# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause

# ignore private usage of variables warning
# pyright: reportPrivateUsage=none

"""Launch Isaac Sim Simulator first."""

from isaaclab.app import AppLauncher

# launch omniverse app
simulation_app = AppLauncher(headless=True, enable_cameras=True).app

"""Rest everything follows."""

import copy
import random

import numpy as np
import pytest
import torch
from flaky import flaky

import omni.replicator.core as rep
from isaacsim.core.prims import SingleGeometryPrim, SingleRigidPrim
from pxr import Gf, UsdGeom

import isaaclab.sim as sim_utils
from isaaclab.sensors.camera import TiledCamera, TiledCameraCfg


@pytest.fixture()
def setup_camera():
    """Create a blank new stage for each test."""
    camera_cfg = TiledCameraCfg(
        height=128,
        width=256,
        offset=TiledCameraCfg.OffsetCfg(pos=(0.0, 0.0, 4.0), rot=(0.0, 0.0, 1.0, 0.0), convention="ros"),
        prim_path="/World/Camera",
        update_period=0,
        data_types=["rgb", "distance_to_camera"],
        spawn=sim_utils.PinholeCameraCfg(
            focal_length=24.0, focus_distance=400.0, horizontal_aperture=20.955, clipping_range=(0.1, 1.0e5)
        ),
    )
    # Create a new stage
    sim_utils.create_new_stage()
    # Simulation time-step
    dt = 0.01
    # Load kit helper
    sim_cfg = sim_utils.SimulationCfg(dt=dt)
    sim = sim_utils.SimulationContext(sim_cfg)
    # populate scene
    _populate_scene()
    # load stage
    sim_utils.update_stage()
    yield camera_cfg, sim, dt
    # Teardown
    rep.vp_manager.destroy_hydra_textures("Replicator")
    # stop simulation
    # note: cannot use self.sim.stop() since it does one render step after stopping!! This doesn't make sense :(
    sim._timeline.stop()
    # clear the stage
    sim.clear_all_callbacks()
    sim.clear_instance()


@pytest.mark.isaacsim_ci
def test_multi_tiled_camera_init(setup_camera):
    """Test initialization of multiple tiled cameras."""
    camera_cfg, sim, dt = setup_camera
    num_tiled_cameras = 3
    num_cameras_per_tiled_camera = 7

    tiled_cameras = []
    for i in range(num_tiled_cameras):
        for j in range(num_cameras_per_tiled_camera):
            sim_utils.create_prim(f"/World/Origin_{i}_{j}", "Xform")

        # Create camera
        camera_cfg = copy.deepcopy(camera_cfg)
        camera_cfg.prim_path = f"/World/Origin_{i}.*/CameraSensor"
        camera = TiledCamera(camera_cfg)
        tiled_cameras.append(camera)

        # Check simulation parameter is set correctly
        assert sim.has_rtx_sensors()

    # Play sim
    sim.reset()

    for i, camera in enumerate(tiled_cameras):
        # Check if camera is initialized
        assert camera.is_initialized
        # Check if camera prim is set correctly and that it is a camera prim
        assert camera._sensor_prims[1].GetPath().pathString == f"/World/Origin_{i}_1/CameraSensor"
        assert isinstance(camera._sensor_prims[0], UsdGeom.Camera)

    # Simulate for a few steps
    # note: This is a workaround to ensure that the textures are loaded.
    #   Check "Known Issues" section in the documentation for more details.
    for _ in range(5):
        sim.step()

    for camera in tiled_cameras:
        # Check buffers that exists and have correct shapes
        assert camera.data.pos_w.shape == (num_cameras_per_tiled_camera, 3)
        assert camera.data.quat_w_ros.shape == (num_cameras_per_tiled_camera, 4)
        assert camera.data.quat_w_world.shape == (num_cameras_per_tiled_camera, 4)
        assert camera.data.quat_w_opengl.shape == (num_cameras_per_tiled_camera, 4)
        assert camera.data.intrinsic_matrices.shape == (num_cameras_per_tiled_camera, 3, 3)
        assert camera.data.image_shape == (camera.cfg.height, camera.cfg.width)

