




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        __lowerCAmelCase					:

			"""simple docstring"""







			snake_case_  						=			field(
			    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}					)
			snake_case_  						=			field(
			    default=A__     ,							metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}     ,							)
			snake_case_  						=			field(
			    default=A__     ,							metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}					)
			snake_case_  						=			field(
			    default=A__     ,							metadata={'''help''': '''Whether to log verbose messages or not.'''}     ,							)
			snake_case_  						=			field(
			    default=2.0     ,							metadata={'''help''': '''Maximum temperature for gumbel softmax.'''}					)
			snake_case_  						=			field(
			    default=0.5     ,							metadata={'''help''': '''Minimum temperature for gumbel softmax.'''}					)
			snake_case_  						=			field(
			    default=0.9_9_9_9_9_5     ,							metadata={'''help''': '''Decay of gumbel temperature during training.'''}					)







def      lowerCamelCase_						(			UpperCamelCase__							: Optional[Any]					,	UpperCamelCase__							: Dict )	->			Optional[Any]:





				"""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 )]					,	)
				__lowerCamelCase									=       logging.WARNING
				if model_args.verbose_logging:
								__lowerCamelCase									=       logging.DEBUG
				elif trainer_utils.is_main_process(training_args.local_rank ):
								__lowerCamelCase									=       logging.INFO
				logger.setLevel(snake_case__ )







@dataclass
class        __lowerCAmelCase					:

			"""simple docstring"""







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







@dataclass
class        __lowerCAmelCase					:

			"""simple docstring"""







			snake_case_  						=			42
			snake_case_  						=			42
			snake_case_  						=			"longest"
			snake_case_  						=			None
			snake_case_  						=			None


			def __call__( self     ,		lowerCamelCase__				)       -> Dict[str, torch.Tensor]:



							'''simple docstring'''
							# reformat list to dict and set to pytorch format
							__lowerCamelCase									=       self.feature_extractor.pad(
							    __UpperCAmelCase     ,		max_length=self.max_length     ,		padding=self.padding     ,		pad_to_multiple_of=self.pad_to_multiple_of     ,		return_tensors='pt'     ,		)
							__lowerCamelCase									=       self.model._get_feat_extract_output_lengths(batch['input_values'].shape[-1]				)

							__lowerCamelCase									=       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
											__lowerCamelCase									=       self.model._get_feat_extract_output_lengths(batch['attention_mask'].sum(-1				)				).to(
											    torch.long				)

											__lowerCamelCase									=       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
											__lowerCamelCase									=       1
											__lowerCamelCase									=       attention_mask.flip([-1]				).cumsum(-1				).flip([-1]				).bool()

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

							return batch







class        __lowerCAmelCase					(			A__					):

			"""simple docstring"""







			def __init__( self     ,		*lowerCamelCase__     ,		lowerCamelCase__=1     ,		lowerCamelCase__=0     ,		lowerCamelCase__=1.0     ,		**lowerCamelCase__				)       -> Optional[Any]:



							'''simple docstring'''
							super().__init__(*__UpperCAmelCase     ,		**__UpperCAmelCase				)
							__lowerCamelCase									=       0
							__lowerCamelCase									=       max_gumbel_temp
							__lowerCamelCase									=       min_gumbel_temp
							__lowerCamelCase									=       gumbel_temp_decay






			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> torch.Tensor:



							'''simple docstring'''
							model.train()
							__lowerCamelCase									=       self._prepare_inputs(__UpperCAmelCase				)

							if self.use_amp:
											with autocast():
															__lowerCamelCase									=       self.compute_loss(__UpperCAmelCase     ,		__UpperCAmelCase				)
							else:
											__lowerCamelCase									=       self.compute_loss(__UpperCAmelCase     ,		__UpperCAmelCase				)

							if self.args.n_gpu > 1 or self.deepspeed:
											if model.module.config.ctc_loss_reduction == "mean":
															__lowerCamelCase									=       loss.mean()
											elif model.module.config.ctc_loss_reduction == "sum":
															__lowerCamelCase									=       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:
											__lowerCamelCase									=       loss / self.args.gradient_accumulation_steps

							if self.use_amp:
											self.scaler.scale(__UpperCAmelCase				).backward()
							elif self.use_apex:
											with amp.scale_loss(__UpperCAmelCase     ,		self.optimizer				) as scaled_loss:
															scaled_loss.backward()
							elif self.deepspeed:
											self.deepspeed.backward(__UpperCAmelCase				)
							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      lowerCamelCase_						(			)	->			Union[str, Any]:





				"""simple docstring"""


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

				__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase									=       parser.parse_args_into_dataclasses()
				configure_logger(snake_case__					,	snake_case__ )

				# Downloading and loading a dataset from the hub.
				__lowerCamelCase									=       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"
								__lowerCamelCase									=       DatasetDict()
								__lowerCamelCase									=       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					,	)
								__lowerCamelCase									=       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"
								__lowerCamelCase									=       DatasetDict()
								__lowerCamelCase									=       load_dataset(
								    data_args.dataset_name					,	data_args.dataset_config_name					,	split='validation'					,	cache_dir=model_args.cache_dir					,	)
								__lowerCamelCase									=       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
				__lowerCamelCase									=       WavaVecaFeatureExtractor.from_pretrained(
				    model_args.model_name_or_path					,	cache_dir=model_args.cache_dir					,	do_normalize=snake_case__ )

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

				# load audio files into numpy arrays
				__lowerCamelCase									=       datasets.map(
				    snake_case__					,	num_proc=data_args.preprocessing_num_workers					,	remove_columns=datasets['train'].column_names )

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

				def normalize(UpperCamelCase__							: Union[str, Any] ):
								return feature_extractor(batch['speech']					,	sampling_rate=feature_extractor.sampling_rate )

				# normalize and transform to `BatchFeatures`
				__lowerCamelCase									=       vectorized_datasets.map(
				    snake_case__					,	batched=snake_case__					,	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
				__lowerCamelCase									=       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\'' )

				__lowerCamelCase									=       WavaVecaForPreTraining(snake_case__ )

				__lowerCamelCase									=       DataCollatorForWavaVecaPretraining(model=snake_case__					,	feature_extractor=snake_case__ )

				__lowerCamelCase									=       WavaVecaPreTrainer(
				    model=snake_case__					,	data_collator=snake_case__					,	args=snake_case__					,	train_dataset=vectorized_datasets['train']					,	eval_dataset=vectorized_datasets['validation']					,	tokenizer=snake_case__					,	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()





"""simple docstring"""




import os
import pickle
import unittest

from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
    VOCAB_FILES_NAMES,
    BertJapaneseTokenizer,
    CharacterTokenizer,
    JumanppTokenizer,
    MecabTokenizer,
    SudachiTokenizer,
    WordpieceTokenizer,
)
from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi

from ...test_tokenization_common import TokenizerTesterMixin





@custom_tokenizers
class  lowerCamelCase    (A__      ,unittest.TestCase      ):
			lowerCamelCase__ :		Union[str, Any]       		=				BertJapaneseTokenizer
			lowerCamelCase__ :		str       		=				False
			lowerCamelCase__ :		Optional[int]       		=				True

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     str:
						super().setUp()

						SCREAMING_SNAKE_CASE__										=   [
						    """[UNK]""",
						    """[CLS]""",
						    """[SEP]""",
						    """こんにちは""",
						    """こん""",
						    """にちは""",
						    """ばんは""",
						    """##こん""",
						    """##にちは""",
						    """##ばんは""",
						    """世界""",
						    """##世界""",
						    """、""",
						    """##、""",
						    """。""",
						    """##。""",
						]

						SCREAMING_SNAKE_CASE__										=   os.path.join(self.tmpdirname     ,					VOCAB_FILES_NAMES["""vocab_file"""]							)
						with open(self.vocab_file     ,					"""w"""     ,					encoding="""utf-8"""							) as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]     ,					__UpperCAmelCase					:    Optional[int]							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   """こんにちは、世界。 \nこんばんは、世界。"""
						SCREAMING_SNAKE_CASE__										=   """こんにちは 、 世界 。 こんばんは 、 世界 。"""
						return input_text, output_text

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]     ,					__UpperCAmelCase					:    Union[str, Any]							) ->     int:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.get_input_output_texts(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   tokenizer.encode(__UpperCAmelCase     ,					add_special_tokens=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   tokenizer.decode(__UpperCAmelCase     ,					clean_up_tokenization_spaces=__UpperCAmelCase							)
						return text, ids

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Dict:
						pass  # TODO add if relevant

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Tuple:
						pass  # TODO add if relevant

			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     Dict:
						pass  # TODO add if relevant

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   self.tokenizer_class(self.vocab_file							)

						SCREAMING_SNAKE_CASE__										=   tokenizer.tokenize("""こんにちは、世界。\nこんばんは、世界。"""							)
						self.assertListEqual(__UpperCAmelCase     ,					["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""]							)
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase							)     ,					[3, 1_2, 1_0, 1_4, 4, 9, 1_2, 1_0, 1_4]							)

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   self.tokenizer_class(self.vocab_file     ,					word_tokenizer_type="""mecab"""							)
						self.assertIsNotNone(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   """こんにちは、世界。\nこんばんは、世界。"""
						SCREAMING_SNAKE_CASE__										=   tokenizer.tokenize(__UpperCAmelCase							)
						self.assertListEqual(__UpperCAmelCase     ,					["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""]							)
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase							)     ,					[3, 1_2, 1_0, 1_4, 4, 9, 1_2, 1_0, 1_4]							)

						SCREAMING_SNAKE_CASE__										=   os.path.join(self.tmpdirname     ,					"""tokenizer.bin"""							)
						with open(__UpperCAmelCase     ,					"""wb"""							) as handle:
									pickle.dump(__UpperCAmelCase     ,					__UpperCAmelCase							)

						with open(__UpperCAmelCase     ,					"""rb"""							) as handle:
									SCREAMING_SNAKE_CASE__										=   pickle.load(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   tokenizer_new.tokenize(__UpperCAmelCase							)

						self.assertListEqual(__UpperCAmelCase     ,					__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     int:
						SCREAMING_SNAKE_CASE__										=   MecabTokenizer(mecab_dic="""ipadic"""							)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)     ,					["""アップルストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""]     ,					)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     Union[str, Any]:
						try:
									SCREAMING_SNAKE_CASE__										=   MecabTokenizer(mecab_dic="""unidic_lite"""							)
						except ModuleNotFoundError:
									return

						self.assertListEqual(
						    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)     ,					["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""]     ,					)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Dict:
						try:
									SCREAMING_SNAKE_CASE__										=   MecabTokenizer(mecab_dic="""unidic"""							)
						except ModuleNotFoundError:
									return

						self.assertListEqual(
						    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)     ,					["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""]     ,					)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   MecabTokenizer(do_lower_case=__UpperCAmelCase     ,					mecab_dic="""ipadic"""							)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)     ,					["""アップルストア""", """で""", """iphone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""]     ,					)

			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     List[str]:
						try:
									SCREAMING_SNAKE_CASE__										=   MecabTokenizer(
									    do_lower_case=__UpperCAmelCase     ,					normalize_text=__UpperCAmelCase     ,					mecab_option="""-d /usr/local/lib/mecab/dic/jumandic"""							)
						except RuntimeError:
									# if dict doesn't exist in the system, previous code raises this error.
									return

						self.assertListEqual(
						    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)     ,					["""ｱｯﾌﾟﾙストア""", """で""", """iPhone""", """８""", """が""", """発売""", """さ""", """れた""", """\u3000""", """。"""]     ,					)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Optional[int]:
						SCREAMING_SNAKE_CASE__										=   MecabTokenizer(normalize_text=__UpperCAmelCase     ,					mecab_dic="""ipadic"""							)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)     ,					["""ｱｯﾌﾟﾙストア""", """で""", """iPhone""", """８""", """が""", """発売""", """さ""", """れ""", """た""", """　""", """。"""]     ,					)

			@require_sudachi
			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   self.tokenizer_class(self.vocab_file     ,					word_tokenizer_type="""sudachi"""							)
						self.assertIsNotNone(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   """こんにちは、世界。\nこんばんは、世界。"""
						SCREAMING_SNAKE_CASE__										=   tokenizer.tokenize(__UpperCAmelCase							)
						self.assertListEqual(__UpperCAmelCase     ,					["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""]							)
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase							)     ,					[3, 1_2, 1_0, 1_4, 4, 9, 1_2, 1_0, 1_4]							)

