


import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


A      = logging.getLogger(__name__)


A      = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
A      = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class     lowercase__  :
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={
          'help': (
              'The model checkpoint for weights initialization. Leave None if you want to train a model from'
              ' scratch.'
          )
      }					,							)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(__SCREAMING_SNAKE_CASE		)}					,							)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Pretrained config name or path if not the same as model_name'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}					,							)




@dataclass
class     lowercase__  :
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'The input training data file (a text file).'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={
          'help': (
              'The input training data files (multiple files in glob format). '
              'Very often splitting large files to smaller files can prevent tokenizer going out of memory'
          )
      }					,							)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}					,							)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'An optional input train ref data file for whole word mask in Chinese.'}					,							)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'An optional input eval ref data file for whole word mask in Chinese.'}					,							)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether distinct lines of text in the dataset are to be handled as distinct sequences.'}					,							)

  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Train with masked-language modeling loss instead of language modeling.'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether ot not to use whole word mask.'}		)
  A__=  field(
      default=0.15					,							metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'}		)
  A__=  field(
      default=1 / 6					,							metadata={
          'help': (
              'Ratio of length of a span of masked tokens to surrounding context length for permutation language'
              ' modeling.'
          )
      }					,							)
  A__=  field(
      default=5					,							metadata={'help': 'Maximum length of a span of masked tokens for permutation language modeling.'}		)

  A__=  field(
      default=-1					,							metadata={
          'help': (
              'Optional input sequence length after tokenization.'
              'The training dataset will be truncated in block of this size for training.'
              'Default to the model max input length for single sentence inputs (take into account special tokens).'
          )
      }					,							)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Overwrite the cached training and evaluation sets'}		)





def __UpperCAmelCase       (		__A ,						__A ,						__A = False ,						__A = None ,						)						->					Optional[int]:



       '''simple docstring'''





       def _dataset(__A ,						__A=None			):
              if args.line_by_line:
                     if ref_path is not None:
                            if not args.whole_word_mask or not args.mlm:
                                   raise ValueError("You need to set world whole masking and mlm to True for Chinese Whole Word Mask"			)
                            return LineByLineWithRefDataset(
                                tokenizer=__A ,						file_path=__A ,						block_size=args.block_size ,						ref_path=__A ,						)

                     return LineByLineTextDataset(tokenizer=__A ,						file_path=__A ,						block_size=args.block_size			)
              else:
                     return TextDataset(
                         tokenizer=__A ,						file_path=__A ,						block_size=args.block_size ,						overwrite_cache=args.overwrite_cache ,						cache_dir=__A ,						)

       if evaluate:
              return _dataset(args.eval_data_file ,						args.eval_ref_file			)
       elif args.train_data_files:
              return ConcatDataset([_dataset(__A			) for f in glob(args.train_data_files			)]			)
       else:
              return _dataset(args.train_data_file ,						args.train_ref_file			)





def __UpperCAmelCase       (		)						->					List[str]:



       '''simple docstring'''





       UpperCAmelCase__										=     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
       UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__										=     parser.parse_args_into_dataclasses()

       if data_args.eval_data_file is None and training_args.do_eval:
              raise ValueError(
                  "Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file "
                  "or remove the --do_eval argument."			)
       if (
           os.path.exists(training_args.output_dir			)
           and os.listdir(training_args.output_dir			)
           and training_args.do_train
           and not training_args.overwrite_output_dir
       ):
              raise ValueError(
                  F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
                  " --overwrite_output_dir to overcome."			)

       # Setup logging
       logging.basicConfig(
           format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" ,						datefmt="%m/%d/%Y %H:%M:%S" ,						level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN ,						)
       logger.warning(
           "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" ,						training_args.local_rank ,						training_args.device ,						training_args.n_gpu ,						bool(training_args.local_rank != -1			) ,						training_args.fpaa ,						)
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(training_args.local_rank			):
              transformers.utils.logging.set_verbosity_info()
              transformers.utils.logging.enable_default_handler()
              transformers.utils.logging.enable_explicit_format()
       logger.info("Training/evaluation parameters %s" ,						__A			)

       # Set seed
       set_seed(training_args.seed			)

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.

