





def    A				(							UpperCAmelCase				):
					return [
					    txt[:a] + txt[a].upper() + txt[a + 1 :]
					    for a in range(len(lowerCamelCase_				)				)
					    if txt[a].isalpha()
					]


if __name__ == "__main__":
							__import__('doctest').testmod()






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 :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')

__lowerCAmelCase :Union[str, Any]     =    list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
__lowerCAmelCase :Optional[Any]     =    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class        _a:
		lowerCamelCase__     :Optional[str]   				=		field(
		    default='cifar10'	,   metadata={'help': 'Name of a dataset from the datasets package'}    )
		lowerCamelCase__     :Optional[str]   				=		field(
		    default=__A	,   metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}    )
		lowerCamelCase__     :Optional[str]   				=		field(
		    default=__A	,   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=__A	,   metadata={'help': 'A folder containing the training data.'}    )
		lowerCamelCase__     :Optional[str]   				=		field(default=__A	,   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=__A	,   metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of training examples to this '
		            'value if set.'
		        )
		    }	,   )
		lowerCamelCase__     :Optional[int]   				=		field(
		    default=__A	,   metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
		            'value if set.'
		        )
		    }	,   )







		def        lowercase       (    self				)      ->      List[Any]:


							'''simple docstring'''

							_snake_case       :					List[Any]				       =   {}
							if self.train_dir is not None:
												_snake_case       :					int				       =   self.train_dir
							if self.validation_dir is not None:
												_snake_case       :					str				       =   self.validation_dir
							_snake_case       :					Optional[int]				       =   data_files if data_files else None






@dataclass
class        _a:
		lowerCamelCase__     :str   				=		field(
		    default=__A	,   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=__A	,   metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(__A    )}	,   )
		lowerCamelCase__     :Optional[str]   				=		field(
		    default=__A	,   metadata={'help': 'Pretrained config name or path if not the same as model_name'}    )
		lowerCamelCase__     :Optional[str]   				=		field(
		    default=__A	,   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=__A	,   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=__A	,   metadata={'help': 'Name or path of preprocessor config.'}    )
		lowerCamelCase__     :bool   				=		field(
		    default=__A	,   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=__A	,   metadata={
		        'help': (
		            'The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'
		        )
		    }	,   )
		lowerCamelCase__     :Optional[int]   				=		field(
		    default=__A	,   metadata={
		        'help': (
		            'The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'
		        )
		    }	,   )
		lowerCamelCase__     :Optional[int]   				=		field(
		    default=__A	,   metadata={'help': 'Stride to use for the encoder.'}	,   )






class        _a:







		def __init__(    self ,						__snake_case=1_9_2 ,						__snake_case=3_2 ,						__snake_case=4 ,						__snake_case=0.6				)      ->      Union[str, Any]:


							'''simple docstring'''

							_snake_case       :					Optional[int]				       =   input_size
							_snake_case       :					Optional[int]				       =   mask_patch_size
							_snake_case       :					Optional[Any]				       =   model_patch_size
							_snake_case       :					Any				       =   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       :					Optional[int]				       =   self.input_size // self.mask_patch_size
							_snake_case       :					Optional[int]				       =   self.mask_patch_size // self.model_patch_size

							_snake_case       :					List[str]				       =   self.rand_size**2
							_snake_case       :					Any				       =   int(np.ceil(self.token_count * self.mask_ratio				)				)







		def __call__(    self				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   np.random.permutation(self.token_count				)[: self.mask_count]
							_snake_case       :					Dict				       =   np.zeros(self.token_count ,						dtype=__snake_case				)
							_snake_case       :					Any				       =   1

							_snake_case       :					Tuple				       =   mask.reshape((self.rand_size, self.rand_size)				)
							_snake_case       :					List[Any]				       =   mask.repeat(self.scale ,						axis=0				).repeat(self.scale ,						axis=1				)

							return torch.tensor(mask.flatten()				)







def    A				(							UpperCAmelCase				):
					_snake_case       :					List[Any]				       =   torch.stack([example["pixel_values"] for example in examples]				)
					_snake_case       :					Any				       =   torch.stack([example["mask"] for example in examples]				)
					return {"pixel_values": pixel_values, "bool_masked_pos": mask}







def    A				(							):
					# 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.

