





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


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





@dataclass
class        _lowerCamelCase      (       _lowercase ):
						UpperCAmelCase_ =			field(
						    default=1E-3  ,			metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."} )



def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


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


def       a__					(					):




		"""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"  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )

		# 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(_SCREAMING_SNAKE_CASE    )
		transformers.utils.logging.set_verbosity(_SCREAMING_SNAKE_CASE    )
		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  ,	_SCREAMING_SNAKE_CASE    ) 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  ,	**_SCREAMING_SNAKE_CASE    )
		elif model_args.model_name_or_path:
				UpperCamelCase   =	ViTMAEConfig.from_pretrained(model_args.model_name_or_path  ,	**_SCREAMING_SNAKE_CASE    )
		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  ,	**_SCREAMING_SNAKE_CASE    )
		elif model_args.model_name_or_path:
				UpperCamelCase   =	ViTImageProcessor.from_pretrained(model_args.model_name_or_path  ,	**_SCREAMING_SNAKE_CASE    )
		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=_SCREAMING_SNAKE_CASE  ,	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(_SCREAMING_SNAKE_CASE    )

		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 _SCREAMING_SNAKE_CASE    : img.convert("RGB"    ) if img.mode != "RGB" else img    ),
		        RandomResizedCrop(_SCREAMING_SNAKE_CASE  ,	scale=(0.2, 1.0)  ,	interpolation=InterpolationMode.BICUBIC    ),
		        RandomHorizontalFlip(),
		        ToTensor(),
		        Normalize(mean=image_processor.image_mean  ,	std=image_processor.image_std    ),
		    ]    )

		def preprocess_images(_SCREAMING_SNAKE_CASE    ):
				UpperCamelCase   =	[transforms(_SCREAMING_SNAKE_CASE    ) 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(_SCREAMING_SNAKE_CASE    )

		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(_SCREAMING_SNAKE_CASE    )

		# 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 / 256

		# Initialize our trainer
		UpperCamelCase   =	Trainer(
		    model=_SCREAMING_SNAKE_CASE  ,	args=_SCREAMING_SNAKE_CASE  ,	train_dataset=ds["train"] if training_args.do_train else None  ,	eval_dataset=ds["validation"] if training_args.do_eval else None  ,	tokenizer=_SCREAMING_SNAKE_CASE  ,	data_collator=_SCREAMING_SNAKE_CASE  ,	)

		# 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=_SCREAMING_SNAKE_CASE    )
				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"  ,	_SCREAMING_SNAKE_CASE    )
				trainer.save_metrics("eval"  ,	_SCREAMING_SNAKE_CASE    )

		# 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(**_SCREAMING_SNAKE_CASE    )
		else:
				trainer.create_model_card(**_SCREAMING_SNAKE_CASE    )


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		main()


if __name__ == "__main__":
						main()













"""simple docstring"""





import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


lowerCAmelCase__     			=			HUGGINGFACE_HUB_CACHE


lowerCAmelCase__     			=			'''config.json'''
lowerCAmelCase__     			=			'''diffusion_pytorch_model.bin'''
lowerCAmelCase__     			=			'''diffusion_flax_model.msgpack'''
lowerCAmelCase__     			=			'''model.onnx'''
lowerCAmelCase__     			=			'''diffusion_pytorch_model.safetensors'''
lowerCAmelCase__     			=			'''weights.pb'''
lowerCAmelCase__     			=			'''https://huggingface.co'''
lowerCAmelCase__     			=			default_cache_path
lowerCAmelCase__     			=			'''diffusers_modules'''
lowerCAmelCase__     			=			os.getenv('''HF_MODULES_CACHE''', os.path.join(hf_cache_home, '''modules'''))
lowerCAmelCase__     			=			['''fp16''', '''non-ema''']
lowerCAmelCase__     			=			'''.self_attn'''






