Update requirements, fixing windows crashes (#13727)

* Re-enable pretraining test

* Require thinc 8.3.4

* Reformat

* Re-enable test

pyproject.toml

@@ -5,7 +5,7 @@ requires = [
     "cymem>=2.0.2,<2.1.0",
     "preshed>=3.0.2,<3.1.0",
     "murmurhash>=0.28.0,<1.1.0",
-    "thinc>=8.3.0,<8.4.0",
+    "thinc>=8.3.4,<8.4.0",
     "numpy>=2.0.0,<3.0.0"
 ]
 build-backend = "setuptools.build_meta"

requirements.txt

@@ -3,7 +3,7 @@ spacy-legacy>=3.0.11,<3.1.0
 spacy-loggers>=1.0.0,<2.0.0
 cymem>=2.0.2,<2.1.0
 preshed>=3.0.2,<3.1.0
-thinc>=8.3.0,<8.4.0
+thinc>=8.3.4,<8.4.0
 ml_datasets>=0.2.0,<0.3.0
 murmurhash>=0.28.0,<1.1.0
 wasabi>=0.9.1,<1.2.0

setup.cfg

@@ -41,7 +41,7 @@ setup_requires =
     cymem>=2.0.2,<2.1.0
     preshed>=3.0.2,<3.1.0
     murmurhash>=0.28.0,<1.1.0
-    thinc>=8.3.0,<8.4.0
+    thinc>=8.3.4,<8.4.0
 install_requires =
     # Our libraries
     spacy-legacy>=3.0.11,<3.1.0
@@ -49,7 +49,7 @@ install_requires =
     murmurhash>=0.28.0,<1.1.0
     cymem>=2.0.2,<2.1.0
     preshed>=3.0.2,<3.1.0
-    thinc>=8.3.0,<8.4.0
+    thinc>=8.3.4,<8.4.0
     wasabi>=0.9.1,<1.2.0
     srsly>=2.4.3,<3.0.0
     catalogue>=2.0.6,<2.1.0

spacy/tests/lang/ca/test_text.py

@@ -1,4 +1,5 @@
 """Test that longer and mixed texts are tokenized correctly."""
+
 import pytest
 
 

spacy/tests/lang/ja/test_lemmatization.py

@@ -3,7 +3,13 @@ import pytest
 
 @pytest.mark.parametrize(
     "word,lemma",
-    [("新しく", "新しい"), ("赤く", "赤い"), ("すごく", "すごい"), ("いただきました", "いただく"), ("なった", "なる")],
+    [
+        ("新しく", "新しい"),
+        ("赤く", "赤い"),
+        ("すごく", "すごい"),
+        ("いただきました", "いただく"),
+        ("なった", "なる"),
+    ],
 )
 def test_ja_lemmatizer_assigns(ja_tokenizer, word, lemma):
     test_lemma = ja_tokenizer(word)[0].lemma_

spacy/tests/lang/ja/test_tokenizer.py

@@ -143,7 +143,12 @@ def test_ja_tokenizer_sub_tokens(
     [
         (
             "取ってつけた",
-            (["五段-ラ行;連用形-促音便"], [], ["下一段-カ行;連用形-一般"], ["助動詞-タ;終止形-一般"]),
+            (
+                ["五段-ラ行;連用形-促音便"],
+                [],
+                ["下一段-カ行;連用形-一般"],
+                ["助動詞-タ;終止形-一般"],
+            ),
             (["トッ"], ["テ"], ["ツケ"], ["タ"]),
         ),
         ("2=3", ([], [], []), (["ニ"], ["_"], ["サン"])),

spacy/tests/lang/ko/test_lemmatization.py

@@ -2,7 +2,14 @@ import pytest
 
 
 @pytest.mark.parametrize(
-    "word,lemma", [("새로운", "새롭"), ("빨간", "빨갛"), ("클수록", "크"), ("뭡니까", "뭣"), ("됐다", "되")]
+    "word,lemma",
+    [
+        ("새로운", "새롭"),
+        ("빨간", "빨갛"),
+        ("클수록", "크"),
+        ("뭡니까", "뭣"),
+        ("됐다", "되"),
+    ],
 )
 def test_ko_lemmatizer_assigns(ko_tokenizer, word, lemma):
     test_lemma = ko_tokenizer(word)[0].lemma_

spacy/tests/lang/pl/test_text.py

@@ -1,4 +1,5 @@
 """Words like numbers are recognized correctly."""
+
 import pytest
 
 

spacy/tests/training/test_pretraining.py

@@ -265,50 +265,50 @@ def test_pretraining_tagger():
 
