import os
import copy
import torch
import unittest

from torch import optim
from torch import nn
from TTS.utils.generic_utils import load_config
from TTS.layers.losses import L1LossMasked
from TTS.models.tacotron import Tacotron
from TTS.models.tacotrongst import TacotronGST

#pylint: disable=unused-variable

torch.manual_seed(1)
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

file_path = os.path.dirname(os.path.realpath(__file__))
c = load_config(os.path.join(file_path, 'test_config.json'))


def count_parameters(model):
    r"""Count number of trainable parameters in a network"""
    return sum(p.numel() for p in model.parameters() if p.requires_grad)


# class TacotronTrainTest(unittest.TestCase):
    # def test_train_step(self):
    #     input = torch.randint(0, 24, (8, 128)).long().to(device)
    #     input_lengths = torch.randint(100, 129, (8, )).long().to(device)
    #     input_lengths[-1] = 128
    #     mel_spec = torch.rand(8, 30, c.audio['num_mels']).to(device)
    #     linear_spec = torch.rand(8, 30, c.audio['num_freq']).to(device)
    #     mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
    #     stop_targets = torch.zeros(8, 30, 1).float().to(device)
    #     speaker_ids = torch.randint(0, 5, (8, )).long().to(device)

    #     for idx in mel_lengths:
    #         stop_targets[:, int(idx.item()):, 0] = 1.0

    #     stop_targets = stop_targets.view(input.shape[0],
    #                                      stop_targets.size(1) // c.r, -1)
    #     stop_targets = (stop_targets.sum(2) >
    #                     0.0).unsqueeze(2).float().squeeze()

    #     criterion = L1LossMasked().to(device)
    #     criterion_st = nn.BCEWithLogitsLoss().to(device)
    #     model = Tacotron(
    #         num_chars=32,
    #         num_speakers=5, 
    #         linear_dim=c.audio['num_freq'],
    #         mel_dim=c.audio['num_mels'],
    #         r=c.r,
    #         memory_size=c.memory_size).to(device) #FIXME: missing num_speakers parameter to Tacotron ctor
    #     model.train()
    #     print(" > Num parameters for Tacotron model:%s"%(count_parameters(model)))
    #     model_ref = copy.deepcopy(model)
    #     count = 0
    #     for param, param_ref in zip(model.parameters(),
    #                                 model_ref.parameters()):
    #         assert (param - param_ref).sum() == 0, param
    #         count += 1
    #     optimizer = optim.Adam(model.parameters(), lr=c.lr)
    #     for _ in range(5):
    #         mel_out, linear_out, align, stop_tokens = model.forward(
    #             input, input_lengths, mel_spec, speaker_ids)
    #         optimizer.zero_grad()
    #         loss = criterion(mel_out, mel_spec, mel_lengths)
    #         stop_loss = criterion_st(stop_tokens, stop_targets)
    #         loss = loss + criterion(linear_out, linear_spec,
    #                                 mel_lengths) + stop_loss
    #         loss.backward()
    #         optimizer.step()
    #     # check parameter changes
    #     count = 0
    #     for param, param_ref in zip(model.parameters(),
    #                                 model_ref.parameters()):
    #         # ignore pre-higway layer since it works conditional
    #         # if count not in [145, 59]:
    #         assert (param != param_ref).any(
    #         ), "param {} with shape {} not updated!! \n{}\n{}".format(
    #             count, param.shape, param, param_ref)
    #         count += 1


class TacotronGSTTrainTest(unittest.TestCase):
    def test_train_step(self):
        input = torch.randint(0, 24, (8, 128)).long().to(device)
        input_lengths = torch.randint(100, 129, (8, )).long().to(device)
        input_lengths[-1] = 128
        mel_spec = torch.rand(8, 120, c.audio['num_mels']).to(device)
        linear_spec = torch.rand(8, 120, c.audio['num_freq']).to(device)
        mel_lengths = torch.randint(20, 120, (8, )).long().to(device)
        stop_targets = torch.zeros(8, 120, 1).float().to(device)
        speaker_ids = torch.randint(0, 5, (8, )).long().to(device)

        for idx in mel_lengths:
            stop_targets[:, int(idx.item()):, 0] = 1.0

        stop_targets = stop_targets.view(input.shape[0],
                                         stop_targets.size(1) // c.r, -1)
        stop_targets = (stop_targets.sum(2) >
                        0.0).unsqueeze(2).float().squeeze()

        criterion = L1LossMasked().to(device)
        criterion_st = nn.BCEWithLogitsLoss().to(device)
        model = TacotronGST(
            num_chars=32,
            num_speakers=5, 
            linear_dim=c.audio['num_freq'],
            mel_dim=c.audio['num_mels'],
            r=c.r,
            memory_size=c.memory_size).to(device) #FIXME: missing num_speakers parameter to Tacotron ctor
        model.train()
        print(model)
        print(" > Num parameters for Tacotron GST model:%s"%(count_parameters(model)))
        model_ref = copy.deepcopy(model)
        count = 0
        for param, param_ref in zip(model.parameters(),
                                    model_ref.parameters()):
            assert (param - param_ref).sum() == 0, param
            count += 1
        optimizer = optim.Adam(model.parameters(), lr=c.lr)
        for _ in range(10):
            mel_out, linear_out, align, stop_tokens = model.forward(
                input, input_lengths, mel_spec, speaker_ids)
            optimizer.zero_grad()
            loss = criterion(mel_out, mel_spec, mel_lengths)
            stop_loss = criterion_st(stop_tokens, stop_targets)
            loss = loss + criterion(linear_out, linear_spec,
                                    mel_lengths) + stop_loss
            loss.backward()
            optimizer.step()
        # check parameter changes
        count = 0
        for param, param_ref in zip(model.parameters(),
                                    model_ref.parameters()):
            # ignore pre-higway layer since it works conditional
            assert (param != param_ref).any(
            ), "param {} with shape {} not updated!! \n{}\n{}".format(
                count, param.shape, param, param_ref)
            count += 1