Tidy up, autoformat, add types

.gitignore

@@ -51,6 +51,7 @@ env3.*/
 .denv
 .pypyenv
 .pytest_cache/
+.mypy_cache/
 
 # Distribution / packaging
 env/

setup.cfg

@@ -108,3 +108,8 @@ exclude =
 [tool:pytest]
 markers =
     slow
+
+[mypy]
+ignore_missing_imports = True
+no_implicit_optional = True
+plugins = pydantic.mypy, thinc.mypy

spacy/cli/convert.py

@@ -1,4 +1,4 @@
-from typing import Optional
+from typing import Optional, Any, List, Union
 from enum import Enum
 from pathlib import Path
 from wasabi import Printer
@@ -66,10 +66,9 @@ def convert_cli(
         file_type = file_type.value
     input_path = Path(input_path)
     output_dir = "-" if output_dir == Path("-") else output_dir
-    cli_args = locals()
     silent = output_dir == "-"
     msg = Printer(no_print=silent)
-    verify_cli_args(msg, **cli_args)
+    verify_cli_args(msg, input_path, output_dir, file_type, converter, ner_map)
     converter = _get_converter(msg, converter, input_path)
     convert(
         input_path,
@@ -89,8 +88,8 @@ def convert_cli(
 
 
 def convert(
-    input_path: Path,
+    input_path: Union[str, Path],
-    output_dir: Path,
+    output_dir: Union[str, Path],
     *,
     file_type: str = "json",
     n_sents: int = 1,
@@ -102,13 +101,12 @@ def convert(
     ner_map: Optional[Path] = None,
     lang: Optional[str] = None,
     silent: bool = True,
-    msg: Optional[Path] = None,
+    msg: Optional[Printer],
 ) -> None:
     if not msg:
         msg = Printer(no_print=silent)
     ner_map = srsly.read_json(ner_map) if ner_map is not None else None
-
+    for input_loc in walk_directory(Path(input_path)):
-    for input_loc in walk_directory(input_path):
         input_data = input_loc.open("r", encoding="utf-8").read()
         # Use converter function to convert data
         func = CONVERTERS[converter]
@@ -140,14 +138,14 @@ def convert(
             msg.good(f"Generated output file ({len(docs)} documents): {output_file}")
 
 
-def _print_docs_to_stdout(data, output_type):
+def _print_docs_to_stdout(data: Any, output_type: str) -> None:
     if output_type == "json":
         srsly.write_json("-", data)
     else:
         sys.stdout.buffer.write(data)
 
 
-def _write_docs_to_file(data, output_file, output_type):
+def _write_docs_to_file(data: Any, output_file: Path, output_type: str) -> None:
     if not output_file.parent.exists():
         output_file.parent.mkdir(parents=True)
     if output_type == "json":
@@ -157,7 +155,7 @@ def _write_docs_to_file(data, output_file, output_type):
             file_.write(data)
 
 
-def autodetect_ner_format(input_data: str) -> str:
+def autodetect_ner_format(input_data: str) -> Optional[str]:
     # guess format from the first 20 lines
     lines = input_data.split("\n")[:20]
     format_guesses = {"ner": 0, "iob": 0}
@@ -176,7 +174,7 @@ def autodetect_ner_format(input_data: str) -> str:
     return None
 
 
-def walk_directory(path):
+def walk_directory(path: Path) -> List[Path]:
     if not path.is_dir():
         return [path]
     paths = [path]
@@ -196,31 +194,25 @@ def walk_directory(path):
 
 
 def verify_cli_args(
-    msg,
+    msg: Printer,
-    input_path,
+    input_path: Union[str, Path],
-    output_dir,
+    output_dir: Union[str, Path],
-    file_type,
+    file_type: FileTypes,
-    n_sents,
+    converter: str,
-    seg_sents,
+    ner_map: Optional[Path],
-    model,
-    morphology,
-    merge_subtokens,
-    converter,
-    ner_map,
-    lang,
 ):
     input_path = Path(input_path)
     if file_type not in FILE_TYPES_STDOUT and output_dir == "-":
-        # TODO: support msgpack via stdout in srsly?
         msg.fail(
-            f"Can't write .{file_type} data to stdout",
+            f"Can't write .{file_type} data to stdout. Please specify an output directory.",
-            "Please specify an output directory.",
             exits=1,
         )
     if not input_path.exists():
         msg.fail("Input file not found", input_path, exits=1)
     if output_dir != "-" and not Path(output_dir).exists():
         msg.fail("Output directory not found", output_dir, exits=1)
+    if ner_map is not None and not Path(ner_map).exists():
+        msg.fail("NER map not found", ner_map, exits=1)
     if input_path.is_dir():
         input_locs = walk_directory(input_path)
         if len(input_locs) == 0:
@@ -229,10 +221,8 @@ def verify_cli_args(
         if len(file_types) >= 2:
             file_types = ",".join(file_types)
             msg.fail("All input files must be same type", file_types, exits=1)
-    converter = _get_converter(msg, converter, input_path)
+    if converter != "auto" and converter not in CONVERTERS:
-    if converter not in CONVERTERS:
         msg.fail(f"Can't find converter for {converter}", exits=1)
-    return converter
 
 
 def _get_converter(msg, converter, input_path):

spacy/cli/evaluate.py

@@ -82,11 +82,11 @@ def evaluate(
         "NER P": "ents_p",
         "NER R": "ents_r",
         "NER F": "ents_f",
-        "Textcat AUC": 'textcat_macro_auc',
+        "Textcat AUC": "textcat_macro_auc",
-        "Textcat F": 'textcat_macro_f',
+        "Textcat F": "textcat_macro_f",
-        "Sent P": 'sents_p',
+        "Sent P": "sents_p",
-        "Sent R": 'sents_r',
+        "Sent R": "sents_r",
-        "Sent F": 'sents_f',
+        "Sent F": "sents_f",
     }
     results = {}
     for metric, key in metrics.items():

spacy/cli/train.py

@@ -14,7 +14,6 @@ import typer
 from ._util import app, Arg, Opt, parse_config_overrides, show_validation_error
 from ._util import import_code
 from ..gold import Corpus, Example
-from ..lookups import Lookups
 from ..language import Language
 from .. import util
 from ..errors import Errors

spacy/compat.py

@@ -1,12 +1,5 @@
-"""
+"""Helpers for Python and platform compatibility."""
-Helpers for Python and platform compatibility. To distinguish them from
-the builtin functions, replacement functions are suffixed with an underscore,
-e.g. `unicode_`.
-
-DOCS: https://spacy.io/api/top-level#compat
-"""
 import sys
-
 from thinc.util import copy_array
 
 try:
@@ -40,21 +33,3 @@ copy_array = copy_array
 is_windows = sys.platform.startswith("win")
 is_linux = sys.platform.startswith("linux")
 is_osx = sys.platform == "darwin"
-
-
-def is_config(windows=None, linux=None, osx=None, **kwargs):
-    """Check if a specific configuration of Python version and operating system
-    matches the user's setup. Mostly used to display targeted error messages.
-
-    windows (bool): spaCy is executed on Windows.
-    linux (bool): spaCy is executed on Linux.
-    osx (bool): spaCy is executed on OS X or macOS.
-    RETURNS (bool): Whether the configuration matches the user's platform.
-
-    DOCS: https://spacy.io/api/top-level#compat.is_config
-    """
-    return (
-        windows in (None, is_windows)
-        and linux in (None, is_linux)
-        and osx in (None, is_osx)
-    )

spacy/displacy/__init__.py

@@ -4,6 +4,7 @@ spaCy's built in visualization suite for dependencies and named entities.
 DOCS: https://spacy.io/api/top-level#displacy
 USAGE: https://spacy.io/usage/visualizers
 """
+from typing import Union, Iterable, Optional, Dict, Any, Callable
 import warnings
 
 from .render import DependencyRenderer, EntityRenderer
@@ -17,11 +18,17 @@ RENDER_WRAPPER = None
 
 
 def render(
-    docs, style="dep", page=False, minify=False, jupyter=None, options={}, manual=False
+    docs: Union[Iterable[Doc], Doc],
-):
+    style: str = "dep",
+    page: bool = False,
+    minify: bool = False,
+    jupyter: Optional[bool] = None,
+    options: Dict[str, Any] = {},
+    manual: bool = False,
+) -> str:
     """Render displaCy visualisation.
 
-    docs (list or Doc): Document(s) to visualise.
+    docs (Union[Iterable[Doc], Doc]): Document(s) to visualise.
     style (str): Visualisation style, 'dep' or 'ent'.
     page (bool): Render markup as full HTML page.
     minify (bool): Minify HTML markup.
@@ -44,8 +51,8 @@ def render(
     docs = [obj if not isinstance(obj, Span) else obj.as_doc() for obj in docs]
     if not all(isinstance(obj, (Doc, Span, dict)) for obj in docs):
         raise ValueError(Errors.E096)
-    renderer, converter = factories[style]
+    renderer_func, converter = factories[style]
-    renderer = renderer(options=options)
+    renderer = renderer_func(options=options)
     parsed = [converter(doc, options) for doc in docs] if not manual else docs
     _html["parsed"] = renderer.render(parsed, page=page, minify=minify).strip()
     html = _html["parsed"]
@@ -61,15 +68,15 @@ def render(
 
 
 def serve(
-    docs,
+    docs: Union[Iterable[Doc], Doc],
-    style="dep",
+    style: str = "dep",
-    page=True,
+    page: bool = True,
-    minify=False,
+    minify: bool = False,
-    options={},
+    options: Dict[str, Any] = {},
-    manual=False,
+    manual: bool = False,
-    port=5000,
+    port: int = 5000,
-    host="0.0.0.0",
+    host: str = "0.0.0.0",
-):
+) -> None:
     """Serve displaCy visualisation.
 
     docs (list or Doc): Document(s) to visualise.
@@ -88,7 +95,6 @@ def serve(
 
     if is_in_jupyter():
         warnings.warn(Warnings.W011)
-
     render(docs, style=style, page=page, minify=minify, options=options, manual=manual)
     httpd = simple_server.make_server(host, port, app)
     print(f"\nUsing the '{style}' visualizer")
@@ -102,14 +108,13 @@ def serve(
 
 
 def app(environ, start_response):
-    # Headers and status need to be bytes in Python 2, see #1227
     headers = [("Content-type", "text/html; charset=utf-8")]
     start_response("200 OK", headers)
     res = _html["parsed"].encode(encoding="utf-8")
     return [res]
 
 
-def parse_deps(orig_doc, options={}):
+def parse_deps(orig_doc: Doc, options: Dict[str, Any] = {}) -> Dict[str, Any]:
     """Generate dependency parse in {'words': [], 'arcs': []} format.
 
     doc (Doc): Document do parse.
@@ -152,7 +157,6 @@ def parse_deps(orig_doc, options={}):
         }
         for w in doc
     ]
-
     arcs = []
     for word in doc:
         if word.i < word.head.i:
@@ -171,7 +175,7 @@ def parse_deps(orig_doc, options={}):
     return {"words": words, "arcs": arcs, "settings": get_doc_settings(orig_doc)}
 
