add VisionTextDualEncoder and CLIP fine-tuning script (#15701)

* begin script

* update script

* fix features and data args

* main

* add requirements

* add column name args

* fix captions

* don't jit transforms

* fix caption

* fix labels, handle attention mask

* convert pixel values to numpy

* labels => input_ids

* transform images on the fly

* use AutoModel class, create the hybird model outside of the script

* fix version message

* add readme

* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* adderss review comments

* add more comments

* allow freezing vision and text models
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

examples/pytorch/contrastive-image-text/README.md

+<!---
+Copyright 2022 The HuggingFace Team. All rights reserved.
+
+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.
+-->
+
+# VisionTextDualEncoder and CLIP model training examples
+
+The following example showcases how to train a CLIP-like vision-text dual encoder model
+using a pre-trained vision and text encoder.
+
+Such a model can be used for natural language image search and potentially zero-shot image classification.
+The model is inspired by [CLIP](https://openai.com/blog/clip/), introduced by Alec Radford et al.
+The idea is to train a vision encoder and a text encoder jointly to project the representation of images and their
+captions into the same embedding space, such that the caption embeddings are located near the embeddings
+of the images they describe.
+
+### Download COCO dataset (2017)
+This example uses COCO dataset (2017) through a custom dataset script, which requires users to manually download the
+COCO dataset before training.
+
+```bash
+mkdir data
+cd data
+wget http://images.cocodataset.org/zips/train2017.zip
+wget http://images.cocodataset.org/zips/val2017.zip
+wget http://images.cocodataset.org/zips/test2017.zip
+wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
+wget http://images.cocodataset.org/annotations/image_info_test2017.zip
+cd ..
+```
+```suggestion
+
+Having downloaded COCO dataset manually you should be able to load with the `ydshieh/coc_dataset_script` dataset loading script:
+
+```py
+COCO_DIR = "data"
+ds = datasets.load_dataset("ydshieh/coco_dataset_script", "2017", data_dir=COCO_DIR)
+### Create a model from a vision encoder model and a text decoder model
+Next, we create a [VisionTextDualEncoderModel](https://huggingface.co/docs/transformers/model_doc/vision-text-dual-encoder#visiontextdualencoder).
+The `VisionTextDualEncoderModel` class let's you load any vision and text encoder model to create a dual encoder. 
+Here is an example of how to load the model using pre-trained vision and text models.
+
+```python3
+from transformers import (
+    VisionTextDualEncoderModel, 
+    VisionTextDualEncoderProcessor, 
+    AutoTokenizer, 
+    AutoFeatureExtractor
+)
+
+model = VisionTextDualEncoderModel.from_vision_text_pretrained(
+    "openai/clip-vit-base-patch32", "roberta-base"
+)
+
+tokenizer = AutoTokenizer.from_pretrained("roberta-base")
+feat_ext = AutoFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32")
+processor = VisionTextDualEncoderProcessor(feat_ext, tokenizer)
+
+# save the model and processor
+model.save_pretrained("clip-roberta")
+processor.save_pretrained("clip-roberta")
+```
+
+This loads both the text and vision encoders using pre-trained weights, the projection layers are randomly
+initialized except for CLIP's vision model. If you use CLIP to initialize the vision model then the vision projection weights are also
+loaded using the pre-trained weights.
+
+### Train the model
+Finally, we can run the example script to train the model:
+
+```bash
+python examples/pytorch/contrastive-image-text/run_clip.py \
+    --output_dir ./clip-roberta-finetuned \
+    --model_name_or_path ./clip-roberta \
+    --data_dir ./data \
+    --dataset_name ydshieh/coco_dataset_script \
+    --dataset_config_name=2017 \
+    --image_column image_path \
+    --caption_column caption \
+    --remove_unused_columns=False \
+    --do_train  --do_eval \
+    --per_device_train_batch_size="64" \
+    --per_device_eval_batch_size="64" \
+    --learning_rate="5e-5" --warmup_steps="0" --weight_decay 0.1 \
+    --overwrite_output_dir \
+    --push_to_hub
+```
\ No newline at end of file

