consistent ignore keys + make private (#8737)

* consistent ignore keys + make private

* style

* - authorized_missing_keys    => _keys_to_ignore_on_load_missing
  - authorized_unexpected_keys => _keys_to_ignore_on_load_unexpected

* move public doc of private attributes to private comment

src/transformers/modeling_tf_pytorch_utils.py

@@ -164,9 +164,9 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
             if allow_missing_keys:
                 missing_keys.append(name)
                 continue
-            elif tf_model.authorized_missing_keys is not None:
+            elif tf_model._keys_to_ignore_on_load_missing is not None:
                 # authorized missing keys don't have to be loaded
-                if any(re.search(pat, name) is not None for pat in tf_model.authorized_missing_keys):
+                if any(re.search(pat, name) is not None for pat in tf_model._keys_to_ignore_on_load_missing):
                     continue
 
             raise AttributeError("{} not found in PyTorch model".format(name))
@@ -209,11 +209,11 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
 
     unexpected_keys = list(all_pytorch_weights)
 
-    if tf_model.authorized_missing_keys is not None:
-        for pat in tf_model.authorized_missing_keys:
+    if tf_model._keys_to_ignore_on_load_missing is not None:
+        for pat in tf_model._keys_to_ignore_on_load_missing:
             missing_keys = [k for k in missing_keys if re.search(pat, k) is None]
-    if tf_model.authorized_unexpected_keys is not None:
-        for pat in tf_model.authorized_unexpected_keys:
+    if tf_model._keys_to_ignore_on_load_unexpected is not None:
+        for pat in tf_model._keys_to_ignore_on_load_unexpected:
             unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
 
     if len(unexpected_keys) > 0:

src/transformers/modeling_tf_utils.py

@@ -343,15 +343,15 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):
           :class:`~transformers.PretrainedConfig` to use as configuration class for this model architecture.
         - **base_model_prefix** (:obj:`str`) -- A string indicating the attribute associated to the base model in
           derived classes of the same architecture adding modules on top of the base model.
-        - **authorized_missing_keys** (:obj:`List[str]`, `optional`) -- A list of re pattern of tensor names to ignore
-          from the model when loading the model weights (and avoid unnecessary warnings).
-        - **authorized_unexpected_keys** (:obj:`List[str]`, `optional`) -- A list of re pattern of tensor names to
-          ignore from the weights when loading the model weights (and avoid unnecessary warnings).
     """
     config_class = None
     base_model_prefix = ""
-    authorized_missing_keys = None
-    authorized_unexpected_keys = None
+    # a list of re pattern of tensor names to ignore from the model when loading the model weights
+    # (and avoid unnecessary warnings).
+    _keys_to_ignore_on_load_missing = None
+    # a list of re pattern of tensor names to ignore from the weights when loading the model weights
+    # (and avoid unnecessary warnings).
+    _keys_to_ignore_on_load_unexpected = None
 
     @property
     def dummy_inputs(self) -> Dict[str, tf.Tensor]:
@@ -742,12 +742,12 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):
 
         model(model.dummy_inputs, training=False)  # Make sure restore ops are run
 
-        if cls.authorized_missing_keys is not None:
-            for pat in cls.authorized_missing_keys:
+        if cls._keys_to_ignore_on_load_missing is not None:
+            for pat in cls._keys_to_ignore_on_load_missing:
                 missing_keys = [k for k in missing_keys if re.search(pat, k) is None]
 
-        if cls.authorized_unexpected_keys is not None:
-            for pat in cls.authorized_unexpected_keys:
+        if cls._keys_to_ignore_on_load_unexpected is not None:
+            for pat in cls._keys_to_ignore_on_load_unexpected:
                 unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
 
         if len(unexpected_keys) > 0:

