"""simple docstring"""






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
lowerCAmelCase__  :  List[str]						     =	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 				snake_case       :




					"""simple docstring"""

					snake_case__				    = field(
					    default="cifar10"      ,    metadata={"help": "Name of a dataset from the datasets package"}				)
					snake_case__				    = field(
					    default=__UpperCAmelCase      ,    metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}				)
					snake_case__				    = field(
					    default=__UpperCAmelCase      ,    metadata={"help": "The column name of the images in the files."}				)
					snake_case__				    = field(default=__UpperCAmelCase      ,    metadata={"help": "A folder containing the training data."}				)
					snake_case__				    = field(default=__UpperCAmelCase      ,    metadata={"help": "A folder containing the validation data."}				)
					snake_case__				    = field(
					    default=0.15      ,    metadata={"help": "Percent to split off of train for validation."}				)
					snake_case__				    = field(
					    default=__UpperCAmelCase      ,    metadata={
					        "help": (
					            "For debugging purposes or quicker training, truncate the number of training examples to this "
					            "value if set."
					        )
					    }      ,    )
					snake_case__				    = field(
					    default=__UpperCAmelCase      ,    metadata={
					        "help": (
					            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
					            "value if set."
					        )
					    }      ,    )







					def 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							{}
									if self.train_dir is not None:
													UpperCAmelCase__             =							self.train_dir
									if self.validation_dir is not None:
													UpperCAmelCase__             =							self.validation_dir
									UpperCAmelCase__             =							data_files if data_files else None






@dataclass
class 				snake_case       :




					"""simple docstring"""

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






@dataclass
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = field(
					    default=1E-3      ,    metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}				)






def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							torch.stack([example['pixel_values'] for example in examples]       )
				return {"pixel_values": pixel_values}



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

				UpperCAmelCase__             =							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.
								UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__             =							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
				else:
								UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__             =							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'					,			lowerCamelCase					,			lowerCamelCase       )

				# 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()

				UpperCAmelCase__             =							training_args.get_process_log_level()
				logger.setLevel(lowerCamelCase       )
				transformers.utils.logging.set_verbosity(lowerCamelCase       )
				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.
				UpperCAmelCase__             =							None
				if os.path.isdir(training_args.output_dir       ) and training_args.do_train and not training_args.overwrite_output_dir:
								UpperCAmelCase__             =							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.
				UpperCAmelCase__             =							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.
				UpperCAmelCase__             =							None if 'validation' in ds.keys() else data_args.train_val_split
				if isinstance(data_args.train_val_split					,			lowerCamelCase       ) and data_args.train_val_split > 0.0:
								UpperCAmelCase__             =							ds['train'].train_test_split(data_args.train_val_split       )
								UpperCAmelCase__             =							split['train']
								UpperCAmelCase__             =							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.
				UpperCAmelCase__             =							{
				    '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:
								UpperCAmelCase__             =							ViTMAEConfig.from_pretrained(model_args.config_name					,			**lowerCamelCase       )
				elif model_args.model_name_or_path:
								UpperCAmelCase__             =							ViTMAEConfig.from_pretrained(model_args.model_name_or_path					,			**lowerCamelCase       )
				else:
								UpperCAmelCase__             =							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:
								UpperCAmelCase__             =							ViTImageProcessor.from_pretrained(model_args.image_processor_name					,			**lowerCamelCase       )
				elif model_args.model_name_or_path:
								UpperCAmelCase__             =							ViTImageProcessor.from_pretrained(model_args.model_name_or_path					,			**lowerCamelCase       )
				else:
								UpperCAmelCase__             =							ViTImageProcessor()

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

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

				if data_args.image_column_name is not None:
								UpperCAmelCase__             =							data_args.image_column_name
				elif "image" in column_names:
								UpperCAmelCase__             =							'image'
				elif "img" in column_names:
								UpperCAmelCase__             =							'img'
				else:
								UpperCAmelCase__             =							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:
								UpperCAmelCase__             =							image_processor.size['shortest_edge']
				else:
								UpperCAmelCase__             =							(image_processor.size['height'], image_processor.size['width'])
				UpperCAmelCase__             =							Compose(
				    [
				        Lambda(lambda lowerCamelCase       : img.convert('RGB'       ) if img.mode != "RGB" else img       ),
				        RandomResizedCrop(lowerCamelCase					,			scale=(0.2, 1.0)					,			interpolation=InterpolationMode.BICUBIC       ),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        Normalize(mean=image_processor.image_mean					,			std=image_processor.image_std       ),
				    ]       )

				def preprocess_images(lowerCamelCase       ):
								UpperCAmelCase__             =							[transforms(lowerCamelCase       ) 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:
												UpperCAmelCase__             =							ds['train'].shuffle(seed=training_args.seed       ).select(range(data_args.max_train_samples       )       )
								# Set the training transforms
								ds["train"].set_transform(lowerCamelCase       )

				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:
												UpperCAmelCase__             =							(
												    ds['validation'].shuffle(seed=training_args.seed       ).select(range(data_args.max_eval_samples       )       )
												)
								# Set the validation transforms
								ds["validation"].set_transform(lowerCamelCase       )

				# Compute absolute learning rate
				UpperCAmelCase__             =							(
				    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
				)
				if training_args.base_learning_rate is not None:
								UpperCAmelCase__             =							training_args.base_learning_rate * total_train_batch_size / 2_5_6

