Initial test of mismatched tokenization

This runs, but the results are nonsense because the indices are off.

spacy/tests/pipeline/test_coref.py

@@ -152,6 +152,62 @@ def test_overfitting_IO(nlp):
     # assert_equal(batch_deps_1, batch_deps_2)
     # assert_equal(batch_deps_1, no_batch_deps)
 
+@pytest.mark.skipif(not has_torch, reason="Torch not available")
+def test_tokenization_mismatch(nlp):
+    train_examples = []
+    for text, annot in TRAIN_DATA:
+        eg = Example.from_dict(nlp.make_doc(text), annot)
+        ref = eg.reference
+        char_spans = {}
+        for key, cluster in ref.spans.items():
+            char_spans[key] = []
+            for span in cluster:
+                char_spans[key].append( (span[0].idx, span[-1].idx + len(span[-1])) )
+        with ref.retokenize() as retokenizer:
+            # merge "many friends"
+            retokenizer.merge(ref[5:7])
+
+        # Note this works because it's the same doc and we know the keys
+        for key, _ in ref.spans.items():
+            spans = char_spans[key]
+            ref.spans[key] = [ref.char_span(*span) for span in spans]
+
+        train_examples.append(eg)
+
+    nlp.add_pipe("coref")
+    optimizer = nlp.initialize()
+    test_text = TRAIN_DATA[0][0]
+    doc = nlp(test_text)
+
+    for i in range(15):
+        losses = {}
+        nlp.update(train_examples, sgd=optimizer, losses=losses)
+        doc = nlp(test_text)
+        print(i, doc.spans)
+
+    # test the trained model
+    doc = nlp(test_text)
+
+    # Also test the results are still the same after IO
+    with make_tempdir() as tmp_dir:
+        nlp.to_disk(tmp_dir)
+        nlp2 = util.load_model_from_path(tmp_dir)
+        doc2 = nlp2(test_text)
+
+    # Make sure that running pipe twice, or comparing to call, always amounts to the same predictions
+    texts = [
+        test_text,
+        "I noticed many friends around me",
+        "They received it. They received the SMS.",
+    ]
+
+    # save the docs so they don't get garbage collected
+    docs = list(nlp.pipe(texts))
+    batch_deps_1 = [doc.spans for doc in docs]
+    docs = list(nlp.pipe(texts))
+    batch_deps_2 = [doc.spans for doc in docs]
+    docs = [nlp(text) for text in texts]
+    no_batch_deps = [doc.spans for doc in docs]
 
 @pytest.mark.skipif(not has_torch, reason="Torch not available")
 def test_crossing_spans():