docarray · JoanFM · Oct 23, 2023 · Oct 18, 2023 · Oct 18, 2023 · Oct 18, 2023
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
@@ -94,7 +94,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: [3.8]
+        python-version: [3.9]
         pydantic-version: ["pydantic-v2", "pydantic-v1"]
         test-path: [tests/integrations, tests/units, tests/documentation]
     steps:
@@ -112,6 +112,7 @@ jobs:
           ./scripts/install_pydantic_v2.sh ${{ matrix.pydantic-version }}
           poetry run pip uninstall -y torch
           poetry run pip install torch
+          poetry run pip install numpy==1.26.1
           sudo apt-get update
           sudo apt-get install --no-install-recommends ffmpeg
 

diff --git a/docarray/base_doc/doc.py b/docarray/base_doc/doc.py
@@ -22,7 +22,7 @@
 import typing_extensions
 from pydantic import BaseModel, Field
 from pydantic.fields import FieldInfo
-from typing_inspect import is_optional_type
+from typing_inspect import get_args, is_optional_type
 
 from docarray.utils._internal.pydantic import is_pydantic_v2
 
@@ -185,7 +185,7 @@ def _get_field_annotation(cls, field: str) -> Type:
             if is_optional_type(
                 annotation
             ):  # this is equivalent to `outer_type_` in pydantic v1
-                return annotation.__args__[0]
+                return get_args(annotation)[0]
             else:
                 return annotation
         else:
@@ -205,12 +205,12 @@ def _get_field_inner_type(cls, field: str) -> Type:
             if is_optional_type(
                 annotation
             ):  # this is equivalent to `outer_type_` in pydantic v1
-                return annotation.__args__[0]
+                return get_args(annotation)[0]
             elif annotation == Tuple:
-                if len(annotation.__args__) == 0:
+                if len(get_args(annotation)) == 0:
                     return Any
                 else:
-                    annotation.__args__[0]
+                    get_args(annotation)[0]
             else:
                 return annotation
         else:

diff --git a/docarray/base_doc/mixins/io.py b/docarray/base_doc/mixins/io.py
@@ -336,7 +336,7 @@ def _get_content_from_node_proto(
                     field_type = None
 
                 if isinstance(field_type, GenericAlias):
-                    field_type = field_type.__args__[0]
+                    field_type = get_args(field_type)[0]
 
                 return_field = arg_to_container[content_key](
                     cls._get_content_from_node_proto(node, field_type=field_type)

diff --git a/docarray/display/document_summary.py b/docarray/display/document_summary.py
@@ -1,4 +1,4 @@
-from typing import Any, List, Optional, Type, Union
+from typing import Any, List, Optional, Type, Union, get_args
 
 from rich.highlighter import RegexHighlighter
 from rich.theme import Theme
@@ -83,7 +83,7 @@ def _get_schema(
 
                 if is_union_type(field_type) or is_optional_type(field_type):
                     sub_tree = Tree(node_name, highlight=True)
-                    for arg in field_type.__args__:
+                    for arg in get_args(field_type):
                         if safe_issubclass(arg, BaseDoc):
                             sub_tree.add(
                                 DocumentSummary._get_schema(

diff --git a/docarray/helper.py b/docarray/helper.py
@@ -15,7 +15,23 @@
     Union,
 )
 
+import numpy as np
+
 from docarray.utils._internal._typing import safe_issubclass
+from docarray.utils._internal.misc import (
+    is_jax_available,
+    is_tf_available,
+    is_torch_available,
+)
+
+if is_torch_available():
+    import torch
+
+if is_jax_available():
+    import jax
+
+if is_tf_available():
+    import tensorflow as tf
 
 if TYPE_CHECKING:
     from docarray import BaseDoc
@@ -54,6 +70,35 @@ def _access_path_to_dict(access_path: str, value) -> Dict[str, Any]:
     return result
 
 
+def _is_none_like(val: Any) -> bool:
+    """
+    :param val: any value
+    :return: true iff `val` equals to `None`, `'None'` or `''`
+    """
+    # Convoluted implementation, but fixes https://github.com/docarray/docarray/issues/1821
+
+    # tensor-like types can have unexpected (= broadcast) `==`/`in` semantics,
+    # so treat separately
+    is_np_arr = isinstance(val, np.ndarray)
+    if is_np_arr:
+        return False
+
+    is_torch_tens = is_torch_available() and isinstance(val, torch.Tensor)
+    if is_torch_tens:
+        return False
+
+    is_tf_tens = is_tf_available() and isinstance(val, tf.Tensor)
+    if is_tf_tens:
+        return False
+
+    is_jax_arr = is_jax_available() and isinstance(val, jax.numpy.ndarray)
+    if is_jax_arr:
+        return False
+
+    # "normal" case
+    return val in ['', 'None', None]
+
+
 def _access_path_dict_to_nested_dict(access_path2val: Dict[str, Any]) -> Dict[Any, Any]:
     """
     Convert a dict, where the keys are access paths ("__"-separated) to a nested dictionary.
@@ -76,7 +121,7 @@ def _access_path_dict_to_nested_dict(access_path2val: Dict[str, Any]) -> Dict[An
     for access_path, value in access_path2val.items():
         field2val = _access_path_to_dict(
             access_path=access_path,
-            value=value if value not in ['', 'None'] else None,
+            value=None if _is_none_like(value) else value,
         )
         _update_nested_dicts(to_update=nested_dict, update_with=field2val)
     return nested_dict

diff --git a/tests/units/typing/tensor/test_ndarray.py b/tests/units/typing/tensor/test_ndarray.py
@@ -200,9 +200,9 @@ def test_parametrized_instance():
 def test_parametrized_equality():
     t1 = parse_obj_as(NdArray[128], np.zeros(128))
     t2 = parse_obj_as(NdArray[128], np.zeros(128))
-    t3 = parse_obj_as(NdArray[256], np.zeros(256))
+    t3 = parse_obj_as(NdArray[128], np.ones(128))
     assert (t1 == t2).all()
-    assert not t1 == t3
+    assert not (t1 == t3).any()
 
 
 def test_parametrized_operations():