Fix issues for Mypy 0.950 and Pydantic 1.9.0

pyproject.toml

@@ -5,8 +5,8 @@ requires = [
     "cymem>=2.0.2,<2.1.0",
     "preshed>=3.0.2,<3.1.0",
     "murmurhash>=0.28.0,<1.1.0",
-    "thinc>=8.0.14,<8.1.0",
-    "blis>=0.4.0,<0.8.0",
+    "thinc>=8.1.0.dev0,<8.2.0",
+    "blis>=0.9.0,<0.10.0",
     "pathy",
     "numpy>=1.15.0",
 ]

requirements.txt

@@ -3,8 +3,8 @@ spacy-legacy>=3.0.9,<3.1.0
 spacy-loggers>=1.0.0,<2.0.0
 cymem>=2.0.2,<2.1.0
 preshed>=3.0.2,<3.1.0
-thinc>=8.0.14,<8.1.0
-blis>=0.4.0,<0.8.0
+thinc>=8.1.0.dev0,<8.2.0
+blis>=0.9.0,<0.10.0
 ml_datasets>=0.2.0,<0.3.0
 murmurhash>=0.28.0,<1.1.0
 wasabi>=0.9.1,<1.1.0
@@ -16,7 +16,7 @@ pathy>=0.3.5
 numpy>=1.15.0
 requests>=2.13.0,<3.0.0
 tqdm>=4.38.0,<5.0.0
-pydantic>=1.7.4,!=1.8,!=1.8.1,<1.9.0
+pydantic>=1.7.4,!=1.8,!=1.8.1,<1.10.0
 jinja2
 langcodes>=3.2.0,<4.0.0
 # Official Python utilities
@@ -31,7 +31,7 @@ pytest-timeout>=1.3.0,<2.0.0
 mock>=2.0.0,<3.0.0
 flake8>=3.8.0,<3.10.0
 hypothesis>=3.27.0,<7.0.0
-mypy==0.910
+mypy>=0.910,<=0.960
 types-dataclasses>=0.1.3; python_version < "3.7"
 types-mock>=0.1.1
 types-requests

setup.cfg

@@ -38,16 +38,16 @@ setup_requires =
     cymem>=2.0.2,<2.1.0
     preshed>=3.0.2,<3.1.0
     murmurhash>=0.28.0,<1.1.0
-    thinc>=8.0.14,<8.1.0
+    thinc>=8.1.0.dev0,<8.2.0
 install_requires =
     # Our libraries
     spacy-legacy>=3.0.9,<3.1.0
     spacy-loggers>=1.0.0,<2.0.0
     murmurhash>=0.28.0,<1.1.0
     cymem>=2.0.2,<2.1.0
     preshed>=3.0.2,<3.1.0
-    thinc>=8.0.14,<8.1.0
-    blis>=0.4.0,<0.8.0
+    thinc>=8.1.0.dev0,<8.2.0
+    blis>=0.9.0,<0.10.0
     wasabi>=0.9.1,<1.1.0
     srsly>=2.4.3,<3.0.0
     catalogue>=2.0.6,<2.1.0
@@ -57,7 +57,7 @@ install_requires =
     tqdm>=4.38.0,<5.0.0
     numpy>=1.15.0
     requests>=2.13.0,<3.0.0
-    pydantic>=1.7.4,!=1.8,!=1.8.1,<1.9.0
+    pydantic>=1.7.4,!=1.8,!=1.8.1,<1.10.0
     jinja2
     # Official Python utilities
     setuptools

spacy/errors.py

@@ -1,4 +1,5 @@
 import warnings
+from .compat import Literal
 
 
 class ErrorsWithCodes(type):
@@ -26,7 +27,10 @@ def setup_default_warnings():
     filter_warning("once", error_msg="[W114]")
 
 
-def filter_warning(action: str, error_msg: str):
+def filter_warning(
+    action: Literal["default", "error", "ignore", "always", "module", "once"],
+    error_msg: str,
+):
     """Customize how spaCy should handle a certain warning.
 
