
'''simple docstring'''




import json
import os
import unittest
from typing import Tuple

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

from ...test_tokenization_common import TokenizerTesterMixin





@require_phonemizer
class 					A							(				__snake_case       ,     unittest.TestCase		):
     __magic_name__          =						WavaVecaPhonemeCTCTokenizer
     __magic_name__          =						False





     def 							__lowerCAmelCase				(				self      )					->							Optional[int]:






         """simple docstring"""






         super().setUp()

         A			:  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(''' '''      )
         A			:  Optional[Any]   =		dict(zip(SCREAMING_SNAKE_CASE		,     range(len(SCREAMING_SNAKE_CASE      )      )      )      )

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

         A			:  Optional[Any]   =		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(SCREAMING_SNAKE_CASE      ) + '''\n'''      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE=20		,     SCREAMING_SNAKE_CASE=5      )					->							Tuple[str, list]:






         """simple docstring"""






         A			:  Tuple   =		[(i, tokenizer.decode([i]		,     clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE      )) for i in range(len(SCREAMING_SNAKE_CASE      )      )]
         A			:  Optional[Any]   =		list(filter(lambda SCREAMING_SNAKE_CASE      : [t[0]] == tokenizer.encode(t[1]		,     do_phonemize=SCREAMING_SNAKE_CASE      )		,     SCREAMING_SNAKE_CASE      )      )
         if max_length is not None and len(SCREAMING_SNAKE_CASE      ) > max_length:
             A			:  Dict   =		toks[:max_length]
         if min_length is not None and len(SCREAMING_SNAKE_CASE      ) < min_length and len(SCREAMING_SNAKE_CASE      ) > 0:
             while len(SCREAMING_SNAKE_CASE      ) < min_length:
                 A			:  Dict   =		toks + toks
        # toks_str = [t[1] for t in toks]
         A			:  Optional[int]   =		[t[0] for t in toks]

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





     def 							__lowerCAmelCase				(				self		,     **SCREAMING_SNAKE_CASE      )					->							List[str]:






         """simple docstring"""






         kwargs.update(self.special_tokens_map      )
         return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname		,     **SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






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

         # check adding a single token
         tokenizer.add_tokens('''xxx'''      )
         A			:  Optional[int]   =		tokenizer('''m xxx ɪ'''		,     do_phonemize=SCREAMING_SNAKE_CASE      ).input_ids
         self.assertEqual(SCREAMING_SNAKE_CASE		,     [13, 392, 17]      )  # xxx should be last token

         tokenizer.add_tokens(['''aaa''', '''bbb''', '''ccc''']      )
         A			:  Dict   =		tokenizer('''m aaa ɪ ccc'''		,     do_phonemize=SCREAMING_SNAKE_CASE      ).input_ids
         self.assertEqual(SCREAMING_SNAKE_CASE		,     [13, 393, 17, 395]      )  # aaa and ccc should be after xxx and 2 after aaa

         A			:  Optional[int]   =		tokenizer('''maɪ c'''		,     do_phonemize=SCREAMING_SNAKE_CASE      ).input_ids
         self.assertEqual(SCREAMING_SNAKE_CASE		,     [3, 200]      )  # mai should be <unk> (=3)





     def 							__lowerCAmelCase				(				self      )					->							List[Any]:






         """simple docstring"""






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

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





     def 							__lowerCAmelCase				(				self      )					->							List[Any]:






         """simple docstring"""






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

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





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






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

         A			:  str   =		tokenizer.decode(tokenizer(SCREAMING_SNAKE_CASE      ).input_ids      )

         self.assertEqual(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							Optional[int]:






         """simple docstring"""






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

         A			:  List[str]   =		[
             [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
             [24, 22, 5, 24, 22, 5, 77],
         ]
         A			:  str   =		tokenizer.decode(sample_ids[0]      )
         A			:  Optional[int]   =		tokenizer.batch_decode(SCREAMING_SNAKE_CASE      )
         self.assertEqual(SCREAMING_SNAKE_CASE		,     batch_tokens[0]      )
         self.assertEqual(SCREAMING_SNAKE_CASE		,     ['''k s ɾ ɾ l ɭʲ''', '''j ð s j ð s oːɹ''']      )





