


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 			UpperCamelCase__			(			__lowerCamelCase				,    unittest.TestCase      ):
      a__ :  List[str]		    =    WavaVecaPhonemeCTCTokenizer
      a__ :  List[Any]		    =    False






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

           UpperCamelCase__				:	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__				:	Optional[int]										=  {'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

           UpperCamelCase__				:	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 						__lowercase(  self       : str,		__lowerCamelCase       : List[Any],		__lowerCamelCase       : str=False,		__lowerCamelCase       : Optional[int]=20,		__lowerCamelCase       : Any=5 )	->			Tuple[str, list]:
           UpperCamelCase__				:	Dict										=  [(i, tokenizer.decode([i],		clean_up_tokenization_spaces=_lowercase )) for i in range(len(_lowercase ) )]
           UpperCamelCase__				:	int										=  list(filter(lambda __lowerCamelCase : [t[0]] == tokenizer.encode(t[1],		do_phonemize=_lowercase ),		_lowercase ) )
           if max_length is not None and len(_lowercase ) > max_length:
                UpperCamelCase__				:	Dict										=  toks[:max_length]
           if min_length is not None and len(_lowercase ) < min_length and len(_lowercase ) > 0:
                while len(_lowercase ) < min_length:
                     UpperCamelCase__				:	Optional[Any]										=  toks + toks
        # toks_str = [t[1] for t in toks]
           UpperCamelCase__				:	Any										=  [t[0] for t in toks]

           # Ensure consistency
           UpperCamelCase__				:	Tuple										=  tokenizer.decode(_lowercase,		clean_up_tokenization_spaces=_lowercase )
           if " " not in output_txt and len(_lowercase ) > 1:
                UpperCamelCase__				:	List[str]										=  (
                    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__				:	Tuple										=  ''' ''' + output_txt
           UpperCamelCase__				:	str										=  tokenizer.encode(_lowercase,		add_special_tokens=_lowercase )
           return output_txt, output_ids






      def 						__lowercase(  self       : Optional[int],		**__lowerCamelCase       : List[Any] )	->			Dict:
           kwargs.update(self.special_tokens_map )
           return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname,		**_lowercase )






      def 						__lowercase(  self       : Any )	->			Optional[Any]:
           UpperCamelCase__				:	Any										=  self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''' )

           # check adding a single token
           tokenizer.add_tokens('''xxx''' )
           UpperCamelCase__				:	Optional[int]										=  tokenizer('''m xxx ɪ''',		do_phonemize=_lowercase ).input_ids
           self.assertEqual(_lowercase,		[13, 3_92, 17] )  # xxx should be last token

           tokenizer.add_tokens(['''aaa''', '''bbb''', '''ccc'''] )
           UpperCamelCase__				:	Union[str, Any]										=  tokenizer('''m aaa ɪ ccc''',		do_phonemize=_lowercase ).input_ids
           self.assertEqual(_lowercase,		[13, 3_93, 17, 3_95] )  # aaa and ccc should be after xxx and 2 after aaa

           UpperCamelCase__				:	Tuple										=  tokenizer('''maɪ c''',		do_phonemize=_lowercase ).input_ids
           self.assertEqual(_lowercase,		[3, 2_00] )  # mai should be <unk> (=3)






      def 						__lowercase(  self       : int )	->			Any:
           UpperCamelCase__				:	str										=  self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''' )

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






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

           UpperCamelCase__				:	str										=  '''Hello how are you'''
           UpperCamelCase__				:	str										=  tokenizer.phonemize(_lowercase,		phonemizer_lang='''en-us''' )
           self.assertEqual(tokenizer(_lowercase ).input_ids,		tokenizer(_lowercase,		do_phonemize=_lowercase ).input_ids )






      def 						__lowercase(  self       : List[Any] )	->			Dict:
           UpperCamelCase__				:	Optional[Any]										=  self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''' )
           UpperCamelCase__				:	Optional[Any]										=  '''Hello how are you'''
           UpperCamelCase__				:	Union[str, Any]										=  tokenizer.phonemize(_lowercase,		phonemizer_lang='''en-us''' )

