# coding: utf-8
# adapted from https://github.com/r9y9/tacotron_pytorch

import torch
from torch import nn

from .attentions import init_attn
from .common_layers import Prenet


class BatchNormConv1d(nn.Module):
    r"""A wrapper for Conv1d with BatchNorm. It sets the activation
    function between Conv and BatchNorm layers. BatchNorm layer
    is initialized with the TF default values for momentum and eps.

    Args:
        in_channels: size of each input sample
        out_channels: size of each output samples
        kernel_size: kernel size of conv filters
        stride: stride of conv filters
        padding: padding of conv filters
        activation: activation function set b/w Conv1d and BatchNorm

    Shapes:
        - input: (B, D)
        - output: (B, D)
    """

    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, activation=None):
        super().__init__()
        self.padding = padding
        self.padder = nn.ConstantPad1d(padding, 0)
        self.conv1d = nn.Conv1d(
            in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=0, bias=False
        )
        # Following tensorflow's default parameters
        self.bn = nn.BatchNorm1d(out_channels, momentum=0.99, eps=1e-3)
        self.activation = activation
        # self.init_layers()

    def init_layers(self):
        if isinstance(self.activation, torch.nn.ReLU):
            w_gain = "relu"
        elif isinstance(self.activation, torch.nn.Tanh):
            w_gain = "tanh"
        elif self.activation is None:
            w_gain = "linear"
        else:
            raise RuntimeError("Unknown activation function")
        torch.nn.init.xavier_uniform_(self.conv1d.weight, gain=torch.nn.init.calculate_gain(w_gain))

    def forward(self, x):
        x = self.padder(x)
        x = self.conv1d(x)
        x = self.bn(x)
        if self.activation is not None:
            x = self.activation(x)
        return x


class Highway(nn.Module):
    r"""Highway layers as explained in https://arxiv.org/abs/1505.00387

    Args:
        in_features (int): size of each input sample
        out_feature (int): size of each output sample

    Shapes:
        - input: (B, *, H_in)
        - output: (B, *, H_out)
    """

    # TODO: Try GLU layer
    def __init__(self, in_features, out_feature):
        super().__init__()
        self.H = nn.Linear(in_features, out_feature)
        self.H.bias.data.zero_()
        self.T = nn.Linear(in_features, out_feature)
        self.T.bias.data.fill_(-1)
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()
        # self.init_layers()

    def init_layers(self):
        torch.nn.init.xavier_uniform_(self.H.weight, gain=torch.nn.init.calculate_gain("relu"))
        torch.nn.init.xavier_uniform_(self.T.weight, gain=torch.nn.init.calculate_gain("sigmoid"))

    def forward(self, inputs):
        H = self.relu(self.H(inputs))
        T = self.sigmoid(self.T(inputs))
        return H * T + inputs * (1.0 - T)


class CBHG(nn.Module):
    """CBHG module: a recurrent neural network composed of:
    - 1-d convolution banks
    - Highway networks + residual connections
    - Bidirectional gated recurrent units

    Args:
        in_features (int): sample size
        K (int): max filter size in conv bank
        projections (list): conv channel sizes for conv projections
        num_highways (int): number of highways layers

    Shapes:
        - input: (B, C, T_in)
        - output: (B, T_in, C*2)
    """

    # pylint: disable=dangerous-default-value
    def __init__(
        self,
        in_features,
        K=16,
        conv_bank_features=128,
        conv_projections=[128, 128],
        highway_features=128,
        gru_features=128,
        num_highways=4,
    ):
        super().__init__()
        self.in_features = in_features
        self.conv_bank_features = conv_bank_features
        self.highway_features = highway_features
        self.gru_features = gru_features
        self.conv_projections = conv_projections
        self.relu = nn.ReLU()
        # list of conv1d bank with filter size k=1...K
        # TODO: try dilational layers instead
        self.conv1d_banks = nn.ModuleList(
            [
                BatchNormConv1d(
                    in_features,
                    conv_bank_features,
                    kernel_size=k,
                    stride=1,
                    padding=[(k - 1) // 2, k // 2],
                    activation=self.relu,
                )
                for k in range(1, K + 1)
            ]
        )
        # max pooling of conv bank, with padding
        # TODO: try average pooling OR larger kernel size
        out_features = [K * conv_bank_features] + conv_projections[:-1]
        activations = [self.relu] * (len(conv_projections) - 1)
        activations += [None]
        # setup conv1d projection layers
        layer_set = []
        for in_size, out_size, ac in zip(out_features, conv_projections, activations):
            layer = BatchNormConv1d(in_size, out_size, kernel_size=3, stride=1, padding=[1, 1], activation=ac)
            layer_set.append(layer)
        self.conv1d_projections = nn.ModuleList(layer_set)
        # setup Highway layers
        if self.highway_features != conv_projections[-1]:
            self.pre_highway = nn.Linear(conv_projections[-1], highway_features, bias=False)
        self.highways = nn.ModuleList([Highway(highway_features, highway_features) for _ in range(num_highways)])
        # bi-directional GPU layer
        self.gru = nn.GRU(gru_features, gru_features, 1, batch_first=True, bidirectional=True)

