# 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 math
import random
from typing import Any, Dict, List, Optional, Tuple

import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.distributed import fsdp_wrap
from fairseq.models import (
    FairseqEncoder,
    FairseqEncoderDecoderModel,
    FairseqIncrementalDecoder,
    register_model,
    register_model_architecture,
)
from fairseq.modules import (
    AdaptiveSoftmax,
    BaseLayer,
    FairseqDropout,
    LayerDropModuleList,
    LayerNorm,
    SinusoidalPositionalEmbedding,
    GradMultiply
)
from fairseq.modules.checkpoint_activations import checkpoint_wrapper
from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_
from torch import Tensor

from .unify_transformer_layer import TransformerEncoderLayer, TransformerDecoderLayer
from .resnet import ResNet


DEFAULT_MAX_SOURCE_POSITIONS = 1024
DEFAULT_MAX_TARGET_POSITIONS = 1024


DEFAULT_MIN_PARAMS_TO_WRAP = int(1e8)


def BatchNorm2d(out_chan, momentum=0.1, eps=1e-3):
    return nn.SyncBatchNorm.convert_sync_batchnorm(
        nn.BatchNorm2d(out_chan, momentum=momentum, eps=eps)
    )


def make_token_bucket_position(bucket_size, max_position=DEFAULT_MAX_SOURCE_POSITIONS):
    context_pos = torch.arange(max_position, dtype=torch.long)[:, None]
    memory_pos = torch.arange(max_position, dtype=torch.long)[None, :]
    relative_pos = context_pos - memory_pos
    sign = torch.sign(relative_pos)
    mid = bucket_size // 2
    abs_pos = torch.where((relative_pos<mid) & (relative_pos > -mid), mid-1, torch.abs(relative_pos))
    log_pos = torch.ceil(torch.log(abs_pos/mid)/math.log((max_position-1)/mid) * (mid-1)) + mid
    log_pos = log_pos.int()
    bucket_pos = torch.where(abs_pos.le(mid), relative_pos, log_pos*sign).long()
    return bucket_pos + bucket_size - 1


def make_image_bucket_position(bucket_size, num_relative_distance):
    coords_h = torch.arange(bucket_size)
    coords_w = torch.arange(bucket_size)
    coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
    coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
    relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
    relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
    relative_coords[:, :, 0] += bucket_size - 1  # shift to start from 0
    relative_coords[:, :, 1] += bucket_size - 1
    relative_coords[:, :, 0] *= 2 * bucket_size - 1
    relative_position_index = torch.zeros(size=(bucket_size * bucket_size + 1,) * 2, dtype=relative_coords.dtype)
    relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
    relative_position_index[0, 0:] = num_relative_distance - 3
    relative_position_index[0:, 0] = num_relative_distance - 2
    relative_position_index[0, 0] = num_relative_distance - 1
    return relative_position_index


@register_model("unify_transformer")
class TransformerModel(FairseqEncoderDecoderModel):
    """
    Transformer model from `"Attention Is All You Need" (Vaswani, et al, 2017)
    <https://arxiv.org/abs/1706.03762>`_.

    Args:
        encoder (TransformerEncoder): the encoder
        decoder (TransformerDecoder): the decoder

    The Transformer model provides the following named architectures and
    command-line arguments:

    .. argparse::
        :ref: fairseq.models.transformer_parser
        :prog:
    """

    def __init__(self, args, encoder, decoder):
        super().__init__(encoder, decoder)
        self.args = args
        self.supports_align_args = True

    @staticmethod
    def add_args(parser):
        """Add model-specific arguments to the parser."""
        # fmt: off
        parser.add_argument('--activation-fn',
                            choices=utils.get_available_activation_fns(),
                            help='activation function to use')
        parser.add_argument('--dropout', type=float, metavar='D',
                            help='dropout probability')
        parser.add_argument('--attention-dropout', type=float, metavar='D',
                            help='dropout probability for attention weights')
        parser.add_argument('--activation-dropout', '--relu-dropout', type=float, metavar='D',
                            help='dropout probability after activation in FFN.')
        parser.add_argument('--encoder-embed-path', type=str, metavar='STR',
                            help='path to pre-trained encoder embedding')
        parser.add_argument('--encoder-embed-dim', type=int, metavar='N',
                            help='encoder embedding dimension')
        parser.add_argument('--encoder-ffn-embed-dim', type=int, metavar='N',
                            help='encoder embedding dimension for FFN')
        parser.add_argument('--encoder-layers', type=int, metavar='N',
                            help='num encoder layers')
        parser.add_argument('--encoder-attention-heads', type=int, metavar='N',
                            help='num encoder attention heads')
        parser.add_argument('--encoder-normalize-before', action='store_true',
                            help='apply layernorm before each encoder block')
        parser.add_argument('--encoder-learned-pos', action='store_true',
                            help='use learned positional embeddings in the encoder')
        parser.add_argument('--decoder-embed-path', type=str, metavar='STR',
                            help='path to pre-trained decoder embedding')
        parser.add_argument('--decoder-embed-dim', type=int, metavar='N',
                            help='decoder embedding dimension')
        parser.add_argument('--decoder-ffn-embed-dim', type=int, metavar='N',
                            help='decoder embedding dimension for FFN')
        parser.add_argument('--decoder-layers', type=int, metavar='N',
                            help='num decoder layers')
        parser.add_argument('--decoder-attention-heads', type=int, metavar='N',
                            help='num decoder attention heads')
        parser.add_argument('--decoder-learned-pos', action='store_true',
                            help='use learned positional embeddings in the decoder')
        parser.add_argument('--decoder-normalize-before', action='store_true',
                            help='apply layernorm before each decoder block')
        parser.add_argument('--decoder-output-dim', type=int, metavar='N',
                            help='decoder output dimension (extra linear layer '
                                 'if different from decoder embed dim')
        parser.add_argument('--share-decoder-input-output-embed', action='store_true',
                            help='share decoder input and output embeddings')
        parser.add_argument('--share-all-embeddings', action='store_true',
                            help='share encoder, decoder and output embeddings'
                                 ' (requires shared dictionary and embed dim)')
        parser.add_argument('--no-token-positional-embeddings', default=False, action='store_true',
                            help='if set, disables positional embeddings (outside self 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('--adaptive-softmax-dropout', type=float, metavar='D',
                            help='sets adaptive softmax dropout for the tail projections')
        parser.add_argument('--layernorm-embedding', action='store_true',
                            help='add layernorm to embedding')
        parser.add_argument('--no-scale-embedding', action='store_true',
                            help='if True, dont scale embeddings')
        parser.add_argument('--checkpoint-activations', action='store_true',
                            help='checkpoint activations at each layer, which saves GPU '
                                 'memory usage at the cost of some additional compute')
        parser.add_argument('--offload-activations', action='store_true',
                            help='checkpoint activations at each layer, then save to gpu. Sets --checkpoint-activations.')
        # args for "Cross+Self-Attention for Transformer Models" (Peitz et al., 2019)
        parser.add_argument('--no-cross-attention', default=False, action='store_true',
                            help='do not perform cross-attention')
        parser.add_argument('--cross-self-attention', default=False, action='store_true',
                            help='perform cross+self-attention')
        # args for "Reducing Transformer Depth on Demand with Structured Dropout" (Fan et al., 2019)
        parser.add_argument('--encoder-layerdrop', type=float, metavar='D', default=0,
                            help='LayerDrop probability for encoder')
        parser.add_argument('--decoder-layerdrop', type=float, metavar='D', default=0,
                            help='LayerDrop probability for decoder')
        parser.add_argument('--encoder-layers-to-keep', default=None,
                            help='which layers to *keep* when pruning as a comma-separated list')
        parser.add_argument('--decoder-layers-to-keep', default=None,
                            help='which layers to *keep* when pruning as a comma-separated list')
        # args for Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020)
        parser.add_argument('--quant-noise-pq', type=float, metavar='D', default=0,
                            help='iterative PQ quantization noise at training time')
        parser.add_argument('--quant-noise-pq-block-size', type=int, metavar='D', default=8,
                            help='block size of quantization noise at training time')
        parser.add_argument('--quant-noise-scalar', type=float, metavar='D', default=0,
                            help='scalar quantization noise and scalar quantization at training time')
        # args for Fully Sharded Data Parallel (FSDP) training
        parser.add_argument(
            '--min-params-to-wrap', type=int, metavar='D', default=DEFAULT_MIN_PARAMS_TO_WRAP,
            help=(
                'minimum number of params for a layer to be wrapped with FSDP() when '
                'training with --ddp-backend=fully_sharded. Smaller values will '
                'improve memory efficiency, but may make torch.distributed '
                'communication less efficient due to smaller input sizes. This option '
                'is set to 0 (i.e., always wrap) when --checkpoint-activations or '
                '--offload-activations are passed.'
            )
        )

