# coding=utf-8
# Copyright 2020 The Facebook AI Research Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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"""PyTorch BART model, ported from the fairseq repo."""
import logging
import math
import random
from typing import Dict, List, Optional, Tuple

import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor, nn

from transformers.activations import ACT2FN
from transformers.configuration_bart import BartConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_callable
from transformers.modeling_utils import PreTrainedModel

from relogic.logickit.dataflow.semtransparse.grammar.keywords import KEYWORDS

logger = logging.getLogger(__name__)


def create_position_ids_from_input_ids(input_ids, padding_idx):
    """ Replace non-padding symbols with their position numbers. Position numbers begin at
    padding_idx+1. Padding symbols are ignored. This is modified from fairseq's
    `utils.make_positions`.

    :param torch.Tensor x:
    :return torch.Tensor:
    """
    # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
    mask = input_ids.ne(padding_idx).int()
    incremental_indices = torch.cumsum(mask, dim=1).type_as(mask) * mask
    return incremental_indices.long() + padding_idx


BART_PRETRAINED_MODEL_ARCHIVE_MAP = {
    "facebook/bart-large": "https://cdn.huggingface.co/facebook/bart-large/pytorch_model.bin",
    "facebook/bart-large-mnli": "https://cdn.huggingface.co/facebook/bart-large-mnli/pytorch_model.bin",
    "facebook/bart-large-cnn": "https://cdn.huggingface.co/facebook/bart-large-cnn/pytorch_model.bin",
    "facebook/bart-large-xsum": "https://cdn.huggingface.co/facebook/bart-large-xsum/pytorch_model.bin",
    "facebook/mbart-large-en-ro": "https://cdn.huggingface.co/facebook/mbart-large-en-ro/pytorch_model.bin",
}

BART_START_DOCSTRING = r"""

    This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class. Use it as a regular PyTorch Module and
    refer to the PyTorch documentation for all matters related to general usage and behavior.

    Parameters:
        config (:class:`~transformers.BartConfig`): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the configuration.
            Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.

"""
BART_GENERATION_EXAMPLE = r"""
    Examples::

        from transformers import BartTokenizer, BartForConditionalGeneration, BartConfig
        # see ``examples/summarization/bart/evaluate_cnn.py`` for a longer example
        model = BartForConditionalGeneration.from_pretrained('bart-large-cnn')
        tokenizer = BartTokenizer.from_pretrained('bart-large-cnn')
        ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
        inputs = tokenizer.batch_encode_plus([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='pt')
        # Generate Summary
        summary_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=5, early_stopping=True)
        print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])

"""

BART_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
               Indices of input sequence tokens in the vocabulary. Use BartTokenizer.encode to produce them.
            Padding will be ignored by default should you provide it.
            Indices can be obtained using :class:`transformers.BartTokenizer.encode(text)`.
        attention_mask (:obj:`torch.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            Mask to avoid performing attention on padding token indices in input_ids.
            Mask values selected in ``[0, 1]``:
            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
        encoder_outputs (:obj:`tuple(tuple(torch.FloatTensor)`, `optional`, defaults to :obj:`None`):
            Tuple consists of (`last_hidden_state`, `optional`: `hidden_states`, `optional`: `attentions`)
            `last_hidden_state` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`) is a sequence of hidden-states at the output of the last layer of the encoder.
            Used in the cross-attention of the decoder.
        decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`, defaults to :obj:`None`):
            Provide for translation and summarization training. By default, the model will create this tensor by shifting the input_ids right, following the paper.
        decoder_attention_mask (:obj:`torch.BoolTensor` of shape :obj:`(batch_size, tgt_seq_len)`, `optional`, defaults to :obj:`None`):
            Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.
            If you want to change padding behavior, you should read :func:`~transformers.modeling_bart._prepare_decoder_inputs` and modify.
            See diagram 1 in the paper for more info on the default strategy
"""
def batched_index_select(input, dim, index):
    views = [input.shape[0]] + \
        [1 if i != dim else -1 for i in range(1, len(input.shape))]
    expanse = list(input.shape)
    expanse[0] = -1
    expanse[dim] = -1
    index = index.view(views).expand(expanse)
    return torch.gather(input, dim, index)

def invert_mask(attention_mask):
    assert attention_mask.dim() == 2
    return attention_mask.eq(0)


