
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	:    Dict          =      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	:    int          =      list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
_lowerCamelCase	:    Optional[int]          =      tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class        SCREAMING_SNAKE_CASE__					:





	'''simple docstring'''



	_UpperCAmelCase	:		Optional[Any]          = field(
	    default="cifar10"							,metadata={"help": "Name of a dataset from the datasets package"} )
	_UpperCAmelCase	:		List[str]          = field(
	    default=UpperCAmelCase							,metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
	_UpperCAmelCase	:		Any          = field(
	    default=UpperCAmelCase							,metadata={"help": "The column name of the images in the files. If not set, will try to use 'image' or 'img'."}							,)
	_UpperCAmelCase	:		List[str]          = field(default=UpperCAmelCase							,metadata={"help": "A folder containing the training data."} )
	_UpperCAmelCase	:		Dict          = field(default=UpperCAmelCase							,metadata={"help": "A folder containing the validation data."} )
	_UpperCAmelCase	:		Optional[int]          = field(
	    default=0.15							,metadata={"help": "Percent to split off of train for validation."} )
	_UpperCAmelCase	:		Optional[int]          = field(default=3_2							,metadata={"help": "The size of the square patches to use for masking."} )
	_UpperCAmelCase	:		str          = field(
	    default=0.6							,metadata={"help": "Percentage of patches to mask."}							,)
	_UpperCAmelCase	:		int          = field(
	    default=UpperCAmelCase							,metadata={
	        "help": (
	            "For debugging purposes or quicker training, truncate the number of training examples to this "
	            "value if set."
	        )
	    }							,)
	_UpperCAmelCase	:		Dict          = field(
	    default=UpperCAmelCase							,metadata={
	        "help": (
	            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
	            "value if set."
	        )
	    }							,)



	def 			A							(  self     :   int     ):


				'''simple docstring'''


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




@dataclass
class        SCREAMING_SNAKE_CASE__					:





	'''simple docstring'''



	_UpperCAmelCase	:		Optional[Any]          = field(
	    default=UpperCAmelCase							,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."
	        )
	    }							,)
	_UpperCAmelCase	:		int          = field(
	    default=UpperCAmelCase							,metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(UpperCAmelCase )}							,)
	_UpperCAmelCase	:		str          = field(
	    default=UpperCAmelCase							,metadata={"help": "Pretrained config name or path if not the same as model_name"} )
	_UpperCAmelCase	:		int          = field(
	    default=UpperCAmelCase							,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[Any]          = field(
	    default=UpperCAmelCase							,metadata={"help": "Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"}							,)
	_UpperCAmelCase	:		int          = field(
	    default="main"							,metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}							,)
	_UpperCAmelCase	:		Any          = field(default=UpperCAmelCase							,metadata={"help": "Name or path of preprocessor config."} )
	_UpperCAmelCase	:		Optional[Any]          = field(
	    default=UpperCAmelCase							,metadata={
	        "help": (
	            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
	            "with private models)."
	        )
	    }							,)
	_UpperCAmelCase	:		Union[str, Any]          = field(
	    default=UpperCAmelCase							,metadata={
	        "help": (
	            "The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
	        )
	    }							,)
	_UpperCAmelCase	:		Optional[int]          = field(
	    default=UpperCAmelCase							,metadata={
	        "help": (
	            "The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
	        )
	    }							,)
	_UpperCAmelCase	:		int          = field(
	    default=UpperCAmelCase							,metadata={"help": "Stride to use for the encoder."}							,)




class        SCREAMING_SNAKE_CASE__					:





	'''simple docstring'''



	def __init__(  self     :   List[Any]      ,  lowercase     :   Any=192      ,  lowercase     :   Any=32      ,  lowercase     :   Optional[Any]=4      ,  lowercase     :   Optional[int]=0.6     ):


				'''simple docstring'''


				_snake_case          =      input_size
				_snake_case          =      mask_patch_size
				_snake_case          =      model_patch_size
				_snake_case          =      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'     )

				_snake_case          =      self.input_size // self.mask_patch_size
				_snake_case          =      self.mask_patch_size // self.model_patch_size

				_snake_case          =      self.rand_size**2
				_snake_case          =      int(np.ceil(self.token_count * self.mask_ratio     )     )





	def __call__(  self     :   Any     ):


				'''simple docstring'''


				_snake_case          =      np.random.permutation(self.token_count     )[: self.mask_count]
				_snake_case          =      np.zeros(self.token_count      ,  dtype=lowerCamelCase_     )
				_snake_case          =      1

