# 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.

import torch
import torch.nn.functional as F

from fairseq import utils
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
    Embedding,
    TransformerDecoder,
    TransformerEncoder,
    TransformerModel,
)
from fairseq.modules.transformer_sentence_encoder import init_bert_params


def _mean_pooling(enc_feats, src_masks):
    # enc_feats: T x B x C
    # src_masks: B x T or None
    if src_masks is None:
        enc_feats = enc_feats.mean(0)
    else:
        src_masks = (~src_masks).transpose(0, 1).type_as(enc_feats)
        enc_feats = (
            (enc_feats / src_masks.sum(0)[None, :, None]) * src_masks[:, :, None]
        ).sum(0)
    return enc_feats


def _argmax(x, dim):
    return (x == x.max(dim, keepdim=True)[0]).type_as(x)


def _uniform_assignment(src_lens, trg_lens):
    max_trg_len = trg_lens.max()
    steps = (src_lens.float() - 1) / (trg_lens.float() - 1)  # step-size
    # max_trg_len
    index_t = utils.new_arange(trg_lens, max_trg_len).float()
    index_t = steps[:, None] * index_t[None, :]  # batch_size X max_trg_len
    index_t = torch.round(index_t).long().detach()
    return index_t


@register_model("nonautoregressive_transformer")
class NATransformerModel(TransformerModel):
    def __init__(self, args, encoder, decoder):
        super().__init__(args, encoder, decoder)
        self.tgt_dict = decoder.dictionary
        self.bos = decoder.dictionary.bos()
        self.eos = decoder.dictionary.eos()
        self.pad = decoder.dictionary.pad()
        self.unk = decoder.dictionary.unk()

    @staticmethod
    def add_args(parser):
        TransformerModel.add_args(parser)
        parser.add_argument(
            "--apply-bert-init",
            action="store_true",
            help="use custom param initialization for BERT",
        )

        # length prediction
        parser.add_argument("--src-embedding-copy", action="store_true",
                            help="copy encoder word embeddings as the initial input of the decoder")
        parser.add_argument("--pred-length-offset", action="store_true",
                            help="predicting the length difference between the target and source sentences")
        parser.add_argument("--sg-length-pred", action="store_true",
                            help="stop the gradients back-propagated from the length predictor")
        parser.add_argument("--length-loss-factor", type=float,
                            help="weights on the length prediction loss")

    @classmethod
    def build_decoder(cls, args, tgt_dict, embed_tokens):
        decoder = NATransformerDecoder(args, tgt_dict, embed_tokens)
        if getattr(args, "apply_bert_init", False):
            decoder.apply(init_bert_params)
        return decoder

    @classmethod
    def build_encoder(cls, args, src_dict, embed_tokens):
        encoder = TransformerEncoder(args, src_dict, embed_tokens)
        if getattr(args, "apply_bert_init", False):
            encoder.apply(init_bert_params)
        return encoder

    def forward(
        self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs
    ):
        # encoding
        encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
        length_out, length_tgt = self.decoder.forward_length_prediction(
            encoder_out, tgt_tokens
        )

        word_ins_out, word_ins_tgt, word_ins_mask = self.decoder(
            prev_output_tokens, encoder_out=encoder_out, tgt_tokens=tgt_tokens
        )

        return {
            "word_ins": {
                "out": word_ins_out, "tgt": word_ins_tgt,
                "mask": word_ins_mask, "ls": self.args.label_smoothing,
                "nll_loss": True
            },
            "length": {
                "out": length_out, "tgt": length_tgt,
                "factor": self.decoder.length_loss_factor
            }
        }
            
    def forward_encoder(self, encoder_inputs):
        return self.encoder(*encoder_inputs)

    def forward_decoder(self, decoder_out, encoder_out, decoding_format=None, **kwargs):
        step = decoder_out.step
        output_tokens = decoder_out.output_tokens
        output_scores = decoder_out.output_scores
        history = decoder_out.history

