





import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
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
lowercase_						=	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-classification/requirements.txt""")

lowercase_						=	list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
lowercase_						=	tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     List[str]:
  with open(_lowerCamelCase	,		'rb'	) as f:
    lowercase__			    =						Image.open(_lowerCamelCase	)
    return im.convert('RGB'	)
@dataclass
class        SCREAMING_SNAKE_CASE     :
   _UpperCamelCase					:	List[Any]				      = field(
       default=_A    ,			metadata={
           'help': 'Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'
       }    ,			)
   _UpperCamelCase					:	Optional[int]				      = field(
       default=_A    ,			metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}   )
   _UpperCamelCase					:	List[str]				      = field(default=_A    ,			metadata={'help': 'A folder containing the training data.'}   )
   _UpperCamelCase					:	Optional[Any]				      = field(default=_A    ,			metadata={'help': 'A folder containing the validation data.'}   )
   _UpperCamelCase					:	List[Any]				      = field(
       default=0.15    ,			metadata={'help': 'Percent to split off of train for validation.'}   )
   _UpperCamelCase					:	Dict				      = field(
       default=_A    ,			metadata={
           'help': (
               'For debugging purposes or quicker training, truncate the number of training examples to this '
               'value if set.'
           )
       }    ,			)
   _UpperCamelCase					:	Tuple				      = field(
       default=_A    ,			metadata={
           'help': (
               'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
               'value if set.'
           )
       }    ,			)


   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     List[Any]:





     """simple docstring"""







     if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
       raise ValueError(
           'You must specify either a dataset name from the hub or a train and/or validation directory.' )






@dataclass
class        SCREAMING_SNAKE_CASE     :
   _UpperCamelCase					:	Tuple				      = field(
       default='google/vit-base-patch16-224-in21k'    ,			metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}    ,			)
   _UpperCamelCase					:	List[Any]				      = field(
       default=_A    ,			metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(_A   )}    ,			)
   _UpperCamelCase					:	List[Any]				      = field(
       default=_A    ,			metadata={'help': 'Pretrained config name or path if not the same as model_name'}   )
   _UpperCamelCase					:	Tuple				      = field(
       default=_A    ,			metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}   )
   _UpperCamelCase					:	Any				      = field(
       default='main'    ,			metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}    ,			)
   _UpperCamelCase					:	Union[str, Any]				      = field(default=_A    ,			metadata={'help': 'Name or path of preprocessor config.'}   )
   _UpperCamelCase					:	Union[str, Any]				      = field(
       default=_A    ,			metadata={
           'help': (
               'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
               'with private models).'
           )
       }    ,			)
   _UpperCamelCase					:	List[Any]				      = field(
       default=_A    ,			metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'}    ,			)






def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     int:
  lowercase__			    =						torch.stack([example['pixel_values'] for example in examples]	)
  lowercase__			    =						torch.tensor([example['labels'] for example in examples]	)
  return {"pixel_values": pixel_values, "labels": labels}




def     __UpperCamelCase						()       ->     Optional[Any]:
  # 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, 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.
    lowercase__			    =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
  else:
    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_image_classification'	,		_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()

  lowercase__			    =						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.
  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.'	)

    # Set seed before initializing model.
  set_seed(training_args.seed	)

  # Initialize our dataset and prepare it for the 'image-classification' task.
  if data_args.dataset_name is not None:
    lowercase__			    =						load_dataset(
        data_args.dataset_name	,		data_args.dataset_config_name	,		cache_dir=model_args.cache_dir	,		task='image-classification'	,		use_auth_token=True if model_args.use_auth_token else None	,		)
  else:
    lowercase__			    =						{}
    if data_args.train_dir is not None:
      lowercase__			    =						os.path.join(data_args.train_dir	,		'**'	)
    if data_args.validation_dir is not None:
      lowercase__			    =						os.path.join(data_args.validation_dir	,		'**'	)
    lowercase__			    =						load_dataset(
        'imagefolder'	,		data_files=_lowerCamelCase	,		cache_dir=model_args.cache_dir	,		task='image-classification'	,		)

  # If we don't have a validation split, split off a percentage of train as validation.
  lowercase__			    =						None if "validation" in dataset.keys() else data_args.train_val_split
  if isinstance(data_args.train_val_split	,		_lowerCamelCase	) and data_args.train_val_split > 0.0:
    lowercase__			    =						dataset["train"].train_test_split(data_args.train_val_split	)
    lowercase__			    =						split["train"]
    lowercase__			    =						split["test"]

  # Prepare label mappings.
  # We'll include these in the model's config to get human readable labels in the Inference API.
  lowercase__			    =						dataset["train"].features["labels"].names
  lowercase__			    =						{}, {}
  for i, label in enumerate(_lowerCamelCase	):
    lowercase__			    =						str(_lowerCamelCase	)
    lowercase__			    =						label