           UpperCamelCase__				:	Optional[int]										=  tokenizer.decode(tokenizer(_lowercase ).input_ids )

           self.assertEqual(_lowercase,		_lowercase )






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

           UpperCamelCase__				:	Union[str, Any]										=  [
               [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
               [24, 22, 5, 24, 22, 5, 77],
           ]
           UpperCamelCase__				:	Union[str, Any]										=  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 						__lowercase(  self       : Tuple )	->			int:
           UpperCamelCase__				:	Dict										=  self.tokenizer_class.from_pretrained(
               '''facebook/wav2vec2-lv-60-espeak-cv-ft''',		word_delimiter_token='''|''' )
           tokenizer.add_tokens('''|''' )

           UpperCamelCase__				:	List[Any]										=  '''Hello how are you'''
           UpperCamelCase__				:	Tuple										=  tokenizer.phonemize(_lowercase,		phonemizer_lang='''en-us''' )
           self.assertEqual(_lowercase,		'''h ə l oʊ | h aʊ | ɑːɹ | j uː |''' )






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

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

           # fmt: off
           UpperCamelCase__				:	Tuple										=  [
               [11, 5, 15, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 15, 8, tokenizer.word_delimiter_token_id, 98],
               [tokenizer.word_delimiter_token_id, 24, 22, tokenizer.word_delimiter_token_id, 5, 24, 22, 5, 77],
           ]
           # fmt: on

           # decode with word_del_token filter
           UpperCamelCase__				:	Dict										=  tokenizer.decode(sample_ids[0] )
           UpperCamelCase__				:	Dict										=  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__				:	Dict										=  tokenizer.decode(sample_ids[0],		filter_word_delimiter_token=_lowercase )
           UpperCamelCase__				:	Tuple										=  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 						__lowercase(  self       : Dict )	->			int:
           UpperCamelCase__				:	Optional[int]										=  self.tokenizer_class.from_pretrained(
               '''facebook/wav2vec2-lv-60-espeak-cv-ft''',		word_delimiter_token='''|''' )
           tokenizer.add_tokens('''|''' )

           UpperCamelCase__				:	int										=  '''Hello how are you'''
           UpperCamelCase__				:	Any										=  tokenizer.phonemize(_lowercase,		phonemizer_lang='''en-us''' )

           UpperCamelCase__				:	str										=  tokenizer.decode(tokenizer(_lowercase ).input_ids,		filter_word_delimiter_token=_lowercase )

           self.assertEqual(_lowercase,		_lowercase )






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

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

           UpperCamelCase__				:	Any										=  tokenizer.decode(tokenizer(_lowercase ).input_ids,		filter_word_delimiter_token=_lowercase )

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






      def 						__lowercase(  self       : Union[str, Any] )	->			int:
           UpperCamelCase__				:	Union[str, Any]										=  self.tokenizer_class.from_pretrained(
               '''facebook/wav2vec2-lv-60-espeak-cv-ft''',		word_delimiter_token=_lowercase )
           UpperCamelCase__				:	Any										=  '''Hello how are you'''

           UpperCamelCase__				:	Optional[Any]										=  tokenizer(_lowercase,		phonemizer_lang='''en-us''' ).input_ids
           UpperCamelCase__				:	Any										=  tokenizer(_lowercase,		phonemizer_lang='''fr-fr''' ).input_ids

           self.assertNotEqual(_lowercase,		_lowercase )

           UpperCamelCase__				:	Union[str, Any]										=  tokenizer.decode(_lowercase )
           UpperCamelCase__				:	Dict										=  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 						__lowercase(  self       : Union[str, Any] )	->			Tuple:
           UpperCamelCase__				:	str										=  self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''' )
           UpperCamelCase__				:	Optional[int]										=  '''Hello how Are you'''
           UpperCamelCase__				:	Dict										=  '''hello how are you'''

