





import logging
import os
import threading
import time


try:
   import warnings
except ImportError:
   _UpperCAmelCase       : str			     =       None

try:
   import msvcrt
except ImportError:
   _UpperCAmelCase       : Optional[Any]			     =       None

try:
   import fcntl
except ImportError:
   _UpperCAmelCase       : Tuple			     =       None


# Backward compatibility
# ------------------------------------------------
try:
   TimeoutError
except NameError:
   _UpperCAmelCase       : str			     =       OSError


# Data
# ------------------------------------------------
_UpperCAmelCase       : Union[str, Any]			     =       [
    """Timeout""",
    """BaseFileLock""",
    """WindowsFileLock""",
    """UnixFileLock""",
    """SoftFileLock""",
    """FileLock""",
]

_UpperCAmelCase       : List[str]			     =       """3.0.12"""


_UpperCAmelCase       : int			     =       None


def  SCREAMING_SNAKE_CASE   ( )    ->  Dict:
    global _logger
    lowerCamelCase__					:   Dict      				=	_logger or logging.getLogger(__name__				)
    return _logger

class      lowerCAmelCase							(      __UpperCamelCase    ):
 def __init__(   self						:   List[str]    ,  UpperCAmelCase						:   List[Any]			)    ->			List[Any]:
     lowerCamelCase__					:   Union[str, Any]      				=	lock_file
     return None







 def __str__(   self						:   Tuple			)    ->			Any:
     lowerCamelCase__					:   Tuple      				=	F"""The file lock '{self.lock_file}' could not be acquired."""
     return temp


class      lowerCAmelCase							:
 def __init__(   self						:   str    ,  UpperCAmelCase						:   Union[str, Any]			)    ->			str:
     lowerCamelCase__					:   List[str]      				=	lock
     return None
 def __enter__(   self						:   Dict			)    ->			Tuple:
     return self.lock







 def __exit__(   self						:   str    ,  UpperCAmelCase						:   Optional[Any]    ,  UpperCAmelCase						:   Any    ,  UpperCAmelCase						:   Tuple			)    ->			Optional[int]:
     self.lock.release()
     return None


class      lowerCAmelCase							:
 def __init__(   self						:   Optional[int]    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   Optional[int]=-1    ,  UpperCAmelCase						:   str=None			)    ->			Tuple:
     lowerCamelCase__					:   Optional[int]      				=	max_filename_length if max_filename_length is not None else 255
     # Hash the filename if it's too long
     lowerCamelCase__					:   Tuple      				=	self.hash_filename_if_too_long(UpperCAmelCase    ,  UpperCAmelCase			)
     # The path to the lock file.
     lowerCamelCase__					:   Dict      				=	lock_file

     # The file descriptor for the *_lock_file* as it is returned by the
     # os.open() function.
     # This file lock is only NOT None, if the object currently holds the
     # lock.
     lowerCamelCase__					:   Optional[int]      				=	None

     # The default timeout value.
     lowerCamelCase__					:   Optional[Any]      				=	timeout

     # We use this lock primarily for the lock counter.
     lowerCamelCase__					:   Optional[int]      				=	threading.Lock()

     # The lock counter is used for implementing the nested locking
     # mechanism. Whenever the lock is acquired, the counter is increased and
     # the lock is only released, when this value is 0 again.
     lowerCamelCase__					:   Tuple      				=	0
     return None
 @property
 def   A_			(   self						:   str			)    ->			Union[str, Any]:
     return self._lock_file
 @property
 def   A_			(   self						:   Optional[int]			)    ->			List[str]:
     return self._timeout
 @timeout.setter
 def   A_			(   self						:   Dict    ,  UpperCAmelCase						:   List[str]			)    ->			Tuple:
     lowerCamelCase__					:   Union[str, Any]      				=	float(UpperCAmelCase			)
     return None
 def   A_			(   self						:   List[Any]			)    ->			int:
     raise NotImplementedError()
 def   A_			(   self						:   Any			)    ->			Optional[Any]:
     raise NotImplementedError()
 @property
 def   A_			(   self						:   Optional[Any]			)    ->			int:
     return self._lock_file_fd is not None
 def   A_			(   self						:   str    ,  UpperCAmelCase						:   Dict=None    ,  UpperCAmelCase						:   List[Any]=0.0_5			)    ->			Optional[int]:
     # Use the default timeout, if no timeout is provided.
     if timeout is None:
         lowerCamelCase__					:   Optional[int]      				=	self.timeout

