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28 lines
1.1 KiB
Python
28 lines
1.1 KiB
Python
import torch
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from transformers.models.bert.modeling_bert import BertModel
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from ...modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions
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from ...processing_utils import Unpack
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from ...utils import TransformersKwargs
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class DummyBertModel(BertModel):
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def forward(
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self,
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input_ids: torch.Tensor | None = None,
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attention_mask: torch.Tensor | None = None,
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token_type_ids: torch.Tensor | None = None,
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position_ids: torch.Tensor | None = None,
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inputs_embeds: torch.Tensor | None = None,
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encoder_hidden_states: torch.Tensor | None = None,
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encoder_attention_mask: torch.Tensor | None = None,
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past_key_values: list[torch.FloatTensor] | None = None,
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use_cache: bool | None = None,
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output_attentions: bool | None = None,
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output_hidden_states: bool | None = None,
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return_dict: bool | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple[torch.Tensor] | BaseModelOutputWithPoolingAndCrossAttentions:
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return super().forward(input_ids, **kwargs)
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