






"""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
A__					: 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""")

A__					: int=	list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
A__					: List[str]=	tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class    __lowerCamelCase    :
   a      :				str 						=field(
       default="""cifar10"""							,	metadata={"""help""": """Name of a dataset from the datasets package"""}  )
   a      :				Optional[int] 						=field(
       default=__A							,	metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}  )
   a      :				int 						=field(
       default=__A							,	metadata={"""help""": """The column name of the images in the files. If not set, will try to use \'image\' or \'img\'."""}							,	)
   a      :				List[Any] 						=field(default=__A							,	metadata={"""help""": """A folder containing the training data."""}  )
   a      :				Any 						=field(default=__A							,	metadata={"""help""": """A folder containing the validation data."""}  )
   a      :				List[Any] 						=field(
       default=0.15							,	metadata={"""help""": """Percent to split off of train for validation."""}  )
   a      :				str 						=field(default=3_2							,	metadata={"""help""": """The size of the square patches to use for masking."""}  )
   a      :				Union[str, Any] 						=field(
       default=0.6							,	metadata={"""help""": """Percentage of patches to mask."""}							,	)
   a      :				int 						=field(
       default=__A							,	metadata={
           """help""": (
               """For debugging purposes or quicker training, truncate the number of training examples to this """
               """value if set."""
           )
       }							,	)
   a      :				int 						=field(
       default=__A							,	metadata={
           """help""": (
               """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
               """value if set."""
           )
       }							,	)

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       UpperCamelCase__         =    {}
       if self.train_dir is not None:
           UpperCamelCase__         =    self.train_dir
       if self.validation_dir is not None:
           UpperCamelCase__         =    self.validation_dir
       UpperCamelCase__         =    data_files if data_files else None







@dataclass
class    __lowerCamelCase    :
   a      :				str 						=field(
       default=__A							,	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      :				Optional[Any] 						=field(
       default=__A							,	metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(__A  )}							,	)
   a      :				str 						=field(
       default=__A							,	metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}  )
   a      :				List[Any] 						=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      :				List[Any] 						=field(
       default=__A							,	metadata={"""help""": """Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"""}							,	)
   a      :				int 						=field(
       default="""main"""							,	metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}							,	)
   a      :				int 						=field(default=__A							,	metadata={"""help""": """Name or path of preprocessor config."""}  )
   a      :				Any 						=field(
       default=__A							,	metadata={
           """help""": (
               """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
               """with private models)."""
           )
       }							,	)
   a      :				int 						=field(
       default=__A							,	metadata={
           """help""": (
               """The size (resolution) of each image. If not specified, will use `image_size` of the configuration."""
           )
       }							,	)
   a      :				List[Any] 						=field(
       default=__A							,	metadata={
           """help""": (
               """The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."""
           )
       }							,	)
   a      :				Tuple 						=field(
       default=__A							,	metadata={"""help""": """Stride to use for the encoder."""}							,	)







class    __lowerCamelCase    :

   def __init__(					self ,				snake_case_=192 ,				snake_case_=32 ,				snake_case_=4 ,				snake_case_=0.6      )		->       List[Any]:
       UpperCamelCase__         =    input_size
       UpperCamelCase__         =    mask_patch_size
       UpperCamelCase__         =    model_patch_size
       UpperCamelCase__         =    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'      )

       UpperCamelCase__         =    self.input_size // self.mask_patch_size
       UpperCamelCase__         =    self.mask_patch_size // self.model_patch_size

       UpperCamelCase__         =    self.rand_size**2
       UpperCamelCase__         =    int(np.ceil(self.token_count * self.mask_ratio      )      )







   def __call__(					self      )		->       str:
       UpperCamelCase__         =    np.random.permutation(self.token_count      )[: self.mask_count]
       UpperCamelCase__         =    np.zeros(self.token_count ,				dtype=snake_case_      )
       UpperCamelCase__         =    1

       UpperCamelCase__         =    mask.reshape((self.rand_size, self.rand_size)      )
       UpperCamelCase__         =    mask.repeat(self.scale ,				axis=0      ).repeat(self.scale ,				axis=1      )

       return torch.tensor(mask.flatten()      )







