# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright 2021-2022 The Alibaba DAMO Duguang Team Authors. All rights reserved.

import math
import os

import torch
import torch.utils.checkpoint
from packaging import version
from torch import nn
from transformers.activations import ACT2FN, gelu
from transformers.configuration_utils import PretrainedConfig
from transformers.file_utils import (add_code_sample_docstrings,
                                     add_start_docstrings,
                                     add_start_docstrings_to_model_forward,
                                     replace_return_docstrings)
from transformers.modeling_outputs import (
    BaseModelOutputWithPastAndCrossAttentions,
    BaseModelOutputWithPoolingAndCrossAttentions,
    CausalLMOutputWithCrossAttentions, MaskedLMOutput,
    MultipleChoiceModelOutput, QuestionAnsweringModelOutput,
    SequenceClassifierOutput, TokenClassifierOutput)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging

from modelscope.utils.torch_utils import (apply_chunking_to_forward,
                                          find_pruneable_heads_and_indices,
                                          prune_linear_layer)

logger = logging.get_logger()


class LayoutRobertaConfig(PretrainedConfig):
    model_type = 'layoutroberta'

    def __init__(self,
                 vocab_size=30522,
                 hidden_size=768,
                 num_hidden_layers=12,
                 num_attention_heads=12,
                 intermediate_size=3072,
                 hidden_act='gelu',
                 hidden_dropout_prob=0.1,
                 attention_probs_dropout_prob=0.1,
                 max_position_embeddings=512,
                 type_vocab_size=2,
                 initializer_range=0.02,
                 layer_norm_eps=1e-12,
                 pad_token_id=1,
                 bos_token_id=0,
                 eos_token_id=2,
                 bbox_scale=100.0,
                 pe_type='crel',
                 position_embedding_type='absolute',
                 use_cache=True,
                 classifier_dropout=None,
                 **kwargs):
        super().__init__(
            vocab_size=vocab_size,
            hidden_size=hidden_size,
            num_hidden_layers=num_hidden_layers,
            num_attention_heads=num_attention_heads,
            intermediate_size=intermediate_size,
            hidden_act=hidden_act,
            hidden_dropout_prob=hidden_dropout_prob,
            attention_probs_dropout_prob=attention_probs_dropout_prob,
            max_position_embeddings=max_position_embeddings,
            type_vocab_size=type_vocab_size,
            initializer_range=initializer_range,
            layer_norm_eps=layer_norm_eps,
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            **kwargs,
        )

        self.bbox_scale = bbox_scale
        self.pe_type = pe_type


class PositionalEmbedding1D(nn.Module):
    # Reference:
    # https://github.com/kimiyoung/transformer-xl/blob/master/pytorch/mem_transformer.py#L15

    def __init__(self, demb):
        super(PositionalEmbedding1D, self).__init__()

        self.demb = demb

        inv_freq = 1 / (10000**(torch.arange(0.0, demb, 2.0) / demb))
        self.register_buffer('inv_freq', inv_freq)

    def forward(self, pos_seq, bsz=None):
        seq_size = pos_seq.size()

        if len(seq_size) == 2:
            b1, b2 = seq_size
            sinusoid_inp = pos_seq.view(b1, b2, 1) * self.inv_freq.view(
                1, 1, self.demb // 2)
        elif len(seq_size) == 3:
            b1, b2, b3 = seq_size
            sinusoid_inp = pos_seq.view(b1, b2, b3, 1) * self.inv_freq.view(
                1, 1, 1, self.demb // 2)
        else:
            raise ValueError(f'Invalid seq_size={len(seq_size)}')

        pos_emb = torch.cat([sinusoid_inp.sin(), sinusoid_inp.cos()], dim=-1)

        return pos_emb


class PositionalEmbedding2D(nn.Module):

    def __init__(self, demb, dim_bbox=8):
        super(PositionalEmbedding2D, self).__init__()

        self.demb = demb
        self.dim_bbox = dim_bbox

        self.x_pos_emb = PositionalEmbedding1D(demb // dim_bbox)
        self.y_pos_emb = PositionalEmbedding1D(demb // dim_bbox)

        inv_freq = 1 / (10000**(torch.arange(0.0, demb, 2.0) / demb))
        self.register_buffer('inv_freq', inv_freq)

    def forward(self, bbox):
        # bbox: [seq_length, batch_size, dim_bbox]
        stack = []
        for i in range(self.dim_bbox):
            if i % 2 == 0:
                stack.append(self.x_pos_emb(bbox[..., i]))
            else:
                stack.append(self.y_pos_emb(bbox[..., i]))
        bbox_pos_emb = torch.cat(stack, dim=-1)
        return bbox_pos_emb


class LayoutRobertaEmbeddings(nn.Module):
    """
    Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
    """

    # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__
    def __init__(self, config):
        super().__init__()
        self.word_embeddings = nn.Embedding(
            config.vocab_size,
            config.hidden_size,
            padding_idx=config.pad_token_id)
        self.position_embeddings = nn.Embedding(config.max_position_embeddings,
                                                config.hidden_size)
        self.token_type_embeddings = nn.Embedding(config.type_vocab_size,
                                                  config.hidden_size)

        # layout-related embeddings
        self.line_rank_embeddings = nn.Embedding(
            config.max_position_embeddings, config.hidden_size)

        self.line_rank_inner_embeddings = nn.Embedding(4, config.hidden_size)

        self.x_position_embeddings = nn.Embedding(
            config.max_2d_position_embeddings, config.coordinate_size)
        self.y_position_embeddings = nn.Embedding(
            config.max_2d_position_embeddings, config.coordinate_size)
        self.h_position_embeddings = nn.Embedding(
            config.max_2d_position_embeddings, config.shape_size)
        self.w_position_embeddings = nn.Embedding(
            config.max_2d_position_embeddings, config.shape_size)

        self.LayerNorm = nn.LayerNorm(
            config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
        self.position_embedding_type = getattr(config,
                                               'position_embedding_type',
                                               'absolute')

        self.register_buffer(
            'position_ids',
            torch.arange(config.max_position_embeddings).expand((1, -1)))

        if version.parse(torch.__version__) > version.parse('1.6.0'):
            self.register_buffer(
                'token_type_ids',
                torch.zeros(self.position_ids.size(), dtype=torch.long),
                persistent=False,
            )

        if config.pe_type == 'pdpdq_ws':
            dim_bbox_sinusoid_emb = config.hidden_size
            dim_bbox_projection = config.hidden_size
        elif config.pe_type == 'crel':
            dim_bbox_sinusoid_emb = config.hidden_size // 4
            dim_bbox_projection = config.hidden_size // config.num_attention_heads
        else:
            raise ValueError(f'Unknown config.pe_type={config.pe_type}')

        self.bbox_sinusoid_emb = PositionalEmbedding2D(
            dim_bbox_sinusoid_emb, dim_bbox=8)
        self.bbox_projection = nn.Linear(
            dim_bbox_sinusoid_emb, dim_bbox_projection, bias=False)

        # End copy
        self.padding_idx = config.pad_token_id
        self.position_embeddings = nn.Embedding(
            config.max_position_embeddings,
            config.hidden_size,
            padding_idx=self.padding_idx)

    def _cal_spatial_position_embeddings(self, bbox):
        try:
            left_position_embeddings = self.x_position_embeddings(bbox[:, :,
                                                                       0])
            upper_position_embeddings = self.y_position_embeddings(bbox[:, :,
                                                                        1])
            right_position_embeddings = self.x_position_embeddings(bbox[:, :,
                                                                        2])
            lower_position_embeddings = self.y_position_embeddings(bbox[:, :,
                                                                        3])
        except IndexError as e:
            raise IndexError(
                'The :obj:`bbox`coordinate values should be within 0-1000 range.'
            ) from e

        h_position_embeddings = self.h_position_embeddings(bbox[:, :, 3]
                                                           - bbox[:, :, 1])
        w_position_embeddings = self.w_position_embeddings(bbox[:, :, 2]
                                                           - bbox[:, :, 0])

        spatial_position_embeddings = torch.cat(
            [
                left_position_embeddings,
                upper_position_embeddings,
                right_position_embeddings,
                lower_position_embeddings,
                h_position_embeddings,
                w_position_embeddings,
            ],
            dim=-1,
        )
        return spatial_position_embeddings

    def forward(self,
                input_ids=None,
                token_type_ids=None,
                position_ids=None,
                inputs_embeds=None,
                past_key_values_length=0,
                **kwargs):
        if position_ids is None:
            if input_ids is not None:
                # Create the position ids from the input token ids. Any padded tokens remain padded.
                position_ids = create_position_ids_from_input_ids(
                    input_ids, self.padding_idx, past_key_values_length)
            else:
                position_ids = self.create_position_ids_from_inputs_embeds(
                    inputs_embeds)

        if input_ids is not None:
            input_shape = input_ids.size()
        else:
            input_shape = inputs_embeds.size()[:-1]

        seq_length = input_shape[1]

