





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

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
UpperCAmelCase_ 							=       logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt""")
@dataclass
class      UpperCamelCase_						:
					lowerCAmelCase_							=		field(
					    default='''cifar10'''							,					metadata={'''help''': '''Name of a dataset from the datasets package'''}   )
					lowerCAmelCase_							=		field(
					    default=_lowerCamelCase							,					metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}   )
					lowerCAmelCase_							=		field(
					    default=_lowerCamelCase							,					metadata={'''help''': '''The column name of the images in the files.'''}   )
					lowerCAmelCase_							=		field(default=_lowerCamelCase							,					metadata={'''help''': '''A folder containing the training data.'''}   )
					lowerCAmelCase_							=		field(default=_lowerCamelCase							,					metadata={'''help''': '''A folder containing the validation data.'''}   )
					lowerCAmelCase_							=		field(
					    default=0.15							,					metadata={'''help''': '''Percent to split off of train for validation.'''}   )
					lowerCAmelCase_							=		field(
					    default=_lowerCamelCase							,					metadata={
					        '''help''': (
					            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
					            '''value if set.'''
					        )
					    }							,					)
					lowerCAmelCase_							=		field(
					    default=_lowerCamelCase							,					metadata={
					        '''help''': (
					            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
					            '''value if set.'''
					        )
					    }							,					)




					def 		lowerCAmelCase     (						self					)						->							List[Any]:
										_snake_case													=			{}
										if self.train_dir is not None:
															_snake_case													=			self.train_dir
										if self.validation_dir is not None:
															_snake_case													=			self.validation_dir
										_snake_case													=			data_files if data_files else None





@dataclass
class      UpperCamelCase_						:
					lowerCAmelCase_							=		field(
					    default=_lowerCamelCase							,					metadata={
					        '''help''': (
					            '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
					        )
					    }							,					)
					lowerCAmelCase_							=		field(
					    default=_lowerCamelCase							,					metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''}   )
					lowerCAmelCase_							=		field(
					    default=_lowerCamelCase							,					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'''
					        )
					    }							,					)
					lowerCAmelCase_							=		field(
					    default=_lowerCamelCase							,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''}   )
					lowerCAmelCase_							=		field(
					    default='''main'''							,					metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}							,					)
					lowerCAmelCase_							=		field(default=_lowerCamelCase							,					metadata={'''help''': '''Name or path of preprocessor config.'''}   )
					lowerCAmelCase_							=		field(
					    default=_lowerCamelCase							,					metadata={
					        '''help''': (
					            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
					            '''with private models).'''
					        )
					    }							,					)
					lowerCAmelCase_							=		field(
					    default=0.75							,					metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''}   )
					lowerCAmelCase_							=		field(
					    default=_lowerCamelCase							,					metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''}   )





@dataclass
class      UpperCamelCase_						(					_lowerCamelCase   ):
					lowerCAmelCase_							=		field(
					    default=1E-3							,					metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''}   )





def   lowerCamelCase__       (       UpperCamelCase__     :    List[Any]  )     ->	Dict:



					'''simple docstring'''




					_snake_case													=			torch.stack([example['pixel_values'] for example in examples]  )
					return {"pixel_values": pixel_values}





def   lowerCamelCase__       (       )     ->	List[str]:



					'''simple docstring'''




					_snake_case													=			HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)  )
					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.
										_snake_case							,       _snake_case							,       _snake_case													=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
					else:
										_snake_case							,       _snake_case							,       _snake_case													=			parser.parse_args_into_dataclasses()

					# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
					# information sent is the one passed as arguments along with your Python/PyTorch versions.
					send_example_telemetry('run_mae'    ,      UpperCamelCase__    ,      UpperCamelCase__  )

					# 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  )]    ,      )

					if training_args.should_log:
										# The default of training_args.log_level is passive, so we set log level at info here to have that default.
										transformers.utils.logging.set_verbosity_info()

					_snake_case													=			training_args.get_process_log_level()
					logger.setLevel(UpperCamelCase__  )
					transformers.utils.logging.set_verbosity(UpperCamelCase__  )
					transformers.utils.logging.enable_default_handler()
					transformers.utils.logging.enable_explicit_format()

					# 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}'''  )
					logger.info(F'''Training/evaluation parameters {training_args}'''  )

