# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

from typing import Dict, List, Optional, Tuple

import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
    FairseqEncoder,
    FairseqEncoderDecoderModel,
    FairseqIncrementalDecoder,
    register_model,
    register_model_architecture,
)
from fairseq.modules import AdaptiveSoftmax, FairseqDropout
from torch import Tensor


DEFAULT_MAX_SOURCE_POSITIONS = 1e5
DEFAULT_MAX_TARGET_POSITIONS = 1e5


@register_model("lstm")
class LSTMModel(FairseqEncoderDecoderModel):
    def __init__(self, encoder, decoder):
        super().__init__(encoder, decoder)

    @staticmethod
    def add_args(parser):
        """Add model-specific arguments to the parser."""
        # fmt: off
        parser.add_argument('--dropout', type=float, metavar='D',
                            help='dropout probability')
        parser.add_argument('--encoder-embed-dim', type=int, metavar='N',
                            help='encoder embedding dimension')
        parser.add_argument('--encoder-embed-path', type=str, metavar='STR',
                            help='path to pre-trained encoder embedding')
        parser.add_argument('--encoder-freeze-embed', action='store_true',
                            help='freeze encoder embeddings')
        parser.add_argument('--encoder-hidden-size', type=int, metavar='N',
                            help='encoder hidden size')
        parser.add_argument('--encoder-layers', type=int, metavar='N',
                            help='number of encoder layers')
        parser.add_argument('--encoder-bidirectional', action='store_true',
                            help='make all layers of encoder bidirectional')
        parser.add_argument('--decoder-embed-dim', type=int, metavar='N',
                            help='decoder embedding dimension')
        parser.add_argument('--decoder-embed-path', type=str, metavar='STR',
                            help='path to pre-trained decoder embedding')
        parser.add_argument('--decoder-freeze-embed', action='store_true',
                            help='freeze decoder embeddings')
        parser.add_argument('--decoder-hidden-size', type=int, metavar='N',
                            help='decoder hidden size')
        parser.add_argument('--decoder-layers', type=int, metavar='N',
                            help='number of decoder layers')
        parser.add_argument('--decoder-out-embed-dim', type=int, metavar='N',
                            help='decoder output embedding dimension')
        parser.add_argument('--decoder-attention', type=str, metavar='BOOL',
                            help='decoder attention')
        parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR',
                            help='comma separated list of adaptive softmax cutoff points. '
                                 'Must be used with adaptive_loss criterion')
        parser.add_argument('--share-decoder-input-output-embed', default=False,
                            action='store_true',
                            help='share decoder input and output embeddings')
        parser.add_argument('--share-all-embeddings', default=False, action='store_true',
                            help='share encoder, decoder and output embeddings'
                                 ' (requires shared dictionary and embed dim)')

        # Granular dropout settings (if not specified these default to --dropout)
        parser.add_argument('--encoder-dropout-in', type=float, metavar='D',
                            help='dropout probability for encoder input embedding')
        parser.add_argument('--encoder-dropout-out', type=float, metavar='D',
                            help='dropout probability for encoder output')
        parser.add_argument('--decoder-dropout-in', type=float, metavar='D',
                            help='dropout probability for decoder input embedding')
        parser.add_argument('--decoder-dropout-out', type=float, metavar='D',
                            help='dropout probability for decoder output')
        # fmt: on

