process_hierarchical_sent_doc.py

#!/usr/bin/env python3
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
"""
Data pre-processing: build vocabularies and binarize training data.
"""

from collections import Counter
from itertools import zip_longest

from fairseq import options, tasks, utils
from fairseq.data import indexed_dataset
from fairseq.binarizer import Binarizer
from multiprocessing import Pool

import os
import shutil


def main(args):
    utils.import_user_module(args)

    print(args)

    os.makedirs(args.destdir, exist_ok=True)
    target = not args.only_source

    task = tasks.get_task(args.task)

    def train_path(lang):
        return "{}{}".format(args.trainpref, ("." + lang) if lang else "")

    def file_name(prefix, lang):
        fname = prefix
        if lang is not None:
            fname += ".{lang}".format(lang=lang)
        return fname

    def dest_path(prefix, lang):
        return os.path.join(args.destdir, file_name(prefix, lang))

    def dict_path(lang):
        return dest_path("dict", lang) + ".txt"

    def build_dictionary(filenames, src=False, tgt=False):
        assert src ^ tgt
        return task.build_dictionary(
            filenames,
            workers=args.workers,
            threshold=args.thresholdsrc if src else args.thresholdtgt,
            nwords=args.nwordssrc if src else args.nwordstgt,
            padding_factor=args.padding_factor,
        )

    if not args.srcdict and os.path.exists(dict_path(args.source_lang)):
        raise FileExistsError(dict_path(args.source_lang))
    if target and not args.tgtdict and os.path.exists(dict_path(args.target_lang)):
        raise FileExistsError(dict_path(args.target_lang))

    if args.joined_dictionary:
        assert not args.srcdict or not args.tgtdict, \
            "cannot use both --srcdict and --tgtdict with --joined-dictionary"

        if args.srcdict:
            src_dict = task.load_dictionary(args.srcdict)
        elif args.tgtdict:
            src_dict = task.load_dictionary(args.tgtdict)
        else:
            assert args.trainpref, "--trainpref must be set if --srcdict is not specified"
            src_dict = build_dictionary(
                {train_path(lang) for lang in [args.source_lang, args.target_lang]}, src=True
            )
        tgt_dict = src_dict
    else:
        if args.srcdict:
            src_dict = task.load_dictionary(args.srcdict)
        else:
            assert args.trainpref, "--trainpref must be set if --srcdict is not specified"
            src_dict = build_dictionary([train_path(args.source_lang)], src=True)

        if target:
            if args.tgtdict:
                tgt_dict = task.load_dictionary(args.tgtdict)
            else:
                assert args.trainpref, "--trainpref must be set if --tgtdict is not specified"
                tgt_dict = build_dictionary([train_path(args.target_lang)], tgt=True)
        else:
            tgt_dict = None

    src_dict.save(dict_path(args.source_lang))
    if target and tgt_dict is not None:
        tgt_dict.save(dict_path(args.target_lang))

    def make_binary_sent_doc_dataset(input_prefix, output_prefix, lang, num_workers):
        n_seq_tok = [0, 0]
        replaced = Counter()

        def merge_result(worker_result):
            replaced.update(worker_result["replaced"])
            n_seq_tok[0] += worker_result["nseq"]
            n_seq_tok[1] += worker_result["ntok"]

        input_file = "{}{}".format(
            input_prefix, ("." + lang) if lang is not None else ""
        )
        offsets = Binarizer.find_offsets(input_file, num_workers)
        pool = None
        if num_workers > 1:
            pool = Pool(processes=num_workers - 1)
            for worker_id in range(1, num_workers):
                prefix = "{}{}".format(output_prefix, worker_id)
                pool.apply_async(
                    binarize_sent_doc,
                    (
                        args,
                        input_file,
                        prefix,
                        lang,
                        offsets[worker_id],
                        offsets[worker_id + 1]
                    ),
                    callback=merge_result
                )
            pool.close()

        ds = indexed_dataset.make_builder(dataset_dest_file(args, output_prefix, lang, "bin"), impl=args.dataset_impl)
        merge_result(
            Binarizer.binarize_sent_doc(
                input_file, lambda t: ds.add_item(t),
                offset=0, end=offsets[1]
            )
        )
        if num_workers > 1:
            pool.join()
            for worker_id in range(1, num_workers):
                prefix = "{}{}".format(output_prefix, worker_id)
                temp_file_path = dataset_dest_prefix(args, prefix, lang)
                ds.merge_file_(temp_file_path)
                os.remove(indexed_dataset.data_file_path(temp_file_path))
                os.remove(indexed_dataset.index_file_path(temp_file_path))

