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





@dataclass
class UpperCAmelCase						:


				'''simple docstring'''






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



						"""simple docstring"""





						A_		:    Optional[Any]         =				{}
						if self.train_dir is not None:
								A_		:    Optional[Any]         =				self.train_dir
						if self.validation_dir is not None:
								A_		:    Union[str, Any]         =				self.validation_dir
						A_		:    Union[str, Any]         =				data_files if data_files else None





@dataclass
class UpperCAmelCase						:


				'''simple docstring'''






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





class UpperCAmelCase						:


				'''simple docstring'''






				def __init__(						self		,     lowercase=1_9_2		,     lowercase=3_2		,     lowercase=4		,     lowercase=0.6 ):



						"""simple docstring"""





						A_		:    List[str]         =				input_size
						A_		:    Dict         =				mask_patch_size
						A_		:    int         =				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_		:    List[Any]         =				self.input_size // self.mask_patch_size
						A_		:    Optional[Any]         =				self.mask_patch_size // self.model_patch_size

						A_		:    Tuple         =				self.rand_size**2
						A_		:    Union[str, Any]         =				int(np.ceil(self.token_count * self.mask_ratio ) )







				def __call__(						self ):



						"""simple docstring"""





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

						A_		:    List[Any]         =				mask.reshape((self.rand_size, self.rand_size) )
						A_		:    Union[str, Any]         =				mask.repeat(self.scale		,     axis=0 ).repeat(self.scale		,     axis=1 )

						return torch.tensor(mask.flatten() )




def     UpperCamelCase						(	__lowercase						:			Union[str, Any]     ):
		'''simple docstring'''




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




def     UpperCamelCase						(	):
		'''simple docstring'''




		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_		:    Tuple         =				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
		else:
				A_		:    List[str]         =				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()

		A_		:    Dict         =				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.
		A_		:    str         =				None
		if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
				A_		:    Optional[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.
		A_		:    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.
		A_		:    List[str]         =				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:
				A_		:    Tuple         =				ds['train'].train_test_split(data_args.train_val_split     )
				A_		:    Union[str, Any]         =				split['train']
				A_		:    Union[str, Any]         =				split['test']

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

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

		# create model
		if model_args.model_name_or_path:
				A_		:    Optional[Any]         =				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'     )
				A_		:    List[Any]         =				AutoModelForMaskedImageModeling.from_config(__lowercase     )

		if training_args.do_train:
				A_		:    Optional[int]         =				ds['train'].column_names
		else:
				A_		:    List[str]         =				ds['validation'].column_names

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

		# transformations as done in original SimMIM paper
		# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
		A_		:    Tuple         =				Compose(
		    [
		        Lambda(lambda __lowercase     : 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_		:    Union[str, 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(__lowercase						:			List[Any]     ):
				A_		:    Dict         =				[transforms(__lowercase     ) for image in examples[image_column_name]]
				A_		:    List[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:
						A_		:    Any         =				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:
						A_		:    Dict         =				(
						    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
		A_		:    Dict         =				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:
				A_		:    int         =				None
				if training_args.resume_from_checkpoint is not None:
						A_		:    int         =				training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						A_		:    Tuple         =				last_checkpoint
				A_		:    Optional[Any]         =				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:
				A_		:    List[Any]         =				trainer.evaluate()
				trainer.log_metrics('eval'						,__lowercase     )
				trainer.save_metrics('eval'						,__lowercase     )

		# 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(**__lowercase     )
		else:
				trainer.create_model_card(**__lowercase     )


if __name__ == "__main__":
	main()







from math import isqrt




def     UpperCamelCase						(	__lowercase						:			int     ):
		'''simple docstring'''




		return all(number % divisor != 0 for divisor in range(2						,isqrt(__lowercase     ) + 1     )     )




def     UpperCamelCase						(	__lowercase						:			int = 10**6     ):
		'''simple docstring'''




		A_		:    Optional[Any]         =				0
		A_		:    List[str]         =				1
		A_		:    Dict         =				7
		while prime_candidate < max_prime:
				primes_count += is_prime(__lowercase     )

				cube_index += 1
				prime_candidate += 6 * cube_index

		return primes_count


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






