usrbinenv python codingutf8 2022 the huggingface team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license training a clip like dual encoder models using text and vision encoders in the library the script can be used to train clip like models for languages other than english by using a text encoder pretrained in the desired language currently this script supports the following vision and text models vision models vithttps huggingface comodels filtervit clip https huggingface comodels filterclip text models bert roberta https huggingface comodels filterfillmask will error if the minimal version of transformers is not installed remove at your own risks arguments pertaining to which modelconfigtokenizer we are going to finetune or train from scratch arguments pertaining to what data we are going to input our model for training and eval we use torchvision for faster image preprocessing the transforms are implemented as nn module so we jit it to be faster x should be an instance of pil image image with torch nograd x self transformsx return x def collatefnexamples pixelvalues torch stackexamplepixelvalues for example in examples inputids torch tensorexampleinputids for example in examples dtypetorch long attentionmask torch tensorexampleattentionmask for example in examples dtypetorch long return pixelvalues pixelvalues inputids inputids attentionmask attentionmask returnloss true def main 1 parse input arguments see all possible arguments in srctransformerstrainingargs py or by passing the help flag to this script we now keep distinct sets of args for a cleaner separation of concerns parser hfargumentparsermodelarguments datatrainingarguments trainingarguments if lensys argv 2 and sys argv1 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 modelargs dataargs trainingargs parser parsejsonfilejsonfileos path abspathsys argv1 else modelargs dataargs trainingargs parser parseargsintodataclasses if modelargs useauthtoken is not none warnings warn the useauthtoken argument is deprecated and will be removed in v4 34 please use token instead futurewarning if modelargs token is not none raise valueerrortoken and useauthtoken are both specified please set only the argument token modelargs token modelargs useauthtoken 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 pythonpytorch versions sendexampletelemetryrunclip modelargs dataargs 2 setup logging logging basicconfig formatasctimes levelnames names messages datefmtmdy h m s handlerslogging streamhandlersys stdout if trainingargs shouldlog the default of trainingargs loglevel is passive so we set log level at info here to have that default transformers utils logging setverbosityinfo loglevel trainingargs getprocessloglevel logger setlevelloglevel transformers utils logging setverbosityloglevel transformers utils logging enabledefaulthandler transformers utils logging enableexplicitformat log on each process the small summary logger warning fprocess rank trainingargs localrank device trainingargs device ngpu trainingargs ngpu fdistributed training trainingargs parallelmode value distributed 16bits training trainingargs fp16 logger infoftrainingevaluation parameters trainingargs 3 detecting last checkpoint and eventualy continue from last checkpoint lastcheckpoint none if os path isdirtrainingargs outputdir and trainingargs dotrain and not trainingargs overwriteoutputdir lastcheckpoint getlastcheckpointtrainingargs outputdir if lastcheckpoint is none and lenos listdirtrainingargs outputdir 0 raise valueerror foutput directory trainingargs outputdir already exists and is not empty use overwriteoutputdir to overcome elif lastcheckpoint is not none and trainingargs resumefromcheckpoint is none logger info fcheckpoint detected resuming training at lastcheckpoint to avoid this behavior change the outputdir or add overwriteoutputdir to train from scratch 4 load dataset get the datasets you can either provide your own csvjson training and evaluation files see below or just provide the name of one of the public datasets available on the hub at https huggingface codatasets the dataset will be downloaded automatically from the datasets hub for csvjson files this script will use the first column for the full image path and the second column for the captions unless you specify column names for this with the imagecolumn and captioncolumn arguments if dataargs datasetname is not none downloading and loading a dataset from the hub dataset loaddataset dataargs datasetname dataargs datasetconfigname cachedirmodelargs cachedir keepinmemoryfalse datadirdataargs datadir tokenmodelargs token else datafiles if dataargs trainfile is not none datafilestrain dataargs trainfile extension dataargs trainfile split 1 if dataargs validationfile is not none datafilesvalidation dataargs validationfile extension dataargs validationfile split 1 if dataargs testfile is not none datafilestest dataargs testfile extension dataargs testfile split 1 dataset loaddataset extension datafilesdatafiles cachedirmodelargs cachedir tokenmodelargs token see more about loading any type of standard or custom dataset from files python dict pandas dataframe etc at https huggingface codocsdatasetsloadingdatasets 5 load pretrained model tokenizer and image processor if modelargs tokenizername tokenizer autotokenizer frompretrained modelargs tokenizername cachedirmodelargs cachedir usefastmodelargs usefasttokenizer tokenmodelargs token trustremotecodemodelargs trustremotecode elif modelargs modelnameorpath tokenizer autotokenizer frompretrained modelargs modelnameorpath cachedirmodelargs cachedir usefastmodelargs usefasttokenizer tokenmodelargs token trustremotecodemodelargs trustremotecode else raise valueerror you are instantiating a new tokenizer from scratch this is not supported by this script you can do it from another script save it and load it from here using tokenizername load imageprocessor in this script we only use this to get the mean and std for normalization imageprocessor autoimageprocessor frompretrained modelargs imageprocessorname or modelargs modelnameorpath cachedirmodelargs