


def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : dict    ):
     __UpperCamelCase   							=set()
     # To detect a back edge, keep track of vertices currently in the recursion stack
     __UpperCamelCase   							=set()
     return any(
         node not in visited and depth_first_search(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
         for node in graph    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : dict      ,					SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : set      ,					SCREAMING_SNAKE_CASE__      : set    ):
     visited.add(SCREAMING_SNAKE_CASE__    )
     rec_stk.add(SCREAMING_SNAKE_CASE__    )

     for node in graph[vertex]:
          if node not in visited:
               if depth_first_search(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ):
                    return True
          elif node in rec_stk:
               return True

    # The node needs to be removed from recursion stack before function ends
     rec_stk.remove(SCREAMING_SNAKE_CASE__    )
     return False


if __name__ == "__main__":
   from doctest import testmod

   testmod()









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			=						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    UpperCAmelCase__	:



    """simple docstring"""






    UpperCAmelCase__						:		Optional[str]              =  field(
        default="cifar10"       ,  metadata={"help": "Name of a dataset from the datasets package"}		)
    UpperCAmelCase__						:		Optional[str]              =  field(
        default=A_       ,  metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}		)
    UpperCAmelCase__						:		Optional[str]              =  field(
        default=A_       ,  metadata={"help": "The column name of the images in the files."}		)
    UpperCAmelCase__						:		Optional[str]              =  field(default=A_       ,  metadata={"help": "A folder containing the training data."}		)
    UpperCAmelCase__						:		Optional[str]              =  field(default=A_       ,  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=A_       ,  metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of training examples to this "
                "value if set."
            )
        }       ,  )
    UpperCAmelCase__						:		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 _a (      self   )  ->	Optional[int]:
         __UpperCamelCase   							={}
         if self.train_dir is not None:
              __UpperCamelCase   							=self.train_dir
         if self.validation_dir is not None:
              __UpperCamelCase   							=self.validation_dir
         __UpperCamelCase   							=data_files if data_files else None






@dataclass
class    UpperCAmelCase__	:



    """simple docstring"""






    UpperCAmelCase__						:		str              =  field(
        default=A_       ,  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=A_       ,  metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}		)
    UpperCAmelCase__						:		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"
            )
        }       ,  )
    UpperCAmelCase__						:		Optional[str]              =  field(
        default=A_       ,  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=A_       ,  metadata={"help": "Name or path of preprocessor config."}		)
    UpperCAmelCase__						:		bool              =  field(
        default=A_       ,  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=A_       ,  metadata={"help": "Whether or not to train with normalized pixel values as target."}		)






@dataclass
class    UpperCAmelCase__	( A_		):



    """simple docstring"""






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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int    ):
     __UpperCamelCase   							=torch.stack([example['pixel_values'] for example in examples]    )
     return {"pixel_values": pixel_values}

def 						_UpperCAmelCase    (      ):
     # 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   							=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   							=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
     else:
          __UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase   							=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'      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_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()

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

     # create model
     if model_args.model_name_or_path:
          __UpperCamelCase   							=ViTMAEForPreTraining.from_pretrained(
              model_args.model_name_or_path      ,					from_tf=bool('.ckpt' in model_args.model_name_or_path    )      ,					config=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      ,					)
     else:
          logger.info('Training new model from scratch'    )
          __UpperCamelCase   							=ViTMAEForPreTraining(SCREAMING_SNAKE_CASE__    )

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

     if data_args.image_column_name is not None:
          __UpperCamelCase   							=data_args.image_column_name
     elif "image" in column_names:
          __UpperCamelCase   							='image'
     elif "img" in column_names:
          __UpperCamelCase   							='img'
     else:
          __UpperCamelCase   							=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   							=image_processor.size['shortest_edge']
     else:
          __UpperCamelCase   							=(image_processor.size['height'], image_processor.size['width'])
     __UpperCamelCase   							=Compose(
         [
             Lambda(lambda SCREAMING_SNAKE_CASE__    : img.convert('RGB'    ) if img.mode != "RGB" else img    ),
             RandomResizedCrop(SCREAMING_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(SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
          __UpperCamelCase   							=[transforms(SCREAMING_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:
               __UpperCamelCase   							=ds['train'].shuffle(seed=training_args.seed    ).select(range(data_args.max_train_samples    )    )
          # Set the training transforms
          ds["train"].set_transform(SCREAMING_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:
               __UpperCamelCase   							=(
                   ds['validation'].shuffle(seed=training_args.seed    ).select(range(data_args.max_eval_samples    )    )
               )
          # Set the validation transforms
          ds["validation"].set_transform(SCREAMING_SNAKE_CASE__    )

     # Compute absolute learning rate
     __UpperCamelCase   							=(
         training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
     )
     if training_args.base_learning_rate is not None:
          __UpperCamelCase   							=training_args.base_learning_rate * total_train_batch_size / 2_56

     # Initialize our trainer
     __UpperCamelCase   							=Trainer(
         model=SCREAMING_SNAKE_CASE__      ,					args=SCREAMING_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=SCREAMING_SNAKE_CASE__      ,					data_collator=SCREAMING_SNAKE_CASE__      ,					)

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

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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int    ):
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
   main()





