def get_text_features(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        text_encoder_inputs_embeds: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.LongTensor] = None,
    ) -> torch.FloatTensor:
        r"""
        This method can be used to extract text_embeds from a text. The text embeddings obtained by applying the
        projection layer to the pooled output of the CLVP text encoder model.

        Args:
            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
                provide it.

                [What are input IDs?](../glossary#input-ids)
            text_encoder_inputs_embeds (`torch.FloatTensor`, *optional*):
                inputs_embeds for the text encoder model passed in place of `input_ids`.
            attention_mask (`torch.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)

        Returns:
            `torch.FloatTensor` of shape `(batch_size, output_dim)`:
                The text embeddings obtained by applying the projection layer to the pooled output of the CLVP Text
                Model.

        Examples:

        ```python
        >>> from transformers import ClvpProcessor, ClvpModelForConditionalGeneration

        >>> # Define the Text
        >>> text = "This is an example text."

        >>> # Define processor and model
        >>> processor = ClvpProcessor.from_pretrained("susnato/clvp_dev")
        >>> model = ClvpModelForConditionalGeneration.from_pretrained("susnato/clvp_dev")

        >>> # Generate processor output and text embeds
        >>> processor_output = processor(text=text, return_tensors="pt")
        >>> text_embeds = model.get_text_features(input_ids=processor_output["input_ids"])
        ```
        """

        outputs = self.text_encoder_model(
            input_ids=input_ids,
            inputs_embeds=text_encoder_inputs_embeds,
            attention_mask=attention_mask,
        )

        return outputs[0]

        This method can be used to extract text_embeds from a text. The text embeddings obtained by applying the
        projection layer to the pooled output of the CLVP text encoder model.

        Args:
            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
                provide it.

                [What are input IDs?](../glossary#input-ids)
            text_encoder_inputs_embeds (`torch.FloatTensor`, *optional*):
                inputs_embeds for the text encoder model passed in place of `input_ids`.
            attention_mask (`torch.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)

        Returns:
            `torch.FloatTensor` of shape `(batch_size, output_dim)`:
                The text embeddings obtained by applying the projection layer to the pooled output of the CLVP Text
                Model.

        Examples:

        ```python
        >>> from transformers import ClvpProcessor, ClvpModelForConditionalGeneration

        >>> # Define the Text
        >>> text = "This is an example text."

        >>> # Define processor and model
        >>> processor = ClvpProcessor.from_pretrained("susnato/clvp_dev")
        >>> model = ClvpModelForConditionalGeneration.from_pretrained("susnato/clvp_dev")

        >>> # Generate processor output and text embeds
        >>> processor_output = processor(text=text, return_tensors="pt")
        >>> text_embeds = model.get_text_features(input_ids=processor_output["input_ids"])
        ```
        
dpkd/transformers/src/transformers/models/clvp/modeling_clvp.py