fix(ci): format with ruff

knowledge_verificator/main.py

@@ -1,19 +1,4 @@
 """Main module with CLI definition."""
-<<<<<<< HEAD
-||||||| parent of 0627c06 (feat(qg): implement basic QG module)
-from knowledge_verificator.nli import infer_relation
-
-if __name__ == '__main__':
-    premise = input("Premise: ")
-    hypothesis = input("Hypothesis: ")
-    print(
-        infer_relation(
-            premise=premise,
-            hypothesis=hypothesis
-        ).value
-    )
-=======
 
 if __name__ == '__main__':
     print('Currently nothing happens here.')
->>>>>>> 0627c06 (feat(qg): implement basic QG module)

knowledge_verificator/nli.py

@@ -3,8 +3,10 @@
 from enum import Enum
 import warnings
 import logging
-from transformers import AutoTokenizer, AutoModelForSequenceClassification  # type: ignore[import-untyped]
-from transformers import AutoTokenizer, AutoModelForSequenceClassification  # type: ignore
+from transformers import (  # type: ignore[import-untyped]
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+)
 import torch
 
 
@@ -77,8 +79,8 @@ def infer(
             torch.Tensor(tokenized_input_seq_pair['input_ids']).long().unsqueeze(0)
         )
 
-        # remember bart doesn't have 'token_type_ids', remove the line below if you are using bart.
         token_type_ids = None
+        # `bart` model does not have `token_type_ids`.
         if self._hg_model_hub_name != self._available_models['bart']:
             token_type_ids = (
                 torch.Tensor(tokenized_input_seq_pair['token_type_ids'])

knowledge_verificator/qg.py

@@ -4,20 +4,20 @@
 from transformers import T5Tokenizer, T5ForConditionalGeneration  # type: ignore[import-untyped]
 
 
-class QuestionGeneration:
+class QuestionGeneration:  # pylint: disable=too-few-public-methods
     """Class for generating question based on supplied context."""
 
     def __init__(self) -> None:
         self.trained_model_path = (
-            "ZhangCheng/T5-Base-Fine-Tuned-for-Question-Generation"
+            'ZhangCheng/T5-Base-Fine-Tuned-for-Question-Generation'
         )
         self.trained_tokenizer_path = (
-            "ZhangCheng/T5-Base-Fine-Tuned-for-Question-Generation"
+            'ZhangCheng/T5-Base-Fine-Tuned-for-Question-Generation'
         )
 
         self.model = T5ForConditionalGeneration.from_pretrained(self.trained_model_path)
         self.tokenizer = T5Tokenizer.from_pretrained(self.trained_tokenizer_path)
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
         self.model = self.model.to(self.device)
         self.model.eval()
 
@@ -32,14 +32,14 @@ def generate(self, answer: str, context: str) -> dict[str, str]:
         Returns:
             dict[str, str]: Dictionary with a generated question, and a provided answer and context.
         """
-        input_text = f"<answer> {answer} <context> {context} "
-        encoding = self.tokenizer.encode_plus(input_text, return_tensors="pt")
-        input_ids = encoding["input_ids"].to(self.device)
-        attention_mask = encoding["attention_mask"].to(self.device)
+        input_text = f'<answer> {answer} <context> {context} '
+        encoding = self.tokenizer.encode_plus(input_text, return_tensors='pt')
+        input_ids = encoding['input_ids'].to(self.device)
+        attention_mask = encoding['attention_mask'].to(self.device)
         outputs = self.model.generate(
             input_ids=input_ids, attention_mask=attention_mask
         )
         question = self.tokenizer.decode(
             outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True
         )
-        return {"question": question, "answer": answer, "context": context}
+        return {'question': question, 'answer': answer, 'context': context}

tests/test_qg.py

@@ -15,16 +15,16 @@ def qg():
 
 
 @pytest.mark.parametrize(
-    "question,answer,context",
-    (("Where is the red apple located?", "Tree", "The red apple is on a tree."),),
+    'question,answer,context',
+    (('Where is the red apple located?', 'Tree', 'The red apple is on a tree.'),),
 )
 def test_basic_question_generation(question: str, answer: str, context: str, qg):
     """Test if generating in very simple case works as expected."""
     output = qg.generate(answer=answer, context=context)
     expected = {
-        "question": question,
-        "answer": answer,
-        "context": context,
+        'question': question,
+        'answer': answer,
+        'context': context,
     }
 
     assert output == expected