


'''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
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  lowerCAmelCase_						:





  """simple docstring"""



  a_       :Optional[str]												=field(
      default="""cifar10"""		,    metadata={"""help""": """Name of a dataset from the datasets package"""}      )
  a_       :Optional[str]												=field(
      default=snake_case__		,    metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}      )
  a_       :Optional[str]												=field(
      default=snake_case__		,    metadata={"""help""": """The column name of the images in the files."""}      )
  a_       :Optional[str]												=field(default=snake_case__		,    metadata={"""help""": """A folder containing the training data."""}      )
  a_       :Optional[str]												=field(default=snake_case__		,    metadata={"""help""": """A folder containing the validation data."""}      )
  a_       :Optional[float]												=field(
      default=0.1_5		,    metadata={"""help""": """Percent to split off of train for validation."""}      )
  a_       :Optional[int]												=field(
      default=snake_case__		,    metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of training examples to this """
              """value if set."""
          )
      }		,    )
  a_       :Optional[int]												=field(
      default=snake_case__		,    metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
              """value if set."""
          )
      }		,    )

  def __a						(					self     :							str						):





       '''simple docstring'''

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


@dataclass
class  lowerCAmelCase_						:





  """simple docstring"""



  a_       :str												=field(
      default=snake_case__		,    metadata={
          """help""": (
              """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
          )
      }		,    )
  a_       :Optional[str]												=field(
      default=snake_case__		,    metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""}      )
  a_       :Optional[str]												=field(
      default=snake_case__		,    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_       :Optional[str]												=field(
      default=snake_case__		,    metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}      )
  a_       :str												=field(
      default="""main"""		,    metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}		,    )
  a_       :str												=field(default=snake_case__		,    metadata={"""help""": """Name or path of preprocessor config."""}      )
  a_       :bool												=field(
      default=snake_case__		,    metadata={
          """help""": (
              """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
              """with private models)."""
          )
      }		,    )
  a_       :float												=field(
      default=0.7_5		,    metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""}      )
  a_       :bool												=field(
      default=snake_case__		,    metadata={"""help""": """Whether or not to train with normalized pixel values as target."""}      )


@dataclass
class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



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




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					Tuple:
     """simple docstring"""




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




def        __lowercase						(      )				->					Optional[Any]:
     """simple docstring"""




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

     __a       =   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.
     __a       =   None
     if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
          __a       =   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.
     __a       =   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.
     __a       =   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:
          __a       =   ds["""train"""].train_test_split(data_args.train_val_split		)
          __a       =   split["""train"""]
          __a       =   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.
     __a       =   {
         """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:
          __a       =   ViTMAEConfig.from_pretrained(model_args.config_name				,  **__SCREAMING_SNAKE_CASE		)
     elif model_args.model_name_or_path:
          __a       =   ViTMAEConfig.from_pretrained(model_args.model_name_or_path				,  **__SCREAMING_SNAKE_CASE		)
     else:
          __a       =   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:
          __a       =   ViTImageProcessor.from_pretrained(model_args.image_processor_name				,  **__SCREAMING_SNAKE_CASE		)
     elif model_args.model_name_or_path:
          __a       =   ViTImageProcessor.from_pretrained(model_args.model_name_or_path				,  **__SCREAMING_SNAKE_CASE		)
     else:
          __a       =   ViTImageProcessor()

     # create model
     if model_args.model_name_or_path:
          __a       =   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"""		)
          __a       =   ViTMAEForPreTraining(__SCREAMING_SNAKE_CASE		)

     if training_args.do_train:
          __a       =   ds["""train"""].column_names
     else:
          __a       =   ds["""validation"""].column_names

     if data_args.image_column_name is not None:
          __a       =   data_args.image_column_name
     elif "image" in column_names:
          __a       =   """image"""
     elif "img" in column_names:
          __a       =   """img"""
     else:
          __a       =   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:
          __a       =   image_processor.size["""shortest_edge"""]
     else:
          __a       =   (image_processor.size["""height"""], image_processor.size["""width"""])
     __a       =   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		):
          __a       =   [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:
               __a       =   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:
               __a       =   (
                   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
     __a       =   (
         training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
     )
     if training_args.base_learning_rate is not None:
          __a       =   training_args.base_learning_rate * total_train_batch_size / 256

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

     # Write model card and (optionally) push to hub
     __a       =   {
         """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()










'''simple docstring'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					str:
     """simple docstring"""




     __a       =   """"""
     for word_or_phrase in separated:
          if not isinstance(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		):
               raise Exception("""join() accepts only strings to be joined"""		)
          joined += word_or_phrase + separator
     return joined.strip(__SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
 from doctest import testmod

 testmod()






