modeling_lxmert.py
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modeling_lxmert.py
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	# coding=utf-8
	# Copyright 2018 Hao Tan, Mohit Bansal, and the HuggingFace 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
	# limitations under the License.
	""" PyTorch LXMERT model. """


	import math
	import os
	import warnings
	from dataclasses import dataclass
	from typing import Optional, Tuple

	import torch
	from torch import nn
	from torch.nn import CrossEntropyLoss, SmoothL1Loss

	from ...activations import ACT2FN, gelu
	from ...file_utils import (
	ModelOutput,
	add_code_sample_docstrings,
	add_start_docstrings,
	add_start_docstrings_to_model_forward,
	replace_return_docstrings,
	)
	from ...modeling_utils import PreTrainedModel
	from ...utils import logging
	from .configuration_lxmert import LxmertConfig


	logger = logging.get_logger(__name__)

	_CHECKPOINT_FOR_DOC = "unc-nlp/lxmert-base-uncased"
	_CONFIG_FOR_DOC = "LxmertConfig"
	_TOKENIZER_FOR_DOC = "LxmertTokenizer"

	LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
	"unc-nlp/lxmert-base-uncased",
	]


	class GeLU(nn.Module):
	def __init__(self):
	super().__init__()

	def forward(self, x):
	return gelu(x)


	@dataclass
	class LxmertModelOutput(ModelOutput):
	"""
	Lxmert's outputs that contain the last hidden states, pooled outputs, and attention probabilities for the language,
	visual, and, cross-modality encoders. (note: the visual encoder in Lxmert is referred to as the "relation-ship"
	encoder")


	Args:
	language_output (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
	Sequence of hidden-states at the output of the last layer of the language encoder.
	vision_output (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
	Sequence of hidden-states at the output of the last layer of the visual encoder.
	pooled_output (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, hidden_size)`):
	Last layer hidden-state of the first token of the sequence (classification, CLS, token) further processed
	by a Linear layer and a Tanh activation function. The Linear
	language_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
	Tuple of :obj:`torch.FloatTensor` (one for input features + one for the output of each cross-modality
	layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`.
	vision_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
	Tuple of :obj:`torch.FloatTensor` (one for input features + one for the output of each cross-modality
	layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`.
	language_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
	Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
	sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the
	weighted average in the self-attention heads.
	vision_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
	Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
	sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the
	weighted average in the self-attention heads.
	cross_encoder_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
	Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
	sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the
	weighted average in the self-attention heads.
	"""

	language_output: Optional[torch.FloatTensor] = None
	vision_output: Optional[torch.FloatTensor] = None
	pooled_output: Optional[torch.FloatTensor] = None
	language_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
	vision_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
	language_attentions: Optional[Tuple[torch.FloatTensor]] = None
	vision_attentions: Optional[Tuple[torch.FloatTensor]] = None
	cross_encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None


	@dataclass
	class LxmertForQuestionAnsweringOutput(ModelOutput):
	"""
	Output type of :class:`~transformers.LxmertForQuestionAnswering`.

	Args:
	loss (`optional`, returned when ``labels`` is provided, ``torch.FloatTensor`` of shape :obj:`(1,)`):
	Total loss as the sum of the masked language modeling loss and the next sequence prediction
	(classification) loss.k.
	question_answering_score: (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, n_qa_answers)`, `optional`):
	Prediction scores of question answering objective (classification).
	language_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
	Tuple of :obj:`torch.FloatTensor` (one for input features + one for the output of each cross-modality
	layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`.
	vision_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
	Tuple of :obj:`torch.FloatTensor` (one for input features + one for the output of each cross-modality
	layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`.
	language_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
	Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
	sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the
	weighted average in the self-attention heads.
	vision_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
	Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
	sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the
	weighted average in the self-attention heads.
	cross_encoder_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
	Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
	sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the
	weighted average in the self-attention heads.
	"""

	loss: Optional[torch.FloatTensor] = None
	question_answering_score: Optional[torch.FloatTensor] = None
	language_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
	vision_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
	language_attentions: Optional[Tuple[torch.FloatTensor]] = None
	vision_attentions: Optional[Tuple[torch.FloatTensor]] = None
	cross_encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None


	@dataclass
	class LxmertForPreTrainingOutput(ModelOutput):
	"""
	Output type of :class:`~transformers.LxmertForPreTraining`.

	Args:
	loss (`optional`, returned when ``labels`` is provided, ``torch.FloatTensor`` of shape :obj:`(1,)`):
	Total loss as the sum of the masked language modeling loss and the next sequence prediction
	(classification) loss.
	prediction_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
	Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
	cross_relationship_score: (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, 2)`):
	Prediction scores of the textual matching objective (classification) head (scores of True/False
	continuation before SoftMax).
	question_answering_score: (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, n_qa_answers)`):
	Prediction scores of question answering objective (classification).
	language_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
	Tuple of :obj:`torch.FloatTensor` (one for input features + one for the output of each cross-modality
	layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`.
	vision_hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
	Tuple of :obj:`torch.FloatTensor` (one for input features + one for the output of each cross-modality
	layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`.
	language_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
	Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
	sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the
	weighted average in the self-attention heads.
	vision_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
	Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
	sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the
	weighted average in the self-attention heads.
	cross_encoder_attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
	Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
	sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the
	weighted average in the self-attention heads.