src/transformers/modeling_utils.py

@@ -404,17 +404,18 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
 
         - **base_model_prefix** (:obj:`str`) -- A string indicating the attribute associated to the base model in
           derived classes of the same architecture adding modules on top of the base model.
-        - **authorized_missing_keys** (:obj:`Optional[List[str]]`) -- A list of re pattern of tensor names to ignore
-          when loading the model (and avoid unnecessary warnings).
-        - **keys_to_never_save** (:obj:`Optional[List[str]]`) -- A list of of tensor names to ignore when saving the
-          model (useful for keys that aren't trained, but which are deterministic)
-
     """
     config_class = None
     base_model_prefix = ""
-    authorized_missing_keys = None
-    authorized_unexpected_keys = None
-    keys_to_never_save = None
+    # a list of re pattern of tensor names to ignore from the model when loading the model weights
+    # (and avoid unnecessary warnings).
+    _keys_to_ignore_on_load_missing = None
+    # a list of re pattern of tensor names to ignore from the weights when loading the model weights
+    # (and avoid unnecessary warnings).
+    _keys_to_ignore_on_load_unexpected = None
+    # a list of of tensor names to ignore when saving the model (useful for keys that aren't
+    # trained, but which are deterministic)
+    _keys_to_ignore_on_save = None
 
     @property
     def dummy_inputs(self) -> Dict[str, torch.Tensor]:
@@ -719,8 +720,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
         state_dict = model_to_save.state_dict()
 
         # Handle the case where some state_dict keys shouldn't be saved
-        if self.keys_to_never_save is not None:
-            state_dict = {k: v for k, v in state_dict.items() if k not in self.keys_to_never_save}
+        if self._keys_to_ignore_on_save is not None:
+            state_dict = {k: v for k, v in state_dict.items() if k not in self._keys_to_ignore_on_save}
 
         # If we save using the predefined names, we can load using `from_pretrained`
         output_model_file = os.path.join(save_directory, WEIGHTS_NAME)
@@ -1034,12 +1035,12 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
 
             # Some models may have keys that are not in the state by design, removing them before needlessly warning
             # the user.
-            if cls.authorized_missing_keys is not None:
-                for pat in cls.authorized_missing_keys:
+            if cls._keys_to_ignore_on_load_missing is not None:
+                for pat in cls._keys_to_ignore_on_load_missing:
                     missing_keys = [k for k in missing_keys if re.search(pat, k) is None]
 
-            if cls.authorized_unexpected_keys is not None:
-                for pat in cls.authorized_unexpected_keys:
+            if cls._keys_to_ignore_on_load_unexpected is not None:
+                for pat in cls._keys_to_ignore_on_load_unexpected:
                     unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
 
             if len(unexpected_keys) > 0:

src/transformers/models/albert/modeling_albert.py

@@ -459,7 +459,7 @@ class AlbertPreTrainedModel(PreTrainedModel):
 
     config_class = AlbertConfig
     base_model_prefix = "albert"
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def _init_weights(self, module):
         """Initialize the weights."""
@@ -851,7 +851,7 @@ class AlbertSOPHead(nn.Module):
 )
 class AlbertForMaskedLM(AlbertPreTrainedModel):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
 
     def __init__(self, config):
         super().__init__(config)
@@ -1021,7 +1021,7 @@ class AlbertForSequenceClassification(AlbertPreTrainedModel):
 )
 class AlbertForTokenClassification(AlbertPreTrainedModel):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
 
     def __init__(self, config):
         super().__init__(config)
@@ -1110,7 +1110,7 @@ class AlbertForTokenClassification(AlbertPreTrainedModel):
 )
 class AlbertForQuestionAnswering(AlbertPreTrainedModel):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
 
     def __init__(self, config):
         super().__init__(config)

src/transformers/models/albert/modeling_tf_albert.py

@@ -843,7 +843,7 @@ class TFAlbertSOPHead(tf.keras.layers.Layer):
 @add_start_docstrings("""Albert Model with a `language modeling` head on top. """, ALBERT_START_DOCSTRING)
 class TFAlbertForMaskedLM(TFAlbertPreTrainedModel, TFMaskedLanguageModelingLoss):
 
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
@@ -1013,7 +1013,7 @@ class TFAlbertForSequenceClassification(TFAlbertPreTrainedModel, TFSequenceClass
 )
 class TFAlbertForTokenClassification(TFAlbertPreTrainedModel, TFTokenClassificationLoss):
 