        parser.add_argument('--resnet-drop-path-rate', type=float,
                            help='resnet drop path rate')
        parser.add_argument('--encoder-drop-path-rate', type=float,
                            help='encoder drop path rate')
        parser.add_argument('--decoder-drop-path-rate', type=float,
                            help='encoder drop path rate')

        parser.add_argument('--token-bucket-size', type=int,
                            help='token bucket size')
        parser.add_argument('--image-bucket-size', type=int,
                            help='image bucket size')

        parser.add_argument('--attn-scale-factor', type=float,
                            help='attention scale factor')
        parser.add_argument('--freeze-resnet', action='store_true',
                            help='freeze resnet')
        parser.add_argument('--freeze-encoder-embedding', action='store_true',
                            help='freeze encoder token embedding')
        parser.add_argument('--freeze-decoder-embedding', action='store_true',
                            help='freeze decoder token embedding')
        parser.add_argument('--add-type-embedding', action='store_true',
                            help='add source/region/patch type embedding')

        parser.add_argument('--resnet-type', choices=['resnet50', 'resnet101', 'resnet152'],
                            help='resnet type')
        parser.add_argument('--resnet-model-path', type=str, metavar='STR',
                            help='path to load resnet')
        parser.add_argument('--code-image-size', type=int,
                            help='code image size')
        parser.add_argument('--patch-layernorm-embedding', action='store_true',
                            help='add layernorm to patch embedding')
        parser.add_argument('--code-layernorm-embedding', action='store_true',
                            help='add layernorm to code embedding')
        parser.add_argument('--entangle-position-embedding', action='store_true',
                            help='entangle position embedding')
        parser.add_argument('--disable-entangle', action='store_true',
                            help='disable entangle')
        parser.add_argument('--sync-bn', action='store_true',
                            help='sync batchnorm')

        parser.add_argument('--scale-attn', action='store_true',
                            help='scale attn')
        parser.add_argument('--scale-fc', action='store_true',
                            help='scale fc')
        parser.add_argument('--scale-heads', action='store_true',
                            help='scale heads')
        parser.add_argument('--scale-resids', action='store_true',
                            help='scale resids')
        # fmt: on

    @classmethod
    def build_model(cls, args, task):
        """Build a new model instance."""

        # make sure all arguments are present in older models
        base_architecture(args)

        if args.encoder_layers_to_keep:
            args.encoder_layers = len(args.encoder_layers_to_keep.split(","))
        if args.decoder_layers_to_keep:
            args.decoder_layers = len(args.decoder_layers_to_keep.split(","))

        if getattr(args, "max_source_positions", None) is None:
            args.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS
        if getattr(args, "max_target_positions", None) is None:
            args.max_target_positions = DEFAULT_MAX_TARGET_POSITIONS

        src_dict, tgt_dict = task.source_dictionary, task.target_dictionary

        if args.share_all_embeddings:
            if src_dict != tgt_dict:
                raise ValueError("--share-all-embeddings requires a joined dictionary")
            if args.encoder_embed_dim != args.decoder_embed_dim:
                raise ValueError(
                    "--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim"
                )
            if args.decoder_embed_path and (
                args.decoder_embed_path != args.encoder_embed_path
            ):
                raise ValueError(
                    "--share-all-embeddings not compatible with --decoder-embed-path"
                )
            encoder_embed_tokens = cls.build_embedding(
                args, src_dict, args.encoder_embed_dim, args.encoder_embed_path
            )
            decoder_embed_tokens = encoder_embed_tokens
            args.share_decoder_input_output_embed = True
        else:
            encoder_embed_tokens = cls.build_embedding(
                args, src_dict, args.encoder_embed_dim, args.encoder_embed_path
            )
            decoder_embed_tokens = cls.build_embedding(
                args, tgt_dict, args.decoder_embed_dim, args.decoder_embed_path
            )
        if getattr(args, "freeze_encoder_embedding", False):
            encoder_embed_tokens.weight.requires_grad = False
        if getattr(args, "freeze_decoder_embedding", False):
            decoder_embed_tokens.weight.requires_grad = False
        if getattr(args, "offload_activations", False):
            args.checkpoint_activations = True  # offloading implies checkpointing
        encoder = cls.build_encoder(args, src_dict, encoder_embed_tokens)
        decoder = cls.build_decoder(args, tgt_dict, decoder_embed_tokens)
        if not args.share_all_embeddings:
            min_params_to_wrap = getattr(
                args, "min_params_to_wrap", DEFAULT_MIN_PARAMS_TO_WRAP
            )
            # fsdp_wrap is a no-op when --ddp-backend != fully_sharded
            encoder = fsdp_wrap(encoder, min_num_params=min_params_to_wrap)
            decoder = fsdp_wrap(decoder, min_num_params=min_params_to_wrap)
        return cls(args, encoder, decoder)

    @classmethod
    def build_embedding(cls, args, dictionary, embed_dim, path=None):
        num_embeddings = len(dictionary)
        padding_idx = dictionary.pad()

        emb = Embedding(num_embeddings, embed_dim, padding_idx)
        # if provided, load from preloaded dictionaries
        if path:
            embed_dict = utils.parse_embedding(path)
            utils.load_embedding(embed_dict, dictionary, emb)
        return emb

    @classmethod
    def build_encoder(cls, args, src_dict, embed_tokens):
        return TransformerEncoder(args, src_dict, embed_tokens)

    @classmethod
    def build_decoder(cls, args, tgt_dict, embed_tokens):
        return TransformerDecoder(
            args,
            tgt_dict,
            embed_tokens,
            no_encoder_attn=getattr(args, "no_cross_attention", False),
        )

    # TorchScript doesn't support optional arguments with variable length (**kwargs).
    # Current workaround is to add union of all arguments in child classes.
    def forward(
        self,
        src_tokens,
        src_lengths,
        prev_output_tokens,
        return_all_hiddens: bool = True,
        features_only: bool = False,
        alignment_layer: Optional[int] = None,
        alignment_heads: Optional[int] = None,
    ):
        """
        Run the forward pass for an encoder-decoder model.

