





'''simple docstring'''

import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
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 : Union[str, Any]											=			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")

__lowerCamelCase : List[str]											=			list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
__lowerCamelCase : List[Any]											=			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class       UpperCAmelCase :
		UpperCAmelCase					:	Optional[int] 			=		field(
		    default='''cifar10'''			,       metadata={'''help''': '''Name of a dataset from the datasets package'''}  )
		UpperCAmelCase					:	int 			=		field(
		    default=_lowercase			,       metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}  )
		UpperCAmelCase					:	Optional[Any] 			=		field(
		    default=_lowercase			,       metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''}			,       )
		UpperCAmelCase					:	Any 			=		field(default=_lowercase			,       metadata={'''help''': '''A folder containing the training data.'''}  )
		UpperCAmelCase					:	Tuple 			=		field(default=_lowercase			,       metadata={'''help''': '''A folder containing the validation data.'''}  )
		UpperCAmelCase					:	Any 			=		field(
		    default=0.15			,       metadata={'''help''': '''Percent to split off of train for validation.'''}  )
		UpperCAmelCase					:	List[Any] 			=		field(default=32			,       metadata={'''help''': '''The size of the square patches to use for masking.'''}  )
		UpperCAmelCase					:	int 			=		field(
		    default=0.6			,       metadata={'''help''': '''Percentage of patches to mask.'''}			,       )
		UpperCAmelCase					:	Tuple 			=		field(
		    default=_lowercase			,       metadata={
		        '''help''': (
		            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
		            '''value if set.'''
		        )
		    }			,       )
		UpperCAmelCase					:	Optional[Any] 			=		field(
		    default=_lowercase			,       metadata={
		        '''help''': (
		            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
		            '''value if set.'''
		        )
		    }			,       )




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




@dataclass
class       UpperCAmelCase :
		UpperCAmelCase					:	str 			=		field(
		    default=_lowercase			,       metadata={
		        '''help''': (
		            '''The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '''
		            '''checkpoint identifier on the hub. '''
		            '''Don\'t set if you want to train a model from scratch.'''
		        )
		    }			,       )
		UpperCAmelCase					:	List[str] 			=		field(
		    default=_lowercase			,       metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(_lowercase  )}			,       )
		UpperCAmelCase					:	str 			=		field(
		    default=_lowercase			,       metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}  )
		UpperCAmelCase					:	Dict 			=		field(
		    default=_lowercase			,       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'''
		        )
		    }			,       )
		UpperCAmelCase					:	Optional[Any] 			=		field(
		    default=_lowercase			,       metadata={'''help''': '''Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'''}			,       )
		UpperCAmelCase					:	Any 			=		field(
		    default='''main'''			,       metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}			,       )
		UpperCAmelCase					:	Optional[Any] 			=		field(default=_lowercase			,       metadata={'''help''': '''Name or path of preprocessor config.'''}  )
		UpperCAmelCase					:	List[str] 			=		field(
		    default=_lowercase			,       metadata={
		        '''help''': (
		            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
		            '''with private models).'''
		        )
		    }			,       )
		UpperCAmelCase					:	int 			=		field(
		    default=_lowercase			,       metadata={
		        '''help''': (
		            '''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
		        )
		    }			,       )
		UpperCAmelCase					:	Optional[Any] 			=		field(
		    default=_lowercase			,       metadata={
		        '''help''': (
		            '''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
		        )
		    }			,       )
		UpperCAmelCase					:	Any 			=		field(
		    default=_lowercase			,       metadata={'''help''': '''Stride to use for the encoder.'''}			,       )




class       UpperCAmelCase :




		def __init__(self      :	Dict							,			A__      :	Tuple=1_9_2							,			A__      :	Optional[Any]=3_2							,			A__      :	Tuple=4							,			A__      :	Tuple=0.6				)					-> Optional[int]:
						lowercase         = input_size
						lowercase         = mask_patch_size
						lowercase         = model_patch_size
						lowercase         = mask_ratio

