




"""simple docstring"""







import json
import os
import unittest
from typing import Tuple

from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
from transformers.testing_utils import require_phonemizer

from ...test_tokenization_common import TokenizerTesterMixin

@require_phonemizer
class 				snake_case_			(					lowerCamelCase_	, unittest.TestCase			):







   """simple docstring"""




   A_				      =       WavaVecaPhonemeCTCTokenizer
   A_				      =       False







   def   UpperCAmelCase__			(		self)				->   List[str]:
       super().setUp()

       UpperCamelCase              =	(
           '''<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(''' ''')
       UpperCamelCase              =	dict(zip(lowerCamelCase_       ,					range(len(lowerCamelCase_))))

       UpperCamelCase              =	{'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

       UpperCamelCase              =	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(lowerCamelCase_) + '''\n''')







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_=False       ,					lowerCamelCase_=2_0       ,					lowerCamelCase_=5)				->   Tuple[str, list]:
       UpperCamelCase              =	[(i, tokenizer.decode([i]       ,					clean_up_tokenization_spaces=lowerCamelCase_)) for i in range(len(lowerCamelCase_))]
       UpperCamelCase              =	list(filter(lambda lowerCamelCase_: [t[0]] == tokenizer.encode(t[1]       ,					do_phonemize=lowerCamelCase_)       ,					lowerCamelCase_))
       if max_length is not None and len(lowerCamelCase_) > max_length:
           UpperCamelCase              =	toks[:max_length]
       if min_length is not None and len(lowerCamelCase_) < min_length and len(lowerCamelCase_) > 0:
           while len(lowerCamelCase_) < min_length:
               UpperCamelCase              =	toks + toks
        # toks_str = [t[1] for t in toks]
       UpperCamelCase              =	[t[0] for t in toks]

       # Ensure consistency
       UpperCamelCase              =	tokenizer.decode(lowerCamelCase_       ,					clean_up_tokenization_spaces=lowerCamelCase_)
       if " " not in output_txt and len(lowerCamelCase_) > 1:
           UpperCamelCase              =	(
               tokenizer.decode([toks_ids[0]]       ,					clean_up_tokenization_spaces=lowerCamelCase_)
               + ''' '''
               + tokenizer.decode(toks_ids[1:]       ,					clean_up_tokenization_spaces=lowerCamelCase_)
           )
       if with_prefix_space:
           UpperCamelCase              =	''' ''' + output_txt
       UpperCamelCase              =	tokenizer.encode(lowerCamelCase_       ,					add_special_tokens=lowerCamelCase_)
       return output_txt, output_ids







   def   UpperCAmelCase__			(		self       ,					**lowerCamelCase_)				->   Optional[int]:
       kwargs.update(self.special_tokens_map)
       return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname       ,					**lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Dict:
       UpperCamelCase              =	self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''')

       # check adding a single token
       tokenizer.add_tokens('''xxx''')
       UpperCamelCase              =	tokenizer('''m xxx ɪ'''       ,					do_phonemize=lowerCamelCase_).input_ids
       self.assertEqual(lowerCamelCase_       ,					[1_3, 3_9_2, 1_7])  # xxx should be last token

       tokenizer.add_tokens(['''aaa''', '''bbb''', '''ccc'''])
       UpperCamelCase              =	tokenizer('''m aaa ɪ ccc'''       ,					do_phonemize=lowerCamelCase_).input_ids
       self.assertEqual(lowerCamelCase_       ,					[1_3, 3_9_3, 1_7, 3_9_5])  # aaa and ccc should be after xxx and 2 after aaa

       UpperCamelCase              =	tokenizer('''maɪ c'''       ,					do_phonemize=lowerCamelCase_).input_ids
       self.assertEqual(lowerCamelCase_       ,					[3, 2_0_0])  # mai should be <unk> (=3)







   def   UpperCAmelCase__			(		self)				->   str:
       UpperCamelCase              =	self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''')

       UpperCamelCase              =	'''Hello how are you'''
       UpperCamelCase              =	tokenizer.phonemize(lowerCamelCase_       ,					phonemizer_lang='''en-us''')
       self.assertEqual(lowerCamelCase_       ,					'''h ə l oʊ h aʊ ɑːɹ j uː''')







   def   UpperCAmelCase__			(		self)				->   Any:
       UpperCamelCase              =	self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''')