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
@@ -1100,7 +1100,7 @@ class TFAlbertForTokenClassification(TFAlbertPreTrainedModel, TFTokenClassificat
 )
 class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel, TFQuestionAnsweringLoss):
 
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)

src/transformers/models/bart/modeling_bart.py

@@ -946,7 +946,7 @@ class BartModel(PretrainedBartModel):
 )
 class BartForConditionalGeneration(PretrainedBartModel):
     base_model_prefix = "model"
-    authorized_missing_keys = [r"final_logits_bias", r"encoder\.version", r"decoder\.version"]
+    _keys_to_ignore_on_load_missing = [r"final_logits_bias", r"encoder\.version", r"decoder\.version"]
 
     def __init__(self, config: BartConfig):
         super().__init__(config)

src/transformers/models/bart/modeling_tf_bart.py

@@ -1020,10 +1020,10 @@ class TFBartModel(TFPretrainedBartModel):
 )
 class TFBartForConditionalGeneration(TFPretrainedBartModel):
     base_model_prefix = "model"
-    authorized_missing_keys = [
+    _keys_to_ignore_on_load_missing = [
         r"final_logits_bias",
     ]
-    authorized_unexpected_keys = [
+    _keys_to_ignore_on_load_unexpected = [
         r"model.encoder.embed_tokens.weight",
         r"model.decoder.embed_tokens.weight",
     ]

src/transformers/models/bert/modeling_bert.py

@@ -598,7 +598,7 @@ class BertPreTrainedModel(PreTrainedModel):
     config_class = BertConfig
     load_tf_weights = load_tf_weights_in_bert
     base_model_prefix = "bert"
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def _init_weights(self, module):
         """ Initialize the weights """
@@ -969,8 +969,8 @@ class BertForPreTraining(BertPreTrainedModel):
 )
 class BertLMHeadModel(BertPreTrainedModel):
 
-    authorized_unexpected_keys = [r"pooler"]
-    authorized_missing_keys = [r"position_ids", r"predictions.decoder.bias"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
 
     def __init__(self, config):
         super().__init__(config)
@@ -1087,8 +1087,8 @@ class BertLMHeadModel(BertPreTrainedModel):
 @add_start_docstrings("""Bert Model with a `language modeling` head on top. """, BERT_START_DOCSTRING)
 class BertForMaskedLM(BertPreTrainedModel):
 
-    authorized_unexpected_keys = [r"pooler"]
-    authorized_missing_keys = [r"position_ids", r"predictions.decoder.bias"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
 
     def __init__(self, config):
         super().__init__(config)
@@ -1469,7 +1469,7 @@ class BertForMultipleChoice(BertPreTrainedModel):
 )
 class BertForTokenClassification(BertPreTrainedModel):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
 
     def __init__(self, config):
         super().__init__(config)
@@ -1560,7 +1560,7 @@ class BertForTokenClassification(BertPreTrainedModel):
 )
 class BertForQuestionAnswering(BertPreTrainedModel):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
 
     def __init__(self, config):
         super().__init__(config)

src/transformers/models/bert/modeling_tf_bert.py

@@ -938,8 +938,8 @@ class TFBertForPreTraining(TFBertPreTrainedModel, TFBertPreTrainingLoss):
 @add_start_docstrings("""Bert Model with a `language modeling` head on top. """, BERT_START_DOCSTRING)
 class TFBertForMaskedLM(TFBertPreTrainedModel, TFMaskedLanguageModelingLoss):
 
-    authorized_unexpected_keys = [r"pooler"]
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
@@ -1023,8 +1023,8 @@ class TFBertForMaskedLM(TFBertPreTrainedModel, TFMaskedLanguageModelingLoss):
 
 class TFBertLMHeadModel(TFBertPreTrainedModel, TFCausalLanguageModelingLoss):
 
-    authorized_unexpected_keys = [r"pooler"]
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
@@ -1416,8 +1416,8 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel, TFMultipleChoiceLoss):
 )
 class TFBertForTokenClassification(TFBertPreTrainedModel, TFTokenClassificationLoss):
 
