






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
UpperCamelCase        =				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_				:







				"""simple docstring"""




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







				def 				__a   ( self     :List[str]     ):
					UpperCamelCase__ :Optional[Any]						=      {}
					if self.train_dir is not None:
						UpperCamelCase__ :int						=      self.train_dir
					if self.validation_dir is not None:
						UpperCamelCase__ :List[str]						=      self.validation_dir
					UpperCamelCase__ :Optional[int]						=      data_files if data_files else None

@dataclass
class        lowerCAmelCase_				:







				"""simple docstring"""




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

@dataclass
class        lowerCAmelCase_				(      lowercase	):







				"""simple docstring"""




				_snake_case				:   float        =			field(
				    default=1e-3						,      metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""}	)




def 				A		(					lowercase__  :  Union[str, Any]  )		-> Dict:
	UpperCamelCase__ :Union[str, Any]						=      torch.stack([example["""pixel_values"""] for example in examples]  )
	return {"pixel_values": pixel_values}




def 				A		(					)		-> Optional[int]:
	# 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.

	UpperCamelCase__ :Optional[int]						=      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.
		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__ :str						=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
	else:
		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__ :Union[str, 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_mae""" ,		lowercase__ ,		lowercase__  )

	# 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()

	UpperCamelCase__ :List[str]						=      training_args.get_process_log_level()
	logger.setLevel(lowercase__  )
	transformers.utils.logging.set_verbosity(lowercase__  )
	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.
	UpperCamelCase__ :Union[str, Any]						=      None
	if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
		UpperCamelCase__ :List[str]						=      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.
	UpperCamelCase__ :Tuple						=      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.
	UpperCamelCase__ :int						=      None if """validation""" in ds.keys() else data_args.train_val_split
	if isinstance(data_args.train_val_split ,		lowercase__  ) and data_args.train_val_split > 0.0:
		UpperCamelCase__ :Optional[Any]						=      ds["""train"""].train_test_split(data_args.train_val_split  )
		UpperCamelCase__ :Union[str, Any]						=      split["""train"""]
		UpperCamelCase__ :Any						=      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.
	UpperCamelCase__ :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:
		UpperCamelCase__ :Any						=      ViTMAEConfig.from_pretrained(model_args.config_name ,		**lowercase__  )
	elif model_args.model_name_or_path:
		UpperCamelCase__ :Union[str, Any]						=      ViTMAEConfig.from_pretrained(model_args.model_name_or_path ,		**lowercase__  )
	else:
		UpperCamelCase__ :Optional[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:
		UpperCamelCase__ :str						=      ViTImageProcessor.from_pretrained(model_args.image_processor_name ,		**lowercase__  )
	elif model_args.model_name_or_path:
		UpperCamelCase__ :Dict						=      ViTImageProcessor.from_pretrained(model_args.model_name_or_path ,		**lowercase__  )
	else:
		UpperCamelCase__ :Tuple						=      ViTImageProcessor()

	# create model
	if model_args.model_name_or_path:
		UpperCamelCase__ :Any						=      ViTMAEForPreTraining.from_pretrained(
		    model_args.model_name_or_path ,		from_tf=bool(""".ckpt""" in model_args.model_name_or_path  ) ,		config=lowercase__ ,		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"""  )
		UpperCamelCase__ :Optional[int]						=      ViTMAEForPreTraining(lowercase__  )

	if training_args.do_train:
		UpperCamelCase__ :Optional[Any]						=      ds["""train"""].column_names
	else:
		UpperCamelCase__ :Union[str, Any]						=      ds["""validation"""].column_names

	if data_args.image_column_name is not None:
		UpperCamelCase__ :Union[str, Any]						=      data_args.image_column_name
	elif "image" in column_names:
		UpperCamelCase__ :Optional[Any]						=      """image"""
	elif "img" in column_names:
		UpperCamelCase__ :List[str]						=      """img"""
	else:
		UpperCamelCase__ :List[Any]						=      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:
		UpperCamelCase__ :List[str]						=      image_processor.size["""shortest_edge"""]
	else:
		UpperCamelCase__ :int						=      (image_processor.size["""height"""], image_processor.size["""width"""])
	UpperCamelCase__ :Any						=      Compose(
	    [
	        Lambda(lambda lowercase__  : img.convert("""RGB"""  ) if img.mode != "RGB" else img  ),
	        RandomResizedCrop(lowercase__ ,		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__  :  Tuple  ):
		UpperCamelCase__ :List[Any]						=      [transforms(lowercase__  ) 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:
			UpperCamelCase__ :Optional[int]						=      ds["""train"""].shuffle(seed=training_args.seed  ).select(range(data_args.max_train_samples  )  )
		# Set the training transforms
		ds["train"].set_transform(lowercase__  )