        # Setting the token_type_ids to the registered buffer in constructor
        # where it is all zeros, which usually occurs
        # when its auto-generated, registered buffer helps users when
        # tracing the model without passing token_type_ids, solves
        # issue #5664
        if token_type_ids is None:
            if hasattr(self, 'token_type_ids'):
                buffered_token_type_ids = self.token_type_ids[:, :seq_length]
                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(
                    input_shape[0], seq_length)
                token_type_ids = buffered_token_type_ids_expanded
            else:
                token_type_ids = torch.zeros(
                    input_shape,
                    dtype=torch.long,
                    device=self.position_ids.device)

        if inputs_embeds is None:
            inputs_embeds = self.word_embeddings(input_ids)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)

        embeddings = inputs_embeds + token_type_embeddings
        if self.position_embedding_type == 'absolute':
            position_embeddings = self.position_embeddings(position_ids)
            embeddings += position_embeddings

        if 'line_bbox' in kwargs:
            embeddings += self._cal_spatial_position_embeddings(
                kwargs['line_bbox'])

        if 'line_rank_id' in kwargs:
            embeddings += self.line_rank_embeddings(kwargs['line_rank_id'])

        if 'line_rank_inner_id' in kwargs:
            embeddings += self.line_rank_inner_embeddings(
                kwargs['line_rank_inner_id'])

        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings

    def calc_bbox_pos_emb(self, bbox, pe_type):
        # bbox_t: [seq_length, batch_size, dim_bbox]
        bbox_t = bbox.transpose(0, 1)

        if pe_type == 'pdpdq_ws':
            bbox_pos = bbox_t
        elif pe_type == 'crel':
            # bbox_pos: [seq_length, seq_length, batch_size, dim_bbox]
            bbox_pos = bbox_t[None, :, :, :] - bbox_t[:, None, :, :]
        else:
            raise ValueError(f'Unknown pe_type={pe_type}')

        bbox_pos_emb = self.bbox_sinusoid_emb(bbox_pos)
        bbox_pos_emb = self.bbox_projection(bbox_pos_emb)

        return bbox_pos_emb

    def create_position_ids_from_inputs_embeds(self, inputs_embeds):
        """
        We are provided embeddings directly.
        We cannot infer which are padded so just generate sequential position ids.

        Args:
            inputs_embeds: torch.Tensor

        Returns: torch.Tensor
        """
        input_shape = inputs_embeds.size()[:-1]
        sequence_length = input_shape[1]

        position_ids = torch.arange(
            self.padding_idx + 1,
            sequence_length + self.padding_idx + 1,
            dtype=torch.long,
            device=inputs_embeds.device)
        return position_ids.unsqueeze(0).expand(input_shape)


# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta
class LayoutRobertaSelfAttention(nn.Module):

    def __init__(self, config, position_embedding_type=None):
        super().__init__()
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(
                config, 'embedding_size'):
            raise ValueError(
                f'The hidden size ({config.hidden_size}) is not a multiple of the number of attention '
                f'heads ({config.num_attention_heads})')

        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size
                                       / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size

        self.query = nn.Linear(config.hidden_size, self.all_head_size)
        self.key = nn.Linear(config.hidden_size, self.all_head_size)
        self.value = nn.Linear(config.hidden_size, self.all_head_size)

        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
        self.position_embedding_type = position_embedding_type or getattr(
            config, 'position_embedding_type', 'absolute')
        if self.position_embedding_type == 'relative_key' or self.position_embedding_type == 'relative_key_query':
            self.max_position_embeddings = config.max_position_embeddings
            self.distance_embedding = nn.Embedding(
                2 * config.max_position_embeddings - 1,
                self.attention_head_size)

        self.is_decoder = config.is_decoder

        self.pe_type = config.pe_type

    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (
            self.num_attention_heads,
            self.attention_head_size,
        )
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_value=None,
        output_attentions=False,
        bbox_pos_emb=None,
        bbox_pos_mask=None,
    ):
        mixed_query_layer = self.query(hidden_states)