def _prepare_bart_decoder_inputs(
    config, input_ids, pad_token_id, decoder_input_ids=None, decoder_padding_mask=None, causal_mask_dtype=torch.float32,
):
    """Prepare masks that ignore padding tokens in the decoder and a causal mask for the decoder if
    none are provided. This mimics the default behavior in fairseq. To override it pass in masks.
    Note: this is not called during generation
    """
    # pad_token_id = config.pad_token_id
    if decoder_input_ids is None:
        decoder_input_ids = shift_tokens_right(input_ids, pad_token_id)
    bsz, tgt_len = decoder_input_ids.size()
    if decoder_padding_mask is None:
        decoder_padding_mask = make_padding_mask(decoder_input_ids, pad_token_id)
    else:
        decoder_padding_mask = invert_mask(decoder_padding_mask)
    causal_mask = torch.triu(fill_with_neg_inf(torch.zeros(tgt_len, tgt_len)), 1).to(
        dtype=causal_mask_dtype, device=decoder_input_ids.device
    )
    return decoder_input_ids, decoder_padding_mask, causal_mask


class PretrainedBartModel(PreTrainedModel):
    config_class = BartConfig
    base_model_prefix = "model"
    pretrained_model_archive_map = BART_PRETRAINED_MODEL_ARCHIVE_MAP

    def _init_weights(self, module):
        std = self.config.init_std
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, SinusoidalPositionalEmbedding):
            pass
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()

    @property
    def dummy_inputs(self):
        pad_token = self.config.pad_token_id
        input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
        dummy_inputs = {
            "attention_mask": input_ids.ne(pad_token),
            "input_ids": input_ids,
        }
        return dummy_inputs


def _make_linear_from_emb(emb):
    vocab_size, emb_size = emb.weight.shape
    lin_layer = nn.Linear(vocab_size, emb_size, bias=False)
    lin_layer.weight.data = emb.weight.data
    return lin_layer


# Helper Functions, mostly for making masks
def _check_shapes(shape_1, shape2):
    if shape_1 != shape2:
        raise AssertionError("shape mismatch: {} != {}".format(shape_1, shape2))


def shift_tokens_right(input_ids, pad_token_id):
    """Shift input ids one token to the right, and wrap the last non pad token (usually <eos>)."""
    prev_output_tokens = input_ids.clone()
    index_of_eos = (input_ids.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1)
    prev_output_tokens[:, 0] = input_ids.gather(1, index_of_eos).squeeze()
    prev_output_tokens[:, 1:] = input_ids[:, :-1]
    return prev_output_tokens


def make_padding_mask(input_ids, padding_idx=1):
    """True for pad tokens"""
    padding_mask = input_ids.eq(padding_idx)
    if not padding_mask.any():
        padding_mask = None
    return padding_mask


# Helper Modules


class EncoderLayer(nn.Module):
    def __init__(self, config: BartConfig):
        super().__init__()
        self.embed_dim = config.d_model
        self.output_attentions = config.output_attentions
        self.self_attn = SelfAttention(
            self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout,
        )
        self.normalize_before = config.normalize_before
        self.self_attn_layer_norm = LayerNorm(self.embed_dim)
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.activation_dropout = config.activation_dropout
        self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
        self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
        self.final_layer_norm = LayerNorm(self.embed_dim)

    def forward(self, x, encoder_padding_mask):
        """
        Args:
            x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
            encoder_padding_mask (ByteTensor): binary ByteTensor of shape
                `(batch, src_len)` where padding elements are indicated by ``1``.
            for t_tgt, t_src is excluded (or masked out), =0 means it is
            included in attention

        Returns:
            encoded output of shape `(seq_len, batch, embed_dim)`
        """
        residual = x
        if self.normalize_before:
            x = self.self_attn_layer_norm(x)
        x, attn_weights = self.self_attn(
            query=x, key=x, key_padding_mask=encoder_padding_mask, need_weights=self.output_attentions
        )
        x = F.dropout(x, p=self.dropout, training=self.training)
        x = residual + x
        if not self.normalize_before:
            x = self.self_attn_layer_norm(x)

        residual = x
        if self.normalize_before:
            x = self.final_layer_norm(x)
        x = self.activation_fn(self.fc1(x))
        x = F.dropout(x, p=self.activation_dropout, training=self.training)
        x = self.fc2(x)
        x = F.dropout(x, p=self.dropout, training=self.training)
        x = residual + x
        if not self.normalize_before:
            x = self.final_layer_norm(x)
        return x, attn_weights

from relogic.pretrainkit.models.relationalsemparse.relational_transformer import RelationalTransformerUpdate
class RelationalBartEncoder(nn.Module):
    """
    Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer
    is a :class:`EncoderLayer`.