    # Simulate physics
    for _ in range(10):
        # Initialize data arrays
        rgbs = []
        distances = []

        # perform rendering
        sim.step()
        for i, camera in enumerate(tiled_cameras):
            # update camera
            camera.update(dt)
            # check image data
            for data_type, im_data in camera.data.output.items():
                if data_type == "rgb":
                    im_data = im_data.clone() / 255.0
                    assert im_data.shape == (num_cameras_per_tiled_camera, camera.cfg.height, camera.cfg.width, 3)
                    for j in range(num_cameras_per_tiled_camera):
                        assert (im_data[j]).mean().item() > 0.0
                    rgbs.append(im_data)
                elif data_type == "distance_to_camera":
                    im_data = im_data.clone()
                    im_data[torch.isinf(im_data)] = 0
                    assert im_data.shape == (num_cameras_per_tiled_camera, camera.cfg.height, camera.cfg.width, 1)
                    for j in range(num_cameras_per_tiled_camera):
                        assert im_data[j].mean().item() > 0.0
                    distances.append(im_data)

        # Check data from tiled cameras are consistent, assumes >1 tiled cameras
        for i in range(1, num_tiled_cameras):
            assert torch.abs(rgbs[0] - rgbs[i]).mean() < 0.05  # images of same color should be below 0.001
            assert torch.abs(distances[0] - distances[i]).mean() < 0.01  # distances of same scene should be 0

    for camera in tiled_cameras:
        del camera


@pytest.mark.isaacsim_ci
def test_all_annotators_multi_tiled_camera(setup_camera):
    """Test initialization of multiple tiled cameras with all supported annotators."""
    camera_cfg, sim, dt = setup_camera
    all_annotator_types = [
        "rgb",
        "rgba",
        "depth",
        "distance_to_camera",
        "distance_to_image_plane",
        "normals",
        "motion_vectors",
        "semantic_segmentation",
        "instance_segmentation_fast",
        "instance_id_segmentation_fast",
    ]

    num_tiled_cameras = 2
    num_cameras_per_tiled_camera = 9

    tiled_cameras = []
    for i in range(num_tiled_cameras):
        for j in range(num_cameras_per_tiled_camera):
            sim_utils.create_prim(f"/World/Origin_{i}_{j}", "Xform")

        # Create camera
        camera_cfg = copy.deepcopy(camera_cfg)
        camera_cfg.data_types = all_annotator_types
        camera_cfg.prim_path = f"/World/Origin_{i}.*/CameraSensor"
        camera = TiledCamera(camera_cfg)
        tiled_cameras.append(camera)

        # Check simulation parameter is set correctly
        assert sim.has_rtx_sensors()

    # Play sim
    sim.reset()

    for i, camera in enumerate(tiled_cameras):
        # Check if camera is initialized
        assert camera.is_initialized
        # Check if camera prim is set correctly and that it is a camera prim
        assert camera._sensor_prims[1].GetPath().pathString == f"/World/Origin_{i}_1/CameraSensor"
        assert isinstance(camera._sensor_prims[0], UsdGeom.Camera)
        assert sorted(camera.data.output.keys()) == sorted(all_annotator_types)

    # Simulate for a few steps
    # note: This is a workaround to ensure that the textures are loaded.
    #   Check "Known Issues" section in the documentation for more details.
    for _ in range(5):
        sim.step()

    for camera in tiled_cameras:
        # Check buffers that exists and have correct shapes
        assert camera.data.pos_w.shape == (num_cameras_per_tiled_camera, 3)
        assert camera.data.quat_w_ros.shape == (num_cameras_per_tiled_camera, 4)
        assert camera.data.quat_w_world.shape == (num_cameras_per_tiled_camera, 4)
        assert camera.data.quat_w_opengl.shape == (num_cameras_per_tiled_camera, 4)
        assert camera.data.intrinsic_matrices.shape == (num_cameras_per_tiled_camera, 3, 3)
        assert camera.data.image_shape == (camera.cfg.height, camera.cfg.width)

