





'''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
lowerCAmelCase       		=      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 		lowerCamelCase  :
	snake_case_      =   field(
	    default="cifar10"      ,   metadata={"help": "Name of a dataset from the datasets package"}       )
	snake_case_      =   field(
	    default=lowerCamelCase__      ,   metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}       )
	snake_case_      =   field(
	    default=lowerCamelCase__      ,   metadata={"help": "The column name of the images in the files."}       )
	snake_case_      =   field(default=lowerCamelCase__      ,   metadata={"help": "A folder containing the training data."}       )
	snake_case_      =   field(default=lowerCamelCase__      ,   metadata={"help": "A folder containing the validation data."}       )
	snake_case_      =   field(
	    default=0.1_5      ,   metadata={"help": "Percent to split off of train for validation."}       )
	snake_case_      =   field(
	    default=lowerCamelCase__      ,   metadata={
	        "help": (
	            "For debugging purposes or quicker training, truncate the number of training examples to this "
	            "value if set."
	        )
	    }      ,   )
	snake_case_      =   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	):
							lowerCAmelCase     :    Any						=   {}
							if self.train_dir is not None:
													lowerCAmelCase     :    int						=   self.train_dir
							if self.validation_dir is not None:
													lowerCAmelCase     :    int						=   self.validation_dir
							lowerCAmelCase     :    Optional[Any]						=   data_files if data_files else None






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






@dataclass
class 		lowerCamelCase  (      lowerCamelCase__       ):
	snake_case_      =   field(
	    default=1e-3      ,   metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}       )






def      __A						(							a_						:  List[Any]	):
						lowerCAmelCase     :    int						=   torch.stack([example["pixel_values"] for example in examples]	)
						return {"pixel_values": pixel_values}






def      __A						(							):
						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     :    Optional[int]						=   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
						else:
												lowerCAmelCase     :    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"      ,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     :    Optional[Any]						=   None
						if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
												lowerCAmelCase     :    List[str]						=   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     :    Optional[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.
						lowerCAmelCase     :    Optional[int]						=   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     :    str						=   ds['''train'''].train_test_split(data_args.train_val_split	)
												lowerCAmelCase     :    Tuple						=   split['''train''']
												lowerCAmelCase     :    Optional[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     :    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:
												lowerCAmelCase     :    Any						=   ViTMAEConfig.from_pretrained(model_args.config_name      ,**lowerCamelCase_	)
						elif model_args.model_name_or_path:
												lowerCAmelCase     :    int						=   ViTMAEConfig.from_pretrained(model_args.model_name_or_path      ,**lowerCamelCase_	)
						else:
												lowerCAmelCase     :    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:
												lowerCAmelCase     :    str						=   ViTImageProcessor.from_pretrained(model_args.image_processor_name      ,**lowerCamelCase_	)
						elif model_args.model_name_or_path:
												lowerCAmelCase     :    Union[str, Any]						=   ViTImageProcessor.from_pretrained(model_args.model_name_or_path      ,**lowerCamelCase_	)
						else:
												lowerCAmelCase     :    Tuple						=   ViTImageProcessor()

						# create model
						if model_args.model_name_or_path:
												lowerCAmelCase     :    Union[str, Any]						=   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     :    List[Any]						=   ViTMAEForPreTraining(lowerCamelCase_	)

						if training_args.do_train:
												lowerCAmelCase     :    Tuple						=   ds['''train'''].column_names
						else:
												lowerCAmelCase     :    Optional[Any]						=   ds['''validation'''].column_names

						if data_args.image_column_name is not None:
												lowerCAmelCase     :    Union[str, Any]						=   data_args.image_column_name
						elif "image" in column_names:
												lowerCAmelCase     :    Dict						=   '''image'''
						elif "img" in column_names:
												lowerCAmelCase     :    str						=   '''img'''
						else:
												lowerCAmelCase     :    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:
												lowerCAmelCase     :    List[str]						=   image_processor.size['''shortest_edge''']
						else:
												lowerCAmelCase     :    int						=   (image_processor.size['''height'''], image_processor.size['''width'''])
						lowerCAmelCase     :    str						=   Compose(
						    [
						        Lambda(lambda a_	: 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(a_						:  Optional[Any]	):
												lowerCAmelCase     :    Union[str, Any]						=   [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     :    List[str]						=   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     :    List[str]						=   (
																		    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     :    List[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     :    int						=   training_args.base_learning_rate * total_train_batch_size / 2_5_6

						# 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[Any]						=   None
												if training_args.resume_from_checkpoint is not None:
																		lowerCAmelCase     :    Tuple						=   training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		lowerCAmelCase     :    Any						=   last_checkpoint
												lowerCAmelCase     :    str						=   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     :    Tuple						=   trainer.evaluate()
												trainer.log_metrics("eval"      ,lowerCamelCase_	)
												trainer.save_metrics("eval"      ,lowerCamelCase_	)

						# Write model card and (optionally) push to hub
						lowerCAmelCase     :    Union[str, 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      __A						(							a_						:  int	):
						main()


if __name__ == "__main__":
					main()










'''simple docstring'''






import numpy as np






def      __A						(							a_						:  np.array	):
		return (2 / (1 + np.exp(-2 * vector	))) - 1


if __name__ == "__main__":
					import doctest

					doctest.testmod()



