# Copyright 2022 The Plenoptix 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.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""
Dataset input structures.
"""

from dataclasses import dataclass
from typing import List, Optional

import torch
from torchtyping import TensorType

from nerfactory.cameras.rays import RayBundle


@dataclass
class PointCloud:
    """_summary_"""

    xyz: TensorType["num_points", 3] = None
    rgb: TensorType["num_points", 3] = None


@dataclass
class Semantics:
    """_summary_"""

    stuff_classes: List[str] = None
    stuff_colors: List[List[int]] = None
    stuff_filenames: List[str] = None
    thing_classes: List[str] = None
    thing_colors: List[List[int]] = None
    thing_filenames: List[str] = None


@dataclass
class SceneBounds:
    """Data to represent the scene bounds.

    aabb: axis-aligned bounding box
        aabb[0] is the minimum (x,y,z) point. aabb[1] is the maximum (x,y,z) point.
    """

    aabb: TensorType[2, 3] = None

    def get_diagonal_length(self):
        """Returns the longest diagonal length."""
        diff = self.aabb[1] - self.aabb[0]
        length = torch.sqrt((diff**2).sum() + 1e-20)
        return length

    def get_center(self):
        """Returns the center of the box."""
        diff = self.aabb[1] - self.aabb[0]
        return self.aabb[0] + diff / 2.0

    def get_centered_and_scaled_scene_bounds(self, scale_factor=1.0):
        """Returns a new box that has been shifted and rescaled to be centered
        about the origin."""
        return SceneBounds(aabb=(self.aabb - self.get_center()) * scale_factor)


@dataclass
class DatasetInputs:
    """Dataset inputs for the image dataset and the ray generator.

    Args:
        image_filenames: Filenames for the images.
        ...
    """

    image_filenames: List[str]
    downscale_factor: int = 1
    intrinsics: torch.tensor = None
    camera_to_world: torch.tensor = None
    mask_filenames: List[str] = None
    depth_filenames: List[str] = None
    scene_bounds: SceneBounds = SceneBounds()
    semantics: Semantics = Semantics()
    point_cloud: PointCloud = PointCloud()
    alpha_color: Optional[TensorType[3]] = None

    def check_inputs(self):
        """Check the inputs to make sure everything is okay."""
        assert self.intrinsics.dtype == torch.float32
        assert self.camera_to_world.dtype == torch.float32

    def save_to_folder_name(self, data_directory: str):
        """Save the dataset inputs."""
        raise NotImplementedError

    def load_from_folder_name(self, data_directory: str):
        """Load the saved dataset inputs."""
        raise NotImplementedError

    def as_dict(self) -> dict:
        """Returns the dataclass as a dictionary."""
        return vars(self)


@dataclass
class BaseDataContainer:
    """A container for data that is not specific to any dataset. It should be everything
    needed by the renderer and loss calculation. Different datasets and models will probably
    need to subclass from this differently, since different Field modules will require different
    data.

    Args:
        rays (RayBundle): The rays for the image.
        ground_truth_pixels (TensorType["num_pixels", 3]): The ground truth pixels for the image.
    """

    rays: RayBundle  # Raybundle and the cameras will be merged into one thing in a later PR
    cameras: torch.tensor
    ground_truth_pixels: Optional[TensorType["num_pixels", 3]] = None