        Copied from the base class, but without ``**kwargs``,
        which are not supported by TorchScript.
        """
        encoder_out = self.encoder(
            src_tokens, src_lengths=src_lengths, return_all_hiddens=return_all_hiddens
        )
        decoder_out = self.decoder(
            prev_output_tokens,
            encoder_out=encoder_out,
            features_only=features_only,
            alignment_layer=alignment_layer,
            alignment_heads=alignment_heads,
            src_lengths=src_lengths,
            return_all_hiddens=return_all_hiddens,
        )
        return decoder_out

    # Since get_normalized_probs is in the Fairseq Model which is not scriptable,
    # I rewrite the get_normalized_probs from Base Class to call the
    # helper function in the Base Class.
    @torch.jit.export
    def get_normalized_probs(
        self,
        net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]],
        log_probs: bool,
        sample: Optional[Dict[str, Tensor]] = None,
    ):
        """Get normalized probabilities (or log probs) from a net's output."""
        return self.get_normalized_probs_scriptable(net_output, log_probs, sample)


class TransformerEncoder(FairseqEncoder):
    """
    Transformer encoder consisting of *args.encoder_layers* layers. Each layer
    is a :class:`TransformerEncoderLayer`.

    Args:
        args (argparse.Namespace): parsed command-line arguments
        dictionary (~fairseq.data.Dictionary): encoding dictionary
        embed_tokens (torch.nn.Embedding): input embedding
    """

    def __init__(self, args, dictionary, embed_tokens):
        self.args = args
        super().__init__(dictionary)
        self.register_buffer("version", torch.Tensor([3]))

        self.dropout_module = FairseqDropout(
            args.dropout, module_name=self.__class__.__name__
        )
        self.encoder_layerdrop = args.encoder_layerdrop

        embed_dim = embed_tokens.embedding_dim
        self.padding_idx = embed_tokens.padding_idx
        self.max_source_positions = args.max_source_positions
        self.num_attention_heads = args.encoder_attention_heads

        self.embed_tokens = embed_tokens

        self.embed_scale = 1.0 if args.no_scale_embedding else math.sqrt(embed_dim)

        if getattr(args, "layernorm_embedding", False):
            self.layernorm_embedding = LayerNorm(embed_dim)
        else:
            self.layernorm_embedding = None

        if getattr(args, "add_type_embedding", False):
            self.type_embedding = Embedding(2, embed_dim, padding_idx=None)
        else:
            self.type_embedding = None

        if getattr(args, "sync_bn", False):
            norm_layer = BatchNorm2d
        else:
            norm_layer = None

        if args.resnet_type == 'resnet101':
            self.embed_images = ResNet([3, 4, 23], norm_layer=norm_layer, drop_path_rate=args.resnet_drop_path_rate)
        elif args.resnet_type == 'resnet152':
            self.embed_images = ResNet([3, 8, 36], norm_layer=norm_layer, drop_path_rate=args.resnet_drop_path_rate)
        else:
            raise NotImplementedError
        self.image_proj = Linear(1024, embed_dim)
        if getattr(args, "resnet_model_path", None):
            print("load resnet {}".format(args.resnet_model_path))
            resnet_state_dict = torch.load(self.args.resnet_model_path)
            self.embed_images.load_state_dict(resnet_state_dict)
        if getattr(args, "patch_layernorm_embedding", False):
            self.patch_layernorm_embedding = LayerNorm(embed_dim)
        else:
            self.patch_layernorm_embedding = None

        self.embed_positions = Embedding(args.max_source_positions + 2, embed_dim)
        self.embed_image_positions = Embedding(args.image_bucket_size ** 2 + 1, embed_dim)
        self.pos_ln = LayerNorm(embed_dim)
        self.image_pos_ln = LayerNorm(embed_dim)
        self.pos_scaling = float(embed_dim / args.encoder_attention_heads * args.attn_scale_factor) ** -0.5
        self.pos_q_linear = nn.Linear(embed_dim, embed_dim)
        self.pos_k_linear = nn.Linear(embed_dim, embed_dim)

        if not args.adaptive_input and args.quant_noise_pq > 0:
            self.quant_noise = apply_quant_noise_(
                nn.Linear(embed_dim, embed_dim, bias=False),
                args.quant_noise_pq,
                args.quant_noise_pq_block_size,
            )
        else:
            self.quant_noise = None

        if self.encoder_layerdrop > 0.0:
            self.layers = LayerDropModuleList(p=self.encoder_layerdrop)
        else:
            self.layers = nn.ModuleList([])

        dpr = [x.item() for x in torch.linspace(0, args.encoder_drop_path_rate, args.encoder_layers)]
        self.layers.extend(
            [self.build_encoder_layer(args, drop_path_rate=dpr[i]) for i in range(args.encoder_layers)]
        )
        self.num_layers = len(self.layers)

        if args.encoder_normalize_before:
            self.layer_norm = LayerNorm(embed_dim)
        else:
            self.layer_norm = None

        token_bucket_size = args.token_bucket_size
        token_num_rel_dis = 2 * token_bucket_size - 1
        token_rp_bucket = make_token_bucket_position(token_bucket_size)
        self.token_rel_pos_table_list = nn.ModuleList(
            [Embedding(token_num_rel_dis, self.num_attention_heads, zero_init=True) for _ in range(args.encoder_layers)]
        )

        image_bucket_size = args.image_bucket_size
        image_num_rel_dis = (2 * image_bucket_size - 1) * (2 * image_bucket_size - 1) + 3
        image_rp_bucket = make_image_bucket_position(image_bucket_size, image_num_rel_dis)
        self.image_rel_pos_table_list = nn.ModuleList(
            [Embedding(image_num_rel_dis, self.num_attention_heads, zero_init=True) for _ in range(args.encoder_layers)]
        )

        self.register_buffer("token_rp_bucket", token_rp_bucket)
        self.register_buffer("image_rp_bucket", image_rp_bucket)
        self.entangle_position_embedding = args.entangle_position_embedding

    def train(self, mode=True):
        super(TransformerEncoder, self).train(mode)
        if getattr(self.args, "freeze_resnet", False):
            for m in self.embed_images.modules():
                if isinstance(m, nn.BatchNorm2d):
                    m.eval()
                    m.weight.requires_grad = False
                    m.bias.requires_grad = False

    def build_encoder_layer(self, args, drop_path_rate=0.0):
        layer = TransformerEncoderLayer(args, drop_path_rate=drop_path_rate)
        checkpoint = getattr(args, "checkpoint_activations", False)
        if checkpoint:
            offload_to_cpu = getattr(args, "offload_activations", False)
            layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu)
        # if we are checkpointing, enforce that FSDP always wraps the
        # checkpointed layer, regardless of layer size
        min_params_to_wrap = (
            getattr(args, "min_params_to_wrap", DEFAULT_MIN_PARAMS_TO_WRAP)
            if not checkpoint else 0
        )
        layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap)
        return layer

    def get_rel_pos_bias(self, x, idx):
        seq_len = x.size(1)
        rp_bucket = self.token_rp_bucket[:seq_len, :seq_len]
        values = F.embedding(rp_bucket, self.token_rel_pos_table_list[idx].weight)
        values = values.unsqueeze(0).expand(x.size(0), -1, -1, -1)
        values = values.permute([0, 3, 1, 2])
        return values.contiguous()

    def get_image_rel_pos_bias(self, image_position_ids, idx):
        bsz, seq_len = image_position_ids.shape
        rp_bucket_size = self.image_rp_bucket.size(1)

        rp_bucket = self.image_rp_bucket.unsqueeze(0).expand(
            bsz, rp_bucket_size, rp_bucket_size
        ).gather(1, image_position_ids[:, :, None].expand(bsz, seq_len, rp_bucket_size)
        ).gather(2, image_position_ids[:, None, :].expand(bsz, seq_len, seq_len))
        values = F.embedding(rp_bucket, self.image_rel_pos_table_list[idx].weight)
        values = values.permute(0, 3, 1, 2)
        return values