       if model_args.config_name:
              UpperCAmelCase__										=     AutoConfig.from_pretrained(model_args.config_name ,						cache_dir=model_args.cache_dir			)
       elif model_args.model_name_or_path:
              UpperCAmelCase__										=     AutoConfig.from_pretrained(model_args.model_name_or_path ,						cache_dir=model_args.cache_dir			)
       else:
              UpperCAmelCase__										=     CONFIG_MAPPING[model_args.model_type]()
              logger.warning("You are instantiating a new config instance from scratch."			)

       if model_args.tokenizer_name:
              UpperCAmelCase__										=     AutoTokenizer.from_pretrained(model_args.tokenizer_name ,						cache_dir=model_args.cache_dir			)
       elif model_args.model_name_or_path:
              UpperCAmelCase__										=     AutoTokenizer.from_pretrained(model_args.model_name_or_path ,						cache_dir=model_args.cache_dir			)
       else:
              raise ValueError(
                  "You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another"
                  " script, save it,and load it from here, using --tokenizer_name"			)

       if model_args.model_name_or_path:
              UpperCAmelCase__										=     AutoModelWithLMHead.from_pretrained(
                  model_args.model_name_or_path ,						from_tf=bool(".ckpt" in model_args.model_name_or_path			) ,						config=__A ,						cache_dir=model_args.cache_dir ,						)
       else:
              logger.info("Training new model from scratch"			)
              UpperCAmelCase__										=     AutoModelWithLMHead.from_config(__A			)

       model.resize_token_embeddings(len(__A			)			)

       if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
              raise ValueError(
                  "BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"
                  "--mlm flag (masked language modeling)."			)

       if data_args.block_size <= 0:
              UpperCAmelCase__										=     tokenizer.max_len
              # Our input block size will be the max possible for the model
       else:
              UpperCAmelCase__										=     min(data_args.block_size ,						tokenizer.max_len			)

       # Get datasets

       UpperCAmelCase__										=     (
           get_dataset(__A ,						tokenizer=__A ,						cache_dir=model_args.cache_dir			) if training_args.do_train else None
       )
       UpperCAmelCase__										=     (
           get_dataset(__A ,						tokenizer=__A ,						evaluate=__A ,						cache_dir=model_args.cache_dir			)
           if training_args.do_eval
           else None
       )
       if config.model_type == "xlnet":
              UpperCAmelCase__										=     DataCollatorForPermutationLanguageModeling(
                  tokenizer=__A ,						plm_probability=data_args.plm_probability ,						max_span_length=data_args.max_span_length ,						)
       else:
              if data_args.mlm and data_args.whole_word_mask:
                     UpperCAmelCase__										=     DataCollatorForWholeWordMask(
                         tokenizer=__A ,						mlm_probability=data_args.mlm_probability			)
              else:
                     UpperCAmelCase__										=     DataCollatorForLanguageModeling(
                         tokenizer=__A ,						mlm=data_args.mlm ,						mlm_probability=data_args.mlm_probability			)

    # Initialize our Trainer
       UpperCAmelCase__										=     Trainer(
           model=__A ,						args=__A ,						data_collator=__A ,						train_dataset=__A ,						eval_dataset=__A ,						prediction_loss_only=__A ,						)

       # Training
       if training_args.do_train:
              UpperCAmelCase__										=     (
                  model_args.model_name_or_path
                  if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path			)
                  else None
              )
              trainer.train(model_path=__A			)
              trainer.save_model()
              # For convenience, we also re-save the tokenizer to the same directory,
              # so that you can share your model easily on huggingface.co/models =)
              if trainer.is_world_master():
                     tokenizer.save_pretrained(training_args.output_dir			)

    # Evaluation
       UpperCAmelCase__										=     {}
       if training_args.do_eval:
              logger.info("*** Evaluate ***"			)

              UpperCAmelCase__										=     trainer.evaluate()

              UpperCAmelCase__										=     math.exp(eval_output["eval_loss"]			)
              UpperCAmelCase__										=     {"perplexity": perplexity}

              UpperCAmelCase__										=     os.path.join(training_args.output_dir ,						"eval_results_lm.txt"			)
              if trainer.is_world_master():
                     with open(__A ,						"w"			) as writer:
                            logger.info("***** Eval results *****"			)
                            for key in sorted(result.keys()			):
                                   logger.info("  %s = %s" ,						__A ,						str(result[key]			)			)
                                   writer.write("%s = %s\n" % (key, str(result[key]			))			)

              results.update(__A			)

       return results





def __UpperCAmelCase       (		__A			)						->					Optional[int]:



       '''simple docstring'''





       main()


if __name__ == "__main__":
    main()









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     lowercase__  (					__SCREAMING_SNAKE_CASE					,							unittest.TestCase		):
  A__=  WavaVecaPhonemeCTCTokenizer
  A__=  False






  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         super().setUp()

         UpperCAmelCase__										=     (
             "<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__										=     dict(zip(_lowercase		,						range(len(_lowercase ) ) ) )