     error_msg (str): e.g. "W006", or a full error message

spacy/lookups.py

@@ -85,7 +85,7 @@ def __setitem__(self, key: Union[str, int], value: Any) -> None:
         value: The value to set.
         """
         key = get_string_id(key)
-        OrderedDict.__setitem__(self, key, value)
+        OrderedDict.__setitem__(self, key, value)  # type:ignore[assignment]
         self.bloom.add(key)
 
     def set(self, key: Union[str, int], value: Any) -> None:
@@ -104,7 +104,7 @@ def __getitem__(self, key: Union[str, int]) -> Any:
         RETURNS: The value.
         """
         key = get_string_id(key)
-        return OrderedDict.__getitem__(self, key)
+        return OrderedDict.__getitem__(self, key)  # type:ignore[index]
 
     def get(self, key: Union[str, int], default: Optional[Any] = None) -> Any:
         """Get the value for a given key. String keys will be hashed.
@@ -114,7 +114,7 @@ def get(self, key: Union[str, int], default: Optional[Any] = None) -> Any:
         RETURNS: The value.
         """
         key = get_string_id(key)
-        return OrderedDict.get(self, key, default)
+        return OrderedDict.get(self, key, default)  # type:ignore[arg-type]
 
     def __contains__(self, key: Union[str, int]) -> bool:  # type: ignore[override]
         """Check whether a key is in the table. String keys will be hashed.

spacy/ml/models/entity_linker.py

@@ -23,7 +23,7 @@ def build_nel_encoder(
             ((tok2vec >> list2ragged()) & build_span_maker())
             >> extract_spans()
             >> reduce_mean()
-            >> residual(Maxout(nO=token_width, nI=token_width, nP=2, dropout=0.0))  # type: ignore[arg-type]
+            >> residual(Maxout(nO=token_width, nI=token_width, nP=2, dropout=0.0))
             >> output_layer
         )
         model.set_ref("output_layer", output_layer)

spacy/ml/models/parser.py

@@ -72,7 +72,7 @@ def build_tb_parser_model(
     t2v_width = tok2vec.get_dim("nO") if tok2vec.has_dim("nO") else None
     tok2vec = chain(
         tok2vec,
-        cast(Model[List["Floats2d"], Floats2d], list2array()),
+        list2array(),
         Linear(hidden_width, t2v_width),
     )
     tok2vec.set_dim("nO", hidden_width)

spacy/ml/models/textcat.py

@@ -1,5 +1,5 @@
+from typing import Optional, List, cast
 from functools import partial
-from typing import Optional, List
 
 from thinc.types import Floats2d
 from thinc.api import Model, reduce_mean, Linear, list2ragged, Logistic
@@ -59,7 +59,8 @@ def build_simple_cnn_text_classifier(
             resizable_layer=resizable_layer,
         )
     model.set_ref("tok2vec", tok2vec)
-    model.set_dim("nO", nO)  # type: ignore  # TODO: remove type ignore once Thinc has been updated
+    if nO is not None:
+        model.set_dim("nO", cast(int, nO))
     model.attrs["multi_label"] = not exclusive_classes
     return model
 
@@ -85,15 +86,16 @@ def build_bow_text_classifier(
         if not no_output_layer:
             fill_defaults["b"] = NEG_VALUE
             output_layer = softmax_activation() if exclusive_classes else Logistic()
-        resizable_layer = resizable(  # type: ignore[var-annotated]
+        resizable_layer: Model[Floats2d, Floats2d] = resizable(
             sparse_linear,
             resize_layer=partial(resize_linear_weighted, fill_defaults=fill_defaults),
         )
         model = extract_ngrams(ngram_size, attr=ORTH) >> resizable_layer
         model = with_cpu(model, model.ops)
         if output_layer:
             model = model >> with_cpu(output_layer, output_layer.ops)
-    model.set_dim("nO", nO)  # type: ignore[arg-type]
+    if nO is not None:
+        model.set_dim("nO", cast(int, nO))
     model.set_ref("output_layer", sparse_linear)
     model.attrs["multi_label"] = not exclusive_classes
     model.attrs["resize_output"] = partial(
@@ -129,8 +131,8 @@ def build_text_classifier_v2(
             output_layer = Linear(nO=nO, nI=nO_double) >> Logistic()
         model = (linear_model | cnn_model) >> output_layer
         model.set_ref("tok2vec", tok2vec)
-    if model.has_dim("nO") is not False:
-        model.set_dim("nO", nO)  # type: ignore[arg-type]
+    if model.has_dim("nO") is not False and nO is not None:
+        model.set_dim("nO", cast(int, nO))
     model.set_ref("output_layer", linear_model.get_ref("output_layer"))
     model.set_ref("attention_layer", attention_layer)
     model.set_ref("maxout_layer", maxout_layer)
@@ -164,7 +166,7 @@ def build_text_classifier_lowdata(
             >> list2ragged()
             >> ParametricAttention(width)
             >> reduce_sum()
-            >> residual(Relu(width, width)) ** 2  # type: ignore[arg-type]
+            >> residual(Relu(width, width)) ** 2
             >> Linear(nO, width)
         )
         if dropout:

spacy/ml/models/tok2vec.py

@@ -1,5 +1,5 @@
 from typing import Optional, List, Union, cast
-from thinc.types import Floats2d, Ints2d, Ragged
+from thinc.types import Floats2d, Ints2d, Ragged, Ints1d
 from thinc.api import chain, clone, concatenate, with_array, with_padded
 from thinc.api import Model, noop, list2ragged, ragged2list, HashEmbed
 from thinc.api import expand_window, residual, Maxout, Mish, PyTorchLSTM
@@ -159,7 +159,7 @@ def make_hash_embed(index):
     embeddings = [make_hash_embed(i) for i in range(len(attrs))]
     concat_size = width * (len(embeddings) + include_static_vectors)
     max_out: Model[Ragged, Ragged] = with_array(
-        Maxout(width, concat_size, nP=3, dropout=0.0, normalize=True)  # type: ignore
+        Maxout(width, concat_size, nP=3, dropout=0.0, normalize=True)
     )
     if include_static_vectors:
         feature_extractor: Model[List[Doc], Ragged] = chain(
@@ -173,15 +173,15 @@ def make_hash_embed(index):
                 StaticVectors(width, dropout=0.0),
             ),
             max_out,
-            cast(Model[Ragged, List[Floats2d]], ragged2list()),
+            ragged2list(),
         )
     else:
         model = chain(
             FeatureExtractor(list(attrs)),
             cast(Model[List[Ints2d], Ragged], list2ragged()),
             with_array(concatenate(*embeddings)),
             max_out,
-            cast(Model[Ragged, List[Floats2d]], ragged2list()),
+            ragged2list(),
         )
     return model
 
@@ -232,12 +232,12 @@ def CharacterEmbed(
     feature_extractor: Model[List[Doc], Ragged] = chain(
         FeatureExtractor([feature]),
         cast(Model[List[Ints2d], Ragged], list2ragged()),
-        with_array(HashEmbed(nO=width, nV=rows, column=0, seed=5)),  # type: ignore
+        with_array(HashEmbed(nO=width, nV=rows, column=0, seed=5)),  # type: ignore[misc]
     )
     max_out: Model[Ragged, Ragged]
     if include_static_vectors:
         max_out = with_array(
-            Maxout(width, nM * nC + (2 * width), nP=3, normalize=True, dropout=0.0)  # type: ignore
+            Maxout(width, nM * nC + (2 * width), nP=3, normalize=True, dropout=0.0)
         )
         model = chain(
             concatenate(
@@ -246,19 +246,19 @@ def CharacterEmbed(
                 StaticVectors(width, dropout=0.0),
             ),
             max_out,
-            cast(Model[Ragged, List[Floats2d]], ragged2list()),
+            ragged2list(),
         )
     else:
         max_out = with_array(
-            Maxout(width, nM * nC + width, nP=3, normalize=True, dropout=0.0)  # type: ignore
+            Maxout(width, nM * nC + width, nP=3, normalize=True, dropout=0.0)
         )
         model = chain(
             concatenate(
                 char_embed,
                 feature_extractor,
             ),
             max_out,
-            cast(Model[Ragged, List[Floats2d]], ragged2list()),
+            ragged2list(),
         )
     return model
 
@@ -289,10 +289,10 @@ def MaxoutWindowEncoder(
             normalize=True,
         ),
     )
-    model = clone(residual(cnn), depth)  # type: ignore[arg-type]
+    model = clone(residual(cnn), depth)
     model.set_dim("nO", width)
     receptive_field = window_size * depth
-    return with_array(model, pad=receptive_field)  # type: ignore[arg-type]
+    return with_array(model, pad=receptive_field)
 
 
 @registry.architectures("spacy.MishWindowEncoder.v2")
@@ -313,9 +313,9 @@ def MishWindowEncoder(
         expand_window(window_size=window_size),
         Mish(nO=width, nI=width * ((window_size * 2) + 1), dropout=0.0, normalize=True),
     )
-    model = clone(residual(cnn), depth)  # type: ignore[arg-type]
+    model = clone(residual(cnn), depth)
     model.set_dim("nO", width)
-    return with_array(model)  # type: ignore[arg-type]
+    return with_array(model)
 