        ds.finalize(dataset_dest_file(args, output_prefix, lang, "idx"))

        print(
            "| [{}] {}: {} sents, {} tokens".format(
                lang,
                input_file,
                n_seq_tok[0],
                n_seq_tok[1],
            )
        )

    def make_binary_dataset_hierarchical(vocab, input_prefix, output_prefix, lang, num_workers):
        print("| [{}] Dictionary: {} types".format(lang, len(vocab) - 1))
        n_seq_tok = [0, 0]
        replaced = Counter()

        def merge_result(worker_result):
            replaced.update(worker_result["replaced"])
            n_seq_tok[0] += worker_result["nseq"]
            n_seq_tok[1] += worker_result["ntok"]

        input_file = "{}{}".format(
            input_prefix, ("." + lang) if lang is not None else ""
        )
        offsets = Binarizer.find_offsets(input_file, num_workers)
        pool = None
        if num_workers > 1:
            pool = Pool(processes=num_workers - 1)
            for worker_id in range(1, num_workers):
                prefix = "{}{}".format(output_prefix, worker_id)
                pool.apply_async(
                    binarize_hierarchical,
                    (
                        args,
                        input_file,
                        vocab,
                        prefix,
                        lang,
                        offsets[worker_id],
                        offsets[worker_id + 1]
                    ),
                    callback=merge_result
                )
            pool.close()

        ds = indexed_dataset.make_builder(dataset_dest_file(args, output_prefix, lang, "bin"), impl=args.dataset_impl)
        merge_result(
            Binarizer.binarize_hierarchical(
                input_file, vocab, lambda t: ds.add_item(t),
                offset=0, end=offsets[1]
            )
        )
        if num_workers > 1:
            pool.join()
            for worker_id in range(1, num_workers):
                prefix = "{}{}".format(output_prefix, worker_id)
                temp_file_path = dataset_dest_prefix(args, prefix, lang)
                ds.merge_file_(temp_file_path)
                os.remove(indexed_dataset.data_file_path(temp_file_path))
                os.remove(indexed_dataset.index_file_path(temp_file_path))

        ds.finalize(dataset_dest_file(args, output_prefix, lang, "idx"))

        print(
            "| [{}] {}: {} sents, {} tokens, {:.3}% replaced by {}".format(
                lang,
                input_file,
                n_seq_tok[0],
                n_seq_tok[1],
                100 * sum(replaced.values()) / n_seq_tok[1],
                vocab.unk_word,
            )
        )

    def make_sent_doc_dataset(input_prefix, output_prefix, lang, num_workers=1):
        make_binary_sent_doc_dataset(input_prefix, output_prefix, lang, num_workers)


    import nltk
    def split_sentence_and_copy(input_file, output_file):
        with open(input_file) as f:
            con = f.readlines()
        sentence_tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
        def split_sentence(paragraph, tokenizer):
            # tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
            sentences = tokenizer.tokenize(paragraph)
            return sentences
        with open(output_file, 'w') as f:
            for line in con:
                line = ' story_separator_special_tag '.join([' sentence_separator_special_tag '.join(split_sentence(paragraph, sentence_tokenizer))
                                                      for paragraph in line.split('story_separator_special_tag') if paragraph])
                f.write(line+'\n')

    def make_dataset_hierarchical(vocab, input_prefix, output_prefix, lang, num_workers=1):
        if args.dataset_impl == "raw":
            # Copy original text file to destination folder
            output_text_file = dest_path(
                output_prefix + ".{}-{}".format(args.source_lang, args.target_lang),
                lang,
            )
            split_sentence_and_copy(file_name(input_prefix, lang), output_text_file)
            # shutil.copyfile(file_name(input_prefix, lang), output_text_file)
        else:
            make_binary_dataset_hierarchical(vocab, input_prefix, output_prefix, lang, num_workers)

    def make_sent(lang):
        if args.trainpref:
            make_sent_doc_dataset(args.trainpref, "train", lang, num_workers=args.workers)
        if args.validpref:
            for k, validpref in enumerate(args.validpref.split(",")):
                outprefix = "valid{}".format(k) if k > 0 else "valid"
                make_sent_doc_dataset(validpref, outprefix, lang, num_workers=args.workers)
        if args.testpref:
            for k, testpref in enumerate(args.testpref.split(",")):
                outprefix = "test{}".format(k) if k > 0 else "test"
                make_sent_doc_dataset(testpref, outprefix, lang, num_workers=args.workers)