					# Detecting last checkpoint.
					_snake_case													=			None
					if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
										_snake_case													=			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 and training_args.resume_from_checkpoint is 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.'  )

    # Initialize our dataset.
					_snake_case													=			load_dataset(
					    data_args.dataset_name    ,      data_args.dataset_config_name    ,      data_files=data_args.data_files    ,      cache_dir=model_args.cache_dir    ,      use_auth_token=True if model_args.use_auth_token else None    ,      )

					# If we don't have a validation split, split off a percentage of train as validation.
					_snake_case													=			None if 'validation' in ds.keys() else data_args.train_val_split
					if isinstance(data_args.train_val_split    ,      UpperCamelCase__  ) and data_args.train_val_split > 0.0:
										_snake_case													=			ds['train'].train_test_split(data_args.train_val_split  )
										_snake_case													=			split['train']
										_snake_case													=			split['test']

					# Load pretrained model and image processor
					#
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.
					_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,
					}
					if model_args.config_name:
										_snake_case													=			ViTMAEConfig.from_pretrained(model_args.config_name    ,      **UpperCamelCase__  )
					elif model_args.model_name_or_path:
										_snake_case													=			ViTMAEConfig.from_pretrained(model_args.model_name_or_path    ,      **UpperCamelCase__  )
					else:
										_snake_case													=			ViTMAEConfig()
										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}'''  )

    # adapt config
					config.update(
					    {
					        'mask_ratio': model_args.mask_ratio,
					        'norm_pix_loss': model_args.norm_pix_loss,
					    }  )

					# create image processor
					if model_args.image_processor_name:
										_snake_case													=			ViTImageProcessor.from_pretrained(model_args.image_processor_name    ,      **UpperCamelCase__  )
					elif model_args.model_name_or_path:
										_snake_case													=			ViTImageProcessor.from_pretrained(model_args.model_name_or_path    ,      **UpperCamelCase__  )
					else:
										_snake_case													=			ViTImageProcessor()

					# create model
					if model_args.model_name_or_path:
										_snake_case													=			ViTMAEForPreTraining.from_pretrained(
										    model_args.model_name_or_path    ,      from_tf=bool('.ckpt' in model_args.model_name_or_path  )    ,      config=UpperCamelCase__    ,      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'  )
										_snake_case													=			ViTMAEForPreTraining(UpperCamelCase__  )

					if training_args.do_train:
										_snake_case													=			ds['train'].column_names
					else:
										_snake_case													=			ds['validation'].column_names

					if data_args.image_column_name is not None:
										_snake_case													=			data_args.image_column_name
					elif "image" in column_names:
										_snake_case													=			'image'
					elif "img" in column_names:
										_snake_case													=			'img'
					else:
										_snake_case													=			column_names[0]

					# transformations as done in original MAE paper
					# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
					if "shortest_edge" in image_processor.size:
										_snake_case													=			image_processor.size['shortest_edge']
					else:
										_snake_case													=			(image_processor.size['height'], image_processor.size['width'])
					_snake_case													=			Compose(
					    [
					        Lambda(lambda UpperCamelCase__  : img.convert('RGB'  ) if img.mode != "RGB" else img  ),
					        RandomResizedCrop(UpperCamelCase__    ,      scale=(0.2, 1.0)    ,      interpolation=InterpolationMode.BICUBIC  ),
					        RandomHorizontalFlip(),
					        ToTensor(),
					        Normalize(mean=image_processor.image_mean    ,      std=image_processor.image_std  ),
					    ]  )

					def preprocess_images(UpperCamelCase__     :    str  ):
										_snake_case													=			[transforms(UpperCamelCase__  ) for image in examples[image_column_name]]
										return examples

					if training_args.do_train:
										if "train" not in ds:
															raise ValueError('--do_train requires a train dataset'  )
										if data_args.max_train_samples is not None:
															_snake_case													=			ds['train'].shuffle(seed=training_args.seed  ).select(range(data_args.max_train_samples  )  )
										# Set the training transforms
										ds["train"].set_transform(UpperCamelCase__  )

					if training_args.do_eval:
										if "validation" not in ds:
															raise ValueError('--do_eval requires a validation dataset'  )
										if data_args.max_eval_samples is not None:
															_snake_case													=			(
															    ds['validation'].shuffle(seed=training_args.seed  ).select(range(data_args.max_eval_samples  )  )
															)
										# Set the validation transforms
										ds["validation"].set_transform(UpperCamelCase__  )

