mirror of
https://github.com/explosion/spaCy.git
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95c0833656
* Add training option to set annotations on update Add a `[training]` option called `set_annotations_on_update` to specify a list of components for which the predicted annotations should be set on `example.predicted` immediately after that component has been updated. The predicted annotations can be accessed by later components in the pipeline during the processing of the batch in the same `update` call. * Rename to annotates / annotating_components * Add test for `annotating_components` when training from config * Add documentation
460 lines
15 KiB
Python
460 lines
15 KiB
Python
import pytest
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from spacy.language import Language
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from spacy.pipeline import TrainablePipe
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from spacy.training import Example
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from spacy.util import SimpleFrozenList, get_arg_names
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from spacy.lang.en import English
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@pytest.fixture
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def nlp():
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return Language()
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@Language.component("new_pipe")
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def new_pipe(doc):
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return doc
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@Language.component("other_pipe")
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def other_pipe(doc):
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return doc
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def test_add_pipe_no_name(nlp):
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nlp.add_pipe("new_pipe")
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assert "new_pipe" in nlp.pipe_names
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def test_add_pipe_duplicate_name(nlp):
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nlp.add_pipe("new_pipe", name="duplicate_name")
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with pytest.raises(ValueError):
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nlp.add_pipe("new_pipe", name="duplicate_name")
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@pytest.mark.parametrize("name", ["parser"])
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def test_add_pipe_first(nlp, name):
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nlp.add_pipe("new_pipe", name=name, first=True)
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assert nlp.pipeline[0][0] == name
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@pytest.mark.parametrize("name1,name2", [("parser", "lambda_pipe")])
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def test_add_pipe_last(nlp, name1, name2):
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Language.component("new_pipe2", func=lambda doc: doc)
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nlp.add_pipe("new_pipe2", name=name2)
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nlp.add_pipe("new_pipe", name=name1, last=True)
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assert nlp.pipeline[0][0] != name1
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assert nlp.pipeline[-1][0] == name1
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def test_cant_add_pipe_first_and_last(nlp):
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with pytest.raises(ValueError):
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nlp.add_pipe("new_pipe", first=True, last=True)
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@pytest.mark.parametrize("name", ["my_component"])
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def test_get_pipe(nlp, name):
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with pytest.raises(KeyError):
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nlp.get_pipe(name)
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nlp.add_pipe("new_pipe", name=name)
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assert nlp.get_pipe(name) == new_pipe
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@pytest.mark.parametrize(
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"name,replacement,invalid_replacement",
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[("my_component", "other_pipe", lambda doc: doc)],
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)
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def test_replace_pipe(nlp, name, replacement, invalid_replacement):
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with pytest.raises(ValueError):
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nlp.replace_pipe(name, new_pipe)
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nlp.add_pipe("new_pipe", name=name)
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with pytest.raises(ValueError):
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nlp.replace_pipe(name, invalid_replacement)
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nlp.replace_pipe(name, replacement)
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assert nlp.get_pipe(name) == nlp.create_pipe(replacement)
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def test_replace_last_pipe(nlp):
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nlp.add_pipe("sentencizer")
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nlp.add_pipe("ner")
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assert nlp.pipe_names == ["sentencizer", "ner"]
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nlp.replace_pipe("ner", "ner")
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assert nlp.pipe_names == ["sentencizer", "ner"]
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def test_replace_pipe_config(nlp):
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nlp.add_pipe("entity_linker")
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nlp.add_pipe("sentencizer")
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assert nlp.get_pipe("entity_linker").incl_prior is True
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nlp.replace_pipe("entity_linker", "entity_linker", config={"incl_prior": False})
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assert nlp.get_pipe("entity_linker").incl_prior is False
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@pytest.mark.parametrize("old_name,new_name", [("old_pipe", "new_pipe")])
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def test_rename_pipe(nlp, old_name, new_name):
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with pytest.raises(ValueError):
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nlp.rename_pipe(old_name, new_name)
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nlp.add_pipe("new_pipe", name=old_name)
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nlp.rename_pipe(old_name, new_name)
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assert nlp.pipeline[0][0] == new_name
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@pytest.mark.parametrize("name", ["my_component"])
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def test_remove_pipe(nlp, name):
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with pytest.raises(ValueError):
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nlp.remove_pipe(name)
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nlp.add_pipe("new_pipe", name=name)
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assert len(nlp.pipeline) == 1
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removed_name, removed_component = nlp.remove_pipe(name)
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assert not len(nlp.pipeline)
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assert removed_name == name
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assert removed_component == new_pipe
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@pytest.mark.parametrize("name", ["my_component"])
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def test_disable_pipes_method(nlp, name):
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nlp.add_pipe("new_pipe", name=name)
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assert nlp.has_pipe(name)
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disabled = nlp.select_pipes(disable=name)
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assert not nlp.has_pipe(name)
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disabled.restore()
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@pytest.mark.parametrize("name", ["my_component"])
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def test_enable_pipes_method(nlp, name):
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nlp.add_pipe("new_pipe", name=name)
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assert nlp.has_pipe(name)
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disabled = nlp.select_pipes(enable=[])
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assert not nlp.has_pipe(name)
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disabled.restore()
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@pytest.mark.parametrize("name", ["my_component"])
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def test_disable_pipes_context(nlp, name):
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"""Test that an enabled component stays enabled after running the context manager."""
