import pytest from spacy.vocab import Vocab from spacy.tokens import Doc, Token from ..util import get_doc def test_doc_retokenize_split(en_vocab): words = ["LosAngeles", "start", "."] heads = [1, 1, 0] doc = get_doc(en_vocab, words=words, heads=heads) assert len(doc) == 3 assert len(str(doc)) == 19 assert doc[0].head.text == "start" assert doc[1].head.text == "." with doc.retokenize() as retokenizer: retokenizer.split( doc[0], ["Los", "Angeles"], [(doc[0], 1), doc[1]], attrs={ "tag": ["NNP"] * 2, "lemma": ["Los", "Angeles"], "ent_type": ["GPE"] * 2, "morph": ["Number=Sing"] * 2, }, ) assert len(doc) == 4 assert doc[0].text == "Los" assert doc[0].head.text == "Angeles" assert doc[0].idx == 0 assert doc[0].morph_ == "Number=Sing" assert doc[1].idx == 3 assert doc[1].text == "Angeles" assert doc[1].head.text == "start" assert doc[1].morph_ == "Number=Sing" assert doc[2].text == "start" assert doc[2].head.text == "." assert doc[3].text == "." assert doc[3].head.text == "." assert len(str(doc)) == 19 def test_doc_retokenize_split_dependencies(en_vocab): doc = Doc(en_vocab, words=["LosAngeles", "start", "."]) dep1 = doc.vocab.strings.add("amod") dep2 = doc.vocab.strings.add("subject") with doc.retokenize() as retokenizer: retokenizer.split( doc[0], ["Los", "Angeles"], [(doc[0], 1), doc[1]], attrs={"dep": [dep1, dep2]}, ) assert doc[0].dep == dep1 assert doc[1].dep == dep2 def test_doc_retokenize_split_heads_error(en_vocab): doc = Doc(en_vocab, words=["LosAngeles", "start", "."]) # Not enough heads with pytest.raises(ValueError): with doc.retokenize() as retokenizer: retokenizer.split(doc[0], ["Los", "Angeles"], [doc[1]]) # Too many heads with pytest.raises(ValueError): with doc.retokenize() as retokenizer: retokenizer.split(doc[0], ["Los", "Angeles"], [doc[1], doc[1], doc[1]]) def test_doc_retokenize_spans_entity_split_iob(): # Test entity IOB stays consistent after merging words = ["abc", "d", "e"] doc = Doc(Vocab(), words=words) doc.ents = [(doc.vocab.strings.add("ent-abcd"), 0, 2)] assert doc[0].ent_iob_ == "B" assert doc[1].ent_iob_ == "I" with doc.retokenize() as retokenizer: retokenizer.split(doc[0], ["a", "b", "c"], [(doc[0], 1), (doc[0], 2), doc[1]]) assert doc[0].ent_iob_ == "B" assert doc[1].ent_iob_ == "I" assert doc[2].ent_iob_ == "I" assert doc[3].ent_iob_ == "I" def test_doc_retokenize_spans_sentence_update_after_split(en_vocab): # fmt: off words = ["StewartLee", "is", "a", "stand", "up", "comedian", ".", "He", "lives", "in", "England", "and", "loves", "JoePasquale", "."] heads = [1, 0, 1, 2, -1, -4, -5, 1, 0, -1, -1, -3, -4, 1, -2] deps = ["nsubj", "ROOT", "det", "amod", "prt", "attr", "punct", "nsubj", "ROOT", "prep", "pobj", "cc", "conj", "compound", "punct"] # fmt: on doc = get_doc(en_vocab, words=words, heads=heads, deps=deps) sent1, sent2 = list(doc.sents) init_len = len(sent1) init_len2 = len(sent2) with doc.retokenize() as retokenizer: retokenizer.split( doc[0], ["Stewart", "Lee"], [(doc[0], 1), doc[1]], attrs={"dep": ["compound", "nsubj"]}, ) retokenizer.split( doc[13], ["Joe", "Pasquale"], [(doc[13], 1), doc[12]], attrs={"dep": ["compound", "dobj"]}, ) sent1, sent2 = list(doc.sents) assert len(sent1) == init_len + 1 assert len(sent2) == init_len2 + 1 def test_doc_retokenize_split_orths_mismatch(en_vocab): """Test