         UpperCAmelCase__										=     {"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}

         UpperCAmelCase__										=     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 			_UpperCAmelCase  (					self      :					str		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					str=False		,						_lowercase      :					Tuple=20		,						_lowercase      :					int=5 ):




         """simple docstring"""







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

         # Ensure consistency
         UpperCAmelCase__										=     tokenizer.decode(_lowercase		,						clean_up_tokenization_spaces=_lowercase )
         if " " not in output_txt and len(_lowercase ) > 1:
                UpperCAmelCase__										=     (
                    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__										=     " " + output_txt
         UpperCAmelCase__										=     tokenizer.encode(_lowercase		,						add_special_tokens=_lowercase )
         return output_txt, output_ids






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						**_lowercase      :					Optional[int] ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft" )

         # check adding a single token
         tokenizer.add_tokens("xxx" )
         UpperCAmelCase__										=     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__										=     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__										=     tokenizer("maɪ c"		,						do_phonemize=_lowercase ).input_ids
         self.assertEqual(_lowercase		,						[3, 2_00] )  # mai should be <unk> (=3)






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         UpperCAmelCase__										=     self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft" )

         UpperCAmelCase__										=     "Hello how are you"
         UpperCAmelCase__										=     tokenizer.phonemize(_lowercase		,						phonemizer_lang="en-us" )
         self.assertEqual(_lowercase		,						"h ə l oʊ h aʊ ɑːɹ j uː" )






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         UpperCAmelCase__										=     self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft" )

         UpperCAmelCase__										=     "Hello how are you"
         UpperCAmelCase__										=     tokenizer.phonemize(_lowercase		,						phonemizer_lang="en-us" )
         self.assertEqual(tokenizer(_lowercase ).input_ids		,						tokenizer(_lowercase		,						do_phonemize=_lowercase ).input_ids )






  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         UpperCAmelCase__										=     self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft" )
         UpperCAmelCase__										=     "Hello how are you"
         UpperCAmelCase__										=     tokenizer.phonemize(_lowercase		,						phonemizer_lang="en-us" )

         UpperCAmelCase__										=     tokenizer.decode(tokenizer(_lowercase ).input_ids )

         self.assertEqual(_lowercase		,						_lowercase )






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft" )

         UpperCAmelCase__										=     [
             [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
             [24, 22, 5, 24, 22, 5, 77],
         ]
         UpperCAmelCase__										=     tokenizer.decode(sample_ids[0] )
         UpperCAmelCase__										=     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 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         UpperCAmelCase__										=     self.tokenizer_class.from_pretrained(
             "facebook/wav2vec2-lv-60-espeak-cv-ft"		,						word_delimiter_token="|" )
         tokenizer.add_tokens("|" )

         UpperCAmelCase__										=     "Hello how are you"
         UpperCAmelCase__										=     tokenizer.phonemize(_lowercase		,						phonemizer_lang="en-us" )
         self.assertEqual(_lowercase		,						"h ə l oʊ | h aʊ | ɑːɹ | j uː |" )






  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.tokenizer_class.from_pretrained(
             "facebook/wav2vec2-lv-60-espeak-cv-ft"		,						word_delimiter_token="|" )
         tokenizer.add_tokens("|" )

         UpperCAmelCase__										=     "Hello how are you"
         UpperCAmelCase__										=     tokenizer.phonemize(_lowercase		,						phonemizer_lang="en-us" )
         self.assertEqual(tokenizer(_lowercase ).input_ids		,						tokenizer(_lowercase		,						do_phonemize=_lowercase ).input_ids )






  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     self.tokenizer_class.from_pretrained(
             "facebook/wav2vec2-lv-60-espeak-cv-ft"		,						word_delimiter_token="|" )
         tokenizer.add_tokens("|" )

         # fmt: off
         UpperCAmelCase__										=     [
             [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__										=     tokenizer.decode(sample_ids[0] )
         UpperCAmelCase__										=     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__										=     tokenizer.decode(sample_ids[0]		,						filter_word_delimiter_token=_lowercase )
         UpperCAmelCase__										=     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 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         UpperCAmelCase__										=     self.tokenizer_class.from_pretrained(
             "facebook/wav2vec2-lv-60-espeak-cv-ft"		,						word_delimiter_token="|" )
         tokenizer.add_tokens("|" )

         UpperCAmelCase__										=     "Hello how are you"
         UpperCAmelCase__										=     tokenizer.phonemize(_lowercase		,						phonemizer_lang="en-us" )