    Args:
        config: BartConfig
    """

    def __init__(self, config: BartConfig, embed_tokens):
        super().__init__()

        self.dropout = config.dropout
        self.layerdrop = config.encoder_layerdrop
        self.output_attentions = config.output_attentions
        self.output_hidden_states = config.output_hidden_states

        embed_dim = embed_tokens.embedding_dim
        self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
        self.padding_idx = embed_tokens.padding_idx
        self.max_source_positions = config.max_position_embeddings

        # print("I am in Encoder padding_idx ", self.padding_idx)

        self.embed_tokens = embed_tokens
        if config.static_position_embeddings:
            self.embed_positions = SinusoidalPositionalEmbedding(
                config.max_position_embeddings, embed_dim, self.padding_idx
            )
        else:
            self.embed_positions = LearnedPositionalEmbedding(
                config.max_position_embeddings, embed_dim, self.padding_idx,
            )
        self.layers = nn.ModuleList([EncoderLayer(config) for _ in range(config.encoder_layers)])
        self.layernorm_embedding = LayerNorm(embed_dim) if config.normalize_embedding else nn.Identity()
        # mbart has one extra layer_norm
        self.layer_norm = LayerNorm(config.d_model) if config.normalize_before else None
        num_heads = 8
        hidden_size = 1024
        num_layers = 8

        self.relational_transformer = RelationalTransformerUpdate(
            num_layers=num_layers,
            num_heads=num_heads,
            hidden_size=hidden_size,
            sc_link=True,
            cv_link=True)
        # self.relational_transformer.load_state_dict(torch.load("data/params/rat_layer_bert.pt"))

        self.use_relation_transformer = True

    def forward(
        self, input_ids, attention_mask, example_info_list

    ):
        """
        Args:
            input_ids (LongTensor): tokens in the source language of shape
                `(batch, src_len)`
            attention_mask (torch.LongTensor): indicating which indices are padding tokens.
        Returns:
            Tuple comprised of:
                - **x** (Tensor): the last encoder layer's output of
                  shape `(src_len, batch, embed_dim)`
                - **encoder_states** (List[Tensor]): all intermediate
                  hidden states of shape `(src_len, batch, embed_dim)`.
                  Only populated if *self.output_hidden_states:* is True.
                - **all_attentions** (List[Tensor]): Attention weights for each layer.
                During training might not be of length n_layers because of layer dropout.
        """
        # check attention mask and invert
        if attention_mask is not None:
            attention_mask = invert_mask(attention_mask)

        inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
        embed_pos, p_idx = self.embed_positions(input_ids)
        # print("I am in Encoder ", p_idx)
        x = inputs_embeds + embed_pos
        x = self.layernorm_embedding(x)
        x = F.dropout(x, p=self.dropout, training=self.training)

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

        encoder_states, all_attentions = [], []
        for encoder_layer in self.layers:
            if self.output_hidden_states:
                encoder_states.append(x)
            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
            dropout_probability = random.uniform(0, 1)
            if self.training and (dropout_probability < self.layerdrop):  # skip the layer
                attn = None
            else:
                x, attn = encoder_layer(x, attention_mask)

            if self.output_attentions:
                all_attentions.append(attn)

        if self.layer_norm:
            x = self.layer_norm(x)
        if self.output_hidden_states:
            encoder_states.append(x)

        # T x B x C -> B x T x C
        encoder_states = [hidden_state.transpose(0, 1) for hidden_state in encoder_states]
        x = x.transpose(0, 1)

        # Apply relational transformer here
        max_q_length = max([example_info["question_end"] - example_info["question_start"] for example_info in example_info_list])
        max_column_length = max([len(example_info["column_start"]) for example_info in example_info_list])
        max_table_length = max([len(example_info["table_start"]) for example_info in example_info_list])

        batch_size, dim = x.size(0), x.size(-1)
        batch_q_enc = x.new_zeros((batch_size, max_q_length, dim))
        batch_q_enc_mask = x.new_zeros((batch_size, max_q_length))
        batch_col_enc = x.new_zeros((batch_size, max_column_length, dim))
        batch_col_enc_mask = x.new_zeros((batch_size, max_column_length))
        batch_tab_enc = x.new_zeros((batch_size, max_table_length, dim))
        batch_tab_enc_mask = x.new_zeros((batch_size, max_table_length))

        for batch_idx, example_info in enumerate(example_info_list):
            q_enc = x[batch_idx][example_info["question_start"]:example_info["question_end"]]
            col_enc_start = x[batch_idx][example_info["column_start"]]
            tab_enc_start = x[batch_idx][example_info["table_start"]]
            col_enc_end = x[batch_idx][example_info["column_end"]-1] # exclusive
            tab_enc_end = x[batch_idx][example_info["table_end"]-1]
            col_enc = (col_enc_start + col_enc_end) / 2.0 # avg the first and last token
            tab_enc = (tab_enc_start + tab_enc_end) / 2.0

