from transformers import Pipeline, T5ForConditionalGeneration, AutoTokenizer class PersianEasePipeline(Pipeline): def _sanitize_parameters(self, **kwargs): preprocess_kwargs = {} if "second_text" in kwargs: preprocess_kwargs["second_text"] = kwargs["second_text"] return preprocess_kwargs, {}, {} def preprocess(self, text, second_text=None): inputs = self.tokenizer.encode("formal: " + text, return_tensors='pt', max_length=128, truncation=True, padding='max_length') return inputs.to(self.device) def _forward(self, model_inputs): return self.model.generate(model_inputs, max_length=128, num_beams=4, temperature=0.7) def postprocess(self, model_outputs): return self.tokenizer.decode(model_outputs[0], skip_special_tokens=True)