examples/pytorch/contrastive-image-text/requirements.txt

+torch>=1.5.0
+torchvision>=0.6.0
+datasets>=1.8.0
\ No newline at end of file

examples/pytorch/contrastive-image-text/run_clip.py

+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2022 The HuggingFace Team All rights reserved.
+#
+# 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)
+"""
+
+import logging
+import os
+import sys
+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 (
+    AutoFeatureExtractor,
+    AutoModel,
+    AutoTokenizer,
+    HfArgumentParser,
+    Trainer,
+    TrainingArguments,
+    set_seed,
+)
+from transformers.trainer_utils import get_last_checkpoint
+from transformers.utils import check_min_version
+from transformers.utils.versions import require_version
+
+
+logger = logging.getLogger(__name__)
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.17.0.dev0")
+
+require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/contrastive-image-text/requirements.txt")
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
+    """
+
+    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"}
+    )
+    feature_extractor_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."},
+    )
+    use_auth_token: bool = field(
+        default=False,
+        metadata={
+            "help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
+            "with private models)."
+        },
+    )
+    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:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    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)."},
+    )
+    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"}
+    )
+    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"),
+}
+
+
+# We use torchvision for faster image pre-processing. The transforms are implemented as nn.Module,
+# so we jit it to be faster.
+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: Image) -> 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():
+    # 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.
+
+    parser = 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.
+        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()
+
+    # 2. 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)],
+    )
+
+    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()
+
+    # 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.fp16}"
+    )
+    logger.info(f"Training/evaluation parameters {training_args}")
+
+    # 3. Detecting last checkpoint and eventualy continue from last checkpoint
+    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."
+            )
+
+    # 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).
+    #
+    if data_args.dataset_name is not None:
+        # Downloading and loading a dataset from the hub.
+        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,
+        )
+    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)
+    # 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.html.
+
+    # 5. Load pretrained model, tokenizer, and feature extractor
+    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
+        )
+    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
+        )
+    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."
+        )
+
+    # Load feature_extractor, in this script we only use this to get the mean and std for normalization.
+    feature_extractor = AutoFeatureExtractor.from_pretrained(
+        model_args.feature_extractor_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,
+    )
+
+    model = AutoModel.from_pretrained(
+        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,
+    )
+    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 for torch dataloaders
+    set_seed(training_args.seed)
+
+    # Preprocessing the datasets.
+    # We need to tokenize inputs and targets.
+    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
+
+    # 6. Get the column names for input/target.
+    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)}"
+            )
+
+    # 7. Preprocessing the datasets.
+    # Initialize torchvision transforms and jit it for faster processing.
+    image_transformations = Transform(
+        config.vision_config.image_size, feature_extractor.image_mean, feature_extractor.image_std
+    )
+    image_transformations = torch.jit.script(image_transformations)
+
+    # Preprocessing the datasets.
+    # We need to tokenize input captions and transform the images.
+    def tokenize_captions(examples):
+        captions = [caption for caption in 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):
+        """remove problematic images"""
+        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:
+            train_dataset = train_dataset.select(range(data_args.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",
+        )
+
+        # Transform images on the fly as doing it on the whole dataset takes too much time.
+        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:
+            eval_dataset = eval_dataset.select(range(data_args.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",
+        )
+
+        # Transform images on the fly as doing it on the whole dataset takes too much time.
+        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:
+            test_dataset = test_dataset.select(range(data_args.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",
+        )
+
+        # Transform images on the fly as doing it on the whole dataset takes too much time.
+        test_dataset.set_transform(transform_images)
+
+    # 8. Initalize our trainer
+    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,
+    )
+
+    # 9. Training
+    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()
+        trainer.log_metrics("train", train_result.metrics)
+        trainer.save_metrics("train", train_result.metrics)
+        trainer.save_state()
+
+    # 10. Evaluation
+    if training_args.do_eval:
+        metrics = trainer.evaluate()
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    # 11. Write Training Stats and push to hub.
+    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()