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Union[str, Any]:
    """simple docstring"""

    UpperCamelCase__         =    torch.stack([example['pixel_values'] for example in examples]					)
    UpperCamelCase__         =    torch.stack([example['mask'] for example in examples]					)
    return {"pixel_values": pixel_values, "bool_masked_pos": mask}







def   lowerCAmelCase_( )					->	Any:
    """simple docstring"""

    UpperCamelCase__         =    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.
        UpperCamelCase__         =    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
    else:
        UpperCamelCase__         =    parser.parse_args_into_dataclasses()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry('run_mim' ,	__lowercase ,	__lowercase					)

    # Setup logging
    logging.basicConfig(
        format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' ,	datefmt='%m/%d/%Y %H:%M:%S' ,	handlers=[logging.StreamHandler(sys.stdout					)] ,	)

    if training_args.should_log:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        transformers.utils.logging.set_verbosity_info()

    UpperCamelCase__         =    training_args.get_process_log_level()
    logger.setLevel(__lowercase					)
    transformers.utils.logging.set_verbosity(__lowercase					)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
        + F'distributed training: {bool(training_args.local_rank != -1					)}, 16-bits training: {training_args.fpaa}'					)
    logger.info(F'Training/evaluation parameters {training_args}'					)

    # Detecting last checkpoint.
    UpperCamelCase__         =    None
    if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
        UpperCamelCase__         =    get_last_checkpoint(training_args.output_dir					)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir					)					) > 0:
            raise ValueError(
                F'Output directory ({training_args.output_dir}) already exists and is not empty. '
                'Use --overwrite_output_dir to overcome.'					)
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
                'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'					)

    # Initialize our dataset.
    UpperCamelCase__         =    load_dataset(
        data_args.dataset_name ,	data_args.dataset_config_name ,	data_files=data_args.data_files ,	cache_dir=model_args.cache_dir ,	use_auth_token=True if model_args.use_auth_token else None ,	)

    # If we don't have a validation split, split off a percentage of train as validation.
    UpperCamelCase__         =    None if 'validation' in ds.keys() else data_args.train_val_split
    if isinstance(data_args.train_val_split ,	__lowercase					) and data_args.train_val_split > 0.0:
        UpperCamelCase__         =    ds['train'].train_test_split(data_args.train_val_split					)
        UpperCamelCase__         =    split['train']
        UpperCamelCase__         =    split['test']

    # Create config
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    UpperCamelCase__         =    {
        'cache_dir': model_args.cache_dir,
        'revision': model_args.model_revision,
        'use_auth_token': True if model_args.use_auth_token else None,
    }
    if model_args.config_name_or_path:
        UpperCamelCase__         =    AutoConfig.from_pretrained(model_args.config_name_or_path ,	**__lowercase					)
    elif model_args.model_name_or_path:
        UpperCamelCase__         =    AutoConfig.from_pretrained(model_args.model_name_or_path ,	**__lowercase					)
    else:
        UpperCamelCase__         =    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(__lowercase ,	'decoder_type'					):
        UpperCamelCase__         =    'simmim'

    # adapt config
    UpperCamelCase__         =    model_args.image_size if model_args.image_size is not None else config.image_size
    UpperCamelCase__         =    model_args.patch_size if model_args.patch_size is not None else config.patch_size
    UpperCamelCase__         =    (
        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:
        UpperCamelCase__         =    AutoImageProcessor.from_pretrained(model_args.image_processor_name ,	**__lowercase					)
    elif model_args.model_name_or_path:
        UpperCamelCase__         =    AutoImageProcessor.from_pretrained(model_args.model_name_or_path ,	**__lowercase					)
    else:
        UpperCamelCase__         =    {
            conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
        }
        UpperCamelCase__         =    IMAGE_PROCESSOR_TYPES[model_args.model_type]()