    @classmethod
    def build_model(cls, args, task):
        """Build a new model instance."""
        # make sure that all args are properly defaulted (in case there are any new ones)
        base_architecture(args)

        if args.encoder_layers != args.decoder_layers:
            raise ValueError("--encoder-layers must match --decoder-layers")

        max_source_positions = getattr(
            args, "max_source_positions", DEFAULT_MAX_SOURCE_POSITIONS
        )
        max_target_positions = getattr(
            args, "max_target_positions", DEFAULT_MAX_TARGET_POSITIONS
        )

        def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim):
            num_embeddings = len(dictionary)
            padding_idx = dictionary.pad()
            embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
            embed_dict = utils.parse_embedding(embed_path)
            utils.print_embed_overlap(embed_dict, dictionary)
            return utils.load_embedding(embed_dict, dictionary, embed_tokens)

        if args.encoder_embed_path:
            pretrained_encoder_embed = load_pretrained_embedding_from_file(
                args.encoder_embed_path, task.source_dictionary, args.encoder_embed_dim
            )
        else:
            num_embeddings = len(task.source_dictionary)
            pretrained_encoder_embed = Embedding(
                num_embeddings, args.encoder_embed_dim, task.source_dictionary.pad()
            )

        if args.share_all_embeddings:
            # double check all parameters combinations are valid
            if task.source_dictionary != task.target_dictionary:
                raise ValueError("--share-all-embeddings requires a joint dictionary")
            if args.decoder_embed_path and (
                args.decoder_embed_path != args.encoder_embed_path
            ):
                raise ValueError(
                    "--share-all-embed not compatible with --decoder-embed-path"
                )
            if args.encoder_embed_dim != args.decoder_embed_dim:
                raise ValueError(
                    "--share-all-embeddings requires --encoder-embed-dim to "
                    "match --decoder-embed-dim"
                )
            pretrained_decoder_embed = pretrained_encoder_embed
            args.share_decoder_input_output_embed = True
        else:
            # separate decoder input embeddings
            pretrained_decoder_embed = None
            if args.decoder_embed_path:
                pretrained_decoder_embed = load_pretrained_embedding_from_file(
                    args.decoder_embed_path,
                    task.target_dictionary,
                    args.decoder_embed_dim,
                )
        # one last double check of parameter combinations
        if args.share_decoder_input_output_embed and (
            args.decoder_embed_dim != args.decoder_out_embed_dim
        ):
            raise ValueError(
                "--share-decoder-input-output-embeddings requires "
                "--decoder-embed-dim to match --decoder-out-embed-dim"
            )

        if args.encoder_freeze_embed:
            pretrained_encoder_embed.weight.requires_grad = False
        if args.decoder_freeze_embed:
            pretrained_decoder_embed.weight.requires_grad = False

        encoder = LSTMEncoder(
            dictionary=task.source_dictionary,
            embed_dim=args.encoder_embed_dim,
            hidden_size=args.encoder_hidden_size,
            num_layers=args.encoder_layers,
            dropout_in=args.encoder_dropout_in,
            dropout_out=args.encoder_dropout_out,
            bidirectional=args.encoder_bidirectional,
            pretrained_embed=pretrained_encoder_embed,
            max_source_positions=max_source_positions,
        )
        decoder = LSTMDecoder(
            dictionary=task.target_dictionary,
            embed_dim=args.decoder_embed_dim,
            hidden_size=args.decoder_hidden_size,
            out_embed_dim=args.decoder_out_embed_dim,
            num_layers=args.decoder_layers,
            dropout_in=args.decoder_dropout_in,
            dropout_out=args.decoder_dropout_out,
            attention=utils.eval_bool(args.decoder_attention),
            encoder_output_units=encoder.output_units,
            pretrained_embed=pretrained_decoder_embed,
            share_input_output_embed=args.share_decoder_input_output_embed,
            adaptive_softmax_cutoff=(
                utils.eval_str_list(args.adaptive_softmax_cutoff, type=int)
                if args.criterion == "adaptive_loss"
                else None
            ),
            max_target_positions=max_target_positions,
            residuals=False,
        )
        return cls(encoder, decoder)

    def forward(
        self,
        src_tokens,
        src_lengths,
        prev_output_tokens,
        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
    ):
        encoder_out = self.encoder(src_tokens, src_lengths=src_lengths)
        decoder_out = self.decoder(
            prev_output_tokens,
            encoder_out=encoder_out,
            incremental_state=incremental_state,
        )
        return decoder_out


class LSTMEncoder(FairseqEncoder):
    """LSTM encoder."""