	"""

	loss: [torch.FloatTensor] = None
	prediction_logits: Optional[torch.FloatTensor] = None
	cross_relationship_score: Optional[torch.FloatTensor] = None
	question_answering_score: Optional[torch.FloatTensor] = None
	language_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
	vision_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
	language_attentions: Optional[Tuple[torch.FloatTensor]] = None
	vision_attentions: Optional[Tuple[torch.FloatTensor]] = None
	cross_encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None


	def load_tf_weights_in_lxmert(model, config, tf_checkpoint_path):
	"""Load tf checkpoints in a pytorch model."""
	try:
	import re

	import numpy as np
	import tensorflow as tf
	except ImportError:
	logger.error(
	"Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
	"https://www.tensorflow.org/install/ for installation instructions."
	)
	raise
	tf_path = os.path.abspath(tf_checkpoint_path)
	logger.info("Converting TensorFlow checkpoint from {}".format(tf_path))
	# Load weights from TF model
	init_vars = tf.train.list_variables(tf_path)
	names = []
	arrays = []
	for name, shape in init_vars:
	logger.info("Loading TF weight {} with shape {}".format(name, shape))
	array = tf.train.load_variable(tf_path, name)
	names.append(name)
	arrays.append(array)

	for name, array in zip(names, arrays):
	name = name.split("/")
	# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
	# which are not required for using pretrained model
	if any(
	n
	in [
	"adam_v",
	"adam_m",
	"AdamWeightDecayOptimizer",
	"AdamWeightDecayOptimizer_1",
	"global_step",
	]
	for n in name
	):
	logger.info("Skipping {}".format("/".join(name)))
	continue
	pointer = model
	for m_name in name:
	if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
	scope_names = re.split(r"_(\d+)", m_name)
	else:
	scope_names = [m_name]
	if scope_names[0] == "kernel" or scope_names[0] == "gamma":
	pointer = getattr(pointer, "weight")
	elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
	pointer = getattr(pointer, "bias")
	elif scope_names[0] == "output_weights":
	pointer = getattr(pointer, "weight")
	elif scope_names[0] == "squad":
	pointer = getattr(pointer, "classifier")
	else:
	try:
	pointer = getattr(pointer, scope_names[0])
	except AttributeError:
	logger.info("Skipping {}".format("/".join(name)))
	continue
	if len(scope_names) >= 2:
	num = int(scope_names[1])
	pointer = pointer[num]
	if m_name[-11:] == "_embeddings":
	pointer = getattr(pointer, "weight")
	elif m_name == "kernel":
	array = np.transpose(array)
	try:
	assert pointer.shape == array.shape
	except AssertionError as e:
	e.args += (pointer.shape, array.shape)
	raise
	logger.info("Initialize PyTorch weight {}".format(name))
	pointer.data = torch.from_numpy(array)
	return model


	class LxmertEmbeddings(nn.Module):
	"""Construct the embeddings from word, position and token_type embeddings."""

	def __init__(self, config):
	super().__init__()
	self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=0)
	self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size, padding_idx=0)
	self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size, padding_idx=0)

	# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
	# any TensorFlow checkpoint file
	self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=1e-12)
	self.dropout = nn.Dropout(config.hidden_dropout_prob)

	def forward(self, input_ids, token_type_ids=None, inputs_embeds=None):
	if input_ids is not None:
	input_shape = input_ids.size()
	device = input_ids.device
	else:
	input_shape = inputs_embeds.size()[:-1]
	device = inputs_embeds.device
	seq_length = input_shape[1]

	position_ids = torch.arange(seq_length, dtype=torch.long, device=device)
	position_ids = position_ids.unsqueeze(0).expand(input_shape)

	if token_type_ids is None:
	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)
	position_embeddings = self.position_embeddings(position_ids)
	token_type_embeddings = self.token_type_embeddings(token_type_ids)

	embeddings = inputs_embeds + position_embeddings + token_type_embeddings
	embeddings = self.LayerNorm(embeddings)
	embeddings = self.dropout(embeddings)
	return embeddings


	class LxmertAttention(nn.Module):
	def __init__(self, config, ctx_dim=None):
	super().__init__()
	if config.hidden_size % config.num_attention_heads != 0:
	raise ValueError(
	"The hidden size (%d) is not a multiple of the number of attention "
	"heads (%d)" % (config.hidden_size, 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.head_size = self.num_attention_heads * self.attention_head_size

	# visual_dim = 2048
	if ctx_dim is None:
	ctx_dim = config.hidden_size
	self.query = nn.Linear(config.hidden_size, self.head_size)
	self.key = nn.Linear(ctx_dim, self.head_size)
	self.value = nn.Linear(ctx_dim, self.head_size)

	self.dropout = nn.Dropout(config.attention_probs_dropout_prob)

	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, context, attention_mask=None, output_attentions=False):
	mixed_query_layer = self.query(hidden_states)
	mixed_key_layer = self.key(context)
	mixed_value_layer = self.value(context)

	query_layer = self.transpose_for_scores(mixed_query_layer)
	key_layer = self.transpose_for_scores(mixed_key_layer)
	value_layer = self.transpose_for_scores(mixed_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))
	attention_scores = attention_scores / math.sqrt(self.attention_head_size)
	# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
	if attention_mask is not None:
	attention_scores = attention_scores + attention_mask

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

	# 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)

	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.head_size,)
	context_layer = context_layer.view(*new_context_layer_shape)

