code: # coding=utf-8
  # Copyright 2019 HuggingFace Inc.
  #
  # 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 os
  import pickle
  import shutil
  import tempfile
  
  from tests.utils import require_tf, require_torch
  
  
  class TokenizerTesterMixin:
  
      tokenizer_class = None
      test_rust_tokenizer = False
  
      def setUp(self):
          self.tmpdirname = tempfile.mkdtemp()
  
      def tearDown(self):
          shutil.rmtree(self.tmpdirname)
  
      def get_tokenizer(self, **kwargs):
          raise NotImplementedError
  
      def get_rust_tokenizer(self, **kwargs):
          raise NotImplementedError
  
      def get_input_output_texts(self):
          raise NotImplementedError
  
      @staticmethod
      def convert_batch_encode_plus_format_to_encode_plus(batch_encode_plus_sequences):
          # Switch from batch_encode_plus format:   {'input_ids': [[...], [...]], ...}
          # to the concatenated encode_plus format: [{'input_ids': [...], ...}, {'input_ids': [...], ...}]
          return [
              {value: batch_encode_plus_sequences[value][i] for value in batch_encode_plus_sequences.keys()}
              for i in range(len(batch_encode_plus_sequences["input_ids"]))
          ]
  
      def test_tokenizers_common_properties(self):
          tokenizer = self.get_tokenizer()
          attributes_list = [
              "bos_token",
              "eos_token",
              "unk_token",
              "sep_token",
              "pad_token",
              "cls_token",
              "mask_token",
          ]
          for attr in attributes_list:
              self.assertTrue(hasattr(tokenizer, attr))
              self.assertTrue(hasattr(tokenizer, attr + "_id"))
  
          self.assertTrue(hasattr(tokenizer, "additional_special_tokens"))
          self.assertTrue(hasattr(tokenizer, "additional_special_tokens_ids"))
  
          attributes_list = ["max_len", "init_inputs", "init_kwargs", "added_tokens_encoder", "added_tokens_decoder"]
          for attr in attributes_list:
              self.assertTrue(hasattr(tokenizer, attr))
  
      def test_save_and_load_tokenizer(self):
          # safety check on max_len default value so we are sure the test works
          tokenizer = self.get_tokenizer()
          self.assertNotEqual(tokenizer.max_len, 42)
  
          # Now let's start the test
          tokenizer = self.get_tokenizer(max_len=42)
  
          before_tokens = tokenizer.encode("He is very happy, UNwant\u00E9d,running", add_special_tokens=False)
  
          with tempfile.TemporaryDirectory() as tmpdirname:
              tokenizer.save_pretrained(tmpdirname)
              tokenizer = self.tokenizer_class.from_pretrained(tmpdirname)
  
              after_tokens = tokenizer.encode("He is very happy, UNwant\u00E9d,running", add_special_tokens=False)
              self.assertListEqual(before_tokens, after_tokens)
  
              self.assertEqual(tokenizer.max_len, 42)
              tokenizer = self.tokenizer_class.from_pretrained(tmpdirname, max_len=43)
              self.assertEqual(tokenizer.max_len, 43)
  
      def test_pickle_tokenizer(self):
          tokenizer = self.get_tokenizer()
          self.assertIsNotNone(tokenizer)
  
          text = "Munich and Berlin are nice cities"
          subwords = tokenizer.tokenize(text)
  
          with tempfile.TemporaryDirectory() as tmpdirname:
  
              filename = os.path.join(tmpdirname, "tokenizer.bin")
              with open(filename, "wb") as handle:
                  pickle.dump(tokenizer, handle)
  
              with open(filename, "rb") as handle:
                  tokenizer_new = pickle.load(handle)
  
          subwords_loaded = tokenizer_new.tokenize(text)
  
          self.assertListEqual(subwords, subwords_loaded)
  
      def test_added_tokens_do_lower_case(self):
          tokenizer = self.get_tokenizer(do_lower_case=True)
  
          special_token = tokenizer.all_special_tokens[0]
  
          text = special_token + " aaaaa bbbbbb low cccccccccdddddddd l " + special_token
          text2 = special_token + " AAAAA BBBBBB low CCCCCCCCCDDDDDDDD l " + special_token
  
          toks0 = tokenizer.tokenize(text)  # toks before adding new_toks
  
          new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd", "AAAAA BBBBBB", "CCCCCCCCCDDDDDDDD"]
          added = tokenizer.add_tokens(new_toks)
          self.assertEqual(added, 2)
  
          toks = tokenizer.tokenize(text)
          toks2 = tokenizer.tokenize(text2)
  
          self.assertEqual(len(toks), len(toks2))
          self.assertNotEqual(len(toks), len(toks0))  # toks0 should be longer
          self.assertListEqual(toks, toks2)
  
          # Check that none of the special tokens are lowercased
          sequence_with_special_tokens = "A " + " yEs ".join(tokenizer.all_special_tokens) + " B"
          tokenized_sequence = tokenizer.tokenize(sequence_with_special_tokens)
  
          for special_token in tokenizer.all_special_tokens:
              self.assertTrue(special_token in tokenized_sequence)
  
          tokenizer = self.get_tokenizer(do_lower_case=False)
  
          added = tokenizer.add_tokens(new_toks)
          self.assertEqual(added, 4)
  
          toks = tokenizer.tokenize(text)
          toks2 = tokenizer.tokenize(text2)
  
          self.assertEqual(len(toks), len(toks2))  # Length should still be the same
          self.assertNotEqual(len(toks), len(toks0))
          self.assertNotEqual(toks[1], toks2[1])  # But at least the first non-special tokens should differ
  
      def test_add_tokens_tokenizer(self):
          tokenizer = self.get_tokenizer()
  
          vocab_size = tokenizer.vocab_size
          all_size = len(tokenizer)
  
          self.assertNotEqual(vocab_size, 0)
          self.assertEqual(vocab_size, all_size)
  
          new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd"]
          added_toks = tokenizer.add_tokens(new_toks)
          vocab_size_2 = tokenizer.vocab_size
          all_size_2 = len(tokenizer)
  
          self.assertNotEqual(vocab_size_2, 0)
          self.assertEqual(vocab_size, vocab_size_2)
          self.assertEqual(added_toks, len(new_toks))
          self.assertEqual(all_size_2, all_size + len(new_toks))
  
          tokens = tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l", add_special_tokens=False)
  
          self.assertGreaterEqual(len(tokens), 4)
          self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
          self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
  
          new_toks_2 = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
          added_toks_2 = tokenizer.add_special_tokens(new_toks_2)
          vocab_size_3 = tokenizer.vocab_size
          all_size_3 = len(tokenizer)
  
          self.assertNotEqual(vocab_size_3, 0)
          self.assertEqual(vocab_size, vocab_size_3)
          self.assertEqual(added_toks_2, len(new_toks_2))
          self.assertEqual(all_size_3, all_size_2 + len(new_toks_2))
  
          tokens = tokenizer.encode(
              ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l", add_special_tokens=False
          )
  
          self.assertGreaterEqual(len(tokens), 6)
          self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
          self.assertGreater(tokens[0], tokens[1])
          self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
          self.assertGreater(tokens[-2], tokens[-3])
          self.assertEqual(tokens[0], tokenizer.eos_token_id)
          self.assertEqual(tokens[-2], tokenizer.pad_token_id)
  
      def test_add_special_tokens(self):
          tokenizer = self.get_tokenizer()
          input_text, output_text = self.get_input_output_texts()
  
          special_token = "[SPECIAL TOKEN]"
  
          tokenizer.add_special_tokens({"cls_token": special_token})
          encoded_special_token = tokenizer.encode(special_token, add_special_tokens=False)
          assert len(encoded_special_token) == 1
  
          text = " ".join([input_text, special_token, output_text])
          encoded = tokenizer.encode(text, add_special_tokens=False)
  
          input_encoded = tokenizer.encode(input_text, add_special_tokens=False)
          output_encoded = tokenizer.encode(" " + output_text, add_special_tokens=False)
          special_token_id = tokenizer.encode(special_token, add_special_tokens=False)
          assert encoded == input_encoded + special_token_id + output_encoded
  
          decoded = tokenizer.decode(encoded, skip_special_tokens=True)
          assert special_token not in decoded
  
      def test_required_methods_tokenizer(self):
          tokenizer = self.get_tokenizer()
          input_text, output_text = self.get_input_output_texts()
  
          tokens = tokenizer.tokenize(input_text)
          ids = tokenizer.convert_tokens_to_ids(tokens)
          ids_2 = tokenizer.encode(input_text, add_special_tokens=False)
          self.assertListEqual(ids, ids_2)
  
          tokens_2 = tokenizer.convert_ids_to_tokens(ids)
          text_2 = tokenizer.decode(ids)
  
          self.assertEqual(text_2, output_text)
  
          self.assertNotEqual(len(tokens_2), 0)
          self.assertIsInstance(text_2, str)
  
      def test_encode_decode_with_spaces(self):
          tokenizer = self.get_tokenizer()
  
          new_toks = ["[ABC]", "[DEF]", "GHI IHG"]
          tokenizer.add_tokens(new_toks)
          input = "[ABC] [DEF] [ABC] GHI IHG [DEF]"
          encoded = tokenizer.encode(input, add_special_tokens=False)
          decoded = tokenizer.decode(encoded)
          self.assertEqual(decoded, input)
  
      def test_pretrained_model_lists(self):
          weights_list = list(self.tokenizer_class.max_model_input_sizes.keys())
          weights_lists_2 = []
          for file_id, map_list in self.tokenizer_class.pretrained_vocab_files_map.items():
              weights_lists_2.append(list(map_list.keys()))
  
          for weights_list_2 in weights_lists_2:
              self.assertListEqual(weights_list, weights_list_2)
  
      def test_mask_output(self):
          tokenizer = self.get_tokenizer()
  
          if (
              tokenizer.build_inputs_with_special_tokens.__qualname__.split(".")[0] != "PreTrainedTokenizer"
              and "token_type_ids" in tokenizer.model_input_names
          ):
              seq_0 = "Test this method."
              seq_1 = "With these inputs."
              information = tokenizer.encode_plus(seq_0, seq_1, add_special_tokens=True)
              sequences, mask = information["input_ids"], information["token_type_ids"]
              self.assertEqual(len(sequences), len(mask))
  
      def test_number_of_added_tokens(self):
          tokenizer = self.get_tokenizer()
  
          seq_0 = "Test this method."
          seq_1 = "With these inputs."
  
          sequences = tokenizer.encode(seq_0, seq_1, add_special_tokens=False)
          attached_sequences = tokenizer.encode(seq_0, seq_1, add_special_tokens=True, add_prefix_space=False)
  
          # Method is implemented (e.g. not GPT-2)
          if len(attached_sequences) != 2:
              self.assertEqual(tokenizer.num_added_tokens(pair=True), len(attached_sequences) - len(sequences))
  
      def test_maximum_encoding_length_single_input(self):
          tokenizer = self.get_tokenizer()
  
          seq_0 = "This is a sentence to be encoded."
          stride = 2
  
          sequence = tokenizer.encode(seq_0, add_special_tokens=False)
          num_added_tokens = tokenizer.num_added_tokens()
          total_length = len(sequence) + num_added_tokens
          information = tokenizer.encode_plus(
              seq_0,
              max_length=total_length - 2,
              add_special_tokens=True,
              stride=stride,
              return_overflowing_tokens=True,
              add_prefix_space=False,
          )
  
          truncated_sequence = information["input_ids"]
          overflowing_tokens = information["overflowing_tokens"]
  
          self.assertEqual(len(overflowing_tokens), 2 + stride)
          self.assertEqual(overflowing_tokens, sequence[-(2 + stride) :])
          self.assertEqual(len(truncated_sequence), total_length - 2)
          self.assertEqual(truncated_sequence, tokenizer.build_inputs_with_special_tokens(sequence[:-2]))
  
      def test_maximum_encoding_length_pair_input(self):
          tokenizer = self.get_tokenizer()
  
          seq_0 = "This is a sentence to be encoded."
          seq_1 = "This is another sentence to be encoded."
          stride = 2
  
          sequence_0_no_special_tokens = tokenizer.encode(seq_0, add_special_tokens=False)
          sequence_1_no_special_tokens = tokenizer.encode(seq_1, add_special_tokens=False)
  
          sequence = tokenizer.encode(seq_0, seq_1, add_special_tokens=True, add_prefix_space=False)
          truncated_second_sequence = tokenizer.build_inputs_with_special_tokens(
              tokenizer.encode(seq_0, add_special_tokens=False), tokenizer.encode(seq_1, add_special_tokens=False)[:-2],
          )
  
          information = tokenizer.encode_plus(
              seq_0,
              seq_1,
              max_length=len(sequence) - 2,
              add_special_tokens=True,
              stride=stride,
              truncation_strategy="only_second",
              return_overflowing_tokens=True,
              add_prefix_space=False,
          )
          information_first_truncated = tokenizer.encode_plus(
              seq_0,
              seq_1,
              max_length=len(sequence) - 2,
              add_special_tokens=True,
              stride=stride,
              truncation_strategy="only_first",
              return_overflowing_tokens=True,
              add_prefix_space=False,
          )
  
          truncated_sequence = information["input_ids"]
          overflowing_tokens = information["overflowing_tokens"]
          overflowing_tokens_first_truncated = information_first_truncated["overflowing_tokens"]
  
          self.assertEqual(len(overflowing_tokens), 2 + stride)
          self.assertEqual(overflowing_tokens, sequence_1_no_special_tokens[-(2 + stride) :])
          self.assertEqual(overflowing_tokens_first_truncated, sequence_0_no_special_tokens[-(2 + stride) :])
          self.assertEqual(len(truncated_sequence), len(sequence) - 2)
          self.assertEqual(truncated_sequence, truncated_second_sequence)
  
      def test_encode_input_type(self):
          tokenizer = self.get_tokenizer()
  
          sequence = "Let's encode this sequence"
  
          tokens = tokenizer.tokenize(sequence)
          input_ids = tokenizer.convert_tokens_to_ids(tokens)
          formatted_input = tokenizer.encode(sequence, add_special_tokens=True, add_prefix_space=False)
  
          self.assertEqual(tokenizer.encode(tokens, add_special_tokens=True), formatted_input)
          self.assertEqual(tokenizer.encode(input_ids, add_special_tokens=True), formatted_input)
  
      def test_swap_special_token(self):
          tokenizer = self.get_tokenizer()
  
          mask = "<mask>"
          sequence = "Encode this sequence"
          sequence_masked_0 = "Encode <mask> sequence"
          sequence_masked_1 = "<mask> this sequence"
  
          # Add tokens so that masked token isn't split
          tokenizer.add_tokens(sequence.split())
          tokenizer.add_special_tokens({"mask_token": mask})
          mask_ind = tokenizer.convert_tokens_to_ids(mask)
          encoded = tokenizer.encode(sequence, add_special_tokens=False)
  
          # Test first masked sequence
          encoded_masked = tokenizer.encode(sequence_masked_0, add_special_tokens=False)
          mask_loc = encoded_masked.index(mask_ind)
          encoded_masked[mask_loc] = encoded[mask_loc]
  
          self.assertEqual(encoded_masked, encoded)
  
          # Test second masked sequence
          encoded_masked = tokenizer.encode(sequence_masked_1, add_special_tokens=False)
          mask_loc = encoded_masked.index(mask_ind)
          encoded_masked[mask_loc] = encoded[mask_loc]
  
          self.assertEqual(encoded_masked, encoded)
  
      def test_special_tokens_mask(self):
          tokenizer = self.get_tokenizer()
  
          sequence_0 = "Encode this."
          sequence_1 = "This one too please."
  
          # Testing single inputs
          encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False)
          encoded_sequence_dict = tokenizer.encode_plus(
              sequence_0, add_special_tokens=True, return_special_tokens_mask=True, add_prefix_space=False
          )
          encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
          special_tokens_mask = encoded_sequence_dict["special_tokens_mask"]
          self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special))
  
          filtered_sequence = [
              (x if not special_tokens_mask[i] else None) for i, x in enumerate(encoded_sequence_w_special)
          ]
          filtered_sequence = [x for x in filtered_sequence if x is not None]
          self.assertEqual(encoded_sequence, filtered_sequence)
  
          # Testing inputs pairs
          encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False)
          encoded_sequence += tokenizer.encode(sequence_1, add_special_tokens=False)
          encoded_sequence_dict = tokenizer.encode_plus(
              sequence_0, sequence_1, add_special_tokens=True, return_special_tokens_mask=True, add_prefix_space=False
          )
          encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
          special_tokens_mask = encoded_sequence_dict["special_tokens_mask"]
          self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special))
  
          filtered_sequence = [
              (x if not special_tokens_mask[i] else None) for i, x in enumerate(encoded_sequence_w_special)
          ]
          filtered_sequence = [x for x in filtered_sequence if x is not None]
          self.assertEqual(encoded_sequence, filtered_sequence)
  
          # Testing with already existing special tokens
          if tokenizer.cls_token_id == tokenizer.unk_token_id and tokenizer.cls_token_id == tokenizer.unk_token_id:
              tokenizer.add_special_tokens({"cls_token": "</s>", "sep_token": "<s>"})
          encoded_sequence_dict = tokenizer.encode_plus(
              sequence_0, add_special_tokens=True, return_special_tokens_mask=True
          )
          encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
          special_tokens_mask_orig = encoded_sequence_dict["special_tokens_mask"]
          special_tokens_mask = tokenizer.get_special_tokens_mask(
              encoded_sequence_w_special, already_has_special_tokens=True
          )
          self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special))
          self.assertEqual(special_tokens_mask_orig, special_tokens_mask)
  
      def test_padding_to_max_length(self):
          tokenizer = self.get_tokenizer()
  
          sequence = "Sequence"
          padding_size = 10
  
          # check correct behaviour if no pad_token_id exists and add it eventually
          self._check_no_pad_token_padding(tokenizer, sequence)
  
          padding_idx = tokenizer.pad_token_id
  
          # RIGHT PADDING - Check that it correctly pads when a maximum length is specified along with the padding flag set to True
          tokenizer.padding_side = "right"
          encoded_sequence = tokenizer.encode(sequence)
          sequence_length = len(encoded_sequence)
          padded_sequence = tokenizer.encode(sequence, max_length=sequence_length + padding_size, pad_to_max_length=True)
          padded_sequence_length = len(padded_sequence)
          assert sequence_length + padding_size == padded_sequence_length
          assert encoded_sequence + [padding_idx] * padding_size == padded_sequence
  
          # LEFT PADDING - Check that it correctly pads when a maximum length is specified along with the padding flag set to True
          tokenizer.padding_side = "left"
          encoded_sequence = tokenizer.encode(sequence)
          sequence_length = len(encoded_sequence)
          padded_sequence = tokenizer.encode(sequence, max_length=sequence_length + padding_size, pad_to_max_length=True)
          padded_sequence_length = len(padded_sequence)
          assert sequence_length + padding_size == padded_sequence_length
          assert [padding_idx] * padding_size + encoded_sequence == padded_sequence
  
          # RIGHT & LEFT PADDING - Check that nothing is done when a maximum length is not specified
          encoded_sequence = tokenizer.encode(sequence)
          sequence_length = len(encoded_sequence)
  
          tokenizer.padding_side = "right"
          padded_sequence_right = tokenizer.encode(sequence, pad_to_max_length=True)
          padded_sequence_right_length = len(padded_sequence_right)
  
          tokenizer.padding_side = "left"
          padded_sequence_left = tokenizer.encode(sequence, pad_to_max_length=True)
          padded_sequence_left_length = len(padded_sequence_left)
  
          assert sequence_length == padded_sequence_right_length
          assert encoded_sequence == padded_sequence_right
          assert sequence_length == padded_sequence_left_length
          assert encoded_sequence == padded_sequence_left
  
      def test_encode_plus_with_padding(self):
          tokenizer = self.get_tokenizer()
  
          sequence = "Sequence"
  
          # check correct behaviour if no pad_token_id exists and add it eventually
          self._check_no_pad_token_padding(tokenizer, sequence)
  
          padding_size = 10
          padding_idx = tokenizer.pad_token_id
          token_type_padding_idx = tokenizer.pad_token_type_id
  
          encoded_sequence = tokenizer.encode_plus(sequence, return_special_tokens_mask=True)
          input_ids = encoded_sequence["input_ids"]
          special_tokens_mask = encoded_sequence["special_tokens_mask"]
          sequence_length = len(input_ids)
  
          # Test right padding
          tokenizer.padding_side = "right"
  
          right_padded_sequence = tokenizer.encode_plus(
              sequence,
              max_length=sequence_length + padding_size,
              pad_to_max_length=True,
              return_special_tokens_mask=True,
          )
          right_padded_input_ids = right_padded_sequence["input_ids"]
  
          right_padded_special_tokens_mask = right_padded_sequence["special_tokens_mask"]
          right_padded_sequence_length = len(right_padded_input_ids)
  
          assert sequence_length + padding_size == right_padded_sequence_length
          assert input_ids + [padding_idx] * padding_size == right_padded_input_ids
          assert special_tokens_mask + [1] * padding_size == right_padded_special_tokens_mask
  
          # Test left padding
          tokenizer.padding_side = "left"
          left_padded_sequence = tokenizer.encode_plus(
              sequence,
              max_length=sequence_length + padding_size,
              pad_to_max_length=True,
              return_special_tokens_mask=True,
          )
          left_padded_input_ids = left_padded_sequence["input_ids"]
          left_padded_special_tokens_mask = left_padded_sequence["special_tokens_mask"]
          left_padded_sequence_length = len(left_padded_input_ids)
  
          assert sequence_length + padding_size == left_padded_sequence_length
          assert [padding_idx] * padding_size + input_ids == left_padded_input_ids
          assert [1] * padding_size + special_tokens_mask == left_padded_special_tokens_mask
  
          if "token_type_ids" in tokenizer.model_input_names:
              token_type_ids = encoded_sequence["token_type_ids"]
              left_padded_token_type_ids = left_padded_sequence["token_type_ids"]
              right_padded_token_type_ids = right_padded_sequence["token_type_ids"]
  
              assert token_type_ids + [token_type_padding_idx] * padding_size == right_padded_token_type_ids
              assert [token_type_padding_idx] * padding_size + token_type_ids == left_padded_token_type_ids
  
          if "attention_mask" in tokenizer.model_input_names:
              attention_mask = encoded_sequence["attention_mask"]
              right_padded_attention_mask = right_padded_sequence["attention_mask"]
              left_padded_attention_mask = left_padded_sequence["attention_mask"]
  
              assert attention_mask + [0] * padding_size == right_padded_attention_mask
              assert [0] * padding_size + attention_mask == left_padded_attention_mask
  
      def test_separate_tokenizers(self):
          # This tests that tokenizers don't impact others. Unfortunately the case where it fails is when
          # we're loading an S3 configuration from a pre-trained identifier, and we have no way of testing those today.
  
          tokenizer = self.get_tokenizer(random_argument=True)
          assert tokenizer.init_kwargs["random_argument"] is True
          new_tokenizer = self.get_tokenizer(random_argument=False)
          assert tokenizer.init_kwargs["random_argument"] is True
          assert new_tokenizer.init_kwargs["random_argument"] is False
  
      def test_get_vocab(self):
          tokenizer = self.get_tokenizer()
          vocab = tokenizer.get_vocab()
  
          self.assertIsInstance(vocab, dict)
          self.assertEqual(len(vocab), len(tokenizer))
  
          for word, ind in vocab.items():
              self.assertEqual(tokenizer.convert_tokens_to_ids(word), ind)
              self.assertEqual(tokenizer.convert_ids_to_tokens(ind), word)
  
          tokenizer.add_tokens(["asdfasdfasdfasdf"])
          vocab = tokenizer.get_vocab()
          self.assertIsInstance(vocab, dict)
          self.assertEqual(len(vocab), len(tokenizer))
  
          for word, ind in vocab.items():
              self.assertEqual(tokenizer.convert_tokens_to_ids(word), ind)
              self.assertEqual(tokenizer.convert_ids_to_tokens(ind), word)
  
      def test_batch_encode_plus_batch_sequence_length(self):
          # Tests that all encoded values have the correct size
          tokenizer = self.get_tokenizer()
          sequences = [
              "Testing batch encode plus",
              "Testing batch encode plus with different sequence lengths",
              "Testing batch encode plus with different sequence lengths correctly pads",
          ]
  
          encoded_sequences = [tokenizer.encode_plus(sequence, pad_to_max_length=False) for sequence in sequences]
          encoded_sequences_batch = tokenizer.batch_encode_plus(sequences)
          self.assertListEqual(
              encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch)
          )
  
          maximum_length = len(max([encoded_sequence["input_ids"] for encoded_sequence in encoded_sequences], key=len))
  
          # check correct behaviour if no pad_token_id exists and add it eventually
          self._check_no_pad_token_padding(tokenizer, sequences)
  
          encoded_sequences_padded = [
              tokenizer.encode_plus(sequence, pad_to_max_length=True, max_length=maximum_length)
              for sequence in sequences
          ]
  
          encoded_sequences_batch_padded = tokenizer.batch_encode_plus(sequences, pad_to_max_length=True)
          self.assertListEqual(
              encoded_sequences_padded,
              self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch_padded),
          )
  
      def test_batch_encode_plus_padding(self):
          # Test that padded sequences are equivalent between batch_encode_plus and encode_plus
  
          # Right padding tests
          tokenizer = self.get_tokenizer()
          sequences = [
              "Testing batch encode plus",
              "Testing batch encode plus with different sequence lengths",
              "Testing batch encode plus with different sequence lengths correctly pads",
          ]
  
          max_length = 100
  
          # check correct behaviour if no pad_token_id exists and add it eventually
          self._check_no_pad_token_padding(tokenizer, sequences)
  
          encoded_sequences = [
              tokenizer.encode_plus(sequence, pad_to_max_length=True, max_length=max_length) for sequence in sequences
          ]
          encoded_sequences_batch = tokenizer.batch_encode_plus(sequences, pad_to_max_length=True, max_length=max_length)
          self.assertListEqual(
              encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch)
          )
  
          # Left padding tests
          tokenizer = self.get_tokenizer()
  
          tokenizer.padding_side = "left"
          sequences = [
              "Testing batch encode plus",
              "Testing batch encode plus with different sequence lengths",
              "Testing batch encode plus with different sequence lengths correctly pads",
          ]
  
          max_length = 100
  
          # check correct behaviour if no pad_token_id exists and add it eventually
          self._check_no_pad_token_padding(tokenizer, sequences)
  
          encoded_sequences = [
              tokenizer.encode_plus(sequence, pad_to_max_length=True, max_length=max_length) for sequence in sequences
          ]
          encoded_sequences_batch = tokenizer.batch_encode_plus(sequences, pad_to_max_length=True, max_length=max_length)
          self.assertListEqual(
              encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch)
          )
  
      @require_torch
      @require_tf
      def test_batch_encode_plus_tensors(self):
          tokenizer = self.get_tokenizer()
          sequences = [
              "Testing batch encode plus",
              "Testing batch encode plus with different sequence lengths",
              "Testing batch encode plus with different sequence lengths correctly pads",
          ]
  
          # A Tensor cannot be build by sequences which are not the same size
          self.assertRaises(ValueError, tokenizer.batch_encode_plus, sequences, return_tensors="pt")
          self.assertRaises(ValueError, tokenizer.batch_encode_plus, sequences, return_tensors="tf")
  
          if tokenizer.pad_token_id is None:
              self.assertRaises(
                  ValueError, tokenizer.batch_encode_plus, sequences, pad_to_max_length=True, return_tensors="pt"
              )
              self.assertRaises(
                  ValueError, tokenizer.batch_encode_plus, sequences, pad_to_max_length=True, return_tensors="tf"
              )
          else:
              pytorch_tensor = tokenizer.batch_encode_plus(sequences, pad_to_max_length=True, return_tensors="pt")
              tensorflow_tensor = tokenizer.batch_encode_plus(sequences, pad_to_max_length=True, return_tensors="tf")
              encoded_sequences = tokenizer.batch_encode_plus(sequences, pad_to_max_length=True)
  
              for key in encoded_sequences.keys():
                  pytorch_value = pytorch_tensor[key].tolist()
                  tensorflow_value = tensorflow_tensor[key].numpy().tolist()
                  encoded_value = encoded_sequences[key]
  
                  self.assertEqual(pytorch_value, tensorflow_value, encoded_value)
  
      def _check_no_pad_token_padding(self, tokenizer, sequences):
          # if tokenizer does not have pad_token_id, an error should be thrown
          if tokenizer.pad_token_id is None:
              with self.assertRaises(ValueError):
                  if isinstance(sequences, list):
                      tokenizer.batch_encode_plus(sequences, pad_to_max_length=True)
                  else:
                      tokenizer.encode_plus(sequences, pad_to_max_length=True)
  
              # add pad_token_id to pass subsequent tests
              tokenizer.add_special_tokens({"pad_token": "<PAD>"})
  