    # Simulate physics
    for _ in range(10):
        # perform rendering
        sim.step()
        for i, camera in enumerate(tiled_cameras):
            # update camera
            camera.update(dt)
            # check image data
            for data_type, im_data in camera.data.output.items():
                if data_type in ["rgb", "normals"]:
                    assert im_data.shape == (num_cameras_per_tiled_camera, camera.cfg.height, camera.cfg.width, 3)
                elif data_type in [
                    "rgba",
                    "semantic_segmentation",
                    "instance_segmentation_fast",
                    "instance_id_segmentation_fast",
                ]:
                    assert im_data.shape == (num_cameras_per_tiled_camera, camera.cfg.height, camera.cfg.width, 4)
                    for i in range(num_cameras_per_tiled_camera):
                        assert (im_data[i] / 255.0).mean().item() > 0.0
                elif data_type in ["motion_vectors"]:
                    assert im_data.shape == (num_cameras_per_tiled_camera, camera.cfg.height, camera.cfg.width, 2)
                    for i in range(num_cameras_per_tiled_camera):
                        assert im_data[i].mean().item() != 0.0
                elif data_type in ["depth", "distance_to_camera", "distance_to_image_plane"]:
                    assert im_data.shape == (num_cameras_per_tiled_camera, camera.cfg.height, camera.cfg.width, 1)
                    for i in range(num_cameras_per_tiled_camera):
                        assert im_data[i].mean().item() > 0.0

    for camera in tiled_cameras:
        # access image data and compare dtype
        output = camera.data.output
        info = camera.data.info
        assert output["rgb"].dtype == torch.uint8
        assert output["rgba"].dtype == torch.uint8
        assert output["depth"].dtype == torch.float
        assert output["distance_to_camera"].dtype == torch.float
        assert output["distance_to_image_plane"].dtype == torch.float
        assert output["normals"].dtype == torch.float
        assert output["motion_vectors"].dtype == torch.float
        assert output["semantic_segmentation"].dtype == torch.uint8
        assert output["instance_segmentation_fast"].dtype == torch.uint8
        assert output["instance_id_segmentation_fast"].dtype == torch.uint8
        assert isinstance(info["semantic_segmentation"], dict)
        assert isinstance(info["instance_segmentation_fast"], dict)
        assert isinstance(info["instance_id_segmentation_fast"], dict)

    for camera in tiled_cameras:
        del camera


@flaky(max_runs=3, min_passes=1)
@pytest.mark.isaacsim_ci
def test_different_resolution_multi_tiled_camera(setup_camera):
    """Test multiple tiled cameras with different resolutions."""
    camera_cfg, sim, dt = setup_camera
    num_tiled_cameras = 2
    num_cameras_per_tiled_camera = 6

    tiled_cameras = []
    resolutions = [(16, 16), (23, 765)]
    for i in range(num_tiled_cameras):
        for j in range(num_cameras_per_tiled_camera):
            sim_utils.create_prim(f"/World/Origin_{i}_{j}", "Xform")

        # Create camera
        camera_cfg = copy.deepcopy(camera_cfg)
        camera_cfg.prim_path = f"/World/Origin_{i}.*/CameraSensor"
        camera_cfg.height, camera_cfg.width = resolutions[i]
        camera = TiledCamera(camera_cfg)
        tiled_cameras.append(camera)

        # Check simulation parameter is set correctly
        assert sim.has_rtx_sensors()

    # Play sim
    sim.reset()

    for i, camera in enumerate(tiled_cameras):
        # Check if camera is initialized
        assert camera.is_initialized
        # Check if camera prim is set correctly and that it is a camera prim
        assert camera._sensor_prims[1].GetPath().pathString == f"/World/Origin_{i}_1/CameraSensor"
        assert isinstance(camera._sensor_prims[0], UsdGeom.Camera)