    def __init__(
        self,
        dictionary,
        embed_dim=512,
        hidden_size=512,
        num_layers=1,
        dropout_in=0.1,
        dropout_out=0.1,
        bidirectional=False,
        left_pad=True,
        pretrained_embed=None,
        padding_idx=None,
        max_source_positions=DEFAULT_MAX_SOURCE_POSITIONS,
    ):
        super().__init__(dictionary)
        self.num_layers = num_layers
        self.dropout_in_module = FairseqDropout(
            dropout_in*1.0, module_name=self.__class__.__name__
        )
        self.dropout_out_module = FairseqDropout(
            dropout_out*1.0, module_name=self.__class__.__name__
        )
        self.bidirectional = bidirectional
        self.hidden_size = hidden_size
        self.max_source_positions = max_source_positions

        num_embeddings = len(dictionary)
        self.padding_idx = padding_idx if padding_idx is not None else dictionary.pad()
        if pretrained_embed is None:
            self.embed_tokens = Embedding(num_embeddings, embed_dim, self.padding_idx)
        else:
            self.embed_tokens = pretrained_embed

        self.lstm = LSTM(
            input_size=embed_dim,
            hidden_size=hidden_size,
            num_layers=num_layers,
            dropout=self.dropout_out_module.p if num_layers > 1 else 0.0,
            bidirectional=bidirectional,
        )
        self.left_pad = left_pad

        self.output_units = hidden_size
        if bidirectional:
            self.output_units *= 2

    def forward(
        self,
        src_tokens: Tensor,
        src_lengths: Tensor,
        enforce_sorted: bool = True,
    ):
        """
        Args:
            src_tokens (LongTensor): tokens in the source language of
                shape `(batch, src_len)`
            src_lengths (LongTensor): lengths of each source sentence of
                shape `(batch)`
            enforce_sorted (bool, optional): if True, `src_tokens` is
                expected to contain sequences sorted by length in a
                decreasing order. If False, this condition is not
                required. Default: True.
        """
        if self.left_pad:
            # nn.utils.rnn.pack_padded_sequence requires right-padding;
            # convert left-padding to right-padding
            src_tokens = utils.convert_padding_direction(
                src_tokens,
                torch.zeros_like(src_tokens).fill_(self.padding_idx),
                left_to_right=True,
            )

        bsz, seqlen = src_tokens.size()

        # embed tokens
        x = self.embed_tokens(src_tokens)
        x = self.dropout_in_module(x)

        # B x T x C -> T x B x C
        x = x.transpose(0, 1)

        # pack embedded source tokens into a PackedSequence
        packed_x = nn.utils.rnn.pack_padded_sequence(
            x, src_lengths.cpu(), enforce_sorted=enforce_sorted
        )

        # apply LSTM
        if self.bidirectional:
            state_size = 2 * self.num_layers, bsz, self.hidden_size
        else:
            state_size = self.num_layers, bsz, self.hidden_size
        h0 = x.new_zeros(*state_size)
        c0 = x.new_zeros(*state_size)
        packed_outs, (final_hiddens, final_cells) = self.lstm(packed_x, (h0, c0))

        # unpack outputs and apply dropout
        x, _ = nn.utils.rnn.pad_packed_sequence(
            packed_outs, padding_value=self.padding_idx * 1.0
        )
        x = self.dropout_out_module(x)
        assert list(x.size()) == [seqlen, bsz, self.output_units]

        if self.bidirectional:
            final_hiddens = self.combine_bidir(final_hiddens, bsz)
            final_cells = self.combine_bidir(final_cells, bsz)

        encoder_padding_mask = src_tokens.eq(self.padding_idx).t()

