

import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
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  :      Optional[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""")

_lowerCAmelCase  :      Optional[Any]			 =list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
_lowerCAmelCase  :      Union[str, Any]			 =tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class __UpperCamelCase     :






					'''simple docstring'''



					__magic_name__        =   field(
					    default="cifar10"					,metadata={"help": "Name of a dataset from the datasets package"}		)
					__magic_name__        =   field(
					    default=_a					,metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}		)
					__magic_name__        =   field(
					    default=_a					,metadata={"help": "The column name of the images in the files. If not set, will try to use 'image' or 'img'."}					,)
					__magic_name__        =   field(default=_a					,metadata={"help": "A folder containing the training data."}		)
					__magic_name__        =   field(default=_a					,metadata={"help": "A folder containing the validation data."}		)
					__magic_name__        =   field(
					    default=0.15					,metadata={"help": "Percent to split off of train for validation."}		)
					__magic_name__        =   field(default=3_2					,metadata={"help": "The size of the square patches to use for masking."}		)
					__magic_name__        =   field(
					    default=0.6					,metadata={"help": "Percentage of patches to mask."}					,)
					__magic_name__        =   field(
					    default=_a					,metadata={
					        "help": (
					            "For debugging purposes or quicker training, truncate the number of training examples to this "
					            "value if set."
					        )
					    }					,)
					__magic_name__        =   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						):
						UpperCAmelCase__:	List[Any]									= {}
						if self.train_dir is not None:
							UpperCAmelCase__:	Optional[Any]									= self.train_dir
						if self.validation_dir is not None:
							UpperCAmelCase__:	Dict									= self.validation_dir
						UpperCAmelCase__:	Any									= data_files if data_files else None

@dataclass
class __UpperCamelCase     :






					'''simple docstring'''



					__magic_name__        =   field(
					    default=_a					,metadata={
					        "help": (
					            "The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a "
					            "checkpoint identifier on the hub. "
					            "Don't set if you want to train a model from scratch."
					        )
					    }					,)
					__magic_name__        =   field(
					    default=_a					,metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(_a		)}					,)
					__magic_name__        =   field(
					    default=_a					,metadata={"help": "Pretrained config name or path if not the same as model_name"}		)
					__magic_name__        =   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"
					        )
					    }					,)
					__magic_name__        =   field(
					    default=_a					,metadata={"help": "Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"}					,)
					__magic_name__        =   field(
					    default="main"					,metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}					,)
					__magic_name__        =   field(default=_a					,metadata={"help": "Name or path of preprocessor config."}		)
					__magic_name__        =   field(
					    default=_a					,metadata={
					        "help": (
					            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
					            "with private models)."
					        )
					    }					,)
					__magic_name__        =   field(
					    default=_a					,metadata={
					        "help": (
					            "The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
					        )
					    }					,)
					__magic_name__        =   field(
					    default=_a					,metadata={
					        "help": (
					            "The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
					        )
					    }					,)
					__magic_name__        =   field(
					    default=_a					,metadata={"help": "Stride to use for the encoder."}					,)

class __UpperCamelCase     :






					'''simple docstring'''



					def __init__(				self				,      lowerCamelCase__=1_9_2				,      lowerCamelCase__=3_2				,      lowerCamelCase__=4				,      lowerCamelCase__=0.6						):
						UpperCAmelCase__:	Dict									= input_size
						UpperCAmelCase__:	List[Any]									= mask_patch_size
						UpperCAmelCase__:	Tuple									= model_patch_size
						UpperCAmelCase__:	Any									= mask_ratio

						if self.input_size % self.mask_patch_size != 0:
							raise ValueError("Input size must be divisible by mask patch size"						)
						if self.mask_patch_size % self.model_patch_size != 0:
							raise ValueError("Mask patch size must be divisible by model patch size"						)

						UpperCAmelCase__:	List[str]									= self.input_size // self.mask_patch_size
						UpperCAmelCase__:	Optional[int]									= self.mask_patch_size // self.model_patch_size

						UpperCAmelCase__:	Union[str, Any]									= self.rand_size**2
						UpperCAmelCase__:	List[str]									= int(np.ceil(self.token_count * self.mask_ratio						)						)





					def __call__(				self						):
						UpperCAmelCase__:	Any									= np.random.permutation(self.token_count						)[: self.mask_count]
						UpperCAmelCase__:	List[str]									= np.zeros(self.token_count				,      dtype=lowerCamelCase__						)
						UpperCAmelCase__:	Any									= 1

						UpperCAmelCase__:	Optional[Any]									= mask.reshape((self.rand_size, self.rand_size)						)
						UpperCAmelCase__:	Tuple									= mask.repeat(self.scale				,      axis=0						).repeat(self.scale				,      axis=1						)

						return torch.tensor(mask.flatten()						)



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[Any]									= torch.stack([example["pixel_values"] for example in examples]      )
	UpperCAmelCase__:	Union[str, Any]									= torch.stack([example["mask"] for example in examples]      )
	return {"pixel_values": pixel_values, "bool_masked_pos": mask}



def 			_A	(      ):
	# 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.