            if self.use_relation_transformer:
                c_boundary = list(range(len(example_info["column_start"]) + 1))
                t_boundary = list(range(len(example_info["table_start"]) + 1))
                # why do we need this information
                q_enc_new, c_enc_new, t_enc_new, _ = self.relational_transformer.forward_unbatched(
                    example_info,
                    q_enc.unsqueeze(1),
                    col_enc.unsqueeze(1),
                    c_boundary,
                    tab_enc.unsqueeze(1),
                    t_boundary)
                # q_enc_new = (q_enc_new + q_enc) / 2.0
                # c_enc_new = (c_enc_new + col_enc) / 2.0
                # t_enc_new = (t_enc_new + tab_enc) / 2.0
            else:
                q_enc_new, c_enc_new, t_enc_new = q_enc, col_enc, tab_enc

            batch_q_enc[batch_idx,:q_enc.size(0)] = q_enc_new
            batch_q_enc_mask[batch_idx,:q_enc.size(0)] = 1
            batch_col_enc[batch_idx, :col_enc.size(0)] = c_enc_new
            batch_col_enc_mask[batch_idx, :col_enc.size(0)] = 1
            batch_tab_enc[batch_idx, :tab_enc.size(0)] = t_enc_new
            batch_tab_enc_mask[batch_idx, :tab_enc.size(0)] = 1

        # return x, encoder_states, all_attentions
        return ((batch_q_enc, batch_q_enc_mask),
                (batch_col_enc, batch_col_enc_mask),
                (batch_tab_enc, batch_tab_enc_mask)), encoder_states, all_attentions

        # return ((x, attention_mask),
        #     (batch_col_enc, batch_col_enc_mask),
        #     (batch_tab_enc, batch_tab_enc_mask)), encoder_states, all_attentions


class DecoderLayer(nn.Module):
    def __init__(self, config: BartConfig):
        super().__init__()
        self.embed_dim = config.d_model
        self.output_attentions = config.output_attentions
        self.self_attn = SelfAttention(
            embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout,
        )
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.activation_dropout = config.activation_dropout
        self.normalize_before = config.normalize_before

        self.self_attn_layer_norm = LayerNorm(self.embed_dim)
        self.encoder_attn = SelfAttention(
            self.embed_dim,
            config.decoder_attention_heads,
            dropout=config.attention_dropout,
            encoder_decoder_attention=True,
        )
        self.encoder_attn_layer_norm = LayerNorm(self.embed_dim)
        self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
        self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
        self.final_layer_norm = LayerNorm(self.embed_dim)

    def forward(
        self,
        x,
        encoder_hidden_states,
        encoder_attn_mask=None,
        layer_state=None,
        causal_mask=None,
        decoder_padding_mask=None,
    ):
        residual = x

        if layer_state is None:
            layer_state = {}
        if self.normalize_before:
            x = self.self_attn_layer_norm(x)
        # Self Attention

        x, self_attn_weights = self.self_attn(
            query=x,
            key=x,
            layer_state=layer_state,  # adds keys to layer state
            key_padding_mask=decoder_padding_mask,
            attn_mask=causal_mask,
            need_weights=self.output_attentions,
        )
        x = F.dropout(x, p=self.dropout, training=self.training)
        x = residual + x
        if not self.normalize_before:
            x = self.self_attn_layer_norm(x)

        # Cross attention
        residual = x
        assert self.encoder_attn.cache_key != self.self_attn.cache_key
        if self.normalize_before:
            x = self.encoder_attn_layer_norm(x)
        x, _ = self.encoder_attn(
            query=x,
            key=encoder_hidden_states,
            key_padding_mask=encoder_attn_mask,
            layer_state=layer_state,  # mutates layer state
        )
        x = F.dropout(x, p=self.dropout, training=self.training)
        x = residual + x
        if not self.normalize_before:
            x = self.encoder_attn_layer_norm(x)

        # Fully Connected
        residual = x
        if self.normalize_before:
            x = self.final_layer_norm(x)
        x = self.activation_fn(self.fc1(x))
        x = F.dropout(x, p=self.activation_dropout, training=self.training)
        x = self.fc2(x)
        x = F.dropout(x, p=self.dropout, training=self.training)
        x = residual + x
        if not self.normalize_before:
            x = self.final_layer_norm(x)
        return (
            x,
            self_attn_weights,
            layer_state,
        )  # just self_attn weights for now, following t5, layer_state = cache for decoding


class BartDecoder(nn.Module):
    """
    Transformer decoder consisting of *config.decoder_layers* layers. Each layer
    is a :class:`DecoderLayer`.
    Args:
        config: BartConfig
        embed_tokens (torch.nn.Embedding): output embedding
    """

