FM / lxmert

/usr/local/lib/python3.6/site-packages/transformers/models/lxmert/

/usr/local/lib/python3.6/site-packages/transformers/models/lxmert

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
configuration_lxmert.py	9226	0644	edit dl rm
convert_lxmert_original_tf_checkpoint_to_pytorch.py	2120	0644	edit dl rm
modeling_lxmert.py	64969	0644	edit dl rm
modeling_tf_lxmert.py	62768	0644	edit dl rm
tokenization_lxmert.py	1675	0644	edit dl rm
tokenization_lxmert_fast.py	2037	0644	edit dl rm
__init__.py	2766	0644	edit dl rm

Edit: /usr/local/lib/python3.6/site-packages/transformers/models/lxmert/modeling_tf_lxmert.py (62768B)

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors, The HuggingFace Inc. team, and the
# Lxmert Authors.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
# 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.
""" TF 2.0 LXMERT model."""

import warnings
from dataclasses import dataclass
from typing import Dict, Optional, Tuple

import tensorflow as tf

from ...activations_tf import get_tf_activation
from ...modeling_tf_utils import TFPreTrainedModel, get_initializer, keras_serializable, shape_list, unpack_inputs
from ...utils import (
    ModelOutput,
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
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"

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

@dataclass
class TFLxmertModelOutput(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 (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the language encoder.
        vision_output (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the visual encoder.
        pooled_output (`tf.Tensor` of shape `(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 (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for input features + one for the output of each cross-modality layer) of shape
            `(batch_size, sequence_length, hidden_size)`.
        vision_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for input features + one for the output of each cross-modality layer) of shape
            `(batch_size, sequence_length, hidden_size)`.
        language_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `tf.Tensor` (one for each layer) of shape `(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 (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `tf.Tensor` (one for each layer) of shape `(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 (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `tf.Tensor` (one for each layer) of shape `(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[tf.Tensor] = None
    vision_output: Optional[tf.Tensor] = None
    pooled_output: Optional[tf.Tensor] = None
    language_hidden_states: Optional[Tuple[tf.Tensor]] = None
    vision_hidden_states: Optional[Tuple[tf.Tensor]] = None
    language_attentions: Optional[Tuple[tf.Tensor]] = None
    vision_attentions: Optional[Tuple[tf.Tensor]] = None
    cross_encoder_attentions: Optional[Tuple[tf.Tensor]] = None

@dataclass
class TFLxmertForPreTrainingOutput(ModelOutput):
    """
    Output type of [`LxmertForPreTraining`].

Args:
        loss (*optional*, returned when `labels` is provided, `tf.Tensor` of shape `(1,)`):
            Total loss as the sum of the masked language modeling loss and the next sequence prediction
            (classification) loss.
        prediction_logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        cross_relationship_score: (`tf.Tensor` of shape `(batch_size, 2)`):
            Prediction scores of the textual matching objective (classification) head (scores of True/False
            continuation before SoftMax).
        question_answering_score: (`tf.Tensor` of shape `(batch_size, n_qa_answers)`):
            Prediction scores of question answering objective (classification).
        language_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for input features + one for the output of each cross-modality layer) of shape
            `(batch_size, sequence_length, hidden_size)`.
        vision_hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (one for input features + one for the output of each cross-modality layer) of shape
            `(batch_size, sequence_length, hidden_size)`.
        language_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `tf.Tensor` (one for each layer) of shape `(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 (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `tf.Tensor` (one for each layer) of shape `(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 (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `tf.Tensor` (one for each layer) of shape `(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[tf.Tensor] = None
    prediction_logits: Optional[tf.Tensor] = None
    cross_relationship_score: Optional[tf.Tensor] = None
    question_answering_score: Optional[tf.Tensor] = None
    language_hidden_states: Optional[Tuple[tf.Tensor]] = None
    vision_hidden_states: Optional[Tuple[tf.Tensor]] = None
    language_attentions: Optional[Tuple[tf.Tensor]] = None
    vision_attentions: Optional[Tuple[tf.Tensor]] = None
    cross_encoder_attentions: Optional[Tuple[tf.Tensor]] = None

class TFLxmertVisualFeatureEncoder(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)