     # Increment the number right at the beginning.
     # We can still undo it, if something fails.
     with self._thread_lock:
         self._lock_counter += 1

     lowerCamelCase__					:   Optional[int]      				=	id(self			)
     lowerCamelCase__					:   List[Any]      				=	self._lock_file
     lowerCamelCase__					:   Any      				=	time.time()
     try:
         while True:
             with self._thread_lock:
                 if not self.is_locked:
                     logger().debug(F"""Attempting to acquire lock {lock_id} on {lock_filename}"""			)
                     self._acquire()

             if self.is_locked:
                 logger().debug(F"""Lock {lock_id} acquired on {lock_filename}"""			)
                 break
             elif timeout >= 0 and time.time() - start_time > timeout:
                 logger().debug(F"""Timeout on acquiring lock {lock_id} on {lock_filename}"""			)
                 raise Timeout(self._lock_file			)
             else:
                 logger().debug(
                     F"""Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ..."""			)
                 time.sleep(UpperCAmelCase			)
     except:  # noqa
         # Something did go wrong, so decrement the counter.
         with self._thread_lock:
             lowerCamelCase__					:   List[Any]      				=	max(0    ,  self._lock_counter - 1			)

         raise
     return _Acquire_ReturnProxy(lock=self			)
 def   A_			(   self						:   Dict    ,  UpperCAmelCase						:   Optional[int]=False			)    ->			str:
     with self._thread_lock:
         if self.is_locked:
             self._lock_counter -= 1

             if self._lock_counter == 0 or force:
                 lowerCamelCase__					:   List[Any]      				=	id(self			)
                 lowerCamelCase__					:   Union[str, Any]      				=	self._lock_file

                 logger().debug(F"""Attempting to release lock {lock_id} on {lock_filename}"""			)
                 self._release()
                 lowerCamelCase__					:   int      				=	0
                 logger().debug(F"""Lock {lock_id} released on {lock_filename}"""			)

     return None
 def __enter__(   self						:   int			)    ->			Optional[int]:
     self.acquire()
     return self
 def __exit__(   self						:   int    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   str    ,  UpperCAmelCase						:   Union[str, Any]			)    ->			Optional[Any]:
     self.release()
     return None
 def __del__(   self						:   Tuple			)    ->			int:
     self.release(force=UpperCAmelCase			)
     return None







 def   A_			(   self						:   List[str]    ,  UpperCAmelCase						:   str    ,  UpperCAmelCase						:   int			)    ->			str:
     lowerCamelCase__					:   Any      				=	os.path.basename(UpperCAmelCase			)
     if len(UpperCAmelCase			) > max_length and max_length > 0:
         lowerCamelCase__					:   Tuple      				=	os.path.dirname(UpperCAmelCase			)
         lowerCamelCase__					:   Optional[Any]      				=	str(hash(UpperCAmelCase			)			)
         lowerCamelCase__					:   Union[str, Any]      				=	filename[: max_length - len(UpperCAmelCase			) - 8] + '...' + hashed_filename + '.lock'
         return os.path.join(UpperCAmelCase    ,  UpperCAmelCase			)
     else:
         return path


class      lowerCAmelCase							(      __UpperCamelCase    ):
 def __init__(   self						:   List[Any]    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   int=-1    ,  UpperCAmelCase						:   List[Any]=None			)    ->			List[str]:
     from .file_utils import relative_to_absolute_path

     super().__init__(UpperCAmelCase    ,  timeout=UpperCAmelCase    ,  max_filename_length=UpperCAmelCase			)
     lowerCamelCase__					:   List[str]      				=	'\\\\?\\' + relative_to_absolute_path(self.lock_file			)
 def   A_			(   self						:   Union[str, Any]			)    ->			Optional[Any]:
     lowerCamelCase__					:   str      				=	os.O_RDWR | os.O_CREAT | os.O_TRUNC

     try:
         lowerCamelCase__					:   List[str]      				=	os.open(self._lock_file    ,  UpperCAmelCase			)
     except OSError:
         pass
     else:
         try:
             msvcrt.locking(UpperCAmelCase    ,  msvcrt.LK_NBLCK    ,  1			)
         except OSError:
             os.close(UpperCAmelCase			)
         else:
             lowerCamelCase__					:   Union[str, Any]      				=	fd
     return None







