<reponame>sumanthd17/indicTrans<filename>scripts/preprocess_translate.py
INDIC_NLP_LIB_HOME = "indic_nlp_library"
INDIC_NLP_RESOURCES = "indic_nlp_resources"
import sys

sys.path.append(r"{}".format(INDIC_NLP_LIB_HOME))
from indicnlp import common

common.set_resources_path(INDIC_NLP_RESOURCES)
from indicnlp import loader

loader.load()
from sacremoses import MosesPunctNormalizer
from sacremoses import MosesTokenizer
from sacremoses import MosesDetokenizer
from collections import defaultdict

from tqdm import tqdm
from joblib import Parallel, delayed

from indicnlp.tokenize import indic_tokenize
from indicnlp.tokenize import indic_detokenize
from indicnlp.normalize import indic_normalize
from indicnlp.transliterate import unicode_transliterate


en_tok = MosesTokenizer(lang="en")
en_normalizer = MosesPunctNormalizer()


def preprocess_line(line, normalizer, lang, transliterate=False):
    if lang == "en":
        return " ".join(
            en_tok.tokenize(en_normalizer.normalize(line.strip()), escape=False)
        )
    elif transliterate:
        # line = indic_detokenize.trivial_detokenize(line.strip(), lang)
        return unicode_transliterate.UnicodeIndicTransliterator.transliterate(
            " ".join(
                indic_tokenize.trivial_tokenize(
                    normalizer.normalize(line.strip()), lang
                )
            ),
            lang,
            "hi",
        ).replace(" ् ", "्")
    else:
        # we only need to transliterate for joint training
        return " ".join(
            indic_tokenize.trivial_tokenize(normalizer.normalize(line.strip()), lang)
        )


def preprocess(infname, outfname, lang, transliterate=False):
    """
    Normalize, tokenize and script convert(for Indic)
    return number of sentences input file

    """

    n = 0
    num_lines = sum(1 for line in open(infname, "r"))
    if lang == "en":
        with open(infname, "r", encoding="utf-8") as infile, open(
            outfname, "w", encoding="utf-8"
        ) as outfile:

            out_lines = Parallel(n_jobs=-1, backend="multiprocessing")(
                delayed(preprocess_line)(line, None, lang)
                for line in tqdm(infile, total=num_lines)
            )

            for line in out_lines:
                outfile.write(line + "\n")
                n += 1

    else:
        normfactory = indic_normalize.IndicNormalizerFactory()
        normalizer = normfactory.get_normalizer(lang)
        # reading
        with open(infname, "r", encoding="utf-8") as infile, open(
            outfname, "w", encoding="utf-8"
        ) as outfile:

            out_lines = Parallel(n_jobs=-1, backend="multiprocessing")(
                delayed(preprocess_line)(line, normalizer, lang, transliterate)
                for line in tqdm(infile, total=num_lines)
            )

            for line in out_lines:
                outfile.write(line + "\n")
                n += 1
    return n


def old_preprocess(infname, outfname, lang):
    """
    Preparing each corpus file:
      - Normalization
      - Tokenization
      - Script coversion to Devanagari for Indic scripts
    """
    n = 0
    num_lines = sum(1 for line in open(infname, "r"))
    # reading
    with open(infname, "r", encoding="utf-8") as infile, open(
        outfname, "w", encoding="utf-8"
    ) as outfile:

        if lang == "en":
            en_tok = MosesTokenizer(lang="en")
            en_normalizer = MosesPunctNormalizer()
            for line in tqdm(infile, total=num_lines):
                outline = " ".join(
                    en_tok.tokenize(en_normalizer.normalize(line.strip()), escape=False)
                )
                outfile.write(outline + "\n")
                n += 1

        else:
            normfactory = indic_normalize.IndicNormalizerFactory()
            normalizer = normfactory.get_normalizer(lang)
            for line in tqdm(infile, total=num_lines):
                outline = (
                    unicode_transliterate.UnicodeIndicTransliterator.transliterate(
                        " ".join(
                            indic_tokenize.trivial_tokenize(
                                normalizer.normalize(line.strip()), lang
                            )
                        ),
                        lang,
                        "hi",
                    ).replace(" ् ", "्")
                )

                outfile.write(outline + "\n")
                n += 1
    return n


if __name__ == "__main__":

