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



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     Union[str, Any]										=	{}
        if self.train_dir is not None:
          a__:     List[str]										=	self.train_dir
        if self.validation_dir is not None:
          a__:     Optional[int]										=	self.validation_dir
        a__:     Optional[Any]										=	data_files if data_files else None







@dataclass
class    __snake_case       :
      a__        =		field(
          default=__lowerCAmelCase       ,      metadata={
              """help""": (
                  """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
              )
          }       ,      )
      a__        =		field(
          default=__lowerCAmelCase       ,      metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""}				)
      a__        =		field(
          default=__lowerCAmelCase       ,      metadata={
              """help""": (
                  """Override some existing default config settings when a model is trained from scratch. Example: """
                  """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
              )
          }       ,      )
      a__        =		field(
          default=__lowerCAmelCase       ,      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=__lowerCAmelCase       ,      metadata={"""help""": """Name or path of preprocessor config."""}				)
      a__        =		field(
          default=__lowerCAmelCase       ,      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=__lowerCAmelCase       ,      metadata={"""help""": """Whether or not to train with normalized pixel values as target."""}				)







@dataclass
class    __snake_case       (     __lowerCAmelCase				):
      a__        =		field(
          default=1e-3       ,      metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""}				)







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->int:
  a__:     int										=	torch.stack([example['pixel_values'] for example in examples]   )
  return {"pixel_values": pixel_values}







def 						__a   (	)	->Dict:
  # 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.

  a__:     Dict										=	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.
    a__							,    a__							,    a__:     Optional[Any]										=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
  else:
    a__							,    a__							,    a__:     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'		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

  # 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()

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

  # create model
  if model_args.model_name_or_path:
    a__:     Tuple										=	ViTMAEForPreTraining.from_pretrained(
        model_args.model_name_or_path		,     from_tf=bool('.ckpt' in model_args.model_name_or_path   )		,     config=_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		,     )
  else:
    logger.info('Training new model from scratch'   )
    a__:     Tuple										=	ViTMAEForPreTraining(_SCREAMING_SNAKE_CASE   )

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

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

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

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

  # Compute absolute learning rate
  a__:     Dict										=	(
      training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
  )
  if training_args.base_learning_rate is not None:
    a__:     List[Any]										=	training_args.base_learning_rate * total_train_batch_size / 256

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

  # Training
  if training_args.do_train:
    a__:     List[Any]										=	None
    if training_args.resume_from_checkpoint is not None:
      a__:     str										=	training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      a__:     List[str]										=	last_checkpoint
    a__:     List[Any]										=	trainer.train(resume_from_checkpoint=_SCREAMING_SNAKE_CASE   )
    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:
    a__:     Optional[Any]										=	trainer.evaluate()
    trainer.log_metrics('eval'		,     _SCREAMING_SNAKE_CASE   )
    trainer.save_metrics('eval'		,     _SCREAMING_SNAKE_CASE   )

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







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Optional[Any]:
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
 main()



"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union


lowercase__					       =	TypeVar('T')

lowercase__					       =	Union[List[T], Tuple[T, ...]]
lowercase__					       =	Union[T, List[T], Dict[str, T]]
lowercase__					       =	Union[str, bytes, os.PathLike]


