
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 		lowercase_    (     A__	)							->							Union[str, Any]:
    """simple docstring"""

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







@dataclass
class   lowerCamelCase	:
     UpperCAmelCase__       :							Optional[str]     =	field(
         default=A_							,				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=A_							,				metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}						)
     UpperCAmelCase__       :							Optional[str]     =	field(default=A_							,				metadata={"help": "A folder containing the training data."}						)
     UpperCAmelCase__       :							Optional[str]     =	field(default=A_							,				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=A_							,				metadata={
             "help": (
                 "For debugging purposes or quicker training, truncate the number of training examples to this "
                 "value if set."
             )
         }							,				)
     UpperCAmelCase__       :							Optional[int]     =	field(
         default=A_							,				metadata={
             "help": (
                 "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                 "value if set."
             )
         }							,				)





     def 	UpperCAmelCase(self   :					str     )		->		Optional[int]:
         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	:
     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=A_							,				metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(A_						)}							,				)
     UpperCAmelCase__       :							Optional[str]     =	field(
         default=A_							,				metadata={"help": "Pretrained config name or path if not the same as model_name"}						)
     UpperCAmelCase__       :							Optional[str]     =	field(
         default=A_							,				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=A_							,				metadata={"help": "Name or path of preprocessor config."}						)
     UpperCAmelCase__       :							bool     =	field(
         default=A_							,				metadata={
             "help": (
                 "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                 "with private models)."
             )
         }							,				)
     UpperCAmelCase__       :							bool     =	field(
         default=A_							,				metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}							,				)



def 		lowercase_    (     A__	)							->							Dict:
    """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 		lowercase_    (     )							->							List[str]:
    """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  ,							snake_case  ,							snake_case				  =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
    else:
        snake_case  ,							snake_case  ,							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"				,	A__				,	A__	)

    # 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(A__	)
    transformers.utils.logging.set_verbosity(A__	)
    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=A__				,	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				,	A__	) 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  ,							snake_case				  =      {}, {}
    for i, label in enumerate(A__	):
        snake_case				  =      str(A__	)
        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(A__	):
        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(A__	)				,	labelaid=A__				,	idalabel=A__				,	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=A__				,	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(A__	),
            RandomHorizontalFlip(),
            ToTensor(),
            normalize,
        ]	)
    snake_case				  =      Compose(
        [
            Resize(A__	),
            CenterCrop(A__	),
            ToTensor(),
            normalize,
        ]	)

    def train_transforms(A__	):
        snake_case				  =      [
            _train_transforms(pil_img.convert("RGB"	)	) for pil_img in example_batch["image"]
        ]
        return example_batch

    def val_transforms(A__	):
        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(A__	)

    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(A__	)

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

    # 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=A__	)
        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"				,	A__	)
        trainer.save_metrics("eval"				,	A__	)

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


if __name__ == "__main__":
   main()



import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()



def 		lowercase_    (     A__				,	A__				,	A__				,	A__				,	A__				,	A__ = None				,	)							->							List[Any]:
    """simple docstring"""

    snake_case				  =      {}

    if train_file is not None:
        snake_case				  =      [train_file]
    if eval_file is not None:
        snake_case				  =      [eval_file]
    if test_file is not None:
        snake_case				  =      [test_file]

    snake_case				  =      datasets.load_dataset("csv"				,	data_files=A__	)
    snake_case				  =      list(ds[list(files.keys()	)[0]].features.keys()	)
    snake_case				  =      features_name.pop(A__	)
    snake_case				  =      list(set(ds[list(files.keys()	)[0]][label_name]	)	)
    snake_case				  =      {label: i for i, label in enumerate(A__	)}
    snake_case				  =      tokenizer.model_input_names
    snake_case				  =      {}

    if len(A__	) == 1:
        for k in files.keys():
            snake_case				  =      ds[k].map(
                lambda A__	: tokenizer.batch_encode_plus(
                    example[features_name[0]]				,	truncation=A__				,	max_length=A__				,	padding="max_length"	)				,	batched=A__				,	)
    elif len(A__	) == 2:
        for k in files.keys():
            snake_case				  =      ds[k].map(
                lambda A__	: tokenizer.batch_encode_plus(
                    (example[features_name[0]], example[features_name[1]])				,	truncation=A__				,	max_length=A__				,	padding="max_length"				,	)				,	batched=A__				,	)

    def gen_train():
        for ex in transformed_ds[datasets.Split.TRAIN]:
            snake_case				  =      {k: v for k, v in ex.items() if k in input_names}
            snake_case				  =      labelaid[ex[label_name]]
            yield (d, label)

    def gen_val():
        for ex in transformed_ds[datasets.Split.VALIDATION]:
            snake_case				  =      {k: v for k, v in ex.items() if k in input_names}
            snake_case				  =      labelaid[ex[label_name]]
            yield (d, label)

    def gen_test():
        for ex in transformed_ds[datasets.Split.TEST]:
            snake_case				  =      {k: v for k, v in ex.items() if k in input_names}
            snake_case				  =      labelaid[ex[label_name]]
            yield (d, label)

