Create roundtrip_mutator.py
Browse files
prompt_injection/mutators/roundtrip_mutator.py
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from prompt_injection.mutators.base import PromptMutator
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from transformers import MarianMTModel, MarianTokenizer
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class RoundTripPromptMutator(PromptMutator):
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def __init__(self,model_name_translate='Helsinki-NLP/opus-mt-en-zh',model_name_inv_translate='Helsinki-NLP/opus-mt-zh-en',label=None):
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self.model_name_translate=model_name_translate
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self.model_name_inv_translate=model_name_inv_translate
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# Load the pre-trained model and tokenizer
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self.model_translate = MarianMTModel.from_pretrained(model_name_translate)
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self.tokenizer_translate = MarianTokenizer.from_pretrained(model_name_translate)
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# Load the pre-trained model and tokenizer
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self.model_inv_translate = MarianMTModel.from_pretrained(model_name_inv_translate)
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self.tokenizer_inv_translate = MarianTokenizer.from_pretrained(model_name_inv_translate)
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if label is None:
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self.label= f'RoundTripPromptMutator-{self.model_name_translate}--{self.model_name_translate}'
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else:
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self.label= f'RoundTripPromptMutator-{label}'
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def to_lang(self,text):
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inputs = self.tokenizer_translate.encode(text, return_tensors='pt', padding=True, truncation=True)
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translated_tokens = self.model_translate.generate(inputs, max_length=40, num_beams=4, early_stopping=True)
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translated_text = self.tokenizer_translate.decode(translated_tokens[0], skip_special_tokens=True)
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return translated_text
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def from_lang(self,text):
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inputs = self.tokenizer_inv_translate.encode(text, return_tensors='pt', padding=True, truncation=True)
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translated_tokens = self.model_inv_translate.generate(inputs, max_length=40, num_beams=4, early_stopping=True)
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translated_text = self.tokenizer_inv_translate.decode(translated_tokens[0], skip_special_tokens=True)
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return translated_text
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def mutate(self,sample:str)->str:
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return self.from_lang(self.to_lang(sample))
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def get_name(self):
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return self.label
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