cachedir revisionmodelargs modelrevision tokenmodelargs token trustremotecodemodelargs trustremotecode model automodel frompretrained modelargs modelnameorpath cachedirmodelargs cachedir revisionmodelargs modelrevision tokenmodelargs token trustremotecodemodelargs trustremotecode config model config def freezeparamsmodule for param in module parameters param requiresgrad false if modelargs freezevisionmodel freezeparamsmodel visionmodel if modelargs freezetextmodel freezeparamsmodel textmodel set seed for torch dataloaders setseedtrainingargs seed preprocessing the datasets we need to tokenize inputs and targets if trainingargs dotrain columnnames datasettrain columnnames elif trainingargs doeval columnnames datasetvalidation columnnames elif trainingargs dopredict columnnames datasettest columnnames else logger infothere is nothing to do please pass dotrain doeval andor dopredict return 6 get the column names for inputtarget datasetcolumns datasetnamemapping getdataargs datasetname none if dataargs imagecolumn is none imagecolumn datasetcolumns0 if datasetcolumns is not none else columnnames0 else imagecolumn dataargs imagecolumn if imagecolumn not in columnnames raise valueerror fimagecolumn value dataargs imagecolumn needs to be one of joincolumnnames if dataargs captioncolumn is none captioncolumn datasetcolumns1 if datasetcolumns is not none else columnnames1 else captioncolumn dataargs captioncolumn if captioncolumn not in columnnames raise valueerror fcaptioncolumn value dataargs captioncolumn needs to be one of joincolumnnames 7 preprocessing the datasets initialize torchvision transforms and jit it for faster processing imagetransformations transform config visionconfig imagesize imageprocessor imagemean imageprocessor imagestd imagetransformations torch jit scriptimagetransformations preprocessing the datasets we need to tokenize input captions and transform the images def tokenizecaptionsexamples captions listexamplescaptioncolumn textinputs tokenizercaptions maxlengthdataargs maxseqlength paddingmaxlength truncationtrue examplesinputids textinputs inputids examplesattentionmask textinputs attentionmask return examples def transformimagesexamples images readimageimagefile modeimagereadmode rgb for imagefile in examplesimagecolumn examplespixelvalues imagetransformationsimage for image in images return examples def filtercorruptimagesexamples transform images on the fly as doing it on the whole dataset takes too much time transform images on the fly as doing it on the whole dataset takes too much time transform images on the fly as doing it on the whole dataset takes too much time 8 initalize our trainer 9 training 10 evaluation 11 write training stats and push to hub usr bin env python coding utf 8 2022 the huggingface team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license training a clip like dual encoder models using text and vision encoders in the library the script can be used to train clip like models for languages other than english by using a text encoder pre trained in the desired language currently this script supports the following vision and text models vision models vit https huggingface co models filter vit clip https huggingface co models filter clip text models bert roberta https huggingface co models filter fill mask will error if the minimal version of transformers is not installed remove at your own risks arguments pertaining to which model config tokenizer we are going to fine tune or train from scratch arguments pertaining to what data we are going to input our model for training and eval we use torchvision for faster image pre processing the transforms are implemented as nn module so we jit it to be faster x should be an instance of pil image image 1 parse input arguments 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 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 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 2 setup logging the default of training_args log_level is passive so we set log level at info here to have that default log on each process the small summary 3 detecting last checkpoint and eventualy continue from last checkpoint 4 load dataset get the datasets you can either provide your own csv json training and evaluation files see below or just provide the name of one of the public datasets available on the hub at https huggingface co datasets the dataset will be downloaded automatically from the datasets hub for csv json files this script will use the first column for the full image path and the second column for the captions unless you specify column names for this with the image_column and caption_column arguments downloading and loading a dataset from the hub see more about loading any type of standard or custom dataset from files python dict pandas dataframe etc at https huggingface co docs datasets loading_datasets 5 load pretrained model tokenizer and image processor load image_processor in this script we only use this to get the mean and std for normalization set seed for torch dataloaders preprocessing the datasets we need to tokenize inputs and targets 6 get the column names for input target 7 preprocessing the datasets initialize torchvision transforms and jit it for faster processing preprocessing the datasets we need to tokenize input captions and transform the images remove problematic images transform images on the fly as doing it on the whole dataset takes too much time transform images on the fly as doing it on the whole dataset takes too much time transform images on the fly as doing it on the whole dataset takes too much time 8 initalize our trainer 9 training 10 evaluation 11 write training stats and push to hub
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from PIL import Image
from torchvision.io import ImageReadMode, read_image
from torchvision.transforms import CenterCrop, ConvertImageDtype, Normalize, Resize
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    AutoImageProcessor,
    AutoModel,
    AutoTokenizer,
    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


