





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
_A   :       Optional[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')

_A   :       List[str]    					=    list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
_A   :       Optional[int]    					=    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class   __SCREAMING_SNAKE_CASE     :
       _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. If not set, will try to use \'image\' or \'img\'."}	,)
       _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 :    int					       =       field(default=3_2	,metadata={"help": "The size of the square patches to use for masking."}   )
       _UpperCAmelCase :    float					       =       field(
           default=0.6	,metadata={"help": "Percentage of patches to mask."}	,)
       _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 		__lowerCamelCase       ( self	:      Union[str, Any]     )  ->Any:
          lowerCamelCase__  :		Tuple     =				{}
          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__  :		Optional[Any]     =				data_files if data_files else None


@dataclass
class   __SCREAMING_SNAKE_CASE     :
       _UpperCAmelCase :    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."
               )
           }	,)
       _UpperCAmelCase :    Optional[str]					       =       field(
           default=lowerCAmelCase_	,metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(lowerCAmelCase_   )}	,)
       _UpperCAmelCase :    Optional[str]					       =       field(
           default=lowerCAmelCase_	,metadata={"help": "Pretrained config name or path if not the same as model_name"}   )
       _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 (cache) the pretrained models/datasets downloaded from the hub"}	,)
       _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 :    Optional[int]					       =       field(
           default=lowerCAmelCase_	,metadata={
               "help": (
                   "The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
               )
           }	,)
       _UpperCAmelCase :    Optional[int]					       =       field(
           default=lowerCAmelCase_	,metadata={
               "help": (
                   "The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
               )
           }	,)
       _UpperCAmelCase :    Optional[int]					       =       field(
           default=lowerCAmelCase_	,metadata={"help": "Stride to use for the encoder."}	,)


class   __SCREAMING_SNAKE_CASE     :







       def __init__( self	:      str						,   A	:      Optional[Any]=1_9_2						,   A	:      Tuple=3_2						,   A	:      str=4						,   A	:      Union[str, Any]=0.6     )  ->Union[str, Any]:
          lowerCamelCase__  :		Tuple     =				input_size
          lowerCamelCase__  :		Tuple     =				mask_patch_size
          lowerCamelCase__  :		Any     =				model_patch_size
          lowerCamelCase__  :		Any     =				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__  :		Union[str, Any]     =				self.input_size // self.mask_patch_size
          lowerCamelCase__  :		Optional[int]     =				self.mask_patch_size // self.model_patch_size

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







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

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

          return torch.tensor(mask.flatten()     )



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







   """simple docstring"""
   lowerCamelCase__  :		int     =				torch.stack([example['''pixel_values'''] for example in examples]	)
   lowerCamelCase__  :		Optional[int]     =				torch.stack([example['''mask'''] for example in examples]	)
   return {"pixel_values": pixel_values, "bool_masked_pos": mask}



def 			_a		(       )							->							Any:







   """simple docstring"""
   lowerCamelCase__  :		str     =				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__  :		List[str]     =				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
   else:
      lowerCamelCase__  ,				lowerCamelCase__  ,				lowerCamelCase__  :		str     =				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'''     ,    __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()

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

   # Create config
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.
   lowerCamelCase__  :		List[Any]     =				{
       '''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     ,    **__a	)
   elif model_args.model_name_or_path:
      lowerCamelCase__  :		Union[str, Any]     =				AutoConfig.from_pretrained(model_args.model_name_or_path     ,    **__a	)
   else:
      lowerCamelCase__  :		str     =				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(__a     ,    '''decoder_type'''	):
      lowerCamelCase__  :		Optional[Any]     =				'''simmim'''