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


	class LxmertAttentionOutput(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=1e-12)
	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


	class LxmertCrossAttentionLayer(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.att = LxmertAttention(config)
	self.output = LxmertAttentionOutput(config)

	def forward(self, input_tensor, ctx_tensor, ctx_att_mask=None, output_attentions=False):
	output = self.att(input_tensor, ctx_tensor, ctx_att_mask, output_attentions=output_attentions)
	if output_attentions:
	attention_probs = output[1]
	attention_output = self.output(output[0], input_tensor)
	outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
	return outputs


	class LxmertSelfAttentionLayer(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.self = LxmertAttention(config)
	self.output = LxmertAttentionOutput(config)

	def forward(self, input_tensor, attention_mask, output_attentions=False):
	# Self attention attends to itself, thus keys and queries are the same (input_tensor).
	output = self.self(
	input_tensor,
	input_tensor,
	attention_mask,
	output_attentions=output_attentions,
	)
	if output_attentions:
	attention_probs = output[1]
	attention_output = self.output(output[0], input_tensor)
	outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
	return outputs


	class LxmertIntermediate(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
	self.intermediate_act_fn = ACT2FN[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


	class LxmertOutput(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=1e-12)
	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


	class LxmertLayer(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.attention = LxmertSelfAttentionLayer(config)
	self.intermediate = LxmertIntermediate(config)
	self.output = LxmertOutput(config)

	def forward(self, hidden_states, attention_mask=None, output_attentions=False):
	outputs = self.attention(hidden_states, attention_mask, output_attentions=output_attentions)
	attention_output = outputs[0]
	intermediate_output = self.intermediate(attention_output)
	layer_output = self.output(intermediate_output, attention_output)
	outputs = (layer_output,) + outputs[1:] # add attentions if we output them
	return outputs


	class LxmertXLayer(nn.Module):
	def __init__(self, config):
	super().__init__()
	# The cross-attention Layer
	self.visual_attention = LxmertCrossAttentionLayer(config)

	# Self-attention Layers
	self.lang_self_att = LxmertSelfAttentionLayer(config)
	self.visn_self_att = LxmertSelfAttentionLayer(config)

	# Intermediate and Output Layers (FFNs)
	self.lang_inter = LxmertIntermediate(config)
	self.lang_output = LxmertOutput(config)
	self.visn_inter = LxmertIntermediate(config)
	self.visn_output = LxmertOutput(config)

	def cross_att(
	self,
	lang_input,
	lang_attention_mask,
	visual_input,
	visual_attention_mask,
	output_x_attentions=False,
	):
	# Cross Attention
	lang_att_output = self.visual_attention(
	lang_input,
	visual_input,
	ctx_att_mask=visual_attention_mask,
	output_attentions=output_x_attentions,
	)
	visual_att_output = self.visual_attention(
	visual_input,
	lang_input,
	ctx_att_mask=lang_attention_mask,
	output_attentions=False,
	)
	return lang_att_output, visual_att_output

	def self_att(self, lang_input, lang_attention_mask, visual_input, visual_attention_mask):
	# Self Attention
	lang_att_output = self.lang_self_att(lang_input, lang_attention_mask, output_attentions=False)
	visual_att_output = self.visn_self_att(visual_input, visual_attention_mask, output_attentions=False)
	return lang_att_output[0], visual_att_output[0]

	def output_fc(self, lang_input, visual_input):
	# FC layers
	lang_inter_output = self.lang_inter(lang_input)
	visual_inter_output = self.visn_inter(visual_input)

	# Layer output
	lang_output = self.lang_output(lang_inter_output, lang_input)
	visual_output = self.visn_output(visual_inter_output, visual_input)

	return lang_output, visual_output

	def forward(
	self,
	lang_feats,
	lang_attention_mask,
	visual_feats,
	visual_attention_mask,
	output_attentions=False,
	):

	lang_att_output, visual_att_output = self.cross_att(
	lang_input=lang_feats,
	lang_attention_mask=lang_attention_mask,
	visual_input=visual_feats,
	visual_attention_mask=visual_attention_mask,
	output_x_attentions=output_attentions,
	)
	attention_probs = lang_att_output[1:]
	lang_att_output, visual_att_output = self.self_att(
	lang_att_output[0],
	lang_attention_mask,
	visual_att_output[0],
	visual_attention_mask,
	)

	lang_output, visual_output = self.output_fc(lang_att_output, visual_att_output)
	return (
	(
	lang_output,
	visual_output,
	attention_probs[0],
	)
	if output_attentions
	else (lang_output, visual_output)
	)


	class LxmertVisualFeatureEncoder(nn.Module):
	def __init__(self, config):
	super().__init__()
	feat_dim = config.visual_feat_dim
	pos_dim = config.visual_pos_dim

	# Object feature encoding
	self.visn_fc = nn.Linear(feat_dim, config.hidden_size)
	self.visn_layer_norm = nn.LayerNorm(config.hidden_size, eps=1e-12)

	# Box position encoding
	self.box_fc = nn.Linear(pos_dim, config.hidden_size)
	self.box_layer_norm = nn.LayerNorm(config.hidden_size, eps=1e-12)

	self.dropout = nn.Dropout(config.hidden_dropout_prob)

	def forward(self, visual_feats, visual_pos):
	x = self.visn_fc(visual_feats)
	x = self.visn_layer_norm(x)
	y = self.box_fc(visual_pos)
	y = self.box_layer_norm(y)
	output = (x + y) / 2

	output = self.dropout(output)
	return output


	class LxmertEncoder(nn.Module):
	def __init__(self, config):
	super().__init__()

	# Obj-level image embedding layer
	self.visn_fc = LxmertVisualFeatureEncoder(config)
	self.config = config

	# Number of layers
	self.num_l_layers = config.l_layers
	self.num_x_layers = config.x_layers
	self.num_r_layers = config.r_layers