         UpperCAmelCase__										=     tokenizer.decode(tokenizer(_lowercase ).input_ids		,						filter_word_delimiter_token=_lowercase )

         self.assertEqual(_lowercase		,						_lowercase )






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.tokenizer_class.from_pretrained(
             "facebook/wav2vec2-lv-60-espeak-cv-ft"		,						word_delimiter_token="|" )
         tokenizer.add_tokens("|" )

         UpperCAmelCase__										=     "Hello how are you"
         UpperCAmelCase__										=     tokenizer.phonemize(_lowercase		,						phonemizer_lang="en-us" )

         UpperCAmelCase__										=     tokenizer.decode(tokenizer(_lowercase ).input_ids		,						filter_word_delimiter_token=_lowercase )

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






  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     self.tokenizer_class.from_pretrained(
             "facebook/wav2vec2-lv-60-espeak-cv-ft"		,						word_delimiter_token=_lowercase )
         UpperCAmelCase__										=     "Hello how are you"

         UpperCAmelCase__										=     tokenizer(_lowercase		,						phonemizer_lang="en-us" ).input_ids
         UpperCAmelCase__										=     tokenizer(_lowercase		,						phonemizer_lang="fr-fr" ).input_ids

         self.assertNotEqual(_lowercase		,						_lowercase )

         UpperCAmelCase__										=     tokenizer.decode(_lowercase )
         UpperCAmelCase__										=     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 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         UpperCAmelCase__										=     self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft" )
         UpperCAmelCase__										=     "Hello how Are you"
         UpperCAmelCase__										=     "hello how are you"

         UpperCAmelCase__										=     tokenizer(_lowercase ).input_ids
         UpperCAmelCase__										=     tokenizer(_lowercase ).input_ids

         self.assertEqual(_lowercase		,						_lowercase )






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







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

         # fmt: off
         UpperCAmelCase__										=     [
             [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__										=     tokenizer.batch_decode(_lowercase )
         self.assertEqual(_lowercase		,						["k s ɾ ɾ l ɭʲ!?!? $$$", "j ð s j ð s oːɹ $$$"] )






  @staticmethod
  def 			_UpperCAmelCase  (					_lowercase      :					int		,						_lowercase      :					Tuple ):




         """simple docstring"""







         UpperCAmelCase__										=     [d[key] for d in offsets]
         return retrieved_list






  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     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__										=     [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__										=     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 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         UpperCAmelCase__										=     self.get_tokenizer(word_delimiter_token="|" )

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

                # transform list to ModelOutput
                UpperCAmelCase__										=     WavaVecaPhonemeCTCTokenizerOutput(
                    {k: [d[k] for d in outputs_list] for k in outputs_list[0]} )

                self.assertListEqual(outputs_batch["text"]		,						outputs_batch_a["text"] )

                def recursive_check(_lowercase      :					Optional[int]		,						_lowercase      :					Optional[int] ):
                       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__										=     [
             [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__										=     tokenizer.batch_decode(_lowercase		,						output_char_offsets=_lowercase )
         UpperCAmelCase__										=     [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 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         pass






  @unittest.skip("Wav2Vec2PhonemeTokenizer always puts spaces between phonemes" )
  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         pass






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




         """simple docstring"""







         pass






  @unittest.skip("Wav2Vec2PhonemeModel has no max model length => no testing" )
  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         pass






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.get_tokenizers(do_lower_case=_lowercase )
         for tokenizer in tokenizers:
                with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
                       UpperCAmelCase__										=     tokenizer.vocab_size
                       UpperCAmelCase__										=     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__										=     ["aaaaa bbbbbb", "cccccccccdddddddd"]
                       UpperCAmelCase__										=     tokenizer.add_tokens(_lowercase )
                       UpperCAmelCase__										=     tokenizer.vocab_size
                       UpperCAmelCase__										=     len(_lowercase )

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

                       UpperCAmelCase__										=     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__										=     {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
                       UpperCAmelCase__										=     tokenizer.add_special_tokens(_lowercase )
                       UpperCAmelCase__										=     tokenizer.vocab_size
                       UpperCAmelCase__										=     len(_lowercase )

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

                       UpperCAmelCase__										=     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 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         pass






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




         """simple docstring"""







         pass






  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.get_tokenizers(fast=_lowercase		,						do_lower_case=_lowercase )
         for tokenizer in tokenizers:
                with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
                       UpperCAmelCase__										=     ["ð", "ɪ", "s", "ɪ", "z", "ɐ", "t", "ɛ", "k", "s", "t"]
                       UpperCAmelCase__										=     tokenizer.convert_tokens_to_string(_lowercase )

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





