
'''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 :   Tuple			    =	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					:





		"""simple docstring"""





		A__	: int    =     field(
		    default='cifar10'	,		metadata={'help': 'Name of a dataset from the datasets package'}			)
		A__	: Tuple    =     field(
		    default=a_	,		metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}			)
		A__	: Dict    =     field(
		    default=a_	,		metadata={'help': 'The column name of the images in the files.'}			)
		A__	: Optional[int]    =     field(default=a_	,		metadata={'help': 'A folder containing the training data.'}			)
		A__	: List[str]    =     field(default=a_	,		metadata={'help': 'A folder containing the validation data.'}			)
		A__	: Optional[int]    =     field(
		    default=0.1_5	,		metadata={'help': 'Percent to split off of train for validation.'}			)
		A__	: List[str]    =     field(
		    default=a_	,		metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of training examples to this '
		            'value if set.'
		        )
		    }	,		)
		A__	: str    =     field(
		    default=a_	,		metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
		            'value if set.'
		        )
		    }	,		)
		def _lowercase	(     self  )					->     Tuple:
				_UpperCamelCase							:			Any     =   {}
				if self.train_dir is not None:
						_UpperCamelCase							:			Union[str, Any]     =   self.train_dir
				if self.validation_dir is not None:
						_UpperCamelCase							:			int     =   self.validation_dir
				_UpperCamelCase							:			Union[str, Any]     =   data_files if data_files else None







@dataclass
class       UpperCAmelCase					:





		"""simple docstring"""





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







@dataclass
class       UpperCAmelCase					(       a_			):





		"""simple docstring"""





		A__	: List[str]    =     field(
		    default=1E-3	,		metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}			)







def     snake_case__						(   UpperCamelCase						)			->   Optional[Any]:
		_UpperCamelCase							:			int     =   torch.stack([example['''pixel_values'''] for example in examples]						)
		return {"pixel_values": pixel_values}







def     snake_case__						(   )			->   Dict:
		# 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							:			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							:			List[str]     =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
		else:
				_UpperCamelCase,				_UpperCamelCase,				_UpperCamelCase							:			Optional[Any]     =   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'''      ,__A      ,__A						)

		# 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							:			int     =   training_args.get_process_log_level()
		logger.setLevel(__A						)
		transformers.utils.logging.set_verbosity(__A						)
		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							:			int     =   None
		if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
				_UpperCamelCase							:			Tuple     =   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							:			Optional[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      ,__A						) and data_args.train_val_split > 0.0:
				_UpperCamelCase							:			int     =   ds['''train'''].train_test_split(data_args.train_val_split						)
				_UpperCamelCase							:			Optional[Any]     =   split['''train''']
				_UpperCamelCase							:			Optional[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							:			Tuple     =   ViTMAEConfig.from_pretrained(model_args.config_name      ,**__A						)
		elif model_args.model_name_or_path:
				_UpperCamelCase							:			int     =   ViTMAEConfig.from_pretrained(model_args.model_name_or_path      ,**__A						)
		else:
				_UpperCamelCase							:			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:
				_UpperCamelCase							:			Optional[int]     =   ViTImageProcessor.from_pretrained(model_args.image_processor_name      ,**__A						)
		elif model_args.model_name_or_path:
				_UpperCamelCase							:			Tuple     =   ViTImageProcessor.from_pretrained(model_args.model_name_or_path      ,**__A						)
		else:
				_UpperCamelCase							:			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=__A      ,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(__A						)

		if training_args.do_train:
				_UpperCamelCase							:			str     =   ds['''train'''].column_names
		else:
				_UpperCamelCase							:			str     =   ds['''validation'''].column_names

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

		def preprocess_images(UpperCamelCase						):
				_UpperCamelCase							:			Any     =   [transforms(__A						) 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(__A						)

		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							:			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(__A						)

		# Compute absolute learning rate
		_UpperCamelCase							:			Optional[int]     =   (
		    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
		)
		if training_args.base_learning_rate is not None:
				_UpperCamelCase							:			int     =   training_args.base_learning_rate * total_train_batch_size / 2_56

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

		# Training
		if training_args.do_train:
				_UpperCamelCase							:			Dict     =   None
				if training_args.resume_from_checkpoint is not None:
						_UpperCamelCase							:			Optional[Any]     =   training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						_UpperCamelCase							:			List[str]     =   last_checkpoint
				_UpperCamelCase							:			str     =   trainer.train(resume_from_checkpoint=__A						)
				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'''      ,__A						)
				trainer.save_metrics('''eval'''      ,__A						)

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







def     snake_case__						(   UpperCamelCase						)			->   str:
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
	main()








from typing import TYPE_CHECKING

from ...utils import _LazyModule


_lowerCamelCase      :					Tuple					    =      {'''tokenization_byt5''': ['''ByT5Tokenizer''']}


if TYPE_CHECKING:
   from .tokenization_byta import ByTaTokenizer
else:
   import sys

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


