Improve dict missing key code (#3071)

* Improve dict missing key code

* remove trailing whitespace

allennlp/data/dataset.py

@@ -64,7 +64,7 @@ def get_padding_lengths(self) -> Dict[str, Dict[str, int]]:
                 all_field_lengths[field_name].append(instance_field_lengths)
         for field_name, field_lengths in all_field_lengths.items():
             for padding_key in field_lengths[0].keys():
-                max_value = max(x[padding_key] if padding_key in x else 0 for x in field_lengths)
+                max_value = max(x.get(padding_key, 0) for x in field_lengths)
                 padding_lengths[field_name][padding_key] = max_value
         return {**padding_lengths}
 
@@ -124,7 +124,7 @@ def as_tensor_dict(self,
         lengths_to_use: Dict[str, Dict[str, int]] = defaultdict(dict)
         for field_name, instance_field_lengths in instance_padding_lengths.items():
             for padding_key in instance_field_lengths.keys():
-                if padding_lengths[field_name].get(padding_key) is not None:
+                if padding_key in padding_lengths[field_name]:
                     lengths_to_use[field_name][padding_key] = padding_lengths[field_name][padding_key]
                 else:
                     lengths_to_use[field_name][padding_key] = instance_field_lengths[padding_key]

allennlp/data/dataset_readers/penn_tree_bank.py

@@ -134,10 +134,7 @@ def text_to_instance(self, # type: ignore
             spans.append(SpanField(start, end, text_field))
 
             if gold_spans is not None:
-                if (start, end) in gold_spans.keys():
-                    gold_labels.append(gold_spans[(start, end)])
-                else:
-                    gold_labels.append("NO-LABEL")
+                gold_labels.append(gold_spans.get((start, end), "NO-LABEL"))
 
         metadata = {"tokens": tokens}
         if gold_tree:

allennlp/data/fields/knowledge_graph_field.py

@@ -198,13 +198,13 @@ def get_padding_lengths(self) -> Dict[str, int]:
             # Iterate over the keys in the first element of the list.  This is fine as for a given
             # indexer, all entities will return the same keys, so we can just use the first one.
             for key in entity_lengths[0].keys():
-                indexer_lengths[key] = max(x[key] if key in x else 0 for x in entity_lengths)
+                indexer_lengths[key] = max(x.get(key, 0) for x in entity_lengths)
             lengths.append(indexer_lengths)
 
         # Get all the keys which have been used for padding.
         padding_keys = {key for d in lengths for key in d.keys()}
         for padding_key in padding_keys:
-            padding_lengths[padding_key] = max(x[padding_key] if padding_key in x else 0 for x in lengths)
+            padding_lengths[padding_key] = max(x.get(padding_key, 0) for x in lengths)
         return padding_lengths
 
     @overrides

allennlp/data/fields/text_field.py

@@ -129,7 +129,7 @@ def get_padding_lengths(self) -> Dict[str, int]:
         # Get all keys which have been used for padding for each indexer and take the max if there are duplicates.
         padding_keys = {key for d in lengths for key in d.keys()}
         for padding_key in padding_keys:
-            padding_lengths[padding_key] = max(x[padding_key] if padding_key in x else 0 for x in lengths)
+            padding_lengths[padding_key] = max(x.get(padding_key, 0) for x in lengths)
         return padding_lengths
 
     @overrides