	# Layers
	# Using self.layer instead of self.l_layer to support loading BERT weights.
	self.layer = nn.ModuleList([LxmertLayer(config) for _ in range(self.num_l_layers)])
	self.x_layers = nn.ModuleList([LxmertXLayer(config) for _ in range(self.num_x_layers)])
	self.r_layers = nn.ModuleList([LxmertLayer(config) for _ in range(self.num_r_layers)])

	def forward(
	self,
	lang_feats,
	lang_attention_mask,
	visual_feats,
	visual_pos,
	visual_attention_mask=None,
	output_attentions=None,
	):

	vision_hidden_states = ()
	language_hidden_states = ()
	vision_attentions = () if output_attentions or self.config.output_attentions else None
	language_attentions = () if output_attentions or self.config.output_attentions else None
	cross_encoder_attentions = () if output_attentions or self.config.output_attentions else None

	visual_feats = self.visn_fc(visual_feats, visual_pos)

	# Run language layers
	for layer_module in self.layer:
	l_outputs = layer_module(lang_feats, lang_attention_mask, output_attentions=output_attentions)
	lang_feats = l_outputs[0]
	language_hidden_states = language_hidden_states + (lang_feats,)
	if language_attentions is not None:
	language_attentions = language_attentions + (l_outputs[1],)

	# Run relational layers
	for layer_module in self.r_layers:
	v_outputs = layer_module(visual_feats, visual_attention_mask, output_attentions=output_attentions)
	visual_feats = v_outputs[0]
	vision_hidden_states = vision_hidden_states + (visual_feats,)
	if vision_attentions is not None:
	vision_attentions = vision_attentions + (v_outputs[1],)

	# Run cross-modality layers
	for layer_module in self.x_layers:
	x_outputs = layer_module(
	lang_feats,
	lang_attention_mask,
	visual_feats,
	visual_attention_mask,
	output_attentions=output_attentions,
	)
	lang_feats, visual_feats = x_outputs[:2]
	vision_hidden_states = vision_hidden_states + (visual_feats,)
	language_hidden_states = language_hidden_states + (lang_feats,)
	if cross_encoder_attentions is not None:
	cross_encoder_attentions = cross_encoder_attentions + (x_outputs[2],)
	visual_encoder_outputs = (
	vision_hidden_states,
	vision_attentions if output_attentions else None,
	)
	lang_encoder_outputs = (
	language_hidden_states,
	language_attentions if output_attentions else None,
	)
	return (
	visual_encoder_outputs,
	lang_encoder_outputs,
	cross_encoder_attentions if output_attentions else None,
	)


	class LxmertPooler(nn.Module):
	def __init__(self, config):
	super(LxmertPooler, self).__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 LxmertPredictionHeadTransform(nn.Module):
	def __init__(self, config):
	super(LxmertPredictionHeadTransform, self).__init__()
	self.dense = nn.Linear(config.hidden_size, config.hidden_size)
	self.transform_act_fn = ACT2FN[config.hidden_act]
	self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=1e-12)

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


	class LxmertLMPredictionHead(nn.Module):
	def __init__(self, config, lxmert_model_embedding_weights):
	super(LxmertLMPredictionHead, self).__init__()
	self.transform = LxmertPredictionHeadTransform(config)

	# The output weights are the same as the input embeddings, but there is
	# an output-only bias for each token.
	self.decoder = nn.Linear(
	lxmert_model_embedding_weights.size(1),
	lxmert_model_embedding_weights.size(0),
	bias=False,
	)
	self.decoder.weight = lxmert_model_embedding_weights
	self.bias = nn.Parameter(torch.zeros(lxmert_model_embedding_weights.size(0)))

	def forward(self, hidden_states):
	hidden_states = self.transform(hidden_states)
	hidden_states = self.decoder(hidden_states) + self.bias
	return hidden_states


	class LxmertVisualAnswerHead(nn.Module):
	def __init__(self, config, num_labels):
	super().__init__()
	hid_dim = config.hidden_size
	self.logit_fc = nn.Sequential(
	nn.Linear(hid_dim, hid_dim * 2),
	GeLU(),
	nn.LayerNorm(hid_dim * 2, eps=1e-12),
	nn.Linear(hid_dim * 2, num_labels),
	)

	def forward(self, hidden_states):
	return self.logit_fc(hidden_states)


	class LxmertVisualObjHead(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.transform = LxmertPredictionHeadTransform(config)
	# Decide the use of visual losses
	visual_losses = {}
	if config.visual_obj_loss:
	visual_losses["obj"] = {"shape": (-1,), "num": config.num_object_labels}
	if config.visual_attr_loss:
	visual_losses["attr"] = {"shape": (-1,), "num": config.num_attr_labels}
	if config.visual_obj_loss:
	visual_losses["feat"] = {
	"shape": (-1, config.visual_feat_dim),
	"num": config.visual_feat_dim,
	}
	self.visual_losses = visual_losses

	# The output weights are the same as the input embeddings, but there is
	# an output-only bias for each token.
	self.decoder_dict = nn.ModuleDict(
	{key: nn.Linear(config.hidden_size, self.visual_losses[key]["num"]) for key in self.visual_losses}
	)

	def forward(self, hidden_states):
	hidden_states = self.transform(hidden_states)
	output = {}
	for key in self.visual_losses:
	output[key] = self.decoder_dict[key](hidden_states)
	return output


	class LxmertPreTrainingHeads(nn.Module):
	def __init__(self, config, lxmert_model_embedding_weights):
	super(LxmertPreTrainingHeads, self).__init__()
	self.predictions = LxmertLMPredictionHead(config, lxmert_model_embedding_weights)
	self.seq_relationship = nn.Linear(config.hidden_size, 2)

	def forward(self, sequence_output, pooled_output):
	prediction_scores = self.predictions(sequence_output)
	seq_relationship_score = self.seq_relationship(pooled_output)
	return prediction_scores, seq_relationship_score