       UpperCamelCase              =	'''Hello how are you'''
       UpperCamelCase              =	tokenizer.phonemize(lowerCamelCase_       ,					phonemizer_lang='''en-us''')
       self.assertEqual(tokenizer(lowerCamelCase_).input_ids       ,					tokenizer(lowerCamelCase_       ,					do_phonemize=lowerCamelCase_).input_ids)







   def   UpperCAmelCase__			(		self)				->   str:
       UpperCamelCase              =	self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''')
       UpperCamelCase              =	'''Hello how are you'''
       UpperCamelCase              =	tokenizer.phonemize(lowerCamelCase_       ,					phonemizer_lang='''en-us''')

       UpperCamelCase              =	tokenizer.decode(tokenizer(lowerCamelCase_).input_ids)

       self.assertEqual(lowerCamelCase_       ,					lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   str:
       UpperCamelCase              =	self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''')

       UpperCamelCase              =	[
           [1_1, 5, 1_5, tokenizer.pad_token_id, 1_5, 8, 9_8],
           [2_4, 2_2, 5, 2_4, 2_2, 5, 7_7],
       ]
       UpperCamelCase              =	tokenizer.decode(sample_ids[0])
       UpperCamelCase              =	tokenizer.batch_decode(lowerCamelCase_)
       self.assertEqual(lowerCamelCase_       ,					batch_tokens[0])
       self.assertEqual(lowerCamelCase_       ,					['''k s ɾ ɾ l ɭʲ''', '''j ð s j ð s oːɹ'''])







   def   UpperCAmelCase__			(		self)				->   Optional[int]:
       UpperCamelCase              =	self.tokenizer_class.from_pretrained(
           '''facebook/wav2vec2-lv-60-espeak-cv-ft'''       ,					word_delimiter_token='''|''')
       tokenizer.add_tokens('''|''')

       UpperCamelCase              =	'''Hello how are you'''
       UpperCamelCase              =	tokenizer.phonemize(lowerCamelCase_       ,					phonemizer_lang='''en-us''')
       self.assertEqual(lowerCamelCase_       ,					'''h ə l oʊ | h aʊ | ɑːɹ | j uː |''')







   def   UpperCAmelCase__			(		self)				->   Any:
       UpperCamelCase              =	self.tokenizer_class.from_pretrained(
           '''facebook/wav2vec2-lv-60-espeak-cv-ft'''       ,					word_delimiter_token='''|''')
       tokenizer.add_tokens('''|''')

       UpperCamelCase              =	'''Hello how are you'''
       UpperCamelCase              =	tokenizer.phonemize(lowerCamelCase_       ,					phonemizer_lang='''en-us''')
       self.assertEqual(tokenizer(lowerCamelCase_).input_ids       ,					tokenizer(lowerCamelCase_       ,					do_phonemize=lowerCamelCase_).input_ids)







   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       UpperCamelCase              =	self.tokenizer_class.from_pretrained(
           '''facebook/wav2vec2-lv-60-espeak-cv-ft'''       ,					word_delimiter_token='''|''')
       tokenizer.add_tokens('''|''')

       # fmt: off
       UpperCamelCase              =	[
           [1_1, 5, 1_5, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 1_5, 8, tokenizer.word_delimiter_token_id, 9_8],
           [tokenizer.word_delimiter_token_id, 2_4, 2_2, tokenizer.word_delimiter_token_id, 5, 2_4, 2_2, 5, 7_7],
       ]
       # fmt: on

       # decode with word_del_token filter
       UpperCamelCase              =	tokenizer.decode(sample_ids[0])
       UpperCamelCase              =	tokenizer.batch_decode(lowerCamelCase_)
       self.assertEqual(lowerCamelCase_       ,					batch_tokens[0])
       self.assertEqual(lowerCamelCase_       ,					['''k s ɾ ɾ l ɭʲ''', '''j ð s j ð s oːɹ'''])

       # decode with no word_del_token filter
       UpperCamelCase              =	tokenizer.decode(sample_ids[0]       ,					filter_word_delimiter_token=lowerCamelCase_)
       UpperCamelCase              =	tokenizer.batch_decode(lowerCamelCase_       ,					filter_word_delimiter_token=lowerCamelCase_)
       self.assertEqual(lowerCamelCase_       ,					batch_tokens[0])
       self.assertEqual(lowerCamelCase_       ,					['''k s ɾ | ɾ l | ɭʲ''', '''| j ð | s j ð s oːɹ'''])







   def   UpperCAmelCase__			(		self)				->   Dict:
       UpperCamelCase              =	self.tokenizer_class.from_pretrained(
           '''facebook/wav2vec2-lv-60-espeak-cv-ft'''       ,					word_delimiter_token='''|''')
       tokenizer.add_tokens('''|''')

