


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 							lowerCamelCase   (  _UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		Union[str, Any]										=						WavaVecaPhonemeCTCTokenizer
	_lowerCAmelCase     :		str										=						False


	def  A(    self):
							super().setUp()

							__UpperCAmelCase			:							Optional[int]	    =	(
							    '''<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			:							Union[str, Any]	    =	dict(zip(lowercase__				,     range(len(lowercase__))))

							__UpperCAmelCase			:							List[Any]	    =	{'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

							__UpperCAmelCase			:							Optional[int]	    =	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(lowercase__) + '''\n''')


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

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


	def  A(    self				,     **lowercase__):
							kwargs.update(self.special_tokens_map)
							return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname				,     **lowercase__)


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

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

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

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


	def  A(    self):
							__UpperCAmelCase			:							List[Any]	    =	self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''')

							__UpperCAmelCase			:							List[str]	    =	'''Hello how are you'''
							__UpperCAmelCase			:							Union[str, Any]	    =	tokenizer.phonemize(lowercase__				,     phonemizer_lang='''en-us''')
							self.assertEqual(lowercase__				,     '''h ə l oʊ h aʊ ɑːɹ j uː''')


	def  A(    self):
							__UpperCAmelCase			:							int	    =	self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''')

							__UpperCAmelCase			:							List[str]	    =	'''Hello how are you'''
							__UpperCAmelCase			:							Optional[Any]	    =	tokenizer.phonemize(lowercase__				,     phonemizer_lang='''en-us''')
							self.assertEqual(tokenizer(lowercase__).input_ids				,     tokenizer(lowercase__				,     do_phonemize=lowercase__).input_ids)


	def  A(    self):
							__UpperCAmelCase			:							int	    =	self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''')
							__UpperCAmelCase			:							Any	    =	'''Hello how are you'''
							__UpperCAmelCase			:							Any	    =	tokenizer.phonemize(lowercase__				,     phonemizer_lang='''en-us''')

							__UpperCAmelCase			:							Union[str, Any]	    =	tokenizer.decode(tokenizer(lowercase__).input_ids)

							self.assertEqual(lowercase__				,     lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							List[str]	    =	self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''')

							__UpperCAmelCase			:							Dict	    =	[
							    [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			:							int	    =	tokenizer.decode(sample_ids[0])
							__UpperCAmelCase			:							Optional[Any]	    =	tokenizer.batch_decode(lowercase__)
							self.assertEqual(lowercase__				,     batch_tokens[0])
							self.assertEqual(lowercase__				,     ['''k s ɾ ɾ l ɭʲ''', '''j ð s j ð s oːɹ'''])


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

							__UpperCAmelCase			:							Optional[Any]	    =	'''Hello how are you'''
							__UpperCAmelCase			:							Union[str, Any]	    =	tokenizer.phonemize(lowercase__				,     phonemizer_lang='''en-us''')
							self.assertEqual(lowercase__				,     '''h ə l oʊ | h aʊ | ɑːɹ | j uː |''')


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

							__UpperCAmelCase			:							Dict	    =	'''Hello how are you'''
							__UpperCAmelCase			:							Dict	    =	tokenizer.phonemize(lowercase__				,     phonemizer_lang='''en-us''')
							self.assertEqual(tokenizer(lowercase__).input_ids				,     tokenizer(lowercase__				,     do_phonemize=lowercase__).input_ids)


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

							# fmt: off
							__UpperCAmelCase			:							Dict	    =	[
							    [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			:							List[str]	    =	tokenizer.decode(sample_ids[0])
							__UpperCAmelCase			:							str	    =	tokenizer.batch_decode(lowercase__)
							self.assertEqual(lowercase__				,     batch_tokens[0])
							self.assertEqual(lowercase__				,     ['''k s ɾ ɾ l ɭʲ''', '''j ð s j ð s oːɹ'''])

							# decode with no word_del_token filter
							__UpperCAmelCase			:							Optional[Any]	    =	tokenizer.decode(sample_ids[0]				,     filter_word_delimiter_token=lowercase__)
							__UpperCAmelCase			:							List[str]	    =	tokenizer.batch_decode(lowercase__				,     filter_word_delimiter_token=lowercase__)
							self.assertEqual(lowercase__				,     batch_tokens[0])
							self.assertEqual(lowercase__				,     ['''k s ɾ | ɾ l | ɭʲ''', '''| j ð | s j ð s oːɹ'''])


	def  A(    self):
							__UpperCAmelCase			:							Tuple	    =	self.tokenizer_class.from_pretrained(
							    '''facebook/wav2vec2-lv-60-espeak-cv-ft'''				,     word_delimiter_token='''|''')
							tokenizer.add_tokens('''|''')

