
"""simple docstring"""





import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						str:


					"""simple docstring"""





					if gpta_config_file == "":
										_UpperCamelCase									=    GPTaConfig()
					else:
										_UpperCamelCase									=    GPTaConfig.from_json_file(__snake_case      )
					_UpperCamelCase									=    GPTaModel(__snake_case      )

					# Load weights from numpy
					load_tf_weights_in_gpta(__snake_case,	__snake_case,	__snake_case      )

					# Save pytorch-model
					_UpperCamelCase									=    pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
					_UpperCamelCase									=    pytorch_dump_folder_path + '''/''' + CONFIG_NAME
					print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''      )
					torch.save(model.state_dict(),	__snake_case      )
					print(F'''Save configuration file to {pytorch_config_dump_path}'''      )
					with open(__snake_case,	'''w''',	encoding='''utf-8'''      ) as f:
										f.write(config.to_json_string()      )


if __name__ == "__main__":
				_a								=						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
				)
				parser.add_argument(
				    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
				)
				parser.add_argument(
				    """--gpt2_config_file""",
				    default="""""",
				    type=str,
				    help=(
				        """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
				        """This specifies the model architecture."""
				    ),
				)
				_a								=						parser.parse_args()
				convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)











'''simple docstring'''





import json
import os
import unittest
from typing import Tuple

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

from ...test_tokenization_common import TokenizerTesterMixin



@require_phonemizer
class  snake_case__						(    SCREAMING_SNAKE_CASE_  ,     unittest.TestCase				):
 A__				= WavaVecaPhonemeCTCTokenizer
 A__				= False
 def A_   (   self						:							Any  )      ->   Optional[Any]:







       '''simple docstring'''
       super().setUp()

       __snake_case :   Optional[Any]													=    (
           '<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(' '  )
       __snake_case :   Tuple													=    dict(zip(__a      ,     range(len(__a  )  )  )  )

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

       __snake_case :   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(__a  ) + '\n'  )
 def A_   (   self						:							Tuple      ,     __a						:							Any      ,     __a						:							str=False      ,     __a						:							Tuple=20      ,     __a						:							int=5  )      ->   Tuple[str, list]:







       '''simple docstring'''
       __snake_case :   Any													=    [(i, tokenizer.decode([i]      ,     clean_up_tokenization_spaces=__a  )) for i in range(len(__a  )  )]
       __snake_case :   Optional[int]													=    list(filter(lambda __a  : [t[0]] == tokenizer.encode(t[1]      ,     do_phonemize=__a  )      ,     __a  )  )
       if max_length is not None and len(__a  ) > max_length:
             __snake_case :   List[str]													=    toks[:max_length]
       if min_length is not None and len(__a  ) < min_length and len(__a  ) > 0:
             while len(__a  ) < min_length:
                   __snake_case :   Optional[int]													=    toks + toks
        # toks_str = [t[1] for t in toks]
       __snake_case :   List[Any]													=    [t[0] for t in toks]

       # Ensure consistency
       __snake_case :   Optional[Any]													=    tokenizer.decode(__a      ,     clean_up_tokenization_spaces=__a  )
       if " " not in output_txt and len(__a  ) > 1:
             __snake_case :   Tuple													=    (
                 tokenizer.decode([toks_ids[0]]      ,     clean_up_tokenization_spaces=__a  )
                 + ' '
                 + tokenizer.decode(toks_ids[1:]      ,     clean_up_tokenization_spaces=__a  )
             )
       if with_prefix_space:
             __snake_case :   Tuple													=    ' ' + output_txt
       __snake_case :   Optional[Any]													=    tokenizer.encode(__a      ,     add_special_tokens=__a  )
       return output_txt, output_ids
 def A_   (   self						:							Union[str, Any]      ,     **__a						:							str  )      ->   int:







       '''simple docstring'''
       kwargs.update(self.special_tokens_map  )
       return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname      ,     **__a  )
 def A_   (   self						:							List[Any]  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'  )

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

       tokenizer.add_tokens(['aaa', 'bbb', 'ccc']  )
       __snake_case :   Union[str, Any]													=    tokenizer('m aaa ɪ ccc'      ,     do_phonemize=__a  ).input_ids
       self.assertEqual(__a      ,     [13, 393, 17, 395]  )  # aaa and ccc should be after xxx and 2 after aaa

       __snake_case :   Dict													=    tokenizer('maɪ c'      ,     do_phonemize=__a  ).input_ids
       self.assertEqual(__a      ,     [3, 200]  )  # mai should be <unk> (=3)
 def A_   (   self						:							Any  )      ->   str:







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

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







       '''simple docstring'''
       __snake_case :   Dict													=    self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'  )