 
 @registry.architectures("spacy.TorchBiLSTMEncoder.v1")

spacy/ml/staticvectors.py

@@ -40,17 +40,15 @@ def forward(
     if not token_count:
         return _handle_empty(model.ops, model.get_dim("nO"))
     key_attr: int = model.attrs["key_attr"]
-    keys: Ints1d = model.ops.flatten(
-        cast(Sequence, [doc.to_array(key_attr) for doc in docs])
-    )
+    keys = model.ops.flatten([cast(Ints1d, doc.to_array(key_attr)) for doc in docs])
     vocab: Vocab = docs[0].vocab
     W = cast(Floats2d, model.ops.as_contig(model.get_param("W")))
     if vocab.vectors.mode == Mode.default:
-        V = cast(Floats2d, model.ops.asarray(vocab.vectors.data))
+        V = model.ops.asarray(vocab.vectors.data)
         rows = vocab.vectors.find(keys=keys)
         V = model.ops.as_contig(V[rows])
     elif vocab.vectors.mode == Mode.floret:
-        V = cast(Floats2d, vocab.vectors.get_batch(keys))
+        V = vocab.vectors.get_batch(keys)
         V = model.ops.as_contig(V)
     else:
         raise RuntimeError(Errors.E896)
@@ -62,9 +60,7 @@ def forward(
         # Convert negative indices to 0-vectors
         # TODO: more options for UNK tokens
         vectors_data[rows < 0] = 0
-    output = Ragged(
-        vectors_data, model.ops.asarray([len(doc) for doc in docs], dtype="i")  # type: ignore
-    )
+    output = Ragged(vectors_data, model.ops.asarray1i([len(doc) for doc in docs]))
     mask = None
     if is_train:
         mask = _get_drop_mask(model.ops, W.shape[0], model.attrs.get("dropout_rate"))
@@ -77,7 +73,9 @@ def backprop(d_output: Ragged) -> List[Doc]:
         model.inc_grad(
             "W",
             model.ops.gemm(
-                cast(Floats2d, d_output.data), model.ops.as_contig(V), trans1=True
+                cast(Floats2d, d_output.data),
+                cast(Floats2d, model.ops.as_contig(V)),
+                trans1=True,
             ),
         )
         return []

spacy/pipeline/edit_tree_lemmatizer.py

@@ -138,7 +138,7 @@ def get_loss(
 
             truths.append(eg_truths)
 
-        d_scores, loss = loss_func(scores, truths)  # type: ignore
+        d_scores, loss = loss_func(scores, truths)
         if self.model.ops.xp.isnan(loss):
             raise ValueError(Errors.E910.format(name=self.name))
 

spacy/pipeline/entityruler.py

@@ -159,10 +159,8 @@ def match(self, doc: Doc):
         self._require_patterns()
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", message="\\[W036")
-            matches = cast(
-                List[Tuple[int, int, int]],
-                list(self.matcher(doc)) + list(self.phrase_matcher(doc)),
-            )
+            matches = list(self.matcher(doc)) + list(self.phrase_matcher(doc))
+
         final_matches = set(
             [(m_id, start, end) for m_id, start, end in matches if start != end]
         )

spacy/pipeline/legacy/entity_linker.py

@@ -213,15 +213,14 @@ def get_loss(self, examples: Iterable[Example], sentence_encodings: Floats2d):
                 if kb_id:
                     entity_encoding = self.kb.get_vector(kb_id)
                     entity_encodings.append(entity_encoding)
-        entity_encodings = self.model.ops.asarray(entity_encodings, dtype="float32")
+        entity_encodings = self.model.ops.asarray2f(entity_encodings)
         if sentence_encodings.shape != entity_encodings.shape:
             err = Errors.E147.format(
                 method="get_loss", msg="gold entities do not match up"
             )
             raise RuntimeError(err)
-        # TODO: fix typing issue here
-        gradients = self.distance.get_grad(sentence_encodings, entity_encodings)  # type: ignore
-        loss = self.distance.get_loss(sentence_encodings, entity_encodings)  # type: ignore
+        gradients = self.distance.get_grad(sentence_encodings, entity_encodings)
+        loss = self.distance.get_loss(sentence_encodings, entity_encodings)
         loss = loss / len(entity_encodings)
         return float(loss), gradients
 

spacy/pipeline/spancat.py

@@ -75,7 +75,7 @@ def ngram_suggester(docs: Iterable[Doc], *, ops: Optional[Ops] = None) -> Ragged
                 if spans:
                     assert spans[-1].ndim == 2, spans[-1].shape
             lengths.append(length)
-        lengths_array = cast(Ints1d, ops.asarray(lengths, dtype="i"))
+        lengths_array = ops.asarray1i(lengths)
         if len(spans) > 0:
             output = Ragged(ops.xp.vstack(spans), lengths_array)
         else:

spacy/schemas.py

@@ -104,7 +104,7 @@ def get_arg_model(
         sig_args[param.name] = (annotation, default)
     is_strict = strict and not has_variable
     sig_args["__config__"] = ArgSchemaConfig if is_strict else ArgSchemaConfigExtra  # type: ignore[assignment]
-    return create_model(name, **sig_args)  # type: ignore[arg-type, return-value]
+    return create_model(name, **sig_args)  # type: ignore[call-overload, arg-type, return-value]
 
 
 def validate_init_settings(