_size
        self._skip_resize = kwargs.pop("_skip_resize", False)

    def forward(
        self, images: List[Tensor], targets: Optional[List[Dict[str, Tensor]]] = None
    ) -> Tuple[ImageList, Optional[List[Dict[str, Tensor]]]]:
        images = [img for img in images]
        if targets is not None:
            # make a copy of targets to avoid modifying it in-place
            # once torchscript supports dict comprehension
            # this can be simplified as follows
            # targets = [{k: v for k,v in t.items()} for t in targets]
            targets_copy: List[Dict[str, Tensor]] = []
            for t in targets:
                data: Dict[str, Tensor] = {}
                for k, v in t.items():
                    data[k] = v
                targets_copy.append(data)
            targets = targets_copy
        for i in range(len(images)):
            image = images[i]
            target_index = targets[i] if targets is not None else None

            if image.dim() != 3:
                raise ValueError(f"images is expected to be a list of 3d tensors of shape [C, H, W], got {image.shape}")
            image = self.normalize(image)
            image, target_index = self.resize(image, target_index)
            images[i] = image
            if targets is not None and target_index is not None:
                targets[i] = target_index

        image_sizes = [img.shape[-2:] for img in images]
        images = self.batch_images(images, size_divisible=self.size_divisible)
        image_sizes_list: List[Tuple[int, int]] = []
        for image_size in image_sizes:
            torch._assert(
                len(image_size) == 2,
                f"Input tensors expected to have in the last two elements H and W, instead got {image_size}",
            )
            image_sizes_list.append((image_size[0], image_size[1]))

        image_list = ImageList(images, image_sizes_list)
        return image_list, targets

    def normalize(self, image: Tensor) -> Tensor:
        if not image.is_floating_point():
            raise TypeError(
                f"Expected input images to be of floating type (in range [0, 1]), "
                f"but found type {image.dtype} instead"
            )
        dtype, device = image.dtype, image.device
        mean = torch.as_tensor(self.image_mean, dtype=dtype, device=device)
        std = torch.as_tensor(self.image_std, dtype=dtype, device=device)
        return (image - mean[:, None, None]) / std[:, None, None]

    def torch_choice(self, k: List[int]) -> int:
        """
        Implements `random.choice` via torch ops, so it can be compiled with
        TorchScript and we use PyTorch's RNG (not native RNG)
        """
        index = int(torch.empty(1).uniform_(0.0, float(len(k))).item())
        return k[index]

    def resize(
        self,
        image: Tensor,
        target: Optional[Dict[str, Tensor]] = None,
    ) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
        h, w = image.shape[-2:]
        if self._skip_resize:
            return image, target
        if self.training:
            size = self.torch_choice(self.min_size)
        else:
            size = self.min_size[-1]
        image, target = _resize_image_and_masks(image, size, self.max_size, target, self.fixed_size)

        if target is None:
            return image, target

        bbox = target["boxes"]
        bbox = resize_boxes(bbox, (h, w), image.shape[-2:])
        target["boxes"] = bbox

        if "keypoints" in target:
            keypoints = target["keypoints"]
            keypoints = resize_keypoints(keypoints, (h, w), image.shape[-2:])
            target["keypoints"] = keypoints
        return image, target

    # _onnx_batch_images() is an implementation of
    # batch_images() that is supported by ONNX tracing.
    @torch.jit.unused
    def _onnx_batch_images(self, images: List[Tensor], size_divisible: int = 32) -> Tensor:
        max_size = []
        for i in range(images[0].dim()):
            max_size_i = torch.max(torch.stack([img.shape[i] for img in images]).to(torch.float32)).to(torch.int64)
            max_size.append(max_size_i)
        stride = size_divisible
        max_size[1] = (torch.ceil((max_size[1].to(torch.float32)) / stride) * stride).to(torch.int64)
        max_size[2] = (torch.ceil((max_size[2].to(torch.float32)) / stride) * stride).to(torch.int64)
        max_size = tuple(max_size)

