


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__						):
 with open(UpperCAmelCase__      ,	'rb'						) as f:
  __lowerCamelCase		:							Union[str, Any]		 =						Image.open(UpperCAmelCase__						)
  return im.convert('RGB'						)


@dataclass
class 	A_ :




      '''simple docstring'''


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




      def 					_snake_case     (    self:	Optional[Any]    ):
       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 	A_ :




      '''simple docstring'''


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



def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							str		 =						torch.stack([example['pixel_values'] for example in examples]						)
 __lowerCamelCase		:							Tuple		 =						torch.tensor([example['labels'] for example in examples]						)
 return {"pixel_values": pixel_values, "labels": labels}
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.

 __lowerCamelCase		:							Optional[int]		 =						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.
  __lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		:							Union[str, Any]		 =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
 else:
  __lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						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'      ,	UpperCAmelCase__      ,	UpperCAmelCase__						)

 # 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		:							Dict		 =						training_args.get_process_log_level()
 logger.setLevel(UpperCAmelCase__						)
 transformers.utils.logging.set_verbosity(UpperCAmelCase__						)
 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		:							Any		 =						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:
  __lowerCamelCase		:							int		 =						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:
  __lowerCamelCase		:							Optional[int]		 =						{}
  if data_args.train_dir is not None:
   __lowerCamelCase		:							Optional[Any]		 =						os.path.join(data_args.train_dir      ,	'**'						)
  if data_args.validation_dir is not None:
   __lowerCamelCase		:							Any		 =						os.path.join(data_args.validation_dir      ,	'**'						)
  __lowerCamelCase		:							Tuple		 =						load_dataset(
      'imagefolder'      ,	data_files=UpperCAmelCase__      ,	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.
 __lowerCamelCase		:							List[str]		 =						None if 'validation' in dataset.keys() else data_args.train_val_split
 if isinstance(data_args.train_val_split      ,	UpperCAmelCase__						) and data_args.train_val_split > 0.0:
  __lowerCamelCase		:							List[Any]		 =						dataset['train'].train_test_split(data_args.train_val_split						)
  __lowerCamelCase		:							Any		 =						split['train']
  __lowerCamelCase		:							Optional[int]		 =						split['test']

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

 # Load the accuracy metric from the datasets package
 __lowerCamelCase		:							Dict		 =						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						)

 __lowerCamelCase		:							str		 =						AutoConfig.from_pretrained(
     model_args.config_name or model_args.model_name_or_path      ,	num_labels=len(UpperCAmelCase__						)      ,	labelaid=UpperCAmelCase__      ,	idalabel=UpperCAmelCase__      ,	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      ,	)
 __lowerCamelCase		:							List[Any]		 =						AutoModelForImageClassification.from_pretrained(
     model_args.model_name_or_path      ,	from_tf=bool('.ckpt' in model_args.model_name_or_path						)      ,	config=UpperCAmelCase__      ,	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      ,	)
 __lowerCamelCase		:							str		 =						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:
  __lowerCamelCase		:							Optional[Any]		 =						image_processor.size['shortest_edge']
 else:
  __lowerCamelCase		:							Dict		 =						(image_processor.size['height'], image_processor.size['width'])
 __lowerCamelCase		:							str		 =						Normalize(mean=image_processor.image_mean      ,	std=image_processor.image_std						)
 __lowerCamelCase		:							str		 =						Compose(
     [
         RandomResizedCrop(UpperCAmelCase__						),
         RandomHorizontalFlip(),
         ToTensor(),
         normalize,
     ]						)
 __lowerCamelCase		:							int		 =						Compose(
     [
         Resize(UpperCAmelCase__						),
         CenterCrop(UpperCAmelCase__						),
         ToTensor(),
         normalize,
     ]						)

 def train_transforms(SCREAMING_SNAKE_CASE__						):
  __lowerCamelCase		:							List[str]		 =						[
      _train_transforms(pil_img.convert('RGB'						)						) for pil_img in example_batch['image']
  ]
  return example_batch

 def val_transforms(SCREAMING_SNAKE_CASE__						):
  __lowerCamelCase		:							Dict		 =						[_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:
   __lowerCamelCase		:							Tuple		 =						(
       dataset['train'].shuffle(seed=training_args.seed						).select(range(data_args.max_train_samples						)						)
   )
  # Set the training transforms
  dataset["train"].set_transform(UpperCAmelCase__						)

 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:
   __lowerCamelCase		:							Any		 =						(
       dataset['validation'].shuffle(seed=training_args.seed						).select(range(data_args.max_eval_samples						)						)
   )
  # Set the validation transforms
  dataset["validation"].set_transform(UpperCAmelCase__						)

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

 # Training
 if training_args.do_train:
  __lowerCamelCase		:							Tuple		 =						None
  if training_args.resume_from_checkpoint is not None:
   __lowerCamelCase		:							Any		 =						training_args.resume_from_checkpoint
  elif last_checkpoint is not None:
   __lowerCamelCase		:							Tuple		 =						last_checkpoint
  __lowerCamelCase		:							Dict		 =						trainer.train(resume_from_checkpoint=UpperCAmelCase__						)
  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		:							List[str]		 =						trainer.evaluate()
  trainer.log_metrics('eval'      ,	UpperCAmelCase__						)
  trainer.save_metrics('eval'      ,	UpperCAmelCase__						)

 # Write model card and (optionally) push to hub
 __lowerCamelCase		:							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(**UpperCAmelCase__						)
 else:
  trainer.create_model_card(**UpperCAmelCase__						)


if __name__ == "__main__":
   main()





from __future__ import annotations
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							List[Any]		 =						str(SCREAMING_SNAKE_CASE__						)
 return len(SCREAMING_SNAKE_CASE__						) == 9 and set(SCREAMING_SNAKE_CASE__						) == set('123456789'						)
def 				UpperCamelCase__					(  ):
 for base_num in range(9_999      ,	4_999      ,	-1						):
  __lowerCamelCase		:							Tuple		 =						100_002 * base_num
  if is_9_pandigital(SCREAMING_SNAKE_CASE__						):
   return candidate

 for base_num in range(333      ,	99      ,	-1						):
  __lowerCamelCase		:							Union[str, Any]		 =						1_002_003 * base_num
  if is_9_pandigital(SCREAMING_SNAKE_CASE__						):
   return candidate

 return None


if __name__ == "__main__":
       print(F"""{solution() = }""")

