


"""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
a__				:							Dict						=     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"""





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




	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	List[Any]:
						__SCREAMING_SNAKE_CASE						   =				{}
						if self.train_dir is not None:
											__SCREAMING_SNAKE_CASE						   =				self.train_dir
						if self.validation_dir is not None:
											__SCREAMING_SNAKE_CASE						   =				self.validation_dir
						__SCREAMING_SNAKE_CASE						   =				data_files if data_files else None




@dataclass
class  UpperCamelCase_	:

	"""simple docstring"""





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




@dataclass
class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	snake_case__				:  float    					=			field(
	    default=1E-3			,  metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."})




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


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




def    UpperCAmelCase__       ():




					'''simple docstring'''


					__SCREAMING_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.
										__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
					else:
										__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_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"  ,						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()

					__SCREAMING_SNAKE_CASE						   =				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.
					__SCREAMING_SNAKE_CASE						   =				None
					if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
										__SCREAMING_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.
					__SCREAMING_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.
					__SCREAMING_SNAKE_CASE						   =				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:
										__SCREAMING_SNAKE_CASE						   =				ds["train"].train_test_split(data_args.train_val_split   )
										__SCREAMING_SNAKE_CASE						   =				split["train"]
										__SCREAMING_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.
					__SCREAMING_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:
										__SCREAMING_SNAKE_CASE						   =				ViTMAEConfig.from_pretrained(model_args.config_name  ,						**lowerCAmelCase_   )
					elif model_args.model_name_or_path:
										__SCREAMING_SNAKE_CASE						   =				ViTMAEConfig.from_pretrained(model_args.model_name_or_path  ,						**lowerCAmelCase_   )
					else:
										__SCREAMING_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:
										__SCREAMING_SNAKE_CASE						   =				ViTImageProcessor.from_pretrained(model_args.image_processor_name  ,						**lowerCAmelCase_   )
					elif model_args.model_name_or_path:
										__SCREAMING_SNAKE_CASE						   =				ViTImageProcessor.from_pretrained(model_args.model_name_or_path  ,						**lowerCAmelCase_   )
					else:
										__SCREAMING_SNAKE_CASE						   =				ViTImageProcessor()

					# create model
					if model_args.model_name_or_path:
										__SCREAMING_SNAKE_CASE						   =				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"   )
										__SCREAMING_SNAKE_CASE						   =				ViTMAEForPreTraining(lowerCAmelCase_   )

					if training_args.do_train:
										__SCREAMING_SNAKE_CASE						   =				ds["train"].column_names
					else:
										__SCREAMING_SNAKE_CASE						   =				ds["validation"].column_names

					if data_args.image_column_name is not None:
										__SCREAMING_SNAKE_CASE						   =				data_args.image_column_name
					elif "image" in column_names:
										__SCREAMING_SNAKE_CASE						   =				"image"
					elif "img" in column_names:
										__SCREAMING_SNAKE_CASE						   =				"img"
					else:
										__SCREAMING_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:
										__SCREAMING_SNAKE_CASE						   =				image_processor.size["shortest_edge"]
					else:
										__SCREAMING_SNAKE_CASE						   =				(image_processor.size["height"], image_processor.size["width"])
					__SCREAMING_SNAKE_CASE						   =				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_   ):
										__SCREAMING_SNAKE_CASE						   =				[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:
															__SCREAMING_SNAKE_CASE						   =				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:
															__SCREAMING_SNAKE_CASE						   =				(
															    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
					__SCREAMING_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:
										__SCREAMING_SNAKE_CASE						   =				training_args.base_learning_rate * total_train_batch_size / 256

					# Initialize our trainer
					__SCREAMING_SNAKE_CASE						   =				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:
										__SCREAMING_SNAKE_CASE						   =				None
										if training_args.resume_from_checkpoint is not None:
															__SCREAMING_SNAKE_CASE						   =				training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															__SCREAMING_SNAKE_CASE						   =				last_checkpoint
										__SCREAMING_SNAKE_CASE						   =				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:
										__SCREAMING_SNAKE_CASE						   =				trainer.evaluate()
										trainer.log_metrics("eval"  ,						lowerCAmelCase_   )
										trainer.save_metrics("eval"  ,						lowerCAmelCase_   )

					# Write model card and (optionally) push to hub
					__SCREAMING_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(**lowerCAmelCase_   )
					else:
										trainer.create_model_card(**lowerCAmelCase_   )




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					main()


if __name__ == "__main__":
		main()




"""simple docstring"""







def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					if number > 0:
										raise ValueError("input must be a negative integer"   )
					__SCREAMING_SNAKE_CASE						   =				len(bin(lowerCAmelCase_   )[3:]   )
					__SCREAMING_SNAKE_CASE						   =				bin(abs(lowerCAmelCase_   ) - (1 << binary_number_length)   )[3:]
					__SCREAMING_SNAKE_CASE						   =				(
					    (
					        "1"
					        + "0" * (binary_number_length - len(lowerCAmelCase_   ))
					        + twos_complement_number
					    )
					    if number < 0
					    else "0"
					)
					return "0b" + twos_complement_number


if __name__ == "__main__":
		import doctest

		doctest.testmod()
