def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPast]:
        r"""
        Returns:

        Example:

        ```python
        >>> from transformers import AutoTokenizer, CTRLModel
        >>> import torch

        >>> tokenizer = AutoTokenizer.from_pretrained("Salesforce/ctrl")
        >>> model = CTRLModel.from_pretrained("Salesforce/ctrl")

        >>> # CTRL was trained with control codes as the first token
        >>> inputs = tokenizer("Opinion My dog is cute", return_tensors="pt")
        >>> assert inputs["input_ids"][0, 0].item() in tokenizer.control_codes.values()

        >>> outputs = model(**inputs)

        >>> last_hidden_states = outputs.last_hidden_state
        >>> list(last_hidden_states.shape)
        [1, 5, 1280]
        ```"""
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        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()
            input_ids = input_ids.view(-1, input_shape[-1])
            batch_size = input_ids.shape[0]
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
            batch_size = inputs_embeds.shape[0]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

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

        if past_key_values is None:
            past_length = 0
            past_key_values = tuple([None] * len(self.h))
        else:
            past_length = past_key_values[0][0].size(-2)
        if position_ids is None:
            position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
            position_ids = position_ids.unsqueeze(0)

        # Attention mask.
        if attention_mask is not None:
            if batch_size <= 0:
                raise ValueError("batch_size has to be defined and > 0")
            attention_mask = attention_mask.view(batch_size, -1)
            # 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.
            attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)

            # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
            # masked positions, this operation will create a tensor which is 0.0 for
            # positions we want to attend and the dtype's smallest value for masked positions.
            # Since we are adding it to the raw scores before the softmax, this is
            # effectively the same as removing these entirely.
            attention_mask = attention_mask.to(dtype=self.dtype)  # fp16 compatibility
            attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min

        # Prepare head mask if needed
        head_mask = self.get_head_mask(head_mask, self.config.n_layer)

        if token_type_ids is not None:
            token_type_ids = token_type_ids.view(-1, input_shape[-1])
            token_type_embeds = self.w(token_type_ids)
            token_type_embeds *= np.sqrt(self.d_model_size)
        else:
            token_type_embeds = 0

        if inputs_embeds is None:
            inputs_embeds = self.w(input_ids)
        # inputs_embeds = embedded.unsqueeze(0) if len(input_ids.shape)<2 else embedded
        seq_len = input_shape[-1]
        mask = torch.triu(torch.ones(seq_len + past_length, seq_len + past_length), 1).to(device)

        inputs_embeds *= np.sqrt(self.d_model_size)

        # `self.pos_encoding` won't be sent to the correct device along the model, so we do it manually.
        self.pos_encoding = self.pos_encoding.to(device)
        pos_embeds = self.pos_encoding[position_ids, :]

        hidden_states = inputs_embeds + pos_embeds + token_type_embeds

        hidden_states = self.dropout(hidden_states)

        presents = () if use_cache else None
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        for i, (h, layer_past) in enumerate(zip(self.h, past_key_values)):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)
            outputs = h(
                hidden_states,
                mask,
                layer_past=layer_past,
                attention_mask=attention_mask,
                head_mask=head_mask[i],
                use_cache=use_cache,
                output_attentions=output_attentions,
            )
            hidden_states, present = outputs[:2]
            if use_cache is True:
                presents = presents + (present,)

            if output_attentions:
                all_attentions += (outputs[2],)

        hidden_states = self.layernorm(hidden_states)
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, presents, all_hidden_states, all_attentions] if v is not None)

        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=presents,
            hidden_states=all_hidden_states,
            attentions=all_attentions,
        )

        Returns:

        Example:

        ```python
        >>> from transformers import AutoTokenizer, CTRLModel
        >>> import torch

        >>> tokenizer = AutoTokenizer.from_pretrained("Salesforce/ctrl")
        >>> model = CTRLModel.from_pretrained("Salesforce/ctrl")

        >>> # CTRL was trained with control codes as the first token
        >>> inputs = tokenizer("Opinion My dog is cute", return_tensors="pt")
        >>> assert inputs["input_ids"][0, 0].item() in tokenizer.control_codes.values()

        >>> outputs = model(**inputs)

        >>> last_hidden_states = outputs.last_hidden_state
        >>> list(last_hidden_states.shape)
        [1, 5, 1280]
        ```
dpkd/transformers/src/transformers/models/ctrl/modeling_ctrl.py