"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
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')

lowercase__					       =	list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
lowercase__					       =	tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@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. If not set, will try to use 'image' or 'img'."""}       ,      )
      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=32       ,      metadata={"""help""": """The size of the square patches to use for masking."""}				)
      a__        =		field(
          default=0.6       ,      metadata={"""help""": """Percentage of patches to mask."""}       ,      )
      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)	->			Union[str, Any]:





        '''simple docstring'''





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







@dataclass
class    __snake_case       :
      a__        =		field(
          default=__lowerCAmelCase       ,      metadata={
              """help""": (
                  """The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a """
                  """checkpoint identifier on the hub. """
                  """Don't set if you want to train a model from scratch."""
              )
          }       ,      )
      a__        =		field(
          default=__lowerCAmelCase       ,      metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(__lowerCAmelCase				)}       ,      )
      a__        =		field(
          default=__lowerCAmelCase       ,      metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}				)
      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 (cache) the pretrained models/datasets downloaded from the hub"""}       ,      )
      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=__lowerCAmelCase       ,      metadata={
              """help""": (
                  """The size (resolution) of each image. If not specified, will use `image_size` of the configuration."""
              )
          }       ,      )
      a__        =		field(
          default=__lowerCAmelCase       ,      metadata={
              """help""": (
                  """The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."""
              )
          }       ,      )
      a__        =		field(
          default=__lowerCAmelCase       ,      metadata={"""help""": """Stride to use for the encoder."""}       ,      )







class    __snake_case       :



      def __init__(						self    ,			lowercase=1_92    ,			lowercase=32    ,			lowercase=4    ,			lowercase=0.6)	->			Optional[Any]:





        '''simple docstring'''





        a__:     int										=	input_size
        a__:     int										=	mask_patch_size
        a__:     Dict										=	model_patch_size
        a__:     int										=	mask_ratio

        if self.input_size % self.mask_patch_size != 0:
          raise ValueError('Input size must be divisible by mask patch size')
        if self.mask_patch_size % self.model_patch_size != 0:
          raise ValueError('Mask patch size must be divisible by model patch size')

        a__:     Tuple										=	self.input_size // self.mask_patch_size
        a__:     List[str]										=	self.mask_patch_size // self.model_patch_size

        a__:     Dict										=	self.rand_size**2
        a__:     Any										=	int(np.ceil(self.token_count * self.mask_ratio))



      def __call__(						self)	->			List[Any]:





        '''simple docstring'''





        a__:     int										=	np.random.permutation(self.token_count)[: self.mask_count]
        a__:     List[str]										=	np.zeros(self.token_count    ,			dtype=lowercase)
        a__:     Optional[int]										=	1

        a__:     Optional[int]										=	mask.reshape((self.rand_size, self.rand_size))
        a__:     List[str]										=	mask.repeat(self.scale    ,			axis=0).repeat(self.scale    ,			axis=1)

        return torch.tensor(mask.flatten())







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







def 						__a   (	)	->List[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.

  a__:     Dict										=	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
  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__:     Dict										=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
  else:
    a__							,    a__							,    a__:     List[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_mim'		,     _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__:     int										=	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__:     Optional[int]										=	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__:     int										=	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__:     str										=	ds['train'].train_test_split(data_args.train_val_split   )
    a__:     Any										=	split['train']
    a__:     Dict										=	split['test']

  # Create config
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.
  a__:     List[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_or_path:
    a__:     Any										=	AutoConfig.from_pretrained(model_args.config_name_or_path		,     **_SCREAMING_SNAKE_CASE   )
  elif model_args.model_name_or_path:
    a__:     str										=	AutoConfig.from_pretrained(model_args.model_name_or_path		,     **_SCREAMING_SNAKE_CASE   )
  else:
    a__:     int										=	CONFIG_MAPPING[model_args.model_type]()
    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}'   )

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
  if hasattr(_SCREAMING_SNAKE_CASE		,     'decoder_type'   ):
    a__:     Union[str, Any]										=	'simmim'

  # adapt config
  a__:     Optional[int]										=	model_args.image_size if model_args.image_size is not None else config.image_size
  a__:     Optional[Any]										=	model_args.patch_size if model_args.patch_size is not None else config.patch_size
  a__:     List[str]										=	(
      model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
  )

  config.update(
      {
          'image_size': model_args.image_size,
          'patch_size': model_args.patch_size,
          'encoder_stride': model_args.encoder_stride,
      }   )

  # create image processor
  if model_args.image_processor_name:
    a__:     Optional[int]										=	AutoImageProcessor.from_pretrained(model_args.image_processor_name		,     **_SCREAMING_SNAKE_CASE   )
  elif model_args.model_name_or_path:
    a__:     Optional[int]										=	AutoImageProcessor.from_pretrained(model_args.model_name_or_path		,     **_SCREAMING_SNAKE_CASE   )
  else:
    a__:     Optional[Any]										=	{
        conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
    }
    a__:     Any										=	IMAGE_PROCESSOR_TYPES[model_args.model_type]()

  # create model
  if model_args.model_name_or_path:
    a__:     Tuple										=	AutoModelForMaskedImageModeling.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__:     Optional[Any]										=	AutoModelForMaskedImageModeling.from_config(_SCREAMING_SNAKE_CASE   )

  if training_args.do_train:
    a__:     Any										=	ds['train'].column_names
  else:
    a__:     List[Any]										=	ds['validation'].column_names

  if data_args.image_column_name is not None:
    a__:     Optional[Any]										=	data_args.image_column_name
  elif "image" in column_names:
    a__:     str										=	'image'
  elif "img" in column_names:
    a__:     List[str]										=	'img'
  else:
    a__:     Tuple										=	column_names[0]

  # transformations as done in original SimMIM paper
  # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
  a__:     Union[str, Any]										=	Compose(
      [
          Lambda(lambda _SCREAMING_SNAKE_CASE   : img.convert('RGB'   ) if img.mode != "RGB" else img   ),
          RandomResizedCrop(model_args.image_size		,     scale=(0.67, 1.0)		,     ratio=(3.0 / 4.0, 4.0 / 3.0)   ),
          RandomHorizontalFlip(),
          ToTensor(),
          Normalize(mean=image_processor.image_mean		,     std=image_processor.image_std   ),
      ]   )

  # create mask generator
  a__:     int										=	MaskGenerator(
      input_size=model_args.image_size		,     mask_patch_size=data_args.mask_patch_size		,     model_patch_size=model_args.patch_size		,     mask_ratio=data_args.mask_ratio		,     )

  def preprocess_images(_SCREAMING_SNAKE_CASE   ):
    a__:     str										=	[transforms(_SCREAMING_SNAKE_CASE   ) for image in examples[image_column_name]]
    a__:     Optional[int]										=	[mask_generator() for i in range(len(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__:     Tuple										=	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__:     Tuple										=	(
          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   )

  # Initialize our trainer
  a__:     List[str]										=	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__:     Optional[int]										=	None
    if training_args.resume_from_checkpoint is not None:
      a__:     List[Any]										=	training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      a__:     List[str]										=	last_checkpoint
    a__:     Optional[int]										=	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__:     Tuple										=	{
      'finetuned_from': model_args.model_name_or_path,
      'tasks': 'masked-image-modeling',
      'dataset': data_args.dataset_name,
      'tags': ['masked-image-modeling'],
  }
  if training_args.push_to_hub:
    trainer.push_to_hub(**_SCREAMING_SNAKE_CASE   )
  else:
    trainer.create_model_card(**_SCREAMING_SNAKE_CASE   )


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]


