



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		(	UpperCAmelCase__		,unittest.TestCase		):
							_a      =					WavaVecaPhonemeCTCTokenizer
							_a      =					False




							def   UpperCAmelCase__     (			self	:			str):
									super().setUp()

									lowerCAmelCase_     :	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(''' ''')
									lowerCAmelCase_     :	Tuple							   =					dict(zip(_UpperCAmelCase							,							range(len(_UpperCAmelCase))))

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

									lowerCAmelCase_     :	Tuple							   =					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(_UpperCAmelCase) + '''\n''')




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

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




							def   UpperCAmelCase__     (			self	:			Tuple							,							**A_	:			List[str]):
									kwargs.update(self.special_tokens_map)
									return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname							,							**_UpperCAmelCase)




							def   UpperCAmelCase__     (			self	:			Optional[int]):
									lowerCAmelCase_     :	List[str]							   =					self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''')

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

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

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




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

									lowerCAmelCase_     :	Tuple							   =					'''Hello how are you'''
									lowerCAmelCase_     :	Any							   =					tokenizer.phonemize(_UpperCAmelCase							,							phonemizer_lang='''en-us''')
									self.assertEqual(_UpperCAmelCase							,							'''h ə l oʊ h aʊ ɑːɹ j uː''')




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

									lowerCAmelCase_     :	List[str]							   =					'''Hello how are you'''
									lowerCAmelCase_     :	int							   =					tokenizer.phonemize(_UpperCAmelCase							,							phonemizer_lang='''en-us''')
									self.assertEqual(tokenizer(_UpperCAmelCase).input_ids							,							tokenizer(_UpperCAmelCase							,							do_phonemize=_UpperCAmelCase).input_ids)




							def   UpperCAmelCase__     (			self	:			Optional[int]):
									lowerCAmelCase_     :	Tuple							   =					self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''')
									lowerCAmelCase_     :	List[str]							   =					'''Hello how are you'''
									lowerCAmelCase_     :	Any							   =					tokenizer.phonemize(_UpperCAmelCase							,							phonemizer_lang='''en-us''')

									lowerCAmelCase_     :	Tuple							   =					tokenizer.decode(tokenizer(_UpperCAmelCase).input_ids)

									self.assertEqual(_UpperCAmelCase							,							_UpperCAmelCase)




							def   UpperCAmelCase__     (			self	:			Union[str, Any]):
									lowerCAmelCase_     :	List[Any]							   =					self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''')

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




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

									lowerCAmelCase_     :	Tuple							   =					'''Hello how are you'''
									lowerCAmelCase_     :	str							   =					tokenizer.phonemize(_UpperCAmelCase							,							phonemizer_lang='''en-us''')
									self.assertEqual(_UpperCAmelCase							,							'''h ə l oʊ | h aʊ | ɑːɹ | j uː |''')




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

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




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

									# fmt: off
									lowerCAmelCase_     :	int							   =					[
									    [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
									lowerCAmelCase_     :	Optional[int]							   =					tokenizer.decode(sample_ids[0])
									lowerCAmelCase_     :	Optional[Any]							   =					tokenizer.batch_decode(_UpperCAmelCase)
									self.assertEqual(_UpperCAmelCase							,							batch_tokens[0])
									self.assertEqual(_UpperCAmelCase							,							['''k s ɾ ɾ l ɭʲ''', '''j ð s j ð s oːɹ'''])

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




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

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

									lowerCAmelCase_     :	str							   =					tokenizer.decode(tokenizer(_UpperCAmelCase).input_ids							,							filter_word_delimiter_token=_UpperCAmelCase)

									self.assertEqual(_UpperCAmelCase							,							_UpperCAmelCase)




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

									lowerCAmelCase_     :	Any							   =					'''Hello how are you'''
									lowerCAmelCase_     :	str							   =					tokenizer.phonemize(_UpperCAmelCase							,							phonemizer_lang='''en-us''')

									lowerCAmelCase_     :	Dict							   =					tokenizer.decode(tokenizer(_UpperCAmelCase).input_ids							,							filter_word_delimiter_token=_UpperCAmelCase)

