# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause

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

from omni.isaac.lab.app import AppLauncher, run_tests

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

"""Rest everything follows."""

import torch
import unittest

import omni.isaac.lab.utils.noise as noise


class TestNoise(unittest.TestCase):
    """Test different noise implementations."""

    def test_gaussian_noise(self):
        """Test guassian_noise function."""

        for device in ["cpu", "cuda"]:
            for noise_device in ["cpu", "cuda"]:
                for op in ["add", "scale", "abs"]:
                    with self.subTest(device=device, noise_device=noise_device, operation=op):
                        # create random data set
                        data = torch.rand(10000, 3, device=device)
                        # define standard deviation and mean
                        std = torch.tensor([0.1, 0.2, 0.3], device=noise_device)
                        mean = torch.tensor([0.4, 0.5, 0.6], device=noise_device)
                        # create noise config
                        noise_cfg = noise.GaussianNoiseCfg(std=std, mean=mean, operation=op)

                        for i in range(10):
                            # apply noise
                            noisy_data = noise_cfg.func(data, cfg=noise_cfg)
                            # calculate resulting noise compared to original data set
                            if op == "add":
                                std_result, mean_result = torch.std_mean(noisy_data - data, dim=0)
                            elif op == "scale":
                                std_result, mean_result = torch.std_mean(noisy_data / data, dim=0)
                            elif op == "abs":
                                std_result, mean_result = torch.std_mean(noisy_data, dim=0)

                            self.assertTrue(noise_cfg.mean.device, device)
                            self.assertTrue(noise_cfg.std.device, device)
                            torch.testing.assert_close(noise_cfg.std, std_result, atol=1e-2, rtol=1e-2)
                            torch.testing.assert_close(noise_cfg.mean, mean_result, atol=1e-2, rtol=1e-2)

    def test_uniform_noise(self):
        """Test uniform_noise function."""
        for device in ["cpu", "cuda"]:
            for noise_device in ["cpu", "cuda"]:
                for op in ["add", "scale", "abs"]:
                    with self.subTest(device=device, noise_device=noise_device, operation=op):
                        # create random data set
                        data = torch.rand(10000, 3, device=device)
                        # define uniform minimum and maximum
                        n_min = torch.tensor([0.1, 0.2, 0.3], device=noise_device)
                        n_max = torch.tensor([0.4, 0.5, 0.6], device=noise_device)
                        # create noise config
                        noise_cfg = noise.UniformNoiseCfg(n_max=n_max, n_min=n_min, operation=op)

                        for i in range(10):
                            # apply noise
                            noisy_data = noise_cfg.func(data, cfg=noise_cfg)
                            # calculate resulting noise compared to original data set
                            if op == "add":
                                min_result, _ = torch.min(noisy_data - data, dim=0)
                                max_result, _ = torch.max(noisy_data - data, dim=0)
                            elif op == "scale":
                                min_result, _ = torch.min(torch.div(noisy_data, data), dim=0)
                                max_result, _ = torch.max(torch.div(noisy_data, data), dim=0)
                            elif op == "abs":
                                min_result, _ = torch.min(noisy_data, dim=0)
                                max_result, _ = torch.max(noisy_data, dim=0)

                            self.assertTrue(noise_cfg.n_min.device, device)
                            self.assertTrue(noise_cfg.n_max.device, device)
                            # add a small epsilon to accommodate for floating point error
                            self.assertTrue(all(torch.le(noise_cfg.n_min - 1e-5, min_result).tolist()))
                            self.assertTrue(all(torch.ge(noise_cfg.n_max + 1e-5, max_result).tolist()))

    def test_constant_noise(self):
        """Test constant_noise"""
        for device in ["cpu", "cuda"]:
            for noise_device in ["cpu", "cuda"]:
                for op in ["add", "scale", "abs"]:
                    with self.subTest(device=device, noise_device=noise_device, operation=op):
                        # create random data set
                        data = torch.rand(10000, 3, device=device)
                        # define a bias
                        bias = torch.tensor([0.1, 0.2, 0.3], device=noise_device)
                        # create noise config
                        noise_cfg = noise.ConstantNoiseCfg(bias=bias, operation=op)

                        for i in range(10):
                            # apply noise
                            noisy_data = noise_cfg.func(data, cfg=noise_cfg)
                            # calculate resulting noise compared to original data set
                            if op == "add":
                                bias_result = noisy_data - data
                            elif op == "scale":
                                bias_result = noisy_data / data
                            elif op == "abs":
                                bias_result = noisy_data

                            self.assertTrue(noise_cfg.bias.device, device)
                            torch.testing.assert_close(noise_cfg.bias.repeat(data.shape[0], 1), bias_result)


if __name__ == "__main__":
    run_tests()