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

	config_class = LxmertConfig
	load_tf_weights = load_tf_weights_in_lxmert
	base_model_prefix = "lxmert"

	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)


	LXMERT_START_DOCSTRING = r"""

	The LXMERT model was proposed in `LXMERT: Learning Cross-Modality Encoder Representations from Transformers
	<https://arxiv.org/abs/1908.07490>`__ by Hao Tan and Mohit Bansal. It's a vision and language transformer model,
	pretrained on a variety of multi-modal datasets comprising of GQA, VQAv2.0, MCSCOCO captions, and Visual genome,
	using a combination of masked language modeling, region of interest feature regression, cross entropy loss for
	question answering attribute prediction, and object tag prediction.

	This model inherits from :class:`~transformers.PreTrainedModel`. Check the superclass documentation for the generic
	methods the library implements for all its model (such as downloading or saving, resizing the input embeddings,
	pruning heads etc.)

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

	Parameters:
	config (:class:`~transformers.LxmertConfig`): 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.
	"""

	LXMERT_INPUTS_DOCSTRING = r"""

	Args:
	input_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`):
	Indices of input sequence tokens in the vocabulary.

	Indices can be obtained using :class:`~transformers.LxmertTokenizer`. See
	:meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
	details.

	`What are input IDs? <../glossary.html#input-ids>`__
	visual_feats: (:obj:`torch.FloatTensor` of shape :obj:՝(batch_size, num_visual_features, visual_feat_dim)՝):
	This input represents visual features. They ROI pooled object features from bounding boxes using a
	faster-RCNN model)

	These are currently not provided by the transformers library.
	visual_pos: (:obj:`torch.FloatTensor` of shape :obj:՝(batch_size, num_visual_features, visual_pos_dim)՝):
	This input represents spacial features corresponding to their relative (via index) visual features. The
	pre-trained LXMERT model expects these spacial features to be normalized bounding boxes on a scale of 0 to
	1.

	These are currently not provided by the transformers library.
	attention_mask (:obj:`torch.FloatTensor` of shape :obj:`({0})`, `optional`):
	Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	`What are attention masks? <../glossary.html#attention-mask>`__
	visual_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`({0})`, `optional`):
	Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	`What are attention masks? <../glossary.html#attention-mask>`__
	token_type_ids (:obj:`torch.LongTensor` of shape :obj:`({0})`, `optional`):
	Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
	1]``:

	- 0 corresponds to a `sentence A` token,
	- 1 corresponds to a `sentence B` token.

	`What are token type IDs? <../glossary.html#token-type-ids>`__
	inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`({0}, hidden_size)`, `optional`):
	Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
	This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
	vectors than the model's internal embedding lookup matrix.
	output_attentions (:obj:`bool`, `optional`):
	Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
	tensors for more detail.
	output_hidden_states (:obj:`bool`, `optional`):
	Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
	more detail.
	return_dict (:obj:`bool`, `optional`):
	Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
	"""


	@add_start_docstrings(
	"The bare Lxmert Model transformer outputting raw hidden-states without any specific head on top.",
	LXMERT_START_DOCSTRING,
	)
	class LxmertModel(LxmertPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.embeddings = LxmertEmbeddings(config)
	self.encoder = LxmertEncoder(config)
	self.pooler = LxmertPooler(config)
	self.init_weights()

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

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

	@add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	tokenizer_class=_TOKENIZER_FOR_DOC,
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=LxmertModelOutput,
	config_class=_CONFIG_FOR_DOC,
	)
	def forward(
	self,
	input_ids=None,
	visual_feats=None,
	visual_pos=None,
	attention_mask=None,
	visual_attention_mask=None,
	token_type_ids=None,
	inputs_embeds=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):

	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 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")

	assert visual_feats is not None, "`visual_feats` cannot be `None`"
	assert visual_pos is not None, "`visual_pos` cannot be `None`"

	device = input_ids.device if input_ids is not None else inputs_embeds.device

	if attention_mask is None:
	attention_mask = torch.ones(input_shape, device=device)
	if token_type_ids is None:
	token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)

	# We create a 3D attention mask from a 2D tensor mask.
	# Sizes are [batch_size, 1, 1, to_seq_length]
	# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
	# this attention mask is more simple than the triangular masking of causal attention
	# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
	extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)

	# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
	# masked positions, this operation will create a tensor which is 0.0 for
	# positions we want to attend and -10000.0 for masked positions.
	# Since we are adding it to the raw scores before the softmax, this is
	# effectively the same as removing these entirely.
	extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)
	extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0

	# Process the visual attention mask
	if visual_attention_mask is not None:
	extended_visual_attention_mask = visual_attention_mask.unsqueeze(1).unsqueeze(2)
	extended_visual_attention_mask = extended_visual_attention_mask.to(dtype=self.dtype)
	extended_visual_attention_mask = (1.0 - extended_visual_attention_mask) * -10000.0
	else:
	extended_visual_attention_mask = None

