

import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


UpperCAmelCase__       :   Tuple 			=			logging.get_logger(__name__)

UpperCAmelCase__       :   int 			=			"▁"

UpperCAmelCase__       :   Dict 			=			{"vocab_file": "vocab.txt", "sentencepiece_model_ckpt": "sentencepiece.bpe.model"}

UpperCAmelCase__       :   Optional[int] 			=			{
    "sentencepiece_model_file": "sentencepiece.bpe.model",
    "vocab_file": "vocab.txt",
}

UpperCAmelCase__       :   Dict 			=			{
    "vocab_file": {
        "ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
        "ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
    },
    "sentencepiece_model_file": {
        "ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
        "ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
    },
}

UpperCAmelCase__       :   Tuple 			=			{
    "ernie-m-base": 514,
    "ernie-m-large": 514,
}

UpperCAmelCase__       :   int 			=			{
    "ernie-m-base": {"do_lower_case": False},
    "ernie-m-large": {"do_lower_case": False},
}







class 				a__				(      A__				):







  """simple docstring"""




  UpperCAmelCase__					:       Optional[int]					=["""input_ids"""]

  UpperCAmelCase__					:       Tuple					=VOCAB_FILES_NAMES
  UpperCAmelCase__					:       Optional[Any]					=PRETRAINED_INIT_CONFIGURATION
  UpperCAmelCase__					:       int					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  UpperCAmelCase__					:       int					=PRETRAINED_VOCAB_FILES_MAP
  UpperCAmelCase__					:       Optional[Any]					=RESOURCE_FILES_NAMES


  def __init__(	self			:       Tuple , UpperCAmelCase__			:       List[Any] , UpperCAmelCase__			:       Dict=None , UpperCAmelCase__			:       Dict=False , UpperCAmelCase__			:       List[Any]="utf8" , UpperCAmelCase__			:       Dict="[UNK]" , UpperCAmelCase__			:       List[Any]="[SEP]" , UpperCAmelCase__			:       List[str]="[PAD]" , UpperCAmelCase__			:       int="[CLS]" , UpperCAmelCase__			:       Dict="[MASK]" , UpperCAmelCase__			:       Optional[Dict[str, Any]] = None , **UpperCAmelCase__			:       Tuple , )    ->None:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       :					List[Any]        =      {} if sp_model_kwargs is None else sp_model_kwargs
        super().__init__(
            do_lower_case=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , vocab_file=_a , encoding=_a , sp_model_kwargs=self.sp_model_kwargs , **_a , )
        SCREAMING_SNAKE_CASE       :					str        =      do_lower_case
        SCREAMING_SNAKE_CASE       :					Any        =      sentencepiece_model_ckpt
        SCREAMING_SNAKE_CASE       :					Dict        =      spm.SentencePieceProcessor(**self.sp_model_kwargs						)
        self.sp_model.Load(_a						)

        # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
        if vocab_file is not None:
              SCREAMING_SNAKE_CASE       :					Dict        =      self.load_vocab(filepath=_a						)
        else:
              SCREAMING_SNAKE_CASE       :					Optional[int]        =      {self.sp_model.id_to_piece(_a						): id for id in range(self.sp_model.get_piece_size()						)}
        SCREAMING_SNAKE_CASE       :					int        =      {v: k for k, v in self.vocab.items()}


  def        _lowercase					(	self			:       Optional[int] , UpperCAmelCase__			:       Any						)    ->List[Any]:
        """simple docstring"""







        if text is None:
              return None

        SCREAMING_SNAKE_CASE       :					str        =      self.tokenize(_a						)
        SCREAMING_SNAKE_CASE   ,		SCREAMING_SNAKE_CASE       :					str        =      """""", []

        for i, ch in enumerate(_a						):
              if ch in self.SP_CHAR_MAPPING:
                    SCREAMING_SNAKE_CASE       :					List[Any]        =      self.SP_CHAR_MAPPING.get(_a						)
              else:
                    SCREAMING_SNAKE_CASE       :					Dict        =      unicodedata.normalize("""NFKC""" , _a						)
              if self.is_whitespace(_a						):
                    continue
              normalized_text += ch
              char_mapping.extend([i] * len(_a						)						)

