
"""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__   					=		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(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
lowerCamelCase__   					=		tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class  A__       :
		A_						:       Optional[str]				   =	field(
		    default='cifar10'       ,     metadata={'help': 'Name of a dataset from the datasets package'})
		A_						:       Optional[str]				   =	field(
		    default=_lowerCamelCase       ,     metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'})
		A_						:       Optional[str]				   =	field(
		    default=_lowerCamelCase       ,     metadata={'help': 'The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'}       ,     )
		A_						:       Optional[str]				   =	field(default=_lowerCamelCase       ,     metadata={'help': 'A folder containing the training data.'})
		A_						:       Optional[str]				   =	field(default=_lowerCamelCase       ,     metadata={'help': 'A folder containing the validation data.'})
		A_						:       Optional[float]				   =	field(
		    default=0.15       ,     metadata={'help': 'Percent to split off of train for validation.'})
		A_						:       int				   =	field(default=3_2       ,     metadata={'help': 'The size of the square patches to use for masking.'})
		A_						:       float				   =	field(
		    default=0.6       ,     metadata={'help': 'Percentage of patches to mask.'}       ,     )
		A_						:       Optional[int]				   =	field(
		    default=_lowerCamelCase       ,     metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of training examples to this '
		            'value if set.'
		        )
		    }       ,     )
		A_						:       Optional[int]				   =	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			:						Optional[int]									=      {}
			if self.train_dir is not None:
				__lowerCAmelCase			:						Dict									=      self.train_dir
			if self.validation_dir is not None:
				__lowerCAmelCase			:						Dict									=      self.validation_dir
			__lowerCAmelCase			:						str									=      data_files if data_files else None




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




class  A__       :

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE=1_92       ,		_SCREAMING_SNAKE_CASE=32       ,		_SCREAMING_SNAKE_CASE=4       ,		_SCREAMING_SNAKE_CASE=0.6 ):
			__lowerCAmelCase			:						List[str]									=      input_size
			__lowerCAmelCase			:						str									=      mask_patch_size
			__lowerCAmelCase			:						Tuple									=      model_patch_size
			__lowerCAmelCase			:						int									=      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' )

			__lowerCAmelCase			:						str									=      self.input_size // self.mask_patch_size
			__lowerCAmelCase			:						Dict									=      self.mask_patch_size // self.model_patch_size

			__lowerCAmelCase			:						Union[str, Any]									=      self.rand_size**2
			__lowerCAmelCase			:						Any									=      int(np.ceil(self.token_count * self.mask_ratio ) )







		def __call__(      self ):
			__lowerCAmelCase			:						List[Any]									=      np.random.permutation(self.token_count )[: self.mask_count]
			__lowerCAmelCase			:						Union[str, Any]									=      np.zeros(self.token_count       ,		dtype=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Optional[Any]									=      1

			__lowerCAmelCase			:						Any									=      mask.reshape((self.rand_size, self.rand_size) )
			__lowerCAmelCase			:						int									=      mask.repeat(self.scale       ,		axis=0 ).repeat(self.scale       ,		axis=1 )

			return torch.tensor(mask.flatten() )



def     __lowerCAmelCase				(_UpperCamelCase		):
	__lowerCAmelCase			:						Optional[int]									=      torch.stack([example['pixel_values'] for example in examples]		)
	__lowerCAmelCase			:						int									=      torch.stack([example['mask'] for example in examples]		)
	return {"pixel_values": pixel_values, "bool_masked_pos": mask}
def     __lowerCAmelCase				():
	# 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.

	__lowerCAmelCase			:						List[Any]									=      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.
		__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			:						int									=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
	else:
		__lowerCAmelCase			,   __lowerCAmelCase			,   __lowerCAmelCase			:						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_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()

	__lowerCAmelCase			:						Dict									=      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.
	__lowerCAmelCase			:						str									=      None
	if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
		__lowerCAmelCase			:						Optional[Any]									=      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			:						str									=      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			:						Dict									=      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:
		__lowerCAmelCase			:						int									=      ds['train'].train_test_split(data_args.train_val_split		)
		__lowerCAmelCase			:						Optional[Any]									=      split['train']
		__lowerCAmelCase			:						Dict									=      split['test']