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




							def   UpperCAmelCase__     (			self	:			Dict):
									lowerCAmelCase_     :	List[Any]							   =					self.tokenizer_class.from_pretrained(
									    '''facebook/wav2vec2-lv-60-espeak-cv-ft'''							,							word_delimiter_token=_UpperCAmelCase)
									lowerCAmelCase_     :	Any							   =					'''Hello how are you'''

									lowerCAmelCase_     :	Optional[int]							   =					tokenizer(_UpperCAmelCase							,							phonemizer_lang='''en-us''').input_ids
									lowerCAmelCase_     :	str							   =					tokenizer(_UpperCAmelCase							,							phonemizer_lang='''fr-fr''').input_ids

									self.assertNotEqual(_UpperCAmelCase							,							_UpperCAmelCase)

									lowerCAmelCase_     :	Tuple							   =					tokenizer.decode(_UpperCAmelCase)
									lowerCAmelCase_     :	Any							   =					tokenizer.decode(_UpperCAmelCase)

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




							def   UpperCAmelCase__     (			self	:			Optional[int]):
									lowerCAmelCase_     :	Optional[int]							   =					self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''')
									lowerCAmelCase_     :	Optional[int]							   =					'''Hello how Are you'''
									lowerCAmelCase_     :	Optional[int]							   =					'''hello how are you'''

									lowerCAmelCase_     :	str							   =					tokenizer(_UpperCAmelCase).input_ids
									lowerCAmelCase_     :	str							   =					tokenizer(_UpperCAmelCase).input_ids

									self.assertEqual(_UpperCAmelCase							,							_UpperCAmelCase)




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

									# fmt: off
									lowerCAmelCase_     :	Dict							   =					[
									    [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

									lowerCAmelCase_     :	Dict							   =					tokenizer.batch_decode(_UpperCAmelCase)
									self.assertEqual(_UpperCAmelCase							,							['''k s ɾ ɾ l ɭʲ!?!? $$$''', '''j ð s j ð s oːɹ $$$'''])




							@staticmethod
							def   UpperCAmelCase__     (			A_	:			Optional[Any]							,							A_	:			str):
									lowerCAmelCase_     :	Any							   =					[d[key] for d in offsets]
									return retrieved_list




							def   UpperCAmelCase__     (			self	:			Dict):
									lowerCAmelCase_     :	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"
									lowerCAmelCase_     :	List[Any]							   =					[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

									lowerCAmelCase_     :	Any							   =					tokenizer.decode(_UpperCAmelCase							,							output_char_offsets=_UpperCAmelCase							,							filter_word_delimiter_token=_UpperCAmelCase)
									# 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(_UpperCAmelCase							,							_UpperCAmelCase))

									# 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	:			int):
									lowerCAmelCase_     :	Any							   =					self.get_tokenizer(word_delimiter_token='''|''')

									def check_list_tuples_equal(A_	:			Optional[int]							,							A_	:			Any):
											self.assertTrue(isinstance(_UpperCAmelCase							,							_UpperCAmelCase))
											self.assertTrue(isinstance(outputs_list[0]							,							_UpperCAmelCase))

											# transform list to ModelOutput
											lowerCAmelCase_     :	Optional[Any]							   =					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(A_	:			List[Any]							,							A_	:			int):
													if isinstance(_UpperCAmelCase							,							_UpperCAmelCase):
															[recursive_check(_UpperCAmelCase							,							_UpperCAmelCase) for la, la in zip(_UpperCAmelCase							,							_UpperCAmelCase)]
													self.assertEqual(_UpperCAmelCase							,							_UpperCAmelCase)

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

											# fmt: off
									lowerCAmelCase_     :	List[Any]							   =					[
									    [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
									lowerCAmelCase_     :	List[Any]							   =					tokenizer.batch_decode(_UpperCAmelCase							,							output_char_offsets=_UpperCAmelCase)
									lowerCAmelCase_     :	List[Any]							   =					[tokenizer.decode(_UpperCAmelCase							,							output_char_offsets=_UpperCAmelCase) for ids in sample_ids]
									check_list_tuples_equal(_UpperCAmelCase							,							_UpperCAmelCase)




							@unittest.skip('''Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes''')
							def   UpperCAmelCase__     (			self	:			List[str]):
									pass