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

				# Training
				if training_args.do_train:
								UpperCAmelCase__             =							None
								if training_args.resume_from_checkpoint is not None:
												UpperCAmelCase__             =							training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												UpperCAmelCase__             =							last_checkpoint
								UpperCAmelCase__             =							trainer.train(resume_from_checkpoint=lowerCamelCase       )
								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:
								UpperCAmelCase__             =							trainer.evaluate()
								trainer.log_metrics('eval'					,			lowerCamelCase       )
								trainer.save_metrics('eval'					,			lowerCamelCase       )

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



def   a_						(					lowerCamelCase       ):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
						main()





import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__snake_case								=  get_tests_dir("""fixtures/test_sentencepiece.model""")




@require_sentencepiece
class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   List[str]				       =XLMProphetNetTokenizer
	A__    :   List[Any]				       =False
	A__    :   Tuple				       =True
	def      A_				(			self							:   str							):
						super().setUp()

						# We have a SentencePiece fixture for testing
						SCREAMING_SNAKE_CASE__													= XLMProphetNetTokenizer(UpperCAmelCase_       ,   keep_accents=UpperCAmelCase_							)
						tokenizer.save_pretrained(self.tmpdirname							)
	def      A_				(			self							:   Tuple							):
						SCREAMING_SNAKE_CASE__													= '[PAD]'
						SCREAMING_SNAKE_CASE__													= 0

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_							)       ,   UpperCAmelCase_							)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_							)       ,   UpperCAmelCase_							)
	def      A_				(			self							:   Tuple							):
						SCREAMING_SNAKE_CASE__													= list(self.get_tokenizer().get_vocab().keys()							)

						self.assertEqual(vocab_keys[0]       ,   '[PAD]'							)
						self.assertEqual(vocab_keys[1]       ,   '[CLS]'							)
						self.assertEqual(vocab_keys[-1]       ,   'j'							)
						self.assertEqual(len(UpperCAmelCase_							)       ,   1012							)
	def      A_				(			self							:   List[Any]							):
						self.assertEqual(self.get_tokenizer().vocab_size       ,   1012							)
	def      A_				(			self							:   Optional[int]							):
						SCREAMING_SNAKE_CASE__													= XLMProphetNetTokenizer(UpperCAmelCase_       ,   keep_accents=UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= tokenizer.tokenize('This is a test'							)
						self.assertListEqual(UpperCAmelCase_       ,   ['▁This', '▁is', '▁a', '▁t', 'est']							)

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)       ,   [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]       ,   )

						SCREAMING_SNAKE_CASE__													= tokenizer.tokenize('I was born in 92000, and this is falsé.'							)
						self.assertListEqual(
						    UpperCAmelCase_       ,   [
						        SPIECE_UNDERLINE + 'I',
						        SPIECE_UNDERLINE + 'was',
						        SPIECE_UNDERLINE + 'b',
						        'or',
						        'n',
						        SPIECE_UNDERLINE + 'in',
						        SPIECE_UNDERLINE + '',
						        '9',
						        '2',
						        '0',
						        '0',
						        '0',
						        ',',
						        SPIECE_UNDERLINE + 'and',
						        SPIECE_UNDERLINE + 'this',
						        SPIECE_UNDERLINE + 'is',
						        SPIECE_UNDERLINE + 'f',
						        'al',
						        's',
						        'é',
						        '.',
						    ]       ,   )
						SCREAMING_SNAKE_CASE__													= tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)
						self.assertListEqual(
						    UpperCAmelCase_       ,   [
						        value + tokenizer.fairseq_offset
						        for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
						    ]       ,   )

						SCREAMING_SNAKE_CASE__													= tokenizer.convert_ids_to_tokens(UpperCAmelCase_							)
						self.assertListEqual(
						    UpperCAmelCase_       ,   [
						        SPIECE_UNDERLINE + 'I',
						        SPIECE_UNDERLINE + 'was',
						        SPIECE_UNDERLINE + 'b',
						        'or',
						        'n',
						        SPIECE_UNDERLINE + 'in',
						        SPIECE_UNDERLINE + '',
						        '[UNK]',
						        '2',
						        '0',
						        '0',
						        '0',
						        ',',
						        SPIECE_UNDERLINE + 'and',
						        SPIECE_UNDERLINE + 'this',
						        SPIECE_UNDERLINE + 'is',
						        SPIECE_UNDERLINE + 'f',
						        'al',
						        's',
						        '[UNK]',
						        '.',
						    ]       ,   )
	@cached_property
	def      A_				(			self							:   Optional[Any]							):
						return XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'							)
	@slow
	def      A_				(			self							:   Any							):
						SCREAMING_SNAKE_CASE__													= 'Hello World!'
						SCREAMING_SNAKE_CASE__													= [35389, 6672, 49, 2]
						self.assertListEqual(UpperCAmelCase_       ,   self.big_tokenizer.encode(UpperCAmelCase_							)							)





	@slow
	def      A_				(			self							:   Tuple							):
						# fmt: off
						SCREAMING_SNAKE_CASE__													= {'input_ids': [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=UpperCAmelCase_       ,   model_name='microsoft/xprophetnet-large-wiki100-cased'       ,   revision='1acad1643ddd54a44df6a1b797ada8373685d90e'       ,   )





