





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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     {}
       if self.train_dir is not None:
        UpperCamelCase			:      Dict 			=     self.train_dir
       if self.validation_dir is not None:
        UpperCamelCase			:      Optional[int] 			=     self.validation_dir
       UpperCamelCase			:      str 			=     data_files if data_files else None



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



@dataclass
class       A__     ( __SCREAMING_SNAKE_CASE    ):
      _UpperCAmelCase			:float							=     field(
          default=1e-3 ,     metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}    )



def 	A_       (  _lowerCAmelCase  )		->				List[str]:
 UpperCamelCase			:      Dict 			=     torch.stack([example["pixel_values"] for example in examples]  )
 return {"pixel_values": pixel_values}



def 	A_       (  )		->				Any:
 # 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			:      int 			=     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			:      Optional[Any] 			=     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
 else:
  UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			:      Optional[int] 			=     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"							,     lowerCAmelCase__							,     lowerCAmelCase__  )

 # 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			:      Dict 			=     training_args.get_process_log_level()
 logger.setLevel(lowerCAmelCase__  )
 transformers.utils.logging.set_verbosity(lowerCAmelCase__  )
 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			:      Tuple 			=     None
 if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
  UpperCamelCase			:      Union[str, Any] 			=     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			:      Dict 			=     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			:      str 			=     None if "validation" in ds.keys() else data_args.train_val_split
 if isinstance(data_args.train_val_split							,     lowerCAmelCase__  ) and data_args.train_val_split > 0.0:
  UpperCamelCase			:      List[str] 			=     ds["train"].train_test_split(data_args.train_val_split  )
  UpperCamelCase			:      List[Any] 			=     split["train"]
  UpperCamelCase			:      Any 			=     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			:      Any 			=     {
     "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			:      Optional[int] 			=     ViTMAEConfig.from_pretrained(model_args.config_name							,     **lowerCAmelCase__  )
 elif model_args.model_name_or_path:
  UpperCamelCase			:      str 			=     ViTMAEConfig.from_pretrained(model_args.model_name_or_path							,     **lowerCAmelCase__  )
 else:
  UpperCamelCase			:      List[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:
  UpperCamelCase			:      str 			=     ViTImageProcessor.from_pretrained(model_args.image_processor_name							,     **lowerCAmelCase__  )
 elif model_args.model_name_or_path:
  UpperCamelCase			:      Union[str, Any] 			=     ViTImageProcessor.from_pretrained(model_args.model_name_or_path							,     **lowerCAmelCase__  )
 else:
  UpperCamelCase			:      Optional[Any] 			=     ViTImageProcessor()

 # create model
 if model_args.model_name_or_path:
  UpperCamelCase			:      Dict 			=     ViTMAEForPreTraining.from_pretrained(
      model_args.model_name_or_path							,     from_tf=bool(".ckpt" in model_args.model_name_or_path  )							,     config=lowerCAmelCase__							,     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			:      Union[str, Any] 			=     ViTMAEForPreTraining(lowerCAmelCase__  )

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

 if data_args.image_column_name is not None:
  UpperCamelCase			:      List[str] 			=     data_args.image_column_name
 elif "image" in column_names:
  UpperCamelCase			:      str 			=     "image"
 elif "img" in column_names:
  UpperCamelCase			:      List[str] 			=     "img"
 else:
  UpperCamelCase			:      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:
  UpperCamelCase			:      Tuple 			=     image_processor.size["shortest_edge"]
 else:
  UpperCamelCase			:      Tuple 			=     (image_processor.size["height"], image_processor.size["width"])
 UpperCamelCase			:      Dict 			=     Compose(
     [
         Lambda(lambda _lowerCAmelCase  : img.convert("RGB"  ) if img.mode != "RGB" else img  ),
         RandomResizedCrop(lowerCAmelCase__							,     scale=(0.2, 1.0)							,     interpolation=InterpolationMode.BICUBIC  ),
         RandomHorizontalFlip(),
         ToTensor(),
         Normalize(mean=image_processor.image_mean							,     std=image_processor.image_std  ),
     ]  )

 def preprocess_images(_lowerCAmelCase  ):
  UpperCamelCase			:      Optional[int] 			=     [transforms(lowerCAmelCase__  ) 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			:      Dict 			=     ds["train"].shuffle(seed=training_args.seed  ).select(range(data_args.max_train_samples  )  )
  # Set the training transforms
  ds["train"].set_transform(lowerCAmelCase__  )

 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			:      Dict 			=     (
       ds["validation"].shuffle(seed=training_args.seed  ).select(range(data_args.max_eval_samples  )  )
   )
  # Set the validation transforms
  ds["validation"].set_transform(lowerCAmelCase__  )

 # Compute absolute learning rate
 UpperCamelCase			:      List[str] 			=     (
     training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
 )
 if training_args.base_learning_rate is not None:
  UpperCamelCase			:      Tuple 			=     training_args.base_learning_rate * total_train_batch_size / 256

