Move usage of deprecated logging.warn to logging.warning (#25310)

The former spelling is deprecated and has been discouraged for a
while. The latter spelling seems to be more common in this project
anyway, so this change ought to be safe.

Fixes https://github.com/huggingface/transformers/issues/25283

src/transformers/modeling_utils.py

@@ -2853,7 +2853,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
             if "special_dtypes" in inspect.signature(infer_auto_device_map).parameters:
                 kwargs["special_dtypes"] = special_dtypes
             elif len(special_dtypes) > 0:
-                logger.warn(
+                logger.warning(
                     "This model has some weights that should be kept in higher precision, you need to upgrade "
                     "`accelerate` to properly deal with them (`pip install --upgrade accelerate`)."
                 )
@@ -3359,7 +3359,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
 
         if len(unexpected_keys) > 0:
             archs = [] if model.config.architectures is None else model.config.architectures
-            warner = logger.warn if model.__class__.__name__ in archs else logger.info
+            warner = logger.warning if model.__class__.__name__ in archs else logger.info
             warner(
                 f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when"
                 f" initializing {model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are"

src/transformers/models/blip/modeling_blip_text.py

@@ -403,7 +403,7 @@ class BlipTextEncoder(nn.Module):
     ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
         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/blip_2/modeling_blip_2.py

@@ -940,7 +940,7 @@ class Blip2QFormerEncoder(nn.Module):
 
             if getattr(self.config, "gradient_checkpointing", False) 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/deprecated/open_llama/modeling_open_llama.py

@@ -39,7 +39,7 @@ try:
     from xformers import ops as xops
 except ImportError:
     xops = None
-    logger.warn(
+    logger.warning(
         "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\npip install xformers."
     )
 

src/transformers/models/instructblip/modeling_instructblip.py

@@ -930,7 +930,7 @@ class InstructBlipQFormerEncoder(nn.Module):
 
             if getattr(self.config, "gradient_checkpointing", False) 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/wav2vec2/modeling_wav2vec2.py

@@ -1273,7 +1273,7 @@ class Wav2Vec2PreTrainedModel(PreTrainedModel):
             raise ValueError(f"Cannot load_adapter for {target_lang} if `config.adapter_attn_dim` is not defined.")
 
         if target_lang == self.target_lang and not force_load:
-            logger.warn(f"Adapter weights are already set to {target_lang}.")
+            logger.warning(f"Adapter weights are already set to {target_lang}.")
             return
 
         cache_dir = kwargs.pop("cache_dir", None)

src/transformers/tools/agents.py

@@ -224,12 +224,12 @@ class Agent:
             self._toolbox.update(additional_tools)
             if len(replacements) > 1:
                 names = "\n".join([f"- {n}: {t}" for n, t in replacements.items()])
-                logger.warn(
+                logger.warning(
                     f"The following tools have been replaced by the ones provided in `additional_tools`:\n{names}."
                 )
             elif len(replacements) == 1:
                 name = list(replacements.keys())[0]
-                logger.warn(f"{name} has been replaced by {replacements[name]} as provided in `additional_tools`.")
+                logger.warning(f"{name} has been replaced by {replacements[name]} as provided in `additional_tools`.")
 
         self.prepare_for_new_chat()
 

src/transformers/tools/base.py

@@ -264,7 +264,7 @@ class Tool:
         if len(tool_class.name) == 0:
             tool_class.name = custom_tool["name"]
         if tool_class.name != custom_tool["name"]:
-            logger.warn(
+            logger.warning(
                 f"{tool_class.__name__} implements a different name in its configuration and class. Using the tool "
                 "configuration name."
             )
@@ -273,7 +273,7 @@ class Tool:
         if len(tool_class.description) == 0:
             tool_class.description = custom_tool["description"]
         if tool_class.description != custom_tool["description"]:
-            logger.warn(
+            logger.warning(
                 f"{tool_class.__name__} implements a different description in its configuration and class. Using the "
                 "tool configuration description."
             )