        return tuple(
            (
                x,  # seq_len x batch x hidden
                final_hiddens,  # num_layers x batch x num_directions*hidden
                final_cells,  # num_layers x batch x num_directions*hidden
                encoder_padding_mask,  # seq_len x batch
            )
        )

    def combine_bidir(self, outs, bsz: int):
        out = outs.view(self.num_layers, 2, bsz, -1).transpose(1, 2).contiguous()
        return out.view(self.num_layers, bsz, -1)

    def reorder_encoder_out(self, encoder_out: Tuple[Tensor, Tensor, Tensor, Tensor], new_order):
        return tuple(
            (
                encoder_out[0].index_select(1, new_order),
                encoder_out[1].index_select(1, new_order),
                encoder_out[2].index_select(1, new_order),
                encoder_out[3].index_select(1, new_order),
            )
        )

    def max_positions(self):
        """Maximum input length supported by the encoder."""
        return self.max_source_positions


class AttentionLayer(nn.Module):
    def __init__(self, input_embed_dim, source_embed_dim, output_embed_dim, bias=False):
        super().__init__()

        self.input_proj = Linear(input_embed_dim, source_embed_dim, bias=bias)
        self.output_proj = Linear(
            input_embed_dim + source_embed_dim, output_embed_dim, bias=bias
        )

    def forward(self, input, source_hids, encoder_padding_mask):
        # input: bsz x input_embed_dim
        # source_hids: srclen x bsz x source_embed_dim

        # x: bsz x source_embed_dim
        x = self.input_proj(input)

        # compute attention
        attn_scores = (source_hids * x.unsqueeze(0)).sum(dim=2)

        # don't attend over padding
        if encoder_padding_mask is not None:
            attn_scores = (
                attn_scores.float()
                .masked_fill_(encoder_padding_mask, float("-inf"))
                .type_as(attn_scores)
            )  # FP16 support: cast to float and back

        attn_scores = F.softmax(attn_scores, dim=0)  # srclen x bsz

        # sum weighted sources
        x = (attn_scores.unsqueeze(2) * source_hids).sum(dim=0)

        x = torch.tanh(self.output_proj(torch.cat((x, input), dim=1)))
        return x, attn_scores


class LSTMDecoder(FairseqIncrementalDecoder):
    """LSTM decoder."""

    def __init__(
        self,
        dictionary,
        embed_dim=512,
        hidden_size=512,
        out_embed_dim=512,
        num_layers=1,
        dropout_in=0.1,
        dropout_out=0.1,
        attention=True,
        encoder_output_units=512,
        pretrained_embed=None,
        share_input_output_embed=False,
        adaptive_softmax_cutoff=None,
        max_target_positions=DEFAULT_MAX_TARGET_POSITIONS,
        residuals=False,
    ):
        super().__init__(dictionary)
        self.dropout_in_module = FairseqDropout(
            dropout_in*1.0, module_name=self.__class__.__name__
        )
        self.dropout_out_module = FairseqDropout(
            dropout_out*1.0, module_name=self.__class__.__name__
        )
        self.hidden_size = hidden_size
        self.share_input_output_embed = share_input_output_embed
        self.need_attn = True
        self.max_target_positions = max_target_positions
        self.residuals = residuals
        self.num_layers = num_layers

        self.adaptive_softmax = None
        num_embeddings = len(dictionary)
        padding_idx = dictionary.pad()
        if pretrained_embed is None:
            self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
        else:
            self.embed_tokens = pretrained_embed

        self.encoder_output_units = encoder_output_units
        if encoder_output_units != hidden_size and encoder_output_units != 0:
            self.encoder_hidden_proj = Linear(encoder_output_units, hidden_size)
            self.encoder_cell_proj = Linear(encoder_output_units, hidden_size)
        else:
            self.encoder_hidden_proj = self.encoder_cell_proj = None

