

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_      =     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 _UpperCamelCase				:
			'''simple docstring'''







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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							{}
				if self.train_dir is not None:
					SCREAMING_SNAKE_CASE :   Dict					  =							self.train_dir
				if self.validation_dir is not None:
					SCREAMING_SNAKE_CASE :   Tuple					  =							self.validation_dir
				SCREAMING_SNAKE_CASE :   Any					  =							data_files if data_files else None






@dataclass
class _UpperCamelCase				:
			'''simple docstring'''







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






@dataclass
class _UpperCamelCase				(				__A ):
			'''simple docstring'''







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

def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   List[Any]					  =							torch.stack([example["pixel_values"] for example in examples])
	return {"pixel_values": pixel_values}

def 			lowerCamelCase__			(       ):
	# 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.

	SCREAMING_SNAKE_CASE :   List[str]					  =							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.
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   str					  =							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
	else:
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Tuple					  =							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()

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

	# create model
	if model_args.model_name_or_path:
		SCREAMING_SNAKE_CASE :   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")
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							ViTMAEForPreTraining(_a)

	if training_args.do_train:
		SCREAMING_SNAKE_CASE :   Optional[Any]					  =							ds["train"].column_names
	else:
		SCREAMING_SNAKE_CASE :   List[Any]					  =							ds["validation"].column_names

	if data_args.image_column_name is not None:
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							data_args.image_column_name
	elif "image" in column_names:
		SCREAMING_SNAKE_CASE :   Tuple					  =							"image"
	elif "img" in column_names:
		SCREAMING_SNAKE_CASE :   str					  =							"img"
	else:
		SCREAMING_SNAKE_CASE :   Union[str, 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:
		SCREAMING_SNAKE_CASE :   List[Any]					  =							image_processor.size["shortest_edge"]
	else:
		SCREAMING_SNAKE_CASE :   Optional[Any]					  =							(image_processor.size["height"], image_processor.size["width"])
	SCREAMING_SNAKE_CASE :   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):
		SCREAMING_SNAKE_CASE :   Any					  =							[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:
			SCREAMING_SNAKE_CASE :   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:
			SCREAMING_SNAKE_CASE :   List[str]					  =							(
			    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
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							(
	    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
	)
	if training_args.base_learning_rate is not None:
		SCREAMING_SNAKE_CASE :   List[Any]					  =							training_args.base_learning_rate * total_train_batch_size / 256

	# Initialize our trainer
	SCREAMING_SNAKE_CASE :   Any					  =							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:
		SCREAMING_SNAKE_CASE :   int					  =							None
		if training_args.resume_from_checkpoint is not None:
			SCREAMING_SNAKE_CASE :   Dict					  =							training_args.resume_from_checkpoint
		elif last_checkpoint is not None:
			SCREAMING_SNAKE_CASE :   Any					  =							last_checkpoint
		SCREAMING_SNAKE_CASE :   Optional[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:
		SCREAMING_SNAKE_CASE :   Dict					  =							trainer.evaluate()
		trainer.log_metrics("eval"				,      _a)
		trainer.save_metrics("eval"				,      _a)

	# Write model card and (optionally) push to hub
	SCREAMING_SNAKE_CASE :   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 			lowerCamelCase__			(       _a):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
			main()


from __future__ import annotations

def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							2
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							[]
	while i * i <= n:
		if n % i:
			i += 1
		else:
			n //= i
			factors.append(_a)
	if n > 1:
		factors.append(_a)
	return factors


if __name__ == "__main__":
			import doctest

			doctest.testmod()