   # adapt config
   lowerCamelCase__  :		Tuple     =				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__  :		int     =				(
       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__  :		Optional[Any]     =				AutoImageProcessor.from_pretrained(model_args.image_processor_name     ,    **__a	)
   elif model_args.model_name_or_path:
      lowerCamelCase__  :		Union[str, Any]     =				AutoImageProcessor.from_pretrained(model_args.model_name_or_path     ,    **__a	)
   else:
      lowerCamelCase__  :		Tuple     =				{
          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[int]     =				AutoModelForMaskedImageModeling.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'''	)
      lowerCamelCase__  :		int     =				AutoModelForMaskedImageModeling.from_config(__a	)

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

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

   # transformations as done in original SimMIM paper
   # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
   lowerCamelCase__  :		Union[str, 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__  :		Dict     =				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__  :		Tuple     =				[transforms(__a	) for image in examples[image_column_name]]
      lowerCamelCase__  :		List[str]     =				[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__  :		List[str]     =				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:
         lowerCamelCase__  :		str     =				(
             ds['''validation'''].shuffle(seed=training_args.seed	).select(range(data_args.max_eval_samples	)	)
         )
      # Set the validation transforms
      ds["validation"].set_transform(__a	)

   # Initialize our trainer
   lowerCamelCase__  :		int     =				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:
      lowerCamelCase__  :		List[Any]     =				None
      if training_args.resume_from_checkpoint is not None:
         lowerCamelCase__  :		Union[str, Any]     =				training_args.resume_from_checkpoint
      elif last_checkpoint is not None:
         lowerCamelCase__  :		Dict     =				last_checkpoint
      lowerCamelCase__  :		Any     =				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:
      lowerCamelCase__  :		List[str]     =				trainer.evaluate()
      trainer.log_metrics('''eval'''     ,    __a	)
      trainer.save_metrics('''eval'''     ,    __a	)

   # Write model card and (optionally) push to hub
   lowerCamelCase__  :		Optional[Any]     =				{
       '''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(**__a	)
   else:
      trainer.create_model_card(**__a	)


if __name__ == "__main__":
       main()










class     A     :
			'''simple docstring'''




			def __init__(     self  :       Optional[int]  )      ->      Dict:







					"""simple docstring"""






					A__     =  {}






			def   a_			(     self  :       Any  )      ->      None:







					"""simple docstring"""






					print(self.vertex  )
					for i in self.vertex:
							print(__lowerCAmelCase				,      """ -> """				,      """ -> """.join([str(__lowerCAmelCase  ) for j in self.vertex[i]]  )  )






			def   a_			(     self  :       Optional[int]				,      __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       int  )      ->      None:







					"""simple docstring"""






					if from_vertex in self.vertex:
							self.vertex[from_vertex].append(__lowerCAmelCase  )
					else:
							# else make a new vertex
							A__     =  [to_vertex]






			def   a_			(     self  :       List[str]  )      ->      None:







					"""simple docstring"""






					A__     =  [False] * len(self.vertex  )

					# call the recursive helper function
					for i in range(len(self.vertex  )  ):
							if not visited[i]:
									self.dfs_recursive(__lowerCAmelCase				,      __lowerCAmelCase  )







			def   a_			(     self  :       List[str]				,      __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       list  )      ->      None:







					"""simple docstring"""






					A__     =  True

					print(__lowerCAmelCase				,      end=""" """  )

					# Recur for all the vertices that are adjacent to this node
					for i in self.vertex:
							if not visited[i]:
									self.dfs_recursive(__lowerCAmelCase				,      __lowerCAmelCase  )


if __name__ == "__main__":
					A       :   Tuple					= Graph()
					g.add_edge(0, 1)
					g.add_edge(0, 2)
					g.add_edge(1, 2)
					g.add_edge(2, 0)
					g.add_edge(2, 3)
					g.add_edge(3, 3)

					g.print_graph()
					print('''DFS:''')
					g.dfs()

					# OUTPUT:
					# 0  ->  1 -> 2
					# 1  ->  2
					# 2  ->  0 -> 3
					# 3  ->  3
					# DFS:
					#  0 1 2 3



