# Copyright 2023-2025 Marigold Team, ETH Zürich. All rights reserved.
# Copyright 2024-2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# --------------------------------------------------------------------------
# More information and citation instructions are available on the
# Marigold project website: https://marigoldcomputervision.github.io
# --------------------------------------------------------------------------
from typing import Any

import numpy as np
import PIL
import torch
import torch.nn.functional as F
from PIL import Image

from ... import ConfigMixin
from ...configuration_utils import register_to_config
from ...image_processor import PipelineImageInput
from ...utils import CONFIG_NAME, logging
from ...utils.import_utils import is_matplotlib_available


logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


class MarigoldImageProcessor(ConfigMixin):
    config_name = CONFIG_NAME

    @register_to_config
    def __init__(
        self,
        vae_scale_factor: int = 8,
        do_normalize: bool = True,
        do_range_check: bool = True,
    ):
        super().__init__()

    @staticmethod
    def expand_tensor_or_array(images: torch.Tensor | np.ndarray) -> torch.Tensor | np.ndarray:
        """
        Expand a tensor or array to a specified number of images.
        """
        if isinstance(images, np.ndarray):
            if images.ndim == 2:  # [H,W] -> [1,H,W,1]
                images = images[None, ..., None]
            if images.ndim == 3:  # [H,W,C] -> [1,H,W,C]
                images = images[None]
        elif isinstance(images, torch.Tensor):
            if images.ndim == 2:  # [H,W] -> [1,1,H,W]
                images = images[None, None]
            elif images.ndim == 3:  # [1,H,W] -> [1,1,H,W]
                images = images[None]
        else:
            raise ValueError(f"Unexpected input type: {type(images)}")
        return images

    @staticmethod
    def pt_to_numpy(images: torch.Tensor) -> np.ndarray:
        """
        Convert a PyTorch tensor to a NumPy image.
        """
        images = images.cpu().permute(0, 2, 3, 1).float().numpy()
        return images

    @staticmethod
    def numpy_to_pt(images: np.ndarray) -> torch.Tensor:
        """
        Convert a NumPy image to a PyTorch tensor.
        """
        if np.issubdtype(images.dtype, np.integer) and not np.issubdtype(images.dtype, np.unsignedinteger):
            raise ValueError(f"Input image dtype={images.dtype} cannot be a signed integer.")
        if np.issubdtype(images.dtype, np.complexfloating):
            raise ValueError(f"Input image dtype={images.dtype} cannot be complex.")
        if np.issubdtype(images.dtype, bool):
            raise ValueError(f"Input image dtype={images.dtype} cannot be boolean.")

        images = torch.from_numpy(images.transpose(0, 3, 1, 2))
        return images

    @staticmethod
    def resize_antialias(
        image: torch.Tensor, size: tuple[int, int], mode: str, is_aa: bool | None = None
    ) -> torch.Tensor:
        if not torch.is_tensor(image):
            raise ValueError(f"Invalid input type={type(image)}.")
        if not torch.is_floating_point(image):
            raise ValueError(f"Invalid input dtype={image.dtype}.")
        if image.dim() != 4:
            raise ValueError(f"Invalid input dimensions; shape={image.shape}.")

        antialias = is_aa and mode in ("bilinear", "bicubic")
        image = F.interpolate(image, size, mode=mode, antialias=antialias)

        return image

    @staticmethod
    def resize_to_max_edge(image: torch.Tensor, max_edge_sz: int, mode: str) -> torch.Tensor:
        if not torch.is_tensor(image):
            raise ValueError(f"Invalid input type={type(image)}.")
        if not torch.is_floating_point(image):
            raise ValueError(f"Invalid input dtype={image.dtype}.")
        if image.dim() != 4:
            raise ValueError(f"Invalid input dimensions; shape={image.shape}.")

