adding ruff auto check for pull requests as well as fixing all ruff errors (#1)

Co-authored-by: Kadir <git@k8n.dev>

vqa/Untitled.ipynb

@@ -24,7 +24,6 @@
    "source": [
     "from transformers import Pix2StructProcessor, Pix2StructForConditionalGeneration\n",
     "from PIL import Image\n",
-    "import pandas as pd\n",
     "import re"
    ]
   },

vqa/main.py

@@ -1,4 +1,4 @@
-from vqa import Blip, MicrosoftGIT, PixStructDocVA, Vilt, Deplot, VQA
+from vqa import MicrosoftGIT, VQA
 from PIL import Image
 from typing import List
 from questions import load_questions

vqa/vqa.py

@@ -1,18 +1,24 @@
 from transformers import BlipProcessor, BlipForQuestionAnswering
 from transformers import ViltProcessor, ViltForQuestionAnswering
 from transformers import Pix2StructProcessor, Pix2StructForConditionalGeneration
-from transformers import GitVisionConfig, GitVisionModel, AutoProcessor, GitProcessor
+from transformers import GitVisionModel, GitProcessor
+from abc import ABC, abstractmethod
+from transformers.processing_utils import ProcessorMixin
 
-class VQA:
+
+class VQA(ABC):
     name = "Not defined"
-    def query(image, question: str) -> str:
-        pass
+    @abstractmethod
+    def query(self, image, question: str) -> str:
+        return "Not implemented"
 
 class Blip(VQA):
     name = "Blip"
     def query(self, image, question):
         processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-capfilt-large")
         model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-capfilt-large")
+        
+        assert processor is ProcessorMixin
         inputs = processor(image, question, return_tensors="pt")
         out = model.generate(max_new_tokens=50000, **inputs)
         return processor.decode(out[0], skip_special_tokens=True)
@@ -25,6 +31,7 @@ class Vilt(VQA):
         model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
 
         # prepare inputs
+        assert processor is ProcessorMixin
         encoding = processor(image, question, return_tensors="pt")
 
         # forward pass
@@ -41,6 +48,7 @@ class Deplot(VQA):
         processor = Pix2StructProcessor.from_pretrained('google/deplot')
         model = Pix2StructForConditionalGeneration.from_pretrained('google/deplot')
 
+        assert processor is ProcessorMixin
         inputs = processor(images=image, text=question, return_tensors="pt")
         predictions = model.generate(**inputs, max_new_tokens=512)
         return processor.decode(predictions[0], skip_special_tokens=True)
@@ -53,6 +61,7 @@ class PixStructDocVA(VQA):
         model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-docvqa-large")
         processor = Pix2StructProcessor.from_pretrained("google/pix2struct-docvqa-large")
 
+        assert processor is ProcessorMixin
         inputs = processor(images=image, text=question, return_tensors="pt")
         predictions = model.generate(**inputs, max_new_tokens=10000)
         answer = processor.decode(predictions[0], skip_special_tokens=True)
@@ -64,6 +73,8 @@ class MicrosoftGIT(VQA):
     def query(self, image, question):
         processor = GitProcessor.from_pretrained("microsoft/git-base")
         model = GitVisionModel.from_pretrained("microsoft/git-base")
+        
+        assert processor is ProcessorMixin
         inputs = processor(images=image, text=question, return_tensors="pt")
         predictions = model.generate(**inputs, max_new_tokens=10000)
         answer = processor.decode(predictions[0], skip_special_tokens=True)