Create styleformer.py

styleformer.py

@@ -0,0 +1,161 @@
+class Styleformer():
+
+    def __init__(
+            self,
+            style=0,
+            ctf_model_tag="jaimin/Informal_to_formal",
+            ftc_model_tag="jaimin/formal_to_informal",
+            atp_model_tag="jaimin/Active_to_passive",
+            pta_model_tag="jaimin/Passive_to_active",
+            adequacy_model_tag="jaimin/parrot_adequacy_model",
+    ):
+        from transformers import AutoTokenizer
+        from transformers import AutoModelForSeq2SeqLM
+        from adequacy import Adequacy
+
+        self.style = style
+        self.adequacy = adequacy_model_tag and Adequacy(model_tag=adequacy_model_tag, use_auth_token="access")
+        self.model_loaded = False
+
+        if self.style == 0:
+            self.ctf_tokenizer = AutoTokenizer.from_pretrained(ctf_model_tag, use_auth_token="access")
+            self.ctf_model = AutoModelForSeq2SeqLM.from_pretrained(ctf_model_tag, use_auth_token="access")
+            print("Casual to Formal model loaded...")
+            self.model_loaded = True
+        elif self.style == 1:
+            self.ftc_tokenizer = AutoTokenizer.from_pretrained(ftc_model_tag, use_auth_token="access")
+            self.ftc_model = AutoModelForSeq2SeqLM.from_pretrained(ftc_model_tag, use_auth_token="access")
+            print("Formal to Casual model loaded...")
+            self.model_loaded = True
+        elif self.style == 2:
+            self.atp_tokenizer = AutoTokenizer.from_pretrained(atp_model_tag,use_auth_token="access")
+            self.atp_model = AutoModelForSeq2SeqLM.from_pretrained(atp_model_tag,use_auth_token="access")
+            print("Active to Passive model loaded...")  
+            self.model_loaded = True
+        elif self.style == 3:
+            self.pta_tokenizer = AutoTokenizer.from_pretrained(pta_model_tag,use_auth_token="access")
+            self.pta_model = AutoModelForSeq2SeqLM.from_pretrained(pta_model_tag,use_auth_token="access")
+            print("Passive to Active model loaded...")        
+            self.model_loaded = True
+        else:
+            print("Only CTF, FTC, ATP and PTA are supported in the pre-release...stay tuned")
+
+    def transfer(self, input_sentence, inference_on=-1, quality_filter=0.95, max_candidates=5):
+        if self.model_loaded:
+            if inference_on == -1:
+                device = "cpu"
+            elif inference_on >= 0 and inference_on < 999:
+                device = "cpu:" + str(inference_on)
+            else:
+                device = "cpu"
+                print("Onnx + Quantisation is not supported in the pre-release...stay tuned.")
+
+            if self.style == 0:
+                output_sentence = self._casual_to_formal(input_sentence, device, quality_filter, max_candidates)
+                return output_sentence
+            elif self.style == 1:
+                output_sentence = self._formal_to_casual(input_sentence, device, quality_filter, max_candidates)
+                return output_sentence
+            elif self.style == 2:
+                output_sentence = self._active_to_passive(input_sentence, device)
+                return output_sentence        
+            elif self.style == 3:
+                output_sentence = self._passive_to_active(input_sentence, device)
+                return output_sentence 
+
+        else:
+            print("Models aren't loaded for this style, please use the right style during init")
+
+    def _formal_to_casual(self, input_sentence, device, quality_filter, max_candidates):
+        ftc_prefix = "transfer Formal to Casual: "
+        src_sentence = input_sentence
+        input_sentence = ftc_prefix + input_sentence
+        input_ids = self.ftc_tokenizer.encode(input_sentence, return_tensors='pt')
+        self.ftc_model = self.ftc_model.to(device)
+        input_ids = input_ids.to(device)
+
+        preds = self.ftc_model.generate(
+            input_ids,
+            do_sample=True,
+            max_length=32,
+            top_k=50,
+            top_p=0.95,
+            early_stopping=True,
+            num_return_sequences=max_candidates)
+
+        gen_sentences = set()
+        for pred in preds:
+            gen_sentences.add(self.ftc_tokenizer.decode(pred, skip_special_tokens=True).strip())
+
+        adequacy_scored_phrases = self.adequacy.score(src_sentence, list(gen_sentences), quality_filter, device)
+        ranked_sentences = sorted(adequacy_scored_phrases.items(), key=lambda x: x[1], reverse=True)
+        if len(ranked_sentences) > 0:
+            return ranked_sentences[0][0]
+        else:
+            return None
+
+    def _casual_to_formal(self, input_sentence, device, quality_filter, max_candidates):
+        ctf_prefix = "transfer Casual to Formal: "
+        src_sentence = input_sentence
+        input_sentence = ctf_prefix + input_sentence
+        input_ids = self.ctf_tokenizer.encode(input_sentence, return_tensors='pt')
+        self.ctf_model = self.ctf_model.to(device)
+        input_ids = input_ids.to(device)
+
+        preds = self.ctf_model.generate(
+            input_ids,
+            do_sample=True,
+            max_length=32,
+            top_k=50,
+            top_p=0.95,
+            early_stopping=True,
+            num_return_sequences=max_candidates)
+
+        gen_sentences = set()
+        for pred in preds:
+            gen_sentences.add(self.ctf_tokenizer.decode(pred, skip_special_tokens=True).strip())
+
+        adequacy_scored_phrases = self.adequacy.score(src_sentence, list(gen_sentences), quality_filter, device)
+        ranked_sentences = sorted(adequacy_scored_phrases.items(), key=lambda x: x[1], reverse=True)
+        if len(ranked_sentences) > 0:
+            return ranked_sentences[0][0]
+        else:
+            return None
+
+    def _active_to_passive(self, input_sentence, device):
+        atp_prefix = "transfer Active to Passive: "
+        src_sentence = input_sentence
+        input_sentence = atp_prefix + input_sentence
+        input_ids = self.atp_tokenizer.encode(input_sentence, return_tensors='pt')
+        self.atp_model = self.atp_model.to(device)
+        input_ids = input_ids.to(device)
+        
+        preds = self.atp_model.generate(
+            input_ids,
+            do_sample=True, 
+            max_length=32, 
+            top_k=50, 
+            top_p=0.95, 
+            early_stopping=True,
+            num_return_sequences=1)
+     
+        return self.atp_tokenizer.decode(preds[0], skip_special_tokens=True).strip()
+
+    def _passive_to_active(self, input_sentence, device):
+        pta_prefix = "transfer Passive to Active: "
+        src_sentence = input_sentence
+        input_sentence = pta_prefix + input_sentence
+        input_ids = self.pta_tokenizer.encode(input_sentence, return_tensors='pt')
+        self.pta_model = self.pta_model.to(device)
+        input_ids = input_ids.to(device)
+        
+        preds = self.pta_model.generate(
+            input_ids,
+            do_sample=True, 
+            max_length=32, 
+            top_k=50, 
+            top_p=0.95, 
+            early_stopping=True,
+            num_return_sequences=1)
+      
+        return self.pta_tokenizer.decode(preds[0], skip_special_tokens=True).strip()