        h, w = image.shape[-2:]
        max_orig = max(h, w)
        new_h = h * max_edge_sz // max_orig
        new_w = w * max_edge_sz // max_orig

        if new_h == 0 or new_w == 0:
            raise ValueError(f"Extreme aspect ratio of the input image: [{w} x {h}]")

        image = MarigoldImageProcessor.resize_antialias(image, (new_h, new_w), mode, is_aa=True)

        return image

    @staticmethod
    def pad_image(image: torch.Tensor, align: int) -> tuple[torch.Tensor, tuple[int, int]]:
        if not torch.is_tensor(image):
            raise ValueError(f"Invalid input type={type(image)}.")
        if not torch.is_floating_point(image):
            raise ValueError(f"Invalid input dtype={image.dtype}.")
        if image.dim() != 4:
            raise ValueError(f"Invalid input dimensions; shape={image.shape}.")

        h, w = image.shape[-2:]
        ph, pw = -h % align, -w % align

        image = F.pad(image, (0, pw, 0, ph), mode="replicate")

        return image, (ph, pw)

    @staticmethod
    def unpad_image(image: torch.Tensor, padding: tuple[int, int]) -> torch.Tensor:
        if not torch.is_tensor(image):
            raise ValueError(f"Invalid input type={type(image)}.")
        if not torch.is_floating_point(image):
            raise ValueError(f"Invalid input dtype={image.dtype}.")
        if image.dim() != 4:
            raise ValueError(f"Invalid input dimensions; shape={image.shape}.")

        ph, pw = padding
        uh = None if ph == 0 else -ph
        uw = None if pw == 0 else -pw

        image = image[:, :, :uh, :uw]

        return image

    @staticmethod
    def load_image_canonical(
        image: torch.Tensor | np.ndarray | Image.Image,
        device: torch.device = torch.device("cpu"),
        dtype: torch.dtype = torch.float32,
    ) -> tuple[torch.Tensor, int]:
        if isinstance(image, Image.Image):
            image = np.array(image)

        image_dtype_max = None
        if isinstance(image, (np.ndarray, torch.Tensor)):
            image = MarigoldImageProcessor.expand_tensor_or_array(image)
            if image.ndim != 4:
                raise ValueError("Input image is not 2-, 3-, or 4-dimensional.")
        if isinstance(image, np.ndarray):
            if np.issubdtype(image.dtype, np.integer) and not np.issubdtype(image.dtype, np.unsignedinteger):
                raise ValueError(f"Input image dtype={image.dtype} cannot be a signed integer.")
            if np.issubdtype(image.dtype, np.complexfloating):
                raise ValueError(f"Input image dtype={image.dtype} cannot be complex.")
            if np.issubdtype(image.dtype, bool):
                raise ValueError(f"Input image dtype={image.dtype} cannot be boolean.")
            if np.issubdtype(image.dtype, np.unsignedinteger):
                image_dtype_max = np.iinfo(image.dtype).max
                image = image.astype(np.float32)  # because torch does not have unsigned dtypes beyond torch.uint8
            image = MarigoldImageProcessor.numpy_to_pt(image)

        if torch.is_tensor(image) and not torch.is_floating_point(image) and image_dtype_max is None:
            if image.dtype != torch.uint8:
                raise ValueError(f"Image dtype={image.dtype} is not supported.")
            image_dtype_max = 255

        if not torch.is_tensor(image):
            raise ValueError(f"Input type unsupported: {type(image)}.")

        if image.shape[1] == 1:
            image = image.repeat(1, 3, 1, 1)  # [N,1,H,W] -> [N,3,H,W]
        if image.shape[1] != 3:
            raise ValueError(f"Input image is not 1- or 3-channel: {image.shape}.")

        image = image.to(device=device, dtype=dtype)

        if image_dtype_max is not None:
            image = image / image_dtype_max

        return image

    @staticmethod
    def check_image_values_range(image: torch.Tensor) -> None:
        if not torch.is_tensor(image):
            raise ValueError(f"Invalid input type={type(image)}.")
        if not torch.is_floating_point(image):
            raise ValueError(f"Invalid input dtype={image.dtype}.")
        if image.min().item() < 0.0 or image.max().item() > 1.0:
            raise ValueError("Input image data is partially outside of the [0,1] range.")