 def   A_			(   self						:   List[Any]			)    ->			List[str]:
     lowerCamelCase__					:   Optional[Any]      				=	self._lock_file_fd
     lowerCamelCase__					:   Tuple      				=	None
     msvcrt.locking(UpperCAmelCase    ,  msvcrt.LK_UNLCK    ,  1			)
     os.close(UpperCAmelCase			)

     try:
         os.remove(self._lock_file			)
     # Probably another instance of the application
     # that acquired the file lock.
     except OSError:
         pass
     return None


class      lowerCAmelCase							(      __UpperCamelCase    ):
 def __init__(   self						:   Dict    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   Any=-1    ,  UpperCAmelCase						:   Optional[Any]=None			)    ->			str:
     lowerCamelCase__					:   int      				=	os.statvfs(os.path.dirname(UpperCAmelCase			)			).f_namemax
     super().__init__(UpperCAmelCase    ,  timeout=UpperCAmelCase    ,  max_filename_length=UpperCAmelCase			)
 def   A_			(   self						:   int			)    ->			List[str]:
     lowerCamelCase__					:   Optional[int]      				=	os.O_RDWR | os.O_CREAT | os.O_TRUNC
     lowerCamelCase__					:   Optional[Any]      				=	os.open(self._lock_file    ,  UpperCAmelCase			)

     try:
         fcntl.flock(UpperCAmelCase    ,  fcntl.LOCK_EX | fcntl.LOCK_NB			)
     except OSError:
         os.close(UpperCAmelCase			)
     else:
         lowerCamelCase__					:   str      				=	fd
     return None







 def   A_			(   self						:   Any			)    ->			Any:
     # Do not remove the lockfile:
     #
     #   https://github.com/benediktschmitt/py-filelock/issues/31
     #   https://stackoverflow.com/questions/17708885/flock-removing-locked-file-without-race-condition
     lowerCamelCase__					:   Union[str, Any]      				=	self._lock_file_fd
     lowerCamelCase__					:   Any      				=	None
     fcntl.flock(UpperCAmelCase    ,  fcntl.LOCK_UN			)
     os.close(UpperCAmelCase			)
     return None


class      lowerCAmelCase							(      __UpperCamelCase    ):
 def   A_			(   self						:   Any			)    ->			List[str]:
     lowerCamelCase__					:   Union[str, Any]      				=	os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
     try:
         lowerCamelCase__					:   Optional[Any]      				=	os.open(self._lock_file    ,  UpperCAmelCase			)
     except OSError:
         pass
     else:
         lowerCamelCase__					:   int      				=	fd
     return None







 def   A_			(   self						:   Optional[int]			)    ->			Tuple:
     os.close(self._lock_file_fd			)
     lowerCamelCase__					:   Any      				=	None

     try:
         os.remove(self._lock_file			)
     # The file is already deleted and that's what we want.
     except OSError:
         pass
     return None


_UpperCAmelCase       : Any			     =       None

if msvcrt:
   _UpperCAmelCase       : Optional[int]			     =       WindowsFileLock
elif fcntl:
   _UpperCAmelCase       : List[Any]			     =       UnixFileLock
else:
   _UpperCAmelCase       : Optional[int]			     =       SoftFileLock

   if warnings is not None:
      warnings.warn("""only soft file lock is available""")












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

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

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_UpperCAmelCase       : 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      lowerCAmelCase							:
 UpperCAmelCase__									=	field(
     default="""cifar10""",						metadata={"""help""": """Name of a dataset from the datasets package"""}    )
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}    )
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={"""help""": """The column name of the images in the files."""}    )
 UpperCAmelCase__									=	field(default=__UpperCamelCase,						metadata={"""help""": """A folder containing the training data."""}    )
 UpperCAmelCase__									=	field(default=__UpperCamelCase,						metadata={"""help""": """A folder containing the validation data."""}    )
 UpperCAmelCase__									=	field(
     default=0.15,						metadata={"""help""": """Percent to split off of train for validation."""}    )
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={
         """help""": (
             """For debugging purposes or quicker training, truncate the number of training examples to this """
             """value if set."""
         )
     },						)
 UpperCAmelCase__									=	field(
     default=__UpperCamelCase,						metadata={
         """help""": (
             """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
             """value if set."""
         )
     },						)
 def   A_			(   self						:   Tuple			)    ->			Optional[Any]:
     lowerCamelCase__					:   Optional[int]      				=	{}
     if self.train_dir is not None:
         lowerCamelCase__					:   int      				=	self.train_dir
     if self.validation_dir is not None:
         lowerCamelCase__					:   Dict      				=	self.validation_dir
     lowerCamelCase__					:   Union[str, Any]      				=	data_files if data_files else None