       UpperCamelCase              =	'''Hello how are you'''
       UpperCamelCase              =	tokenizer.phonemize(lowerCamelCase_       ,					phonemizer_lang='''en-us''')

       UpperCamelCase              =	tokenizer.decode(tokenizer(lowerCamelCase_).input_ids       ,					filter_word_delimiter_token=lowerCamelCase_)

       self.assertEqual(lowerCamelCase_       ,					lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   int:
       UpperCamelCase              =	self.tokenizer_class.from_pretrained(
           '''facebook/wav2vec2-lv-60-espeak-cv-ft'''       ,					word_delimiter_token='''|''')
       tokenizer.add_tokens('''|''')

       UpperCamelCase              =	'''Hello how are you'''
       UpperCamelCase              =	tokenizer.phonemize(lowerCamelCase_       ,					phonemizer_lang='''en-us''')

       UpperCamelCase              =	tokenizer.decode(tokenizer(lowerCamelCase_).input_ids       ,					filter_word_delimiter_token=lowerCamelCase_)

       self.assertEqual(''' '''.join([p.strip() for p in phonemes.split(''' |''')]).strip()       ,					lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   int:
       UpperCamelCase              =	self.tokenizer_class.from_pretrained(
           '''facebook/wav2vec2-lv-60-espeak-cv-ft'''       ,					word_delimiter_token=lowerCamelCase_)
       UpperCamelCase              =	'''Hello how are you'''

       UpperCamelCase              =	tokenizer(lowerCamelCase_       ,					phonemizer_lang='''en-us''').input_ids
       UpperCamelCase              =	tokenizer(lowerCamelCase_       ,					phonemizer_lang='''fr-fr''').input_ids

       self.assertNotEqual(lowerCamelCase_       ,					lowerCamelCase_)

       UpperCamelCase              =	tokenizer.decode(lowerCamelCase_)
       UpperCamelCase              =	tokenizer.decode(lowerCamelCase_)

       self.assertEqual(lowerCamelCase_       ,					'''h ə l oʊ h aʊ ɑːɹ j uː''')
       self.assertEqual(lowerCamelCase_       ,					'''ɛ l o h aʊ a ʁ j u''')







   def   UpperCAmelCase__			(		self)				->   Any:
       UpperCamelCase              =	self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''')
       UpperCamelCase              =	'''Hello how Are you'''
       UpperCamelCase              =	'''hello how are you'''

       UpperCamelCase              =	tokenizer(lowerCamelCase_).input_ids
       UpperCamelCase              =	tokenizer(lowerCamelCase_).input_ids

       self.assertEqual(lowerCamelCase_       ,					lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   List[Any]:
       UpperCamelCase              =	self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''')
       tokenizer.add_tokens(['''!''', '''?'''])
       tokenizer.add_special_tokens({'''cls_token''': '''$$$'''})

       # fmt: off
       UpperCamelCase              =	[
           [1_1, 5, 1_5, tokenizer.pad_token_id, 1_5, 8, 9_8, 3_9_2, 3_9_2, 3_9_3, 3_9_2, 3_9_2, 3_9_3, 3_9_4, 3_9_4],
           [2_4, 2_2, 5, 2_4, 2_2, 5, 7_7, tokenizer.pad_token_id, 3_9_4, 3_9_4],
       ]
       # fmt: on

       UpperCamelCase              =	tokenizer.batch_decode(lowerCamelCase_)
       self.assertEqual(lowerCamelCase_       ,					['''k s ɾ ɾ l ɭʲ!?!? $$$''', '''j ð s j ð s oːɹ $$$'''])







   @staticmethod
   def   UpperCAmelCase__			(		lowerCamelCase_       ,					lowerCamelCase_)				->   List[str]:
       UpperCamelCase              =	[d[key] for d in offsets]
       return retrieved_list







   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       UpperCamelCase              =	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"
       UpperCamelCase              =	[1_1, 5, 5, 5, 1_5, 1_5, tokenizer.pad_token_id, 1_5, 1_5, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 1_5, 8, 8, 8, tokenizer.word_delimiter_token_id, 9_8]
       # fmt: on

