



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
_lowerCAmelCase	:   List[str]    =   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 	__snake_case   :
		SCREAMING_SNAKE_CASE__								=	field(
		    default='cifar10'							, metadata={'help': 'Name of a dataset from the datasets package'} )
		SCREAMING_SNAKE_CASE__								=	field(
		    default=__snake_case							, metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
		SCREAMING_SNAKE_CASE__								=	field(
		    default=__snake_case							, metadata={'help': 'The column name of the images in the files.'} )
		SCREAMING_SNAKE_CASE__								=	field(default=__snake_case							, metadata={'help': 'A folder containing the training data.'} )
		SCREAMING_SNAKE_CASE__								=	field(default=__snake_case							, metadata={'help': 'A folder containing the validation data.'} )
		SCREAMING_SNAKE_CASE__								=	field(
		    default=0.1_5							, metadata={'help': 'Percent to split off of train for validation.'} )
		SCREAMING_SNAKE_CASE__								=	field(
		    default=__snake_case							, metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of training examples to this '
		            'value if set.'
		        )
		    }							, )
		SCREAMING_SNAKE_CASE__								=	field(
		    default=__snake_case							, metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
		            'value if set.'
		        )
		    }							, )
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  {}
						if self.train_dir is not None:
										lowerCAmelCase__		  =  self.train_dir
						if self.validation_dir is not None:
										lowerCAmelCase__		  =  self.validation_dir
						lowerCAmelCase__		  =  data_files if data_files else None



@dataclass
class 	__snake_case   :
		SCREAMING_SNAKE_CASE__								=	field(
		    default=__snake_case							, metadata={
		        'help': (
		            'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
		        )
		    }							, )
		SCREAMING_SNAKE_CASE__								=	field(
		    default=__snake_case							, metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'} )
		SCREAMING_SNAKE_CASE__								=	field(
		    default=__snake_case							, 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'
		        )
		    }							, )
		SCREAMING_SNAKE_CASE__								=	field(
		    default=__snake_case							, metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'} )
		SCREAMING_SNAKE_CASE__								=	field(
		    default='main'							, metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}							, )
		SCREAMING_SNAKE_CASE__								=	field(default=__snake_case							, metadata={'help': 'Name or path of preprocessor config.'} )
		SCREAMING_SNAKE_CASE__								=	field(
		    default=__snake_case							, metadata={
		        'help': (
		            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
		            'with private models).'
		        )
		    }							, )
		SCREAMING_SNAKE_CASE__								=	field(
		    default=0.7_5							, metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'} )
		SCREAMING_SNAKE_CASE__								=	field(
		    default=__snake_case							, metadata={'help': 'Whether or not to train with normalized pixel values as target.'} )



@dataclass
class 	__snake_case   ( __snake_case ):
		SCREAMING_SNAKE_CASE__								=	field(
		    default=1E-3							, metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'} )







def     UpperCAmelCase_		(					snake_case__					)	->     List[Any]:



				"""simple docstring"""





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







def     UpperCAmelCase_		(					)	->     Optional[Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  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.
								lowerCAmelCase__		  =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
				else:
								lowerCAmelCase__		  =  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'    ,			_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__		  =  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__		  =  None
				if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
								lowerCAmelCase__		  =  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__		  =  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__		  =  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__		  =  ds["train"].train_test_split(data_args.train_val_split					)
								lowerCAmelCase__		  =  split["train"]
								lowerCAmelCase__		  =  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.
				lowerCAmelCase__		  =  {
				    "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:
								lowerCAmelCase__		  =  ViTMAEConfig.from_pretrained(model_args.config_name    ,			**_UpperCAmelCase					)
				elif model_args.model_name_or_path:
								lowerCAmelCase__		  =  ViTMAEConfig.from_pretrained(model_args.model_name_or_path    ,			**_UpperCAmelCase					)
				else:
								lowerCAmelCase__		  =  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:
								lowerCAmelCase__		  =  ViTImageProcessor.from_pretrained(model_args.image_processor_name    ,			**_UpperCAmelCase					)
				elif model_args.model_name_or_path:
								lowerCAmelCase__		  =  ViTImageProcessor.from_pretrained(model_args.model_name_or_path    ,			**_UpperCAmelCase					)
				else:
								lowerCAmelCase__		  =  ViTImageProcessor()

