#12789 Replace assert statements with exceptions (#13909)

* #12789 Replace assert statements with exceptions

* fix-copies: made copy changes to utils_qa.py in examples/pytorch/question-answering and examples/tensorflow/question-answering

* minor refactor for clarity

Author: djroxx2000

examples/flax/question-answering/utils_qa.py

@@ -73,10 +73,12 @@ def postprocess_qa_predictions(
         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)."
+    if len(predictions) != 2:
+        raise ValueError("`predictions` should be a tuple with two elements (start_logits, end_logits).")
     all_start_logits, all_end_logits = predictions
 
-    assert len(predictions[0]) == len(features), f"Got {len(predictions[0])} predictions and {len(features)} features."
+    if len(predictions[0]) != len(features):
+        raise ValueError(f"Got {len(predictions[0])} predictions and {len(features)} features.")
 
     # Build a map example to its corresponding features.
     example_id_to_index = {k: i for i, k in enumerate(examples["id"])}
@@ -212,7 +214,8 @@ def postprocess_qa_predictions(
 
     # If we have an output_dir, let's save all those dicts.
     if output_dir is not None:
-        assert os.path.isdir(output_dir), f"{output_dir} is not a directory."
+        if not os.path.isdir(output_dir):
+            raise EnvironmentError(f"{output_dir} is not a directory.")
 
         prediction_file = os.path.join(
             output_dir, "predictions.json" if prefix is None else f"{prefix}_predictions.json"
@@ -283,12 +286,12 @@ def postprocess_qa_predictions_with_beam_search(
         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."
+    if len(predictions) != 5:
+        raise ValueError("`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
 
-    assert len(predictions[0]) == len(
-        features
-    ), f"Got {len(predictions[0])} predicitions and {len(features)} features."
+    if len(predictions[0]) != len(features):
+        raise ValueError(f"Got {len(predictions[0])} predictions and {len(features)} features.")
 
     # Build a map example to its corresponding features.
     example_id_to_index = {k: i for i, k in enumerate(examples["id"])}
@@ -400,7 +403,8 @@ def postprocess_qa_predictions_with_beam_search(
 
     # If we have an output_dir, let's save all those dicts.
     if output_dir is not None:
-        assert os.path.isdir(output_dir), f"{output_dir} is not a directory."
+        if not os.path.isdir(output_dir):
+            raise EnvironmentError(f"{output_dir} is not a directory.")
 
         prediction_file = os.path.join(
             output_dir, "predictions.json" if prefix is None else f"{prefix}_predictions.json"

examples/pytorch/question-answering/utils_qa.py

@@ -73,10 +73,12 @@ def postprocess_qa_predictions(
         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)."
+    if len(predictions) != 2:
+        raise ValueError("`predictions` should be a tuple with two elements (start_logits, end_logits).")
     all_start_logits, all_end_logits = predictions
 
-    assert len(predictions[0]) == len(features), f"Got {len(predictions[0])} predictions and {len(features)} features."
+    if len(predictions[0]) != len(features):
+        raise ValueError(f"Got {len(predictions[0])} predictions and {len(features)} features.")
 
     # Build a map example to its corresponding features.
     example_id_to_index = {k: i for i, k in enumerate(examples["id"])}
@@ -212,7 +214,8 @@ def postprocess_qa_predictions(
 
     # If we have an output_dir, let's save all those dicts.
     if output_dir is not None:
-        assert os.path.isdir(output_dir), f"{output_dir} is not a directory."
+        if not os.path.isdir(output_dir):
+            raise EnvironmentError(f"{output_dir} is not a directory.")
 
         prediction_file = os.path.join(
             output_dir, "predictions.json" if prefix is None else f"{prefix}_predictions.json"
@@ -283,12 +286,12 @@ def postprocess_qa_predictions_with_beam_search(
         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."
+    if len(predictions) != 5:
+        raise ValueError("`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
 
-    assert len(predictions[0]) == len(
-        features
-    ), f"Got {len(predictions[0])} predicitions and {len(features)} features."
+    if len(predictions[0]) != len(features):
+        raise ValueError(f"Got {len(predictions[0])} predictions and {len(features)} features.")
 
     # Build a map example to its corresponding features.
     example_id_to_index = {k: i for i, k in enumerate(examples["id"])}
@@ -400,7 +403,8 @@ def postprocess_qa_predictions_with_beam_search(
 
     # If we have an output_dir, let's save all those dicts.
     if output_dir is not None:
-        assert os.path.isdir(output_dir), f"{output_dir} is not a directory."
+        if not os.path.isdir(output_dir):
+            raise EnvironmentError(f"{output_dir} is not a directory.")
 
         prediction_file = os.path.join(
             output_dir, "predictions.json" if prefix is None else f"{prefix}_predictions.json"

examples/tensorflow/question-answering/utils_qa.py

@@ -73,10 +73,12 @@ def postprocess_qa_predictions(
         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)."
+    if len(predictions) != 2:
+        raise ValueError("`predictions` should be a tuple with two elements (start_logits, end_logits).")
     all_start_logits, all_end_logits = predictions
 
-    assert len(predictions[0]) == len(features), f"Got {len(predictions[0])} predictions and {len(features)} features."
+    if len(predictions[0]) != len(features):
+        raise ValueError(f"Got {len(predictions[0])} predictions and {len(features)} features.")
 
     # Build a map example to its corresponding features.
     example_id_to_index = {k: i for i, k in enumerate(examples["id"])}
@@ -212,7 +214,8 @@ def postprocess_qa_predictions(
 
     # If we have an output_dir, let's save all those dicts.
     if output_dir is not None:
-        assert os.path.isdir(output_dir), f"{output_dir} is not a directory."
+        if not os.path.isdir(output_dir):
+            raise EnvironmentError(f"{output_dir} is not a directory.")
 
         prediction_file = os.path.join(
             output_dir, "predictions.json" if prefix is None else f"{prefix}_predictions.json"
@@ -283,12 +286,12 @@ def postprocess_qa_predictions_with_beam_search(
         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."
+    if len(predictions) != 5:
+        raise ValueError("`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
 
-    assert len(predictions[0]) == len(
-        features
-    ), f"Got {len(predictions[0])} predicitions and {len(features)} features."
+    if len(predictions[0]) != len(features):
+        raise ValueError(f"Got {len(predictions[0])} predictions and {len(features)} features.")
 
     # Build a map example to its corresponding features.
     example_id_to_index = {k: i for i, k in enumerate(examples["id"])}
@@ -400,7 +403,8 @@ def postprocess_qa_predictions_with_beam_search(
 
     # If we have an output_dir, let's save all those dicts.
     if output_dir is not None:
-        assert os.path.isdir(output_dir), f"{output_dir} is not a directory."
+        if not os.path.isdir(output_dir):
+            raise EnvironmentError(f"{output_dir} is not a directory.")
 
         prediction_file = os.path.join(
             output_dir, "predictions.json" if prefix is None else f"{prefix}_predictions.json"