


import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
    from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
    A      = True
    from torch.cuda.amp import autocast


A      = logging.getLogger(__name__)


@dataclass
class     lowercase__  :
  A__=  field(
      metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}					,							)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to freeze the feature extractor layers of the model.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to log verbose messages or not.'}					,							)
  A__=  field(
      default=2.0					,							metadata={'help': 'Maximum temperature for gumbel softmax.'}		)
  A__=  field(
      default=0.5					,							metadata={'help': 'Minimum temperature for gumbel softmax.'}		)
  A__=  field(
      default=0.999_995					,							metadata={'help': 'Decay of gumbel temperature during training.'}		)





def __UpperCAmelCase       (		__A ,						__A			)						->					List[str]:



       '''simple docstring'''





       logging.basicConfig(
           format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" ,						datefmt="%m/%d/%Y %H:%M:%S" ,						handlers=[logging.StreamHandler(sys.stdout			)] ,						)
       UpperCAmelCase__										=     logging.WARNING
       if model_args.verbose_logging:
              UpperCAmelCase__										=     logging.DEBUG
       elif trainer_utils.is_main_process(training_args.local_rank			):
              UpperCAmelCase__										=     logging.INFO
       logger.setLevel(__A			)


@dataclass
class     lowercase__  :
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'The name of the dataset to use (via the datasets library).'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}		)
  A__=  field(
      default='train'					,							metadata={
          'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
      }					,							)
  A__=  field(
      default='validation'					,							metadata={
          'help': (
              'The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''
          )
      }					,							)
  A__=  field(
      default='file'					,							metadata={'help': 'Column in the dataset that contains speech file path. Defaults to \'file\''}					,							)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Overwrite the cached preprocessed datasets or not.'}		)
  A__=  field(
      default=1					,							metadata={
          'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
      }					,							)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'The number of processes to use for the preprocessing.'}					,							)
  A__=  field(
      default=20.0					,							metadata={'help': 'Filter audio files that are longer than `max_duration_in_seconds` seconds'}		)




@dataclass
class     lowercase__  :
  A__=  42
  A__=  42
  A__=  "longest"
  A__=  None
  A__=  None






  def __call__(					self      :					str		,						_lowercase      :					List[Dict[str, Union[List[int], torch.Tensor]]] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.feature_extractor.pad(
             _lowercase		,						max_length=self.max_length		,						padding=self.padding		,						pad_to_multiple_of=self.pad_to_multiple_of		,						return_tensors="pt"		,						)
         UpperCAmelCase__										=     self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1] )

         UpperCAmelCase__										=     batch["input_values"].shape[0]

         # make sure that no loss is computed on padded inputs
         if batch["attention_mask"] is not None:
                # compute real output lengths according to convolution formula
                UpperCAmelCase__										=     self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1 ) ).to(
                    torch.long )

                UpperCAmelCase__										=     torch.zeros(
                    (batch_size, mask_indices_seq_length)		,						dtype=torch.long		,						device=batch["input_values"].device )

                # these two operations makes sure that all values
                # before the output lengths indices are attended to
                UpperCAmelCase__										=     1
                UpperCAmelCase__										=     attention_mask.flip([-1] ).cumsum(-1 ).flip([-1] ).bool()

         # sample randomly masked indices
         UpperCAmelCase__										=     _compute_mask_indices(
             (batch_size, mask_indices_seq_length)		,						self.model.config.mask_time_prob		,						self.model.config.mask_time_length		,						attention_mask=_lowercase		,						min_masks=2		,						)

         return batch




class     lowercase__  (					__SCREAMING_SNAKE_CASE		):






  def __init__(					self      :					str		,						*_lowercase      :					Tuple		,						_lowercase      :					Dict=1		,						_lowercase      :					int=0		,						_lowercase      :					str=1.0		,						**_lowercase      :					Any ):




         """simple docstring"""







         super().__init__(*_lowercase		,						**_lowercase )
         UpperCAmelCase__										=     0
         UpperCAmelCase__										=     max_gumbel_temp
         UpperCAmelCase__										=     min_gumbel_temp
         UpperCAmelCase__										=     gumbel_temp_decay






  def 			_UpperCAmelCase  (					self      :					List[str]		,						_lowercase      :					nn.Module		,						_lowercase      :					Dict[str, Union[torch.Tensor, Any]] ):




