I'm so confused

translation.py

@@ -23,131 +23,46 @@ LANGUAGES = {
     "Татар | Tatar | Татарский": "tat_Cyrl",
     "Тыва | Тувинский | Tuvan ": "tyv_Cyrl",
 }
-L1 = "rus_Cyrl"
-L2 = "eng_Latn"
-
+L1, L2 = "rus_Cyrl", "eng_Latn"
 
 def get_non_printing_char_replacer(replace_by: str = " ") -> tp.Callable[[str], str]:
-    non_printable_map = {
-        ord(c): replace_by
-        for c in (chr(i) for i in range(sys.maxunicode + 1))
-        # same as \p{C} in perl
-        # see https://www.unicode.org/reports/tr44/#General_Category_Values
-        if unicodedata.category(c) in {"C", "Cc", "Cf", "Cs", "Co", "Cn"}
-    }
-
-    def replace_non_printing_char(line) -> str:
-        return line.translate(non_printable_map)
-
-    return replace_non_printing_char
-
+    return lambda line: line.translate({ord(c): replace_by for c in (chr(i) for i in range(sys.maxunicode + 1)) if unicodedata.category(c) in {"C", "Cc", "Cf", "Cs", "Co", "Cn"}})
 
 class TextPreprocessor:
-    """
-    Mimic the text preprocessing made for the NLLB model.
-    This code is adapted from the Stopes repo of the NLLB team:
-    https://github.com/facebookresearch/stopes/blob/main/stopes/pipelines/monolingual/monolingual_line_processor.py#L214
-    """
-
     def __init__(self, lang="en"):
         self.mpn = MosesPunctNormalizer(lang=lang)
-        self.mpn.substitutions = [
-            (re.compile(r), sub) for r, sub in self.mpn.substitutions
-        ]
+        self.mpn.substitutions = [(re.compile(r), sub) for r, sub in self.mpn.substitutions]
         self.replace_nonprint = get_non_printing_char_replacer(" ")
 
     def __call__(self, text: str) -> str:
-        clean = self.mpn.normalize(text)
-        clean = self.replace_nonprint(clean)
-        # replace 𝓕𝔯𝔞𝔫𝔠𝔢𝔰𝔠𝔞 by Francesca
-        clean = unicodedata.normalize("NFKC", clean)
-        return clean
-
+        return unicodedata.normalize("NFKC", self.replace_nonprint(self.mpn.normalize(text)))
 
 def sentenize_with_fillers(text, splitter, fix_double_space=True, ignore_errors=False):
-    """Apply a sentence splitter and return the sentences and all separators before and after them"""
     if fix_double_space:
         text = re.sub(" +", " ", text)
     sentences = splitter.split(text)
-    fillers = []
-    i = 0
-    for sentence in sentences:
-        start_idx = text.find(sentence, i)
-        if ignore_errors and start_idx == -1:
-            # print(f"sent not found after {i}: `{sentence}`")
-            start_idx = i + 1
-        assert start_idx != -1, f"sent not found after {i}: `{sentence}`"
-        fillers.append(text[i:start_idx])
-        i = start_idx + len(sentence)
-    fillers.append(text[i:])
+    fillers = [text[i:text.find(s, i)] for i, s in enumerate(sentences)]
+    fillers.append(text[text.find(sentences[-1], 0) + len(sentences[-1]):])
     return sentences, fillers
 
-
 class Translator:
     def __init__(self):
         self.model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_URL, low_cpu_mem_usage=True)
-        if torch.cuda.is_available():
-            self.model.cuda()
         self.tokenizer = NllbTokenizer.from_pretrained(MODEL_URL)
-
         self.splitter = SentenceSplitter("ru")
         self.preprocessor = TextPreprocessor()
-
         self.languages = LANGUAGES
 
-    def translate(
-        self,
-        text,
-        src_lang=L1,
-        tgt_lang=L2,
-        max_length="auto",
-        num_beams=4,
-        by_sentence=True,
-        preprocess=True,
-        **kwargs,
-    ):
-        """Translate a text sentence by sentence, preserving the fillers around the sentences."""
-        if by_sentence:
-            sents, fillers = sentenize_with_fillers(
-                text, splitter=self.splitter, ignore_errors=True
-            )
-        else:
-            sents = [text]
-            fillers = ["", ""]
-        if preprocess:
-            sents = [self.preprocessor(sent) for sent in sents]
-        results = []
-        for sent, sep in zip(sents, fillers):
-            results.append(sep)
-            results.append(
-                self.translate_single(
-                    sent,
-                    src_lang=src_lang,
-                    tgt_lang=tgt_lang,
-                    max_length=max_length,
-                    num_beams=num_beams,
-                    **kwargs,
-                )
-            )
-        results.append(fillers[-1])
-        return "".join(results)
+    def translate(self, text, src_lang=L1, tgt_lang=L2, max_length="auto", num_beams=4, by_sentence=True, preprocess=True, **kwargs):
+        sents, fillers = (sentenize_with_fillers(text, self.splitter, ignore_errors=True) if by_sentence else ([text], ["", ""]))
+        sents = [self.preprocessor(sent) for sent in sents] if preprocess else sents
+        results = [sep + self.translate_single(sent, src_lang, tgt_lang, max_length, num_beams, **kwargs) for sent, sep in zip(sents, fillers)]
+        return "".join(results + [fillers[-1]])
 
-    def translate_single(
-        self,
-        text,
-        src_lang=L1,
-        tgt_lang=L2,
-        max_length="auto",
-        num_beams=4,
-        n_out=None,
-        **kwargs,
-    ):
+    def translate_single(self, text, src_lang=L1, tgt_lang=L2, max_length="auto", num_beams=4, n_out=None, **kwargs):
         self.tokenizer.src_lang = src_lang
-        encoded = self.tokenizer(
-            text, return_tensors="pt", truncation=True, max_length=512
-        )
-        if max_length == "auto":
-            max_length = int(32 + 2.0 * encoded.input_ids.shape[1])
+        encoded = self.tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+        max_length = int(32 + 2.0 * encoded.input_ids.shape[1]) if max_length == "auto" else max_length
         generated_tokens = self.model.generate(
             **encoded.to(self.model.device),
             forced_bos_token_id=self.tokenizer.lang_code_to_id[tgt_lang],
@@ -156,13 +71,9 @@ class Translator:
             num_return_sequences=n_out or 1,
             **kwargs,
         )
-        out = self.tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
-        if isinstance(text, str) and n_out is None:
-            return out[0]
-        return out
-
+        return self.tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0] if n_out is None else self.tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
 
 if __name__ == "__main__":
     print("Initializing a translator to pre-download models...")
     translator = Translator()
-    print("Initialization successful!")
+    print("Initialization successful!")