					# Compute absolute learning rate
					_snake_case													=			(
					    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
					)
					if training_args.base_learning_rate is not None:
										_snake_case													=			training_args.base_learning_rate * total_train_batch_size / 256

					# Initialize our trainer
					_snake_case													=			Trainer(
					    model=UpperCamelCase__    ,      args=UpperCamelCase__    ,      train_dataset=ds['train'] if training_args.do_train else None    ,      eval_dataset=ds['validation'] if training_args.do_eval else None    ,      tokenizer=UpperCamelCase__    ,      data_collator=UpperCamelCase__    ,      )

					# Training
					if training_args.do_train:
										_snake_case													=			None
										if training_args.resume_from_checkpoint is not None:
															_snake_case													=			training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															_snake_case													=			last_checkpoint
										_snake_case													=			trainer.train(resume_from_checkpoint=UpperCamelCase__  )
										trainer.save_model()
										trainer.log_metrics('train'    ,      train_result.metrics  )
										trainer.save_metrics('train'    ,      train_result.metrics  )
										trainer.save_state()

					# Evaluation
					if training_args.do_eval:
										_snake_case													=			trainer.evaluate()
										trainer.log_metrics('eval'    ,      UpperCamelCase__  )
										trainer.save_metrics('eval'    ,      UpperCamelCase__  )

					# Write model card and (optionally) push to hub
					_snake_case													=			{
					    'tasks': 'masked-auto-encoding',
					    'dataset': data_args.dataset_name,
					    'tags': ['masked-auto-encoding'],
					}
					if training_args.push_to_hub:
										trainer.push_to_hub(**UpperCamelCase__  )
					else:
										trainer.create_model_card(**UpperCamelCase__  )





def   lowerCamelCase__       (       UpperCamelCase__     :    Dict  )     ->	Dict:



					'''simple docstring'''




					main()


if __name__ == "__main__":
					main()










import os
import unittest

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    BertTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class      UpperCamelCase_						(					_lowerCamelCase							,					unittest.TestCase   ):
					lowerCAmelCase_							=		BertTokenizer
					lowerCAmelCase_							=		BertTokenizerFast
					lowerCAmelCase_							=		True
					lowerCAmelCase_							=		True
					lowerCAmelCase_							=		filter_non_english




					def 		lowerCAmelCase     (						self					)						->							Optional[int]:
										super().setUp()

										_snake_case													=			[
										    '[UNK]',
										    '[CLS]',
										    '[SEP]',
										    '[PAD]',
										    '[MASK]',
										    'want',
										    '##want',
										    '##ed',
										    'wa',
										    'un',
										    'runn',
										    '##ing',
										    ',',
										    'low',
										    'lowest',
										]
										_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 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Optional[Any]:
										_snake_case													=			'UNwant\u00E9d,running'
										_snake_case													=			'unwanted, running'
										return input_text, output_text




					def 		lowerCAmelCase     (						self					)						->							List[Any]:
										_snake_case													=			self.tokenizer_class(self.vocab_file					)

										_snake_case													=			tokenizer.tokenize('UNwant\u00E9d,running'					)
										self.assertListEqual(lowerCAmelCase_			,  ['un', '##want', '##ed', ',', 'runn', '##ing']					)
										self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_					)			,  [9, 6, 7, 12, 10, 11]					)




					def 		lowerCAmelCase     (						self					)						->							Tuple:
										if not self.test_rust_tokenizer:
															return

										_snake_case													=			self.get_tokenizer()
										_snake_case													=			self.get_rust_tokenizer()

										_snake_case													=			'UNwant\u00E9d,running'

										_snake_case													=			tokenizer.tokenize(lowerCAmelCase_					)
										_snake_case													=			rust_tokenizer.tokenize(lowerCAmelCase_					)
										self.assertListEqual(lowerCAmelCase_			,  lowerCAmelCase_					)