							@unittest.skip('''Wav2Vec2PhonemeTokenizer always puts spaces between phonemes''')
							def   UpperCAmelCase__     (			self	:			int):
									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	:			List[Any]):
									pass




							def   UpperCAmelCase__     (			self	:			Tuple):
									lowerCAmelCase_     :	Optional[Any]							   =					self.get_tokenizers(do_lower_case=_UpperCAmelCase)
									for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""):
													lowerCAmelCase_     :	str							   =					tokenizer.vocab_size
													lowerCAmelCase_     :	int							   =					len(_UpperCAmelCase)

													self.assertNotEqual(_UpperCAmelCase							,							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)

													lowerCAmelCase_     :	int							   =					['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
													lowerCAmelCase_     :	str							   =					tokenizer.add_tokens(_UpperCAmelCase)
													lowerCAmelCase_     :	List[str]							   =					tokenizer.vocab_size
													lowerCAmelCase_     :	List[str]							   =					len(_UpperCAmelCase)

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

													lowerCAmelCase_     :	Optional[int]							   =					tokenizer.encode('''aaaaa bbbbbb low cccccccccdddddddd l'''							,							add_special_tokens=_UpperCAmelCase)

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

													lowerCAmelCase_     :	Union[str, Any]							   =					{'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''}
													lowerCAmelCase_     :	Union[str, Any]							   =					tokenizer.add_special_tokens(_UpperCAmelCase)
													lowerCAmelCase_     :	Union[str, Any]							   =					tokenizer.vocab_size
													lowerCAmelCase_     :	Union[str, Any]							   =					len(_UpperCAmelCase)

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

													lowerCAmelCase_     :	str							   =					tokenizer.encode(
													    '''>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l'''							,							add_special_tokens=_UpperCAmelCase)

													self.assertGreaterEqual(len(_UpperCAmelCase)							,							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	:			List[str]):
									pass




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







							def   UpperCAmelCase__     (			self	:			Union[str, 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.
									lowerCAmelCase_     :	int							   =					self.get_tokenizers(fast=_UpperCAmelCase							,							do_lower_case=_UpperCAmelCase)
									for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""):
													lowerCAmelCase_     :	int							   =					['''ð''', '''ɪ''', '''s''', '''ɪ''', '''z''', '''ɐ''', '''t''', '''ɛ''', '''k''', '''s''', '''t''']
													lowerCAmelCase_     :	Union[str, Any]							   =					tokenizer.convert_tokens_to_string(_UpperCAmelCase)

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













# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput


@dataclass
class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = 42
  A_						       = 42




class     _a							(	UpperCAmelCase__    ,							UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = 1




  @register_to_config
  def __init__(  self		,		_UpperCAmelCase = 2000		,		_UpperCAmelCase = 0.1_5		,		_UpperCAmelCase = 0.0_1		,		_UpperCAmelCase = 1_3_4_8.0		,		_UpperCAmelCase = 1e-5		,		_UpperCAmelCase = 1		,		)      ->			Tuple:
     # standard deviation of the initial noise distribution
     UpperCamelCase_						=  sigma_max

     # setable values
     UpperCamelCase_						=  None

     self.set_sigmas(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None			)      ->			torch.FloatTensor:
     return sample




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None			)      ->			str:
     UpperCamelCase_						=  sampling_eps if sampling_eps is not None else self.config.sampling_eps

     UpperCamelCase_						=  torch.linspace(1		,		_UpperCAmelCase		,		_UpperCAmelCase		,		device=_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None			)      ->			Any:
     UpperCamelCase_						=  sigma_min if sigma_min is not None else self.config.sigma_min
     UpperCamelCase_						=  sigma_max if sigma_max is not None else self.config.sigma_max
     UpperCamelCase_						=  sampling_eps if sampling_eps is not None else self.config.sampling_eps
     if self.timesteps is None:
        self.set_timesteps(_UpperCAmelCase		,		_UpperCAmelCase			)

     UpperCamelCase_						=  sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
     UpperCamelCase_						=  torch.exp(torch.linspace(math.log(_UpperCAmelCase			)		,		math.log(_UpperCAmelCase			)		,		_UpperCAmelCase			)			)
     UpperCamelCase_						=  torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			List[Any]:
     return torch.where(
         timesteps == 0		,		torch.zeros_like(t.to(timesteps.device			)			)		,		self.discrete_sigmas[timesteps - 1].to(timesteps.device			)		,		)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = True		,		)      ->			Union[SdeVeOutput, Tuple]:
     if self.timesteps is None:
        raise ValueError(
            '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'			)

