
"""simple docstring"""




import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


UpperCAmelCase__   :					Optional[int]												=				logging.getLogger(__name__)
UpperCAmelCase__   :					Optional[Any]												=				list(MODEL_FOR_MASKED_LM_MAPPING.keys())
UpperCAmelCase__   :					List[str]												=				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class 			lowerCAmelCase_		:


				"""simple docstring"""



				__UpperCamelCase   :		Optional[str]       =     field(
				    default=a__				,      metadata={
				        '''help''': (
				            '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
				        )
				    }				,      )
				__UpperCamelCase   :		Optional[str]       =     field(
				    default=a__				,      metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(a__						)}				,      )
				__UpperCamelCase   :		Optional[str]       =     field(
				    default=a__				,      metadata={
				        '''help''': (
				            '''Override some existing default config settings when a model is trained from scratch. Example: '''
				            '''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
				        )
				    }				,      )
				__UpperCamelCase   :		Optional[str]       =     field(
				    default=a__				,      metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}						)
				__UpperCamelCase   :		Optional[str]       =     field(
				    default=a__				,      metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}						)
				__UpperCamelCase   :		Optional[str]       =     field(
				    default=a__				,      metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}				,      )
				__UpperCamelCase   :		bool       =     field(
				    default=a__				,      metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}				,      )
				__UpperCamelCase   :		str       =     field(
				    default='''main'''				,      metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}				,      )
				__UpperCamelCase   :		bool       =     field(
				    default=a__				,      metadata={
				        '''help''': (
				            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
				            '''with private models).'''
				        )
				    }				,      )



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



											"""simple docstring"""




											if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
																		raise ValueError(
																		    """--config_overrides can't be used in combination with --config_name or --model_name_or_path"""					)






@dataclass
class 			lowerCAmelCase_		:


				"""simple docstring"""



				__UpperCamelCase   :		Optional[str]       =     field(
				    default=a__				,      metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}						)
				__UpperCamelCase   :		Optional[str]       =     field(
				    default=a__				,      metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}						)
				__UpperCamelCase   :		Optional[str]       =     field(default=a__				,      metadata={'''help''': '''The input training data file (a text file).'''}						)
				__UpperCamelCase   :		Optional[str]       =     field(
				    default=a__				,      metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}				,      )
				__UpperCamelCase   :		Optional[str]       =     field(
				    default=a__				,      metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}				,      )
				__UpperCamelCase   :		Optional[str]       =     field(
				    default=a__				,      metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}				,      )
				__UpperCamelCase   :		bool       =     field(
				    default=a__				,      metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}						)
				__UpperCamelCase   :		Optional[int]       =     field(
				    default=5				,      metadata={
				        '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
				    }				,      )
				__UpperCamelCase   :		Optional[int]       =     field(
				    default=a__				,      metadata={
				        '''help''': (
				            '''The maximum total input sequence length after tokenization. Sequences longer '''
				            '''than this will be truncated. Default to the max input length of the model.'''
				        )
				    }				,      )
				__UpperCamelCase   :		Optional[int]       =     field(
				    default=a__				,      metadata={'''help''': '''The number of processes to use for the preprocessing.'''}				,      )
				__UpperCamelCase   :		float       =     field(
				    default=0.15				,      metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}						)
				__UpperCamelCase   :		bool       =     field(
				    default=a__				,      metadata={
				        '''help''': (
				            '''Whether to pad all samples to `max_seq_length`. '''
				            '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
				        )
				    }				,      )



				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											if self.train_file is not None:
																		SCREAMING_SNAKE_CASE__		: str     =     self.train_file.split("""."""					)[-1]
																		assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
											if self.validation_file is not None:
																		SCREAMING_SNAKE_CASE__		: str     =     self.validation_file.split("""."""					)[-1]
																		assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."






def lowercase_						(     _snake_case     ,_snake_case		):
							with open(_snake_case     ,"""r"""     ,encoding="""utf-8"""		) as f:
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     [json.loads(_snake_case		) for line in f.read().splitlines() if (len(_snake_case		) > 0 and not line.isspace())]
							assert len(_snake_case		) == len(_snake_case		)

							SCREAMING_SNAKE_CASE__		: Tuple     =     {c: dataset[c] for c in dataset.column_names}
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     refs
							return Dataset.from_dict(_snake_case		)

def lowercase_						(     ):
							# See all possible arguments in src/transformers/training_args.py
							# or by passing the --help flag to this script.
							# We now keep distinct sets of args, for a cleaner separation of concerns.