 
-def parse_ents(doc, options={}):
+def parse_ents(doc: Doc, options: Dict[str, Any] = {}) -> Dict[str, Any]:
     """Generate named entities in [{start: i, end: i, label: 'label'}] format.
 
     doc (Doc): Document do parse.
@@ -188,7 +192,7 @@ def parse_ents(doc, options={}):
     return {"text": doc.text, "ents": ents, "title": title, "settings": settings}
 
 
-def set_render_wrapper(func):
+def set_render_wrapper(func: Callable[[str], str]) -> None:
     """Set an optional wrapper function that is called around the generated
     HTML markup on displacy.render. This can be used to allow integration into
     other platforms, similar to Jupyter Notebooks that require functions to be
@@ -205,7 +209,7 @@ def set_render_wrapper(func):
     RENDER_WRAPPER = func
 
 
-def get_doc_settings(doc):
+def get_doc_settings(doc: Doc) -> Dict[str, Any]:
     return {
         "lang": doc.lang_,
         "direction": doc.vocab.writing_system.get("direction", "ltr"),

spacy/displacy/render.py

@@ -1,19 +1,36 @@
+from typing import Dict, Any, List, Optional, Union
 import uuid
 
-from .templates import (
+from .templates import TPL_DEP_SVG, TPL_DEP_WORDS, TPL_DEP_WORDS_LEMMA, TPL_DEP_ARCS
-    TPL_DEP_SVG,
-    TPL_DEP_WORDS,
-    TPL_DEP_WORDS_LEMMA,
-    TPL_DEP_ARCS,
-    TPL_ENTS,
-)
 from .templates import TPL_ENT, TPL_ENT_RTL, TPL_FIGURE, TPL_TITLE, TPL_PAGE
+from .templates import TPL_ENTS
 from ..util import minify_html, escape_html, registry
 from ..errors import Errors
 
 
 DEFAULT_LANG = "en"
 DEFAULT_DIR = "ltr"
+DEFAULT_ENTITY_COLOR = "#ddd"
+DEFAULT_LABEL_COLORS = {
+    "ORG": "#7aecec",
+    "PRODUCT": "#bfeeb7",
+    "GPE": "#feca74",
+    "LOC": "#ff9561",
+    "PERSON": "#aa9cfc",
+    "NORP": "#c887fb",
+    "FACILITY": "#9cc9cc",
+    "EVENT": "#ffeb80",
+    "LAW": "#ff8197",
+    "LANGUAGE": "#ff8197",
+    "WORK_OF_ART": "#f0d0ff",
+    "DATE": "#bfe1d9",
+    "TIME": "#bfe1d9",
+    "MONEY": "#e4e7d2",
+    "QUANTITY": "#e4e7d2",
+    "ORDINAL": "#e4e7d2",
+    "CARDINAL": "#e4e7d2",
+    "PERCENT": "#e4e7d2",
+}
 
 
 class DependencyRenderer:
@@ -21,7 +38,7 @@ class DependencyRenderer:
 
     style = "dep"
 
-    def __init__(self, options={}):
+    def __init__(self, options: Dict[str, Any] = {}) -> None:
         """Initialise dependency renderer.
 
         options (dict): Visualiser-specific options (compact, word_spacing,
@@ -41,7 +58,9 @@ class DependencyRenderer:
         self.direction = DEFAULT_DIR
         self.lang = DEFAULT_LANG
 
-    def render(self, parsed, page=False, minify=False):
+    def render(
+        self, parsed: List[Dict[str, Any]], page: bool = False, minify: bool = False
+    ) -> str:
         """Render complete markup.
 
         parsed (list): Dependency parses to render.
@@ -72,10 +91,15 @@ class DependencyRenderer:
             return minify_html(markup)
         return markup
 
-    def render_svg(self, render_id, words, arcs):
+    def render_svg(
+        self,
+        render_id: Union[int, str],
+        words: List[Dict[str, Any]],
+        arcs: List[Dict[str, Any]],
+    ) -> str:
         """Render SVG.
 
-        render_id (int): Unique ID, typically index of document.
+        render_id (Union[int, str]): Unique ID, typically index of document.
         words (list): Individual words and their tags.
         arcs (list): Individual arcs and their start, end, direction and label.
         RETURNS (str): Rendered SVG markup.
@@ -86,15 +110,15 @@ class DependencyRenderer:
         self.width = self.offset_x + len(words) * self.distance
         self.height = self.offset_y + 3 * self.word_spacing
         self.id = render_id
-        words = [
+        words_svg = [
             self.render_word(w["text"], w["tag"], w.get("lemma", None), i)
             for i, w in enumerate(words)
         ]
-        arcs = [
+        arcs_svg = [
             self.render_arrow(a["label"], a["start"], a["end"], a["dir"], i)
             for i, a in enumerate(arcs)
         ]
-        content = "".join(words) + "".join(arcs)
+        content = "".join(words_svg) + "".join(arcs_svg)
         return TPL_DEP_SVG.format(
             id=self.id,
             width=self.width,
@@ -107,9 +131,7 @@ class DependencyRenderer:
             lang=self.lang,
         )
 
-    def render_word(
+    def render_word(self, text: str, tag: str, lemma: str, i: int) -> str:
-        self, text, tag, lemma, i,
-    ):
         """Render individual word.
 
         text (str): Word text.
@@ -128,7 +150,9 @@ class DependencyRenderer:
             )
         return TPL_DEP_WORDS.format(text=html_text, tag=tag, x=x, y=y)
 
-    def render_arrow(self, label, start, end, direction, i):
+    def render_arrow(
+        self, label: str, start: int, end: int, direction: str, i: int
+    ) -> str:
         """Render individual arrow.
 
         label (str): Dependency label.
@@ -172,7 +196,7 @@ class DependencyRenderer:
             arc=arc,
         )
 
-    def get_arc(self, x_start, y, y_curve, x_end):
+    def get_arc(self, x_start: int, y: int, y_curve: int, x_end: int) -> str:
         """Render individual arc.
 
         x_start (int): X-coordinate of arrow start point.
@@ -186,7 +210,7 @@ class DependencyRenderer:
             template = "M{x},{y} {x},{c} {e},{c} {e},{y}"
         return template.format(x=x_start, y=y, c=y_curve, e=x_end)
 
-    def get_arrowhead(self, direction, x, y, end):
+    def get_arrowhead(self, direction: str, x: int, y: int, end: int) -> str:
         """Render individual arrow head.
 
         direction (str): Arrow direction, 'left' or 'right'.
@@ -196,24 +220,12 @@ class DependencyRenderer:
         RETURNS (str): Definition of the arrow head path ('d' attribute).
         """
         if direction == "left":
-            pos1, pos2, pos3 = (x, x - self.arrow_width + 2, x + self.arrow_width - 2)
+            p1, p2, p3 = (x, x - self.arrow_width + 2, x + self.arrow_width - 2)
         else:
-            pos1, pos2, pos3 = (
+            p1, p2, p3 = (end, end + self.arrow_width - 2, end - self.arrow_width + 2)
-                end,
+        return f"M{p1},{y + 2} L{p2},{y - self.arrow_width} {p3},{y - self.arrow_width}"
-                end + self.arrow_width - 2,
-                end - self.arrow_width + 2,
-            )
-        arrowhead = (
-            pos1,
-            y + 2,
-            pos2,
-            y - self.arrow_width,
-            pos3,
-            y - self.arrow_width,
-        )
-        return "M{},{} L{},{} {},{}".format(*arrowhead)
 
-    def get_levels(self, arcs):
+    def get_levels(self, arcs: List[Dict[str, Any]]) -> List[int]:
         """Calculate available arc height "levels".
         Used to calculate arrow heights dynamically and without wasting space.
 
@@ -229,41 +241,21 @@ class EntityRenderer:
 
     style = "ent"
 
-    def __init__(self, options={}):
+    def __init__(self, options: Dict[str, Any] = {}) -> None:
         """Initialise dependency renderer.
 
         options (dict): Visualiser-specific options (colors, ents)
         """
-        colors = {
+        colors = dict(DEFAULT_LABEL_COLORS)
-            "ORG": "#7aecec",
-            "PRODUCT": "#bfeeb7",
-            "GPE": "#feca74",
-            "LOC": "#ff9561",
-            "PERSON": "#aa9cfc",
-            "NORP": "#c887fb",
-            "FACILITY": "#9cc9cc",
-            "EVENT": "#ffeb80",
-            "LAW": "#ff8197",
-            "LANGUAGE": "#ff8197",
-            "WORK_OF_ART": "#f0d0ff",
-            "DATE": "#bfe1d9",
-            "TIME": "#bfe1d9",
-            "MONEY": "#e4e7d2",
-            "QUANTITY": "#e4e7d2",
-            "ORDINAL": "#e4e7d2",
-            "CARDINAL": "#e4e7d2",
-            "PERCENT": "#e4e7d2",
-        }
         user_colors = registry.displacy_colors.get_all()
         for user_color in user_colors.values():
             colors.update(user_color)
         colors.update(options.get("colors", {}))
-        self.default_color = "#ddd"
+        self.default_color = DEFAULT_ENTITY_COLOR
         self.colors = colors
         self.ents = options.get("ents", None)
         self.direction = DEFAULT_DIR
         self.lang = DEFAULT_LANG
-
         template = options.get("template")
         if template:
             self.ent_template = template
@@ -273,7 +265,9 @@ class EntityRenderer:
             else:
                 self.ent_template = TPL_ENT
 
-    def render(self, parsed, page=False, minify=False):
+    def render(
+        self, parsed: List[Dict[str, Any]], page: bool = False, minify: bool = False
+    ) -> str:
         """Render complete markup.
 
         parsed (list): Dependency parses to render.
@@ -297,7 +291,9 @@ class EntityRenderer:
             return minify_html(markup)
         return markup
 
-    def render_ents(self, text, spans, title):
+    def render_ents(
+        self, text: str, spans: List[Dict[str, Any]], title: Optional[str]
+    ) -> str:
         """Render entities in text.
 
         text (str): Original text.

spacy/errors.py

@@ -483,6 +483,8 @@ class Errors:
     E199 = ("Unable to merge 0-length span at doc[{start}:{end}].")
 