that the regular retokenizer.split raises an error if the orths don't match the original token text. There might still be a method that allows this, but for the default use cases, merging and splitting should always conform with spaCy's non-destructive tokenization policy. Otherwise, it can lead to very confusing and unexpected results. """ doc = Doc(en_vocab, words=["LosAngeles", "start", "."]) with pytest.raises(ValueError): with doc.retokenize() as retokenizer: retokenizer.split(doc[0], ["L", "A"], [(doc[0], 0), (doc[0], 0)]) def test_doc_retokenize_split_extension_attrs(en_vocab): Token.set_extension("a", default=False, force=True) Token.set_extension("b", default="nothing", force=True) doc = Doc(en_vocab, words=["LosAngeles", "start"]) with doc.retokenize() as retokenizer: heads = [(doc[0], 1), doc[1]] underscore = [{"a": True, "b": "1"}, {"b": "2"}] attrs = {"lemma": ["los", "angeles"], "_": underscore} retokenizer.split(doc[0], ["Los", "Angeles"], heads, attrs=attrs) assert doc[0].lemma_ == "los" assert doc[0]._.a is True assert doc[0]._.b == "1" assert doc[1].lemma_ == "angeles" assert doc[1]._.a is False assert doc[1]._.b == "2" @pytest.mark.parametrize( "underscore_attrs", [ [{"a": "x"}, {}], # Overwriting getter without setter [{"b": "x"}, {}], # Overwriting method [{"c": "x"}, {}], # Overwriting nonexistent attribute [{"a": "x"}, {"x": "x"}], # Combination [{"a": "x", "x": "x"}, {"x": "x"}], # Combination {"x": "x"}, # Not a list of dicts ], ) def test_doc_retokenize_split_extension_attrs_invalid(en_vocab, underscore_attrs): Token.set_extension("x", default=False, force=True) Token.set_extension("a", getter=lambda x: x, force=True) Token.set_extension("b", method=lambda x: x, force=True) doc = Doc(en_vocab, words=["LosAngeles", "start"]) attrs = {"_": underscore_attrs} with pytest.raises(ValueError): with doc.retokenize() as retokenizer: heads = [(doc[0], 1), doc[1]] retokenizer.split(doc[0], ["Los", "Angeles"], heads, attrs=attrs) def test_doc_retokenizer_split_lex_attrs(en_vocab): """Test that retokenization also sets attributes on the lexeme if they're lexical attributes. For example, if a user sets IS_STOP, it should mean that "all tokens with that lexeme" are marked as a stop word, so the ambiguity here is acceptable. Also see #2390. """ assert not Doc(en_vocab, words=["Los"])[0].is_stop assert not Doc(en_vocab, words=["Angeles"])[0].is_stop doc = Doc(en_vocab, words=["LosAngeles", "start"]) assert not doc[0].is_stop with doc.retokenize() as retokenizer: attrs = {"is_stop": [True, False]} heads = [(doc[0], 1), doc[1]] retokenizer.split(doc[0], ["Los", "Angeles"], heads, attrs=attrs) assert doc[0].is_stop assert not doc[1].is_stop def test_doc_retokenizer_realloc(en_vocab): """#4604: realloc correctly when new tokens outnumber original tokens""" text = "Hyperglycemic adverse events following antipsychotic drug administration in the" doc = Doc(en_vocab, words=text.split()[:-1]) with doc.retokenize() as retokenizer: token = doc[0] heads = [(token, 0)] * len(token) retokenizer.split(doc[token.i], list(token.text), heads=heads) doc = Doc(en_vocab, words=text.split()) with doc.retokenize() as retokenizer: token = doc[0] heads = [(token, 0)] * len(token) retokenizer.split(doc[token.i], list(token.text), heads=heads)