[ViTDet] Fix doc tests (#25880)

Fix docstrings

src/transformers/models/vitdet/modeling_vitdet.py

@@ -27,7 +27,6 @@ from ...activations import ACT2FN
 from ...modeling_outputs import BackboneOutput, BaseModelOutput
 from ...modeling_utils import PreTrainedModel
 from ...utils import (
-    add_code_sample_docstrings,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
     logging,
@@ -42,10 +41,6 @@ logger = logging.get_logger(__name__)
 # General docstring
 _CONFIG_FOR_DOC = "VitDetConfig"
 
-# Base docstring
-_CHECKPOINT_FOR_DOC = "facebook/vit-det-base"
-_EXPECTED_OUTPUT_SHAPE = [1, 197, 768]
-
 
 VITDET_PRETRAINED_MODEL_ARCHIVE_LIST = [
     "facebook/vit-det-base",
@@ -737,13 +732,7 @@ class VitDetModel(VitDetPreTrainedModel):
             self.encoder.layer[layer].attention.prune_heads(heads)
 
     @add_start_docstrings_to_model_forward(VITDET_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
+    @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC)
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=BaseModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-        modality="vision",
-        expected_output=_EXPECTED_OUTPUT_SHAPE,
-    )
     def forward(
         self,
         pixel_values: Optional[torch.Tensor] = None,
@@ -752,6 +741,27 @@ class VitDetModel(VitDetPreTrainedModel):
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
     ) -> Union[Tuple, BaseModelOutput]:
+        """
+        Returns:
+
+        Examples:
+
+        ```python
+        >>> from transformers import VitDetConfig, VitDetModel
+        >>> import torch
+
+        >>> config = VitDetConfig()
+        >>> model = VitDetModel(config)
+
+        >>> pixel_values = torch.randn(1, 3, 224, 224)
+
+        >>> with torch.no_grad():
+        ...     outputs = model(pixel_values)
+
+        >>> last_hidden_states = outputs.last_hidden_state
+        >>> list(last_hidden_states.shape)
+        [1, 768, 14, 14]
+        ```"""
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@@ -825,19 +835,20 @@ class VitDetBackbone(VitDetPreTrainedModel, BackboneMixin):
         Examples:
 
         ```python
-        >>> from transformers import AutoImageProcessor, AutoBackbone
+        >>> from transformers import VitDetConfig, VitDetBackbone
         >>> import torch
-        >>> from PIL import Image
-        >>> import requests
 
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> config = VitDetConfig()
-        >>> image = Image.open(requests.get(url, stream=True).raw)
+        >>> model = VitDetBackbone(config)
 
-        >>> processor = AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224")
+        >>> pixel_values = torch.randn(1, 3, 224, 224)
-        >>> model = AutoBackbone.from_pretrained("facebook/convnext-tiny-224")
 
-        >>> inputs = processor(image, return_tensors="pt")
+        >>> with torch.no_grad():
-        >>> outputs = model(**inputs)
+        ...     outputs = model(pixel_values)
+
+        >>> feature_maps = outputs.feature_maps
+        >>> list(feature_maps[-1].shape)
+        [1, 768, 14, 14]
         ```"""
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
         output_hidden_states = (