"""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
SCREAMING_SNAKE_CASE__					  =     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")

SCREAMING_SNAKE_CASE__					  =     list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
SCREAMING_SNAKE_CASE__					  =     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class      lowerCAmelCase_			:



				"""simple docstring"""
				_lowerCAmelCase   :			Optional[str]	     =  field(
				    default="""cifar10"""   ,			metadata={"""help""": """Name of a dataset from the datasets package"""}    )
				_lowerCAmelCase   :			Optional[str]	     =  field(
				    default=UpperCamelCase__   ,			metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}    )
				_lowerCAmelCase   :			Optional[str]	     =  field(
				    default=UpperCamelCase__   ,			metadata={"""help""": """The column name of the images in the files. If not set, will try to use 'image' or 'img'."""}   ,			)
				_lowerCAmelCase   :			Optional[str]	     =  field(default=UpperCamelCase__   ,			metadata={"""help""": """A folder containing the training data."""}    )
				_lowerCAmelCase   :			Optional[str]	     =  field(default=UpperCamelCase__   ,			metadata={"""help""": """A folder containing the validation data."""}    )
				_lowerCAmelCase   :			Optional[float]	     =  field(
				    default=0.15   ,			metadata={"""help""": """Percent to split off of train for validation."""}    )
				_lowerCAmelCase   :			int	     =  field(default=32   ,			metadata={"""help""": """The size of the square patches to use for masking."""}    )
				_lowerCAmelCase   :			float	     =  field(
				    default=0.6   ,			metadata={"""help""": """Percentage of patches to mask."""}   ,			)
				_lowerCAmelCase   :			Optional[int]	     =  field(
				    default=UpperCamelCase__   ,			metadata={
				        """help""": (
				            """For debugging purposes or quicker training, truncate the number of training examples to this """
				            """value if set."""
				        )
				    }   ,			)
				_lowerCAmelCase   :			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 						snake_case       ( self			):






										"""simple docstring"""

										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      lowerCAmelCase_			:



				"""simple docstring"""
				_lowerCAmelCase   :			str	     =  field(
				    default=UpperCamelCase__   ,			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."""
				        )
				    }   ,			)
				_lowerCAmelCase   :			Optional[str]	     =  field(
				    default=UpperCamelCase__   ,			metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(UpperCamelCase__    )}   ,			)
				_lowerCAmelCase   :			Optional[str]	     =  field(
				    default=UpperCamelCase__   ,			metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}    )
				_lowerCAmelCase   :			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"""
				        )
				    }   ,			)
				_lowerCAmelCase   :			Optional[str]	     =  field(
				    default=UpperCamelCase__   ,			metadata={"""help""": """Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"""}   ,			)
				_lowerCAmelCase   :			str	     =  field(
				    default="""main"""   ,			metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}   ,			)
				_lowerCAmelCase   :			str	     =  field(default=UpperCamelCase__   ,			metadata={"""help""": """Name or path of preprocessor config."""}    )
				_lowerCAmelCase   :			bool	     =  field(
				    default=UpperCamelCase__   ,			metadata={
				        """help""": (
				            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
				            """with private models)."""
				        )
				    }   ,			)
				_lowerCAmelCase   :			Optional[int]	     =  field(
				    default=UpperCamelCase__   ,			metadata={
				        """help""": (
				            """The size (resolution) of each image. If not specified, will use `image_size` of the configuration."""
				        )
				    }   ,			)
				_lowerCAmelCase   :			Optional[int]	     =  field(
				    default=UpperCamelCase__   ,			metadata={
				        """help""": (
				            """The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."""
				        )
				    }   ,			)
				_lowerCAmelCase   :			Optional[int]	     =  field(
				    default=UpperCamelCase__   ,			metadata={"""help""": """Stride to use for the encoder."""}   ,			)





class      lowerCAmelCase_			:



				"""simple docstring"""

				def __init__( self						,			lowerCAmelCase=1_92						,			lowerCAmelCase=32						,			lowerCAmelCase=4						,			lowerCAmelCase=0.6			):






										"""simple docstring"""

										snake_case	    =	input_size
										snake_case	    =	mask_patch_size
										snake_case	    =	model_patch_size
										snake_case	    =	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'			)

										snake_case	    =	self.input_size // self.mask_patch_size
										snake_case	    =	self.mask_patch_size // self.model_patch_size

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

				def __call__( self			):






