





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
SCREAMING_SNAKE_CASE_						       =	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 		UpperCamelCase__			:



     '''simple docstring'''

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Any:




           '''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 		UpperCamelCase__			:



     '''simple docstring'''

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

@dataclass
class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

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



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       Optional[Any]:


      '''simple docstring'''


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



def        __lowercase       (				)			->       int:


      '''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"""			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )

      # 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(_SCREAMING_SNAKE_CASE     )
      transformers.utils.logging.set_verbosity(_SCREAMING_SNAKE_CASE     )
      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			,						_SCREAMING_SNAKE_CASE     ) 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			,						**_SCREAMING_SNAKE_CASE     )
      elif model_args.model_name_or_path:
            SCREAMING_SNAKE_CASE		=						ViTMAEConfig.from_pretrained(model_args.model_name_or_path			,						**_SCREAMING_SNAKE_CASE     )
      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			,						**_SCREAMING_SNAKE_CASE     )
      elif model_args.model_name_or_path:
            SCREAMING_SNAKE_CASE		=						ViTImageProcessor.from_pretrained(model_args.model_name_or_path			,						**_SCREAMING_SNAKE_CASE     )
      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=_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			,						)
      else:
            logger.info("""Training new model from scratch"""     )
            SCREAMING_SNAKE_CASE		=						ViTMAEForPreTraining(_SCREAMING_SNAKE_CASE     )

      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 _SCREAMING_SNAKE_CASE     : img.convert("""RGB"""     ) if img.mode != "RGB" else img     ),
              RandomResizedCrop(_SCREAMING_SNAKE_CASE			,						scale=(0.2, 1.0)			,						interpolation=InterpolationMode.BICUBIC     ),
              RandomHorizontalFlip(),
              ToTensor(),
              Normalize(mean=image_processor.image_mean			,						std=image_processor.image_std     ),
          ]     )

      def preprocess_images(_SCREAMING_SNAKE_CASE     ):
            SCREAMING_SNAKE_CASE		=						[transforms(_SCREAMING_SNAKE_CASE     ) 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(_SCREAMING_SNAKE_CASE     )

      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(_SCREAMING_SNAKE_CASE     )

      # 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=_SCREAMING_SNAKE_CASE			,						args=_SCREAMING_SNAKE_CASE			,						train_dataset=ds["""train"""] if training_args.do_train else None			,						eval_dataset=ds["""validation"""] if training_args.do_eval else None			,						tokenizer=_SCREAMING_SNAKE_CASE			,						data_collator=_SCREAMING_SNAKE_CASE			,						)

      # 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=_SCREAMING_SNAKE_CASE     )
            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"""			,						_SCREAMING_SNAKE_CASE     )
            trainer.save_metrics("""eval"""			,						_SCREAMING_SNAKE_CASE     )

      # 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(**_SCREAMING_SNAKE_CASE     )
      else:
            trainer.create_model_card(**_SCREAMING_SNAKE_CASE     )



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       Optional[int]:


      '''simple docstring'''


      main()


if __name__ == "__main__":
      main()









from itertools import permutations



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       bool:


      '''simple docstring'''


      if num[3] % 2 != 0:
            return False

      if (num[2] + num[3] + num[4]) % 3 != 0:
            return False

      if num[5] % 5 != 0:
            return False

      SCREAMING_SNAKE_CASE		=						[7, 11, 13, 17]
      for i, test in enumerate(_SCREAMING_SNAKE_CASE     ):
            if (num[i + 4] * 1_00 + num[i + 5] * 10 + num[i + 6]) % test != 0:
                  return False
      return True



def        __lowercase       (				_SCREAMING_SNAKE_CASE = 10     )			->       int:


      '''simple docstring'''


      return sum(
          int("""""".join(map(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )     )     )
          for num in permutations(range(_SCREAMING_SNAKE_CASE     )     )
          if is_substring_divisible(_SCREAMING_SNAKE_CASE     )     )


if __name__ == "__main__":
      print(F'''{solution() = }''')