						if self.input_size % self.mask_patch_size != 0:
										raise ValueError("Input size must be divisible by mask patch size"				)
						if self.mask_patch_size % self.model_patch_size != 0:
										raise ValueError("Mask patch size must be divisible by model patch size"				)

						lowercase         = self.input_size // self.mask_patch_size
						lowercase         = self.mask_patch_size // self.model_patch_size

						lowercase         = self.rand_size**2
						lowercase         = int(np.ceil(self.token_count * self.mask_ratio				)				)





		def __call__(self      :	Dict				)					-> List[str]:
						lowercase         = np.random.permutation(self.token_count				)[: self.mask_count]
						lowercase         = np.zeros(self.token_count							,			dtype=__UpperCamelCase				)
						lowercase         = 1

						lowercase         = mask.reshape((self.rand_size, self.rand_size)				)
						lowercase         = mask.repeat(self.scale							,			axis=0				).repeat(self.scale							,			axis=1				)

						return torch.tensor(mask.flatten()				)







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = torch.stack([example["pixel_values"] for example in examples]							)
				lowercase         = torch.stack([example["mask"] for example in examples]							)
				return {"pixel_values": pixel_values, "bool_masked_pos": mask}







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
				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.
								lowercase   ,							lowercase   ,							lowercase         = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
				else:
								lowercase   ,							lowercase   ,							lowercase         = 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_mim"		,							_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE							)

				# 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()

				lowercase         = training_args.get_process_log_level()
				logger.setLevel(_SCREAMING_SNAKE_CASE							)
				transformers.utils.logging.set_verbosity(_SCREAMING_SNAKE_CASE							)
				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.
				lowercase         = None
				if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
								lowercase         = 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.
				lowercase         = 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.
				lowercase         = None if "validation" in ds.keys() else data_args.train_val_split
				if isinstance(data_args.train_val_split		,							_SCREAMING_SNAKE_CASE							) and data_args.train_val_split > 0.0:
								lowercase         = ds["train"].train_test_split(data_args.train_val_split							)
								lowercase         = split["train"]
								lowercase         = split["test"]

				# Create config
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				lowercase         = {
				    "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_or_path:
								lowercase         = AutoConfig.from_pretrained(model_args.config_name_or_path		,							**_SCREAMING_SNAKE_CASE							)
				elif model_args.model_name_or_path:
								lowercase         = AutoConfig.from_pretrained(model_args.model_name_or_path		,							**_SCREAMING_SNAKE_CASE							)
				else:
								lowercase         = CONFIG_MAPPING[model_args.model_type]()
								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}'							)

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
				if hasattr(_SCREAMING_SNAKE_CASE		,							"decoder_type"							):
								lowercase         = "simmim"

				# adapt config
				lowercase         = model_args.image_size if model_args.image_size is not None else config.image_size
				lowercase         = model_args.patch_size if model_args.patch_size is not None else config.patch_size
				lowercase         = (
				    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
				)

				config.update(
				    {
				        "image_size": model_args.image_size,
				        "patch_size": model_args.patch_size,
				        "encoder_stride": model_args.encoder_stride,
				    }							)

				# create image processor
				if model_args.image_processor_name:
								lowercase         = AutoImageProcessor.from_pretrained(model_args.image_processor_name		,							**_SCREAMING_SNAKE_CASE							)
				elif model_args.model_name_or_path:
								lowercase         = AutoImageProcessor.from_pretrained(model_args.model_name_or_path		,							**_SCREAMING_SNAKE_CASE							)
				else:
								lowercase         = {
								    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
								}
								lowercase         = IMAGE_PROCESSOR_TYPES[model_args.model_type]()

