






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
_SCREAMING_SNAKE_CASE   =  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 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


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





       def 				UpperCamelCase__						(				self	:Optional[Any]):



          """simple docstring"""

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






@dataclass
class 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


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






@dataclass
class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


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





def        _snake_case   (_snake_case				:       List[str])					->	Any:
   _lowercase              =torch.stack([example['pixel_values'] for example in examples])
   return {"pixel_values": pixel_values}





def        _snake_case   ()					->	List[str]:
   # 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.

   _lowercase              =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.
      _lowercase       ,       _lowercase       ,       _lowercase              =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
   else:
      _lowercase       ,       _lowercase       ,       _lowercase              =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()

   _lowercase              =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.
   _lowercase              =None
   if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
      _lowercase              =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.
   _lowercase              =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.
   _lowercase              =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:
      _lowercase              =ds['train'].train_test_split(data_args.train_val_split)
      _lowercase              =split['train']
      _lowercase              =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.
   _lowercase              ={
       '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:
      _lowercase              =ViTMAEConfig.from_pretrained(model_args.config_name      ,    **_snake_case)
   elif model_args.model_name_or_path:
      _lowercase              =ViTMAEConfig.from_pretrained(model_args.model_name_or_path      ,    **_snake_case)
   else:
      _lowercase              =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:
      _lowercase              =ViTImageProcessor.from_pretrained(model_args.image_processor_name      ,    **_snake_case)
   elif model_args.model_name_or_path:
      _lowercase              =ViTImageProcessor.from_pretrained(model_args.model_name_or_path      ,    **_snake_case)
   else:
      _lowercase              =ViTImageProcessor()

   # create model
   if model_args.model_name_or_path:
      _lowercase              =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')
      _lowercase              =ViTMAEForPreTraining(_snake_case)

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

   if data_args.image_column_name is not None:
      _lowercase              =data_args.image_column_name
   elif "image" in column_names:
      _lowercase              ='image'
   elif "img" in column_names:
      _lowercase              ='img'
   else:
      _lowercase              =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:
      _lowercase              =image_processor.size['shortest_edge']
   else:
      _lowercase              =(image_processor.size['height'], image_processor.size['width'])
   _lowercase              =Compose(
       [
           Lambda(lambda _snake_case: 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(_snake_case				:       Any):
      _lowercase              =[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:
         _lowercase              =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:
         _lowercase              =(
             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
   _lowercase              =(
       training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
   )
   if training_args.base_learning_rate is not None:
      _lowercase              =training_args.base_learning_rate * total_train_batch_size / 256

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

   # Write model card and (optionally) push to hub
   _lowercase              ={
       '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        _snake_case   (_snake_case				:       Any)					->	Optional[Any]:
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
    main()










class 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


       def __init__(				self	:List[Any]):



          """simple docstring"""

          _lowercase              ={}





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:Any,      snake_case	:int,      snake_case	:int):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

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

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





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:int,      snake_case	:list):



          """simple docstring"""

          _lowercase              =True

          print(snake_case,      end=' ')

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


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE   =  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