	# Create config
	# 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_or_path:
		__lowerCAmelCase			:						Union[str, Any]									=      AutoConfig.from_pretrained(model_args.config_name_or_path       ,  **_UpperCamelCase		)
	elif model_args.model_name_or_path:
		__lowerCAmelCase			:						Dict									=      AutoConfig.from_pretrained(model_args.model_name_or_path       ,  **_UpperCamelCase		)
	else:
		__lowerCAmelCase			:						Optional[int]									=      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'		):
		__lowerCAmelCase			:						List[str]									=      'simmim'

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

	# create model
	if model_args.model_name_or_path:
		__lowerCAmelCase			:						Optional[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'		)
		__lowerCAmelCase			:						Optional[Any]									=      AutoModelForMaskedImageModeling.from_config(_UpperCamelCase		)

	if training_args.do_train:
		__lowerCAmelCase			:						Any									=      ds['train'].column_names
	else:
		__lowerCAmelCase			:						List[str]									=      ds['validation'].column_names

	if data_args.image_column_name is not None:
		__lowerCAmelCase			:						List[Any]									=      data_args.image_column_name
	elif "image" in column_names:
		__lowerCAmelCase			:						Dict									=      'image'
	elif "img" in column_names:
		__lowerCAmelCase			:						Optional[int]									=      'img'
	else:
		__lowerCAmelCase			:						Dict									=      column_names[0]

	# transformations as done in original SimMIM paper
	# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
	__lowerCAmelCase			:						Optional[Any]									=      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
	__lowerCAmelCase			:						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		):
		__lowerCAmelCase			:						List[Any]									=      [transforms(_UpperCamelCase		) for image in examples[image_column_name]]
		__lowerCAmelCase			:						int									=      [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:
			__lowerCAmelCase			:						Optional[Any]									=      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:
			__lowerCAmelCase			:						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
	__lowerCAmelCase			:						List[str]									=      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:
		__lowerCAmelCase			:						Optional[int]									=      None
		if training_args.resume_from_checkpoint is not None:
			__lowerCAmelCase			:						List[str]									=      training_args.resume_from_checkpoint
		elif last_checkpoint is not None:
			__lowerCAmelCase			:						str									=      last_checkpoint
		__lowerCAmelCase			:						Union[str, 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:
		__lowerCAmelCase			:						List[str]									=      trainer.evaluate()
		trainer.log_metrics('eval'       ,  _UpperCamelCase		)
		trainer.save_metrics('eval'       ,  _UpperCamelCase		)

	# Write model card and (optionally) push to hub
	__lowerCAmelCase			:						int									=      {
	    '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()





"""simple docstring"""






import functools
from typing import Any







def      _snake_case			(				lowercase__							,    lowercase__		):

				# Validation
				if not isinstance(lowercase__							,    lowercase__		) or len(lowercase__		) == 0:
								raise ValueError('the string should be not empty string'		)

				if not isinstance(lowercase__							,    lowercase__		) or not all(
				    isinstance(lowercase__							,    lowercase__		) and len(lowercase__		) > 0 for item in words		):
								raise ValueError('the words should be a list of non-empty strings'		)

				# Build trie
				_lowerCamelCase     :			dict[str, Any]    =    {}
				_lowerCamelCase     :			List[Any]    =    'WORD_KEEPER'

				for word in words:
								_lowerCamelCase     :			Dict    =    trie
								for c in word:
												if c not in trie_node:
																_lowerCamelCase     :			Any    =    {}

												_lowerCamelCase     :			str    =    trie_node[c]

								_lowerCamelCase     :			Optional[Any]    =    True

				_lowerCamelCase     :			Dict    =    len(lowercase__		)

				# Dynamic programming method
				@functools.cache
				def is_breakable(lowercase__		) -> bool:
								if index == len_string:
												return True

								_lowerCamelCase     :			List[Any]    =    trie
								for i in range(lowercase__							,    lowercase__		):
												_lowerCamelCase     :			Any    =    trie_node.get(string[i]							,    lowercase__		)

												if trie_node is None:
																return False

												if trie_node.get(lowercase__							,    lowercase__		) and is_breakable(i + 1		):
																return True

								return False

				return is_breakable(0		)


if __name__ == "__main__":
			import doctest

			doctest.testmod()