





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




					def 	lowerCAmelCase			(		self  )		->							int:
										_snake_case									=				{}
										if self.train_dir is not None:
															_snake_case									=				self.train_dir
										if self.validation_dir is not None:
															_snake_case									=				self.validation_dir
										_snake_case									=				data_files if data_files else None





@dataclass
class 			UpperCamelCase_		:
					lowerCAmelCase_							=	field(
					    default=_lowerCamelCase							,  metadata={
					        '''help''': (
					            '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
					        )
					    }							,  )
					lowerCAmelCase_							=	field(
					    default=_lowerCamelCase							,  metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''}					)
					lowerCAmelCase_							=	field(
					    default=_lowerCamelCase							,  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=_lowerCamelCase							,  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=_lowerCamelCase							,  metadata={'''help''': '''Name or path of preprocessor config.'''}					)
					lowerCAmelCase_							=	field(
					    default=_lowerCamelCase							,  metadata={
					        '''help''': (
					            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
					            '''with private models).'''
					        )
					    }							,  )
					lowerCAmelCase_							=	field(
					    default=0.75							,  metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''}					)
					lowerCAmelCase_							=	field(
					    default=_lowerCamelCase							,  metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''}					)





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





def 						lowerCamelCase__     (     UpperCamelCase__			:       Dict						)			-> List[Any]:



					'''simple docstring'''




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





def 						lowerCamelCase__     (     )			-> Dict:



					'''simple docstring'''




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

					# 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()

					_snake_case									=				training_args.get_process_log_level()
					logger.setLevel(UpperCamelCase__						)
					transformers.utils.logging.set_verbosity(UpperCamelCase__						)
					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.
					_snake_case									=				None
					if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
										_snake_case									=				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.
					_snake_case									=				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.
					_snake_case									=				None if 'validation' in ds.keys() else data_args.train_val_split
					if isinstance(data_args.train_val_split       ,    UpperCamelCase__						) and data_args.train_val_split > 0.0:
										_snake_case									=				ds['train'].train_test_split(data_args.train_val_split						)
										_snake_case									=				split['train']
										_snake_case									=				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.
					_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,
					}
					if model_args.config_name:
										_snake_case									=				ViTMAEConfig.from_pretrained(model_args.config_name       ,    **UpperCamelCase__						)
					elif model_args.model_name_or_path:
										_snake_case									=				ViTMAEConfig.from_pretrained(model_args.model_name_or_path       ,    **UpperCamelCase__						)
					else:
										_snake_case									=				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:
										_snake_case									=				ViTImageProcessor.from_pretrained(model_args.image_processor_name       ,    **UpperCamelCase__						)
					elif model_args.model_name_or_path:
										_snake_case									=				ViTImageProcessor.from_pretrained(model_args.model_name_or_path       ,    **UpperCamelCase__						)
					else:
										_snake_case									=				ViTImageProcessor()

					# create model
					if model_args.model_name_or_path:
										_snake_case									=				ViTMAEForPreTraining.from_pretrained(
										    model_args.model_name_or_path       ,    from_tf=bool('.ckpt' in model_args.model_name_or_path						)       ,    config=UpperCamelCase__       ,    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'						)
										_snake_case									=				ViTMAEForPreTraining(UpperCamelCase__						)

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

					if data_args.image_column_name is not None:
										_snake_case									=				data_args.image_column_name
					elif "image" in column_names:
										_snake_case									=				'image'
					elif "img" in column_names:
										_snake_case									=				'img'
					else:
										_snake_case									=				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:
										_snake_case									=				image_processor.size['shortest_edge']
					else:
										_snake_case									=				(image_processor.size['height'], image_processor.size['width'])
					_snake_case									=				Compose(
					    [
					        Lambda(lambda UpperCamelCase__						: img.convert('RGB'						) if img.mode != "RGB" else img						),
					        RandomResizedCrop(UpperCamelCase__       ,    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__			:       Tuple						):
										_snake_case									=				[transforms(UpperCamelCase__						) 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:
															_snake_case									=				ds['train'].shuffle(seed=training_args.seed						).select(range(data_args.max_train_samples						)						)
										# Set the training transforms
										ds["train"].set_transform(UpperCamelCase__						)

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

					# Compute absolute learning rate
					_snake_case									=				(
					    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
					)
					if training_args.base_learning_rate is not None:
										_snake_case									=				training_args.base_learning_rate * total_train_batch_size / 256

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

					# Training
					if training_args.do_train:
										_snake_case									=				None
										if training_args.resume_from_checkpoint is not None:
															_snake_case									=				training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															_snake_case									=				last_checkpoint
										_snake_case									=				trainer.train(resume_from_checkpoint=UpperCamelCase__						)
										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:
										_snake_case									=				trainer.evaluate()
										trainer.log_metrics('eval'       ,    UpperCamelCase__						)
										trainer.save_metrics('eval'       ,    UpperCamelCase__						)

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





def 						lowerCamelCase__     (     UpperCamelCase__			:       Optional[int]						)			-> List[str]:



					'''simple docstring'''




					main()


if __name__ == "__main__":
					main()










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_      							=     {
    """configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
    """tokenization_luke""": ["""LukeTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase_      							=     [
					    """LUKE_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LukeForEntityClassification""",
					    """LukeForEntityPairClassification""",
					    """LukeForEntitySpanClassification""",
					    """LukeForMultipleChoice""",
					    """LukeForQuestionAnswering""",
					    """LukeForSequenceClassification""",
					    """LukeForTokenClassification""",
					    """LukeForMaskedLM""",
					    """LukeModel""",
					    """LukePreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
					from .tokenization_luke import LukeTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_luke import (
										    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LukeForEntityClassification,
										    LukeForEntityPairClassification,
										    LukeForEntitySpanClassification,
										    LukeForMaskedLM,
										    LukeForMultipleChoice,
										    LukeForQuestionAnswering,
										    LukeForSequenceClassification,
										    LukeForTokenClassification,
										    LukeModel,
										    LukePreTrainedModel,
										)

else:
					import sys

					UpperCAmelCase_      							=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



