

UpperCAmelCase__       :   Optional[Any] 			=			256
# Modulus to hash a string
UpperCAmelCase__       :   int 			=			1_000_003






def     __lowercase					(   _A  ,			_A	)   ->  bool:
      SCREAMING_SNAKE_CASE       :					str        =      len(_UpperCamelCase	)
      SCREAMING_SNAKE_CASE       :					Dict        =      len(_UpperCamelCase	)
      if p_len > t_len:
            return False

      SCREAMING_SNAKE_CASE       :					Tuple        =      0
      SCREAMING_SNAKE_CASE       :					List[str]        =      0
      SCREAMING_SNAKE_CASE       :					List[Any]        =      1

      # Calculating the hash of pattern and substring of text
      for i in range(_UpperCamelCase	):
            SCREAMING_SNAKE_CASE       :					List[Any]        =      (ord(pattern[i]	) + p_hash * alphabet_size) % modulus
            SCREAMING_SNAKE_CASE       :					Tuple        =      (ord(text[i]	) + text_hash * alphabet_size) % modulus
            if i == p_len - 1:
                  continue
            SCREAMING_SNAKE_CASE       :					Tuple        =      (modulus_power * alphabet_size) % modulus

      for i in range(0  ,			t_len - p_len + 1	):
            if text_hash == p_hash and text[i : i + p_len] == pattern:
                  return True
            if i == t_len - p_len:
                  continue
            # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
            SCREAMING_SNAKE_CASE       :					int        =      (
                (text_hash - ord(text[i]	) * modulus_power) * alphabet_size
                + ord(text[i + p_len]	)
            ) % modulus
      return False






def     __lowercase					(   )   ->  None:
      SCREAMING_SNAKE_CASE       :					str        =      """abc1abc12"""
      SCREAMING_SNAKE_CASE       :					Optional[int]        =      """alskfjaldsabc1abc1abc12k23adsfabcabc"""
      SCREAMING_SNAKE_CASE       :					Dict        =      """alskfjaldsk23adsfabcabc"""
      assert rabin_karp(_UpperCamelCase  ,			_UpperCamelCase	) and not rabin_karp(_UpperCamelCase  ,			_UpperCamelCase	)

      # Test 2)
      SCREAMING_SNAKE_CASE       :					Optional[Any]        =      """ABABX"""
      SCREAMING_SNAKE_CASE       :					Tuple        =      """ABABZABABYABABX"""
      assert rabin_karp(_UpperCamelCase  ,			_UpperCamelCase	)

      # Test 3)
      SCREAMING_SNAKE_CASE       :					int        =      """AAAB"""
      SCREAMING_SNAKE_CASE       :					Optional[Any]        =      """ABAAAAAB"""
      assert rabin_karp(_UpperCamelCase  ,			_UpperCamelCase	)

      # Test 4)
      SCREAMING_SNAKE_CASE       :					Union[str, Any]        =      """abcdabcy"""
      SCREAMING_SNAKE_CASE       :					int        =      """abcxabcdabxabcdabcdabcy"""
      assert rabin_karp(_UpperCamelCase  ,			_UpperCamelCase	)

      # Test 5)
      SCREAMING_SNAKE_CASE       :					Union[str, Any]        =      """Lü"""
      SCREAMING_SNAKE_CASE       :					List[Any]        =      """Lüsai"""
      assert rabin_karp(_UpperCamelCase  ,			_UpperCamelCase	)
      SCREAMING_SNAKE_CASE       :					Optional[int]        =      """Lue"""
      assert not rabin_karp(_UpperCamelCase  ,			_UpperCamelCase	)
      print("""Success."""	)


if __name__ == "__main__":
  test_rabin_karp()







'''simple docstring'''
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
lowerCamelCase :      Optional[int]					 =     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__    :
  A__		 =				field(
      default='cifar10'     ,   metadata={'help': 'Name of a dataset from the datasets package'}  )
  A__		 =				field(
      default=A__     ,   metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}  )
  A__		 =				field(
      default=A__     ,   metadata={'help': 'The column name of the images in the files.'}  )
  A__		 =				field(default=A__     ,   metadata={'help': 'A folder containing the training data.'}  )
  A__		 =				field(default=A__     ,   metadata={'help': 'A folder containing the validation data.'}  )
  A__		 =				field(
      default=0.15     ,   metadata={'help': 'Percent to split off of train for validation.'}  )
  A__		 =				field(
      default=A__     ,   metadata={
          'help': (
              'For debugging purposes or quicker training, truncate the number of training examples to this '
              'value if set.'
          )
      }     ,   )
  A__		 =				field(
      default=A__     ,   metadata={
          'help': (
              'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
              'value if set.'
          )
      }     ,   )





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





       '''simple docstring'''






