import re
import importlib
import numpy as np
from matplotlib import pyplot as plt

from TTS.tts.utils.visual import plot_spectrogram


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_wavernn(c):
    print(" > Model: WaveRNN")
    MyModel = importlib.import_module("TTS.vocoder.models.wavernn")
    MyModel = getattr(MyModel, "WaveRNN")
    model = MyModel(
        rnn_dims=512,
        fc_dims=512,
        mode=c.mode,
        mulaw=c.mulaw,
        pad=c.padding,
        use_aux_net=c.use_aux_net,
        use_upsample_net=c.use_upsample_net,
        upsample_factors=c.upsample_factors,
        feat_dims=80,
        compute_dims=128,
        res_out_dims=128,
        res_blocks=10,
        hop_length=c.audio["hop_length"],
        sample_rate=c.audio["sample_rate"],
    )
    return model


def setup_generator(c):
    print(" > Generator Model: {}".format(c.generator_model))
    MyModel = importlib.import_module("TTS.vocoder.models." + c.generator_model.lower())
    MyModel = getattr(MyModel, to_camel(c.generator_model))
    if c.generator_model 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"],
        )
    if c.generator_model in "melgan_fb_generator":
        pass
    if c.generator_model 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"],
        )
    if c.generator_model 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"],
        )
    if c.generator_model 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"],
        )
    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 "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