import torch
import torch.nn as nn
import torch.nn.functional as F
from torchlibrosa.stft import Spectrogram, LogmelFilterBank
from torchlibrosa.augmentation import SpecAugmentation

def init_layer(layer):
    """Initialize a Linear or Convolutional layer. """
    nn.init.xavier_uniform_(layer.weight)

    if hasattr(layer, 'bias'):
        if layer.bias is not None:
            layer.bias.data.fill_(0.)

def init_bn(bn):
    """Initialize a Batchnorm layer. """
    bn.bias.data.fill_(0.)
    bn.weight.data.fill_(1.)

class ConvBlock(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(ConvBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=in_channels,
                               out_channels=out_channels,
                               kernel_size=(3, 3), stride=(1, 1),
                               padding=(1, 1), bias=False)
        self.conv2 = nn.Conv2d(in_channels=out_channels,
                               out_channels=out_channels,
                               kernel_size=(3, 3), stride=(1, 1),
                               padding=(1, 1), bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.init_weight()
    def init_weight(self):
        init_layer(self.conv1)
        init_layer(self.conv2)
        init_bn(self.bn1)
        init_bn(self.bn2)
    def forward(self, input, pool_size=(2, 2), pool_type='avg'):
        x = input
        x = F.relu_(self.bn1(self.conv1(x)))
        x = F.relu_(self.bn2(self.conv2(x)))
        if pool_type == 'max':
            x = F.max_pool2d(x, kernel_size=pool_size)
        elif pool_type == 'avg':
            x = F.avg_pool2d(x, kernel_size=pool_size)
        elif pool_type == 'avg+max':
            x1 = F.avg_pool2d(x, kernel_size=pool_size)
            x2 = F.max_pool2d(x, kernel_size=pool_size)
            x = x1 + x2
        else:
            raise Exception('Incorrect argument!')
        return x

class CNN10(nn.Module):
    def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin,
                 fmax, classes_num):
        super(CNN10, self).__init__()
        window = 'hann'
        center = True
        pad_mode = 'reflect'
        ref = 1.0
        amin = 1e-10
        top_db = None
        # Spectrogram extractor
        self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size,
                                                 win_length=window_size, window=window, center=center,
                                                 pad_mode=pad_mode,
                                                 freeze_parameters=True)
        # Logmel feature extractor
        self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size,
                                                 n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin,
                                                 top_db=top_db,
                                                 freeze_parameters=True)
        # Spec augmenter
        self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2,
                                               freq_drop_width=8, freq_stripes_num=2)

        self.bn0 = nn.BatchNorm2d(64)
        self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
        self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
        self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
        self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
        self.fc1 = nn.Linear(512, 512, bias=True)
        self.fc_audioset = nn.Linear(512, classes_num, bias=True)
        self.init_weight()

    def init_weight(self):
        init_bn(self.bn0)
        init_layer(self.fc1)
        init_layer(self.fc_audioset)

    def forward(self, input):
        """
        Input: (batch_size, data_length)"""
        x = self.spectrogram_extractor(input)  # (batch_size, 1, time_steps, freq_bins)
        x = self.logmel_extractor(x)  # (batch_size, 1, time_steps, mel_bins)

        x = x.transpose(1, 3)
        x = self.bn0(x)
        x = x.transpose(1, 3)

        x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg')
        x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')
        x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg')
        x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg')
        x = torch.mean(x, dim=3)

        (x1, _) = torch.max(x, dim=2)
        x2 = torch.mean(x, dim=2)
        x = x1 + x2
        x = F.relu_(self.fc1(x))
        embedding = x

        return embedding
