logger.warn --> logger.warning (#15572)

* change logger.warn to logger.warning

* make style
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

src/transformers/configuration_utils.py

@@ -517,7 +517,7 @@ class PretrainedConfig(PushToHubMixin):
         ```"""
         config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
         if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
-            logger.warn(
+            logger.warning(
                 f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                 f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
             )

src/transformers/modeling_utils.py

@@ -388,7 +388,7 @@ class ModuleUtilsMixin:
         if self.main_input_name in input_dict:
             return input_dict[self.main_input_name].numel()
         else:
-            logger.warn(
+            logger.warning(
                 "Could not estimate the number of tokens of the input, floating-point operations will not be computed"
             )
             return 0

src/transformers/models/auto/auto_factory.py

@@ -398,7 +398,7 @@ class _BaseAutoModelClass:
                     "the option `trust_remote_code=True` to remove this error."
                 )
             if kwargs.get("revision", None) is None:
-                logger.warn(
+                logger.warning(
                     "Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure "
                     "no malicious code has been contributed in a newer revision."
                 )
@@ -432,7 +432,7 @@ class _BaseAutoModelClass:
                     "the option `trust_remote_code=True` to remove this error."
                 )
             if kwargs.get("revision", None) is None:
-                logger.warn(
+                logger.warning(
                     "Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure "
                     "no malicious code has been contributed in a newer revision."
                 )

src/transformers/models/auto/configuration_auto.py

@@ -626,7 +626,7 @@ class AutoConfig:
                     "the option `trust_remote_code=True` to remove this error."
                 )
             if kwargs.get("revision", None) is None:
-                logger.warn(
+                logger.warning(
                     "Explicitly passing a `revision` is encouraged when loading a configuration with custom code to "
                     "ensure no malicious code has been contributed in a newer revision."
                 )

src/transformers/models/auto/tokenization_auto.py

@@ -492,7 +492,7 @@ class AutoTokenizer:
                         "the option `trust_remote_code=True` to remove this error."
                     )
                 if kwargs.get("revision", None) is None:
-                    logger.warn(
+                    logger.warning(
                         "Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure "
                         "no malicious code has been contributed in a newer revision."
                     )

src/transformers/models/megatron_bert/modeling_megatron_bert.py

@@ -540,7 +540,7 @@ class MegatronBertEncoder(nn.Module):
             if self.gradient_checkpointing and self.training:
 
                 if use_cache:
-                    logger.warn(
+                    logger.warning(
                         "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                     )
                     use_cache = False

src/transformers/models/rembert/modeling_rembert.py

@@ -535,7 +535,7 @@ class RemBertEncoder(nn.Module):
             if self.gradient_checkpointing and self.training:
 
                 if use_cache:
-                    logger.warn(
+                    logger.warning(
                         "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                     )
                     use_cache = False

src/transformers/models/t5/modeling_t5.py

@@ -984,7 +984,7 @@ class T5Stack(T5PreTrainedModel):
 
             if self.gradient_checkpointing and self.training:
                 if use_cache:
-                    logger.warn(
+                    logger.warning(
                         "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                     )
                     use_cache = False

src/transformers/trainer.py

@@ -952,7 +952,7 @@ class Trainer:
 
         for key, value in params.items():
             if not hasattr(self.args, key):
-                logger.warn(
+                logger.warning(
                     f"Trying to set {key} in the hyperparameter search but there is no corresponding field in `TrainingArguments`."
                 )
                 continue
@@ -1165,7 +1165,7 @@ class Trainer:
                 config = PretrainedConfig.from_json_file(os.path.join(resume_from_checkpoint, CONFIG_NAME))
                 checkpoint_version = config.transformers_version
                 if checkpoint_version is not None and checkpoint_version != __version__:
-                    logger.warn(
+                    logger.warning(
                         f"You are resuming training from a checkpoint trained with {checkpoint_version} of "
                         f"Transformers but your current version is {__version__}. This is not recommended and could "
                         "yield to errors or unwanted behaviors."
@@ -1534,7 +1534,7 @@ class Trainer:
                     # If the model is on the GPU, it still works!
                     self._load_state_dict_in_model(state_dict)
             else:
-                logger.warn(
+                logger.warning(
                     f"Could not locate the best model at {best_model_path}, if you are running a distributed training "
                     "on multiple nodes, you should activate `--save_on_each_node`."
                 )
@@ -1567,9 +1567,11 @@ class Trainer:
             ):
                 self.model.tie_weights()
             else:
-                logger.warn(f"There were missing keys in the checkpoint model loaded: {load_result.missing_keys}.")
+                logger.warning(f"There were missing keys in the checkpoint model loaded: {load_result.missing_keys}.")
         if len(load_result.unexpected_keys) != 0:
-            logger.warn(f"There were unexpected keys in the checkpoint model loaded: {load_result.unexpected_keys}.")
+            logger.warning(
+                f"There were unexpected keys in the checkpoint model loaded: {load_result.unexpected_keys}."
+            )
 
     def _maybe_log_save_evaluate(self, tr_loss, model, trial, epoch, ignore_keys_for_eval):
         if self.control.should_log: