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

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
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""")

UpperCAmelCase_ 							=       list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
UpperCAmelCase_ 							=       tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@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. If not set, will try to use \'image\' or \'img\'.'''}							,					)
					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=32							,					metadata={'''help''': '''The size of the square patches to use for masking.'''}   )
					lowerCAmelCase_							=		field(
					    default=0.6							,					metadata={'''help''': '''Percentage of patches to mask.'''}							,					)
					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					)						->							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. Can be a local path to a pytorch_model.bin or a '''
					            '''checkpoint identifier on the hub. '''
					            '''Don\'t set if you want to train a model from scratch.'''
					        )
					    }							,					)
					lowerCAmelCase_							=		field(
					    default=_lowerCamelCase							,					metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(_lowerCamelCase   )}							,					)
					lowerCAmelCase_							=		field(
					    default=_lowerCamelCase							,					metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}   )
					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 (cache) the pretrained models/datasets downloaded from the hub'''}							,					)
					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=_lowerCamelCase							,					metadata={
					        '''help''': (
					            '''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
					        )
					    }							,					)
					lowerCAmelCase_							=		field(
					    default=_lowerCamelCase							,					metadata={
					        '''help''': (
					            '''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
					        )
					    }							,					)
					lowerCAmelCase_							=		field(
					    default=_lowerCamelCase							,					metadata={'''help''': '''Stride to use for the encoder.'''}							,					)





class      UpperCamelCase_						:




					def __init__(						self			,  lowerCAmelCase_=192			,  lowerCAmelCase_=32			,  lowerCAmelCase_=4			,  lowerCAmelCase_=0.6					)						->							Tuple:
										_snake_case													=			input_size
										_snake_case													=			mask_patch_size
										_snake_case													=			model_patch_size
										_snake_case													=			mask_ratio

										if self.input_size % self.mask_patch_size != 0:
															raise ValueError('Input size must be divisible by mask patch size'					)
										if self.mask_patch_size % self.model_patch_size != 0:
															raise ValueError('Mask patch size must be divisible by model patch size'					)

										_snake_case													=			self.input_size // self.mask_patch_size
										_snake_case													=			self.mask_patch_size // self.model_patch_size

										_snake_case													=			self.rand_size**2
										_snake_case													=			int(np.ceil(self.token_count * self.mask_ratio					)					)






					def __call__(						self					)						->							Any:
										_snake_case													=			np.random.permutation(self.token_count					)[: self.mask_count]
										_snake_case													=			np.zeros(self.token_count			,  dtype=lowerCAmelCase_					)
										_snake_case													=			1

										_snake_case													=			mask.reshape((self.rand_size, self.rand_size)					)
										_snake_case													=			mask.repeat(self.scale			,  axis=0					).repeat(self.scale			,  axis=1					)

										return torch.tensor(mask.flatten()					)





def   lowerCamelCase__       (       UpperCamelCase__     :    Union[str, Any]  )     ->	Tuple:



					'''simple docstring'''




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





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



					'''simple docstring'''




					_snake_case													=			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.
										_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_mim'    ,      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']

					# Create config
					# 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_or_path:
										_snake_case													=			AutoConfig.from_pretrained(model_args.config_name_or_path    ,      **UpperCamelCase__  )
					elif model_args.model_name_or_path:
										_snake_case													=			AutoConfig.from_pretrained(model_args.model_name_or_path    ,      **UpperCamelCase__  )
					else:
										_snake_case													=			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}'''  )

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
					if hasattr(UpperCamelCase__    ,      'decoder_type'  ):
										_snake_case													=			'simmim'

					# adapt config
					_snake_case													=			model_args.image_size if model_args.image_size is not None else config.image_size
					_snake_case													=			model_args.patch_size if model_args.patch_size is not None else config.patch_size
					_snake_case													=			(
					    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
					)

					config.update(
					    {
					        'image_size': model_args.image_size,
					        'patch_size': model_args.patch_size,
					        'encoder_stride': model_args.encoder_stride,
					    }  )

					# create image processor
					if model_args.image_processor_name:
										_snake_case													=			AutoImageProcessor.from_pretrained(model_args.image_processor_name    ,      **UpperCamelCase__  )
					elif model_args.model_name_or_path:
										_snake_case													=			AutoImageProcessor.from_pretrained(model_args.model_name_or_path    ,      **UpperCamelCase__  )
					else:
										_snake_case													=			{
										    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
										}
										_snake_case													=			IMAGE_PROCESSOR_TYPES[model_args.model_type]()

					# create model
					if model_args.model_name_or_path:
										_snake_case													=			AutoModelForMaskedImageModeling.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													=			AutoModelForMaskedImageModeling.from_config(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 SimMIM paper
					# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
					_snake_case													=			Compose(
					    [
					        Lambda(lambda UpperCamelCase__  : img.convert('RGB'  ) if img.mode != "RGB" else img  ),
					        RandomResizedCrop(model_args.image_size    ,      scale=(0.67, 1.0)    ,      ratio=(3.0 / 4.0, 4.0 / 3.0)  ),
					        RandomHorizontalFlip(),
					        ToTensor(),
					        Normalize(mean=image_processor.image_mean    ,      std=image_processor.image_std  ),
					    ]  )

					# create mask generator
					_snake_case													=			MaskGenerator(
					    input_size=model_args.image_size    ,      mask_patch_size=data_args.mask_patch_size    ,      model_patch_size=model_args.patch_size    ,      mask_ratio=data_args.mask_ratio    ,      )

					def preprocess_images(UpperCamelCase__     :    Dict  ):
										_snake_case													=			[transforms(UpperCamelCase__  ) for image in examples[image_column_name]]
										_snake_case													=			[mask_generator() for i in range(len(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__  )

					# 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													=			{
					    'finetuned_from': model_args.model_name_or_path,
					    'tasks': 'masked-image-modeling',
					    'dataset': data_args.dataset_name,
					    'tags': ['masked-image-modeling'],
					}
					if training_args.push_to_hub:
										trainer.push_to_hub(**UpperCamelCase__  )
					else:
										trainer.create_model_card(**UpperCamelCase__  )


if __name__ == "__main__":
					main()










import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class      UpperCamelCase_						(					_lowerCamelCase							,					unittest.TestCase   ):
					lowerCAmelCase_							=		LEDTokenizer
					lowerCAmelCase_							=		LEDTokenizerFast
					lowerCAmelCase_							=		True




					def 		lowerCAmelCase     (						self					)						->							List[str]:
										super().setUp()
										_snake_case													=			[
										    'l',
										    'o',
										    'w',
										    'e',
										    'r',
										    's',
										    't',
										    'i',
										    'd',
										    'n',
										    '\u0120',
										    '\u0120l',
										    '\u0120n',
										    '\u0120lo',
										    '\u0120low',
										    'er',
										    '\u0120lowest',
										    '\u0120newer',
										    '\u0120wider',
										    '<unk>',
										]
										_snake_case													=			dict(zip(lowerCAmelCase_			,  range(len(lowerCAmelCase_					)					)					)					)
										_snake_case													=			['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
										_snake_case													=			{'unk_token': '<unk>'}

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




					def 		lowerCAmelCase     (						self			,  **lowerCAmelCase_					)						->							List[str]:
										kwargs.update(self.special_tokens_map					)
										return self.tokenizer_class.from_pretrained(self.tmpdirname			,  **lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self			,  **lowerCAmelCase_					)						->							str:
										kwargs.update(self.special_tokens_map					)
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,  **lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Union[str, Any]:
										return "lower newer", "lower newer"




					@cached_property
					def 		lowerCAmelCase     (						self					)						->							Optional[Any]:
										return LEDTokenizer.from_pretrained('allenai/led-base-16384'					)




					@cached_property
					def 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'					)




					@require_torch
					def 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										_snake_case													=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
										_snake_case													=			[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_snake_case													=			tokenizer(lowerCAmelCase_			,  max_length=len(lowerCAmelCase_					)			,  padding=lowerCAmelCase_			,  return_tensors='pt'					)
															self.assertIsInstance(lowerCAmelCase_			,  lowerCAmelCase_					)

															self.assertEqual((2, 9)			,  batch.input_ids.shape					)
															self.assertEqual((2, 9)			,  batch.attention_mask.shape					)
															_snake_case													=			batch.input_ids.tolist()[0]
															self.assertListEqual(lowerCAmelCase_			,  lowerCAmelCase_					)




					@require_torch
					def 		lowerCAmelCase     (						self					)						->							Optional[Any]:
										_snake_case													=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_snake_case													=			tokenizer(lowerCAmelCase_			,  padding=lowerCAmelCase_			,  return_tensors='pt'					)
															self.assertIn('input_ids'			,  lowerCAmelCase_					)
															self.assertIn('attention_mask'			,  lowerCAmelCase_					)
															self.assertNotIn('labels'			,  lowerCAmelCase_					)
															self.assertNotIn('decoder_attention_mask'			,  lowerCAmelCase_					)




					@require_torch
					def 		lowerCAmelCase     (						self					)						->							Optional[int]:
										_snake_case													=			[
										    'Summary of the text.',
										    'Another summary.',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_snake_case													=			tokenizer(text_target=lowerCAmelCase_			,  max_length=32			,  padding='max_length'			,  return_tensors='pt'					)
															self.assertEqual(32			,  targets['input_ids'].shape[1]					)




					@require_torch
					def 		lowerCAmelCase     (						self					)						->							List[str]:
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_snake_case													=			tokenizer(
															    ['I am a small frog' * 1024, 'I am a small frog']			,  padding=lowerCAmelCase_			,  truncation=lowerCAmelCase_			,  return_tensors='pt'					)
															self.assertIsInstance(lowerCAmelCase_			,  lowerCAmelCase_					)
															self.assertEqual(batch.input_ids.shape			,  (2, 5122)					)




					@require_torch
					def 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										_snake_case													=			['A long paragraph for summarization.']
										_snake_case													=			[
										    'Summary of the text.',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_snake_case													=			tokenizer(lowerCAmelCase_			,  return_tensors='pt'					)
															_snake_case													=			tokenizer(text_target=lowerCAmelCase_			,  return_tensors='pt'					)
															_snake_case													=			inputs['input_ids']
															_snake_case													=			targets['input_ids']
															self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()					)
															self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()					)
															self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()					)
															self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()					)




					@require_torch
					def 		lowerCAmelCase     (						self					)						->							List[str]:
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_snake_case													=			['Summary of the text.', 'Another summary.']
															_snake_case													=			[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

															_snake_case													=			tokenizer(lowerCAmelCase_			,  padding=lowerCAmelCase_					)
															_snake_case													=			[[0] * len(lowerCAmelCase_					) for x in encoded_output['input_ids']]
															_snake_case													=			tokenizer.pad(lowerCAmelCase_					)
															self.assertSequenceEqual(outputs['global_attention_mask']			,  lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self					)						->							Tuple:
										pass






					def 		lowerCAmelCase     (						self					)						->							str:
										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													=			self.tokenizer_class.from_pretrained(lowerCAmelCase_			,  **lowerCAmelCase_					)
																				_snake_case													=			'A, <mask> AllenNLP sentence.'
																				_snake_case													=			tokenizer_r.encode_plus(lowerCAmelCase_			,  add_special_tokens=lowerCAmelCase_			,  return_token_type_ids=lowerCAmelCase_					)
																				_snake_case													=			tokenizer_p.encode_plus(lowerCAmelCase_			,  add_special_tokens=lowerCAmelCase_			,  return_token_type_ids=lowerCAmelCase_					)
																				self.assertEqual(sum(tokens_r['token_type_ids']					)			,  sum(tokens_p['token_type_ids']					)					)
																				self.assertEqual(
																				    sum(tokens_r['attention_mask']					) / len(tokens_r['attention_mask']					)			,  sum(tokens_p['attention_mask']					) / len(tokens_p['attention_mask']					)			,  )

																				_snake_case													=			tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']					)
																				_snake_case													=			tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']					)
																				self.assertSequenceEqual(tokens_p['input_ids']			,  [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]					)
																				self.assertSequenceEqual(tokens_r['input_ids']			,  [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]					)

																				self.assertSequenceEqual(
																				    lowerCAmelCase_			,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)
																				self.assertSequenceEqual(
																				    lowerCAmelCase_			,  ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)



