



'''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
a     :    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""")

@dataclass
class     UpperCamelCase_				:
	lowercase    =				field(
	    default='cifar10'   ,  metadata={'help': 'Name of a dataset from the datasets package'}    )
	lowercase    =				field(
	    default=__magic_name__   ,  metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}    )
	lowercase    =				field(
	    default=__magic_name__   ,  metadata={'help': 'The column name of the images in the files.'}    )
	lowercase    =				field(default=__magic_name__   ,  metadata={'help': 'A folder containing the training data.'}    )
	lowercase    =				field(default=__magic_name__   ,  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=__magic_name__   ,  metadata={
	        'help': (
	            'For debugging purposes or quicker training, truncate the number of training examples to this '
	            'value if set.'
	        )
	    }   ,  )
	lowercase    =				field(
	    default=__magic_name__   ,  metadata={
	        'help': (
	            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
	            'value if set.'
	        )
	    }   ,  )





	def 				_lowercase(						self					)    ->    Tuple:
								UpperCAmelCase  :	Optional[Any]							  =	{}
								if self.train_dir is not None:
															UpperCAmelCase  :	List[Any]							  =	self.train_dir
								if self.validation_dir is not None:
															UpperCAmelCase  :	Tuple							  =	self.validation_dir
								UpperCAmelCase  :	Tuple							  =	data_files if data_files else None




@dataclass
class     UpperCamelCase_				:
	lowercase    =				field(
	    default=__magic_name__   ,  metadata={
	        'help': (
	            'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
	        )
	    }   ,  )
	lowercase    =				field(
	    default=__magic_name__   ,  metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'}    )
	lowercase    =				field(
	    default=__magic_name__   ,  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=__magic_name__   ,  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=__magic_name__   ,  metadata={'help': 'Name or path of preprocessor config.'}    )
	lowercase    =				field(
	    default=__magic_name__   ,  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=__magic_name__   ,  metadata={'help': 'Whether or not to train with normalized pixel values as target.'}    )




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




def    __lowerCamelCase						(      _lowercase	)   ->    int:
							UpperCAmelCase  :	str							  =	torch.stack([example["""pixel_values"""] for example in examples]	)
							return {"pixel_values": pixel_values}



def    __lowerCamelCase						(      )   ->    Union[str, 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  :	str							  =	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  :	int							  =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
							else:
														UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  :	List[str]							  =	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"""			,       _lowercase			,       _lowercase	)

							# 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(_lowercase	)
							transformers.utils.logging.set_verbosity(_lowercase	)
							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  :	int							  =	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  :	Union[str, Any]							  =	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  :	Tuple							  =	None if """validation""" in ds.keys() else data_args.train_val_split
							if isinstance(data_args.train_val_split			,       _lowercase	) and data_args.train_val_split > 0.0:
														UpperCAmelCase  :	Optional[int]							  =	ds["""train"""].train_test_split(data_args.train_val_split	)
														UpperCAmelCase  :	Union[str, Any]							  =	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  :	Optional[int]							  =	{
							    """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  :	Dict							  =	ViTMAEConfig.from_pretrained(model_args.config_name			,       **_lowercase	)
							elif model_args.model_name_or_path:
														UpperCAmelCase  :	List[str]							  =	ViTMAEConfig.from_pretrained(model_args.model_name_or_path			,       **_lowercase	)
							else:
														UpperCAmelCase  :	Tuple							  =	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  :	List[str]							  =	ViTImageProcessor.from_pretrained(model_args.image_processor_name			,       **_lowercase	)
							elif model_args.model_name_or_path:
														UpperCAmelCase  :	Union[str, Any]							  =	ViTImageProcessor.from_pretrained(model_args.model_name_or_path			,       **_lowercase	)
							else:
														UpperCAmelCase  :	Optional[Any]							  =	ViTImageProcessor()

							# create model
							if model_args.model_name_or_path:
														UpperCAmelCase  :	int							  =	ViTMAEForPreTraining.from_pretrained(
														    model_args.model_name_or_path			,       from_tf=bool(""".ckpt""" in model_args.model_name_or_path	)			,       config=_lowercase			,       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  :	Dict							  =	ViTMAEForPreTraining(_lowercase	)

							if training_args.do_train:
														UpperCAmelCase  :	List[Any]							  =	ds["""train"""].column_names
							else:
														UpperCAmelCase  :	Optional[int]							  =	ds["""validation"""].column_names