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nlp.add_pipe("new_pipe", name=name)
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assert nlp.has_pipe(name)
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with nlp.select_pipes(disable=name):
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assert not nlp.has_pipe(name)
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assert nlp.has_pipe(name)
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@pytest.mark.parametrize("name", ["my_component"])
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def test_disable_pipes_context_restore(nlp, name):
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"""Test that a disabled component stays disabled after running the context manager."""
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nlp.add_pipe("new_pipe", name=name)
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assert nlp.has_pipe(name)
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nlp.disable_pipe(name)
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assert not nlp.has_pipe(name)
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with nlp.select_pipes(disable=name):
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assert not nlp.has_pipe(name)
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assert not nlp.has_pipe(name)
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def test_select_pipes_list_arg(nlp):
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for name in ["c1", "c2", "c3"]:
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nlp.add_pipe("new_pipe", name=name)
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assert nlp.has_pipe(name)
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with nlp.select_pipes(disable=["c1", "c2"]):
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assert not nlp.has_pipe("c1")
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assert not nlp.has_pipe("c2")
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assert nlp.has_pipe("c3")
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with nlp.select_pipes(enable="c3"):
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assert not nlp.has_pipe("c1")
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assert not nlp.has_pipe("c2")
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assert nlp.has_pipe("c3")
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with nlp.select_pipes(enable=["c1", "c2"], disable="c3"):
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assert nlp.has_pipe("c1")
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assert nlp.has_pipe("c2")
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assert not nlp.has_pipe("c3")
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with nlp.select_pipes(enable=[]):
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assert not nlp.has_pipe("c1")
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assert not nlp.has_pipe("c2")
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assert not nlp.has_pipe("c3")
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with nlp.select_pipes(enable=["c1", "c2", "c3"], disable=[]):
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assert nlp.has_pipe("c1")
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assert nlp.has_pipe("c2")
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assert nlp.has_pipe("c3")
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with nlp.select_pipes(disable=["c1", "c2", "c3"], enable=[]):
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assert not nlp.has_pipe("c1")
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assert not nlp.has_pipe("c2")
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assert not nlp.has_pipe("c3")
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def test_select_pipes_errors(nlp):
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for name in ["c1", "c2", "c3"]:
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nlp.add_pipe("new_pipe", name=name)
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assert nlp.has_pipe(name)
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with pytest.raises(ValueError):
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nlp.select_pipes()
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with pytest.raises(ValueError):
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nlp.select_pipes(enable=["c1", "c2"], disable=["c1"])
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with pytest.raises(ValueError):
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nlp.select_pipes(enable=["c1", "c2"], disable=[])
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with pytest.raises(ValueError):
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nlp.select_pipes(enable=[], disable=["c3"])
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disabled = nlp.select_pipes(disable=["c2"])
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nlp.remove_pipe("c2")
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with pytest.raises(ValueError):
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disabled.restore()
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@pytest.mark.parametrize("n_pipes", [100])
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def test_add_lots_of_pipes(nlp, n_pipes):
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Language.component("n_pipes", func=lambda doc: doc)
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for i in range(n_pipes):
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nlp.add_pipe("n_pipes", name=f"pipe_{i}")
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assert len(nlp.pipe_names) == n_pipes
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@pytest.mark.parametrize("component", [lambda doc: doc, {"hello": "world"}])
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def test_raise_for_invalid_components(nlp, component):
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with pytest.raises(ValueError):
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nlp.add_pipe(component)
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@pytest.mark.parametrize("component", ["ner", "tagger", "parser", "textcat"])
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def test_pipe_base_class_add_label(nlp, component):
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label = "TEST"
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pipe = nlp.create_pipe(component)
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pipe.add_label(label)
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if component == "tagger":
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# Tagger always has the default coarse-grained label scheme
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assert label in pipe.labels
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else:
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assert pipe.labels == (label,)
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def test_pipe_labels(nlp):
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input_labels = {
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"ner": ["PERSON", "ORG", "GPE"],
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"textcat": ["POSITIVE", "NEGATIVE"],
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}
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for name, labels in input_labels.items():
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nlp.add_pipe(name)
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pipe = nlp.get_pipe(name)
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for label in labels:
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pipe.add_label(label)
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assert len(pipe.labels) == len(labels)
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assert len(nlp.pipe_labels) == len(input_labels)
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for name, labels in nlp.pipe_labels.items():
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assert sorted(input_labels[name]) == sorted(labels)
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def test_add_pipe_before_after():
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"""Test that before/after works with strings and ints."""
