






"""simple docstring"""






def       _A    (__a							)				-> int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						int							=	hex_num.strip()
    if not hex_num:
        raise ValueError('''No value was passed to the function'''							)

    SCREAMING_SNAKE_CASE_   :						Dict							=	hex_num[0] == '''-'''
    if is_negative:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	hex_num[1:]

    try:
        SCREAMING_SNAKE_CASE_   :						Tuple							=	int(__A					,       16							)
    except ValueError:
        raise ValueError('''Invalid value was passed to the function'''							)

    SCREAMING_SNAKE_CASE_   :						int							=	''''''
    while int_num > 0:
        SCREAMING_SNAKE_CASE_   :						Any							=	str(int_num % 2							) + bin_str
        int_num >>= 1

    return int(('''-''' + bin_str) if is_negative else bin_str							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()










"""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
UpperCAmelCase_     :				Optional[Any]         =				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""")

UpperCAmelCase_     :				Dict         =				list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
UpperCAmelCase_     :				int         =				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class 							lowerCAmelCase__       :






      '''simple docstring'''

      __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. If not set, will try to use 'image' or 'img'."}       ,	)
      __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=0.1_5       ,	metadata={"help": "Percent to split off of train for validation."}				)
      __UpperCamelCase							=						field(default=3_2       ,	metadata={"help": "The size of the square patches to use for masking."}				)
      __UpperCamelCase							=						field(
          default=0.6       ,	metadata={"help": "Percentage of patches to mask."}       ,	)
      __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 _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


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




@dataclass
class 							lowerCAmelCase__       :






      '''simple docstring'''

      __UpperCamelCase							=						field(
          default=UpperCAmelCase__       ,	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."
              )
          }       ,	)
      __UpperCamelCase							=						field(
          default=UpperCAmelCase__       ,	metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(UpperCAmelCase__				)}       ,	)
      __UpperCamelCase							=						field(
          default=UpperCAmelCase__       ,	metadata={"help": "Pretrained config name or path if not the same as model_name"}				)
      __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 (cache) the pretrained models/datasets downloaded from the hub"}       ,	)
      __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=UpperCAmelCase__       ,	metadata={
              "help": (
                  "The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
              )
          }       ,	)
      __UpperCamelCase							=						field(
          default=UpperCAmelCase__       ,	metadata={
              "help": (
                  "The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
              )
          }       ,	)
      __UpperCamelCase							=						field(
          default=UpperCAmelCase__       ,	metadata={"help": "Stride to use for the encoder."}       ,	)




class 							lowerCAmelCase__       :






      '''simple docstring'''

      def __init__(		self  :						int		,			lowercase_  :						Tuple=192		,			lowercase_  :						Tuple=32		,			lowercase_  :						Optional[Any]=4		,			lowercase_  :						Optional[int]=0.6):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	input_size
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	mask_patch_size
          SCREAMING_SNAKE_CASE_   :						Dict							=	model_patch_size
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	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''')

          SCREAMING_SNAKE_CASE_   :						int							=	self.input_size // self.mask_patch_size
          SCREAMING_SNAKE_CASE_   :						str							=	self.mask_patch_size // self.model_patch_size

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.rand_size**2
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	int(np.ceil(self.token_count * self.mask_ratio))



      def __call__(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.random.permutation(self.token_count)[: self.mask_count]
          SCREAMING_SNAKE_CASE_   :						Any							=	np.zeros(self.token_count		,			dtype=lowercase_)
          SCREAMING_SNAKE_CASE_   :						Tuple							=	1

          SCREAMING_SNAKE_CASE_   :						Tuple							=	mask.reshape((self.rand_size, self.rand_size))
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	mask.repeat(self.scale		,			axis=0).repeat(self.scale		,			axis=1)

          return torch.tensor(mask.flatten())



def       _A    (__a							)				-> str:
    """simple docstring"""






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



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






    SCREAMING_SNAKE_CASE_   :						List[str]							=	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.
        SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
    else:
        SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Dict							=	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'''					,       __a					,       __a							)

    # 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_   :						Tuple							=	training_args.get_process_log_level()
    logger.setLevel(__a							)
    transformers.utils.logging.set_verbosity(__a							)
    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_   :						Optional[int]							=	None
    if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
        SCREAMING_SNAKE_CASE_   :						Union[str, 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.
    SCREAMING_SNAKE_CASE_   :						str							=	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_   :						List[str]							=	None if '''validation''' in ds.keys() else data_args.train_val_split
    if isinstance(data_args.train_val_split					,       __a							) and data_args.train_val_split > 0.0:
        SCREAMING_SNAKE_CASE_   :						Tuple							=	ds['''train'''].train_test_split(data_args.train_val_split							)
        SCREAMING_SNAKE_CASE_   :						List[str]							=	split['''train''']
        SCREAMING_SNAKE_CASE_   :						Any							=	split['''test''']

    # Create config
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    SCREAMING_SNAKE_CASE_   :						List[str]							=	{
        '''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:
        SCREAMING_SNAKE_CASE_   :						str							=	AutoConfig.from_pretrained(model_args.config_name_or_path					,       **__a							)
    elif model_args.model_name_or_path:
        SCREAMING_SNAKE_CASE_   :						str							=	AutoConfig.from_pretrained(model_args.model_name_or_path					,       **__a							)
    else:
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	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(__a					,       '''decoder_type'''							):
        SCREAMING_SNAKE_CASE_   :						Any							=	'''simmim'''

    # adapt config
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	model_args.image_size if model_args.image_size is not None else config.image_size
    SCREAMING_SNAKE_CASE_   :						List[str]							=	model_args.patch_size if model_args.patch_size is not None else config.patch_size
    SCREAMING_SNAKE_CASE_   :						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:
        SCREAMING_SNAKE_CASE_   :						str							=	AutoImageProcessor.from_pretrained(model_args.image_processor_name					,       **__a							)
    elif model_args.model_name_or_path:
        SCREAMING_SNAKE_CASE_   :						Any							=	AutoImageProcessor.from_pretrained(model_args.model_name_or_path					,       **__a							)
    else:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	{
            conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
        }
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	IMAGE_PROCESSOR_TYPES[model_args.model_type]()

    # create model
    if model_args.model_name_or_path:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	AutoModelForMaskedImageModeling.from_pretrained(
            model_args.model_name_or_path					,       from_tf=bool('''.ckpt''' in model_args.model_name_or_path							)					,       config=__a					,       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_   :						Optional[Any]							=	AutoModelForMaskedImageModeling.from_config(__a							)

    if training_args.do_train:
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	ds['''train'''].column_names
    else:
        SCREAMING_SNAKE_CASE_   :						Tuple							=	ds['''validation'''].column_names

    if data_args.image_column_name is not None:
        SCREAMING_SNAKE_CASE_   :						Dict							=	data_args.image_column_name
    elif "image" in column_names:
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	'''image'''
    elif "img" in column_names:
        SCREAMING_SNAKE_CASE_   :						int							=	'''img'''
    else:
        SCREAMING_SNAKE_CASE_   :						Any							=	column_names[0]

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

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

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

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

    # Write model card and (optionally) push to hub
    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	{
        '''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(**__a							)
    else:
        trainer.create_model_card(**__a							)


if __name__ == "__main__":
  main()