     UpperCamelCase_						=  timestep * torch.ones(
         sample.shape[0]		,		device=sample.device			)  # torch.repeat_interleave(timestep, sample.shape[0])
     UpperCamelCase_						=  (timestep * (len(self.timesteps			) - 1)).long()

     # mps requires indices to be in the same device, so we use cpu as is the default with cuda
     UpperCamelCase_						=  timesteps.to(self.discrete_sigmas.device			)

     UpperCamelCase_						=  self.discrete_sigmas[timesteps].to(sample.device			)
     UpperCamelCase_						=  self.get_adjacent_sigma(_UpperCAmelCase		,		_UpperCAmelCase			).to(sample.device			)
     UpperCamelCase_						=  torch.zeros_like(_UpperCAmelCase			)
     UpperCamelCase_						=  (sigma**2 - adjacent_sigma**2) ** 0.5

     # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
     # also equation 47 shows the analog from SDE models to ancestral sampling methods
     UpperCamelCase_						=  diffusion.flatten()
     while len(diffusion.shape			) < len(sample.shape			):
        UpperCamelCase_						=  diffusion.unsqueeze(-1			)
     UpperCamelCase_						=  drift - diffusion**2 * model_output

     #  equation 6: sample noise for the diffusion term of
     UpperCamelCase_						=  randn_tensor(
         sample.shape		,		layout=sample.layout		,		generator=_UpperCAmelCase		,		device=sample.device		,		dtype=sample.dtype			)
     UpperCamelCase_						=  sample - drift  # subtract because `dt` is a small negative timestep
     # TODO is the variable diffusion the correct scaling term for the noise?
     UpperCamelCase_						=  prev_sample_mean + diffusion * noise  # add impact of diffusion field g

     if not return_dict:
        return (prev_sample, prev_sample_mean)

     return SdeVeOutput(prev_sample=_UpperCAmelCase		,		prev_sample_mean=_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = True		,		)      ->			Union[SchedulerOutput, Tuple]:
     if self.timesteps is None:
        raise ValueError(
            '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'			)

     # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
     # sample noise for correction
     UpperCamelCase_						=  randn_tensor(sample.shape		,		layout=sample.layout		,		generator=_UpperCAmelCase			).to(sample.device			)

     # compute step size from the model_output, the noise, and the snr
     UpperCamelCase_						=  torch.norm(model_output.reshape(model_output.shape[0]		,		-1			)		,		dim=-1			).mean()
     UpperCamelCase_						=  torch.norm(noise.reshape(noise.shape[0]		,		-1			)		,		dim=-1			).mean()
     UpperCamelCase_						=  (self.config.snr * noise_norm / grad_norm) ** 2 * 2
     UpperCamelCase_						=  step_size * torch.ones(sample.shape[0]			).to(sample.device			)
     # self.repeat_scalar(step_size, sample.shape[0])

     # compute corrected sample: model_output term and noise term
     UpperCamelCase_						=  step_size.flatten()
     while len(step_size.shape			) < len(sample.shape			):
        UpperCamelCase_						=  step_size.unsqueeze(-1			)
     UpperCamelCase_						=  sample + step_size * model_output
     UpperCamelCase_						=  prev_sample_mean + ((step_size * 2) ** 0.5) * noise

     if not return_dict:
        return (prev_sample,)

     return SchedulerOutput(prev_sample=_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		)      ->			torch.FloatTensor:
     # Make sure sigmas and timesteps have the same device and dtype as original_samples
     UpperCamelCase_						=  timesteps.to(original_samples.device			)
     UpperCamelCase_						=  self.discrete_sigmas.to(original_samples.device			)[timesteps]
     UpperCamelCase_						=  (
         noise * sigmas[:, None, None, None]
         if noise is not None
         else torch.randn_like(_UpperCAmelCase			) * sigmas[:, None, None, None]
     )
     UpperCamelCase_						=  noise + original_samples
     return noisy_samples




  def __len__(  self			)      ->			Optional[int]:
     return self.config.num_train_timesteps