							SCREAMING_SNAKE_CASE__		: List[Any]     =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
							if len(sys.argv		) == 2 and sys.argv[1].endswith(""".json"""		):
														# If we pass only one argument to the script and it's the path to a json file,
														# let's parse it to get our arguments.
														SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Dict     =     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
							else:
														SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Dict     =     parser.parse_args_into_dataclasses()

							# Detecting last checkpoint.
							SCREAMING_SNAKE_CASE__		: Dict     =     None
							if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     get_last_checkpoint(training_args.output_dir		)
														if last_checkpoint is None and len(os.listdir(training_args.output_dir		)		) > 0:
																					raise ValueError(
																					    f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
																					    """Use --overwrite_output_dir to overcome."""		)
														elif last_checkpoint is not None:
																					logger.info(
																					    f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
																					    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""		)

    # Setup logging
							logging.basicConfig(
							    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""     ,datefmt="""%m/%d/%Y %H:%M:%S"""     ,handlers=[logging.StreamHandler(sys.stdout		)]     ,)
							logger.setLevel(logging.INFO if is_main_process(training_args.local_rank		) else logging.WARN		)

							# Log on each process the small summary:
							logger.warning(
							    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
							    + f'''distributed training: {bool(training_args.local_rank != -1		)}, 16-bits training: {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"""     ,_snake_case		)

							# Set seed before initializing model.
							set_seed(training_args.seed		)

							# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
							# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
							# (the dataset will be downloaded automatically from the datasets Hub).
							#
							# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
							# 'text' is found. You can easily tweak this behavior (see below).
							#
							# In distributed training, the load_dataset function guarantee that only one local process can concurrently
							# download the dataset.
							if data_args.dataset_name is not None:
														# Downloading and loading a dataset from the hub.
														SCREAMING_SNAKE_CASE__		: List[Any]     =     load_dataset(data_args.dataset_name     ,data_args.dataset_config_name		)
														if "validation" not in datasets.keys():
																					SCREAMING_SNAKE_CASE__		: List[str]     =     load_dataset(
																					    data_args.dataset_name     ,data_args.dataset_config_name     ,split=f'''train[:{data_args.validation_split_percentage}%]'''     ,)
																					SCREAMING_SNAKE_CASE__		: Any     =     load_dataset(
																					    data_args.dataset_name     ,data_args.dataset_config_name     ,split=f'''train[{data_args.validation_split_percentage}%:]'''     ,)
							else:
														SCREAMING_SNAKE_CASE__		: int     =     {}
														if data_args.train_file is not None:
																					SCREAMING_SNAKE_CASE__		: List[Any]     =     data_args.train_file
														if data_args.validation_file is not None:
																					SCREAMING_SNAKE_CASE__		: List[Any]     =     data_args.validation_file
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     data_args.train_file.split("""."""		)[-1]
														if extension == "txt":
																					SCREAMING_SNAKE_CASE__		: int     =     """text"""
														SCREAMING_SNAKE_CASE__		: Any     =     load_dataset(_snake_case     ,data_files=_snake_case		)
							# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
							# https://huggingface.co/docs/datasets/loading_datasets.html.

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.
							SCREAMING_SNAKE_CASE__		: Dict     =     {
							    """cache_dir""": model_args.cache_dir,
							    """revision""": model_args.model_revision,
							    """use_auth_token""": True if model_args.use_auth_token else None,
							}
							if model_args.config_name:
														SCREAMING_SNAKE_CASE__		: List[str]     =     AutoConfig.from_pretrained(model_args.config_name     ,**_snake_case		)
							elif model_args.model_name_or_path:
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     AutoConfig.from_pretrained(model_args.model_name_or_path     ,**_snake_case		)
							else:
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     CONFIG_MAPPING[model_args.model_type]()
														logger.warning("""You are instantiating a new config instance from scratch."""		)
														if model_args.config_overrides is not None:
																					logger.info(f'''Overriding config: {model_args.config_overrides}'''		)
																					config.update_from_string(model_args.config_overrides		)
																					logger.info(f'''New config: {config}'''		)

