


'''simple docstring'''

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
a_  :				List[Any]						= 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""")

a_  :				Optional[int]						= list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
a_  :				str						= tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class      snake_case    :





  """simple docstring"""




  _lowerCamelCase		    =  field(
      default="cifar10"			,						metadata={"help": "Name of a dataset from the datasets package"}			)
  _lowerCamelCase		    =  field(
      default=__lowerCAmelCase			,						metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}			)
  _lowerCamelCase		    =  field(
      default=__lowerCAmelCase			,						metadata={"help": "The column name of the images in the files. If not set, will try to use 'image' or 'img'."}			,						)
  _lowerCamelCase		    =  field(default=__lowerCAmelCase			,						metadata={"help": "A folder containing the training data."}			)
  _lowerCamelCase		    =  field(default=__lowerCAmelCase			,						metadata={"help": "A folder containing the validation data."}			)
  _lowerCamelCase		    =  field(
      default=0.15			,						metadata={"help": "Percent to split off of train for validation."}			)
  _lowerCamelCase		    =  field(default=32			,						metadata={"help": "The size of the square patches to use for masking."}			)
  _lowerCamelCase		    =  field(
      default=0.6			,						metadata={"help": "Percentage of patches to mask."}			,						)
  _lowerCamelCase		    =  field(
      default=__lowerCAmelCase			,						metadata={
          "help": (
              "For debugging purposes or quicker training, truncate the number of training examples to this "
              "value if set."
          )
      }			,						)
  _lowerCamelCase		    =  field(
      default=__lowerCAmelCase			,						metadata={
          "help": (
              "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
              "value if set."
          )
      }			,						)






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  {}
   if self.train_dir is not None:
    lowerCamelCase_      =  self.train_dir
   if self.validation_dir is not None:
    lowerCamelCase_      =  self.validation_dir
   lowerCamelCase_      =  data_files if data_files else None





@dataclass
class      snake_case    :





  """simple docstring"""




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





class      snake_case    :





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase=192			,  UpperCamelCase=32			,  UpperCamelCase=4			,  UpperCamelCase=0.6     ):







   """simple docstring"""
   lowerCamelCase_      =  input_size
   lowerCamelCase_      =  mask_patch_size
   lowerCamelCase_      =  model_patch_size
   lowerCamelCase_      =  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"     )

   lowerCamelCase_      =  self.input_size // self.mask_patch_size
   lowerCamelCase_      =  self.mask_patch_size // self.model_patch_size

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







  def __call__(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  np.random.permutation(self.token_count     )[: self.mask_count]
   lowerCamelCase_      =  np.zeros(self.token_count			,  dtype=UpperCamelCase     )
   lowerCamelCase_      =  1

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

   return torch.tensor(mask.flatten()     )





def __snake_case					(      UpperCAmelCase_						:						str		):
 lowerCamelCase_      =  torch.stack([example["pixel_values"] for example in examples]		)
 lowerCamelCase_      =  torch.stack([example["mask"] for example in examples]		)
 return {"pixel_values": pixel_values, "bool_masked_pos": mask}





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.

 lowerCamelCase_      =  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.
  lowerCamelCase_      =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
 else:
  lowerCamelCase_      =  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"					,				_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()

 lowerCamelCase_      =  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.
 lowerCamelCase_      =  None
 if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
  lowerCamelCase_      =  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.
 lowerCamelCase_      =  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.
 lowerCamelCase_      =  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:
  lowerCamelCase_      =  ds['train'].train_test_split(data_args.train_val_split		)
  lowerCamelCase_      =  split['train']
  lowerCamelCase_      =  split['test']

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

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

 # create model
 if model_args.model_name_or_path:
  lowerCamelCase_      =  AutoModelForMaskedImageModeling.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"		)
  lowerCamelCase_      =  AutoModelForMaskedImageModeling.from_config(_SCREAMING_SNAKE_CASE		)

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

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

 # transformations as done in original SimMIM paper
 # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
 lowerCamelCase_      =  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
 lowerCamelCase_      =  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_						:						Any		):
  lowerCamelCase_      =  [transforms(_SCREAMING_SNAKE_CASE		) for image in examples[image_column_name]]
  lowerCamelCase_      =  [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:
   lowerCamelCase_      =  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:
   lowerCamelCase_      =  (
       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		)

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

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


if __name__ == "__main__":
     main()




"""simple docstring"""
def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Union[str, Any]:
  if height >= 1:
    move_tower(height - 1		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
    move_disk(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
    move_tower(height - 1		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Union[str, Any]:
  print('moving disk from'		,     _SCREAMING_SNAKE_CASE		,     'to'		,     _SCREAMING_SNAKE_CASE   )







def 						__a   (	)	->List[str]:
  a__:     Dict										=	int(input('Height of hanoi: '   ).strip()   )
  move_tower(_SCREAMING_SNAKE_CASE		,     'A'		,     'B'		,     'C'   )


if __name__ == "__main__":
 main()