										"""simple docstring"""

										snake_case	    =	np.random.permutation(self.token_count			)[: self.mask_count]
										snake_case	    =	np.zeros(self.token_count						,			dtype=__a			)
										snake_case	    =	1

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

										return torch.tensor(mask.flatten()			)





def       lowerCAmelCase__			(	_UpperCamelCase				: List[Any]    )    -> Union[str, Any]:





						"""simple docstring"""






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





def       lowerCAmelCase__			(	)    -> List[Any]:





						"""simple docstring"""






						snake_case	    =	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.
												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_mim'       ,	snake_case_       ,	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()

						snake_case	    =	training_args.get_process_log_level()
						logger.setLevel(snake_case_    )
						transformers.utils.logging.set_verbosity(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.
						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       ,	snake_case_    ) 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']

						# Create config
						# 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_or_path:
												snake_case	    =	AutoConfig.from_pretrained(model_args.config_name_or_path       ,	**snake_case_    )
						elif model_args.model_name_or_path:
												snake_case	    =	AutoConfig.from_pretrained(model_args.model_name_or_path       ,	**snake_case_    )
						else:
												snake_case	    =	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(snake_case_       ,	'decoder_type'    ):
												snake_case	    =	'simmim'

						# adapt config
						snake_case	    =	model_args.image_size if model_args.image_size is not None else config.image_size
						snake_case	    =	model_args.patch_size if model_args.patch_size is not None else config.patch_size
						snake_case	    =	(
						    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:
												snake_case	    =	AutoImageProcessor.from_pretrained(model_args.image_processor_name       ,	**snake_case_    )
						elif model_args.model_name_or_path:
												snake_case	    =	AutoImageProcessor.from_pretrained(model_args.model_name_or_path       ,	**snake_case_    )
						else:
												snake_case	    =	{
												    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
												}
												snake_case	    =	IMAGE_PROCESSOR_TYPES[model_args.model_type]()

						# create model
						if model_args.model_name_or_path:
												snake_case	    =	AutoModelForMaskedImageModeling.from_pretrained(
												    model_args.model_name_or_path       ,	from_tf=bool('.ckpt' in model_args.model_name_or_path    )       ,	config=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'    )
												snake_case	    =	AutoModelForMaskedImageModeling.from_config(snake_case_    )

						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 SimMIM paper
						# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
						snake_case	    =	Compose(
						    [
						        Lambda(lambda _UpperCamelCase    : 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
						snake_case	    =	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(_UpperCamelCase				: List[str]    ):
												snake_case	    =	[transforms(snake_case_    ) for image in examples[image_column_name]]
												snake_case	    =	[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:
																		snake_case	    =	ds['train'].shuffle(seed=training_args.seed    ).select(range(data_args.max_train_samples    )    )
												# Set the training transforms
												ds["train"].set_transform(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:
																		snake_case	    =	(
																		    ds['validation'].shuffle(seed=training_args.seed    ).select(range(data_args.max_eval_samples    )    )
																		)
												# Set the validation transforms
												ds["validation"].set_transform(snake_case_    )

						# Initialize our trainer
						snake_case	    =	Trainer(
						    model=snake_case_       ,	args=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=snake_case_       ,	data_collator=snake_case_       ,	)

						# 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=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:
												snake_case	    =	trainer.evaluate()
												trainer.log_metrics('eval'       ,	snake_case_    )
												trainer.save_metrics('eval'       ,	snake_case_    )

						# Write model card and (optionally) push to hub
						snake_case	    =	{
						    '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(**snake_case_    )
						else:
												trainer.create_model_card(**snake_case_    )


if __name__ == "__main__":
					main()



'''simple docstring'''





def 	lowerCAmelCase_				(					snake_case_       : int					,    snake_case_       : int			)    ->      str:


     '''simple docstring'''




     if a < 0 or b < 0:
          raise ValueError("the value of both inputs must be positive"			)
     UpperCAmelCase_           =   str(bin(snake_case_			)			)[2:]  # remove the leading "0b"
     UpperCAmelCase_           =   str(bin(snake_case_			)			)[2:]
     UpperCAmelCase_           =   max(len(snake_case_			)					,    len(snake_case_			)			)
     return "0b" + "".join(
         str(int("1" in (char_a, char_b)			)			)
         for char_a, char_b in zip(a_binary.zfill(snake_case_			)					,    b_binary.zfill(snake_case_			)			)			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()