        # If this is instantiated as a cross-attention module, the keys
        # and values come from an encoder; the attention mask needs to be
        # such that the encoder's padding tokens are not attended to.
        is_cross_attention = encoder_hidden_states is not None

        if is_cross_attention and past_key_value is not None:
            # reuse k,v, cross_attentions
            key_layer = past_key_value[0]
            value_layer = past_key_value[1]
            attention_mask = encoder_attention_mask
        elif is_cross_attention:
            key_layer = self.transpose_for_scores(
                self.key(encoder_hidden_states))
            value_layer = self.transpose_for_scores(
                self.value(encoder_hidden_states))
            attention_mask = encoder_attention_mask
        elif past_key_value is not None:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))
            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
        else:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))

        query_layer = self.transpose_for_scores(mixed_query_layer)

        if self.is_decoder:
            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
            # Further calls to cross_attention layer can then reuse all cross-attention
            # key/value_states (first "if" case)
            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
            # all previous decoder key/value_states. Further calls to uni-directional self-attention
            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
            # if encoder bi-directional self-attention `past_key_value` is always `None`
            past_key_value = (key_layer, value_layer)

        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = torch.matmul(query_layer,
                                        key_layer.transpose(-1, -2))

        if (self.position_embedding_type == 'relative_key'
                or self.position_embedding_type == 'relative_key_query'):
            seq_length = hidden_states.size()[1]
            position_ids_l = torch.arange(
                seq_length, dtype=torch.long,
                device=hidden_states.device).view(-1, 1)
            position_ids_r = torch.arange(
                seq_length, dtype=torch.long,
                device=hidden_states.device).view(1, -1)
            distance = position_ids_l - position_ids_r
            positional_embedding = self.distance_embedding(
                distance + self.max_position_embeddings - 1)
            positional_embedding = positional_embedding.to(
                dtype=query_layer.dtype)  # fp16 compatibility

            if self.position_embedding_type == 'relative_key':
                relative_position_scores = torch.einsum(
                    'bhld,lrd->bhlr', query_layer, positional_embedding)
                attention_scores = attention_scores + relative_position_scores
            elif self.position_embedding_type == 'relative_key_query':
                relative_position_scores_query = torch.einsum(
                    'bhld,lrd->bhlr', query_layer, positional_embedding)
                relative_position_scores_key = torch.einsum(
                    'bhrd,lrd->bhlr', key_layer, positional_embedding)
                attention_scores = (
                    attention_scores + relative_position_scores_query
                    + relative_position_scores_key)

        # bbox positional encoding
        batch_size, n_head, seq_length, d_head = query_layer.shape
        if self.pe_type == 'pdpdq_ws':
            head_q_pos = self.query(bbox_pos_emb)
            head_k_pos = self.key(bbox_pos_emb)
            head_q_pos = head_q_pos.view(seq_length, batch_size, n_head,
                                         d_head)
            head_k_pos = head_k_pos.view(seq_length, batch_size, n_head,
                                         d_head)
            head_q_pos = head_q_pos.permute([1, 2, 0, 3])
            head_k_pos = head_k_pos.permute([1, 2, 0, 3])

            bbox_pos_scores_1 = torch.einsum(
                'bnid,bnjd->bnij',
                (torch.mul(query_layer, head_q_pos), head_k_pos))
            bbox_pos_scores_2 = torch.einsum('bnid,bnjd->bnij',
                                             (head_q_pos, head_k_pos))
            bbox_pos_scores = bbox_pos_scores_1 + bbox_pos_scores_2
        elif self.pe_type == 'crel':
            bbox_pos_emb = bbox_pos_emb.view(seq_length, seq_length,
                                             batch_size, d_head)
            bbox_pos_emb = bbox_pos_emb.permute([2, 0, 1, 3])
            bbox_pos_scores = torch.einsum('bnid,bijd->bnij',
                                           (query_layer, bbox_pos_emb))
        else:
            raise ValueError(f'Unknown self.pe_type={self.pe_type}')

        if bbox_pos_mask is not None:
            # bbox_pos_mask is [batch_size, seq_length]
            bbox_pos_mask = 1 - bbox_pos_mask
            # [batch_size, 1, seq_length]
            M1 = bbox_pos_mask.unsqueeze(1)
            # [batch_size, seq_length, 1]
            MT = M1.permute(0, 2, 1)
            # [batch_size, seq_length, seq_length]
            bbox_pos_mask_final = torch.matmul(
                MT.to(bbox_pos_scores.dtype), M1.to(bbox_pos_scores.dtype))
        else:
            bbox_pos_mask_final = None

        if bbox_pos_mask_final is not None:
            bbox_pos_scores = torch.mul(bbox_pos_scores,
                                        bbox_pos_mask_final.unsqueeze(1))

