






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__      :	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""")


@dataclass
class 						_a       :






  """simple docstring"""





  A_						=   field(
      default="""cifar10"""						,       metadata={"""help""": """Name of a dataset from the datasets package"""}    )
  A_						=   field(
      default=UpperCAmelCase__						,       metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}    )
  A_						=   field(
      default=UpperCAmelCase__						,       metadata={"""help""": """The column name of the images in the files."""}    )
  A_						=   field(default=UpperCAmelCase__						,       metadata={"""help""": """A folder containing the training data."""}    )
  A_						=   field(default=UpperCAmelCase__						,       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=UpperCAmelCase__						,       metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of training examples to this """
              """value if set."""
          )
      }						,       )
  A_						=   field(
      default=UpperCAmelCase__						,       metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
              """value if set."""
          )
      }						,       )




  def    _UpperCAmelCase     (     self )			-> Any:
     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 						_a       :






  """simple docstring"""





  A_						=   field(
      default=UpperCAmelCase__						,       metadata={
          """help""": (
              """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
          )
      }						,       )
  A_						=   field(
      default=UpperCAmelCase__						,       metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""}    )
  A_						=   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"""
          )
      }						,       )
  A_						=   field(
      default=UpperCAmelCase__						,       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=UpperCAmelCase__						,       metadata={"""help""": """Name or path of preprocessor config."""}    )
  A_						=   field(
      default=UpperCAmelCase__						,       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=UpperCAmelCase__						,       metadata={"""help""": """Whether or not to train with normalized pixel values as target."""}    )




@dataclass
class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





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






def     _snake_case    (__lowercase):
   UpperCamelCase_									=     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.

   UpperCamelCase_									=     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.
      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_mae'    ,      __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()

   UpperCamelCase_									=     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.
   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    ,      __lowercase) 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']

   # Load pretrained model and image processor
   #
   # 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:
      UpperCamelCase_									=     ViTMAEConfig.from_pretrained(model_args.config_name    ,      **__lowercase)
   elif model_args.model_name_or_path:
      UpperCamelCase_									=     ViTMAEConfig.from_pretrained(model_args.model_name_or_path    ,      **__lowercase)
   else:
      UpperCamelCase_									=     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:
      UpperCamelCase_									=     ViTImageProcessor.from_pretrained(model_args.image_processor_name    ,      **__lowercase)
   elif model_args.model_name_or_path:
      UpperCamelCase_									=     ViTImageProcessor.from_pretrained(model_args.model_name_or_path    ,      **__lowercase)
   else:
      UpperCamelCase_									=     ViTImageProcessor()

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

   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 MAE paper
   # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
   if "shortest_edge" in image_processor.size:
      UpperCamelCase_									=     image_processor.size['shortest_edge']
   else:
      UpperCamelCase_									=     (image_processor.size['height'], image_processor.size['width'])
   UpperCamelCase_									=     Compose(
       [
           Lambda(lambda __lowercase: img.convert('RGB') if img.mode != "RGB" else img),
           RandomResizedCrop(__lowercase    ,      scale=(0.2, 1.0)    ,      interpolation=InterpolationMode.BICUBIC),
           RandomHorizontalFlip(),
           ToTensor(),
           Normalize(mean=image_processor.image_mean    ,      std=image_processor.image_std),
       ])

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

   # Compute absolute learning rate
   UpperCamelCase_									=     (
       training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
   )
   if training_args.base_learning_rate is not None:
      UpperCamelCase_									=     training_args.base_learning_rate * total_train_batch_size / 256

   # Initialize our trainer
   UpperCamelCase_									=     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:
      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=__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:
      UpperCamelCase_									=     trainer.evaluate()
      trainer.log_metrics('eval'    ,      __lowercase)
      trainer.save_metrics('eval'    ,      __lowercase)

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






def     _snake_case    (__lowercase):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
 main()













def     _snake_case    (__lowercase = 1000):
   UpperCamelCase_		,      UpperCamelCase_									=     1, 1
   UpperCamelCase_									=     2
   while True:
      UpperCamelCase_									=     0
      UpperCamelCase_									=     fa + fa
      UpperCamelCase_		,      UpperCamelCase_									=     fa, f
      index += 1
      for _ in str(__lowercase):
         i += 1
      if i == n:
         break
   return index


if __name__ == "__main__":
 print(solution(int(str(input()).strip())))