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nlp = Language()
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nlp.add_pipe("ner")
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with pytest.raises(ValueError):
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nlp.add_pipe("textcat", before="parser")
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nlp.add_pipe("textcat", before="ner")
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assert nlp.pipe_names == ["textcat", "ner"]
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with pytest.raises(ValueError):
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nlp.add_pipe("parser", before=3)
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with pytest.raises(ValueError):
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nlp.add_pipe("parser", after=3)
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nlp.add_pipe("parser", after=0)
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assert nlp.pipe_names == ["textcat", "parser", "ner"]
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nlp.add_pipe("tagger", before=2)
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assert nlp.pipe_names == ["textcat", "parser", "tagger", "ner"]
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with pytest.raises(ValueError):
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nlp.add_pipe("entity_ruler", after=1, first=True)
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with pytest.raises(ValueError):
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nlp.add_pipe("entity_ruler", before="ner", after=2)
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with pytest.raises(ValueError):
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nlp.add_pipe("entity_ruler", before=True)
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with pytest.raises(ValueError):
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nlp.add_pipe("entity_ruler", first=False)
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def test_disable_enable_pipes():
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name = "test_disable_enable_pipes"
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results = {}
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def make_component(name):
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results[name] = ""
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def component(doc):
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nonlocal results
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results[name] = doc.text
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return doc
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return component
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c1 = Language.component(f"{name}1", func=make_component(f"{name}1"))
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c2 = Language.component(f"{name}2", func=make_component(f"{name}2"))
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nlp = Language()
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nlp.add_pipe(f"{name}1")
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nlp.add_pipe(f"{name}2")
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assert results[f"{name}1"] == ""
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assert results[f"{name}2"] == ""
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assert nlp.pipeline == [(f"{name}1", c1), (f"{name}2", c2)]
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assert nlp.pipe_names == [f"{name}1", f"{name}2"]
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nlp.disable_pipe(f"{name}1")
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assert nlp.disabled == [f"{name}1"]
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assert nlp.component_names == [f"{name}1", f"{name}2"]
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assert nlp.pipe_names == [f"{name}2"]
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assert nlp.config["nlp"]["disabled"] == [f"{name}1"]
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nlp("hello")
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assert results[f"{name}1"] == "" # didn't run
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assert results[f"{name}2"] == "hello" # ran
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nlp.enable_pipe(f"{name}1")
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assert nlp.disabled == []
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assert nlp.pipe_names == [f"{name}1", f"{name}2"]
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assert nlp.config["nlp"]["disabled"] == []
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nlp("world")
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assert results[f"{name}1"] == "world"
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assert results[f"{name}2"] == "world"
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nlp.disable_pipe(f"{name}2")
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nlp.remove_pipe(f"{name}2")
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assert nlp.components == [(f"{name}1", c1)]
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assert nlp.pipeline == [(f"{name}1", c1)]
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assert nlp.component_names == [f"{name}1"]
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assert nlp.pipe_names == [f"{name}1"]
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assert nlp.disabled == []
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assert nlp.config["nlp"]["disabled"] == []
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nlp.rename_pipe(f"{name}1", name)
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assert nlp.components == [(name, c1)]
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assert nlp.component_names == [name]
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nlp("!")
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assert results[f"{name}1"] == "!"
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assert results[f"{name}2"] == "world"
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with pytest.raises(ValueError):
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nlp.disable_pipe(f"{name}2")
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nlp.disable_pipe(name)
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assert nlp.component_names == [name]
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assert nlp.pipe_names == []
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assert nlp.config["nlp"]["disabled"] == [name]
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nlp("?")
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assert results[f"{name}1"] == "!"
