






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


				def 						__a					(					self      ):
											_lowercase							:   Optional[int] 							=   {}
											if self.train_dir is not None:
																		_lowercase							:   Optional[int] 							=   self.train_dir
											if self.validation_dir is not None:
																		_lowercase							:   int 							=   self.validation_dir
											_lowercase							:   Any 							=   data_files if data_files else None




@dataclass
class 				lowerCAmelCase_	:
				_UpperCamelCase					:       str							       = field(
				    default=__lowerCamelCase   ,		metadata={
				        "help": (
				            "The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch."
				        )
				    }   ,		)
				_UpperCamelCase					:       Optional[str]							       = field(
				    default=__lowerCamelCase   ,		metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}			)
				_UpperCamelCase					:       Optional[str]							       = 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"
				        )
				    }   ,		)
				_UpperCamelCase					:       Optional[str]							       = field(
				    default=__lowerCamelCase   ,		metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}			)
				_UpperCamelCase					:       str							       = field(
				    default="main"   ,		metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}   ,		)
				_UpperCamelCase					:       str							       = field(default=__lowerCamelCase   ,		metadata={"help": "Name or path of preprocessor config."}			)
				_UpperCamelCase					:       bool							       = field(
				    default=__lowerCamelCase   ,		metadata={
				        "help": (
				            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
				            "with private models)."
				        )
				    }   ,		)
				_UpperCamelCase					:       float							       = field(
				    default=0.7_5   ,		metadata={"help": "The ratio of the number of masked tokens in the input sequence."}			)
				_UpperCamelCase					:       bool							       = field(
				    default=__lowerCamelCase   ,		metadata={"help": "Whether or not to train with normalized pixel values as target."}			)




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






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    Dict:
							_lowercase							:   Tuple 							=   torch.stack([example['pixel_values'] for example in examples]				)
							return {"pixel_values": pixel_values}






def    __magic_name__    (	)     ->    Union[str, Any]:
							_lowercase							:   Optional[int] 							=   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							:   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'     ,     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()

							_lowercase							:   str 							=   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.
							_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							:   Tuple 							=   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							:   int 							=   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:
														_lowercase							:   Any 							=   ds['train'].train_test_split(data_args.train_val_split				)
														_lowercase							:   List[Any] 							=   split['train']
														_lowercase							:   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.
							_lowercase							:   List[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							:   Union[str, Any] 							=   ViTMAEConfig.from_pretrained(model_args.config_name     ,     **SCREAMING_SNAKE_CASE				)
							elif model_args.model_name_or_path:
														_lowercase							:   Dict 							=   ViTMAEConfig.from_pretrained(model_args.model_name_or_path     ,     **SCREAMING_SNAKE_CASE				)
							else:
														_lowercase							:   Union[str, 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:
														_lowercase							:   Any 							=   ViTImageProcessor.from_pretrained(model_args.image_processor_name     ,     **SCREAMING_SNAKE_CASE				)
							elif model_args.model_name_or_path:
														_lowercase							:   List[str] 							=   ViTImageProcessor.from_pretrained(model_args.model_name_or_path     ,     **SCREAMING_SNAKE_CASE				)
							else:
														_lowercase							:   List[Any] 							=   ViTImageProcessor()

							# create model
							if model_args.model_name_or_path:
														_lowercase							:   List[Any] 							=   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'				)
														_lowercase							:   Dict 							=   ViTMAEForPreTraining(SCREAMING_SNAKE_CASE				)

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

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

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

							# Write model card and (optionally) push to hub
							_lowercase							:   int 							=   {
							    '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    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    Tuple:
							main()


if __name__ == "__main__":
							main()






from PIL import Image


def    a      ( a	)     ->Image:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= image.size
			SCREAMING_SNAKE_CASE     						= 0
			SCREAMING_SNAKE_CASE     						= image.load()
			for i in range(a	):
						for j in range(a	):
									SCREAMING_SNAKE_CASE     						= pixels[j, i]
									mean += pixel
			mean //= width * height

			for j in range(a	):
						for i in range(a	):
									SCREAMING_SNAKE_CASE     						= 255 if pixels[i, j] > mean else 0
			return image


if __name__ == "__main__":
							__lowerCAmelCase					   =       mean_threshold(Image.open('path_to_image').convert('L'))
							image.save('output_image_path')