BERT_explainability/roberta2.py.rej

--- modeling_roberta.py	2022-06-28 11:59:19.974278244 +0200
+++ roberta2.py	2022-06-28 14:13:05.765050058 +0200
@@ -23,14 +23,14 @@
 from torch import nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
-from ...activations import ACT2FN, gelu
-from ...file_utils import (
+from transformers.activations import ACT2FN, gelu
+from transformers.file_utils import (
     add_code_sample_docstrings,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
     replace_return_docstrings,
 )
-from ...modeling_outputs import (
+from transformers.modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
     BaseModelOutputWithPoolingAndCrossAttentions,
     CausalLMOutputWithCrossAttentions,
@@ -40,14 +40,14 @@
     SequenceClassifierOutput,
     TokenClassifierOutput,
 )
-from ...modeling_utils import (
+from transformers.modeling_utils import (
     PreTrainedModel,
     apply_chunking_to_forward,
     find_pruneable_heads_and_indices,
     prune_linear_layer,
 )
-from ...utils import logging
-from .configuration_roberta import RobertaConfig
+from transformers.utils import logging
+from transformers.models.roberta.configuration_roberta import RobertaConfig
 
 
 logger = logging.get_logger(__name__)
@@ -183,6 +183,24 @@
 
         self.is_decoder = config.is_decoder
 
+    def get_attn(self):
+        return self.attn
+
+    def save_attn(self, attn):
+        self.attn = attn
+
+    def save_attn_cam(self, cam):
+        self.attn_cam = cam
+
+    def get_attn_cam(self):
+        return self.attn_cam
+
+    def save_attn_gradients(self, attn_gradients):
+        self.attn_gradients = attn_gradients
+
+    def get_attn_gradients(self):
+        return self.attn_gradients
+
     def transpose_for_scores(self, x):
         new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
         x = x.view(*new_x_shape)