	# Positional Word Embeddings
	embedding_output = self.embeddings(input_ids, token_type_ids, inputs_embeds)

	# Run Lxmert encoder
	encoder_outputs = self.encoder(
	embedding_output,
	extended_attention_mask,
	visual_feats=visual_feats,
	visual_pos=visual_pos,
	visual_attention_mask=extended_visual_attention_mask,
	output_attentions=output_attentions,
	)

	visual_encoder_outputs, lang_encoder_outputs = encoder_outputs[:2]
	vision_hidden_states = visual_encoder_outputs[0]
	language_hidden_states = lang_encoder_outputs[0]

	all_attentions = ()
	if output_attentions:
	language_attentions = lang_encoder_outputs[1]
	vision_attentions = visual_encoder_outputs[1]
	cross_encoder_attentions = encoder_outputs[2]
	all_attentions = (
	language_attentions,
	vision_attentions,
	cross_encoder_attentions,
	)

	hidden_states = (language_hidden_states, vision_hidden_states) if output_hidden_states else ()

	visual_output = vision_hidden_states[-1]
	lang_output = language_hidden_states[-1]
	pooled_output = self.pooler(lang_output)

	if not return_dict:
	return (lang_output, visual_output, pooled_output) + hidden_states + all_attentions

	return LxmertModelOutput(
	pooled_output=pooled_output,
	language_output=lang_output,
	vision_output=visual_output,
	language_hidden_states=language_hidden_states if output_hidden_states else None,
	vision_hidden_states=vision_hidden_states if output_hidden_states else None,
	language_attentions=language_attentions if output_attentions else None,
	vision_attentions=vision_attentions if output_attentions else None,
	cross_encoder_attentions=cross_encoder_attentions if output_attentions else None,
	)


	@add_start_docstrings(
	"""Lxmert Model with a specified pretraining head on top. """,
	LXMERT_START_DOCSTRING,
	)
	class LxmertForPreTraining(LxmertPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	# Configuration
	self.config = config
	self.num_qa_labels = config.num_qa_labels
	self.visual_loss_normalizer = config.visual_loss_normalizer

	# Use of pretraining tasks
	self.task_mask_lm = config.task_mask_lm
	self.task_obj_predict = config.task_obj_predict
	self.task_matched = config.task_matched
	self.task_qa = config.task_qa

	# Lxmert backbone
	self.lxmert = LxmertModel(config)

	# Pre-training heads
	self.cls = LxmertPreTrainingHeads(config, self.lxmert.embeddings.word_embeddings.weight)
	if self.task_obj_predict:
	self.obj_predict_head = LxmertVisualObjHead(config)
	if self.task_qa:
	self.answer_head = LxmertVisualAnswerHead(config, self.num_qa_labels)

	# Weight initialization
	self.init_weights()

	# Loss functions
	self.loss_fcts = {
	"l2": SmoothL1Loss(reduction="none"),
	"visual_ce": CrossEntropyLoss(reduction="none"),
	"ce": CrossEntropyLoss(),
	}

	visual_losses = {}
	if config.visual_obj_loss:
	visual_losses["obj"] = {
	"shape": (-1,),
	"num": config.num_object_labels,
	"loss": "visual_ce",
	}
	if config.visual_attr_loss:
	visual_losses["attr"] = {
	"shape": (-1,),
	"num": config.num_attr_labels,
	"loss": "visual_ce",
	}
	if config.visual_obj_loss:
	visual_losses["feat"] = {
	"shape": (-1, config.visual_feat_dim),
	"num": config.visual_feat_dim,
	"loss": "l2",
	}
	self.visual_losses = visual_losses

	def resize_num_qa_labels(self, num_labels):
	"""
	Build a resized question answering linear layer Module from a provided new linear layer. Increasing the size
	will add newly initialized weights. Reducing the size will remove weights from the end

	Args:
	num_labels (:obj:`int`, `optional`):
	New number of labels in the linear layer weight matrix. Increasing the size will add newly initialized
	weights at the end. Reducing the size will remove weights from the end. If not provided or :obj:`None`,
	just returns a pointer to the qa labels :obj:`torch.nn.Linear`` module of the model without doing
	anything.

	Return:
	:obj:`torch.nn.Linear`: Pointer to the resized Linear layer or the old Linear layer
	"""

	cur_qa_logit_layer = self.get_qa_logit_layer()
	if num_labels is None or cur_qa_logit_layer is None:
	return
	new_qa_logit_layer = self._resize_qa_labels(num_labels)
	self.config.num_qa_labels = num_labels
	self.num_qa_labels = num_labels

	return new_qa_logit_layer

	def _resize_qa_labels(self, num_labels):
	cur_qa_logit_layer = self.get_qa_logit_layer()
	new_qa_logit_layer = self._get_resized_qa_labels(cur_qa_logit_layer, num_labels)
	self._set_qa_logit_layer(new_qa_logit_layer)
	return self.get_qa_logit_layer()

	def get_qa_logit_layer(self) -> nn.Module:
	"""
	Returns the the linear layer that produces question answering logits.

	Returns:
	:obj:`nn.Module`: A torch module mapping the question answering prediction hidden states or :obj:`None` if
	LXMERT does not have a visual answering head.
	"""
	if hasattr(self, "answer_head"):
	return self.answer_head.logit_fc[-1]

	def _set_qa_logit_layer(self, qa_logit_layer):
	self.answer_head.logit_fc[-1] = qa_logit_layer

	def _get_resized_qa_labels(self, cur_qa_logit_layer, num_labels):

	if num_labels is None:
	return cur_qa_logit_layer

	cur_qa_labels, hidden_dim = cur_qa_logit_layer.weight.size()
	if cur_qa_labels == num_labels:
	return cur_qa_logit_layer

	# Build new linear output
	if getattr(cur_qa_logit_layer, "bias", None) is not None:
	new_qa_logit_layer = nn.Linear(hidden_dim, num_labels)
	else:
	new_qa_logit_layer = nn.Linear(hidden_dim, num_labels, bias=False)

	new_qa_logit_layer.to(cur_qa_logit_layer.weight.device)

