text: from pathlib import Path
  from typing import Iterator, List, Tuple, Union
  
  import numpy as np
  from xlib import onnxruntime as lib_ort
  from xlib import path as lib_path
  from xlib.image import ImageProcessor
  from xlib.net import ThreadFileDownloader
  from xlib.onnxruntime.device import ORTDeviceInfo
  
  
  class DFMModelInfo:
      def __init__(self, name : str, model_path : Path, url : str = None):
          self._name = name
          self._model_path = model_path
          self._url = url
  
      def get_name(self) -> str: return self._name
      def get_model_path(self) -> Path: return self._model_path
      def get_url(self) -> Union[str, None]: return self._url
  
      def __eq__(self, other):
          if self is not None and other is not None and isinstance(self, DFMModelInfo) and isinstance(other, DFMModelInfo):
              return self._name == other._name
          return False
  
      def __hash__(self):
          return self._name.__hash__()
  
      def __str__(self):
          return self._name
  
  
  def get_available_models_info(models_path : Path) -> List[DFMModelInfo]:
      # predefined list of celebs with urls
      dfm_models = [
              DFMModelInfo(name='Ava de Addario', model_path=models_path / f'Ava_de_Addario.dfm', url=rf'https://github.com/iperov/DeepFaceLive/releases/download/AVA_DE_ADDARIO/Ava_de_Addario.dfm'),
              DFMModelInfo(name='Bryan Greynolds', model_path=models_path / f'Bryan_Greynolds.dfm', url=rf'https://github.com/iperov/DeepFaceLive/releases/download/BRYAN_GREYNOLDS/Bryan_Greynolds.dfm'),
              DFMModelInfo(name='David Kovalniy', model_path=models_path / f'David_Kovalniy.dfm', url=rf'https://github.com/iperov/DeepFaceLive/releases/download/DAVID_KOVALNIY/David_Kovalniy.dfm'),
              DFMModelInfo(name='Dilraba Dilmurat', model_path=models_path / f'Dilraba_Dilmurat.dfm', url=rf'https://github.com/iperov/DeepFaceLive/releases/download/DILRABA_DILMURAT/Dilraba_Dilmurat.dfm'),
              DFMModelInfo(name='Ewon Spice', model_path=models_path / f'Ewon_Spice.dfm', url=rf'https://github.com/iperov/DeepFaceLive/releases/download/EWON_SPICE/Ewon_Spice.dfm'),
              DFMModelInfo(name='Kim Jarrey', model_path=models_path / f'Kim_Jarrey.dfm', url=rf'https://github.com/iperov/DeepFaceLive/releases/download/KIM_JARREY/Kim_Jarrey.dfm'),
              DFMModelInfo(name='Matilda Bobbie', model_path=models_path / f'Matilda_Bobbie.dfm', url=rf'https://github.com/iperov/DeepFaceLive/releases/download/MATILDA_BOBBIE/Matilda_Bobbie.dfm'),
              DFMModelInfo(name='Silwan Stillwone', model_path=models_path / f'Silwan_Stillwone.dfm', url=rf'https://github.com/iperov/DeepFaceLive/releases/download/SILWAN_STILLWONE/Silwan_Stillwone.dfm'),
              DFMModelInfo(name='Tim Chrys', model_path=models_path / f'Tim_Chrys.dfm', url=rf'https://github.com/iperov/DeepFaceLive/releases/download/TIM_CHRYS/Tim_Chrys.dfm'),
              DFMModelInfo(name='Tim Norland', model_path=models_path / f'Tim_Norland.dfm', url=rf'https://github.com/iperov/DeepFaceLive/releases/download/TIM_NORLAND/Tim_Norland.dfm'),
              DFMModelInfo(name='Yohanna Coralson', model_path=models_path / f'Yohanna_Coralson.dfm', url=rf'https://github.com/iperov/DeepFaceLive/releases/download/YOHANNA_CORALSON/Yohanna_Coralson.dfm'),
              DFMModelInfo(name='Zahar Lupin', model_path=models_path / f'Zahar_Lupin.dfm', url=rf'https://github.com/iperov/DeepFaceLive/releases/download/ZAHAR_LUPIN/Zahar_Lupin.dfm'),
          ]
  
      # scan additional models in directory
      dfm_model_paths = [ celeb.get_model_path() for celeb in dfm_models]
  
      for dfm_path in lib_path.get_files_paths(models_path, extensions=['.dfm']):
          if dfm_path not in dfm_model_paths:
              dfm_models.append( DFMModelInfo(dfm_path.stem, model_path=dfm_path, ) )
  
      return dfm_models
  
  def get_available_devices() -> List[ORTDeviceInfo]:
      """
      """
      return lib_ort.get_available_devices_info()
  
  
  class DFMModel:
      def __init__(self, model_path : Path, device : ORTDeviceInfo = None):
          if device is None:
              device = lib_ort.get_cpu_device_info()
          self._model_path = model_path
  
          sess = self._sess = lib_ort.InferenceSession_with_device(str(model_path), device)
  
          inputs = sess.get_inputs()
  
          if len(inputs) == 0:
              raise Exception(f'Invalid model {model_path}')
          else:
              if 'in_face' not in inputs[0].name:
                  raise Exception(f'Invalid model {model_path}')
              else:
                  self._input_height, self._input_width = inputs[0].shape[1:3]
                  self._model_type = 1
                  if len(inputs) == 2:
                      if 'morph_value' not in inputs[1].name:
                          raise Exception(f'Invalid model {model_path}')
                      self._model_type = 2
                  elif len(inputs) > 2:
                      raise Exception(f'Invalid model {model_path}')
  
