import regex as re from joblib import Parallel, delayed from nltk.tokenize import sent_tokenize from typing import List, Tuple, Union from sacremoses import MosesPunctNormalizer from indicnlp.normalize import indic_normalize from sacremoses import MosesTokenizer, MosesDetokenizer from indicnlp.transliterate import unicode_transliterate from indicnlp.tokenize import indic_tokenize, indic_detokenize from indicnlp.tokenize.sentence_tokenize import sentence_split, DELIM_PAT_NO_DANDA en_tok = MosesTokenizer(lang="en") en_normalizer = MosesPunctNormalizer() en_detok = MosesDetokenizer(lang="en") xliterator = unicode_transliterate.UnicodeIndicTransliterator() flores_codes = { "asm_Beng": "as", "awa_Deva": "hi", "ben_Beng": "bn", "bho_Deva": "hi", "brx_Deva": "hi", "doi_Deva": "hi", "eng_Latn": "en", "gom_Deva": "kK", "guj_Gujr": "gu", "hin_Deva": "hi", "hne_Deva": "hi", "kan_Knda": "kn", "kas_Arab": "ur", "kas_Deva": "hi", "kha_Latn": "en", "lus_Latn": "en", "mag_Deva": "hi", "mai_Deva": "hi", "mal_Mlym": "ml", "mar_Deva": "mr", "mni_Beng": "bn", "mni_Mtei": "hi", "npi_Deva": "ne", "ory_Orya": "or", "pan_Guru": "pa", "san_Deva": "hi", "sat_Olck": "or", "snd_Arab": "ur", "snd_Deva": "hi", "tam_Taml": "ta", "tel_Telu": "te", "urd_Arab": "ur", } flores_to_iso = { "asm_Beng": "as", "awa_Deva": "awa", "ben_Beng": "bn", "bho_Deva": "bho", "brx_Deva": "brx", "doi_Deva": "doi", "eng_Latn": "en", "gom_Deva": "gom", "guj_Gujr": "gu", "hin_Deva": "hi", "hne_Deva": "hne", "kan_Knda": "kn", "kas_Arab": "ksa", "kas_Deva": "ksd", "kha_Latn": "kha", "lus_Latn": "lus", "mag_Deva": "mag", "mai_Deva": "mai", "mal_Mlym": "ml", "mar_Deva": "mr", "mni_Beng": "mnib", "mni_Mtei": "mnim", "npi_Deva": "ne", "ory_Orya": "or", "pan_Guru": "pa", "san_Deva": "sa", "sat_Olck": "sat", "snd_Arab": "sda", "snd_Deva": "sdd", "tam_Taml": "ta", "tel_Telu": "te", "urd_Arab": "ur", } INDIC_NUM_MAP = { "\u09e6": "0", "0": "0", "\u0ae6": "0", "\u0ce6": "0", "\u0966": "0", "\u0660": "0", "\uabf0": "0", "\u0b66": "0", "\u0a66": "0", "\u1c50": "0", "\u06f0": "0", "\u09e7": "1", "1": "1", "\u0ae7": "1", "\u0967": "1", "\u0ce7": "1", "\u06f1": "1", "\uabf1": "1", "\u0b67": "1", "\u0a67": "1", "\u1c51": "1", "\u0c67": "1", "\u09e8": "2", "2": "2", "\u0ae8": "2", "\u0968": "2", "\u0ce8": "2", "\u06f2": "2", "\uabf2": "2", "\u0b68": "2", "\u0a68": "2", "\u1c52": "2", "\u0c68": "2", "\u09e9": "3", "3": "3", "\u0ae9": "3", "\u0969": "3", "\u0ce9": "3", "\u06f3": "3", "\uabf3": "3", "\u0b69": "3", "\u0a69": "3", "\u1c53": "3", "\u0c69": "3", "\u09ea": "4", "4": "4", "\u0aea": "4", "\u096a": "4", "\u0cea": "4", "\u06f4": "4", "\uabf4": "4", "\u0b6a": "4", "\u0a6a": "4", "\u1c54": "4", "\u0c6a": "4", "\u09eb": "5", "5": "5", "\u0aeb": "5", "\u096b": "5", "\u0ceb": "5", "\u06f5": "5", "\uabf5": "5", "\u0b6b": "5", "\u0a6b": "5", "\u1c55": "5", "\u0c6b": "5", "\u09ec": "6", "6": "6", "\u0aec": "6", "\u096c": "6", "\u0cec": "6", "\u06f6": "6", "\uabf6": "6", "\u0b6c": "6", "\u0a6c": "6", "\u1c56": "6", "\u0c6c": "6", "\u09ed": "7", "7": "7", "\u0aed": "7", "\u096d": "7", "\u0ced": "7", "\u06f7": "7", "\uabf7": "7", "\u0b6d": "7", "\u0a6d": "7", "\u1c57": "7", "\u0c6d": "7", "\u09ee": "8", "8": "8", "\u0aee": "8", "\u096e": "8", "\u0cee": "8", "\u06f8": "8", "\uabf8": "8", "\u0b6e": "8", "\u0a6e": "8", "\u1c58": "8", "\u0c6e": "8", "\u09ef": "9", "9": "9", "\u0aef": "9", "\u096f": "9", "\u0cef": "9", "\u06f9": "9", "\uabf9": "9", "\u0b6f": "9", "\u0a6f": "9", "\u1c59": "9", "\u0c6f": "9", } multispace_regex = re.compile("[ ]{2,}") end_bracket_space_punc_regex = re.compile(r"\) ([\.!:?;,])") digit_space_percent = re.compile(r"(\d) %") double_quot_punc = re.compile(r"\"([,\.]