


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
lowerCAmelCase           =		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''')

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





@dataclass
class       A    :
							UpperCamelCase_   :							Optional[str]       =field(
							    default='''cifar10'''					,				metadata={'''help''': '''Name of a dataset from the datasets package'''} )
							UpperCamelCase_   :							Optional[str]       =field(
							    default=A_					,				metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
							UpperCamelCase_   :							Optional[str]       =field(
							    default=A_					,				metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''}					,				)
							UpperCamelCase_   :							Optional[str]       =field(default=A_					,				metadata={'''help''': '''A folder containing the training data.'''} )
							UpperCamelCase_   :							Optional[str]       =field(default=A_					,				metadata={'''help''': '''A folder containing the validation data.'''} )
							UpperCamelCase_   :							Optional[float]       =field(
							    default=0.15					,				metadata={'''help''': '''Percent to split off of train for validation.'''} )
							UpperCamelCase_   :							int       =field(default=32					,				metadata={'''help''': '''The size of the square patches to use for masking.'''} )
							UpperCamelCase_   :							float       =field(
							    default=0.6					,				metadata={'''help''': '''Percentage of patches to mask.'''}					,				)
							UpperCamelCase_   :							Optional[int]       =field(
							    default=A_					,				metadata={
							        '''help''': (
							            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
							            '''value if set.'''
							        )
							    }					,				)
							UpperCamelCase_   :							Optional[int]       =field(
							    default=A_					,				metadata={
							        '''help''': (
							            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
							            '''value if set.'''
							        )
							    }					,				)


							def 			_A       (self					):
									__lowercase=			{}
									if self.train_dir is not None:
											__lowercase=			self.train_dir
									if self.validation_dir is not None:
											__lowercase=			self.validation_dir
									__lowercase=			data_files if data_files else None





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





class       A    :


							def __init__(self      ,				lowerCAmelCase=1_9_2      ,				lowerCAmelCase=3_2      ,				lowerCAmelCase=4      ,				lowerCAmelCase=0.6					):
									__lowercase=			input_size
									__lowercase=			mask_patch_size
									__lowercase=			model_patch_size
									__lowercase=			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'					)

									__lowercase=			self.input_size // self.mask_patch_size
									__lowercase=			self.mask_patch_size // self.model_patch_size

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







							def __call__(self					):
									__lowercase=			np.random.permutation(self.token_count					)[: self.mask_count]
									__lowercase=			np.zeros(self.token_count      ,				dtype=lowerCAmelCase					)
									__lowercase=			1

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

									return torch.tensor(mask.flatten()					)







def        _lowerCamelCase(	lowercase__	)						->	Dict:

		'''simple docstring'''







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







def        _lowerCamelCase(	)						->	Tuple:

		'''simple docstring'''







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

		__lowercase=			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.
		__lowercase=			None
		if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
				__lowercase=			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.
		__lowercase=			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.
		__lowercase=			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:
				__lowercase=			ds['train'].train_test_split(data_args.train_val_split	)
				__lowercase=			split['train']
				__lowercase=			split['test']

		# Create config
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		__lowercase=			{
		    '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:
				__lowercase=			AutoConfig.from_pretrained(model_args.config_name_or_path   , **lowercase__	)
		elif model_args.model_name_or_path:
				__lowercase=			AutoConfig.from_pretrained(model_args.model_name_or_path   , **lowercase__	)
		else:
				__lowercase=			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'	):
				__lowercase=			'simmim'

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

		# create model
		if model_args.model_name_or_path:
				__lowercase=			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'	)
				__lowercase=			AutoModelForMaskedImageModeling.from_config(lowercase__	)

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

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

		# transformations as done in original SimMIM paper
		# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
		__lowercase=			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
		__lowercase=			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__	):
				__lowercase=			[transforms(lowercase__	) for image in examples[image_column_name]]
				__lowercase=			[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:
						__lowercase=			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:
						__lowercase=			(
						    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
		__lowercase=			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:
				__lowercase=			None
				if training_args.resume_from_checkpoint is not None:
						__lowercase=			training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						__lowercase=			last_checkpoint
				__lowercase=			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:
				__lowercase=			trainer.evaluate()
				trainer.log_metrics('eval'   , lowercase__	)
				trainer.save_metrics('eval'   , lowercase__	)

		# Write model card and (optionally) push to hub
		__lowercase=			{
		    '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        _lowerCamelCase(	lowercase__	)						->	bool:

		'''simple docstring'''







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







def        _lowerCamelCase(	lowercase__ = 1_0**6	)						->	int:

		'''simple docstring'''







		__lowercase=			0
		__lowercase=			1
		__lowercase=			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() = }')






