import importlib
import re

import numpy as np
import torch
from matplotlib import pyplot as plt

from TTS.tts.utils.visual import plot_spectrogram


def interpolate_vocoder_input(scale_factor, spec):
    """Interpolate spectrogram by the scale factor.
    It is mainly used to match the sampling rates of
    the tts and vocoder models.

    Args:
        scale_factor (float): scale factor to interpolate the spectrogram
        spec (np.array): spectrogram to be interpolated

    Returns:
        torch.tensor: interpolated spectrogram.
    """
    print(" > before interpolation :", spec.shape)
    spec = torch.tensor(spec).unsqueeze(0).unsqueeze(0)  # pylint: disable=not-callable
    spec = torch.nn.functional.interpolate(
        spec, scale_factor=scale_factor, recompute_scale_factor=True, mode="bilinear", align_corners=False
    ).squeeze(0)
    print(" > after interpolation :", spec.shape)
    return spec


def plot_results(y_hat, y, ap, global_step, name_prefix):
    """Plot vocoder model results"""

    # select an instance from batch
    y_hat = y_hat[0].squeeze(0).detach().cpu().numpy()
    y = y[0].squeeze(0).detach().cpu().numpy()

    spec_fake = ap.melspectrogram(y_hat).T
    spec_real = ap.melspectrogram(y).T
    spec_diff = np.abs(spec_fake - spec_real)

    # plot figure and save it
    fig_wave = plt.figure()
    plt.subplot(2, 1, 1)
    plt.plot(y)
    plt.title("groundtruth speech")
    plt.subplot(2, 1, 2)
    plt.plot(y_hat)
    plt.title(f"generated speech @ {global_step} steps")
    plt.tight_layout()
    plt.close()

    figures = {
        name_prefix + "spectrogram/fake": plot_spectrogram(spec_fake),
        name_prefix + "spectrogram/real": plot_spectrogram(spec_real),
        name_prefix + "spectrogram/diff": plot_spectrogram(spec_diff),
        name_prefix + "speech_comparison": fig_wave,
    }
    return figures


def to_camel(text):
    text = text.capitalize()
    return re.sub(r"(?!^)_([a-zA-Z])", lambda m: m.group(1).upper(), text)


def setup_generator(c):
    print(" > Generator Model: {}".format(c.generator_model))
    MyModel = importlib.import_module("TTS.vocoder.models." + c.generator_model.lower())
    # this is to preserve the WaveRNN class name (instead of Wavernn)
    if c.generator_model.lower() == "wavernn":
        MyModel = getattr(MyModel, "WaveRNN")
    else:
        MyModel = getattr(MyModel, to_camel(c.generator_model))
    if c.generator_model.lower() in "wavernn":
        model = MyModel(
            rnn_dims=c.wavernn_model_params["rnn_dims"],
            fc_dims=c.wavernn_model_params["fc_dims"],
            mode=c.mode,
            mulaw=c.mulaw,
            pad=c.padding,
            use_aux_net=c.wavernn_model_params["use_aux_net"],
            use_upsample_net=c.wavernn_model_params["use_upsample_net"],
            upsample_factors=c.wavernn_model_params["upsample_factors"],
            feat_dims=c.audio["num_mels"],
            compute_dims=c.wavernn_model_params["compute_dims"],
            res_out_dims=c.wavernn_model_params["res_out_dims"],
            num_res_blocks=c.wavernn_model_params["num_res_blocks"],
            hop_length=c.audio["hop_length"],
            sample_rate=c.audio["sample_rate"],
        )
    elif c.generator_model.lower() in "hifigan_generator":
        model = MyModel(in_channels=c.audio["num_mels"], out_channels=1, **c.generator_model_params)
    elif c.generator_model.lower() in "melgan_generator":
        model = MyModel(
            in_channels=c.audio["num_mels"],
            out_channels=1,
            proj_kernel=7,
            base_channels=512,
            upsample_factors=c.generator_model_params["upsample_factors"],
            res_kernel=3,
            num_res_blocks=c.generator_model_params["num_res_blocks"],
        )
    elif c.generator_model in "melgan_fb_generator":
        raise ValueError("melgan_fb_generator is now fullband_melgan_generator")
    elif c.generator_model.lower() in "multiband_melgan_generator":
        model = MyModel(
            in_channels=c.audio["num_mels"],
            out_channels=4,
            proj_kernel=7,
            base_channels=384,
            upsample_factors=c.generator_model_params["upsample_factors"],
            res_kernel=3,
            num_res_blocks=c.generator_model_params["num_res_blocks"],
        )
    elif c.generator_model.lower() in "fullband_melgan_generator":
        model = MyModel(
            in_channels=c.audio["num_mels"],
            out_channels=1,
            proj_kernel=7,
            base_channels=512,
            upsample_factors=c.generator_model_params["upsample_factors"],
            res_kernel=3,
            num_res_blocks=c.generator_model_params["num_res_blocks"],
        )
    elif c.generator_model.lower() in "parallel_wavegan_generator":
        model = MyModel(
            in_channels=1,
            out_channels=1,
            kernel_size=3,
            num_res_blocks=c.generator_model_params["num_res_blocks"],
            stacks=c.generator_model_params["stacks"],
            res_channels=64,
            gate_channels=128,
            skip_channels=64,
            aux_channels=c.audio["num_mels"],
            dropout=0.0,
            bias=True,
            use_weight_norm=True,
            upsample_factors=c.generator_model_params["upsample_factors"],
        )
    elif c.generator_model.lower() in "wavegrad":
        model = MyModel(
            in_channels=c["audio"]["num_mels"],
            out_channels=1,
            use_weight_norm=c["model_params"]["use_weight_norm"],
            x_conv_channels=c["model_params"]["x_conv_channels"],
            y_conv_channels=c["model_params"]["y_conv_channels"],
            dblock_out_channels=c["model_params"]["dblock_out_channels"],
            ublock_out_channels=c["model_params"]["ublock_out_channels"],
            upsample_factors=c["model_params"]["upsample_factors"],
            upsample_dilations=c["model_params"]["upsample_dilations"],
        )
    else:
        raise NotImplementedError(f"Model {c.generator_model} not implemented!")
    return model


