



"""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
_lowercase : int      						=						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	:


					'''simple docstring'''





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



					def 		snake_case       (       self							:  Union[str, Any]     )->					List[str]:
								lowerCamelCase__		:		int			={}
								if self.train_dir is not None:
											lowerCamelCase__		:		str			=self.train_dir
								if self.validation_dir is not None:
											lowerCamelCase__		:		List[Any]			=self.validation_dir
								lowerCamelCase__		:		List[str]			=data_files if data_files else None





@dataclass
class        __SCREAMING_SNAKE_CASE	:


					'''simple docstring'''





					_a   			=							field(
					    default=lowerCAmelCase_		,   metadata={
					        'help': (
					            'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
					        )
					    }		,   )
					_a   			=							field(
					    default=lowerCAmelCase_		,   metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'}   )
					_a   			=							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'
					        )
					    }		,   )
					_a   			=							field(
					    default=lowerCAmelCase_		,   metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}   )
					_a   			=							field(
					    default='main'		,   metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}		,   )
					_a   			=							field(default=lowerCAmelCase_		,   metadata={'help': 'Name or path of preprocessor config.'}   )
					_a   			=							field(
					    default=lowerCAmelCase_		,   metadata={
					        'help': (
					            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
					            'with private models).'
					        )
					    }		,   )
					_a   			=							field(
					    default=0.75		,   metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'}   )
					_a   			=							field(
					    default=lowerCAmelCase_		,   metadata={'help': 'Whether or not to train with normalized pixel values as target.'}   )





@dataclass
class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							field(
					    default=1e-3		,   metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}   )






def      snake_case__   (		__lowerCamelCase				:    Any	):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]			=torch.stack([example['''pixel_values'''] for example in examples]	)
			return {"pixel_values": pixel_values}






def      snake_case__   (		):
			"""simple docstring"""
			# 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.

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

			# 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()

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

			# create model
			if model_args.model_name_or_path:
						lowerCamelCase__		:		Tuple			=ViTMAEForPreTraining.from_pretrained(
						    model_args.model_name_or_path							,		from_tf=bool('''.ckpt''' in model_args.model_name_or_path	)							,		config=__lowerCamelCase							,		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'''	)
						lowerCamelCase__		:		str			=ViTMAEForPreTraining(__lowerCamelCase	)

			if training_args.do_train:
						lowerCamelCase__		:		List[str]			=ds['''train'''].column_names
			else:
						lowerCamelCase__		:		Optional[int]			=ds['''validation'''].column_names

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

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

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

			# Compute absolute learning rate
			lowerCamelCase__		:		Optional[Any]			=(
			    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
			)
			if training_args.base_learning_rate is not None:
						lowerCamelCase__		:		Dict			=training_args.base_learning_rate * total_train_batch_size / 256

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

			# Training
			if training_args.do_train:
						lowerCamelCase__		:		Optional[int]			=None
						if training_args.resume_from_checkpoint is not None:
									lowerCamelCase__		:		List[Any]			=training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									lowerCamelCase__		:		Dict			=last_checkpoint
						lowerCamelCase__		:		Tuple			=trainer.train(resume_from_checkpoint=__lowerCamelCase	)
						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:
						lowerCamelCase__		:		str			=trainer.evaluate()
						trainer.log_metrics('''eval'''							,		__lowerCamelCase	)
						trainer.save_metrics('''eval'''							,		__lowerCamelCase	)

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






def      snake_case__   (		__lowerCamelCase				:    Union[str, Any]	):
			"""simple docstring"""
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
		main()









"""simple docstring"""






class        __SCREAMING_SNAKE_CASE	:


					'''simple docstring'''





					def __init__(       self							:  Dict,  lowerCamelCase							:  list     )->					None:
								lowerCamelCase__		:		Tuple			=set_counts
								lowerCamelCase__		:		Dict			=max(lowerCamelCase     )
								lowerCamelCase__		:		Union[str, Any]			=len(lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=[1] * num_sets
								lowerCamelCase__		:		str			=list(range(lowerCamelCase     )     )



					def 		snake_case       (       self							:  Tuple,  lowerCamelCase							:  int,  lowerCamelCase							:  int     )->					bool:
								lowerCamelCase__		:		List[Any]			=self.get_parent(lowerCamelCase     )
								lowerCamelCase__		:		str			=self.get_parent(lowerCamelCase     )

								if src_parent == dst_parent:
											return False

								if self.ranks[dst_parent] >= self.ranks[src_parent]:
											self.set_counts[dst_parent] += self.set_counts[src_parent]
											lowerCamelCase__		:		Tuple			=0
											lowerCamelCase__		:		int			=dst_parent
											if self.ranks[dst_parent] == self.ranks[src_parent]:
														self.ranks[dst_parent] += 1
											lowerCamelCase__		:		str			=self.set_counts[dst_parent]
								else:
											self.set_counts[src_parent] += self.set_counts[dst_parent]
											lowerCamelCase__		:		List[Any]			=0
											lowerCamelCase__		:		List[str]			=src_parent
											lowerCamelCase__		:		Dict			=self.set_counts[src_parent]

								lowerCamelCase__		:		str			=max(self.max_set,  lowerCamelCase     )
								return True




					def 		snake_case       (       self							:  Union[str, Any],  lowerCamelCase							:  int     )->					int:
								if self.parents[disj_set] == disj_set:
											return disj_set
								lowerCamelCase__		:		Tuple			=self.get_parent(self.parents[disj_set]     )
								return self.parents[disj_set]




