text: # coding=utf-8
  # Copyright 2018 Google T5 Authors and HuggingFace Inc. team.
  #
  # 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 tempfile
  import unittest
  
  from transformers import SPIECE_UNDERLINE, AddedToken, BatchEncoding, T5Tokenizer, T5TokenizerFast
  from transformers.file_utils import cached_property, is_tf_available, is_torch_available
  from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
  
  from .test_tokenization_common import TokenizerTesterMixin
  
  
  SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
  
  if is_torch_available():
      FRAMEWORK = "pt"
  elif is_tf_available():
      FRAMEWORK = "tf"
  else:
      FRAMEWORK = "jax"
  
  
  @require_sentencepiece
  @require_tokenizers
  class T5TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
  
      tokenizer_class = T5Tokenizer
      rust_tokenizer_class = T5TokenizerFast
      test_rust_tokenizer = True
      test_sentencepiece = True
  
      def setUp(self):
          super().setUp()
  
          # We have a SentencePiece fixture for testing
          tokenizer = T5Tokenizer(SAMPLE_VOCAB)
          tokenizer.save_pretrained(self.tmpdirname)
  
      def test_convert_token_and_id(self):
          """Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
          token = "<s>"
          token_id = 1
  
          self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
  
      def test_get_vocab(self):
          vocab_keys = list(self.get_tokenizer().get_vocab().keys())
  
          self.assertEqual(vocab_keys[0], "<unk>")
          self.assertEqual(vocab_keys[1], "<s>")
          self.assertEqual(vocab_keys[-1], "<pad>")
          self.assertEqual(len(vocab_keys), 1_101)
  
      def test_vocab_size(self):
          self.assertEqual(self.get_tokenizer().vocab_size, 1_100)
  
      def test_full_tokenizer(self):
          tokenizer = T5Tokenizer(SAMPLE_VOCAB)
  
          tokens = tokenizer.tokenize("This is a test")
          self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
  
          self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [285, 46, 10, 170, 382])
  
          tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
          self.assertListEqual(
              tokens,
              [
                  SPIECE_UNDERLINE + "I",
                  SPIECE_UNDERLINE + "was",
                  SPIECE_UNDERLINE + "b",
                  "or",
                  "n",
                  SPIECE_UNDERLINE + "in",
                  SPIECE_UNDERLINE + "",
                  "9",
                  "2",
                  "0",
                  "0",
                  "0",
                  ",",
                  SPIECE_UNDERLINE + "and",
                  SPIECE_UNDERLINE + "this",
                  SPIECE_UNDERLINE + "is",
                  SPIECE_UNDERLINE + "f",
                  "al",
                  "s",
                  "é",
                  ".",
              ],
          )
          ids = tokenizer.convert_tokens_to_ids(tokens)
          self.assertListEqual(ids, [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4])
  
          back_tokens = tokenizer.convert_ids_to_tokens(ids)
          self.assertListEqual(
              back_tokens,
              [
                  SPIECE_UNDERLINE + "I",
                  SPIECE_UNDERLINE + "was",
                  SPIECE_UNDERLINE + "b",
                  "or",
                  "n",
                  SPIECE_UNDERLINE + "in",
                  SPIECE_UNDERLINE + "",
                  "<unk>",
                  "2",
                  "0",
                  "0",
                  "0",
                  ",",
                  SPIECE_UNDERLINE + "and",
                  SPIECE_UNDERLINE + "this",
                  SPIECE_UNDERLINE + "is",
                  SPIECE_UNDERLINE + "f",
                  "al",
                  "s",
                  "<unk>",
                  ".",
              ],
          )
  
      @cached_property
      def t5_base_tokenizer(self):
          return T5Tokenizer.from_pretrained("t5-base")
  
      @cached_property
      def t5_base_tokenizer_fast(self):
          return T5TokenizerFast.from_pretrained("t5-base")
  
      def get_tokenizer(self, **kwargs) -> T5Tokenizer:
          return self.tokenizer_class.from_pretrained(self.tmpdirname, pad_token=None, **kwargs)
  
      def get_rust_tokenizer(self, **kwargs) -> T5TokenizerFast:
          return self.rust_tokenizer_class.from_pretrained(self.tmpdirname, pad_token=None, **kwargs)
  
      def test_rust_and_python_full_tokenizers(self):
          if not self.test_rust_tokenizer:
              return
  
          tokenizer = self.get_tokenizer()
          rust_tokenizer = self.get_rust_tokenizer()
  
          sequence = "I was born in 92000, and this is falsé."
  
          tokens = tokenizer.tokenize(sequence)
          rust_tokens = rust_tokenizer.tokenize(sequence)
          self.assertListEqual(tokens, rust_tokens)
  
          ids = tokenizer.encode(sequence, add_special_tokens=False)
          rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
          self.assertListEqual(ids, rust_ids)
  
          rust_tokenizer = self.get_rust_tokenizer()
          ids = tokenizer.encode(sequence)
          rust_ids = rust_tokenizer.encode(sequence)
          self.assertListEqual(ids, rust_ids)
  
      def test_eos_treatment(self):
          tokenizer = self.t5_base_tokenizer
          batch_with_eos_added = tokenizer(["hi</s>", "I went to the gym</s>", "</s>"])
          batch_without_eos_added = tokenizer(["hi", "I went to the gym", ""])
          self.assertListEqual(batch_with_eos_added["input_ids"], batch_without_eos_added["input_ids"])
  
      def test_prepare_batch(self):
          tokenizer = self.t5_base_tokenizer
          src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
          expected_src_tokens = [71, 307, 8986, 21, 4505, 1635, 1707, 5, tokenizer.eos_token_id]
          batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK)
          self.assertIsInstance(batch, BatchEncoding)
  
          if FRAMEWORK != "jax":
              result = list(batch.input_ids.numpy()[0])
          else:
              result = list(batch.input_ids.tolist()[0])
  
          self.assertListEqual(expected_src_tokens, result)
  
          self.assertEqual((2, 9), batch.input_ids.shape)
          self.assertEqual((2, 9), batch.attention_mask.shape)
  
      def test_empty_target_text(self):
          tokenizer = self.t5_base_tokenizer
          src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
          batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK)
          # check if input_ids are returned and no decoder_input_ids
          self.assertIn("input_ids", batch)
          self.assertIn("attention_mask", batch)
          self.assertNotIn("decoder_input_ids", batch)
          self.assertNotIn("decoder_attention_mask", batch)
  
      def test_max_length(self):
          tokenizer = self.t5_base_tokenizer
          tgt_text = [
              "Summary of the text.",
              "Another summary.",
          ]
          with tokenizer.as_target_tokenizer():
              targets = tokenizer(
                  tgt_text, max_length=32, padding="max_length", truncation=True, return_tensors=FRAMEWORK
              )
          self.assertEqual(32, targets["input_ids"].shape[1])
  
      def test_outputs_not_longer_than_maxlen(self):
          tokenizer = self.t5_base_tokenizer
  
          batch = tokenizer(
              ["I am a small frog" * 1000, "I am a small frog"], padding=True, truncation=True, return_tensors=FRAMEWORK
          )
          self.assertIsInstance(batch, BatchEncoding)
          self.assertEqual(batch.input_ids.shape, (2, 512))
  
      def test_eos_in_input(self):
          tokenizer = self.t5_base_tokenizer
          src_text = ["A long paragraph for summarization. </s>"]
          tgt_text = ["Summary of the text. </s>"]
          expected_src_tokens = [71, 307, 8986, 21, 4505, 1635, 1707, 5, 1]
          expected_tgt_tokens = [20698, 13, 8, 1499, 5, 1]
  
          batch = tokenizer(src_text)
          with tokenizer.as_target_tokenizer():
              targets = tokenizer(tgt_text)
  
          self.assertEqual(expected_src_tokens, batch["input_ids"][0])
          self.assertEqual(expected_tgt_tokens, targets["input_ids"][0])
  
      def test_token_type_ids(self):
          src_text_1 = ["A first paragraph for summarization."]
          src_text_2 = ["A second paragraph for summarization."]
  
          fast_token_type_ids = self.t5_base_tokenizer_fast(
              src_text_1, src_text_2, add_special_tokens=True, return_token_type_ids=True
          ).token_type_ids
          slow_token_type_ids = self.t5_base_tokenizer(
              src_text_1, src_text_2, add_special_tokens=True, return_token_type_ids=True
          ).token_type_ids
  
          self.assertEqual(slow_token_type_ids, fast_token_type_ids)
          self.assertEqual(len(slow_token_type_ids[0]), 18)
  
      def test_fast_and_slow_same_result(self):
          src_text = "<pad> Today is <unk> nice day </s>"
          tgt_ids = [0, 1960, 19, 2, 1245, 239, 1]
          tgt_text = "<pad> Today is<unk> nice day</s>"
  
          fast_ids = self.t5_base_tokenizer_fast(src_text, add_special_tokens=False).input_ids
          slow_ids = self.t5_base_tokenizer(src_text, add_special_tokens=False).input_ids
          self.assertEqual(tgt_ids, fast_ids)
          self.assertEqual(tgt_ids, slow_ids)
  
          fast_text = self.t5_base_tokenizer_fast.decode(fast_ids)
          slow_text = self.t5_base_tokenizer.decode(fast_ids)
          self.assertEqual(tgt_text, fast_text)
          self.assertEqual(tgt_text, slow_text)
  
      def test_special_tokens_initialization(self):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
  
                  added_tokens = [f"<extra_id_{i}>" for i in range(100)] + [AddedToken("<special>", lstrip=True)]
  
                  tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                      pretrained_name, additional_special_tokens=added_tokens, **kwargs
                  )
                  tokenizer_cr = self.rust_tokenizer_class.from_pretrained(
                      pretrained_name, additional_special_tokens=added_tokens, **kwargs, from_slow=True
                  )
                  tokenizer_p = self.tokenizer_class.from_pretrained(
                      pretrained_name, additional_special_tokens=added_tokens, **kwargs
                  )
  
                  p_output = tokenizer_p.encode("Hey this is a <special> token")
                  r_output = tokenizer_r.encode("Hey this is a <special> token")
                  cr_output = tokenizer_cr.encode("Hey this is a <special> token")
  
                  special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0]
  
                  self.assertEqual(p_output, r_output)
                  self.assertEqual(cr_output, r_output)
                  self.assertTrue(special_token_id in p_output)
                  self.assertTrue(special_token_id in r_output)
                  self.assertTrue(special_token_id in cr_output)
  
      def test_special_tokens_initialization_with_non_empty_additional_special_tokens(self):
          tokenizer_list = []
          if self.test_slow_tokenizer:
              tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))
  
          if self.test_rust_tokenizer:
              tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))
  
          for tokenizer_class, tokenizer_utils in tokenizer_list:
  
              with tempfile.TemporaryDirectory() as tmp_dir:
                  tokenizer_utils.save_pretrained(tmp_dir)
  
                  with open(os.path.join(tmp_dir, "special_tokens_map.json"), encoding="utf-8") as json_file:
                      special_tokens_map = json.load(json_file)
  
                  with open(os.path.join(tmp_dir, "tokenizer_config.json"), encoding="utf-8") as json_file:
                      tokenizer_config = json.load(json_file)
  
                  added_tokens_extra_ids = [f"<extra_id_{i}>" for i in range(100)]
  
                  special_tokens_map["additional_special_tokens"] = added_tokens_extra_ids + [
                      "an_additional_special_token"
                  ]
                  tokenizer_config["additional_special_tokens"] = added_tokens_extra_ids + [
                      "an_additional_special_token"
                  ]
  
                  with open(os.path.join(tmp_dir, "special_tokens_map.json"), "w", encoding="utf-8") as outfile:
                      json.dump(special_tokens_map, outfile)
                  with open(os.path.join(tmp_dir, "tokenizer_config.json"), "w", encoding="utf-8") as outfile:
                      json.dump(tokenizer_config, outfile)
  
                  # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                  # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                  # "special_tokens_map.json" files
                  tokenizer_without_change_in_init = tokenizer_class.from_pretrained(
                      tmp_dir,
                  )
                  self.assertIn(
                      "an_additional_special_token", tokenizer_without_change_in_init.additional_special_tokens
                  )
                  # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                  self.assertEqual(
                      ["an_additional_special_token"],
                      tokenizer_without_change_in_init.convert_ids_to_tokens(
                          tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"])
                      ),
                  )
  
                  # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                  new_added_tokens = added_tokens_extra_ids + [AddedToken("a_new_additional_special_token", lstrip=True)]
                  tokenizer = tokenizer_class.from_pretrained(
                      tmp_dir,
                      additional_special_tokens=new_added_tokens,
                  )
  
                  self.assertIn("a_new_additional_special_token", tokenizer.additional_special_tokens)
                  self.assertEqual(
                      ["a_new_additional_special_token"],
                      tokenizer.convert_ids_to_tokens(
                          tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"])
                      ),
                  )
  
      @slow
      def test_tokenizer_integration(self):
          # fmt: off
          expected_encoding = {'input_ids': [[31220, 7, 41, 14034, 801, 38, 3, 102, 63, 17, 127, 524, 18, 7031, 2032, 277, 11, 3, 102, 63, 17, 127, 524, 18, 2026, 17, 10761, 18, 7041, 61, 795, 879, 18, 19681, 4648, 7, 41, 12920, 382, 6, 350, 6383, 4949, 6, 2158, 12920, 382, 9, 6, 3, 4, 11160, 6, 2043, 17153, 279, 49, 17, 6, 3, 4, 434, 9688, 11439, 21, 6869, 10509, 17725, 41, 567, 9138, 61, 11, 6869, 10509, 11946, 41, 18207, 517, 61, 28, 147, 3538, 1220, 7140, 10761, 2250, 16, 910, 1220, 8024, 11, 1659, 1413, 32, 883, 2020, 344, 2215, 226, 6, 12901, 382, 127, 524, 11, 4738, 7, 127, 15390, 5, 1], [272, 24203, 19, 876, 12, 554, 18, 9719, 1659, 2647, 26352, 6497, 7, 45, 73, 9339, 400, 26, 1499, 57, 22801, 10760, 30, 321, 646, 11, 269, 2625, 16, 66, 7500, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [37, 1704, 4216, 3, 20400, 4418, 7, 147, 8, 19743, 1782, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
          # fmt: on
  
          self.tokenizer_integration_test_util(
              expected_encoding=expected_encoding,
              model_name="t5-base",
              revision="5a7ff2d8f5117c194c7e32ec1ccbf04642cca99b",
          )
  