# Object feature encoding
        self.visn_fc = tf.keras.layers.Dense(
            config.hidden_size,
            kernel_initializer=get_initializer(config.initializer_range),
            name="visn_fc",
        )
        self.visn_layer_norm = tf.keras.layers.LayerNormalization(
            epsilon=config.layer_norm_eps, name="visn_layer_norm"
        )

# Box position encoding
        self.box_fc = tf.keras.layers.Dense(
            config.hidden_size,
            kernel_initializer=get_initializer(config.initializer_range),
            name="box_fc",
        )
        self.box_layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="box_layer_norm")

self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)

def call(self, visn_input, training=False):
        feats, boxes = visn_input

x = self.visn_fc(feats)
        x = self.visn_layer_norm(x)
        y = self.box_fc(boxes)
        y = self.box_layer_norm(y)
        output = (x + y) / 2

output = self.dropout(output, training=training)
        return output

class TFLxmertEmbeddings(tf.keras.layers.Layer):
    """Construct the embeddings from word, position and token_type embeddings."""

def __init__(self, config, **kwargs):
        super().__init__(**kwargs)

self.vocab_size = config.vocab_size
        self.type_vocab_size = config.type_vocab_size
        self.hidden_size = config.hidden_size
        self.max_position_embeddings = config.max_position_embeddings
        self.initializer_range = config.initializer_range
        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)

def build(self, input_shape):
        with tf.name_scope("word_embeddings"):
            self.weight = self.add_weight(
                name="weight",
                shape=[self.vocab_size, self.hidden_size],
                initializer=get_initializer(initializer_range=self.initializer_range),
            )

with tf.name_scope("token_type_embeddings"):
            self.token_type_embeddings = self.add_weight(
                name="embeddings",
                shape=[self.type_vocab_size, self.hidden_size],
                initializer=get_initializer(initializer_range=self.initializer_range),
            )

with tf.name_scope("position_embeddings"):
            self.position_embeddings = self.add_weight(
                name="embeddings",
                shape=[self.max_position_embeddings, self.hidden_size],
                initializer=get_initializer(initializer_range=self.initializer_range),
            )

super().build(input_shape)

def call(self, input_ids=None, token_type_ids=None, inputs_embeds=None, training=False):
        """
        Applies embedding based on inputs tensor.

Returns:
            final_embeddings (`tf.Tensor`): output embedding tensor.
        """
        assert not (input_ids is None and inputs_embeds is None)

if input_ids is not None:
            inputs_embeds = tf.gather(params=self.weight, indices=input_ids)

input_shape = shape_list(inputs_embeds)[:-1]

if token_type_ids is None:
            token_type_ids = tf.fill(dims=input_shape, value=0)

position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0)
        position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
        token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
        final_embeddings = inputs_embeds + position_embeds + token_type_embeds
        final_embeddings = self.LayerNorm(inputs=final_embeddings)
        final_embeddings = self.dropout(inputs=final_embeddings, training=training)

return final_embeddings

class TFLxmertAttention(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        if config.hidden_size % config.num_attention_heads != 0:
            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
        assert config.hidden_size % config.num_attention_heads == 0
        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 = tf.keras.layers.Dense(
            self.all_head_size,
            kernel_initializer=get_initializer(config.initializer_range),
            name="query",
        )
        self.key = tf.keras.layers.Dense(
            self.all_head_size,
            kernel_initializer=get_initializer(config.initializer_range),
            name="key",
        )
        self.value = tf.keras.layers.Dense(
            self.all_head_size,
            kernel_initializer=get_initializer(config.initializer_range),
            name="value",
        )

self.dropout = tf.keras.layers.Dropout(config.attention_probs_dropout_prob)

def transpose_for_scores(self, x, batch_size):
        # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
        x = tf.reshape(x, (batch_size, -1, self.num_attention_heads, self.attention_head_size))
        return tf.transpose(x, perm=[0, 2, 1, 3])

def call(self, hidden_states, context, attention_mask, output_attentions, training=False):
        batch_size = shape_list(hidden_states)[0]
        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, batch_size)
        key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
        value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)

# Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = tf.matmul(
            query_layer, key_layer, transpose_b=True
        )  # (batch size, num_heads, seq_len_q, seq_len_k)
        dk = tf.cast(shape_list(key_layer)[-1], dtype=attention_scores.dtype)  # scale attention_scores
        attention_scores = attention_scores / tf.math.sqrt(dk)

if attention_mask is not None:
            # Apply the attention mask is (precomputed for all layers in TFLxmertModel call() function)
            attention_mask = tf.cast(attention_mask, dtype=attention_scores.dtype)
            attention_scores = attention_scores + attention_mask

# Normalize the attention scores to probabilities.
        attention_probs = tf.nn.softmax(attention_scores, axis=-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, training=training)
        context_layer = tf.matmul(attention_probs, value_layer)

context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3])
        context_layer = tf.reshape(
            context_layer, (batch_size, -1, self.all_head_size)
        )  # (batch_size, seq_len_q, all_head_size)

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

class TFLxmertIntermediate(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.dense = tf.keras.layers.Dense(
            config.intermediate_size,
            kernel_initializer=get_initializer(config.initializer_range),
            name="dense",
        )
        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = get_tf_activation(config.hidden_act)
        else:
            self.intermediate_act_fn = config.hidden_act

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

class TFLxmertOutput(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.dense = tf.keras.layers.Dense(
            config.hidden_size,
            kernel_initializer=get_initializer(config.initializer_range),
            name="dense",
        )

self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)

def call(self, hidden_states, input_tensor, training=False):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states, training)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states

class TFLxmertAttentionOutput(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.dense = tf.keras.layers.Dense(
            config.hidden_size,
            kernel_initializer=get_initializer(config.initializer_range),
            name="dense",
        )
        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)

def call(self, hidden_states, input_tensor, training=False):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states, training=training)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states

class TFLxmertSelfAttentionLayer(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.self = TFLxmertAttention(config, name="self")
        self.attention_output = TFLxmertAttentionOutput(config, name="output")

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

class TFLxmertCrossAttentionLayer(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.att = TFLxmertAttention(config, name="att")
        self.attention_output = TFLxmertAttentionOutput(config, name="output")

def call(
        self,
        input_tensor,
        ctx_tensor,
        ctx_att_mask,
        output_attentions=False,
        training=False,
    ):
        output = self.att(input_tensor, ctx_tensor, ctx_att_mask, output_attentions, training=training)
        if output_attentions:
            attention_probs = output[1]
        attention_output = self.attention_output(output[0], input_tensor, training=training)
        outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
        return outputs

class TFLxmertLayer(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.attention = TFLxmertSelfAttentionLayer(config, name="attention")
        self.intermediate = TFLxmertIntermediate(config, name="intermediate")
        self.transformer_output = TFLxmertOutput(config, name="output")

def call(self, hidden_states, attention_mask, output_attentions, training=False):
        attention_outputs = self.attention(hidden_states, attention_mask, output_attentions, training=training)
        attention_output = attention_outputs[0]
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.transformer_output(intermediate_output, attention_output, training=training)
        outputs = (layer_output,) + attention_outputs[1:]  # add attentions if we output them
        return outputs

class TFLxmertXLayer(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.visual_attention = TFLxmertCrossAttentionLayer(config, name="visual_attention")

# Self-attention Layers
        self.lang_self_att = TFLxmertSelfAttentionLayer(config, name="lang_self_att")
        self.visn_self_att = TFLxmertSelfAttentionLayer(config, name="visn_self_att")

# Intermediate and Output Layers (FFNs)
        self.lang_inter = TFLxmertIntermediate(config, name="lang_inter")
        self.lang_output = TFLxmertOutput(config, name="lang_output")
        self.visn_inter = TFLxmertIntermediate(config, name="visn_inter")
        self.visn_output = TFLxmertOutput(config, name="visn_output")

def cross_att(
        self,
        lang_input,
        lang_attention_mask,
        visn_input,
        visn_attention_mask,
        output_attentions,
        training=False,
    ):
        # Cross Attention