           UpperCamelCase__				:	Tuple										=  tokenizer(_lowercase ).input_ids
           UpperCamelCase__				:	Optional[Any]										=  tokenizer(_lowercase ).input_ids

           self.assertEqual(_lowercase,		_lowercase )






      def 						__lowercase(  self       : List[str] )	->			Optional[int]:
           UpperCamelCase__				:	Tuple										=  self.tokenizer_class.from_pretrained('''facebook/wav2vec2-lv-60-espeak-cv-ft''' )
           tokenizer.add_tokens(['''!''', '''?'''] )
           tokenizer.add_special_tokens({'''cls_token''': '''$$$'''} )

           # fmt: off
           UpperCamelCase__				:	Union[str, Any]										=  [
               [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 3_92, 3_92, 3_93, 3_92, 3_92, 3_93, 3_94, 3_94],
               [24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 3_94, 3_94],
           ]
           # fmt: on

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






      @staticmethod
      def 						__lowercase(  __lowerCamelCase       : List[str],		__lowerCamelCase       : List[str] )	->			str:
           UpperCamelCase__				:	Any										=  [d[key] for d in offsets]
           return retrieved_list






      def 						__lowercase(  self       : Any )	->			Union[str, Any]:
           UpperCamelCase__				:	List[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__				:	str										=  [11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98]
           # fmt: on

           UpperCamelCase__				:	str										=  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, 11, 12, 15, 16] )
           self.assertListEqual(
               self.get_from_offsets(outputs['''char_offsets'''],		'''end_offset''' ),		[1, 4, 6, 9, 10, 12, 15, 16, 17] )






      def 						__lowercase(  self       : Optional[Any] )	->			Any:
           UpperCamelCase__				:	Optional[Any]										=  self.get_tokenizer(word_delimiter_token='''|''' )

           def check_list_tuples_equal(__lowerCamelCase       : Union[str, Any],		__lowerCamelCase       : List[Any] ):
                self.assertTrue(isinstance(_lowercase,		_lowercase ) )
                self.assertTrue(isinstance(outputs_list[0],		_lowercase ) )

                # transform list to ModelOutput
                UpperCamelCase__				:	Optional[int]										=  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       : Optional[Any],		__lowerCamelCase       : List[Any] ):
                     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										=  [
               [11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
               [24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34],
           ]
           # fmt: on

           # We assume that `decode` works as expected. All we will check now is
           # the output type is correct and the output is identical to `decode`

           # char
           UpperCamelCase__				:	List[str]										=  tokenizer.batch_decode(_lowercase,		output_char_offsets=_lowercase )
           UpperCamelCase__				:	Tuple										=  [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 						__lowercase(  self       : Tuple )	->			Optional[int]:
           pass






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






      @unittest.skip('''encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency''' )
      def 						__lowercase(  self       : Union[str, Any] )	->			Tuple:
           pass






      @unittest.skip('''Wav2Vec2PhonemeModel has no max model length => no testing''' )
      def 						__lowercase(  self       : Dict )	->			Union[str, Any]:
           pass






      def 						__lowercase(  self       : int )	->			Optional[Any]:
           UpperCamelCase__				:	str										=  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__				:	Optional[int]										=  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__				:	Any										=  ['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
                     UpperCamelCase__				:	Tuple										=  tokenizer.add_tokens(_lowercase )
                     UpperCamelCase__				:	List[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 + len(_lowercase ) )

                     UpperCamelCase__				:	Optional[int]										=  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__				:	int										=  tokenizer.add_special_tokens(_lowercase )
                     UpperCamelCase__				:	List[Any]										=  tokenizer.vocab_size
                     UpperCamelCase__				:	Dict										=  len(_lowercase )

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

                     UpperCamelCase__				:	Union[str, Any]										=  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 						__lowercase(  self       : Union[str, Any] )	->			Union[str, Any]:
           pass






