
'''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
lowercase					:							Optional[int]   =      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   :
							__lowercase   	= field(
							    default="""cifar10"""		,     metadata={"""help""": """Name of a dataset from the datasets package"""}		)
							__lowercase   	= field(
							    default=_lowerCamelCase		,     metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}		)
							__lowercase   	= field(
							    default=_lowerCamelCase		,     metadata={"""help""": """The column name of the images in the files."""}		)
							__lowercase   	= field(default=_lowerCamelCase		,     metadata={"""help""": """A folder containing the training data."""}		)
							__lowercase   	= field(default=_lowerCamelCase		,     metadata={"""help""": """A folder containing the validation data."""}		)
							__lowercase   	= field(
							    default=0.1_5		,     metadata={"""help""": """Percent to split off of train for validation."""}		)
							__lowercase   	= field(
							    default=_lowerCamelCase		,     metadata={
							        """help""": (
							            """For debugging purposes or quicker training, truncate the number of training examples to this """
							            """value if set."""
							        )
							    }		,     )
							__lowercase   	= field(
							    default=_lowerCamelCase		,     metadata={
							        """help""": (
							            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
							            """value if set."""
							        )
							    }		,     )


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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


@dataclass
class 		__UpperCAmelCase   :
							__lowercase   	= field(
							    default=_lowerCamelCase		,     metadata={
							        """help""": (
							            """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
							        )
							    }		,     )
							__lowercase   	= field(
							    default=_lowerCamelCase		,     metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""}		)
							__lowercase   	= 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"""
							        )
							    }		,     )
							__lowercase   	= field(
							    default=_lowerCamelCase		,     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=_lowerCamelCase		,     metadata={"""help""": """Name or path of preprocessor config."""}		)
							__lowercase   	= field(
							    default=_lowerCamelCase		,     metadata={
							        """help""": (
							            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
							            """with private models)."""
							        )
							    }		,     )
							__lowercase   	= field(
							    default=0.7_5		,     metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""}		)
							__lowercase   	= field(
							    default=_lowerCamelCase		,     metadata={"""help""": """Whether or not to train with normalized pixel values as target."""}		)


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







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			Optional[Any]:
	_snake_case     		=	torch.stack([example['pixel_values'] for example in examples]			)
	return {"pixel_values": pixel_values}







def        SCREAMING_SNAKE_CASE__			(     ) ->			str:
	# 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.

	_snake_case     		=	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.
		_snake_case		,				_snake_case		,				_snake_case     		=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
	else:
		_snake_case		,				_snake_case		,				_snake_case     		=	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()

	_snake_case     		=	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.
	_snake_case     		=	None
	if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
		_snake_case     		=	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.
	_snake_case     		=	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.
	_snake_case     		=	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:
		_snake_case     		=	ds['train'].train_test_split(data_args.train_val_split			)
		_snake_case     		=	split['train']
		_snake_case     		=	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.
	_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,
	}
	if model_args.config_name:
		_snake_case     		=	ViTMAEConfig.from_pretrained(model_args.config_name    ,       **__A			)
	elif model_args.model_name_or_path:
		_snake_case     		=	ViTMAEConfig.from_pretrained(model_args.model_name_or_path    ,       **__A			)
	else:
		_snake_case     		=	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:
		_snake_case     		=	ViTImageProcessor.from_pretrained(model_args.image_processor_name    ,       **__A			)
	elif model_args.model_name_or_path:
		_snake_case     		=	ViTImageProcessor.from_pretrained(model_args.model_name_or_path    ,       **__A			)
	else:
		_snake_case     		=	ViTImageProcessor()

	# create model
	if model_args.model_name_or_path:
		_snake_case     		=	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'			)
		_snake_case     		=	ViTMAEForPreTraining(__A			)

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

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

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

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


if __name__ == "__main__":
					main()







'''simple docstring'''







import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize


class 		__UpperCAmelCase   :


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_=sys.maxsize						):





								"""simple docstring"""






								_snake_case     		=	'bilinear'
								_snake_case     		=	max_size
								_snake_case     		=	short_edge_length




							def __call__( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	[]
								for img in imgs:
									_snake_case		,				_snake_case     		=	img.shape[:2]
									# later: provide list and randomly choose index for resize
									_snake_case     		=	np.random.randint(self.short_edge_length[0]					,     self.short_edge_length[1] + 1						)
									if size == 0:
										return img
									_snake_case     		=	size * 1.0 / min(lowerCAmelCase_					,     lowerCAmelCase_						)
									if h < w:
										_snake_case		,				_snake_case     		=	size, scale * w
									else:
										_snake_case		,				_snake_case     		=	scale * h, size
									if max(lowerCAmelCase_					,     lowerCAmelCase_						) > self.max_size:
										_snake_case     		=	self.max_size * 1.0 / max(lowerCAmelCase_					,     lowerCAmelCase_						)
										_snake_case     		=	newh * scale
										_snake_case     		=	neww * scale
									_snake_case     		=	int(neww + 0.5						)
									_snake_case     		=	int(newh + 0.5						)

									if img.dtype == np.uinta:
										_snake_case     		=	Image.fromarray(lowerCAmelCase_						)
										_snake_case     		=	pil_image.resize((neww, newh)					,     PILImageResampling.BILINEAR						)
										_snake_case     		=	np.asarray(lowerCAmelCase_						)
									else:
										_snake_case     		=	img.permute(2					,     0					,     1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
										_snake_case     		=	nn.functional.interpolate(
										    lowerCAmelCase_					,     (newh, neww)					,     mode=self.interp_method					,     align_corners=lowerCAmelCase_						).squeeze(0						)
									img_augs.append(lowerCAmelCase_						)

								return img_augs


class 		__UpperCAmelCase   :


							def __init__( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]					,     cfg.INPUT.MAX_SIZE_TEST						)
								_snake_case     		=	cfg.INPUT.FORMAT
								_snake_case     		=	cfg.SIZE_DIVISIBILITY
								_snake_case     		=	cfg.PAD_VALUE
								_snake_case     		=	cfg.INPUT.MAX_SIZE_TEST
								_snake_case     		=	cfg.MODEL.DEVICE
								_snake_case     		=	torch.tensor(cfg.MODEL.PIXEL_STD						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)					,     1					,     1						)
								_snake_case     		=	torch.tensor(cfg.MODEL.PIXEL_MEAN						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)					,     1					,     1						)
								_snake_case     		=	lambda lowerCAmelCase_						: (x - self.pixel_mean) / self.pixel_std


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	tuple(max(lowerCAmelCase_						) for s in zip(*[img.shape for img in images]						)						)
								_snake_case     		=	[im.shape[-2:] for im in images]
								_snake_case     		=	[
								    nn.functional.pad(
								        lowerCAmelCase_					,     [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]					,     value=self.pad_value					,     )
								    for size, im in zip(lowerCAmelCase_					,     lowerCAmelCase_						)
								]

								return torch.stack(lowerCAmelCase_						), torch.tensor(lowerCAmelCase_						)




							def __call__( self					,     lowerCAmelCase_					,     lowerCAmelCase_=False						):





								"""simple docstring"""






								with torch.no_grad():
									if not isinstance(lowerCAmelCase_					,     lowerCAmelCase_						):
										_snake_case     		=	[images]
									if single_image:
										assert len(lowerCAmelCase_						) == 1
									for i in range(len(lowerCAmelCase_						)						):
										if isinstance(images[i]					,     torch.Tensor						):
											images.insert(lowerCAmelCase_					,     images.pop(lowerCAmelCase_						).to(self.device						).float()						)
										elif not isinstance(images[i]					,     torch.Tensor						):
											images.insert(
											    lowerCAmelCase_					,     torch.as_tensor(img_tensorize(images.pop(lowerCAmelCase_						)					,     input_format=self.input_format						)						)
											    .to(self.device						)
											    .float()					,     )
            # resize smallest edge
									_snake_case     		=	torch.tensor([im.shape[:2] for im in images]						)
									_snake_case     		=	self.aug(lowerCAmelCase_						)
									# transpose images and convert to torch tensors
									# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
									# now normalize before pad to avoid useless arithmetic
									_snake_case     		=	[self.normalizer(lowerCAmelCase_						) for x in images]
									# now pad them to do the following operations
									_snake_case		,				_snake_case     		=	self.pad(lowerCAmelCase_						)
									# Normalize

									if self.size_divisibility > 0:
										raise NotImplementedError()
									# pad
									_snake_case     		=	torch.true_divide(lowerCAmelCase_					,     lowerCAmelCase_						)
									if single_image:
										return images[0], sizes[0], scales_yx[0]
									else:
										return images, sizes, scales_yx







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			List[Any]:
	boxes[:, 0::2] *= scale_yx[:, 1]
	boxes[:, 1::2] *= scale_yx[:, 0]
	return boxes







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			List[Any]:
	assert torch.isfinite(__A			).all(), "Box tensor contains infinite or NaN!"
	_snake_case		,				_snake_case     		=	box_size
	tensor[:, 0].clamp_(min=0    ,       max=__A			)
	tensor[:, 1].clamp_(min=0    ,       max=__A			)
	tensor[:, 2].clamp_(min=0    ,       max=__A			)
	tensor[:, 3].clamp_(min=0    ,       max=__A			)