				# create model
				if model_args.model_name_or_path:
								lowercase         = AutoModelForMaskedImageModeling.from_pretrained(
								    model_args.model_name_or_path		,							from_tf=bool(".ckpt" in model_args.model_name_or_path							)		,							config=_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		,							)
				else:
								logger.info("Training new model from scratch"							)
								lowercase         = AutoModelForMaskedImageModeling.from_config(_SCREAMING_SNAKE_CASE							)

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

				if data_args.image_column_name is not None:
								lowercase         = data_args.image_column_name
				elif "image" in column_names:
								lowercase         = "image"
				elif "img" in column_names:
								lowercase         = "img"
				else:
								lowercase         = column_names[0]

				# transformations as done in original SimMIM paper
				# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
				lowercase         = Compose(
				    [
				        Lambda(lambda lowerCAmelCase_							: img.convert("RGB"							) if img.mode != "RGB" else img							),
				        RandomResizedCrop(model_args.image_size		,							scale=(0.67, 1.0)		,							ratio=(3.0 / 4.0, 4.0 / 3.0)							),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        Normalize(mean=image_processor.image_mean		,							std=image_processor.image_std							),
				    ]							)

				# create mask generator
				lowercase         = MaskGenerator(
				    input_size=model_args.image_size		,							mask_patch_size=data_args.mask_patch_size		,							model_patch_size=model_args.patch_size		,							mask_ratio=data_args.mask_ratio		,							)

				def preprocess_images(lowerCAmelCase_							):
								lowercase         = [transforms(_SCREAMING_SNAKE_CASE							) for image in examples[image_column_name]]
								lowercase         = [mask_generator() for i in range(len(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:
												lowercase         = ds["train"].shuffle(seed=training_args.seed							).select(range(data_args.max_train_samples							)							)
								# Set the training transforms
								ds["train"].set_transform(_SCREAMING_SNAKE_CASE							)

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

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

				# Training
				if training_args.do_train:
								lowercase         = None
								if training_args.resume_from_checkpoint is not None:
												lowercase         = training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												lowercase         = last_checkpoint
								lowercase         = trainer.train(resume_from_checkpoint=_SCREAMING_SNAKE_CASE							)
								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:
								lowercase         = trainer.evaluate()
								trainer.log_metrics("eval"		,							_SCREAMING_SNAKE_CASE							)
								trainer.save_metrics("eval"		,							_SCREAMING_SNAKE_CASE							)

				# Write model card and (optionally) push to hub
				lowercase         = {
				    "finetuned_from": model_args.model_name_or_path,
				    "tasks": "masked-image-modeling",
				    "dataset": data_args.dataset_name,
				    "tags": ["masked-image-modeling"],
				}
				if training_args.push_to_hub:
								trainer.push_to_hub(**_SCREAMING_SNAKE_CASE							)
				else:
								trainer.create_model_card(**_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
			main()











'''simple docstring'''

import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate

@dataclass(frozen=_lowercase  )
class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	str 			=		field(default='''image-classification'''			,       metadata={'''include_in_asdict_even_if_is_default''': True}  )
		UpperCAmelCase					:	ClassVar[Features] 			=		Features({'''image''': Image()}  )
		UpperCAmelCase					:	ClassVar[Features] 			=		Features({'''labels''': ClassLabel}  )
		UpperCAmelCase					:	str 			=		"image"
		UpperCAmelCase					:	str 			=		"labels"




		def 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	Union[str, Any]				)					-> Union[str, Any]:
						if self.label_column not in features:
										raise ValueError(f'Column {self.label_column} is not present in features.'				)
						if not isinstance(features[self.label_column]							,			A__				):
										raise ValueError(f'Column {self.label_column} is not a ClassLabel.'				)
						lowercase         = copy.deepcopy(self				)
						lowercase         = self.label_schema.copy()
						lowercase         = features[self.label_column]
						lowercase         = label_schema
						return task_template





		@property
		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Dict[str, str]:
						return {
						    self.image_column: "image",
						    self.label_column: "labels",
						}




