
"""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__      :	Any          =    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class   _lowercase						:


     '''simple docstring'''





     _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  )->					str:
          UpperCAmelCase__							: List[str] 							=						{}
          if self.train_dir is not None:
               UpperCAmelCase__							: Any 							=						self.train_dir
          if self.validation_dir is not None:
               UpperCAmelCase__							: List[str] 							=						self.validation_dir
          UpperCAmelCase__							: List[Any] 							=						data_files if data_files else None







@dataclass
class   _lowercase						:


     '''simple docstring'''





     _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   _lowercase						:


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase=1_92 ,			__UpperCamelCase=32 ,			__UpperCamelCase=4 ,			__UpperCamelCase=0.6  )->					Union[str, Any]:
          UpperCAmelCase__							: List[Any] 							=						input_size
          UpperCAmelCase__							: Union[str, Any] 							=						mask_patch_size
          UpperCAmelCase__							: Optional[int] 							=						model_patch_size
          UpperCAmelCase__							: Tuple 							=						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__							: Dict 							=						self.input_size // self.mask_patch_size
          UpperCAmelCase__							: Dict 							=						self.mask_patch_size // self.model_patch_size

          UpperCAmelCase__							: int 							=						self.rand_size**2
          UpperCAmelCase__							: List[str] 							=						int(np.ceil(self.token_count * self.mask_ratio  )  )



     def __call__(      self  )->					int:
          UpperCAmelCase__							: Any 							=						np.random.permutation(self.token_count  )[: self.mask_count]
          UpperCAmelCase__							: Any 							=						np.zeros(self.token_count ,			dtype=__UpperCamelCase  )
          UpperCAmelCase__							: int 							=						1

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

          return torch.tensor(mask.flatten()  )





def 							a__ (				lowerCAmelCase	:       Optional[Any]		):






     '''simple docstring'''
     UpperCAmelCase__							: Union[str, Any] 							=						torch.stack([example["pixel_values"] for example in examples]		)
     UpperCAmelCase__							: List[Any] 							=						torch.stack([example["mask"] for example in examples]		)
     return {"pixel_values": pixel_values, "bool_masked_pos": mask}



def 							a__ (				):






     '''simple docstring'''
     # 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__							: Any 							=						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__							: Any 							=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
     else:
          UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: Optional[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"       ,    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__							: 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__							: Dict 							=						None
     if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
          UpperCAmelCase__							: List[str] 							=						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__							: Union[str, 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.
     UpperCAmelCase__							: int 							=						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__							: str 							=						split["train"]
          UpperCAmelCase__							: str 							=						split["test"]

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

     # adapt config
     UpperCAmelCase__							: Dict 							=						model_args.image_size if model_args.image_size is not None else config.image_size
     UpperCAmelCase__							: Union[str, Any] 							=						model_args.patch_size if model_args.patch_size is not None else config.patch_size
     UpperCAmelCase__							: Union[str, 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__							: Optional[Any] 							=						AutoImageProcessor.from_pretrained(model_args.image_processor_name       ,    **lowerCAmelCase		)
     elif model_args.model_name_or_path:
          UpperCAmelCase__							: Any 							=						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__							: Optional[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__							: Tuple 							=						AutoModelForMaskedImageModeling.from_config(lowerCAmelCase		)

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

     if data_args.image_column_name is not None:
          UpperCAmelCase__							: Union[str, Any] 							=						data_args.image_column_name
     elif "image" in column_names:
          UpperCAmelCase__							: Union[str, Any] 							=						"image"
     elif "img" in column_names:
          UpperCAmelCase__							: Tuple 							=						"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__							: 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__							: Optional[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(lowerCAmelCase	:       List[Any]		):
          UpperCAmelCase__							: Optional[int] 							=						[transforms(lowerCAmelCase		) for image in examples[image_column_name]]
          UpperCAmelCase__							: 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:
               UpperCAmelCase__							: Optional[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__							: Optional[int] 							=						(
                   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__							: Tuple 							=						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__							: int 							=						None
          if training_args.resume_from_checkpoint is not None:
               UpperCAmelCase__							: Optional[int] 							=						training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               UpperCAmelCase__							: Optional[int] 							=						last_checkpoint
          UpperCAmelCase__							: List[str] 							=						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__							: Any 							=						trainer.evaluate()
          trainer.log_metrics("eval"       ,    lowerCAmelCase		)
          trainer.save_metrics("eval"       ,    lowerCAmelCase		)

     # Write model card and (optionally) push to hub
     UpperCAmelCase__							: Optional[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"""




from math import isqrt



def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						[True] * max_number
     for i in range(2       ,    isqrt(max_number - 1		) + 1		):
          if is_prime[i]:
               for j in range(i**2       ,    lowerCAmelCase       ,    lowerCAmelCase		):
                    UpperCAmelCase__							: List[Any] 							=						False

     return [i for i in range(2       ,    lowerCAmelCase		) if is_prime[i]]



def 							a__ (				lowerCAmelCase	:       int = 10**8		):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						calculate_prime_numbers(max_number // 2		)

     UpperCAmelCase__							: Optional[int] 							=						0
     UpperCAmelCase__							: Optional[int] 							=						0
     UpperCAmelCase__							: Tuple 							=						len(lowerCAmelCase		) - 1
     while left <= right:
          while prime_numbers[left] * prime_numbers[right] >= max_number:
               right -= 1
          semiprimes_count += right - left + 1
          left += 1

     return semiprimes_count


if __name__ == "__main__":
    print(f"""{solution() = }""")