-    authorized_unexpected_keys = [r"pooler"]
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
@@ -1502,8 +1502,8 @@ class TFBertForTokenClassification(TFBertPreTrainedModel, TFTokenClassificationL
 )
 class TFBertForQuestionAnswering(TFBertPreTrainedModel, TFQuestionAnsweringLoss):
 
-    authorized_unexpected_keys = [r"pooler"]
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)

src/transformers/models/bert_generation/modeling_bert_generation.py

@@ -173,7 +173,7 @@ class BertGenerationPreTrainedModel(PreTrainedModel):
 
     config_class = BertGenerationConfig
     base_model_prefix = "bert"
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def _init_weights(self, module):
         """ Initialize the weights """

src/transformers/models/deberta/modeling_deberta.py

@@ -756,7 +756,7 @@ class DebertaPreTrainedModel(PreTrainedModel):
 
     config_class = DebertaConfig
     base_model_prefix = "deberta"
-    authorized_missing_keys = ["position_ids"]
+    _keys_to_ignore_on_load_missing = ["position_ids"]
 
     def _init_weights(self, module):
         """ Initialize the weights """

src/transformers/models/dpr/modeling_dpr.py

@@ -279,7 +279,7 @@ class DPRPretrainedContextEncoder(PreTrainedModel):
     config_class = DPRConfig
     load_tf_weights = None
     base_model_prefix = "ctx_encoder"
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def init_weights(self):
         self.ctx_encoder.init_weights()
@@ -294,7 +294,7 @@ class DPRPretrainedQuestionEncoder(PreTrainedModel):
     config_class = DPRConfig
     load_tf_weights = None
     base_model_prefix = "question_encoder"
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def init_weights(self):
         self.question_encoder.init_weights()
@@ -309,7 +309,7 @@ class DPRPretrainedReader(PreTrainedModel):
     config_class = DPRConfig
     load_tf_weights = None
     base_model_prefix = "span_predictor"
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def init_weights(self):
         self.span_predictor.encoder.init_weights()

src/transformers/models/electra/modeling_electra.py

@@ -544,8 +544,8 @@ class ElectraPreTrainedModel(PreTrainedModel):
     config_class = ElectraConfig
     load_tf_weights = load_tf_weights_in_electra
     base_model_prefix = "electra"
-    authorized_missing_keys = [r"position_ids"]
-    authorized_unexpected_keys = [r"electra\.embeddings_project\.weight", r"electra\.embeddings_project\.bias"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
+    _keys_to_ignore_on_load_unexpected = [r"electra\.embeddings_project\.weight", r"electra\.embeddings_project\.bias"]
 
     # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
     def _init_weights(self, module):

src/transformers/models/fsmt/modeling_fsmt.py

@@ -1005,11 +1005,11 @@ class FSMTModel(PretrainedFSMTModel):
 )
 class FSMTForConditionalGeneration(PretrainedFSMTModel):
     base_model_prefix = "model"
-    authorized_missing_keys = [
+    _keys_to_ignore_on_load_missing = [
         "model.encoder.embed_positions.weight",
         "model.decoder.embed_positions.weight",
     ]
-    keys_to_never_save = [
+    _keys_to_ignore_on_save = [
         "model.encoder.embed_positions.weight",
         "model.decoder.embed_positions.weight",
     ]

src/transformers/models/gpt2/modeling_gpt2.py

@@ -780,7 +780,7 @@ class GPT2Model(GPT2PreTrainedModel):
     GPT2_START_DOCSTRING,
 )
 class GPT2LMHeadModel(GPT2PreTrainedModel):
-    authorized_missing_keys = [r"h\.\d+\.attn\.masked_bias", r"lm_head\.weight"]
+    _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.masked_bias", r"lm_head\.weight"]
 
     def __init__(self, config):
         super().__init__(config)
@@ -1097,7 +1097,7 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
     GPT2_START_DOCSTRING,
 )
 class GPT2ForSequenceClassification(GPT2PreTrainedModel):
-    authorized_missing_keys = [r"h\.\d+\.attn\.masked_bias", r"lm_head\.weight"]
+    _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.masked_bias", r"lm_head\.weight"]
 
