Fix ImageGPT doc example (#24317)

* Fix ImageGPT doc example

* Update src/transformers/models/imagegpt/image_processing_imagegpt.py

* Fix types

src/transformers/models/imagegpt/image_processing_imagegpt.py

@@ -60,9 +60,9 @@ class ImageGPTImageProcessor(BaseImageProcessor):
     (color clusters).
 
     Args:
-        clusters (`np.ndarray`, *optional*):
+        clusters (`np.ndarray` or `List[List[int]]`, *optional*):
-            The color clusters to use, as a `np.ndarray` of shape `(n_clusters, 3)` when color quantizing. Can be
+            The color clusters to use, of shape `(n_clusters, 3)` when color quantizing. Can be overriden by `clusters`
-            overriden by `clusters` in `preprocess`.
+            in `preprocess`.
         do_resize (`bool`, *optional*, defaults to `True`):
             Whether to resize the image's dimensions to `(size["height"], size["width"])`. Can be overridden by
             `do_resize` in `preprocess`.
@@ -82,7 +82,7 @@ class ImageGPTImageProcessor(BaseImageProcessor):
     def __init__(
         self,
         # clusters is a first argument to maintain backwards compatibility with the old ImageGPTFeatureExtractor
-        clusters: Optional[np.ndarray] = None,
+        clusters: Optional[Union[List[List[int]], np.ndarray]] = None,
         do_resize: bool = True,
         size: Dict[str, int] = None,
         resample: PILImageResampling = PILImageResampling.BILINEAR,
@@ -93,7 +93,7 @@ class ImageGPTImageProcessor(BaseImageProcessor):
         super().__init__(**kwargs)
         size = size if size is not None else {"height": 256, "width": 256}
         size = get_size_dict(size)
-        self.clusters = clusters
+        self.clusters = np.array(clusters) if clusters is not None else None
         self.do_resize = do_resize
         self.size = size
         self.resample = resample
@@ -154,7 +154,7 @@ class ImageGPTImageProcessor(BaseImageProcessor):
         resample: PILImageResampling = None,
         do_normalize: bool = None,
         do_color_quantize: Optional[bool] = None,
-        clusters: Optional[Union[int, List[int]]] = None,
+        clusters: Optional[Union[List[List[int]], np.ndarray]] = None,
         return_tensors: Optional[Union[str, TensorType]] = None,
         data_format: Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST,
         **kwargs,
@@ -176,7 +176,7 @@ class ImageGPTImageProcessor(BaseImageProcessor):
                 Whether to normalize the image
             do_color_quantize (`bool`, *optional*, defaults to `self.do_color_quantize`):
                 Whether to color quantize the image.
-            clusters (`np.ndarray`, *optional*, defaults to `self.clusters`):
+            clusters (`np.ndarray` or `List[List[int]]`, *optional*, defaults to `self.clusters`):
                 Clusters used to quantize the image of shape `(n_clusters, 3)`. Only has an effect if
                 `do_color_quantize` is set to `True`.
             return_tensors (`str` or `TensorType`, *optional*):
@@ -199,6 +199,7 @@ class ImageGPTImageProcessor(BaseImageProcessor):
         do_normalize = do_normalize if do_normalize is not None else self.do_normalize
         do_color_quantize = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
         clusters = clusters if clusters is not None else self.clusters
+        clusters = np.array(clusters)
 
         images = make_list_of_images(images)
 
@@ -227,7 +228,6 @@ class ImageGPTImageProcessor(BaseImageProcessor):
             images = [to_channel_dimension_format(image, ChannelDimension.LAST) for image in images]
             # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
             images = np.array(images)
-            clusters = np.array(clusters)
             images = color_quantize(images, clusters).reshape(images.shape[:-1])
 
             # flatten to (batch_size, height*width)

src/transformers/models/imagegpt/modeling_imagegpt.py

@@ -983,9 +983,9 @@ class ImageGPTForCausalImageModeling(ImageGPTPreTrainedModel):
         >>> model.to(device)
 
         >>> # unconditional generation of 8 images
-        >>> batch_size = 8
+        >>> batch_size = 4
         >>> context = torch.full((batch_size, 1), model.config.vocab_size - 1)  # initialize with SOS token
-        >>> context = torch.tensor(context).to(device)
+        >>> context = context.to(device)
         >>> output = model.generate(
         ...     input_ids=context, max_length=model.config.n_positions + 1, temperature=1.0, do_sample=True, top_k=40
         ... )

utils/documentation_tests.txt

@@ -102,6 +102,7 @@ src/transformers/models/groupvit/modeling_groupvit.py
 src/transformers/models/groupvit/modeling_tf_groupvit.py
 src/transformers/models/hubert/modeling_hubert.py
 src/transformers/models/imagegpt/configuration_imagegpt.py
+src/transformers/models/imagegpt/modeling_imagegpt.py
 src/transformers/models/layoutlm/configuration_layoutlm.py
 src/transformers/models/layoutlm/modeling_layoutlm.py
 src/transformers/models/layoutlm/modeling_tf_layoutlm.py