     # TODO: fix numbering after merging develop into master
+    E953 = ("Mismatched IDs received by the Tok2Vec listener: {id1} vs. {id2}")
+    E954 = ("The Tok2Vec listener did not receive a valid input.")
     E955 = ("Can't find table '{table}' for language '{lang}' in spacy-lookups-data.")
     E956 = ("Can't find component '{name}' in [components] block in the config. "
             "Available components: {opts}")

spacy/gold/align.py

@@ -15,7 +15,7 @@ class Alignment:
         x2y = _make_ragged(x2y)
         y2x = _make_ragged(y2x)
         return Alignment(x2y=x2y, y2x=y2x)
-    
+
     @classmethod
     def from_strings(cls, A: List[str], B: List[str]) -> "Alignment":
         x2y, y2x = tokenizations.get_alignments(A, B)

spacy/language.py

@@ -163,7 +163,7 @@ class Language:
         else:
             if (self.lang and vocab.lang) and (self.lang != vocab.lang):
                 raise ValueError(Errors.E150.format(nlp=self.lang, vocab=vocab.lang))
-        self.vocab = vocab
+        self.vocab: Vocab = vocab
         if self.lang is None:
             self.lang = self.vocab.lang
         self.pipeline = []

spacy/ml/_precomputable_affine.py

@@ -8,7 +8,7 @@ def PrecomputableAffine(nO, nI, nF, nP, dropout=0.1):
         init=init,
         dims={"nO": nO, "nI": nI, "nF": nF, "nP": nP},
         params={"W": None, "b": None, "pad": None},
-        attrs={"dropout_rate": dropout}
+        attrs={"dropout_rate": dropout},
     )
     return model
 

spacy/ml/models/tok2vec.py

@@ -154,16 +154,30 @@ def LayerNormalizedMaxout(width, maxout_pieces):
 def MultiHashEmbed(
     columns, width, rows, use_subwords, pretrained_vectors, mix, dropout
 ):
-    norm = HashEmbed(nO=width, nV=rows, column=columns.index("NORM"), dropout=dropout, seed=6)
+    norm = HashEmbed(
+        nO=width, nV=rows, column=columns.index("NORM"), dropout=dropout, seed=6
+    )
     if use_subwords:
         prefix = HashEmbed(
-            nO=width, nV=rows // 2, column=columns.index("PREFIX"), dropout=dropout, seed=7
+            nO=width,
+            nV=rows // 2,
+            column=columns.index("PREFIX"),
+            dropout=dropout,
+            seed=7,
         )
         suffix = HashEmbed(
-            nO=width, nV=rows // 2, column=columns.index("SUFFIX"), dropout=dropout, seed=8
+            nO=width,
+            nV=rows // 2,
+            column=columns.index("SUFFIX"),
+            dropout=dropout,
+            seed=8,
         )
         shape = HashEmbed(
-            nO=width, nV=rows // 2, column=columns.index("SHAPE"), dropout=dropout, seed=9
+            nO=width,
+            nV=rows // 2,
+            column=columns.index("SHAPE"),
+            dropout=dropout,
+            seed=9,
         )
 
     if pretrained_vectors:
@@ -192,7 +206,9 @@ def MultiHashEmbed(
 
 @registry.architectures.register("spacy.CharacterEmbed.v1")
 def CharacterEmbed(columns, width, rows, nM, nC, features, dropout):
-    norm = HashEmbed(nO=width, nV=rows, column=columns.index("NORM"), dropout=dropout, seed=5)
+    norm = HashEmbed(
+        nO=width, nV=rows, column=columns.index("NORM"), dropout=dropout, seed=5
+    )
     chr_embed = _character_embed.CharacterEmbed(nM=nM, nC=nC)
     with Model.define_operators({">>": chain, "|": concatenate}):
         embed_layer = chr_embed | features >> with_array(norm)
@@ -263,21 +279,29 @@ def build_Tok2Vec_model(
     cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
     with Model.define_operators({">>": chain, "|": concatenate, "**": clone}):
         norm = HashEmbed(
-            nO=width, nV=embed_size, column=cols.index(NORM), dropout=None,
+            nO=width, nV=embed_size, column=cols.index(NORM), dropout=None, seed=0
-            seed=0
         )
         if subword_features:
             prefix = HashEmbed(
-                nO=width, nV=embed_size // 2, column=cols.index(PREFIX), dropout=None,
+                nO=width,
-                seed=1
+                nV=embed_size // 2,
+                column=cols.index(PREFIX),
+                dropout=None,
+                seed=1,
             )
             suffix = HashEmbed(
-                nO=width, nV=embed_size // 2, column=cols.index(SUFFIX), dropout=None,
+                nO=width,
-                seed=2
+                nV=embed_size // 2,
+                column=cols.index(SUFFIX),
+                dropout=None,
+                seed=2,
             )
             shape = HashEmbed(
-                nO=width, nV=embed_size // 2, column=cols.index(SHAPE), dropout=None,
+                nO=width,
-                seed=3
+                nV=embed_size // 2,
+                column=cols.index(SHAPE),
+                dropout=None,
+                seed=3,
             )
         else:
             prefix, suffix, shape = (None, None, None)
@@ -294,11 +318,7 @@ def build_Tok2Vec_model(
                 embed = uniqued(
                     (glove | norm | prefix | suffix | shape)
                     >> Maxout(
-                        nO=width,
+                        nO=width, nI=width * columns, nP=3, dropout=0.0, normalize=True,
-                        nI=width * columns,
-                        nP=3,
-                        dropout=0.0,
-                        normalize=True,
                     ),
                     column=cols.index(ORTH),
                 )
@@ -307,11 +327,7 @@ def build_Tok2Vec_model(
                 embed = uniqued(
                     (glove | norm)
                     >> Maxout(
-                        nO=width,
+                        nO=width, nI=width * columns, nP=3, dropout=0.0, normalize=True,
-                        nI=width * columns,
-                        nP=3,
-                        dropout=0.0,
-                        normalize=True,
                     ),
                     column=cols.index(ORTH),
                 )
@@ -320,11 +336,7 @@ def build_Tok2Vec_model(
             embed = uniqued(
                 concatenate(norm, prefix, suffix, shape)
                 >> Maxout(
-                    nO=width,
+                    nO=width, nI=width * columns, nP=3, dropout=0.0, normalize=True,
-                    nI=width * columns,
-                    nP=3,
-                    dropout=0.0,
-                    normalize=True,
                 ),
                 column=cols.index(ORTH),
             )
@@ -333,11 +345,7 @@ def build_Tok2Vec_model(
                 cols
             ) >> with_array(norm)
             reduce_dimensions = Maxout(
-                nO=width,
+                nO=width, nI=nM * nC + width, nP=3, dropout=0.0, normalize=True,
-                nI=nM * nC + width,
-                nP=3,
-                dropout=0.0,
-                normalize=True,
             )
         else:
             embed = norm

spacy/pipeline/__init__.py

@@ -10,7 +10,6 @@ from .simple_ner import SimpleNER
 from .tagger import Tagger
 from .textcat import TextCategorizer
 from .tok2vec import Tok2Vec
-from .hooks import SentenceSegmenter, SimilarityHook
 from .functions import merge_entities, merge_noun_chunks, merge_subtokens
 
 __all__ = [
@@ -21,9 +20,7 @@ __all__ = [
     "Morphologizer",
     "Pipe",
     "SentenceRecognizer",
-    "SentenceSegmenter",
     "Sentencizer",
-    "SimilarityHook",
     "SimpleNER",
     "Tagger",
     "TextCategorizer",

spacy/pipeline/entityruler.py

@@ -63,7 +63,7 @@ class EntityRuler:
         overwrite_ents: bool = False,
         ent_id_sep: str = DEFAULT_ENT_ID_SEP,
         patterns: Optional[List[PatternType]] = None,
-    ):
+    ) -> None:
         """Initialize the entitiy ruler. If patterns are supplied here, they
         need to be a list of dictionaries with a `"label"` and `"pattern"`
         key. A pattern can either be a token pattern (list) or a phrase pattern
@@ -239,7 +239,6 @@ class EntityRuler:
             phrase_pattern_labels = []
             phrase_pattern_texts = []
             phrase_pattern_ids = []
-
             for entry in patterns:
                 if isinstance(entry["pattern"], str):
                     phrase_pattern_labels.append(entry["label"])
@@ -247,7 +246,6 @@ class EntityRuler:
                     phrase_pattern_ids.append(entry.get("id"))
                 elif isinstance(entry["pattern"], list):
                     token_patterns.append(entry)
-
             phrase_patterns = []
             for label, pattern, ent_id in zip(
                 phrase_pattern_labels,
@@ -258,7 +256,6 @@ class EntityRuler:
                 if ent_id:
                     phrase_pattern["id"] = ent_id
                 phrase_patterns.append(phrase_pattern)
-
             for entry in token_patterns + phrase_patterns:
                 label = entry["label"]
                 if "id" in entry:
@@ -266,7 +263,6 @@ class EntityRuler:
                     label = self._create_label(label, entry["id"])
                     key = self.matcher._normalize_key(label)
                     self._ent_ids[key] = (ent_label, entry["id"])
-
                 pattern = entry["pattern"]
                 if isinstance(pattern, Doc):
                     self.phrase_patterns[label].append(pattern)
@@ -323,13 +319,13 @@ class EntityRuler:
         self.clear()
         if isinstance(cfg, dict):
             self.add_patterns(cfg.get("patterns", cfg))
-            self.overwrite = cfg.get("overwrite")
+            self.overwrite = cfg.get("overwrite", False)
             self.phrase_matcher_attr = cfg.get("phrase_matcher_attr", None)
             if self.phrase_matcher_attr is not None:
                 self.phrase_matcher = PhraseMatcher(
                     self.nlp.vocab, attr=self.phrase_matcher_attr
                 )
-            self.ent_id_sep = cfg.get("ent_id_sep")
+            self.ent_id_sep = cfg.get("ent_id_sep", DEFAULT_ENT_ID_SEP)
         else:
             self.add_patterns(cfg)
         return self
@@ -375,9 +371,9 @@ class EntityRuler:
             }
             deserializers_cfg = {"cfg": lambda p: cfg.update(srsly.read_json(p))}
             from_disk(path, deserializers_cfg, {})
-            self.overwrite = cfg.get("overwrite")
+            self.overwrite = cfg.get("overwrite", False)
             self.phrase_matcher_attr = cfg.get("phrase_matcher_attr")
-            self.ent_id_sep = cfg.get("ent_id_sep")
+            self.ent_id_sep = cfg.get("ent_id_sep", DEFAULT_ENT_ID_SEP)
 
             if self.phrase_matcher_attr is not None:
                 self.phrase_matcher = PhraseMatcher(

