import torch

from diffusers import SASolverScheduler

from ..testing_utils import require_torchsde, torch_device
from .test_schedulers import SchedulerCommonTest


@require_torchsde
class SASolverSchedulerTest(SchedulerCommonTest):
    scheduler_classes = (SASolverScheduler,)
    forward_default_kwargs = (("num_inference_steps", 10),)
    num_inference_steps = 10

    def get_scheduler_config(self, **kwargs):
        config = {
            "num_train_timesteps": 1100,
            "beta_start": 0.0001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }

        config.update(**kwargs)
        return config

    def test_step_shape(self):
        kwargs = dict(self.forward_default_kwargs)

        num_inference_steps = kwargs.pop("num_inference_steps", None)

        for scheduler_class in self.scheduler_classes:
            scheduler_config = self.get_scheduler_config()
            scheduler = scheduler_class(**scheduler_config)

            sample = self.dummy_sample
            residual = 0.1 * sample

            if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
                scheduler.set_timesteps(num_inference_steps)
            elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
                kwargs["num_inference_steps"] = num_inference_steps

            # copy over dummy past residuals (must be done after set_timesteps)
            dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]
            scheduler.model_outputs = dummy_past_residuals[
                : max(
                    scheduler.config.predictor_order,
                    scheduler.config.corrector_order - 1,
                )
            ]

            time_step_0 = scheduler.timesteps[5]
            time_step_1 = scheduler.timesteps[6]

            output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample
            output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample

            self.assertEqual(output_0.shape, sample.shape)
            self.assertEqual(output_0.shape, output_1.shape)

    def test_timesteps(self):
        for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=timesteps)

    def test_betas(self):
        for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]):
            self.check_over_configs(beta_start=beta_start, beta_end=beta_end)

    def test_schedules(self):
        for schedule in ["linear", "scaled_linear"]:
            self.check_over_configs(beta_schedule=schedule)

    def test_prediction_type(self):
        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=prediction_type)

    def test_full_loop_no_noise(self):
        scheduler_class = self.scheduler_classes[0]
        scheduler_config = self.get_scheduler_config()
        scheduler = scheduler_class(**scheduler_config)

        scheduler.set_timesteps(self.num_inference_steps)

        model = self.dummy_model()
        sample = self.dummy_sample_deter * scheduler.init_noise_sigma
        sample = sample.to(torch_device)
        generator = torch.manual_seed(0)

        for i, t in enumerate(scheduler.timesteps):
            sample = scheduler.scale_model_input(sample, t, generator=generator)

            model_output = model(sample, t)

            output = scheduler.step(model_output, t, sample)
            sample = output.prev_sample

        result_sum = torch.sum(torch.abs(sample))
        result_mean = torch.mean(torch.abs(sample))

        if torch_device in ["cpu"]:
            assert abs(result_sum.item() - 337.394287109375) < 1e-2
            assert abs(result_mean.item() - 0.43931546807289124) < 1e-3
        elif torch_device in ["cuda"]:
            assert abs(result_sum.item() - 329.1999816894531) < 1e-2
            assert abs(result_mean.item() - 0.4286458194255829) < 1e-3

    def test_full_loop_with_v_prediction(self):
        scheduler_class = self.scheduler_classes[0]
        scheduler_config = self.get_scheduler_config(prediction_type="v_prediction")
        scheduler = scheduler_class(**scheduler_config)

        scheduler.set_timesteps(self.num_inference_steps)

        model = self.dummy_model()
        sample = self.dummy_sample_deter * scheduler.init_noise_sigma
        sample = sample.to(torch_device)
        generator = torch.manual_seed(0)

        for i, t in enumerate(scheduler.timesteps):
            sample = scheduler.scale_model_input(sample, t, generator=generator)

            model_output = model(sample, t)

            output = scheduler.step(model_output, t, sample)
            sample = output.prev_sample

        result_sum = torch.sum(torch.abs(sample))
        result_mean = torch.mean(torch.abs(sample))

        if torch_device in ["cpu"]:
            assert abs(result_sum.item() - 193.1467742919922) < 1e-2
            assert abs(result_mean.item() - 0.2514931857585907) < 1e-3
        elif torch_device in ["cuda"]:
            assert abs(result_sum.item() - 193.4154052734375) < 1e-2
            assert abs(result_mean.item() - 0.2518429756164551) < 1e-3

    def test_full_loop_device(self):
        scheduler_class = self.scheduler_classes[0]
        scheduler_config = self.get_scheduler_config()
        scheduler = scheduler_class(**scheduler_config)

        scheduler.set_timesteps(self.num_inference_steps, device=torch_device)

        model = self.dummy_model()
        sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma
        generator = torch.manual_seed(0)

        for t in scheduler.timesteps:
            sample = scheduler.scale_model_input(sample, t)

            model_output = model(sample, t)

            output = scheduler.step(model_output, t, sample, generator=generator)
            sample = output.prev_sample

        result_sum = torch.sum(torch.abs(sample))
        result_mean = torch.mean(torch.abs(sample))

        if torch_device in ["cpu"]:
            assert abs(result_sum.item() - 337.394287109375) < 1e-2
            assert abs(result_mean.item() - 0.43931546807289124) < 1e-3
        elif torch_device in ["cuda"]:
            assert abs(result_sum.item() - 337.394287109375) < 1e-2
            assert abs(result_mean.item() - 0.4393154978752136) < 1e-3

    def test_full_loop_device_karras_sigmas(self):
        scheduler_class = self.scheduler_classes[0]
        scheduler_config = self.get_scheduler_config()
        scheduler = scheduler_class(**scheduler_config, use_karras_sigmas=True)

        scheduler.set_timesteps(self.num_inference_steps, device=torch_device)

        model = self.dummy_model()
        sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma
        sample = sample.to(torch_device)
        generator = torch.manual_seed(0)

        for t in scheduler.timesteps:
            sample = scheduler.scale_model_input(sample, t)

            model_output = model(sample, t)

            output = scheduler.step(model_output, t, sample, generator=generator)
            sample = output.prev_sample

        result_sum = torch.sum(torch.abs(sample))
        result_mean = torch.mean(torch.abs(sample))

        if torch_device in ["cpu"]:
            assert abs(result_sum.item() - 837.2554931640625) < 1e-2
            assert abs(result_mean.item() - 1.0901764631271362) < 1e-2
        elif torch_device in ["cuda"]:
            assert abs(result_sum.item() - 837.25537109375) < 1e-2
            assert abs(result_mean.item() - 1.0901763439178467) < 1e-2

    def test_beta_sigmas(self):
        self.check_over_configs(use_beta_sigmas=True)

    def test_exponential_sigmas(self):
        self.check_over_configs(use_exponential_sigmas=True)