+)") digit_nbsp_digit = re.compile(r"(\d) (\d)") def punc_norm(text, lang="en"): text = ( text.replace("\r", "") .replace("(", " (") .replace(")", ") ") .replace("( ", "(") .replace(" )", ")") .replace(" :", ":") .replace(" ;", ";") .replace("`", "'") .replace("„", '"') .replace("“", '"') .replace("”", '"') .replace("–", "-") .replace("—", " - ") .replace("´", "'") .replace("‘", "'") .replace("‚", "'") .replace("’", "'") .replace("''", '"') .replace("´´", '"') .replace("…", "...") .replace(" « ", ' "') .replace("« ", '"') .replace("«", '"') .replace(" » ", '" ') .replace(" »", '"') .replace("»", '"') .replace(" %", "%") .replace("nº ", "nº ") .replace(" :", ":") .replace(" ºC", " ºC") .replace(" cm", " cm") .replace(" ?", "?") .replace(" !", "!") .replace(" ;", ";") .replace(", ", ", ") ) text = multispace_regex.sub(" ", text) text = end_bracket_space_punc_regex.sub(r")\1", text) text = digit_space_percent.sub(r"\1%", text) text = double_quot_punc.sub( r'\1"', text ) # English "quotation," followed by comma, style text = digit_nbsp_digit.sub(r"\1.\2", text) # What does it mean? return text.strip(" ") URL_PATTERN = r"\b(? Tuple[str, dict]: """ Wraps substrings with matched patterns in the given text with placeholders and returns the modified text along with a mapping of the placeholders to their original value. Args: text (str): an input string which needs to be wrapped with the placeholders. pattern (list): list of patterns to search for in the input string. Returns: Tuple[str, dict]: a tuple containing the modified text and a dictionary mapping placeholders to their original values. """ serial_no = 1 placeholder_entity_map = dict() for pattern in patterns: matches = set(re.findall(pattern, text)) # wrap common match with placeholder tags for match in matches: if pattern == URL_PATTERN: # Avoids false positive URL matches for names with initials. temp = match.replace(".", "") if len(temp) < 4: continue if pattern == NUMERAL_PATTERN: # Short numeral patterns do not need placeholder based handling. temp = match.replace(" ", "").replace(".", "").replace(":", "") if len(temp) < 4: continue # Set of Translations of "ID" in all the suppported languages have been collated. # This has been added to deal with edge cases where placeholders might get translated. indic_failure_cases = [ "آی ڈی ", "ꯑꯥꯏꯗꯤ", "आईडी", "आई . डी . ", "आई . डी .", "आई. डी. ", "आई. डी.", "ऐटि", "آئی ڈی ", "ᱟᱭᱰᱤ ᱾", "आयडी", "ऐडि", "आइडि", ] placeholder = "".format(serial_no) alternate_placeholder = "< ID{} >".format(serial_no) placeholder_entity_map[placeholder] = match placeholder_entity_map[alternate_placeholder] = match placeholder = "/", ">") text = text.replace("]/", "]") return text, placeholder_entity_map def normalize( text: str, patterns: list = [EMAIL_PATTERN, URL_PATTERN, NUMERAL_PATTERN, OTHER_PATTERN], ) -> Tuple[str, dict]: """ Normalizes and wraps the spans of input string with placeholder tags. It first normalizes the Indic numerals in the input string to Roman script. Later, it uses the input string with normalized Indic numerals to wrap the spans of text matching the pattern with placeholder tags. Args: text (str): input