    def make_doc(lang):
        if args.trainpref:
            make_sent_doc_dataset(args.trainpref, "train", lang, num_workers=args.workers)
        if args.validpref:
            for k, validpref in enumerate(args.validpref.split(",")):
                outprefix = "valid{}".format(k) if k > 0 else "valid"
                make_sent_doc_dataset(validpref, outprefix, lang, num_workers=args.workers)
        if args.testpref:
            for k, testpref in enumerate(args.testpref.split(",")):
                outprefix = "test{}".format(k) if k > 0 else "test"
                make_sent_doc_dataset(testpref, outprefix, lang, num_workers=args.workers)

    def make_all_hierarchical(lang, vocab):
        if args.trainpref:
            make_dataset_hierarchical(vocab, args.trainpref, "train", lang, num_workers=args.workers)
        if args.validpref:
            for k, validpref in enumerate(args.validpref.split(",")):
                outprefix = "valid{}".format(k) if k > 0 else "valid"
                make_dataset_hierarchical(vocab, validpref, outprefix, lang, num_workers=args.workers)
        if args.testpref:
            for k, testpref in enumerate(args.testpref.split(",")):
                outprefix = "test{}".format(k) if k > 0 else "test"
                make_dataset_hierarchical(vocab, testpref, outprefix, lang, num_workers=args.workers)

    make_all_hierarchical(args.source_lang, src_dict)
    make_sent(args.sent_lang)
    make_doc(args.doc_lang)
    if target:
        def make_binary_dataset(vocab, input_prefix, output_prefix, lang, num_workers):
            print("| [{}] Dictionary: {} types".format(lang, len(vocab) - 1))
            n_seq_tok = [0, 0]
            replaced = Counter()

            def merge_result(worker_result):
                replaced.update(worker_result["replaced"])
                n_seq_tok[0] += worker_result["nseq"]
                n_seq_tok[1] += worker_result["ntok"]

            input_file = "{}{}".format(
                input_prefix, ("." + lang) if lang is not None else ""
            )
            offsets = Binarizer.find_offsets(input_file, num_workers)
            pool = None
            if num_workers > 1:
                pool = Pool(processes=num_workers - 1)
                for worker_id in range(1, num_workers):
                    prefix = "{}{}".format(output_prefix, worker_id)
                    pool.apply_async(
                        binarize,
                        (
                            args,
                            input_file,
                            vocab,
                            prefix,
                            lang,
                            offsets[worker_id],
                            offsets[worker_id + 1]
                        ),
                        callback=merge_result
                    )
                pool.close()

            ds = indexed_dataset.make_builder(dataset_dest_file(args, output_prefix, lang, "bin"),
                                              impl=args.dataset_impl)
            merge_result(
                Binarizer.binarize(
                    input_file, vocab, lambda t: ds.add_item(t),
                    offset=0, end=offsets[1]
                )
            )
            if num_workers > 1:
                pool.join()
                for worker_id in range(1, num_workers):
                    prefix = "{}{}".format(output_prefix, worker_id)
                    temp_file_path = dataset_dest_prefix(args, prefix, lang)
                    ds.merge_file_(temp_file_path)
                    os.remove(indexed_dataset.data_file_path(temp_file_path))
                    os.remove(indexed_dataset.index_file_path(temp_file_path))

            ds.finalize(dataset_dest_file(args, output_prefix, lang, "idx"))

            print(
                "| [{}] {}: {} sents, {} tokens, {:.3}% replaced by {}".format(
                    lang,
                    input_file,
                    n_seq_tok[0],
                    n_seq_tok[1],
                    100 * sum(replaced.values()) / n_seq_tok[1],
                    vocab.unk_word,
                )
            )

        def make_dataset(vocab, input_prefix, output_prefix, lang, num_workers=1):
            if args.dataset_impl == "raw":
                # Copy original text file to destination folder
                output_text_file = dest_path(
                    output_prefix + ".{}-{}".format(args.source_lang, args.target_lang),
                    lang,
                )
                shutil.copyfile(file_name(input_prefix, lang), output_text_file)
            else:
                make_binary_dataset(vocab, input_prefix, output_prefix, lang, num_workers)

        def make_all(lang, vocab):
            if args.trainpref:
                make_dataset(vocab, args.trainpref, "train", lang, num_workers=args.workers)
            if args.validpref:
                for k, validpref in enumerate(args.validpref.split(",")):
                    outprefix = "valid{}".format(k) if k > 0 else "valid"
                    make_dataset(vocab, validpref, outprefix, lang, num_workers=args.workers)
            if args.testpref:
                for k, testpref in enumerate(args.testpref.split(",")):
                    outprefix = "test{}".format(k) if k > 0 else "test"
                    make_dataset(vocab, testpref, outprefix, lang, num_workers=args.workers)