    def forward(self, inputs):
        # (B, in_features, T_in)
        x = inputs
        # (B, hid_features*K, T_in)
        # Concat conv1d bank outputs
        outs = []
        for conv1d in self.conv1d_banks:
            out = conv1d(x)
            outs.append(out)
        x = torch.cat(outs, dim=1)
        assert x.size(1) == self.conv_bank_features * len(self.conv1d_banks)
        for conv1d in self.conv1d_projections:
            x = conv1d(x)
        x += inputs
        x = x.transpose(1, 2)
        if self.highway_features != self.conv_projections[-1]:
            x = self.pre_highway(x)
        # Residual connection
        # TODO: try residual scaling as in Deep Voice 3
        # TODO: try plain residual layers
        for highway in self.highways:
            x = highway(x)
        # (B, T_in, hid_features*2)
        # TODO: replace GRU with convolution as in Deep Voice 3
        self.gru.flatten_parameters()
        outputs, _ = self.gru(x)
        return outputs


class EncoderCBHG(nn.Module):
    r"""CBHG module with Encoder specific arguments"""

    def __init__(self):
        super().__init__()
        self.cbhg = CBHG(
            128,
            K=16,
            conv_bank_features=128,
            conv_projections=[128, 128],
            highway_features=128,
            gru_features=128,
            num_highways=4,
        )

    def forward(self, x):
        return self.cbhg(x)


class Encoder(nn.Module):
    r"""Stack Prenet and CBHG module for encoder
    Args:
        inputs (FloatTensor): embedding features

    Shapes:
        - inputs: (B, T, D_in)
        - outputs: (B, T, 128 * 2)
    """

    def __init__(self, in_features):
        super().__init__()
        self.prenet = Prenet(in_features, out_features=[256, 128])
        self.cbhg = EncoderCBHG()

    def forward(self, inputs):
        # B x T x prenet_dim
        outputs = self.prenet(inputs)
        outputs = self.cbhg(outputs.transpose(1, 2))
        return outputs


class PostCBHG(nn.Module):
    def __init__(self, mel_dim):
        super().__init__()
        self.cbhg = CBHG(
            mel_dim,
            K=8,
            conv_bank_features=128,
            conv_projections=[256, mel_dim],
            highway_features=128,
            gru_features=128,
            num_highways=4,
        )

    def forward(self, x):
        return self.cbhg(x)


class Decoder(nn.Module):
    """Tacotron decoder.

    Args:
        in_channels (int): number of input channels.
        frame_channels (int): number of feature frame channels.
        r (int): number of outputs per time step (reduction rate).
        memory_size (int): size of the past window. if <= 0 memory_size = r
        attn_type (string): type of attention used in decoder.
        attn_windowing (bool): if true, define an attention window centered to maximum
            attention response. It provides more robust attention alignment especially
            at interence time.
        attn_norm (string): attention normalization function. 'sigmoid' or 'softmax'.
        prenet_type (string): 'original' or 'bn'.
        prenet_dropout (float): prenet dropout rate.
        forward_attn (bool): if true, use forward attention method. https://arxiv.org/abs/1807.06736
        trans_agent (bool): if true, use transition agent. https://arxiv.org/abs/1807.06736
        forward_attn_mask (bool): if true, mask attention values smaller than a threshold.
        location_attn (bool): if true, use location sensitive attention.
        attn_K (int): number of attention heads for GravesAttention.
        separate_stopnet (bool): if true, detach stopnet input to prevent gradient flow.
        d_vector_dim (int): size of speaker embedding vector, for multi-speaker training.
        max_decoder_steps (int): Maximum number of steps allowed for the decoder. Defaults to 500.
    """