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






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

         A			:  int   =		'''Hello how are you'''
         A			:  List[str]   =		tokenizer.phonemize(SCREAMING_SNAKE_CASE		,     phonemizer_lang='''en-us'''      )
         self.assertEqual(SCREAMING_SNAKE_CASE		,     '''h ə l oʊ | h aʊ | ɑːɹ | j uː |'''      )





     def 							__lowerCAmelCase				(				self      )					->							Optional[int]:






         """simple docstring"""






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

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





     def 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






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

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

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





     def 							__lowerCAmelCase				(				self      )					->							Optional[int]:






         """simple docstring"""






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

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

         A			:  Dict   =		tokenizer.decode(tokenizer(SCREAMING_SNAKE_CASE      ).input_ids		,     filter_word_delimiter_token=SCREAMING_SNAKE_CASE      )

         self.assertEqual(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							Union[str, Any]:






         """simple docstring"""






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

         A			:  str   =		'''Hello how are you'''
         A			:  Optional[Any]   =		tokenizer.phonemize(SCREAMING_SNAKE_CASE		,     phonemizer_lang='''en-us'''      )

         A			:  Union[str, Any]   =		tokenizer.decode(tokenizer(SCREAMING_SNAKE_CASE      ).input_ids		,     filter_word_delimiter_token=SCREAMING_SNAKE_CASE      )

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





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






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

         A			:  Tuple   =		tokenizer(SCREAMING_SNAKE_CASE		,     phonemizer_lang='''en-us'''      ).input_ids
         A			:  Dict   =		tokenizer(SCREAMING_SNAKE_CASE		,     phonemizer_lang='''fr-fr'''      ).input_ids

         self.assertNotEqual(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )

         A			:  Optional[int]   =		tokenizer.decode(SCREAMING_SNAKE_CASE      )
         A			:  int   =		tokenizer.decode(SCREAMING_SNAKE_CASE      )

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





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






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

         A			:  Optional[Any]   =		tokenizer(SCREAMING_SNAKE_CASE      ).input_ids
         A			:  int   =		tokenizer(SCREAMING_SNAKE_CASE      ).input_ids

         self.assertEqual(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							List[Any]:






         """simple docstring"""






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

         # fmt: off
         A			:  List[Any]   =		[
             [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 392, 392, 393, 392, 392, 393, 394, 394],
             [24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 394, 394],
         ]
         # fmt: on

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





     @staticmethod
     def 							__lowerCAmelCase				(				SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							Tuple:






         """simple docstring"""






         A			:  Tuple   =		[d[key] for d in offsets]
         return retrieved_list





     def 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






         A			:  Optional[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"
         A			:  Tuple   =		[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

         A			:  List[Any]   =		tokenizer.decode(SCREAMING_SNAKE_CASE		,     output_char_offsets=SCREAMING_SNAKE_CASE		,     filter_word_delimiter_token=SCREAMING_SNAKE_CASE      )
         # 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(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )      )

         # 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 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         A			:  Optional[Any]   =		self.get_tokenizer(word_delimiter_token='''|'''      )

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

             # transform list to ModelOutput
             A			:  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(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      ):
                 if isinstance(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      ):
                     [recursive_check(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      ) for la, la in zip(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )]
                 self.assertEqual(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )

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

        # fmt: off
         A			:  Union[str, Any]   =		[
             [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
         A			:  Optional[int]   =		tokenizer.batch_decode(SCREAMING_SNAKE_CASE		,     output_char_offsets=SCREAMING_SNAKE_CASE      )
         A			:  str   =		[tokenizer.decode(SCREAMING_SNAKE_CASE		,     output_char_offsets=SCREAMING_SNAKE_CASE      ) for ids in sample_ids]
         check_list_tuples_equal(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )





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






         """simple docstring"""






         pass





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






         """simple docstring"""






         pass





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






         """simple docstring"""






         pass





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






         """simple docstring"""






         pass





     def 							__lowerCAmelCase				(				self      )					->							List[Any]:






         """simple docstring"""






         A			:  Dict   =		self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE      )
         for tokenizer in tokenizers:
             with self.subTest(F'{tokenizer.__class__.__name__}'      ):
                 A			:  List[str]   =		tokenizer.vocab_size
                 A			:  Union[str, Any]   =		len(SCREAMING_SNAKE_CASE      )

                 self.assertNotEqual(SCREAMING_SNAKE_CASE		,     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)