        # disable input feeding if there is no encoder
        # input feeding is described in arxiv.org/abs/1508.04025
        input_feed_size = 0 if encoder_output_units == 0 else hidden_size
        self.layers = nn.ModuleList(
            [
                LSTMCell(
                    input_size=input_feed_size + embed_dim
                    if layer == 0
                    else hidden_size,
                    hidden_size=hidden_size,
                )
                for layer in range(num_layers)
            ]
        )

        if attention:
            # TODO make bias configurable
            self.attention = AttentionLayer(
                hidden_size, encoder_output_units, hidden_size, bias=False
            )
        else:
            self.attention = None

        if hidden_size != out_embed_dim:
            self.additional_fc = Linear(hidden_size, out_embed_dim)

        if adaptive_softmax_cutoff is not None:
            # setting adaptive_softmax dropout to dropout_out for now but can be redefined
            self.adaptive_softmax = AdaptiveSoftmax(
                num_embeddings,
                hidden_size,
                adaptive_softmax_cutoff,
                dropout=dropout_out,
            )
        elif not self.share_input_output_embed:
            self.fc_out = Linear(out_embed_dim, num_embeddings, dropout=dropout_out)

    def forward(
        self,
        prev_output_tokens,
        encoder_out: Optional[Tuple[Tensor, Tensor, Tensor, Tensor]] = None,
        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
        src_lengths: Optional[Tensor] = None,
    ):
        x, attn_scores = self.extract_features(
            prev_output_tokens, encoder_out, incremental_state
        )
        return self.output_layer(x), attn_scores

    def extract_features(
        self,
        prev_output_tokens,
        encoder_out: Optional[Tuple[Tensor, Tensor, Tensor, Tensor]] = None,
        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
    ):
        """
        Similar to *forward* but only return features.
        """
        # get outputs from encoder
        if encoder_out is not None:
            encoder_outs = encoder_out[0]
            encoder_hiddens = encoder_out[1]
            encoder_cells = encoder_out[2]
            encoder_padding_mask = encoder_out[3]
        else:
            encoder_outs = torch.empty(0)
            encoder_hiddens = torch.empty(0)
            encoder_cells = torch.empty(0)
            encoder_padding_mask = torch.empty(0)
        srclen = encoder_outs.size(0)

        if incremental_state is not None and len(incremental_state) > 0:
            prev_output_tokens = prev_output_tokens[:, -1:]

        bsz, seqlen = prev_output_tokens.size()

        # embed tokens
        x = self.embed_tokens(prev_output_tokens)
        x = self.dropout_in_module(x)

        # B x T x C -> T x B x C
        x = x.transpose(0, 1)

        # initialize previous states (or get from cache during incremental generation)
        if incremental_state is not None and len(incremental_state) > 0:
            prev_hiddens, prev_cells, input_feed = self.get_cached_state(
                incremental_state
            )
        elif encoder_out is not None:
            # setup recurrent cells
            prev_hiddens = [encoder_hiddens[i] for i in range(self.num_layers)]
            prev_cells = [encoder_cells[i] for i in range(self.num_layers)]
            if self.encoder_hidden_proj is not None:
                prev_hiddens = [self.encoder_hidden_proj(y) for y in prev_hiddens]
                prev_cells = [self.encoder_cell_proj(y) for y in prev_cells]
            input_feed = x.new_zeros(bsz, self.hidden_size)
        else:
            # setup zero cells, since there is no encoder
            zero_state = x.new_zeros(bsz, self.hidden_size)
            prev_hiddens = [zero_state for i in range(self.num_layers)]
            prev_cells = [zero_state for i in range(self.num_layers)]
            input_feed = None

        assert (
            srclen > 0 or self.attention is None
        ), "attention is not supported if there are no encoder outputs"
        attn_scores: Optional[Tensor] = (
            x.new_zeros(srclen, seqlen, bsz) if self.attention is not None else None
        )
        outs = []
        for j in range(seqlen):
            # input feeding: concatenate context vector from previous time step
            if input_feed is not None:
                input = torch.cat((x[j, :, :], input_feed), dim=1)
            else:
                input = x[j]

            for i, rnn in enumerate(self.layers):
                # recurrent cell
                hidden, cell = rnn(input, (prev_hiddens[i], prev_cells[i]))