    # Pylint gets confused by PyTorch conventions here
    # pylint: disable=attribute-defined-outside-init

    def __init__(
        self,
        in_channels,
        frame_channels,
        r,
        memory_size,
        attn_type,
        attn_windowing,
        attn_norm,
        prenet_type,
        prenet_dropout,
        forward_attn,
        trans_agent,
        forward_attn_mask,
        location_attn,
        attn_K,
        separate_stopnet,
        max_decoder_steps,
    ):
        super().__init__()
        self.r_init = r
        self.r = r
        self.in_channels = in_channels
        self.max_decoder_steps = max_decoder_steps
        self.use_memory_queue = memory_size > 0
        self.memory_size = memory_size if memory_size > 0 else r
        self.frame_channels = frame_channels
        self.separate_stopnet = separate_stopnet
        self.query_dim = 256
        # memory -> |Prenet| -> processed_memory
        prenet_dim = frame_channels * self.memory_size if self.use_memory_queue else frame_channels
        self.prenet = Prenet(prenet_dim, prenet_type, prenet_dropout, out_features=[256, 128])
        # processed_inputs, processed_memory -> |Attention| -> Attention, attention, RNN_State
        # attention_rnn generates queries for the attention mechanism
        self.attention_rnn = nn.GRUCell(in_channels + 128, self.query_dim)
        self.attention = init_attn(
            attn_type=attn_type,
            query_dim=self.query_dim,
            embedding_dim=in_channels,
            attention_dim=128,
            location_attention=location_attn,
            attention_location_n_filters=32,
            attention_location_kernel_size=31,
            windowing=attn_windowing,
            norm=attn_norm,
            forward_attn=forward_attn,
            trans_agent=trans_agent,
            forward_attn_mask=forward_attn_mask,
            attn_K=attn_K,
        )
        # (processed_memory | attention context) -> |Linear| -> decoder_RNN_input
        self.project_to_decoder_in = nn.Linear(256 + in_channels, 256)
        # decoder_RNN_input -> |RNN| -> RNN_state
        self.decoder_rnns = nn.ModuleList([nn.GRUCell(256, 256) for _ in range(2)])
        # RNN_state -> |Linear| -> mel_spec
        self.proj_to_mel = nn.Linear(256, frame_channels * self.r_init)
        # learn init values instead of zero init.
        self.stopnet = StopNet(256 + frame_channels * self.r_init)

    def set_r(self, new_r):
        self.r = new_r

    def _reshape_memory(self, memory):
        """
        Reshape the spectrograms for given 'r'
        """
        # Grouping multiple frames if necessary
        if memory.size(-1) == self.frame_channels:
            memory = memory.view(memory.shape[0], memory.size(1) // self.r, -1)
        # Time first (T_decoder, B, frame_channels)
        memory = memory.transpose(0, 1)
        return memory

    def _init_states(self, inputs):
        """
        Initialization of decoder states
        """
        B = inputs.size(0)
        # go frame as zeros matrix
        if self.use_memory_queue:
            self.memory_input = torch.zeros(1, device=inputs.device).repeat(B, self.frame_channels * self.memory_size)
        else:
            self.memory_input = torch.zeros(1, device=inputs.device).repeat(B, self.frame_channels)
        # decoder states
        self.attention_rnn_hidden = torch.zeros(1, device=inputs.device).repeat(B, 256)
        self.decoder_rnn_hiddens = [
            torch.zeros(1, device=inputs.device).repeat(B, 256) for idx in range(len(self.decoder_rnns))
        ]
        self.context_vec = inputs.data.new(B, self.in_channels).zero_()
        # cache attention inputs
        self.processed_inputs = self.attention.preprocess_inputs(inputs)

    def _parse_outputs(self, outputs, attentions, stop_tokens):
        # Back to batch first
        attentions = torch.stack(attentions).transpose(0, 1)
        stop_tokens = torch.stack(stop_tokens).transpose(0, 1)
        outputs = torch.stack(outputs).transpose(0, 1).contiguous()
        outputs = outputs.view(outputs.size(0), -1, self.frame_channels)
        outputs = outputs.transpose(1, 2)
        return outputs, attentions, stop_tokens

    def decode(self, inputs, mask=None):
        # Prenet
        processed_memory = self.prenet(self.memory_input)
        # Attention RNN
        self.attention_rnn_hidden = self.attention_rnn(
            torch.cat((processed_memory, self.context_vec), -1), self.attention_rnn_hidden
        )
        self.context_vec = self.attention(self.attention_rnn_hidden, inputs, self.processed_inputs, mask)
        # Concat RNN output and attention context vector
        decoder_input = self.project_to_decoder_in(torch.cat((self.attention_rnn_hidden, self.context_vec), -1))

