





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
lowerCAmelCase_						   =    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""")

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


def     lowerCamelCase_	(      lowerCAmelCase:	str				)->		int:
		with open(snake_case_    ,							'rb'				) as f:
				_snake_case   :				List[Any]														=	Image.open(snake_case_				)
				return im.convert('RGB'				)



@dataclass
class        _lowerCAmelCase    :


		'''simple docstring'''
		a_							:       List[str]      					=field(
		    default=UpperCamelCase_							,    metadata={
		        """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
		    }							,    )
		a_							:       Optional[Any]      					=field(
		    default=UpperCamelCase_							,    metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}							)
		a_							:       Optional[int]      					=field(default=UpperCamelCase_							,    metadata={"""help""": """A folder containing the training data."""}							)
		a_							:       Optional[Any]      					=field(default=UpperCamelCase_							,    metadata={"""help""": """A folder containing the validation data."""}							)
		a_							:       Union[str, Any]      					=field(
		    default=0.15							,    metadata={"""help""": """Percent to split off of train for validation."""}							)
		a_							:       List[Any]      					=field(
		    default=UpperCamelCase_							,    metadata={
		        """help""": (
		            """For debugging purposes or quicker training, truncate the number of training examples to this """
		            """value if set."""
		        )
		    }							,    )
		a_							:       Optional[Any]      					=field(
		    default=UpperCamelCase_							,    metadata={
		        """help""": (
		            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
		            """value if set."""
		        )
		    }							,    )







		def UpperCamelCase_ (      self      :					Tuple							):


				'''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        _lowerCAmelCase    :


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




def     lowerCamelCase_	(      lowerCAmelCase:	Any				)->		Union[str, Any]:
		_snake_case   :				List[str]														=	torch.stack([example['pixel_values'] for example in examples]				)
		_snake_case   :				List[Any]														=	torch.tensor([example['labels'] for example in examples]				)
		return {"pixel_values": pixel_values, "labels": labels}


def     lowerCamelCase_	(      )->		Optional[int]:
		# 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.

		_snake_case   :				Tuple														=	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.
				_snake_case   :				Optional[int]														=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
		else:
				_snake_case   :				Optional[int]														=	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'    ,							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()

		_snake_case   :				Tuple														=	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.
		_snake_case   :				int														=	None
		if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
				_snake_case   :				int														=	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:
				_snake_case   :				List[str]														=	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:
				_snake_case   :				Any														=	{}
				if data_args.train_dir is not None:
						_snake_case   :				str														=	os.path.join(data_args.train_dir    ,							'**'				)
				if data_args.validation_dir is not None:
						_snake_case   :				Tuple														=	os.path.join(data_args.validation_dir    ,							'**'				)
				_snake_case   :				int														=	load_dataset(
				    'imagefolder'    ,							data_files=snake_case_    ,							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.
		_snake_case   :				Tuple														=	None if '''validation''' in dataset.keys() else data_args.train_val_split
		if isinstance(data_args.train_val_split    ,							snake_case_				) and data_args.train_val_split > 0.0:
				_snake_case   :				Optional[int]														=	dataset['''train'''].train_test_split(data_args.train_val_split				)
				_snake_case   :				List[Any]														=	split['''train''']
				_snake_case   :				List[Any]														=	split['''test''']

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

		# Load the accuracy metric from the datasets package
		_snake_case   :				List[Any]														=	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(lowerCAmelCase:	Tuple				):
				return metric.compute(predictions=np.argmax(p.predictions    ,							axis=1				)    ,							references=p.label_ids				)

		_snake_case   :				int														=	AutoConfig.from_pretrained(
		    model_args.config_name or model_args.model_name_or_path    ,							num_labels=len(snake_case_				)    ,							labelaid=snake_case_    ,							idalabel=snake_case_    ,							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    ,							)
		_snake_case   :				int														=	AutoModelForImageClassification.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    ,							ignore_mismatched_sizes=model_args.ignore_mismatched_sizes    ,							)
		_snake_case   :				Optional[int]														=	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:
				_snake_case   :				Optional[Any]														=	image_processor.size['''shortest_edge''']
		else:
				_snake_case   :				Optional[Any]														=	(image_processor.size['''height'''], image_processor.size['''width'''])
		_snake_case   :				int														=	Normalize(mean=image_processor.image_mean    ,							std=image_processor.image_std				)
		_snake_case   :				Tuple														=	Compose(
		    [
		        RandomResizedCrop(snake_case_				),
		        RandomHorizontalFlip(),
		        ToTensor(),
		        normalize,
		    ]				)
		_snake_case   :				Union[str, Any]														=	Compose(
		    [
		        Resize(snake_case_				),
		        CenterCrop(snake_case_				),
		        ToTensor(),
		        normalize,
		    ]				)

		def train_transforms(lowerCAmelCase:	Any				):
				_snake_case   :				Any														=	[
				    _train_transforms(pil_img.convert('RGB'				)				) for pil_img in example_batch['''image''']
				]
				return example_batch

		def val_transforms(lowerCAmelCase:	int				):
				_snake_case   :				int														=	[_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:
						_snake_case   :				Optional[Any]														=	(
						    dataset['''train'''].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
						)
				# Set the training transforms
				dataset["train"].set_transform(snake_case_				)