spacy/pipeline/hooks.py

@@ -1,95 +0,0 @@
-from thinc.api import concatenate, reduce_max, reduce_mean, siamese, CauchySimilarity
-
-from .pipe import Pipe
-from ..util import link_vectors_to_models
-
-
-# TODO: do we want to keep these?
-
-
-class SentenceSegmenter:
-    """A simple spaCy hook, to allow custom sentence boundary detection logic
-    (that doesn't require the dependency parse). To change the sentence
-    boundary detection strategy, pass a generator function `strategy` on
-    initialization, or assign a new strategy to the .strategy attribute.
-    Sentence detection strategies should be generators that take `Doc` objects
-    and yield `Span` objects for each sentence.
-    """
-
-    def __init__(self, vocab, strategy=None):
-        self.vocab = vocab
-        if strategy is None or strategy == "on_punct":
-            strategy = self.split_on_punct
-        self.strategy = strategy
-
-    def __call__(self, doc):
-        doc.user_hooks["sents"] = self.strategy
-        return doc
-
-    @staticmethod
-    def split_on_punct(doc):
-        start = 0
-        seen_period = False
-        for i, token in enumerate(doc):
-            if seen_period and not token.is_punct:
-                yield doc[start : token.i]
-                start = token.i
-                seen_period = False
-            elif token.text in [".", "!", "?"]:
-                seen_period = True
-        if start < len(doc):
-            yield doc[start : len(doc)]
-
-
-class SimilarityHook(Pipe):
-    """
-    Experimental: A pipeline component to install a hook for supervised
-    similarity into `Doc` objects.
-    The similarity model can be any object obeying the Thinc `Model`
-    interface. By default, the model concatenates the elementwise mean and
-    elementwise max of the two tensors, and compares them using the
-    Cauchy-like similarity function from Chen (2013):
-
-        >>> similarity = 1. / (1. + (W * (vec1-vec2)**2).sum())
-
-    Where W is a vector of dimension weights, initialized to 1.
-    """
-
-    def __init__(self, vocab, model=True, **cfg):
-        self.vocab = vocab
-        self.model = model
-        self.cfg = dict(cfg)
-
-    @classmethod
-    def Model(cls, length):
-        return siamese(
-            concatenate(reduce_max(), reduce_mean()), CauchySimilarity(length * 2)
-        )
-
-    def __call__(self, doc):
-        """Install similarity hook"""
-        doc.user_hooks["similarity"] = self.predict
-        return doc
-
-    def pipe(self, docs, **kwargs):
-        for doc in docs:
-            yield self(doc)
-
-    def predict(self, doc1, doc2):
-        return self.model.predict([(doc1, doc2)])
-
-    def update(self, doc1_doc2, golds, sgd=None, drop=0.0):
-        sims, bp_sims = self.model.begin_update(doc1_doc2)
-
-    def begin_training(self, _=tuple(), pipeline=None, sgd=None, **kwargs):
-        """Allocate model, using nO from the first model in the pipeline.
-
-        gold_tuples (iterable): Gold-standard training data.
-        pipeline (list): The pipeline the model is part of.
-        """
-        if self.model is True:
-            self.model = self.Model(pipeline[0].model.get_dim("nO"))
-            link_vectors_to_models(self.vocab)
-        if sgd is None:
-            sgd = self.create_optimizer()
-        return sgd

spacy/pipeline/textcat.py

@@ -1,4 +1,4 @@
-from typing import Iterable, Tuple, Optional, Dict, List, Callable
+from typing import Iterable, Tuple, Optional, Dict, List, Callable, Iterator, Any
 from thinc.api import get_array_module, Model, Optimizer, set_dropout_rate, Config
 import numpy
 
@@ -97,7 +97,7 @@ class TextCategorizer(Pipe):
     def labels(self, value: Iterable[str]) -> None:
         self.cfg["labels"] = tuple(value)
 
-    def pipe(self, stream, batch_size=128):
+    def pipe(self, stream: Iterator[str], batch_size: int = 128) -> Iterator[Doc]:
         for docs in util.minibatch(stream, size=batch_size):
             scores = self.predict(docs)
             self.set_annotations(docs, scores)
@@ -252,8 +252,17 @@ class TextCategorizer(Pipe):
             sgd = self.create_optimizer()
         return sgd
 
-    def score(self, examples, positive_label=None, **kwargs):
+    def score(
-        return Scorer.score_cats(examples, "cats", labels=self.labels,
+        self,
+        examples: Iterable[Example],
+        positive_label: Optional[str] = None,
+        **kwargs,
+    ) -> Dict[str, Any]:
+        return Scorer.score_cats(
+            examples,
+            "cats",
+            labels=self.labels,
             multi_label=self.model.attrs["multi_label"],
-            positive_label=positive_label, **kwargs
+            positive_label=positive_label,
+            **kwargs,
         )

spacy/pipeline/tok2vec.py

@@ -6,6 +6,7 @@ from ..gold import Example
 from ..tokens import Doc
 from ..vocab import Vocab
 from ..language import Language
+from ..errors import Errors
 from ..util import link_vectors_to_models, minibatch
 
 
@@ -150,7 +151,7 @@ class Tok2Vec(Pipe):
             self.set_annotations(docs, tokvecs)
         return losses
 
-    def get_loss(self, examples, scores):
+    def get_loss(self, examples, scores) -> None:
         pass
 
     def begin_training(
@@ -184,26 +185,26 @@ class Tok2VecListener(Model):
         self._backprop = None
 
     @classmethod
-    def get_batch_id(cls, inputs):
+    def get_batch_id(cls, inputs) -> int:
         return sum(sum(token.orth for token in doc) for doc in inputs)
 
-    def receive(self, batch_id, outputs, backprop):
+    def receive(self, batch_id: int, outputs, backprop) -> None:
         self._batch_id = batch_id
         self._outputs = outputs
         self._backprop = backprop
 
-    def verify_inputs(self, inputs):
+    def verify_inputs(self, inputs) -> bool:
         if self._batch_id is None and self._outputs is None:
-            raise ValueError("The Tok2Vec listener did not receive valid input.")
+            raise ValueError(Errors.E954)
         else:
             batch_id = self.get_batch_id(inputs)
             if batch_id != self._batch_id:
-                raise ValueError(f"Mismatched IDs! {batch_id} vs {self._batch_id}")
+                raise ValueError(Errors.E953.format(id1=batch_id, id2=self._batch_id))
             else:
                 return True
 
 
-def forward(model: Tok2VecListener, inputs, is_train):
+def forward(model: Tok2VecListener, inputs, is_train: bool):
     if is_train:
         model.verify_inputs(inputs)
         return model._outputs, model._backprop

spacy/schemas.py

@@ -1,4 +1,4 @@
-from typing import Dict, List, Union, Optional, Sequence, Any, Callable
+from typing import Dict, List, Union, Optional, Sequence, Any, Callable, Type
 from enum import Enum
 from pydantic import BaseModel, Field, ValidationError, validator
 from pydantic import StrictStr, StrictInt, StrictFloat, StrictBool
@@ -9,12 +9,12 @@ from thinc.api import Optimizer
 from .attrs import NAMES
 
 
-def validate(schema, obj):
+def validate(schema: Type[BaseModel], obj: Dict[str, Any]) -> List[str]:
     """Validate data against a given pydantic schema.
 
-    obj (dict): JSON-serializable data to validate.
+    obj (Dict[str, Any]): JSON-serializable data to validate.
     schema (pydantic.BaseModel): The schema to validate against.
-    RETURNS (list): A list of error messages, if available.
+    RETURNS (List[str]): A list of error messages, if available.
     """
     try:
         schema(**obj)
@@ -31,7 +31,7 @@ def validate(schema, obj):
 # Matcher token patterns
 
 
-def validate_token_pattern(obj):
+def validate_token_pattern(obj: list) -> List[str]:
     # Try to convert non-string keys (e.g. {ORTH: "foo"} -> {"ORTH": "foo"})
     get_key = lambda k: NAMES[k] if isinstance(k, int) and k < len(NAMES) else k
     if isinstance(obj, list):

spacy/tests/doc/test_add_entities.py

@@ -15,7 +15,8 @@ def test_doc_add_entities_set_ents_iob(en_vocab):
         "min_action_freq": 30,
         "update_with_oracle_cut_size": 100,
     }
-    model = registry.make_from_config({"model": DEFAULT_NER_MODEL}, validate=True)["model"]
+    cfg = {"model": DEFAULT_NER_MODEL}
+    model = registry.make_from_config(cfg, validate=True)["model"]
     ner = EntityRecognizer(en_vocab, model, **config)
     ner.begin_training([])
     ner(doc)
@@ -37,7 +38,8 @@ def test_ents_reset(en_vocab):
         "min_action_freq": 30,
         "update_with_oracle_cut_size": 100,
     }
-    model = registry.make_from_config({"model": DEFAULT_NER_MODEL}, validate=True)["model"]
+    cfg = {"model": DEFAULT_NER_MODEL}
+    model = registry.make_from_config(cfg, validate=True)["model"]
     ner = EntityRecognizer(en_vocab, model, **config)
     ner.begin_training([])
     ner(doc)

spacy/tests/lang/en/test_sbd.py

@@ -14,7 +14,9 @@ def test_en_sbd_single_punct(en_tokenizer, text, punct):
     assert sum(len(sent) for sent in doc.sents) == len(doc)
 
 
-@pytest.mark.skip(reason="The step_through API was removed (but should be brought back)")
+@pytest.mark.skip(
+    reason="The step_through API was removed (but should be brought back)"
+)
 def test_en_sentence_breaks(en_tokenizer, en_parser):
     # fmt: off
     text = "This is a sentence . This is another one ."

spacy/tests/lang/ja/test_tokenizer.py

@@ -32,6 +32,25 @@ SENTENCE_TESTS = [
     ("あれ。これ。", ["あれ。", "これ。"]),
     ("「伝染るんです。」という漫画があります。", ["「伝染るんです。」という漫画があります。"]),
 ]
+
+tokens1 = [
+    DetailedToken(surface="委員", tag="名詞-普通名詞-一般", inf="", lemma="委員", reading="イイン", sub_tokens=None),
+    DetailedToken(surface="会", tag="名詞-普通名詞-一般", inf="", lemma="会", reading="カイ", sub_tokens=None),
+]
+tokens2 = [
+    DetailedToken(surface="選挙", tag="名詞-普通名詞-サ変可能", inf="", lemma="選挙", reading="センキョ", sub_tokens=None),
+    DetailedToken(surface="管理", tag="名詞-普通名詞-サ変可能", inf="", lemma="管理", reading="カンリ", sub_tokens=None),
+    DetailedToken(surface="委員", tag="名詞-普通名詞-一般", inf="", lemma="委員", reading="イイン", sub_tokens=None),
+    DetailedToken(surface="会", tag="名詞-普通名詞-一般", inf="", lemma="会", reading="カイ", sub_tokens=None),
+]
+tokens3 = [
+    DetailedToken(surface="選挙", tag="名詞-普通名詞-サ変可能", inf="", lemma="選挙", reading="センキョ", sub_tokens=None),
+    DetailedToken(surface="管理", tag="名詞-普通名詞-サ変可能", inf="", lemma="管理", reading="カンリ", sub_tokens=None),
+    DetailedToken(surface="委員会", tag="名詞-普通名詞-一般", inf="", lemma="委員会", reading="イインカイ", sub_tokens=None),
+]
+SUB_TOKEN_TESTS = [
+    ("選挙管理委員会", [None, None, None, None], [None, None, [tokens1]], [[tokens2, tokens3]])
+]
 # fmt: on
 
 
@@ -92,33 +111,12 @@ def test_ja_tokenizer_split_modes(ja_tokenizer, text, len_a, len_b, len_c):
     assert len(nlp_c(text)) == len_c
 