    def preprocess(
        self,
        image: PipelineImageInput,
        processing_resolution: int | None = None,
        resample_method_input: str = "bilinear",
        device: torch.device = torch.device("cpu"),
        dtype: torch.dtype = torch.float32,
    ):
        if isinstance(image, list):
            images = None
            for i, img in enumerate(image):
                img = self.load_image_canonical(img, device, dtype)  # [N,3,H,W]
                if images is None:
                    images = img
                else:
                    if images.shape[2:] != img.shape[2:]:
                        raise ValueError(
                            f"Input image[{i}] has incompatible dimensions {img.shape[2:]} with the previous images "
                            f"{images.shape[2:]}"
                        )
                    images = torch.cat((images, img), dim=0)
            image = images
            del images
        else:
            image = self.load_image_canonical(image, device, dtype)  # [N,3,H,W]

        original_resolution = image.shape[2:]

        if self.config.do_range_check:
            self.check_image_values_range(image)

        if self.config.do_normalize:
            image = image * 2.0 - 1.0

        if processing_resolution is not None and processing_resolution > 0:
            image = self.resize_to_max_edge(image, processing_resolution, resample_method_input)  # [N,3,PH,PW]

        image, padding = self.pad_image(image, self.config.vae_scale_factor)  # [N,3,PPH,PPW]

        return image, padding, original_resolution

    @staticmethod
    def colormap(
        image: np.ndarray | torch.Tensor,
        cmap: str = "Spectral",
        bytes: bool = False,
        _force_method: str | None = None,
    ) -> np.ndarray | torch.Tensor:
        """
        Converts a monochrome image into an RGB image by applying the specified colormap. This function mimics the
        behavior of matplotlib.colormaps, but allows the user to use the most discriminative color maps ("Spectral",
        "binary") without having to install or import matplotlib. For all other cases, the function will attempt to use
        the native implementation.

        Args:
            image: 2D tensor of values between 0 and 1, either as np.ndarray or torch.Tensor.
            cmap: Colormap name.
            bytes: Whether to return the output as uint8 or floating point image.
            _force_method:
                Can be used to specify whether to use the native implementation (`"matplotlib"`), the efficient custom
                implementation of the select color maps (`"custom"`), or rely on autodetection (`None`, default).

        Returns:
            An RGB-colorized tensor corresponding to the input image.
        """
        if not (torch.is_tensor(image) or isinstance(image, np.ndarray)):
            raise ValueError("Argument must be a numpy array or torch tensor.")
        if _force_method not in (None, "matplotlib", "custom"):
            raise ValueError("_force_method must be either `None`, `'matplotlib'` or `'custom'`.")

        supported_cmaps = {
            "binary": [
                (1.0, 1.0, 1.0),
                (0.0, 0.0, 0.0),
            ],
            "Spectral": [  # Taken from matplotlib/_cm.py
                (0.61960784313725492, 0.003921568627450980, 0.25882352941176473),  # 0.0 -> [0]
                (0.83529411764705885, 0.24313725490196078, 0.30980392156862746),
                (0.95686274509803926, 0.42745098039215684, 0.2627450980392157),
                (0.99215686274509807, 0.68235294117647061, 0.38039215686274508),
                (0.99607843137254903, 0.8784313725490196, 0.54509803921568623),
                (1.0, 1.0, 0.74901960784313726),
                (0.90196078431372551, 0.96078431372549022, 0.59607843137254901),
                (0.6705882352941176, 0.8666666666666667, 0.64313725490196083),
                (0.4, 0.76078431372549016, 0.6470588235294118),
                (0.19607843137254902, 0.53333333333333333, 0.74117647058823533),
                (0.36862745098039218, 0.30980392156862746, 0.63529411764705879),  # 1.0 -> [K-1]
            ],
        }

        def method_matplotlib(image, cmap, bytes=False):
            if is_matplotlib_available():
                import matplotlib
            else:
                return None

            arg_is_pt, device = torch.is_tensor(image), None
            if arg_is_pt:
                image, device = image.cpu().numpy(), image.device

            if cmap not in matplotlib.colormaps:
                raise ValueError(
                    f"Unexpected color map {cmap}; available options are: {', '.join(list(matplotlib.colormaps.keys()))}"
                )

            cmap = matplotlib.colormaps[cmap]
            out = cmap(image, bytes=bytes)  # [?,4]
            out = out[..., :3]  # [?,3]

            if arg_is_pt:
                out = torch.tensor(out, device=device)