						SCREAMING_SNAKE_CASE__										=   os.path.join(self.tmpdirname     ,					"""tokenizer.bin"""							)
						with open(__UpperCAmelCase     ,					"""wb"""							) as handle:
									pickle.dump(__UpperCAmelCase     ,					__UpperCAmelCase							)

						with open(__UpperCAmelCase     ,					"""rb"""							) as handle:
									SCREAMING_SNAKE_CASE__										=   pickle.load(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   tokenizer_new.tokenize(__UpperCAmelCase							)

						self.assertListEqual(__UpperCAmelCase     ,					__UpperCAmelCase							)

			@require_sudachi
			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Dict:
						SCREAMING_SNAKE_CASE__										=   SudachiTokenizer(sudachi_dict_type="""core"""							)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)     ,					[""" """, """\t""", """アップル""", """ストア""", """で""", """iPhone""", """8""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """ """, """。""", """ """, """ """]     ,					)

			@require_sudachi
			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   SudachiTokenizer(sudachi_dict_type="""core"""     ,					sudachi_split_mode="""A"""							)

						self.assertListEqual(tokenizer.tokenize("""外国人参政権"""							)     ,					["""外国""", """人""", """参政""", """権"""]							)

			@require_sudachi
			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   SudachiTokenizer(sudachi_dict_type="""core"""     ,					sudachi_split_mode="""B"""							)

						self.assertListEqual(tokenizer.tokenize("""外国人参政権"""							)     ,					["""外国人""", """参政権"""]							)

			@require_sudachi
			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     Optional[int]:
						SCREAMING_SNAKE_CASE__										=   SudachiTokenizer(sudachi_dict_type="""core"""     ,					sudachi_split_mode="""C"""							)

						self.assertListEqual(tokenizer.tokenize("""外国人参政権"""							)     ,					["""外国人参政権"""]							)

			@require_sudachi
			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   SudachiTokenizer(do_lower_case=__UpperCAmelCase     ,					sudachi_dict_type="""core"""							)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)     ,					[""" """, """\t""", """アップル""", """ストア""", """で""", """iphone""", """8""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """ """, """。""", """ """, """ """]     ,					)

			@require_sudachi
			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     str:
						SCREAMING_SNAKE_CASE__										=   SudachiTokenizer(normalize_text=__UpperCAmelCase     ,					sudachi_dict_type="""core"""							)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)     ,					[""" """, """\t""", """ｱｯﾌﾟﾙ""", """ストア""", """で""", """iPhone""", """８""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """\u3000""", """。""", """ """, """ """]     ,					)

			@require_sudachi
			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   SudachiTokenizer(trim_whitespace=__UpperCAmelCase     ,					sudachi_dict_type="""core"""							)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)     ,					["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""]     ,					)

			@require_jumanpp
			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     Dict:
						SCREAMING_SNAKE_CASE__										=   self.tokenizer_class(self.vocab_file     ,					word_tokenizer_type="""jumanpp"""							)
						self.assertIsNotNone(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   """こんにちは、世界。\nこんばんは、世界。"""
						SCREAMING_SNAKE_CASE__										=   tokenizer.tokenize(__UpperCAmelCase							)
						self.assertListEqual(__UpperCAmelCase     ,					["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""]							)
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase							)     ,					[3, 1_2, 1_0, 1_4, 4, 9, 1_2, 1_0, 1_4]							)

						SCREAMING_SNAKE_CASE__										=   os.path.join(self.tmpdirname     ,					"""tokenizer.bin"""							)
						with open(__UpperCAmelCase     ,					"""wb"""							) as handle:
									pickle.dump(__UpperCAmelCase     ,					__UpperCAmelCase							)

						with open(__UpperCAmelCase     ,					"""rb"""							) as handle:
									SCREAMING_SNAKE_CASE__										=   pickle.load(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   tokenizer_new.tokenize(__UpperCAmelCase							)

						self.assertListEqual(__UpperCAmelCase     ,					__UpperCAmelCase							)

			@require_jumanpp
			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   JumanppTokenizer()

						self.assertListEqual(
						    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)     ,					["""アップル""", """ストア""", """で""", """iPhone""", """8""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""]     ,					)

			@require_jumanpp
			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     int:
						SCREAMING_SNAKE_CASE__										=   JumanppTokenizer(do_lower_case=__UpperCAmelCase							)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)     ,					["""アップル""", """ストア""", """で""", """iphone""", """8""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""]     ,					)

