

'''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 					_snake_case		:
			__A  :	Optional[str]        =field(
			    default="cifar10"			,			metadata={"help": "Name of a dataset from the datasets package"})
			__A  :	Optional[str]        =field(
			    default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "The configuration name of the dataset to use (via the datasets library)."})
			__A  :	Optional[str]        =field(
			    default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "The column name of the images in the files."})
			__A  :	Optional[str]        =field(default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "A folder containing the training data."})
			__A  :	Optional[str]        =field(default=__SCREAMING_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=__SCREAMING_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=__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     ):
				UpperCAmelCase_     :      int						=  {}
				if self.train_dir is not None:
					UpperCAmelCase_     :      List[str]						=  self.train_dir
				if self.validation_dir is not None:
					UpperCAmelCase_     :      str						=  self.validation_dir
				UpperCAmelCase_     :      Tuple						=  data_files if data_files else None




@dataclass
class 					_snake_case		:
			__A  :	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."
			        )
			    }			,			)
			__A  :	Optional[str]        =field(
			    default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"})
			__A  :	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"
			        )
			    }			,			)
			__A  :	Optional[str]        =field(
			    default=__SCREAMING_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=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Name or path of preprocessor config."})
			__A  :	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)."
			        )
			    }			,			)
			__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=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Whether or not to train with normalized pixel values as target."})




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




def  a__       (    _SCREAMING_SNAKE_CASE   :       Dict				)     ->					Any:


	"""simple docstring"""





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


def  a__       (    )     ->					Any:


	"""simple docstring"""





	UpperCAmelCase_     :      Union[str, 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.
		UpperCAmelCase_					,					UpperCAmelCase_					,					UpperCAmelCase_     :      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"	,				_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()

	UpperCAmelCase_     :      List[Any]						=  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.
	UpperCAmelCase_     :      Dict						=  None
	if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
		UpperCAmelCase_     :      str						=  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_     :      int						=  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_     :      List[str]						=  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:
		UpperCAmelCase_     :      str						=  ds["train"].train_test_split(data_args.train_val_split				)
		UpperCAmelCase_     :      int						=  split["train"]
		UpperCAmelCase_     :      List[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_     :      int						=  ViTMAEConfig.from_pretrained(model_args.config_name	,				**_SCREAMING_SNAKE_CASE				)
	elif model_args.model_name_or_path:
		UpperCAmelCase_     :      List[Any]						=  ViTMAEConfig.from_pretrained(model_args.model_name_or_path	,				**_SCREAMING_SNAKE_CASE				)
	else:
		UpperCAmelCase_     :      List[str]						=  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_     :      Tuple						=  ViTImageProcessor.from_pretrained(model_args.image_processor_name	,				**_SCREAMING_SNAKE_CASE				)
	elif model_args.model_name_or_path:
		UpperCAmelCase_     :      List[Any]						=  ViTImageProcessor.from_pretrained(model_args.model_name_or_path	,				**_SCREAMING_SNAKE_CASE				)
	else:
		UpperCAmelCase_     :      Optional[Any]						=  ViTImageProcessor()

	# create model
	if model_args.model_name_or_path:
		UpperCAmelCase_     :      Optional[int]						=  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"				)
		UpperCAmelCase_     :      Optional[int]						=  ViTMAEForPreTraining(_SCREAMING_SNAKE_CASE				)

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

	if data_args.image_column_name is not None:
		UpperCAmelCase_     :      Union[str, Any]						=  data_args.image_column_name
	elif "image" in column_names:
		UpperCAmelCase_     :      Tuple						=  "image"
	elif "img" in column_names:
		UpperCAmelCase_     :      int						=  "img"
	else:
		UpperCAmelCase_     :      int						=  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_     :      Optional[Any]						=  image_processor.size["shortest_edge"]
	else:
		UpperCAmelCase_     :      str						=  (image_processor.size["height"], image_processor.size["width"])
	UpperCAmelCase_     :      Union[str, Any]						=  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   :       str				):
		UpperCAmelCase_     :      List[Any]						=  [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:
			UpperCAmelCase_     :      Dict						=  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:
			UpperCAmelCase_     :      Optional[int]						=  (
			    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
	UpperCAmelCase_     :      Union[str, Any]						=  (
	    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
	)
	if training_args.base_learning_rate is not None:
		UpperCAmelCase_     :      Dict						=  training_args.base_learning_rate * total_train_batch_size / 2_56

	# Initialize our trainer
	UpperCAmelCase_     :      List[str]						=  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:
		UpperCAmelCase_     :      str						=  None
		if training_args.resume_from_checkpoint is not None:
			UpperCAmelCase_     :      List[Any]						=  training_args.resume_from_checkpoint
		elif last_checkpoint is not None:
			UpperCAmelCase_     :      List[Any]						=  last_checkpoint
		UpperCAmelCase_     :      List[Any]						=  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:
		UpperCAmelCase_     :      Dict						=  trainer.evaluate()
		trainer.log_metrics("eval"	,				_SCREAMING_SNAKE_CASE				)
		trainer.save_metrics("eval"	,				_SCREAMING_SNAKE_CASE				)

	# Write model card and (optionally) push to hub
	UpperCAmelCase_     :      List[str]						=  {
	    "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  a__       (    _SCREAMING_SNAKE_CASE   :       Any				)     ->					Union[str, Any]:


	"""simple docstring"""





	main()


if __name__ == "__main__":
					main()











from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    {
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__							:      Tuple         =    [
      """AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """AutoformerForPrediction""",
      """AutoformerModel""",
      """AutoformerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_autoformer import (
      AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      AutoformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_autoformer import (
        AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        AutoformerForPrediction,
        AutoformerModel,
        AutoformerPreTrainedModel,
    )

else:
  import sys

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

