


import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__    :str       =				logging.get_logger(__name__)

lowerCAmelCase__    :List[Any]       =				'''▁'''

lowerCAmelCase__    :List[Any]       =				{'''vocab_file''': '''prophetnet.tokenizer'''}

lowerCAmelCase__    :List[str]       =				{
    '''vocab_file''': {
        '''microsoft/xprophetnet-large-wiki100-cased''': (
            '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
        ),
    }
}

lowerCAmelCase__    :str       =				{
    '''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}

lowerCAmelCase__    :int       =				{
    '''microsoft/xprophetnet-large-wiki100-cased''': 5_1_2,
}

def 			lowerCAmelCase__					(     a__:						Any			)    ->						Optional[int]:


							'''simple docstring'''





							_UpperCAmelCase											=     collections.OrderedDict()
							with open(a__     ,    'r'     ,    encoding='utf-8'			) as reader:
														_UpperCAmelCase											=     reader.readlines()
							for index, token in enumerate(a__			):
														_UpperCAmelCase											=     token.rstrip('\n'			)
														_UpperCAmelCase											=     index
							return vocab







class 						__a (							UpperCAmelCase     ):
		_a      :    Union[str, Any]           =      VOCAB_FILES_NAMES
		_a      :    int           =      PRETRAINED_VOCAB_FILES_MAP
		_a      :    str           =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a      :    Optional[Any]           =      ['input_ids', 'attention_mask']
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE="[SEP]"     ,       _SCREAMING_SNAKE_CASE="[SEP]"     ,       _SCREAMING_SNAKE_CASE="[SEP]"     ,       _SCREAMING_SNAKE_CASE="[UNK]"     ,       _SCREAMING_SNAKE_CASE="[PAD]"     ,       _SCREAMING_SNAKE_CASE="[CLS]"     ,       _SCREAMING_SNAKE_CASE="[MASK]"     ,       _SCREAMING_SNAKE_CASE = None     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			None:




									"""simple docstring"""





									_UpperCAmelCase											=     {} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    bos_token=_SCREAMING_SNAKE_CASE     ,       eos_token=_SCREAMING_SNAKE_CASE     ,       sep_token=_SCREAMING_SNAKE_CASE     ,       unk_token=_SCREAMING_SNAKE_CASE     ,       pad_token=_SCREAMING_SNAKE_CASE     ,       cls_token=_SCREAMING_SNAKE_CASE     ,       mask_token=_SCREAMING_SNAKE_CASE     ,       sp_model_kwargs=self.sp_model_kwargs     ,       **_SCREAMING_SNAKE_CASE     ,       )

									try:
																import sentencepiece as spm
									except ImportError:
																logger.warning(
																    'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
																    ' pip install sentencepiece'		)
																raise

									_UpperCAmelCase											=     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
									self.sp_model.Load(str(_SCREAMING_SNAKE_CASE		)		)
									_UpperCAmelCase											=     vocab_file

									# Original fairseq vocab and spm vocab must be "aligned":
									# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
									# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
									# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
									# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

									# put special tokens and [unused] tokens into the vocab
									_UpperCAmelCase											=     {'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4}

									for i in range(10		):
																_UpperCAmelCase											=     f'''[unused{i}]'''
																_UpperCAmelCase											=     5 + i

									# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
									_UpperCAmelCase											=     12
									_UpperCAmelCase											=     {v: k for k, v in self.fairseq_tokens_to_ids.items()}
									for k in self.fairseq_tokens_to_ids.keys():
																self.unique_no_split_tokens.append(_SCREAMING_SNAKE_CASE		)
		def __getstate__(     self		)					->			Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.__dict__.copy()
									_UpperCAmelCase											=     None
									return state
		def __setstate__(     self     ,       _SCREAMING_SNAKE_CASE		)					->			List[str]:




									"""simple docstring"""





									_UpperCAmelCase											=     d
									try:
																import sentencepiece as spm
									except ImportError:
																logger.warning(
																    'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
																    ' pip install sentencepiece'		)
																raise

									# for backward compatibility
									if not hasattr(self     ,       'sp_model_kwargs'		):
																_UpperCAmelCase											=     {}

									_UpperCAmelCase											=     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
									self.sp_model.Load(self.vocab_file		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = False		)					->			List[int]:




									"""simple docstring"""





									if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=_SCREAMING_SNAKE_CASE     ,       token_ids_a=_SCREAMING_SNAKE_CASE     ,       already_has_special_tokens=_SCREAMING_SNAKE_CASE		)

									if token_ids_a is None:
																return ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
									return ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = None		)					->			List[int]:




									"""simple docstring"""





									_UpperCAmelCase											=     [self.sep_token_id]

									if token_ids_a is None:
																return len(token_ids_a + sep		) * [0]
									return len(token_ids_a + sep + sep + token_ids_a + sep		) * [0]
		@property
		def       UpperCAmelCase__							(     self		)					->			Dict:




									"""simple docstring"""





									return len(self.sp_model		) + self.fairseq_offset
		def       UpperCAmelCase__							(     self		)					->			Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE		): i for i in range(self.vocab_size		)}
									vocab.update(self.added_tokens_encoder		)
									return vocab
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			str:




									"""simple docstring"""





									return self.sp_model.encode(_SCREAMING_SNAKE_CASE     ,       out_type=_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			int:




									"""simple docstring"""





									if token in self.fairseq_tokens_to_ids:
																return self.fairseq_tokens_to_ids[token]
									_UpperCAmelCase											=     self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE		)

									# Need to return unknown token if the SP model returned 0
									return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			Dict:




