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from dataclasses import dataclass

import pytest
import torch

from nemo.collections.audio.parts.submodules.schroedinger_bridge import SBNoiseScheduleVE, SBNoiseScheduleVP, SBSampler

NUM_STEPS = [1, 5, 10, 20, 100]


@pytest.mark.parametrize("num_steps", NUM_STEPS)
@pytest.mark.parametrize("process", ["sde", "ode"])
@pytest.mark.parametrize("noise_schedule_type", ["ve", "vp"])
def test_sb_sampler_nfe(num_steps, process, noise_schedule_type):
    """
    For this specific solver the number of steps should be equal to the number of function (estimator) evaluations
    """
    if noise_schedule_type == "ve":
        noise_schedule = SBNoiseScheduleVE(k=2.0, c=0.5, num_steps=num_steps)
    elif noise_schedule_type == "vp":
        noise_schedule = SBNoiseScheduleVP(beta_0=0.1, beta_1=1.0, c=0.5, num_steps=num_steps)
    else:
        raise ValueError(f"Invalid noise schedule type: {noise_schedule_type}")

    class IdentityEstimator(torch.nn.Module):
        def forward(self, input, input_length, condition):
            return input, input_length

    @dataclass
    class ForwardCounterHook:
        counter: int = 0

        def __call__(self, *args, **kwargs):
            self.counter += 1

    estimator = IdentityEstimator()
    counter_hook = ForwardCounterHook()
    estimator.register_forward_hook(counter_hook)

    sampler = SBSampler(
        noise_schedule=noise_schedule,
        estimator=estimator,
        estimator_output='data_prediction',
        process=process,
        num_steps=num_steps,
    )

    b, c, d, l = 2, 3, 4, 5
    lengths = [5, 3]
    init_state = torch.randn(b, c, d, l)
    init_state_length = torch.LongTensor(lengths)

    sampler.forward(prior_mean=init_state, estimator_condition=None, state_length=init_state_length)

    assert counter_hook.counter == sampler.num_steps