

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
A_       : Tuple         =			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')

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



@dataclass
class     _a						:


					'''simple docstring'''






					UpperCAmelCase__:   Optional[str]  							=       field(
					    default='''cifar10'''		,   metadata={'''help''': '''Name of a dataset from the datasets package'''}				)
					UpperCAmelCase__:   Optional[str]  							=       field(
					    default=__magic_name__		,   metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}				)
					UpperCAmelCase__:   Optional[str]  							=       field(
					    default=__magic_name__		,   metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''}		,   )
					UpperCAmelCase__:   Optional[str]  							=       field(default=__magic_name__		,   metadata={'''help''': '''A folder containing the training data.'''}				)
					UpperCAmelCase__:   Optional[str]  							=       field(default=__magic_name__		,   metadata={'''help''': '''A folder containing the validation data.'''}				)
					UpperCAmelCase__:   Optional[float]  							=       field(
					    default=0.15		,   metadata={'''help''': '''Percent to split off of train for validation.'''}				)
					UpperCAmelCase__:   int  							=       field(default=32		,   metadata={'''help''': '''The size of the square patches to use for masking.'''}				)
					UpperCAmelCase__:   float  							=       field(
					    default=0.6		,   metadata={'''help''': '''Percentage of patches to mask.'''}		,   )
					UpperCAmelCase__:   Optional[int]  							=       field(
					    default=__magic_name__		,   metadata={
					        '''help''': (
					            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
					            '''value if set.'''
					        )
					    }		,   )
					UpperCAmelCase__:   Optional[int]  							=       field(
					    default=__magic_name__		,   metadata={
					        '''help''': (
					            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
					            '''value if set.'''
					        )
					    }		,   )






					def   __A			( self			):
						A__							:    int        =		{}
						if self.train_dir is not None:
							A__							:    Optional[int]        =		self.train_dir
						if self.validation_dir is not None:
							A__							:    Dict        =		self.validation_dir
						A__							:    List[str]        =		data_files if data_files else None



@dataclass
class     _a						:


					'''simple docstring'''






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



class     _a						:


					'''simple docstring'''






					def __init__( self    ,       A__=192    ,       A__=32    ,       A__=4    ,       A__=0.6			):
						A__							:    str        =		input_size
						A__							:    Optional[Any]        =		mask_patch_size
						A__							:    Dict        =		model_patch_size
						A__							:    Union[str, 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"""			)

						A__							:    int        =		self.input_size // self.mask_patch_size
						A__							:    Optional[int]        =		self.mask_patch_size // self.model_patch_size

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






					def __call__( self			):
						A__							:    int        =		np.random.permutation(self.token_count			)[: self.mask_count]
						A__							:    str        =		np.zeros(self.token_count    ,       dtype=A__			)
						A__							:    Tuple        =		1

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

						return torch.tensor(mask.flatten()			)



def 						UpperCamelCase					(lowercase_: Optional[Any]					)     ->      Dict:
	A__							:    Union[str, Any]        =		torch.stack([example["""pixel_values"""] for example in examples]					)
	A__							:    str        =		torch.stack([example["""mask"""] for example in examples]					)
	return {"pixel_values": pixel_values, "bool_masked_pos": mask}

def 						UpperCamelCase					()     ->      int:
	# 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.

	A__							:    Optional[int]        =		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.
		A__   ,   A__   ,   A__							:    List[Any]        =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
	else:
		A__   ,   A__   ,   A__							:    Optional[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_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()

	A__							:    int        =		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.
	A__							:    Tuple        =		None
	if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
		A__							:    List[str]        =		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.
	A__							:    List[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.
	A__							:    Tuple        =		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:
		A__							:    List[Any]        =		ds["""train"""].train_test_split(data_args.train_val_split					)
		A__							:    int        =		split["""train"""]
		A__							:    Union[str, Any]        =		split["""test"""]

	# Create config
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.
	A__							:    Optional[Any]        =		{
	    """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:
		A__							:    Optional[Any]        =		AutoConfig.from_pretrained(model_args.config_name_or_path    ,      **lowercase_					)
	elif model_args.model_name_or_path:
		A__							:    List[str]        =		AutoConfig.from_pretrained(model_args.model_name_or_path    ,      **lowercase_					)
	else:
		A__							:    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(lowercase_    ,      """decoder_type"""					):
		A__							:    Any        =		"""simmim"""

	# adapt config
	A__							:    Tuple        =		model_args.image_size if model_args.image_size is not None else config.image_size
	A__							:    Dict        =		model_args.patch_size if model_args.patch_size is not None else config.patch_size
	A__							:    List[str]        =		(
	    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:
		A__							:    List[Any]        =		AutoImageProcessor.from_pretrained(model_args.image_processor_name    ,      **lowercase_					)
	elif model_args.model_name_or_path:
		A__							:    str        =		AutoImageProcessor.from_pretrained(model_args.model_name_or_path    ,      **lowercase_					)
	else:
		A__							:    Any        =		{
		    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
		}
		A__							:    Dict        =		IMAGE_PROCESSOR_TYPES[model_args.model_type]()

	# create model
	if model_args.model_name_or_path:
		A__							:    str        =		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"""					)
		A__							:    int        =		AutoModelForMaskedImageModeling.from_config(lowercase_					)

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

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

	# transformations as done in original SimMIM paper
	# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
	A__							:    Any        =		Compose(
	    [
	        Lambda(lambda lowercase_					: 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
	A__							:    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(lowercase_: int					):
		A__							:    List[str]        =		[transforms(lowercase_					) for image in examples[image_column_name]]
		A__							:    Tuple        =		[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:
			A__							:    str        =		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:
			A__							:    Optional[Any]        =		(
			    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
	A__							:    int        =		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:
		A__							:    Any        =		None
		if training_args.resume_from_checkpoint is not None:
			A__							:    Optional[int]        =		training_args.resume_from_checkpoint
		elif last_checkpoint is not None:
			A__							:    Optional[int]        =		last_checkpoint
		A__							:    List[Any]        =		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:
		A__							:    Dict        =		trainer.evaluate()
		trainer.log_metrics("""eval"""    ,      lowercase_					)
		trainer.save_metrics("""eval"""    ,      lowercase_					)

	# Write model card and (optionally) push to hub
	A__							:    Tuple        =		{
	    """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 string

def 						UpperCamelCase					(lowercase_: str					)     ->      None:
	for key in range(len(string.ascii_uppercase					)					):
		A__							:    Dict        =		""""""
		for symbol in message:
			if symbol in string.ascii_uppercase:
				A__							:    Dict        =		string.ascii_uppercase.find(lowercase_					)
				A__							:    Optional[int]        =		num - key
				if num < 0:
					A__							:    Optional[int]        =		num + len(string.ascii_uppercase					)
				A__							:    Any        =		translated + string.ascii_uppercase[num]
			else:
				A__							:    Optional[Any]        =		translated + symbol
		print(f"""Decryption using Key #{key}: {translated}"""					)

def 						UpperCamelCase					()     ->      None:
	A__							:    Optional[Any]        =		input("""Encrypted message: """					)
	A__							:    Optional[Any]        =		message.upper()
	decrypt(lowercase_					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()



