text: # coding=utf-8
  # Copyright 2018 The Google AI Language Team 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.
  
  
  import json
  import os
  import unittest
  
  from transformers.file_utils import cached_property
  from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
  from transformers.testing_utils import slow
  
  from .test_tokenization_common import TokenizerTesterMixin
  
  
  # using a different tiny model than the one used for default params defined in init to ensure proper testing
  FSMT_TINY2 = "stas/tiny-wmt19-en-ru"
  
  
  class FSMTTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
      tokenizer_class = FSMTTokenizer
  
      def setUp(self):
          super().setUp()
  
          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          vocab = [
              "l",
              "o",
              "w",
              "e",
              "r",
              "s",
              "t",
              "i",
              "d",
              "n",
              "w</w>",
              "r</w>",
              "t</w>",
              "lo",
              "low",
              "er</w>",
              "low</w>",
              "lowest</w>",
              "newer</w>",
              "wider</w>",
              "<unk>",
          ]
          vocab_tokens = dict(zip(vocab, range(len(vocab))))
          merges = ["l o 123", "lo w 1456", "e r</w> 1789", ""]
  
          self.langs = ["en", "ru"]
          config = {
              "langs": self.langs,
              "src_vocab_size": 10,
              "tgt_vocab_size": 20,
          }
  
          self.src_vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["src_vocab_file"])
          self.tgt_vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["tgt_vocab_file"])
          config_file = os.path.join(self.tmpdirname, "tokenizer_config.json")
          self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
          with open(self.src_vocab_file, "w") as fp:
              fp.write(json.dumps(vocab_tokens))
          with open(self.tgt_vocab_file, "w") as fp:
              fp.write(json.dumps(vocab_tokens))
          with open(self.merges_file, "w") as fp:
              fp.write("\n".join(merges))
          with open(config_file, "w") as fp:
              fp.write(json.dumps(config))
  
      @cached_property
      def tokenizer_ru_en(self):
          return FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en")
  
      @cached_property
      def tokenizer_en_ru(self):
          return FSMTTokenizer.from_pretrained("facebook/wmt19-en-ru")
  
      def test_online_tokenizer_config(self):
          """this just tests that the online tokenizer files get correctly fetched and
          loaded via its tokenizer_config.json and it's not slow so it's run by normal CI
          """
          tokenizer = FSMTTokenizer.from_pretrained(FSMT_TINY2)
          self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang], ["en", "ru"])
          self.assertEqual(tokenizer.src_vocab_size, 21)
          self.assertEqual(tokenizer.tgt_vocab_size, 21)
  
      def test_full_tokenizer(self):
          """ Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt """
          tokenizer = FSMTTokenizer(self.langs, self.src_vocab_file, self.tgt_vocab_file, self.merges_file)
  
          text = "lower"
          bpe_tokens = ["low", "er</w>"]
          tokens = tokenizer.tokenize(text)
          self.assertListEqual(tokens, bpe_tokens)
  
          input_tokens = tokens + ["<unk>"]
          input_bpe_tokens = [14, 15, 20]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
  
      @slow
      def test_sequence_builders(self):
          tokenizer = self.tokenizer_ru_en
  
          text = tokenizer.encode("sequence builders", add_special_tokens=False)
          text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False)
  
          encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
          encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
  
          assert encoded_sentence == text + [2]
          assert encoded_pair == text + [2] + text_2 + [2]
  
      @slow
      def test_match_encode_decode(self):
          tokenizer_enc = self.tokenizer_en_ru
          tokenizer_dec = self.tokenizer_ru_en
  
          targets = [
              [
                  "Here's a little song I wrote. Don't worry, be happy.",
                  [2470, 39, 11, 2349, 7222, 70, 5979, 7, 8450, 1050, 13160, 5, 26, 6445, 7, 2],
              ],
              ["This is it. No more. I'm done!", [132, 21, 37, 7, 1434, 86, 7, 70, 6476, 1305, 427, 2]],
          ]
  
          # if data needs to be recreated or added, run:
          # import torch
          # model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
          # for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")
  
          for src_text, tgt_input_ids in targets:
              encoded_ids = tokenizer_enc.encode(src_text, return_tensors=None)
              self.assertListEqual(encoded_ids, tgt_input_ids)
  
              # and decode backward, using the reversed languages model
              decoded_text = tokenizer_dec.decode(encoded_ids, skip_special_tokens=True)
              self.assertEqual(decoded_text, src_text)
  
      @slow
      def test_tokenizer_lower(self):
          tokenizer = FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en", do_lower_case=True)
          tokens = tokenizer.tokenize("USA is United States of America")
          expected = ["us", "a</w>", "is</w>", "un", "i", "ted</w>", "st", "ates</w>", "of</w>", "am", "er", "ica</w>"]
          self.assertListEqual(tokens, expected)
  
      @unittest.skip("FSMTConfig.__init__  requires non-optional args")
      def test_torch_encode_plus_sent_to_model(self):
          pass
  
      @unittest.skip("FSMTConfig.__init__  requires non-optional args")
      def test_np_encode_plus_sent_to_model(self):
          pass
  