										_snake_case													=			tokenizer.encode(lowerCAmelCase_			,  add_special_tokens=lowerCAmelCase_					)
										_snake_case													=			rust_tokenizer.encode(lowerCAmelCase_			,  add_special_tokens=lowerCAmelCase_					)
										self.assertListEqual(lowerCAmelCase_			,  lowerCAmelCase_					)

										_snake_case													=			self.get_rust_tokenizer()
										_snake_case													=			tokenizer.encode(lowerCAmelCase_					)
										_snake_case													=			rust_tokenizer.encode(lowerCAmelCase_					)
										self.assertListEqual(lowerCAmelCase_			,  lowerCAmelCase_					)

										# With lower casing
										_snake_case													=			self.get_tokenizer(do_lower_case=lowerCAmelCase_					)
										_snake_case													=			self.get_rust_tokenizer(do_lower_case=lowerCAmelCase_					)

										_snake_case													=			'UNwant\u00E9d,running'

										_snake_case													=			tokenizer.tokenize(lowerCAmelCase_					)
										_snake_case													=			rust_tokenizer.tokenize(lowerCAmelCase_					)
										self.assertListEqual(lowerCAmelCase_			,  lowerCAmelCase_					)

										_snake_case													=			tokenizer.encode(lowerCAmelCase_			,  add_special_tokens=lowerCAmelCase_					)
										_snake_case													=			rust_tokenizer.encode(lowerCAmelCase_			,  add_special_tokens=lowerCAmelCase_					)
										self.assertListEqual(lowerCAmelCase_			,  lowerCAmelCase_					)

										_snake_case													=			self.get_rust_tokenizer()
										_snake_case													=			tokenizer.encode(lowerCAmelCase_					)
										_snake_case													=			rust_tokenizer.encode(lowerCAmelCase_					)
										self.assertListEqual(lowerCAmelCase_			,  lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self					)						->							List[str]:
										_snake_case													=			BasicTokenizer()

										self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'					)			,  ['ah', '\u535A', '\u63A8', 'zz']					)




					def 		lowerCAmelCase     (						self					)						->							Optional[Any]:
										_snake_case													=			BasicTokenizer(do_lower_case=lowerCAmelCase_					)

										self.assertListEqual(
										    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '					)			,  ['hello', '!', 'how', 'are', 'you', '?']					)
										self.assertListEqual(tokenizer.tokenize('H\u00E9llo'					)			,  ['hello']					)




					def 		lowerCAmelCase     (						self					)						->							List[Any]:
										_snake_case													=			BasicTokenizer(do_lower_case=lowerCAmelCase_			,  strip_accents=lowerCAmelCase_					)

										self.assertListEqual(
										    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '					)			,  ['hällo', '!', 'how', 'are', 'you', '?']					)
										self.assertListEqual(tokenizer.tokenize('H\u00E9llo'					)			,  ['h\u00E9llo']					)




					def 		lowerCAmelCase     (						self					)						->							Any:
										_snake_case													=			BasicTokenizer(do_lower_case=lowerCAmelCase_			,  strip_accents=lowerCAmelCase_					)

										self.assertListEqual(
										    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '					)			,  ['hallo', '!', 'how', 'are', 'you', '?']					)
										self.assertListEqual(tokenizer.tokenize('H\u00E9llo'					)			,  ['hello']					)




					def 		lowerCAmelCase     (						self					)						->							List[str]:
										_snake_case													=			BasicTokenizer(do_lower_case=lowerCAmelCase_					)

										self.assertListEqual(
										    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '					)			,  ['hallo', '!', 'how', 'are', 'you', '?']					)
										self.assertListEqual(tokenizer.tokenize('H\u00E9llo'					)			,  ['hello']					)




					def 		lowerCAmelCase     (						self					)						->							Tuple:
										_snake_case													=			BasicTokenizer(do_lower_case=lowerCAmelCase_					)

										self.assertListEqual(
										    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '					)			,  ['HeLLo', '!', 'how', 'Are', 'yoU', '?']					)




					def 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										_snake_case													=			BasicTokenizer(do_lower_case=lowerCAmelCase_			,  strip_accents=lowerCAmelCase_					)

										self.assertListEqual(
										    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '					)			,  ['HäLLo', '!', 'how', 'Are', 'yoU', '?']					)