        # work around for
        # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
        # which is not yet supported in onnx
        padded_imgs = []
        for img in images:
            padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]
            padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))
            padded_imgs.append(padded_img)

        return torch.stack(padded_imgs)

    def max_by_axis(self, the_list: List[List[int]]) -> List[int]:
        maxes = the_list[0]
        for sublist in the_list[1:]:
            for index, item in enumerate(sublist):
                maxes[index] = max(maxes[index], item)
        return maxes

    def batch_images(self, images: List[Tensor], size_divisible: int = 32) -> Tensor:
        if torchvision._is_tracing():
            # batch_images() does not export well to ONNX
            # call _onnx_batch_images() instead
            return self._onnx_batch_images(images, size_divisible)

        max_size = self.max_by_axis([list(img.shape) for img in images])
        stride = float(size_divisible)
        max_size = list(max_size)
        max_size[1] = int(math.ceil(float(max_size[1]) / stride) * stride)
        max_size[2] = int(math.ceil(float(max_size[2]) / stride) * stride)

        batch_shape = [len(images)] + max_size
        batched_imgs = images[0].new_full(batch_shape, 0)
        for i in range(batched_imgs.shape[0]):
            img = images[i]
            batched_imgs[i, : img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)

        return batched_imgs

    def postprocess(
        self,
        result: List[Dict[str, Tensor]],
        image_shapes: List[Tuple[int, int]],
        original_image_sizes: List[Tuple[int, int]],
    ) -> List[Dict[str, Tensor]]:
        if self.training:
            return result
        for i, (pred, im_s, o_im_s) in enumerate(zip(result, image_shapes, original_image_sizes)):
            boxes = pred["boxes"]
            boxes = resize_boxes(boxes, im_s, o_im_s)
            result[i]["boxes"] = boxes
            if "masks" in pred:
                masks = pred["masks"]
                masks = paste_masks_in_image(masks, boxes, o_im_s)
                result[i]["masks"] = masks
            if "keypoints" in pred:
                keypoints = pred["keypoints"]
                keypoints = resize_keypoints(keypoints, im_s, o_im_s)
                result[i]["keypoints"] = keypoints
        return result

    def __repr__(self) -> str:
        format_string = f"{self.__class__.__name__}("
        _indent = "\n    "
        format_string += f"{_indent}Normalize(mean={self.image_mean}, std={self.image_std})"
        format_string += f"{_indent}Resize(min_size={self.min_size}, max_size={self.max_size}, mode='bilinear')"
        format_string += "\n)"
        return format_string


def resize_keypoints(keypoints: Tensor, original_size: List[int], new_size: List[int]) -> Tensor:
    ratios = [
        torch.tensor(s, dtype=torch.float32, device=keypoints.device)
        / torch.tensor(s_orig, dtype=torch.float32, device=keypoints.device)
        for s, s_orig in zip(new_size, original_size)
    ]
    ratio_h, ratio_w = ratios
    resized_data = keypoints.clone()
    if torch._C._get_tracing_state():
        resized_data_0 = resized_data[:, :, 0] * ratio_w
        resized_data_1 = resized_data[:, :, 1] * ratio_h
        resized_data = torch.stack((resized_data_0, resized_data_1, resized_data[:, :, 2]), dim=2)
    else:
        resized_data[..., 0] *= ratio_w
        resized_data[..., 1] *= ratio_h
    return resized_data


def resize_boxes(boxes: Tensor, original_size: List[int], new_size: List[int]) -> Tensor:
    ratios = [
        torch.tensor(s, dtype=torch.float32, device=boxes.device)
        / torch.tensor(s_orig, dtype=torch.float32, device=boxes.device)
        for s, s_orig in zip(new_size, original_size)
    ]
    ratio_height, ratio_width = ratios
    xmin, ymin, xmax, ymax = boxes.unbind(1)

    xmin = xmin * ratio_width
    xmax = xmax * ratio_width
    ymin = ymin * ratio_height
    ymax = ymax * ratio_height
    return torch.stack((xmin, ymin, xmax, ymax), dim=1)
