text: # Copyright The PyTorch Lightning 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.
  
  from flash.core.data.utils import download_data
  from flash.core.utilities.flash_cli import FlashCLI
  from flash.image import ImageClassificationData, ImageClassifier
  
  __all__ = ["image_classification"]
  
  
  def from_hymenoptera(
      batch_size: int = 4,
      num_workers: int = 0,
      **preprocess_kwargs,
  ) -> ImageClassificationData:
      """Downloads and loads the Hymenoptera (Ants, Bees) data set."""
      download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip", "./data")
      return ImageClassificationData.from_folders(
          train_folder="data/hymenoptera_data/train/",
          val_folder="data/hymenoptera_data/val/",
          batch_size=batch_size,
          num_workers=num_workers,
          **preprocess_kwargs,
      )
  
  
  def from_movie_posters(
      batch_size: int = 4,
      num_workers: int = 0,
      **preprocess_kwargs,
  ) -> ImageClassificationData:
      """Downloads and loads the movie posters genre classification data set."""
      download_data("https://pl-flash-data.s3.amazonaws.com/movie_posters.zip", "./data")
      return ImageClassificationData.from_csv(
          "Id",
          ["Action", "Romance", "Crime", "Thriller", "Adventure"],
          train_file="data/movie_posters/train/metadata.csv",
          val_file="data/movie_posters/val/metadata.csv",
          batch_size=batch_size,
          num_workers=num_workers,
          **preprocess_kwargs,
      )
  
  
  def image_classification():
      """Classify images."""
      cli = FlashCLI(
          ImageClassifier,
          ImageClassificationData,
          default_datamodule_builder=from_hymenoptera,
          additional_datamodule_builders=[from_movie_posters],
          default_arguments={
              "trainer.max_epochs": 3,
          },
          datamodule_attributes={"num_classes", "multi_label"},
      )
  
      cli.trainer.save_checkpoint("image_classification_model.pt")
  
  
  if __name__ == "__main__":
      image_classification()
  