							SCREAMING_SNAKE_CASE__		: Tuple     =     {
							    """cache_dir""": model_args.cache_dir,
							    """use_fast""": model_args.use_fast_tokenizer,
							    """revision""": model_args.model_revision,
							    """use_auth_token""": True if model_args.use_auth_token else None,
							}
							if model_args.tokenizer_name:
														SCREAMING_SNAKE_CASE__		: str     =     AutoTokenizer.from_pretrained(model_args.tokenizer_name     ,**_snake_case		)
							elif model_args.model_name_or_path:
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     AutoTokenizer.from_pretrained(model_args.model_name_or_path     ,**_snake_case		)
							else:
														raise ValueError(
														    """You are instantiating a new tokenizer from scratch. This is not supported by this script."""
														    """You can do it from another script, save it, and load it from here, using --tokenizer_name."""		)

							if model_args.model_name_or_path:
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     AutoModelForMaskedLM.from_pretrained(
														    model_args.model_name_or_path     ,from_tf=bool(""".ckpt""" in model_args.model_name_or_path		)     ,config=_snake_case     ,cache_dir=model_args.cache_dir     ,revision=model_args.model_revision     ,use_auth_token=True if model_args.use_auth_token else None     ,)
							else:
														logger.info("""Training new model from scratch"""		)
														SCREAMING_SNAKE_CASE__		: Dict     =     AutoModelForMaskedLM.from_config(_snake_case		)

							model.resize_token_embeddings(len(_snake_case		)		)

							# Preprocessing the datasets.
							# First we tokenize all the texts.
							if training_args.do_train:
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     datasets["""train"""].column_names
							else:
														SCREAMING_SNAKE_CASE__		: int     =     datasets["""validation"""].column_names
							SCREAMING_SNAKE_CASE__		: List[str]     =     """text""" if """text""" in column_names else column_names[0]

							SCREAMING_SNAKE_CASE__		: Any     =     """max_length""" if data_args.pad_to_max_length else False

							def tokenize_function(_snake_case		):
														# Remove empty lines
														SCREAMING_SNAKE_CASE__		: List[str]     =     [line for line in examples["""text"""] if len(_snake_case		) > 0 and not line.isspace()]
														return tokenizer(examples["""text"""]     ,padding=_snake_case     ,truncation=_snake_case     ,max_length=data_args.max_seq_length		)

							SCREAMING_SNAKE_CASE__		: Dict     =     datasets.map(
							    _snake_case     ,batched=_snake_case     ,num_proc=data_args.preprocessing_num_workers     ,remove_columns=[text_column_name]     ,load_from_cache_file=not data_args.overwrite_cache     ,)

							# Add the chinese references if provided
							if data_args.train_ref_file is not None:
														SCREAMING_SNAKE_CASE__		: int     =     add_chinese_references(tokenized_datasets["""train"""]     ,data_args.train_ref_file		)
							if data_args.validation_ref_file is not None:
														SCREAMING_SNAKE_CASE__		: List[str]     =     add_chinese_references(
														    tokenized_datasets["""validation"""]     ,data_args.validation_ref_file		)
							# If we have ref files, need to avoid it removed by trainer
							SCREAMING_SNAKE_CASE__		: Any     =     data_args.train_ref_file or data_args.validation_ref_file
							if has_ref:
														SCREAMING_SNAKE_CASE__		: int     =     False

							# Data collator
							# This one will take care of randomly masking the tokens.
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     DataCollatorForWholeWordMask(tokenizer=_snake_case     ,mlm_probability=data_args.mlm_probability		)

							# Initialize our Trainer
							SCREAMING_SNAKE_CASE__		: Any     =     Trainer(
							    model=_snake_case     ,args=_snake_case     ,train_dataset=tokenized_datasets["""train"""] if training_args.do_train else None     ,eval_dataset=tokenized_datasets["""validation"""] if training_args.do_eval else None     ,tokenizer=_snake_case     ,data_collator=_snake_case     ,)

							# Training
							if training_args.do_train:
														if last_checkpoint is not None:
																					SCREAMING_SNAKE_CASE__		: Dict     =     last_checkpoint
														elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path		):
																					SCREAMING_SNAKE_CASE__		: str     =     model_args.model_name_or_path
														else:
																					SCREAMING_SNAKE_CASE__		: str     =     None
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     trainer.train(resume_from_checkpoint=_snake_case		)
														trainer.save_model()  # Saves the tokenizer too for easy upload

														SCREAMING_SNAKE_CASE__		: str     =     os.path.join(training_args.output_dir     ,"""train_results.txt"""		)
														if trainer.is_world_process_zero():
																					with open(_snake_case     ,"""w"""		) as writer:
																												logger.info("""***** Train results *****"""		)
																												for key, value in sorted(train_result.metrics.items()		):
																																			logger.info(f'''  {key} = {value}'''		)
																																			writer.write(f'''{key} = {value}\n'''		)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
																					trainer.state.save_to_json(os.path.join(training_args.output_dir     ,"""trainer_state.json"""		)		)

