* Delete test_token_references, which checked a flakey strategy for preventing orphan tokens from a while ago. Now orphan tokens simply hold a reference to Pool, preventing the memory from being freed underneath them. This means that we don't need to run this slow test.

spacy/tests/tokens/test_token_references.py

@@ -1,54 +0,0 @@
-from __future__ import unicode_literals
-import pytest
-import gc
-
-from spacy.en import English
-import os
-
-# Let this have its own instances, as we have to be careful about memory here
-# that's the point, after all
-
-@pytest.mark.models
-def get_orphan_token(text, i):
-    nlp = English()
-    tokens = nlp(text)
-    gc.collect()
-    token = tokens[i]
-    del tokens
-    return token
-
-
-@pytest.mark.models
-def test_orphan():
-    orphan = get_orphan_token('An orphan token', 1)
-    gc.collect()
-    dummy = get_orphan_token('Load and flush the memory', 0)
-    dummy = get_orphan_token('Load again...', 0)
-    assert orphan.orth_ == 'orphan'
-    assert orphan.pos_ in ('ADJ', 'NOUN')
-    assert orphan.head.orth_ == 'token'
-
-
-def _orphan_from_list(toks):
-    ''' Take the tokens from nlp(), append them to a list, return the list '''
-    lst = []
-    for tok in toks:
-        lst.append(tok)
-    return lst
-
-
-@pytest.mark.models
-def test_list_orphans():
-    # Test case from NSchrading
-    nlp = English()
-    samples = ["a", "test blah wat okay"]
-    lst = []
-    for sample in samples:
-        # Go through all the samples, call nlp() on each to get tokens,
-        # pass those tokens to the _orphan_from_list() function, get a list back
-        # and put all results in another list
-        lst.extend(_orphan_from_list(nlp(sample)))
-    # go through the list of all tokens and try to print orth_
-    orths = ['a', 'test', 'blah', 'wat', 'okay']
-    for i, l in enumerate(lst):
-        assert l.orth_  == orths[i]