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

							def preprocess_images(_lowercase	):
														UpperCAmelCase  :	Any							  =	[transforms(_lowercase	) 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  :	int							  =	ds["""train"""].shuffle(seed=training_args.seed	).select(range(data_args.max_train_samples	)	)
														# Set the training transforms
														ds["train"].set_transform(_lowercase	)

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

							# Compute absolute learning rate
							UpperCAmelCase  :	Dict							  =	(
							    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 / 2_5_6

							# Initialize our trainer
							UpperCAmelCase  :	Union[str, Any]							  =	Trainer(
							    model=_lowercase			,       args=_lowercase			,       train_dataset=ds["""train"""] if training_args.do_train else None			,       eval_dataset=ds["""validation"""] if training_args.do_eval else None			,       tokenizer=_lowercase			,       data_collator=_lowercase			,       )

							# Training
							if training_args.do_train:
														UpperCAmelCase  :	Any							  =	None
														if training_args.resume_from_checkpoint is not None:
																					UpperCAmelCase  :	Optional[int]							  =	training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					UpperCAmelCase  :	int							  =	last_checkpoint
														UpperCAmelCase  :	Tuple							  =	trainer.train(resume_from_checkpoint=_lowercase	)
														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"""			,       _lowercase	)
														trainer.save_metrics("""eval"""			,       _lowercase	)

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



def    __lowerCamelCase						(      _lowercase	)   ->    int:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
							main()






'''simple docstring'''




from typing import TYPE_CHECKING

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


a     :    Optional[Any]      						=					{
    """configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
    """tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    Dict      						=					["""BertTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    List[Any]      						=					[
							    """BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """BertForMaskedLM""",
							    """BertForMultipleChoice""",
							    """BertForNextSentencePrediction""",
							    """BertForPreTraining""",
							    """BertForQuestionAnswering""",
							    """BertForSequenceClassification""",
							    """BertForTokenClassification""",
							    """BertLayer""",
							    """BertLMHeadModel""",
							    """BertModel""",
							    """BertPreTrainedModel""",
							    """load_tf_weights_in_bert""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    List[str]      						=					[
							    """TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFBertEmbeddings""",
							    """TFBertForMaskedLM""",
							    """TFBertForMultipleChoice""",
							    """TFBertForNextSentencePrediction""",
							    """TFBertForPreTraining""",
							    """TFBertForQuestionAnswering""",
							    """TFBertForSequenceClassification""",
							    """TFBertForTokenClassification""",
							    """TFBertLMHeadModel""",
							    """TFBertMainLayer""",
							    """TFBertModel""",
							    """TFBertPreTrainedModel""",
							]
try:
							if not is_tensorflow_text_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    int      						=					["""TFBertTokenizer"""]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    Optional[Any]      						=					[
							    """FlaxBertForCausalLM""",
							    """FlaxBertForMaskedLM""",
							    """FlaxBertForMultipleChoice""",
							    """FlaxBertForNextSentencePrediction""",
							    """FlaxBertForPreTraining""",
							    """FlaxBertForQuestionAnswering""",
							    """FlaxBertForSequenceClassification""",
							    """FlaxBertForTokenClassification""",
							    """FlaxBertModel""",
							    """FlaxBertPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
							from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bert_fast import BertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bert import (
														    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BertForMaskedLM,
														    BertForMultipleChoice,
														    BertForNextSentencePrediction,
														    BertForPreTraining,
														    BertForQuestionAnswering,
														    BertForSequenceClassification,
														    BertForTokenClassification,
														    BertLayer,
														    BertLMHeadModel,
														    BertModel,
														    BertPreTrainedModel,
														    load_tf_weights_in_bert,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_bert import (
														    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFBertEmbeddings,
														    TFBertForMaskedLM,
														    TFBertForMultipleChoice,
														    TFBertForNextSentencePrediction,
														    TFBertForPreTraining,
														    TFBertForQuestionAnswering,
														    TFBertForSequenceClassification,
														    TFBertForTokenClassification,
														    TFBertLMHeadModel,
														    TFBertMainLayer,
														    TFBertModel,
														    TFBertPreTrainedModel,
														)

							try:
														if not is_tensorflow_text_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bert_tf import TFBertTokenizer

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_bert import (
														    FlaxBertForCausalLM,
														    FlaxBertForMaskedLM,
														    FlaxBertForMultipleChoice,
														    FlaxBertForNextSentencePrediction,
														    FlaxBertForPreTraining,
														    FlaxBertForQuestionAnswering,
														    FlaxBertForSequenceClassification,
														    FlaxBertForTokenClassification,
														    FlaxBertModel,
														    FlaxBertPreTrainedModel,
														)

else:
							import sys

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

