[Examples] Replicates the new --log_level feature to all trainer-based pytorch (#12359)

* added log_level

* fix comment

* fixed log_level

* Trigger CI

* Unfied logging

* simplified args for log_level

examples/pytorch/text-classification/run_xnli.py

@@ -24,6 +24,7 @@ import sys
 from dataclasses import dataclass, field
 from typing import Optional
 
+import datasets
 import numpy as np
 from datasets import load_dataset, load_metric
 
@@ -174,19 +175,19 @@ def main():
         datefmt="%m/%d/%Y %H:%M:%S",
         handlers=[logging.StreamHandler(sys.stdout)],
     )
-    logger.setLevel(logging.INFO if training_args.should_log else logging.WARN)
+
+    log_level = training_args.get_process_log_level()
+    logger.setLevel(log_level)
+    datasets.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
 
     # Log on each process the small summary:
     logger.warning(
         f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
         + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
     )
-
-    # Set the verbosity to info of the Transformers logger (on main process only):
-    if training_args.should_log:
-        transformers.utils.logging.set_verbosity_info()
-        transformers.utils.logging.enable_default_handler()
-        transformers.utils.logging.enable_explicit_format()
     logger.info(f"Training/evaluation parameters {training_args}")
 
     # Detecting last checkpoint.