					def 		lowerCAmelCase     (						self					)						->							Tuple:
										_snake_case													=			BasicTokenizer(do_lower_case=lowerCAmelCase_			,  strip_accents=lowerCAmelCase_					)

										self.assertListEqual(
										    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '					)			,  ['HaLLo', '!', 'how', 'Are', 'yoU', '?']					)




					def 		lowerCAmelCase     (						self					)						->							Dict:
										_snake_case													=			BasicTokenizer(do_lower_case=lowerCAmelCase_			,  never_split=['[UNK]']					)

										self.assertListEqual(
										    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'					)			,  ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']					)




					def 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										_snake_case													=			BasicTokenizer()
										_snake_case													=			'a\n\'ll !!to?\'d of, can\'t.'
										_snake_case													=			['a', '\'', 'll', '!', '!', 'to', '?', '\'', 'd', 'of', ',', 'can', '\'', 't', '.']
										self.assertListEqual(tokenizer.tokenize(lowerCAmelCase_					)			,  lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										_snake_case													=			['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

										_snake_case													=			{}
										for i, token in enumerate(lowerCAmelCase_					):
															_snake_case													=			i
										_snake_case													=			WordpieceTokenizer(vocab=lowerCAmelCase_			,  unk_token='[UNK]'					)

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

										self.assertListEqual(tokenizer.tokenize('unwanted running'					)			,  ['un', '##want', '##ed', 'runn', '##ing']					)

										self.assertListEqual(tokenizer.tokenize('unwantedX running'					)			,  ['[UNK]', 'runn', '##ing']					)




					def 		lowerCAmelCase     (						self					)						->							Tuple:
										self.assertTrue(_is_whitespace(' '					)					)
										self.assertTrue(_is_whitespace('\t'					)					)
										self.assertTrue(_is_whitespace('\r'					)					)
										self.assertTrue(_is_whitespace('\n'					)					)
										self.assertTrue(_is_whitespace('\u00A0'					)					)

										self.assertFalse(_is_whitespace('A'					)					)
										self.assertFalse(_is_whitespace('-'					)					)




					def 		lowerCAmelCase     (						self					)						->							Dict:
										self.assertTrue(_is_control('\u0005'					)					)

										self.assertFalse(_is_control('A'					)					)
										self.assertFalse(_is_control(' '					)					)
										self.assertFalse(_is_control('\t'					)					)
										self.assertFalse(_is_control('\r'					)					)




					def 		lowerCAmelCase     (						self					)						->							int:
										self.assertTrue(_is_punctuation('-'					)					)
										self.assertTrue(_is_punctuation('$'					)					)
										self.assertTrue(_is_punctuation('`'					)					)
										self.assertTrue(_is_punctuation('.'					)					)

										self.assertFalse(_is_punctuation('A'					)					)
										self.assertFalse(_is_punctuation(' '					)					)




					def 		lowerCAmelCase     (						self					)						->							Tuple:
										_snake_case													=			self.get_tokenizer()
										_snake_case													=			self.get_rust_tokenizer()

										# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
										self.assertListEqual([tokenizer.tokenize(lowerCAmelCase_					) for t in ['Test', '\xad', 'test']]			,  [['[UNK]'], [], ['[UNK]']]					)

										self.assertListEqual(
										    [rust_tokenizer.tokenize(lowerCAmelCase_					) for t in ['Test', '\xad', 'test']]			,  [['[UNK]'], [], ['[UNK]']]					)




					@slow
					def 		lowerCAmelCase     (						self					)						->							Optional[Any]:
										_snake_case													=			self.tokenizer_class.from_pretrained('bert-base-uncased'					)

										_snake_case													=			tokenizer.encode('sequence builders'			,  add_special_tokens=lowerCAmelCase_					)
										_snake_case													=			tokenizer.encode('multi-sequence build'			,  add_special_tokens=lowerCAmelCase_					)

										_snake_case													=			tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_					)
										_snake_case													=			tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_			,  lowerCAmelCase_					)

										assert encoded_sentence == [101] + text + [102]
										assert encoded_pair == [101] + text + [102] + text_a + [102]




					def 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
																				_snake_case													=			self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_			,  **lowerCAmelCase_					)

