diff --git a/src/transformers/models/camembert/modeling_camembert.py b/src/transformers/models/camembert/modeling_camembert.py
index 1449304cb93451..e19488bbb47447 100644
--- a/src/transformers/models/camembert/modeling_camembert.py
+++ b/src/transformers/models/camembert/modeling_camembert.py
@@ -24,10 +24,6 @@
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN, gelu
-from ...modeling_attn_mask_utils import (
-    _prepare_4d_attention_mask_for_sdpa,
-    _prepare_4d_causal_attention_mask_for_sdpa,
-)
 from ...modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
     BaseModelOutputWithPoolingAndCrossAttentions,
@@ -795,7 +791,6 @@ class PreTrainedModel
         output_type=BaseModelOutputWithPoolingAndCrossAttentions,
         config_class=_CONFIG_FOR_DOC,
     )
-    # Copied from transformers.models.bert.modeling_bert.BertModel.forward
     def forward(
         self,
         input_ids: Optional[torch.Tensor] = None,
@@ -867,43 +862,12 @@ def forward(
             else:
                 token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
 
-        embedding_output = self.embeddings(
-            input_ids=input_ids,
-            position_ids=position_ids,
-            token_type_ids=token_type_ids,
-            inputs_embeds=inputs_embeds,
-            past_key_values_length=past_key_values_length,
-        )
-
         if attention_mask is None:
             attention_mask = torch.ones((batch_size, seq_length + past_key_values_length), device=device)
 
-        use_sdpa_attention_masks = (
-            self._use_sdpa
-            and self.position_embedding_type == "absolute"
-            and head_mask is None
-            and not output_attentions
-        )
-
-        # Expand the attention mask
-        if use_sdpa_attention_masks:
-            # Expand the attention mask for SDPA.
-            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-            if self.config.is_decoder:
-                extended_attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
-                    attention_mask,
-                    input_shape,
-                    embedding_output,
-                    past_key_values_length,
-                )
-            else:
-                extended_attention_mask = _prepare_4d_attention_mask_for_sdpa(
-                    attention_mask, embedding_output.dtype, tgt_len=seq_length
-                )
-        else:
-            # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
-            # ourselves in which case we just need to make it broadcastable to all heads.
-            extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape)
+        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case we just need to make it broadcastable to all heads.
+        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
 
         # If a 2D or 3D attention mask is provided for the cross-attention
         # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
@@ -913,14 +877,7 @@ def forward(
             if encoder_attention_mask is None:
                 encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
 
-            if use_sdpa_attention_masks:
-                # Expand the attention mask for SDPA.
-                # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-                encoder_extended_attention_mask = _prepare_4d_attention_mask_for_sdpa(
-                    encoder_attention_mask, embedding_output.dtype, tgt_len=seq_length
-                )
-            else:
-                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
+            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
         else:
             encoder_extended_attention_mask = None
 
@@ -931,6 +888,13 @@ def forward(
         # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
         head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
 
+        embedding_output = self.embeddings(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            token_type_ids=token_type_ids,
+            inputs_embeds=inputs_embeds,
+            past_key_values_length=past_key_values_length,
+        )
         encoder_outputs = self.encoder(
             embedding_output,
             attention_mask=extended_attention_mask,