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modeling_dplm.py

@@ -367,9 +367,9 @@ import torch
 import torch.nn as nn
 from torch.nn import functional as F
 from dataclasses import dataclass
-from typing import Dict, List, Optional, Tuple, Union
+from typing import List, Optional, Tuple, Union
 
-from transformers import AutoTokenizer, EsmTokenizer
+from transformers import EsmTokenizer
 from transformers.modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
     BaseModelOutputWithPoolingAndCrossAttentions,
@@ -1056,7 +1056,7 @@ class DPLMForMaskedLM(DPLMPreTrainedModel, EmbeddingMixin):
         self.tokenizer = self.__class__.tokenizer
         if isinstance(config._name_or_path, str) and len(config._name_or_path) > 0:
             try:
-                self.tokenizer = AutoTokenizer.from_pretrained(config._name_or_path)
+                self.tokenizer = EsmTokenizer.from_pretrained(config._name_or_path)
             except Exception:
                 self.tokenizer = self.__class__.tokenizer