            return out

        def method_custom(image, cmap, bytes=False):
            arg_is_np = isinstance(image, np.ndarray)
            if arg_is_np:
                image = torch.tensor(image)
            if image.dtype == torch.uint8:
                image = image.float() / 255
            else:
                image = image.float()

            is_cmap_reversed = cmap.endswith("_r")
            if is_cmap_reversed:
                cmap = cmap[:-2]

            if cmap not in supported_cmaps:
                raise ValueError(
                    f"Only {list(supported_cmaps.keys())} color maps are available without installing matplotlib."
                )

            cmap = supported_cmaps[cmap]
            if is_cmap_reversed:
                cmap = cmap[::-1]
            cmap = torch.tensor(cmap, dtype=torch.float, device=image.device)  # [K,3]
            K = cmap.shape[0]

            pos = image.clamp(min=0, max=1) * (K - 1)
            left = pos.long()
            right = (left + 1).clamp(max=K - 1)

            d = (pos - left.float()).unsqueeze(-1)
            left_colors = cmap[left]
            right_colors = cmap[right]

            out = (1 - d) * left_colors + d * right_colors

            if bytes:
                out = (out * 255).to(torch.uint8)

            if arg_is_np:
                out = out.numpy()

            return out

        if _force_method is None and torch.is_tensor(image) and cmap == "Spectral":
            return method_custom(image, cmap, bytes)

        out = None
        if _force_method != "custom":
            out = method_matplotlib(image, cmap, bytes)

        if _force_method == "matplotlib" and out is None:
            raise ImportError("Make sure to install matplotlib if you want to use a color map other than 'Spectral'.")

        if out is None:
            out = method_custom(image, cmap, bytes)

        return out

    @staticmethod
    def visualize_depth(
        depth: PIL.Image.Image
        | np.ndarray
        | torch.Tensor
        | list[PIL.Image.Image]
        | list[np.ndarray]
        | list[torch.Tensor],
        val_min: float = 0.0,
        val_max: float = 1.0,
        color_map: str = "Spectral",
    ) -> list[PIL.Image.Image]:
        """
        Visualizes depth maps, such as predictions of the `MarigoldDepthPipeline`.

        Args:
            depth (`PIL.Image.Image | np.ndarray | torch.Tensor | list[PIL.Image.Image, list[np.ndarray],
                list[torch.Tensor]]`): Depth maps.
            val_min (`float`, *optional*, defaults to `0.0`): Minimum value of the visualized depth range.
            val_max (`float`, *optional*, defaults to `1.0`): Maximum value of the visualized depth range.
            color_map (`str`, *optional*, defaults to `"Spectral"`): Color map used to convert a single-channel
                      depth prediction into colored representation.

        Returns: `list[PIL.Image.Image]` with depth maps visualization.
        """
        if val_max <= val_min:
            raise ValueError(f"Invalid values range: [{val_min}, {val_max}].")

        def visualize_depth_one(img, idx=None):
            prefix = "Depth" + (f"[{idx}]" if idx else "")
            if isinstance(img, PIL.Image.Image):
                if img.mode != "I;16":
                    raise ValueError(f"{prefix}: invalid PIL mode={img.mode}.")
                img = np.array(img).astype(np.float32) / (2**16 - 1)
            if isinstance(img, np.ndarray) or torch.is_tensor(img):
                if img.ndim != 2:
                    raise ValueError(f"{prefix}: unexpected shape={img.shape}.")
                if isinstance(img, np.ndarray):
                    img = torch.from_numpy(img)
                if not torch.is_floating_point(img):
                    raise ValueError(f"{prefix}: unexpected dtype={img.dtype}.")
            else:
                raise ValueError(f"{prefix}: unexpected type={type(img)}.")
            if val_min != 0.0 or val_max != 1.0:
                img = (img - val_min) / (val_max - val_min)
            img = MarigoldImageProcessor.colormap(img, cmap=color_map, bytes=True)  # [H,W,3]
            img = PIL.Image.fromarray(img.cpu().numpy())
            return img