    def get_patch_images_info(self, patch_images, sample_patch_num, device):
        image_embed = self.embed_images(patch_images)
        h, w = image_embed.shape[-2:]
        image_num_patches = h * w
        image_padding_mask = patch_images.new_zeros((patch_images.size(0), image_num_patches)).bool()
        image_position_idx = torch.arange(w).unsqueeze(0).expand(h, w) + \
                             torch.arange(h).unsqueeze(1) * self.args.image_bucket_size + 1
        image_position_idx = image_position_idx.view(-1).to(device)
        image_position_ids = image_position_idx[None, :].expand(patch_images.size(0), image_num_patches)

        image_embed = image_embed.flatten(2).transpose(1, 2)
        if sample_patch_num is not None:
            patch_orders = [
                random.sample(range(image_num_patches), k=sample_patch_num)
                for _ in range(patch_images.size(0))
            ]
            patch_orders = torch.LongTensor(patch_orders).to(device)
            image_embed = image_embed.gather(
                1, patch_orders.unsqueeze(2).expand(-1, -1, image_embed.size(2))
            )
            image_num_patches = sample_patch_num
            image_padding_mask = image_padding_mask.gather(1, patch_orders)
            image_position_ids = image_position_ids.gather(1, patch_orders)
        image_pos_embed = self.embed_image_positions(image_position_ids)

        return image_embed, image_num_patches, image_padding_mask, image_position_ids, image_pos_embed

    def forward_embedding(
        self,
        src_tokens,
        image_embed: Optional[torch.Tensor] = None,
        image_embed_2: Optional[torch.Tensor] = None,
        token_embedding: Optional[torch.Tensor] = None,
        pos_embed: Optional[torch.Tensor] = None,
        image_pos_embed: Optional[torch.Tensor] = None,
        image_pos_embed_2: Optional[torch.Tensor] = None
    ):
        # embed tokens and positions
        if token_embedding is None:
            token_embedding = self.embed_tokens(src_tokens)
        x = embed = self.embed_scale * token_embedding
        if self.entangle_position_embedding and pos_embed is not None:
            x += pos_embed
        if self.type_embedding is not None:
            x += self.type_embedding(src_tokens.new_zeros(x.size()[:2]))
        if self.layernorm_embedding is not None:
            x = self.layernorm_embedding(x)
        x = self.dropout_module(x)
        if self.quant_noise is not None:
            x = self.quant_noise(x)

        # embed raw images
        if image_embed is not None:
            image_embed = self.image_proj(image_embed)
            image_x = image_embed = self.embed_scale * image_embed
            if self.entangle_position_embedding and image_pos_embed is not None:
                image_x += image_pos_embed
            if self.type_embedding is not None:
                image_x += self.type_embedding(src_tokens.new_ones(image_x.size()[:2]))
            if self.patch_layernorm_embedding is not None:
                image_x = self.patch_layernorm_embedding(image_x)
            image_x = self.dropout_module(image_x)
            if self.quant_noise is not None:
                image_x = self.quant_noise(image_x)
            x = torch.cat([image_x, x], dim=1)
            embed = torch.cat([image_embed, embed], dim=1)

        if image_embed_2 is not None:
            assert self.type_embedding is not None
            image_embed_2 = self.image_proj(image_embed_2)
            image_x_2 = image_embed_2 = self.embed_scale * image_embed_2
            if self.entangle_position_embedding and image_pos_embed_2 is not None:
                image_x_2 += image_pos_embed_2
            if self.type_embedding is not None:
                image_x_2 += self.type_embedding(src_tokens.new_full(image_x_2.size()[:2], fill_value=2))
            if self.patch_layernorm_embedding is not None:
                image_x_2 = self.patch_layernorm_embedding(image_x_2)
            image_x_2 = self.dropout_module(image_x_2)
            if self.quant_noise is not None:
                image_x_2 = self.quant_noise(image_x_2)
            x = torch.cat([image_x_2, x], dim=1)
            embed = torch.cat([image_embed_2, embed], dim=1)

        return x, embed

    def forward(
        self,
        src_tokens,
        src_lengths,
        patch_images: Optional[torch.Tensor] = None,
        patch_images_2: Optional[torch.Tensor] = None,
        patch_masks: Optional[torch.Tensor] = None,
        code_masks: Optional[torch.Tensor] = None,
        return_all_hiddens: bool = False,
        token_embeddings: Optional[torch.Tensor] = None,
        sample_patch_num: Optional[int] = None
    ):
        """
        Args:
            src_tokens (LongTensor): tokens in the source language of shape
                `(batch, src_len)`
            src_lengths (torch.LongTensor): lengths of each source sentence of
                shape `(batch)`
            return_all_hiddens (bool, optional): also return all of the
                intermediate hidden states (default: False).
            token_embeddings (torch.Tensor, optional): precomputed embeddings
                default `None` will recompute embeddings

        Returns:
            dict:
                - **encoder_out** (Tensor): the last encoder layer's output of
                  shape `(src_len, batch, embed_dim)`
                - **encoder_padding_mask** (ByteTensor): the positions of
                  padding elements of shape `(batch, src_len)`
                - **encoder_embedding** (Tensor): the (scaled) embedding lookup
                  of shape `(batch, src_len, embed_dim)`
                - **encoder_states** (List[Tensor]): all intermediate
                  hidden states of shape `(src_len, batch, embed_dim)`.
                  Only populated if *return_all_hiddens* is True.
        """
        return self.forward_scriptable(src_tokens,
                                       src_lengths,
                                       patch_images,
                                       patch_images_2,
                                       patch_masks,
                                       return_all_hiddens,
                                       token_embeddings,
                                       sample_patch_num)

    # TorchScript doesn't support super() method so that the scriptable Subclass
    # can't access the base class model in Torchscript.
    # Current workaround is to add a helper function with different name and
    # call the helper function from scriptable Subclass.
    def forward_scriptable(
        self,
        src_tokens,
        src_lengths,
        patch_images: Optional[torch.Tensor] = None,
        patch_images_2: Optional[torch.Tensor] = None,
        patch_masks: Optional[torch.Tensor] = None,
        return_all_hiddens: bool = False,
        token_embeddings: Optional[torch.Tensor] = None,
        sample_patch_num: Optional[int] = None
    ):
        """
        Args:
            src_tokens (LongTensor): tokens in the source language of shape
                `(batch, src_len)`
            src_lengths (torch.LongTensor): lengths of each source sentence of
                shape `(batch)`
            return_all_hiddens (bool, optional): also return all of the
                intermediate hidden states (default: False).
            token_embeddings (torch.Tensor, optional): precomputed embeddings
                default `None` will recompute embeddings

        Returns:
            dict:
                - **encoder_out** (Tensor): the last encoder layer's output of
                  shape `(src_len, batch, embed_dim)`
                - **encoder_padding_mask** (ByteTensor): the positions of
                  padding elements of shape `(batch, src_len)`
                - **encoder_embedding** (Tensor): the (scaled) embedding lookup
                  of shape `(batch, src_len, embed_dim)`
                - **encoder_states** (List[Tensor]): all intermediate
                  hidden states of shape `(src_len, batch, embed_dim)`.
                  Only populated if *return_all_hiddens* is True.
        """
        image_embed = None
        image_embed_2 = None
        image_pos_embed = None
        image_pos_embed_2 = None
        if patch_images is not None:
            image_embed, image_num_patches, image_padding_mask, image_position_ids, image_pos_embed = \
                self.get_patch_images_info(patch_images, sample_patch_num, src_tokens.device)
            image_padding_mask[~patch_masks] = True
        if patch_images_2 is not None:
            image_embed_2, image_num_patches_2, image_padding_mask_2, image_position_ids_2, image_pos_embed_2 = \
                self.get_patch_images_info(patch_images_2, sample_patch_num, src_tokens.device)
            image_padding_mask_2[~patch_masks] = True

        encoder_padding_mask = src_tokens.eq(self.padding_idx)
        if patch_images is not None:
            encoder_padding_mask = torch.cat([image_padding_mask, encoder_padding_mask], dim=1)
        if patch_images_2 is not None:
            encoder_padding_mask = torch.cat([image_padding_mask_2, encoder_padding_mask], dim=1)
        has_pads = (src_tokens.device.type == "xla" or encoder_padding_mask.any())

        pos_embed = self.embed_positions(utils.new_arange(src_tokens))
        x, encoder_embedding = self.forward_embedding(
            src_tokens, image_embed, image_embed_2, token_embeddings,
            pos_embed, image_pos_embed, image_pos_embed_2
        )

