text: #   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
  #
  # 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.
  """
  """
  
  from __future__ import print_function
  
  import os
  import six
  import tarfile
  import numpy as np
  from collections import defaultdict
  
  import paddle
  from paddle.io import Dataset
  import paddle.compat as cpt
  from paddle.dataset.common import _check_exists_and_download
  
  DATA_URL = ("http://paddlemodels.bj.bcebos.com/wmt/wmt16.tar.gz")
  DATA_MD5 = "0c38be43600334966403524a40dcd81e"
  
  TOTAL_EN_WORDS = 11250
  TOTAL_DE_WORDS = 19220
  
  START_MARK = "<s>"
  END_MARK = "<e>"
  UNK_MARK = "<unk>"
  
  
  class WMT16(Dataset):
      """
      Implementation of `WMT16 <http://www.statmt.org/wmt16/>`_ test dataset.
      ACL2016 Multimodal Machine Translation. Please see this website for more
      details: http://www.statmt.org/wmt16/multimodal-task.html#task1
  
      If you use the dataset created for your task, please cite the following paper:
      Multi30K: Multilingual English-German Image Descriptions.
  
      .. code-block:: text
  
          @article{elliott-EtAl:2016:VL16,
           author    = {{Elliott}, D. and {Frank}, S. and {Sima"an}, K. and {Specia}, L.},
           title     = {Multi30K: Multilingual English-German Image Descriptions},
           booktitle = {Proceedings of the 6th Workshop on Vision and Language},
           year      = {2016},
           pages     = {70--74},
           year      = 2016
          }
  
      Args:
          data_file(str): path to data tar file, can be set None if
              :attr:`download` is True. Default None
          mode(str): 'train', 'test' or 'val'. Default 'train'
          src_dict_size(int): word dictionary size for source language word. Default -1.
          trg_dict_size(int): word dictionary size for target language word. Default -1.
          lang(str): source language, 'en' or 'de'. Default 'en'.
          download(bool): whether to download dataset automatically if
              :attr:`data_file` is not set. Default True
  
      Returns:
          Dataset: instance of WMT16 dataset
  
      Examples:
  
          .. code-block:: python
  
              import paddle
              from paddle.text.datasets import WMT16
  
              class SimpleNet(paddle.nn.Layer):
                  def __init__(self):
                      super(SimpleNet, self).__init__()
  
                  def forward(self, src_ids, trg_ids, trg_ids_next):
                      return paddle.sum(src_ids), paddle.sum(trg_ids), paddle.sum(trg_ids_next)
  
              paddle.disable_static()
  
              wmt16 = WMT16(mode='train', src_dict_size=50, trg_dict_size=50)
  
              for i in range(10):
                  src_ids, trg_ids, trg_ids_next = wmt16[i]
                  src_ids = paddle.to_tensor(src_ids)
                  trg_ids = paddle.to_tensor(trg_ids)
                  trg_ids_next = paddle.to_tensor(trg_ids_next)
  
                  model = SimpleNet()
                  src_ids, trg_ids, trg_ids_next = model(src_ids, trg_ids, trg_ids_next)
                  print(src_ids.numpy(), trg_ids.numpy(), trg_ids_next.numpy())
  
      """
  
      def __init__(self,
                   data_file=None,
                   mode='train',
                   src_dict_size=-1,
                   trg_dict_size=-1,
                   lang='en',
                   download=True):
          assert mode.lower() in ['train', 'test', 'val'], \
              "mode should be 'train', 'test' or 'val', but got {}".format(mode)
          self.mode = mode.lower()
  
          self.data_file = data_file
          if self.data_file is None:
              assert download, "data_file is not set and downloading automatically is disabled"
              self.data_file = _check_exists_and_download(
                  data_file, DATA_URL, DATA_MD5, 'wmt16', download)
  
          self.lang = lang
          assert src_dict_size > 0, "dict_size should be set as positive number"
          assert trg_dict_size > 0, "dict_size should be set as positive number"
          self.src_dict_size = min(src_dict_size, (TOTAL_EN_WORDS if lang == "en"
                                                   else TOTAL_DE_WORDS))
          self.trg_dict_size = min(trg_dict_size, (TOTAL_DE_WORDS if lang == "en"
                                                   else TOTAL_EN_WORDS))
  
          # load source and target word dict
          self.src_dict = self._load_dict(lang, src_dict_size)
          self.trg_dict = self._load_dict("de" if lang == "en" else "en",
                                          trg_dict_size)
  