                 A			:  List[Any]   =		['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
                 A			:  int   =		tokenizer.add_tokens(SCREAMING_SNAKE_CASE      )
                 A			:  Optional[Any]   =		tokenizer.vocab_size
                 A			:  str   =		len(SCREAMING_SNAKE_CASE      )

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

                 A			:  str   =		tokenizer.encode('''aaaaa bbbbbb low cccccccccdddddddd l'''		,     add_special_tokens=SCREAMING_SNAKE_CASE      )

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

                 A			:  Tuple   =		{'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''}
                 A			:  int   =		tokenizer.add_special_tokens(SCREAMING_SNAKE_CASE      )
                 A			:  Dict   =		tokenizer.vocab_size
                 A			:  List[Any]   =		len(SCREAMING_SNAKE_CASE      )

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

                 A			:  Tuple   =		tokenizer.encode(
                     '''>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l'''		,     add_special_tokens=SCREAMING_SNAKE_CASE      )

                 self.assertGreaterEqual(len(SCREAMING_SNAKE_CASE      )		,     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 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






         pass





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






         """simple docstring"""






         pass





     def 							__lowerCAmelCase				(				self      )					->							List[str]:






         """simple docstring"""






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

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






'''simple docstring'''




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()
lowercase     :							List[str]       						=     logging.get_logger('transformers.models.encodec')

lowercase     :							str       						=     {
    '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',
}
lowercase     :							Optional[int]       						=     {
    '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',
}
lowercase     :							int       						=     {
    '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',
}
lowercase     :							int       						=     {
    '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',
}
lowercase     :							List[Any]       						=     {
    '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',
}
lowercase     :							Dict       						=     {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
lowercase     :							List[str]       						=     {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
lowercase     :							Union[str, Any]       						=     []
lowercase     :							int       						=     []







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    for attribute in key.split('''.'''		):
        A			:  int   =		getattr(snake_case__						,							snake_case__		)

    if weight_type is not None:
        A			:  int   =		getattr(snake_case__						,							snake_case__		).shape
    else:
        A			:  List[str]   =		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":
        A			:  str   =		value
    elif weight_type == "weight_g":
        A			:  Union[str, Any]   =		value
    elif weight_type == "weight_v":
        A			:  List[Any]   =		value
    elif weight_type == "bias":
        A			:  Union[str, Any]   =		value
    elif weight_type == "running_mean":
        A			:  Union[str, Any]   =		value
    elif weight_type == "running_var":
        A			:  List[str]   =		value
    elif weight_type == "num_batches_tracked":
        A			:  List[Any]   =		value
    elif weight_type == "weight_ih_l0":
        A			:  Optional[int]   =		value
    elif weight_type == "weight_hh_l0":
        A			:  List[Any]   =		value
    elif weight_type == "bias_ih_l0":
        A			:  List[Any]   =		value
    elif weight_type == "bias_hh_l0":
        A			:  int   =		value
    elif weight_type == "weight_ih_l1":
        A			:  Tuple   =		value
    elif weight_type == "weight_hh_l1":
        A			:  List[str]   =		value
    elif weight_type == "bias_ih_l1":
        A			:  Any   =		value
    elif weight_type == "bias_hh_l1":
        A			:  Tuple   =		value
    else:
        A			:  str   =		value

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







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



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







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  List[Any]   =		[]

    if model_name == "encodec_24khz" or "encodec_32khz":
        A			:  str   =		MAPPING_24K
    elif model_name == "encodec_48khz":
        A			:  List[Any]   =		MAPPING_48K
    else:
        raise ValueError(F'Unsupported model: {model_name}'		)

    for name, value in orig_dict.items():
        if should_ignore(snake_case__						,							snake_case__		):
            logger.info(F'{name} was ignored'		)
            continue

        A			:  int   =		False
        for key, mapped_key in MAPPING.items():
            if "*" in key:
                A,    A			:  Optional[int]   =		key.split('''.*.'''		)
                if prefix in name and suffix in name:
                    A			:  Dict   =		suffix

