# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
#

import math
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F

from fairseq.data import LanguagePairDataset
from fairseq.modules import BeamableMM, GradMultiply, LinearizedConvolution

from . import FairseqEncoder, FairseqIncrementalDecoder, FairseqModel


def make_positions(tokens, padding_idx, left_pad, offset=0):
    seqlen = tokens.size(1)
    if not hasattr(make_positions, 'range'):
        make_positions.range = tokens.new()
    if make_positions.range.numel() < offset + seqlen:
        # offset positions by the padding index
        torch.arange(padding_idx + 1, padding_idx + 1 + offset + seqlen,
                     out=make_positions.range)
    mask = tokens.ne(padding_idx)
    positions = make_positions.range[offset:offset+seqlen].expand_as(tokens)
    if left_pad:
        positions = positions - mask.size(1) + mask.long().sum(dim=1).unsqueeze(1)
    return tokens.clone().masked_scatter_(mask, positions[mask])


class FConvModel(FairseqModel):
    def __init__(self, encoder, decoder):
        super().__init__(encoder, decoder)
        self.encoder.num_attention_layers = sum(layer is not None for layer in decoder.attention)


class FConvEncoder(FairseqEncoder):
    """Convolutional encoder"""
    def __init__(self, dictionary, embed_dim=512, max_positions=1024,
                 convolutions=((512, 3),) * 20, dropout=0.1):
        super().__init__()
        self.dictionary = dictionary
        self.dropout = dropout
        self.num_attention_layers = None

        num_embeddings = len(dictionary)
        padding_idx = dictionary.pad()
        self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
        self.embed_positions = Embedding(max_positions, embed_dim, padding_idx)

        in_channels = convolutions[0][0]
        self.fc1 = Linear(embed_dim, in_channels, dropout=dropout)
        self.projections = nn.ModuleList()
        self.convolutions = nn.ModuleList()
        for (out_channels, kernel_size) in convolutions:
            pad = (kernel_size - 1) / 2
            self.projections.append(Linear(in_channels, out_channels)
                                    if in_channels != out_channels else None)
            self.convolutions.append(
                ConvTBC(in_channels, out_channels * 2, kernel_size, padding=pad,
                        dropout=dropout))
            in_channels = out_channels
        self.fc2 = Linear(in_channels, embed_dim)

    def forward(self, src_tokens):
        positions = Variable(make_positions(src_tokens.data, self.dictionary.pad(),
                                            left_pad=LanguagePairDataset.LEFT_PAD_SOURCE))

        # embed tokens and positions
        x = self.embed_tokens(src_tokens) + self.embed_positions(positions)
        x = F.dropout(x, p=self.dropout, training=self.training)
        input_embedding = x

        # project to size of convolution
        x = self.fc1(x)

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

        # temporal convolutions
        for proj, conv in zip(self.projections, self.convolutions):
            residual = x if proj is None else proj(x)
            x = F.dropout(x, p=self.dropout, training=self.training)
            x = conv(x)
            x = F.glu(x, dim=-1)
            x = (x + residual) * math.sqrt(0.5)

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

        # project back to size of embedding
        x = self.fc2(x)

        # scale gradients (this only affects backward, not forward)
        x = GradMultiply.apply(x, 1.0 / (2.0 * self.num_attention_layers))

        # add output to input embedding for attention
        y = (x + input_embedding) * math.sqrt(0.5)

        return x, y

    def max_positions(self):
        """Maximum input length supported by the encoder."""
        return self.embed_positions.num_embeddings - self.dictionary.pad() - 1


class AttentionLayer(nn.Module):
    def __init__(self, conv_channels, embed_dim, bmm=None):
        super().__init__()
        # projects from output of convolution to embedding dimension
        self.in_projection = Linear(conv_channels, embed_dim)
        # projects from embedding dimension to convolution size
        self.out_projection = Linear(embed_dim, conv_channels)

        self.bmm = bmm if bmm is not None else torch.bmm

    def forward(self, x, target_embedding, encoder_out):
        residual = x

        # attention
        x = (self.in_projection(x) + target_embedding) * math.sqrt(0.5)
        x = self.bmm(x, encoder_out[0])

        # softmax over last dim
        sz = x.size()
        x = F.softmax(x.view(sz[0] * sz[1], sz[2]))
        x = x.view(sz)
        attn_scores = x

        x = self.bmm(x, encoder_out[1])