			@require_jumanpp
			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   JumanppTokenizer(normalize_text=__UpperCAmelCase							)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)     ,					["""ｱ""", """ｯ""", """ﾌ""", """ﾟ""", """ﾙ""", """ストア""", """で""", """iPhone""", """８""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""]     ,					)

			@require_jumanpp
			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   JumanppTokenizer(trim_whitespace=__UpperCAmelCase							)

						self.assertListEqual(
						    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)     ,					["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れた""", """。"""]     ,					)

			@require_jumanpp
			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   JumanppTokenizer()

						self.assertListEqual(
						    tokenizer.tokenize("""ありがとうございますm(_ _)ｍ見つけるのが大変です。"""							)     ,					["""ありがとう""", """ございます""", """m(_ _)m""", """見つける""", """の""", """が""", """大変です""", """。"""]     ,					)

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   ["""[UNK]""", """[CLS]""", """[SEP]""", """こんにちは""", """こん""", """にちは""", """ばんは""", """##こん""", """##にちは""", """##ばんは"""]

						SCREAMING_SNAKE_CASE__										=   {}
						for i, token in enumerate(__UpperCAmelCase							):
									SCREAMING_SNAKE_CASE__										=   i
						SCREAMING_SNAKE_CASE__										=   WordpieceTokenizer(vocab=__UpperCAmelCase     ,					unk_token="""[UNK]"""							)

						self.assertListEqual(tokenizer.tokenize(""""""							)     ,					[]							)

						self.assertListEqual(tokenizer.tokenize("""こんにちは"""							)     ,					["""こんにちは"""]							)

						self.assertListEqual(tokenizer.tokenize("""こんばんは"""							)     ,					["""こん""", """##ばんは"""]							)

						self.assertListEqual(tokenizer.tokenize("""こんばんは こんばんにちは こんにちは"""							)     ,					["""こん""", """##ばんは""", """[UNK]""", """こんにちは"""]							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   BertJapaneseTokenizer.from_pretrained("""nlp-waseda/roberta-base-japanese-with-auto-jumanpp"""							)
						SCREAMING_SNAKE_CASE__										=   tokenizer.subword_tokenizer

						SCREAMING_SNAKE_CASE__										=   subword_tokenizer.tokenize("""国境 の 長い トンネル を 抜ける と 雪国 であった 。"""							)
						self.assertListEqual(__UpperCAmelCase     ,					["""▁国境""", """▁の""", """▁長い""", """▁トンネル""", """▁を""", """▁抜ける""", """▁と""", """▁雪""", """国""", """▁であった""", """▁。"""]							)

						SCREAMING_SNAKE_CASE__										=   subword_tokenizer.tokenize("""こんばんは こんばん にち は こんにちは"""							)
						self.assertListEqual(__UpperCAmelCase     ,					["""▁こん""", """ばん""", """は""", """▁こん""", """ばん""", """▁に""", """ち""", """▁は""", """▁こんにちは"""]							)





			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   self.tokenizer_class.from_pretrained("""cl-tohoku/bert-base-japanese"""							)

						SCREAMING_SNAKE_CASE__										=   tokenizer.encode("""ありがとう。"""     ,					add_special_tokens=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   tokenizer.encode("""どういたしまして。"""     ,					add_special_tokens=__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase     ,					__UpperCAmelCase							)

						# 2 is for "[CLS]", 3 is for "[SEP]"
						assert encoded_sentence == [2] + text + [3]
						assert encoded_pair == [2] + text + [3] + text_a + [3]





@custom_tokenizers
class  lowerCamelCase    (A__      ,unittest.TestCase      ):
			lowerCamelCase__ :		Dict       		=				BertJapaneseTokenizer
			lowerCamelCase__ :		Tuple       		=				False

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     List[Any]:
						super().setUp()

						SCREAMING_SNAKE_CASE__										=   ["""[UNK]""", """[CLS]""", """[SEP]""", """こ""", """ん""", """に""", """ち""", """は""", """ば""", """世""", """界""", """、""", """。"""]

