






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      :       List[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")

__a      :       Optional[int]				  =   list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__a      :       Dict				  =   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str    )						->      Dict:





    """simple docstring"""

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


@dataclass
class       __lowercase      :







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




     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """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       __lowercase      :







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



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[Any]    )						->      Tuple:





    """simple docstring"""

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



def 			_SCREAMING_SNAKE_CASE						( )						->      List[str]:





    """simple docstring"""

    __A         =	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.
        __A    ,	__A    ,	__A         =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
    else:
        __A    ,	__A    ,	__A         =	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"""			,				__lowercase			,				__lowercase    )

    # 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()

    __A         =	training_args.get_process_log_level()
    logger.setLevel(__lowercase    )
    transformers.utils.logging.set_verbosity(__lowercase    )
    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.
    __A         =	None
    if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
        __A         =	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:
        __A         =	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:
        __A         =	{}
        if data_args.train_dir is not None:
            __A         =	os.path.join(data_args.train_dir			,				"""**"""    )
        if data_args.validation_dir is not None:
            __A         =	os.path.join(data_args.validation_dir			,				"""**"""    )
        __A         =	load_dataset(
            """imagefolder"""			,				data_files=__lowercase			,				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.
    __A         =	None if """validation""" in dataset.keys() else data_args.train_val_split
    if isinstance(data_args.train_val_split			,				__lowercase    ) and data_args.train_val_split > 0.0:
        __A         =	dataset["""train"""].train_test_split(data_args.train_val_split    )
        __A         =	split["""train"""]
        __A         =	split["""test"""]

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

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

    __A         =	AutoConfig.from_pretrained(
        model_args.config_name or model_args.model_name_or_path			,				num_labels=len(__lowercase    )			,				labelaid=__lowercase			,				idalabel=__lowercase			,				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			,				)
    __A         =	AutoModelForImageClassification.from_pretrained(
        model_args.model_name_or_path			,				from_tf=bool(""".ckpt""" in model_args.model_name_or_path    )			,				config=__lowercase			,				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			,				)
    __A         =	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:
        __A         =	image_processor.size["""shortest_edge"""]
    else:
        __A         =	(image_processor.size["""height"""], image_processor.size["""width"""])
    __A         =	Normalize(mean=image_processor.image_mean			,				std=image_processor.image_std    )
    __A         =	Compose(
        [
            RandomResizedCrop(__lowercase    ),
            RandomHorizontalFlip(),
            ToTensor(),
            normalize,
        ]    )
    __A         =	Compose(
        [
            Resize(__lowercase    ),
            CenterCrop(__lowercase    ),
            ToTensor(),
            normalize,
        ]    )

    def train_transforms(__lowercase   :			Dict    ):
        __A         =	[
            _train_transforms(pil_img.convert("""RGB"""    )    ) for pil_img in example_batch["""image"""]
        ]
        return example_batch

    def val_transforms(__lowercase   :			int    ):
        __A         =	[_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:
            __A         =	(
                dataset["""train"""].shuffle(seed=training_args.seed    ).select(range(data_args.max_train_samples    )    )
            )
        # Set the training transforms
        dataset["train"].set_transform(__lowercase    )

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

    # Initalize our trainer
    __A         =	Trainer(
        model=__lowercase			,				args=__lowercase			,				train_dataset=dataset["""train"""] if training_args.do_train else None			,				eval_dataset=dataset["""validation"""] if training_args.do_eval else None			,				compute_metrics=__lowercase			,				tokenizer=__lowercase			,				data_collator=__lowercase			,				)

    # Training
    if training_args.do_train:
        __A         =	None
        if training_args.resume_from_checkpoint is not None:
            __A         =	training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            __A         =	last_checkpoint
        __A         =	trainer.train(resume_from_checkpoint=__lowercase    )
        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:
        __A         =	trainer.evaluate()
        trainer.log_metrics("""eval"""			,				__lowercase    )
        trainer.save_metrics("""eval"""			,				__lowercase    )

    # Write model card and (optionally) push to hub
    __A         =	{
        """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(**__lowercase    )
    else:
        trainer.create_model_card(**__lowercase    )


if __name__ == "__main__":
  main()












from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				["image_processor", "tokenizer"]
     SCREAMING_SNAKE_CASE   						=				"AutoImageProcessor"
     SCREAMING_SNAKE_CASE   						=				"AutoTokenizer"




     def __init__(				self       :			str ,	UpperCamelCase_       :			Any ,	UpperCamelCase_       :			str				):


         """simple docstring"""



         super().__init__(UpperCamelCase_ ,	UpperCamelCase_				)
         __A         =	self.image_processor




     def __call__(				self       :			Optional[Any] ,	UpperCamelCase_       :			Optional[Any]=None ,	UpperCamelCase_       :			Any=None ,	UpperCamelCase_       :			List[Any]=None ,	**UpperCamelCase_       :			int				):


         """simple docstring"""



         if text is None and images is None:
             raise ValueError("""You have to specify either text or images. Both cannot be none."""				)

         if text is not None:
             __A         =	self.tokenizer(UpperCamelCase_ ,	return_tensors=UpperCamelCase_ ,	**UpperCamelCase_				)

         if images is not None:
             __A         =	self.image_processor(UpperCamelCase_ ,	return_tensors=UpperCamelCase_ ,	**UpperCamelCase_				)

         if text is not None and images is not None:
             __A         =	image_features.pixel_values
             return encoding
         elif text is not None:
             return encoding
         else:
             return BatchEncoding(data=dict(**UpperCamelCase_				) ,	tensor_type=UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			List[str] ,	*UpperCamelCase_       :			Tuple ,	**UpperCamelCase_       :			List[str]				):


         """simple docstring"""



         return self.tokenizer.batch_decode(*UpperCamelCase_ ,	**UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Any ,	*UpperCamelCase_       :			int ,	**UpperCamelCase_       :			Tuple				):


         """simple docstring"""



         return self.tokenizer.decode(*UpperCamelCase_ ,	**UpperCamelCase_				)





     @property
     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         return ["input_ids", "attention_mask", "pixel_values"]