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


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


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  Tuple:
    lowerCamelCase__					:   str      				=	torch.stack([example['pixel_values'] for example in examples]				)
    return {"pixel_values": pixel_values}


def  SCREAMING_SNAKE_CASE   ( )    ->  Union[str, Any]:
    # 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.

    lowerCamelCase__					:   Tuple      				=	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.
        lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   List[str]      				=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
    else:
        lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   Union[str, Any]      				=	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()

    lowerCamelCase__					:   Optional[Any]      				=	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.
    lowerCamelCase__					:   Optional[Any]      				=	None
    if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
        lowerCamelCase__					:   str      				=	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.
    lowerCamelCase__					:   List[Any]      				=	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.
    lowerCamelCase__					:   List[Any]      				=	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:
        lowerCamelCase__					:   Optional[int]      				=	ds['train'].train_test_split(data_args.train_val_split				)
        lowerCamelCase__					:   List[str]      				=	split['train']
        lowerCamelCase__					:   List[str]      				=	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.
    lowerCamelCase__					:   Union[str, Any]      				=	{
        '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:
        lowerCamelCase__					:   Tuple      				=	ViTMAEConfig.from_pretrained(model_args.config_name		,			**_UpperCAmelCase				)
    elif model_args.model_name_or_path:
        lowerCamelCase__					:   Tuple      				=	ViTMAEConfig.from_pretrained(model_args.model_name_or_path		,			**_UpperCAmelCase				)
    else:
        lowerCamelCase__					:   List[Any]      				=	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:
        lowerCamelCase__					:   Optional[Any]      				=	ViTImageProcessor.from_pretrained(model_args.image_processor_name		,			**_UpperCAmelCase				)
    elif model_args.model_name_or_path:
        lowerCamelCase__					:   Any      				=	ViTImageProcessor.from_pretrained(model_args.model_name_or_path		,			**_UpperCAmelCase				)
    else:
        lowerCamelCase__					:   List[Any]      				=	ViTImageProcessor()

    # create model
    if model_args.model_name_or_path:
        lowerCamelCase__					:   Dict      				=	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'				)
        lowerCamelCase__					:   Dict      				=	ViTMAEForPreTraining(_UpperCAmelCase				)

    if training_args.do_train:
        lowerCamelCase__					:   Union[str, Any]      				=	ds['train'].column_names
    else:
        lowerCamelCase__					:   Any      				=	ds['validation'].column_names

    if data_args.image_column_name is not None:
        lowerCamelCase__					:   str      				=	data_args.image_column_name
    elif "image" in column_names:
        lowerCamelCase__					:   Tuple      				=	'image'
    elif "img" in column_names:
        lowerCamelCase__					:   int      				=	'img'
    else:
        lowerCamelCase__					:   str      				=	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:
        lowerCamelCase__					:   List[Any]      				=	image_processor.size['shortest_edge']
    else:
        lowerCamelCase__					:   Optional[int]      				=	(image_processor.size['height'], image_processor.size['width'])
    lowerCamelCase__					:   Optional[Any]      				=	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				):
        lowerCamelCase__					:   Tuple      				=	[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:
            lowerCamelCase__					:   Optional[int]      				=	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:
            lowerCamelCase__					:   List[str]      				=	(
                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
    lowerCamelCase__					:   Union[str, Any]      				=	(
        training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
    )
    if training_args.base_learning_rate is not None:
        lowerCamelCase__					:   Tuple      				=	training_args.base_learning_rate * total_train_batch_size / 256

    # Initialize our trainer
    lowerCamelCase__					:   str      				=	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:
        lowerCamelCase__					:   List[str]      				=	None
        if training_args.resume_from_checkpoint is not None:
            lowerCamelCase__					:   Optional[int]      				=	training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            lowerCamelCase__					:   Any      				=	last_checkpoint
        lowerCamelCase__					:   Optional[int]      				=	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:
        lowerCamelCase__					:   Dict      				=	trainer.evaluate()
        trainer.log_metrics('eval'		,			_UpperCAmelCase				)
        trainer.save_metrics('eval'		,			_UpperCAmelCase				)

    # Write model card and (optionally) push to hub
    lowerCamelCase__					:   Union[str, Any]      				=	{
        '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  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  Optional[int]:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
    main()





