Upload processor

processing_taivisionlm.py

@@ -285,32 +285,4 @@ class TaiVisionProcessor(ProcessorMixin):
     def model_input_names(self):
         tokenizer_input_names = self.tokenizer.model_input_names
         image_processor_input_names = self.image_processor.model_input_names
-        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
-    
-    
-    
-# if __name__ == '__main__':
-#     from configuration_taivisionlm import TaiVisionLMConfig
-#     import transformers
-#     import torch
-#     config = TaiVisionLMConfig.from_pretrained("./")
-#     preprocessor = transformers.SiglipImageProcessor.from_pretrained("google/siglip-base-patch16-224")
-#     preprocessor.image_seq_length = config.num_image_tokens
-#     tokenizer = transformers.AutoTokenizer.from_pretrained("benchang1110/Taiwan-tinyllama-v1.0-chat")
-#     processor = TaiVisionProcessor(tokenizer=tokenizer, image_processor=preprocessor)
-#     processor.save_pretrained("./")
-    
-#     from PIL import Image
-#     import requests
-#     processor = TaiVisionProcessor.from_pretrained("./")
-#     url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"
-#     image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
-#     text = "Hello< what is your name?"
-#     suffix = "I am fine, thank you."
-#     inputs = processor(text=text,suffix=suffix,images=image, return_tensors="pt",padding="max_length",max_length=512)
-#     print(inputs['attention_mask'].shape)
-#     print(inputs['input_ids'].shape)
-#     print(inputs['token_type_ids'].shape)
-#     # print number of 0 in token_type_ids
-#     print(torch.sum(inputs['token_type_ids']==0))
-#     print(inputs)
+        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))