    def __init__(self, config: BartConfig, embed_tokens: nn.Embedding):
        super().__init__()
        self.output_attentions = config.output_attentions
        self.output_hidden_states = config.output_hidden_states
        self.dropout = config.dropout
        self.layerdrop = config.decoder_layerdrop
        self.padding_idx = embed_tokens.padding_idx
        # print("I am in Decoder, padding_idx ", self.padding_idx)
        # self.padding_idx = 0
        self.max_target_positions = config.max_position_embeddings
        self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
        self.embed_tokens = embed_tokens
        if config.static_position_embeddings:
            self.embed_positions = SinusoidalPositionalEmbedding(
                config.max_position_embeddings, config.d_model, config.pad_token_id
            )
        else:
            self.embed_positions = LearnedPositionalEmbedding(
                config.max_position_embeddings, config.d_model, self.padding_idx,
            )
        self.layers = nn.ModuleList(
            [DecoderLayer(config) for _ in range(config.decoder_layers)]
        )  # type: List[DecoderLayer]
        self.layernorm_embedding = LayerNorm(config.d_model) if config.normalize_embedding else nn.Identity()
        self.layer_norm = LayerNorm(config.d_model) if config.add_final_layer_norm else None

    def forward(
        self,
        input_ids,
        input_embed,
        encoder_hidden_states,
        encoder_padding_mask,
        decoder_padding_mask,
        decoder_causal_mask,
        decoder_cached_states=None,
        use_cache=False,
        **unused
    ):
        """
        Includes several features from "Jointly Learning to Align and
        Translate with Transformer Models" (Garg et al., EMNLP 2019).

        Args:
            input_ids (LongTensor): previous decoder outputs of shape
                `(batch, tgt_len)`, for teacher forcing
            encoder_hidden_states: output from the encoder, used for
                encoder-side attention
            encoder_padding_mask: for ignoring pad tokens
            decoder_cached_states (dict or None): dictionary used for storing state during generation

        Returns:
            tuple:
                - the decoder's features of shape `(batch, tgt_len, embed_dim)`
                - hidden states
                - attentions
        """
        # check attention mask and invert
        if encoder_padding_mask is not None:
            encoder_padding_mask = invert_mask(encoder_padding_mask)

        # embed positions
        positions, p_idx = self.embed_positions(input_ids, use_cache=use_cache)
        # print("I am in decoder, use_cache", use_cache, p_idx)

        if use_cache:
            input_ids = input_ids[:, -1:]
            input_embed = input_embed[:, -1:]
            positions = positions[:, -1:]  # happens after we embed them
            # assert input_ids.ne(self.padding_idx).any()

        # x = self.embed_tokens(input_ids) * self.embed_scale
        x = input_embed * self.embed_scale
        x += positions
        x = self.layernorm_embedding(x)
        x = F.dropout(x, p=self.dropout, training=self.training)

        # Convert to Bart output format: (seq_len, BS, model_dim) -> (BS, seq_len, model_dim)
        x = x.transpose(0, 1)
        encoder_hidden_states = encoder_hidden_states.transpose(0, 1)

        # decoder layers
        all_hidden_states = ()
        all_self_attns = ()
        next_decoder_cache = []
        for idx, decoder_layer in enumerate(self.layers):
            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
            if self.output_hidden_states:
                all_hidden_states += (x,)
            dropout_probability = random.uniform(0, 1)
            if self.training and (dropout_probability < self.layerdrop):
                continue

            layer_state = decoder_cached_states[idx] if decoder_cached_states is not None else None

            x, layer_self_attn, layer_past = decoder_layer(
                x,
                encoder_hidden_states,
                encoder_attn_mask=encoder_padding_mask,
                decoder_padding_mask=decoder_padding_mask,
                layer_state=layer_state,
                causal_mask=decoder_causal_mask,
            )

            if use_cache:
                next_decoder_cache.append(layer_past.copy())

            if self.layer_norm and (idx == len(self.layers) - 1):  # last layer of mbart
                x = self.layer_norm(x)
            if self.output_attentions:
                all_self_attns += (layer_self_attn,)

        # Convert to standard output format: (seq_len, BS, model_dim) -> (BS, seq_len, model_dim)
        all_hidden_states = [hidden_state.transpose(0, 1) for hidden_state in all_hidden_states]
        x = x.transpose(0, 1)
        encoder_hidden_states = encoder_hidden_states.transpose(0, 1)

        if use_cache:
            # next_cache = ((encoder_hidden_states, encoder_padding_mask), next_decoder_cache)
            next_cache = (next_decoder_cache, )
            # The cache is more than we predefined.
        else:
            next_cache = None
        return x, next_cache, all_hidden_states, list(all_self_attns)