					_snake_case       :					Optional[int]				       =   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       :					int				       =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
					else:
										_snake_case       , _snake_case       , _snake_case       :					Dict				       =   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"  ,  UpperCAmelCase  ,  UpperCAmelCase				)

					# 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       :					Any				       =   training_args.get_process_log_level()
					logger.setLevel(UpperCAmelCase				)
					transformers.utils.logging.set_verbosity(UpperCAmelCase				)
					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       :					Tuple				       =   None
					if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
										_snake_case       :					Optional[int]				       =   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       :					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.
					_snake_case       :					str				       =   None if "validation" in ds.keys() else data_args.train_val_split
					if isinstance(data_args.train_val_split  ,  UpperCAmelCase				) and data_args.train_val_split > 0.0:
										_snake_case       :					Tuple				       =   ds["train"].train_test_split(data_args.train_val_split				)
										_snake_case       :					List[str]				       =   split["train"]
										_snake_case       :					Optional[Any]				       =   split["test"]

					# Create config
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.
					_snake_case       :					Optional[int]				       =   {
					    "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       :					Union[str, Any]				       =   AutoConfig.from_pretrained(model_args.config_name_or_path  ,  **UpperCAmelCase				)
					elif model_args.model_name_or_path:
										_snake_case       :					int				       =   AutoConfig.from_pretrained(model_args.model_name_or_path  ,  **UpperCAmelCase				)
					else:
										_snake_case       :					Dict				       =   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(UpperCAmelCase  ,  "decoder_type"				):
										_snake_case       :					Tuple				       =   "simmim"

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

					# create model
					if model_args.model_name_or_path:
										_snake_case       :					Union[str, Any]				       =   AutoModelForMaskedImageModeling.from_pretrained(
										    model_args.model_name_or_path  ,  from_tf=bool(".ckpt" in model_args.model_name_or_path				)  ,  config=UpperCAmelCase  ,  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       :					Any				       =   AutoModelForMaskedImageModeling.from_config(UpperCAmelCase				)

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

					if data_args.image_column_name is not None:
										_snake_case       :					Dict				       =   data_args.image_column_name
					elif "image" in column_names:
										_snake_case       :					Tuple				       =   "image"
					elif "img" in column_names:
										_snake_case       :					str				       =   "img"
					else:
										_snake_case       :					Dict				       =   column_names[0]

					# transformations as done in original SimMIM paper
					# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
					_snake_case       :					str				       =   Compose(
					    [
					        Lambda(lambda UpperCAmelCase				: img.convert("RGB"				) if img.mode != "RGB" else img				),
					        RandomResizedCrop(model_args.image_size  ,  scale=(0.6_7, 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       :					List[str]				       =   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				):
										_snake_case       :					str				       =   [transforms(UpperCAmelCase				) for image in examples[image_column_name]]
										_snake_case       :					List[Any]				       =   [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       :					Dict				       =   ds["train"].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
										# Set the training transforms
										ds["train"].set_transform(UpperCAmelCase				)

					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       :					Union[str, Any]				       =   (
															    ds["validation"].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
															)
										# Set the validation transforms
										ds["validation"].set_transform(UpperCAmelCase				)

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

					# Training
					if training_args.do_train:
										_snake_case       :					Dict				       =   None
										if training_args.resume_from_checkpoint is not None:
															_snake_case       :					List[Any]				       =   training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															_snake_case       :					Optional[int]				       =   last_checkpoint
										_snake_case       :					Optional[Any]				       =   trainer.train(resume_from_checkpoint=UpperCAmelCase				)
										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       :					Optional[Any]				       =   trainer.evaluate()
										trainer.log_metrics("eval"  ,  UpperCAmelCase				)
										trainer.save_metrics("eval"  ,  UpperCAmelCase				)

					# Write model card and (optionally) push to hub
					_snake_case       :					Optional[Any]				       =   {
					    "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(**UpperCAmelCase				)
					else:
										trainer.create_model_card(**UpperCAmelCase				)


if __name__ == "__main__":
							main()