def __call__()

in optimum/intel/openvino/modeling_diffusion.py [0:0]

• 55 lines of code
• 25 McCabe index (conditional complexity)

    def __call__(self, *args, **kwargs):
        # we do this to keep numpy random states support for now
        # TODO: deprecate and add warnings when a random state is passed

        args = list(args)
        for i in range(len(args)):
            args[i] = np_to_pt_generators(args[i], self.device)

        for k, v in kwargs.items():
            kwargs[k] = np_to_pt_generators(v, self.device)

        height, width = None, None
        height_idx, width_idx = None, None
        shapes_overridden = False
        sig = inspect.signature(self.auto_model_class.__call__)
        sig_height_idx = list(sig.parameters).index("height") if "height" in sig.parameters else len(sig.parameters)
        sig_width_idx = list(sig.parameters).index("width") if "width" in sig.parameters else len(sig.parameters)
        if "height" in kwargs:
            height = kwargs["height"]
        elif len(args) > sig_height_idx:
            height = args[sig_height_idx]
            height_idx = sig_height_idx

        if "width" in kwargs:
            width = kwargs["width"]
        elif len(args) > sig_width_idx:
            width = args[sig_width_idx]
            width_idx = sig_width_idx

        if self.height != -1:
            if height is not None and height != self.height:
                logger.warning(f"Incompatible height argument provided {height}. Pipeline only support {self.height}.")
                height = self.height
            else:
                height = self.height

            if height_idx is not None:
                args[height_idx] = height
            else:
                kwargs["height"] = height

            shapes_overridden = True

        if self.width != -1:
            if width is not None and width != self.width:
                logger.warning(f"Incompatible widtth argument provided {width}. Pipeline only support {self.width}.")
                width = self.width
            else:
                width = self.width

            if width_idx is not None:
                args[width_idx] = width
            else:
                kwargs["width"] = width
            shapes_overridden = True

        # Sana generates images in specific resolution grid size and then resize to requested size by default, it may contradict with pipeline height / width
        # Disable this behavior for static shape pipeline
        if self.auto_model_class.__name__.startswith("Sana") and shapes_overridden:
            sig_resolution_bining_idx = (
                list(sig.parameters).index("use_resolution_binning")
                if "use_resolution_binning" in sig.parameters
                else len(sig.parameters)
            )
            if len(args) > sig_resolution_bining_idx:
                args[sig_resolution_bining_idx] = False
            else:
                kwargs["use_resolution_binning"] = False
        # we use auto_model_class.__call__ here because we can't call super().__call__
        # as OptimizedModel already defines a __call__ which is the first in the MRO
        return self.auto_model_class.__call__(self, *args, **kwargs)