     def __init__(self, config):
         super().__init__(config)

src/transformers/models/layoutlm/modeling_layoutlm.py

@@ -509,7 +509,7 @@ class LayoutLMPreTrainedModel(PreTrainedModel):
 
     config_class = LayoutLMConfig
     base_model_prefix = "layoutlm"
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def _init_weights(self, module):
         """ Initialize the weights """

src/transformers/models/longformer/modeling_longformer.py

@@ -1303,7 +1303,7 @@ class LongformerPreTrainedModel(PreTrainedModel):
 
     config_class = LongformerConfig
     base_model_prefix = "longformer"
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def _init_weights(self, module):
         """ Initialize the weights """
@@ -1621,7 +1621,7 @@ class LongformerModel(LongformerPreTrainedModel):
 @add_start_docstrings("""Longformer Model with a `language modeling` head on top. """, LONGFORMER_START_DOCSTRING)
 class LongformerForMaskedLM(LongformerPreTrainedModel):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
 
     def __init__(self, config):
         super().__init__(config)
@@ -1718,7 +1718,7 @@ class LongformerForMaskedLM(LongformerPreTrainedModel):
 )
 class LongformerForSequenceClassification(LongformerPreTrainedModel):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
 
     def __init__(self, config):
         super().__init__(config)
@@ -1827,7 +1827,7 @@ class LongformerClassificationHead(nn.Module):
 )
 class LongformerForQuestionAnswering(LongformerPreTrainedModel):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
 
     def __init__(self, config):
         super().__init__(config)
@@ -1961,7 +1961,7 @@ class LongformerForQuestionAnswering(LongformerPreTrainedModel):
 )
 class LongformerForTokenClassification(LongformerPreTrainedModel):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
 
     def __init__(self, config):
         super().__init__(config)

src/transformers/models/longformer/modeling_tf_longformer.py

@@ -1961,7 +1961,7 @@ class TFLongformerModel(TFLongformerPreTrainedModel):
 )
 class TFLongformerForMaskedLM(TFLongformerPreTrainedModel, TFMaskedLanguageModelingLoss):
 
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
@@ -2048,7 +2048,7 @@ class TFLongformerForMaskedLM(TFLongformerPreTrainedModel, TFMaskedLanguageModel
 )
 class TFLongformerForQuestionAnswering(TFLongformerPreTrainedModel, TFQuestionAnsweringLoss):
 
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
@@ -2199,7 +2199,7 @@ class TFLongformerClassificationHead(tf.keras.layers.Layer):
 )
 class TFLongformerForSequenceClassification(TFLongformerPreTrainedModel, TFSequenceClassificationLoss):
 
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
@@ -2443,7 +2443,7 @@ class TFLongformerForMultipleChoice(TFLongformerPreTrainedModel, TFMultipleChoic
 )
 class TFLongformerForTokenClassification(TFLongformerPreTrainedModel, TFTokenClassificationLoss):
 
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)

src/transformers/models/marian/modeling_marian.py

@@ -47,11 +47,11 @@ class MarianMTModel(BartForConditionalGeneration):
 
     """
     config_class = MarianConfig
-    authorized_missing_keys = [
+    _keys_to_ignore_on_load_missing = [
         "model.encoder.embed_positions.weight",
         "model.decoder.embed_positions.weight",
     ]
-    keys_to_never_save = [
+    _keys_to_ignore_on_save = [
         "model.encoder.embed_positions.weight",
         "model.decoder.embed_positions.weight",
     ]

src/transformers/models/marian/modeling_tf_marian.py

@@ -37,7 +37,7 @@ logger = logging.get_logger(__name__)
 
 @add_start_docstrings("Marian model for machine translation", START_DOCSTRING)
 class TFMarianMTModel(TFBartForConditionalGeneration):
-    authorized_missing_keys = [
+    _keys_to_ignore_on_load_missing = [
         r"model.encoder.embed_positions.weight",
         r"model.decoder.embed_positions.weight",
     ]