



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

    def 					_UpperCAmelCase	(				self			:				str):







      """simple docstring"""







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







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







@dataclass
class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   field(
        default=1e-3      ,  metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}   )







def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Dict:







  '''simple docstring'''


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


def 			_SCREAMING_SNAKE_CASE						() ->						Optional[Any]:







  '''simple docstring'''


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

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

  # create model
  if model_args.model_name_or_path:
    lowercase_								= 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"""							)
    lowercase_								= ViTMAEForPreTraining(__lowerCAmelCase							)

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

  if data_args.image_column_name is not None:
    lowercase_								= data_args.image_column_name
  elif "image" in column_names:
    lowercase_								= """image"""
  elif "img" in column_names:
    lowercase_								= """img"""
  else:
    lowercase_								= 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:
    lowercase_								= image_processor.size["""shortest_edge"""]
  else:
    lowercase_								= (image_processor.size["""height"""], image_processor.size["""width"""])
  lowercase_								= 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							):
    lowercase_								= [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:
      lowercase_								= 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:
      lowercase_								= (
          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
  lowercase_								= (
      training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
  )
  if training_args.base_learning_rate is not None:
    lowercase_								= training_args.base_learning_rate * total_train_batch_size / 2_56

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

  # Write model card and (optionally) push to hub
  lowercase_								= {
      """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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						List[str]:







  '''simple docstring'''


  main()


if __name__ == "__main__":
       main()






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase						:		Union[str, Any]			= {
    "configuration_longformer": [
        "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LongformerConfig",
        "LongformerOnnxConfig",
    ],
    "tokenization_longformer": ["LongformerTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		List[Any]			= ["LongformerTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Tuple			= [
           "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "LongformerForMaskedLM",
           "LongformerForMultipleChoice",
           "LongformerForQuestionAnswering",
           "LongformerForSequenceClassification",
           "LongformerForTokenClassification",
           "LongformerModel",
           "LongformerPreTrainedModel",
           "LongformerSelfAttention",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		List[Any]			= [
           "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFLongformerForMaskedLM",
           "TFLongformerForMultipleChoice",
           "TFLongformerForQuestionAnswering",
           "TFLongformerForSequenceClassification",
           "TFLongformerForTokenClassification",
           "TFLongformerModel",
           "TFLongformerPreTrainedModel",
           "TFLongformerSelfAttention",
       ]


if TYPE_CHECKING:
       from .configuration_longformer import (
           LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
           LongformerConfig,
           LongformerOnnxConfig,
       )
       from .tokenization_longformer import LongformerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_longformer_fast import LongformerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_longformer import (
                  LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LongformerForMaskedLM,
                  LongformerForMultipleChoice,
                  LongformerForQuestionAnswering,
                  LongformerForSequenceClassification,
                  LongformerForTokenClassification,
                  LongformerModel,
                  LongformerPreTrainedModel,
                  LongformerSelfAttention,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_longformer import (
                  TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLongformerForMaskedLM,
                  TFLongformerForMultipleChoice,
                  TFLongformerForQuestionAnswering,
                  TFLongformerForSequenceClassification,
                  TFLongformerForTokenClassification,
                  TFLongformerModel,
                  TFLongformerPreTrainedModel,
                  TFLongformerSelfAttention,
              )

else:
       import sys

       UpperCAmelCase						:		Optional[Any]			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