 
-@pytest.mark.parametrize("text,sub_tokens_list_a,sub_tokens_list_b,sub_tokens_list_c",
+@pytest.mark.parametrize(
-    [
+    "text,sub_tokens_list_a,sub_tokens_list_b,sub_tokens_list_c", SUB_TOKEN_TESTS,
-        (
-            "選挙管理委員会",
-            [None, None, None, None],
-            [None, None, [
-                [
-                    DetailedToken(surface='委員', tag='名詞-普通名詞-一般', inf='', lemma='委員', reading='イイン', sub_tokens=None),
-                    DetailedToken(surface='会', tag='名詞-普通名詞-一般', inf='', lemma='会', reading='カイ', sub_tokens=None),
-                ]
-            ]],
-            [[
-                [
-                    DetailedToken(surface='選挙', tag='名詞-普通名詞-サ変可能', inf='', lemma='選挙', reading='センキョ', sub_tokens=None),
-                    DetailedToken(surface='管理', tag='名詞-普通名詞-サ変可能', inf='', lemma='管理', reading='カンリ', sub_tokens=None),
-                    DetailedToken(surface='委員', tag='名詞-普通名詞-一般', inf='', lemma='委員', reading='イイン', sub_tokens=None),
-                    DetailedToken(surface='会', tag='名詞-普通名詞-一般', inf='', lemma='会', reading='カイ', sub_tokens=None),
-                ], [
-                    DetailedToken(surface='選挙', tag='名詞-普通名詞-サ変可能', inf='', lemma='選挙', reading='センキョ', sub_tokens=None),
-                    DetailedToken(surface='管理', tag='名詞-普通名詞-サ変可能', inf='', lemma='管理', reading='カンリ', sub_tokens=None),
-                    DetailedToken(surface='委員会', tag='名詞-普通名詞-一般', inf='', lemma='委員会', reading='イインカイ', sub_tokens=None),
-                ]
-            ]]
-        ),
-    ]
 )
-def test_ja_tokenizer_sub_tokens(ja_tokenizer, text, sub_tokens_list_a, sub_tokens_list_b, sub_tokens_list_c):
+def test_ja_tokenizer_sub_tokens(
+    ja_tokenizer, text, sub_tokens_list_a, sub_tokens_list_b, sub_tokens_list_c
+):
     nlp_a = Japanese.from_config({"nlp": {"tokenizer": {"split_mode": "A"}}})
     nlp_b = Japanese.from_config({"nlp": {"tokenizer": {"split_mode": "B"}}})
     nlp_c = Japanese.from_config({"nlp": {"tokenizer": {"split_mode": "C"}}})
@@ -129,16 +127,19 @@ def test_ja_tokenizer_sub_tokens(ja_tokenizer, text, sub_tokens_list_a, sub_toke
     assert nlp_c(text).user_data["sub_tokens"] == sub_tokens_list_c
 
 
-@pytest.mark.parametrize("text,inflections,reading_forms",
+@pytest.mark.parametrize(
+    "text,inflections,reading_forms",
     [
         (
             "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"),
             ("トッ", "テ", "ツケ", "タ"),
         ),
-    ]
+    ],
 )
-def test_ja_tokenizer_inflections_reading_forms(ja_tokenizer, text, inflections, reading_forms):
+def test_ja_tokenizer_inflections_reading_forms(
+    ja_tokenizer, text, inflections, reading_forms
+):
     assert ja_tokenizer(text).user_data["inflections"] == inflections
     assert ja_tokenizer(text).user_data["reading_forms"] == reading_forms
 

spacy/tests/lang/ne/test_text.py

@@ -11,9 +11,8 @@ def test_ne_tokenizer_handlers_long_text(ne_tokenizer):
 
 
 @pytest.mark.parametrize(
-    "text,length",
+    "text,length", [("समय जान कति पनि बेर लाग्दैन ।", 7), ("म ठूलो हुँदै थिएँ ।", 5)],
-    [("समय जान कति पनि बेर लाग्दैन ।", 7), ("म ठूलो हुँदै थिएँ ।", 5)],
 )
 def test_ne_tokenizer_handles_cnts(ne_tokenizer, text, length):
     tokens = ne_tokenizer(text)
-    assert len(tokens) == length
+    assert len(tokens) == length

spacy/tests/lang/zh/test_serialize.py

@@ -30,10 +30,7 @@ def test_zh_tokenizer_serialize_pkuseg_with_processors(zh_tokenizer_pkuseg):
     nlp = Chinese(
         meta={
             "tokenizer": {
-                "config": {
+                "config": {"segmenter": "pkuseg", "pkuseg_model": "medicine",}
-                    "segmenter": "pkuseg",
-                    "pkuseg_model": "medicine",
-                }
             }
         }
     )

spacy/tests/parser/test_add_label.py

@@ -22,7 +22,8 @@ def parser(vocab):
         "min_action_freq": 30,
         "update_with_oracle_cut_size": 100,
     }
-    model = registry.make_from_config({"model": DEFAULT_PARSER_MODEL}, validate=True)["model"]
+    cfg = {"model": DEFAULT_PARSER_MODEL}
+    model = registry.make_from_config(cfg, validate=True)["model"]
     parser = DependencyParser(vocab, model, **config)
     return parser
 
@@ -68,7 +69,8 @@ def test_add_label_deserializes_correctly():
         "min_action_freq": 30,
         "update_with_oracle_cut_size": 100,
     }
-    model = registry.make_from_config({"model": DEFAULT_NER_MODEL}, validate=True)["model"]
+    cfg = {"model": DEFAULT_NER_MODEL}
+    model = registry.make_from_config(cfg, validate=True)["model"]
     ner1 = EntityRecognizer(Vocab(), model, **config)
     ner1.add_label("C")
     ner1.add_label("B")
@@ -86,7 +88,10 @@ def test_add_label_deserializes_correctly():
 
 @pytest.mark.parametrize(
     "pipe_cls,n_moves,model_config",
-    [(DependencyParser, 5, DEFAULT_PARSER_MODEL), (EntityRecognizer, 4, DEFAULT_NER_MODEL)],
+    [
+        (DependencyParser, 5, DEFAULT_PARSER_MODEL),
+        (EntityRecognizer, 4, DEFAULT_NER_MODEL),
+    ],
 )
 def test_add_label_get_label(pipe_cls, n_moves, model_config):
     """Test that added labels are returned correctly. This test was added to

spacy/tests/parser/test_arc_eager_oracle.py

@@ -126,7 +126,8 @@ def test_get_oracle_actions():
         "min_action_freq": 0,
         "update_with_oracle_cut_size": 100,
     }
-    model = registry.make_from_config({"model": DEFAULT_PARSER_MODEL}, validate=True)["model"]
+    cfg = {"model": DEFAULT_PARSER_MODEL}
+    model = registry.make_from_config(cfg, validate=True)["model"]
     parser = DependencyParser(doc.vocab, model, **config)
     parser.moves.add_action(0, "")
     parser.moves.add_action(1, "")

spacy/tests/parser/test_neural_parser.py

@@ -24,7 +24,8 @@ def arc_eager(vocab):
 
 @pytest.fixture
 def tok2vec():
-    tok2vec = registry.make_from_config({"model": DEFAULT_TOK2VEC_MODEL}, validate=True)["model"]
+    cfg = {"model": DEFAULT_TOK2VEC_MODEL}
+    tok2vec = registry.make_from_config(cfg, validate=True)["model"]
     tok2vec.initialize()
     return tok2vec
 
@@ -36,13 +37,15 @@ def parser(vocab, arc_eager):
         "min_action_freq": 30,
         "update_with_oracle_cut_size": 100,
     }
-    model = registry.make_from_config({"model": DEFAULT_PARSER_MODEL}, validate=True)["model"]
+    cfg = {"model": DEFAULT_PARSER_MODEL}
+    model = registry.make_from_config(cfg, validate=True)["model"]
     return Parser(vocab, model, moves=arc_eager, **config)
 
 
 @pytest.fixture
 def model(arc_eager, tok2vec, vocab):
-    model = registry.make_from_config({"model": DEFAULT_PARSER_MODEL}, validate=True)["model"]
+    cfg = {"model": DEFAULT_PARSER_MODEL}
+    model = registry.make_from_config(cfg, validate=True)["model"]
     model.attrs["resize_output"](model, arc_eager.n_moves)
     model.initialize()
     return model
@@ -68,7 +71,8 @@ def test_build_model(parser, vocab):
         "min_action_freq": 0,
         "update_with_oracle_cut_size": 100,
     }
-    model = registry.make_from_config({"model": DEFAULT_PARSER_MODEL}, validate=True)["model"]
+    cfg = {"model": DEFAULT_PARSER_MODEL}
+    model = registry.make_from_config(cfg, validate=True)["model"]
     parser.model = Parser(vocab, model=model, moves=parser.moves, **config).model
     assert parser.model is not None
 

spacy/tests/parser/test_parse.py

@@ -33,7 +33,9 @@ def test_parser_root(en_tokenizer):
         assert t.dep != 0, t.text
 
 
-@pytest.mark.skip(reason="The step_through API was removed (but should be brought back)")
+@pytest.mark.skip(
+    reason="The step_through API was removed (but should be brought back)"
+)
 @pytest.mark.parametrize("text", ["Hello"])
 def test_parser_parse_one_word_sentence(en_tokenizer, en_parser, text):
     tokens = en_tokenizer(text)
@@ -47,7 +49,9 @@ def test_parser_parse_one_word_sentence(en_tokenizer, en_parser, text):
     assert doc[0].dep != 0
 
 
-@pytest.mark.skip(reason="The step_through API was removed (but should be brought back)")
+@pytest.mark.skip(
+    reason="The step_through API was removed (but should be brought back)"
+)
 def test_parser_initial(en_tokenizer, en_parser):
     text = "I ate the pizza with anchovies."
     # heads = [1, 0, 1, -2, -3, -1, -5]
@@ -92,7 +96,9 @@ def test_parser_merge_pp(en_tokenizer):
     assert doc[3].text == "occurs"
 
 
-@pytest.mark.skip(reason="The step_through API was removed (but should be brought back)")
+@pytest.mark.skip(
+    reason="The step_through API was removed (but should be brought back)"
+)
 def test_parser_arc_eager_finalize_state(en_tokenizer, en_parser):
     text = "a b c d e"
 

spacy/tests/parser/test_preset_sbd.py

@@ -21,7 +21,8 @@ def parser(vocab):
         "min_action_freq": 30,
         "update_with_oracle_cut_size": 100,
     }
-    model = registry.make_from_config({"model": DEFAULT_PARSER_MODEL}, validate=True)["model"]
+    cfg = {"model": DEFAULT_PARSER_MODEL}
+    model = registry.make_from_config(cfg, validate=True)["model"]
     parser = DependencyParser(vocab, model, **config)
     parser.cfg["token_vector_width"] = 4
     parser.cfg["hidden_width"] = 32

spacy/tests/parser/test_space_attachment.py

@@ -28,7 +28,9 @@ def test_parser_sentence_space(en_tokenizer):
     assert len(list(doc.sents)) == 2
 
 
-@pytest.mark.skip(reason="The step_through API was removed (but should be brought back)")
+@pytest.mark.skip(
+    reason="The step_through API was removed (but should be brought back)"
+)
 def test_parser_space_attachment_leading(en_tokenizer, en_parser):
     text = "\t \n This is a sentence ."
     heads = [1, 1, 0, 1, -2, -3]
@@ -44,7 +46,9 @@ def test_parser_space_attachment_leading(en_tokenizer, en_parser):
     assert stepwise.stack == set([2])
 