       UpperCamelCase              =	tokenizer.decode(lowerCamelCase_       ,					output_char_offsets=lowerCamelCase_       ,					filter_word_delimiter_token=lowerCamelCase_)
       # 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(lowerCamelCase_       ,					lowerCamelCase_))

       # 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, 1_1, 1_2, 1_5, 1_6])
       self.assertListEqual(
           self.get_from_offsets(outputs['''char_offsets''']       ,					'''end_offset''')       ,					[1, 4, 6, 9, 1_0, 1_2, 1_5, 1_6, 1_7])







   def   UpperCAmelCase__			(		self)				->   Any:
       UpperCamelCase              =	self.get_tokenizer(word_delimiter_token='''|''')

       def check_list_tuples_equal(lowerCamelCase_       ,					lowerCamelCase_):
           self.assertTrue(isinstance(lowerCamelCase_       ,					lowerCamelCase_))
           self.assertTrue(isinstance(outputs_list[0]       ,					lowerCamelCase_))

           # transform list to ModelOutput
           UpperCamelCase              =	WavaVecaPhonemeCTCTokenizerOutput(
               {k: [d[k] for d in outputs_list] for k in outputs_list[0]})

           self.assertListEqual(outputs_batch['''text''']       ,					outputs_batch_a['''text'''])

           def recursive_check(lowerCamelCase_       ,					lowerCamelCase_):
               if isinstance(lowerCamelCase_       ,					lowerCamelCase_):
                   [recursive_check(lowerCamelCase_       ,					lowerCamelCase_) for la, la in zip(lowerCamelCase_       ,					lowerCamelCase_)]
               self.assertEqual(lowerCamelCase_       ,					lowerCamelCase_)

           if "char_offsets" in outputs_batch:
               recursive_check(outputs_batch['''char_offsets''']       ,					outputs_batch_a['''char_offsets'''])

        # fmt: off
       UpperCamelCase              =	[
           [1_1, 5, 1_5, tokenizer.pad_token_id, 1_5, 4, 8, 9_8, 3_2, 3_2, 3_2, 3_2, 4, 3_3, tokenizer.word_delimiter_token_id, 3_2, 3_2, 3_3, 3_4, 3_4],
           [2_4, 2_2, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 2_4, 2_2, 2_2, 2_2, 4, 5, 7_7, tokenizer.pad_token_id, 2_2, 2_2, 4, 3_4, 3_4, 3_4, 3_4],
       ]
       # 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
       UpperCamelCase              =	tokenizer.batch_decode(lowerCamelCase_       ,					output_char_offsets=lowerCamelCase_)
       UpperCamelCase              =	[tokenizer.decode(lowerCamelCase_       ,					output_char_offsets=lowerCamelCase_) for ids in sample_ids]
       check_list_tuples_equal(lowerCamelCase_       ,					lowerCamelCase_)







   @unittest.skip('''Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes''')
   def   UpperCAmelCase__			(		self)				->   Tuple:
       pass







   @unittest.skip('''Wav2Vec2PhonemeTokenizer always puts spaces between phonemes''')
   def   UpperCAmelCase__			(		self)				->   Dict:
       pass







   @unittest.skip('''encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency''')
   def   UpperCAmelCase__			(		self)				->   int:
       pass







   @unittest.skip('''Wav2Vec2PhonemeModel has no max model length => no testing''')
   def   UpperCAmelCase__			(		self)				->   Tuple:
       pass







   def   UpperCAmelCase__			(		self)				->   Tuple:
       UpperCamelCase              =	self.get_tokenizers(do_lower_case=lowerCamelCase_)
       for tokenizer in tokenizers:
           with self.subTest(F'{tokenizer.__class__.__name__}'):
               UpperCamelCase              =	tokenizer.vocab_size
               UpperCamelCase              =	len(lowerCamelCase_)

               self.assertNotEqual(lowerCamelCase_       ,					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)

               UpperCamelCase              =	['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
               UpperCamelCase              =	tokenizer.add_tokens(lowerCamelCase_)
               UpperCamelCase              =	tokenizer.vocab_size
               UpperCamelCase              =	len(lowerCamelCase_)

               self.assertNotEqual(lowerCamelCase_       ,					0)
               self.assertEqual(lowerCamelCase_       ,					lowerCamelCase_)
               self.assertEqual(lowerCamelCase_       ,					len(lowerCamelCase_))
               self.assertEqual(lowerCamelCase_       ,					all_size + len(lowerCamelCase_))

