



'''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
__magic_name__   :						Union[str, Any]    =			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""")

__magic_name__   :						str    =			list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__magic_name__   :						Union[str, Any]    =			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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


@dataclass
class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






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



     '''simple docstring'''






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







def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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




def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				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     		=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
  else:
    _snake_case     		=				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     		=				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     		=				None
  if os.path.isdir(training_args.output_dir       ) and training_args.do_train and not training_args.overwrite_output_dir:
    _snake_case     		=				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     		=				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     		=				{}
    if data_args.train_dir is not None:
      _snake_case     		=				os.path.join(data_args.train_dir					,	"**"       )
    if data_args.validation_dir is not None:
      _snake_case     		=				os.path.join(data_args.validation_dir					,	"**"       )
    _snake_case     		=				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     		=				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     		=				dataset['train'].train_test_split(data_args.train_val_split       )
    _snake_case     		=				split['train']
    _snake_case     		=				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     		=				dataset['train'].features['labels'].names
  _snake_case     		=				{}, {}
  for i, label in enumerate(SCREAMING_SNAKE_CASE__       ):
    _snake_case     		=				str(SCREAMING_SNAKE_CASE__       )
    _snake_case     		=				label

  # Load the accuracy metric from the datasets package
  _snake_case     		=				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     		=				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     		=				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     		=				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     		=				image_processor.size['shortest_edge']
  else:
    _snake_case     		=				(image_processor.size['height'], image_processor.size['width'])
  _snake_case     		=				Normalize(mean=image_processor.image_mean					,	std=image_processor.image_std       )
  _snake_case     		=				Compose(
      [
          RandomResizedCrop(SCREAMING_SNAKE_CASE__       ),
          RandomHorizontalFlip(),
          ToTensor(),
          normalize,
      ]       )
  _snake_case     		=				Compose(
      [
          Resize(SCREAMING_SNAKE_CASE__       ),
          CenterCrop(SCREAMING_SNAKE_CASE__       ),
          ToTensor(),
          normalize,
      ]       )

  def train_transforms(SCREAMING_SNAKE_CASE__       ):
    _snake_case     		=				[
        _train_transforms(pil_img.convert("RGB"       )       ) for pil_img in example_batch['image']
    ]
    return example_batch

  def val_transforms(SCREAMING_SNAKE_CASE__       ):
    _snake_case     		=				[_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     		=				(
          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     		=				(
          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     		=				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     		=				None
    if training_args.resume_from_checkpoint is not None:
      _snake_case     		=				training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      _snake_case     		=				last_checkpoint
    _snake_case     		=				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     		=				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     		=				{
      '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 __future__ import annotations

import numpy as np




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  return np.maximum(0					,	SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
 print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