        # [batch_size, d_head, seq_length, seq_length]
        attention_scores = attention_scores + bbox_pos_scores

        attention_scores = attention_scores / math.sqrt(
            self.attention_head_size)
        if attention_mask is not None:
            # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function)
            attention_scores = attention_scores + attention_mask

        # Normalize the attention scores to probabilities.
        attention_probs = nn.functional.softmax(attention_scores, dim=-1)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.dropout(attention_probs)

        # Mask heads if we want to
        if head_mask is not None:
            attention_probs = attention_probs * head_mask

        context_layer = torch.matmul(attention_probs, value_layer)

        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (
            self.all_head_size, )
        context_layer = context_layer.view(new_context_layer_shape)

        outputs = (context_layer,
                   attention_probs) if output_attentions else (context_layer, )

        if self.is_decoder:
            outputs = outputs + (past_key_value, )
        return outputs


# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
class LayoutRobertaSelfOutput(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(
            config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta
class LayoutRobertaAttention(nn.Module):

    def __init__(self, config, position_embedding_type=None):
        super().__init__()
        self.self = LayoutRobertaSelfAttention(
            config, position_embedding_type=position_embedding_type)
        self.output = LayoutRobertaSelfOutput(config)
        self.pruned_heads = set()

    def prune_heads(self, heads):
        if len(heads) == 0:
            return
        heads, index = find_pruneable_heads_and_indices(
            heads, self.self.num_attention_heads,
            self.self.attention_head_size, self.pruned_heads)

        # Prune linear layers
        self.self.query = prune_linear_layer(self.self.query, index)
        self.self.key = prune_linear_layer(self.self.key, index)
        self.self.value = prune_linear_layer(self.self.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)

        # Update hyper params and store pruned heads
        self.self.num_attention_heads = self.self.num_attention_heads - len(
            heads)
        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_value=None,
        output_attentions=False,
        bbox_pos_emb=None,
        bbox_pos_mask=None,
    ):
        self_outputs = self.self(
            hidden_states,
            attention_mask,
            head_mask,
            encoder_hidden_states,
            encoder_attention_mask,
            past_key_value,
            output_attentions,
            bbox_pos_emb=bbox_pos_emb,
            bbox_pos_mask=bbox_pos_mask,
        )
        attention_output = self.output(self_outputs[0], hidden_states)
        outputs = (attention_output,
                   ) + self_outputs[1:]  # add attentions if we output them
        return outputs


# Copied from transformers.models.bert.modeling_bert.BertIntermediate
class LayoutRobertaIntermediate(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else:
            self.intermediate_act_fn = config.hidden_act

    def forward(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states


# Copied from transformers.models.bert.modeling_bert.BertOutput
class LayoutRobertaOutput(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(
            config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta
class LayoutRobertaLayer(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = LayoutRobertaAttention(config)
        self.is_decoder = config.is_decoder
        self.add_cross_attention = config.add_cross_attention
        if self.add_cross_attention:
            if not self.is_decoder:
                raise ValueError(
                    f'{self} should be used as a decoder model if cross attention is added'
                )
            self.crossattention = LayoutRobertaAttention(
                config, position_embedding_type='absolute')
        self.intermediate = LayoutRobertaIntermediate(config)
        self.output = LayoutRobertaOutput(config)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        bbox_pos_emb=None,
        bbox_pos_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_value=None,
        output_attentions=False,
    ):
        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
        self_attn_past_key_value = past_key_value[:
                                                  2] if past_key_value is not None else None
        self_attention_outputs = self.attention(
            hidden_states,
            attention_mask,
            head_mask,
            output_attentions=output_attentions,
            past_key_value=self_attn_past_key_value,
            bbox_pos_emb=bbox_pos_emb,
            bbox_pos_mask=bbox_pos_mask,
        )
        attention_output = self_attention_outputs[0]

        # if decoder, the last output is tuple of self-attn cache
        if self.is_decoder:
            outputs = self_attention_outputs[1:-1]
            present_key_value = self_attention_outputs[-1]
        else:
            outputs = self_attention_outputs[
                1:]  # add self attentions if we output attention weights

        cross_attn_present_key_value = None
        if self.is_decoder and encoder_hidden_states is not None:
            if not hasattr(self, 'crossattention'):
                raise ValueError(
                    f'If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers'
                    ' by setting `config.add_cross_attention=True`')

            # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
            cross_attn_past_key_value = past_key_value[
                -2:] if past_key_value is not None else None
            cross_attention_outputs = self.crossattention(
                attention_output,
                attention_mask,
                head_mask,
                encoder_hidden_states,
                encoder_attention_mask,
                cross_attn_past_key_value,
                output_attentions,
            )
            attention_output = cross_attention_outputs[0]
            outputs = outputs + cross_attention_outputs[
                1:-1]  # add cross attentions if we output attention weights

            # add cross-attn cache to positions 3,4 of present_key_value tuple
            cross_attn_present_key_value = cross_attention_outputs[-1]
            present_key_value = present_key_value + cross_attn_present_key_value

        layer_output = apply_chunking_to_forward(
            self.feed_forward_chunk,
            self.chunk_size_feed_forward,
            self.seq_len_dim,
            attention_output,
        )
        outputs = (layer_output, ) + outputs

        # if decoder, return the attn key/values as the last output
        if self.is_decoder:
            outputs = outputs + (present_key_value, )

        return outputs

    def feed_forward_chunk(self, attention_output):
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output)
        return layer_output


# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta
class LayoutRobertaEncoder(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([
            LayoutRobertaLayer(config) for _ in range(config.num_hidden_layers)
        ])
        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_values=None,
        use_cache=None,
        output_attentions=False,
        output_hidden_states=False,
        return_dict=True,
        bbox_pos_emb=None,
        bbox_pos_mask=None,
    ):
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        all_cross_attentions = (
        ) if output_attentions and self.config.add_cross_attention else None

        next_decoder_cache = () if use_cache else None
        for i, layer_module in enumerate(self.layer):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states, )

            layer_head_mask = head_mask[i] if head_mask is not None else None
            past_key_value = past_key_values[
                i] if past_key_values is not None else None

            if self.gradient_checkpointing and self.training:

                if use_cache:
                    logger.warning(
                        '`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...'
                    )
                    use_cache = False

                def create_custom_forward(module):

                    def custom_forward(*inputs):
                        return module(*inputs, past_key_value,
                                      output_attentions)

                    return custom_forward

                layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(layer_module),
                    hidden_states,
                    attention_mask,
                    bbox_pos_emb,
                    bbox_pos_mask,
                    layer_head_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                )
            else:
                layer_outputs = layer_module(
                    hidden_states,
                    attention_mask,
                    bbox_pos_emb,
                    bbox_pos_mask,
                    layer_head_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                    past_key_value,
                    output_attentions,
                )

            hidden_states = layer_outputs[0]
            if use_cache:
                next_decoder_cache += (layer_outputs[-1], )
            if output_attentions:
                all_self_attentions = all_self_attentions + (
                    layer_outputs[1], )
                if self.config.add_cross_attention:
                    all_cross_attentions = all_cross_attentions + (
                        layer_outputs[2], )

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states, )

        if not return_dict:
            return tuple(v for v in [
                hidden_states,
                next_decoder_cache,
                all_hidden_states,
                all_self_attentions,
                all_cross_attentions,
            ] if v is not None)
        return BaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            past_key_values=next_decoder_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
            cross_attentions=all_cross_attentions,
        )


# Copied from transformers.models.bert.modeling_bert.BertPooler
class LayoutRobertaPooler(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.activation = nn.Tanh()

    def forward(self, hidden_states):
        # We "pool" the model by simply taking the hidden state corresponding
        # to the first token.
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(first_token_tensor)
        pooled_output = self.activation(pooled_output)
        return pooled_output


class LayoutRobertaPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = LayoutRobertaConfig
    base_model_prefix = 'layoutroberta'
    supports_gradient_checkpointing = True
    _keys_to_ignore_on_load_missing = [r'position_ids']

    # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, nn.Linear):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(
                mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(
                mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, LayoutRobertaEncoder):
            module.gradient_checkpointing = value

    def update_keys_to_ignore(self, config, del_keys_to_ignore):
        """Remove some keys from ignore list"""
        if not config.tie_word_embeddings:
            # must make a new list, or the class variable gets modified!
            self._keys_to_ignore_on_save = [
                k for k in self._keys_to_ignore_on_save
                if k not in del_keys_to_ignore
            ]
            self._keys_to_ignore_on_load_missing = [
                k for k in self._keys_to_ignore_on_load_missing
                if k not in del_keys_to_ignore
            ]


class LayoutRobertaModel(LayoutRobertaPreTrainedModel):
    """