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

														SCREAMING_SNAKE_CASE__		: List[Any]     =     trainer.evaluate()

														SCREAMING_SNAKE_CASE__		: str     =     math.exp(eval_output["""eval_loss"""]		)
														SCREAMING_SNAKE_CASE__		: List[str]     =     perplexity

														SCREAMING_SNAKE_CASE__		: List[str]     =     os.path.join(training_args.output_dir     ,"""eval_results_mlm_wwm.txt"""		)
														if trainer.is_world_process_zero():
																					with open(_snake_case     ,"""w"""		) as writer:
																												logger.info("""***** Eval results *****"""		)
																												for key, value in sorted(results.items()		):
																																			logger.info(f'''  {key} = {value}'''		)
																																			writer.write(f'''{key} = {value}\n'''		)

							return results

def lowercase_						(     _snake_case		):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
					main()






"""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 			lowerCAmelCase_		(a__				,      unittest.TestCase						):


				"""simple docstring"""



				__UpperCamelCase   :		List[str]       =     WavaVecaPhonemeCTCTokenizer
				__UpperCamelCase   :		int       =     False



				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




											super().setUp()

											SCREAMING_SNAKE_CASE__		: List[str]     =     (
											    """<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(""" """					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     dict(zip(SCREAMING_SNAKE_CASE__     , range(len(SCREAMING_SNAKE_CASE__					)					)					)					)

											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     {"""pad_token""": """<pad>""", """unk_token""": """<unk>""", """bos_token""": """<s>""", """eos_token""": """</s>"""}

											SCREAMING_SNAKE_CASE__		: Dict     =     os.path.join(self.tmpdirname     , VOCAB_FILES_NAMES["""vocab_file"""]					)
											with open(self.vocab_file     , """w"""     , encoding="""utf-8"""					) as fp:
																		fp.write(json.dumps(SCREAMING_SNAKE_CASE__					) + """\n"""					)



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



											"""simple docstring"""




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

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



				def 			__magic_name__					(self     , **SCREAMING_SNAKE_CASE__					)      ->	Dict:



											"""simple docstring"""




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



				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




											SCREAMING_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"""					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     tokenizer("""m xxx ɪ"""     , do_phonemize=SCREAMING_SNAKE_CASE__					).input_ids
											self.assertEqual(SCREAMING_SNAKE_CASE__     , [13, 3_92, 17]					)  # xxx should be last token

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

											SCREAMING_SNAKE_CASE__		: List[Any]     =     tokenizer("""maɪ c"""     , do_phonemize=SCREAMING_SNAKE_CASE__					).input_ids
											self.assertEqual(SCREAMING_SNAKE_CASE__     , [3, 2_00]					)  # mai should be <unk> (=3)



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



											"""simple docstring"""




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

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



				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




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

											SCREAMING_SNAKE_CASE__		: int     =     """Hello how are you"""
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     tokenizer.phonemize(SCREAMING_SNAKE_CASE__     , phonemizer_lang="""en-us"""					)
											self.assertEqual(tokenizer(SCREAMING_SNAKE_CASE__					).input_ids     , tokenizer(SCREAMING_SNAKE_CASE__     , do_phonemize=SCREAMING_SNAKE_CASE__					).input_ids					)



				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[str]     =     self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     """Hello how are you"""
											SCREAMING_SNAKE_CASE__		: List[Any]     =     tokenizer.phonemize(SCREAMING_SNAKE_CASE__     , phonemizer_lang="""en-us"""					)

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

											self.assertEqual(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Dict:



											"""simple docstring"""




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

											SCREAMING_SNAKE_CASE__		: Dict     =     [
											    [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
											    [24, 22, 5, 24, 22, 5, 77],
											]
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     tokenizer.decode(sample_ids[0]					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     tokenizer.batch_decode(SCREAMING_SNAKE_CASE__					)
											self.assertEqual(SCREAMING_SNAKE_CASE__     , batch_tokens[0]					)
											self.assertEqual(SCREAMING_SNAKE_CASE__     , ["""k s ɾ ɾ l ɭʲ""", """j ð s j ð s oːɹ"""]					)



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



											"""simple docstring"""