# Keras saving and loading model *does not work* with the same inputs for two layers.
        lang_attention_lang_input = tf.identity(lang_input)
        visn_attention_lang_input = tf.identity(lang_input)
        lang_attention_visn_input = tf.identity(visn_input)
        visn_attention_visn_input = tf.identity(visn_input)

lang_att_output = self.visual_attention(
            lang_attention_lang_input,
            lang_attention_visn_input,
            visn_attention_mask,
            output_attentions=output_attentions,
            training=training,
        )
        visn_att_output = self.visual_attention(
            visn_attention_visn_input,
            visn_attention_lang_input,
            lang_attention_mask,
            output_attentions=output_attentions,
            training=training,
        )
        return lang_att_output, visn_att_output

def self_att(
        self,
        lang_input,
        lang_attention_mask,
        visn_input,
        visn_attention_mask,
        training=False,
    ):
        # Self Attention
        output_attentions = False
        lang_att_output = self.lang_self_att(lang_input, lang_attention_mask, output_attentions, training=training)
        visn_att_output = self.visn_self_att(visn_input, visn_attention_mask, output_attentions, training=training)
        return lang_att_output[0], visn_att_output[0]

def output_fc(self, lang_input, visn_input, training=False):
        # FC layers
        lang_inter_output = self.lang_inter(lang_input)
        visn_inter_output = self.visn_inter(visn_input)

# Layer output
        lang_output = self.lang_output(lang_inter_output, lang_input, training)
        visn_output = self.visn_output(visn_inter_output, visn_input, training)
        return lang_output, visn_output

def call(
        self,
        lang_feats,
        lang_attention_mask,
        visn_feats,
        visn_attention_mask,
        output_attentions,
        training=False,
    ):
        lang_att_output = lang_feats
        visn_att_output = visn_feats

lang_att_output, visn_att_output = self.cross_att(
            lang_att_output,
            lang_attention_mask,
            visn_att_output,
            visn_attention_mask,
            output_attentions,
            training=training,
        )
        attention_probs = lang_att_output[1:]
        lang_att_output, visn_att_output = self.self_att(
            lang_att_output[0],
            lang_attention_mask,
            visn_att_output[0],
            visn_attention_mask,
            training=training,
        )
        lang_output, visn_output = self.output_fc(lang_att_output, visn_att_output, training=training)

return (lang_output, visn_output, attention_probs[0]) if output_attentions else (lang_output, visn_output)

class TFLxmertEncoder(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)

self.visn_fc = TFLxmertVisualFeatureEncoder(config, name="visn_fc")

# 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 = [TFLxmertLayer(config, name=f"layer_._{i}") for i in range(self.num_l_layers)]
        self.x_layers = [TFLxmertXLayer(config, name=f"x_layers_._{i}") for i in range(self.num_x_layers)]
        self.r_layers = [TFLxmertLayer(config, name=f"r_layers_._{i}") for i in range(self.num_r_layers)]
        self.config = config

def call(
        self,
        lang_feats=None,
        lang_attention_mask=None,
        visual_feats=None,
        visual_pos=None,
        visual_attention_mask=None,
        output_attentions=None,
        training=False,
    ):
        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], training=training)

# Run language layers
        for layer_module in self.layer:
            l_outputs = layer_module(lang_feats, lang_attention_mask, output_attentions, training=training)
            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,
                training=training,
            )
            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,
                training=training,
            )
            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,
        )

@keras_serializable
class TFLxmertMainLayer(tf.keras.layers.Layer):
    config_class = LxmertConfig

@property
    def dummy_inputs(self):
        """
        Dummy inputs to build the network.

Returns:
            tf.Tensor with dummy inputs
        """
        batch_size = 2
        num_visual_features = 10
        input_ids = tf.constant([[3, 5, 6], [2, 3, 4]])
        visual_feats = tf.random.uniform((batch_size, num_visual_features, self.config.visual_feat_dim))
        visual_pos = tf.random.uniform((batch_size, num_visual_features, 4))

return {
            "input_ids": input_ids,
            "visual_feats": visual_feats,
            "visual_pos": visual_pos,
        }

def __init__(self, config, **kwargs):
        super().__init__(**kwargs)