        # account for padding while computing the representation
        if has_pads:
            x = x * (1 - encoder_padding_mask.unsqueeze(-1).type_as(x))

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

        pos_embed = self.pos_ln(pos_embed)
        if patch_images is not None:
            image_pos_embed = self.image_pos_ln(image_pos_embed)
            pos_embed = torch.cat([image_pos_embed, pos_embed], dim=1)
        if patch_images_2 is not None:
            image_pos_embed_2 = self.image_pos_ln(image_pos_embed_2)
            pos_embed = torch.cat([image_pos_embed_2, pos_embed], dim=1)

        pos_q = self.pos_q_linear(pos_embed).view(
            x.size(1), x.size(0), self.num_attention_heads, -1
        ).transpose(1, 2) * self.pos_scaling
        pos_k = self.pos_k_linear(pos_embed).view(
            x.size(1), x.size(0), self.num_attention_heads, -1
        ).transpose(1, 2)
        abs_pos_bias = torch.matmul(pos_q, pos_k.transpose(2, 3))

        encoder_states = []

        if return_all_hiddens:
            encoder_states.append(x)

        # encoder layers
        for idx, layer in enumerate(self.layers):
            self_attn_bias = abs_pos_bias.clone()
            self_attn_bias[:, :, -src_tokens.size(1):, -src_tokens.size(1):] += self.get_rel_pos_bias(src_tokens, idx)
            if patch_images_2 is not None:
                self_attn_bias[:, :, :image_num_patches_2, :image_num_patches_2] += \
                    self.get_image_rel_pos_bias(image_position_ids_2, idx)
                self_attn_bias[:, :, image_num_patches_2:image_num_patches_2+image_num_patches, image_num_patches_2:image_num_patches_2+image_num_patches] += \
                    self.get_image_rel_pos_bias(image_position_ids, idx)
            elif patch_images is not None:
                self_attn_bias[:, :, :x.size(0) - src_tokens.size(1), :x.size(0) - src_tokens.size(1)] += \
                    self.get_image_rel_pos_bias(image_position_ids, idx)
            self_attn_bias = self_attn_bias.reshape(-1, x.size(0), x.size(0))

            x = layer(
                x, encoder_padding_mask=encoder_padding_mask if has_pads else None, self_attn_bias=self_attn_bias
            )
            if return_all_hiddens:
                assert encoder_states is not None
                encoder_states.append(x)

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

        # The Pytorch Mobile lite interpreter does not supports returning NamedTuple in
        # `forward` so we use a dictionary instead.
        # TorchScript does not support mixed values so the values are all lists.
        # The empty list is equivalent to None.
        return {
            "encoder_out": [x],  # T x B x C
            "encoder_padding_mask": [encoder_padding_mask],  # B x T
            "encoder_embedding": [],  # B x T x C
            "encoder_states": encoder_states,  # List[T x B x C]
            "src_tokens": [],
            "src_lengths": [],
            "position_embeddings": [pos_embed],  # B x T x C
        }

    @torch.jit.export
    def reorder_encoder_out(self, encoder_out: Dict[str, List[Tensor]], new_order):
        """
        Reorder encoder output according to *new_order*.

        Args:
            encoder_out: output from the ``forward()`` method
            new_order (LongTensor): desired order

        Returns:
            *encoder_out* rearranged according to *new_order*
        """
        if len(encoder_out["encoder_out"]) == 0:
            new_encoder_out = []
        else:
            new_encoder_out = [encoder_out["encoder_out"][0].index_select(1, new_order)]
        if len(encoder_out["encoder_padding_mask"]) == 0:
            new_encoder_padding_mask = []
        else:
            new_encoder_padding_mask = [
                encoder_out["encoder_padding_mask"][0].index_select(0, new_order)
            ]
        if len(encoder_out["encoder_embedding"]) == 0:
            new_encoder_embedding = []
        else:
            new_encoder_embedding = [
                encoder_out["encoder_embedding"][0].index_select(0, new_order)
            ]

        if len(encoder_out["src_tokens"]) == 0:
            new_src_tokens = []
        else:
            new_src_tokens = [(encoder_out["src_tokens"][0]).index_select(0, new_order)]

        if len(encoder_out["src_lengths"]) == 0:
            new_src_lengths = []
        else:
            new_src_lengths = [(encoder_out["src_lengths"][0]).index_select(0, new_order)]

        if len(encoder_out["position_embeddings"]) == 0:
            new_position_embeddings = []
        else:
            new_position_embeddings = [(encoder_out["position_embeddings"][0]).index_select(0, new_order)]

        encoder_states = encoder_out["encoder_states"]
        if len(encoder_states) > 0:
            for idx, state in enumerate(encoder_states):
                encoder_states[idx] = state.index_select(1, new_order)

        return {
            "encoder_out": new_encoder_out,  # T x B x C
            "encoder_padding_mask": new_encoder_padding_mask,  # B x T
            "encoder_embedding": new_encoder_embedding,  # B x T x C
            "encoder_states": encoder_states,  # List[T x B x C]
            "src_tokens": new_src_tokens,  # B x T
            "src_lengths": new_src_lengths,  # B x 1
            "position_embeddings": new_position_embeddings,  # B x T x C
        }

    def max_positions(self):
        """Maximum input length supported by the encoder."""
        if self.embed_positions is None:
            return self.max_source_positions
        return self.max_source_positions

    def upgrade_state_dict_named(self, state_dict, name):
        """Upgrade a (possibly old) state dict for new versions of fairseq."""
        if isinstance(self.embed_positions, SinusoidalPositionalEmbedding):
            weights_key = "{}.embed_positions.weights".format(name)
            if weights_key in state_dict:
                print("deleting {0}".format(weights_key))
                del state_dict[weights_key]
            state_dict[
                "{}.embed_positions._float_tensor".format(name)
            ] = torch.FloatTensor(1)
        for i in range(self.num_layers):
            # update layer norms
            self.layers[i].upgrade_state_dict_named(
                state_dict, "{}.layers.{}".format(name, i)
            )

        # version_key = "{}.version".format(name)
        # if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) < 2:
        #     # earlier checkpoints did not normalize after the stack of layers
        #     self.layer_norm = None
        #     self.normalize = False
        #     state_dict[version_key] = torch.Tensor([1])

        prefix = name + "." if name != "" else ""
        for param_name, param_tensor in self.state_dict().items():
            if (prefix + param_name) not in state_dict and param_name in self.state_dict():
                state_dict[prefix + param_name] = self.state_dict()[param_name]

        if len(state_dict["encoder.embed_image_positions.weight"]) < len(self.state_dict()["embed_image_positions.weight"]):
            num_posids_to_add = len(self.state_dict()["embed_image_positions.weight"]) - len(state_dict["encoder.embed_image_positions.weight"])
            embed_dim = state_dict["encoder.embed_image_positions.weight"].size(1)
            new_pos_embed_to_add = torch.zeros(num_posids_to_add, embed_dim)
            nn.init.normal_(new_pos_embed_to_add, mean=0, std=embed_dim ** -0.5)
            new_pos_embed_to_add = new_pos_embed_to_add.to(
                dtype=state_dict["encoder.embed_image_positions.weight"].dtype,
            )
            state_dict["encoder.embed_image_positions.weight"] = torch.cat(
                [state_dict["encoder.embed_image_positions.weight"], new_pos_embed_to_add]
            )
        return state_dict


class TransformerDecoder(FairseqIncrementalDecoder):
    """
    Transformer decoder consisting of *args.decoder_layers* layers. Each layer
    is a :class:`TransformerDecoderLayer`.