						SCREAMING_SNAKE_CASE__										=   os.path.join(self.tmpdirname     ,					VOCAB_FILES_NAMES["""vocab_file"""]							)
						with open(self.vocab_file     ,					"""w"""     ,					encoding="""utf-8"""							) as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]     ,					**__UpperCAmelCase					:    List[Any]							) ->     List[Any]:
						return BertJapaneseTokenizer.from_pretrained(self.tmpdirname     ,					subword_tokenizer_type="""character"""     ,					**__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Any     ,					__UpperCAmelCase					:    Any							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   """こんにちは、世界。 \nこんばんは、世界。"""
						SCREAMING_SNAKE_CASE__										=   """こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。"""
						return input_text, output_text

			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     Dict:
						pass  # TODO add if relevant

			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     Optional[Any]:
						pass  # TODO add if relevant

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     Union[str, Any]:
						pass  # TODO add if relevant

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   self.tokenizer_class(self.vocab_file     ,					subword_tokenizer_type="""character"""							)

						SCREAMING_SNAKE_CASE__										=   tokenizer.tokenize("""こんにちは、世界。 \nこんばんは、世界。"""							)
						self.assertListEqual(
						    __UpperCAmelCase     ,					["""こ""", """ん""", """に""", """ち""", """は""", """、""", """世""", """界""", """。""", """こ""", """ん""", """ば""", """ん""", """は""", """、""", """世""", """界""", """。"""]							)
						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(__UpperCAmelCase							)     ,					[3, 4, 5, 6, 7, 1_1, 9, 1_0, 1_2, 3, 4, 8, 4, 7, 1_1, 9, 1_0, 1_2]							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   ["""[UNK]""", """[CLS]""", """[SEP]""", """こ""", """ん""", """に""", """ち""", """は""", """ば""", """世""", """界""", """、""", """。"""]

						SCREAMING_SNAKE_CASE__										=   {}
						for i, token in enumerate(__UpperCAmelCase							):
									SCREAMING_SNAKE_CASE__										=   i
						SCREAMING_SNAKE_CASE__										=   CharacterTokenizer(vocab=__UpperCAmelCase     ,					unk_token="""[UNK]"""							)

						self.assertListEqual(tokenizer.tokenize(""""""							)     ,					[]							)

						self.assertListEqual(tokenizer.tokenize("""こんにちは"""							)     ,					["""こ""", """ん""", """に""", """ち""", """は"""]							)

						self.assertListEqual(tokenizer.tokenize("""こんにちほ"""							)     ,					["""こ""", """ん""", """に""", """ち""", """[UNK]"""]							)





			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     str:
						SCREAMING_SNAKE_CASE__										=   self.tokenizer_class.from_pretrained("""cl-tohoku/bert-base-japanese-char"""							)

						SCREAMING_SNAKE_CASE__										=   tokenizer.encode("""ありがとう。"""     ,					add_special_tokens=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   tokenizer.encode("""どういたしまして。"""     ,					add_special_tokens=__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase     ,					__UpperCAmelCase							)

						# 2 is for "[CLS]", 3 is for "[SEP]"
						assert encoded_sentence == [2] + text + [3]
						assert encoded_pair == [2] + text + [3] + text_a + [3]





@custom_tokenizers
class  lowerCamelCase    (unittest.TestCase      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     Dict:
						SCREAMING_SNAKE_CASE__										=   """cl-tohoku/bert-base-japanese"""
						SCREAMING_SNAKE_CASE__										=   AutoTokenizer.from_pretrained(__UpperCAmelCase							)
						self.assertIsInstance(__UpperCAmelCase     ,					__UpperCAmelCase							)





class  lowerCamelCase    (unittest.TestCase      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   """cl-tohoku/bert-base-japanese"""
						with self.assertLogs("""transformers"""     ,					level="""WARNING"""							) as cm:
									BertTokenizer.from_pretrained(__UpperCAmelCase							)
									self.assertTrue(
									    cm.records[0].message.startswith(
									        """The tokenizer class you load from this checkpoint is not the same type as the class this function"""
									        """ is called from."""							)							)
						SCREAMING_SNAKE_CASE__										=   """bert-base-cased"""
						with self.assertLogs("""transformers"""     ,					level="""WARNING"""							) as cm:
									BertJapaneseTokenizer.from_pretrained(__UpperCAmelCase							)
									self.assertTrue(
									    cm.records[0].message.startswith(
									        """The tokenizer class you load from this checkpoint is not the same type as the class this function"""
									        """ is called from."""							)							)