logger = logging.getLogger(__name__)


check_min_version("4.36.0.dev0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/contrastive-image-text/requirements.txt")


@dataclass
class ModelArguments:
    

    model_name_or_path: str = field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"},
    )
    config_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
    )
    tokenizer_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
    )
    image_processor_name: str = field(default=None, metadata={"help": "Name or path of preprocessor config."})
    cache_dir: Optional[str] = field(
        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
    )
    model_revision: str = field(
        default="main",
        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
    )
    use_fast_tokenizer: bool = field(
        default=True,
        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
    )
    token: str = field(
        default=None,
        metadata={
            "help": (
                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
            )
        },
    )
    use_auth_token: bool = field(
        default=None,
        metadata={
            "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
        },
    )
    trust_remote_code: bool = field(
        default=False,
        metadata={
            "help": (
                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
                "should only be set to `True` for repositories you trust and in which you have read the code, as it will "
                "execute code present on the Hub on your local machine."
            )
        },
    )
    freeze_vision_model: bool = field(
        default=False, metadata={"help": "Whether to freeze the vision model parameters or not."}
    )
    freeze_text_model: bool = field(
        default=False, metadata={"help": "Whether to freeze the text model parameters or not."}
    )


@dataclass
class DataTrainingArguments:
    

    dataset_name: Optional[str] = field(
        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
    )
    dataset_config_name: Optional[str] = field(
        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
    )
    data_dir: Optional[str] = field(default=None, metadata={"help": "The data directory containing input files."})
    image_column: Optional[str] = field(
        default="image_path",
        metadata={"help": "The name of the column in the datasets containing the full image file paths."},
    )
    caption_column: Optional[str] = field(
        default="caption",
        metadata={"help": "The name of the column in the datasets containing the image captions."},
    )
    train_file: Optional[str] = field(
        default=None, metadata={"help": "The input training data file (a jsonlines file)."}
    )
    validation_file: Optional[str] = field(
        default=None,
        metadata={"help": "An optional input evaluation data file (a jsonlines file)."},
    )
    test_file: Optional[str] = field(
        default=None,
        metadata={"help": "An optional input testing data file (a jsonlines file)."},
    )
    max_seq_length: Optional[int] = field(
        default=128,
        metadata={
            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        },
    )
    max_train_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of training examples to this "
                "value if set."
            )
        },
    )
    max_eval_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                "value if set."
            )
        },
    )
    overwrite_cache: bool = field(
        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
    )
    preprocessing_num_workers: Optional[int] = field(
        default=None,
        metadata={"help": "The number of processes to use for the preprocessing."},
    )

    def __post_init__(self):
        if self.dataset_name is None and self.train_file is None and self.validation_file is None:
            raise ValueError("Need either a dataset name or a training/validation file.")
        else:
            if self.train_file is not None:
                extension = self.train_file.split(".")[-1]
                assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
            if self.validation_file is not None:
                extension = self.validation_file.split(".")[-1]
                assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
            if self.validation_file is not None:
                extension = self.validation_file.split(".")[-1]
                assert extension == "json", "`validation_file` should be a json file."