        # execute the decoder
        output_masks = output_tokens.ne(self.pad)
        _scores, _tokens = self.decoder(
            output_tokens,
            encoder_out=encoder_out,
            decoding_format=decoding_format,
            step=step,
        )
        output_tokens.masked_scatter_(output_masks, _tokens[output_masks])
        output_scores.masked_scatter_(output_masks, _scores[output_masks])    
        if history is not None:
            history.append(output_tokens.clone())

        return decoder_out._replace(
            output_tokens=output_tokens,
            output_scores=output_scores,
            attn=None,
            history=history
        )

    def initialize_output_tokens(self, encoder_out, src_tokens):
        # length prediction
        _, length_tgt = self.decoder.forward_length_prediction(encoder_out)
        max_length = length_tgt.clamp_(min=2).max()
        idx_length = utils.new_arange(src_tokens, max_length)

        initial_output_tokens = src_tokens.new_zeros(
            src_tokens.size(0), max_length
        ).fill_(self.pad)
        initial_output_tokens.masked_fill_(
            idx_length[None, :] < length_tgt[:, None], self.unk
        )
        initial_output_tokens[:, 0] = self.bos
        initial_output_tokens.scatter_(1, length_tgt[:, None] - 1, self.eos)

        initial_output_scores = initial_output_tokens.new_zeros(
            *initial_output_tokens.size()
        ).type_as(encoder_out.encoder_out)

        return DecoderOut(
            output_tokens=initial_output_tokens,
            output_scores=initial_output_scores,
            attn=None,
            step=0,
            max_step=0,
            history=None
        )


class NATransformerDecoder(TransformerDecoder):
    def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False):
        super().__init__(
            args, dictionary, embed_tokens, no_encoder_attn=no_encoder_attn
        )

        self.dictionary = dictionary
        self.bos = dictionary.bos()
        self.unk = dictionary.unk()
        self.eos = dictionary.eos()

        self.encoder_embed_dim = args.encoder_embed_dim
        self.sg_length_pred = getattr(args, "sg_length_pred", False)
        self.pred_length_offset = getattr(args, "pred_length_offset", False)
        self.length_loss_factor = getattr(args, "length_loss_factor", 0.1)
        self.src_embedding_copy = getattr(args, "src_embedding_copy", False)
        self.embed_length = Embedding(256, self.encoder_embed_dim, None)

    def forward(
        self,
        prev_output_tokens,
        encoder_out=None,
        tgt_tokens=None,
        decoding_format=None,
        step=0,
        **kwargs
    ):

        features, _ = self.extract_features(
            prev_output_tokens,
            encoder_out=encoder_out,
            embedding_copy=(step == 0) & self.src_embedding_copy,
        )

        if tgt_tokens is not None:
            word_ins_mask = tgt_tokens.ne(self.padding_idx)
            word_ins_tgt = tgt_tokens
            return self.output_layer(features), word_ins_tgt, word_ins_mask

        else:
            return F.log_softmax(self.output_layer(features), -1).max(-1)

    def extract_features(
        self,
        prev_output_tokens,
        encoder_out=None,
        early_exit=None,
        embedding_copy=False,
        **unused
    ):
        """
        Similar to *forward* but only return features.

        Inputs:
            prev_output_tokens: Tensor(B, T)
            encoder_out: a dictionary of hidden states and masks

        Returns:
            tuple:
                - the decoder's features of shape `(batch, tgt_len, embed_dim)`
                - a dictionary with any model-specific outputs
            the LevenshteinTransformer decoder has full-attention to all generated tokens
        """
        # embedding
        if embedding_copy:
            src_embd = encoder_out.encoder_embedding
            src_mask = encoder_out.encoder_padding_mask
            src_mask = (
                ~src_mask
                if src_mask is not None
                else prev_output_tokens.new_ones(*src_embd.size()[:2]).bool()
            )

            x, decoder_padding_mask = self.forward_embedding(
                prev_output_tokens,
                self.forward_copying_source(
                    src_embd, src_mask, prev_output_tokens.ne(self.padding_idx)
                ),
            )

        else:

            x, decoder_padding_mask = self.forward_embedding(prev_output_tokens)

        # B x T x C -> T x B x C
        x = x.transpose(0, 1)
        attn = None
        inner_states = [x]

        # decoder layers
        for i, layer in enumerate(self.layers):

            # early exit from the decoder.
            if (early_exit is not None) and (i >= early_exit):
                break

            x, attn = layer(
                x,
                encoder_out.encoder_out if encoder_out is not None else None,
                encoder_out.encoder_padding_mask if encoder_out is not None else None,
                self_attn_mask=None,
                self_attn_padding_mask=decoder_padding_mask,
            )
            inner_states.append(x)

        if self.layer_norm:
            x = self.layer_norm(x)