       __snake_case :   Optional[Any]													=    'Hello how are you'
       __snake_case :   List[str]													=    tokenizer.phonemize(__a      ,     phonemizer_lang='en-us'  )
       self.assertEqual(tokenizer(__a  ).input_ids      ,     tokenizer(__a      ,     do_phonemize=__a  ).input_ids  )
 def A_   (   self						:							Optional[Any]  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   Optional[int]													=    self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'  )
       __snake_case :   Tuple													=    'Hello how are you'
       __snake_case :   Tuple													=    tokenizer.phonemize(__a      ,     phonemizer_lang='en-us'  )

       __snake_case :   str													=    tokenizer.decode(tokenizer(__a  ).input_ids  )

       self.assertEqual(__a      ,     __a  )
 def A_   (   self						:							Union[str, Any]  )      ->   List[str]:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'  )

       __snake_case :   Union[str, Any]													=    [
           [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
           [24, 22, 5, 24, 22, 5, 77],
       ]
       __snake_case :   Tuple													=    tokenizer.decode(sample_ids[0]  )
       __snake_case :   str													=    tokenizer.batch_decode(__a  )
       self.assertEqual(__a      ,     batch_tokens[0]  )
       self.assertEqual(__a      ,     ['k s ɾ ɾ l ɭʲ', 'j ð s j ð s oːɹ']  )
 def A_   (   self						:							Tuple  )      ->   str:







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

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







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

       __snake_case :   Tuple													=    'Hello how are you'
       __snake_case :   List[str]													=    tokenizer.phonemize(__a      ,     phonemizer_lang='en-us'  )
       self.assertEqual(tokenizer(__a  ).input_ids      ,     tokenizer(__a      ,     do_phonemize=__a  ).input_ids  )
 def A_   (   self						:							Tuple  )      ->   Dict:







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

       # fmt: off
       __snake_case :   int													=    [
           [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
       __snake_case :   Dict													=    tokenizer.decode(sample_ids[0]  )
       __snake_case :   Tuple													=    tokenizer.batch_decode(__a  )
       self.assertEqual(__a      ,     batch_tokens[0]  )
       self.assertEqual(__a      ,     ['k s ɾ ɾ l ɭʲ', 'j ð s j ð s oːɹ']  )

       # decode with no word_del_token filter
       __snake_case :   Union[str, Any]													=    tokenizer.decode(sample_ids[0]      ,     filter_word_delimiter_token=__a  )
       __snake_case :   Optional[int]													=    tokenizer.batch_decode(__a      ,     filter_word_delimiter_token=__a  )
       self.assertEqual(__a      ,     batch_tokens[0]  )
       self.assertEqual(__a      ,     ['k s ɾ | ɾ l | ɭʲ', '| j ð | s j ð s oːɹ']  )
 def A_   (   self						:							Optional[Any]  )      ->   Tuple:







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

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

       __snake_case :   Union[str, Any]													=    tokenizer.decode(tokenizer(__a  ).input_ids      ,     filter_word_delimiter_token=__a  )

       self.assertEqual(__a      ,     __a  )
 def A_   (   self						:							Dict  )      ->   List[str]:







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

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

       __snake_case :   Union[str, Any]													=    tokenizer.decode(tokenizer(__a  ).input_ids      ,     filter_word_delimiter_token=__a  )

       self.assertEqual(' '.join([p.strip() for p in phonemes.split(' |'  )]  ).strip()      ,     __a  )
 def A_   (   self						:							int  )      ->   Optional[Any]:







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

       __snake_case :   Union[str, Any]													=    tokenizer(__a      ,     phonemizer_lang='en-us'  ).input_ids
       __snake_case :   Union[str, Any]													=    tokenizer(__a      ,     phonemizer_lang='fr-fr'  ).input_ids

       self.assertNotEqual(__a      ,     __a  )

       __snake_case :   str													=    tokenizer.decode(__a  )
       __snake_case :   int													=    tokenizer.decode(__a  )

       self.assertEqual(__a      ,     'h ə l oʊ h aʊ ɑːɹ j uː'  )
       self.assertEqual(__a      ,     'ɛ l o h aʊ a ʁ j u'  )
 def A_   (   self						:							str  )      ->   str:







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

       __snake_case :   Union[str, Any]													=    tokenizer(__a  ).input_ids
       __snake_case :   Any													=    tokenizer(__a  ).input_ids

       self.assertEqual(__a      ,     __a  )
 def A_   (   self						:							List[Any]  )      ->   Any:







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

       # fmt: off
       __snake_case :   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

       __snake_case :   str													=    tokenizer.batch_decode(__a  )
       self.assertEqual(__a      ,     ['k s ɾ ɾ l ɭʲ!?!? $$$', 'j ð s j ð s oːɹ $$$']  )
 @staticmethod
 def A_   (   __a						:							Any      ,     __a						:							Dict  )      ->   Tuple:







       '''simple docstring'''
       __snake_case :   str													=    [d[key] for d in offsets]
       return retrieved_list
 def A_   (   self						:							str  )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   Optional[int]													=    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"
       __snake_case :   int													=    [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

       __snake_case :   Any													=    tokenizer.decode(__a      ,     output_char_offsets=__a      ,     filter_word_delimiter_token=__a  )
       # 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(__a      ,     __a  )  )

       # 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 A_   (   self						:							Union[str, Any]  )      ->   str:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    self.get_tokenizer(word_delimiter_token='|'  )

       def check_list_tuples_equal(__a						:							int      ,     __a						:							Union[str, Any]  ):
             self.assertTrue(isinstance(__a      ,     __a  )  )
             self.assertTrue(isinstance(outputs_list[0]      ,     __a  )  )

             # transform list to ModelOutput
             __snake_case :   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(__a						:							Any      ,     __a						:							str  ):
                   if isinstance(__a      ,     __a  ):
                         [recursive_check(__a      ,     __a  ) for la, la in zip(__a      ,     __a  )]
                   self.assertEqual(__a      ,     __a  )

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

        # fmt: off
       __snake_case :   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
       __snake_case :   List[str]													=    tokenizer.batch_decode(__a      ,     output_char_offsets=__a  )
       __snake_case :   str													=    [tokenizer.decode(__a      ,     output_char_offsets=__a  ) for ids in sample_ids]
       check_list_tuples_equal(__a      ,     __a  )
 @unittest.skip('Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes'  )
 def A_   (   self						:							Any  )      ->   Optional[int]:







       '''simple docstring'''
       pass
 @unittest.skip('Wav2Vec2PhonemeTokenizer always puts spaces between phonemes'  )
 def A_   (   self						:							Union[str, Any]  )      ->   Union[str, Any]:







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







       '''simple docstring'''
       pass
 @unittest.skip('Wav2Vec2PhonemeModel has no max model length => no testing'  )
 def A_   (   self						:							Tuple  )      ->   Optional[Any]:







       '''simple docstring'''
       pass
 def A_   (   self						:							Optional[int]  )      ->   str:







       '''simple docstring'''
       __snake_case :   int													=    self.get_tokenizers(do_lower_case=__a  )
       for tokenizer in tokenizers:
             with self.subTest(f'''{tokenizer.__class__.__name__}'''  ):
                   __snake_case :   int													=    tokenizer.vocab_size
                   __snake_case :   List[Any]													=    len(__a  )

                   self.assertNotEqual(__a      ,     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)

                   __snake_case :   Optional[Any]													=    ['aaaaa bbbbbb', 'cccccccccdddddddd']
                   __snake_case :   Optional[int]													=    tokenizer.add_tokens(__a  )
                   __snake_case :   Optional[int]													=    tokenizer.vocab_size
                   __snake_case :   Tuple													=    len(__a  )

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

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

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

                   __snake_case :   Tuple													=    {'eos_token': '>>>>|||<||<<|<<', 'pad_token': '<<<<<|||>|>>>>|>'}
                   __snake_case :   Optional[Any]													=    tokenizer.add_special_tokens(__a  )
                   __snake_case :   int													=    tokenizer.vocab_size
                   __snake_case :   Any													=    len(__a  )

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

                   __snake_case :   List[str]													=    tokenizer.encode(
                       '>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l'      ,     add_special_tokens=__a  )

                   self.assertGreaterEqual(len(__a  )      ,     6  )
                   self.assertGreater(tokens[0]      ,     tokenizer.vocab_size - 1  )
                   self.assertGreater(tokens[0]      ,     tokens[1]  )
                   self.assertGreater(tokens[-3]      ,     tokenizer.vocab_size - 1  )
                   self.assertGreater(tokens[-3]      ,     tokens[-4]  )
                   self.assertEqual(tokens[0]      ,     tokenizer.eos_token_id  )
                   self.assertEqual(tokens[-3]      ,     tokenizer.pad_token_id  )
 @unittest.skip('The tokenizer shouldn\'t be used to encode input IDs (except for labels), only to decode.'  )
 def A_   (   self						:							Dict  )      ->   List[str]:







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







       '''simple docstring'''
       pass





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







       '''simple docstring'''
       # The default common tokenizer tests assumes that the output of `convert_tokens_to_string` is a string which
       # is not the case for Wav2Vec2PhonemeCTCTokenizer.
       __snake_case :   Optional[int]													=    self.get_tokenizers(fast=__a      ,     do_lower_case=__a  )
       for tokenizer in tokenizers:
             with self.subTest(f'''{tokenizer.__class__.__name__}'''  ):
                   __snake_case :   Union[str, Any]													=    ['ð', 'ɪ', 's', 'ɪ', 'z', 'ɐ', 't', 'ɛ', 'k', 's', 't']
                   __snake_case :   List[Any]													=    tokenizer.convert_tokens_to_string(__a  )

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



