Fix confusing warnings during TF2 import from PyTorch (#6623)

1. Swapped missing_keys and unexpected_keys.

2. Copy&paste error caused these warnings to say "from TF 2.0" when it's actually "from PyTorch".

src/transformers/modeling_tf_pytorch_utils.py

@@ -148,7 +148,7 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
     tf_loaded_numel = 0
     weight_value_tuples = []
     all_pytorch_weights = set(list(pt_state_dict.keys()))
-    unexpected_keys = []
+    missing_keys = []
     for symbolic_weight in symbolic_weights:
         sw_name = symbolic_weight.name
         name, transpose = convert_tf_weight_name_to_pt_weight_name(
@@ -158,7 +158,7 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
         # Find associated numpy array in pytorch model state dict
         if name not in pt_state_dict:
             if allow_missing_keys:
-                unexpected_keys.append(name)
+                missing_keys.append(name)
                 continue
 
             raise AttributeError("{} not found in PyTorch model".format(name))
@@ -192,28 +192,28 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
 
     logger.info("Loaded {:,} parameters in the TF 2.0 model.".format(tf_loaded_numel))
 
-    missing_keys = list(all_pytorch_weights)
+    unexpected_keys = list(all_pytorch_weights)
 
     if len(unexpected_keys) > 0:
         logger.warning(
             f"Some weights of the PyTorch model were not used when "
             f"initializing the TF 2.0 model {tf_model.__class__.__name__}: {unexpected_keys}\n"
-            f"- This IS expected if you are initializing {tf_model.__class__.__name__} from a TF 2.0 model trained on another task "
-            f"or with another architecture (e.g. initializing a BertForSequenceClassification model from a TFBertForPretraining model).\n"
-            f"- This IS NOT expected if you are initializing {tf_model.__class__.__name__} from a TF 2.0 model that you expect "
-            f"to be exactly identical (e.g. initializing a BertForSequenceClassification model from a TFBertForSequenceClassification model)."
+            f"- This IS expected if you are initializing {tf_model.__class__.__name__} from a PyTorch model trained on another task "
+            f"or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPretraining model).\n"
+            f"- This IS NOT expected if you are initializing {tf_model.__class__.__name__} from a PyTorch model that you expect "
+            f"to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model)."
         )
     else:
         logger.warning(f"All PyTorch model weights were used when initializing {tf_model.__class__.__name__}.\n")
     if len(missing_keys) > 0:
         logger.warning(
-            f"Some weights or buffers of the PyTorch model {tf_model.__class__.__name__} were not initialized from the TF 2.0 model "
+            f"Some weights or buffers of the TF 2.0 model {tf_model.__class__.__name__} were not initialized from the PyTorch model "
             f"and are newly initialized: {missing_keys}\n"
             f"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference."
         )
     else:
         logger.warning(
-            f"All the weights of {tf_model.__class__.__name__} were initialized from the TF 2.0 model.\n"
+            f"All the weights of {tf_model.__class__.__name__} were initialized from the PyTorch model.\n"
             f"If your task is similar to the task the model of the ckeckpoint was trained on, "
             f"you can already use {tf_model.__class__.__name__} for predictions without further training."
         )