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






      def 						__lowercase(  self       : int )	->			Tuple:
           # 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__				:	Dict										=  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__				:	str										=  tokenizer.convert_tokens_to_string(_lowercase )

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







import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE		:List[str]						=     logging.get_logger("""transformers.models.encodec""")

SCREAMING_SNAKE_CASE		:Tuple						=     {
    """quantizer.vq.layers.*._codebook.inited""": """quantizer.layers.*.codebook.inited""",
    """quantizer.vq.layers.*._codebook.cluster_size""": """quantizer.layers.*.codebook.cluster_size""",
    """quantizer.vq.layers.*._codebook.embed""": """quantizer.layers.*.codebook.embed""",
    """quantizer.vq.layers.*._codebook.embed_avg""": """quantizer.layers.*.codebook.embed_avg""",
}
SCREAMING_SNAKE_CASE		:List[str]						=     {
    """encoder.model.0.conv.conv""": """encoder.layers.0.conv""",
    """encoder.model.1.block.1.conv.conv""": """encoder.layers.1.block.1.conv""",
    """encoder.model.1.block.3.conv.conv""": """encoder.layers.1.block.3.conv""",
    """encoder.model.1.shortcut.conv.conv""": """encoder.layers.1.shortcut.conv""",
    """encoder.model.3.conv.conv""": """encoder.layers.3.conv""",
    """encoder.model.4.block.1.conv.conv""": """encoder.layers.4.block.1.conv""",
    """encoder.model.4.block.3.conv.conv""": """encoder.layers.4.block.3.conv""",
    """encoder.model.4.shortcut.conv.conv""": """encoder.layers.4.shortcut.conv""",
    """encoder.model.6.conv.conv""": """encoder.layers.6.conv""",
    """encoder.model.7.block.1.conv.conv""": """encoder.layers.7.block.1.conv""",
    """encoder.model.7.block.3.conv.conv""": """encoder.layers.7.block.3.conv""",
    """encoder.model.7.shortcut.conv.conv""": """encoder.layers.7.shortcut.conv""",
    """encoder.model.9.conv.conv""": """encoder.layers.9.conv""",
    """encoder.model.10.block.1.conv.conv""": """encoder.layers.10.block.1.conv""",
    """encoder.model.10.block.3.conv.conv""": """encoder.layers.10.block.3.conv""",
    """encoder.model.10.shortcut.conv.conv""": """encoder.layers.10.shortcut.conv""",
    """encoder.model.12.conv.conv""": """encoder.layers.12.conv""",
    """encoder.model.13.lstm""": """encoder.layers.13.lstm""",
    """encoder.model.15.conv.conv""": """encoder.layers.15.conv""",
}
SCREAMING_SNAKE_CASE		:Union[str, Any]						=     {
    """encoder.model.0.conv.norm""": """encoder.layers.0.norm""",
    """encoder.model.1.block.1.conv.norm""": """encoder.layers.1.block.1.norm""",
    """encoder.model.1.block.3.conv.norm""": """encoder.layers.1.block.3.norm""",
    """encoder.model.1.shortcut.conv.norm""": """encoder.layers.1.shortcut.norm""",
    """encoder.model.3.conv.norm""": """encoder.layers.3.norm""",
    """encoder.model.4.block.1.conv.norm""": """encoder.layers.4.block.1.norm""",
    """encoder.model.4.block.3.conv.norm""": """encoder.layers.4.block.3.norm""",
    """encoder.model.4.shortcut.conv.norm""": """encoder.layers.4.shortcut.norm""",
    """encoder.model.6.conv.norm""": """encoder.layers.6.norm""",
    """encoder.model.7.block.1.conv.norm""": """encoder.layers.7.block.1.norm""",
    """encoder.model.7.block.3.conv.norm""": """encoder.layers.7.block.3.norm""",
    """encoder.model.7.shortcut.conv.norm""": """encoder.layers.7.shortcut.norm""",
    """encoder.model.9.conv.norm""": """encoder.layers.9.norm""",
    """encoder.model.10.block.1.conv.norm""": """encoder.layers.10.block.1.norm""",