				_snake_case          =      mask.reshape((self.rand_size, self.rand_size)     )
				_snake_case          =      mask.repeat(self.scale      ,  axis=0     ).repeat(self.scale      ,  axis=1     )

				return torch.tensor(mask.flatten()     )






def 	a_		(	__lowercase       :     Optional[int]			)			->  Dict:
			_snake_case          =      torch.stack([example['pixel_values'] for example in examples]			)
			_snake_case          =      torch.stack([example['mask'] for example in examples]			)
			return {"pixel_values": pixel_values, "bool_masked_pos": mask}






def 	a_		(	)			->  List[str]:
			_snake_case          =      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.
						_snake_case     , _snake_case     , _snake_case          =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
			else:
						_snake_case     , _snake_case     , _snake_case          =      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'	, __lowercase	, __lowercase			)

			# 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()

			_snake_case          =      training_args.get_process_log_level()
			logger.setLevel(__lowercase			)
			transformers.utils.logging.set_verbosity(__lowercase			)
			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.
			_snake_case          =      None
			if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
						_snake_case          =      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.
			_snake_case          =      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.
			_snake_case          =      None if 'validation' in ds.keys() else data_args.train_val_split
			if isinstance(data_args.train_val_split	, __lowercase			) and data_args.train_val_split > 0.0:
						_snake_case          =      ds['train'].train_test_split(data_args.train_val_split			)
						_snake_case          =      split['train']
						_snake_case          =      split['test']

			# Create config
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.
			_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,
			}
			if model_args.config_name_or_path:
						_snake_case          =      AutoConfig.from_pretrained(model_args.config_name_or_path	, **__lowercase			)
			elif model_args.model_name_or_path:
						_snake_case          =      AutoConfig.from_pretrained(model_args.model_name_or_path	, **__lowercase			)
			else:
						_snake_case          =      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(__lowercase	, 'decoder_type'			):
						_snake_case          =      'simmim'

			# adapt config
			_snake_case          =      model_args.image_size if model_args.image_size is not None else config.image_size
			_snake_case          =      model_args.patch_size if model_args.patch_size is not None else config.patch_size
			_snake_case          =      (
			    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:
						_snake_case          =      AutoImageProcessor.from_pretrained(model_args.image_processor_name	, **__lowercase			)
			elif model_args.model_name_or_path:
						_snake_case          =      AutoImageProcessor.from_pretrained(model_args.model_name_or_path	, **__lowercase			)
			else:
						_snake_case          =      {
						    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
						}
						_snake_case          =      IMAGE_PROCESSOR_TYPES[model_args.model_type]()

			# create model
			if model_args.model_name_or_path:
						_snake_case          =      AutoModelForMaskedImageModeling.from_pretrained(
						    model_args.model_name_or_path	, from_tf=bool('.ckpt' in model_args.model_name_or_path			)	, config=__lowercase	, 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'			)
						_snake_case          =      AutoModelForMaskedImageModeling.from_config(__lowercase			)

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

			if data_args.image_column_name is not None:
						_snake_case          =      data_args.image_column_name
			elif "image" in column_names:
						_snake_case          =      'image'
			elif "img" in column_names:
						_snake_case          =      'img'
			else:
						_snake_case          =      column_names[0]

			# transformations as done in original SimMIM paper
			# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
			_snake_case          =      Compose(
			    [
			        Lambda(lambda __lowercase			: img.convert('RGB'			) if img.mode != "RGB" else img			),
			        RandomResizedCrop(model_args.image_size	, scale=(0.6_7, 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
			_snake_case          =      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(__lowercase       :     Optional[int]			):
						_snake_case          =      [transforms(__lowercase			) for image in examples[image_column_name]]
						_snake_case          =      [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:
									_snake_case          =      ds['train'].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
						# Set the training transforms
						ds["train"].set_transform(__lowercase			)

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

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

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

			# Write model card and (optionally) push to hub
			_snake_case          =      {
			    '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(**__lowercase			)
			else:
						trainer.create_model_card(**__lowercase			)


if __name__ == "__main__":
					main()

import baseaa






def 	a_		(	__lowercase       :     str			)			->  bytes:
			return baseaa.aaaencode(string.encode('utf-8'			)			)






def 	a_		(	__lowercase       :     bytes			)			->  str:
			return baseaa.aaadecode(__lowercase			).decode('utf-8'			)


if __name__ == "__main__":
			import doctest

			doctest.testmod()