          # load data
          self.data = self._load_data()
  
      def _load_dict(self, lang, dict_size, reverse=False):
          dict_path = os.path.join(paddle.dataset.common.DATA_HOME,
                                   "wmt16/%s_%d.dict" % (lang, dict_size))
          dict_found = False
          if os.path.exists(dict_path):
              with open(dict_path, "rb") as d:
                  dict_found = len(d.readlines()) == dict_size
          if not dict_found:
              self._build_dict(dict_path, dict_size, lang)
  
          word_dict = {}
          with open(dict_path, "rb") as fdict:
              for idx, line in enumerate(fdict):
                  if reverse:
                      word_dict[idx] = cpt.to_text(line.strip())
                  else:
                      word_dict[cpt.to_text(line.strip())] = idx
          return word_dict
  
      def _build_dict(self, dict_path, dict_size, lang):
          word_dict = defaultdict(int)
          with tarfile.open(self.data_file, mode="r") as f:
              for line in f.extractfile("wmt16/train"):
                  line = cpt.to_text(line)
                  line_split = line.strip().split("\t")
                  if len(line_split) != 2: continue
                  sen = line_split[0] if self.lang == "en" else line_split[1]
                  for w in sen.split():
                      word_dict[w] += 1
  
          with open(dict_path, "wb") as fout:
              fout.write(
                  cpt.to_bytes("%s\n%s\n%s\n" % (START_MARK, END_MARK, UNK_MARK)))
              for idx, word in enumerate(
                      sorted(
                          six.iteritems(word_dict),
                          key=lambda x: x[1],
                          reverse=True)):
                  if idx + 3 == dict_size: break
                  fout.write(cpt.to_bytes(word[0]))
                  fout.write(cpt.to_bytes('\n'))
  
      def _load_data(self):
          # the index for start mark, end mark, and unk are the same in source
          # language and target language. Here uses the source language
          # dictionary to determine their indices.
          start_id = self.src_dict[START_MARK]
          end_id = self.src_dict[END_MARK]
          unk_id = self.src_dict[UNK_MARK]
  
          src_col = 0 if self.lang == "en" else 1
          trg_col = 1 - src_col
  
          self.src_ids = []
          self.trg_ids = []
          self.trg_ids_next = []
          with tarfile.open(self.data_file, mode="r") as f:
              for line in f.extractfile("wmt16/{}".format(self.mode)):
                  line = cpt.to_text(line)
                  line_split = line.strip().split("\t")
                  if len(line_split) != 2:
                      continue
                  src_words = line_split[src_col].split()
                  src_ids = [start_id] + [
                      self.src_dict.get(w, unk_id) for w in src_words
                  ] + [end_id]
  
                  trg_words = line_split[trg_col].split()
                  trg_ids = [self.trg_dict.get(w, unk_id) for w in trg_words]
  
                  trg_ids_next = trg_ids + [end_id]
                  trg_ids = [start_id] + trg_ids
  
                  self.src_ids.append(src_ids)
                  self.trg_ids.append(trg_ids)
                  self.trg_ids_next.append(trg_ids_next)
  
      def __getitem__(self, idx):
          return (np.array(self.src_ids[idx]), np.array(self.trg_ids[idx]),
                  np.array(self.trg_ids_next[idx]))
  
      def __len__(self):
          return len(self.src_ids)
  
      def get_dict(self, lang, reverse=False):
          """
          return the word dictionary for the specified language.
  
          Args:
              lang(string): A string indicating which language is the source
                            language. Available options are: "en" for English
                            and "de" for Germany.
              reverse(bool): If reverse is set to False, the returned python
                             dictionary will use word as key and use index as value.
                             If reverse is set to True, the returned python
                             dictionary will use index as key and word as value.
  
          Returns:
              dict: The word dictionary for the specific language.
  
          Examples:
      
              .. code-block:: python
      
                  from paddle.text.datasets import WMT16
                  wmt16 = WMT16(mode='train', src_dict_size=50, trg_dict_size=50)
                  en_dict = wmt16.get_dict('en')
  
          """
          dict_size = self.src_dict_size if lang == self.lang else self.trg_dict_size
  
          dict_path = os.path.join(paddle.dataset.common.DATA_HOME,
                                   "wmt16/%s_%d.dict" % (lang, dict_size))
          assert os.path.exists(dict_path), "Word dictionary does not exist. "
          "Please invoke paddle.dataset.wmt16.train/test/validation first "
          "to build the dictionary."
          return self._load_dict(lang, dict_size)
  