	UpperCAmelCase__:	List[Any]									= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
	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__:	Optional[int]									= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
	else:
		UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	Dict									= 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_mim"	,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__:	Dict									= 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__:	List[str]									= None
	if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
		UpperCAmelCase__:	Any									= 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__:	Any									= 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__:	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:
		UpperCAmelCase__:	Dict									= ds["train"].train_test_split(data_args.train_val_split      )
		UpperCAmelCase__:	Optional[int]									= split["train"]
		UpperCAmelCase__:	Tuple									= split["test"]

	# Create config
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.
	UpperCAmelCase__:	Optional[int]									= {
	    "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_or_path:
		UpperCAmelCase__:	Tuple									= AutoConfig.from_pretrained(model_args.config_name_or_path	,**SCREAMING_SNAKE_CASE      )
	elif model_args.model_name_or_path:
		UpperCAmelCase__:	Optional[Any]									= AutoConfig.from_pretrained(model_args.model_name_or_path	,**SCREAMING_SNAKE_CASE      )
	else:
		UpperCAmelCase__:	List[Any]									= CONFIG_MAPPING[model_args.model_type]()
		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}"      )

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
	if hasattr(SCREAMING_SNAKE_CASE	,"decoder_type"      ):
		UpperCAmelCase__:	List[Any]									= "simmim"

	# adapt config
	UpperCAmelCase__:	str									= model_args.image_size if model_args.image_size is not None else config.image_size
	UpperCAmelCase__:	List[Any]									= model_args.patch_size if model_args.patch_size is not None else config.patch_size
	UpperCAmelCase__:	Dict									= (
	    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
	)

	config.update(
	    {
	        "image_size": model_args.image_size,
	        "patch_size": model_args.patch_size,
	        "encoder_stride": model_args.encoder_stride,
	    }      )

	# create image processor
	if model_args.image_processor_name:
		UpperCAmelCase__:	int									= AutoImageProcessor.from_pretrained(model_args.image_processor_name	,**SCREAMING_SNAKE_CASE      )
	elif model_args.model_name_or_path:
		UpperCAmelCase__:	Optional[int]									= AutoImageProcessor.from_pretrained(model_args.model_name_or_path	,**SCREAMING_SNAKE_CASE      )
	else:
		UpperCAmelCase__:	List[Any]									= {
		    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
		}
		UpperCAmelCase__:	Optional[int]									= IMAGE_PROCESSOR_TYPES[model_args.model_type]()

	# create model
	if model_args.model_name_or_path:
		UpperCAmelCase__:	str									= AutoModelForMaskedImageModeling.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__:	Tuple									= AutoModelForMaskedImageModeling.from_config(SCREAMING_SNAKE_CASE      )

	if training_args.do_train:
		UpperCAmelCase__:	Optional[int]									= ds["train"].column_names
	else:
		UpperCAmelCase__:	int									= ds["validation"].column_names

	if data_args.image_column_name is not None:
		UpperCAmelCase__:	Tuple									= data_args.image_column_name
	elif "image" in column_names:
		UpperCAmelCase__:	Optional[int]									= "image"
	elif "img" in column_names:
		UpperCAmelCase__:	str									= "img"
	else:
		UpperCAmelCase__:	List[str]									= column_names[0]

	# transformations as done in original SimMIM paper
	# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
	UpperCAmelCase__:	str									= Compose(
	    [
	        Lambda(lambda SCREAMING_SNAKE_CASE      : img.convert("RGB"      ) if img.mode != "RGB" else img      ),
	        RandomResizedCrop(model_args.image_size	,scale=(0.67, 1.0)	,ratio=(3.0 / 4.0, 4.0 / 3.0)      ),
	        RandomHorizontalFlip(),
	        ToTensor(),
	        Normalize(mean=image_processor.image_mean	,std=image_processor.image_std      ),
	    ]      )

	# create mask generator
	UpperCAmelCase__:	List[str]									= MaskGenerator(
	    input_size=model_args.image_size	,mask_patch_size=data_args.mask_patch_size	,model_patch_size=model_args.patch_size	,mask_ratio=data_args.mask_ratio	,)

	def preprocess_images(SCREAMING_SNAKE_CASE      ):
		UpperCAmelCase__:	Union[str, Any]									= [transforms(SCREAMING_SNAKE_CASE      ) for image in examples[image_column_name]]
		UpperCAmelCase__:	List[Any]									= [mask_generator() for i in range(len(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__:	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:
			UpperCAmelCase__:	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      )

	# Initialize our trainer
	UpperCAmelCase__:	Any									= 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__:	Optional[int]									= None
		if training_args.resume_from_checkpoint is not None:
			UpperCAmelCase__:	Union[str, Any]									= training_args.resume_from_checkpoint
		elif last_checkpoint is not None:
			UpperCAmelCase__:	Optional[int]									= last_checkpoint
		UpperCAmelCase__:	Union[str, 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:
		UpperCAmelCase__:	Dict									= 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__:	Union[str, Any]									= {
	    "finetuned_from": model_args.model_name_or_path,
	    "tasks": "masked-image-modeling",
	    "dataset": data_args.dataset_name,
	    "tags": ["masked-image-modeling"],
	}
	if training_args.push_to_hub:
		trainer.push_to_hub(**SCREAMING_SNAKE_CASE      )
	else:
		trainer.create_model_card(**SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
					main()


def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__							,						UpperCAmelCase__:	int									= [], []
	while len(SCREAMING_SNAKE_CASE      ) > 1:
		UpperCAmelCase__							,						UpperCAmelCase__:	str									= min(SCREAMING_SNAKE_CASE      ), max(SCREAMING_SNAKE_CASE      )
		start.append(SCREAMING_SNAKE_CASE      )
		end.append(SCREAMING_SNAKE_CASE      )
		collection.remove(SCREAMING_SNAKE_CASE      )
		collection.remove(SCREAMING_SNAKE_CASE      )
	end.reverse()
	return start + collection + end


if __name__ == "__main__":
					_lowerCAmelCase  :      Union[str, Any]			 =input("""Enter numbers separated by a comma:\n""").strip()
					_lowerCAmelCase  :      Dict			 =[int(item) for item in user_input.split(""",""")]
					print(*merge_sort(unsorted), sep=""",""")