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def test_pipe_methods_frozen():
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"""Test that spaCy raises custom error messages if "frozen" properties are
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accessed. We still want to use a list here to not break backwards
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compatibility, but users should see an error if they're trying to append
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to nlp.pipeline etc."""
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nlp = Language()
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ner = nlp.add_pipe("ner")
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assert nlp.pipe_names == ["ner"]
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for prop in [
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nlp.pipeline,
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nlp.pipe_names,
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nlp.components,
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nlp.component_names,
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nlp.disabled,
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nlp.factory_names,
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]:
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assert isinstance(prop, list)
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assert isinstance(prop, SimpleFrozenList)
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with pytest.raises(NotImplementedError):
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nlp.pipeline.append(("ner2", ner))
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with pytest.raises(NotImplementedError):
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nlp.pipe_names.pop()
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with pytest.raises(NotImplementedError):
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nlp.components.sort()
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with pytest.raises(NotImplementedError):
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nlp.component_names.clear()
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@pytest.mark.parametrize(
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"pipe", ["tagger", "parser", "ner", "textcat", "morphologizer"]
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)
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def test_pipe_label_data_exports_labels(pipe):
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nlp = Language()
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pipe = nlp.add_pipe(pipe)
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# Make sure pipe has pipe labels
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assert getattr(pipe, "label_data", None) is not None
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# Make sure pipe can be initialized with labels
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initialize = getattr(pipe, "initialize", None)
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assert initialize is not None
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assert "labels" in get_arg_names(initialize)
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@pytest.mark.parametrize("pipe", ["senter", "entity_linker"])
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def test_pipe_label_data_no_labels(pipe):
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nlp = Language()
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pipe = nlp.add_pipe(pipe)
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assert getattr(pipe, "label_data", None) is None
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initialize = getattr(pipe, "initialize", None)
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if initialize is not None:
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assert "labels" not in get_arg_names(initialize)
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def test_warning_pipe_begin_training():
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with pytest.warns(UserWarning, match="begin_training"):
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class IncompatPipe(TrainablePipe):
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def __init__(self):
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...
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def begin_training(*args, **kwargs):
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...
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def test_pipe_methods_initialize():
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"""Test that the [initialize] config reflects the components correctly."""
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nlp = Language()
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nlp.add_pipe("tagger")
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assert "tagger" not in nlp.config["initialize"]["components"]
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nlp.config["initialize"]["components"]["tagger"] = {"labels": ["hello"]}
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assert nlp.config["initialize"]["components"]["tagger"] == {"labels": ["hello"]}
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nlp.remove_pipe("tagger")
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assert "tagger" not in nlp.config["initialize"]["components"]
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nlp.add_pipe("tagger")
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assert "tagger" not in nlp.config["initialize"]["components"]
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nlp.config["initialize"]["components"]["tagger"] = {"labels": ["hello"]}
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nlp.rename_pipe("tagger", "my_tagger")
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assert "tagger" not in nlp.config["initialize"]["components"]
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assert nlp.config["initialize"]["components"]["my_tagger"] == {"labels": ["hello"]}
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nlp.config["initialize"]["components"]["test"] = {"foo": "bar"}
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nlp.add_pipe("ner", name="test")
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assert "test" in nlp.config["initialize"]["components"]
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nlp.remove_pipe("test")
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assert "test" not in nlp.config["initialize"]["components"]
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def test_update_with_annotates():
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name = "test_with_annotates"
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results = {}
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def make_component(name):
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results[name] = ""
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def component(doc):
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nonlocal results
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results[name] += doc.text
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return doc
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return component
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c1 = Language.component(f"{name}1", func=make_component(f"{name}1"))
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c2 = Language.component(f"{name}2", func=make_component(f"{name}2"))
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components = set([f"{name}1", f"{name}2"])
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nlp = English()
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texts = ["a", "bb", "ccc"]
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examples = []
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for text in texts:
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examples.append(Example(nlp.make_doc(text), nlp.make_doc(text)))
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for components_to_annotate in [[], [f"{name}1"], [f"{name}1", f"{name}2"], [f"{name}2", f"{name}1"]]:
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for key in results:
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results[key] = ""
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nlp = English(vocab=nlp.vocab)
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nlp.add_pipe(f"{name}1")
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nlp.add_pipe(f"{name}2")
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nlp.update(examples, annotates=components_to_annotate)
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for component in components_to_annotate:
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assert results[component] == "".join(eg.predicted.text for eg in examples)
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for component in components - set(components_to_annotate):
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assert results[component] == ""
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