    """encoder.model.10.block.3.conv.norm""": """encoder.layers.10.block.3.norm""",
    """encoder.model.10.shortcut.conv.norm""": """encoder.layers.10.shortcut.norm""",
    """encoder.model.12.conv.norm""": """encoder.layers.12.norm""",
    """encoder.model.15.conv.norm""": """encoder.layers.15.norm""",
}
SCREAMING_SNAKE_CASE		:Optional[Any]						=     {
    """decoder.model.0.conv.conv""": """decoder.layers.0.conv""",
    """decoder.model.1.lstm""": """decoder.layers.1.lstm""",
    """decoder.model.3.convtr.convtr""": """decoder.layers.3.conv""",
    """decoder.model.4.block.1.conv.conv""": """decoder.layers.4.block.1.conv""",
    """decoder.model.4.block.3.conv.conv""": """decoder.layers.4.block.3.conv""",
    """decoder.model.4.shortcut.conv.conv""": """decoder.layers.4.shortcut.conv""",
    """decoder.model.6.convtr.convtr""": """decoder.layers.6.conv""",
    """decoder.model.7.block.1.conv.conv""": """decoder.layers.7.block.1.conv""",
    """decoder.model.7.block.3.conv.conv""": """decoder.layers.7.block.3.conv""",
    """decoder.model.7.shortcut.conv.conv""": """decoder.layers.7.shortcut.conv""",
    """decoder.model.9.convtr.convtr""": """decoder.layers.9.conv""",
    """decoder.model.10.block.1.conv.conv""": """decoder.layers.10.block.1.conv""",
    """decoder.model.10.block.3.conv.conv""": """decoder.layers.10.block.3.conv""",
    """decoder.model.10.shortcut.conv.conv""": """decoder.layers.10.shortcut.conv""",
    """decoder.model.12.convtr.convtr""": """decoder.layers.12.conv""",
    """decoder.model.13.block.1.conv.conv""": """decoder.layers.13.block.1.conv""",
    """decoder.model.13.block.3.conv.conv""": """decoder.layers.13.block.3.conv""",
    """decoder.model.13.shortcut.conv.conv""": """decoder.layers.13.shortcut.conv""",
    """decoder.model.15.conv.conv""": """decoder.layers.15.conv""",
}
SCREAMING_SNAKE_CASE		:Optional[int]						=     {
    """decoder.model.0.conv.norm""": """decoder.layers.0.norm""",
    """decoder.model.3.convtr.norm""": """decoder.layers.3.norm""",
    """decoder.model.4.block.1.conv.norm""": """decoder.layers.4.block.1.norm""",
    """decoder.model.4.block.3.conv.norm""": """decoder.layers.4.block.3.norm""",
    """decoder.model.4.shortcut.conv.norm""": """decoder.layers.4.shortcut.norm""",
    """decoder.model.6.convtr.norm""": """decoder.layers.6.norm""",
    """decoder.model.7.block.1.conv.norm""": """decoder.layers.7.block.1.norm""",
    """decoder.model.7.block.3.conv.norm""": """decoder.layers.7.block.3.norm""",
    """decoder.model.7.shortcut.conv.norm""": """decoder.layers.7.shortcut.norm""",
    """decoder.model.9.convtr.norm""": """decoder.layers.9.norm""",
    """decoder.model.10.block.1.conv.norm""": """decoder.layers.10.block.1.norm""",
    """decoder.model.10.block.3.conv.norm""": """decoder.layers.10.block.3.norm""",
    """decoder.model.10.shortcut.conv.norm""": """decoder.layers.10.shortcut.norm""",
    """decoder.model.12.convtr.norm""": """decoder.layers.12.norm""",
    """decoder.model.13.block.1.conv.norm""": """decoder.layers.13.block.1.norm""",
    """decoder.model.13.block.3.conv.norm""": """decoder.layers.13.block.3.norm""",
    """decoder.model.13.shortcut.conv.norm""": """decoder.layers.13.shortcut.norm""",
    """decoder.model.15.conv.norm""": """decoder.layers.15.norm""",
}
SCREAMING_SNAKE_CASE		:List[Any]						=     {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
SCREAMING_SNAKE_CASE		:int						=     {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
SCREAMING_SNAKE_CASE		:Dict						=     []
SCREAMING_SNAKE_CASE		:int						=     []




