"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss, KLDivLoss, LogSoftmax
from ...activations import ACT2FN
from ...modeling_outputs import (BaseModelOutput, BaseModelOutputWithPooling, MultipleChoiceModelOutput, SequenceClassifierOutput)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings)
from .configuration_visual_bert import VisualBertConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "VisualBertConfig"
_CHECKPOINT_FOR_DOC = "uclanlp/visualbert-vqa-coco-pre"
class VisualBertEmbeddings(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False)
        self.visual_token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
        self.visual_position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
        if config.special_visual_initialize:
            self.visual_token_type_embeddings.weight.data = nn.Parameter(self.token_type_embeddings.weight.data.clone(), requires_grad=True)
            self.visual_position_embeddings.weight.data = nn.Parameter(self.position_embeddings.weight.data.clone(), requires_grad=True)
        self.visual_projection = nn.Linear(config.visual_embedding_dim, config.hidden_size)
    def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, visual_embeds=None, visual_token_type_ids=None, image_text_alignment=None):
        if input_ids is not None: input_shape = input_ids.size()
        else: input_shape = inputs_embeds.size()[:-1]
        seq_length = input_shape[1]
        if position_ids is None: position_ids = self.position_ids[:, :seq_length]
        if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids)
        if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)
        embeddings = inputs_embeds + token_type_embeddings
        position_embeddings = self.position_embeddings(position_ids)
        embeddings += position_embeddings
        if visual_embeds is not None:
            if visual_token_type_ids is None: visual_token_type_ids = torch.ones(visual_embeds.size()[:-1], dtype=torch.long, device=self.position_ids.device)
            visual_embeds = self.visual_projection(visual_embeds)
            visual_token_type_embeddings = self.visual_token_type_embeddings(visual_token_type_ids)
            if image_text_alignment is not None:
                dtype = token_type_embeddings.dtype
                image_text_alignment_mask = (image_text_alignment != -1).long()
                image_text_alignment = image_text_alignment_mask * image_text_alignment
                visual_position_embeddings = self.position_embeddings(image_text_alignment)
                visual_position_embeddings *= image_text_alignment_mask.to(dtype=dtype).unsqueeze(-1)
                visual_position_embeddings = visual_position_embeddings.sum(2)
                image_text_alignment_mask = image_text_alignment_mask.to(dtype=dtype).sum(2)
                if (image_text_alignment_mask == 0).sum() != 0:
                    image_text_alignment_mask[image_text_alignment_mask == 0] = 1
                    logger.warning("Found 0 values in `image_text_alignment_mask`. Setting them to 1 to avoid divide-by-zero error.")
                visual_position_embeddings = visual_position_embeddings / image_text_alignment_mask.unsqueeze(-1)
                visual_position_ids = torch.zeros(*visual_embeds.size()[:-1], dtype=torch.long, device=visual_embeds.device)
                if visual_position_embeddings.size(1) != visual_embeds.size(1):
                    if visual_position_embeddings.size(1) < visual_embeds.size(1): raise ValueError(f"Visual position embeddings length: {visual_position_embeddings.size(1)} should be the same as `visual_embeds` length: {visual_embeds.size(1)}")
                    visual_position_embeddings = visual_position_embeddings[:, : visual_embeds.size(1), :]
                visual_position_embeddings = visual_position_embeddings + self.visual_position_embeddings(visual_position_ids)
            else:
                visual_position_ids = torch.zeros(*visual_embeds.size()[:-1], dtype=torch.long, device=visual_embeds.device)
                visual_position_embeddings = self.visual_position_embeddings(visual_position_ids)
            visual_embeddings = visual_embeds + visual_position_embeddings + visual_token_type_embeddings
            embeddings = torch.cat((embeddings, visual_embeddings), dim=1)
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings
class VisualBertSelfAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError(f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention heads ({config.num_attention_heads})")
        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.query = nn.Linear(config.hidden_size, self.all_head_size)
        self.key = nn.Linear(config.hidden_size, self.all_head_size)
        self.value = nn.Linear(config.hidden_size, self.all_head_size)
        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)
    def forward(self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False):
        mixed_query_layer = self.query(hidden_states)
        key_layer = self.transpose_for_scores(self.key(hidden_states))
        value_layer = self.transpose_for_scores(self.value(hidden_states))
        query_layer = self.transpose_for_scores(mixed_query_layer)
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        if attention_mask is not None: attention_scores = attention_scores + attention_mask
        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
        attention_probs = self.dropout(attention_probs)
        if head_mask is not None: attention_probs = attention_probs * head_mask
        context_layer = torch.matmul(attention_probs, value_layer)
        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(*new_context_layer_shape)
        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
        return outputs
class VisualBertSelfOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.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 VisualBertAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.self = VisualBertSelfAttention(config)
        self.output = VisualBertSelfOutput(config)
        self.pruned_heads = set()
    def prune_heads(self, heads):
        if len(heads) == 0: return
        heads, index = find_pruneable_heads_and_indices(heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads)
