text: # -*- coding: utf-8 -*-
  """Catalyst-data scripts.
  
  Examples:
  
      1. **tag2label** prepares a dataset to json like
      `{"class_id":  class_column_from_dataset}`
  
      .. code:: bash
  
          $ catalyst-data tag2label \\
              --in-dir=./data/dataset \\
              --out-dataset=./data/dataset_raw.csv \\
              --out-labeling=./data/tag2cls.json
  
      2. **check-images** checks images in your data
      to be non-broken and writes a flag:
      `true` if image opened without an error and `false` otherwise
  
      .. code:: bash
  
          $ catalyst-data check-images \\
              --in-csv=./data/dataset_raw.csv \\
              --img-datapath=./data/dataset \\
              --img-col="tag" \\
              --out-csv=./data/dataset_checked.csv \\
              --n-cpu=4
  
      3. **split-dataframe** split your dataset into train/valid folds
  
       .. code:: bash
  
           $ catalyst-data split-dataframe \\
              --in-csv=./data/dataset_raw.csv \\
              --tag2class=./data/tag2cls.json \\
              --tag-column=tag \\
              --class-column=class \\
              --n-folds=5 \\
              --train-folds=0,1,2,3 \\
              --out-csv=./data/dataset.csv
  
      4. **image2embedding** embeds images from your csv
      or image directory with specified neural net architecture
  
      .. code:: bash
  
          $ catalyst-data image2embedding \\
              --in-csv=./data/input.csv \\
              --img-col="filename" \\
              --img-size=64 \\
              --out-npy=./embeddings.npy \\
              --arch=resnet34 \\
              --pooling=GlobalMaxPool2d \\
              --batch-size=8 \\
              --num-workers=16 \\
              --verbose
  
  """
  
  from argparse import ArgumentParser, RawTextHelpFormatter
  from collections import OrderedDict
  
  from catalyst.__version__ import __version__
  from catalyst.contrib.scripts import process_images, \
      image2embedding, tag2label, split_dataframe
  
  COMMANDS = OrderedDict(
      [
          ("tag2label", tag2label),
          ("process-images", process_images),
          ("split-dataframe", split_dataframe),
          ("image2embedding", image2embedding),
      ]
  )
  
  
  def build_parser() -> ArgumentParser:
      parser = ArgumentParser(
          "catalyst-data", formatter_class=RawTextHelpFormatter
      )
      parser.add_argument(
          "-v", "--version", action="version", version=f"%(prog)s {__version__}"
      )
      all_commands = ", \n".join(map(lambda x: f"    {x}", COMMANDS.keys()))
  
      subparsers = parser.add_subparsers(
          metavar="{command}",
          dest="command",
          help=f"available commands: \n{all_commands}",
      )
      subparsers.required = True
  
      for key, value in COMMANDS.items():
          value.build_args(subparsers.add_parser(key))
  
      return parser
  
  
  def main():
      parser = build_parser()
  
      args, uargs = parser.parse_known_args()
  
      COMMANDS[args.command].main(args, uargs)
  
  
  if __name__ == "__main__":
      main()
  