def setup_discriminator(c):
    print(" > Discriminator Model: {}".format(c.discriminator_model))
    if "parallel_wavegan" in c.discriminator_model:
        MyModel = importlib.import_module("TTS.vocoder.models.parallel_wavegan_discriminator")
    else:
        MyModel = importlib.import_module("TTS.vocoder.models." + c.discriminator_model.lower())
    MyModel = getattr(MyModel, to_camel(c.discriminator_model.lower()))
    if c.discriminator_model in "hifigan_discriminator":
        model = MyModel()
    if c.discriminator_model in "random_window_discriminator":
        model = MyModel(
            cond_channels=c.audio["num_mels"],
            hop_length=c.audio["hop_length"],
            uncond_disc_donwsample_factors=c.discriminator_model_params["uncond_disc_donwsample_factors"],
            cond_disc_downsample_factors=c.discriminator_model_params["cond_disc_downsample_factors"],
            cond_disc_out_channels=c.discriminator_model_params["cond_disc_out_channels"],
            window_sizes=c.discriminator_model_params["window_sizes"],
        )
    if c.discriminator_model in "melgan_multiscale_discriminator":
        model = MyModel(
            in_channels=1,
            out_channels=1,
            kernel_sizes=(5, 3),
            base_channels=c.discriminator_model_params["base_channels"],
            max_channels=c.discriminator_model_params["max_channels"],
            downsample_factors=c.discriminator_model_params["downsample_factors"],
        )
    if c.discriminator_model == "residual_parallel_wavegan_discriminator":
        model = MyModel(
            in_channels=1,
            out_channels=1,
            kernel_size=3,
            num_layers=c.discriminator_model_params["num_layers"],
            stacks=c.discriminator_model_params["stacks"],
            res_channels=64,
            gate_channels=128,
            skip_channels=64,
            dropout=0.0,
            bias=True,
            nonlinear_activation="LeakyReLU",
            nonlinear_activation_params={"negative_slope": 0.2},
        )
    if c.discriminator_model == "parallel_wavegan_discriminator":
        model = MyModel(
            in_channels=1,
            out_channels=1,
            kernel_size=3,
            num_layers=c.discriminator_model_params["num_layers"],
            conv_channels=64,
            dilation_factor=1,
            nonlinear_activation="LeakyReLU",
            nonlinear_activation_params={"negative_slope": 0.2},
            bias=True,
        )
    return model


# def check_config(c):
#     c = None
#     pass