              if not all(img in training_images for img in filenames):
                  raise FaceswapError(f"All images in the Timelapse folder '{folder}' must exist in "
                                      f"the training folder '{training_folder}'")
  
          TKey = Literal["input_a", "input_b", "output"]
          kwargs = {cast(TKey, "input_a"): self._args.timelapse_input_a,
                    cast(TKey, "input_b"): self._args.timelapse_input_b,
                    cast(TKey, "output"): timelapse_output}
          logger.debug("Timelapse enabled: %s", kwargs)
          return kwargs
  
      def process(self) -> None:
          """ The entry point for triggering the Training Process.
 
</s> remove             side = cast(Literal["a", "b"], side)
 </s> add             side = T.cast(T.Literal["a", "b"], side) </s> remove             retval = dict(faces_dir=faces,
                          frames_dir=[None for _ in range(len(faces))],
                          alignments_file=[None for _ in range(len(faces))])
 </s> add             retval = {"faces_dir": faces,
                      "frames_dir": [None for _ in range(len(faces))],
                      "alignments_file": [None for _ in range(len(faces))]} </s> remove     def sorted_items(self) -> Union[List[Dict[str, str]],
                                    List[Tuple[str, "PNGHeaderDict"]]]:
 </s> add     def sorted_items(self) -> list[dict[str, str]] | list[tuple[str, PNGHeaderDict]]: </s> remove         retval: Dict[int, str] = {}
 </s> add         retval: dict[int, str] = {} </s> remove                        viewer: T.Optional[T.Callable[[np.ndarray, str], None]],
                       timelapse_kwargs: T.Optional[T.Dict[Literal["input_a",
                                                                   "input_b",
                                                                   "output"], str]]) -> None:
 </s> add                        viewer: Callable[[np.ndarray, str], None] | None,
                       timelapse_kwargs: dict[T.Literal["input_a", "input_b", "output"],
                                              str] | None) -> None:
        TKey = T.Literal["input_a", "input_b", "output"]
        kwargs = {T.cast(TKey, "input_a"): self._args.timelapse_input_a,
                  T.cast(TKey, "input_b"): self._args.timelapse_input_b,
                  T.cast(TKey, "output"): timelapse_output}
              if not all(img in training_images for img in filenames):
                  raise FaceswapError(f"All images in the Timelapse folder '{folder}' must exist in "
                                      f"the training folder '{training_folder}'")
  
        TKey = T.Literal["input_a", "input_b", "output"]
        kwargs = {T.cast(TKey, "input_a"): self._args.timelapse_input_a,
                  T.cast(TKey, "input_b"): self._args.timelapse_input_b,
                  T.cast(TKey, "output"): timelapse_output}
        TKey = T.Literal["input_a", "input_b", "output"]
        kwargs = {T.cast(TKey, "input_a"): self._args.timelapse_input_a,
                  T.cast(TKey, "input_b"): self._args.timelapse_input_b,
                  T.cast(TKey, "output"): timelapse_output}
        TKey = T.Literal["input_a", "input_b", "output"]
        kwargs = {T.cast(TKey, "input_a"): self._args.timelapse_input_a,
                  T.cast(TKey, "input_b"): self._args.timelapse_input_b,
                  T.cast(TKey, "output"): timelapse_output}
        TKey = T.Literal["input_a", "input_b", "output"]
        kwargs = {T.cast(TKey, "input_a"): self._args.timelapse_input_a,
                  T.cast(TKey, "input_b"): self._args.timelapse_input_b,
                  T.cast(TKey, "output"): timelapse_output}
          logger.debug("Timelapse enabled: %s", kwargs)
          return kwargs
  
      def process(self) -> None:
          """ The entry point for triggering the Training Process.
keep keep keep keep replace replace replace replace keep keep keep keep keep
 Rebase code (#1326)

* Remove tensorflow_probability requirement

* setup.py - fix progress bars

* requirements.txt: Remove pre python 3.9 packages

* update apple requirements.txt

* update INSTALL.md

* Remove python<3.9 code

* setup.py - fix Windows Installer

* typing: python3.9 compliant

* Update pytest and readthedocs python versions

* typing fixes

* Python Version updates
  - Reduce max version to 3.10
  - Default to 3.10 in installers
  - Remove incompatible 3.11 tests

* Update dependencies

* Downgrade imageio dep for Windows

* typing: merge optional unions and fixes

* Updates
  - min python version 3.10
  - typing to python 3.10 spec
  - remove pre-tf2.10 code
  - Add conda tests

* train: re-enable optimizer saving

* Update dockerfiles

* Update setup.py
  - Apple Conda deps to setup.py
  - Better Cuda + dependency handling

* bugfix: Patch logging to prevent Autograph errors

* Update dockerfiles

* Setup.py - Setup.py - stdout to utf-8

* Add more OSes to github Actions

* suppress mac-os end to end test 
scripts/train.py