def _reorder_buffer(attn_cache, new_order):
    for k, input_buffer_k in attn_cache.items():
        if input_buffer_k is not None:
            attn_cache[k] = input_buffer_k.index_select(0, new_order)
    return attn_cache


class SelfAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(
        self,
        embed_dim,
        num_heads,
        dropout=0.0,
        bias=True,
        encoder_decoder_attention=False,  # otherwise self_attention
    ):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads
        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
        self.scaling = self.head_dim ** -0.5

        self.encoder_decoder_attention = encoder_decoder_attention
        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.cache_key = "encoder_decoder" if self.encoder_decoder_attention else "self"

    def _shape(self, tensor, dim_0, bsz):
        return tensor.contiguous().view(dim_0, bsz * self.num_heads, self.head_dim).transpose(0, 1)

    def forward(
        self,
        query,
        key: Optional[Tensor],
        key_padding_mask: Optional[Tensor] = None,
        layer_state: Optional[Dict[str, Optional[Tensor]]] = None,
        attn_mask: Optional[Tensor] = None,
        need_weights=False,
    ) -> Tuple[Tensor, Optional[Tensor]]:
        """Input shape: Time(SeqLen) x Batch x Channel"""
        static_kv: bool = self.encoder_decoder_attention
        tgt_len, bsz, embed_dim = query.size()
        assert embed_dim == self.embed_dim
        assert list(query.size()) == [tgt_len, bsz, embed_dim]
        # get here for encoder decoder cause of static_kv
        if layer_state is not None:  # reuse k,v and encoder_padding_mask
            saved_state = layer_state.get(self.cache_key, {})
            if "prev_key" in saved_state:
                # previous time steps are cached - no need to recompute key and value if they are static
                if static_kv:
                    key = None
        else:
            saved_state = None
            layer_state = {}

        q = self.q_proj(query) * self.scaling
        if static_kv:
            if key is None:
                k = v = None
            else:
                k = self.k_proj(key)
                v = self.v_proj(key)
        else:
            k = self.k_proj(query)
            v = self.v_proj(query)

        q = self._shape(q, tgt_len, bsz)
        if k is not None:
            k = self._shape(k, -1, bsz)
        if v is not None:
            v = self._shape(v, -1, bsz)

        if saved_state is not None:
            k, v, key_padding_mask = self._use_saved_state(k, v, saved_state, key_padding_mask, static_kv, bsz)

        # Update cache
        layer_state[self.cache_key] = {
            "prev_key": k.view(bsz, self.num_heads, -1, self.head_dim),
            "prev_value": v.view(bsz, self.num_heads, -1, self.head_dim),
            "prev_key_padding_mask": key_padding_mask if not static_kv else None,
        }

        assert k is not None
        src_len = k.size(1)
        attn_weights = torch.bmm(q, k.transpose(1, 2))
        assert attn_weights.size() == (bsz * self.num_heads, tgt_len, src_len)

        if attn_mask is not None:
            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_mask
            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)

        # This is part of a workaround to get around fork/join parallelism not supporting Optional types.
        if key_padding_mask is not None and key_padding_mask.dim() == 0:
            key_padding_mask = None
        assert key_padding_mask is None or key_padding_mask.size()[:2] == (bsz, src_len,)

        if key_padding_mask is not None:  # don't attend to padding symbols
            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
            reshaped = key_padding_mask.unsqueeze(1).unsqueeze(2)
            attn_weights = attn_weights.masked_fill(reshaped, float("-inf"))
            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
        attn_weights = F.softmax(attn_weights, dim=-1)
        attn_probs = F.dropout(attn_weights, p=self.dropout, training=self.training,)

        assert v is not None
        attn_output = torch.bmm(attn_probs, v)
        assert attn_output.size() == (bsz * self.num_heads, tgt_len, self.head_dim)
        attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
        attn_output = self.out_proj(attn_output)
        if need_weights:
            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
        else:
            attn_weights = None
        return attn_output, attn_weights

    def _use_saved_state(self, k, v, saved_state, key_padding_mask, static_kv, bsz):
        # saved states are stored with shape (bsz, num_heads, seq_len, head_dim)
        if "prev_key" in saved_state:
            _prev_key = saved_state["prev_key"]
            assert _prev_key is not None
            prev_key = _prev_key.view(bsz * self.num_heads, -1, self.head_dim)
            if static_kv:
                k = prev_key
            else:
                assert k is not None
                k = torch.cat([prev_key, k], dim=1)
        if "prev_value" in saved_state:
            _prev_value = saved_state["prev_value"]
            assert _prev_value is not None
            prev_value = _prev_value.view(bsz * self.num_heads, -1, self.head_dim)
            if static_kv:
                v = prev_value
            else:
                assert v is not None
                v = torch.cat([prev_value, v], dim=1)
        assert k is not None and v is not None
        prev_key_padding_mask: Optional[Tensor] = saved_state.get("prev_key_padding_mask", None)
        key_padding_mask = self._cat_prev_key_padding_mask(
            key_padding_mask, prev_key_padding_mask, bsz, k.size(1), static_kv
        )
        return k, v, key_padding_mask