        SCREAMING_SNAKE_CASE   ,		SCREAMING_SNAKE_CASE   ,		SCREAMING_SNAKE_CASE       :					List[str]        =      normalized_text, [], 0

        if self.do_lower_case:
              SCREAMING_SNAKE_CASE       :					int        =      text.lower()

        for token in split_tokens:
              if token[:1] == "▁":
                    SCREAMING_SNAKE_CASE       :					Optional[Any]        =      token[1:]
              SCREAMING_SNAKE_CASE       :					Union[str, Any]        =      text[offset:].index(_a						) + offset
              SCREAMING_SNAKE_CASE       :					List[Any]        =      start + len(_a						)

              token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)						)
              SCREAMING_SNAKE_CASE       :					str        =      end
        return token_mapping


  @property
  def        _lowercase					(	self			:       List[str]						)    ->Union[str, Any]:
        """simple docstring"""







        return len(self.vocab						)


  def        _lowercase					(	self			:       int						)    ->Optional[Any]:
        """simple docstring"""







        return dict(self.vocab , **self.added_tokens_encoder						)


  def __getstate__(	self			:       Optional[int]						)    ->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       :					List[str]        =      self.__dict__.copy()
        SCREAMING_SNAKE_CASE       :					int        =      None
        return state


  def __setstate__(	self			:       Any , UpperCAmelCase__			:       List[Any]						)    ->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       :					Any        =      d

        # for backward compatibility
        if not hasattr(self , """sp_model_kwargs"""						):
              SCREAMING_SNAKE_CASE       :					Dict        =      {}

        SCREAMING_SNAKE_CASE       :					int        =      spm.SentencePieceProcessor(**self.sp_model_kwargs						)
        self.sp_model.Load(self.sentencepiece_model_ckpt						)


  def        _lowercase					(	self			:       Any , UpperCAmelCase__			:       int						)    ->Optional[Any]:
        """simple docstring"""







        return "".join((self.SP_CHAR_MAPPING.get(_a , _a						) for c in text)						)


  def        _lowercase					(	self			:       List[Any] , UpperCAmelCase__			:       int , UpperCAmelCase__			:       Optional[int]=False , UpperCAmelCase__			:       Any=6_4 , UpperCAmelCase__			:       List[str]=0.1						)    ->Optional[Any]:
        """simple docstring"""







        if self.sp_model_kwargs.get("""enable_sampling"""						) is True:
              SCREAMING_SNAKE_CASE       :					Any        =      True
        if self.sp_model_kwargs.get("""alpha"""						) is not None:
              SCREAMING_SNAKE_CASE       :					Tuple        =      self.sp_model_kwargs.get("""alpha"""						)
        if self.sp_model_kwargs.get("""nbest_size"""						) is not None:
              SCREAMING_SNAKE_CASE       :					Optional[int]        =      self.sp_model_kwargs.get("""nbest_size"""						)

        if not enable_sampling:
              SCREAMING_SNAKE_CASE       :					str        =      self.sp_model.EncodeAsPieces(_a						)
        else:
              SCREAMING_SNAKE_CASE       :					Tuple        =      self.sp_model.SampleEncodeAsPieces(_a , _a , _a						)
        SCREAMING_SNAKE_CASE       :					Tuple        =      []
        for pi, piece in enumerate(_a						):
              if piece == SPIECE_UNDERLINE:
                    if not pieces[pi + 1].startswith(_a						) and pi != 0:
                          new_pieces.append(_a						)
                          continue
                    else:
                          continue
              SCREAMING_SNAKE_CASE       :					List[str]        =      0
              for i, chunk in enumerate(_a						):
                    if chunk == SPIECE_UNDERLINE:
                          continue
                    if self.is_ch_char(_a						) or self.is_punct(_a						):
                          if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                                new_pieces.append(piece[lst_i:i]						)
                          new_pieces.append(_a						)
                          SCREAMING_SNAKE_CASE       :					List[str]        =      i + 1
                    elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
                          if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                                new_pieces.append(piece[lst_i:i]						)
                          SCREAMING_SNAKE_CASE       :					List[Any]        =      i
                    elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
                          if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                                new_pieces.append(piece[lst_i:i]						)
                          SCREAMING_SNAKE_CASE       :					Tuple        =      i
              if len(_a						) > lst_i:
                    new_pieces.append(piece[lst_i:]						)
        return new_pieces


  def        _lowercase					(	self			:       List[str] , UpperCAmelCase__			:       List[str]						)    ->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       :					List[str]        =      """""".join(_a						).replace(_a , """ """						).strip()
        return out_string


  def        _lowercase					(	self			:       str , UpperCAmelCase__			:       int						)    ->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       :					Any        =      self.convert_ids_to_tokens(_a						)
        SCREAMING_SNAKE_CASE       :					Optional[int]        =      """""".join(_a						).replace(_a , """ """						).strip()
        return out_string