									"""simple docstring"""





									if index in self.fairseq_ids_to_tokens:
																return self.fairseq_ids_to_tokens[index]
									return self.sp_model.IdToPiece(index - self.fairseq_offset		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     ''.join(_SCREAMING_SNAKE_CASE		).replace(_SCREAMING_SNAKE_CASE     ,       ' '		).strip()
									return out_string
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = None		)					->			Tuple[str]:




									"""simple docstring"""





									if not os.path.isdir(_SCREAMING_SNAKE_CASE		):
																logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
																return
									_UpperCAmelCase											=     os.path.join(
									    _SCREAMING_SNAKE_CASE     ,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

									if os.path.abspath(self.vocab_file		) != os.path.abspath(_SCREAMING_SNAKE_CASE		) and os.path.isfile(self.vocab_file		):
																copyfile(self.vocab_file     ,       _SCREAMING_SNAKE_CASE		)
									elif not os.path.isfile(self.vocab_file		):
																with open(_SCREAMING_SNAKE_CASE     ,       'wb'		) as fi:
																							_UpperCAmelCase											=     self.sp_model.serialized_model_proto()
																							fi.write(_SCREAMING_SNAKE_CASE		)

									return (out_vocab_file,)






		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = None		)					->			List[int]:




									"""simple docstring"""





									if token_ids_a is None:
																return token_ids_a + [self.sep_token_id]
									_UpperCAmelCase											=     [self.sep_token_id]
									return token_ids_a + sep + token_ids_a + sep




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 						__a :
		_a      :    Optional[str]           =      field(
		    default='cifar10'       , metadata={'help': 'Name of a dataset from the datasets package'}     )
		_a      :    Optional[str]           =      field(
		    default=UpperCAmelCase       , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}     )
		_a      :    Optional[str]           =      field(
		    default=UpperCAmelCase       , metadata={'help': 'The column name of the images in the files.'}     )
		_a      :    Optional[str]           =      field(default=UpperCAmelCase       , metadata={'help': 'A folder containing the training data.'}     )
		_a      :    Optional[str]           =      field(default=UpperCAmelCase       , metadata={'help': 'A folder containing the validation data.'}     )
		_a      :    Optional[float]           =      field(
		    default=0.1_5       , metadata={'help': 'Percent to split off of train for validation.'}     )
		_a      :    Optional[int]           =      field(
		    default=UpperCAmelCase       , metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of training examples to this '
		            'value if set.'
		        )
		    }       , )
		_a      :    Optional[int]           =      field(
		    default=UpperCAmelCase       , metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
		            'value if set.'
		        )
		    }       , )
		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





									_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 						__a :
		_a      :    str           =      field(
		    default=UpperCAmelCase       , metadata={
		        'help': (
		            'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
		        )
		    }       , )
		_a      :    Optional[str]           =      field(
		    default=UpperCAmelCase       , metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'}     )
		_a      :    Optional[str]           =      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'
		        )
		    }       , )
		_a      :    Optional[str]           =      field(
		    default=UpperCAmelCase       , metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}     )
		_a      :    str           =      field(
		    default='main'       , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}       , )
		_a      :    str           =      field(default=UpperCAmelCase       , metadata={'help': 'Name or path of preprocessor config.'}     )
		_a      :    bool           =      field(
		    default=UpperCAmelCase       , metadata={
		        'help': (
		            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
		            'with private models).'
		        )
		    }       , )
		_a      :    float           =      field(
		    default=0.7_5       , metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'}     )
		_a      :    bool           =      field(
		    default=UpperCAmelCase       , metadata={'help': 'Whether or not to train with normalized pixel values as target.'}     )



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



def 			lowerCAmelCase__					(     a__:						Optional[Any]			)    ->						Union[str, Any]:


							'''simple docstring'''





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

def 			lowerCAmelCase__					(     )    ->						Tuple:


							'''simple docstring'''





							_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'     ,    a__     ,    a__			)

							# 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(a__			)
							transformers.utils.logging.set_verbosity(a__			)
							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     ,    a__			) 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     ,    **a__			)
							elif model_args.model_name_or_path:
														_UpperCAmelCase											=     ViTMAEConfig.from_pretrained(model_args.model_name_or_path     ,    **a__			)
							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     ,    **a__			)
							elif model_args.model_name_or_path:
														_UpperCAmelCase											=     ViTImageProcessor.from_pretrained(model_args.model_name_or_path     ,    **a__			)
							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=a__     ,    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(a__			)

							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 a__			: img.convert('RGB'			) if img.mode != "RGB" else img			),
							        RandomResizedCrop(a__     ,    scale=(0.2, 1.0)     ,    interpolation=InterpolationMode.BICUBIC			),
							        RandomHorizontalFlip(),
							        ToTensor(),
							        Normalize(mean=image_processor.image_mean     ,    std=image_processor.image_std			),
							    ]			)

							def preprocess_images(a__:						Optional[int]			):
														_UpperCAmelCase											=     [transforms(a__			) 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(a__			)

							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(a__			)

							# 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=a__     ,    args=a__     ,    train_dataset=ds['train'] if training_args.do_train else None     ,    eval_dataset=ds['validation'] if training_args.do_eval else None     ,    tokenizer=a__     ,    data_collator=a__     ,    )

							# 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=a__			)
														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'     ,    a__			)
														trainer.save_metrics('eval'     ,    a__			)

							# 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(**a__			)
							else:
														trainer.create_model_card(**a__			)

def 			lowerCAmelCase__					(     a__:						Tuple			)    ->						Optional[Any]:


							'''simple docstring'''





							main()


if __name__ == "__main__":
		main()