    @staticmethod
    def _cat_prev_key_padding_mask(
        key_padding_mask: Optional[Tensor],
        prev_key_padding_mask: Optional[Tensor],
        batch_size: int,
        src_len: int,
        static_kv: bool,
    ) -> Optional[Tensor]:
        # saved key padding masks have shape (bsz, seq_len)
        if prev_key_padding_mask is not None:
            if static_kv:
                new_key_padding_mask = prev_key_padding_mask
            else:
                new_key_padding_mask = torch.cat([prev_key_padding_mask, key_padding_mask], dim=1)

        elif key_padding_mask is not None:
            filler = torch.zeros(
                batch_size,
                src_len - key_padding_mask.size(1),
                dtype=key_padding_mask.dtype,
                device=key_padding_mask.device,
            )
            new_key_padding_mask = torch.cat([filler, key_padding_mask], dim=1)
        else:
            new_key_padding_mask = prev_key_padding_mask
        return new_key_padding_mask


class BartClassificationHead(nn.Module):
    """Head for sentence-level classification tasks."""

    # This can trivially be shared with RobertaClassificationHead

    def __init__(
        self, input_dim, inner_dim, num_classes, pooler_dropout,
    ):
        super().__init__()
        self.dense = nn.Linear(input_dim, inner_dim)
        self.dropout = nn.Dropout(p=pooler_dropout)
        self.out_proj = nn.Linear(inner_dim, num_classes)

    def forward(self, x):
        x = self.dropout(x)
        x = self.dense(x)
        x = torch.tanh(x)
        x = self.dropout(x)
        x = self.out_proj(x)
        return x


class LearnedPositionalEmbedding(nn.Embedding):
    """
    This module learns positional embeddings up to a fixed maximum size.
    Padding ids are ignored by either offsetting based on padding_idx
    or by setting padding_idx to None and ensuring that the appropriate
    position ids are passed to the forward function.
    """

    def __init__(
        self, num_embeddings: int, embedding_dim: int, padding_idx: int,
    ):
        # if padding_idx is specified then offset the embedding ids by
        # this index and adjust num_embeddings appropriately
        assert padding_idx is not None
        num_embeddings += padding_idx + 1  # WHY?
        super().__init__(num_embeddings, embedding_dim, padding_idx=padding_idx)

    def forward(self, input, use_cache=False):
        """Input is expected to be of size [bsz x seqlen]."""
        if use_cache:  # the position is our current step in the decoded sequence
            pos = int(self.padding_idx + input.size(1))
            positions = input.data.new(1, 1).fill_(pos)
        else:
            positions = create_position_ids_from_input_ids(input, self.padding_idx)
        return super().forward(positions), positions


def LayerNorm(normalized_shape, eps=1e-5, elementwise_affine=True):
    if torch.cuda.is_available():
        try:
            from apex.normalization import FusedLayerNorm

            return FusedLayerNorm(normalized_shape, eps, elementwise_affine)
        except ImportError:
            pass
    return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine)


def fill_with_neg_inf(t):
    """FP16-compatible function that fills a input_ids with -inf."""
    return t.float().fill_(float("-inf")).type_as(t)


def _filter_out_falsey_values(tup) -> Tuple:
    """Remove entries that are None or [] from an iterable."""
    return tuple(x for x in tup if isinstance(x, torch.Tensor) or x)


# Public API
def _get_shape(t):
    return getattr(t, "shape", None)


@add_start_docstrings(
    "The bare BART Model outputting raw hidden-states without any specific head on top.", BART_START_DOCSTRING,
)
class RelationalBartModel(PretrainedBartModel):
    def __init__(self, config: BartConfig):
        super().__init__(config)
        self.output_attentions = config.output_attentions
        self.output_hidden_states = config.output_hidden_states

        padding_idx, vocab_size = config.pad_token_id, config.vocab_size
        self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)
        # self.keyword_embedding = nn.Embedding(len(KEYWORDS), config.d_model)
        self.keyword_embedding = nn.Embedding(len(KEYWORDS), config.d_model)

        self.encoder = RelationalBartEncoder(config, self.shared)
        self.decoder = BartDecoder(config, self.shared)

        self.init_weights()

    @add_start_docstrings_to_callable(BART_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids,
        example_info_list,
        attention_mask=None,
        decoder_input_ids=None,
        encoder_outputs: Optional[Tuple] = None,
        decoder_attention_mask=None,
        decoder_cached_states=None,
        use_cache=False,
    ):

