



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
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")

UpperCAmelCase__       =	list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
UpperCAmelCase__       =	tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







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



			def    _lowerCamelCase	(						self       :  Any)       ->   Union[str, Any]:



						"""simple docstring"""






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







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







class       __lowerCAmelCase   :



			def __init__(						self       :  Any			,		A       :  int=1_92			,		A       :  Tuple=32			,		A       :  List[str]=4			,		A       :  str=0.6)       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       input_size
						_UpperCAmelCase			=       mask_patch_size
						_UpperCAmelCase			=       model_patch_size
						_UpperCAmelCase			=       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			=       self.input_size // self.mask_patch_size
						_UpperCAmelCase			=       self.mask_patch_size // self.model_patch_size

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



			def __call__(						self       :  Optional[int])       ->   Tuple:



						"""simple docstring"""






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

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

						return torch.tensor(mask.flatten())





def 							A     (					_UpperCAmelCase    :       Union[str, Any]   )    ->			int:







			'''simple docstring'''
			_UpperCAmelCase			=       torch.stack([example['pixel_values'] for example in examples]   )
			_UpperCAmelCase			=       torch.stack([example['mask'] for example in examples]   )
			return {"pixel_values": pixel_values, "bool_masked_pos": mask}





def 							A     (					)    ->			Dict:







			'''simple docstring'''
			_UpperCAmelCase			=       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			=       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
			else:
						_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       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'							,       _UpperCAmelCase							,       _UpperCAmelCase   )

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

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

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

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

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

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

			# transformations as done in original SimMIM paper
			# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
			_UpperCAmelCase			=       Compose(
			    [
			        Lambda(lambda _UpperCAmelCase   : 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			=       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(_UpperCAmelCase    :       List[Any]   ):
						_UpperCAmelCase			=       [transforms(_UpperCAmelCase   ) for image in examples[image_column_name]]
						_UpperCAmelCase			=       [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			=       ds['train'].shuffle(seed=training_args.seed   ).select(range(data_args.max_train_samples   )   )
						# Set the training transforms
						ds["train"].set_transform(_UpperCAmelCase   )

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

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

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

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


if __name__ == "__main__":
						main()





import string

import numpy





def 							A     (					_UpperCAmelCase    :       int							,       _UpperCAmelCase    :       int   )    ->			int:







			'''simple docstring'''
			return b if a == 0 else greatest_common_divisor(b % a							,       _UpperCAmelCase   )







class       __lowerCAmelCase   :
			UpperCamelCase        =					string.ascii_uppercase + string.digits
			# This cipher takes alphanumerics into account
			# i.e. a total of 36 characters

			# take x and return x % len(key_string)
			UpperCamelCase        =					numpy.vectorize(lambda A						: x % 3_6						)

			UpperCamelCase        =					numpy.vectorize(A						)



			def __init__(						self       :  Tuple			,		A       :  numpy.ndarray)       ->   None:



						"""simple docstring"""






						_UpperCAmelCase			=       self.modulus(A)  # mod36 calc's on the encrypt key
						self.check_determinant()  # validate the determinant of the encryption key
						_UpperCAmelCase			=       encrypt_key.shape[0]



			def    _lowerCamelCase	(						self       :  str			,		A       :  str)       ->   int:



						"""simple docstring"""






						return self.key_string.index(A)



			def    _lowerCamelCase	(						self       :  Any			,		A       :  int)       ->   str:



						"""simple docstring"""






						return self.key_string[round(A)]



			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   None:



						"""simple docstring"""






						_UpperCAmelCase			=       round(numpy.linalg.det(self.encrypt_key))

						if det < 0:
									_UpperCAmelCase			=       det % len(self.key_string)

						_UpperCAmelCase			=       len(self.key_string)
						if greatest_common_divisor(A			,		len(self.key_string)) != 1:
									_UpperCAmelCase			=       (
									    F"determinant modular {req_l} of encryption key({det}) "
									    F"is not co prime w.r.t {req_l}.\nTry another key."
									)
									raise ValueError(A)



			def    _lowerCamelCase	(						self       :  Union[str, Any]			,		A       :  str)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       [char for char in text.upper() if char in self.key_string]

						_UpperCAmelCase			=       chars[-1]
						while len(A) % self.break_key != 0:
									chars.append(A)

						return "".join(A)



			def    _lowerCamelCase	(						self       :  Union[str, Any]			,		A       :  str)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       self.process_text(text.upper())
						_UpperCAmelCase			=       ''

						for i in range(0			,		len(A) - self.break_key + 1			,		self.break_key):
									_UpperCAmelCase			=       text[i : i + self.break_key]
									_UpperCAmelCase			=       [self.replace_letters(A) for char in batch]
									_UpperCAmelCase			=       numpy.array([vec]).T
									_UpperCAmelCase			=       self.modulus(self.encrypt_key.dot(A)).T.tolist()[
									    0
									]
									_UpperCAmelCase			=       ''.join(
									    self.replace_digits(A) for num in batch_encrypted)
									encrypted += encrypted_batch

						return encrypted



			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   numpy.ndarray:



						"""simple docstring"""






						_UpperCAmelCase			=       round(numpy.linalg.det(self.encrypt_key))

						if det < 0:
									_UpperCAmelCase			=       det % len(self.key_string)
						_UpperCAmelCase			=       None
						for i in range(len(self.key_string)):
									if (det * i) % len(self.key_string) == 1:
												_UpperCAmelCase			=       i
												break

						_UpperCAmelCase			=       (
						    det_inv
						    * numpy.linalg.det(self.encrypt_key)
						    * numpy.linalg.inv(self.encrypt_key)
						)

						return self.to_int(self.modulus(A))



			def    _lowerCamelCase	(						self       :  Tuple			,		A       :  str)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       self.make_decrypt_key()
						_UpperCAmelCase			=       self.process_text(text.upper())
						_UpperCAmelCase			=       ''

						for i in range(0			,		len(A) - self.break_key + 1			,		self.break_key):
									_UpperCAmelCase			=       text[i : i + self.break_key]
									_UpperCAmelCase			=       [self.replace_letters(A) for char in batch]
									_UpperCAmelCase			=       numpy.array([vec]).T
									_UpperCAmelCase			=       self.modulus(decrypt_key.dot(A)).T.tolist()[0]
									_UpperCAmelCase			=       ''.join(
									    self.replace_digits(A) for num in batch_decrypted)
									decrypted += decrypted_batch

						return decrypted





def 							A     (					)    ->			None:







			'''simple docstring'''
			_UpperCAmelCase			=       int(input('Enter the order of the encryption key: '   )   )
			_UpperCAmelCase			=       []

			print('Enter each row of the encryption key with space separated integers'   )
			for _ in range(_UpperCAmelCase   ):
						_UpperCAmelCase			=       [int(_UpperCAmelCase   ) for x in input().split()]
						hill_matrix.append(_UpperCAmelCase   )

			_UpperCAmelCase			=       HillCipher(numpy.array(_UpperCAmelCase   )   )

			print('Would you like to encrypt or decrypt some text? (1 or 2)'   )
			_UpperCAmelCase			=       input('\n1. Encrypt\n2. Decrypt\n'   )
			if option == "1":
						_UpperCAmelCase			=       input('What text would you like to encrypt?: '   )
						print('Your encrypted text is:'   )
						print(hc.encrypt(_UpperCAmelCase   )   )
			elif option == "2":
						_UpperCAmelCase			=       input('What text would you like to decrypt?: '   )
						print('Your decrypted text is:'   )
						print(hc.decrypt(_UpperCAmelCase   )   )


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							main()
