switch order of consuming __arrow_c_array__ and __arrow_c_device_array__

python/pyarrow/array.pxi

@@ -246,25 +246,25 @@ def array(object obj, type=None, mask=None, size=None, from_pandas=None,
 
     if hasattr(obj, '__arrow_array__'):
         return _handle_arrow_array_protocol(obj, type, mask, size)
-    elif hasattr(obj, '__arrow_c_array__'):
+    elif hasattr(obj, '__arrow_c_device_array__'):
         if type is not None:
             requested_type = type.__arrow_c_schema__()
         else:
             requested_type = None
-        schema_capsule, array_capsule = obj.__arrow_c_array__(requested_type)
-        out_array = Array._import_from_c_capsule(schema_capsule, array_capsule)
+        schema_capsule, array_capsule = obj.__arrow_c_device_array__(requested_type)
+        out_array = Array._import_from_c_device_capsule(schema_capsule, array_capsule)
         if type is not None and out_array.type != type:
             # PyCapsule interface type coercion is best effort, so we need to
             # check the type of the returned array and cast if necessary
             out_array = array.cast(type, safe=safe, memory_pool=memory_pool)
         return out_array
-    elif hasattr(obj, '__arrow_c_device_array__'):
+    elif hasattr(obj, '__arrow_c_array__'):
         if type is not None:
             requested_type = type.__arrow_c_schema__()
         else:
             requested_type = None
-        schema_capsule, array_capsule = obj.__arrow_c_device_array__(requested_type)
-        out_array = Array._import_from_c_device_capsule(schema_capsule, array_capsule)
+        schema_capsule, array_capsule = obj.__arrow_c_array__(requested_type)
+        out_array = Array._import_from_c_capsule(schema_capsule, array_capsule)
         if type is not None and out_array.type != type:
             # PyCapsule interface type coercion is best effort, so we need to
             # check the type of the returned array and cast if necessary

python/pyarrow/table.pxi

@@ -5715,27 +5715,27 @@ def record_batch(data, names=None, schema=None, metadata=None):
             raise ValueError(
                 "The 'names' argument is not valid when passing a dictionary")
         return RecordBatch.from_pydict(data, schema=schema, metadata=metadata)
-    elif hasattr(data, "__arrow_c_array__"):
+    elif hasattr(data, "__arrow_c_device_array__"):
         if schema is not None:
             requested_schema = schema.__arrow_c_schema__()
         else:
             requested_schema = None
-        schema_capsule, array_capsule = data.__arrow_c_array__(requested_schema)
-        batch = RecordBatch._import_from_c_capsule(schema_capsule, array_capsule)
+        schema_capsule, array_capsule = data.__arrow_c_device_array__(requested_schema)
+        batch = RecordBatch._import_from_c_device_capsule(schema_capsule, array_capsule)
         if schema is not None and batch.schema != schema:
-            # __arrow_c_array__ coerces schema with best effort, so we might
+            # __arrow_c_device_array__ coerces schema with best effort, so we might
             # need to cast it if the producer wasn't able to cast to exact schema.
             batch = batch.cast(schema)
         return batch
-    elif hasattr(data, "__arrow_c_device_array__"):
+    elif hasattr(data, "__arrow_c_array__"):
         if schema is not None:
             requested_schema = schema.__arrow_c_schema__()
         else:
             requested_schema = None
-        schema_capsule, array_capsule = data.__arrow_c_device_array__(requested_schema)
-        batch = RecordBatch._import_from_c_device_capsule(schema_capsule, array_capsule)
+        schema_capsule, array_capsule = data.__arrow_c_array__(requested_schema)
+        batch = RecordBatch._import_from_c_capsule(schema_capsule, array_capsule)
         if schema is not None and batch.schema != schema:
-            # __arrow_c_device_array__ coerces schema with best effort, so we might
+            # __arrow_c_array__ coerces schema with best effort, so we might
             # need to cast it if the producer wasn't able to cast to exact schema.
             batch = batch.cast(schema)
         return batch