From 493c77462a236fae204920e8a3fa22d70833d2fc Mon Sep 17 00:00:00 2001
From: Leander Fiedler <lfiedler@users.noreply.github.com>
Date: Mon, 6 Apr 2020 18:46:51 +0200
Subject: [PATCH] issue5230: test cases

covering known sources of resource warnings
---
 spacy/tests/regression/test_issue5230.py | 112 +++++++++++++++++++++++
 1 file changed, 112 insertions(+)
 create mode 100644 spacy/tests/regression/test_issue5230.py

diff --git a/spacy/tests/regression/test_issue5230.py b/spacy/tests/regression/test_issue5230.py
new file mode 100644
index 000000000..e3d7c7e82
--- /dev/null
+++ b/spacy/tests/regression/test_issue5230.py
@@ -0,0 +1,112 @@
+import warnings
+
+import numpy
+import pytest
+import srsly
+
+from spacy.kb import KnowledgeBase
+from spacy.vectors import Vectors
+from spacy.language import Language
+from spacy.pipeline import Pipe
+from spacy.tests.util import make_tempdir
+
+
+@pytest.mark.xfail
+def test_language_to_disk_resource_warning():
+    nlp = Language()
+    with make_tempdir() as d:
+        with warnings.catch_warnings(record=True) as w:
+            # catch only warnings raised in spacy.language since there may be others from other components or pipelines
+            warnings.filterwarnings(
+                "always", module="spacy.language", category=ResourceWarning
+            )
+            nlp.to_disk(d)
+            assert len(w) == 0
+
+
+@pytest.mark.xfail
+def test_vectors_to_disk_resource_warning():
+    data = numpy.zeros((3, 300), dtype="f")
+    keys = ["cat", "dog", "rat"]
+    vectors = Vectors(data=data, keys=keys)
+    with make_tempdir() as d:
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings("always", category=ResourceWarning)
+            vectors.to_disk(d)
+            assert len(w) == 0
+
+
+@pytest.mark.xfail
+def test_custom_pipes_to_disk_resource_warning():
+    # create dummy pipe partially implementing interface -- only want to test to_disk
+    class SerializableDummy(object):
+        def __init__(self, **cfg):
+            if cfg:
+                self.cfg = cfg
+            else:
+                self.cfg = None
+            super(SerializableDummy, self).__init__()
+
+        def to_bytes(self, exclude=tuple(), disable=None, **kwargs):
+            return srsly.msgpack_dumps({"dummy": srsly.json_dumps(None)})
+
+        def from_bytes(self, bytes_data, exclude):
+            return self
+
+        def to_disk(self, path, exclude=tuple(), **kwargs):
+            pass
+
+        def from_disk(self, path, exclude=tuple(), **kwargs):
+            return self
+
+    class MyPipe(Pipe):
+        def __init__(self, vocab, model=True, **cfg):
+            if cfg:
+                self.cfg = cfg
+            else:
+                self.cfg = None
+            self.model = SerializableDummy()
+            self.vocab = SerializableDummy()
+
+    pipe = MyPipe(None)
+    with make_tempdir() as d:
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings("always", category=ResourceWarning)
+            pipe.to_disk(d)
+            assert len(w) == 0
+
+
+@pytest.mark.xfail
+def test_tagger_to_disk_resource_warning():
+    nlp = Language()
+    nlp.add_pipe(nlp.create_pipe("tagger"))
+    tagger = nlp.get_pipe("tagger")
+    # need to add model for two reasons:
+    # 1. no model leads to error in serialization,
+    # 2. the affected line is the one for model serialization
+    tagger.begin_training(pipeline=nlp.pipeline)
+
+    with make_tempdir() as d:
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings("always", category=ResourceWarning)
+            tagger.to_disk(d)
+            assert len(w) == 0
+
+
+@pytest.mark.xfail
+def test_entity_linker_to_disk_resource_warning():
+    nlp = Language()
+    nlp.add_pipe(nlp.create_pipe("entity_linker"))
+    entity_linker = nlp.get_pipe("entity_linker")
+    # need to add model for two reasons:
+    # 1. no model leads to error in serialization,
+    # 2. the affected line is the one for model serialization
+    kb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+    entity_linker.set_kb(kb)
+    entity_linker.begin_training(pipeline=nlp.pipeline)
+
+    with make_tempdir() as d:
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings("always", category=ResourceWarning)
+            entity_linker.to_disk(d)
+            assert len(w) == 0