
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
lowerCamelCase  =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")

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


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__		):
      with open(UpperCamelCase__			,		'''rb'''		) as f:
            UpperCamelCase__     : Tuple											=					Image.open(UpperCamelCase__		)
            return im.convert('''RGB'''		)




@dataclass
class 					_lowerCamelCase			:





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	field(
         default=UpperCamelCase_						,				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=UpperCamelCase_						,				metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}			)
     SCREAMING_SNAKE_CASE_		 =	field(default=UpperCamelCase_						,				metadata={'''help''': '''A folder containing the training data.'''}			)
     SCREAMING_SNAKE_CASE_		 =	field(default=UpperCamelCase_						,				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=UpperCamelCase_						,				metadata={
             '''help''': (
                 '''For debugging purposes or quicker training, truncate the number of training examples to this '''
                 '''value if set.'''
             )
         }						,				)
     SCREAMING_SNAKE_CASE_		 =	field(
         default=UpperCamelCase_						,				metadata={
             '''help''': (
                 '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
                 '''value if set.'''
             )
         }						,				)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """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=UpperCamelCase_						,				metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(UpperCamelCase_			)}						,				)
     SCREAMING_SNAKE_CASE_		 =	field(
         default=UpperCamelCase_						,				metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}			)
     SCREAMING_SNAKE_CASE_		 =	field(
         default=UpperCamelCase_						,				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=UpperCamelCase_						,				metadata={'''help''': '''Name or path of preprocessor config.'''}			)
     SCREAMING_SNAKE_CASE_		 =	field(
         default=UpperCamelCase_						,				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=UpperCamelCase_						,				metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''}						,				)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__		):
      UpperCamelCase__     : Union[str, Any]											=					torch.stack([example['''pixel_values'''] for example in examples]		)
      UpperCamelCase__     : Any											=					torch.tensor([example['''labels'''] for example in examples]		)
      return {"pixel_values": pixel_values, "labels": labels}


def 				SCREAMING_SNAKE_CASE_   (      ):
      # 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[Any]											=					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'''			,		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()

      UpperCamelCase__     : List[Any]											=					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.
      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__     : 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:
            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__     : int											=					os.path.join(data_args.train_dir			,		'''**'''		)
            if data_args.validation_dir is not None:
                  UpperCamelCase__     : Union[str, Any]											=					os.path.join(data_args.validation_dir			,		'''**'''		)
            UpperCamelCase__     : Dict											=					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.
      UpperCamelCase__     : Tuple											=					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:
            UpperCamelCase__     : str											=					dataset['''train'''].train_test_split(data_args.train_val_split		)
            UpperCamelCase__     : List[str]											=					split['''train''']
            UpperCamelCase__     : int											=					split['''test''']

      # Prepare label mappings.
      # We'll include these in the model's config to get human readable labels in the Inference API.
      UpperCamelCase__     : Optional[int]											=					dataset['''train'''].features['''labels'''].names
      UpperCamelCase__					,UpperCamelCase__     : Optional[int]											=					{}, {}
      for i, label in enumerate(UpperCamelCase__		):
            UpperCamelCase__     : Any											=					str(UpperCamelCase__		)
            UpperCamelCase__     : 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(UpperCamelCase__		):
            return metric.compute(predictions=np.argmax(p.predictions			,		axis=1		)			,		references=p.label_ids		)

      UpperCamelCase__     : List[Any]											=					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			,		)
      UpperCamelCase__     : List[str]											=					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			,		)
      UpperCamelCase__     : Tuple											=					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__     : Optional[Any]											=					image_processor.size['''shortest_edge''']
      else:
            UpperCamelCase__     : Union[str, Any]											=					(image_processor.size['''height'''], image_processor.size['''width'''])
      UpperCamelCase__     : str											=					Normalize(mean=image_processor.image_mean			,		std=image_processor.image_std		)
      UpperCamelCase__     : List[Any]											=					Compose(
          [
              RandomResizedCrop(UpperCamelCase__		),
              RandomHorizontalFlip(),
              ToTensor(),
              normalize,
          ]		)
      UpperCamelCase__     : Union[str, Any]											=					Compose(
          [
              Resize(UpperCamelCase__		),
              CenterCrop(UpperCamelCase__		),
              ToTensor(),
              normalize,
          ]		)

      def train_transforms(UpperCamelCase__		):
            UpperCamelCase__     : Optional[int]											=					[
                _train_transforms(pil_img.convert('''RGB'''		)		) for pil_img in example_batch['''image''']
            ]
            return example_batch

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

      # 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(**UpperCamelCase__		)
      else:
            trainer.create_model_card(**UpperCamelCase__		)


if __name__ == "__main__":
    main()




def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__ = " "		):
      UpperCamelCase__     : Optional[int]											=					[]

      UpperCamelCase__     : int											=					0
      for index, char in enumerate(UpperCamelCase__		):
            if char == separator:
                  split_words.append(string[last_index:index]		)
                  UpperCamelCase__     : int											=					index + 1
            elif index + 1 == len(UpperCamelCase__		):
                  split_words.append(string[last_index : index + 1]		)
      return split_words


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

    testmod()


