'''simple docstring'''





from __future__ import annotations

SCREAMING_SNAKE_CASE_:	str    =[True] * 1_00_00_01
SCREAMING_SNAKE_CASE_:	List[Any]    =2
while i * i <= 1_00_00_00:
    if seive[i]:
        for j in range(i * i, 1_00_00_01, i):
            SCREAMING_SNAKE_CASE_:	str    =False
    i += 1





def       lowerCAmelCase_							(   snake_case_			:					int     )       -> bool:


     '''simple docstring'''




     return seive[n]





def       lowerCAmelCase_							(   snake_case_			:					int     )       -> bool:


     '''simple docstring'''




     return any(digit in "02468" for digit in str(snake_case_     )     )





def       lowerCAmelCase_							(   snake_case_			:					int = 1_00_00_00     )       -> list[int]:


     '''simple docstring'''




     UpperCAmelCase_			       =  [2]  # result already includes the number 2.
     for num in range(3   ,       limit + 1   ,       2     ):
          if is_prime(snake_case_     ) and not contains_an_even_digit(snake_case_     ):
               UpperCAmelCase_			       =  str(snake_case_     )
               UpperCAmelCase_			       =  [int(str_num[j:] + str_num[:j]     ) for j in range(len(snake_case_     )     )]
               if all(is_prime(snake_case_     ) for i in list_nums     ):
                    result.append(snake_case_     )
     return result





def       lowerCAmelCase_							(   )       -> int:


     '''simple docstring'''




     return len(find_circular_primes()     )


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








import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
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__    :List[str]       =				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''')

@dataclass
class 						__a :
		_a      :    Optional[str]           =      field(
		    default='cifar10'       , metadata={'help': 'Name of a dataset from the datasets package'}     )
		_a      :    Optional[str]           =      field(
		    default=UpperCAmelCase       , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}     )
		_a      :    Optional[str]           =      field(
		    default=UpperCAmelCase       , metadata={'help': 'The column name of the images in the files.'}     )
		_a      :    Optional[str]           =      field(default=UpperCAmelCase       , metadata={'help': 'A folder containing the training data.'}     )
		_a      :    Optional[str]           =      field(default=UpperCAmelCase       , metadata={'help': 'A folder containing the validation data.'}     )
		_a      :    Optional[float]           =      field(
		    default=0.1_5       , metadata={'help': 'Percent to split off of train for validation.'}     )
		_a      :    Optional[int]           =      field(
		    default=UpperCAmelCase       , metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of training examples to this '
		            'value if set.'
		        )
		    }       , )
		_a      :    Optional[int]           =      field(
		    default=UpperCAmelCase       , metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
		            'value if set.'
		        )
		    }       , )
		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     {}
									if self.train_dir is not None:
																_UpperCAmelCase											=     self.train_dir
									if self.validation_dir is not None:
																_UpperCAmelCase											=     self.validation_dir
									_UpperCAmelCase											=     data_files if data_files else None



@dataclass
class 						__a :
		_a      :    str           =      field(
		    default=UpperCAmelCase       , metadata={
		        'help': (
		            'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
		        )
		    }       , )
		_a      :    Optional[str]           =      field(
		    default=UpperCAmelCase       , metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'}     )
		_a      :    Optional[str]           =      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'
		        )
		    }       , )
		_a      :    Optional[str]           =      field(
		    default=UpperCAmelCase       , metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}     )
		_a      :    str           =      field(
		    default='main'       , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}       , )
		_a      :    str           =      field(default=UpperCAmelCase       , metadata={'help': 'Name or path of preprocessor config.'}     )
		_a      :    bool           =      field(
		    default=UpperCAmelCase       , metadata={
		        'help': (
		            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
		            'with private models).'
		        )
		    }       , )
		_a      :    float           =      field(
		    default=0.7_5       , metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'}     )
		_a      :    bool           =      field(
		    default=UpperCAmelCase       , metadata={'help': 'Whether or not to train with normalized pixel values as target.'}     )



@dataclass
class 						__a (							UpperCAmelCase     ):
		_a      :    float           =      field(
		    default=1E-3       , metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}     )



def 			lowerCAmelCase__					(     a__:						Optional[Any]			)    ->						Union[str, Any]:


							'''simple docstring'''





							_UpperCAmelCase											=     torch.stack([example['pixel_values'] for example in examples]			)
							return {"pixel_values": pixel_values}

def 			lowerCAmelCase__					(     )    ->						Tuple:


							'''simple docstring'''





							_UpperCAmelCase											=     HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)			)
							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											=     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
							else:
														_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     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_mae'     ,    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()

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

							# Load pretrained model and image processor
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.
							_UpperCAmelCase											=     {
							    '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:
														_UpperCAmelCase											=     ViTMAEConfig.from_pretrained(model_args.config_name     ,    **a__			)
							elif model_args.model_name_or_path:
														_UpperCAmelCase											=     ViTMAEConfig.from_pretrained(model_args.model_name_or_path     ,    **a__			)
							else:
														_UpperCAmelCase											=     ViTMAEConfig()
														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}'''			)

    # adapt config
							config.update(
							    {
							        'mask_ratio': model_args.mask_ratio,
							        'norm_pix_loss': model_args.norm_pix_loss,
							    }			)

							# create image processor
							if model_args.image_processor_name:
														_UpperCAmelCase											=     ViTImageProcessor.from_pretrained(model_args.image_processor_name     ,    **a__			)
							elif model_args.model_name_or_path:
														_UpperCAmelCase											=     ViTImageProcessor.from_pretrained(model_args.model_name_or_path     ,    **a__			)
							else:
														_UpperCAmelCase											=     ViTImageProcessor()

							# create model
							if model_args.model_name_or_path:
														_UpperCAmelCase											=     ViTMAEForPreTraining.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'			)
														_UpperCAmelCase											=     ViTMAEForPreTraining(a__			)

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

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

							# transformations as done in original MAE paper
							# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
							if "shortest_edge" in image_processor.size:
														_UpperCAmelCase											=     image_processor.size['shortest_edge']
							else:
														_UpperCAmelCase											=     (image_processor.size['height'], image_processor.size['width'])
							_UpperCAmelCase											=     Compose(
							    [
							        Lambda(lambda a__			: img.convert('RGB'			) if img.mode != "RGB" else img			),
							        RandomResizedCrop(a__     ,    scale=(0.2, 1.0)     ,    interpolation=InterpolationMode.BICUBIC			),
							        RandomHorizontalFlip(),
							        ToTensor(),
							        Normalize(mean=image_processor.image_mean     ,    std=image_processor.image_std			),
							    ]			)

							def preprocess_images(a__:						Optional[int]			):
														_UpperCAmelCase											=     [transforms(a__			) for image in 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											=     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:
																					_UpperCAmelCase											=     (
																					    ds['validation'].shuffle(seed=training_args.seed			).select(range(data_args.max_eval_samples			)			)
																					)
														# Set the validation transforms
														ds["validation"].set_transform(a__			)

							# Compute absolute learning rate
							_UpperCAmelCase											=     (
							    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
							)
							if training_args.base_learning_rate is not None:
														_UpperCAmelCase											=     training_args.base_learning_rate * total_train_batch_size / 2_5_6

							# Initialize our trainer
							_UpperCAmelCase											=     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:
														_UpperCAmelCase											=     None
														if training_args.resume_from_checkpoint is not None:
																					_UpperCAmelCase											=     training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					_UpperCAmelCase											=     last_checkpoint
														_UpperCAmelCase											=     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:
														_UpperCAmelCase											=     trainer.evaluate()
														trainer.log_metrics('eval'     ,    a__			)
														trainer.save_metrics('eval'     ,    a__			)

							# Write model card and (optionally) push to hub
							_UpperCAmelCase											=     {
							    'tasks': 'masked-auto-encoding',
							    'dataset': data_args.dataset_name,
							    'tags': ['masked-auto-encoding'],
							}
							if training_args.push_to_hub:
														trainer.push_to_hub(**a__			)
							else:
														trainer.create_model_card(**a__			)

def 			lowerCAmelCase__					(     a__:						Tuple			)    ->						Optional[Any]:


							'''simple docstring'''





							main()


if __name__ == "__main__":
		main()
