


"""simple docstring"""






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


  def 							_lowercase					(			self:						Optional[Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				Dict							=     {}
     if self.train_dir is not None:
        _lowerCamelCase   :				Optional[Any]							=     self.train_dir
     if self.validation_dir is not None:
        _lowerCamelCase   :				str							=     self.validation_dir
     _lowerCamelCase   :				Any							=     data_files if data_files else None




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




@dataclass
class 							A_      (       _a ):
  lowerCAmelCase__      			=	field(
      default=1E-3     ,	metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'} )







def        lowerCamelCase_(		_lowerCamelCase       )    ->     Union[str, Any]:
   '''simple docstring'''





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







def        lowerCamelCase_(		)    ->     Any:
   '''simple docstring'''





   _lowerCamelCase   :				Dict							=     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.
      _lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase   :				Any							=     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
   else:
      _lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase   :				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"			,		_lowerCamelCase			,		_lowerCamelCase       )

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

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

   if training_args.do_train:
      _lowerCamelCase   :				int							=     ds["train"].column_names
   else:
      _lowerCamelCase   :				List[Any]							=     ds["validation"].column_names

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

   def preprocess_images(_lowerCamelCase       ):
      _lowerCamelCase   :				Optional[Any]							=     [transforms(_lowerCamelCase       ) 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:
         _lowerCamelCase   :				int							=     ds["train"].shuffle(seed=training_args.seed       ).select(range(data_args.max_train_samples       )       )
      # Set the training transforms
      ds["train"].set_transform(_lowerCamelCase       )

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

   # Compute absolute learning rate
   _lowerCamelCase   :				List[str]							=     (
       training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
   )
   if training_args.base_learning_rate is not None:
      _lowerCamelCase   :				int							=     training_args.base_learning_rate * total_train_batch_size / 256

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

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

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







def        lowerCamelCase_(		_lowerCamelCase       )    ->     str:
   '''simple docstring'''





   main()


if __name__ == "__main__":
  main()



import math
class 		__magic_name__						:





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]     ,		_a:list[list[float]]     ,		_a:list[int]   ):
       snake_case__						= 0.0
       snake_case__						= 0.0
       for i in range(len(_a   )   ):
         da += math.pow((sample[i] - weights[0][i])     ,		2   )
         da += math.pow((sample[i] - weights[1][i])     ,		2   )
         return 0 if da > da else 1
       return 0







     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		_a:list[list[int | float]]     ,		_a:list[int]     ,		_a:int     ,		_a:float   ):
       for i in range(len(_a   )   ):
         weights[j][i] += alpha * (sample[i] - weights[j][i])
       return weights







def SCREAMING_SNAKE_CASE	(			)    ->				None:
  # Training Examples ( m, n )
  snake_case__						= [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

  # weight initialization ( n, C )
  snake_case__						= [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

  # training
  snake_case__						= SelfOrganizingMap()
  snake_case__						= 3
  snake_case__						= 0.5

  for _ in range(__lowerCAmelCase				):
    for j in range(len(__lowerCAmelCase				)				):
      # training sample
      snake_case__						= training_samples[j]

      # Compute the winning vector
      snake_case__						= self_organizing_map.get_winner(__lowerCAmelCase							,   __lowerCAmelCase				)

      # Update the winning vector
      snake_case__						= self_organizing_map.update(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)

    # classify test sample
  snake_case__						= [0, 0, 0, 1]
  snake_case__						= self_organizing_map.get_winner(__lowerCAmelCase							,   __lowerCAmelCase				)

  # results
  print(F"""Clusters that the test sample belongs to : {winner}"""				)
  print(F"""Weights that have been trained : {weights}"""				)


# running the main() function
if __name__ == "__main__":
   main()