    Args:
        args (argparse.Namespace): parsed command-line arguments
        dictionary (~fairseq.data.Dictionary): decoding dictionary
        embed_tokens (torch.nn.Embedding): output embedding
        no_encoder_attn (bool, optional): whether to attend to encoder outputs
            (default: False).
    """

    def __init__(
        self,
        args,
        dictionary,
        embed_tokens,
        no_encoder_attn=False,
        output_projection=None,
    ):
        self.args = args
        super().__init__(dictionary)
        self.register_buffer("version", torch.Tensor([3]))
        self._future_mask = torch.empty(0)

        self.dropout_module = FairseqDropout(
            args.dropout, module_name=self.__class__.__name__
        )
        self.decoder_layerdrop = args.decoder_layerdrop
        self.share_input_output_embed = args.share_decoder_input_output_embed
        self.num_attention_heads = args.decoder_attention_heads

        input_embed_dim = embed_tokens.embedding_dim
        embed_dim = args.decoder_embed_dim
        self.embed_dim = embed_dim
        self.output_embed_dim = args.decoder_output_dim

        self.padding_idx = embed_tokens.padding_idx
        self.max_target_positions = args.max_target_positions

        self.embed_tokens = embed_tokens

        self.embed_scale = 1.0 if args.no_scale_embedding else math.sqrt(embed_dim)

        if not args.adaptive_input and args.quant_noise_pq > 0:
            self.quant_noise = apply_quant_noise_(
                nn.Linear(embed_dim, embed_dim, bias=False),
                args.quant_noise_pq,
                args.quant_noise_pq_block_size,
            )
        else:
            self.quant_noise = None

        self.project_in_dim = (
            Linear(input_embed_dim, embed_dim, bias=False)
            if embed_dim != input_embed_dim
            else None
        )

        if getattr(args, "layernorm_embedding", False):
            self.layernorm_embedding = LayerNorm(embed_dim)
        else:
            self.layernorm_embedding = None

        self.window_size = args.code_image_size // 8

        self.embed_positions = Embedding(args.max_target_positions + 2, embed_dim)
        self.embed_image_positions = Embedding(args.image_bucket_size ** 2 + 1, embed_dim)
        self.pos_ln = LayerNorm(embed_dim)
        self.image_pos_ln = LayerNorm(embed_dim)
        self.pos_scaling = float(embed_dim / self.num_attention_heads * args.attn_scale_factor) ** -0.5
        self.self_pos_q_linear = nn.Linear(embed_dim, embed_dim)
        self.self_pos_k_linear = nn.Linear(embed_dim, embed_dim)
        self.cross_pos_q_linear = nn.Linear(embed_dim, embed_dim)
        self.cross_pos_k_linear = nn.Linear(embed_dim, embed_dim)

        if getattr(args, "code_layernorm_embedding", False):
            self.code_layernorm_embedding = LayerNorm(embed_dim)
        else:
            self.code_layernorm_embedding = None

        self.cross_self_attention = getattr(args, "cross_self_attention", False)

        if self.decoder_layerdrop > 0.0:
            self.layers = LayerDropModuleList(p=self.decoder_layerdrop)
        else:
            self.layers = nn.ModuleList([])

        dpr = [x.item() for x in torch.linspace(0, args.decoder_drop_path_rate, args.decoder_layers)]
        self.layers.extend(
            [
                self.build_decoder_layer(args, no_encoder_attn, drop_path_rate=dpr[i])
                for i in range(args.decoder_layers)
            ]
        )
        self.num_layers = len(self.layers)

        if args.decoder_normalize_before:
            self.layer_norm = LayerNorm(embed_dim)
        else:
            self.layer_norm = None

        self.project_out_dim = (
            Linear(embed_dim, self.output_embed_dim, bias=False)
            if embed_dim != self.output_embed_dim and not args.tie_adaptive_weights
            else None
        )

        self.adaptive_softmax = None
        self.output_projection = output_projection
        if self.output_projection is None:
            self.build_output_projection(args, dictionary, embed_tokens)

        token_bucket_size = args.token_bucket_size
        token_num_rel_dis = 2 * token_bucket_size - 1
        token_rp_bucket = make_token_bucket_position(token_bucket_size)
        self.token_rel_pos_table_list = nn.ModuleList(
            [Embedding(token_num_rel_dis, self.num_attention_heads, zero_init=True) for _ in range(args.decoder_layers)]
        )

        image_bucket_size = args.image_bucket_size
        image_num_rel_dis = (2 * image_bucket_size - 1) * (2 * image_bucket_size - 1) + 3
        image_rp_bucket = make_image_bucket_position(image_bucket_size, image_num_rel_dis)
        image_position_idx = torch.arange(self.window_size).unsqueeze(0).expand(self.window_size, self.window_size) + \
                             torch.arange(self.window_size).unsqueeze(1) * image_bucket_size + 1
        image_position_idx = torch.cat([torch.tensor([0]), image_position_idx.view(-1)])
        image_position_idx = torch.cat([image_position_idx, torch.tensor([1024] * 768)])
        self.image_rel_pos_table_list = nn.ModuleList(
            [Embedding(image_num_rel_dis, self.num_attention_heads, zero_init=True) for _ in range(args.decoder_layers)]
        )

        self.register_buffer("token_rp_bucket", token_rp_bucket)
        self.register_buffer("image_rp_bucket", image_rp_bucket)
        self.register_buffer("image_position_idx", image_position_idx)
        self.entangle_position_embedding = args.entangle_position_embedding

    def build_output_projection(self, args, dictionary, embed_tokens):
        if args.adaptive_softmax_cutoff is not None:
            self.adaptive_softmax = AdaptiveSoftmax(
                len(dictionary),
                self.output_embed_dim,
                utils.eval_str_list(args.adaptive_softmax_cutoff, type=int),
                dropout=args.adaptive_softmax_dropout,
                adaptive_inputs=embed_tokens if args.tie_adaptive_weights else None,
                factor=args.adaptive_softmax_factor,
                tie_proj=args.tie_adaptive_proj,
            )
        elif self.share_input_output_embed:
            self.output_projection = nn.Linear(
                self.embed_tokens.weight.shape[1],
                self.embed_tokens.weight.shape[0],
                bias=False,
            )
            self.output_projection.weight = self.embed_tokens.weight
        else:
            self.output_projection = nn.Linear(
                self.output_embed_dim, len(dictionary), bias=False
            )
            nn.init.normal_(
                self.output_projection.weight, mean=0, std=self.output_embed_dim ** -0.5
            )
        num_base_layers = getattr(args, "base_layers", 0)
        for i in range(num_base_layers):
            self.layers.insert(((i+1) * args.decoder_layers) // (num_base_layers + 1), BaseLayer(args))

    def build_decoder_layer(self, args, no_encoder_attn=False, drop_path_rate=0.0):
        layer = TransformerDecoderLayer(args, no_encoder_attn, drop_path_rate=drop_path_rate)
        checkpoint = getattr(args, "checkpoint_activations", False)
        if checkpoint:
            offload_to_cpu = getattr(args, "offload_activations", False)
            layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu)
        # if we are checkpointing, enforce that FSDP always wraps the
        # checkpointed layer, regardless of layer size
        min_params_to_wrap = (
            getattr(args, "min_params_to_wrap", DEFAULT_MIN_PARAMS_TO_WRAP)
            if not checkpoint else 0
        )
        layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap)
        return layer