        # Pass through the decoder RNNs
        for idx, decoder_rnn in enumerate(self.decoder_rnns):
            self.decoder_rnn_hiddens[idx] = decoder_rnn(decoder_input, self.decoder_rnn_hiddens[idx])
            # Residual connection
            decoder_input = self.decoder_rnn_hiddens[idx] + decoder_input
        decoder_output = decoder_input

        # predict mel vectors from decoder vectors
        output = self.proj_to_mel(decoder_output)
        # output = torch.sigmoid(output)
        # predict stop token
        stopnet_input = torch.cat([decoder_output, output], -1)
        if self.separate_stopnet:
            stop_token = self.stopnet(stopnet_input.detach())
        else:
            stop_token = self.stopnet(stopnet_input)
        output = output[:, : self.r * self.frame_channels]
        return output, stop_token, self.attention.attention_weights

    def _update_memory_input(self, new_memory):
        if self.use_memory_queue:
            if self.memory_size > self.r:
                # memory queue size is larger than number of frames per decoder iter
                self.memory_input = torch.cat(
                    [new_memory, self.memory_input[:, : (self.memory_size - self.r) * self.frame_channels].clone()],
                    dim=-1,
                )
            else:
                # memory queue size smaller than number of frames per decoder iter
                self.memory_input = new_memory[:, : self.memory_size * self.frame_channels]
        else:
            # use only the last frame prediction
            # assert new_memory.shape[-1] == self.r * self.frame_channels
            self.memory_input = new_memory[:, self.frame_channels * (self.r - 1) :]

    def forward(self, inputs, memory, mask):
        """
        Args:
            inputs: Encoder outputs.
            memory: Decoder memory (autoregression. If None (at eval-time),
              decoder outputs are used as decoder inputs. If None, it uses the last
              output as the input.
            mask: Attention mask for sequence padding.

        Shapes:
            - inputs: (B, T, D_out_enc)
            - memory: (B, T_mel, D_mel)
        """
        # Run greedy decoding if memory is None
        memory = self._reshape_memory(memory)
        outputs = []
        attentions = []
        stop_tokens = []
        t = 0
        self._init_states(inputs)
        self.attention.init_states(inputs)
        while len(outputs) < memory.size(0):
            if t > 0:
                new_memory = memory[t - 1]
                self._update_memory_input(new_memory)

            output, stop_token, attention = self.decode(inputs, mask)
            outputs += [output]
            attentions += [attention]
            stop_tokens += [stop_token.squeeze(1)]
            t += 1
        return self._parse_outputs(outputs, attentions, stop_tokens)

    def inference(self, inputs):
        """
        Args:
            inputs: encoder outputs.
        Shapes:
            - inputs: batch x time x encoder_out_dim
        """
        outputs = []
        attentions = []
        stop_tokens = []
        t = 0
        self._init_states(inputs)
        self.attention.init_states(inputs)
        while True:
            if t > 0:
                new_memory = outputs[-1]
                self._update_memory_input(new_memory)
            output, stop_token, attention = self.decode(inputs, None)
            stop_token = torch.sigmoid(stop_token.data)
            outputs += [output]
            attentions += [attention]
            stop_tokens += [stop_token]
            t += 1
            if t > inputs.shape[1] / 4 and (stop_token > 0.6 or attention[:, -1].item() > 0.6):
                break
            if t > self.max_decoder_steps:
                print("   | > Decoder stopped with 'max_decoder_steps")
                break
        return self._parse_outputs(outputs, attentions, stop_tokens)


class StopNet(nn.Module):
    r"""Stopnet signalling decoder to stop inference.
    Args:
        in_features (int): feature dimension of input.
    """

    def __init__(self, in_features):
        super().__init__()
        self.dropout = nn.Dropout(0.1)
        self.linear = nn.Linear(in_features, 1)
        torch.nn.init.xavier_uniform_(self.linear.weight, gain=torch.nn.init.calculate_gain("linear"))

    def forward(self, inputs):
        outputs = self.dropout(inputs)
        outputs = self.linear(outputs)
        return outputs