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

        if self.project_out_dim is not None:
            x = self.project_out_dim(x)

        return x, {"attn": attn, "inner_states": inner_states}

    def forward_embedding(self, prev_output_tokens, states=None):
        # embed positions
        positions = (
            self.embed_positions(prev_output_tokens)
            if self.embed_positions is not None
            else None
        )

        # embed tokens and positions
        if states is None:
            x = self.embed_scale * self.embed_tokens(prev_output_tokens)
            if self.project_in_dim is not None:
                x = self.project_in_dim(x)
        else:
            x = states

        if positions is not None:
            x += positions
        x = F.dropout(x, p=self.dropout, training=self.training)
        decoder_padding_mask = prev_output_tokens.eq(self.padding_idx)
        return x, decoder_padding_mask

    def forward_copying_source(self, src_embeds, src_masks, tgt_masks):
        length_sources = src_masks.sum(1)
        length_targets = tgt_masks.sum(1)
        mapped_inputs = _uniform_assignment(length_sources, length_targets).masked_fill(
            ~tgt_masks, 0
        )
        copied_embedding = torch.gather(
            src_embeds,
            1,
            mapped_inputs.unsqueeze(-1).expand(
                *mapped_inputs.size(), src_embeds.size(-1)
            ),
        )
        return copied_embedding

    def forward_length_prediction(self, encoder_out, tgt_tokens=None):
        enc_feats = encoder_out.encoder_out  # T x B x C
        src_masks = encoder_out.encoder_padding_mask  # B x T or None

        if self.pred_length_offset:
            if src_masks is None:
                src_lengs = enc_feats.new_ones(enc_feats.size(1)).fill_(
                    enc_feats.size(0)
                )
            else:
                src_lengs = (~src_masks).transpose(0, 1).type_as(enc_feats).sum(0)
            src_lengs = src_lengs.long()

        enc_feats = _mean_pooling(enc_feats, src_masks)
        if self.sg_length_pred:
            enc_feats = enc_feats.detach()

        length_out = F.linear(enc_feats, self.embed_length.weight)

        if tgt_tokens is not None:
            # obtain the length target
            tgt_lengs = tgt_tokens.ne(self.padding_idx).sum(1).long()
            if self.pred_length_offset:
                length_tgt = tgt_lengs - src_lengs + 128
            else:
                length_tgt = tgt_lengs
            length_tgt = length_tgt.clamp(min=0, max=255)

        else:
            # predict the length target (greedy for now)
            # TODO: implementing length-beam
            pred_lengs = length_out.max(-1)[1]
            if self.pred_length_offset:
                length_tgt = pred_lengs - 128 + src_lengs
            else:
                length_tgt = pred_lengs

        return length_out, length_tgt


@register_model_architecture(
    "nonautoregressive_transformer", "nonautoregressive_transformer"
)
def base_architecture(args):
    args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
    args.encoder_layers = getattr(args, "encoder_layers", 6)
    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
    args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
    args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
    args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
    args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
    args.decoder_ffn_embed_dim = getattr(
        args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
    )
    args.decoder_layers = getattr(args, "decoder_layers", 6)
    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
    args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
    args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
    args.attention_dropout = getattr(args, "attention_dropout", 0.0)
    args.activation_dropout = getattr(args, "activation_dropout", 0.0)
    args.activation_fn = getattr(args, "activation_fn", "relu")
    args.dropout = getattr(args, "dropout", 0.1)
    args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
    args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
    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.no_token_positional_embeddings = getattr(
        args, "no_token_positional_embeddings", False
    )
    args.adaptive_input = getattr(args, "adaptive_input", False)
    args.apply_bert_init = getattr(args, "apply_bert_init", False)

    args.decoder_output_dim = getattr(
        args, "decoder_output_dim", args.decoder_embed_dim
    )
    args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)

    # --- special arguments ---
    args.sg_length_pred = getattr(args, "sg_length_pred", False)
    args.pred_length_offset = getattr(args, "pred_length_offset", False)
    args.length_loss_factor = getattr(args, "length_loss_factor", 0.1)
    args.src_embedding_copy = getattr(args, "src_embedding_copy", False)


@register_model_architecture(
    "nonautoregressive_transformer", "nonautoregressive_transformer_wmt_en_de"
)
def nonautoregressive_transformer_wmt_en_de(args):
    base_architecture(args)
