# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Mainly copy-paste from https://github.com/facebookresearch/detr/blob/master/models/transformer.py
"""
"""
DETR Transformer class.

Copy-paste from torch.nn.Transformer with modifications:
    * positional encodings are passed in MHattention
    * extra LN at the end of encoder is removed
    * decoder returns a stack of activations from all decoding layers
"""
import copy
from typing import Optional, List

import torch
import torch.nn.functional as F
from torch import nn, Tensor


class TransformerEncoder(nn.Module):

    def __init__(self, encoder_layer, num_layers, norm=None):
        super().__init__()
        self.layers = _get_clones(encoder_layer, num_layers)
        self.num_layers = num_layers
        self.norm = norm

    def get_last_attentions(self, src,
                mask: Optional[Tensor] = None,
                src_key_padding_mask: Optional[Tensor] = None,
                pos: Optional[Tensor] = None):

        output = src

        for i, layer in enumerate(self.layers):
            if i < len(self.layers) - 1:
                output = layer(output, src_mask=mask, 
                            src_key_padding_mask=src_key_padding_mask, pos=pos)
            else:
                # return attention of the last block
                return layer(output, src_mask=mask,
                            src_key_padding_mask=src_key_padding_mask, pos=pos, return_attention=True)

    def forward(self, src,
                mask: Optional[Tensor] = None,
                src_key_padding_mask: Optional[Tensor] = None,
                pos: Optional[Tensor] = None):
        output = src

        for layer in self.layers:
            output = layer(output, src_mask=mask,
                           src_key_padding_mask=src_key_padding_mask, pos=pos)

        if self.norm is not None:
            output = self.norm(output)

        return output


class TransformerEncoderLayer(nn.Module):

    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
                 activation="relu", normalize_before=False):
        super().__init__()
        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
        # Implementation of Feedforward model
        self.linear1 = nn.Linear(d_model, dim_feedforward)
        self.dropout = nn.Dropout(dropout)
        self.linear2 = nn.Linear(dim_feedforward, d_model)

        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)

        self.activation = _get_activation_fn(activation)
        self.normalize_before = normalize_before

    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
        return tensor if pos is None else tensor + pos

    def forward_post(self,
                     src,
                     src_mask: Optional[Tensor] = None,
                     src_key_padding_mask: Optional[Tensor] = None,
                     pos: Optional[Tensor] = None,
                     return_attention=False):
        q = k = self.with_pos_embed(src, pos)
        src2, attn = self.self_attn(q, k, value=src, attn_mask=src_mask,
                              key_padding_mask=src_key_padding_mask)
        if return_attention:
            return attn
        src = src + self.dropout1(src2)
        src = self.norm1(src)
        src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
        src = src + self.dropout2(src2)
        src = self.norm2(src)
        return src

    def forward_pre(self, src,
                    src_mask: Optional[Tensor] = None,
                    src_key_padding_mask: Optional[Tensor] = None,
                    pos: Optional[Tensor] = None,
                    return_attention=False):
        src2 = self.norm1(src)
        q = k = self.with_pos_embed(src2, pos)
        src2, attn = self.self_attn(q, k, value=src2, attn_mask=src_mask,
                              key_padding_mask=src_key_padding_mask)
        if return_attention:
            return attn
        src = src + self.dropout1(src2)
        src2 = self.norm2(src)
        src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))
        src = src + self.dropout2(src2)
        return src

    def forward(self, src,
                src_mask: Optional[Tensor] = None,
                src_key_padding_mask: Optional[Tensor] = None,
                pos: Optional[Tensor] = None,
                return_attention=False):
        if self.normalize_before:
            return self.forward_pre(src, src_mask, src_key_padding_mask, pos, return_attention)
        return self.forward_post(src, src_mask, src_key_padding_mask, pos, return_attention)


def _get_clones(module, N):
    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])


def _get_activation_fn(activation):
    """Return an activation function given a string"""
    if activation == "relu":
        return F.relu
    if activation == "gelu":
        return F.gelu
    if activation == "glu":
        return F.glu
    raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