							__UpperCAmelCase			:							Any	    =	'''Hello how are you'''
							__UpperCAmelCase			:							List[str]	    =	tokenizer.phonemize(lowercase__				,     phonemizer_lang='''en-us''')

							__UpperCAmelCase			:							Union[str, Any]	    =	tokenizer.decode(tokenizer(lowercase__).input_ids				,     filter_word_delimiter_token=lowercase__)

							self.assertEqual(lowercase__				,     lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							List[str]	    =	self.tokenizer_class.from_pretrained(
							    '''facebook/wav2vec2-lv-60-espeak-cv-ft'''				,     word_delimiter_token='''|''')
							tokenizer.add_tokens('''|''')

							__UpperCAmelCase			:							Optional[int]	    =	'''Hello how are you'''
							__UpperCAmelCase			:							Optional[Any]	    =	tokenizer.phonemize(lowercase__				,     phonemizer_lang='''en-us''')

							__UpperCAmelCase			:							Optional[int]	    =	tokenizer.decode(tokenizer(lowercase__).input_ids				,     filter_word_delimiter_token=lowercase__)

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


	def  A(    self):
							__UpperCAmelCase			:							List[str]	    =	self.tokenizer_class.from_pretrained(
							    '''facebook/wav2vec2-lv-60-espeak-cv-ft'''				,     word_delimiter_token=lowercase__)
							__UpperCAmelCase			:							Optional[Any]	    =	'''Hello how are you'''

							__UpperCAmelCase			:							Optional[int]	    =	tokenizer(lowercase__				,     phonemizer_lang='''en-us''').input_ids
							__UpperCAmelCase			:							Tuple	    =	tokenizer(lowercase__				,     phonemizer_lang='''fr-fr''').input_ids

							self.assertNotEqual(lowercase__				,     lowercase__)

							__UpperCAmelCase			:							Union[str, Any]	    =	tokenizer.decode(lowercase__)
							__UpperCAmelCase			:							Any	    =	tokenizer.decode(lowercase__)

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


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

							__UpperCAmelCase			:							Optional[int]	    =	tokenizer(lowercase__).input_ids
							__UpperCAmelCase			:							str	    =	tokenizer(lowercase__).input_ids

							self.assertEqual(lowercase__				,     lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							int	    =	self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''')
							tokenizer.add_tokens(['''!''', '''?'''])
							tokenizer.add_special_tokens({'''cls_token''': '''$$$'''})

							# fmt: off
							__UpperCAmelCase			:							Optional[Any]	    =	[
							    [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			:							Any	    =	tokenizer.batch_decode(lowercase__)
							self.assertEqual(lowercase__				,     ['''k s ɾ ɾ l ɭʲ!?!? $$$''', '''j ð s j ð s oːɹ $$$'''])


	@staticmethod
	def  A(    lowercase__				,     lowercase__):
							__UpperCAmelCase			:							Tuple	    =	[d[key] for d in offsets]
							return retrieved_list


	def  A(    self):
							__UpperCAmelCase			:							Any	    =	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			:							Dict	    =	[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			:							Tuple	    =	tokenizer.decode(lowercase__				,     output_char_offsets=lowercase__				,     filter_word_delimiter_token=lowercase__)
							# 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(lowercase__				,     lowercase__))

							# 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  A(    self):
							__UpperCAmelCase			:							Dict	    =	self.get_tokenizer(word_delimiter_token='''|''')

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

													# transform list to ModelOutput
													__UpperCAmelCase			:							str	    =	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(lowercase__				,     lowercase__):
																			if isinstance(lowercase__				,     lowercase__):
																									[recursive_check(lowercase__				,     lowercase__) for la, la in zip(lowercase__				,     lowercase__)]
																			self.assertEqual(lowercase__				,     lowercase__)

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

        # fmt: off
							__UpperCAmelCase			:							int	    =	[
							    [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			:							List[Any]	    =	tokenizer.batch_decode(lowercase__				,     output_char_offsets=lowercase__)
							__UpperCAmelCase			:							str	    =	[tokenizer.decode(lowercase__				,     output_char_offsets=lowercase__) for ids in sample_ids]
							check_list_tuples_equal(lowercase__				,     lowercase__)


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


	@unittest.skip('''Wav2Vec2PhonemeTokenizer always puts spaces between phonemes''')
	def  A(    self):
							pass