        # make masks if user doesn't supply
        if not use_cache:
            decoder_input_ids, decoder_padding_mask, causal_mask = _prepare_bart_decoder_inputs(
                self.config,
                input_ids,
                KEYWORDS.index("<pad>"),
                decoder_input_ids=decoder_input_ids,
                decoder_padding_mask=decoder_attention_mask,
                causal_mask_dtype=self.shared.weight.dtype,
            )
        else:
            decoder_padding_mask, causal_mask = None, None

        assert decoder_input_ids is not None
        if encoder_outputs is None:
            encoder_outputs = self.encoder(input_ids=input_ids, example_info_list=example_info_list, attention_mask=attention_mask)
        assert isinstance(encoder_outputs, tuple)

        # We want to use extra relational transformer here
        # Because the output of the encoder_outputs is ((question, column, column_mask, table), _, _)
        question, question_mask = encoder_outputs[0][0][0], encoder_outputs[0][0][1]
        columns, columns_mask = encoder_outputs[0][1][0], encoder_outputs[0][1][1]
        tables, tables_mask = encoder_outputs[0][2][0], encoder_outputs[0][2][1]

        encoder_output_for_decoder = question
        attention_mask_for_decoder = question_mask

        batch_size = question.size(0)
        keyword_size = len(KEYWORDS)
        dim = self.config.d_model

        keyword_vocab_embed = self.keyword_embedding.weight.unsqueeze(0).expand(batch_size, keyword_size, dim)
        if columns is None:
            # This is for sketch prediction
            weight = keyword_vocab_embed
        else:
            weight = torch.cat([keyword_vocab_embed, columns], dim=1)
        decoder_input_embed = batched_index_select(weight, dim=1, index=decoder_input_ids)
        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        decoder_outputs = self.decoder(
            decoder_input_ids,
            decoder_input_embed,
            encoder_output_for_decoder,
            attention_mask_for_decoder,
            decoder_padding_mask,
            decoder_causal_mask=causal_mask,
            decoder_cached_states=decoder_cached_states,
            use_cache=use_cache,
        ) # x, next_cache, all_hidden_states, list(all_self_attns)
        # Attention and hidden_states will be [] or None if they aren't needed
        decoder_outputs: Tuple = _filter_out_falsey_values(decoder_outputs)
        assert isinstance(decoder_outputs[0], torch.Tensor)
        encoder_outputs: Tuple = _filter_out_falsey_values(encoder_outputs)
        return decoder_outputs + encoder_outputs + (weight,)

    def get_input_embeddings(self):
        return self.shared

    def set_input_embeddings(self, value):
        self.shared = value
        self.encoder.embed_tokens = self.shared
        self.decoder.embed_tokens = self.shared

    def get_output_embeddings(self):
        return _make_linear_from_emb(self.shared)  # make it on the fly

def fill_tensor(base, values, spans):
    for idx, ex_spans in enumerate(spans):
        for token_idx, span in enumerate(ex_spans):
            if span[0] > 0:
                base[idx, span[0]:span[1]] = values[idx, token_idx]
    return base


class SinusoidalPositionalEmbedding(nn.Embedding):
    """This module produces sinusoidal positional embeddings of any length."""

    def __init__(self, num_positions, embedding_dim, padding_idx=None):
        super().__init__(num_positions, embedding_dim)
        if embedding_dim % 2 != 0:
            raise NotImplementedError(f"odd embedding_dim {embedding_dim} not supported")
        self.weight = self._init_weight(self.weight)

    @staticmethod
    def _init_weight(out: nn.Parameter):
        """Identical to the XLM create_sinusoidal_embeddings except features are not interleaved.
            The cos features are in the 2nd half of the vector. [dim // 2:]
        """
        n_pos, dim = out.shape
        position_enc = np.array(
            [[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)]
        )
        out[:, 0 : dim // 2] = torch.FloatTensor(np.sin(position_enc[:, 0::2]))  # This line breaks for odd n_pos
        out[:, dim // 2 :] = torch.FloatTensor(np.cos(position_enc[:, 1::2]))
        out.detach_()
        out.requires_grad = False
        return out

    @torch.no_grad()
    def forward(self, input_ids, use_cache=False):
        """Input is expected to be of size [bsz x seqlen]."""
        bsz, seq_len = input_ids.shape[:2]
        if use_cache:
            positions = input_ids.data.new(1, 1).fill_(seq_len - 1)  # called before slicing
        else:
            # starts at 0, ends at 1-seq_len
            positions = torch.arange(seq_len, dtype=torch.long, device=self.weight.device)
        return super().forward(positions)