self.config = config
        self.num_l_layers = config.l_layers
        self.num_x_layers = config.x_layers
        self.num_r_layers = config.r_layers
        self.initializer_range = config.initializer_range
        self.output_attentions = config.output_attentions
        self.output_hidden_states = config.output_hidden_states
        self.return_dict = config.use_return_dict
        self.embeddings = TFLxmertEmbeddings(config, name="embeddings")
        self.encoder = TFLxmertEncoder(config, name="encoder")
        self.pooler = TFLxmertPooler(config, name="pooler")
        self.config = config

def get_input_embeddings(self):
        return self.embeddings

def set_input_embeddings(self, value):
        self.embeddings.weight = value
        self.embeddings.vocab_size = shape_list(value)[0]

def _prune_heads(self, heads_to_prune):
        raise NotImplementedError

@unpack_inputs
    def call(
        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,
        training=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 = shape_list(input_ids)
        elif inputs_embeds is not None:
            input_shape = shape_list(inputs_embeds)[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")
        if visual_pos is None or visual_feats is None:
            raise ValueError("visual_feats and visual_pos cannot be `None` in LXMERT's `call` method.")

if attention_mask is None:
            attention_mask = tf.fill(input_shape, 1)

if token_type_ids is None:
            token_type_ids = tf.fill(input_shape, 0)

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

# 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 = tf.reshape(attention_mask, (input_shape[0], 1, 1, input_shape[1]))

# 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 = tf.cast(extended_attention_mask, dtype=embedding_output.dtype)
        one_cst = tf.constant(1.0, dtype=embedding_output.dtype)
        ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype)
        extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst)

if visual_attention_mask is not None:
            extended_visual_attention_mask = tf.reshape(visual_attention_mask, (input_shape[0], 1, 1, input_shape[1]))
            extended_visual_attention_mask = tf.expand_dims(tf.expand_dims(visual_attention_mask, axis=1), axis=1)

extended_visual_attention_mask = tf.cast(extended_visual_attention_mask, dtype=embedding_output.dtype)
            extended_visual_attention_mask = tf.multiply(
                tf.subtract(one_cst, extended_visual_attention_mask), ten_thousand_cst
            )
        else:
            extended_visual_attention_mask = None

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

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

config_class = LxmertConfig
    base_model_prefix = "lxmert"

@property
    def dummy_inputs(self) -> Dict[str, tf.Tensor]:
        return getattr(self, self.base_model_prefix).dummy_inputs

@tf.function(
        input_signature=[
            {
                "input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
                "attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
                "visual_feats": tf.TensorSpec((None, None, None), tf.float32, name="visual_feats"),
                "visual_pos": tf.TensorSpec((None, None, None), tf.float32, name="visual_pos"),
                "visual_attention_mask": tf.TensorSpec((None, None), tf.int32, name="visual_attention_mask"),
                "token_type_ids": tf.TensorSpec((None, None), tf.int32, name="token_type_ids"),
            }
        ]
    )
    def serving(self, inputs):
        output = self.call(inputs)

return self.serving_output(output)

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, pre-trained 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 is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
    as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
    behavior.

<Tip>

TF 2.0 models accepts two formats as inputs:

- having all inputs as keyword arguments (like PyTorch models), or
    - having all inputs as a list, tuple or dict in the first positional arguments.

This second option is useful when using [`tf.keras.Model.fit`] method which currently requires having all the
    tensors in the first argument of the model call function: `model(inputs)`.

If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the
    first positional argument :

- a single Tensor with `input_ids` only and nothing else: `model(inputs_ids)`
    - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
    `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
    - a dictionary with one or several input Tensors associated to the input names given in the docstring:
    `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`

</Tip>

Parameters:
        config ([`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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

LXMERT_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

Indices can be obtained using [`LxmertTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
            [`PreTrainedTokenizer.encode`] for details.