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


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


	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	self.get_tokenizers(do_lower_case=lowercase__)
							for tokenizer in tokenizers:
													with self.subTest(F"{tokenizer.__class__.__name__}"):
																			__UpperCAmelCase			:							List[str]	    =	tokenizer.vocab_size
																			__UpperCAmelCase			:							List[Any]	    =	len(lowercase__)

																			self.assertNotEqual(lowercase__				,     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			:							List[str]	    =	['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
																			__UpperCAmelCase			:							int	    =	tokenizer.add_tokens(lowercase__)
																			__UpperCAmelCase			:							Tuple	    =	tokenizer.vocab_size
																			__UpperCAmelCase			:							str	    =	len(lowercase__)

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

																			__UpperCAmelCase			:							List[Any]	    =	tokenizer.encode('''aaaaa bbbbbb low cccccccccdddddddd l'''				,     add_special_tokens=lowercase__)

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

																			__UpperCAmelCase			:							List[str]	    =	{'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''}
																			__UpperCAmelCase			:							str	    =	tokenizer.add_special_tokens(lowercase__)
																			__UpperCAmelCase			:							Union[str, Any]	    =	tokenizer.vocab_size
																			__UpperCAmelCase			:							Tuple	    =	len(lowercase__)

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

																			__UpperCAmelCase			:							int	    =	tokenizer.encode(
																			    '''>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l'''				,     add_special_tokens=lowercase__)

																			self.assertGreaterEqual(len(lowercase__)				,     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  A(    self):
							pass


	@unittest.skip('''The tokenizer shouldn\'t be used to encode input IDs (except for labels), only to decode.''')
	def  A(    self):
							pass


	def  A(    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.
							__UpperCAmelCase			:							List[Any]	    =	self.get_tokenizers(fast=lowercase__				,     do_lower_case=lowercase__)
							for tokenizer in tokenizers:
													with self.subTest(F"{tokenizer.__class__.__name__}"):
																			__UpperCAmelCase			:							int	    =	['''ð''', '''ɪ''', '''s''', '''ɪ''', '''z''', '''ɐ''', '''t''', '''ɛ''', '''k''', '''s''', '''t''']
																			__UpperCAmelCase			:							Tuple	    =	tokenizer.convert_tokens_to_string(lowercase__)

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





import coval  # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator

import datasets


lowerCAmelCase       		=    datasets.logging.get_logger(__name__)


lowerCAmelCase       		=    """\
@InProceedings{moosavi2019minimum,
    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},
    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},
    year = {2019},
    booktitle = {Proceedings of the 57th Annual Meeting of
        the Association for Computational Linguistics (Volume 1: Long Papers)},
    publisher = {Association for Computational Linguistics},
    address = {Florence, Italy},
}

@inproceedings{10.3115/1072399.1072405,
author = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},
title = {A Model-Theoretic Coreference Scoring Scheme},
year = {1995},
isbn = {1558604022},
publisher = {Association for Computational Linguistics},
address = {USA},
url = {https://doi.org/10.3115/1072399.1072405},
doi = {10.3115/1072399.1072405},
booktitle = {Proceedings of the 6th Conference on Message Understanding},
pages = {45–52},
numpages = {8},
location = {Columbia, Maryland},
series = {MUC6 ’95}
}

@INPROCEEDINGS{Bagga98algorithmsfor,
    author = {Amit Bagga and Breck Baldwin},
    title = {Algorithms for Scoring Coreference Chains},
    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},
    year = {1998},
    pages = {563--566}
}

@INPROCEEDINGS{Luo05oncoreference,
    author = {Xiaoqiang Luo},
    title = {On coreference resolution performance metrics},
    booktitle = {In Proc. of HLT/EMNLP},
    year = {2005},
    pages = {25--32},
    publisher = {URL}
}

@inproceedings{moosavi-strube-2016-coreference,
    title = \"Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric\",
    author = \"Moosavi, Nafise Sadat  and
      Strube, Michael\",
    booktitle = \"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",
    month = aug,
    year = \"2016\",
    address = \"Berlin, Germany\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/P16-1060\",
    doi = \"10.18653/v1/P16-1060\",
    pages = \"632--642\",
}

"""

lowerCAmelCase       		=    """\
CoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which
implements of the common evaluation metrics including MUC [Vilain et al, 1995],
B-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],
LEA [Moosavi and Strube, 2016] and the averaged CoNLL score
(the average of the F1 values of MUC, B-cubed and CEAFe)
[Denis and Baldridge, 2009a; Pradhan et al., 2011].