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						List[Any]:



    """simple docstring"""






    for attribute in key.split("."			):
        UpperCamelCase_			  =						getattr(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)

    if weight_type is not None:
        UpperCamelCase_			  =						getattr(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			).shape
    else:
        UpperCamelCase_			  =						hf_pointer.shape

    if hf_shape != value.shape:
        raise ValueError(
            f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
            f" {value.shape} for {full_name}"			)

    if weight_type == "weight":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_g":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_v":
        UpperCamelCase_			  =						value
    elif weight_type == "bias":
        UpperCamelCase_			  =						value
    elif weight_type == "running_mean":
        UpperCamelCase_			  =						value
    elif weight_type == "running_var":
        UpperCamelCase_			  =						value
    elif weight_type == "num_batches_tracked":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_ih_l0":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_hh_l0":
        UpperCamelCase_			  =						value
    elif weight_type == "bias_ih_l0":
        UpperCamelCase_			  =						value
    elif weight_type == "bias_hh_l0":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_ih_l1":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_hh_l1":
        UpperCamelCase_			  =						value
    elif weight_type == "bias_ih_l1":
        UpperCamelCase_			  =						value
    elif weight_type == "bias_hh_l1":
        UpperCamelCase_			  =						value
    else:
        UpperCamelCase_			  =						value

    logger.info(f"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}."			)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






    for key in ignore_keys:
        if key.endswith(".*"			):
            if name.startswith(key[:-1]			):
                return True
        elif ".*." in key:
            UpperCamelCase_  ,       UpperCamelCase_			  =						key.split(".*."			)
            if prefix in name and suffix in name:
                return True
        elif key in name:
            return True
    return False




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						[]

    if model_name == "encodec_24khz" or "encodec_32khz":
        UpperCamelCase_			  =						MAPPING_24K
    elif model_name == "encodec_48khz":
        UpperCamelCase_			  =						MAPPING_48K
    else:
        raise ValueError(f"Unsupported model: {model_name}"			)

    for name, value in orig_dict.items():
        if should_ignore(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			):
            logger.info(f"{name} was ignored"			)
            continue

        UpperCamelCase_			  =						False
        for key, mapped_key in MAPPING.items():
            if "*" in key:
                UpperCamelCase_  ,       UpperCamelCase_			  =						key.split(".*."			)
                if prefix in name and suffix in name:
                    UpperCamelCase_			  =						suffix

            if key in name:
                # HACK otherwise .embed gets initialized with .embed_avg too
                if key.endswith("embed"			) and name.endswith("embed_avg"			):
                    continue

