





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
UpperCAmelCase						:   Any			  =     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:


				"""simple docstring"""


				UpperCamelCase    :  Optional[int]					    =	field(
				    default='''cifar10'''				, metadata={'''help''': '''Name of a dataset from the datasets package'''}   )
				UpperCamelCase    :  Any					    =	field(
				    default=_lowerCamelCase				, metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}   )
				UpperCamelCase    :  Dict					    =	field(
				    default=_lowerCamelCase				, metadata={'''help''': '''The column name of the images in the files.'''}   )
				UpperCamelCase    :  str					    =	field(default=_lowerCamelCase				, metadata={'''help''': '''A folder containing the training data.'''}   )
				UpperCamelCase    :  List[Any]					    =	field(default=_lowerCamelCase				, metadata={'''help''': '''A folder containing the validation data.'''}   )
				UpperCamelCase    :  str					    =	field(
				    default=0.15				, metadata={'''help''': '''Percent to split off of train for validation.'''}   )
				UpperCamelCase    :  int					    =	field(
				    default=_lowerCamelCase				, metadata={
				        '''help''': (
				            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
				            '''value if set.'''
				        )
				    }				, )
				UpperCamelCase    :  List[Any]					    =	field(
				    default=_lowerCamelCase				, metadata={
				        '''help''': (
				            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
				            '''value if set.'''
				        )
				    }				, )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						List[str]       =				{}
									if self.train_dir is not None:
														__A	:						Any       =				self.train_dir
									if self.validation_dir is not None:
														__A	:						Optional[int]       =				self.validation_dir
									__A	:						List[str]       =				data_files if data_files else None





@dataclass
class 						_A:


				"""simple docstring"""


				UpperCamelCase    :  Any					    =	field(
				    default=_lowerCamelCase				, metadata={
				        '''help''': (
				            '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
				        )
				    }				, )
				UpperCamelCase    :  str					    =	field(
				    default=_lowerCamelCase				, metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''}   )
				UpperCamelCase    :  str					    =	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'''
				        )
				    }				, )
				UpperCamelCase    :  Optional[int]					    =	field(
				    default=_lowerCamelCase				, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''}   )
				UpperCamelCase    :  List[str]					    =	field(
				    default='''main'''				, metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}				, )
				UpperCamelCase    :  Union[str, Any]					    =	field(default=_lowerCamelCase				, metadata={'''help''': '''Name or path of preprocessor config.'''}   )
				UpperCamelCase    :  Union[str, Any]					    =	field(
				    default=_lowerCamelCase				, metadata={
				        '''help''': (
				            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
				            '''with private models).'''
				        )
				    }				, )
				UpperCamelCase    :  Tuple					    =	field(
				    default=0.75				, metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''}   )
				UpperCamelCase    :  Any					    =	field(
				    default=_lowerCamelCase				, metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''}   )





@dataclass
class 						_A(					_lowerCamelCase   ):


				"""simple docstring"""


				UpperCamelCase    :  Dict					    =	field(
				    default=1E-3				, metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''}   )







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Optional[int]:
					__A	:						Union[str, Any]       =				torch.stack([example['pixel_values'] for example in examples]  )
					return {"pixel_values": pixel_values}







def  _SCREAMING_SNAKE_CASE				(   )      ->      Optional[int]:
					__A	:						Tuple       =				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.
										__A    ,				__A    ,				__A	:						Optional[Any]       =				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
					else:
										__A    ,				__A    ,				__A	:						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_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()

					__A	:						List[Any]       =				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.
					__A	:						Optional[int]       =				None
					if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
										__A	:						Dict       =				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	:						Optional[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	:						Union[str, Any]       =				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:
										__A	:						Union[str, Any]       =				ds['train'].train_test_split(data_args.train_val_split  )
										__A	:						Tuple       =				split['train']
										__A	:						List[Any]       =				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.
					__A	:						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:
										__A	:						List[str]       =				ViTMAEConfig.from_pretrained(model_args.config_name					,						**a  )
					elif model_args.model_name_or_path:
										__A	:						Tuple       =				ViTMAEConfig.from_pretrained(model_args.model_name_or_path					,						**a  )
					else:
										__A	:						List[Any]       =				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:
										__A	:						Any       =				ViTImageProcessor.from_pretrained(model_args.image_processor_name					,						**a  )
					elif model_args.model_name_or_path:
										__A	:						str       =				ViTImageProcessor.from_pretrained(model_args.model_name_or_path					,						**a  )
					else:
										__A	:						Optional[Any]       =				ViTImageProcessor()

					# create model
					if model_args.model_name_or_path:
										__A	:						Union[str, Any]       =				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'  )
										__A	:						Union[str, Any]       =				ViTMAEForPreTraining(a  )

					if training_args.do_train:
										__A	:						Union[str, Any]       =				ds['train'].column_names
					else:
										__A	:						int       =				ds['validation'].column_names

					if data_args.image_column_name is not None:
										__A	:						List[Any]       =				data_args.image_column_name
					elif "image" in column_names:
										__A	:						Tuple       =				'image'
					elif "img" in column_names:
										__A	:						str       =				'img'
					else:
										__A	:						Any       =				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:
										__A	:						List[str]       =				image_processor.size['shortest_edge']
					else:
										__A	:						Any       =				(image_processor.size['height'], image_processor.size['width'])
					__A	:						Optional[Any]       =				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  ):
										__A	:						Dict       =				[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:
															__A	:						int       =				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:
															__A	:						Union[str, Any]       =				(
															    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
					__A	:						Any       =				(
					    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
					)
					if training_args.base_learning_rate is not None:
										__A	:						Tuple       =				training_args.base_learning_rate * total_train_batch_size / 2_56

					# Initialize our trainer
					__A	:						int       =				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:
										__A	:						Optional[int]       =				None
										if training_args.resume_from_checkpoint is not None:
															__A	:						List[str]       =				training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															__A	:						Optional[Any]       =				last_checkpoint
										__A	:						int       =				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:
										__A	:						List[str]       =				trainer.evaluate()
										trainer.log_metrics('eval'					,						a  )
										trainer.save_metrics('eval'					,						a  )

					# Write model card and (optionally) push to hub
					__A	:						List[str]       =				{
					    '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  _SCREAMING_SNAKE_CASE				(   a  )      ->      int:
					main()


if __name__ == "__main__":
				main()











from itertools import permutations



def _a				(	lowerCamelCase:   tuple )	->						bool:







      '''simple docstring'''






      if num[3] % 2 != 0:
            return False

      if (num[2] + num[3] + num[4]) % 3 != 0:
            return False

      if num[5] % 5 != 0:
            return False

      __A          =				[7, 11, 13, 17]
      for i, test in enumerate(lowerCamelCase ):
            if (num[i + 4] * 1_00 + num[i + 5] * 10 + num[i + 6]) % test != 0:
                  return False
      return True



def _a				(	lowerCamelCase:   int = 10 )	->						int:







      '''simple docstring'''






      return sum(
          int(''''''.join(map(lowerCamelCase					,				lowerCamelCase ) ) )
          for num in permutations(range(lowerCamelCase ) )
          if is_substring_divisible(lowerCamelCase ) )


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