        make_all(args.target_lang, tgt_dict)

    print("| Wrote preprocessed data to {}".format(args.destdir))

    if args.alignfile:
        assert args.trainpref, "--trainpref must be set if --alignfile is specified"
        src_file_name = train_path(args.source_lang)
        tgt_file_name = train_path(args.target_lang)
        freq_map = {}
        with open(args.alignfile, "r", encoding='utf-8') as align_file:
            with open(src_file_name, "r", encoding='utf-8') as src_file:
                with open(tgt_file_name, "r", encoding='utf-8') as tgt_file:
                    for a, s, t in zip_longest(align_file, src_file, tgt_file):
                        si = src_dict.encode_line(s, add_if_not_exist=False)
                        ti = tgt_dict.encode_line(t, add_if_not_exist=False)
                        ai = list(map(lambda x: tuple(x.split("-")), a.split()))
                        for sai, tai in ai:
                            srcidx = si[int(sai)]
                            tgtidx = ti[int(tai)]
                            if srcidx != src_dict.unk() and tgtidx != tgt_dict.unk():
                                assert srcidx != src_dict.pad()
                                assert srcidx != src_dict.eos()
                                assert tgtidx != tgt_dict.pad()
                                assert tgtidx != tgt_dict.eos()

                                if srcidx not in freq_map:
                                    freq_map[srcidx] = {}
                                if tgtidx not in freq_map[srcidx]:
                                    freq_map[srcidx][tgtidx] = 1
                                else:
                                    freq_map[srcidx][tgtidx] += 1

        align_dict = {}
        for srcidx in freq_map.keys():
            align_dict[srcidx] = max(freq_map[srcidx], key=freq_map[srcidx].get)

        with open(
                os.path.join(
                    args.destdir,
                    "alignment.{}-{}.txt".format(args.source_lang, args.target_lang),
                ),
                "w", encoding='utf-8'
        ) as f:
            for k, v in align_dict.items():
                print("{} {}".format(src_dict[k], tgt_dict[v]), file=f)


def binarize(args, filename, vocab, output_prefix, lang, offset, end, append_eos=True):
    ds = indexed_dataset.make_builder(dataset_dest_file(args, output_prefix, lang, "bin"), impl=args.dataset_impl)

    def consumer(tensor):
        ds.add_item(tensor)

    res = Binarizer.binarize(filename, vocab, consumer, append_eos=append_eos,
                             offset=offset, end=end)
    ds.finalize(dataset_dest_file(args, output_prefix, lang, "idx"))
    return res


def binarize_hierarchical(args, filename, vocab, output_prefix, lang, offset, end, append_eos=True):
    ds = indexed_dataset.make_builder(dataset_dest_file(args, output_prefix, lang, "bin"), impl=args.dataset_impl)

    def consumer(tensor):
        ds.add_item(tensor)

    res = Binarizer.binarize_hierarchical(filename, vocab, consumer, append_eos=append_eos,
                             offset=offset, end=end)
    ds.finalize(dataset_dest_file(args, output_prefix, lang, "idx"))
    return res

def binarize_sent_doc(args, filename, output_prefix, lang, offset, end, append_eos=True):
    ds = indexed_dataset.make_builder(dataset_dest_file(args, output_prefix, lang, "bin"), impl=args.dataset_impl)

    def consumer(tensor):
        ds.add_item(tensor)

    res = Binarizer.binarize_sent_doc(filename, consumer, append_eos=append_eos,
                             offset=offset, end=end)
    ds.finalize(dataset_dest_file(args, output_prefix, lang, "idx"))
    return res

def dataset_dest_prefix(args, output_prefix, lang):
    base = "{}/{}".format(args.destdir, output_prefix)
    lang_part = (
        ".{}-{}.{}".format(args.source_lang, args.target_lang, lang) if lang is not None else ""
    )
    return "{}{}".format(base, lang_part)


def dataset_dest_file(args, output_prefix, lang, extension):
    base = dataset_dest_prefix(args, output_prefix, lang)
    return "{}.{}".format(base, extension)


def get_offsets(input_file, num_workers):
    return Binarizer.find_offsets(input_file, num_workers)


def cli_main():
    parser = options.get_preprocessing_parser()
    args = parser.parse_args()
    main(args)


if __name__ == "__main__":
    cli_main()