optimum/exporters/openvino/model_patcher.py [3682:3707]:
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    position_ids: Optional[torch.FloatTensor] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
    from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask

    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

    batch_size = pixel_values.size(0)
    if patch_attention_mask is None:
        patch_attention_mask = torch.ones(
            size=(
                batch_size,
                pixel_values.size(2) // self.config.patch_size,
                pixel_values.size(3) // self.config.patch_size,
            ),
            dtype=torch.bool,
            device=pixel_values.device,
        )

    hidden_states = self.embeddings(
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optimum/exporters/openvino/model_patcher.py [6079:6104]:
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            position_ids: Optional[torch.FloatTensor] = None,
            output_attentions: Optional[bool] = None,
            output_hidden_states: Optional[bool] = None,
            return_dict: Optional[bool] = None,
        ) -> Union[Tuple, BaseModelOutputWithPooling]:
            from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask

            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

            batch_size = pixel_values.size(0)
            if patch_attention_mask is None:
                patch_attention_mask = torch.ones(
                    size=(
                        batch_size,
                        pixel_values.size(2) // self.config.patch_size,
                        pixel_values.size(3) // self.config.patch_size,
                    ),
                    dtype=torch.bool,
                    device=pixel_values.device,
                )

            hidden_states = self.embeddings(
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