    snake_case				  =      (
        tf.data.Dataset.from_generator(
            A__				,	({k: tf.intaa for k in input_names}, tf.intaa)				,	({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))				,	)
        if datasets.Split.TRAIN in transformed_ds
        else None
    )

    if train_ds is not None:
        snake_case				  =      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]	)	)	)

    snake_case				  =      (
        tf.data.Dataset.from_generator(
            A__				,	({k: tf.intaa for k in input_names}, tf.intaa)				,	({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))				,	)
        if datasets.Split.VALIDATION in transformed_ds
        else None
    )

    if val_ds is not None:
        snake_case				  =      val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]	)	)	)

    snake_case				  =      (
        tf.data.Dataset.from_generator(
            A__				,	({k: tf.intaa for k in input_names}, tf.intaa)				,	({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))				,	)
        if datasets.Split.TEST in transformed_ds
        else None
    )

    if test_ds is not None:
        snake_case				  =      test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]	)	)	)

    return train_ds, val_ds, test_ds, labelaid


_A						      =				logging.getLogger(__name__)







@dataclass
class   lowerCamelCase	:
     UpperCAmelCase__       :							int     =	field(metadata={"help": "Which column contains the label"}						)
     UpperCAmelCase__       :							str     =	field(default=A_							,				metadata={"help": "The path of the training file"}						)
     UpperCAmelCase__       :							Optional[str]     =	field(default=A_							,				metadata={"help": "The path of the development file"}						)
     UpperCAmelCase__       :							Optional[str]     =	field(default=A_							,				metadata={"help": "The path of the test file"}						)
     UpperCAmelCase__       :							int     =	field(
         default=1_28							,				metadata={
             "help": (
                 "The maximum total input sequence length after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )
         }							,				)
     UpperCAmelCase__       :							bool     =	field(
         default=A_							,				metadata={"help": "Overwrite the cached training and evaluation sets"}						)







@dataclass
class   lowerCamelCase	:
     UpperCAmelCase__       :							str     =	field(
         metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}						)
     UpperCAmelCase__       :							Optional[str]     =	field(
         default=A_							,				metadata={"help": "Pretrained config name or path if not the same as model_name"}						)
     UpperCAmelCase__       :							Optional[str]     =	field(
         default=A_							,				metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}						)
     UpperCAmelCase__       :							bool     =	field(default=A_							,				metadata={"help": "Set this flag to use fast tokenization."}						)
     # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
     # or just modify its tokenizer_config.json.
     UpperCAmelCase__       :							Optional[str]     =	field(
         default=A_							,				metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}							,				)



def 		lowercase_    (     )							->							Dict:
    """simple docstring"""

    snake_case				  =      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
    snake_case  ,							snake_case  ,							snake_case				  =      parser.parse_args_into_dataclasses()

    if (
        os.path.exists(training_args.output_dir	)
        and os.listdir(training_args.output_dir	)
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
            " --overwrite_output_dir to overcome."	)

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				,	datefmt="%m/%d/%Y %H:%M:%S"				,	level=logging.INFO				,	)
    logger.info(
        F'n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1	)}, '
        F'16-bits training: {training_args.fpaa}'	)
    logger.info(F'Training/evaluation parameters {training_args}'	)

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    snake_case				  =      AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				,	cache_dir=model_args.cache_dir				,	)

    snake_case  ,							snake_case  ,							snake_case  ,							snake_case				  =      get_tfds(
        train_file=data_args.train_file				,	eval_file=data_args.dev_file				,	test_file=data_args.test_file				,	tokenizer=A__				,	label_column_id=data_args.label_column_id				,	max_seq_length=data_args.max_seq_length				,	)

    snake_case				  =      AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path				,	num_labels=len(A__	)				,	labelaid=A__				,	idalabel={id: label for label, id in labelaid.items()}				,	finetuning_task="text-classification"				,	cache_dir=model_args.cache_dir				,	)

    with training_args.strategy.scope():
        snake_case				  =      TFAutoModelForSequenceClassification.from_pretrained(
            model_args.model_name_or_path				,	from_pt=bool(".bin" in model_args.model_name_or_path	)				,	config=A__				,	cache_dir=model_args.cache_dir				,	)

    def compute_metrics(A__	) -> Dict:
        snake_case				  =      np.argmax(p.predictions				,	axis=1	)

        return {"acc": (preds == p.label_ids).mean()}

    # Initialize our Trainer
    snake_case				  =      TFTrainer(
        model=A__				,	args=A__				,	train_dataset=A__				,	eval_dataset=A__				,	compute_metrics=A__				,	)

    # Training
    if training_args.do_train:
        trainer.train()
        trainer.save_model()
        tokenizer.save_pretrained(training_args.output_dir	)

    # Evaluation
    snake_case				  =      {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***"	)
        snake_case				  =      trainer.evaluate()
        snake_case				  =      os.path.join(training_args.output_dir				,	"eval_results.txt"	)

        with open(A__				,	"w"	) as writer:
            logger.info("***** Eval results *****"	)

            for key, value in result.items():
                logger.info(F'  {key} = {value}'	)
                writer.write(F'{key} = {value}\n'	)

            results.update(A__	)

    return results


if __name__ == "__main__":
   main()

