

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

@dataclass
class       lowerCamelCase	:


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  field(
      default='cifar10'					,					metadata={'help': 'Name of a dataset from the datasets package'}   )
  SCREAMING_SNAKE_CASE					      =  field(
      default=lowerCAmelCase__					,					metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}   )
  SCREAMING_SNAKE_CASE					      =  field(
      default=lowerCAmelCase__					,					metadata={'help': 'The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'}					,					)
  SCREAMING_SNAKE_CASE					      =  field(default=lowerCAmelCase__					,					metadata={'help': 'A folder containing the training data.'}   )
  SCREAMING_SNAKE_CASE					      =  field(default=lowerCAmelCase__					,					metadata={'help': 'A folder containing the validation data.'}   )
  SCREAMING_SNAKE_CASE					      =  field(
      default=0.15					,					metadata={'help': 'Percent to split off of train for validation.'}   )
  SCREAMING_SNAKE_CASE					      =  field(default=3_2					,					metadata={'help': 'The size of the square patches to use for masking.'}   )
  SCREAMING_SNAKE_CASE					      =  field(
      default=0.6					,					metadata={'help': 'Percentage of patches to mask.'}					,					)
  SCREAMING_SNAKE_CASE					      =  field(
      default=lowerCAmelCase__					,					metadata={
          'help': (
              'For debugging purposes or quicker training, truncate the number of training examples to this '
              'value if set.'
          )
      }					,					)
  SCREAMING_SNAKE_CASE					      =  field(
      default=lowerCAmelCase__					,					metadata={
          'help': (
              'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
              'value if set.'
          )
      }					,					)



  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[int]							=    {}
        if self.train_dir is not None:
              UpperCAmelCase__			:							Dict							=    self.train_dir
        if self.validation_dir is not None:
              UpperCAmelCase__			:							Any							=    self.validation_dir
        UpperCAmelCase__			:							Any							=    data_files if data_files else None




@dataclass
class       lowerCamelCase	:


  '''simple docstring'''







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




class       lowerCamelCase	:


  '''simple docstring'''







  def __init__(self							,       _lowerCamelCase=192							,       _lowerCamelCase=32							,       _lowerCamelCase=4							,       _lowerCamelCase=0.6	):


        """simple docstring"""

        UpperCAmelCase__			:							Any							=    input_size
        UpperCAmelCase__			:							Any							=    mask_patch_size
        UpperCAmelCase__			:							str							=    model_patch_size
        UpperCAmelCase__			:							List[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"""	)

        UpperCAmelCase__			:							Tuple							=    self.input_size // self.mask_patch_size
        UpperCAmelCase__			:							int							=    self.mask_patch_size // self.model_patch_size

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




  def __call__(self	):


        """simple docstring"""

        UpperCAmelCase__			:							Any							=    np.random.permutation(self.token_count	)[: self.mask_count]
        UpperCAmelCase__			:							int							=    np.zeros(self.token_count							,       dtype=_lowerCamelCase	)
        UpperCAmelCase__			:							Tuple							=    1

        UpperCAmelCase__			:							Any							=    mask.reshape((self.rand_size, self.rand_size)	)
        UpperCAmelCase__			:							Any							=    mask.repeat(self.scale							,       axis=0	).repeat(self.scale							,       axis=1	)

        return torch.tensor(mask.flatten()	)




def 					a__						(						lowerCAmelCase    )	-> Union[str, Any]:
      UpperCAmelCase__			:							int							=    torch.stack([example["""pixel_values"""] for example in examples]    )
      UpperCAmelCase__			:							List[str]							=    torch.stack([example["""mask"""] for example in examples]    )
      return {"pixel_values": pixel_values, "bool_masked_pos": mask}




def 					a__						(						)	-> str:
      # 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.

