






'''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:      Dict  				=				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:      int  				=				list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
A:      List[Any]  				=				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






def 	_UpperCAmelCase    (  a		:	Optional[int]							)			->			Optional[int]:





      """simple docstring"""
      with open(_lowerCamelCase						,			'rb'							) as f:
            lowercase_      :   str        =			Image.open(_lowerCamelCase							)
            return im.convert('RGB'							)






@dataclass
class 							__magic_name__     :







 """simple docstring"""



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







 """simple docstring"""



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






def 	_UpperCAmelCase    (  a		:	Union[str, Any]							)			->			Union[str, Any]:





      """simple docstring"""
      lowercase_      :   Dict        =			torch.stack([example['pixel_values'] for example in examples]							)
      lowercase_      :   Union[str, Any]        =			torch.tensor([example['labels'] for example in examples]							)
      return {"pixel_values": pixel_values, "labels": labels}






def 	_UpperCAmelCase    (  )			->			Optional[int]:





      """simple docstring"""
      # 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.

      lowercase_      :   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.
            lowercase_      :   Tuple        =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
      else:
            lowercase_      :   Tuple        =			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'						,			_lowerCamelCase						,			_lowerCamelCase							)

      # 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_      :   int        =			training_args.get_process_log_level()
      logger.setLevel(_lowerCamelCase							)
      transformers.utils.logging.set_verbosity(_lowerCamelCase							)
      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_      :   Optional[int]        =			None
      if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
            lowercase_      :   List[str]        =			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_      :   Union[str, 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_      :   Union[str, Any]        =			{}
            if data_args.train_dir is not None:
                  lowercase_      :   Union[str, Any]        =			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_      :   Any        =			load_dataset(
                'imagefolder'						,			data_files=_lowerCamelCase						,			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_      :   Union[str, Any]        =			None if "validation" in dataset.keys() else data_args.train_val_split
      if isinstance(data_args.train_val_split						,			_lowerCamelCase							) and data_args.train_val_split > 0.0:
            lowercase_      :   int        =			dataset["train"].train_test_split(data_args.train_val_split							)
            lowercase_      :   Tuple        =			split["train"]
            lowercase_      :   List[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_      :   str        =			dataset["train"].features["labels"].names
      lowercase_      :   int        =			{}, {}
      for i, label in enumerate(_lowerCamelCase							):
            lowercase_      :   Union[str, Any]        =			str(_lowerCamelCase							)
            lowercase_      :   Dict        =			label

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

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

      def train_transforms(a		:	Tuple							):
            lowercase_      :   Union[str, Any]        =			[
                _train_transforms(pil_img.convert('RGB'							)							) for pil_img in example_batch["image"]
            ]
            return example_batch

      def val_transforms(a		:	Optional[Any]							):
            lowercase_      :   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:
                  lowercase_      :   List[str]        =			(
                      dataset["train"].shuffle(seed=training_args.seed							).select(range(data_args.max_train_samples							)							)
                  )
            # Set the training transforms
            dataset["train"].set_transform(_lowerCamelCase							)

      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_      :   Tuple        =			(
                      dataset["validation"].shuffle(seed=training_args.seed							).select(range(data_args.max_eval_samples							)							)
                  )
            # Set the validation transforms
            dataset["validation"].set_transform(_lowerCamelCase							)

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

      # Training
      if training_args.do_train:
            lowercase_      :   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_      :   int        =			last_checkpoint
            lowercase_      :   int        =			trainer.train(resume_from_checkpoint=_lowerCamelCase							)
            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_      :   str        =			trainer.evaluate()
            trainer.log_metrics('eval'						,			_lowerCamelCase							)
            trainer.save_metrics('eval'						,			_lowerCamelCase							)

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


if __name__ == "__main__":
      main()








'''simple docstring'''

import os
from distutils.util import strtobool






def 	_UpperCAmelCase    (  a		:	Any						,			a		:	int							)			->			Any:





						"""simple docstring"""
						for e in env_keys:
												lowercase_      :   Optional[Any]        =			int(os.environ.get(a						,			-1							)							)
												if val >= 0:
																		return val
						return default






def 	_UpperCAmelCase    (  a		:	List[Any]						,			a		:	Dict=False							)			->			Optional[Any]:





						"""simple docstring"""
						lowercase_      :   Optional[int]        =			os.environ.get(a						,			str(a							)							)
						return strtobool(a							) == 1  # As its name indicates `strtobool` actually returns an int...






def 	_UpperCAmelCase    (  a		:	List[Any]						,			a		:	Dict="no"							)			->			str:





						"""simple docstring"""
						lowercase_      :   List[Any]        =			os.environ.get(a						,			str(a							)							)
						return value
