# coding=utf-8
# Copyright 2023 The TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""TED talk multilingual data set."""

import csv
import os

from etils import epath
import tensorflow_datasets.public_api as tfds

_DATA_URL = 'http://phontron.com/data/ted_talks.tar.gz'

_LANGUAGES = (
    'en',
    'es',
    'pt-br',
    'fr',
    'ru',
    'he',
    'ar',
    'ko',
    'zh-cn',
    'it',
    'ja',
    'zh-tw',
    'nl',
    'ro',
    'tr',
    'de',
    'vi',
    'pl',
    'pt',
    'bg',
    'el',
    'fa',
    'sr',
    'hu',
    'hr',
    'uk',
    'cs',
    'id',
    'th',
    'sv',
    'sk',
    'sq',
    'lt',
    'da',
    'calv',
    'my',
    'sl',
    'mk',
    'fr-ca',
    'fi',
    'hy',
    'hi',
    'nb',
    'ka',
    'mn',
    'et',
    'ku',
    'gl',
    'mr',
    'zh',
    'ur',
    'eo',
    'ms',
    'az',
    'ta',
    'bn',
    'kk',
    'be',
    'eu',
    'bs',
)


class Builder(tfds.core.GeneratorBasedBuilder):
  """TED talk multilingual data set."""

  VERSION = tfds.core.Version('1.1.0')

  def _info(self):
    return self.dataset_info_from_configs(
        features=tfds.features.FeaturesDict({
            'translations': tfds.features.TranslationVariableLanguages(
                languages=_LANGUAGES
            ),
            'talk_name': tfds.features.Text(),
        }),
        homepage='https://github.com/neulab/word-embeddings-for-nmt',
    )

  def _split_generators(self, dl_manager):
    dl_dir = dl_manager.download_and_extract(_DATA_URL)

    return [
        tfds.core.SplitGenerator(
            name=tfds.Split.TRAIN,
            gen_kwargs={
                'data_file': os.path.join(dl_dir, 'all_talks_train.tsv')
            },
        ),
        tfds.core.SplitGenerator(
            name=tfds.Split.VALIDATION,
            gen_kwargs={'data_file': os.path.join(dl_dir, 'all_talks_dev.tsv')},
        ),
        tfds.core.SplitGenerator(
            name=tfds.Split.TEST,
            gen_kwargs={
                'data_file': os.path.join(dl_dir, 'all_talks_test.tsv')
            },
        ),
    ]

  def _generate_examples(self, data_file):
    """This function returns the examples in the raw (text) form."""
    with epath.Path(data_file).open() as f:
      reader = csv.DictReader(f, delimiter='\t', quoting=csv.QUOTE_NONE)
      for idx, row in enumerate(reader):
        # Everything in the row except for 'talk_name' will be a translation.
        # Missing/incomplete translations will contain the string "__NULL__" or
        # "_ _ NULL _ _".
        yield idx, {
            'translations': {
                lang: text
                for lang, text in row.items()
                if lang != 'talk_name' and _is_translation_complete(text)
            },
            'talk_name': row['talk_name'],
        }


def _is_translation_complete(text):
  return text and '__NULL__' not in text and '_ _ NULL _ _' not in text