      UpperCAmelCase__			:							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.
            UpperCAmelCase__					,		UpperCAmelCase__					,		UpperCAmelCase__			:							Dict							=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
      else:
            UpperCAmelCase__					,		UpperCAmelCase__					,		UpperCAmelCase__			:							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"""			,	lowerCAmelCase			,	lowerCAmelCase    )

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

      # Create config
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
      UpperCAmelCase__			:							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_or_path:
            UpperCAmelCase__			:							Dict							=    AutoConfig.from_pretrained(model_args.config_name_or_path			,	**lowerCAmelCase    )
      elif model_args.model_name_or_path:
            UpperCAmelCase__			:							int							=    AutoConfig.from_pretrained(model_args.model_name_or_path			,	**lowerCAmelCase    )
      else:
            UpperCAmelCase__			:							Tuple							=    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(lowerCAmelCase			,	"""decoder_type"""    ):
            UpperCAmelCase__			:							Dict							=    """simmim"""

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

      # create model
      if model_args.model_name_or_path:
            UpperCAmelCase__			:							List[Any]							=    AutoModelForMaskedImageModeling.from_pretrained(
                model_args.model_name_or_path			,	from_tf=bool(""".ckpt""" in model_args.model_name_or_path    )			,	config=lowerCAmelCase			,	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__			:							int							=    AutoModelForMaskedImageModeling.from_config(lowerCAmelCase    )

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

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

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

      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__			:							List[str]							=    (
                      ds["""validation"""].shuffle(seed=training_args.seed    ).select(range(data_args.max_eval_samples    )    )
                  )
            # Set the validation transforms
            ds["validation"].set_transform(lowerCAmelCase    )

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

      # Training
      if training_args.do_train:
            UpperCAmelCase__			:							Optional[int]							=    None
            if training_args.resume_from_checkpoint is not None:
                  UpperCAmelCase__			:							str							=    training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  UpperCAmelCase__			:							Union[str, Any]							=    last_checkpoint
            UpperCAmelCase__			:							Optional[int]							=    trainer.train(resume_from_checkpoint=lowerCAmelCase    )
            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__			:							Dict							=    trainer.evaluate()
            trainer.log_metrics("""eval"""			,	lowerCAmelCase    )
            trainer.save_metrics("""eval"""			,	lowerCAmelCase    )

      # Write model card and (optionally) push to hub
      UpperCAmelCase__			:							Any							=    {
          """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(**lowerCAmelCase    )
      else:
            trainer.create_model_card(**lowerCAmelCase    )


if __name__ == "__main__":
    main()




"""simple docstring"""
import math




def 					a__						(						lowerCAmelCase    )	-> list[int]:
      UpperCAmelCase__			:							Optional[int]							=    []
      UpperCAmelCase__			:							Union[str, Any]							=    2
      UpperCAmelCase__			:							List[Any]							=    int(math.sqrt(lowerCAmelCase    )    )  # Size of every segment
      UpperCAmelCase__			:							Dict							=    [True] * (end + 1)
      UpperCAmelCase__			:							str							=    []

      while start <= end:
            if temp[start] is True:
                  in_prime.append(lowerCAmelCase    )
                  for i in range(start * start			,	end + 1			,	lowerCAmelCase    ):
                        UpperCAmelCase__			:							Optional[Any]							=    False
            start += 1
      prime += in_prime

      UpperCAmelCase__			:							Optional[Any]							=    end + 1
      UpperCAmelCase__			:							Dict							=    min(2 * end			,	lowerCAmelCase    )

      while low <= n:
            UpperCAmelCase__			:							Optional[int]							=    [True] * (high - low + 1)
            for each in in_prime:
                  UpperCAmelCase__			:							Optional[int]							=    math.floor(low / each    ) * each
                  if t < low:
                        t += each

                  for j in range(lowerCAmelCase			,	high + 1			,	lowerCAmelCase    ):
                        UpperCAmelCase__			:							int							=    False

            for j in range(len(lowerCAmelCase    )    ):
                  if temp[j] is True:
                        prime.append(j + low    )

            UpperCAmelCase__			:							Dict							=    high + 1
            UpperCAmelCase__			:							int							=    min(high + end			,	lowerCAmelCase    )

      return prime





print(sieve(10**6))

