Fix copies

examples/tensorflow/question-answering/utils_qa.py

@@ -38,7 +38,7 @@ def postprocess_qa_predictions(
     null_score_diff_threshold: float = 0.0,
     output_dir: Optional[str] = None,
     prefix: Optional[str] = None,
-    is_world_process_zero: bool = True,
+    log_level: Optional[int] = logging.WARNING,
 ):
     """
     Post-processes the predictions of a question-answering model to convert them to answers that are substrings of the
@@ -70,8 +70,8 @@ def postprocess_qa_predictions(
             answers, are saved in `output_dir`.
         prefix (:obj:`str`, `optional`):
             If provided, the dictionaries mentioned above are saved with `prefix` added to their names.
-        is_world_process_zero (:obj:`bool`, `optional`, defaults to :obj:`True`):
-            Whether this process is the main process or not (used to determine if logging/saves should be done).
+        log_level (:obj:`int`, `optional`, defaults to ``logging.WARNING``):
+            ``logging`` log level (e.g., ``logging.WARNING``)
     """
     assert len(predictions) == 2, "`predictions` should be a tuple with two elements (start_logits, end_logits)."
     all_start_logits, all_end_logits = predictions
@@ -91,7 +91,7 @@ def postprocess_qa_predictions(
         scores_diff_json = collections.OrderedDict()
 
     # Logging.
-    logger.setLevel(logging.INFO if is_world_process_zero else logging.WARN)
+    logger.setLevel(log_level)
     logger.info(f"Post-processing {len(examples)} example predictions split into {len(features)} features.")
 
     # Let's loop over all the examples!
@@ -250,7 +250,7 @@ def postprocess_qa_predictions_with_beam_search(
     end_n_top: int = 5,
     output_dir: Optional[str] = None,
     prefix: Optional[str] = None,
-    is_world_process_zero: bool = True,
+    log_level: Optional[int] = logging.WARNING,
 ):
     """
     Post-processes the predictions of a question-answering model with beam search to convert them to answers that are substrings of the
@@ -280,8 +280,8 @@ def postprocess_qa_predictions_with_beam_search(
             answers, are saved in `output_dir`.
         prefix (:obj:`str`, `optional`):
             If provided, the dictionaries mentioned above are saved with `prefix` added to their names.
-        is_world_process_zero (:obj:`bool`, `optional`, defaults to :obj:`True`):
-            Whether this process is the main process or not (used to determine if logging/saves should be done).
+        log_level (:obj:`int`, `optional`, defaults to ``logging.WARNING``):
+            ``logging`` log level (e.g., ``logging.WARNING``)
     """
     assert len(predictions) == 5, "`predictions` should be a tuple with five elements."
     start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits = predictions
@@ -302,7 +302,7 @@ def postprocess_qa_predictions_with_beam_search(
     scores_diff_json = collections.OrderedDict() if version_2_with_negative else None
 
     # Logging.
-    logger.setLevel(logging.INFO if is_world_process_zero else logging.WARN)
+    logger.setLevel(log_level)
     logger.info(f"Post-processing {len(examples)} example predictions split into {len(features)} features.")
 
     # Let's loop over all the examples!
@@ -413,14 +413,14 @@ def postprocess_qa_predictions_with_beam_search(
                 output_dir, "null_odds.json" if prefix is None else f"{prefix}_null_odds.json"
             )
 
-        print(f"Saving predictions to {prediction_file}.")
+        logger.info(f"Saving predictions to {prediction_file}.")
         with open(prediction_file, "w") as writer:
             writer.write(json.dumps(all_predictions, indent=4) + "\n")
-        print(f"Saving nbest_preds to {nbest_file}.")
+        logger.info(f"Saving nbest_preds to {nbest_file}.")
         with open(nbest_file, "w") as writer:
             writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
         if version_2_with_negative:
-            print(f"Saving null_odds to {null_odds_file}.")
+            logger.info(f"Saving null_odds to {null_odds_file}.")
             with open(null_odds_file, "w") as writer:
                 writer.write(json.dumps(scores_diff_json, indent=4) + "\n")