.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples_apps/datapreproc/datapreproc.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_examples_apps_datapreproc_datapreproc.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_examples_apps_datapreproc_datapreproc.py:


Data Preprocessing App Example
====================================

This is a simple TorchX app that downloads some data via HTTP, normalizes the
images via torchvision and then reuploads it via fsspec.

.. GENERATED FROM PYTHON SOURCE LINES 15-120

.. code-block:: default


    import argparse
    import os
    import sys
    import tarfile
    import tempfile
    import zipfile
    from typing import List

    import fsspec
    import requests
    from PIL import Image
    from torchvision import transforms
    from torchvision.datasets.folder import is_image_file
    from tqdm import tqdm


    def parse_args(argv: List[str]) -> argparse.Namespace:
        parser = argparse.ArgumentParser(
            description="example data preprocessing",
        )
        parser.add_argument(
            "--input_path",
            type=str,
            help="dataset to download",
            default="http://cs231n.stanford.edu/tiny-imagenet-200.zip",
        )
        parser.add_argument(
            "--input_md5",
            type=str,
            help="dataset to download",
            default="90528d7ca1a48142e341f4ef8d21d0de",
        )
        parser.add_argument(
            "--output_path",
            type=str,
            help="remote path to save the .tar.gz data to",
            required=True,
        )
        return parser.parse_args(argv)


    def download_and_extract_zip_archive(url: str, path: str) -> None:
        temp_file = os.path.join(path, "dest.zip")
        chunk_size_bytes: int = 1024 * 1024 * 32  # 32 MB
        with requests.get(url, allow_redirects=True, stream=True) as r:
            r.raise_for_status()
            with open(temp_file, "wb") as fp:
                for chunk in r.iter_content(chunk_size=chunk_size_bytes):
                    fp.write(chunk)

        with zipfile.ZipFile(temp_file, "r") as zip_ref:
            zip_ref.extractall(path)

        os.remove(temp_file)


    def main(argv: List[str]) -> None:
        args = parse_args(argv)
        with tempfile.TemporaryDirectory() as tmpdir:
            print(f"downloading {args.input_path} to {tmpdir}...")
            download_and_extract_zip_archive(args.input_path, tmpdir)

            img_root = os.path.join(
                tmpdir,
                os.path.splitext(os.path.basename(args.input_path))[0],
            )
            print(f"img_root={img_root}")

            print("transforming images...")
            transform = transforms.Compose(
                [
                    transforms.ToTensor(),
                    transforms.Normalize((0.5,), (0.5,)),
                    transforms.ToPILImage(),
                ]
            )

            image_files = []
            for root, _, fnames in os.walk(img_root):
                for fname in fnames:
                    path = os.path.join(root, fname)
                    if not is_image_file(path):
                        continue
                    image_files.append(path)
            for path in tqdm(image_files, miniters=int(len(image_files) / 2000)):
                f = Image.open(path)
                f = transform(f)
                f.save(path)

            tar_path = os.path.join(tmpdir, "out.tar.gz")
            print(f"packing images into {tar_path}...")
            with tarfile.open(tar_path, mode="w:gz") as f:
                f.add(img_root, arcname="")

            print(f"uploading dataset to {args.output_path}...")
            fs, _, rpaths = fsspec.get_fs_token_paths(args.output_path)
            assert len(rpaths) == 1, "must have single output path"
            if fs.exists(rpaths[0]):
                fs.rm(rpaths[0])
            fs.put(tar_path, rpaths[0])


    if __name__ == "__main__" and "NOTEBOOK" not in globals():
        main(sys.argv[1:])


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_examples_apps_datapreproc_datapreproc.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: datapreproc.py <datapreproc.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: datapreproc.ipynb <datapreproc.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_