

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
A_ :				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')



@dataclass
class 					_a		:


					'''simple docstring'''






					UpperCAmelCase__:     Tuple													= field(
					    default='''cifar10'''	,     metadata={'''help''': '''Name of a dataset from the datasets package'''}       )
					UpperCAmelCase__:     Union[str, Any]													= field(
					    default=UpperCamelCase_	,     metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}       )
					UpperCAmelCase__:     Any													= field(
					    default=UpperCamelCase_	,     metadata={'''help''': '''The column name of the images in the files.'''}       )
					UpperCAmelCase__:     List[Any]													= field(default=UpperCamelCase_	,     metadata={'''help''': '''A folder containing the training data.'''}       )
					UpperCAmelCase__:     int													= field(default=UpperCamelCase_	,     metadata={'''help''': '''A folder containing the validation data.'''}       )
					UpperCAmelCase__:     Optional[Any]													= field(
					    default=0.15	,     metadata={'''help''': '''Percent to split off of train for validation.'''}       )
					UpperCAmelCase__:     int													= field(
					    default=UpperCamelCase_	,     metadata={
					        '''help''': (
					            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
					            '''value if set.'''
					        )
					    }	,     )
					UpperCAmelCase__:     Dict													= field(
					    default=UpperCamelCase_	,     metadata={
					        '''help''': (
					            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
					            '''value if set.'''
					        )
					    }	,     )






					def 						__A    (				self    ):
						A__							:					Optional[Any]       =	{}
						if self.train_dir is not None:
							A__							:					List[str]       =	self.train_dir
						if self.validation_dir is not None:
							A__							:					str       =	self.validation_dir
						A__							:					Union[str, Any]       =	data_files if data_files else None



@dataclass
class 					_a		:


					'''simple docstring'''






					UpperCAmelCase__:     List[Any]													= field(
					    default=UpperCamelCase_	,     metadata={
					        '''help''': (
					            '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
					        )
					    }	,     )
					UpperCAmelCase__:     List[str]													= field(
					    default=UpperCamelCase_	,     metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''}       )
					UpperCAmelCase__:     Union[str, Any]													= 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'''
					        )
					    }	,     )
					UpperCAmelCase__:     Dict													= field(
					    default=UpperCamelCase_	,     metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''}       )
					UpperCAmelCase__:     Optional[Any]													= field(
					    default='''main'''	,     metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}	,     )
					UpperCAmelCase__:     Tuple													= field(default=UpperCamelCase_	,     metadata={'''help''': '''Name or path of preprocessor config.'''}       )
					UpperCAmelCase__:     str													= field(
					    default=UpperCamelCase_	,     metadata={
					        '''help''': (
					            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
					            '''with private models).'''
					        )
					    }	,     )
					UpperCAmelCase__:     Dict													= field(
					    default=0.75	,     metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''}       )
					UpperCAmelCase__:     List[Any]													= field(
					    default=UpperCamelCase_	,     metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''}       )



@dataclass
class 					_a		(UpperCamelCase_       ):


					'''simple docstring'''






					UpperCAmelCase__:     Optional[int]													= field(
					    default=1E-3	,     metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''}       )



def 		UpperCamelCase      (lowercase_:				int      )  ->   Any:
	A__							:					Optional[int]       =	torch.stack([example["""pixel_values"""] for example in examples]      )
	return {"pixel_values": pixel_values}

def 		UpperCamelCase      ()  ->   Dict:
	# 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.

	A__							:					List[Any]       =	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.
		A__							:					Optional[int]       =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
	else:
		A__							:					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_mae"""					,   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()

	A__							:					Optional[Any]       =	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.
	A__							:					List[str]       =	None
	if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
		A__							:					Tuple       =	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.
	A__							:					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.
	A__							:					Any       =	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:
		A__							:					List[Any]       =	ds['''train'''].train_test_split(data_args.train_val_split      )
		A__							:					int       =	split['''train''']
		A__							:					Optional[int]       =	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.
	A__							:					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:
		A__							:					Dict       =	ViTMAEConfig.from_pretrained(model_args.config_name					,   **snake_case_      )
	elif model_args.model_name_or_path:
		A__							:					Any       =	ViTMAEConfig.from_pretrained(model_args.model_name_or_path					,   **snake_case_      )
	else:
		A__							:					Any       =	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:
		A__							:					List[str]       =	ViTImageProcessor.from_pretrained(model_args.image_processor_name					,   **snake_case_      )
	elif model_args.model_name_or_path:
		A__							:					Optional[Any]       =	ViTImageProcessor.from_pretrained(model_args.model_name_or_path					,   **snake_case_      )
	else:
		A__							:					Any       =	ViTImageProcessor()

	# create model
	if model_args.model_name_or_path:
		A__							:					int       =	ViTMAEForPreTraining.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"""      )
		A__							:					Union[str, Any]       =	ViTMAEForPreTraining(snake_case_      )

	if training_args.do_train:
		A__							:					int       =	ds['''train'''].column_names
	else:
		A__							:					Optional[Any]       =	ds['''validation'''].column_names

	if data_args.image_column_name is not None:
		A__							:					Optional[int]       =	data_args.image_column_name
	elif "image" in column_names:
		A__							:					List[Any]       =	'''image'''
	elif "img" in column_names:
		A__							:					Dict       =	'''img'''
	else:
		A__							:					List[str]       =	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:
		A__							:					Optional[int]       =	image_processor.size['''shortest_edge''']
	else:
		A__							:					Union[str, Any]       =	(image_processor.size['''height'''], image_processor.size['''width'''])
	A__							:					Union[str, Any]       =	Compose(
	    [
	        Lambda(lambda lowercase_      : img.convert("""RGB"""      ) if img.mode != "RGB" else img      ),
	        RandomResizedCrop(snake_case_					,   scale=(0.2, 1.0)					,   interpolation=InterpolationMode.BICUBIC      ),
	        RandomHorizontalFlip(),
	        ToTensor(),
	        Normalize(mean=image_processor.image_mean					,   std=image_processor.image_std      ),
	    ]      )

	def preprocess_images(lowercase_:				List[Any]      ):
		A__							:					Any       =	[transforms(snake_case_      ) 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:
			A__							:					Any       =	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:
			A__							:					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(snake_case_      )

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

	# Initialize our trainer
	A__							:					Dict       =	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:
		A__							:					Optional[Any]       =	None
		if training_args.resume_from_checkpoint is not None:
			A__							:					List[str]       =	training_args.resume_from_checkpoint
		elif last_checkpoint is not None:
			A__							:					List[str]       =	last_checkpoint
		A__							:					Any       =	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:
		A__							:					Dict       =	trainer.evaluate()
		trainer.log_metrics("""eval"""					,   snake_case_      )
		trainer.save_metrics("""eval"""					,   snake_case_      )

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

def 		UpperCamelCase      (lowercase_:				Tuple      )  ->   Dict:
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
	main()







def 		UpperCamelCase      (lowercase_:				str					,   lowercase_:				str      )  ->   bool:
	A__							:					Union[str, Any]       =	len(lowercase_      )
	A__							:					List[Any]       =	len(lowercase_      )
	A__							:					List[Any]       =	[[False for _ in range(m + 1      )] for _ in range(n + 1      )]
	A__							:					str       =	True
	for i in range(lowercase_      ):
		for j in range(m + 1      ):
			if dp[i][j]:
				if j < m and a[i].upper() == b[j]:
					A__							:					int       =	True
				if a[i].islower():
					A__							:					Dict       =	True
	return dp[n][m]


if __name__ == "__main__":
	import doctest

	doctest.testmod()



