def generate(
        self,
        pixel_values: torch.FloatTensor,
        qformer_input_ids: Optional[torch.LongTensor] = None,
        qformer_attention_mask: Optional[torch.LongTensor] = None,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.LongTensor] = None,
        **generate_kwargs,
    ) -> torch.LongTensor:
        """
        Overrides `generate` function to be able to use the model as a conditional generator.

        Args:
            pixel_values (`torch.FloatTensor` of shape (batch_size, num_channels, height, width)):
                Input images to be processed.
            qformer_input_ids (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):
                The sequence used as a prompt to be fed to the Q-Former module.
            qformer_attention_mask (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):
                Mask to avoid performing attention on padding token indices.
            input_ids (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):
                The sequence used as a prompt for the generation.
            attention_mask (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):
                Mask to avoid performing attention on padding token indices.

        Returns:
            captions (list): A list of strings of length batch_size * num_captions.
        """
        if hasattr(self, "hf_device_map"):
            # preprocess for `accelerate`
            self._preprocess_accelerate()

        batch_size = pixel_values.shape[0]
        image_embeds = self.vision_model(pixel_values, return_dict=True).last_hidden_state

        image_attention_mask = torch.ones(image_embeds.size()[:-1], dtype=torch.long, device=image_embeds.device)

        query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
        query_attention_mask = torch.ones(query_tokens.size()[:-1], dtype=torch.long, device=image_embeds.device)
        if qformer_attention_mask is None:
            qformer_attention_mask = torch.ones_like(qformer_input_ids)
        qformer_attention_mask = torch.cat([query_attention_mask, qformer_attention_mask], dim=1)
        query_outputs = self.qformer(
            input_ids=qformer_input_ids,
            attention_mask=qformer_attention_mask,
            query_embeds=query_tokens,
            encoder_hidden_states=image_embeds,
            encoder_attention_mask=image_attention_mask,
            return_dict=True,
        )
        query_output = query_outputs.last_hidden_state[:, : query_tokens.size(1), :]

        language_model_inputs = self.language_projection(query_output)
        language_attention_mask = torch.ones(
            language_model_inputs.size()[:-1], dtype=torch.long, device=language_model_inputs.device
        )

        if input_ids is None:
            input_ids = (
                torch.LongTensor([[self.config.text_config.bos_token_id]])
                .repeat(batch_size, 1)
                .to(image_embeds.device)
            )
        if attention_mask is None:
            attention_mask = torch.ones_like(input_ids)
        attention_mask = torch.cat([language_attention_mask, attention_mask.to(language_attention_mask.device)], dim=1)

        # concatenate query embeddings with prompt embeddings
        inputs_embeds = self.get_input_embeddings()(input_ids)
        inputs_embeds = torch.cat([language_model_inputs, inputs_embeds.to(language_model_inputs.device)], dim=1)

        outputs = self.language_model.generate(
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
            **generate_kwargs,
        )

        # the InstructBLIP authors used inconsistent tokenizer/model files during training,
        # with the tokenizer's bos token being set to </s> which has ID=2,
        # whereas the model's text config has bos token id = 0
        if self.config.text_config.architectures[0] == "LLaMAForCausalLM":
            if isinstance(outputs, torch.Tensor):
                outputs[outputs == 0] = 2
            else:
                outputs.sequences[outputs.sequences == 0] = 2

        return outputs

        Overrides `generate` function to be able to use the model as a conditional generator.

        Args:
            pixel_values (`torch.FloatTensor` of shape (batch_size, num_channels, height, width)):
                Input images to be processed.
            qformer_input_ids (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):
                The sequence used as a prompt to be fed to the Q-Former module.
            qformer_attention_mask (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):
                Mask to avoid performing attention on padding token indices.
            input_ids (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):
                The sequence used as a prompt for the generation.
            attention_mask (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):
                Mask to avoid performing attention on padding token indices.

        Returns:
            captions (list): A list of strings of length batch_size * num_captions.
        
dpkd/transformers/src/transformers/models/instructblip/modeling_instructblip.py