    # INDIC_NLP_LIB_HOME = "indic_nlp_library"
    # INDIC_NLP_RESOURCES = "indic_nlp_resources"
    # sys.path.append(r'{}'.format(INDIC_NLP_LIB_HOME))
    # common.set_resources_path(INDIC_NLP_RESOURCES)

    # data_dir = '../joint_training/v1'
    # new_dir = data_dir + '.norm'
    # for path, subdirs, files in os.walk(data_dir):
    #     for name in files:
    #         infile = os.path.join(path, name)
    #         lang = infile.split('.')[-1]
    #         outfile = os.path.join(path.replace(data_dir, new_dir), name)
    #         preprocess(infile, outfile, lang)
    # loader.load()

    infname = sys.argv[1]
    outfname = sys.argv[2]
    lang = sys.argv[3]

    if len(sys.argv) == 4:
        transliterate = False
    elif len(sys.argv) == 5:
        transliterate = sys.argv[4]
        if transliterate.lower() == "true":
            transliterate = True
        else:
            transliterate = False
    else:
        print(f"Invalid arguments: {sys.argv}")
        exit()
    print(preprocess(infname, outfname, lang, transliterate))

INDIC_NLP_LIB_HOME = "indic_nlp_library"
INDIC_NLP_RESOURCES = "indic_nlp_resources"
import sys

sys.path.append(r"{}".format(INDIC_NLP_LIB_HOME))
from indicnlp import common

common.set_resources_path(INDIC_NLP_RESOURCES)
from indicnlp import loader

loader.load()
from sacremoses import MosesPunctNormalizer
from sacremoses import MosesTokenizer
from sacremoses import MosesDetokenizer
from collections import defaultdict

from tqdm import tqdm
from joblib import Parallel, delayed

from indicnlp.tokenize import indic_tokenize
from indicnlp.tokenize import indic_detokenize
from indicnlp.normalize import indic_normalize
from indicnlp.transliterate import unicode_transliterate


en_tok = MosesTokenizer(lang="en")
en_normalizer = MosesPunctNormalizer()


def preprocess_line(line, normalizer, lang, transliterate=False):
    if lang == "en":
        return " ".join(
            en_tok.tokenize(en_normalizer.normalize(line.strip()), escape=False)
        )
    elif transliterate:
        # line = indic_detokenize.trivial_detokenize(line.strip(), lang)
        return unicode_transliterate.UnicodeIndicTransliterator.transliterate(
            " ".join(
                indic_tokenize.trivial_tokenize(
                    normalizer.normalize(line.strip()), lang
                )
            ),
            lang,
            "hi",
        ).replace(" ् ", "्")
    else:
        # we only need to transliterate for joint training
        return " ".join(
            indic_tokenize.trivial_tokenize(normalizer.normalize(line.strip()), lang)
        )


def preprocess(infname, outfname, lang, transliterate=False):
    """
    Normalize, tokenize and script convert(for Indic)
    return number of sentences input file

    """

    n = 0
    num_lines = sum(1 for line in open(infname, "r"))
    if lang == "en":
        with open(infname, "r", encoding="utf-8") as infile, open(
            outfname, "w", encoding="utf-8"
        ) as outfile:

            out_lines = Parallel(n_jobs=-1, backend="multiprocessing")(
                delayed(preprocess_line)(line, None, lang)
                for line in tqdm(infile, total=num_lines)
            )

            for line in out_lines:
                outfile.write(line + "\n")
                n += 1