         """simple docstring"""







         model.train()
         UpperCAmelCase__										=     self._prepare_inputs(_lowercase )

         if self.use_amp:
                with autocast():
                       UpperCAmelCase__										=     self.compute_loss(_lowercase		,						_lowercase )
         else:
                UpperCAmelCase__										=     self.compute_loss(_lowercase		,						_lowercase )

         if self.args.n_gpu > 1 or self.deepspeed:
                if model.module.config.ctc_loss_reduction == "mean":
                       UpperCAmelCase__										=     loss.mean()
                elif model.module.config.ctc_loss_reduction == "sum":
                       UpperCAmelCase__										=     loss.sum() / (inputs["mask_time_indices"]).sum()
                else:
                       raise ValueError(F"""{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']""" )

         if self.args.gradient_accumulation_steps > 1:
                UpperCAmelCase__										=     loss / self.args.gradient_accumulation_steps

         if self.use_amp:
                self.scaler.scale(_lowercase ).backward()
         elif self.use_apex:
                with amp.scale_loss(_lowercase		,						self.optimizer ) as scaled_loss:
                       scaled_loss.backward()
         elif self.deepspeed:
                self.deepspeed.backward(_lowercase )
         else:
                loss.backward()

         self.num_update_step += 1
         # make sure gumbel softmax temperature is decayed
         if self.args.n_gpu > 1 or self.deepspeed:
                model.module.set_gumbel_temperature(
                    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step		,						self.min_gumbel_temp ) )
         else:
                model.set_gumbel_temperature(
                    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step		,						self.min_gumbel_temp ) )

         return loss.detach()





def __UpperCAmelCase       (		)						->					List[Any]:



       '''simple docstring'''





       UpperCAmelCase__										=     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)

       UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__										=     parser.parse_args_into_dataclasses()
       configure_logger(__A ,						__A			)

       # Downloading and loading a dataset from the hub.
       UpperCAmelCase__										=     load_dataset(data_args.dataset_name ,						data_args.dataset_config_name ,						cache_dir=model_args.cache_dir			)

       if "validation" not in datasets.keys():
              # make sure only "validation" and "train" keys remain"
              UpperCAmelCase__										=     DatasetDict()
              UpperCAmelCase__										=     load_dataset(
                  data_args.dataset_name ,						data_args.dataset_config_name ,						split=F"""{data_args.train_split_name}[:{data_args.validation_split_percentage}%]""" ,						cache_dir=model_args.cache_dir ,						)
              UpperCAmelCase__										=     load_dataset(
                  data_args.dataset_name ,						data_args.dataset_config_name ,						split=F"""{data_args.train_split_name}[{data_args.validation_split_percentage}%:]""" ,						cache_dir=model_args.cache_dir ,						)
       else:
              # make sure only "validation" and "train" keys remain"
              UpperCAmelCase__										=     DatasetDict()
              UpperCAmelCase__										=     load_dataset(
                  data_args.dataset_name ,						data_args.dataset_config_name ,						split="validation" ,						cache_dir=model_args.cache_dir ,						)
              UpperCAmelCase__										=     load_dataset(
                  data_args.dataset_name ,						data_args.dataset_config_name ,						split=F"""{data_args.train_split_name}""" ,						cache_dir=model_args.cache_dir ,						)

       # only normalized-inputs-training is supported
       UpperCAmelCase__										=     WavaVecaFeatureExtractor.from_pretrained(
           model_args.model_name_or_path ,						cache_dir=model_args.cache_dir ,						do_normalize=__A			)

       def prepare_dataset(__A			):
              # check that all files have the correct sampling rate
              UpperCAmelCase__						,			UpperCAmelCase__										=     librosa.load(batch[data_args.speech_file_column] ,						sr=feature_extractor.sampling_rate			)
              return batch

       # load audio files into numpy arrays
       UpperCAmelCase__										=     datasets.map(
           __A ,						num_proc=data_args.preprocessing_num_workers ,						remove_columns=datasets["train"].column_names			)

       # filter audio files that are too long
       UpperCAmelCase__										=     vectorized_datasets.filter(
           lambda __A			: len(data["speech"]			) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate			)			)

       def normalize(__A			):
              return feature_extractor(batch["speech"] ,						sampling_rate=feature_extractor.sampling_rate			)

       # normalize and transform to `BatchFeatures`
       UpperCAmelCase__										=     vectorized_datasets.map(
           __A ,						batched=__A ,						num_proc=data_args.preprocessing_num_workers ,						load_from_cache_file=not data_args.overwrite_cache ,						remove_columns=vectorized_datasets["train"].column_names ,						)

       # pretraining is only supported for "newer" stable layer norm architecture
       # apply_spec_augment has to be True, mask_feature_prob has to be 0.0
       UpperCAmelCase__										=     WavaVecaConfig.from_pretrained(
           model_args.model_name_or_path ,						cache_dir=model_args.cache_dir ,						gradient_checkpointing=training_args.gradient_checkpointing ,						)

       if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
              raise ValueError(
                  "PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"
                  " ``config.feat_extract_norm='layer'"			)

       UpperCAmelCase__										=     WavaVecaForPreTraining(__A			)

       UpperCAmelCase__										=     DataCollatorForWavaVecaPretraining(model=__A ,						feature_extractor=__A			)

       UpperCAmelCase__										=     WavaVecaPreTrainer(
           model=__A ,						data_collator=__A ,						args=__A ,						train_dataset=vectorized_datasets["train"] ,						eval_dataset=vectorized_datasets["validation"] ,						tokenizer=__A ,						max_gumbel_temp=model_args.max_gumbel_temperature ,						min_gumbel_temp=model_args.min_gumbel_temperature ,						gumbel_temp_decay=model_args.gumbel_temperature_decay ,						)
       trainer.train()


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 )





