





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 					__A    :

			'''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=0.1_5			,				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 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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






@dataclass
class 					__A    :

			'''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=0.7_5			,				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 					__A    (    _a ):

			'''simple docstring'''







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





def lowerCAmelCase__(__snake_case							)				->      List[str]:







	'''simple docstring'''







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





def lowerCAmelCase__()				->      Dict:







	'''simple docstring'''







	lowerCamelCase__								=		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__								=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
	else:
		lowerCamelCase__								=		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'''       ,lowerCamelCase_       ,lowerCamelCase_							)

	# 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__								=		training_args.get_process_log_level()
	logger.setLevel(lowerCamelCase_							)
	transformers.utils.logging.set_verbosity(lowerCamelCase_							)
	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__								=		None
	if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
		lowerCamelCase__								=		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__								=		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__								=		None if 'validation' in ds.keys() else data_args.train_val_split
	if isinstance(data_args.train_val_split       ,lowerCamelCase_							) and data_args.train_val_split > 0.0:
		lowerCamelCase__								=		ds['train'].train_test_split(data_args.train_val_split							)
		lowerCamelCase__								=		split['train']
		lowerCamelCase__								=		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__								=		{
	    '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__								=		ViTMAEConfig.from_pretrained(model_args.config_name       ,**lowerCamelCase_							)
	elif model_args.model_name_or_path:
		lowerCamelCase__								=		ViTMAEConfig.from_pretrained(model_args.model_name_or_path       ,**lowerCamelCase_							)
	else:
		lowerCamelCase__								=		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__								=		ViTImageProcessor.from_pretrained(model_args.image_processor_name       ,**lowerCamelCase_							)
	elif model_args.model_name_or_path:
		lowerCamelCase__								=		ViTImageProcessor.from_pretrained(model_args.model_name_or_path       ,**lowerCamelCase_							)
	else:
		lowerCamelCase__								=		ViTImageProcessor()

	# create model
	if model_args.model_name_or_path:
		lowerCamelCase__								=		ViTMAEForPreTraining.from_pretrained(
		    model_args.model_name_or_path       ,from_tf=bool('''.ckpt''' in model_args.model_name_or_path							)       ,config=lowerCamelCase_       ,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__								=		ViTMAEForPreTraining(lowerCamelCase_							)

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

	if data_args.image_column_name is not None:
		lowerCamelCase__								=		data_args.image_column_name
	elif "image" in column_names:
		lowerCamelCase__								=		'image'
	elif "img" in column_names:
		lowerCamelCase__								=		'img'
	else:
		lowerCamelCase__								=		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__								=		image_processor.size['shortest_edge']
	else:
		lowerCamelCase__								=		(image_processor.size['height'], image_processor.size['width'])
	lowerCamelCase__								=		Compose(
	    [
	        Lambda(lambda __snake_case							: img.convert('''RGB'''							) if img.mode != "RGB" else img							),
	        RandomResizedCrop(lowerCamelCase_       ,scale=(0.2, 1.0)       ,interpolation=InterpolationMode.BICUBIC							),
	        RandomHorizontalFlip(),
	        ToTensor(),
	        Normalize(mean=image_processor.image_mean       ,std=image_processor.image_std							),
	    ]							)

	def preprocess_images(__snake_case							):
		lowerCamelCase__								=		[transforms(lowerCamelCase_							) 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__								=		ds['train'].shuffle(seed=training_args.seed							).select(range(data_args.max_train_samples							)							)
		# Set the training transforms
		ds["train"].set_transform(lowerCamelCase_							)

	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__								=		(
			    ds['validation'].shuffle(seed=training_args.seed							).select(range(data_args.max_eval_samples							)							)
			)
		# Set the validation transforms
		ds["validation"].set_transform(lowerCamelCase_							)

	# Compute absolute learning rate
	lowerCamelCase__								=		(
	    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
	)
	if training_args.base_learning_rate is not None:
		lowerCamelCase__								=		training_args.base_learning_rate * total_train_batch_size / 256

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

	# Training
	if training_args.do_train:
		lowerCamelCase__								=		None
		if training_args.resume_from_checkpoint is not None:
			lowerCamelCase__								=		training_args.resume_from_checkpoint
		elif last_checkpoint is not None:
			lowerCamelCase__								=		last_checkpoint
		lowerCamelCase__								=		trainer.train(resume_from_checkpoint=lowerCamelCase_							)
		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__								=		trainer.evaluate()
		trainer.log_metrics('''eval'''       ,lowerCamelCase_							)
		trainer.save_metrics('''eval'''       ,lowerCamelCase_							)

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





def lowerCAmelCase__(__snake_case							)				->      Tuple:







	'''simple docstring'''







	main()


if __name__ == "__main__":
			main()







from __future__ import annotations

from math import ceil, floor, sqrt



def 				UpperCamelCase_(      lowerCamelCase_ = 200_0000 )    ->						int:
    _lowercase       :							list[int]      		=		[0]
    _lowercase       :							int

    for idx in range(1							, ceil(sqrt(target * 2 ) * 1.1 ) ):
        triangle_numbers.append(triangle_numbers[-1] + idx )

    # we want this to be as close as possible to target
    _lowercase       :							int      		=		0
    # the area corresponding to the grid that gives the product closest to target
    _lowercase       :							int      		=		0
    # an estimate of b, using the quadratic formula
    _lowercase       :							float
    # the largest integer less than b_estimate
    _lowercase       :							int
    # the largest integer less than b_estimate
    _lowercase       :							int
    # the triangle number corresponding to b_floor
    _lowercase       :							int
    # the triangle number corresponding to b_ceil
    _lowercase       :							int

    for idx_a, triangle_a in enumerate(triangle_numbers[1:]							, 1 ):
        _lowercase       :							Optional[int]      		=		(-1 + sqrt(1 + 8 * target / triangle_a )) / 2
        _lowercase       :							List[str]      		=		floor(lowerCamelCase_ )
        _lowercase       :							Dict      		=		ceil(lowerCamelCase_ )
        _lowercase       :							List[str]      		=		triangle_numbers[b_floor]
        _lowercase       :							List[str]      		=		triangle_numbers[b_ceil]

        if abs(target - triangle_b_first_guess * triangle_a ) < abs(
            target - best_product ):
            _lowercase       :							Union[str, Any]      		=		triangle_b_first_guess * triangle_a
            _lowercase       :							Union[str, Any]      		=		idx_a * b_floor

        if abs(target - triangle_b_second_guess * triangle_a ) < abs(
            target - best_product ):
            _lowercase       :							Any      		=		triangle_b_second_guess * triangle_a
            _lowercase       :							Optional[Any]      		=		idx_a * b_ceil

    return area


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


