# coding=utf-8
# Copyright 2023 The Tensor2Tensor 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.

"""Data generators for translation data-sets."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

from tensor2tensor.data_generators import problem
from tensor2tensor.data_generators import text_encoder
from tensor2tensor.data_generators import text_problems
from tensor2tensor.data_generators import translate
from tensor2tensor.data_generators import wiki_lm
from tensor2tensor.utils import registry


# End-of-sentence marker.
EOS = text_encoder.EOS_ID

_ENFR_TRAIN_SMALL_DATA = [
    [
        "https://s3.amazonaws.com/opennmt-trainingdata/baseline-1M-enfr.tgz",
        ("baseline-1M-enfr/baseline-1M_train.en",
         "baseline-1M-enfr/baseline-1M_train.fr")
    ],
]
_ENFR_TEST_SMALL_DATA = [
    [
        "https://s3.amazonaws.com/opennmt-trainingdata/baseline-1M-enfr.tgz",
        ("baseline-1M-enfr/baseline-1M_valid.en",
         "baseline-1M-enfr/baseline-1M_valid.fr")
    ],
]
_ENFR_TRAIN_LARGE_DATA = [
    [
        "http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz",
        ("commoncrawl.fr-en.en", "commoncrawl.fr-en.fr")
    ],
    [
        "http://www.statmt.org/wmt13/training-parallel-europarl-v7.tgz",
        ("training/europarl-v7.fr-en.en", "training/europarl-v7.fr-en.fr")
    ],
    [
        "http://www.statmt.org/wmt14/training-parallel-nc-v9.tgz",
        ("training/news-commentary-v9.fr-en.en",
         "training/news-commentary-v9.fr-en.fr")
    ],
    [
        "http://www.statmt.org/wmt10/training-giga-fren.tar",
        ("giga-fren.release2.fixed.en.gz",
         "giga-fren.release2.fixed.fr.gz")
    ],
    [
        "http://www.statmt.org/wmt13/training-parallel-un.tgz",
        ("un/undoc.2000.fr-en.en", "un/undoc.2000.fr-en.fr")
    ],
]
_ENFR_TEST_LARGE_DATA = [
    [
        "http://data.statmt.org/wmt17/translation-task/dev.tgz",
        ("dev/newstest2013.en", "dev/newstest2013.fr")
    ],
]


@registry.register_problem
class TranslateEnfrWmtSmall8k(translate.TranslateProblem):
  """Problem spec for WMT En-Fr translation."""

  @property
  def approx_vocab_size(self):
    return 2**13  # 8192

  @property
  def use_small_dataset(self):
    return True

  def source_data_files(self, dataset_split):
    train = dataset_split == problem.DatasetSplit.TRAIN
    if self.use_small_dataset:
      datasets = _ENFR_TRAIN_SMALL_DATA if train else _ENFR_TEST_SMALL_DATA
    else:
      datasets = _ENFR_TRAIN_LARGE_DATA if train else _ENFR_TEST_LARGE_DATA
    return datasets

  def vocab_data_files(self):
    return (_ENFR_TRAIN_SMALL_DATA if self.use_small_dataset
            else _ENFR_TRAIN_LARGE_DATA)


@registry.register_problem
class TranslateEnfrWmtSmall32k(TranslateEnfrWmtSmall8k):

  @property
  def approx_vocab_size(self):
    return 2**15  # 32768


@registry.register_problem
class TranslateEnfrWmt8k(TranslateEnfrWmtSmall8k):

  @property
  def use_small_dataset(self):
    return False


@registry.register_problem
class TranslateEnfrWmt32k(TranslateEnfrWmtSmall32k):

  @property
  def use_small_dataset(self):
    return False


@registry.register_problem
class TranslateEnfrWmt32kPacked(TranslateEnfrWmt32k):

  @property
  def packed_length(self):
    return 256

  @property
  def use_vocab_from_other_problem(self):
    return TranslateEnfrWmt32k()


@registry.register_problem
class TranslateEnfrWmt32kWithBacktranslateFr(TranslateEnfrWmt32k):
  """En-Fr translation with added French data, back-translated."""