    # create model
    if model_args.model_name_or_path:
        UpperCamelCase__         =    AutoModelForMaskedImageModeling.from_pretrained(
            model_args.model_name_or_path ,	from_tf=bool('.ckpt' in model_args.model_name_or_path					) ,	config=__lowercase ,	cache_dir=model_args.cache_dir ,	revision=model_args.model_revision ,	use_auth_token=True if model_args.use_auth_token else None ,	)
    else:
        logger.info('Training new model from scratch'					)
        UpperCamelCase__         =    AutoModelForMaskedImageModeling.from_config(__lowercase					)

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

    if data_args.image_column_name is not None:
        UpperCamelCase__         =    data_args.image_column_name
    elif "image" in column_names:
        UpperCamelCase__         =    'image'
    elif "img" in column_names:
        UpperCamelCase__         =    'img'
    else:
        UpperCamelCase__         =    column_names[0]

    # transformations as done in original SimMIM paper
    # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
    UpperCamelCase__         =    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
    UpperCamelCase__         =    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					):
        UpperCamelCase__         =    [transforms(__lowercase					) for image in examples[image_column_name]]
        UpperCamelCase__         =    [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:
            UpperCamelCase__         =    ds['train'].shuffle(seed=training_args.seed					).select(range(data_args.max_train_samples					)					)
        # Set the training transforms
        ds["train"].set_transform(__lowercase					)

    if training_args.do_eval:
        if "validation" not in ds:
            raise ValueError('--do_eval requires a validation dataset'					)
        if data_args.max_eval_samples is not None:
            UpperCamelCase__         =    (
                ds['validation'].shuffle(seed=training_args.seed					).select(range(data_args.max_eval_samples					)					)
            )
        # Set the validation transforms
        ds["validation"].set_transform(__lowercase					)

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

    # Training
    if training_args.do_train:
        UpperCamelCase__         =    None
        if training_args.resume_from_checkpoint is not None:
            UpperCamelCase__         =    training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            UpperCamelCase__         =    last_checkpoint
        UpperCamelCase__         =    trainer.train(resume_from_checkpoint=__lowercase					)
        trainer.save_model()
        trainer.log_metrics('train' ,	train_result.metrics					)
        trainer.save_metrics('train' ,	train_result.metrics					)
        trainer.save_state()

    # Evaluation
    if training_args.do_eval:
        UpperCamelCase__         =    trainer.evaluate()
        trainer.log_metrics('eval' ,	__lowercase					)
        trainer.save_metrics('eval' ,	__lowercase					)

    # Write model card and (optionally) push to hub
    UpperCamelCase__         =    {
        '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(**__lowercase					)
    else:
        trainer.create_model_card(**__lowercase					)


if __name__ == "__main__":
  main()











"""simple docstring"""





import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
  A__					: str=	{
      """linear""": PIL.Image.Resampling.BILINEAR,
      """bilinear""": PIL.Image.Resampling.BILINEAR,
      """bicubic""": PIL.Image.Resampling.BICUBIC,
      """lanczos""": PIL.Image.Resampling.LANCZOS,
      """nearest""": PIL.Image.Resampling.NEAREST,
  }
else:
  A__					: str=	{
      """linear""": PIL.Image.LINEAR,
      """bilinear""": PIL.Image.BILINEAR,
      """bicubic""": PIL.Image.BICUBIC,
      """lanczos""": PIL.Image.LANCZOS,
      """nearest""": PIL.Image.NEAREST,
  }







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    (images / 2 + 0.5).clamp(0 ,	1					)
    UpperCamelCase__         =    images.cpu().permute(0 ,	2 ,	3 ,	1					).float().numpy()
    UpperCamelCase__         =    numpy_to_pil(SCREAMING_SNAKE_CASE					)
    return images







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Optional[int]:
    """simple docstring"""

    if images.ndim == 3:
        UpperCamelCase__         =    images[None, ...]
    UpperCamelCase__         =    (images * 2_55).round().astype('uint8'					)
    if images.shape[-1] == 1:
        # special case for grayscale (single channel) images
        UpperCamelCase__         =    [Image.fromarray(image.squeeze() ,	mode='L'					) for image in images]
    else:
        UpperCamelCase__         =    [Image.fromarray(SCREAMING_SNAKE_CASE					) for image in images]

    return pil_images




