chore: import upstream snapshot with attribution

python/ray/air/_internal/tensorflow_utils.py

@@ -0,0 +1,73 @@
+from typing import Dict, Optional, Union
+
+import numpy as np
+import tensorflow as tf
+
+from ray.air.util.data_batch_conversion import _unwrap_ndarray_object_type_if_needed
+
+
+def convert_ndarray_to_tf_tensor(
+    ndarray: np.ndarray,
+    dtype: Optional[tf.dtypes.DType] = None,
+    type_spec: Optional[tf.TypeSpec] = None,
+) -> tf.Tensor:
+    """Convert a NumPy ndarray to a TensorFlow Tensor.
+
+    Args:
+        ndarray: A NumPy ndarray that we wish to convert to a TensorFlow Tensor.
+        dtype: A TensorFlow dtype for the created tensor; if None, the dtype will be
+            inferred from the NumPy ndarray data.
+        type_spec: A type spec that specifies the shape and dtype of the returned
+            tensor. If you specify ``dtype``, the dtype stored in the type spec is
+            ignored.
+
+    Returns:
+        A TensorFlow Tensor.
+    """
+    if dtype is None and type_spec is not None:
+        dtype = type_spec.dtype
+
+    is_ragged = isinstance(type_spec, tf.RaggedTensorSpec)
+    ndarray = _unwrap_ndarray_object_type_if_needed(ndarray)
+    if is_ragged:
+        return tf.ragged.constant(ndarray, dtype=dtype)
+    else:
+        return tf.convert_to_tensor(ndarray, dtype=dtype)
+
+
+def convert_ndarray_batch_to_tf_tensor_batch(
+    ndarrays: Union[np.ndarray, Dict[str, np.ndarray]],
+    dtypes: Optional[Union[tf.dtypes.DType, Dict[str, tf.dtypes.DType]]] = None,
+) -> Union[tf.Tensor, Dict[str, tf.Tensor]]:
+    """Convert a NumPy ndarray batch to a TensorFlow Tensor batch.
+
+    Args:
+        ndarrays: A (dict of) NumPy ndarray(s) that we wish to convert to a TensorFlow
+            Tensor.
+        dtypes: A (dict of) TensorFlow dtype(s) for the created tensor; if None, the
+            dtype will be inferred from the NumPy ndarray data.
+
+    Returns:
+        A (dict of) TensorFlow Tensor(s).
+    """
+    if isinstance(ndarrays, np.ndarray):
+        # Single-tensor case.
+        if isinstance(dtypes, dict):
+            if len(dtypes) != 1:
+                raise ValueError(
+                    "When constructing a single-tensor batch, only a single dtype "
+                    f"should be given, instead got: {dtypes}"
+                )
+            dtypes = next(iter(dtypes.values()))
+        batch = convert_ndarray_to_tf_tensor(ndarrays, dtypes)
+    else:
+        # Multi-tensor case.
+        batch = {
+            col_name: convert_ndarray_to_tf_tensor(
+                col_ndarray,
+                dtype=dtypes[col_name] if isinstance(dtypes, dict) else dtypes,
+            )
+            for col_name, col_ndarray in ndarrays.items()
+        }
+
+    return batch