


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
SCREAMING_SNAKE_CASE       :      Union[str, 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 _lowerCamelCase:
       lowercase_ :	Optional[str]     =			field(
           default="""cifar10""", metadata={"""help""": """Name of a dataset from the datasets package"""}   )
       lowercase_ :	Optional[str]     =			field(
           default=_a, metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}   )
       lowercase_ :	Optional[str]     =			field(
           default=_a, metadata={"""help""": """The column name of the images in the files."""}   )
       lowercase_ :	Optional[str]     =			field(default=_a, metadata={"""help""": """A folder containing the training data."""}   )
       lowercase_ :	Optional[str]     =			field(default=_a, metadata={"""help""": """A folder containing the validation data."""}   )
       lowercase_ :	Optional[float]     =			field(
           default=0.15, metadata={"""help""": """Percent to split off of train for validation."""}   )
       lowercase_ :	Optional[int]     =			field(
           default=_a, metadata={
               """help""": (
                   """For debugging purposes or quicker training, truncate the number of training examples to this """
                   """value if set."""
               )
           }, )
       lowercase_ :	Optional[int]     =			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)       ->   Dict:




           """simple docstring"""

           _lowercase       :							List[str]      		=		{}
           if self.train_dir is not None:
               _lowercase       :							Optional[int]      		=		self.train_dir
           if self.validation_dir is not None:
               _lowercase       :							Union[str, Any]      		=		self.validation_dir
           _lowercase       :							Any      		=		data_files if data_files else None






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






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



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						List[str]:
    _lowercase       :							Any      		=		torch.stack([example['pixel_values'] for example in examples] )
    return {"pixel_values": pixel_values}



def 				UpperCamelCase_(      )    ->						Dict:
    # 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.

    _lowercase       :							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.
        _lowercase		,				_lowercase		,				_lowercase       :							Any      		=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
    else:
        _lowercase		,				_lowercase		,				_lowercase       :							Union[str, 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'							, 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()

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

    # create model
    if model_args.model_name_or_path:
        _lowercase       :							Dict      		=		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' )
        _lowercase       :							Any      		=		ViTMAEForPreTraining(lowerCamelCase_ )

    if training_args.do_train:
        _lowercase       :							int      		=		ds['train'].column_names
    else:
        _lowercase       :							List[str]      		=		ds['validation'].column_names

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

    # Initialize our trainer
    _lowercase       :							Optional[Any]      		=		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:
        _lowercase       :							str      		=		None
        if training_args.resume_from_checkpoint is not None:
            _lowercase       :							str      		=		training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            _lowercase       :							Tuple      		=		last_checkpoint
        _lowercase       :							Dict      		=		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:
        _lowercase       :							Union[str, Any]      		=		trainer.evaluate()
        trainer.log_metrics('eval'							, lowerCamelCase_ )
        trainer.save_metrics('eval'							, lowerCamelCase_ )

    # Write model card and (optionally) push to hub
    _lowercase       :							Tuple      		=		{
        '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 				UpperCamelCase_(      lowerCamelCase_ )    ->						Tuple:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
 main()






from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						bool:
    _lowercase       :							int      		=		int(number**0.5 )
    return number == sq * sq



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						tuple[int, int]:
    _lowercase       :							int      		=		x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
    _lowercase       :							int      		=		x_den * y_den * z_den
    _lowercase       :							int      		=		gcd(lowerCamelCase_							, lowerCamelCase_ )
    top //= hcf
    bottom //= hcf
    return top, bottom



def 				UpperCamelCase_(      lowerCamelCase_ = 35 )    ->						int:
    _lowercase       :							set      		=		set()
    _lowercase       :							int
    _lowercase       :							Fraction      		=		Fraction(0 )
    _lowercase       :							tuple[int, int]

    for x_num in range(1							, order + 1 ):
        for x_den in range(x_num + 1							, order + 1 ):
            for y_num in range(1							, order + 1 ):
                for y_den in range(y_num + 1							, order + 1 ):
                    # n=1
                    _lowercase       :							int      		=		x_num * y_den + x_den * y_num
                    _lowercase       :							int      		=		x_den * y_den
                    _lowercase       :							str      		=		gcd(lowerCamelCase_							, lowerCamelCase_ )
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        _lowercase       :							List[Any]      		=		add_three(
                            lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )
                        unique_s.add(lowerCamelCase_ )

                    # n=2
                    _lowercase       :							Dict      		=		(
                        x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
                    )
                    _lowercase       :							List[Any]      		=		x_den * x_den * y_den * y_den
                    if is_sq(lowerCamelCase_ ) and is_sq(lowerCamelCase_ ):
                        _lowercase       :							Tuple      		=		int(sqrt(lowerCamelCase_ ) )
                        _lowercase       :							int      		=		int(sqrt(lowerCamelCase_ ) )
                        _lowercase       :							Any      		=		gcd(lowerCamelCase_							, lowerCamelCase_ )
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            _lowercase       :							Optional[int]      		=		add_three(
                                lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )
                            unique_s.add(lowerCamelCase_ )

                    # n=-1
                    _lowercase       :							Any      		=		x_num * y_num
                    _lowercase       :							str      		=		x_den * y_num + x_num * y_den
                    _lowercase       :							Any      		=		gcd(lowerCamelCase_							, lowerCamelCase_ )
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        _lowercase       :							int      		=		add_three(
                            lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )
                        unique_s.add(lowerCamelCase_ )

                    # n=2
                    _lowercase       :							str      		=		x_num * x_num * y_num * y_num
                    _lowercase       :							Optional[Any]      		=		(
                        x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
                    )
                    if is_sq(lowerCamelCase_ ) and is_sq(lowerCamelCase_ ):
                        _lowercase       :							Tuple      		=		int(sqrt(lowerCamelCase_ ) )
                        _lowercase       :							List[str]      		=		int(sqrt(lowerCamelCase_ ) )
                        _lowercase       :							Union[str, Any]      		=		gcd(lowerCamelCase_							, lowerCamelCase_ )
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            _lowercase       :							Tuple      		=		add_three(
                                lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )
                            unique_s.add(lowerCamelCase_ )

    for num, den in unique_s:
        total += Fraction(lowerCamelCase_							, lowerCamelCase_ )

    return total.denominator + total.numerator


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


