Add MetadataField

combo/data/fields/metadata_field.py

+"""
+Adapted from AllenNLP
+https://github.com/allenai/allennlp/blob/main/allennlp/data/fields/metadata_field.py
+"""
+from typing import Any, Dict, List, Mapping
+
+
+from combo.data.fields.field import DataArray, Field
+
+
+class MetadataField(Field[DataArray], Mapping[str, Any]):
+    """
+    A `MetadataField` is a `Field` that does not get converted into tensors.  It just carries
+    side information that might be needed later on, for computing some third-party metric, or
+    outputting debugging information, or whatever else you need.  We use this in the BiDAF model,
+    for instance, to keep track of question IDs and passage token offsets, so we can more easily
+    use the official evaluation script to compute metrics.
+    We don't try to do any kind of smart combination of this field for batched input - when you use
+    this `Field` in a model, you'll get a list of metadata objects, one for each instance in the
+    batch.
+    # Parameters
+    metadata : `Any`
+        Some object containing the metadata that you want to store.  It's likely that you'll want
+        this to be a dictionary, but it could be anything you want.
+    """
+
+    __slots__ = ["metadata"]
+
+    def __init__(self, metadata: Any) -> None:
+        self.metadata = metadata
+
+    def __getitem__(self, key: str) -> Any:
+        try:
+            return self.metadata[key]  # type: ignore
+        except TypeError:
+            raise TypeError("your metadata is not a dict")
+
+    def __iter__(self):
+        try:
+            return iter(self.metadata)
+        except TypeError:
+            raise TypeError("your metadata is not iterable")
+
+    def __len__(self):
+        try:
+            return len(self.metadata)
+        except TypeError:
+            raise TypeError("your metadata has no length")
+
+    def get_padding_lengths(self) -> Dict[str, int]:
+        return {}
+
+    def as_tensor(self, padding_lengths: Dict[str, int]) -> DataArray:
+
+        return self.metadata  # type: ignore
+
+    def empty_field(self) -> "MetadataField":
+        return MetadataField(None)
+
+    def batch_tensors(self, tensor_list: List[DataArray]) -> List[DataArray]:  # type: ignore
+        return tensor_list
+
+    def __str__(self) -> str:
+        return "MetadataField (print field.metadata to see specific information)."
+
+    def human_readable_repr(self):
+        if hasattr(self.metadata, "human_readable_repr"):
+            return self.metadata.human_readable_repr()
+        return self.metadata