  @property
  def use_vocab_from_other_problem(self):
    return TranslateEnfrWmt32k()

  @property
  def already_shuffled(self):
    return True

  @property
  def skip_random_fraction_when_training(self):
    return False

  @property
  def backtranslate_data_filenames(self):
    """List of pairs of files with matched back-translated data."""
    # Files must be placed in tmp_dir, each similar size to authentic data.
    return [("fr_mono_en.txt", "fr_mono_fr.txt")]

  @property
  def dataset_splits(self):
    """Splits of data to produce and number of output shards for each."""
    return [{
        "split": problem.DatasetSplit.TRAIN,
        "shards": 1,  # Use just 1 shard so as to not mix data.
    }, {
        "split": problem.DatasetSplit.EVAL,
        "shards": 1,
    }]

  def generate_samples(self, data_dir, tmp_dir, dataset_split):
    datasets = self.source_data_files(dataset_split)
    tag = "train" if dataset_split == problem.DatasetSplit.TRAIN else "dev"
    data_path = translate.compile_data(
        tmp_dir, datasets, "%s-compiled-%s" % (self.name, tag))
    # For eval, use authentic data.
    if dataset_split != problem.DatasetSplit.TRAIN:
      for example in text_problems.text2text_txt_iterator(
          data_path + ".lang1", data_path + ".lang2"):
        yield example
    else:  # For training, mix synthetic and authentic data as follows.
      for (file1, file2) in self.backtranslate_data_filenames:
        path1 = os.path.join(tmp_dir, file1)
        path2 = os.path.join(tmp_dir, file2)
        # Synthetic data first.
        for example in text_problems.text2text_txt_iterator(path1, path2):
          yield example
        # Now authentic data.
        for example in text_problems.text2text_txt_iterator(
            data_path + ".lang1", data_path + ".lang2"):
          yield example


@registry.register_problem
class TranslateEnfrWmt32kWithBacktranslateEn(
    TranslateEnfrWmt32kWithBacktranslateFr):
  """En-Fr translation with added English data, back-translated."""

  @property
  def backtranslate_data_filenames(self):
    """List of pairs of files with matched back-translated data."""
    # Files must be placed in tmp_dir, each similar size to authentic data.
    return [("en_mono_en.txt%d" % i, "en_mono_fr.txt%d" % i) for i in [0, 1, 2]]


@registry.register_problem
class TranslateEnfrWmtSmallCharacters(translate.TranslateProblem):
  """Problem spec for WMT En-Fr translation."""

  @property
  def vocab_type(self):
    return text_problems.VocabType.CHARACTER

  @property
  def use_small_dataset(self):
    return True

  def source_data_files(self, dataset_split):
    train = dataset_split == problem.DatasetSplit.TRAIN
    if self.use_small_dataset:
      datasets = _ENFR_TRAIN_SMALL_DATA if train else _ENFR_TEST_SMALL_DATA
    else:
      datasets = _ENFR_TRAIN_LARGE_DATA if train else _ENFR_TEST_LARGE_DATA
    return datasets


@registry.register_problem
class TranslateEnfrWmtCharacters(TranslateEnfrWmtSmallCharacters):

  @property
  def use_small_dataset(self):
    return False


@registry.register_problem
class TranslateEnfrWmtMulti64k(TranslateEnfrWmtSmall32k):
  """Translation with muli-lingual vocabulary."""

  @property
  def use_small_dataset(self):
    return False

  @property
  def use_vocab_from_other_problem(self):
    return wiki_lm.LanguagemodelDeEnFrRoWiki64k()


@registry.register_problem
class TranslateEnfrWmtMulti64kPacked1k(TranslateEnfrWmtMulti64k):
  """Translation with muli-lingual vocabulary."""

  @property
  def packed_length(self):
    return 1024

  @property
  def num_training_examples(self):
    return 1760600

  @property
  def inputs_prefix(self):
    return "translate English French "

  @property
  def targets_prefix(self):
    return "translate French English "