This wrapper of CoVal currently only work with CoNLL line format:
The CoNLL format has one word per line with all the annotation for this word in column separated by spaces:
Column	Type	Description
1	Document ID	This is a variation on the document filename
2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
3	Word number
4	Word itself	This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.
5	Part-of-Speech
6	Parse bit	This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the \"([pos] [word])\" string (or leaf) and concatenating the items in the rows of that column.
7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a \"-\"
8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.
9	Word sense	This is the word sense of the word in Column 3.
10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.
11	Named Entities	These columns identifies the spans representing various named entities.
12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.
N	Coreference	Coreference chain information encoded in a parenthesis structure.
More informations on the format can be found here (section \"*_conll File Format\"): http://www.conll.cemantix.org/2012/data.html

Details on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md

CoVal code was written by @ns-moosavi.
Some parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py
The test suite is taken from https://github.com/conll/reference-coreference-scorers/
Mention evaluation and the test suite are added by @andreasvc.
Parsing CoNLL files is developed by Leo Born.
"""

lowerCAmelCase       		=    """
Calculates coreference evaluation metrics.
Args:
    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.
        Each prediction is a word with its annotations as a string made of columns joined with spaces.
        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
        See the details on the format in the description of the metric.
    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.
        Each reference is a word with its annotations as a string made of columns joined with spaces.
        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
        See the details on the format in the description of the metric.
    keep_singletons: After extracting all mentions of key or system files,
        mentions whose corresponding coreference chain is of size one,
        are considered as singletons. The default evaluation mode will include
        singletons in evaluations if they are included in the key or the system files.
        By setting 'keep_singletons=False', all singletons in the key and system files
        will be excluded from the evaluation.
    NP_only: Most of the recent coreference resolvers only resolve NP mentions and
        leave out the resolution of VPs. By setting the 'NP_only' option, the scorer will only evaluate the resolution of NPs.
    min_span: By setting 'min_span', the scorer reports the results based on automatically detected minimum spans.
        Minimum spans are determined using the MINA algorithm.

Returns:
    'mentions': mentions
    'muc': MUC metric [Vilain et al, 1995]
    'bcub': B-cubed [Bagga and Baldwin, 1998]
    'ceafe': CEAFe [Luo et al., 2005]
    'lea': LEA [Moosavi and Strube, 2016]
    'conll_score': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)

Examples:

    >>> coval = datasets.load_metric('coval')
    >>> words = ['bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -',
    ... 'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)',
    ... 'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)',
    ... 'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -',
    ... 'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -',
    ... 'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -']
    >>> references = [words]
    >>> predictions = [words]
    >>> results = coval.compute(predictions=predictions, references=references)
    >>> print(results) # doctest:+ELLIPSIS
    {'mentions/recall': 1.0,[...] 'conll_score': 100.0}
"""




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_=False      ,       lowercase_=False      ,       lowercase_=True      ,       lowercase_=False      ,       lowercase_="dummy_doc"      )		->     Dict:




						'''simple docstring'''







						__UpperCAmelCase			:							List[str]	    =	{doc: key_lines}
						__UpperCAmelCase			:							Tuple	    =	{doc: sys_lines}

						__UpperCAmelCase			:							Union[str, Any]	    =	{}

						__UpperCAmelCase			:							Union[str, Any]	    =	0
						__UpperCAmelCase			:							str	    =	0
						__UpperCAmelCase			:							int	    =	0
						__UpperCAmelCase			:							int	    =	0
						__UpperCAmelCase			:							Union[str, Any]	    =	0
						__UpperCAmelCase			:							Any	    =	0

						__UpperCAmelCase    ,      __UpperCAmelCase			:							Union[str, Any]	    =	reader.get_doc_mentions(lowercase_      ,       key_doc_lines[doc]      ,       lowercase_      )
						key_singletons_num += singletons_num

						if NP_only or min_span:
												__UpperCAmelCase			:							List[Any]	    =	reader.set_annotated_parse_trees(lowercase_      ,       key_doc_lines[doc]      ,       lowercase_      ,       lowercase_      )

						__UpperCAmelCase    ,      __UpperCAmelCase			:							List[Any]	    =	reader.get_doc_mentions(lowercase_      ,       sys_doc_lines[doc]      ,       lowercase_      )
						sys_singletons_num += singletons_num

						if NP_only or min_span:
												__UpperCAmelCase			:							Union[str, Any]	    =	reader.set_annotated_parse_trees(lowercase_      ,       key_doc_lines[doc]      ,       lowercase_      ,       lowercase_      )

						if remove_nested:
												__UpperCAmelCase    ,      __UpperCAmelCase			:							Union[str, Any]	    =	reader.remove_nested_coref_mentions(lowercase_      ,       lowercase_      )
												key_nested_coref_num += nested_mentions
												key_removed_nested_clusters += removed_clusters