  def        _lowercase					(	self			:       List[str] , UpperCAmelCase__			:       Optional[int]						)    ->Union[str, Any]:
        """simple docstring"""







        return self.vocab.get(_a , self.vocab.get(self.unk_token						)						)


  def        _lowercase					(	self			:       List[Any] , UpperCAmelCase__			:       Optional[Any]						)    ->Any:
        """simple docstring"""







        return self.reverse_vocab.get(_a , self.unk_token						)


  def        _lowercase					(	self			:       Optional[Any] , UpperCAmelCase__			:       Union[str, Any] , UpperCAmelCase__			:       Dict=None						)    ->Optional[int]:
        """simple docstring"""







        if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        SCREAMING_SNAKE_CASE       :					int        =      [self.cls_token_id]
        SCREAMING_SNAKE_CASE       :					Optional[int]        =      [self.sep_token_id]
        return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep


  def        _lowercase					(	self			:       List[Any] , UpperCAmelCase__			:       Dict , UpperCAmelCase__			:       Optional[Any]=None						)    ->Optional[int]:
        """simple docstring"""







        if offset_mapping_a is None:
              return [(0, 0)] + offset_mapping_a + [(0, 0)]

        return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]


  def        _lowercase					(	self			:       int , UpperCAmelCase__			:       Tuple , UpperCAmelCase__			:       Optional[int]=None , UpperCAmelCase__			:       Tuple=False						)    ->List[Any]:
        """simple docstring"""







        if already_has_special_tokens:
              if token_ids_a is not None:
                    raise ValueError(
                        """You should not supply a second sequence if the provided sequence of """
                        """ids is already formatted with special tokens for the model."""						)
              return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

        if token_ids_a is not None:
              return [1] + ([0] * len(_a						)) + [1, 1] + ([0] * len(_a						)) + [1]
        return [1] + ([0] * len(_a						)) + [1]


  def        _lowercase					(	self			:       List[str] , UpperCAmelCase__			:       List[int] , UpperCAmelCase__			:       Optional[List[int]] = None						)    ->List[int]:
        """simple docstring"""







        if token_ids_a is None:
              # [CLS] X [SEP]
              return (len(_a						) + 2) * [0]

        # [CLS] A [SEP] [SEP] B [SEP]
        return [0] * (len(_a						) + 1) + [1] * (len(_a						) + 3)


  def        _lowercase					(	self			:       List[Any] , UpperCAmelCase__			:       str						)    ->Optional[int]:
        """simple docstring"""







        if "\u4e00" <= char <= "\u9fff":
              return True
        return False


  def        _lowercase					(	self			:       Tuple , UpperCAmelCase__			:       Tuple						)    ->Optional[Any]:
        """simple docstring"""







        if ("a" <= char <= "z") or ("A" <= char <= "Z"):
              return True
        return False


  def        _lowercase					(	self			:       Tuple , UpperCAmelCase__			:       Optional[int]						)    ->Any:
        """simple docstring"""







        if char in ",;:.?!~，；：。？！《》【】":
              return True
        return False


  def        _lowercase					(	self			:       str , UpperCAmelCase__			:       int						)    ->List[Any]:
        """simple docstring"""







        if char == " " or char == "\t" or char == "\n" or char == "\r":
              return True
        if len(_a						) == 1:
              SCREAMING_SNAKE_CASE       :					Dict        =      unicodedata.category(_a						)
              if cat == "Zs":
                    return True
        return False


  def        _lowercase					(	self			:       str , UpperCAmelCase__			:       List[Any]						)    ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       :					Optional[int]        =      {}
        with io.open(_a , """r""" , encoding="""utf-8"""						) as f:
              for index, line in enumerate(_a						):
                    SCREAMING_SNAKE_CASE       :					Any        =      line.rstrip("""\n"""						)
                    SCREAMING_SNAKE_CASE       :					List[str]        =      int(_a						)

        return token_to_idx




  def        _lowercase					(	self			:       str , UpperCAmelCase__			:       str , UpperCAmelCase__			:       Optional[str] = None						)    ->Tuple[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       :					Union[str, Any]        =      0
        if os.path.isdir(_a						):
              SCREAMING_SNAKE_CASE       :					List[str]        =      os.path.join(
                  _a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)
        else:
              SCREAMING_SNAKE_CASE       :					Tuple        =      (filename_prefix + """-""" if filename_prefix else """""") + save_directory
        with open(_a , """w""" , encoding="""utf-8"""						) as writer:
              for token, token_index in sorted(self.vocab.items() , key=lambda UpperCAmelCase__						: kv[1]						):
                    if index != token_index:
                          logger.warning(
                              f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
                              """ Please check that the vocabulary is not corrupted!"""						)
                          SCREAMING_SNAKE_CASE       :					Union[str, Any]        =      token_index
                    writer.write(token + """\n"""						)
                    index += 1

