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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= {}
				if self.train_dir is not None:
							SCREAMING_SNAKE_CASE     						= self.train_dir
				if self.validation_dir is not None:
							SCREAMING_SNAKE_CASE     						= self.validation_dir
				SCREAMING_SNAKE_CASE     						= data_files if data_files else None





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





@dataclass
class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					float    		=	field(
	    default=1e-3 ,		metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}					)



def    a      ( a	)     ->Dict:




			'''simple docstring'''


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


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




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= 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.
						SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
			else:
						SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE						,							SCREAMING_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_mae'''		,						a		,						a	)

			# 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()

			SCREAMING_SNAKE_CASE     						= training_args.get_process_log_level()
			logger.setLevel(a	)
			transformers.utils.logging.set_verbosity(a	)
			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.
			SCREAMING_SNAKE_CASE     						= None
			if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
						SCREAMING_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.
			SCREAMING_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.
			SCREAMING_SNAKE_CASE     						= None if '''validation''' in ds.keys() else data_args.train_val_split
			if isinstance(data_args.train_val_split		,						a	) and data_args.train_val_split > 0.0:
						SCREAMING_SNAKE_CASE     						= ds['''train'''].train_test_split(data_args.train_val_split	)
						SCREAMING_SNAKE_CASE     						= split['''train''']
						SCREAMING_SNAKE_CASE     						= 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.
			SCREAMING_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:
						SCREAMING_SNAKE_CASE     						= ViTMAEConfig.from_pretrained(model_args.config_name		,						**a	)
			elif model_args.model_name_or_path:
						SCREAMING_SNAKE_CASE     						= ViTMAEConfig.from_pretrained(model_args.model_name_or_path		,						**a	)
			else:
						SCREAMING_SNAKE_CASE     						= 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:
						SCREAMING_SNAKE_CASE     						= ViTImageProcessor.from_pretrained(model_args.image_processor_name		,						**a	)
			elif model_args.model_name_or_path:
						SCREAMING_SNAKE_CASE     						= ViTImageProcessor.from_pretrained(model_args.model_name_or_path		,						**a	)
			else:
						SCREAMING_SNAKE_CASE     						= ViTImageProcessor()

			# create model
			if model_args.model_name_or_path:
						SCREAMING_SNAKE_CASE     						= ViTMAEForPreTraining.from_pretrained(
						    model_args.model_name_or_path		,						from_tf=bool('''.ckpt''' in model_args.model_name_or_path	)		,						config=a		,						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'''	)
						SCREAMING_SNAKE_CASE     						= ViTMAEForPreTraining(a	)

			if training_args.do_train:
						SCREAMING_SNAKE_CASE     						= ds['''train'''].column_names
			else:
						SCREAMING_SNAKE_CASE     						= ds['''validation'''].column_names

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

			def preprocess_images(a	):
						SCREAMING_SNAKE_CASE     						= [transforms(a	) 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:
									SCREAMING_SNAKE_CASE     						= ds['''train'''].shuffle(seed=training_args.seed	).select(range(data_args.max_train_samples	)	)
						# Set the training transforms
						ds["train"].set_transform(a	)

			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:
									SCREAMING_SNAKE_CASE     						= (
									    ds['''validation'''].shuffle(seed=training_args.seed	).select(range(data_args.max_eval_samples	)	)
									)
						# Set the validation transforms
						ds["validation"].set_transform(a	)

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

			# Initialize our trainer
			SCREAMING_SNAKE_CASE     						= Trainer(
			    model=a		,						args=a		,						train_dataset=ds['''train'''] if training_args.do_train else None		,						eval_dataset=ds['''validation'''] if training_args.do_eval else None		,						tokenizer=a		,						data_collator=a		,						)

			# Training
			if training_args.do_train:
						SCREAMING_SNAKE_CASE     						= None
						if training_args.resume_from_checkpoint is not None:
									SCREAMING_SNAKE_CASE     						= training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									SCREAMING_SNAKE_CASE     						= last_checkpoint
						SCREAMING_SNAKE_CASE     						= trainer.train(resume_from_checkpoint=a	)
						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:
						SCREAMING_SNAKE_CASE     						= trainer.evaluate()
						trainer.log_metrics('''eval'''		,						a	)
						trainer.save_metrics('''eval'''		,						a	)

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


def    a      ( a	)     ->Tuple:




			'''simple docstring'''


			main()


if __name__ == "__main__":
							main()

import os


def    a      ( a = "matrix.txt"	)     ->int:




			'''simple docstring'''


			with open(os.path.join(os.path.dirname(a	)		,						a	)	) as in_file:
						SCREAMING_SNAKE_CASE     						= in_file.read()

			SCREAMING_SNAKE_CASE     						= [[int(a	) for cell in row.split(''','''	)] for row in data.strip().splitlines()]
			SCREAMING_SNAKE_CASE     						= [[0 for cell in row] for row in grid]
			SCREAMING_SNAKE_CASE     						= len(grid[0]	)

			SCREAMING_SNAKE_CASE     						= [[0 for i in range(a	)] for j in range(a	)]
			SCREAMING_SNAKE_CASE     						= grid[0][0]
			for i in range(1		,						a	):
						SCREAMING_SNAKE_CASE     						= grid[0][i] + dp[0][i - 1]
			for i in range(1		,						a	):
						SCREAMING_SNAKE_CASE     						= grid[i][0] + dp[i - 1][0]

			for i in range(1		,						a	):
						for j in range(1		,						a	):
									SCREAMING_SNAKE_CASE     						= grid[i][j] + min(dp[i - 1][j]		,						dp[i][j - 1]	)

			return dp[-1][-1]


if __name__ == "__main__":
							print(F'''{solution() = }''')