    BROS + Roberta

    """

    # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta
    def __init__(self, config, add_pooling_layer=True):
        super().__init__(config)
        self.config = config

        self.embeddings = LayoutRobertaEmbeddings(config)
        self.encoder = LayoutRobertaEncoder(config)

        self.pooler = LayoutRobertaPooler(
            config) if add_pooling_layer else None

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    # Copied from transformers.models.bert.modeling_bert.BertModel.forward
    def forward(self,
                input_ids=None,
                bbox=None,
                bbox_mask=None,
                attention_mask=None,
                token_type_ids=None,
                position_ids=None,
                head_mask=None,
                inputs_embeds=None,
                encoder_hidden_states=None,
                encoder_attention_mask=None,
                past_key_values=None,
                use_cache=None,
                output_attentions=None,
                output_hidden_states=None,
                return_dict=None,
                **kwargs):
        r"""
        encoder_hidden_states  (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
            the model is configured as a decoder.
        encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.
        past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple
            having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.

            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        """
        output_attentions = (
            output_attentions if output_attentions is not None else
            self.config.output_attentions)
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else
            self.config.output_hidden_states)
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if self.config.is_decoder:
            use_cache = use_cache if use_cache is not None else self.config.use_cache
        else:
            use_cache = False

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError(
                'You cannot specify both input_ids and inputs_embeds at the same time'
            )
        elif input_ids is not None:
            input_shape = input_ids.size()
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            raise ValueError(
                'You have to specify either input_ids or inputs_embeds')

        batch_size, seq_length = input_shape
        device = input_ids.device if input_ids is not None else inputs_embeds.device

        # past_key_values_length
        past_key_values_length = past_key_values[0][0].shape[
            2] if past_key_values is not None else 0

        if attention_mask is None:
            attention_mask = torch.ones(
                ((batch_size, seq_length + past_key_values_length)),
                device=device)

        if token_type_ids is None:
            if hasattr(self.embeddings, 'token_type_ids'):
                buffered_token_type_ids = self.embeddings.token_type_ids[:, :
                                                                         seq_length]
                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(
                    batch_size, seq_length)
                token_type_ids = buffered_token_type_ids_expanded
            else:
                token_type_ids = torch.zeros(
                    input_shape, dtype=torch.long, device=device)

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
            attention_mask, input_shape, device)

        # If a 2D or 3D attention mask is provided for the cross-attention
        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
        if self.config.is_decoder and encoder_hidden_states is not None:
            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size(
            )
            encoder_hidden_shape = (encoder_batch_size,
                                    encoder_sequence_length)
            if encoder_attention_mask is None:
                encoder_attention_mask = torch.ones(
                    encoder_hidden_shape, device=device)
            encoder_extended_attention_mask = self.invert_attention_mask(
                encoder_attention_mask)
        else:
            encoder_extended_attention_mask = None

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        head_mask = self.get_head_mask(head_mask,
                                       self.config.num_hidden_layers)

        embedding_output = self.embeddings(
            input_ids=input_ids,
            position_ids=position_ids,
            token_type_ids=token_type_ids,
            inputs_embeds=inputs_embeds,
            past_key_values_length=past_key_values_length,
            **kwargs)

        scaled_bbox = bbox * self.config.bbox_scale
        bbox_pos_emb = self.embeddings.calc_bbox_pos_emb(
            scaled_bbox, self.config.pe_type)

        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask=extended_attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_extended_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            bbox_pos_emb=bbox_pos_emb,
            bbox_pos_mask=bbox_mask,
        )
        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(
            sequence_output) if self.pooler is not None else None

        if not return_dict:
            return (sequence_output, pooled_output) + encoder_outputs[1:]

        return BaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            past_key_values=encoder_outputs.past_key_values,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
            cross_attentions=encoder_outputs.cross_attentions,
        )


def create_position_ids_from_input_ids(input_ids,
                                       padding_idx,
                                       past_key_values_length=0):
    """
    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`.

    Args:
        x: torch.Tensor x:

    Returns: 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)
                           + past_key_values_length) * mask
    return incremental_indices.long() + padding_idx
