Scaffold empty component based on morphologizer

Closes #1

spacy-phonologizer/pipeline/__init__.py

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+from .phonologizer import Phonologizer
+
+__all__ = ["Phonologizer"]

spacy-phonologizer/pipeline/phonologizer.py

@@ -0,0 +1,114 @@
+from typing import Callable, Dict, Optional, Union, Any
+
+from spacy.language import Language
+from spacy.pipeline.tagger import Tagger
+from spacy.scorer import Scorer
+from spacy.util import registry
+from spacy.vocab import Vocab
+from thinc.api import Config, Model
+
+default_model_config = """
+[model]
+@architectures = "spacy.Tagger.v1"
+
+[model.tok2vec]
+@architectures = "spacy.Tok2Vec.v2"
+
+[model.tok2vec.embed]
+@architectures = "spacy.CharacterEmbed.v2"
+width = 128
+rows = 7000
+nM = 64
+nC = 8
+include_static_vectors = false
+
+[model.tok2vec.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v2"
+width = 128
+depth = 4
+window_size = 1
+maxout_pieces = 3
+"""
+
+DEFAULT_MORPH_MODEL = Config().from_str(default_model_config)["model"]
+
+
+@Language.factory(
+    "phonologizer",
+    assigns=["token.phon"],
+    default_config={
+        "model": DEFAULT_MORPH_MODEL,
+        "scorer": {"@scorers": "spacy.phonologizer_scorer.v1"},
+    },
+    default_score_weights={"phon_acc": 0.5, "phon_per_feat": None},
+)
+def make_phonologizer(
+    nlp: Language,
+    model: Model,
+    name: str,
+    scorer: Optional[Callable],
+):
+    return Phonologizer(nlp.vocab, model, name, scorer=scorer)
+
+
+def phonologizer_score(examples, **kwargs):
+    def phon_key_getter(token, attr):
+        return getattr(token, attr).key
+
+    results = {}
+    results.update(Scorer.score_token_attr(examples, "pos", **kwargs))
+    results.update(
+        Scorer.score_token_attr(examples, "phon", getter=phon_key_getter, **kwargs)
+    )
+    results.update(
+        Scorer.score_token_attr_per_feat(
+            examples, "phon", getter=phon_key_getter, **kwargs
+        )
+    )
+    return results
+
+
+@registry.scorers("spacy.phonologizer_scorer.v1")
+def make_phonologizer_scorer():
+    return phonologizer_score
+
+
+class Phonologizer(Tagger):
+    def __init__(
+        self,
+        vocab: Vocab,
+        model: Model,
+        name: str = "phonologizer",
+        *,
+        scorer: Optional[Callable] = phonologizer_score,
+    ):
+        self.vocab = vocab
+        self.model = model
+        self.name = name
+        self.scorer = scorer
+        self._rehearsal_model = None
+        self.labels_phon: Any = {}
+
+    @property
+    def labels(self):
+        """The labels currently added to the component."""
+        return tuple(self.labels_phon.keys())
+
+    @property
+    def label_data(self) -> Dict[str, Dict[str, Union[str, float, int, None]]]:
+        """A dictionary with all labels data."""
+        return self.labels_phon
+
+    def add_label(self, label):
+        """Add a new label to the pipe."""
+        pass
+
+    def initialize(self, get_examples, *, nlp=None, labels=None):
+        """Initialize the pipe."""
+        pass
+
+    def set_annotations(self, docs, batch_tag_ids):
+        pass
+
+    def get_loss(self, examples, scores):
+        pass