 
-@pytest.mark.skip(reason="The step_through API was removed (but should be brought back)")
+@pytest.mark.skip(
+    reason="The step_through API was removed (but should be brought back)"
+)
 def test_parser_space_attachment_intermediate_trailing(en_tokenizer, en_parser):
     text = "This is \t a \t\n \n sentence . \n\n \n"
     heads = [1, 0, -1, 2, -1, -4, -5, -1]
@@ -64,7 +68,9 @@ def test_parser_space_attachment_intermediate_trailing(en_tokenizer, en_parser):
 
 
 @pytest.mark.parametrize("text,length", [(["\n"], 1), (["\n", "\t", "\n\n", "\t"], 4)])
-@pytest.mark.skip(reason="The step_through API was removed (but should be brought back)")
+@pytest.mark.skip(
+    reason="The step_through API was removed (but should be brought back)"
+)
 def test_parser_space_attachment_space(en_tokenizer, en_parser, text, length):
     doc = Doc(en_parser.vocab, words=text)
     assert len(doc) == length

spacy/tests/pipeline/test_textcat.py

@@ -117,7 +117,9 @@ def test_overfitting_IO():
         assert cats2["POSITIVE"] + cats2["NEGATIVE"] == pytest.approx(1.0, 0.1)
 
     # Test scoring
-    scores = nlp.evaluate(train_examples, component_cfg={"scorer": {"positive_label": "POSITIVE"}})
+    scores = nlp.evaluate(
+        train_examples, component_cfg={"scorer": {"positive_label": "POSITIVE"}}
+    )
     assert scores["cats_f"] == 1.0
 
 

spacy/tests/regression/test_issue3001-3500.py

@@ -201,7 +201,8 @@ def test_issue3345():
         "min_action_freq": 30,
         "update_with_oracle_cut_size": 100,
     }
-    model = registry.make_from_config({"model": DEFAULT_NER_MODEL}, validate=True)["model"]
+    cfg = {"model": DEFAULT_NER_MODEL}
+    model = registry.make_from_config(cfg, validate=True)["model"]
     ner = EntityRecognizer(doc.vocab, model, **config)
     # Add the OUT action. I wouldn't have thought this would be necessary...
     ner.moves.add_action(5, "")

spacy/tests/serialize/test_serialize_pipeline.py

@@ -20,7 +20,8 @@ def parser(en_vocab):
         "min_action_freq": 30,
         "update_with_oracle_cut_size": 100,
     }
-    model = registry.make_from_config({"model": DEFAULT_PARSER_MODEL}, validate=True)["model"]
+    cfg = {"model": DEFAULT_PARSER_MODEL}
+    model = registry.make_from_config(cfg, validate=True)["model"]
     parser = DependencyParser(en_vocab, model, **config)
     parser.add_label("nsubj")
     return parser
@@ -33,14 +34,16 @@ def blank_parser(en_vocab):
         "min_action_freq": 30,
         "update_with_oracle_cut_size": 100,
     }
-    model = registry.make_from_config({"model": DEFAULT_PARSER_MODEL}, validate=True)["model"]
+    cfg = {"model": DEFAULT_PARSER_MODEL}
+    model = registry.make_from_config(cfg, validate=True)["model"]
     parser = DependencyParser(en_vocab, model, **config)
     return parser
 
 
 @pytest.fixture
 def taggers(en_vocab):
-    model = registry.make_from_config({"model": DEFAULT_TAGGER_MODEL}, validate=True)["model"]
+    cfg = {"model": DEFAULT_TAGGER_MODEL}
+    model = registry.make_from_config(cfg, validate=True)["model"]
     tagger1 = Tagger(en_vocab, model, set_morphology=True)
     tagger2 = Tagger(en_vocab, model, set_morphology=True)
     return tagger1, tagger2
@@ -53,7 +56,8 @@ def test_serialize_parser_roundtrip_bytes(en_vocab, Parser):
         "min_action_freq": 0,
         "update_with_oracle_cut_size": 100,
     }
-    model = registry.make_from_config({"model": DEFAULT_PARSER_MODEL}, validate=True)["model"]
+    cfg = {"model": DEFAULT_PARSER_MODEL}
+    model = registry.make_from_config(cfg, validate=True)["model"]
     parser = Parser(en_vocab, model, **config)
     new_parser = Parser(en_vocab, model, **config)
     new_parser = new_parser.from_bytes(parser.to_bytes(exclude=["vocab"]))
@@ -70,7 +74,8 @@ def test_serialize_parser_roundtrip_disk(en_vocab, Parser):
         "min_action_freq": 0,
         "update_with_oracle_cut_size": 100,
     }
-    model = registry.make_from_config({"model": DEFAULT_PARSER_MODEL}, validate=True)["model"]
+    cfg = {"model": DEFAULT_PARSER_MODEL}
+    model = registry.make_from_config(cfg, validate=True)["model"]
     parser = Parser(en_vocab, model, **config)
     with make_tempdir() as d:
         file_path = d / "parser"
@@ -88,7 +93,6 @@ def test_to_from_bytes(parser, blank_parser):
     assert blank_parser.model is not True
     assert blank_parser.moves.n_moves != parser.moves.n_moves
     bytes_data = parser.to_bytes(exclude=["vocab"])
-
     # the blank parser needs to be resized before we can call from_bytes
     blank_parser.model.attrs["resize_output"](blank_parser.model, parser.moves.n_moves)
     blank_parser.from_bytes(bytes_data)
@@ -104,7 +108,8 @@ def test_serialize_tagger_roundtrip_bytes(en_vocab, taggers):
     tagger1_b = tagger1.to_bytes()
     tagger1 = tagger1.from_bytes(tagger1_b)
     assert tagger1.to_bytes() == tagger1_b
-    model = registry.make_from_config({"model": DEFAULT_TAGGER_MODEL}, validate=True)["model"]
+    cfg = {"model": DEFAULT_TAGGER_MODEL}
+    model = registry.make_from_config(cfg, validate=True)["model"]
     new_tagger1 = Tagger(en_vocab, model).from_bytes(tagger1_b)
     new_tagger1_b = new_tagger1.to_bytes()
     assert len(new_tagger1_b) == len(tagger1_b)
@@ -118,7 +123,8 @@ def test_serialize_tagger_roundtrip_disk(en_vocab, taggers):
         file_path2 = d / "tagger2"
         tagger1.to_disk(file_path1)
         tagger2.to_disk(file_path2)
-        model = registry.make_from_config({"model": DEFAULT_TAGGER_MODEL}, validate=True)["model"]
+        cfg = {"model": DEFAULT_TAGGER_MODEL}
+        model = registry.make_from_config(cfg, validate=True)["model"]
         tagger1_d = Tagger(en_vocab, model, set_morphology=True).from_disk(file_path1)
         tagger2_d = Tagger(en_vocab, model, set_morphology=True).from_disk(file_path2)
         assert tagger1_d.to_bytes() == tagger2_d.to_bytes()
@@ -126,21 +132,22 @@ def test_serialize_tagger_roundtrip_disk(en_vocab, taggers):
 
 def test_serialize_textcat_empty(en_vocab):
     # See issue #1105
-    model = registry.make_from_config({"model": DEFAULT_TEXTCAT_MODEL}, validate=True)["model"]
+    cfg = {"model": DEFAULT_TEXTCAT_MODEL}
-    textcat = TextCategorizer(
+    model = registry.make_from_config(cfg, validate=True)["model"]
-        en_vocab, model, labels=["ENTITY", "ACTION", "MODIFIER"]
+    textcat = TextCategorizer(en_vocab, model, labels=["ENTITY", "ACTION", "MODIFIER"])
-    )
     textcat.to_bytes(exclude=["vocab"])
 
 
 @pytest.mark.parametrize("Parser", test_parsers)
 def test_serialize_pipe_exclude(en_vocab, Parser):
-    model = registry.make_from_config({"model": DEFAULT_PARSER_MODEL}, validate=True)["model"]
+    cfg = {"model": DEFAULT_PARSER_MODEL}
+    model = registry.make_from_config(cfg, validate=True)["model"]
     config = {
         "learn_tokens": False,
         "min_action_freq": 0,
         "update_with_oracle_cut_size": 100,
     }
+
     def get_new_parser():
         new_parser = Parser(en_vocab, model, **config)
         return new_parser
@@ -160,7 +167,8 @@ def test_serialize_pipe_exclude(en_vocab, Parser):
 
 
 def test_serialize_sentencerecognizer(en_vocab):
-    model = registry.make_from_config({"model": DEFAULT_SENTER_MODEL}, validate=True)["model"]
+    cfg = {"model": DEFAULT_SENTER_MODEL}
+    model = registry.make_from_config(cfg, validate=True)["model"]
     sr = SentenceRecognizer(en_vocab, model)
     sr_b = sr.to_bytes()
     sr_d = SentenceRecognizer(en_vocab, model).from_bytes(sr_b)

spacy/tests/test_gold.py

@@ -466,7 +466,6 @@ def test_iob_to_biluo():
 
 
 def test_roundtrip_docs_to_docbin(doc):
-    nlp = English()
     text = doc.text
     idx = [t.idx for t in doc]
     tags = [t.tag_ for t in doc]

spacy/tests/test_new_example.py

@@ -7,11 +7,11 @@ from spacy.vocab import Vocab
 def test_Example_init_requires_doc_objects():
     vocab = Vocab()
     with pytest.raises(TypeError):
-        example = Example(None, None)
+        Example(None, None)
     with pytest.raises(TypeError):
-        example = Example(Doc(vocab, words=["hi"]), None)
+        Example(Doc(vocab, words=["hi"]), None)
     with pytest.raises(TypeError):
-        example = Example(None, Doc(vocab, words=["hi"]))
+        Example(None, Doc(vocab, words=["hi"]))
 
 
 def test_Example_from_dict_basic():

spacy/tests/test_scorer.py

@@ -105,7 +105,11 @@ def test_tokenization(sented_doc):
     assert scores["token_acc"] == 1.0
 
     nlp = English()
-    example.predicted = Doc(nlp.vocab, words=["One", "sentence.", "Two", "sentences.", "Three", "sentences."], spaces=[True, True, True, True, True, False])
+    example.predicted = Doc(
+        nlp.vocab,
+        words=["One", "sentence.", "Two", "sentences.", "Three", "sentences."],
+        spaces=[True, True, True, True, True, False],
+    )
     example.predicted[1].is_sent_start = False
     scores = scorer.score([example])
     assert scores["token_acc"] == approx(0.66666666)

spacy/util.py

@@ -1,12 +1,13 @@
 from typing import List, Union, Dict, Any, Optional, Iterable, Callable, Tuple
-from typing import Iterator, TYPE_CHECKING
+from typing import Iterator, Type, Pattern, Sequence, TYPE_CHECKING
+from types import ModuleType
 import os
 import importlib
 import importlib.util
 import re
 from pathlib import Path
 import thinc
-from thinc.api import NumpyOps, get_current_ops, Adam, Config
+from thinc.api import NumpyOps, get_current_ops, Adam, Config, Optimizer
 import functools
 import itertools
 import numpy.random
@@ -49,6 +50,8 @@ from . import about
 if TYPE_CHECKING:
     # This lets us add type hints for mypy etc. without causing circular imports
     from .language import Language  # noqa: F401
+    from .tokens import Doc, Span  # noqa: F401
+    from .vocab import Vocab  # noqa: F401
 