        if depth is None or isinstance(depth, list) and any(o is None for o in depth):
            raise ValueError("Input depth is `None`")
        if isinstance(depth, (np.ndarray, torch.Tensor)):
            depth = MarigoldImageProcessor.expand_tensor_or_array(depth)
            if isinstance(depth, np.ndarray):
                depth = MarigoldImageProcessor.numpy_to_pt(depth)  # [N,H,W,1] -> [N,1,H,W]
            if not (depth.ndim == 4 and depth.shape[1] == 1):  # [N,1,H,W]
                raise ValueError(f"Unexpected input shape={depth.shape}, expecting [N,1,H,W].")
            return [visualize_depth_one(img[0], idx) for idx, img in enumerate(depth)]
        elif isinstance(depth, list):
            return [visualize_depth_one(img, idx) for idx, img in enumerate(depth)]
        else:
            raise ValueError(f"Unexpected input type: {type(depth)}")

    @staticmethod
    def export_depth_to_16bit_png(
        depth: np.ndarray | torch.Tensor | list[np.ndarray] | list[torch.Tensor],
        val_min: float = 0.0,
        val_max: float = 1.0,
    ) -> list[PIL.Image.Image]:
        def export_depth_to_16bit_png_one(img, idx=None):
            prefix = "Depth" + (f"[{idx}]" if idx else "")
            if not isinstance(img, np.ndarray) and not torch.is_tensor(img):
                raise ValueError(f"{prefix}: unexpected type={type(img)}.")
            if img.ndim != 2:
                raise ValueError(f"{prefix}: unexpected shape={img.shape}.")
            if torch.is_tensor(img):
                img = img.cpu().numpy()
            if not np.issubdtype(img.dtype, np.floating):
                raise ValueError(f"{prefix}: unexpected dtype={img.dtype}.")
            if val_min != 0.0 or val_max != 1.0:
                img = (img - val_min) / (val_max - val_min)
            img = (img * (2**16 - 1)).astype(np.uint16)
            img = PIL.Image.fromarray(img, mode="I;16")
            return img

        if depth is None or isinstance(depth, list) and any(o is None for o in depth):
            raise ValueError("Input depth is `None`")
        if isinstance(depth, (np.ndarray, torch.Tensor)):
            depth = MarigoldImageProcessor.expand_tensor_or_array(depth)
            if isinstance(depth, np.ndarray):
                depth = MarigoldImageProcessor.numpy_to_pt(depth)  # [N,H,W,1] -> [N,1,H,W]
            if not (depth.ndim == 4 and depth.shape[1] == 1):
                raise ValueError(f"Unexpected input shape={depth.shape}, expecting [N,1,H,W].")
            return [export_depth_to_16bit_png_one(img[0], idx) for idx, img in enumerate(depth)]
        elif isinstance(depth, list):
            return [export_depth_to_16bit_png_one(img, idx) for idx, img in enumerate(depth)]
        else:
            raise ValueError(f"Unexpected input type: {type(depth)}")

    @staticmethod
    def visualize_normals(
        normals: np.ndarray | torch.Tensor | list[np.ndarray] | list[torch.Tensor],
        flip_x: bool = False,
        flip_y: bool = False,
        flip_z: bool = False,
    ) -> list[PIL.Image.Image]:
        """
        Visualizes surface normals, such as predictions of the `MarigoldNormalsPipeline`.

        Args:
            normals (`np.ndarray | torch.Tensor | list[np.ndarray, list[torch.Tensor]]`):
                Surface normals.
            flip_x (`bool`, *optional*, defaults to `False`): Flips the X axis of the normals frame of reference.
                      Default direction is right.
            flip_y (`bool`, *optional*, defaults to `False`):  Flips the Y axis of the normals frame of reference.
                      Default direction is top.
            flip_z (`bool`, *optional*, defaults to `False`): Flips the Z axis of the normals frame of reference.
                      Default direction is facing the observer.