	# initialize all new labels
	self._init_weights(new_qa_logit_layer)

	# Copy labels from the previous weights
	num_labels_to_copy = min(cur_qa_labels, num_labels)
	new_qa_logit_layer.weight.data[:num_labels_to_copy, :] = cur_qa_logit_layer.weight.data[:num_labels_to_copy, :]
	if getattr(cur_qa_logit_layer, "bias", None) is not None:
	new_qa_logit_layer.bias.data[:num_labels_to_copy] = cur_qa_logit_layer.bias.data[:num_labels_to_copy]

	return new_qa_logit_layer

	@add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@replace_return_docstrings(output_type=LxmertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
	def forward(
	self,
	input_ids=None,
	visual_feats=None,
	visual_pos=None,
	attention_mask=None,
	visual_attention_mask=None,
	token_type_ids=None,
	inputs_embeds=None,
	labels=None,
	obj_labels=None,
	matched_label=None,
	ans=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	**kwargs,
	):
	r"""
	labels (``torch.LongTensor`` of shape ``(batch_size, sequence_length)``, `optional`):
	Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
	config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
	(masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
	obj_labels: (``Dict[Str: Tuple[Torch.FloatTensor, Torch.FloatTensor]]``, `optional`):
	each key is named after each one of the visual losses and each element of the tuple is of the shape
	``(batch_size, num_features)`` and ``(batch_size, num_features, visual_feature_dim)`` for each the label id
	and the label score respectively
	matched_label (``torch.LongTensor`` of shape ``(batch_size,)``, `optional`):
	Labels for computing the whether or not the text input matches the image (classification) loss. Input
	should be a sequence pair (see :obj:`input_ids` docstring) Indices should be in ``[0, 1]``:

	- 0 indicates that the sentence does not match the image,
	- 1 indicates that the sentence does match the image.
	ans: (``Torch.Tensor`` of shape ``(batch_size)``, `optional`):
	a one hot representation hof the correct answer `optional`

	Returns:
	"""

	if "masked_lm_labels" in kwargs:
	warnings.warn(
	"The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.",
	FutureWarning,
	)
	labels = kwargs.pop("masked_lm_labels")

	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	device = input_ids.device if input_ids is not None else inputs_embeds.device
	lxmert_output = self.lxmert(
	input_ids=input_ids,
	visual_feats=visual_feats,
	visual_pos=visual_pos,
	token_type_ids=token_type_ids,
	attention_mask=attention_mask,
	visual_attention_mask=visual_attention_mask,
	inputs_embeds=inputs_embeds,
	output_hidden_states=output_hidden_states,
	output_attentions=output_attentions,
	return_dict=return_dict,
	)

	lang_output, visual_output, pooled_output = (
	lxmert_output[0],
	lxmert_output[1],
	lxmert_output[2],
	)
	lang_prediction_scores, cross_relationship_score = self.cls(lang_output, pooled_output)
	if self.task_qa:
	answer_score = self.answer_head(pooled_output)
	else:
	answer_score = pooled_output[0][0]

	total_loss = (
	None
	if (labels is None and matched_label is None and obj_labels is None and ans is None)
	else torch.tensor(0.0, device=device)
	)
	if labels is not None and self.task_mask_lm:
	masked_lm_loss = self.loss_fcts["ce"](
	lang_prediction_scores.view(-1, self.config.vocab_size),
	labels.view(-1),
	)
	total_loss += masked_lm_loss
	if matched_label is not None and self.task_matched:
	matched_loss = self.loss_fcts["ce"](cross_relationship_score.view(-1, 2), matched_label.view(-1))
	total_loss += matched_loss
	if obj_labels is not None and self.task_obj_predict:
	total_visual_loss = torch.tensor(0.0, device=input_ids.device)
	visual_prediction_scores_dict = self.obj_predict_head(visual_output)
	for key, key_info in self.visual_losses.items():
	label, mask_conf = obj_labels[key]
	output_dim = key_info["num"]
	loss_fct_name = key_info["loss"]
	label_shape = key_info["shape"]
	weight = self.visual_loss_normalizer
	visual_loss_fct = self.loss_fcts[loss_fct_name]
	visual_prediction_scores = visual_prediction_scores_dict[key]
	visual_loss = visual_loss_fct(
	visual_prediction_scores.view(-1, output_dim),
	label.view(*label_shape),
	)
	if visual_loss.dim() > 1: # Regression Losses
	visual_loss = visual_loss.mean(1)
	visual_loss = (visual_loss * mask_conf.view(-1)).mean() * weight
	total_visual_loss += visual_loss
	total_loss += total_visual_loss
	if ans is not None and self.task_qa:
	answer_loss = self.loss_fcts["ce"](answer_score.view(-1, self.num_qa_labels), ans.view(-1))
	total_loss += answer_loss

	if not return_dict:
	output = (
	lang_prediction_scores,
	cross_relationship_score,
	answer_score,
	) + lxmert_output[3:]
	return ((total_loss,) + output) if total_loss is not None else output

	return LxmertForPreTrainingOutput(
	loss=total_loss,
	prediction_logits=lang_prediction_scores,
	cross_relationship_score=cross_relationship_score,
	question_answering_score=answer_score,
	language_hidden_states=lxmert_output.language_hidden_states,
	vision_hidden_states=lxmert_output.vision_hidden_states,
	language_attentions=lxmert_output.language_attentions,
	vision_attentions=lxmert_output.vision_attentions,
	cross_encoder_attentions=lxmert_output.cross_encoder_attentions,
	)


