




'''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
A_			      =		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")

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






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Dict:
      with open(__SCREAMING_SNAKE_CASE				,      'rb'		) as f:
            snake_case__       :   Optional[int]	      =    Image.open(__SCREAMING_SNAKE_CASE		)
            return im.convert('RGB'		)






@dataclass
class        lowercase_      :
     A_											=			field(
         default=UpperCAmelCase__						,       metadata={
             "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
         }						,       )
     A_											=			field(
         default=UpperCAmelCase__						,       metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}							)
     A_											=			field(default=UpperCAmelCase__						,       metadata={"help": "A folder containing the training data."}							)
     A_											=			field(default=UpperCAmelCase__						,       metadata={"help": "A folder containing the validation data."}							)
     A_											=			field(
         default=0.1_5						,       metadata={"help": "Percent to split off of train for validation."}							)
     A_											=			field(
         default=UpperCAmelCase__						,       metadata={
             "help": (
                 "For debugging purposes or quicker training, truncate the number of training examples to this "
                 "value if set."
             )
         }						,       )
     A_											=			field(
         default=UpperCAmelCase__						,       metadata={
             "help": (
                 "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                 "value if set."
             )
         }						,       )





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






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






def 		UpperCamelCase__      (		)    ->   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__       :   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.
            snake_case__      ,							snake_case__      ,							snake_case__       :   Union[str, Any]	      =    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
      else:
            snake_case__      ,							snake_case__      ,							snake_case__       :   Optional[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_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()

      snake_case__       :   List[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.
      snake_case__       :   Union[str, Any]	      =    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__       :   int	      =    {}
            if data_args.train_dir is not None:
                  snake_case__       :   Any	      =    os.path.join(data_args.train_dir				,      '**'		)
            if data_args.validation_dir is not None:
                  snake_case__       :   Any	      =    os.path.join(data_args.validation_dir				,      '**'		)
            snake_case__       :   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.
      snake_case__       :   Union[str, 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:
            snake_case__       :   List[Any]	      =    dataset['train'].train_test_split(data_args.train_val_split		)
            snake_case__       :   str	      =    split['train']
            snake_case__       :   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__       :   Optional[int]	      =    dataset['train'].features['labels'].names
      snake_case__      ,							snake_case__       :   Dict	      =    {}, {}
      for i, label in enumerate(__SCREAMING_SNAKE_CASE		):
            snake_case__       :   Union[str, Any]	      =    str(__SCREAMING_SNAKE_CASE		)
            snake_case__       :   Tuple	      =    label

      # Load the accuracy metric from the datasets package
      snake_case__       :   Optional[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(__SCREAMING_SNAKE_CASE		):
            return metric.compute(predictions=np.argmax(p.predictions				,      axis=1		)				,      references=p.label_ids		)

      snake_case__       :   str	      =    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				,      )
      snake_case__       :   Any	      =    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				,      )
      snake_case__       :   Union[str, 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:
            snake_case__       :   List[str]	      =    image_processor.size['shortest_edge']
      else:
            snake_case__       :   Optional[Any]	      =    (image_processor.size['height'], image_processor.size['width'])
      snake_case__       :   Optional[Any]	      =    Normalize(mean=image_processor.image_mean				,      std=image_processor.image_std		)
      snake_case__       :   int	      =    Compose(
          [
              RandomResizedCrop(__SCREAMING_SNAKE_CASE		),
              RandomHorizontalFlip(),
              ToTensor(),
              normalize,
          ]		)
      snake_case__       :   str	      =    Compose(
          [
              Resize(__SCREAMING_SNAKE_CASE		),
              CenterCrop(__SCREAMING_SNAKE_CASE		),
              ToTensor(),
              normalize,
          ]		)

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

      def val_transforms(__SCREAMING_SNAKE_CASE		):
            snake_case__       :   List[Any]	      =    [_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__       :   int	      =    (
                      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:
                  snake_case__       :   int	      =    (
                      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
      snake_case__       :   Optional[Any]	      =    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:
            snake_case__       :   Any	      =    None
            if training_args.resume_from_checkpoint is not None:
                  snake_case__       :   Optional[Any]	      =    training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  snake_case__       :   Tuple	      =    last_checkpoint
            snake_case__       :   Dict	      =    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:
            snake_case__       :   Optional[Any]	      =    trainer.evaluate()
            trainer.log_metrics('eval'				,      __SCREAMING_SNAKE_CASE		)
            trainer.save_metrics('eval'				,      __SCREAMING_SNAKE_CASE		)

      # Write model card and (optionally) push to hub
      snake_case__       :   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()






'''simple docstring'''


from collections import defaultdict
def 						lowerCamelCase__   (				a    ,	a							):
						__snake_case						 =   first_str.lower().strip()
						__snake_case						 =   second_str.lower().strip()

						# Remove whitespace
						__snake_case						 =   first_str.replace(' '    ,	''							)
						__snake_case						 =   second_str.replace(' '    ,	''							)

						# Strings of different lengths are not anagrams
						if len(a							) != len(a							):
												return False

						# Default values for count should be 0
						__snake_case						 =   defaultdict(a							)

						# For each character in input strings,
						# increment count in the corresponding
						for i in range(len(a							)							):
												count[first_str[i]] += 1
												count[second_str[i]] -= 1

						return all(_count == 0 for _count in count.values()							)


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	_lowercase  =			input("""Enter the first string """).strip()
	_lowercase  =			input("""Enter the second string """).strip()

	_lowercase  =			check_anagrams(input_a, input_b)
	print(f'''{input_a} and {input_b} are {'' if status else 'not '}anagrams.''')

