Remove usage of modern type hints in annotator signatures for compati…

…bility

CHANGELOG.md

@@ -12,7 +12,6 @@
 - Sparv now respects the `JAVA_HOME` environment variable.
 - Added autocomplete instructions for `zsh`.
 - Added 'misc:fake_text_headtail' annotator.
-- You can now use `from __future__ import annotations` in Sparv modules.
 
 ### Changed
 

pyproject.toml

@@ -109,6 +109,7 @@ preview = true
 
 [tool.ruff.lint.per-file-ignores]
 "__init__.py" = ["F401"]  # Pyflakes: unused-import
+"sparv/modules/*.py" = ["FA100", "UP045"]  # For Python 3.9 compatibility in modules
 
 [tool.ruff.lint.pydocstyle]
 convention = "google"

sparv/modules/lexical_classes/token.py

@@ -1,5 +1,5 @@
 """Annotate words with lexical classes from Blingbring or SweFN."""
-from __future__ import annotations
+from typing import Optional
 
 from sparv.api import Annotation, Config, Model, Output, annotator, get_logger, util
 from sparv.api.util.constants import AFFIX, DELIM, SCORESEP
@@ -23,7 +23,7 @@ def blingbring_words(out: Output = Output("<token>:lexical_classes.blingbring",
                      delimiter: str = DELIM,
                      affix: str = AFFIX,
                      scoresep: str = SCORESEP,
-                     lexicon: util.misc.PickledLexicon | None = None):
+                     lexicon: Optional[util.misc.PickledLexicon] = None):
     """Blingbring specific wrapper for annotate_words. See annotate_words for more info."""
     # pos_limit="NN VB JJ AB" | None
 
@@ -62,7 +62,7 @@ def swefn_words(out: Output = Output("<token>:lexical_classes.swefn",
                 delimiter: str = DELIM,
                 affix: str = AFFIX,
                 scoresep: str = SCORESEP,
-                lexicon: util.misc.PickledLexicon | None = None):
+                lexicon: Optional[util.misc.PickledLexicon] = None):
     """Swefn specific wrapper for annotate_words. See annotate_words for more info."""
 
     # SweFN annotation function

sparv/modules/malt/malt.py

@@ -1,7 +1,6 @@
 """Dependency parsing using MaltParser."""
-from __future__ import annotations
-
 import re
+from typing import Optional
 
 from sparv.api import Annotation, Binary, Config, Model, ModelOutput, Output, annotator, get_logger, modelbuilder, util
 
@@ -61,7 +60,7 @@ def annotate(maltjar: Binary = Binary("[malt.jar]"),
              sentence: Annotation = Annotation("<sentence>"),
              token: Annotation = Annotation("<token>"),
              encoding: str = util.constants.UTF8,
-             process_dict: dict | None = None):
+             process_dict: Optional[dict] = None):
     """
     Run the malt parser, in an already started process defined in process_dict, or start a new process (default).
 

sparv/modules/saldo/compound.py

@@ -1,12 +1,11 @@
 """Compound analysis."""
-from __future__ import annotations
-
 import itertools
 import operator
 import pathlib
 import pickle
 import re
 import time
+from typing import Optional
 import xml.etree.ElementTree as etree
 from functools import reduce
 
@@ -82,7 +81,7 @@ def annotate(out_complemgrams: Output = Output("<token>:saldo.complemgram",
              compdelim: str = util.constants.COMPSEP,
              affix: str = util.constants.AFFIX,
              cutoff: bool = True,
-             preloaded_models: tuple | None = None):
+             preloaded_models: Optional[tuple] = None):
     """Divide compound words into prefix(es) and suffix.
 
     - out_complemgram is the resulting annotation file for compound lemgrams

sparv/modules/sensaldo/sensaldo.py

@@ -1,5 +1,5 @@
 """Sentiment annotation per token using SenSALDO."""
-from __future__ import annotations
+from typing import Optional
 
 from sparv.api import Annotation, Config, Model, ModelOutput, Output, annotator, get_logger, modelbuilder, util
 
@@ -19,7 +19,7 @@ def annotate(sense: Annotation = Annotation("<token:sense>"),
              out_scores: Output = Output("<token>:sensaldo.sentiment_score", description="SenSALDO sentiment score"),
              out_labels: Output = Output("<token>:sensaldo.sentiment_label", description="SenSALDO sentiment label"),
              model: Model = Model("[sensaldo.model]"),
-             lexicon: util.misc.PickledLexicon | None = None):
+             lexicon: Optional[util.misc.PickledLexicon] = None):
     """Assign sentiment values to tokens based on their sense annotation.
 
     When more than one sense is possible, calulate a weighted mean.

sparv/modules/swener/swener.py

@@ -1,7 +1,6 @@
 """Named entity tagging with SweNER."""
-from __future__ import annotations
-
 import re
+from typing import Optional
 import xml.etree.ElementTree as etree
 import xml.sax.saxutils
 
@@ -29,7 +28,7 @@ def annotate(out_ne: Output = Output("swener.ne", cls="named_entity", descriptio
              sentence: Annotation = Annotation("<sentence>"),
              token: Annotation = Annotation("<token>"),
              binary: Binary = Binary("[swener.binary]"),
-             process_dict: dict | None = None):
+             process_dict: Optional[dict] = None):
     """Tag named entities using HFST-SweNER.
 
     SweNER is either run in an already started process defined in