  # Load the accuracy metric from the datasets package
  lowercase__			    =						evaluate.load('accuracy'	)

  # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
  # predictions and label_ids field) and has to return a dictionary string to float.
  def compute_metrics(_SCREAMING_SNAKE_CASE	):
    return metric.compute(predictions=np.argmax(p.predictions	,		axis=1	)	,		references=p.label_ids	)

  lowercase__			    =						AutoConfig.from_pretrained(
      model_args.config_name or model_args.model_name_or_path	,		num_labels=len(_lowerCamelCase	)	,		labelaid=_lowerCamelCase	,		idalabel=_lowerCamelCase	,		finetuning_task='image-classification'	,		cache_dir=model_args.cache_dir	,		revision=model_args.model_revision	,		use_auth_token=True if model_args.use_auth_token else None	,		)
  lowercase__			    =						AutoModelForImageClassification.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	,		ignore_mismatched_sizes=model_args.ignore_mismatched_sizes	,		)
  lowercase__			    =						AutoImageProcessor.from_pretrained(
      model_args.image_processor_name or model_args.model_name_or_path	,		cache_dir=model_args.cache_dir	,		revision=model_args.model_revision	,		use_auth_token=True if model_args.use_auth_token else None	,		)

  # Define torchvision transforms to be applied to each image.
  if "shortest_edge" in image_processor.size:
    lowercase__			    =						image_processor.size["shortest_edge"]
  else:
    lowercase__			    =						(image_processor.size["height"], image_processor.size["width"])
  lowercase__			    =						Normalize(mean=image_processor.image_mean	,		std=image_processor.image_std	)
  lowercase__			    =						Compose(
      [
          RandomResizedCrop(_lowerCamelCase	),
          RandomHorizontalFlip(),
          ToTensor(),
          normalize,
      ]	)
  lowercase__			    =						Compose(
      [
          Resize(_lowerCamelCase	),
          CenterCrop(_lowerCamelCase	),
          ToTensor(),
          normalize,
      ]	)

  def train_transforms(_SCREAMING_SNAKE_CASE	):
    lowercase__			    =						[
        _train_transforms(pil_img.convert('RGB'	)	) for pil_img in example_batch["image"]
    ]
    return example_batch

  def val_transforms(_SCREAMING_SNAKE_CASE	):
    lowercase__			    =						[_val_transforms(pil_img.convert('RGB'	)	) for pil_img in example_batch["image"]]
    return example_batch

  if training_args.do_train:
    if "train" not in dataset:
      raise ValueError('--do_train requires a train dataset'	)
    if data_args.max_train_samples is not None:
      lowercase__			    =						(
          dataset["train"].shuffle(seed=training_args.seed	).select(range(data_args.max_train_samples	)	)
      )
    # Set the training transforms
    dataset["train"].set_transform(_lowerCamelCase	)

  if training_args.do_eval:
    if "validation" not in dataset:
      raise ValueError('--do_eval requires a validation dataset'	)
    if data_args.max_eval_samples is not None:
      lowercase__			    =						(
          dataset["validation"].shuffle(seed=training_args.seed	).select(range(data_args.max_eval_samples	)	)
      )
    # Set the validation transforms
    dataset["validation"].set_transform(_lowerCamelCase	)

  # Initalize our trainer
  lowercase__			    =						Trainer(
      model=_lowerCamelCase	,		args=_lowerCamelCase	,		train_dataset=dataset['train'] if training_args.do_train else None	,		eval_dataset=dataset['validation'] if training_args.do_eval else None	,		compute_metrics=_lowerCamelCase	,		tokenizer=_lowerCamelCase	,		data_collator=_lowerCamelCase	,		)

  # 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=_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:
    lowercase__			    =						trainer.evaluate()
    trainer.log_metrics('eval'	,		_lowerCamelCase	)
    trainer.save_metrics('eval'	,		_lowerCamelCase	)

  # Write model card and (optionally) push to hub
  lowercase__			    =						{
      "finetuned_from": model_args.model_name_or_path,
      "tasks": "image-classification",
      "dataset": data_args.dataset_name,
      "tags": ["image-classification", "vision"],
  }
  if training_args.push_to_hub:
    trainer.push_to_hub(**_lowerCamelCase	)
  else:
    trainer.create_model_card(**_lowerCamelCase	)


if __name__ == "__main__":
      main()









def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     List[Any]:
  stooge(_SCREAMING_SNAKE_CASE	,		0	,		len(_SCREAMING_SNAKE_CASE	) - 1	)
  return arr




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     int:
  if i >= h:
    return

  # If first element is smaller than the last then swap them
  if arr[i] > arr[h]:
    lowercase__    ,					lowercase__			    =						arr[h], arr[i]

  # If there are more than 2 elements in the array
  if h - i + 1 > 2:
    lowercase__			    =						(int)((h - i + 1) / 3	)

    # Recursively sort first 2/3 elements
    stooge(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		(h - t)	)

    # Recursively sort last 2/3 elements
    stooge(_SCREAMING_SNAKE_CASE	,		i + t	,		(_SCREAMING_SNAKE_CASE)	)

    # Recursively sort first 2/3 elements
    stooge(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		(h - t)	)


if __name__ == "__main__":
      lowercase_					     =	input("""Enter numbers separated by a comma:\n""").strip()
      lowercase_					     =	[int(item) for item in user_input.split(""",""")]
      print(stooge_sort(unsorted))


