#!/usr/bin/env python3
# -*- coding: utf-8 -*-

# Copyright 2019 Shigeki Karita
#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)

"""Encoder self-attention layer definition."""

from espnet_onnx.utils.torch_function import normalize
import torch

from torch import nn

from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm


class OnnxDecoderLayer(nn.Module):
    """Encoder layer module.

    Args:
        size (int): Input dimension.
        self_attn (torch.nn.Module): Self-attention module instance.
            `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` instance
            can be used as the argument.
        feed_forward (torch.nn.Module): Feed-forward module instance.
            `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
            can be used as the argument.
        dropout_rate (float): Dropout rate.
        normalize_before (bool): Whether to use layer_norm before the first block.
        concat_after (bool): Whether to concat attention layer's input and output.
            if True, additional linear will be applied.
            i.e. x -> x + linear(concat(x, att(x)))
            if False, no additional linear will be applied. i.e. x -> x + att(x)
        stochastic_depth_rate (float): Proability to skip this layer.
            During training, the layer may skip residual computation and return input
            as-is with given probability.
    """

    def __init__(
        self,
        model
    ):
        """Construct an EncoderLayer object."""
        super().__init__()
        self.self_attn = model.self_attn
        self.src_attn = model.src_attn
        self.feed_forward = model.feed_forward
        self.norm1 = model.norm1
        self.norm2 = model.norm2
        self.norm3 = model.norm3
        self.size = model.size
        self.normalize_before = model.normalize_before
        self.concat_after = model.concat_after
        if self.concat_after:
            self.concat_linear1 = model.concat_linear1
            self.concat_linear2 = model.concat_linear2

    def forward(self, x, mask, memory, memory_mask, cache=None):
        """Compute encoded features.

        Args:
            x_input (torch.Tensor): Input tensor (#batch, time, size).
            mask (torch.Tensor): Mask tensor for the input (#batch, time).
            cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).

        Returns:
            torch.Tensor: Output tensor (#batch, time, size).
            torch.Tensor: Mask tensor (#batch, time).

        """
        residual = x

        if self.normalize_before:
            x = self.norm1(x)

        if cache is not None:
            x_q = x[:, -1:, :]
            residual = residual[:, -1:, :]
            tgt_q_mask = None
            if mask is not None:
                tgt_q_mask = mask[:, :, -1:]
        else:
            x_q = x
            tgt_q_mask = mask

        if self.concat_after:
            x_concat = torch.cat((x, self.self_attn(x_q, x, x, tgt_q_mask)), dim=-1)
            x = self.concat_linear(x_concat) + residual
        else:
            x = self.self_attn(x_q, x, x, tgt_q_mask) + residual
            
        if not self.normalize_before:
            x = self.norm1(x)

        residual = x
        
        if self.normalize_before:
            x = self.norm2(x)
        if self.concat_after:
            x_concat = torch.cat(
                (x, self.src_attn(x, memory, memory, memory_mask)), dim=-1
            )
            x = self.concat_linear2(x_concat) + residual
        else:
            x = self.src_attn(x, memory, memory, memory_mask) + residual
        if not self.normalize_before:
            x = self.norm2(x)
        
        residual = x
        if self.normalize_before:
            x = self.norm3(x)
            
        x = self.feed_forward(x) + residual
        if not self.normalize_before:
            x = self.norm3(x)
        
        if cache is not None:
            x = torch.cat([cache, x], dim=1)

        return x, mask
