def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        pixel_values: Optional[torch.FloatTensor] = None,
        pixel_mask: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        image_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[MaskedLMOutput, Tuple[torch.FloatTensor]]:
        r"""
        labels (*torch.LongTensor* of shape *(batch_size, sequence_length)*, *optional*):
            Labels for computing the masked language modeling loss. Indices should be in *[-100, 0, ...,
            config.vocab_size]* (see *input_ids* docstring) Tokens with indices set to *-100* are ignored (masked), the
            loss is only computed for the tokens with labels in *[0, ..., config.vocab_size]*

        Returns:

        Examples:

        ```python
        >>> from transformers import ViltProcessor, ViltForMaskedLM
        >>> import requests
        >>> from PIL import Image
        >>> import re
        >>> import torch

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)
        >>> text = "a bunch of [MASK] laying on a [MASK]."

        >>> processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-mlm")
        >>> model = ViltForMaskedLM.from_pretrained("dandelin/vilt-b32-mlm")

        >>> # prepare inputs
        >>> encoding = processor(image, text, return_tensors="pt")

        >>> # forward pass
        >>> outputs = model(**encoding)

        >>> tl = len(re.findall("\[MASK\]", text))
        >>> inferred_token = [text]

        >>> # gradually fill in the MASK tokens, one by one
        >>> with torch.no_grad():
        ...     for i in range(tl):
        ...         encoded = processor.tokenizer(inferred_token)
        ...         input_ids = torch.tensor(encoded.input_ids)
        ...         encoded = encoded["input_ids"][0][1:-1]
        ...         outputs = model(input_ids=input_ids, pixel_values=encoding.pixel_values)
        ...         mlm_logits = outputs.logits[0]  # shape (seq_len, vocab_size)
        ...         # only take into account text features (minus CLS and SEP token)
        ...         mlm_logits = mlm_logits[1 : input_ids.shape[1] - 1, :]
        ...         mlm_values, mlm_ids = mlm_logits.softmax(dim=-1).max(dim=-1)
        ...         # only take into account text
        ...         mlm_values[torch.tensor(encoded) != 103] = 0
        ...         select = mlm_values.argmax().item()
        ...         encoded[select] = mlm_ids[select].item()
        ...         inferred_token = [processor.decode(encoded)]

        >>> selected_token = ""
        >>> encoded = processor.tokenizer(inferred_token)
        >>> output = processor.decode(encoded.input_ids[0], skip_special_tokens=True)
        >>> print(output)
        a bunch of cats laying on a couch.
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.vilt(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            pixel_values=pixel_values,
            pixel_mask=pixel_mask,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            image_embeds=image_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output, pooled_output = outputs[:2]
        # split up final hidden states into text and image features
        text_seq_len = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
        text_features, _ = (sequence_output[:, :text_seq_len], sequence_output[:, text_seq_len:])

        mlm_logits = self.mlm_score(text_features)

        masked_lm_loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()  # -100 index = padding token
            # move labels to correct device to enable PP
            labels = labels.to(mlm_logits.device)
            masked_lm_loss = loss_fct(mlm_logits.view(-1, self.config.vocab_size), labels.view(-1))

        if not return_dict:
            output = (mlm_logits,) + outputs[2:]
            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output

        return MaskedLMOutput(
            loss=masked_lm_loss,
            logits=mlm_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

        labels (*torch.LongTensor* of shape *(batch_size, sequence_length)*, *optional*):
            Labels for computing the masked language modeling loss. Indices should be in *[-100, 0, ...,
            config.vocab_size]* (see *input_ids* docstring) Tokens with indices set to *-100* are ignored (masked), the
            loss is only computed for the tokens with labels in *[0, ..., config.vocab_size]*

        Returns:

        Examples:

        ```python
        >>> from transformers import ViltProcessor, ViltForMaskedLM
        >>> import requests
        >>> from PIL import Image
        >>> import re
        >>> import torch

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)
        >>> text = "a bunch of [MASK] laying on a [MASK]."

        >>> processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-mlm")
        >>> model = ViltForMaskedLM.from_pretrained("dandelin/vilt-b32-mlm")

        >>> # prepare inputs
        >>> encoding = processor(image, text, return_tensors="pt")

        >>> # forward pass
        >>> outputs = model(**encoding)

        >>> tl = len(re.findall("\[MASK\]", text))
        >>> inferred_token = [text]

        >>> # gradually fill in the MASK tokens, one by one
        >>> with torch.no_grad():
        ...     for i in range(tl):
        ...         encoded = processor.tokenizer(inferred_token)
        ...         input_ids = torch.tensor(encoded.input_ids)
        ...         encoded = encoded["input_ids"][0][1:-1]
        ...         outputs = model(input_ids=input_ids, pixel_values=encoding.pixel_values)
        ...         mlm_logits = outputs.logits[0]  # shape (seq_len, vocab_size)
        ...         # only take into account text features (minus CLS and SEP token)
        ...         mlm_logits = mlm_logits[1 : input_ids.shape[1] - 1, :]
        ...         mlm_values, mlm_ids = mlm_logits.softmax(dim=-1).max(dim=-1)
        ...         # only take into account text
        ...         mlm_values[torch.tensor(encoded) != 103] = 0
        ...         select = mlm_values.argmax().item()
        ...         encoded[select] = mlm_ids[select].item()
        ...         inferred_token = [processor.decode(encoded)]

        >>> selected_token = ""
        >>> encoded = processor.tokenizer(inferred_token)
        >>> output = processor.decode(encoded.input_ids[0], skip_special_tokens=True)
        >>> print(output)
        a bunch of cats laying on a couch.
        ```
dpkd/transformers/src/transformers/models/vilt/modeling_vilt.py