 
 _PRINT_ENV = False
@@ -102,12 +105,12 @@ class SimpleFrozenDict(dict):
         raise NotImplementedError(self.error)
 
 
-def set_env_log(value):
+def set_env_log(value: bool) -> None:
     global _PRINT_ENV
     _PRINT_ENV = value
 
 
-def lang_class_is_loaded(lang):
+def lang_class_is_loaded(lang: str) -> bool:
     """Check whether a Language class is already loaded. Language classes are
     loaded lazily, to avoid expensive setup code associated with the language
     data.
@@ -118,7 +121,7 @@ def lang_class_is_loaded(lang):
     return lang in registry.languages
 
 
-def get_lang_class(lang):
+def get_lang_class(lang: str) -> "Language":
     """Import and load a Language class.
 
     lang (str): Two-letter language code, e.g. 'en'.
@@ -136,7 +139,7 @@ def get_lang_class(lang):
     return registry.languages.get(lang)
 
 
-def set_lang_class(name, cls):
+def set_lang_class(name: str, cls: Type["Language"]) -> None:
     """Set a custom Language class name that can be loaded via get_lang_class.
 
     name (str): Name of Language class.
@@ -145,10 +148,10 @@ def set_lang_class(name, cls):
     registry.languages.register(name, func=cls)
 
 
-def ensure_path(path):
+def ensure_path(path: Any) -> Any:
     """Ensure string is converted to a Path.
 
-    path: Anything. If string, it's converted to Path.
+    path (Any): Anything. If string, it's converted to Path.
     RETURNS: Path or original argument.
     """
     if isinstance(path, str):
@@ -157,7 +160,7 @@ def ensure_path(path):
         return path
 
 
-def load_language_data(path):
+def load_language_data(path: Union[str, Path]) -> Union[dict, list]:
     """Load JSON language data using the given path as a base. If the provided
     path isn't present, will attempt to load a gzipped version before giving up.
 
@@ -173,7 +176,12 @@ def load_language_data(path):
     raise ValueError(Errors.E160.format(path=path))
 
 
-def get_module_path(module):
+def get_module_path(module: ModuleType) -> Path:
+    """Get the path of a Python module.
+
+    module (ModuleType): The Python module.
+    RETURNS (Path): The path.
+    """
     if not hasattr(module, "__module__"):
         raise ValueError(Errors.E169.format(module=repr(module)))
     return Path(sys.modules[module.__module__].__file__).parent
@@ -183,7 +191,7 @@ def load_model(
     name: Union[str, Path],
     disable: Iterable[str] = tuple(),
     component_cfg: Dict[str, Dict[str, Any]] = SimpleFrozenDict(),
-):
+) -> "Language":
     """Load a model from a package or data path.
 
     name (str): Package name or model path.
@@ -209,7 +217,7 @@ def load_model_from_package(
     name: str,
     disable: Iterable[str] = tuple(),
     component_cfg: Dict[str, Dict[str, Any]] = SimpleFrozenDict(),
-):
+) -> "Language":
     """Load a model from an installed package."""
     cls = importlib.import_module(name)
     return cls.load(disable=disable, component_cfg=component_cfg)
@@ -220,7 +228,7 @@ def load_model_from_path(
     meta: Optional[Dict[str, Any]] = None,
     disable: Iterable[str] = tuple(),
     component_cfg: Dict[str, Dict[str, Any]] = SimpleFrozenDict(),
-):
+) -> "Language":
     """Load a model from a data directory path. Creates Language class with
     pipeline from config.cfg and then calls from_disk() with path."""
     if not model_path.exists():
@@ -269,7 +277,7 @@ def load_model_from_init_py(
     init_file: Union[Path, str],
     disable: Iterable[str] = tuple(),
     component_cfg: Dict[str, Dict[str, Any]] = SimpleFrozenDict(),
-):
+) -> "Language":
     """Helper function to use in the `load()` method of a model package's
     __init__.py.
 
@@ -288,15 +296,15 @@ def load_model_from_init_py(
     )
 
 
-def get_installed_models():
+def get_installed_models() -> List[str]:
     """List all model packages currently installed in the environment.
 
-    RETURNS (list): The string names of the models.
+    RETURNS (List[str]): The string names of the models.
     """
     return list(registry.models.get_all().keys())
 
 
-def get_package_version(name):
+def get_package_version(name: str) -> Optional[str]:
     """Get the version of an installed package. Typically used to get model
     package versions.
 
@@ -309,7 +317,9 @@ def get_package_version(name):
         return None
 
 
-def is_compatible_version(version, constraint, prereleases=True):
+def is_compatible_version(
+    version: str, constraint: str, prereleases: bool = True
+) -> Optional[bool]:
     """Check if a version (e.g. "2.0.0") is compatible given a version
     constraint (e.g. ">=1.9.0,<2.2.1"). If the constraint is a specific version,
     it's interpreted as =={version}.
@@ -333,7 +343,9 @@ def is_compatible_version(version, constraint, prereleases=True):
     return version in spec
 
 
-def is_unconstrained_version(constraint, prereleases=True):
+def is_unconstrained_version(
+    constraint: str, prereleases: bool = True
+) -> Optional[bool]:
     # We have an exact version, this is the ultimate constrained version
     if constraint[0].isdigit():
         return False
@@ -358,7 +370,7 @@ def is_unconstrained_version(constraint, prereleases=True):
     return True
 
 
-def get_model_version_range(spacy_version):
+def get_model_version_range(spacy_version: str) -> str:
     """Generate a version range like >=1.2.3,<1.3.0 based on a given spaCy
     version. Models are always compatible across patch versions but not
     across minor or major versions.
@@ -367,7 +379,7 @@ def get_model_version_range(spacy_version):
     return f">={spacy_version},<{release[0]}.{release[1] + 1}.0"
 
 
-def get_base_version(version):
+def get_base_version(version: str) -> str:
     """Generate the base version without any prerelease identifiers.
 
     version (str): The version, e.g. "3.0.0.dev1".
@@ -376,11 +388,11 @@ def get_base_version(version):
     return Version(version).base_version
 
 
-def get_model_meta(path):
+def get_model_meta(path: Union[str, Path]) -> Dict[str, Any]:
     """Get model meta.json from a directory path and validate its contents.
 
     path (str / Path): Path to model directory.
-    RETURNS (dict): The model's meta data.
+    RETURNS (Dict[str, Any]): The model's meta data.
     """
     model_path = ensure_path(path)
     if not model_path.exists():
@@ -412,7 +424,7 @@ def get_model_meta(path):
     return meta
 
 
-def is_package(name):
+def is_package(name: str) -> bool:
     """Check if string maps to a package installed via pip.
 
     name (str): Name of package.
@@ -425,7 +437,7 @@ def is_package(name):
         return False
 
 
-def get_package_path(name):
+def get_package_path(name: str) -> Path:
     """Get the path to an installed package.
 
     name (str): Package name.
@@ -495,7 +507,7 @@ def working_dir(path: Union[str, Path]) -> None:
 
 
 @contextmanager
-def make_tempdir():
+def make_tempdir() -> None:
     """Execute a block in a temporary directory and remove the directory and
     its contents at the end of the with block.
 
@@ -518,7 +530,7 @@ def is_cwd(path: Union[Path, str]) -> bool:
     return str(Path(path).resolve()).lower() == str(Path.cwd().resolve()).lower()
 
 
-def is_in_jupyter():
+def is_in_jupyter() -> bool:
     """Check if user is running spaCy from a Jupyter notebook by detecting the
     IPython kernel. Mainly used for the displaCy visualizer.
     RETURNS (bool): True if in Jupyter, False if not.
@@ -548,7 +560,9 @@ def get_object_name(obj: Any) -> str:
     return repr(obj)
 
 
-def get_cuda_stream(require=False, non_blocking=True):
+def get_cuda_stream(
+    require: bool = False, non_blocking: bool = True
+) -> Optional[CudaStream]:
     ops = get_current_ops()
     if CudaStream is None:
         return None
@@ -567,7 +581,7 @@ def get_async(stream, numpy_array):
         return array
 
 
-def env_opt(name, default=None):
+def env_opt(name: str, default: Optional[Any] = None) -> Optional[Any]:
     if type(default) is float:
         type_convert = float
     else:
@@ -588,7 +602,7 @@ def env_opt(name, default=None):
         return default
 
 
-def read_regex(path):
+def read_regex(path: Union[str, Path]) -> Pattern:
     path = ensure_path(path)
     with path.open(encoding="utf8") as file_:
         entries = file_.read().split("\n")
@@ -598,37 +612,40 @@ def read_regex(path):
     return re.compile(expression)
 
 
-def compile_prefix_regex(entries):
+def compile_prefix_regex(entries: Iterable[Union[str, Pattern]]) -> Pattern:
     """Compile a sequence of prefix rules into a regex object.
 
-    entries (tuple): The prefix rules, e.g. spacy.lang.punctuation.TOKENIZER_PREFIXES.
+    entries (Iterable[Union[str, Pattern]]): The prefix rules, e.g.
-    RETURNS (regex object): The regex object. to be used for Tokenizer.prefix_search.
+        spacy.lang.punctuation.TOKENIZER_PREFIXES.
+    RETURNS (Pattern): The regex object. to be used for Tokenizer.prefix_search.
     """
     expression = "|".join(["^" + piece for piece in entries if piece.strip()])
     return re.compile(expression)
 
 
-def compile_suffix_regex(entries):
+def compile_suffix_regex(entries: Iterable[Union[str, Pattern]]) -> Pattern:
     """Compile a sequence of suffix rules into a regex object.
 
-    entries (tuple): The suffix rules, e.g. spacy.lang.punctuation.TOKENIZER_SUFFIXES.
+    entries (Iterable[Union[str, Pattern]]): The suffix rules, e.g.
-    RETURNS (regex object): The regex object. to be used for Tokenizer.suffix_search.
+        spacy.lang.punctuation.TOKENIZER_SUFFIXES.
+    RETURNS (Pattern): The regex object. to be used for Tokenizer.suffix_search.
     """
     expression = "|".join([piece + "$" for piece in entries if piece.strip()])
     return re.compile(expression)
 
 
-def compile_infix_regex(entries):
+def compile_infix_regex(entries: Iterable[Union[str, Pattern]]) -> Pattern:
     """Compile a sequence of infix rules into a regex object.
 
