| from typing import Optional, Union | |
| import torch | |
| from transformers.models.bert.modeling_bert import BertModel | |
| from ...modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions | |
| class DummyBertModel(BertModel): | |
| def forward( | |
| self, | |
| input_ids: Optional[torch.Tensor] = None, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| token_type_ids: Optional[torch.Tensor] = None, | |
| position_ids: Optional[torch.Tensor] = None, | |
| head_mask: Optional[torch.Tensor] = None, | |
| inputs_embeds: Optional[torch.Tensor] = None, | |
| encoder_hidden_states: Optional[torch.Tensor] = None, | |
| encoder_attention_mask: Optional[torch.Tensor] = None, | |
| past_key_values: Optional[list[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], BaseModelOutputWithPoolingAndCrossAttentions]: | |
| return super().forward(input_ids) | |