# coding=utf-8
# Copyright 2019 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.

"""This module implements the ice_parsing_* problems."""

# These parse plain text into flattened parse trees and POS tags.
# The training data is stored in files named `parsing_train.pairs`
# and `parsing_dev.pairs`. These files are UTF-8 text files where
# each line contains an input sentence and a target parse tree,
# separated by a tab character.

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

import os
from tensor2tensor.data_generators import generator_utils
from tensor2tensor.data_generators import problem
from tensor2tensor.data_generators import text_encoder
from tensor2tensor.data_generators import text_problems
from tensor2tensor.layers import modalities
from tensor2tensor.utils import registry


def tabbed_parsing_token_generator(data_dir, tmp_dir, train, prefix,
                                   source_vocab_size, target_vocab_size):
  """Generate source and target data from a single file."""
  filename = "parsing_{0}.pairs".format("train" if train else "dev")
  source_vocab = generator_utils.get_or_generate_tabbed_vocab(
      data_dir, tmp_dir, filename, 0,
      prefix + "_source.tokens.vocab.%d" % source_vocab_size, source_vocab_size)
  target_vocab = generator_utils.get_or_generate_tabbed_vocab(
      data_dir, tmp_dir, filename, 1,
      prefix + "_target.tokens.vocab.%d" % target_vocab_size, target_vocab_size)
  pair_filepath = os.path.join(tmp_dir, filename)
  return text_problems.text2text_generate_encoded(
      text_problems.text2text_txt_tab_iterator(pair_filepath), source_vocab,
      target_vocab)


def tabbed_parsing_character_generator(tmp_dir, train):
  """Generate source and target data from a single file."""
  character_vocab = text_encoder.ByteTextEncoder()
  filename = "parsing_{0}.pairs".format("train" if train else "dev")
  pair_filepath = os.path.join(tmp_dir, filename)
  return text_problems.text2text_generate_encoded(
      text_problems.text2text_txt_tab_iterator(pair_filepath), character_vocab)


@registry.register_problem
class ParsingIcelandic16k(problem.Problem):
  """Problem spec for parsing tokenized Icelandic text to constituency trees."""

  @property
  def source_vocab_size(self):
    return 2**14  # 16384

  @property
  def targeted_vocab_size(self):
    return 2**8  # 256

  @property
  def input_space_id(self):
    return problem.SpaceID.ICE_TOK

  @property
  def target_space_id(self):
    return problem.SpaceID.ICE_PARSE_TOK

  @property
  def num_shards(self):
    return 10

  def feature_encoders(self, data_dir):
    source_vocab_filename = os.path.join(
        data_dir, "ice_source.tokens.vocab.%d" % self.source_vocab_size)
    target_vocab_filename = os.path.join(
        data_dir, "ice_target.tokens.vocab.%d" % self.targeted_vocab_size)
    source_subtokenizer = text_encoder.SubwordTextEncoder(source_vocab_filename)
    target_subtokenizer = text_encoder.SubwordTextEncoder(target_vocab_filename)
    return {
        "inputs": source_subtokenizer,
        "targets": target_subtokenizer,
    }

  def generate_data(self, data_dir, tmp_dir, task_id=-1):
    generator_utils.generate_dataset_and_shuffle(
        tabbed_parsing_token_generator(data_dir, tmp_dir, True, "ice",
                                       self.source_vocab_size,
                                       self.targeted_vocab_size),
        self.training_filepaths(data_dir, self.num_shards, shuffled=False),
        tabbed_parsing_token_generator(data_dir, tmp_dir, False, "ice",
                                       self.source_vocab_size,
                                       self.targeted_vocab_size),
        self.dev_filepaths(data_dir, 1, shuffled=False))

  def hparams(self, defaults, unused_model_hparams):
    p = defaults
    p.modality = {"inputs": modalities.ModalityType.SYMBOL,
                  "targets": modalities.ModalityType.SYMBOL}
    p.vocab_size = {"inputs": self._encoders["inputs"].vocab_size,
                    "targets": self.targeted_vocab_size}
    p.input_space_id = self.input_space_id
    p.target_space_id = self.target_space_id
    p.loss_multiplier = 2.5  # Rough estimate of avg number of tokens per word
