

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







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       int								={}
										if self.train_dir is not None:
																lowercase   :       str								=self.train_dir
										if self.validation_dir is not None:
																lowercase   :       str								=self.validation_dir
										lowercase   :       Any								=data_files if data_files else None





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





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






def 							_lowerCAmelCase (      __magic_name__	:    int	)							->     Optional[Any]:
						lowercase   :       Dict								=torch.stack([example['''pixel_values'''] for example in examples]	)
						return {"pixel_values": pixel_values}






def 							_lowerCAmelCase (      )							->     Tuple:
						# 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.

						lowercase   :       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.
												lowercase   :       Optional[Any]								=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
						else:
												lowercase   :       Tuple								=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'''      ,   __magic_name__      ,   __magic_name__	)

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

						# create model
						if model_args.model_name_or_path:
												lowercase   :       Optional[Any]								=ViTMAEForPreTraining.from_pretrained(
												    model_args.model_name_or_path      ,   from_tf=bool('''.ckpt''' in model_args.model_name_or_path	)      ,   config=__magic_name__      ,   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   :       Dict								=ViTMAEForPreTraining(__magic_name__	)

						if training_args.do_train:
												lowercase   :       Union[str, Any]								=ds['''train'''].column_names
						else:
												lowercase   :       Any								=ds['''validation'''].column_names

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

						def preprocess_images(__magic_name__	:    int	):
												lowercase   :       str								=[transforms(__magic_name__	) 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   :       Optional[Any]								=ds['''train'''].shuffle(seed=training_args.seed	).select(range(data_args.max_train_samples	)	)
												# Set the training transforms
												ds["train"].set_transform(__magic_name__	)

						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   :       Union[str, Any]								=(
																		    ds['''validation'''].shuffle(seed=training_args.seed	).select(range(data_args.max_eval_samples	)	)
																		)
												# Set the validation transforms
												ds["validation"].set_transform(__magic_name__	)

						# Compute absolute learning rate
						lowercase   :       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:
												lowercase   :       str								=training_args.base_learning_rate * total_train_batch_size / 256

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

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

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






def 							_lowerCAmelCase (      __magic_name__	:    int	)							->     Tuple:
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
							main()








'''simple docstring'''





from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments






def 							_lowerCAmelCase (      )							->     List[Any]:
						lowercase   :       Tuple								=HfArgumentParser(__magic_name__	)
						lowercase   :       Union[str, Any]								=parser.parse_args_into_dataclasses()[0]
						lowercase   :       Any								=TensorFlowBenchmark(args=__magic_name__	)
						try:
												lowercase   :       List[Any]								=parser.parse_args_into_dataclasses()[0]
						except ValueError as e:
												lowercase   :       List[Any]								='''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
												lowercase   :       Any								=''' '''.join(str(__magic_name__	).split(''' '''	)[:-1]	)
												lowercase   :       Optional[Any]								=''''''
												lowercase   :       List[str]								=eval(str(__magic_name__	).split(''' '''	)[-1]	)
												lowercase   :       Optional[Any]								=[]
												for arg in depreciated_args:
																		# arg[2:] removes '--'
																		if arg[2:] in TensorFlowBenchmark.deprecated_args:
																								# arg[5:] removes '--no_'
																								full_error_msg += arg_error_msg.format(arg[5:]	)
																		else:
																								wrong_args.append(__magic_name__	)
												if len(__magic_name__	) > 0:
																		lowercase   :       int								=full_error_msg + begin_error_msg + str(__magic_name__	)
												raise ValueError(__magic_name__	)
						benchmark.run()


if __name__ == "__main__":
							main()