                # hidden state becomes the input to the next layer
                input = self.dropout_out_module(hidden)
                if self.residuals:
                    input = input + prev_hiddens[i]

                # save state for next time step
                prev_hiddens[i] = hidden
                prev_cells[i] = cell

            # apply attention using the last layer's hidden state
            if self.attention is not None:
                assert attn_scores is not None
                out, attn_scores[:, j, :] = self.attention(
                    hidden, encoder_outs, encoder_padding_mask
                )
            else:
                out = hidden
            out = self.dropout_out_module(out)

            # input feeding
            if input_feed is not None:
                input_feed = out

            # save final output
            outs.append(out)

        # Stack all the necessary tensors together and store
        prev_hiddens_tensor = torch.stack(prev_hiddens)
        prev_cells_tensor = torch.stack(prev_cells)
        cache_state = torch.jit.annotate(
            Dict[str, Optional[Tensor]],
            {
                "prev_hiddens": prev_hiddens_tensor,
                "prev_cells": prev_cells_tensor,
                "input_feed": input_feed,
            },
        )
        self.set_incremental_state(incremental_state, "cached_state", cache_state)

        # collect outputs across time steps
        x = torch.cat(outs, dim=0).view(seqlen, bsz, self.hidden_size)

        # T x B x C -> B x T x C
        x = x.transpose(1, 0)

        if hasattr(self, "additional_fc") and self.adaptive_softmax is None:
            x = self.additional_fc(x)
            x = self.dropout_out_module(x)
        # srclen x tgtlen x bsz -> bsz x tgtlen x srclen
        if not self.training and self.need_attn and self.attention is not None:
            assert attn_scores is not None
            attn_scores = attn_scores.transpose(0, 2)
        else:
            attn_scores = None
        return x, attn_scores

    def output_layer(self, x):
        """Project features to the vocabulary size."""
        if self.adaptive_softmax is None:
            if self.share_input_output_embed:
                x = F.linear(x, self.embed_tokens.weight)
            else:
                x = self.fc_out(x)
        return x

    def get_cached_state(
        self,
        incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
    ) -> Tuple[List[Tensor], List[Tensor], Optional[Tensor]]:
        cached_state = self.get_incremental_state(incremental_state, "cached_state")
        assert cached_state is not None
        prev_hiddens_ = cached_state["prev_hiddens"]
        assert prev_hiddens_ is not None
        prev_cells_ = cached_state["prev_cells"]
        assert prev_cells_ is not None
        prev_hiddens = [prev_hiddens_[i] for i in range(self.num_layers)]
        prev_cells = [prev_cells_[j] for j in range(self.num_layers)]
        input_feed = cached_state[
            "input_feed"
        ]  # can be None for decoder-only language models
        return prev_hiddens, prev_cells, input_feed

    def reorder_incremental_state(
        self,
        incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
        new_order: Tensor,
    ):
        if incremental_state is None or len(incremental_state) == 0:
            return
        prev_hiddens, prev_cells, input_feed = self.get_cached_state(incremental_state)
        prev_hiddens = [p.index_select(0, new_order) for p in prev_hiddens]
        prev_cells = [p.index_select(0, new_order) for p in prev_cells]
        if input_feed is not None:
            input_feed = input_feed.index_select(0, new_order)
        cached_state_new = torch.jit.annotate(
            Dict[str, Optional[Tensor]],
            {
                "prev_hiddens": torch.stack(prev_hiddens),
                "prev_cells": torch.stack(prev_cells),
                "input_feed": input_feed,
            },
        )
        self.set_incremental_state(incremental_state, "cached_state", cached_state_new),
        return

    def max_positions(self):
        """Maximum output length supported by the decoder."""
        return self.max_target_positions