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

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



				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




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

											SCREAMING_SNAKE_CASE__		: List[str]     =     """Hello how are you"""
											SCREAMING_SNAKE_CASE__		: Any     =     tokenizer.phonemize(SCREAMING_SNAKE_CASE__     , phonemizer_lang="""en-us"""					)
											self.assertEqual(tokenizer(SCREAMING_SNAKE_CASE__					).input_ids     , tokenizer(SCREAMING_SNAKE_CASE__     , do_phonemize=SCREAMING_SNAKE_CASE__					).input_ids					)



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



											"""simple docstring"""




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

											# fmt: off
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     [
											    [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
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     tokenizer.decode(sample_ids[0]					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     tokenizer.batch_decode(SCREAMING_SNAKE_CASE__					)
											self.assertEqual(SCREAMING_SNAKE_CASE__     , batch_tokens[0]					)
											self.assertEqual(SCREAMING_SNAKE_CASE__     , ["""k s ɾ ɾ l ɭʲ""", """j ð s j ð s oːɹ"""]					)

											# decode with no word_del_token filter
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     tokenizer.decode(sample_ids[0]     , filter_word_delimiter_token=SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Dict     =     tokenizer.batch_decode(SCREAMING_SNAKE_CASE__     , filter_word_delimiter_token=SCREAMING_SNAKE_CASE__					)
											self.assertEqual(SCREAMING_SNAKE_CASE__     , batch_tokens[0]					)
											self.assertEqual(SCREAMING_SNAKE_CASE__     , ["""k s ɾ | ɾ l | ɭʲ""", """| j ð | s j ð s oːɹ"""]					)



				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




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

											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     """Hello how are you"""
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     tokenizer.phonemize(SCREAMING_SNAKE_CASE__     , phonemizer_lang="""en-us"""					)

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

											self.assertEqual(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)



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



											"""simple docstring"""




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

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     """Hello how are you"""
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     tokenizer.phonemize(SCREAMING_SNAKE_CASE__     , phonemizer_lang="""en-us"""					)

											SCREAMING_SNAKE_CASE__		: int     =     tokenizer.decode(tokenizer(SCREAMING_SNAKE_CASE__					).input_ids     , filter_word_delimiter_token=SCREAMING_SNAKE_CASE__					)

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



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     self.tokenizer_class.from_pretrained(
											    """facebook/wav2vec2-lv-60-espeak-cv-ft"""     , word_delimiter_token=SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: str     =     """Hello how are you"""

											SCREAMING_SNAKE_CASE__		: List[str]     =     tokenizer(SCREAMING_SNAKE_CASE__     , phonemizer_lang="""en-us"""					).input_ids
											SCREAMING_SNAKE_CASE__		: Tuple     =     tokenizer(SCREAMING_SNAKE_CASE__     , phonemizer_lang="""fr-fr"""					).input_ids

											self.assertNotEqual(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     tokenizer.decode(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     tokenizer.decode(SCREAMING_SNAKE_CASE__					)

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



				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""					)
											SCREAMING_SNAKE_CASE__		: Tuple     =     """Hello how Are you"""
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     """hello how are you"""

											SCREAMING_SNAKE_CASE__		: str     =     tokenizer(SCREAMING_SNAKE_CASE__					).input_ids
											SCREAMING_SNAKE_CASE__		: List[str]     =     tokenizer(SCREAMING_SNAKE_CASE__					).input_ids

											self.assertEqual(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




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

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

											SCREAMING_SNAKE_CASE__		: str     =     tokenizer.batch_decode(SCREAMING_SNAKE_CASE__					)
											self.assertEqual(SCREAMING_SNAKE_CASE__     , ["""k s ɾ ɾ l ɭʲ!?!? $$$""", """j ð s j ð s oːɹ $$$"""]					)



				@staticmethod
				def 			__magic_name__					(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[Any]     =     [d[key] for d in offsets]
											return retrieved_list



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     self.get_tokenizer(word_delimiter_token="""|"""					)
											tokenizer.add_tokens("""|"""					)

											# fmt: off
											# ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ"
											SCREAMING_SNAKE_CASE__		: List[str]     =     [11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98]
											# fmt: on

											SCREAMING_SNAKE_CASE__		: int     =     tokenizer.decode(SCREAMING_SNAKE_CASE__     , output_char_offsets=SCREAMING_SNAKE_CASE__     , filter_word_delimiter_token=SCREAMING_SNAKE_CASE__					)
											# check Wav2Vec2CTCTokenizerOutput keys for char
											self.assertEqual(len(outputs.keys()					)     , 2					)
											self.assertTrue("""text""" in outputs					)
											self.assertTrue("""char_offsets""" in outputs					)
											self.assertTrue(isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)					)

