# --------------------------------------------------------
# The YiTrans End-to-End Speech Translation System for IWSLT 2022 Offline Shared Task (https://arxiv.org/abs/2206.05777)
# Github source: https://github.com/microsoft/SpeechT5/tree/main/YiTrans
# Copyright (c) 2022 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Based on fairseq code bases
# https://github.com/facebookresearch/fairseq
# --------------------------------------------------------
"""
    Modified from https://github.com/facebookresearch/fairseq/blob/main/fairseq/models/transformer/transformer_decoder.py  
"""

import math
from typing import Any, Dict, List, Optional

import torch
import torch.nn as nn
from fairseq import utils
from fairseq.distributed import fsdp_wrap
from fairseq.models import FairseqIncrementalDecoder
from fairseq.models.transformer import TransformerConfig
from fairseq.models.transformer.transformer_decoder import module_name_fordropout, Linear
from fairseq.modules import (
    AdaptiveSoftmax,
    BaseLayer,
    FairseqDropout,
    LayerDropModuleList,
    LayerNorm,
    PositionalEmbedding,
    SinusoidalPositionalEmbedding,
)
from fairseq.modules.checkpoint_activations import checkpoint_wrapper
from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_
from torch import Tensor

import yitrans_iwslt22.modules.transformer_decoder_layer as transformer_layer
from yitrans_iwslt22.modules.relative_pos_enc import RelativePositionalEncoding

class MultimodalTransformerDecoderBase(FairseqIncrementalDecoder):
    """
    Transformer decoder consisting of *cfg.decoder.layers* layers. Each layer
    is a :class:`TransformerDecoderLayer`.

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

    def __init__(
        self,
        cfg,
        dictionaries,
        embed_tokens_list,
        no_encoder_attn=False,
        output_projection=None,
        use_rel_pos_enc=False,
    ):
        assert all([embed_tokens.padding_idx == embed_tokens_list[0].padding_idx for embed_tokens in embed_tokens_list])
        assert all([embed_tokens.embedding_dim == embed_tokens_list[0].embedding_dim for embed_tokens in embed_tokens_list])
        self.cfg = cfg
        super().__init__(dictionaries)
        self.register_buffer("version", torch.Tensor([3]))
        self._future_mask = torch.empty(0)

        self.dropout_module = FairseqDropout(
            cfg.dropout, module_name=module_name_fordropout(self.__class__.__name__)
        )
        self.decoder_layerdrop = cfg.decoder.layerdrop
        self.share_input_output_embed = cfg.share_decoder_input_output_embed

        input_embed_dim = embed_tokens_list[0].embedding_dim
        embed_dim = cfg.decoder.embed_dim
        self.embed_dim = embed_dim
        self.output_embed_dim = cfg.decoder.output_dim

        self.padding_idx = embed_tokens_list[0].padding_idx
        self.max_target_positions = cfg.max_target_positions

        self.embed_tokens_list = nn.ModuleList(embed_tokens_list)

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

        if not cfg.adaptive_input and cfg.quant_noise.pq > 0:
            self.quant_noise = apply_quant_noise_(
                nn.Linear(embed_dim, embed_dim, bias=False),
                cfg.quant_noise.pq,
                cfg.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
        )
        self.embed_positions = (
            PositionalEmbedding(
                self.max_target_positions,
                embed_dim,
                self.padding_idx,
                learned=cfg.decoder.learned_pos,
            )
            if not cfg.no_token_positional_embeddings
            else None
        )
        if cfg.layernorm_embedding:
            self.layernorm_embedding = LayerNorm(embed_dim, export=cfg.export)
        else:
            self.layernorm_embedding = None

        self.cross_self_attention = cfg.cross_self_attention

        if self.decoder_layerdrop > 0.0:
            self.layers = LayerDropModuleList(p=self.decoder_layerdrop)
        else:
            self.layers = nn.ModuleList([])
        self.use_rel_pos_enc = use_rel_pos_enc
        self.layers.extend(
            [
                self.build_decoder_layer(cfg, no_encoder_attn)
                for _ in range(cfg.decoder.layers)
            ]
        )
        self.num_layers = len(self.layers)

        if cfg.decoder.normalize_before and not cfg.no_decoder_final_norm:
            self.layer_norm = LayerNorm(embed_dim, export=cfg.export)
        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 cfg.tie_adaptive_weights
            else None
        )

        self.adaptive_softmax = None
        self.output_projection = output_projection
        if self.output_projection is None:
            self.build_output_projection(cfg, dictionaries, embed_tokens_list)
        if self.use_rel_pos_enc:
            self.pos_emb = RelativePositionalEncoding(embed_dim // cfg.decoder.attention_heads, 24)