      def get_model_path(self) -> Path: return self._model_path
      def get_input_res(self) -> Tuple[int, int]:
          return self._input_width, self._input_height
  
      def has_morph_value(self) -> bool:
          return self._model_type == 2
  
      def convert(self, img, morph_factor=0.75):
          """
           img    np.ndarray  HW,HWC,NHWC uint8,float32
  
           morph_factor   float   used if model supports it
  
          returns
  
           img        NHW3  same dtype as img
           celeb_mask NHW1  same dtype as img
           face_mask  NHW1  same dtype as img
          """
  
          ip = ImageProcessor(img)
  
          N,H,W,C = ip.get_dims()
          dtype = ip.get_dtype()
  
          img = ip.resize( (self._input_width,self._input_height) ).ch(3).to_ufloat32().get_image('NHWC')
  
          if self._model_type == 1:
              out_face_mask, out_celeb, out_celeb_mask = self._sess.run(None, {'in_face:0': img})
          elif self._model_type == 2:
              out_face_mask, out_celeb, out_celeb_mask = self._sess.run(None, {'in_face:0': img, 'morph_value:0':np.float32([morph_factor]) })
  
          out_celeb      = ImageProcessor(out_celeb).resize((W,H)).ch(3).to_dtype(dtype).get_image('NHWC')
          out_celeb_mask = ImageProcessor(out_celeb_mask).resize((W,H)).ch(1).to_dtype(dtype).get_image('NHWC')
          out_face_mask  = ImageProcessor(out_face_mask).resize((W,H)).ch(1).to_dtype(dtype).get_image('NHWC')
  
          return out_celeb, out_celeb_mask, out_face_mask
  
  
  
  class DFMModelInitializer:
      """
      class to initialize DFMModel from DFMModelInfo
  
      use .process_events() to iterate initialization process with events
      """
  
      class Events:
          prev_status_initializing : bool = False
          prev_status_downloading  : bool = False
          prev_status_initialized  : bool = False
          prev_status_error        : bool = False
  
          new_status_initializing : bool = False
          new_status_downloading  : bool = False
          new_status_initialized  : bool = False
          new_status_error        : bool = False
  
          download_progress : float = None
          error : str = None
          dfm_model : DFMModel = None
  
      def __init__(self, dfm_model_info : DFMModelInfo, device : ORTDeviceInfo = None ): self._gen = self._generator(dfm_model_info, device)
      def process_events(self) -> 'DFMModelInitializer.Events': return next(self._gen)
      def _generator(self, dfm_model_info : DFMModelInfo, device : ORTDeviceInfo = None) -> Iterator['DFMModelInitializer.Events']:
          """
          Creates a generator object to initialize DFM model from provided parameters
  
          """
  
          INITIALIZING, DOWNLOADING, INITIALIZED, ERROR, = range(4)
          downloader : ThreadFileDownloader = None
          url = dfm_model_info.get_url()
          status = None
          while True:
              events = DFMModelInitializer.Events()
  
              new_status = status
  
              if status is None:
                  new_status = INITIALIZING
  
              elif status == INITIALIZING:
                  model_path = dfm_model_info.get_model_path()
  
                  if not model_path.exists():
                      
                      if url is None:
                          new_status = ERROR
                          events.error = 'Model file is not found and URL is not defined.'
                      else:
                          downloader = ThreadFileDownloader(url=url, savepath=model_path)
                          new_status = DOWNLOADING
                  else:
                      error = None
                      try:
                          dfm_model = DFMModel(model_path, device)
                      except Exception as e:
                          error = str(e)
  
                      if error is None:
                          new_status = INITIALIZED
                          events.dfm_model = dfm_model
                      else:
                          new_status = ERROR
                          events.error = error
  
              elif status == DOWNLOADING:
                  error = downloader.get_error()
                  if error is None:
                      progress = downloader.get_progress()
                      if progress == 100.0:
                          new_status = INITIALIZING
                      else:
                          events.download_progress = progress
                  else:
                      new_status = ERROR
                      events.error = f'Unable to download {url}, error:' + error
  
              if new_status != status:
                  events.prev_status_initializing = status == INITIALIZING
                  events.prev_status_downloading  = status == DOWNLOADING
                  events.prev_status_initialized  = status == INITIALIZED
                  events.prev_status_error        = status == ERROR
  
                  events.new_status_initializing = new_status == INITIALIZING
                  events.new_status_downloading  = new_status == DOWNLOADING
                  events.new_status_initialized  = new_status == INITIALIZED
                  events.new_status_error        = new_status == ERROR
                  status = new_status
  
              yield events
  
  def DFMModel_from_path(model_path : Path, device : ORTDeviceInfo = None) -> DFMModel:
      """
      instantiates DFMModel
      """
      return DFMModel(model_path=model_path, device=device, __check=1)
  
  def DFMModel_from_info(dfm_model_info : DFMModelInfo, device : ORTDeviceInfo = None) -> DFMModelInitializer:
      """
      instantiates DFMModelInitializer
      """
      return DFMModelInitializer(dfm_model_info=dfm_model_info, device=device)
  