RHOAIENG-12605: Update JPype to 1.5.0 (#216)

* Update JPype to 1.5.0
* Add Python 3.11 to GHAs
* Fix lint error
* Workaround Pylint error
* Fix styling

Author: ruivieira

.github/workflows/workflow.yml

@@ -7,7 +7,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [ 3.8, 3.9 ]
+        python-version: [ 3.8, 3.9, 3.11 ]
         java-version: [ 17 ]
         maven-version: [ '3.8.6' ]
     steps:

pyproject.toml

@@ -22,7 +22,7 @@ classifiers = [
 ]
 
 dependencies = [
-    "Jpype1==1.4.1",
+    "Jpype1==1.5.0",
     "pyarrow==17.0.0",
     "matplotlib~=3.6.3",
     "pandas~=1.5.3",
@@ -32,7 +32,7 @@ dependencies = [
 
 [project.optional-dependencies]
 dev = [
-    "JPype1==1.4.1",
+    "JPype1==1.5.0",
     "black~=22.12.0",
     "click==8.0.4",
     "joblib~=1.2.0",

src/trustyai/language/detoxify/tmarco.py

@@ -104,14 +104,16 @@ def __init__(
                     "cuda" if torch.cuda.is_available() else "cpu"
                 )
 
-        def load_models(self, experts: list[str] = None, expert_weights: list = None):
+        def load_models(
+            self, experts: list[str] = None, expert_weights: list = None
+        ):  # pylint: disable=unsubscriptable-object
             """Load expert models."""
             if expert_weights is not None:
                 self.expert_weights = expert_weights
             expert_models = []
             for expert in experts:
                 # Load TMaRCO models
-                if (expert == "trustyai/gplus" or expert == "trustyai/gminus"):
+                if expert in ["trustyai/gplus", "trustyai/gminus"]:
                     expert = BartForConditionalGeneration.from_pretrained(
                         expert,
                         forced_bos_token_id=self.tokenizer.bos_token_id,
@@ -122,14 +124,14 @@ def load_models(self, experts: list[str] = None, expert_weights: list = None):
                     expert = AutoModelForMaskedLM.from_pretrained(
                         expert,
                         forced_bos_token_id=self.tokenizer.bos_token_id,
-                        device_map = "auto"
+                        device_map="auto",
                     )
                 # Load HuggingFace models
                 else:
                     expert = AutoModelForCausalLM.from_pretrained(
                         expert,
                         forced_bos_token_id=self.tokenizer.bos_token_id,
-                        device_map = "auto"
+                        device_map="auto",
                     )
                 expert_models.append(expert)
             self.experts = expert_models