    # Simulate for a few steps
    # note: This is a workaround to ensure that the textures are loaded.
    #   Check "Known Issues" section in the documentation for more details.
    for _ in range(5):
        sim.step()

    for camera in tiled_cameras:
        # Check buffers that exists and have correct shapes
        assert camera.data.pos_w.shape == (num_cameras_per_tiled_camera, 3)
        assert camera.data.quat_w_ros.shape == (num_cameras_per_tiled_camera, 4)
        assert camera.data.quat_w_world.shape == (num_cameras_per_tiled_camera, 4)
        assert camera.data.quat_w_opengl.shape == (num_cameras_per_tiled_camera, 4)
        assert camera.data.intrinsic_matrices.shape == (num_cameras_per_tiled_camera, 3, 3)
        assert camera.data.image_shape == (camera.cfg.height, camera.cfg.width)

    # Simulate physics
    for _ in range(10):
        # perform rendering
        sim.step()
        for i, camera in enumerate(tiled_cameras):
            # update camera
            camera.update(dt)
            # check image data
            for data_type, im_data in camera.data.output.items():
                if data_type == "rgb":
                    im_data = im_data.clone() / 255.0
                    assert im_data.shape == (num_cameras_per_tiled_camera, camera.cfg.height, camera.cfg.width, 3)
                    for j in range(num_cameras_per_tiled_camera):
                        assert (im_data[j]).mean().item() > 0.0
                elif data_type == "distance_to_camera":
                    im_data = im_data.clone()
                    assert im_data.shape == (num_cameras_per_tiled_camera, camera.cfg.height, camera.cfg.width, 1)
                    for j in range(num_cameras_per_tiled_camera):
                        assert im_data[j].mean().item() > 0.0

    for camera in tiled_cameras:
        del camera


@pytest.mark.isaacsim_ci
def test_frame_offset_multi_tiled_camera(setup_camera):
    """Test frame offset issue with multiple tiled cameras"""
    camera_cfg, sim, dt = setup_camera
    num_tiled_cameras = 4
    num_cameras_per_tiled_camera = 4

    tiled_cameras = []
    for i in range(num_tiled_cameras):
        for j in range(num_cameras_per_tiled_camera):
            sim_utils.create_prim(f"/World/Origin_{i}_{j}", "Xform")

        # Create camera
        camera_cfg = copy.deepcopy(camera_cfg)
        camera_cfg.prim_path = f"/World/Origin_{i}.*/CameraSensor"
        camera = TiledCamera(camera_cfg)
        tiled_cameras.append(camera)

    # modify scene to be less stochastic
    stage = sim_utils.get_current_stage()
    for i in range(10):
        prim = stage.GetPrimAtPath(f"/World/Objects/Obj_{i:02d}")
        color = Gf.Vec3f(1, 1, 1)
        UsdGeom.Gprim(prim).GetDisplayColorAttr().Set([color])

    # play sim
    sim.reset()

    # simulate some steps first to make sure objects are settled
    for i in range(100):
        # step simulation
        sim.step()
        # update cameras
        for camera in tiled_cameras:
            camera.update(dt)

    # collect image data
    image_befores = [camera.data.output["rgb"].clone() / 255.0 for camera in tiled_cameras]

    # update scene
    for i in range(10):
        prim = stage.GetPrimAtPath(f"/World/Objects/Obj_{i:02d}")
        color = Gf.Vec3f(0, 0, 0)
        UsdGeom.Gprim(prim).GetDisplayColorAttr().Set([color])

    # update rendering
    sim.step()

    # update cameras
    for camera in tiled_cameras:
        camera.update(dt)

    # make sure the image is different
    image_afters = [camera.data.output["rgb"].clone() / 255.0 for camera in tiled_cameras]

    # check difference is above threshold
    for i in range(num_tiled_cameras):
        image_before = image_befores[i]
        image_after = image_afters[i]
        assert torch.abs(image_after - image_before).mean() > 0.02  # images of same color should be below 0.001

    for camera in tiled_cameras:
        del camera


@flaky(max_runs=3, min_passes=1)
@pytest.mark.isaacsim_ci
def test_frame_different_poses_multi_tiled_camera(setup_camera):
    """Test multiple tiled cameras placed at different poses render different images."""
    camera_cfg, sim, dt = setup_camera
    num_tiled_cameras = 3
    num_cameras_per_tiled_camera = 4
    positions = [(0.0, 0.0, 4.0), (0.0, 0.0, 2.0), (0.0, 0.0, 3.0)]
    rotations = [(0.0, 0.0, 1.0, 0.0), (0.0, 0.0, 1.0, 0.0), (0.0, 0.0, 1.0, 0.0)]

    tiled_cameras = []
    for i in range(num_tiled_cameras):
        for j in range(num_cameras_per_tiled_camera):
            sim_utils.create_prim(f"/World/Origin_{i}_{j}", "Xform")