        self.self.query = prune_linear_layer(self.self.query, index)
        self.self.key = prune_linear_layer(self.self.key, index)
        self.self.value = prune_linear_layer(self.self.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)
    def forward(self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False):
        self_outputs = self.self(hidden_states, attention_mask, head_mask, output_attentions)
        attention_output = self.output(self_outputs[0], hidden_states)
        outputs = (attention_output,) + self_outputs[1:]
        return outputs
class VisualBertIntermediate(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
        if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else: self.intermediate_act_fn = config.hidden_act
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states
class VisualBertOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.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 VisualBertLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = VisualBertAttention(config)
        self.intermediate = VisualBertIntermediate(config)
        self.output = VisualBertOutput(config)
    def forward(self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False):
        self_attention_outputs = self.attention(hidden_states, attention_mask, head_mask, output_attentions=output_attentions)
        attention_output = self_attention_outputs[0]
        outputs = self_attention_outputs[1:]
        layer_output = apply_chunking_to_forward(self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output)
        outputs = (layer_output,) + outputs
        return outputs
    def feed_forward_chunk(self, attention_output):
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output)
        return layer_output
class VisualBertEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([VisualBertLayer(config) for _ in range(config.num_hidden_layers)])
        self.gradient_checkpointing = False
    def forward(self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False, output_hidden_states=False, return_dict=True):
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        for i, layer_module in enumerate(self.layer):
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
            layer_head_mask = head_mask[i] if head_mask is not None else None
            if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func(layer_module.__call__, hidden_states, attention_mask, layer_head_mask, output_attentions)
            else: layer_outputs = layer_module(hidden_states, attention_mask, layer_head_mask, output_attentions)
            hidden_states = layer_outputs[0]
            if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],)
        if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
        if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
        return BaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions)
class VisualBertPooler(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.activation = nn.Tanh()
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(first_token_tensor)
        pooled_output = self.activation(pooled_output)
        return pooled_output
class VisualBertPredictionHeadTransform(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act]
        else: self.transform_act_fn = config.hidden_act
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.transform_act_fn(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)
        return hidden_states
class VisualBertLMPredictionHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.transform = VisualBertPredictionHeadTransform(config)
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
        self.decoder.bias = self.bias
    def _tie_weights(self): self.decoder.bias = self.bias
    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
class VisualBertPreTrainingHeads(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.predictions = VisualBertLMPredictionHead(config)
        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 VisualBertPreTrainedModel(PreTrainedModel):
    config_class = VisualBertConfig
    base_model_prefix = "visual_bert"
    supports_gradient_checkpointing = True
    def _init_weights(self, module):
        if isinstance(module, (nn.Linear, nn.Embedding)): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        if isinstance(module, nn.Linear) and module.bias is not None: module.bias.data.zero_()
@dataclass
class VisualBertForPreTrainingOutput(ModelOutput):
    """Args:"""
    loss: Optional[torch.FloatTensor] = None
    prediction_logits: torch.FloatTensor = None
    seq_relationship_logits: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
VISUAL_BERT_START_DOCSTRING = r"""
    This model inherits from [`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 ([`VisualBertConfig`]): 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.
"""
VISUAL_BERT_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(0)`):
            Indices of input sequence tokens in the vocabulary.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.FloatTensor` of shape `(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#attention-mask)
        token_type_ids (`torch.LongTensor` of shape `(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#token-type-ids)
        position_ids (`torch.LongTensor` of shape `(0)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.
            [What are position IDs?](../glossary#position-ids)
        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        inputs_embeds (`torch.FloatTensor` of shape `(0, 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.
        visual_embeds (`torch.FloatTensor` of shape `(batch_size, visual_seq_length, visual_embedding_dim)`, *optional*):
            The embedded representation of the visual inputs, generally derived using using an object detector.
        visual_attention_mask (`torch.FloatTensor` of shape `(batch_size, visual_seq_length)`, *optional*):
            Mask to avoid performing attention on visual embeddings. 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_token_type_ids (`torch.LongTensor` of shape `(batch_size, visual_seq_length)`, *optional*):
            Segment token indices to indicate different portions of the visual embeds.