        Returns: `list[PIL.Image.Image]` with surface normals visualization.
        """
        flip_vec = None
        if any((flip_x, flip_y, flip_z)):
            flip_vec = torch.tensor(
                [
                    (-1) ** flip_x,
                    (-1) ** flip_y,
                    (-1) ** flip_z,
                ],
                dtype=torch.float32,
            )

        def visualize_normals_one(img, idx=None):
            img = img.permute(1, 2, 0)
            if flip_vec is not None:
                img *= flip_vec.to(img.device)
            img = (img + 1.0) * 0.5
            img = (img * 255).to(dtype=torch.uint8, device="cpu").numpy()
            img = PIL.Image.fromarray(img)
            return img

        if normals is None or isinstance(normals, list) and any(o is None for o in normals):
            raise ValueError("Input normals is `None`")
        if isinstance(normals, (np.ndarray, torch.Tensor)):
            normals = MarigoldImageProcessor.expand_tensor_or_array(normals)
            if isinstance(normals, np.ndarray):
                normals = MarigoldImageProcessor.numpy_to_pt(normals)  # [N,3,H,W]
            if not (normals.ndim == 4 and normals.shape[1] == 3):
                raise ValueError(f"Unexpected input shape={normals.shape}, expecting [N,3,H,W].")
            return [visualize_normals_one(img, idx) for idx, img in enumerate(normals)]
        elif isinstance(normals, list):
            return [visualize_normals_one(img, idx) for idx, img in enumerate(normals)]
        else:
            raise ValueError(f"Unexpected input type: {type(normals)}")

    @staticmethod
    def visualize_intrinsics(
        prediction: np.ndarray | torch.Tensor | list[np.ndarray] | list[torch.Tensor],
        target_properties: dict[str, Any],
        color_map: str | dict[str, str] = "binary",
    ) -> list[dict[str, PIL.Image.Image]]:
        """
        Visualizes intrinsic image decomposition, such as predictions of the `MarigoldIntrinsicsPipeline`.

        Args:
            prediction (`np.ndarray | torch.Tensor | list[np.ndarray, list[torch.Tensor]]`):
                Intrinsic image decomposition.
            target_properties (`dict[str, Any]`):
                Decomposition properties. Expected entries: `target_names: list[str]` and a dictionary with keys
                `prediction_space: str`, `sub_target_names: list[str | Null]` (must have 3 entries, null for missing
                modalities), `up_to_scale: bool`, one for each target and sub-target.
            color_map (`str | dict[str, str]`, *optional*, defaults to `"Spectral"`):
                Color map used to convert a single-channel predictions into colored representations. When a dictionary
                is passed, each modality can be colored with its own color map.

        Returns: `list[dict[str, PIL.Image.Image]]` with intrinsic image decomposition visualization.
        """
        if "target_names" not in target_properties:
            raise ValueError("Missing `target_names` in target_properties")
        if not isinstance(color_map, str) and not (
            isinstance(color_map, dict)
            and all(isinstance(k, str) and isinstance(v, str) for k, v in color_map.items())
        ):
            raise ValueError("`color_map` must be a string or a dictionary of strings")
        n_targets = len(target_properties["target_names"])

        def visualize_targets_one(images, idx=None):
            # img: [T, 3, H, W]
            out = {}
            for target_name, img in zip(target_properties["target_names"], images):
                img = img.permute(1, 2, 0)  # [H, W, 3]
                prediction_space = target_properties[target_name].get("prediction_space", "srgb")
                if prediction_space == "stack":
                    sub_target_names = target_properties[target_name]["sub_target_names"]
                    if len(sub_target_names) != 3 or any(
                        not (isinstance(s, str) or s is None) for s in sub_target_names
                    ):
                        raise ValueError(f"Unexpected target sub-names {sub_target_names} in {target_name}")
                    for i, sub_target_name in enumerate(sub_target_names):
                        if sub_target_name is None:
                            continue
                        sub_img = img[:, :, i]
                        sub_prediction_space = target_properties[sub_target_name].get("prediction_space", "srgb")
                        if sub_prediction_space == "linear":
                            sub_up_to_scale = target_properties[sub_target_name].get("up_to_scale", False)
                            if sub_up_to_scale:
                                sub_img = sub_img / max(sub_img.max().item(), 1e-6)
                            sub_img = sub_img ** (1 / 2.2)
                        cmap_name = (
                            color_map if isinstance(color_map, str) else color_map.get(sub_target_name, "binary")
                        )
                        sub_img = MarigoldImageProcessor.colormap(sub_img, cmap=cmap_name, bytes=True)
                        sub_img = PIL.Image.fromarray(sub_img.cpu().numpy())
                        out[sub_target_name] = sub_img
                elif prediction_space == "linear":
                    up_to_scale = target_properties[target_name].get("up_to_scale", False)
                    if up_to_scale:
                        img = img / max(img.max().item(), 1e-6)
                    img = img ** (1 / 2.2)
                elif prediction_space == "srgb":
                    pass
                img = (img * 255).to(dtype=torch.uint8, device="cpu").numpy()
                img = PIL.Image.fromarray(img)
                out[target_name] = img
            return out

