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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




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







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







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



def     __lowerCAmelCase			(			a__  )       ->  Tuple:
				__a										=    torch.stack([example['''pixel_values'''] for example in examples]  )
				return {"pixel_values": pixel_values}


def     __lowerCAmelCase			(			)       ->  int:
				# 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.

				__a										=    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										=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
				else:
								__a							,     __a							,     __a										=    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										=    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										=    None
				if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
								__a										=    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										=    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										=    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										=    ds['''train'''].train_test_split(data_args.train_val_split  )
								__a										=    split['''train''']
								__a										=    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										=    {
				    '''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										=    ViTMAEConfig.from_pretrained(model_args.config_name						,    **a__  )
				elif model_args.model_name_or_path:
								__a										=    ViTMAEConfig.from_pretrained(model_args.model_name_or_path						,    **a__  )
				else:
								__a										=    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										=    ViTImageProcessor.from_pretrained(model_args.image_processor_name						,    **a__  )
				elif model_args.model_name_or_path:
								__a										=    ViTImageProcessor.from_pretrained(model_args.model_name_or_path						,    **a__  )
				else:
								__a										=    ViTImageProcessor()

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

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

				if data_args.image_column_name is not None:
								__a										=    data_args.image_column_name
				elif "image" in column_names:
								__a										=    '''image'''
				elif "img" in column_names:
								__a										=    '''img'''
				else:
								__a										=    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										=    image_processor.size['''shortest_edge''']
				else:
								__a										=    (image_processor.size['''height'''], image_processor.size['''width'''])
				__a										=    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										=    [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										=    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										=    (
												    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										=    (
				    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
				)
				if training_args.base_learning_rate is not None:
								__a										=    training_args.base_learning_rate * total_train_batch_size / 256

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

				# Write model card and (optionally) push to hub
				__a										=    {
				    '''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__  )       ->  Optional[int]:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
					main()







from itertools import permutations
def 					snake_case_						(   lowerCAmelCase_				:  tuple					):
							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

							__lowercase					:							Dict			=   [7, 11, 13, 17]
							for i, test in enumerate(lowerCAmelCase_					):
														if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
																					return False
							return True
def 					snake_case_						(   lowerCAmelCase_				:  int = 10					):
							return sum(
							    int("""""".join(map(lowerCAmelCase_				,				lowerCAmelCase_					)					)					)
							    for num in permutations(range(lowerCAmelCase_					)					)
							    if is_substring_divisible(lowerCAmelCase_					)					)


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