 
 # Try to debug segfault on windows
-#def test_pretraining_training():
-#    """Test that training can use a pretrained Tok2Vec model"""
-#    config = Config().from_str(pretrain_string_internal)
-#    nlp = util.load_model_from_config(config, auto_fill=True, validate=False)
-#    filled = nlp.config
-#    pretrain_config = util.load_config(DEFAULT_CONFIG_PRETRAIN_PATH)
-#    filled = pretrain_config.merge(filled)
-#    train_config = util.load_config(DEFAULT_CONFIG_PATH)
-#    filled = train_config.merge(filled)
-#    with make_tempdir() as tmp_dir:
-#        pretrain_dir = tmp_dir / "pretrain"
-#        pretrain_dir.mkdir()
-#        file_path = write_sample_jsonl(pretrain_dir)
-#        filled["paths"]["raw_text"] = file_path
-#        filled["pretraining"]["component"] = "tagger"
-#        filled["pretraining"]["layer"] = "tok2vec"
-#        train_dir = tmp_dir / "train"
-#        train_dir.mkdir()
-#        train_path, dev_path = write_sample_training(train_dir)
-#        filled["paths"]["train"] = train_path
-#        filled["paths"]["dev"] = dev_path
-#        filled = filled.interpolate()
-#        P = filled["pretraining"]
-#        nlp_base = init_nlp(filled)
-#        model_base = (
-#            nlp_base.get_pipe(P["component"]).model.get_ref(P["layer"]).get_ref("embed")
-#        )
-#        embed_base = None
-#        for node in model_base.walk():
-#            if node.name == "hashembed":
-#                embed_base = node
-#        pretrain(filled, pretrain_dir)
-#        pretrained_model = Path(pretrain_dir / "model3.bin")
-#        assert pretrained_model.exists()
-#        filled["initialize"]["init_tok2vec"] = str(pretrained_model)
-#        nlp = init_nlp(filled)
-#        model = nlp.get_pipe(P["component"]).model.get_ref(P["layer"]).get_ref("embed")
-#        embed = None
-#        for node in model.walk():
-#            if node.name == "hashembed":
-#                embed = node
-#        # ensure that the tok2vec weights are actually changed by the pretraining
-#        assert np.any(np.not_equal(embed.get_param("E"), embed_base.get_param("E")))
-#        train(nlp, train_dir)
+def test_pretraining_training():
+    """Test that training can use a pretrained Tok2Vec model"""
+    config = Config().from_str(pretrain_string_internal)
+    nlp = util.load_model_from_config(config, auto_fill=True, validate=False)
+    filled = nlp.config
+    pretrain_config = util.load_config(DEFAULT_CONFIG_PRETRAIN_PATH)
+    filled = pretrain_config.merge(filled)
+    train_config = util.load_config(DEFAULT_CONFIG_PATH)
+    filled = train_config.merge(filled)
+    with make_tempdir() as tmp_dir:
+        pretrain_dir = tmp_dir / "pretrain"
+        pretrain_dir.mkdir()
+        file_path = write_sample_jsonl(pretrain_dir)
+        filled["paths"]["raw_text"] = file_path
+        filled["pretraining"]["component"] = "tagger"
+        filled["pretraining"]["layer"] = "tok2vec"
+        train_dir = tmp_dir / "train"
+        train_dir.mkdir()
+        train_path, dev_path = write_sample_training(train_dir)
+        filled["paths"]["train"] = train_path
+        filled["paths"]["dev"] = dev_path
+        filled = filled.interpolate()
+        P = filled["pretraining"]
+        nlp_base = init_nlp(filled)
+        model_base = (
+            nlp_base.get_pipe(P["component"]).model.get_ref(P["layer"]).get_ref("embed")
+        )
+        embed_base = None
+        for node in model_base.walk():
+            if node.name == "hashembed":
+                embed_base = node
+        pretrain(filled, pretrain_dir)
+        pretrained_model = Path(pretrain_dir / "model3.bin")
+        assert pretrained_model.exists()
+        filled["initialize"]["init_tok2vec"] = str(pretrained_model)
+        nlp = init_nlp(filled)
+        model = nlp.get_pipe(P["component"]).model.get_ref(P["layer"]).get_ref("embed")
+        embed = None
+        for node in model.walk():
+            if node.name == "hashembed":
+                embed = node
+        # ensure that the tok2vec weights are actually changed by the pretraining
+        assert np.any(np.not_equal(embed.get_param("E"), embed_base.get_param("E")))
+        train(nlp, train_dir)
 
 
 def write_sample_jsonl(tmp_dir):