

"""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 					a__						(						lowerCAmelCase    )	-> Optional[Any]:
      with open(lowerCAmelCase			,	"""rb"""    ) as f:
            UpperCAmelCase__			:							Any							=    Image.open(lowerCAmelCase    )
            return im.convert("""RGB"""    )

@dataclass
class       lowerCamelCase	:


  '''simple docstring'''







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







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




def 					a__						(						lowerCAmelCase    )	-> Dict:
      UpperCAmelCase__			:							int							=    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 					a__						(						)	-> Dict:
      # 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.

      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__			:							List[str]							=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
      else:
            UpperCAmelCase__					,		UpperCAmelCase__					,		UpperCAmelCase__			:							int							=    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()

      UpperCAmelCase__			:							Union[str, Any]							=    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.
      UpperCAmelCase__			:							Any							=    None
      if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
            UpperCAmelCase__			:							List[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__			:							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:
            UpperCAmelCase__			:							int							=    {}
            if data_args.train_dir is not None:
                  UpperCAmelCase__			:							str							=    os.path.join(data_args.train_dir			,	"""**"""    )
            if data_args.validation_dir is not None:
                  UpperCAmelCase__			:							Any							=    os.path.join(data_args.validation_dir			,	"""**"""    )
            UpperCAmelCase__			:							Tuple							=    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.
      UpperCAmelCase__			:							int							=    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:
            UpperCAmelCase__			:							str							=    dataset["""train"""].train_test_split(data_args.train_val_split    )
            UpperCAmelCase__			:							str							=    split["""train"""]
            UpperCAmelCase__			:							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.
      UpperCAmelCase__			:							str							=    dataset["""train"""].features["""labels"""].names
      UpperCAmelCase__					,		UpperCAmelCase__			:							str							=    {}, {}
      for i, label in enumerate(lowerCAmelCase    ):
            UpperCAmelCase__			:							str							=    str(lowerCAmelCase    )
            UpperCAmelCase__			:							Optional[Any]							=    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(lowerCAmelCase    ):
            return metric.compute(predictions=np.argmax(p.predictions			,	axis=1    )			,	references=p.label_ids    )

      UpperCAmelCase__			:							Union[str, Any]							=    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			,	)
      UpperCAmelCase__			:							Optional[Any]							=    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			,	)
      UpperCAmelCase__			:							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:
            UpperCAmelCase__			:							Union[str, Any]							=    image_processor.size["""shortest_edge"""]
      else:
            UpperCAmelCase__			:							Union[str, Any]							=    (image_processor.size["""height"""], image_processor.size["""width"""])
      UpperCAmelCase__			:							Dict							=    Normalize(mean=image_processor.image_mean			,	std=image_processor.image_std    )
      UpperCAmelCase__			:							Union[str, Any]							=    Compose(
          [
              RandomResizedCrop(lowerCAmelCase    ),
              RandomHorizontalFlip(),
              ToTensor(),
              normalize,
          ]    )
      UpperCAmelCase__			:							List[str]							=    Compose(
          [
              Resize(lowerCAmelCase    ),
              CenterCrop(lowerCAmelCase    ),
              ToTensor(),
              normalize,
          ]    )

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

      def val_transforms(lowerCAmelCase    ):
            UpperCAmelCase__			:							List[str]							=    [_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__			:							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:
                  UpperCAmelCase__			:							Optional[Any]							=    (
                      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
      UpperCAmelCase__			:							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:
            UpperCAmelCase__			:							str							=    None
            if training_args.resume_from_checkpoint is not None:
                  UpperCAmelCase__			:							Optional[int]							=    training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  UpperCAmelCase__			:							Union[str, Any]							=    last_checkpoint
            UpperCAmelCase__			:							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:
            UpperCAmelCase__			:							str							=    trainer.evaluate()
            trainer.log_metrics("""eval"""			,	lowerCAmelCase    )
            trainer.save_metrics("""eval"""			,	lowerCAmelCase    )

      # Write model card and (optionally) push to hub
      UpperCAmelCase__			:							Union[str, 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 requests




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> None:
      UpperCAmelCase__			:							List[str]							=    {"""Content-Type""": """application/json"""}
      UpperCAmelCase__			:							List[str]							=    requests.post(lowerCAmelCase			,	json={"""text""": message_body}			,	headers=lowerCAmelCase    )
      if response.status_code != 2_00:
            UpperCAmelCase__			:							str							=    (
                """Request to slack returned an error """
                F"""{response.status_code}, the response is:\n{response.text}"""
            )
            raise ValueError(lowerCAmelCase    )


if __name__ == "__main__":
    # Set the slack url to the one provided by Slack when you create the webhook at
    # https://my.slack.com/services/new/incoming-webhook/
    send_slack_message("""<YOUR MESSAGE BODY>""", """<SLACK CHANNEL URL>""")