    def get_rel_pos_bias(self, x, idx):
        seq_len = x.size(1)
        rp_bucket = self.token_rp_bucket[:seq_len, :seq_len]
        values = F.embedding(rp_bucket, self.token_rel_pos_table_list[idx].weight)
        values = values.permute([2, 0, 1])
        return values.contiguous()

    def get_image_rel_pos_bias(self, x, idx):
        seq_len = x.size(1)
        image_position_idx = self.image_position_idx[:seq_len]
        rp_bucket = self.image_rp_bucket[image_position_idx][:, image_position_idx]
        values = F.embedding(rp_bucket, self.image_rel_pos_table_list[idx].weight)
        values = values.permute(2, 0, 1)
        return values

    def get_pos_info(self, tokens, tgt_pos_embed, src_pos_embed=None, use_image=False):
        batch_size = tokens.size(0)
        tgt_len = tokens.size(1)
        tgt_pos_embed = self.image_pos_ln(tgt_pos_embed) if use_image else self.pos_ln(tgt_pos_embed)
        if src_pos_embed is not None:
            src_len = src_pos_embed.size(1)
            pos_q = self.cross_pos_q_linear(tgt_pos_embed).view(
                batch_size, tgt_len, self.num_attention_heads, -1
            ).transpose(1, 2) * self.pos_scaling
            pos_k = self.cross_pos_k_linear(src_pos_embed).view(
                batch_size, src_len, self.num_attention_heads, -1
            ).transpose(1, 2)
        else:
            src_len = tgt_pos_embed.size(1)
            pos_q = self.self_pos_q_linear(tgt_pos_embed).view(
                batch_size, tgt_len, self.num_attention_heads, -1
            ).transpose(1, 2) * self.pos_scaling
            pos_k = self.self_pos_k_linear(tgt_pos_embed).view(
                batch_size, src_len, self.num_attention_heads, -1
            ).transpose(1, 2)
        abs_pos_bias = torch.matmul(pos_q, pos_k.transpose(2, 3))
        return abs_pos_bias

    def forward(
        self,
        prev_output_tokens,
        code_masks: Optional[torch.Tensor] = None,
        encoder_out: Optional[Dict[str, List[Tensor]]] = None,
        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
        features_only: bool = False,
        full_context_alignment: bool = False,
        alignment_layer: Optional[int] = None,
        alignment_heads: Optional[int] = None,
        src_lengths: Optional[Any] = None,
        return_all_hiddens: bool = False,
    ):
        """
        Args:
            prev_output_tokens (LongTensor): previous decoder outputs of shape
                `(batch, tgt_len)`, for teacher forcing
            encoder_out (optional): output from the encoder, used for
                encoder-side attention, should be of size T x B x C
            incremental_state (dict): dictionary used for storing state during
                :ref:`Incremental decoding`
            features_only (bool, optional): only return features without
                applying output layer (default: False).
            full_context_alignment (bool, optional): don't apply
                auto-regressive mask to self-attention (default: False).

        Returns:
            tuple:
                - the decoder's output of shape `(batch, tgt_len, vocab)`
                - a dictionary with any model-specific outputs
        """

        x, extra = self.extract_features(
            prev_output_tokens,
            code_masks=code_masks,
            encoder_out=encoder_out,
            incremental_state=incremental_state,
            full_context_alignment=full_context_alignment,
            alignment_layer=alignment_layer,
            alignment_heads=alignment_heads,
        )

        if not features_only:
            x = self.output_layer(x)
        return x, extra

    def extract_features(
        self,
        prev_output_tokens,
        code_masks: Optional[torch.Tensor],
        encoder_out: Optional[Dict[str, List[Tensor]]],
        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
        full_context_alignment: bool = False,
        alignment_layer: Optional[int] = None,
        alignment_heads: Optional[int] = None,
    ):
        return self.extract_features_scriptable(
            prev_output_tokens,
            code_masks,
            encoder_out,
            incremental_state,
            full_context_alignment,
            alignment_layer,
            alignment_heads,
        )

    """
    A scriptable subclass of this class has an extract_features method and calls
    super().extract_features, but super() is not supported in torchscript. A copy of
    this function is made to be used in the subclass instead.
    """

    def extract_features_scriptable(
        self,
        prev_output_tokens,
        code_masks: Optional[torch.Tensor],
        encoder_out: Optional[Dict[str, List[Tensor]]],
        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
        full_context_alignment: bool = False,
        alignment_layer: Optional[int] = None,
        alignment_heads: Optional[int] = None,
    ):
        """
        Similar to *forward* but only return features.

        Includes several features from "Jointly Learning to Align and
        Translate with Transformer Models" (Garg et al., EMNLP 2019).

        Args:
            full_context_alignment (bool, optional): don't apply
                auto-regressive mask to self-attention (default: False).
            alignment_layer (int, optional): return mean alignment over
                heads at this layer (default: last layer).
            alignment_heads (int, optional): only average alignment over
                this many heads (default: all heads).

        Returns:
            tuple:
                - the decoder's features of shape `(batch, tgt_len, embed_dim)`
                - a dictionary with any model-specific outputs
        """
        bs, slen = prev_output_tokens.size()
        if alignment_layer is None:
            alignment_layer = self.num_layers - 1

        enc: Optional[Tensor] = None
        padding_mask: Optional[Tensor] = None
        if encoder_out is not None and len(encoder_out["encoder_out"]) > 0:
            enc = encoder_out["encoder_out"][0]
            assert (
                enc.size()[1] == bs
            ), f"Expected enc.shape == (t, {bs}, c) got {enc.shape}"
        if encoder_out is not None and len(encoder_out["encoder_padding_mask"]) > 0:
            padding_mask = encoder_out["encoder_padding_mask"][0]

        bsz, tgt_len = prev_output_tokens.shape
        token_position_idx = utils.new_arange(prev_output_tokens)
        tgt_pos_embed = self.embed_positions(token_position_idx)
        if code_masks is not None and torch.any(code_masks):
            image_position_idx = self.image_position_idx[:prev_output_tokens.size(1)].unsqueeze(0).expand(bsz, tgt_len)
            tgt_pos_embed[code_masks] = self.embed_image_positions(image_position_idx)[code_masks]

        # self attn position bias
        self_abs_pos_bias = self.get_pos_info(prev_output_tokens, tgt_pos_embed, use_image=False)
        if code_masks is not None and torch.any(code_masks):
            self_image_abs_pos_bias = self.get_pos_info(prev_output_tokens, tgt_pos_embed, use_image=True)
            self_abs_pos_bias[code_masks] = self_image_abs_pos_bias[code_masks]
        # cross attn position bias
        src_pos_embed = encoder_out['position_embeddings'][0]
        cross_abs_pos_bias = self.get_pos_info(prev_output_tokens, tgt_pos_embed, src_pos_embed=src_pos_embed)
        if code_masks is not None and torch.any(code_masks):
            cross_image_abs_pos_bias = self.get_pos_info(prev_output_tokens, tgt_pos_embed, src_pos_embed=src_pos_embed, use_image=True)
            cross_abs_pos_bias[code_masks] = cross_image_abs_pos_bias[code_masks]
        cross_abs_pos_bias = cross_abs_pos_bias.reshape(-1, *cross_abs_pos_bias.size()[-2:])

        all_prev_output_tokens = prev_output_tokens.clone()
        if incremental_state is not None:
            prev_output_tokens = prev_output_tokens[:, -1:]
            cross_abs_pos_bias = cross_abs_pos_bias[:, -1:, :]
            tgt_pos_embed = tgt_pos_embed[:, -1:, :]