            [What are token type IDs?](../glossary#token-type-ids) The authors of VisualBERT set the
            *visual_token_type_ids* to *1* for all tokens.
        image_text_alignment (`torch.LongTensor` of shape `(batch_size, visual_seq_length, alignment_number)`, *optional*):
            Image-Text alignment uses to decide the position IDs of the visual embeddings.
        output_attentions (`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 (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings("The bare VisualBert Model transformer outputting raw hidden-states without any specific head on top.", VISUAL_BERT_START_DOCSTRING)
class VisualBertModel(VisualBertPreTrainedModel):
    def __init__(self, config, add_pooling_layer=True):
        super().__init__(config)
        self.config = config
        self.embeddings = VisualBertEmbeddings(config)
        self.encoder = VisualBertEncoder(config)
        self.pooler = VisualBertPooler(config) if add_pooling_layer else None
        self.bypass_transformer = config.bypass_transformer
        if self.bypass_transformer: self.additional_layer = VisualBertLayer(config)
        self.post_init()
    def get_input_embeddings(self): return self.embeddings.word_embeddings
    def set_input_embeddings(self, value): self.embeddings.word_embeddings = value
    def _prune_heads(self, heads_to_prune):
        for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads)
    @add_start_docstrings_to_model_forward(VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, visual_embeds: Optional[torch.FloatTensor] = None,
    visual_attention_mask: Optional[torch.LongTensor] = None, visual_token_type_ids: Optional[torch.LongTensor] = None, image_text_alignment: Optional[torch.LongTensor] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPooling]:
        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:
            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
            input_shape = input_ids.size()
        elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1]
        else: raise ValueError("You have to specify either input_ids or inputs_embeds")
        batch_size, seq_length = input_shape
        device = input_ids.device if input_ids is not None else inputs_embeds.device
        if visual_embeds is not None: visual_input_shape = visual_embeds.size()[:-1]
        if attention_mask is None: attention_mask = torch.ones(input_shape, device=device)
        if visual_embeds is not None and visual_attention_mask is None: visual_attention_mask = torch.ones(visual_input_shape, device=device)
        if visual_embeds is not None:
            combined_attention_mask = torch.cat((attention_mask, visual_attention_mask), dim=-1)
            extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(combined_attention_mask, (batch_size, input_shape + visual_input_shape))
        else: extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, (batch_size, input_shape))
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
        embedding_output = self.embeddings(input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds,
        visual_embeds=visual_embeds, visual_token_type_ids=visual_token_type_ids, image_text_alignment=image_text_alignment)
        if self.bypass_transformer and visual_embeds is not None:
            text_length = input_ids.size(1)
            text_embedding_output = embedding_output[:, :text_length, :]
            visual_embedding_output = embedding_output[:, text_length:, :]
            text_extended_attention_mask = extended_attention_mask[:, :, text_length, :text_length]
            encoded_outputs = self.encoder(text_embedding_output, attention_mask=text_extended_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
            sequence_output = encoded_outputs[0]
            concatenated_input = torch.cat((sequence_output, visual_embedding_output), dim=1)
            sequence_output = self.additional_layer(concatenated_input, extended_attention_mask)
            pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
        else:
            encoder_outputs = self.encoder(embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
            sequence_output = encoder_outputs[0]
            pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
        if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:]
        return BaseModelOutputWithPooling(last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions)
@add_start_docstrings("VisualBert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `sentence-image prediction (classification)` head.", VISUAL_BERT_START_DOCSTRING)
class VisualBertForPreTraining(VisualBertPreTrainedModel):
    _tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
    def __init__(self, config):
        super().__init__(config)
        self.visual_bert = VisualBertModel(config)
        self.cls = VisualBertPreTrainingHeads(config)
        self.post_init()
    def get_output_embeddings(self): return self.cls.predictions.decoder
    def set_output_embeddings(self, new_embeddings):
        self.cls.predictions.decoder = new_embeddings
        self.cls.predictions.bias = new_embeddings.bias
    @add_start_docstrings_to_model_forward(VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, visual_embeds: Optional[torch.FloatTensor] = None,
    visual_attention_mask: Optional[torch.LongTensor] = None, visual_token_type_ids: Optional[torch.LongTensor] = None, image_text_alignment: Optional[torch.LongTensor] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[torch.LongTensor] = None,
    sentence_image_labels: Optional[torch.LongTensor] = None) -> Union[Tuple[torch.Tensor], VisualBertForPreTrainingOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if labels is not None:
            total_size = attention_mask.size(-1) + visual_attention_mask.size(-1)
            if labels.size(-1) != total_size: raise ValueError(f"The labels provided should have same sequence length as total attention mask. Found labels with sequence length {labels.size(-1)}, expected {total_size}.")
