chore: import upstream snapshot with attribution

spacy/tests/parser/test_neural_parser.py

@@ -0,0 +1,110 @@
+import pytest
+from thinc.api import Model
+
+from spacy import registry
+from spacy.pipeline._parser_internals.arc_eager import ArcEager
+from spacy.pipeline.dep_parser import DEFAULT_PARSER_MODEL
+from spacy.pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
+from spacy.pipeline.transition_parser import Parser
+from spacy.tokens.doc import Doc
+from spacy.training import Example
+from spacy.vocab import Vocab
+
+
+@pytest.fixture
+def vocab():
+    return Vocab()
+
+
+@pytest.fixture
+def arc_eager(vocab):
+    actions = ArcEager.get_actions(left_labels=["L"], right_labels=["R"])
+    return ArcEager(vocab.strings, actions)
+
+
+@pytest.fixture
+def tok2vec():
+    cfg = {"model": DEFAULT_TOK2VEC_MODEL}
+    tok2vec = registry.resolve(cfg, validate=True)["model"]
+    tok2vec.initialize()
+    return tok2vec
+
+
+@pytest.fixture
+def parser(vocab, arc_eager):
+    config = {
+        "learn_tokens": False,
+        "min_action_freq": 30,
+        "update_with_oracle_cut_size": 100,
+    }
+    cfg = {"model": DEFAULT_PARSER_MODEL}
+    model = registry.resolve(cfg, validate=True)["model"]
+    return Parser(vocab, model, moves=arc_eager, **config)
+
+
+@pytest.fixture
+def model(arc_eager, tok2vec, vocab):
+    cfg = {"model": DEFAULT_PARSER_MODEL}
+    model = registry.resolve(cfg, validate=True)["model"]
+    model.attrs["resize_output"](model, arc_eager.n_moves)
+    model.initialize()
+    return model
+
+
+@pytest.fixture
+def doc(vocab):
+    return Doc(vocab, words=["a", "b", "c"])
+
+
+@pytest.fixture
+def gold(doc):
+    return {"heads": [1, 1, 1], "deps": ["L", "ROOT", "R"]}
+
+
+def test_can_init_nn_parser(parser):
+    assert isinstance(parser.model, Model)
+
+
+def test_build_model(parser, vocab):
+    config = {
+        "learn_tokens": False,
+        "min_action_freq": 0,
+        "update_with_oracle_cut_size": 100,
+    }
+    cfg = {"model": DEFAULT_PARSER_MODEL}
+    model = registry.resolve(cfg, validate=True)["model"]
+    parser.model = Parser(vocab, model=model, moves=parser.moves, **config).model
+    assert parser.model is not None
+
+
+def test_predict_doc(parser, tok2vec, model, doc):
+    doc.tensor = tok2vec.predict([doc])[0]
+    parser.model = model
+    parser(doc)
+
+
+def test_update_doc(parser, model, doc, gold):
+    parser.model = model
+
+    def optimize(key, weights, gradient):
+        weights -= 0.001 * gradient
+        return weights, gradient
+
+    example = Example.from_dict(doc, gold)
+    parser.update([example], sgd=optimize)
+
+
+@pytest.mark.skip(reason="No longer supported")
+def test_predict_doc_beam(parser, model, doc):
+    parser.model = model
+    parser(doc, beam_width=32, beam_density=0.001)
+
+
+@pytest.mark.skip(reason="No longer supported")
+def test_update_doc_beam(parser, model, doc, gold):
+    parser.model = model
+
+    def optimize(weights, gradient, key=None):
+        weights -= 0.001 * gradient
+
+    parser.update_beam((doc, gold), sgd=optimize)