        # Create camera
        camera_cfg = copy.deepcopy(camera_cfg)
        camera_cfg.prim_path = f"/World/Origin_{i}.*/CameraSensor"
        camera_cfg.offset = TiledCameraCfg.OffsetCfg(pos=positions[i], rot=rotations[i], convention="ros")
        camera = TiledCamera(camera_cfg)
        tiled_cameras.append(camera)

    # Play sim
    sim.reset()

    # Simulate for a few steps
    # note: This is a workaround to ensure that the textures are loaded.
    #   Check "Known Issues" section in the documentation for more details.
    for _ in range(5):
        sim.step()

    # Simulate physics
    for _ in range(10):
        # Initialize data arrays
        rgbs = []
        distances = []

        # perform rendering
        sim.step()
        for i, camera in enumerate(tiled_cameras):
            # update camera
            camera.update(dt)
            # check image data
            for data_type, im_data in camera.data.output.items():
                if data_type == "rgb":
                    im_data = im_data.clone() / 255.0
                    assert im_data.shape == (num_cameras_per_tiled_camera, camera.cfg.height, camera.cfg.width, 3)
                    for j in range(num_cameras_per_tiled_camera):
                        assert (im_data[j]).mean().item() > 0.0
                    rgbs.append(im_data)
                elif data_type == "distance_to_camera":
                    im_data = im_data.clone()
                    # replace inf with 0
                    im_data[torch.isinf(im_data)] = 0
                    assert im_data.shape == (num_cameras_per_tiled_camera, camera.cfg.height, camera.cfg.width, 1)
                    for j in range(num_cameras_per_tiled_camera):
                        assert im_data[j].mean().item() > 0.0
                    distances.append(im_data)

        # Check data from tiled cameras are different, assumes >1 tiled cameras
        for i in range(1, num_tiled_cameras):
            assert torch.abs(rgbs[0] - rgbs[i]).mean() > 0.04  # images of same color should be below 0.001
            assert torch.abs(distances[0] - distances[i]).mean() > 0.01  # distances of same scene should be 0

    for camera in tiled_cameras:
        del camera


"""
Helper functions.
"""


def _populate_scene():
    """Add prims to the scene."""
    # TODO: this causes hang with Kit 107.3???
    # # Ground-plane
    # cfg = sim_utils.GroundPlaneCfg()
    # cfg.func("/World/defaultGroundPlane", cfg)
    # Lights
    cfg = sim_utils.SphereLightCfg()
    cfg.func("/World/Light/GreySphere", cfg, translation=(4.5, 3.5, 10.0))
    cfg.func("/World/Light/WhiteSphere", cfg, translation=(-4.5, 3.5, 10.0))
    # Random objects
    random.seed(0)
    for i in range(10):
        # sample random position
        position = np.random.rand(3) - np.asarray([0.05, 0.05, -1.0])
        position *= np.asarray([1.5, 1.5, 0.5])
        # create prim
        prim_type = random.choice(["Cube", "Sphere", "Cylinder"])
        prim = sim_utils.create_prim(
            f"/World/Objects/Obj_{i:02d}",
            prim_type,
            translation=position,
            scale=(0.25, 0.25, 0.25),
            semantic_label=prim_type,
        )
        # cast to geom prim
        geom_prim = getattr(UsdGeom, prim_type)(prim)
        # set random color
        color = Gf.Vec3f(random.random(), random.random(), random.random())
        geom_prim.CreateDisplayColorAttr()
        geom_prim.GetDisplayColorAttr().Set([color])
        # add rigid properties
        SingleGeometryPrim(f"/World/Objects/Obj_{i:02d}", collision=True)
        SingleRigidPrim(f"/World/Objects/Obj_{i:02d}", mass=5.0)