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

                A			:  Union[str, Any]   =		True
                if "*" in mapped_key:
                    A			:  List[str]   =		name.split(snake_case__		)[0].split('''.'''		)[-2]
                    A			:  Optional[Any]   =		mapped_key.replace('''*'''						,							snake_case__		)
                if "weight_g" in name:
                    A			:  Dict   =		'''weight_g'''
                elif "weight_v" in name:
                    A			:  List[str]   =		'''weight_v'''
                elif "weight_ih_l0" in name:
                    A			:  int   =		'''weight_ih_l0'''
                elif "weight_hh_l0" in name:
                    A			:  Any   =		'''weight_hh_l0'''
                elif "bias_ih_l0" in name:
                    A			:  Dict   =		'''bias_ih_l0'''
                elif "bias_hh_l0" in name:
                    A			:  List[Any]   =		'''bias_hh_l0'''
                elif "weight_ih_l1" in name:
                    A			:  Any   =		'''weight_ih_l1'''
                elif "weight_hh_l1" in name:
                    A			:  Tuple   =		'''weight_hh_l1'''
                elif "bias_ih_l1" in name:
                    A			:  Dict   =		'''bias_ih_l1'''
                elif "bias_hh_l1" in name:
                    A			:  List[str]   =		'''bias_hh_l1'''
                elif "bias" in name:
                    A			:  int   =		'''bias'''
                elif "weight" in name:
                    A			:  Optional[Any]   =		'''weight'''
                elif "running_mean" in name:
                    A			:  Optional[Any]   =		'''running_mean'''
                elif "running_var" in name:
                    A			:  Dict   =		'''running_var'''
                elif "num_batches_tracked" in name:
                    A			:  List[str]   =		'''num_batches_tracked'''
                else:
                    A			:  int   =		None
                set_recursively(snake_case__						,							snake_case__						,							snake_case__						,							snake_case__						,							snake_case__		)
            continue
        if not is_used:
            unused_weights.append(snake_case__		)

    logger.warning(F'Unused weights: {unused_weights}'		)







@torch.no_grad()
def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__						,							snake_case__=None						,							snake_case__=None						,							):




    '''simple docstring'''



    if config_path is not None:
        A			:  Dict   =		EncodecConfig.from_pretrained(snake_case__		)
    else:
        A			:  Dict   =		EncodecConfig()

    if model_name == "encodec_24khz":
        pass  # config is already correct
    elif model_name == "encodec_32khz":
        A			:  Optional[Any]   =		[8, 5, 4, 4]
        A			:  List[Any]   =		[2.2]
        A			:  Tuple   =		64
        A			:  str   =		3_2000
        A			:  Optional[Any]   =		2048
        A			:  Optional[int]   =		False
        A			:  Optional[int]   =		False
        A			:  List[Any]   =		False
    elif model_name == "encodec_48khz":
        A			:  List[str]   =		[8, 5, 4, 2]
        A			:  Any   =		[3.0, 6.0, 12.0, 24.0]
        A			:  Tuple   =		4_8000
        A			:  Optional[int]   =		2
        A			:  Dict   =		False
        A			:  Union[str, Any]   =		'''time_group_norm'''
        A			:  List[Any]   =		True
        A			:  Union[str, Any]   =		1.0
        A			:  Union[str, Any]   =		0.01
    else:
        raise ValueError(F'Unknown model name: {model_name}'		)

    A			:  Optional[Any]   =		EncodecModel(snake_case__		)

    A			:  Optional[Any]   =		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(snake_case__		)

    A			:  Optional[int]   =		torch.load(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
        A			:  Union[str, Any]   =		original_checkpoint['''best_state''']
    recursively_load_weights(snake_case__						,							snake_case__						,							snake_case__		)
    model.save_pretrained(snake_case__		)

    if repo_id:
        print('''Pushing to the hub...'''		)
        feature_extractor.push_to_hub(snake_case__		)
        model.push_to_hub(snake_case__		)


if __name__ == "__main__":
      lowercase     :							str       						=     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.'
      )

      lowercase     :							Union[str, Any]       						=     parser.parse_args()
      convert_checkpoint(
          args.model,
          args.checkpoint_path,
          args.pytorch_dump_folder_path,
          args.config_path,
          args.push_to_hub,
      )




