text: # Copyright 2020 The HuggingFace Team. 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.
  
  import unittest
  
  from transformers import PegasusTokenizer, PegasusTokenizerFast
  from transformers.file_utils import cached_property
  from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch
  
  from .test_tokenization_common import TokenizerTesterMixin
  
  
  SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece_no_bos.model")
  
  
  @require_sentencepiece
  @require_tokenizers
  class PegasusTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
  
      tokenizer_class = PegasusTokenizer
      rust_tokenizer_class = PegasusTokenizerFast
      test_rust_tokenizer = True
  
      def setUp(self):
          super().setUp()
  
          # We have a SentencePiece fixture for testing
          tokenizer = PegasusTokenizer(SAMPLE_VOCAB)
          tokenizer.save_pretrained(self.tmpdirname)
  
      @cached_property
      def _large_tokenizer(self):
          return PegasusTokenizer.from_pretrained("google/pegasus-large")
  
      def get_tokenizer(self, **kwargs) -> PegasusTokenizer:
          return PegasusTokenizer.from_pretrained(self.tmpdirname, **kwargs)
  
      def get_input_output_texts(self, tokenizer):
          return ("This is a test", "This is a test")
  
      def test_mask_tokens_rust_pegasus(self):
          rust_tokenizer = self.rust_tokenizer_class.from_pretrained(self.tmpdirname)
          py_tokenizer = self.tokenizer_class.from_pretrained(self.tmpdirname)
          raw_input_str = "Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important </s> <pad> <pad> <pad>"
          rust_ids = rust_tokenizer([raw_input_str], return_tensors=None, add_special_tokens=False).input_ids[0]
          py_ids = py_tokenizer([raw_input_str], return_tensors=None, add_special_tokens=False).input_ids[0]
          # TODO: (Thom, Patrick) - this fails because the rust tokenizer does not know about the <mask_1>, <mask_2>, and those <unk_token_x> yet
          self.assertListEqual(py_ids, rust_ids)
  
      def test_large_mask_tokens(self):
          tokenizer = self._large_tokenizer
          # <mask_1> masks whole sentence while <mask_2> masks single word
          raw_input_str = "<mask_1> To ensure a <mask_2> flow of bank resolutions."
          desired_result = [2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
          ids = tokenizer([raw_input_str], return_tensors=None).input_ids[0]
          self.assertListEqual(desired_result, ids)
  
      def test_large_tokenizer_settings(self):
          tokenizer = self._large_tokenizer
          # The tracebacks for the following asserts are **better** without messages or self.assertEqual
          assert tokenizer.vocab_size == 96103
          assert tokenizer.pad_token_id == 0
          assert tokenizer.eos_token_id == 1
          assert tokenizer.offset == 103
          assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
          assert tokenizer.unk_token == "<unk>"
          assert tokenizer.model_max_length == 1024
          raw_input_str = "To ensure a smooth flow of bank resolutions."
          desired_result = [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
          ids = tokenizer([raw_input_str], return_tensors=None).input_ids[0]
          self.assertListEqual(desired_result, ids)
          assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
  
      @require_torch
      def test_large_seq2seq_truncation(self):
          src_texts = ["This is going to be way too long." * 150, "short example"]
          tgt_texts = ["not super long but more than 5 tokens", "tiny"]
          batch = self._large_tokenizer.prepare_seq2seq_batch(
              src_texts, tgt_texts=tgt_texts, max_target_length=5, return_tensors="pt"
          )
          assert batch.input_ids.shape == (2, 1024)
          assert batch.attention_mask.shape == (2, 1024)
          assert "labels" in batch  # because tgt_texts was specified
          assert batch.labels.shape == (2, 5)
          assert len(batch) == 3  # input_ids, attention_mask, labels. Other things make by BartModel
  