typing: add initial types

pyproject.toml

@@ -28,7 +28,7 @@ Source = "https://github.com/sloria/TextBlob"
 [project.optional-dependencies]
 docs = ["sphinx==8.1.3", "sphinx-issues==5.0.0", "PyYAML==6.0.2"]
 tests = ["pytest", "numpy"]
-dev = ["textblob[tests]", "tox", "pre-commit>=3.5,<5.0"]
+dev = ["textblob[tests]", "tox", "pre-commit>=3.5,<5.0", "pyright", "ruff"]
 
 [build-system]
 requires = ["flit_core<4"]
@@ -86,6 +86,7 @@ select = [
   "I",  # isort
   "UP", # pyupgrade
   "W",  # pycodestyle warning
+  "TC", # flake8-typechecking
 ]
 
 [tool.ruff.lint.per-file-ignores]
@@ -96,3 +97,6 @@ markers = [
   "slow: marks tests as slow (deselect with '-m \"not slow\"')",
   "numpy: marks tests that require numpy",
 ]
+
+[tool.pyright]
+include = ["src/**", "tests/**"]

src/textblob/_text.py

@@ -124,7 +124,7 @@ def keys(self):
     def values(self):
         return self._lazy("values")
 
-    def update(self, *args):
+    def update(self, *args, **kwargs):
         return self._lazy("update", *args)
 
     def pop(self, *args):
@@ -324,10 +324,10 @@ def penntreebank2universal(token, tag):
     ("cry", -1.00): set((":'(", ":'''(", ";'(")),
 }
 
-RE_EMOTICONS = [
+TEMP_RE_EMOTICONS = [
     r" ?".join([re.escape(each) for each in e]) for v in EMOTICONS.values() for e in v
 ]
-RE_EMOTICONS = re.compile(r"(%s)($|\s)" % "|".join(RE_EMOTICONS))
+RE_EMOTICONS = re.compile(r"(%s)($|\s)" % "|".join(TEMP_RE_EMOTICONS))
 
 # Handle sarcasm punctuation (!).
 RE_SARCASM = re.compile(r"\( ?\! ?\)")
@@ -490,9 +490,9 @@ class Lexicon(lazydict):
     def __init__(
         self,
         path="",
-        morphology=None,
-        context=None,
-        entities=None,
+        morphology="",
+        context="",
+        entities="",
         NNP="NNP",
         language=None,
     ):
@@ -724,7 +724,7 @@ def apply(self, tokens):
                     t[i] = [t[i][0], r[1]]
         return t[len(o) : -len(o)]
 
-    def insert(self, i, tag1, tag2, cmd="prevtag", x=None, y=None):
+    def insert(self, i, tag1, tag2, cmd="prevtag", x=None, y=None, *args):
         """Inserts a new rule that updates words with tag1 to tag2,
         given constraints x and y, e.g., Context.append("TO < NN", "VB")
         """
@@ -739,7 +739,7 @@ def insert(self, i, tag1, tag2, cmd="prevtag", x=None, y=None):
     def append(self, *args, **kwargs):
         self.insert(len(self) - 1, *args, **kwargs)
 
-    def extend(self, rules=None):
+    def extend(self, rules=None, *args):
         if rules is None:
             rules = []
         for r in rules:
@@ -1570,9 +1570,8 @@ def parse(
 
 TOKENS = "tokens"
 
-
 class TaggedString(str):
-    def __new__(self, string, tags=None, language=None):
+    def __new__(cls, string, tags=None, language=None):
         """Unicode string with tags and language attributes.
         For example: TaggedString("cat/NN/NP", tags=["word", "pos", "chunk"]).
         """
@@ -1588,7 +1587,7 @@ def __new__(self, string, tags=None, language=None):
                 for s in string
             ]
             string = "\n".join(" ".join("/".join(token) for token in s) for s in string)
-        s = str.__new__(self, string)
+        s = str.__new__(cls, string)
         s.tags = list(tags)
         s.language = language
         return s
@@ -1634,7 +1633,7 @@ def language(self):
         return self._language
 
     @classmethod
-    def train(self, s, path="spelling.txt"):
+    def train(cls, s, path="spelling.txt"):
         """Counts the words in the given string and saves the probabilities at the given path.
         This can be used to generate a new model for the Spelling() constructor.
         """

src/textblob/base.py

@@ -5,10 +5,16 @@
     All base classes are defined in the same module, ``textblob.base``.
 """
 
+from __future__ import annotations
+
 from abc import ABCMeta, abstractmethod
+from typing import TYPE_CHECKING
 
 import nltk
 
+if TYPE_CHECKING:
+    from typing import Any, AnyStr
+
 ##### POS TAGGERS #####
 
 
@@ -19,11 +25,11 @@ class BaseTagger(metaclass=ABCMeta):
     """
 
     @abstractmethod
-    def tag(self, text, tokenize=True):
+    def tag(self, text: str, tokenize=True) -> list[tuple[str, str]]:
         """Return a list of tuples of the form (word, tag)
         for a given set of text or BaseBlob instance.
         """
-        return
+        ...
 
