



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
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   _A   :


						'''simple docstring'''





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



						def     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



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

@dataclass
class   _A   :


						'''simple docstring'''





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

@dataclass
class   _A   ( _lowercase	):


						'''simple docstring'''





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



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			torch.stack([example["pixel_values"] for example in examples]   )
	return {"pixel_values": pixel_values}



def    snake_case_				(    ):
	# 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		       =			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		       =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
	else:
		__lowercase       ,    __lowercase       ,    __lowercase		       =			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		       =			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		       =			None
	if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
		__lowercase		       =			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		       =			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		       =			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		       =			ds["train"].train_test_split(data_args.train_val_split   )
		__lowercase		       =			split["train"]
		__lowercase		       =			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		       =			{
	    "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		       =			ViTMAEConfig.from_pretrained(model_args.config_name  ,	**_SCREAMING_SNAKE_CASE   )
	elif model_args.model_name_or_path:
		__lowercase		       =			ViTMAEConfig.from_pretrained(model_args.model_name_or_path  ,	**_SCREAMING_SNAKE_CASE   )
	else:
		__lowercase		       =			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		       =			ViTImageProcessor.from_pretrained(model_args.image_processor_name  ,	**_SCREAMING_SNAKE_CASE   )
	elif model_args.model_name_or_path:
		__lowercase		       =			ViTImageProcessor.from_pretrained(model_args.model_name_or_path  ,	**_SCREAMING_SNAKE_CASE   )
	else:
		__lowercase		       =			ViTImageProcessor()

	# create model
	if model_args.model_name_or_path:
		__lowercase		       =			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		       =			ViTMAEForPreTraining(_SCREAMING_SNAKE_CASE   )

	if training_args.do_train:
		__lowercase		       =			ds["train"].column_names
	else:
		__lowercase		       =			ds["validation"].column_names

	if data_args.image_column_name is not None:
		__lowercase		       =			data_args.image_column_name
	elif "image" in column_names:
		__lowercase		       =			"image"
	elif "img" in column_names:
		__lowercase		       =			"img"
	else:
		__lowercase		       =			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		       =			image_processor.size["shortest_edge"]
	else:
		__lowercase		       =			(image_processor.size["height"], image_processor.size["width"])
	__lowercase		       =			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		       =			[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		       =			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		       =			(
			    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		       =			(
	    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
	)
	if training_args.base_learning_rate is not None:
		__lowercase		       =			training_args.base_learning_rate * total_train_batch_size / 2_5_6

	# Initialize our trainer
	__lowercase		       =			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		       =			None
		if training_args.resume_from_checkpoint is not None:
			__lowercase		       =			training_args.resume_from_checkpoint
		elif last_checkpoint is not None:
			__lowercase		       =			last_checkpoint
		__lowercase		       =			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		       =			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		       =			{
	    "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    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
	main()








from __future__ import annotations

import bisect



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 0  ,	_SCREAMING_SNAKE_CASE = -1   ):
	if hi < 0:
		__lowercase		       =			len(_SCREAMING_SNAKE_CASE   )

	while lo < hi:
		__lowercase		       =			lo + (hi - lo) // 2
		if sorted_collection[mid] < item:
			__lowercase		       =			mid + 1
		else:
			__lowercase		       =			mid

	return lo



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 0  ,	_SCREAMING_SNAKE_CASE = -1   ):
	if hi < 0:
		__lowercase		       =			len(_SCREAMING_SNAKE_CASE   )

	while lo < hi:
		__lowercase		       =			lo + (hi - lo) // 2
		if sorted_collection[mid] <= item:
			__lowercase		       =			mid + 1
		else:
			__lowercase		       =			mid

	return lo



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 0  ,	_SCREAMING_SNAKE_CASE = -1   ):
	sorted_collection.insert(bisect_left(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )  ,	_SCREAMING_SNAKE_CASE   )



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 0  ,	_SCREAMING_SNAKE_CASE = -1   ):
	sorted_collection.insert(bisect_right(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )  ,	_SCREAMING_SNAKE_CASE   )



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			0
	__lowercase		       =			len(_SCREAMING_SNAKE_CASE   ) - 1

	while left <= right:
		__lowercase		       =			left + (right - left) // 2
		__lowercase		       =			sorted_collection[midpoint]
		if current_item == item:
			return midpoint
		elif item < current_item:
			__lowercase		       =			midpoint - 1
		else:
			__lowercase		       =			midpoint + 1
	return None



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			bisect.bisect_left(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
	if index != len(_SCREAMING_SNAKE_CASE   ) and sorted_collection[index] == item:
		return index
	return None



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	if right < left:
		return None

	__lowercase		       =			left + (right - left) // 2

	if sorted_collection[midpoint] == item:
		return midpoint
	elif sorted_collection[midpoint] > item:
		return binary_search_by_recursion(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	midpoint - 1   )
	else:
		return binary_search_by_recursion(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	midpoint + 1  ,	_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	snake_case__					:     Optional[Any]    		=   input("""Enter numbers separated by comma:\n""").strip()
	snake_case__					:     Any    		=   sorted(int(item) for item in user_input.split(""","""))
	snake_case__					:     Any    		=   int(input("""Enter a single number to be found in the list:\n"""))
	snake_case__					:     List[Any]    		=   binary_search(collection, target)
	if result is None:
		print(F'''{target} was not found in {collection}.''')
	else:
		print(F'''{target} was found at position {result} in {collection}.''')