                UpperCamelCase_			  =						True
                if "*" in mapped_key:
                    UpperCamelCase_			  =						name.split(SCREAMING_SNAKE_CASE_			)[0].split("."			)[-2]
                    UpperCamelCase_			  =						mapped_key.replace("*"			,						SCREAMING_SNAKE_CASE_			)
                if "weight_g" in name:
                    UpperCamelCase_			  =						"weight_g"
                elif "weight_v" in name:
                    UpperCamelCase_			  =						"weight_v"
                elif "weight_ih_l0" in name:
                    UpperCamelCase_			  =						"weight_ih_l0"
                elif "weight_hh_l0" in name:
                    UpperCamelCase_			  =						"weight_hh_l0"
                elif "bias_ih_l0" in name:
                    UpperCamelCase_			  =						"bias_ih_l0"
                elif "bias_hh_l0" in name:
                    UpperCamelCase_			  =						"bias_hh_l0"
                elif "weight_ih_l1" in name:
                    UpperCamelCase_			  =						"weight_ih_l1"
                elif "weight_hh_l1" in name:
                    UpperCamelCase_			  =						"weight_hh_l1"
                elif "bias_ih_l1" in name:
                    UpperCamelCase_			  =						"bias_ih_l1"
                elif "bias_hh_l1" in name:
                    UpperCamelCase_			  =						"bias_hh_l1"
                elif "bias" in name:
                    UpperCamelCase_			  =						"bias"
                elif "weight" in name:
                    UpperCamelCase_			  =						"weight"
                elif "running_mean" in name:
                    UpperCamelCase_			  =						"running_mean"
                elif "running_var" in name:
                    UpperCamelCase_			  =						"running_var"
                elif "num_batches_tracked" in name:
                    UpperCamelCase_			  =						"num_batches_tracked"
                else:
                    UpperCamelCase_			  =						None
                set_recursively(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)
            continue
        if not is_used:
            unused_weights.append(SCREAMING_SNAKE_CASE_			)

    logger.warning(f"Unused weights: {unused_weights}"			)




@torch.no_grad()
def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=None			,						SCREAMING_SNAKE_CASE_=None			,						)->						Union[str, Any]:



    """simple docstring"""






    if config_path is not None:
        UpperCamelCase_			  =						EncodecConfig.from_pretrained(SCREAMING_SNAKE_CASE_			)
    else:
        UpperCamelCase_			  =						EncodecConfig()

    if model_name == "encodec_24khz":
        pass  # config is already correct
    elif model_name == "encodec_32khz":
        UpperCamelCase_			  =						[8, 5, 4, 4]
        UpperCamelCase_			  =						[2.2]
        UpperCamelCase_			  =						6_4
        UpperCamelCase_			  =						3_2_0_0_0
        UpperCamelCase_			  =						2_0_4_8
        UpperCamelCase_			  =						False
        UpperCamelCase_			  =						False
        UpperCamelCase_			  =						False
    elif model_name == "encodec_48khz":
        UpperCamelCase_			  =						[8, 5, 4, 2]
        UpperCamelCase_			  =						[3.0, 6.0, 12.0, 24.0]
        UpperCamelCase_			  =						4_8_0_0_0
        UpperCamelCase_			  =						2
        UpperCamelCase_			  =						False
        UpperCamelCase_			  =						"time_group_norm"
        UpperCamelCase_			  =						True
        UpperCamelCase_			  =						1.0
        UpperCamelCase_			  =						0.01
    else:
        raise ValueError(f"Unknown model name: {model_name}"			)

    UpperCamelCase_			  =						EncodecModel(SCREAMING_SNAKE_CASE_			)

    UpperCamelCase_			  =						EncodecFeatureExtractor(
        feature_size=config.audio_channels			,						sampling_rate=config.sampling_rate			,						chunk_length_s=config.chunk_length_s			,						overlap=config.overlap			,						)
    feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_			)

    UpperCamelCase_			  =						torch.load(SCREAMING_SNAKE_CASE_			)
    if "best_state" in original_checkpoint:
        # we might have a training state saved, in which case discard the yaml results and just retain the weights
        UpperCamelCase_			  =						original_checkpoint["best_state"]
    recursively_load_weights(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)
    model.save_pretrained(SCREAMING_SNAKE_CASE_			)

    if repo_id:
        print("Pushing to the hub..."			)
        feature_extractor.push_to_hub(SCREAMING_SNAKE_CASE_			)
        model.push_to_hub(SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE		:Tuple						=     argparse.ArgumentParser()
 parser.add_argument(
     """--model""",
     default="""encodec_24khz""",
     type=str,
     help="""The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.""",
 )
 parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
 parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
 parser.add_argument(
     """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
 )
 parser.add_argument(
     """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
 )

 SCREAMING_SNAKE_CASE		:Dict						=     parser.parse_args()
 convert_checkpoint(
     args.model,
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.push_to_hub,
 )