dataset_name_mapping = {
    "image_caption_dataset.py": ("image_path", "caption"),
}




class Transform(torch.nn.Module):
    def __init__(self, image_size, mean, std):
        super().__init__()
        self.transforms = torch.nn.Sequential(
            Resize([image_size], interpolation=InterpolationMode.BICUBIC),
            CenterCrop(image_size),
            ConvertImageDtype(torch.float),
            Normalize(mean, std),
        )

    def forward(self, x) -> torch.Tensor:
        
        with torch.no_grad():
            x = self.transforms(x)
        return x


def collate_fn(examples):
    pixel_values = torch.stack([example["pixel_values"] for example in examples])
    input_ids = torch.tensor([example["input_ids"] for example in examples], dtype=torch.long)
    attention_mask = torch.tensor([example["attention_mask"] for example in examples], dtype=torch.long)
    return {
        "pixel_values": pixel_values,
        "input_ids": input_ids,
        "attention_mask": attention_mask,
        "return_loss": True,
    }


def main():
    
    
    
    

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        
        
        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    if model_args.use_auth_token is not None:
        warnings.warn(
            "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
            FutureWarning,
        )
        if model_args.token is not None:
            raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
        model_args.token = model_args.use_auth_token

    
    
    send_example_telemetry("run_clip", model_args, data_args)

    
    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:
        
        transformers.utils.logging.set_verbosity_info()

    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
        + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
    )
    logger.info(f"Training/evaluation parameters {training_args}")

    
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = 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."
            )

    
    
    
    
    
    
    
    
    if data_args.dataset_name is not None:
        
        dataset = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            cache_dir=model_args.cache_dir,
            keep_in_memory=False,
            data_dir=data_args.data_dir,
            token=model_args.token,
        )
    else:
        data_files = {}
        if data_args.train_file is not None:
            data_files["train"] = data_args.train_file
            extension = data_args.train_file.split(".")[-1]
        if data_args.validation_file is not None:
            data_files["validation"] = data_args.validation_file
            extension = data_args.validation_file.split(".")[-1]
        if data_args.test_file is not None:
            data_files["test"] = data_args.test_file
            extension = data_args.test_file.split(".")[-1]
        dataset = load_dataset(
            extension,
            data_files=data_files,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
        )
    
    

    
    if model_args.tokenizer_name:
        tokenizer = AutoTokenizer.from_pretrained(
            model_args.tokenizer_name,
            cache_dir=model_args.cache_dir,
            use_fast=model_args.use_fast_tokenizer,
            token=model_args.token,
            trust_remote_code=model_args.trust_remote_code,
        )
    elif model_args.model_name_or_path:
        tokenizer = AutoTokenizer.from_pretrained(
            model_args.model_name_or_path,
            cache_dir=model_args.cache_dir,
            use_fast=model_args.use_fast_tokenizer,
            token=model_args.token,
            trust_remote_code=model_args.trust_remote_code,
        )
    else:
        raise ValueError(
            "You are instantiating a new tokenizer from scratch. This is not supported by this script. "
            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
        )

    
    image_processor = 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,
        token=model_args.token,
        trust_remote_code=model_args.trust_remote_code,
    )

    model = AutoModel.from_pretrained(
        model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        token=model_args.token,
        trust_remote_code=model_args.trust_remote_code,
    )
    config = model.config

    def _freeze_params(module):
        for param in module.parameters():
            param.requires_grad = False

    if model_args.freeze_vision_model:
        _freeze_params(model.vision_model)

    if model_args.freeze_text_model:
        _freeze_params(model.text_model)

    
    set_seed(training_args.seed)

    
    
    if training_args.do_train:
        column_names = dataset["train"].column_names
    elif training_args.do_eval:
        column_names = dataset["validation"].column_names
    elif training_args.do_predict:
        column_names = dataset["test"].column_names
    else:
        logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
        return

    
    dataset_columns = dataset_name_mapping.get(data_args.dataset_name, None)
    if data_args.image_column is None:
        image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
    else:
        image_column = data_args.image_column
        if image_column not in column_names:
            raise ValueError(
                f"--image_column' value '{data_args.image_column}' needs to be one of: {', '.join(column_names)}"
            )
    if data_args.caption_column is None:
        caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
    else:
        caption_column = data_args.caption_column
        if caption_column not in column_names:
            raise ValueError(
                f"--caption_column' value '{data_args.caption_column}' needs to be one of: {', '.join(column_names)}"
            )

    
    
    image_transformations = Transform(
        config.vision_config.image_size, image_processor.image_mean, image_processor.image_std
    )
    image_transformations = torch.jit.script(image_transformations)

