import math
import random
from unittest.mock import patch

import numpy as np
import torch
import torchaudio.prototype.transforms as T
from parameterized import parameterized
from scipy import signal
from torchaudio.functional import lfilter
from torchaudio.prototype.functional import preemphasis
from torchaudio_unittest.common_utils import get_spectrogram, get_whitenoise, nested_params, TestBaseMixin


def _get_ratio(mat):
    return (mat.sum() / mat.numel()).item()


class TransformsTestImpl(TestBaseMixin):
    @nested_params(
        [(10, 4), (4, 3, 1, 2), (2,), ()],
        [(100, 43), (21, 45)],
        ["full", "valid", "same"],
    )
    def test_Convolve(self, leading_dims, lengths, mode):
        """Check that convolve returns values identical to those that SciPy produces."""
        L_x, L_y = lengths

        x = torch.rand(*(leading_dims + (L_x,)), dtype=self.dtype, device=self.device)
        y = torch.rand(*(leading_dims + (L_y,)), dtype=self.dtype, device=self.device)

        convolve = T.Convolve(mode=mode).to(self.device)
        actual = convolve(x, y)

        num_signals = torch.tensor(leading_dims).prod() if leading_dims else 1
        x_reshaped = x.reshape((num_signals, L_x))
        y_reshaped = y.reshape((num_signals, L_y))
        expected = [
            signal.convolve(x_reshaped[i].detach().cpu().numpy(), y_reshaped[i].detach().cpu().numpy(), mode=mode)
            for i in range(num_signals)
        ]
        expected = torch.tensor(np.array(expected))
        expected = expected.reshape(leading_dims + (-1,))

        self.assertEqual(expected, actual)

    @nested_params(
        [(10, 4), (4, 3, 1, 2), (2,), ()],
        [(100, 43), (21, 45)],
        ["full", "valid", "same"],
    )
    def test_FFTConvolve(self, leading_dims, lengths, mode):
        """Check that fftconvolve returns values identical to those that SciPy produces."""
        L_x, L_y = lengths

        x = torch.rand(*(leading_dims + (L_x,)), dtype=self.dtype, device=self.device)
        y = torch.rand(*(leading_dims + (L_y,)), dtype=self.dtype, device=self.device)

        convolve = T.FFTConvolve(mode=mode).to(self.device)
        actual = convolve(x, y)

        expected = signal.fftconvolve(x.detach().cpu().numpy(), y.detach().cpu().numpy(), axes=-1, mode=mode)
        expected = torch.tensor(expected)

        self.assertEqual(expected, actual)

    def test_InverseBarkScale(self):
        """Gauge the quality of InverseBarkScale transform.

        As InverseBarkScale is currently implemented with
        random initialization + iterative optimization,
        it is not practically possible to assert the difference between
        the estimated spectrogram and the original spectrogram as a whole.
        Estimated spectrogram has very huge descrepency locally.
        Thus in this test we gauge what percentage of elements are bellow
        certain tolerance.
        At the moment, the quality of estimated spectrogram is worse than the
        one obtained for Inverse MelScale.
        When implementation is changed in a way it makes the quality even worse,
        this test will fail.
        """
        n_fft = 400
        power = 1
        n_barks = 64
        sample_rate = 8000

        n_stft = n_fft // 2 + 1

        # Generate reference spectrogram and input mel-scaled spectrogram
        expected = get_spectrogram(
            get_whitenoise(sample_rate=sample_rate, duration=1, n_channels=2), n_fft=n_fft, power=power
        ).to(self.device, self.dtype)
        input = T.BarkScale(n_barks=n_barks, sample_rate=sample_rate, n_stft=n_stft).to(self.device, self.dtype)(
            expected
        )

        # Run transform
        transform = T.InverseBarkScale(n_stft, n_barks=n_barks, sample_rate=sample_rate).to(self.device, self.dtype)
        result = transform(input)

        # Compare
        epsilon = 1e-60
        relative_diff = torch.abs((result - expected) / (expected + epsilon))

        for tol in [1e-1, 1e-3, 1e-5, 1e-10]:
            print(f"Ratio of relative diff smaller than {tol:e} is " f"{_get_ratio(relative_diff < tol)}")
        assert _get_ratio(relative_diff < 1e-1) > 0.2
        assert _get_ratio(relative_diff < 1e-3) > 2e-3

    def test_Speed_identity(self):
        """speed of 1.0 does not alter input waveform and length"""
        leading_dims = (5, 4, 2)
        time = 1000
        waveform = torch.rand(*leading_dims, time)
        lengths = torch.randint(1, 1000, leading_dims)
        speed = T.Speed(1000, 1.0)
        actual_waveform, actual_lengths = speed(waveform, lengths)
        self.assertEqual(waveform, actual_waveform)
        self.assertEqual(lengths, actual_lengths)

