



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_    :  Tuple						  =		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[str]						  =		list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
a_    :  List[Any]						  =		tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      with open(UpperCAmelCase__ , "rb"			) as f:
            lowerCamelCase             =		Image.open(UpperCAmelCase__			)
            return im.convert("RGB"			)





@dataclass
class  lowerCamelCase__	:
     """simple docstring"""



     _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=0.15							,     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     _a   (self     ):






           '''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      =      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      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		torch.stack([example["pixel_values"] for example in examples]			)
      lowerCamelCase             =		torch.tensor([example["labels"] for example in examples]			)
      return {"pixel_values": pixel_values, "labels": labels}

def      __lowercase(						):


      """simple docstring"""



      lowerCamelCase             =		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             =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
      else:
            lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase             =		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             =		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             =		None
      if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
            lowerCamelCase             =		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             =		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             =		{}
            if data_args.train_dir is not None:
                  lowerCamelCase             =		os.path.join(data_args.train_dir , "**"			)
            if data_args.validation_dir is not None:
                  lowerCamelCase             =		os.path.join(data_args.validation_dir , "**"			)
            lowerCamelCase             =		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             =		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             =		dataset["train"].train_test_split(data_args.train_val_split			)
            lowerCamelCase             =		split["train"]
            lowerCamelCase             =		split["test"]

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

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

      lowerCamelCase             =		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             =		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             =		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             =		image_processor.size["shortest_edge"]
      else:
            lowerCamelCase             =		(image_processor.size["height"], image_processor.size["width"])
      lowerCamelCase             =		Normalize(mean=image_processor.image_mean , std=image_processor.image_std			)
      lowerCamelCase             =		Compose(
          [
              RandomResizedCrop(UpperCAmelCase__			),
              RandomHorizontalFlip(),
              ToTensor(),
              normalize,
          ]			)
      lowerCamelCase             =		Compose(
          [
              Resize(UpperCAmelCase__			),
              CenterCrop(UpperCAmelCase__			),
              ToTensor(),
              normalize,
          ]			)

      def train_transforms(UpperCAmelCase__			):
            lowerCamelCase             =		[
                _train_transforms(pil_img.convert("RGB"			)			) for pil_img in example_batch["image"]
            ]
            return example_batch

      def val_transforms(UpperCAmelCase__			):
            lowerCamelCase             =		[_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             =		(
                      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             =		(
                      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             =		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             =		None
            if training_args.resume_from_checkpoint is not None:
                  lowerCamelCase             =		training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  lowerCamelCase             =		last_checkpoint
            lowerCamelCase             =		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             =		trainer.evaluate()
            trainer.log_metrics("eval" , UpperCAmelCase__			)
            trainer.save_metrics("eval" , UpperCAmelCase__			)

      # Write model card and (optionally) push to hub
      lowerCamelCase             =		{
          "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()




a_    :  Dict						  =		8.314462  # Unit - J mol-1 K-1

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      if moles < 0 or kelvin < 0 or volume < 0:
            raise ValueError("Invalid inputs. Enter positive value."			)
      return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      if moles < 0 or kelvin < 0 or pressure < 0:
            raise ValueError("Invalid inputs. Enter positive value."			)
      return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


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

     testmod()