	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:
			UpperCamelCase__ :Optional[Any]						=      (
			    ds["""validation"""].shuffle(seed=training_args.seed  ).select(range(data_args.max_eval_samples  )  )
			)
		# Set the validation transforms
		ds["validation"].set_transform(lowercase__  )

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

	# Initialize our trainer
	UpperCamelCase__ :Union[str, Any]						=      Trainer(
	    model=lowercase__ ,		args=lowercase__ ,		train_dataset=ds["""train"""] if training_args.do_train else None ,		eval_dataset=ds["""validation"""] if training_args.do_eval else None ,		tokenizer=lowercase__ ,		data_collator=lowercase__ ,		)

	# Training
	if training_args.do_train:
		UpperCamelCase__ :Any						=      None
		if training_args.resume_from_checkpoint is not None:
			UpperCamelCase__ :int						=      training_args.resume_from_checkpoint
		elif last_checkpoint is not None:
			UpperCamelCase__ :Dict						=      last_checkpoint
		UpperCamelCase__ :Union[str, Any]						=      trainer.train(resume_from_checkpoint=lowercase__  )
		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:
		UpperCamelCase__ :int						=      trainer.evaluate()
		trainer.log_metrics("""eval""" ,		lowercase__  )
		trainer.save_metrics("""eval""" ,		lowercase__  )

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




def 				A		(					lowercase__  :  Union[str, Any]  )		-> Dict:
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
							main()



'''simple docstring'''



from __future__ import annotations

import os
from collections.abc import Mapping

A__						:   Optional[Any]				=    tuple[int, int]





class   UpperCAmelCase_    :





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	None:
         __lowerCamelCase  :      set[int]														=						vertices
         __lowerCamelCase  :      dict[EdgeT, int]														=						{
             (min(SCREAMING_SNAKE_CASE_      ), max(SCREAMING_SNAKE_CASE_      )): weight for edge, weight in edges.items()
         }







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	None:
         self.vertices.add(edge[0]      )
         self.vertices.add(edge[1]      )
         __lowerCamelCase  :      Union[str, Any]														=						weight







    def  lowercase_							(				self      )							->	Graph:
         __lowerCamelCase  :      Graph														=						Graph({min(self.vertices      )}     ,	{}      )
         __lowerCamelCase  :      EdgeT
         __lowerCamelCase  :      int
         __lowerCamelCase  :      EdgeT
         __lowerCamelCase  :      int

         while len(subgraph.vertices      ) < len(self.vertices      ):
              __lowerCamelCase  :      Any														=						max(self.edges.values()      ) + 1
              for edge, weight in self.edges.items():
                   if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                        if weight < min_weight:
                             __lowerCamelCase  :      Optional[int]														=						edge
                             __lowerCamelCase  :      List[str]														=						weight

              subgraph.add_edge(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )

         return subgraph







def      UpperCAmelCase__   (      UpperCAmelCase_       :							str = "p107_network.txt"				)	->						int:
     __lowerCamelCase  :      str														=						os.path.abspath(os.path.dirname(UpperCAmelCase_				)				)
     __lowerCamelCase  :      str														=						os.path.join(UpperCAmelCase_ ,       UpperCAmelCase_				)
     __lowerCamelCase  :      dict[EdgeT, int]														=						{}
     __lowerCamelCase  :      list[str]
     __lowerCamelCase  :      int
     __lowerCamelCase  :      int

     with open(UpperCAmelCase_				) as f:
          __lowerCamelCase  :      Any														=						f.read().strip().split('\n'				)

     __lowerCamelCase  :      Any														=						[line.split(','				) for line in data]

     for edgea in range(1 ,       len(UpperCAmelCase_				)				):
          for edgea in range(UpperCAmelCase_				):
               if adjaceny_matrix[edgea][edgea] != "-":
                    __lowerCamelCase  :      int														=						int(adjaceny_matrix[edgea][edgea]				)

     __lowerCamelCase  :      Graph														=						Graph(set(range(len(UpperCAmelCase_				)				)				) ,       UpperCAmelCase_				)

     __lowerCamelCase  :      Graph														=						graph.prims_algorithm()

     __lowerCamelCase  :      int														=						sum(graph.edges.values()				)
     __lowerCamelCase  :      int														=						sum(subgraph.edges.values()				)

     return initial_total - optimal_total


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