-    entries (tuple): The infix rules, e.g. spacy.lang.punctuation.TOKENIZER_INFIXES.
+    entries (Iterable[Union[str, Pattern]]): The infix rules, e.g.
+        spacy.lang.punctuation.TOKENIZER_INFIXES.
     RETURNS (regex object): The regex object. to be used for Tokenizer.infix_finditer.
     """
     expression = "|".join([piece for piece in entries if piece.strip()])
     return re.compile(expression)
 
 
-def add_lookups(default_func, *lookups):
+def add_lookups(default_func: Callable[[str], Any], *lookups) -> Callable[[str], Any]:
     """Extend an attribute function with special cases. If a word is in the
     lookups, the value is returned. Otherwise the previous function is used.
 
@@ -641,19 +658,23 @@ def add_lookups(default_func, *lookups):
     return functools.partial(_get_attr_unless_lookup, default_func, lookups)
 
 
-def _get_attr_unless_lookup(default_func, lookups, string):
+def _get_attr_unless_lookup(
+    default_func: Callable[[str], Any], lookups: Dict[str, Any], string: str
+) -> Any:
     for lookup in lookups:
         if string in lookup:
             return lookup[string]
     return default_func(string)
 
 
-def update_exc(base_exceptions, *addition_dicts):
+def update_exc(
+    base_exceptions: Dict[str, List[dict]], *addition_dicts
+) -> Dict[str, List[dict]]:
     """Update and validate tokenizer exceptions. Will overwrite exceptions.
 
-    base_exceptions (dict): Base exceptions.
+    base_exceptions (Dict[str, List[dict]]): Base exceptions.
-    *addition_dicts (dict): Exceptions to add to the base dict, in order.
+    *addition_dicts (Dict[str, List[dict]]): Exceptions to add to the base dict, in order.
-    RETURNS (dict): Combined tokenizer exceptions.
+    RETURNS (Dict[str, List[dict]]): Combined tokenizer exceptions.
     """
     exc = dict(base_exceptions)
     for additions in addition_dicts:
@@ -668,14 +689,16 @@ def update_exc(base_exceptions, *addition_dicts):
     return exc
 
 
-def expand_exc(excs, search, replace):
+def expand_exc(
+    excs: Dict[str, List[dict]], search: str, replace: str
+) -> Dict[str, List[dict]]:
     """Find string in tokenizer exceptions, duplicate entry and replace string.
     For example, to add additional versions with typographic apostrophes.
 
-    excs (dict): Tokenizer exceptions.
+    excs (Dict[str, List[dict]]): Tokenizer exceptions.
     search (str): String to find and replace.
     replace (str): Replacement.
-    RETURNS (dict): Combined tokenizer exceptions.
+    RETURNS (Dict[str, List[dict]]): Combined tokenizer exceptions.
     """
 
     def _fix_token(token, search, replace):
@@ -692,7 +715,9 @@ def expand_exc(excs, search, replace):
     return new_excs
 
 
-def normalize_slice(length, start, stop, step=None):
+def normalize_slice(
+    length: int, start: int, stop: int, step: Optional[int] = None
+) -> Tuple[int, int]:
     if not (step is None or step == 1):
         raise ValueError(Errors.E057)
     if start is None:
@@ -708,7 +733,9 @@ def normalize_slice(length, start, stop, step=None):
     return start, stop
 
 
-def minibatch(items, size=8):
+def minibatch(
+    items: Iterable[Any], size: Union[Iterator[int], int] = 8
+) -> Iterator[Any]:
     """Iterate over batches of items. `size` may be an iterator,
     so that batch-size can vary on each step.
     """
@@ -725,7 +752,12 @@ def minibatch(items, size=8):
         yield list(batch)
 
 
-def minibatch_by_padded_size(docs, size, buffer=256, discard_oversize=False):
+def minibatch_by_padded_size(
+    docs: Iterator["Doc"],
+    size: Union[Iterator[int], int],
+    buffer: int = 256,
+    discard_oversize: bool = False,
+) -> Iterator[Iterator["Doc"]]:
     if isinstance(size, int):
         size_ = itertools.repeat(size)
     else:
@@ -742,7 +774,7 @@ def minibatch_by_padded_size(docs, size, buffer=256, discard_oversize=False):
                 yield subbatch
 
 
-def _batch_by_length(seqs, max_words):
+def _batch_by_length(seqs: Sequence[Any], max_words: int) -> List[List[Any]]:
     """Given a list of sequences, return a batched list of indices into the
     list, where the batches are grouped by length, in descending order.
 
@@ -783,14 +815,12 @@ def minibatch_by_words(docs, size, tolerance=0.2, discard_oversize=False):
         size_ = iter(size)
     else:
         size_ = size
-
     target_size = next(size_)
     tol_size = target_size * tolerance
     batch = []
     overflow = []
     batch_size = 0
     overflow_size = 0
-
     for doc in docs:
         if isinstance(doc, Example):
             n_words = len(doc.reference)
@@ -803,17 +833,14 @@ def minibatch_by_words(docs, size, tolerance=0.2, discard_oversize=False):
         if n_words > target_size + tol_size:
             if not discard_oversize:
                 yield [doc]
-
         # add the example to the current batch if there's no overflow yet and it still fits
         elif overflow_size == 0 and (batch_size + n_words) <= target_size:
             batch.append(doc)
             batch_size += n_words
-
         # add the example to the overflow buffer if it fits in the tolerance margin
         elif (batch_size + overflow_size + n_words) <= (target_size + tol_size):
             overflow.append(doc)
             overflow_size += n_words
-
         # yield the previous batch and start a new one. The new one gets the overflow examples.
         else:
             if batch:
@@ -824,17 +851,14 @@ def minibatch_by_words(docs, size, tolerance=0.2, discard_oversize=False):
             batch_size = overflow_size
             overflow = []
             overflow_size = 0
-
             # this example still fits
             if (batch_size + n_words) <= target_size:
                 batch.append(doc)
                 batch_size += n_words
-
             # this example fits in overflow
             elif (batch_size + n_words) <= (target_size + tol_size):
                 overflow.append(doc)
                 overflow_size += n_words
-
             # this example does not fit with the previous overflow: start another new batch
             else:
                 if batch:
@@ -843,20 +867,19 @@ def minibatch_by_words(docs, size, tolerance=0.2, discard_oversize=False):
                 tol_size = target_size * tolerance
                 batch = [doc]
                 batch_size = n_words
-
     batch.extend(overflow)
     if batch:
         yield batch
 
 
-def filter_spans(spans):
+def filter_spans(spans: Iterable["Span"]) -> List["Span"]:
     """Filter a sequence of spans and remove duplicates or overlaps. Useful for
     creating named entities (where one token can only be part of one entity) or
     when merging spans with `Retokenizer.merge`. When spans overlap, the (first)
     longest span is preferred over shorter spans.
 
-    spans (iterable): The spans to filter.
+    spans (Iterable[Span]): The spans to filter.
-    RETURNS (list): The filtered spans.
+    RETURNS (List[Span]): The filtered spans.
     """
     get_sort_key = lambda span: (span.end - span.start, -span.start)
     sorted_spans = sorted(spans, key=get_sort_key, reverse=True)
@@ -871,15 +894,21 @@ def filter_spans(spans):
     return result
 
 
-def to_bytes(getters, exclude):
+def to_bytes(getters: Dict[str, Callable[[], bytes]], exclude: Iterable[str]) -> bytes:
     return srsly.msgpack_dumps(to_dict(getters, exclude))
 
 
-def from_bytes(bytes_data, setters, exclude):
+def from_bytes(
+    bytes_data: bytes,
+    setters: Dict[str, Callable[[bytes], Any]],
+    exclude: Iterable[str],
+) -> None:
     return from_dict(srsly.msgpack_loads(bytes_data), setters, exclude)
 
 
-def to_dict(getters, exclude):
+def to_dict(
+    getters: Dict[str, Callable[[], Any]], exclude: Iterable[str]
+) -> Dict[str, Any]:
     serialized = {}
     for key, getter in getters.items():
         # Split to support file names like meta.json
@@ -888,7 +917,11 @@ def to_dict(getters, exclude):
     return serialized
 
 
-def from_dict(msg, setters, exclude):
+def from_dict(
+    msg: Dict[str, Any],
+    setters: Dict[str, Callable[[Any], Any]],
+    exclude: Iterable[str],
+) -> Dict[str, Any]:
     for key, setter in setters.items():
         # Split to support file names like meta.json
         if key.split(".")[0] not in exclude and key in msg:
@@ -896,7 +929,11 @@ def from_dict(msg, setters, exclude):
     return msg
 
 
-def to_disk(path, writers, exclude):
+def to_disk(
+    path: Union[str, Path],
+    writers: Dict[str, Callable[[Path], None]],
+    exclude: Iterable[str],
+) -> Path:
     path = ensure_path(path)
     if not path.exists():
         path.mkdir()
@@ -907,7 +944,11 @@ def to_disk(path, writers, exclude):
     return path
 
 
-def from_disk(path, readers, exclude):
+def from_disk(
+    path: Union[str, Path],
+    readers: Dict[str, Callable[[Path], None]],
+    exclude: Iterable[str],
+) -> Path:
     path = ensure_path(path)
     for key, reader in readers.items():
         # Split to support file names like meta.json
@@ -916,7 +957,7 @@ def from_disk(path, readers, exclude):
     return path
 
 
-def import_file(name, loc):
+def import_file(name: str, loc: Union[str, Path]) -> ModuleType:
     """Import module from a file. Used to load models from a directory.
 
     name (str): Name of module to load.
@@ -930,7 +971,7 @@ def import_file(name, loc):
     return module
 
 
-def minify_html(html):
+def minify_html(html: str) -> str:
     """Perform a template-specific, rudimentary HTML minification for displaCy.
     Disclaimer: NOT a general-purpose solution, only removes indentation and
     newlines.
@@ -941,7 +982,7 @@ def minify_html(html):
     return html.strip().replace("    ", "").replace("\n", "")
 
 
-def escape_html(text):
+def escape_html(text: str) -> str:
     """Replace <, >, &, " with their HTML encoded representation. Intended to
     prevent HTML errors in rendered displaCy markup.
 
@@ -955,7 +996,9 @@ def escape_html(text):
     return text
 
 
-def get_words_and_spaces(words, text):
+def get_words_and_spaces(
+    words: Iterable[str], text: str
+) -> Tuple[List[str], List[bool]]:
     if "".join("".join(words).split()) != "".join(text.split()):
         raise ValueError(Errors.E194.format(text=text, words=words))
     text_words = []
@@ -1103,7 +1146,7 @@ class DummyTokenizer:
         return self
 
 
-def link_vectors_to_models(vocab):
+def link_vectors_to_models(vocab: "Vocab") -> None:
     vectors = vocab.vectors
     if vectors.name is None:
         vectors.name = VECTORS_KEY
@@ -1119,7 +1162,7 @@ def link_vectors_to_models(vocab):
 VECTORS_KEY = "spacy_pretrained_vectors"
 
 
-def create_default_optimizer():
+def create_default_optimizer() -> Optimizer:
     learn_rate = env_opt("learn_rate", 0.001)
     beta1 = env_opt("optimizer_B1", 0.9)
     beta2 = env_opt("optimizer_B2", 0.999)