

"""simple docstring"""




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
snake_case_        =			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''')

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

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   str      ):



     '''simple docstring'''





     with open(SCREAMING_SNAKE_CASE_     ,					'rb'      ) as f:
          lowercase__   :       List[str]				=	Image.open(SCREAMING_SNAKE_CASE_      )
          return im.convert('RGB'      )


@dataclass
class 				SCREAMING_SNAKE_CASE__ :
    __lowerCamelCase						:	Optional[str]														=					field(
        default=__snake_case		,	metadata={
            """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
        }		,	)
    __lowerCamelCase						:	Optional[str]														=					field(
        default=__snake_case		,	metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}					)
    __lowerCamelCase						:	Optional[str]														=					field(default=__snake_case		,	metadata={"""help""": """A folder containing the training data."""}					)
    __lowerCamelCase						:	Optional[str]														=					field(default=__snake_case		,	metadata={"""help""": """A folder containing the validation data."""}					)
    __lowerCamelCase						:	Optional[float]														=					field(
        default=0.15		,	metadata={"""help""": """Percent to split off of train for validation."""}					)
    __lowerCamelCase						:	Optional[int]														=					field(
        default=__snake_case		,	metadata={
            """help""": (
                """For debugging purposes or quicker training, truncate the number of training examples to this """
                """value if set."""
            )
        }		,	)
    __lowerCamelCase						:	Optional[int]														=					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):
         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__ :
    __lowerCamelCase						:	str														=					field(
        default="""google/vit-base-patch16-224-in21k"""		,	metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}		,	)
    __lowerCamelCase						:	Optional[str]														=					field(
        default=__snake_case		,	metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(__snake_case					)}		,	)
    __lowerCamelCase						:	Optional[str]														=					field(
        default=__snake_case		,	metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}					)
    __lowerCamelCase						:	Optional[str]														=					field(
        default=__snake_case		,	metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}					)
    __lowerCamelCase						:	str														=					field(
        default="""main"""		,	metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}		,	)
    __lowerCamelCase						:	str														=					field(default=__snake_case		,	metadata={"""help""": """Name or path of preprocessor config."""}					)
    __lowerCamelCase						:	bool														=					field(
        default=__snake_case		,	metadata={
            """help""": (
                """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                """with private models)."""
            )
        }		,	)
    __lowerCamelCase						:	bool														=					field(
        default=__snake_case		,	metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}		,	)



def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     lowercase__   :       str				=	torch.stack([example['pixel_values'] for example in examples]      )
     lowercase__   :       List[Any]				=	torch.tensor([example['labels'] for example in examples]      )
     return {"pixel_values": pixel_values, "labels": labels}

def      snake_case__      (   ):



     '''simple docstring'''





     lowercase__   :       List[str]				=	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__   :       List[str]				=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
     else:
          lowercase__   :       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'     ,					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()

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

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

     # Load the accuracy metric from the datasets package
     lowercase__   :       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_		:   Dict      ):
          return metric.compute(predictions=np.argmax(p.predictions     ,					axis=1      )     ,					references=p.label_ids      )

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

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

     def val_transforms(SCREAMING_SNAKE_CASE_		:   List[str]      ):
          lowercase__   :       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:
               lowercase__   :       str				=	(
                   dataset['train'].shuffle(seed=training_args.seed      ).select(range(data_args.max_train_samples      )      )
               )
          # Set the training transforms
          dataset["train"].set_transform(SCREAMING_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:
               lowercase__   :       Optional[Any]				=	(
                   dataset['validation'].shuffle(seed=training_args.seed      ).select(range(data_args.max_eval_samples      )      )
               )
          # Set the validation transforms
          dataset["validation"].set_transform(SCREAMING_SNAKE_CASE_      )

     # Initalize our trainer
     lowercase__   :       Tuple				=	Trainer(
         model=SCREAMING_SNAKE_CASE_     ,					args=SCREAMING_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=SCREAMING_SNAKE_CASE_     ,					tokenizer=SCREAMING_SNAKE_CASE_     ,					data_collator=SCREAMING_SNAKE_CASE_     ,					)

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

     # Write model card and (optionally) push to hub
     lowercase__   :       Optional[int]				=	{
         '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(**SCREAMING_SNAKE_CASE_      )
     else:
          trainer.create_model_card(**SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
       main()




from __future__ import annotations

import math

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5     ,					int(math.sqrt(SCREAMING_SNAKE_CASE_      ) + 1      )     ,					6      ):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     lowercase__   :       int				=	str(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Union[str, Any]				=	[n]
     for i in range(1     ,					len(SCREAMING_SNAKE_CASE_      )      ):
          list_nums.append(int(str_num[i:]      )      )
          list_nums.append(int(str_num[:-i]      )      )
     return list_nums

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     if len(str(SCREAMING_SNAKE_CASE_      )      ) > 3:
          if not is_prime(int(str(SCREAMING_SNAKE_CASE_      )[-3:]      )      ) or not is_prime(int(str(SCREAMING_SNAKE_CASE_      )[:3]      )      ):
               return False
     return True

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int = 11      ):



     '''simple docstring'''





     lowercase__   :       list[int]				=	[]
     lowercase__   :       Tuple				=	13
     while len(SCREAMING_SNAKE_CASE_      ) != count:
          if validate(SCREAMING_SNAKE_CASE_      ):
               lowercase__   :       Optional[int]				=	list_truncated_nums(SCREAMING_SNAKE_CASE_      )
               if all(is_prime(SCREAMING_SNAKE_CASE_      ) for i in list_nums      ):
                    list_truncated_primes.append(SCREAMING_SNAKE_CASE_      )
          num += 2
     return list_truncated_primes

def      snake_case__      (   ):



     '''simple docstring'''





     return sum(compute_truncated_primes(11      )      )


if __name__ == "__main__":
       print(F'''{sum(compute_truncated_primes(11)) = }''')