        # embed tokens and positions
        x = self.embed_scale * self.embed_tokens(prev_output_tokens)

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

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

        if self.entangle_position_embedding is not None and not self.args.disable_entangle:
            x += tgt_pos_embed

        if self.layernorm_embedding is not None:
            if code_masks is None or not code_masks.any() or not getattr(self, "code_layernorm_embedding", False):
                x = self.layernorm_embedding(x)
            elif code_masks is not None and code_masks.all():
                x = self.code_layernorm_embedding(x)
            else:
                x[~code_masks] = self.layernorm_embedding(x[~code_masks])
                x[code_masks] = self.code_layernorm_embedding(x[code_masks])

        x = self.dropout_module(x)

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

        self_attn_padding_mask: Optional[Tensor] = None
        if self.cross_self_attention or prev_output_tokens.eq(self.padding_idx).any():
            self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx)

        # decoder layers
        attn: Optional[Tensor] = None
        inner_states: List[Optional[Tensor]] = [x]
        for idx, layer in enumerate(self.layers):
            if incremental_state is None and not full_context_alignment:
                self_attn_mask = self.buffered_future_mask(x)
            else:
                self_attn_mask = None

            self_attn_bias = self_abs_pos_bias.clone()
            if code_masks is None or not code_masks.any():
                self_attn_bias += self.get_rel_pos_bias(all_prev_output_tokens, idx).unsqueeze(0)
            elif code_masks is not None and code_masks.all():
                self_attn_bias += self.get_image_rel_pos_bias(all_prev_output_tokens, idx).unsqueeze(0)
            else:
                self_attn_bias[~code_masks] += self.get_rel_pos_bias(all_prev_output_tokens, idx).unsqueeze(0)
                self_attn_bias[code_masks] += self.get_image_rel_pos_bias(all_prev_output_tokens, idx).unsqueeze(0)
            self_attn_bias = self_attn_bias.reshape(-1, *self_attn_bias.size()[-2:])
            if incremental_state is not None:
                self_attn_bias = self_attn_bias[:, -1:, :]

            x, layer_attn, _ = layer(
                x,
                enc,
                padding_mask,
                incremental_state,
                self_attn_mask=self_attn_mask,
                self_attn_padding_mask=self_attn_padding_mask,
                need_attn=bool((idx == alignment_layer)),
                need_head_weights=bool((idx == alignment_layer)),
                self_attn_bias=self_attn_bias,
                cross_attn_bias=cross_abs_pos_bias
            )
            inner_states.append(x)
            if layer_attn is not None and idx == alignment_layer:
                attn = layer_attn.float().to(x)

        if attn is not None:
            if alignment_heads is not None:
                attn = attn[:alignment_heads]

            # average probabilities over heads
            attn = attn.mean(dim=0)

        if self.layer_norm is not None:
            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 output_layer(self, features):
        """Project features to the vocabulary size."""
        if self.adaptive_softmax is None:
            # project back to size of vocabulary
            return self.output_projection(features)
        else:
            return features

    def max_positions(self):
        """Maximum output length supported by the decoder."""
        if self.embed_positions is None:
            return self.max_target_positions
        return self.max_target_positions

    def buffered_future_mask(self, tensor):
        dim = tensor.size(0)
        # self._future_mask.device != tensor.device is not working in TorchScript. This is a workaround.
        if (
            self._future_mask.size(0) == 0
            or (not self._future_mask.device == tensor.device)
            or self._future_mask.size(0) < dim
        ):
            self._future_mask = torch.triu(
                utils.fill_with_neg_inf(torch.zeros([dim, dim])), 1
            )
        self._future_mask = self._future_mask.to(tensor)
        return self._future_mask[:dim, :dim]

    def upgrade_state_dict_named(self, state_dict, name):
        """Upgrade a (possibly old) state dict for new versions of fairseq."""
        if isinstance(self.embed_positions, SinusoidalPositionalEmbedding):
            weights_key = "{}.embed_positions.weights".format(name)
            if weights_key in state_dict:
                del state_dict[weights_key]
            state_dict[
                "{}.embed_positions._float_tensor".format(name)
            ] = torch.FloatTensor(1)

        if f"{name}.output_projection.weight" not in state_dict:
            if self.share_input_output_embed:
                embed_out_key = f"{name}.embed_tokens.weight"
            else:
                embed_out_key = f"{name}.embed_out"
            if embed_out_key in state_dict:
                state_dict[f"{name}.output_projection.weight"] = state_dict[
                    embed_out_key
                ]
                if not self.share_input_output_embed:
                    del state_dict[embed_out_key]

        for i in range(self.num_layers):
            # update layer norms
            self.layers[i].upgrade_state_dict_named(
                state_dict, "{}.layers.{}".format(name, i)
            )

        # version_key = "{}.version".format(name)
        # if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) <= 2:
        #     # earlier checkpoints did not normalize after the stack of layers
        #     self.layer_norm = None
        #     self.normalize = False
        #     state_dict[version_key] = torch.Tensor([1])

        prefix = name + "." if name != "" else ""
        image_params = ["image_position_idx"]
        for image_param in image_params:
            state_dict[prefix + image_param] = self.state_dict()[image_param]
        for param_name, param_tensor in self.state_dict().items():
            if (prefix + param_name) not in state_dict and param_name in self.state_dict():
                state_dict[prefix + param_name] = self.state_dict()[param_name]

        if len(state_dict["decoder.embed_image_positions.weight"]) < len(self.state_dict()["embed_image_positions.weight"]):
            num_posids_to_add = len(self.state_dict()["embed_image_positions.weight"]) - len(state_dict["decoder.embed_image_positions.weight"])
            embed_dim = state_dict["decoder.embed_image_positions.weight"].size(1)
            new_pos_embed_to_add = torch.zeros(num_posids_to_add, embed_dim)
            nn.init.normal_(new_pos_embed_to_add, mean=0, std=embed_dim ** -0.5)
            new_pos_embed_to_add = new_pos_embed_to_add.to(
                dtype=state_dict["decoder.embed_image_positions.weight"].dtype,
            )
            state_dict["decoder.embed_image_positions.weight"] = torch.cat(
                [state_dict["decoder.embed_image_positions.weight"], new_pos_embed_to_add]
            )
        return state_dict


def Embedding(num_embeddings, embedding_dim, padding_idx=None, zero_init=False):
    m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
    nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
    if padding_idx is not None:
        nn.init.constant_(m.weight[padding_idx], 0)
    if zero_init:
        nn.init.constant_(m.weight, 0)
    return m


def Linear(in_features, out_features, bias=True):
    m = nn.Linear(in_features, out_features, bias)
    nn.init.xavier_uniform_(m.weight)
    if bias:
        nn.init.constant_(m.bias, 0.0)
    return m


@register_model_architecture("unify_transformer", "unify_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.no_cross_attention = getattr(args, "no_cross_attention", False)
    args.cross_self_attention = getattr(args, "cross_self_attention", 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)

    args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
    args.layernorm_embedding = getattr(args, "layernorm_embedding", False)
    args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False)
    args.checkpoint_activations = getattr(args, "checkpoint_activations", False)
    args.offload_activations = getattr(args, "offload_activations", False)
    if args.offload_activations:
        args.checkpoint_activations = True
    args.encoder_layers_to_keep = getattr(args, "encoder_layers_to_keep", None)
    args.decoder_layers_to_keep = getattr(args, "decoder_layers_to_keep", None)
    args.encoder_layerdrop = getattr(args, "encoder_layerdrop", 0)
    args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0)
    args.quant_noise_pq = getattr(args, "quant_noise_pq", 0)
    args.quant_noise_pq_block_size = getattr(args, "quant_noise_pq_block_size", 8)
    args.quant_noise_scalar = getattr(args, "quant_noise_scalar", 0)