		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:
						_snake_case   :				List[str]														=	(
						    dataset['''validation'''].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
						)
				# Set the validation transforms
				dataset["validation"].set_transform(snake_case_				)

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

		# Training
		if training_args.do_train:
				_snake_case   :				List[str]														=	None
				if training_args.resume_from_checkpoint is not None:
						_snake_case   :				str														=	training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						_snake_case   :				List[Any]														=	last_checkpoint
				_snake_case   :				str														=	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:
				_snake_case   :				Optional[int]														=	trainer.evaluate()
				trainer.log_metrics('eval'    ,							snake_case_				)
				trainer.save_metrics('eval'    ,							snake_case_				)

		# Write model card and (optionally) push to hub
		_snake_case   :				List[Any]														=	{
		    '''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(**snake_case_				)
		else:
				trainer.create_model_card(**snake_case_				)


if __name__ == "__main__":
						main()











from __future__ import annotations

from random import random



class        _lowerCAmelCase    :


  '''simple docstring'''







  def __init__(      self      :					Dict      ,				UpperCamelCase      :					int | None = None							):


    '''simple docstring'''





    _snake_case   :				str														=	value
    _snake_case   :				List[Any]														=	random()
    _snake_case   :				Node | None														=	None
    _snake_case   :				Node | None														=	None







  def __repr__(      self      :					Optional[Any]							):


    '''simple docstring'''





    from pprint import pformat

    if self.left is None and self.right is None:
      return f"""'{self.value}: {self.prior:.5}'"""
    else:
      return pformat(
          {f"""{self.value}: {self.prior:.5}""": (self.left, self.right)}      ,				indent=1							)







  def __str__(      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	str(self.value							) + ' '
    _snake_case   :				List[Any]														=	str(self.left or ''							)
    _snake_case   :				int														=	str(self.right or ''							)
    return value + left + right




def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	int				)->		tuple[Node | None, Node | None]:
  if root is None:  # None tree is split into 2 Nones
    return None, None
  elif root.value is None:
    return None, None
  else:
    if value < root.value:
      _snake_case							,					_snake_case   :				Optional[Any]														=	split(root.left    ,							lowerCAmelCase				)
      return left, root
    else:
      _snake_case							,					_snake_case   :				List[str]														=	split(root.right    ,							lowerCAmelCase				)
      return root, right


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	Node | None				)->		Node | None:
  if (not left) or (not right):  # If one node is None, return the other
    return left or right
  elif left.prior < right.prior:
    _snake_case   :				str														=	merge(left.right    ,							lowerCAmelCase				)
    return left
  else:
    _snake_case   :				Union[str, Any]														=	merge(lowerCAmelCase    ,							right.left				)
    return right


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	int				)->		Node | None:
  _snake_case   :				Tuple														=	Node(lowerCAmelCase				)
  _snake_case							,					_snake_case   :				Optional[int]														=	split(lowerCAmelCase    ,							lowerCAmelCase				)
  return merge(merge(lowerCAmelCase    ,							lowerCAmelCase				)    ,							lowerCAmelCase				)


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	int				)->		Node | None:
  _snake_case							,					_snake_case   :				Optional[int]														=	split(lowerCAmelCase    ,							value - 1				)
  _snake_case							,					_snake_case   :				List[str]														=	split(lowerCAmelCase    ,							lowerCAmelCase				)
  return merge(lowerCAmelCase    ,							lowerCAmelCase				)


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None				)->		None:
  if not root:  # None
    return
  else:
    inorder(root.left				)
    print(root.value    ,							end=','				)
    inorder(root.right				)


def     lowerCamelCase_	(      lowerCAmelCase:	Node | None    ,							lowerCAmelCase:	str				)->		Node | None:
  for arg in args.split():
    if arg[0] == "+":
      _snake_case   :				List[str]														=	insert(lowerCAmelCase    ,							int(arg[1:]				)				)

    elif arg[0] == "-":
      _snake_case   :				Any														=	erase(lowerCAmelCase    ,							int(arg[1:]				)				)

    else:
      print('Unknown command'				)

  return root


def     lowerCamelCase_	(      )->		None:
  _snake_case   :				Tuple														=	None
  print(
      'enter numbers to create a tree, + value to add value into treap, '
      '- value to erase all nodes with value. \'q\' to quit. '				)

  _snake_case   :				List[Any]														=	input()
  while args != "q":
    _snake_case   :				int														=	interact_treap(lowerCAmelCase    ,							lowerCAmelCase				)
    print(lowerCAmelCase				)
    _snake_case   :				Tuple														=	input()

  print('good by!'				)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()