												__UpperCAmelCase    ,      __UpperCAmelCase			:							str	    =	reader.remove_nested_coref_mentions(lowercase_      ,       lowercase_      )
												sys_nested_coref_num += nested_mentions
												sys_removed_nested_clusters += removed_clusters

						__UpperCAmelCase			:							List[Any]	    =	reader.get_mention_assignments(lowercase_      ,       lowercase_      )
						__UpperCAmelCase			:							Optional[Any]	    =	reader.get_mention_assignments(lowercase_      ,       lowercase_      )

						__UpperCAmelCase			:							Dict	    =	(key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)

						if remove_nested:
												logger.info(
												    '''Number of removed nested coreferring mentions in the key '''
												    f"annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}"      )
												logger.info(
												    '''Number of resulting singleton clusters in the key '''
												    f"annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}"      )

						if not keep_singletons:
												logger.info(
												    f"{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system "
												    '''files, respectively'''      )

						return doc_coref_infos




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      )		->     Any:




						'''simple docstring'''







						__UpperCAmelCase			:							List[Any]	    =	get_coref_infos(lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      )

						__UpperCAmelCase			:							Union[str, Any]	    =	{}
						__UpperCAmelCase			:							Union[str, Any]	    =	0
						__UpperCAmelCase			:							str	    =	0

						for name, metric in metrics:
												__UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase			:							Dict	    =	evaluator.evaluate_documents(lowercase_      ,       lowercase_      ,       beta=1      )
												if name in ["muc", "bcub", "ceafe"]:
																		conll += fa
																		conll_subparts_num += 1
												output_scores.update({f"{name}/recall": recall, f"{name}/precision": precision, f"{name}/f1": fa}      )

												logger.info(
												    name.ljust(10      )      ,       f"Recall: {recall * 100:.2f}"      ,       f" Precision: {precision * 100:.2f}"      ,       f" F1: {fa * 100:.2f}"      ,       )

						if conll_subparts_num == 3:
												__UpperCAmelCase			:							Optional[int]	    =	(conll / 3) * 100
												logger.info(f"CoNLL score: {conll:.2f}"      )
												output_scores.update({'''conll_score''': conll}      )

						return output_scores




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     Any:




						'''simple docstring'''







						__UpperCAmelCase			:							Union[str, Any]	    =	False
						for line in key_lines:
												if not line.startswith('''#'''      ):
																		if len(line.split()      ) > 6:
																								__UpperCAmelCase			:							List[Any]	    =	line.split()[5]
																								if not parse_col == "-":
																														__UpperCAmelCase			:							Optional[Any]	    =	True
																														break
																								else:
																														break
						return has_gold_parse





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,						_KWARGS_DESCRIPTION )
class 							lowerCamelCase   (  datasets.Metric ):


	def  A(    self):
							return datasets.MetricInfo(
							    description=_DESCRIPTION				,     citation=_CITATION				,     inputs_description=_KWARGS_DESCRIPTION				,     features=datasets.Features(
							        {
							            '''predictions''': datasets.Sequence(datasets.Value('''string''')),
							            '''references''': datasets.Sequence(datasets.Value('''string''')),
							        })				,     codebase_urls=['''https://github.com/ns-moosavi/coval''']				,     reference_urls=[
							        '''https://github.com/ns-moosavi/coval''',
							        '''https://www.aclweb.org/anthology/P16-1060''',
							        '''http://www.conll.cemantix.org/2012/data.html''',
							    ]				,     )


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__=True				,     lowercase__=False				,     lowercase__=False				,     lowercase__=False):
							__UpperCAmelCase			:							List[Any]	    =	[
							    ('''mentions''', evaluator.mentions),
							    ('''muc''', evaluator.muc),
							    ('''bcub''', evaluator.b_cubed),
							    ('''ceafe''', evaluator.ceafe),
							    ('''lea''', evaluator.lea),
							]

							if min_span:
													__UpperCAmelCase			:							Optional[Any]	    =	util.check_gold_parse_annotation(lowercase__)
													if not has_gold_parse:
																			raise NotImplementedError('''References should have gold parse annotation to use \'min_span\'.''')
																			# util.parse_key_file(key_file)
																			# key_file = key_file + ".parsed"

							__UpperCAmelCase			:							Tuple	    =	evaluate(
							    key_lines=lowercase__				,     sys_lines=lowercase__				,     metrics=lowercase__				,     NP_only=lowercase__				,     remove_nested=lowercase__				,     keep_singletons=lowercase__				,     min_span=lowercase__				,     )

							return score