               UpperCamelCase              =	tokenizer.encode('''aaaaa bbbbbb low cccccccccdddddddd l'''       ,					add_special_tokens=lowerCamelCase_)

               self.assertGreaterEqual(len(lowerCamelCase_)       ,					4)
               self.assertGreater(tokens[0]       ,					tokenizer.vocab_size - 1)
               self.assertGreater(tokens[-3]       ,					tokenizer.vocab_size - 1)

               UpperCamelCase              =	{'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''}
               UpperCamelCase              =	tokenizer.add_special_tokens(lowerCamelCase_)
               UpperCamelCase              =	tokenizer.vocab_size
               UpperCamelCase              =	len(lowerCamelCase_)

               self.assertNotEqual(lowerCamelCase_       ,					0)
               self.assertEqual(lowerCamelCase_       ,					lowerCamelCase_)
               self.assertEqual(lowerCamelCase_       ,					len(lowerCamelCase_))
               self.assertEqual(lowerCamelCase_       ,					all_size_a + len(lowerCamelCase_))

               UpperCamelCase              =	tokenizer.encode(
                   '''>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l'''       ,					add_special_tokens=lowerCamelCase_)

               self.assertGreaterEqual(len(lowerCamelCase_)       ,					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   UpperCAmelCase__			(		self)				->   Tuple:
       pass







   @unittest.skip('''The tokenizer shouldn\'t be used to encode input IDs (except for labels), only to decode.''')
   def   UpperCAmelCase__			(		self)				->   Optional[int]:
       pass







   def   UpperCAmelCase__			(		self)				->   Optional[Any]:
       # The default common tokenizer tests assumes that the output of `convert_tokens_to_string` is a string which
       # is not the case for Wav2Vec2PhonemeCTCTokenizer.
       UpperCamelCase              =	self.get_tokenizers(fast=lowerCamelCase_       ,					do_lower_case=lowerCamelCase_)
       for tokenizer in tokenizers:
           with self.subTest(F'{tokenizer.__class__.__name__}'):
               UpperCamelCase              =	['''ð''', '''ɪ''', '''s''', '''ɪ''', '''z''', '''ɐ''', '''t''', '''ɛ''', '''k''', '''s''', '''t''']
               UpperCamelCase              =	tokenizer.convert_tokens_to_string(lowerCamelCase_)

               self.assertIsInstance(output['''text''']       ,					lowerCamelCase_)







from jiwer import compute_measures

import datasets


A__      :     Tuple       =					'\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'

A__      :     Optional[int]       =					'\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'

A__      :     Dict       =					'\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = ["this is the prediction", "there is an other sample"]\n    >>> references = ["this is the reference", "there is another one"]\n    >>> wer = datasets.load_metric("wer")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,_KWARGS_DESCRIPTION      )
class  _UpperCAmelCase   (			datasets.Metric      ):

				"""simple docstring"""




				def 							lowercase__  (	self		:       Union[str, Any]    ):

								'''simple docstring'''
								return datasets.MetricInfo(
								    description=_DESCRIPTION,    citation=_CITATION,    inputs_description=_KWARGS_DESCRIPTION,    features=datasets.Features(
								        {
								            '''predictions''': datasets.Value('''string''',    id='''sequence'''    ),
								            '''references''': datasets.Value('''string''',    id='''sequence'''    ),
								        }    ),    codebase_urls=['''https://github.com/jitsi/jiwer/'''],    reference_urls=[
								        '''https://en.wikipedia.org/wiki/Word_error_rate''',
								    ],    )




				def 							lowercase__  (	self		:       Union[str, Any],    lowerCamelCase		:       str=None,    lowerCamelCase		:       Union[str, Any]=None,    lowerCamelCase		:       List[str]=False    ):

								'''simple docstring'''
								if concatenate_texts:
												return compute_measures(lowerCamelCase,    lowerCamelCase    )["wer"]
								else:
												lowercase__  					=     0
												lowercase__  					=     0
												for prediction, reference in zip(lowerCamelCase,    lowerCamelCase    ):
																lowercase__  					=     compute_measures(lowerCamelCase,    lowerCamelCase    )
																incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																total += measures["substitutions"] + measures["deletions"] + measures["hits"]
												return incorrect / total