[What are input IDs?](../glossary#input-ids)
        visual_feats: (`tf.Tensor` of shape `(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: (`tf.Tensor` of shape `(batch_size, num_visual_features, visual_feat_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 (`tf.Tensor` of shape `(batch_size, sequence_length)`, *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#attention-mask)
        visual_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            MMask 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#attention-mask)
        token_type_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *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#token-type-ids)
        inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
            config will be used instead.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
            used instead.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
            eager mode, in graph mode the value will always be set to True.
        training (`bool`, *optional*, defaults to `False`):
            Whether or not to use the model in training mode (some modules like dropout modules have different
            behaviors between training and evaluation).
"""

@add_start_docstrings(
    "The bare Lxmert Model transformer outputting raw hidden-states without any specific head on top.",
    LXMERT_START_DOCSTRING,
)
class TFLxmertModel(TFLxmertPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.lxmert = TFLxmertMainLayer(config, name="lxmert")

@unpack_inputs
    @add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        processor_class=_TOKENIZER_FOR_DOC,
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFLxmertModelOutput,
        config_class=_CONFIG_FOR_DOC,
    )
    def call(
        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,
        training=False,
    ):
        outputs = self.lxmert(
            input_ids,
            visual_feats,
            visual_pos,
            attention_mask,
            visual_attention_mask,
            token_type_ids,
            inputs_embeds,
            output_attentions,
            output_hidden_states,
            return_dict,
            training,
        )

return outputs

def serving_output(self, output):
        l_hs = tf.convert_to_tensor(output.language_hidden_states) if self.config.output_hidden_states else None
        v_hs = tf.convert_to_tensor(output.vision_hidden_states) if self.config.output_hidden_states else None
        l_attns = tf.convert_to_tensor(output.language_attentions) if self.config.output_attentions else None
        v_attns = tf.convert_to_tensor(output.vision_attentions) if self.config.output_attentions else None
        c_enc_attns = tf.convert_to_tensor(output.cross_encoder_attentions) if self.config.output_attentions else None

return TFLxmertModelOutput(
            pooled_output=output.pooled_output,
            language_output=output.language_output,
            vision_output=output.vision_output,
            language_hidden_states=l_hs,
            vision_hidden_states=v_hs,
            language_attentions=l_attns,
            vision_attentions=v_attns,
            cross_encoder_attentions=c_enc_attns,
        )

class TFLxmertPooler(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.dense = tf.keras.layers.Dense(
            config.hidden_size,
            kernel_initializer=get_initializer(config.initializer_range),
            activation="tanh",
            name="dense",
        )

def call(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)
        return pooled_output

# Copied from transformers.models.bert.modeling_tf_bert.TFBertPredictionHeadTransform with Bert->Lxmert
class TFLxmertPredictionHeadTransform(tf.keras.layers.Layer):
    def __init__(self, config: LxmertConfig, **kwargs):
        super().__init__(**kwargs)

self.dense = tf.keras.layers.Dense(
            units=config.hidden_size,
            kernel_initializer=get_initializer(config.initializer_range),
            name="dense",
        )

if isinstance(config.hidden_act, str):
            self.transform_act_fn = get_tf_activation(config.hidden_act)
        else:
            self.transform_act_fn = config.hidden_act

self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")

def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
        hidden_states = self.dense(inputs=hidden_states)
        hidden_states = self.transform_act_fn(hidden_states)
        hidden_states = self.LayerNorm(inputs=hidden_states)

return hidden_states

# Copied from transformers.models.bert.modeling_tf_bert.TFBertLMPredictionHead with Bert->Lxmert
class TFLxmertLMPredictionHead(tf.keras.layers.Layer):
    def __init__(self, config: LxmertConfig, input_embeddings: tf.keras.layers.Layer, **kwargs):
        super().__init__(**kwargs)

self.vocab_size = config.vocab_size
        self.hidden_size = config.hidden_size

self.transform = TFLxmertPredictionHeadTransform(config, name="transform")

