






'''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
UpperCamelCase      					=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")

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



def 				snake_case       (  a_				:			str						)							->					Optional[int]:





      """simple docstring"""

      with open(a_     , """rb"""						) as f:
            UpperCamelCase_					: List[Any]            =				Image.open(a_						)
            return im.convert("""RGB"""						)




@dataclass
class      A     :






 """simple docstring"""





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






 """simple docstring"""





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






def 				snake_case       (  a_				:			Any						)							->					Dict:





      """simple docstring"""

      UpperCamelCase_					: List[Any]            =				torch.stack([example["""pixel_values"""] for example in examples]						)
      UpperCamelCase_					: Optional[int]            =				torch.tensor([example["""labels"""] for example in examples]						)
      return {"pixel_values": pixel_values, "labels": labels}



def 				snake_case       (  )							->					List[str]:





      """simple docstring"""

      UpperCamelCase_					: 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.
            UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: Tuple            =				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
      else:
            UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: 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"""     , a_     , a_						)

      # 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()

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

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

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

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

      def train_transforms(a_				:			List[Any]						):
            UpperCamelCase_					: Optional[Any]            =				[
                _train_transforms(pil_img.convert("""RGB"""						)						) for pil_img in example_batch["""image"""]
            ]
            return example_batch

      def val_transforms(a_				:			int						):
            UpperCamelCase_					: Tuple            =				[_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:
                  UpperCamelCase_					: Any            =				(
                      dataset["""train"""].shuffle(seed=training_args.seed						).select(range(data_args.max_train_samples						)						)
                  )
            # Set the training transforms
            dataset["train"].set_transform(a_						)

      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:
                  UpperCamelCase_					: int            =				(
                      dataset["""validation"""].shuffle(seed=training_args.seed						).select(range(data_args.max_eval_samples						)						)
                  )
            # Set the validation transforms
            dataset["validation"].set_transform(a_						)

      # Initalize our trainer
      UpperCamelCase_					: str            =				Trainer(
          model=a_     , args=a_     , train_dataset=dataset["""train"""] if training_args.do_train else None     , eval_dataset=dataset["""validation"""] if training_args.do_eval else None     , compute_metrics=a_     , tokenizer=a_     , data_collator=a_     , )

      # Training
      if training_args.do_train:
            UpperCamelCase_					: Optional[int]            =				None
            if training_args.resume_from_checkpoint is not None:
                  UpperCamelCase_					: int            =				training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  UpperCamelCase_					: Tuple            =				last_checkpoint
            UpperCamelCase_					: List[str]            =				trainer.train(resume_from_checkpoint=a_						)
            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:
            UpperCamelCase_					: Optional[int]            =				trainer.evaluate()
            trainer.log_metrics("""eval"""     , a_						)
            trainer.save_metrics("""eval"""     , a_						)

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


if __name__ == "__main__":
      main()














'''simple docstring'''

from __future__ import annotations

from math import pi, sqrt



def 				snake_case       (  a_				:			float     , a_				:			float						)							->					tuple:





      """simple docstring"""

      if inductance <= 0:
            raise ValueError("""Inductance cannot be 0 or negative"""						)

      elif capacitance <= 0:
            raise ValueError("""Capacitance cannot be 0 or negative"""						)

      else:
            return (
                "Resonant frequency",
                float(1 / (2 * pi * (sqrt(inductance * capacitance						)))						),
            )


if __name__ == "__main__":
      import doctest

      doctest.testmod()






