

"""simple docstring"""





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  :      Optional[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 				SCREAMING_SNAKE_CASE					:






					"""simple docstring"""



					lowercase__				 =       field(
					    default="cifar10"    ,							metadata={"help": "Name of a dataset from the datasets package"}       )
					lowercase__				 =       field(
					    default=A_    ,							metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}       )
					lowercase__				 =       field(
					    default=A_    ,							metadata={"help": "The column name of the images in the files."}       )
					lowercase__				 =       field(default=A_    ,							metadata={"help": "A folder containing the training data."}       )
					lowercase__				 =       field(default=A_    ,							metadata={"help": "A folder containing the validation data."}       )
					lowercase__				 =       field(
					    default=0.15    ,							metadata={"help": "Percent to split off of train for validation."}       )
					lowercase__				 =       field(
					    default=A_    ,							metadata={
					        "help": (
					            "For debugging purposes or quicker training, truncate the number of training examples to this "
					            "value if set."
					        )
					    }    ,							)
					lowercase__				 =       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			:				Union[str, Any]		):
						lowerCAmelCase__	:						int  				=	{}
						if self.train_dir is not None:
							lowerCAmelCase__	:						Any  				=	self.train_dir
						if self.validation_dir is not None:
							lowerCAmelCase__	:						Tuple  				=	self.validation_dir
						lowerCAmelCase__	:						str  				=	data_files if data_files else None







@dataclass
class 				SCREAMING_SNAKE_CASE					:






					"""simple docstring"""



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







@dataclass
class 				SCREAMING_SNAKE_CASE					(		A_       ):






					"""simple docstring"""



					lowercase__				 =       field(
					    default=1e-3    ,							metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}       )

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__	:						List[Any]  				=	torch.stack([example['''pixel_values'''] for example in examples] )
	return {"pixel_values": pixel_values}

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						Optional[int]  				=	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.
		lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
	else:
		lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	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'''						,     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()

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

	# create model
	if model_args.model_name_or_path:
		lowerCAmelCase__	:						List[Any]  				=	ViTMAEForPreTraining.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''' )
		lowerCAmelCase__	:						Union[str, Any]  				=	ViTMAEForPreTraining(lowercase__ )

	if training_args.do_train:
		lowerCAmelCase__	:						Dict  				=	ds['''train'''].column_names
	else:
		lowerCAmelCase__	:						Optional[int]  				=	ds['''validation'''].column_names

	if data_args.image_column_name is not None:
		lowerCAmelCase__	:						List[str]  				=	data_args.image_column_name
	elif "image" in column_names:
		lowerCAmelCase__	:						List[str]  				=	'''image'''
	elif "img" in column_names:
		lowerCAmelCase__	:						Dict  				=	'''img'''
	else:
		lowerCAmelCase__	:						List[str]  				=	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:
		lowerCAmelCase__	:						Optional[int]  				=	image_processor.size['''shortest_edge''']
	else:
		lowerCAmelCase__	:						Union[str, Any]  				=	(image_processor.size['''height'''], image_processor.size['''width'''])
	lowerCAmelCase__	:						Optional[Any]  				=	Compose(
	    [
	        Lambda(lambda A_ : img.convert('''RGB''' ) if img.mode != "RGB" else img ),
	        RandomResizedCrop(lowercase__						,     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_ ):
		lowerCAmelCase__	:						int  				=	[transforms(lowercase__ ) 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:
			lowerCAmelCase__	:						List[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:
			lowerCAmelCase__	:						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(lowercase__ )

	# Compute absolute learning rate
	lowerCAmelCase__	:						Optional[Any]  				=	(
	    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
	)
	if training_args.base_learning_rate is not None:
		lowerCAmelCase__	:						str  				=	training_args.base_learning_rate * total_train_batch_size / 2_56

	# Initialize our trainer
	lowerCAmelCase__	:						List[str]  				=	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:
		lowerCAmelCase__	:						str  				=	None
		if training_args.resume_from_checkpoint is not None:
			lowerCAmelCase__	:						Dict  				=	training_args.resume_from_checkpoint
		elif last_checkpoint is not None:
			lowerCAmelCase__	:						Tuple  				=	last_checkpoint
		lowerCAmelCase__	:						Tuple  				=	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:
		lowerCAmelCase__	:						Dict  				=	trainer.evaluate()
		trainer.log_metrics('''eval'''						,     lowercase__ )
		trainer.save_metrics('''eval'''						,     lowercase__ )

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

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	main()


if __name__ == "__main__":
							main()








from __future__ import annotations







def        _lowerCamelCase(	lowercase__	)						->	list[int]:

		'''simple docstring'''







		__lowercase=			2
		__lowercase=			[]
		while i * i <= n:
				if n % i:
						i += 1
				else:
						n //= i
						factors.append(lowercase__	)
		if n > 1:
				factors.append(lowercase__	)
		return factors


if __name__ == "__main__":
	import doctest

	doctest.testmod()