	@add_start_docstrings(
	"""Lxmert Model with a visual-answering head on top for downstream QA tasks""",
	LXMERT_START_DOCSTRING,
	)
	class LxmertForQuestionAnswering(LxmertPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	# Configuration
	self.config = config
	self.num_qa_labels = config.num_qa_labels
	self.visual_loss_normalizer = config.visual_loss_normalizer

	# Lxmert backbone
	self.lxmert = LxmertModel(config)

	self.answer_head = LxmertVisualAnswerHead(config, self.num_qa_labels)

	# Weight initialization
	self.init_weights()

	# Loss function
	self.loss = CrossEntropyLoss()

	def resize_num_qa_labels(self, num_labels):
	"""
	Build a resized question answering linear layer Module from a provided new linear layer. Increasing the size
	will add newly initialized weights. Reducing the size will remove weights from the end

	Args:
	num_labels (:obj:`int`, `optional`):
	New number of labels in the linear layer weight matrix. Increasing the size will add newly initialized
	weights at the end. Reducing the size will remove weights from the end. If not provided or :obj:`None`,
	just returns a pointer to the qa labels :obj:`torch.nn.Linear`` module of the model without doing
	anything.

	Return:
	:obj:`torch.nn.Linear`: Pointer to the resized Linear layer or the old Linear layer
	"""

	cur_qa_logit_layer = self.get_qa_logit_layer()
	if num_labels is None or cur_qa_logit_layer is None:
	return
	new_qa_logit_layer = self._resize_qa_labels(num_labels)
	self.config.num_qa_labels = num_labels
	self.num_qa_labels = num_labels

	return new_qa_logit_layer

	def _resize_qa_labels(self, num_labels):
	cur_qa_logit_layer = self.get_qa_logit_layer()
	new_qa_logit_layer = self._get_resized_qa_labels(cur_qa_logit_layer, num_labels)
	self._set_qa_logit_layer(new_qa_logit_layer)
	return self.get_qa_logit_layer()

	def get_qa_logit_layer(self) -> nn.Module:
	"""
	Returns the the linear layer that produces question answering logits

	Returns:
	:obj:`nn.Module`: A torch module mapping the question answering prediction hidden states. :obj:`None`: A
	NoneType object if Lxmert does not have the visual answering head.
	"""

	if hasattr(self, "answer_head"):
	return self.answer_head.logit_fc[-1]

	def _set_qa_logit_layer(self, qa_logit_layer):
	self.answer_head.logit_fc[-1] = qa_logit_layer

	def _get_resized_qa_labels(self, cur_qa_logit_layer, num_labels):

	if num_labels is None:
	return cur_qa_logit_layer

	cur_qa_labels, hidden_dim = cur_qa_logit_layer.weight.size()
	if cur_qa_labels == num_labels:
	return cur_qa_logit_layer

	# Build new linear output
	if getattr(cur_qa_logit_layer, "bias", None) is not None:
	new_qa_logit_layer = nn.Linear(hidden_dim, num_labels)
	else:
	new_qa_logit_layer = nn.Linear(hidden_dim, num_labels, bias=False)

	new_qa_logit_layer.to(cur_qa_logit_layer.weight.device)

	# initialize all new labels
	self._init_weights(new_qa_logit_layer)

	# Copy labels from the previous weights
	num_labels_to_copy = min(cur_qa_labels, num_labels)
	new_qa_logit_layer.weight.data[:num_labels_to_copy, :] = cur_qa_logit_layer.weight.data[:num_labels_to_copy, :]
	if getattr(cur_qa_logit_layer, "bias", None) is not None:
	new_qa_logit_layer.bias.data[:num_labels_to_copy] = cur_qa_logit_layer.bias.data[:num_labels_to_copy]

	return new_qa_logit_layer

	@add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
	@add_code_sample_docstrings(
	tokenizer_class=_TOKENIZER_FOR_DOC,
	checkpoint=_CHECKPOINT_FOR_DOC,
	output_type=LxmertForQuestionAnsweringOutput,
	config_class=_CONFIG_FOR_DOC,
	)
	def forward(
	self,
	input_ids=None,
	visual_feats=None,
	visual_pos=None,
	attention_mask=None,
	visual_attention_mask=None,
	token_type_ids=None,
	inputs_embeds=None,
	labels=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	r"""
	labels: (``Torch.Tensor`` of shape ``(batch_size)``, `optional`):
	A one-hot representation of the correct answer

	Returns:
	"""
	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	lxmert_output = self.lxmert(
	input_ids=input_ids,
	visual_feats=visual_feats,
	visual_pos=visual_pos,
	token_type_ids=token_type_ids,
	attention_mask=attention_mask,
	visual_attention_mask=visual_attention_mask,
	inputs_embeds=inputs_embeds,
	output_hidden_states=output_hidden_states,
	output_attentions=output_attentions,
	return_dict=return_dict,
	)

	pooled_output = lxmert_output[2]
	answer_score = self.answer_head(pooled_output)
	loss = None
	if labels is not None:
	loss = self.loss(answer_score.view(-1, self.num_qa_labels), labels.view(-1))

	if not return_dict:
	output = (answer_score,) + lxmert_output[3:]
	return (loss,) + output if loss is not None else output

	return LxmertForQuestionAnsweringOutput(
	loss=loss,
	question_answering_score=answer_score,
	language_hidden_states=lxmert_output.language_hidden_states,
	vision_hidden_states=lxmert_output.vision_hidden_states,
	language_attentions=lxmert_output.language_attentions,
	vision_attentions=lxmert_output.vision_attentions,
	cross_encoder_attentions=lxmert_output.cross_encoder_attentions,
	)

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