 # Initialize our trainer
 UpperCamelCase			:      Tuple 			=     Trainer(
     model=lowerCAmelCase__							,     args=lowerCAmelCase__							,     train_dataset=ds["train"] if training_args.do_train else None							,     eval_dataset=ds["validation"] if training_args.do_eval else None							,     tokenizer=lowerCAmelCase__							,     data_collator=lowerCAmelCase__							,     )

 # Training
 if training_args.do_train:
  UpperCamelCase			:      int 			=     None
  if training_args.resume_from_checkpoint is not None:
   UpperCamelCase			:      List[str] 			=     training_args.resume_from_checkpoint
  elif last_checkpoint is not None:
   UpperCamelCase			:      Union[str, Any] 			=     last_checkpoint
  UpperCamelCase			:      int 			=     trainer.train(resume_from_checkpoint=lowerCAmelCase__  )
  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			:      Optional[int] 			=     trainer.evaluate()
  trainer.log_metrics("eval"							,     lowerCAmelCase__  )
  trainer.save_metrics("eval"							,     lowerCAmelCase__  )

 # Write model card and (optionally) push to hub
 UpperCamelCase			:      Union[str, Any] 			=     {
     "tasks": "masked-auto-encoding",
     "dataset": data_args.dataset_name,
     "tags": ["masked-auto-encoding"],
 }
 if training_args.push_to_hub:
  trainer.push_to_hub(**lowerCAmelCase__  )
 else:
  trainer.create_model_card(**lowerCAmelCase__  )



def 	A_       (  _lowerCAmelCase  )		->				Optional[Any]:
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
 main()







import math

import tensorflow as tf
from packaging import version



def 	A_       (  _lowerCAmelCase  )		->				Any:
 UpperCamelCase			:      List[Any] 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      Any 			=     0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0  )							,     x.dtype  )  ))

 return x * cdf



def 	A_       (  _lowerCAmelCase  )		->				Dict:
 UpperCamelCase			:      Union[str, Any] 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      List[Any] 			=     tf.cast(math.pi							,     x.dtype  )
 UpperCamelCase			:      Optional[Any] 			=     tf.cast(0.044_715							,     x.dtype  )
 UpperCamelCase			:      int 			=     0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi  ) * (x + coeff * tf.pow(_lowerCAmelCase							,     3  ))  ))

 return x * cdf



def 	A_       (  _lowerCAmelCase  )		->				List[Any]:
 UpperCamelCase			:      str 			=     tf.convert_to_tensor(_lowerCAmelCase  )

 return x * tf.tanh(tf.math.softplus(_lowerCAmelCase  )  )



def 	A_       (  _lowerCAmelCase  )		->				List[Any]:
 UpperCamelCase			:      Tuple 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      List[Any] 			=     tf.cast(0.044_715							,     x.dtype  )
 UpperCamelCase			:      Optional[Any] 			=     tf.cast(0.7_978_845_608							,     x.dtype  )

 return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)  ))



def 	A_       (  _lowerCAmelCase  )		->				Optional[Any]:
 UpperCamelCase			:      Any 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      List[Any] 			=     tf.cast(1.702							,     x.dtype  )
 return x * tf.math.sigmoid(coeff * x  )



def 	A_       (  _lowerCAmelCase  )		->				List[Any]:
 return tf.clip_by_value(_gelu(_lowerCAmelCase  )							,     -10							,     10  )



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase=-1  )		->				str:
 UpperCamelCase			,					UpperCamelCase			:      List[Any] 			=     tf.split(_lowerCAmelCase							,     2							,     axis=_lowerCAmelCase  )
 return a * tf.math.sigmoid(_lowerCAmelCase  )


if version.parse(tf.version.VERSION) >= version.parse("""2.4"""):



    def 	A_       (  _lowerCAmelCase  )		->				Any:
     return tf.keras.activations.gelu(_lowerCAmelCase							,     approximate=_lowerCAmelCase  )

    __lowerCamelCase :  Optional[int]										=  tf.keras.activations.gelu


    __lowerCamelCase :  int										=  approximate_gelu_wrap
else:
    __lowerCamelCase :  List[Any]										=  _gelu
    __lowerCamelCase :  Optional[Any]										=  _gelu_new


__lowerCamelCase :  Any										=  {
    """gelu""": gelu,
    """gelu_10""": gelu_aa,
    """gelu_fast""": gelu_fast,
    """gelu_new""": gelu_new,
    """glu""": glu,
    """mish""": mish,
    """quick_gelu""": quick_gelu,
    """relu""": tf.keras.activations.relu,
    """sigmoid""": tf.keras.activations.sigmoid,
    """silu""": tf.keras.activations.swish,
    """swish""": tf.keras.activations.swish,
    """tanh""": tf.keras.activations.tanh,
}



def 	A_       (  _lowerCAmelCase  )		->				Optional[Any]:
 if activation_string in ACTaFN:
  return ACTaFN[activation_string]
 else:
  raise KeyError(F"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()  )}"""  )