																				_snake_case													=			F'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
																				_snake_case													=			tokenizer_r.encode_plus(
																				    lowerCAmelCase_			,  return_attention_mask=lowerCAmelCase_			,  return_token_type_ids=lowerCAmelCase_			,  return_offsets_mapping=lowerCAmelCase_			,  add_special_tokens=lowerCAmelCase_			,  )

																				_snake_case													=			tokenizer_r.do_lower_case if hasattr(lowerCAmelCase_			,  'do_lower_case'					) else False
																				_snake_case													=			(
																				    [
																				        ((0, 0), tokenizer_r.cls_token),
																				        ((0, 1), 'A'),
																				        ((1, 2), ','),
																				        ((3, 5), 'na'),
																				        ((5, 6), '##ï'),
																				        ((6, 8), '##ve'),
																				        ((9, 15), tokenizer_r.mask_token),
																				        ((16, 21), 'Allen'),
																				        ((21, 23), '##NL'),
																				        ((23, 24), '##P'),
																				        ((25, 33), 'sentence'),
																				        ((33, 34), '.'),
																				        ((0, 0), tokenizer_r.sep_token),
																				    ]
																				    if not do_lower_case
																				    else [
																				        ((0, 0), tokenizer_r.cls_token),
																				        ((0, 1), 'a'),
																				        ((1, 2), ','),
																				        ((3, 8), 'naive'),
																				        ((9, 15), tokenizer_r.mask_token),
																				        ((16, 21), 'allen'),
																				        ((21, 23), '##nl'),
																				        ((23, 24), '##p'),
																				        ((25, 33), 'sentence'),
																				        ((33, 34), '.'),
																				        ((0, 0), tokenizer_r.sep_token),
																				    ]
																				)

																				self.assertEqual(
																				    [e[1] for e in expected_results]			,  tokenizer_r.convert_ids_to_tokens(tokens['input_ids']					)					)
																				self.assertEqual([e[0] for e in expected_results]			,  tokens['offset_mapping']					)






					def 		lowerCAmelCase     (						self					)						->							str:
										_snake_case													=			['的', '人', '有']
										_snake_case													=			''.join(lowerCAmelCase_					)
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
																				_snake_case													=			True
																				_snake_case													=			self.tokenizer_class.from_pretrained(lowerCAmelCase_			,  **lowerCAmelCase_					)
																				_snake_case													=			self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_			,  **lowerCAmelCase_					)

																				_snake_case													=			tokenizer_p.encode(lowerCAmelCase_			,  add_special_tokens=lowerCAmelCase_					)
																				_snake_case													=			tokenizer_r.encode(lowerCAmelCase_			,  add_special_tokens=lowerCAmelCase_					)

																				_snake_case													=			tokenizer_r.convert_ids_to_tokens(lowerCAmelCase_					)
																				_snake_case													=			tokenizer_p.convert_ids_to_tokens(lowerCAmelCase_					)

																				# it is expected that each Chinese character is not preceded by "##"
																				self.assertListEqual(lowerCAmelCase_			,  lowerCAmelCase_					)
																				self.assertListEqual(lowerCAmelCase_			,  lowerCAmelCase_					)

																				_snake_case													=			False
																				_snake_case													=			self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_			,  **lowerCAmelCase_					)
																				_snake_case													=			self.tokenizer_class.from_pretrained(lowerCAmelCase_			,  **lowerCAmelCase_					)

																				_snake_case													=			tokenizer_r.encode(lowerCAmelCase_			,  add_special_tokens=lowerCAmelCase_					)
																				_snake_case													=			tokenizer_p.encode(lowerCAmelCase_			,  add_special_tokens=lowerCAmelCase_					)

																				_snake_case													=			tokenizer_r.convert_ids_to_tokens(lowerCAmelCase_					)
																				_snake_case													=			tokenizer_p.convert_ids_to_tokens(lowerCAmelCase_					)

																				# it is expected that only the first Chinese character is not preceded by "##".
																				_snake_case													=			[
																				    F'''##{token}''' if idx != 0 else token for idx, token in enumerate(lowerCAmelCase_					)
																				]
																				self.assertListEqual(lowerCAmelCase_			,  lowerCAmelCase_					)
																				self.assertListEqual(lowerCAmelCase_			,  lowerCAmelCase_					)