        if prediction is None or isinstance(prediction, list) and any(o is None for o in prediction):
            raise ValueError("Input prediction is `None`")
        if isinstance(prediction, (np.ndarray, torch.Tensor)):
            prediction = MarigoldImageProcessor.expand_tensor_or_array(prediction)
            if isinstance(prediction, np.ndarray):
                prediction = MarigoldImageProcessor.numpy_to_pt(prediction)  # [N*T,3,H,W]
            if not (prediction.ndim == 4 and prediction.shape[1] == 3 and prediction.shape[0] % n_targets == 0):
                raise ValueError(f"Unexpected input shape={prediction.shape}, expecting [N*T,3,H,W].")
            N_T, _, H, W = prediction.shape
            N = N_T // n_targets
            prediction = prediction.reshape(N, n_targets, 3, H, W)
            return [visualize_targets_one(img, idx) for idx, img in enumerate(prediction)]
        elif isinstance(prediction, list):
            return [visualize_targets_one(img, idx) for idx, img in enumerate(prediction)]
        else:
            raise ValueError(f"Unexpected input type: {type(prediction)}")

    @staticmethod
    def visualize_uncertainty(
        uncertainty: np.ndarray | torch.Tensor | list[np.ndarray] | list[torch.Tensor],
        saturation_percentile=95,
    ) -> list[PIL.Image.Image]:
        """
        Visualizes dense uncertainties, such as produced by `MarigoldDepthPipeline`, `MarigoldNormalsPipeline`, or
        `MarigoldIntrinsicsPipeline`.

        Args:
            uncertainty (`np.ndarray | torch.Tensor | list[np.ndarray, list[torch.Tensor]]`):
                Uncertainty maps.
            saturation_percentile (`int`, *optional*, defaults to `95`):
                Specifies the percentile uncertainty value visualized with maximum intensity.

        Returns: `list[PIL.Image.Image]` with uncertainty visualization.
        """

        def visualize_uncertainty_one(img, idx=None):
            prefix = "Uncertainty" + (f"[{idx}]" if idx else "")
            if img.min() < 0:
                raise ValueError(f"{prefix}: unexpected data range, min={img.min()}.")
            img = img.permute(1, 2, 0)  # [H,W,C]
            img = img.squeeze(2).cpu().numpy()  # [H,W] or [H,W,3]
            saturation_value = np.percentile(img, saturation_percentile)
            img = np.clip(img * 255 / saturation_value, 0, 255)
            img = img.astype(np.uint8)
            img = PIL.Image.fromarray(img)
            return img

        if uncertainty is None or isinstance(uncertainty, list) and any(o is None for o in uncertainty):
            raise ValueError("Input uncertainty is `None`")
        if isinstance(uncertainty, (np.ndarray, torch.Tensor)):
            uncertainty = MarigoldImageProcessor.expand_tensor_or_array(uncertainty)
            if isinstance(uncertainty, np.ndarray):
                uncertainty = MarigoldImageProcessor.numpy_to_pt(uncertainty)  # [N,C,H,W]
            if not (uncertainty.ndim == 4 and uncertainty.shape[1] in (1, 3)):
                raise ValueError(f"Unexpected input shape={uncertainty.shape}, expecting [N,C,H,W] with C in (1,3).")
            return [visualize_uncertainty_one(img, idx) for idx, img in enumerate(uncertainty)]
        elif isinstance(uncertainty, list):
            return [visualize_uncertainty_one(img, idx) for idx, img in enumerate(uncertainty)]
        else:
            raise ValueError(f"Unexpected input type: {type(uncertainty)}")
