



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



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[Any]     			={}
     if self.train_dir is not None:
        SCREAMING_SNAKE_CASE_: List[str]     			=self.train_dir
     if self.validation_dir is not None:
        SCREAMING_SNAKE_CASE_: str     			=self.validation_dir
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=data_files if data_files else None







@dataclass
class 					a	:
  UpperCamelCase	:				Optional[int]     						=					field(
      default=UpperCamelCase_	,      metadata={
          'help': (
              'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
          )
      }	,      )
  UpperCamelCase	:				List[str]     						=					field(
      default=UpperCamelCase_	,      metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'}       )
  UpperCamelCase	:				str     						=					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'
          )
      }	,      )
  UpperCamelCase	:				Union[str, Any]     						=					field(
      default=UpperCamelCase_	,      metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}       )
  UpperCamelCase	:				Optional[Any]     						=					field(
      default='main'	,      metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}	,      )
  UpperCamelCase	:				List[str]     						=					field(default=UpperCamelCase_	,      metadata={'help': 'Name or path of preprocessor config.'}       )
  UpperCamelCase	:				Any     						=					field(
      default=UpperCamelCase_	,      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=0.75	,      metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'}       )
  UpperCamelCase	:				Tuple     						=					field(
      default=UpperCamelCase_	,      metadata={'help': 'Whether or not to train with normalized pixel values as target.'}       )







@dataclass
class 					a	(  UpperCamelCase_       ):
  UpperCamelCase	:				List[Any]     						=					field(
      default=1E-3	,      metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}       )







def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Optional[int]     			=torch.stack([example["""pixel_values"""] for example in examples] )
   return {"pixel_values": pixel_values}





def      __magic_name__				(							):
   # 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.

   SCREAMING_SNAKE_CASE_: List[Any]     			=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_: Optional[int]     			=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
   else:
      SCREAMING_SNAKE_CASE_: Dict     			=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"""							,			snake_case_							,			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_: Optional[Any]     			=training_args.get_process_log_level()
   logger.setLevel(snake_case_ )
   transformers.utils.logging.set_verbosity(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_: List[str]     			=None
   if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
      SCREAMING_SNAKE_CASE_: Tuple     			=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_: str     			=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_: Any     			=None if '''validation''' in ds.keys() else data_args.train_val_split
   if isinstance(data_args.train_val_split							,			snake_case_ ) and data_args.train_val_split > 0.0:
      SCREAMING_SNAKE_CASE_: List[Any]     			=ds['''train'''].train_test_split(data_args.train_val_split )
      SCREAMING_SNAKE_CASE_: int     			=split['''train''']
      SCREAMING_SNAKE_CASE_: Optional[int]     			=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_: Optional[int]     			={
       '''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_: Dict     			=ViTMAEConfig.from_pretrained(model_args.config_name							,			**snake_case_ )
   elif model_args.model_name_or_path:
      SCREAMING_SNAKE_CASE_: Any     			=ViTMAEConfig.from_pretrained(model_args.model_name_or_path							,			**snake_case_ )
   else:
      SCREAMING_SNAKE_CASE_: Any     			=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_: List[str]     			=ViTImageProcessor.from_pretrained(model_args.image_processor_name							,			**snake_case_ )
   elif model_args.model_name_or_path:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=ViTImageProcessor.from_pretrained(model_args.model_name_or_path							,			**snake_case_ )
   else:
      SCREAMING_SNAKE_CASE_: Any     			=ViTImageProcessor()

   # create model
   if model_args.model_name_or_path:
      SCREAMING_SNAKE_CASE_: int     			=ViTMAEForPreTraining.from_pretrained(
          model_args.model_name_or_path							,			from_tf=bool(""".ckpt""" in model_args.model_name_or_path )							,			config=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_: Union[str, Any]     			=ViTMAEForPreTraining(snake_case_ )

   if training_args.do_train:
      SCREAMING_SNAKE_CASE_: int     			=ds['''train'''].column_names
   else:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=ds['''validation'''].column_names

   if data_args.image_column_name is not None:
      SCREAMING_SNAKE_CASE_: Optional[int]     			=data_args.image_column_name
   elif "image" in column_names:
      SCREAMING_SNAKE_CASE_: List[Any]     			='''image'''
   elif "img" in column_names:
      SCREAMING_SNAKE_CASE_: Dict     			='''img'''
   else:
      SCREAMING_SNAKE_CASE_: List[str]     			=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_: Optional[int]     			=image_processor.size['''shortest_edge''']
   else:
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=(image_processor.size['''height'''], image_processor.size['''width'''])
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=Compose(
       [
           Lambda(lambda lowercase : img.convert("""RGB""" ) if img.mode != "RGB" else img ),
           RandomResizedCrop(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(lowercase ):
      SCREAMING_SNAKE_CASE_: Any     			=[transforms(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_: Any     			=ds['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
      # Set the training transforms
      ds["train"].set_transform(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_: Union[str, Any]     			=(
             ds['''validation'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
         )
      # Set the validation transforms
      ds["validation"].set_transform(snake_case_ )

   # Compute absolute learning rate
   SCREAMING_SNAKE_CASE_: Dict     			=(
       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_: int     			=training_args.base_learning_rate * total_train_batch_size / 256

   # Initialize our trainer
   SCREAMING_SNAKE_CASE_: Dict     			=Trainer(
       model=snake_case_							,			args=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=snake_case_							,			data_collator=snake_case_							,			)

   # Training
   if training_args.do_train:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=None
      if training_args.resume_from_checkpoint is not None:
         SCREAMING_SNAKE_CASE_: List[str]     			=training_args.resume_from_checkpoint
      elif last_checkpoint is not None:
         SCREAMING_SNAKE_CASE_: List[str]     			=last_checkpoint
      SCREAMING_SNAKE_CASE_: Any     			=trainer.train(resume_from_checkpoint=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_: Dict     			=trainer.evaluate()
      trainer.log_metrics("""eval"""							,			snake_case_ )
      trainer.save_metrics("""eval"""							,			snake_case_ )

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





def      __magic_name__				(							lowercase ):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
   main()








"""simple docstring"""
import string





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=""""""
   for i in sequence:
      SCREAMING_SNAKE_CASE_: List[Any]     			=ord(lowercase )
      if 65 <= extract <= 90:
         output += chr(155 - extract )
      elif 97 <= extract <= 122:
         output += chr(219 - extract )
      else:
         output += i
   return output





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Any     			=string.ascii_letters
   SCREAMING_SNAKE_CASE_: Tuple     			=string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
   return "".join(
       letters_reversed[letters.index(lowercase )] if c in letters else c for c in sequence )





def      __magic_name__				(							):
   from timeit import timeit

   print("""Running performance benchmarks...""" )
   SCREAMING_SNAKE_CASE_: int     			="""from string import printable ; from __main__ import atbash, atbash_slow"""
   print(f'''> atbash_slow(): {timeit("atbash_slow(printable)"							,			setup=lowercase )} seconds''' )
   print(f'''>      atbash(): {timeit("atbash(printable)"							,			setup=lowercase )} seconds''' )


if __name__ == "__main__":
   for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
      print(f"""{example} encrypted in atbash: {atbash(example)}""")
   benchmark()



