text: # coding=utf-8
  # Copyright 2021 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 typing import Tuple
  
  from transformers.models.mluke.tokenization_mluke import MLukeTokenizer
  from transformers.testing_utils import require_torch, slow
  
  from .test_tokenization_common import TokenizerTesterMixin
  
  
  @slow
  class MLukeTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
      tokenizer_class = MLukeTokenizer
      test_rust_tokenizer = False
      from_pretrained_kwargs = {"cls_token": "<s>"}
  
      def setUp(self):
          super().setUp()
  
          self.special_tokens_map = {"entity_token_1": "<ent>", "entity_token_2": "<ent2>"}
  
      def get_tokenizer(self, task=None, **kwargs):
          kwargs.update(self.special_tokens_map)
          kwargs.update({"task": task})
          return self.tokenizer_class.from_pretrained("studio-ousia/mluke-base", **kwargs)
  
      def get_input_output_texts(self, tokenizer):
          input_text = "lower newer"
          output_text = "lower newer"
          return input_text, output_text
  
      def test_full_tokenizer(self):
          tokenizer = self.tokenizer_class.from_pretrained("studio-ousia/mluke-base")
          text = "lower newer"
          spm_tokens = ["▁lower", "▁new", "er"]
          tokens = tokenizer.tokenize(text)
          self.assertListEqual(tokens, spm_tokens)
  
          input_tokens = tokens + [tokenizer.unk_token]
          input_spm_tokens = [92319, 3525, 56, 3]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_spm_tokens)
  
      def mluke_dict_integration_testing(self):
          tokenizer = self.get_tokenizer()
  
          self.assertListEqual(tokenizer.encode("Hello world!", add_special_tokens=False), [35378, 8999, 38])
          self.assertListEqual(
              tokenizer.encode("Hello world! cécé herlolip 418", add_special_tokens=False),
              [35378, 8999, 38, 33273, 11676, 604, 365, 21392, 201, 1819],
          )
  
      @slow
      def test_sequence_builders(self):
          tokenizer = self.tokenizer_class.from_pretrained("studio-ousia/mluke-base")
  
          text = tokenizer.encode("sequence builders", add_special_tokens=False)
          text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False)
  
          encoded_text_from_decode = tokenizer.encode(
              "sequence builders", add_special_tokens=True, add_prefix_space=False
          )
          encoded_pair_from_decode = tokenizer.encode(
              "sequence builders", "multi-sequence build", add_special_tokens=True, add_prefix_space=False
          )
  
          encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
          encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
  
          self.assertEqual(encoded_sentence, encoded_text_from_decode)
          self.assertEqual(encoded_pair, encoded_pair_from_decode)
  
      def get_clean_sequence(self, tokenizer, max_length=20) -> Tuple[str, list]:
          txt = "Beyonce lives in Los Angeles"
          ids = tokenizer.encode(txt, add_special_tokens=False)
          return txt, ids
  
      def test_pretokenized_inputs(self):
          pass
  
      def test_embeded_special_tokens(self):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest("{} ({})".format(tokenizer.__class__.__name__, pretrained_name)):
                  tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                  tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                  sentence = "A, <mask> AllenNLP sentence."
                  tokens_r = tokenizer_r.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True)
                  tokens_p = tokenizer_p.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True)
  
                  # token_type_ids should put 0 everywhere
                  self.assertEqual(sum(tokens_r["token_type_ids"]), sum(tokens_p["token_type_ids"]))
  
                  # token_type_ids should put 0 everywhere
                  self.assertEqual(sum(tokens_r["token_type_ids"]), sum(tokens_p["token_type_ids"]))
  
                  # attention_mask should put 1 everywhere, so sum over length should be 1
                  self.assertEqual(
                      sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"]),
                  )
  
                  tokens_p_str = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])
  
                  # Rust correctly handles the space before the mask while python doesnt
                  self.assertSequenceEqual(tokens_p["input_ids"], [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])
  
                  self.assertSequenceEqual(
                      tokens_p_str, ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]
                  )
  
      def test_padding_entity_inputs(self):
          tokenizer = self.get_tokenizer()
  
          sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan."
          span = (15, 34)
          pad_id = tokenizer.entity_vocab["[PAD]"]
          mask_id = tokenizer.entity_vocab["[MASK]"]
  
          encoding = tokenizer([sentence, sentence], entity_spans=[[span], [span, span]], padding=True)
          self.assertEqual(encoding["entity_ids"], [[mask_id, pad_id], [mask_id, mask_id]])
  
          # test with a sentence with no entity
          encoding = tokenizer([sentence, sentence], entity_spans=[[], [span, span]], padding=True)
          self.assertEqual(encoding["entity_ids"], [[pad_id, pad_id], [mask_id, mask_id]])
  
      def test_if_tokenize_single_text_raise_error_with_invalid_inputs(self):
          tokenizer = self.get_tokenizer()
  
          sentence = "ISO 639-3 uses the code fas for the dialects spoken across Iran and Afghanistan."
          entities = ["en:ISO 639-3"]
          spans = [(0, 9)]
  
          with self.assertRaises(ValueError):
              tokenizer(sentence, entities=tuple(entities), entity_spans=spans)
  
          with self.assertRaises(ValueError):
              tokenizer(sentence, entities=entities, entity_spans=tuple(spans))
  
          with self.assertRaises(ValueError):
              tokenizer(sentence, entities=[0], entity_spans=spans)
  
          with self.assertRaises(ValueError):
              tokenizer(sentence, entities=entities, entity_spans=[0])
  
          with self.assertRaises(ValueError):
              tokenizer(sentence, entities=entities, entity_spans=spans + [(0, 9)])
  
      def test_if_tokenize_entity_classification_raise_error_with_invalid_inputs(self):
          tokenizer = self.get_tokenizer(task="entity_classification")
  
          sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan."
          span = (15, 34)
  
          with self.assertRaises(ValueError):
              tokenizer(sentence, entity_spans=[])
  
          with self.assertRaises(ValueError):
              tokenizer(sentence, entity_spans=[span, span])
  
          with self.assertRaises(ValueError):
              tokenizer(sentence, entity_spans=[0])
  
      def test_if_tokenize_entity_pair_classification_raise_error_with_invalid_inputs(self):
          tokenizer = self.get_tokenizer(task="entity_pair_classification")
  
          sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan."
          # head and tail information
  
          with self.assertRaises(ValueError):
              tokenizer(sentence, entity_spans=[])
  
          with self.assertRaises(ValueError):
              tokenizer(sentence, entity_spans=[0, 0])
  
      def test_if_tokenize_entity_span_classification_raise_error_with_invalid_inputs(self):
          tokenizer = self.get_tokenizer(task="entity_span_classification")
  
          sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan."
  
          with self.assertRaises(ValueError):
              tokenizer(sentence, entity_spans=[])
  
          with self.assertRaises(ValueError):
              tokenizer(sentence, entity_spans=[0, 0, 0])
  
  
  @slow
  @require_torch
  class MLukeTokenizerIntegrationTests(unittest.TestCase):
      tokenizer_class = MLukeTokenizer
      from_pretrained_kwargs = {"cls_token": "<s>"}
  
      @classmethod
      def setUpClass(cls):
          cls.tokenizer = MLukeTokenizer.from_pretrained("studio-ousia/mluke-base", return_token_type_ids=True)
          cls.entity_classification_tokenizer = MLukeTokenizer.from_pretrained(
              "studio-ousia/mluke-base", return_token_type_ids=True, task="entity_classification"
          )
          cls.entity_pair_tokenizer = MLukeTokenizer.from_pretrained(
              "studio-ousia/mluke-base", return_token_type_ids=True, task="entity_pair_classification"
          )
  
          cls.entity_span_tokenizer = MLukeTokenizer.from_pretrained(
              "studio-ousia/mluke-base", return_token_type_ids=True, task="entity_span_classification"
          )
  
      def test_single_text_no_padding_or_truncation(self):
          tokenizer = self.tokenizer
          sentence = "ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
          entities = ["en:ISO 639-3", "DUMMY_ENTITY", "ja:アフガニスタン", "en:Afghanistan"]
          spans = [(0, 9), (59, 63), (68, 75), (77, 88)]
  
          encoding = tokenizer(sentence, entities=entities, entity_spans=spans, return_token_type_ids=True)
  
          self.assertEqual(
              tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False),
              "<s> ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン ( Afghanistan ).</s>",
          )
          self.assertEqual(
              tokenizer.decode(encoding["input_ids"][1:5], spaces_between_special_tokens=False), "ISO 639-3"
          )
          self.assertEqual(tokenizer.decode(encoding["input_ids"][17], spaces_between_special_tokens=False), "Iran")
          self.assertEqual(
              tokenizer.decode(encoding["input_ids"][19:25], spaces_between_special_tokens=False), "アフガニスタン"
          )
          self.assertEqual(
              tokenizer.decode(encoding["input_ids"][26], spaces_between_special_tokens=False), "Afghanistan"
          )
  
          self.assertEqual(
              encoding["entity_ids"],
              [
                  tokenizer.entity_vocab["en:ISO 639-3"],
                  tokenizer.entity_vocab["[UNK]"],
                  tokenizer.entity_vocab["ja:アフガニスタン"],
                  tokenizer.entity_vocab["en:Afghanistan"],
              ],
          )
          self.assertEqual(encoding["entity_attention_mask"], [1, 1, 1, 1])
          self.assertEqual(encoding["entity_token_type_ids"], [0, 0, 0, 0])
          # fmt: off
          self.assertEqual(
              encoding["entity_position_ids"],
              [
                  [1, 2, 3, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
                  [17, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
                  [19, 20, 21, 22, 23, 24, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
                  [26, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
              ]
          )
          # fmt: on
  
      def test_single_text_only_entity_spans_no_padding_or_truncation(self):
          tokenizer = self.tokenizer
  
          sentence = "ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
          entities = ["en:ISO 639-3", "DUMMY_ENTITY", "ja:アフガニスタン", "en:Afghanistan"]
          spans = [(0, 9), (59, 63), (68, 75), (77, 88)]
  
          encoding = tokenizer(sentence, entities=entities, entity_spans=spans, return_token_type_ids=True)
  
          self.assertEqual(
              tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False),
              "<s> ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン ( Afghanistan ).</s>",
          )
          self.assertEqual(
              tokenizer.decode(encoding["input_ids"][1:5], spaces_between_special_tokens=False), "ISO 639-3"
          )
          self.assertEqual(tokenizer.decode(encoding["input_ids"][17], spaces_between_special_tokens=False), "Iran")
          self.assertEqual(
              tokenizer.decode(encoding["input_ids"][20:25], spaces_between_special_tokens=False), "アフガニスタン"
          )
          self.assertEqual(
              tokenizer.decode(encoding["input_ids"][26], spaces_between_special_tokens=False), "Afghanistan"
          )
  
          self.assertEqual(
              encoding["entity_ids"],
              [
                  tokenizer.entity_vocab["en:ISO 639-3"],
                  tokenizer.entity_vocab["[UNK]"],
                  tokenizer.entity_vocab["ja:アフガニスタン"],
                  tokenizer.entity_vocab["en:Afghanistan"],
              ],
          )
          self.assertEqual(encoding["entity_attention_mask"], [1, 1, 1, 1])
          self.assertEqual(encoding["entity_token_type_ids"], [0, 0, 0, 0])
          # fmt: off
          self.assertEqual(
              encoding["entity_position_ids"],
              [
                  [1, 2, 3, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
                  [17, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
                  [19, 20, 21, 22, 23, 24, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
                  [26, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
              ]
          )
          # fmt: on
  
      def test_single_text_padding_pytorch_tensors(self):
          tokenizer = self.tokenizer
  
          sentence = "ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
          entities = ["en:ISO 639-3", "DUMMY_ENTITY", "ja:アフガニスタン", "en:Afghanistan"]
          spans = [(0, 9), (59, 63), (68, 75), (77, 88)]
  
          encoding = tokenizer(
              sentence,
              entities=entities,
              entity_spans=spans,
              return_token_type_ids=True,
              padding="max_length",
              max_length=30,
              max_entity_length=16,
              return_tensors="pt",
          )
  
          # test words
          self.assertEqual(encoding["input_ids"].shape, (1, 30))
          self.assertEqual(encoding["attention_mask"].shape, (1, 30))
          self.assertEqual(encoding["token_type_ids"].shape, (1, 30))
  
          # test entities
          self.assertEqual(encoding["entity_ids"].shape, (1, 16))
          self.assertEqual(encoding["entity_attention_mask"].shape, (1, 16))
          self.assertEqual(encoding["entity_token_type_ids"].shape, (1, 16))
          self.assertEqual(encoding["entity_position_ids"].shape, (1, 16, tokenizer.max_mention_length))
  
      def test_text_pair_no_padding_or_truncation(self):
          tokenizer = self.tokenizer
  
          sentence = "ISO 639-3 uses the code fas"
          sentence_pair = "for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
          entities = ["en:ISO 639-3"]
          entities_pair = ["DUMMY_ENTITY", "ja:アフガニスタン", "en:Afghanistan"]
          spans = [(0, 9)]
          spans_pair = [(31, 35), (40, 47), (49, 60)]
  
          encoding = tokenizer(
              sentence,
              sentence_pair,
              entities=entities,
              entities_pair=entities_pair,
              entity_spans=spans,
              entity_spans_pair=spans_pair,
              return_token_type_ids=True,
          )
  
          self.assertEqual(
              tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False),
              "<s> ISO 639-3 uses the code fas</s></s> for the dialects spoken across Iran and アフガニスタン ( Afghanistan ).</s>",
          )
          self.assertEqual(
              tokenizer.decode(encoding["input_ids"][1:5], spaces_between_special_tokens=False), "ISO 639-3"
          )
          self.assertEqual(tokenizer.decode(encoding["input_ids"][19], spaces_between_special_tokens=False), "Iran")
          self.assertEqual(
              tokenizer.decode(encoding["input_ids"][21:27], spaces_between_special_tokens=False), "アフガニスタン"
          )
          self.assertEqual(
              tokenizer.decode(encoding["input_ids"][28], spaces_between_special_tokens=False), "Afghanistan"
          )
  
          self.assertEqual(
              encoding["entity_ids"],
              [
                  tokenizer.entity_vocab["en:ISO 639-3"],
                  tokenizer.entity_vocab["[UNK]"],
                  tokenizer.entity_vocab["ja:アフガニスタン"],
                  tokenizer.entity_vocab["en:Afghanistan"],
              ],
          )
          self.assertEqual(encoding["entity_attention_mask"], [1, 1, 1, 1])
          self.assertEqual(encoding["entity_token_type_ids"], [0, 0, 0, 0])
          # fmt: off
          self.assertEqual(
              encoding["entity_position_ids"],
              [
                  [1, 2, 3, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
                  [19, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
                  [21, 22, 23, 24, 25, 26, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
                  [28, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
              ]
          )
          # fmt: on
  
      def test_text_pair_only_entity_spans_no_padding_or_truncation(self):
          tokenizer = self.tokenizer
  
          sentence = "ISO 639-3 uses the code fas"
          sentence_pair = "for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
          entities = ["en:ISO 639-3"]
          entities_pair = ["DUMMY_ENTITY", "ja:アフガニスタン", "en:Afghanistan"]
          spans = [(0, 9)]
          spans_pair = [(31, 35), (40, 47), (49, 60)]
  
          encoding = tokenizer(
              sentence,
              sentence_pair,
              entities=entities,
              entities_pair=entities_pair,
              entity_spans=spans,
              entity_spans_pair=spans_pair,
              return_token_type_ids=True,
          )
  
          self.assertEqual(
              tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False),
              "<s> ISO 639-3 uses the code fas</s></s> for the dialects spoken across Iran and アフガニスタン ( Afghanistan ).</s>",
          )
          self.assertEqual(
              tokenizer.decode(encoding["input_ids"][1:5], spaces_between_special_tokens=False), "ISO 639-3"
          )
          self.assertEqual(tokenizer.decode(encoding["input_ids"][19], spaces_between_special_tokens=False), "Iran")
          self.assertEqual(
              tokenizer.decode(encoding["input_ids"][21:27], spaces_between_special_tokens=False), "アフガニスタン"
          )
          self.assertEqual(
              tokenizer.decode(encoding["input_ids"][28], spaces_between_special_tokens=False), "Afghanistan"
          )
  
          self.assertEqual(
              encoding["entity_ids"],
              [
                  tokenizer.entity_vocab["en:ISO 639-3"],
                  tokenizer.entity_vocab["[UNK]"],
                  tokenizer.entity_vocab["ja:アフガニスタン"],
                  tokenizer.entity_vocab["en:Afghanistan"],
              ],
          )
          # fmt: off
          self.assertEqual(
              encoding["entity_position_ids"],
              [
                  [1, 2, 3, 4, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
                  [19, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
                  [21, 22, 23, 24, 25, 26, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
                  [28, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
              ]
          )
          # fmt: on
  
      def test_text_pair_padding_pytorch_tensors(self):
          tokenizer = self.tokenizer
  
          sentence = "ISO 639-3 uses the code fas"
          sentence_pair = "for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
          entities = ["en:ISO 639-3"]
          entities_pair = ["DUMMY_ENTITY", "ja:アフガニスタン", "en:Afghanistan"]
          spans = [(0, 9)]
          spans_pair = [(31, 35), (40, 47), (49, 60)]
  
          encoding = tokenizer(
              sentence,
              sentence_pair,
              entities=entities,
              entities_pair=entities_pair,
              entity_spans=spans,
              entity_spans_pair=spans_pair,
              return_token_type_ids=True,
              padding="max_length",
              max_length=40,
              max_entity_length=16,
              return_tensors="pt",
          )
  
          # test words
          self.assertEqual(encoding["input_ids"].shape, (1, 40))
          self.assertEqual(encoding["attention_mask"].shape, (1, 40))
          self.assertEqual(encoding["token_type_ids"].shape, (1, 40))
  
          # test entities
          self.assertEqual(encoding["entity_ids"].shape, (1, 16))
          self.assertEqual(encoding["entity_attention_mask"].shape, (1, 16))
          self.assertEqual(encoding["entity_token_type_ids"].shape, (1, 16))
          self.assertEqual(encoding["entity_position_ids"].shape, (1, 16, tokenizer.max_mention_length))
  
      def test_entity_classification_no_padding_or_truncation(self):
          tokenizer = self.entity_classification_tokenizer
  
          sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan."
          span = (15, 34)
  
          encoding = tokenizer(sentence, entity_spans=[span], return_token_type_ids=True)
  
          # test words
          self.assertEqual(len(encoding["input_ids"]), 23)
          self.assertEqual(len(encoding["attention_mask"]), 23)
          self.assertEqual(len(encoding["token_type_ids"]), 23)
          self.assertEqual(
              tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False),
              "<s> Japanese is an<ent>East Asian language<ent>spoken by about 128 million people, primarily in Japan.</s>",
          )
          self.assertEqual(
              tokenizer.decode(encoding["input_ids"][4:9], spaces_between_special_tokens=False),
              "<ent>East Asian language<ent>",
          )
  
          # test entities
          mask_id = tokenizer.entity_vocab["[MASK]"]
          self.assertEqual(encoding["entity_ids"], [mask_id])
          self.assertEqual(encoding["entity_attention_mask"], [1])
          self.assertEqual(encoding["entity_token_type_ids"], [0])
          # fmt: off
          self.assertEqual(
              encoding["entity_position_ids"],
              [[4, 5, 6, 7, 8, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]]
          )
          # fmt: on
  
      def test_entity_classification_padding_pytorch_tensors(self):
          tokenizer = self.entity_classification_tokenizer
  
          sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan."
          span = (15, 34)
  
          encoding = tokenizer(
              sentence, entity_spans=[span], return_token_type_ids=True, padding="max_length", return_tensors="pt"
          )
  
          # test words
          self.assertEqual(encoding["input_ids"].shape, (1, 512))
          self.assertEqual(encoding["attention_mask"].shape, (1, 512))
          self.assertEqual(encoding["token_type_ids"].shape, (1, 512))
  
          # test entities
          self.assertEqual(encoding["entity_ids"].shape, (1, 1))
          self.assertEqual(encoding["entity_attention_mask"].shape, (1, 1))
          self.assertEqual(encoding["entity_token_type_ids"].shape, (1, 1))
          self.assertEqual(
              encoding["entity_position_ids"].shape, (1, tokenizer.max_entity_length, tokenizer.max_mention_length)
          )
  
      def test_entity_pair_classification_no_padding_or_truncation(self):
          tokenizer = self.entity_pair_tokenizer
  
          sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan."
          # head and tail information
          spans = [(0, 8), (84, 89)]
  
          encoding = tokenizer(sentence, entity_spans=spans, return_token_type_ids=True)
  
          self.assertEqual(
              tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False),
              "<s><ent>Japanese<ent>is an East Asian language spoken by about 128 million people, primarily in<ent2>Japan<ent2>.</s>",
          )
          self.assertEqual(
              tokenizer.decode(encoding["input_ids"][1:4], spaces_between_special_tokens=False),
              "<ent>Japanese<ent>",
          )
          self.assertEqual(
              tokenizer.decode(encoding["input_ids"][20:23], spaces_between_special_tokens=False), "<ent2>Japan<ent2>"
          )
  
          mask_id = tokenizer.entity_vocab["[MASK]"]
          mask2_id = tokenizer.entity_vocab["[MASK2]"]
          self.assertEqual(encoding["entity_ids"], [mask_id, mask2_id])
          self.assertEqual(encoding["entity_attention_mask"], [1, 1])
          self.assertEqual(encoding["entity_token_type_ids"], [0, 0])
          # fmt: off
          self.assertEqual(
              encoding["entity_position_ids"],
              [
                  [1, 2, 3, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
                  [20, 21, 22, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
              ]
          )
          # fmt: on
  
      def test_entity_pair_classification_padding_pytorch_tensors(self):
          tokenizer = self.entity_pair_tokenizer
  
          sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan."
          # head and tail information
          spans = [(0, 8), (84, 89)]
  
          encoding = tokenizer(
              sentence,
              entity_spans=spans,
              return_token_type_ids=True,
              padding="max_length",
              max_length=30,
              return_tensors="pt",
          )
  
          # test words
          self.assertEqual(encoding["input_ids"].shape, (1, 30))
          self.assertEqual(encoding["attention_mask"].shape, (1, 30))
          self.assertEqual(encoding["token_type_ids"].shape, (1, 30))
  
          # test entities
          self.assertEqual(encoding["entity_ids"].shape, (1, 2))
          self.assertEqual(encoding["entity_attention_mask"].shape, (1, 2))
          self.assertEqual(encoding["entity_token_type_ids"].shape, (1, 2))
          self.assertEqual(
              encoding["entity_position_ids"].shape, (1, tokenizer.max_entity_length, tokenizer.max_mention_length)
          )
  
      def test_entity_span_classification_no_padding_or_truncation(self):
          tokenizer = self.entity_span_tokenizer
  
          sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan."
          spans = [(0, 8), (15, 34), (84, 89)]
  
          encoding = tokenizer(sentence, entity_spans=spans, return_token_type_ids=True)
  
          self.assertEqual(
              tokenizer.decode(encoding["input_ids"], spaces_between_special_tokens=False),
              "<s> Japanese is an East Asian language spoken by about 128 million people, primarily in Japan.</s>",
          )
  
          mask_id = tokenizer.entity_vocab["[MASK]"]
          self.assertEqual(encoding["entity_ids"], [mask_id, mask_id, mask_id])
          self.assertEqual(encoding["entity_attention_mask"], [1, 1, 1])
          self.assertEqual(encoding["entity_token_type_ids"], [0, 0, 0])
          # fmt: off
          self.assertEqual(
              encoding["entity_position_ids"],
              [
                  [1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
                  [4, 5, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
                  [18, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]]
          )
          # fmt: on
          self.assertEqual(encoding["entity_start_positions"], [1, 4, 18])
          self.assertEqual(encoding["entity_end_positions"], [1, 6, 18])
  
      def test_entity_span_classification_padding_pytorch_tensors(self):
          tokenizer = self.entity_span_tokenizer
  
          sentence = "Japanese is an East Asian language spoken by about 128 million people, primarily in Japan."
          spans = [(0, 8), (15, 34), (84, 89)]
  
          encoding = tokenizer(
              sentence,
              entity_spans=spans,
              return_token_type_ids=True,
              padding="max_length",
              max_length=30,
              max_entity_length=16,
              return_tensors="pt",
          )
  
          # test words
          self.assertEqual(encoding["input_ids"].shape, (1, 30))
          self.assertEqual(encoding["attention_mask"].shape, (1, 30))
          self.assertEqual(encoding["token_type_ids"].shape, (1, 30))
  
          # test entities
          self.assertEqual(encoding["entity_ids"].shape, (1, 16))
          self.assertEqual(encoding["entity_attention_mask"].shape, (1, 16))
          self.assertEqual(encoding["entity_token_type_ids"].shape, (1, 16))
          self.assertEqual(encoding["entity_position_ids"].shape, (1, 16, tokenizer.max_mention_length))
          self.assertEqual(encoding["entity_start_positions"].shape, (1, 16))
          self.assertEqual(encoding["entity_end_positions"].shape, (1, 16))
  