											# check that order of chars is correct and identical for both outputs
											self.assertEqual(""" """.join(self.get_from_offsets(outputs["""char_offsets"""]     , """char"""					)					)     , outputs.text					)
											self.assertListEqual(
											    self.get_from_offsets(outputs["""char_offsets"""]     , """char"""					)     , ["""k""", """s""", """ɾ""", """ɾ""", """|""", """ɾ""", """l""", """|""", """ɭʲ"""]					)

											# check that offsets are actually correct for char
											# 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token,
											# 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98
											self.assertListEqual(
											    self.get_from_offsets(outputs["""char_offsets"""]     , """start_offset"""					)     , [0, 1, 4, 7, 9, 11, 12, 15, 16]					)
											self.assertListEqual(
											    self.get_from_offsets(outputs["""char_offsets"""]     , """end_offset"""					)     , [1, 4, 6, 9, 10, 12, 15, 16, 17]					)



				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Dict     =     self.get_tokenizer(word_delimiter_token="""|"""					)

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

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

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

        # fmt: off
											SCREAMING_SNAKE_CASE__		: List[Any]     =     [
											    [11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
											    [24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34],
											]
											# fmt: on

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

											# char
											SCREAMING_SNAKE_CASE__		: int     =     tokenizer.batch_decode(SCREAMING_SNAKE_CASE__     , output_char_offsets=SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     [tokenizer.decode(SCREAMING_SNAKE_CASE__     , output_char_offsets=SCREAMING_SNAKE_CASE__					) for ids in sample_ids]
											check_list_tuples_equal(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)



				@unittest.skip("""Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes"""					)
				def 			__magic_name__					(self					)      ->	Optional[int]:



											"""simple docstring"""




											pass



				@unittest.skip("""Wav2Vec2PhonemeTokenizer always puts spaces between phonemes"""					)
				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											pass



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



											"""simple docstring"""




											pass



				@unittest.skip("""Wav2Vec2PhonemeModel has no max model length => no testing"""					)
				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											pass



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




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

																									self.assertNotEqual(SCREAMING_SNAKE_CASE__     , 0					)

																									# We usually have added tokens from the start in tests because our vocab fixtures are
																									# smaller than the original vocabs - let's not assert this
																									# self.assertEqual(vocab_size, all_size)

																									SCREAMING_SNAKE_CASE__		: Tuple     =     ["""aaaaa bbbbbb""", """cccccccccdddddddd"""]
																									SCREAMING_SNAKE_CASE__		: Tuple     =     tokenizer.add_tokens(SCREAMING_SNAKE_CASE__					)
																									SCREAMING_SNAKE_CASE__		: List[Any]     =     tokenizer.vocab_size
																									SCREAMING_SNAKE_CASE__		: List[Any]     =     len(SCREAMING_SNAKE_CASE__					)

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

																									SCREAMING_SNAKE_CASE__		: Tuple     =     tokenizer.encode("""aaaaa bbbbbb low cccccccccdddddddd l"""     , add_special_tokens=SCREAMING_SNAKE_CASE__					)

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

																									SCREAMING_SNAKE_CASE__		: List[Any]     =     {"""eos_token""": """>>>>|||<||<<|<<""", """pad_token""": """<<<<<|||>|>>>>|>"""}
																									SCREAMING_SNAKE_CASE__		: Any     =     tokenizer.add_special_tokens(SCREAMING_SNAKE_CASE__					)
																									SCREAMING_SNAKE_CASE__		: Any     =     tokenizer.vocab_size
																									SCREAMING_SNAKE_CASE__		: Any     =     len(SCREAMING_SNAKE_CASE__					)

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

																									SCREAMING_SNAKE_CASE__		: List[Any]     =     tokenizer.encode(
																									    """>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"""     , add_special_tokens=SCREAMING_SNAKE_CASE__					)

																									self.assertGreaterEqual(len(SCREAMING_SNAKE_CASE__					)     , 6					)
																									self.assertGreater(tokens[0]     , tokenizer.vocab_size - 1					)
																									self.assertGreater(tokens[0]     , tokens[1]					)
																									self.assertGreater(tokens[-3]     , tokenizer.vocab_size - 1					)
																									self.assertGreater(tokens[-3]     , tokens[-4]					)
																									self.assertEqual(tokens[0]     , tokenizer.eos_token_id					)
																									self.assertEqual(tokens[-3]     , tokenizer.pad_token_id					)



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



											"""simple docstring"""




											pass



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



											"""simple docstring"""




											pass



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



											"""simple docstring"""




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

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