# The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.input_embeddings = input_embeddings

def build(self, input_shape: tf.TensorShape):
        self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")

super().build(input_shape)

def get_output_embeddings(self) -> tf.keras.layers.Layer:
        return self.input_embeddings

def set_output_embeddings(self, value: tf.Variable):
        self.input_embeddings.weight = value
        self.input_embeddings.vocab_size = shape_list(value)[0]

def get_bias(self) -> Dict[str, tf.Variable]:
        return {"bias": self.bias}

def set_bias(self, value: tf.Variable):
        self.bias = value["bias"]
        self.vocab_size = shape_list(value["bias"])[0]

def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
        hidden_states = self.transform(hidden_states=hidden_states)
        seq_length = shape_list(hidden_states)[1]
        hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
        hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True)
        hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size])
        hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)

return hidden_states

# Copied from transformers.models.bert.modeling_tf_bert.TFBertMLMHead with Bert->Lxmert
class TFLxmertMLMHead(tf.keras.layers.Layer):
    def __init__(self, config: LxmertConfig, input_embeddings: tf.keras.layers.Layer, **kwargs):
        super().__init__(**kwargs)

self.predictions = TFLxmertLMPredictionHead(config, input_embeddings, name="predictions")

def call(self, sequence_output: tf.Tensor) -> tf.Tensor:
        prediction_scores = self.predictions(hidden_states=sequence_output)

return prediction_scores

class TFLxmertPreTrainingHeads(tf.keras.layers.Layer):
    def __init__(self, config, input_embeddings, **kwargs):
        super().__init__(**kwargs)
        self.predictions = TFLxmertLMPredictionHead(config, input_embeddings, name="predictions")

self.seq_relationship = tf.keras.layers.Dense(
            2,
            kernel_initializer=get_initializer(config.initializer_range),
            name="seq_relationship",
        )

def call(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 TFLxmertVisualAnswerHead(tf.keras.layers.Layer):
    def __init__(self, config, num_labels, **kwargs):
        super().__init__(**kwargs)
        hid_dim = config.hidden_size
        self.dense = tf.keras.layers.Dense(
            hid_dim * 2,
            kernel_initializer=get_initializer(config.initializer_range),
            name="logit_fc_._0",
        )
        self.activation = get_tf_activation("gelu")
        self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="logit_fc_._2")
        self.dense_1 = tf.keras.layers.Dense(
            num_labels,
            kernel_initializer=get_initializer(config.initializer_range),
            name="logit_fc_._3",
        )

def call(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.activation(hidden_states)
        hidden_states = self.layer_norm(hidden_states)
        hidden_states = self.dense_1(hidden_states)

return hidden_states

class TFLxmertVisualObjHead(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.transform = TFLxmertPredictionHeadTransform(config, name="transform")

# 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, 2048), "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 = {
            key: tf.keras.layers.Dense(
                self.visual_losses[key]["num"],
                kernel_initializer=get_initializer(config.initializer_range),
                name=f"decoder_dict.{key}",
            )
            for key in self.visual_losses
        }

def call(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

@add_start_docstrings("""Lxmert Model with a `language modeling` head on top.""", LXMERT_START_DOCSTRING)
class TFLxmertForPreTraining(TFLxmertPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

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 = TFLxmertMainLayer(config, name="lxmert")

# Pre-training heads
        self.cls = TFLxmertPreTrainingHeads(config, self.lxmert.embeddings, name="cls")
        if self.task_obj_predict:
            self.obj_predict_head = TFLxmertVisualObjHead(config, name="obj_predict_head")
        if self.task_qa:
            self.answer_head = TFLxmertVisualAnswerHead(config, self.num_qa_labels, name="answer_head")

# Loss functions
        self.loss_fcts = {
            "l2": tf.keras.losses.Huber(delta=1.0, name="huber_loss"),
            "visn_ce": tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
            "ce": tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
        }

visual_losses = {}
        if config.visual_obj_loss:
            visual_losses["obj"] = {
                "shape": (-1,),
                "num": config.num_object_labels,
                "loss": "visn_ce",
            }
        if config.visual_attr_loss:
            visual_losses["attr"] = {
                "shape": (-1,),
                "num": config.num_attr_labels,
                "loss": "visn_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

@property
    def dummy_inputs(self):
        """
        Dummy inputs to build the network.