    @nested_params(
        [0.8, 1.1, 1.2],
    )
    def test_Speed_accuracy(self, factor):
        """sinusoidal waveform is properly compressed by factor"""
        n_to_trim = 20

        sample_rate = 1000
        freq = 2
        times = torch.arange(0, 5, 1.0 / sample_rate)
        waveform = torch.cos(2 * math.pi * freq * times).unsqueeze(0).to(self.device, self.dtype)
        lengths = torch.tensor([waveform.size(1)])

        speed = T.Speed(sample_rate, factor).to(self.device, self.dtype)
        output, output_lengths = speed(waveform, lengths)
        self.assertEqual(output.size(1), output_lengths[0])

        new_times = torch.arange(0, 5 / factor, 1.0 / sample_rate)
        expected_waveform = torch.cos(2 * math.pi * freq * factor * new_times).unsqueeze(0).to(self.device, self.dtype)

        self.assertEqual(
            expected_waveform[..., n_to_trim:-n_to_trim], output[..., n_to_trim:-n_to_trim], atol=1e-1, rtol=1e-4
        )

    def test_SpeedPerturbation(self):
        """sinusoidal waveform is properly compressed by sampled factors"""
        n_to_trim = 20

        sample_rate = 1000
        freq = 2
        times = torch.arange(0, 5, 1.0 / sample_rate)
        waveform = torch.cos(2 * math.pi * freq * times).unsqueeze(0).to(self.device, self.dtype)
        lengths = torch.tensor([waveform.size(1)])

        factors = [0.8, 1.1, 1.0]
        indices = random.choices(range(len(factors)), k=5)

        speed_perturb = T.SpeedPerturbation(sample_rate, factors).to(self.device, self.dtype)

        with patch("torch.randint", side_effect=indices):
            for idx in indices:
                output, output_lengths = speed_perturb(waveform, lengths)
                self.assertEqual(output.size(1), output_lengths[0])
                factor = factors[idx]
                new_times = torch.arange(0, 5 / factor, 1.0 / sample_rate)
                expected_waveform = (
                    torch.cos(2 * math.pi * freq * factor * new_times).unsqueeze(0).to(self.device, self.dtype)
                )
                self.assertEqual(
                    expected_waveform[..., n_to_trim:-n_to_trim],
                    output[..., n_to_trim:-n_to_trim],
                    atol=1e-1,
                    rtol=1e-4,
                )

    def test_AddNoise_broadcast(self):
        """Check that add_noise produces correct outputs when broadcasting input dimensions."""
        leading_dims = (5, 2, 3)
        L = 51

        waveform = torch.rand(*leading_dims, L, dtype=self.dtype, device=self.device)
        noise = torch.rand(5, 1, 1, L, dtype=self.dtype, device=self.device)
        lengths = torch.rand(5, 1, 3, dtype=self.dtype, device=self.device)
        snr = torch.rand(1, 1, 1, dtype=self.dtype, device=self.device) * 10

        add_noise = T.AddNoise()
        actual = add_noise(waveform, noise, snr, lengths)

        noise_expanded = noise.expand(*leading_dims, L)
        snr_expanded = snr.expand(*leading_dims)
        lengths_expanded = lengths.expand(*leading_dims)
        expected = add_noise(waveform, noise_expanded, snr_expanded, lengths_expanded)

        self.assertEqual(expected, actual)

    @parameterized.expand(
        [((5, 2, 3), (2, 1, 1), (5, 2), (5, 2, 3)), ((2, 1), (5,), (5,), (5,)), ((3,), (5, 2, 3), (2, 1, 1), (5, 2))]
    )
    def test_AddNoise_leading_dim_check(self, waveform_dims, noise_dims, lengths_dims, snr_dims):
        """Check that add_noise properly rejects inputs with different leading dimension lengths."""
        L = 51

        waveform = torch.rand(*waveform_dims, L, dtype=self.dtype, device=self.device)
        noise = torch.rand(*noise_dims, L, dtype=self.dtype, device=self.device)
        lengths = torch.rand(*lengths_dims, dtype=self.dtype, device=self.device)
        snr = torch.rand(*snr_dims, dtype=self.dtype, device=self.device) * 10

        add_noise = T.AddNoise()

        with self.assertRaisesRegex(ValueError, "Input leading dimensions"):
            add_noise(waveform, noise, snr, lengths)

    def test_AddNoise_length_check(self):
        """Check that add_noise properly rejects inputs that have inconsistent length dimensions."""
        leading_dims = (5, 2, 3)
        L = 51

        waveform = torch.rand(*leading_dims, L, dtype=self.dtype, device=self.device)
        noise = torch.rand(*leading_dims, 50, dtype=self.dtype, device=self.device)
        lengths = torch.rand(*leading_dims, dtype=self.dtype, device=self.device)
        snr = torch.rand(*leading_dims, dtype=self.dtype, device=self.device) * 10

        add_noise = T.AddNoise()

        with self.assertRaisesRegex(ValueError, "Length dimensions"):
            add_noise(waveform, noise, snr, lengths)

    @nested_params(
        [(2, 1, 31)],
        [0.97, 0.72],
    )
    def test_Preemphasis(self, input_shape, coeff):
        waveform = torch.rand(*input_shape, dtype=self.dtype, device=self.device)
        preemphasis = T.Preemphasis(coeff=coeff).to(dtype=self.dtype, device=self.device)
        actual = preemphasis(waveform)

        a_coeffs = torch.tensor([1.0, 0.0], device=self.device, dtype=self.dtype)
        b_coeffs = torch.tensor([1.0, -coeff], device=self.device, dtype=self.dtype)
        expected = lfilter(waveform, a_coeffs=a_coeffs, b_coeffs=b_coeffs)
        self.assertEqual(actual, expected)

    @nested_params(
        [(2, 1, 31)],
        [0.97, 0.72],
    )
    def test_Deemphasis(self, input_shape, coeff):
        waveform = torch.rand(*input_shape, dtype=self.dtype, device=self.device)
        preemphasized = preemphasis(waveform, coeff=coeff)
        deemphasis = T.Deemphasis(coeff=coeff).to(dtype=self.dtype, device=self.device)
        deemphasized = deemphasis(preemphasized)
        self.assertEqual(deemphasized, waveform)
