Generate dummy inputs function is not necessary.

src/transformers/models/donut/configuration_donut_swin.py

@@ -164,43 +164,3 @@ def atol_for_validation(self) -> float:
     @property
     def default_onnx_opset(self) -> int:
         return 16
-
-    def generate_dummy_inputs(
-        self,
-        processor: "FeatureExtractionMixin",
-        batch_size: int = -1,
-        seq_length: int = -1,
-        is_pair: bool = False,
-        framework: Optional["TensorType"] = None,
-        num_channels: int = 3,
-        image_width: int = 40,
-        image_height: int = 40,
-    ) -> Mapping[str, Any]:
-        """
-        Generate inputs to provide to the ONNX exporter for the specific framework
-
-        Args:
-            processor ([`ProcessorMixin`]):
-                The processor associated with this model configuration.
-            batch_size (`int`, *optional*, defaults to -1):
-                The batch size to export the model for (-1 means dynamic axis).
-            seq_length (`int`, *optional*, defaults to -1):
-                The sequence length to export the model for (-1 means dynamic axis).
-            is_pair (`bool`, *optional*, defaults to `False`):
-                Indicate if the input is a pair (sentence 1, sentence 2).
-            framework (`TensorType`, *optional*, defaults to `None`):
-                The framework (PyTorch or TensorFlow) that the processor will generate tensors for.
-            num_channels (`int`, *optional*, defaults to 3):
-                The number of channels of the generated images.
-            image_width (`int`, *optional*, defaults to 40):
-                The width of the generated images.
-            image_height (`int`, *optional*, defaults to 40):
-                The height of the generated images.
-
-        Returns:
-            Mapping[str, Any]: holding the kwargs to provide to the model's forward function
-        """
-
-        batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
-        dummy_input = self._generate_dummy_images(batch_size, num_channels, image_height, image_width)
-        return dict(processor(images=dummy_input, return_tensors=framework))