if self.config.task_obj_predict:
            obj_labels = {}
        if self.config.visual_attr_loss and self.config.task_obj_predict:
            obj_labels["attr"] = (
                tf.ones([batch_size, num_visual_features]),
                tf.ones([batch_size, num_visual_features]),
            )
        if self.config.visual_feat_loss and self.config.task_obj_predict:
            obj_labels["feat"] = (
                tf.ones([batch_size, num_visual_features, self.config.visual_feat_dim]),
                tf.ones([batch_size, num_visual_features]),
            )
        if self.config.visual_obj_loss and self.config.task_obj_predict:
            obj_labels["obj"] = (
                tf.ones([batch_size, num_visual_features]),
                tf.ones([batch_size, num_visual_features]),
            )

return {
            **{
                "input_ids": input_ids,
                "visual_feats": visual_feats,
                "visual_pos": visual_pos,
            },
            **({"obj_labels": obj_labels} if self.config.task_obj_predict else {}),
        }

def get_lm_head(self):
        return self.cls.predictions

def get_prefix_bias_name(self):
        warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
        return self.name + "/" + self.cls.name + "/" + self.cls.predictions.name

@unpack_inputs
    @add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=TFLxmertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
    def call(
        self,
        input_ids=None,
        visual_feats=None,
        visual_pos=None,
        attention_mask=None,
        visual_attention_mask=None,
        token_type_ids=None,
        inputs_embeds=None,
        masked_lm_labels=None,
        obj_labels=None,
        matched_label=None,
        ans=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        training=False,
    ):
        r"""
        masked_lm_labels (`tf.Tensor` 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[tf.Tensor, tf.Tensor]]`, *optional*, defaults to `None`):
            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 (`tf.Tensor` 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 `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*, defaults to `None`):
            a one hot representation hof the correct answer *optional*

Returns:
        """

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

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 (masked_lm_labels is None and matched_label is None and obj_labels is None and ans is None)
            else tf.constant(0.0)
        )
        losses = ()
        if masked_lm_labels is not None and self.task_mask_lm:
            masked_lm_loss = self.loss_fcts["ce"](
                tf.reshape(masked_lm_labels, [-1]),
                tf.reshape(lang_prediction_scores, [-1, self.config.vocab_size]),
            )
            total_loss += masked_lm_loss
            losses += (masked_lm_loss,)
        if matched_label is not None and self.task_matched:
            matched_loss = self.loss_fcts["ce"](
                tf.reshape(matched_label, [-1]),
                tf.reshape(cross_relationship_score, [-1, 2]),
            )
            total_loss += matched_loss
            losses += (matched_loss,)
        if obj_labels is not None and self.task_obj_predict:
            total_visn_loss = 0.0
            visn_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
                visn_loss_fct = self.loss_fcts[loss_fct_name]
                visn_prediction_scores = visn_prediction_scores_dict[key]
                visn_loss = visn_loss_fct(
                    tf.reshape(label, label_shape),
                    tf.reshape(visn_prediction_scores, [-1, output_dim]),
                )

if visn_loss.ndim > 1:  # Regression Losses
                    visn_loss = tf.reduce_mean(visn_loss)
                visn_loss = tf.reduce_mean(visn_loss * tf.cast(tf.reshape(mask_conf, [-1]), visn_loss.dtype)) * weight
                total_visn_loss += visn_loss
                losses += (visn_loss,)
            total_loss += total_visn_loss
        if ans is not None and self.task_qa:
            answer_loss = self.loss_fcts["ce"](
                tf.reshape(ans, [-1]), tf.reshape(answer_score, [-1, self.num_qa_labels])
            )
            # exclude "*2" here to match the effect of QA losses.
            # Previous: (loss *0) for 6 epochs, (loss *2) for 6 epochs.   (Used 10 instead of 6 in EMNLP paper)
            # Now     : (loss *1) for 12 epochs
            #
            # * 2       # Multiply by 2 because > half of the data will not have label
            total_loss += answer_loss
            losses += (answer_loss,)
        # return total_loss, tf.stack(losses)[tf.new_axis, ...], answer_score.detach()

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 TFLxmertForPreTrainingOutput(
            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,
        )

return TFLxmertForPreTrainingOutput(
            prediction_logits=output.prediction_logits,
            cross_relationship_score=output.cross_relationship_score,
            question_answering_score=output.question_answering_score,
            language_hidden_states=l_hs,
            vision_hidden_states=v_hs,
            language_attentions=l_attns,
            vision_attentions=v_attns,
            cross_encoder_attentions=c_enc_attns,
        )

cmd: