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.
  """Tests for the Wav2Vec2Phoneme tokenizer."""
  import json
  import os
  import unittest
  from typing import Tuple
  
  from transformers import Wav2Vec2PhonemeCTCTokenizer
  from transformers.models.wav2vec2.tokenization_wav2vec2 import VOCAB_FILES_NAMES
  from transformers.models.wav2vec2_phoneme.tokenization_wav2vec2_phoneme import Wav2Vec2PhonemeCTCTokenizerOutput
  from transformers.testing_utils import require_phonemizer
  
  from ..test_tokenization_common import TokenizerTesterMixin
  
  
  @require_phonemizer
  class Wav2Vec2PhonemeCTCTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
      tokenizer_class = Wav2Vec2PhonemeCTCTokenizer
      test_rust_tokenizer = False
  
      def setUp(self):
          super().setUp()
  
          vocab = (
              "<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː "
              "ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
              "ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
              "oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
              "pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
              "yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
              'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
              "ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
              "ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
              "uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
              "ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
              "ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
              "ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
          ).split(" ")
          vocab_tokens = dict(zip(vocab, range(len(vocab))))
  
          self.special_tokens_map = {"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
  
          self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
          with open(self.vocab_file, "w", encoding="utf-8") as fp:
              fp.write(json.dumps(vocab_tokens) + "\n")
  
      # overwrite since phonemes require specific creation
      def get_clean_sequence(self, tokenizer, with_prefix_space=False, max_length=20, min_length=5) -> Tuple[str, list]:
          toks = [(i, tokenizer.decode([i], clean_up_tokenization_spaces=False)) for i in range(len(tokenizer))]
          toks = list(filter(lambda t: [t[0]] == tokenizer.encode(t[1], do_phonemize=False), toks))
          if max_length is not None and len(toks) > max_length:
              toks = toks[:max_length]
          if min_length is not None and len(toks) < min_length and len(toks) > 0:
              while len(toks) < min_length:
                  toks = toks + toks
          # toks_str = [t[1] for t in toks]
          toks_ids = [t[0] for t in toks]
  
          # Ensure consistency
          output_txt = tokenizer.decode(toks_ids, clean_up_tokenization_spaces=False)
          if " " not in output_txt and len(toks_ids) > 1:
              output_txt = (
                  tokenizer.decode([toks_ids[0]], clean_up_tokenization_spaces=False)
                  + " "
                  + tokenizer.decode(toks_ids[1:], clean_up_tokenization_spaces=False)
              )
          if with_prefix_space:
              output_txt = " " + output_txt
          output_ids = tokenizer.encode(output_txt, add_special_tokens=False)
          return output_txt, output_ids
  
      def get_tokenizer(self, **kwargs):
          kwargs.update(self.special_tokens_map)
          return Wav2Vec2PhonemeCTCTokenizer.from_pretrained(self.tmpdirname, **kwargs)
  
      def test_tokenizer_add_new_tokens(self):
          tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
  
          # check adding a single token
          tokenizer.add_tokens("xxx")
          token_ids = tokenizer("m xxx ɪ", do_phonemize=False).input_ids
          self.assertEqual(token_ids, [13, 392, 17])  # xxx should be last token
  
          tokenizer.add_tokens(["aaa", "bbb", "ccc"])
          token_ids = tokenizer("m aaa ɪ ccc", do_phonemize=False).input_ids
          self.assertEqual(token_ids, [13, 393, 17, 395])  # aaa and ccc should be after xxx and 2 after aaa
  
          token_ids = tokenizer("maɪ c", do_phonemize=False).input_ids
          self.assertEqual(token_ids, [3, 200])  # mai should be <unk> (=3)
  
      def test_phonemize(self):
          tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
  
          input_text = "Hello how are you"
          phonemes = tokenizer.phonemize(input_text, phonemizer_lang="en-us")
          self.assertEqual(phonemes, "h ə l oʊ h aʊ ɑːɹ j uː")
  
      def test_encode(self):
          tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
  
          input_text = "Hello how are you"
          phonemes = tokenizer.phonemize(input_text, phonemizer_lang="en-us")
          self.assertEqual(tokenizer(input_text).input_ids, tokenizer(phonemes, do_phonemize=False).input_ids)
  
      def test_encode_decode(self):
          tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
          input_text = "Hello how are you"
          phonemes = tokenizer.phonemize(input_text, phonemizer_lang="en-us")
  
          phonemes_enc_dec = tokenizer.decode(tokenizer(input_text).input_ids)
  
          self.assertEqual(phonemes, phonemes_enc_dec)
  
      def test_decode(self):
          tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
  
          sample_ids = [
              [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
              [24, 22, 5, 24, 22, 5, 77],
          ]
          tokens = tokenizer.decode(sample_ids[0])
          batch_tokens = tokenizer.batch_decode(sample_ids)
          self.assertEqual(tokens, batch_tokens[0])
          self.assertEqual(batch_tokens, ["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"])
  
      def test_phonemize_with_word_del(self):
          tokenizer = self.tokenizer_class.from_pretrained(
              "facebook/wav2vec2-lv-60-espeak-cv-ft", word_delimiter_token="|"
          )
          tokenizer.add_tokens("|")
  
          input_text = "Hello how are you"
          phonemes = tokenizer.phonemize(input_text, phonemizer_lang="en-us")
          self.assertEqual(phonemes, "h ə l oʊ | h aʊ | ɑːɹ | j uː |")
  
      def test_encode_with_del(self):
          tokenizer = self.tokenizer_class.from_pretrained(
              "facebook/wav2vec2-lv-60-espeak-cv-ft", word_delimiter_token="|"
          )
          tokenizer.add_tokens("|")
  
          input_text = "Hello how are you"
          phonemes = tokenizer.phonemize(input_text, phonemizer_lang="en-us")
          self.assertEqual(tokenizer(input_text).input_ids, tokenizer(phonemes, do_phonemize=False).input_ids)
  
      def test_decode_with_del(self):
          tokenizer = self.tokenizer_class.from_pretrained(
              "facebook/wav2vec2-lv-60-espeak-cv-ft", word_delimiter_token="|"
          )
          tokenizer.add_tokens("|")
  
          # fmt: off
          sample_ids = [
              [11, 5, 15, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 15, 8, tokenizer.word_delimiter_token_id, 98],
              [tokenizer.word_delimiter_token_id, 24, 22, tokenizer.word_delimiter_token_id, 5, 24, 22, 5, 77],
          ]
          # fmt: on
  
          # decode with word_del_token filter
          tokens = tokenizer.decode(sample_ids[0])
          batch_tokens = tokenizer.batch_decode(sample_ids)
          self.assertEqual(tokens, batch_tokens[0])
          self.assertEqual(batch_tokens, ["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"])
  
          # decode with no word_del_token filter
          tokens = tokenizer.decode(sample_ids[0], filter_word_delimiter_token=False)
          batch_tokens = tokenizer.batch_decode(sample_ids, filter_word_delimiter_token=False)
          self.assertEqual(tokens, batch_tokens[0])
          self.assertEqual(batch_tokens, ["k s ɾ | ɾ l | ɭʲ", "| j ð | s j ð s oːɹ"])
  
      def test_encode_decode_with_del(self):
          tokenizer = self.tokenizer_class.from_pretrained(
              "facebook/wav2vec2-lv-60-espeak-cv-ft", word_delimiter_token="|"
          )
          tokenizer.add_tokens("|")
  
          input_text = "Hello how are you"
          phonemes = tokenizer.phonemize(input_text, phonemizer_lang="en-us")
  
          phonemes_enc_dec = tokenizer.decode(tokenizer(input_text).input_ids, filter_word_delimiter_token=False)
  
          self.assertEqual(phonemes, phonemes_enc_dec)
  
      def test_encode_decode_with_del_filter(self):
          tokenizer = self.tokenizer_class.from_pretrained(
              "facebook/wav2vec2-lv-60-espeak-cv-ft", word_delimiter_token="|"
          )
          tokenizer.add_tokens("|")
  
          input_text = "Hello how are you"
          phonemes = tokenizer.phonemize(input_text, phonemizer_lang="en-us")
  
          phonemes_enc_dec = tokenizer.decode(tokenizer(input_text).input_ids, filter_word_delimiter_token=True)
  
          self.assertEqual(" ".join([p.strip() for p in phonemes.split(" |")]).strip(), phonemes_enc_dec)
  
      def test_change_phonemizer_lang(self):
          tokenizer = self.tokenizer_class.from_pretrained(
              "facebook/wav2vec2-lv-60-espeak-cv-ft", word_delimiter_token=None
          )
          input_text = "Hello how are you"
  
          input_ids_en = tokenizer(input_text, phonemizer_lang="en-us").input_ids
          input_ids_fr = tokenizer(input_text, phonemizer_lang="fr-fr").input_ids
  
          self.assertNotEqual(input_ids_en, input_ids_fr)
  
          text_en = tokenizer.decode(input_ids_en)
          text_fr = tokenizer.decode(input_ids_fr)
  
          self.assertEqual(text_en, "h ə l oʊ h aʊ ɑːɹ j uː")
          self.assertEqual(text_fr, "ɛ l o h aʊ a ʁ j u")
  
      def test_case_insensitive(self):
          tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
          input_text_up = "Hello how Are you"
          input_text_low = "hello how are you"
  
          input_ids_up = tokenizer(input_text_up).input_ids
          input_ids_low = tokenizer(input_text_low).input_ids
  
          self.assertEqual(input_ids_up, input_ids_low)
  
      def test_tokenizer_decode_added_tokens(self):
          tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
          tokenizer.add_tokens(["!", "?"])
          tokenizer.add_special_tokens({"cls_token": "$$$"})
  
          # fmt: off
          sample_ids = [
              [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 392, 392, 393, 392, 392, 393, 394, 394],
              [24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 394, 394],
          ]
          # fmt: on
  
          batch_tokens = tokenizer.batch_decode(sample_ids)
          self.assertEqual(batch_tokens, ["k s ɾ ɾ l ɭʲ!?!? $$$", "j ð s j ð s oːɹ $$$"])
  
      @staticmethod
      def get_from_offsets(offsets, key):
          retrieved_list = [d[key] for d in offsets]
          return retrieved_list
  
      def test_offsets(self):
          tokenizer = self.get_tokenizer(word_delimiter_token="|")
          tokenizer.add_tokens("|")
  
          # fmt: off
          # ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ"
          sample_ids = [11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98]
          # fmt: on
  
          outputs = tokenizer.decode(sample_ids, output_char_offsets=True, filter_word_delimiter_token=False)
          # check Wav2Vec2CTCTokenizerOutput keys for char
          self.assertEqual(len(outputs.keys()), 2)
          self.assertTrue("text" in outputs)
          self.assertTrue("char_offsets" in outputs)
          self.assertTrue(isinstance(outputs, Wav2Vec2PhonemeCTCTokenizerOutput))
  
          # check that order of chars is correct and identical for both outputs
          self.assertEqual(" ".join(self.get_from_offsets(outputs["char_offsets"], "char")), outputs.text)
          self.assertListEqual(
              self.get_from_offsets(outputs["char_offsets"], "char"), ["k", "s", "ɾ", "ɾ", "|", "ɾ", "l", "|", "ɭʲ"]
          )
  
          # check that offsets are actually correct for char
          # 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token,
          # 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98
          self.assertListEqual(
              self.get_from_offsets(outputs["char_offsets"], "start_offset"), [0, 1, 4, 7, 9, 11, 12, 15, 16]
          )
          self.assertListEqual(
              self.get_from_offsets(outputs["char_offsets"], "end_offset"), [1, 4, 6, 9, 10, 12, 15, 16, 17]
          )
  
      def test_offsets_batch(self):
          tokenizer = self.get_tokenizer(word_delimiter_token="|")
  
          def check_list_tuples_equal(outputs_batch, outputs_list):
              self.assertTrue(isinstance(outputs_batch, Wav2Vec2PhonemeCTCTokenizerOutput))
              self.assertTrue(isinstance(outputs_list[0], Wav2Vec2PhonemeCTCTokenizerOutput))
  
              # transform list to ModelOutput
              outputs_batch_2 = Wav2Vec2PhonemeCTCTokenizerOutput(
                  {k: [d[k] for d in outputs_list] for k in outputs_list[0]}
              )
  
              self.assertListEqual(outputs_batch["text"], outputs_batch_2["text"])
  
              def recursive_check(list_or_dict_1, list_or_dict_2):
                  if isinstance(list_or_dict_1, list):
                      [recursive_check(l1, l2) for l1, l2 in zip(list_or_dict_1, list_or_dict_2)]
                  self.assertEqual(list_or_dict_1, list_or_dict_2)
  
              if "char_offsets" in outputs_batch:
                  recursive_check(outputs_batch["char_offsets"], outputs_batch_2["char_offsets"])
  
          # fmt: off
          sample_ids = [
              [11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
              [24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34],
          ]
          # fmt: on
  
          # We assume that `decode` works as expected. All we will check now is
          # the output type is correct and the output is identical to `decode`
  
          # char
          outputs_char_batch = tokenizer.batch_decode(sample_ids, output_char_offsets=True)
          outputs_char = [tokenizer.decode(ids, output_char_offsets=True) for ids in sample_ids]
          check_list_tuples_equal(outputs_char_batch, outputs_char)
  
      @unittest.skip("Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes")
      def test_added_tokens_do_lower_case(self):
          pass
  
      @unittest.skip("Wav2Vec2PhonemeTokenizer always puts spaces between phonemes")
      def test_encode_decode_with_spaces(self):
          pass
  
      @unittest.skip("encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency")
      def test_internal_consistency(self):
          pass
  
      @unittest.skip("Wav2Vec2PhonemeModel has no max model length => no testing")
      def test_pretrained_model_lists(self):
          pass
  
      # overwrite common
      def test_add_tokens_tokenizer(self):
          tokenizers = self.get_tokenizers(do_lower_case=False)
          for tokenizer in tokenizers:
              with self.subTest(f"{tokenizer.__class__.__name__}"):
                  vocab_size = tokenizer.vocab_size
                  all_size = len(tokenizer)
  
                  self.assertNotEqual(vocab_size, 0)
  
                  # We usually have added tokens from the start in tests because our vocab fixtures are
                  # smaller than the original vocabs - let's not assert this
                  # 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[-3], 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[-3], tokenizer.vocab_size - 1)
                  self.assertGreater(tokens[-3], tokens[-4])
                  self.assertEqual(tokens[0], tokenizer.eos_token_id)
                  self.assertEqual(tokens[-3], tokenizer.pad_token_id)
  
      @unittest.skip("The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode.")
      def test_tf_encode_plus_sent_to_model(self):
          pass
  
      @unittest.skip("The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode.")
      def test_torch_encode_plus_sent_to_model(self):
          pass
  
      def test_convert_tokens_to_string_format(self):
          # The default common tokenizer tests assumes that the output of `convert_tokens_to_string` is a string which
          # is not the case for Wav2Vec2PhonemeCTCTokenizer.
          tokenizers = self.get_tokenizers(fast=True, do_lower_case=True)
          for tokenizer in tokenizers:
              with self.subTest(f"{tokenizer.__class__.__name__}"):
                  tokens = ["ð", "ɪ", "s", "ɪ", "z", "ɐ", "t", "ɛ", "k", "s", "t"]
                  output = tokenizer.convert_tokens_to_string(tokens)
  
                  self.assertIsInstance(output["text"], str)
  