    else:
        normfactory = indic_normalize.IndicNormalizerFactory()
        normalizer = normfactory.get_normalizer(lang)
        # reading
        with open(infname, "r", encoding="utf-8") as infile, open(
            outfname, "w", encoding="utf-8"
        ) as outfile:

            out_lines = Parallel(n_jobs=-1, backend="multiprocessing")(
                delayed(preprocess_line)(line, normalizer, lang, transliterate)
                for line in tqdm(infile, total=num_lines)
            )

            for line in out_lines:
                outfile.write(line + "\n")
                n += 1
    return n


def old_preprocess(infname, outfname, lang):
    """
    Preparing each corpus file:
      - Normalization
      - Tokenization
      - Script coversion to Devanagari for Indic scripts
    """
    n = 0
    num_lines = sum(1 for line in open(infname, "r"))
    # reading
    with open(infname, "r", encoding="utf-8") as infile, open(
        outfname, "w", encoding="utf-8"
    ) as outfile:

        if lang == "en":
            en_tok = MosesTokenizer(lang="en")
            en_normalizer = MosesPunctNormalizer()
            for line in tqdm(infile, total=num_lines):
                outline = " ".join(
                    en_tok.tokenize(en_normalizer.normalize(line.strip()), escape=False)
                )
                outfile.write(outline + "\n")
                n += 1

        else:
            normfactory = indic_normalize.IndicNormalizerFactory()
            normalizer = normfactory.get_normalizer(lang)
            for line in tqdm(infile, total=num_lines):
                outline = (
                    unicode_transliterate.UnicodeIndicTransliterator.transliterate(
                        " ".join(
                            indic_tokenize.trivial_tokenize(
                                normalizer.normalize(line.strip()), lang
                            )
                        ),
                        lang,
                        "hi",
                    ).replace(" ् ", "्")
                )

                outfile.write(outline + "\n")
                n += 1
    return n


if __name__ == "__main__":

    # INDIC_NLP_LIB_HOME = "indic_nlp_library"
    # INDIC_NLP_RESOURCES = "indic_nlp_resources"
    # sys.path.append(r'{}'.format(INDIC_NLP_LIB_HOME))
    # common.set_resources_path(INDIC_NLP_RESOURCES)

    # data_dir = '../joint_training/v1'
    # new_dir = data_dir + '.norm'
    # for path, subdirs, files in os.walk(data_dir):
    #     for name in files:
    #         infile = os.path.join(path, name)
    #         lang = infile.split('.')[-1]
    #         outfile = os.path.join(path.replace(data_dir, new_dir), name)
    #         preprocess(infile, outfile, lang)
    # loader.load()

    infname = sys.argv[1]
    outfname = sys.argv[2]
    lang = sys.argv[3]

    if len(sys.argv) == 4:
        transliterate = False
    elif len(sys.argv) == 5:
        transliterate = sys.argv[4]
        if transliterate.lower() == "true":
            transliterate = True
        else:
            transliterate = False
    else:
        print(f"Invalid arguments: {sys.argv}")
        exit()
    print(preprocess(infname, outfname, lang, transliterate))
# line = indic_detokenize.trivial_detokenize(line.strip(), lang) # we only need to transliterate for joint training Normalize, tokenize and script convert(for Indic)     return number of sentences input file # reading Preparing each corpus file:       - Normalization       - Tokenization       - Script coversion to Devanagari for Indic scripts # reading # INDIC_NLP_LIB_HOME = "indic_nlp_library" # INDIC_NLP_RESOURCES = "indic_nlp_resources" # sys.path.append(r'{}'.format(INDIC_NLP_LIB_HOME)) # common.set_resources_path(INDIC_NLP_RESOURCES) # data_dir = '../joint_training/v1' # new_dir = data_dir + '.norm' # for path, subdirs, files in os.walk(data_dir): #     for name in files: #         infile = os.path.join(path, name) #         lang = infile.split('.')[-1] #         outfile = os.path.join(path.replace(data_dir, new_dir), name) #         preprocess(infile, outfile, lang) # loader.load()