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







@dataclass
class 							A__    :
  A__		 =				field(
      default=A__     ,   metadata={
          'help': (
              'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
          )
      }     ,   )
  A__		 =				field(
      default=A__     ,   metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'}  )
  A__		 =				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'
          )
      }     ,   )
  A__		 =				field(
      default=A__     ,   metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}  )
  A__		 =				field(
      default='main'     ,   metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}     ,   )
  A__		 =				field(default=A__     ,   metadata={'help': 'Name or path of preprocessor config.'}  )
  A__		 =				field(
      default=A__     ,   metadata={
          'help': (
              'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
              'with private models).'
          )
      }     ,   )
  A__		 =				field(
      default=0.75     ,   metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'}  )
  A__		 =				field(
      default=A__     ,   metadata={'help': 'Whether or not to train with normalized pixel values as target.'}  )







@dataclass
class 							A__    (       A__  ):
  A__		 =				field(
      default=1E-3     ,   metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}  )


def        _lowerCAmelCase					(						_UpperCamelCase						:     int				)      ->			Tuple:
     """simple docstring"""




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


def        _lowerCAmelCase					(						)      ->			Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =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.
          _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
     else:
          _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_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_mae'							, _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()

     _SCREAMING_SNAKE_CASE							    =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.
     _SCREAMING_SNAKE_CASE							    =None
     if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
          _SCREAMING_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.
     _SCREAMING_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.
     _SCREAMING_SNAKE_CASE							    =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:
          _SCREAMING_SNAKE_CASE							    =ds['train'].train_test_split(data_args.train_val_split				)
          _SCREAMING_SNAKE_CASE							    =split['train']
          _SCREAMING_SNAKE_CASE							    =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.
     _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,
     }
     if model_args.config_name:
          _SCREAMING_SNAKE_CASE							    =ViTMAEConfig.from_pretrained(model_args.config_name							, **_UpperCamelCase				)
     elif model_args.model_name_or_path:
          _SCREAMING_SNAKE_CASE							    =ViTMAEConfig.from_pretrained(model_args.model_name_or_path							, **_UpperCamelCase				)
     else:
          _SCREAMING_SNAKE_CASE							    =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:
          _SCREAMING_SNAKE_CASE							    =ViTImageProcessor.from_pretrained(model_args.image_processor_name							, **_UpperCamelCase				)
     elif model_args.model_name_or_path:
          _SCREAMING_SNAKE_CASE							    =ViTImageProcessor.from_pretrained(model_args.model_name_or_path							, **_UpperCamelCase				)
     else:
          _SCREAMING_SNAKE_CASE							    =ViTImageProcessor()

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

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

     if data_args.image_column_name is not None:
          _SCREAMING_SNAKE_CASE							    =data_args.image_column_name
     elif "image" in column_names:
          _SCREAMING_SNAKE_CASE							    ='image'
     elif "img" in column_names:
          _SCREAMING_SNAKE_CASE							    ='img'
     else:
          _SCREAMING_SNAKE_CASE							    =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:
          _SCREAMING_SNAKE_CASE							    =image_processor.size['shortest_edge']
     else:
          _SCREAMING_SNAKE_CASE							    =(image_processor.size['height'], image_processor.size['width'])
     _SCREAMING_SNAKE_CASE							    =Compose(
         [
             Lambda(lambda _UpperCamelCase				: img.convert('RGB'				) if img.mode != "RGB" else img				),
             RandomResizedCrop(_UpperCamelCase							, scale=(0.2, 1.0)							, interpolation=InterpolationMode.BICUBIC				),
             RandomHorizontalFlip(),
             ToTensor(),
             Normalize(mean=image_processor.image_mean							, std=image_processor.image_std				),
         ]				)

     def preprocess_images(_UpperCamelCase						:     Dict				):
          _SCREAMING_SNAKE_CASE							    =[transforms(_UpperCamelCase				) 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:
               _SCREAMING_SNAKE_CASE							    =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:
               _SCREAMING_SNAKE_CASE							    =(
                   ds['validation'].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
               )
          # Set the validation transforms
          ds["validation"].set_transform(_UpperCamelCase				)

     # Compute absolute learning rate
     _SCREAMING_SNAKE_CASE							    =(
         training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
     )
     if training_args.base_learning_rate is not None:
          _SCREAMING_SNAKE_CASE							    =training_args.base_learning_rate * total_train_batch_size / 2_56

     # Initialize our trainer
     _SCREAMING_SNAKE_CASE							    =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:
          _SCREAMING_SNAKE_CASE							    =None
          if training_args.resume_from_checkpoint is not None:
               _SCREAMING_SNAKE_CASE							    =training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               _SCREAMING_SNAKE_CASE							    =last_checkpoint
          _SCREAMING_SNAKE_CASE							    =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:
          _SCREAMING_SNAKE_CASE							    =trainer.evaluate()
          trainer.log_metrics('eval'							, _UpperCamelCase				)
          trainer.save_metrics('eval'							, _UpperCamelCase				)

     # Write model card and (optionally) push to hub
     _SCREAMING_SNAKE_CASE							    ={
         'tasks': 'masked-auto-encoding',
         'dataset': data_args.dataset_name,
         'tags': ['masked-auto-encoding'],
     }
     if training_args.push_to_hub:
          trainer.push_to_hub(**_UpperCamelCase				)
     else:
          trainer.create_model_card(**_UpperCamelCase				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     List[str]				)      ->			Optional[int]:
     """simple docstring"""




     main()


if __name__ == "__main__":
     main()