    
    
    def tokenize_captions(examples):
        captions = list(examples[caption_column])
        text_inputs = tokenizer(captions, max_length=data_args.max_seq_length, padding="max_length", truncation=True)
        examples["input_ids"] = text_inputs.input_ids
        examples["attention_mask"] = text_inputs.attention_mask
        return examples

    def transform_images(examples):
        images = [read_image(image_file, mode=ImageReadMode.RGB) for image_file in examples[image_column]]
        examples["pixel_values"] = [image_transformations(image) for image in images]
        return examples

    def filter_corrupt_images(examples):
        
        valid_images = []
        for image_file in examples[image_column]:
            try:
                Image.open(image_file)
                valid_images.append(True)
            except Exception:
                valid_images.append(False)
        return valid_images

    if training_args.do_train:
        if "train" not in dataset:
            raise ValueError("--do_train requires a train dataset")
        train_dataset = dataset["train"]
        if data_args.max_train_samples is not None:
            max_train_samples = min(len(train_dataset), data_args.max_train_samples)
            train_dataset = train_dataset.select(range(max_train_samples))

        train_dataset = train_dataset.filter(
            filter_corrupt_images, batched=True, num_proc=data_args.preprocessing_num_workers
        )
        train_dataset = train_dataset.map(
            function=tokenize_captions,
            batched=True,
            remove_columns=[col for col in column_names if col != image_column],
            num_proc=data_args.preprocessing_num_workers,
            load_from_cache_file=not data_args.overwrite_cache,
            desc="Running tokenizer on train dataset",
        )

        
        train_dataset.set_transform(transform_images)

    if training_args.do_eval:
        if "validation" not in dataset:
            raise ValueError("--do_eval requires a train validation")
        eval_dataset = dataset["validation"]
        if data_args.max_eval_samples is not None:
            max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
            eval_dataset = eval_dataset.select(range(max_eval_samples))

        eval_dataset = eval_dataset.filter(
            filter_corrupt_images, batched=True, num_proc=data_args.preprocessing_num_workers
        )
        eval_dataset = eval_dataset.map(
            function=tokenize_captions,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            remove_columns=[col for col in column_names if col != image_column],
            load_from_cache_file=not data_args.overwrite_cache,
            desc="Running tokenizer on validation dataset",
        )

        
        eval_dataset.set_transform(transform_images)

    if training_args.do_predict:
        if "test" not in dataset:
            raise ValueError("--do_predict requires a test dataset")
        test_dataset = dataset["test"]
        if data_args.max_eval_samples is not None:
            max_eval_samples = min(len(test_dataset), data_args.max_eval_samples)
            test_dataset = test_dataset.select(range(max_eval_samples))

        test_dataset = test_dataset.filter(
            filter_corrupt_images, batched=True, num_proc=data_args.preprocessing_num_workers
        )
        test_dataset = test_dataset.map(
            function=tokenize_captions,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            remove_columns=[col for col in column_names if col != image_column],
            load_from_cache_file=not data_args.overwrite_cache,
            desc="Running tokenizer on test dataset",
        )

        
        test_dataset.set_transform(transform_images)

    
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset if training_args.do_train else None,
        eval_dataset=eval_dataset if training_args.do_eval else None,
        data_collator=collate_fn,
    )

    
    if training_args.do_train:
        checkpoint = None
        if training_args.resume_from_checkpoint is not None:
            checkpoint = training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            checkpoint = last_checkpoint
        train_result = trainer.train(resume_from_checkpoint=checkpoint)
        trainer.save_model()
        tokenizer.save_pretrained(training_args.output_dir)
        image_processor.save_pretrained(training_args.output_dir)
        trainer.log_metrics("train", train_result.metrics)
        trainer.save_metrics("train", train_result.metrics)
        trainer.save_state()

    
    if training_args.do_eval:
        metrics = trainer.evaluate()
        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)

    
    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "contrastive-image-text-modeling"}
    if data_args.dataset_name is not None:
        kwargs["dataset_tags"] = data_args.dataset_name
        if data_args.dataset_config_name is not None:
            kwargs["dataset_args"] = data_args.dataset_config_name
            kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
        else:
            kwargs["dataset"] = data_args.dataset_name

    if training_args.push_to_hub:
        trainer.push_to_hub(**kwargs)
    else:
        trainer.create_model_card(**kwargs)


if __name__ == "__main__":
    main()