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

    def build_decoder_layer(self, cfg, no_encoder_attn=False):
        layer = transformer_layer.TransformerDecoderLayerBase(cfg, no_encoder_attn, has_relative_attention_bias=self.use_rel_pos_enc)
        checkpoint = cfg.checkpoint_activations
        if checkpoint:
            offload_to_cpu = cfg.offload_activations
            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 = cfg.min_params_to_wrap if not checkpoint else 0
        layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap)
        return layer

    def forward(
        self,
        prev_output_tokens,
        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,
        modal_idx=0,
    ):
        """
        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,
            encoder_out=encoder_out,
            incremental_state=incremental_state,
            full_context_alignment=full_context_alignment,
            alignment_layer=alignment_layer,
            alignment_heads=alignment_heads,
            modal_idx=modal_idx,
        )

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

    def extract_features(
        self,
        prev_output_tokens,
        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,
        modal_idx=0,
    ):
        return self.extract_features_scriptable(
            prev_output_tokens,
            encoder_out,
            incremental_state,
            full_context_alignment,
            alignment_layer,
            alignment_heads,
            modal_idx=modal_idx,
        )

    """
    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,
        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,
        modal_idx=0,
    ):
        """
        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]

        # embed positions
        positions = None
        if self.embed_positions is not None:
            positions = self.embed_positions(
                prev_output_tokens, incremental_state=incremental_state
            )

        if incremental_state is not None:
            prev_output_tokens = prev_output_tokens[:, -1:]
            if positions is not None:
                positions = positions[:, -1:]

        # embed tokens and positions
        x = self.embed_scale * self.embed_tokens_list[modal_idx](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 positions is not None:
            x += positions

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

        x = self.dropout_module(x)

        # B x T x C -> T x B x C
        x = x.transpose(0, 1)
        if self.use_rel_pos_enc:
            pos_seq = torch.arange(0, slen).long().to(x.device)
            pos_seq = pos_seq[:, None] - pos_seq[None, :]
            pos_k, _ = self.pos_emb(pos_seq, incremental_state)
        else:
            pos_k = None

        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

            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)),
                pos_bias=pos_k,
            )
            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, modal_idx):
        """Project features to the vocabulary size."""
        if self.adaptive_softmax is None:
            # project back to size of vocabulary
            return self.output_projection[modal_idx](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 min(self.max_target_positions, self.embed_positions.max_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
            layer_norm_map = {
                "0": "self_attn_layer_norm",
                "1": "encoder_attn_layer_norm",
                "2": "final_layer_norm",
            }
            for old, new in layer_norm_map.items():
                for m in ("weight", "bias"):
                    k = "{}.layers.{}.layer_norms.{}.{}".format(name, i, old, m)
                    if k in state_dict:
                        state_dict[
                            "{}.layers.{}.{}.{}".format(name, i, new, m)
                        ] = state_dict[k]
                        del state_dict[k]

        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])

        return state_dict


class MultimodalTransformerDecoder(MultimodalTransformerDecoderBase):
    def __init__(
        self,
        args,
        dictionaries,
        embed_tokens_list,
        no_encoder_attn=False,
        output_projection=None,
    ):

        self.args = args
        super().__init__(
            TransformerConfig.from_namespace(args),
            dictionaries,
            embed_tokens_list,
            no_encoder_attn=no_encoder_attn,
            output_projection=output_projection,
            use_rel_pos_enc=getattr(args, "use_rel_pos_enc", False),
        )

    def build_output_projection(self, args, dictionaries, embed_tokens_list):
        super().build_output_projection(
            TransformerConfig.from_namespace(args), dictionaries, embed_tokens_list
        )

    def build_decoder_layer(self, args, no_encoder_attn=False):
        return super().build_decoder_layer(
            TransformerConfig.from_namespace(args), no_encoder_attn=no_encoder_attn
        )
    
    def extract_features(
        self,
        prev_output_tokens,
        encoder_out: Optional[Dict[str, List[Tensor]]] = None,
        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,
        modal_idx=0,
    ):
        # call scriptable method from parent class
        x, _ = self.extract_features_scriptable(
            prev_output_tokens,
            encoder_out,
            incremental_state,
            full_context_alignment,
            alignment_layer,
            alignment_heads,
            modal_idx=modal_idx,
        )
        return x, None