 
 ##### NOUN PHRASE EXTRACTORS #####
@@ -36,29 +42,29 @@ class BaseNPExtractor(metaclass=ABCMeta):
     """
 
     @abstractmethod
-    def extract(self, text):
+    def extract(self, text: str) -> list[str]:
         """Return a list of noun phrases (strings) for a body of text."""
-        return
+        ...
 
 
 ##### TOKENIZERS #####
 
 
-class BaseTokenizer(nltk.tokenize.api.TokenizerI, metaclass=ABCMeta):
+class BaseTokenizer(nltk.tokenize.api.TokenizerI, metaclass=ABCMeta):  # pyright: ignore
     """Abstract base class from which all Tokenizer classes inherit.
     Descendant classes must implement a ``tokenize(text)`` method
     that returns a list of noun phrases as strings.
     """
 
     @abstractmethod
-    def tokenize(self, text):
+    def tokenize(self, text: str) -> list[str]:
         """Return a list of tokens (strings) for a body of text.
 
         :rtype: list
         """
-        return
+        ...
 
-    def itokenize(self, text, *args, **kwargs):
+    def itokenize(self, text: str, *args, **kwargs):
         """Return a generator that generates tokens "on-demand".
 
         .. versionadded:: 0.6.0
@@ -81,6 +87,8 @@ class BaseSentimentAnalyzer(metaclass=ABCMeta):
     results of analysis.
     """
 
+    _trained: bool
+
     kind = DISCRETE
 
     def __init__(self):
@@ -91,7 +99,7 @@ def train(self):
         self._trained = True
 
     @abstractmethod
-    def analyze(self, text):
+    def analyze(self, text) -> Any:
         """Return the result of of analysis. Typically returns either a
         tuple, float, or dictionary.
         """
@@ -111,6 +119,6 @@ class BaseParser(metaclass=ABCMeta):
     """
 
     @abstractmethod
-    def parse(self, text):
+    def parse(self, text: AnyStr):
         """Parses the text."""
-        return
+        ...

src/textblob/blob.py

@@ -138,9 +138,9 @@ def lemmatize(self, pos=None):
         lemmatizer = nltk.stem.WordNetLemmatizer()
         return lemmatizer.lemmatize(self.string, tag)
 
-    PorterStemmer = nltk.stem.porter.PorterStemmer()
-    LancasterStemmer = nltk.stem.lancaster.LancasterStemmer()
-    SnowballStemmer = nltk.stem.snowball.SnowballStemmer("english")
+    PorterStemmer = nltk.stem.PorterStemmer()
+    LancasterStemmer = nltk.stem.LancasterStemmer()
+    SnowballStemmer = nltk.stem.SnowballStemmer("english")
 
     # added 'stemmer' on lines of lemmatizer
     # based on nltk
@@ -308,7 +308,7 @@ def _initialize_models(
     obj.tokenizer = _validated_param(
         tokenizer,
         "tokenizer",
-        base_class=(BaseTokenizer, nltk.tokenize.api.TokenizerI),
+        base_class=(BaseTokenizer, nltk.tokenize.api.TokenizerI),  # pyright: ignore
         default=BaseBlob.tokenizer,
         base_class_name="BaseTokenizer",
     )

src/textblob/classifiers.py

@@ -510,8 +510,8 @@ def update(
 
 
 class MaxEntClassifier(NLTKClassifier):
-    __doc__ = nltk.classify.maxent.MaxentClassifier.__doc__
-    nltk_class = nltk.classify.maxent.MaxentClassifier
+    __doc__ = nltk.classify.MaxentClassifier.__doc__
+    nltk_class = nltk.classify.MaxentClassifier
 
     def prob_classify(self, text):
         """Return the label probability distribution for classifying a string

src/textblob/decorators.py

@@ -1,9 +1,18 @@
 """Custom decorators."""
 
+from __future__ import annotations
+
 from functools import wraps
+from typing import TYPE_CHECKING
 
 from textblob.exceptions import MissingCorpusError
 
+if TYPE_CHECKING:
+    from collections.abc import Callable
+    from typing import TypeVar
+
+    ReturnType = TypeVar("ReturnType")
+
 
 class cached_property:
     """A property that is only computed once per instance and then replaces
@@ -24,7 +33,9 @@ def __get__(self, obj, cls):
         return value
 
 
-def requires_nltk_corpus(func):
+def requires_nltk_corpus(
+    func: Callable[..., ReturnType],
+) -> Callable[..., ReturnType]:
     """Wraps a function that requires an NLTK corpus. If the corpus isn't found,
     raise a :exc:`MissingCorpusError`.
     """