Fix tests

spacy/tests/conftest.py

@@ -127,7 +127,7 @@ def he_tokenizer():
 
 @pytest.fixture(scope="session")
 def hi_tokenizer():
-    return get_lang_class("hi").Defaults.create_tokenizer()
+    return get_lang_class("hi")().tokenizer
 
 
 @pytest.fixture(scope="session")
@@ -245,14 +245,6 @@ def tr_tokenizer():
     return get_lang_class("tr")().tokenizer
 
 
-@pytest.fixture(scope="session")
-def tr_vocab():
-    return get_lang_class("tr").Defaults.create_vocab()
-
-@pytest.fixture(scope="session")
-def tr_vocab():
-    return get_lang_class("tr").Defaults.create_vocab()
-
 @pytest.fixture(scope="session")
 def tt_tokenizer():
     return get_lang_class("tt")().tokenizer
@@ -305,11 +297,7 @@ def zh_tokenizer_pkuseg():
                 "segmenter": "pkuseg",
             }
         },
-        "initialize": {
-            "tokenizer": {
-                "pkuseg_model": "web",
-            }
-        },
+        "initialize": {"tokenizer": {"pkuseg_model": "web",}},
     }
     nlp = get_lang_class("zh").from_config(config)
     nlp.initialize()

spacy/tests/lang/hi/test_lex_attrs.py

@@ -1,6 +1,3 @@
-# coding: utf-8
-from __future__ import unicode_literals
-
 import pytest
 from spacy.lang.hi.lex_attrs import norm, like_num
 

spacy/tests/regression/test_issue6177.py

@@ -1,35 +0,0 @@
-# coding: utf8
-from __future__ import unicode_literals
-
-from spacy.lang.en import English
-from spacy.util import fix_random_seed
-
-
-def test_issue6177():
-    """Test that after fixing the random seed, the results of the pipeline are truly identical"""
-
-    # NOTE: no need to transform this code to v3 when 'master' is merged into 'develop'.
-    # A similar test exists already for v3: test_issue5551
-    # This is just a backport
-
-    results = []
-    for i in range(3):
-        fix_random_seed(0)
-        nlp = English()
-        example = (
-            "Once hot, form ping-pong-ball-sized balls of the mixture, each weighing roughly 25 g.",
-            {"cats": {"Labe1": 1.0, "Label2": 0.0, "Label3": 0.0}},
-        )
-        textcat = nlp.create_pipe("textcat")
-        nlp.add_pipe(textcat)
-        for label in set(example[1]["cats"]):
-            textcat.add_label(label)
-        nlp.begin_training()
-        # Store the result of each iteration
-        result = textcat.model.predict([nlp.make_doc(example[0])])
-        results.append(list(result[0]))
-
-    # All results should be the same because of the fixed seed
-    assert len(results) == 3
-    assert results[0] == results[1]
-    assert results[0] == results[2]