				# create model
				if model_args.model_name_or_path:
								lowerCAmelCase__		  =  ViTMAEForPreTraining.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__		  =  ViTMAEForPreTraining(_UpperCAmelCase					)

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

				if data_args.image_column_name is not None:
								lowerCAmelCase__		  =  data_args.image_column_name
				elif "image" in column_names:
								lowerCAmelCase__		  =  "image"
				elif "img" in column_names:
								lowerCAmelCase__		  =  "img"
				else:
								lowerCAmelCase__		  =  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:
								lowerCAmelCase__		  =  image_processor.size["shortest_edge"]
				else:
								lowerCAmelCase__		  =  (image_processor.size["height"], image_processor.size["width"])
				lowerCAmelCase__		  =  Compose(
				    [
				        Lambda(lambda snake_case__					: img.convert('RGB'					) if img.mode != "RGB" else img					),
				        RandomResizedCrop(_UpperCAmelCase    ,			scale=(0.2, 1.0)    ,			interpolation=InterpolationMode.BICUBIC					),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        Normalize(mean=image_processor.image_mean    ,			std=image_processor.image_std					),
				    ]					)

				def preprocess_images(snake_case__					):
								lowerCAmelCase__		  =  [transforms(_UpperCAmelCase					) 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:
												lowerCAmelCase__		  =  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__		  =  (
												    ds["validation"].shuffle(seed=training_args.seed					).select(range(data_args.max_eval_samples					)					)
												)
								# Set the validation transforms
								ds["validation"].set_transform(_UpperCAmelCase					)

				# Compute absolute learning rate
				lowerCAmelCase__		  =  (
				    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
				)
				if training_args.base_learning_rate is not None:
								lowerCAmelCase__		  =  training_args.base_learning_rate * total_train_batch_size / 256

				# Initialize our trainer
				lowerCAmelCase__		  =  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__		  =  None
								if training_args.resume_from_checkpoint is not None:
												lowerCAmelCase__		  =  training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												lowerCAmelCase__		  =  last_checkpoint
								lowerCAmelCase__		  =  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__		  =  trainer.evaluate()
								trainer.log_metrics('eval'    ,			_UpperCAmelCase					)
								trainer.save_metrics('eval'    ,			_UpperCAmelCase					)

				# Write model card and (optionally) push to hub
				lowerCAmelCase__		  =  {
				    "tasks": "masked-auto-encoding",
				    "dataset": data_args.dataset_name,
				    "tags": ["masked-auto-encoding"],
				}
				if training_args.push_to_hub:
								trainer.push_to_hub(**_UpperCAmelCase					)
				else:
								trainer.create_model_card(**_UpperCAmelCase					)







def     UpperCAmelCase_		(					snake_case__					)	->     Union[str, Any]:



				"""simple docstring"""





				main()


if __name__ == "__main__":
	main()


import functools
from typing import Any



def 		UpperCamelCase     (       _UpperCAmelCase  :   str ,			_UpperCAmelCase  :   list[str]  )   ->			bool:






						'''simple docstring'''

						if not isinstance(_UpperCAmelCase ,			_UpperCAmelCase  ) or len(_UpperCAmelCase  ) == 0:
												raise ValueError("the string should be not empty string"  )

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

						# Build trie
						_lowercase       :	dict[str, Any]           =   {}
						_lowercase       :	List[Any]           =   "WORD_KEEPER"

						for word in words:
												_lowercase       :	Optional[Any]           =   trie
												for c in word:
																		if c not in trie_node:
																								_lowercase       :	List[Any]           =   {}

																		_lowercase       :	Optional[int]           =   trie_node[c]

												_lowercase       :	str           =   True

						_lowercase       :	int           =   len(_UpperCAmelCase  )

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

												_lowercase       :	Union[str, Any]           =   trie
												for i in range(_UpperCAmelCase ,			_UpperCAmelCase  ):
																		_lowercase       :	List[str]           =   trie_node.get(string[i] ,			_UpperCAmelCase  )

																		if trie_node is None:
																								return False

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

												return False

						return is_breakable(0  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