    def make_generation_fast_(self, need_attn=False, **kwargs):
        self.need_attn = need_attn


def Embedding(num_embeddings, embedding_dim, padding_idx):
    m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
    nn.init.uniform_(m.weight, -0.1, 0.1)
    nn.init.constant_(m.weight[padding_idx], 0)
    return m


def LSTM(input_size, hidden_size, **kwargs):
    m = nn.LSTM(input_size, hidden_size, **kwargs)
    for name, param in m.named_parameters():
        if "weight" in name or "bias" in name:
            param.data.uniform_(-0.1, 0.1)
    return m


def LSTMCell(input_size, hidden_size, **kwargs):
    m = nn.LSTMCell(input_size, hidden_size, **kwargs)
    for name, param in m.named_parameters():
        if "weight" in name or "bias" in name:
            param.data.uniform_(-0.1, 0.1)
    return m


def Linear(in_features, out_features, bias=True, dropout=0.0):
    """Linear layer (input: N x T x C)"""
    m = nn.Linear(in_features, out_features, bias=bias)
    m.weight.data.uniform_(-0.1, 0.1)
    if bias:
        m.bias.data.uniform_(-0.1, 0.1)
    return m


@register_model_architecture("lstm", "lstm")
def base_architecture(args):
    args.dropout = getattr(args, "dropout", 0.1)
    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
    args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
    args.encoder_freeze_embed = getattr(args, "encoder_freeze_embed", False)
    args.encoder_hidden_size = getattr(
        args, "encoder_hidden_size", args.encoder_embed_dim
    )
    args.encoder_layers = getattr(args, "encoder_layers", 1)
    args.encoder_bidirectional = getattr(args, "encoder_bidirectional", False)
    args.encoder_dropout_in = getattr(args, "encoder_dropout_in", args.dropout)
    args.encoder_dropout_out = getattr(args, "encoder_dropout_out", args.dropout)
    args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
    args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
    args.decoder_freeze_embed = getattr(args, "decoder_freeze_embed", False)
    args.decoder_hidden_size = getattr(
        args, "decoder_hidden_size", args.decoder_embed_dim
    )
    args.decoder_layers = getattr(args, "decoder_layers", 1)
    args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512)
    args.decoder_attention = getattr(args, "decoder_attention", "1")
    args.decoder_dropout_in = getattr(args, "decoder_dropout_in", args.dropout)
    args.decoder_dropout_out = getattr(args, "decoder_dropout_out", args.dropout)
    args.share_decoder_input_output_embed = getattr(
        args, "share_decoder_input_output_embed", False
    )
    args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
    args.adaptive_softmax_cutoff = getattr(
        args, "adaptive_softmax_cutoff", "10000,50000,200000"
    )


@register_model_architecture("lstm", "lstm_wiseman_iwslt_de_en")
def lstm_wiseman_iwslt_de_en(args):
    args.dropout = getattr(args, "dropout", 0.1)
    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256)
    args.encoder_dropout_in = getattr(args, "encoder_dropout_in", 0)
    args.encoder_dropout_out = getattr(args, "encoder_dropout_out", 0)
    args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256)
    args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 256)
    args.decoder_dropout_in = getattr(args, "decoder_dropout_in", 0)
    args.decoder_dropout_out = getattr(args, "decoder_dropout_out", args.dropout)
    base_architecture(args)


@register_model_architecture("lstm", "lstm_luong_wmt_en_de")
def lstm_luong_wmt_en_de(args):
    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1000)
    args.encoder_layers = getattr(args, "encoder_layers", 4)
    args.encoder_dropout_out = getattr(args, "encoder_dropout_out", 0)
    args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1000)
    args.decoder_layers = getattr(args, "decoder_layers", 4)
    args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 1000)
    args.decoder_dropout_out = getattr(args, "decoder_dropout_out", 0)
    base_architecture(args)