string. pattern (list): list of patterns to search for in the input string. Returns: Tuple[str, dict]: a tuple containing the modified text and a dictionary mapping placeholders to their original values. """ text = normalize_indic_numerals(text.strip("\n")) text, placeholder_entity_map = wrap_with_placeholders(text, patterns) return text, placeholder_entity_map def split_sentences(paragraph: str, lang: str) -> List[str]: """ Splits the input text paragraph into sentences. It uses `moses` for English and `indic-nlp` for Indic languages. Args: paragraph (str): input text paragraph. lang (str): flores language code. Returns: List[str] -> list of sentences. """ # fails to handle sentence splitting in case of # with MosesSentenceSplitter(lang) as splitter: # return splitter([paragraph]) return ( sent_tokenize(paragraph) if lang == "eng_Latn" else sentence_split( paragraph, lang=flores_codes[lang], delim_pat=DELIM_PAT_NO_DANDA ) ) def apply_lang_tags(sents: List[str], src_lang: str, tgt_lang: str) -> List[str]: """ Add special tokens indicating source and target language to the start of the each input sentence. Each resulting input sentence will have the format: "`{src_lang} {tgt_lang} {input_sentence}`". Args: sent (str): input sentence to be translated. src_lang (str): flores lang code of the input sentence. tgt_lang (str): flores lang code in which the input sentence will be translated. Returns: List[str]: list of input sentences with the special tokens added to the start. """ return Parallel(n_jobs=-1)( delayed(lambda x: f"{src_lang} {tgt_lang} {x.strip()}")(sent) for sent in sents ) def preprocess_sent( sent: str, normalizer: Union[MosesPunctNormalizer, indic_normalize.IndicNormalizerFactory], lang: str, ) -> str: """ Preprocess an input text sentence by normalizing, tokenization, and possibly transliterating it. Args: sent (str): input text sentence to preprocess. normalizer (Union[MosesPunctNormalizer, indic_normalize.IndicNormalizerFactory]): an object that performs normalization on the text. lang (str): flores language code of the input text sentence. Returns: Tuple[str, dict]: a tuple of preprocessed input text sentence and also a corresponding dictionary mapping placeholders to their original values. """ iso_lang = flores_codes[lang] sent = punc_norm(sent, iso_lang) sent, placeholder_entity_map = normalize(sent) transliterate = True if lang.split("_")[1] in ["Arab", "Aran", "Olck", "Mtei", "Latn"]: transliterate = False if iso_lang == "en": processed_sent = " ".join( en_tok.tokenize(en_normalizer.normalize(sent.strip()), escape=False) ) elif transliterate: # transliterates from the any specific language to devanagari # which is why we specify lang2_code as "hi". processed_sent = xliterator.transliterate( " ".join( indic_tokenize.trivial_tokenize( normalizer.normalize(sent.strip()), iso_lang ) ), iso_lang, "hi", ).replace(" ् ", "्") else: # we only need to transliterate for joint training processed_sent = " ".join( indic_tokenize.trivial_tokenize( normalizer.normalize(sent.strip()), iso_lang ) ) return processed_sent, placeholder_entity_map def preprocess(sents: List[str], lang: str): """ Preprocess an array of sentences by normalizing, tokenization, and possibly transliterating it. Args: batch (List[str]): input list of sentences to preprocess. lang (str): flores language code of the input text sentences. Returns: Tuple[List[str], List[dict]]: a tuple of list of preprocessed input text sentences and also a corresponding list of dictionary mapping placeholders to their original values. """ normalizer = ( indic_normalize.IndicNormalizerFactory().get_normalizer(flores_codes[lang]) if lang != "eng_Latn" else None ) processed_sents, placeholder_entity_map_sents = zip( *[preprocess_sent(sent, normalizer, lang) for sent in sents] ) return processed_sents, placeholder_entity_map_sents def preprocess_batch(batch: List[str], src_lang: str, tgt_lang: str) -> List[str]: """ Preprocess an array of sentences by normalizing, tokenization, and possibly transliterating it. It also tokenizes the normalized text sequences using sentence piece tokenizer and also adds language tags. Args: batch (List[str]): input list of sentences to preprocess. src_lang (str): flores language code of the input text sentences. tgt_lang (str): flores language code of the output text sentences. Returns: Tuple[List[str], List[dict]]: a tuple of list of preprocessed input text sentences and also a corresponding list of dictionary mapping placeholders to their original values. """ preprocessed_sents, placeholder_entity_map_sents = preprocess(batch, lang=src_lang) tagged_sents = apply_lang_tags(preprocessed_sents, src_lang, tgt_lang) return tagged_sents, placeholder_entity_map_sents def postprocess_batch( sents: List[str], placeholder_entity_map: List[dict], lang: str, common_lang: str = "hin_Deva", ) -> List[str]: """ Postprocesses a batch of input sentences after the translation generations. Args: sents (List[str]): batch of translated sentences to postprocess. placeholder_entity_map (List[dict]): dictionary mapping placeholders to the original entity values. lang (str): flores language code of the input sentences. common_lang (str, optional): flores language code of the transliterated language (defaults: hin_Deva). Returns: List[str]: postprocessed batch of input sentences. """ lang_code, script_code = lang.split("_") for i in range(len(sents)): sents[i] = sents[i].replace(" ", "").replace("▁", " ").strip() # Fixes for Perso-Arabic scripts # TODO: Move these normalizations inside indic-nlp-library if script_code in {"Arab", "Aran"}: # UrduHack adds space before punctuations. Since the model was trained without fixing this issue, let's fix it now sents[i] = sents[i].replace(" ؟", "؟").replace(" ۔", "۔").replace(" ،", "،") # Kashmiri bugfix for palatalization: https://github.com/AI4Bharat/IndicTrans2/issues/11 sents[i] = sents[i].replace("ٮ۪", "ؠ") # Oriya bug: indic-nlp-library produces ଯ଼ instead of ୟ when converting from Devanagari to Odia # TODO: Find out what's the issue with unicode transliterator for Oriya and fix it if lang_code == "or": sents[i] = sents[i].replace("ଯ଼", "ୟ") assert len(sents) == len(placeholder_entity_map) # Replace the placeholders entity for i in range(0, len(sents)): for key in placeholder_entity_map[i].keys(): sents[i] = sents[i].replace(key, placeholder_entity_map[i][key]) # Detokenize and transliterate to native scripts if applicable if lang == "eng_Latn": postprocessed_sents = [en_detok.detokenize(sent.split(" ")) for sent in sents] else: postprocessed_sents = [ indic_detokenize.trivial_detokenize( xliterator.transliterate( s, flores_codes[common_lang], flores_codes[lang] ), flores_codes[lang], ) for s in sents ] assert len(postprocessed_sents) == len(placeholder_entity_map) return postprocessed_sents