        SCREAMING_SNAKE_CASE       :					str        =      os.path.join(_a , """sentencepiece.bpe.model"""						)
        with open(_a , """wb"""						) as fi:
              SCREAMING_SNAKE_CASE       :					int        =      self.sp_model.serialized_model_proto()
              fi.write(_a						)

        return (vocab_file,)







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





  def 						A  (		self      :	Union[str, Any] )	->    int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    ={}
       if self.train_dir is not None:
            _SCREAMING_SNAKE_CASE							    =self.train_dir
       if self.validation_dir is not None:
            _SCREAMING_SNAKE_CASE							    =self.validation_dir
       _SCREAMING_SNAKE_CASE							    =data_files if data_files else None







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







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


def        _lowerCAmelCase					(						_UpperCamelCase						:     int				)      ->			Tuple:
     """simple docstring"""




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


def        _lowerCAmelCase					(						)      ->			Dict:
     """simple docstring"""




     _SCREAMING_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.
          _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
     else:
          _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_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()

     _SCREAMING_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.
     _SCREAMING_SNAKE_CASE							    =None
     if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
          _SCREAMING_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.
     _SCREAMING_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.
     _SCREAMING_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:
          _SCREAMING_SNAKE_CASE							    =ds['train'].train_test_split(data_args.train_val_split				)
          _SCREAMING_SNAKE_CASE							    =split['train']
          _SCREAMING_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.
     _SCREAMING_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:
          _SCREAMING_SNAKE_CASE							    =ViTMAEConfig.from_pretrained(model_args.config_name							, **_UpperCamelCase				)
     elif model_args.model_name_or_path:
          _SCREAMING_SNAKE_CASE							    =ViTMAEConfig.from_pretrained(model_args.model_name_or_path							, **_UpperCamelCase				)
     else:
          _SCREAMING_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:
          _SCREAMING_SNAKE_CASE							    =ViTImageProcessor.from_pretrained(model_args.image_processor_name							, **_UpperCamelCase				)
     elif model_args.model_name_or_path:
          _SCREAMING_SNAKE_CASE							    =ViTImageProcessor.from_pretrained(model_args.model_name_or_path							, **_UpperCamelCase				)
     else:
          _SCREAMING_SNAKE_CASE							    =ViTImageProcessor()

     # create model
     if model_args.model_name_or_path:
          _SCREAMING_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'				)
          _SCREAMING_SNAKE_CASE							    =ViTMAEForPreTraining(_UpperCamelCase				)

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

     if data_args.image_column_name is not None:
          _SCREAMING_SNAKE_CASE							    =data_args.image_column_name
     elif "image" in column_names:
          _SCREAMING_SNAKE_CASE							    ='image'
     elif "img" in column_names:
          _SCREAMING_SNAKE_CASE							    ='img'
     else:
          _SCREAMING_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:
          _SCREAMING_SNAKE_CASE							    =image_processor.size['shortest_edge']
     else:
          _SCREAMING_SNAKE_CASE							    =(image_processor.size['height'], image_processor.size['width'])
     _SCREAMING_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						:     Dict				):
          _SCREAMING_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:
               _SCREAMING_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:
               _SCREAMING_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
     _SCREAMING_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:
          _SCREAMING_SNAKE_CASE							    =training_args.base_learning_rate * total_train_batch_size / 2_56

     # Initialize our trainer
     _SCREAMING_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:
          _SCREAMING_SNAKE_CASE							    =None
          if training_args.resume_from_checkpoint is not None:
               _SCREAMING_SNAKE_CASE							    =training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               _SCREAMING_SNAKE_CASE							    =last_checkpoint
          _SCREAMING_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:
          _SCREAMING_SNAKE_CASE							    =trainer.evaluate()
          trainer.log_metrics('eval'							, _UpperCamelCase				)
          trainer.save_metrics('eval'							, _UpperCamelCase				)

     # Write model card and (optionally) push to hub
     _SCREAMING_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						:     List[str]				)      ->			Optional[int]:
     """simple docstring"""




     main()


if __name__ == "__main__":
     main()