        outputs = self.visual_bert(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds,
        visual_embeds=visual_embeds, visual_attention_mask=visual_attention_mask, visual_token_type_ids=visual_token_type_ids, image_text_alignment=image_text_alignment,
        output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output, pooled_output = outputs[:2]
        prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
        total_loss = None
        if labels is not None and sentence_image_labels is not None:
            loss_fct = CrossEntropyLoss()
            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
            sentence_image_loss = loss_fct(seq_relationship_score.view(-1, 2), sentence_image_labels.view(-1))
            total_loss = masked_lm_loss + sentence_image_loss
        elif labels is not None:
            loss_fct = CrossEntropyLoss()
            total_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
        if not return_dict:
            output = (prediction_scores, seq_relationship_score) + outputs[2:]
            return ((total_loss,) + output) if total_loss is not None else output
        return VisualBertForPreTrainingOutput(loss=total_loss, prediction_logits=prediction_scores, seq_relationship_logits=seq_relationship_score, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("VisualBert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for VCR tasks.", VISUAL_BERT_START_DOCSTRING)
class VisualBertForMultipleChoice(VisualBertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.visual_bert = VisualBertModel(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.cls = nn.Linear(config.hidden_size, 1)
        self.post_init()
    @add_start_docstrings_to_model_forward(VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, visual_embeds: Optional[torch.FloatTensor] = None,
    visual_attention_mask: Optional[torch.LongTensor] = None, visual_token_type_ids: Optional[torch.LongTensor] = None, image_text_alignment: Optional[torch.LongTensor] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None,
    labels: Optional[torch.LongTensor] = None) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
        input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
        attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
        token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
        position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
        inputs_embeds = (inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None)
        visual_embeds = (visual_embeds.view(-1, visual_embeds.size(-2), visual_embeds.size(-1)) if visual_embeds is not None else None)
        visual_attention_mask = (visual_attention_mask.view(-1, visual_attention_mask.size(-1)) if visual_attention_mask is not None else None)
        visual_token_type_ids = (visual_token_type_ids.view(-1, visual_token_type_ids.size(-1)) if visual_token_type_ids is not None else None)
        outputs = self.visual_bert(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask,
        inputs_embeds=inputs_embeds, visual_embeds=visual_embeds, visual_attention_mask=visual_attention_mask, visual_token_type_ids=visual_token_type_ids,
        image_text_alignment=image_text_alignment, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        _, pooled_output = outputs[0], outputs[1]
        pooled_output = self.dropout(pooled_output)
        logits = self.cls(pooled_output)
        reshaped_logits = logits.view(-1, num_choices)
        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(reshaped_logits, labels)
        if not return_dict:
            output = (reshaped_logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return MultipleChoiceModelOutput(loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("VisualBert Model with a classification/regression head on top (a dropout and a linear layer on top of the pooled output) for VQA.", VISUAL_BERT_START_DOCSTRING)
class VisualBertForQuestionAnswering(VisualBertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.visual_bert = VisualBertModel(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.cls = nn.Linear(config.hidden_size, config.num_labels)
        self.post_init()
    @add_start_docstrings_to_model_forward(VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, visual_embeds: Optional[torch.FloatTensor] = None,
    visual_attention_mask: Optional[torch.LongTensor] = None, visual_token_type_ids: Optional[torch.LongTensor] = None, image_text_alignment: Optional[torch.LongTensor] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[torch.LongTensor] = None) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        index_to_gather = attention_mask.sum(1) - 2
        outputs = self.visual_bert(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds,
        visual_embeds=visual_embeds, visual_attention_mask=visual_attention_mask, visual_token_type_ids=visual_token_type_ids, image_text_alignment=image_text_alignment,
        output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = outputs[0]
        index_to_gather = (index_to_gather.unsqueeze(-1).unsqueeze(-1).expand(index_to_gather.size(0), 1, sequence_output.size(-1)))
        pooled_output = torch.gather(sequence_output, 1, index_to_gather)
        pooled_output = self.dropout(pooled_output)
        logits = self.cls(pooled_output)
        reshaped_logits = logits.view(-1, self.num_labels)
        loss = None
        if labels is not None:
            loss_fct = nn.KLDivLoss(reduction="batchmean")
            log_softmax = nn.LogSoftmax(dim=-1)
            reshaped_logits = log_softmax(reshaped_logits)
            loss = loss_fct(reshaped_logits, labels.contiguous())
        if not return_dict:
            output = (reshaped_logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return SequenceClassifierOutput(loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("VisualBert Model with a sequence classification head on top (a dropout and a linear layer on top of the pooled output) for Visual Reasoning e.g. for NLVR task.", VISUAL_BERT_START_DOCSTRING)
class VisualBertForVisualReasoning(VisualBertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.visual_bert = VisualBertModel(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.cls = nn.Linear(config.hidden_size, config.num_labels)  # 2
        self.post_init()
    @add_start_docstrings_to_model_forward(VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, visual_embeds: Optional[torch.FloatTensor] = None,
    visual_attention_mask: Optional[torch.LongTensor] = None, visual_token_type_ids: Optional[torch.LongTensor] = None, image_text_alignment: Optional[torch.LongTensor] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[torch.LongTensor] = None) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.visual_bert(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds,
        visual_embeds=visual_embeds, visual_attention_mask=visual_attention_mask, visual_token_type_ids=visual_token_type_ids, image_text_alignment=image_text_alignment,
        output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        pooled_output = outputs[1]
        pooled_output = self.dropout(pooled_output)
        logits = self.cls(pooled_output)
        reshaped_logits = logits.contiguous()
        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(reshaped_logits, labels.view(-1))
        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return SequenceClassifierOutput(loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
class VisualBertRegionToPhraseAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        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 heads ({config.num_attention_heads})")
        self.num_attention_heads = 1
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.query = nn.Linear(config.hidden_size, self.all_head_size)
        self.key = nn.Linear(config.hidden_size, self.all_head_size)
        self.value = nn.Linear(config.hidden_size, self.all_head_size)
        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
    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, query, key, attention_mask):
        attention_mask = attention_mask.to(query.dtype)
        attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
        attention_mask = (1.0 - attention_mask) * torch.finfo(query.dtype).min
        mixed_query_layer = self.query(query)
        mixed_key_layer = self.key(key)
        query_layer = self.transpose_for_scores(mixed_query_layer)
        key_layer = self.transpose_for_scores(mixed_key_layer)
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        attention_scores = attention_scores + attention_mask
        attention_scores = attention_scores.squeeze(1)
        return attention_scores
@add_start_docstrings("VisualBert Model with a Masked Language Modeling head and an attention layer on top for Region-to-Phrase Alignment e.g. for Flickr30 Entities task.", VISUAL_BERT_START_DOCSTRING)
class VisualBertForRegionToPhraseAlignment(VisualBertPreTrainedModel):
    _tied_weights_keys = ["cls.predictions.decoder.bias"]
    def __init__(self, config):
        super().__init__(config)
        self.visual_bert = VisualBertModel(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.cls = VisualBertPreTrainingHeads(config)
        self.attention = VisualBertRegionToPhraseAttention(config)
        self.post_init()
    @add_start_docstrings_to_model_forward(VISUAL_BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, visual_embeds: Optional[torch.FloatTensor] = None,
    visual_attention_mask: Optional[torch.LongTensor] = None, visual_token_type_ids: Optional[torch.LongTensor] = None, image_text_alignment: Optional[torch.LongTensor] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, region_to_phrase_position: Optional[torch.LongTensor] = None,
    labels: Optional[torch.LongTensor] = None) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
        if region_to_phrase_position is None: raise ValueError("`region_to_phrase_position` should not be None when using Flickr Model.")
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.visual_bert(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds,
        visual_embeds=visual_embeds, visual_attention_mask=visual_attention_mask, visual_token_type_ids=visual_token_type_ids, image_text_alignment=image_text_alignment,
        output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = outputs[0]
        region_to_phrase_position_mask = (region_to_phrase_position != -1).long()
        region_to_phrase_position = region_to_phrase_position * region_to_phrase_position_mask
        expanded_region_to_phrase_positions = region_to_phrase_position.unsqueeze(2).expand(region_to_phrase_position.size(0), region_to_phrase_position.size(1), sequence_output.size(2))
        selected_positions = sequence_output.gather(1, expanded_region_to_phrase_positions)
        visual_features = sequence_output[:, attention_mask.size(1) :]
        if visual_features.size(1) != visual_attention_mask.size(1): raise ValueError(f"Visual features length :{visual_features.size(1)} should be the same as visual attention mask length: {visual_attention_mask.size(1)}.")
        logits = self.attention(selected_positions, visual_features, visual_attention_mask)
        loss = None
        if labels is not None:
            loss_fct = KLDivLoss(reduction="batchmean")
            log_softmax = LogSoftmax(dim=-1)
            scores = log_softmax(logits)
            labels = labels.contiguous()
            loss = loss_fct(scores, labels)
        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return SequenceClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