        # scale attention output
        s = encoder_out[1].size(1)
        x = x * (s * math.sqrt(1.0 / s))

        # project back
        x = (self.out_projection(x) + residual) * math.sqrt(0.5)
        return x, attn_scores

    def make_generation_fast_(self, beamable_mm_beam_size=None, **kwargs):
        """Replace torch.bmm with BeamableMM."""
        if beamable_mm_beam_size is not None:
            del self.bmm
            self.add_module('bmm', BeamableMM(beamable_mm_beam_size))


class FConvDecoder(FairseqIncrementalDecoder):
    """Convolutional decoder"""
    def __init__(self, dictionary, embed_dim=512, out_embed_dim=256,
                 max_positions=1024, convolutions=((512, 3),) * 20,
                 attention=True, dropout=0.1):
        super().__init__()
        self.register_buffer('version', torch.Tensor([2]))
        self.dictionary = dictionary
        self.dropout = dropout

        in_channels = convolutions[0][0]
        if isinstance(attention, bool):
            # expand True into [True, True, ...] and do the same with False
            attention = [attention] * len(convolutions)

        num_embeddings = len(dictionary)
        padding_idx = dictionary.pad()
        self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
        self.embed_positions = Embedding(max_positions, embed_dim, padding_idx)

        self.fc1 = Linear(embed_dim, in_channels, dropout=dropout)
        self.projections = nn.ModuleList()
        self.convolutions = nn.ModuleList()
        self.attention = nn.ModuleList()
        for i, (out_channels, kernel_size) in enumerate(convolutions):
            pad = kernel_size - 1
            self.projections.append(Linear(in_channels, out_channels)
                                    if in_channels != out_channels else None)
            self.convolutions.append(
                LinearizedConv1d(in_channels, out_channels * 2, kernel_size,
                                 padding=pad, dropout=dropout))
            self.attention.append(AttentionLayer(out_channels, embed_dim)
                                  if attention[i] else None)
            in_channels = out_channels
        self.fc2 = Linear(in_channels, out_embed_dim)
        self.fc3 = Linear(out_embed_dim, num_embeddings, dropout=dropout)

    def forward(self, input_tokens, encoder_out):
        if self._is_incremental_eval:
            return self.incremental_forward(input_tokens, encoder_out)
        else:
            return self.batch_forward(input_tokens, encoder_out)

    def batch_forward(self, input_tokens, encoder_out):
        """Forward pass for decoding multiple time steps in batch mode."""
        positions = Variable(make_positions(input_tokens.data, self.dictionary.pad(),
                                            left_pad=LanguagePairDataset.LEFT_PAD_TARGET))
        return self._forward(input_tokens, positions, encoder_out)

    def incremental_forward(self, input_tokens, encoder_out):
        """Forward pass for one time step."""
        # positions is the same for every token when decoding a single step
        positions = Variable(input_tokens.data.new(1, 1).fill_(
            self.dictionary.pad() + input_tokens.size(1)))

        # keep only the last token for incremental forward pass
        return self._forward(input_tokens[:, -1:], positions, encoder_out)

    def _forward(self, input_tokens, positions, encoder_out):
        # split and transpose encoder outputs
        encoder_a, encoder_b = self._split_encoder_out(encoder_out)

        # embed tokens and positions
        x = self.embed_tokens(input_tokens) + self.embed_positions(positions)
        x = F.dropout(x, p=self.dropout, training=self.training)
        target_embedding = x

        # project to size of convolution
        x = self.fc1(x)

        # B x T x C -> T x B x C
        x = self._transpose_unless_incremental_eval(x)

        # temporal convolutions
        avg_attn_scores = None
        num_attn_layers = len(self.attention)
        for proj, conv, attention in zip(self.projections, self.convolutions, self.attention):
            residual = x if proj is None else proj(x)

            x = F.dropout(x, p=self.dropout, training=self.training)
            x = conv(x)
            x = conv.remove_future_timesteps(x)
            x = F.glu(x)

            # attention
            if attention is not None:
                x = self._transpose_unless_incremental_eval(x)

                x, attn_scores = attention(x, target_embedding, (encoder_a, encoder_b))
                attn_scores = attn_scores / num_attn_layers
                if avg_attn_scores is None:
                    avg_attn_scores = attn_scores
                else:
                    avg_attn_scores.add_(attn_scores)

                x = self._transpose_unless_incremental_eval(x)

            # residual
            x = (x + residual) * math.sqrt(0.5)

        # T x B x C -> B x T x C
        x = self._transpose_unless_incremental_eval(x)

        # project back to size of vocabulary
        x = self.fc2(x)
        x = F.dropout(x, p=self.dropout, training=self.training)
        x = self.fc3(x)

        return x, avg_attn_scores

    def reorder_incremental_state(self, new_order):
        """Reorder buffered internal state (for incremental generation)."""
        super().reorder_incremental_state(new_order)

    def max_positions(self):
        """Maximum output length supported by the decoder."""
        return self.embed_positions.num_embeddings - self.dictionary.pad() - 1

    def upgrade_state_dict(self, state_dict):
        if state_dict.get('decoder.version', torch.Tensor([1]))[0] < 2:
            # old models use incorrect weight norm dimension
            for i, conv in enumerate(self.convolutions):
                # reconfigure weight norm
                nn.utils.remove_weight_norm(conv)
                self.convolutions[i] = nn.utils.weight_norm(conv, dim=0)
            state_dict['decoder.version'] = torch.Tensor([1])
        return state_dict

    def _split_encoder_out(self, encoder_out):
        """Split and transpose encoder outputs.

        This is cached when doing incremental inference.
        """
        cached_result = self.get_incremental_state('encoder_out')
        if cached_result:
            return cached_result

        # transpose only once to speed up attention layers
        encoder_a, encoder_b = encoder_out
        encoder_a = encoder_a.transpose(1, 2).contiguous()
        result = (encoder_a, encoder_b)

        return self.set_incremental_state('encoder_out', result)

    def _transpose_unless_incremental_eval(self, x):
        if self._is_incremental_eval:
            return x
        return x.transpose(0, 1)


def Embedding(num_embeddings, embedding_dim, padding_idx):
    m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
    m.weight.data.normal_(0, 0.1)
    return m


def Linear(in_features, out_features, dropout=0):
    """Weight-normalized Linear layer (input: N x T x C)"""
    m = nn.Linear(in_features, out_features)
    m.weight.data.normal_(mean=0, std=math.sqrt((1 - dropout) / in_features))
    m.bias.data.zero_()
    return nn.utils.weight_norm(m)


def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0, **kwargs):
    """Weight-normalized Conv1d layer optimized for decoding"""
    m = LinearizedConvolution(in_channels, out_channels, kernel_size, **kwargs)
    std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels))
    m.weight.data.normal_(mean=0, std=std)
    m.bias.data.zero_()
    return nn.utils.weight_norm(m, dim=2)


def ConvTBC(in_channels, out_channels, kernel_size, dropout=0, **kwargs):
    """Weight-normalized Conv1d layer"""
    from fairseq.modules import ConvTBC
    m = ConvTBC(in_channels, out_channels, kernel_size, **kwargs)
    std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels))
    m.weight.data.normal_(mean=0, std=std)
    m.bias.data.zero_()
    return nn.utils.weight_norm(m, dim=2)


def get_archs():
    return [
        'fconv', 'fconv_iwslt_de_en', 'fconv_wmt_en_ro', 'fconv_wmt_en_de', 'fconv_wmt_en_fr',
    ]


def _check_arch(args):
    """Check that the specified architecture is valid and not ambiguous."""
    if args.arch not in get_archs():
        raise ValueError('Unknown fconv model architecture: {}'.format(args.arch))
    if args.arch != 'fconv':
        # check that architecture is not ambiguous
        for a in ['encoder_embed_dim', 'encoder_layers', 'decoder_embed_dim', 'decoder_layers',
                  'decoder_out_embed_dim']:
            if hasattr(args, a):
                raise ValueError('--{} cannot be combined with --arch={}'.format(a, args.arch))


def parse_arch(args):
    _check_arch(args)

    if args.arch == 'fconv_iwslt_de_en':
        args.encoder_embed_dim = 256
        args.encoder_layers = '[(256, 3)] * 4'
        args.decoder_embed_dim = 256
        args.decoder_layers = '[(256, 3)] * 3'
        args.decoder_out_embed_dim = 256
    elif args.arch == 'fconv_wmt_en_ro':
        args.encoder_embed_dim = 512
        args.encoder_layers = '[(512, 3)] * 20'
        args.decoder_embed_dim = 512
        args.decoder_layers = '[(512, 3)] * 20'
        args.decoder_out_embed_dim = 512
    elif args.arch == 'fconv_wmt_en_de':
        convs = '[(512, 3)] * 9'       # first 9 layers have 512 units
        convs += ' + [(1024, 3)] * 4'  # next 4 layers have 1024 units
        convs += ' + [(2048, 1)] * 2'  # final 2 layers use 1x1 convolutions
        args.encoder_embed_dim = 768
        args.encoder_layers = convs
        args.decoder_embed_dim = 768
        args.decoder_layers = convs
        args.decoder_out_embed_dim = 512
    elif args.arch == 'fconv_wmt_en_fr':
        convs = '[(512, 3)] * 6'       # first 6 layers have 512 units
        convs += ' + [(768, 3)] * 4'   # next 4 layers have 768 units
        convs += ' + [(1024, 3)] * 3'  # next 3 layers have 1024 units
        convs += ' + [(2048, 1)] * 1'  # next 1 layer uses 1x1 convolutions
        convs += ' + [(4096, 1)] * 1'  # final 1 layer uses 1x1 convolutions
        args.encoder_embed_dim = 768
        args.encoder_layers = convs
        args.decoder_embed_dim = 768
        args.decoder_layers = convs
        args.decoder_out_embed_dim = 512
    else:
        assert args.arch == 'fconv'

    # default architecture
    args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512)
    args.encoder_layers = getattr(args, 'encoder_layers', '[(512, 3)] * 20')
    args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
    args.decoder_layers = getattr(args, 'decoder_layers', '[(512, 3)] * 20')
    args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 256)
    args.decoder_attention = getattr(args, 'decoder_attention', 'True')
    return args


def build_model(args, src_dict, dst_dict):
    encoder = FConvEncoder(
        src_dict,
        embed_dim=args.encoder_embed_dim,
        convolutions=eval(args.encoder_layers),
        dropout=args.dropout,
        max_positions=args.max_source_positions,
    )
    decoder = FConvDecoder(
        dst_dict,
        embed_dim=args.decoder_embed_dim,
        convolutions=eval(args.decoder_layers),
        out_embed_dim=args.decoder_out_embed_dim,
        attention=eval(args.decoder_attention),
        dropout=args.dropout,
        max_positions=args.max_target_positions,
    )
    return FConvModel(encoder, decoder)
