[Swin2SR] Add doc tests (#20829)

* Fix doc tests

* Use Auto API

* Apply suggestion

* Revert "Apply suggestion"

This reverts commit cd9507a86644b4877c3e4a3d6c2d5919d9272dd7.
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MBP.localdomain>

src/transformers/models/swin2sr/modeling_swin2sr.py

@@ -43,11 +43,11 @@ logger = logging.get_logger(__name__)
 
 # General docstring
 _CONFIG_FOR_DOC = "Swin2SRConfig"
-_FEAT_EXTRACTOR_FOR_DOC = "AutoFeatureExtractor"
+_FEAT_EXTRACTOR_FOR_DOC = "AutoImageProcessor"
 
 # Base docstring
-_CHECKPOINT_FOR_DOC = "caidas/swin2sr-classicalsr-x2-64"
-_EXPECTED_OUTPUT_SHAPE = [1, 64, 768]
+_CHECKPOINT_FOR_DOC = "caidas/swin2SR-classical-sr-x2-64"
+_EXPECTED_OUTPUT_SHAPE = [1, 180, 488, 648]
 
 
 SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST = [
@@ -1141,19 +1141,28 @@ class Swin2SRForImageSuperResolution(Swin2SRPreTrainedModel):
         Example:
          ```python
          >>> import torch
-         >>> from transformers import Swin2SRFeatureExtractor, Swin2SRForImageSuperResolution
-         >>> from datasets import load_dataset
-
-         >>> feature_extractor = Swin2SRFeatureExtractor.from_pretrained("openai/whisper-base")
-         >>> model = Swin2SRForImageSuperResolution.from_pretrained("openai/whisper-base")
-
-         >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
-         >>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt")
-         >>> input_features = inputs.input_features
-         >>> decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id
-         >>> last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state
-         >>> list(last_hidden_state.shape)
-         [1, 2, 512]
+         >>> import numpy as np
+         >>> from PIL import Image
+         >>> import requests
+
+         >>> from transformers import AutoImageProcessor, Swin2SRForImageSuperResolution
+
+         >>> processor = AutoImageProcessor.from_pretrained("caidas/swin2SR-classical-sr-x2-64")
+         >>> model = Swin2SRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64")
+
+         >>> url = "https://huggingface.co/spaces/jjourney1125/swin2sr/resolve/main/samples/butterfly.jpg"
+         >>> image = Image.open(requests.get(url, stream=True).raw)
+         >>> # prepare image for the model
+         >>> inputs = processor(image, return_tensors="pt")
+
+         >>> # forward pass
+         >>> with torch.no_grad():
+         ...     outputs = model(**inputs)
+
+         >>> output = outputs.reconstruction.data.squeeze().float().cpu().clamp_(0, 1).numpy()
+         >>> output = np.moveaxis(output, source=0, destination=-1)
+         >>> output = (output * 255.0).round().astype(np.uint8)  # float32 to uint8
+         >>> # you can visualize `output` with `Image.fromarray`
          ```"""
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 

utils/documentation_tests.txt

@@ -166,6 +166,7 @@ src/transformers/models/segformer/modeling_tf_segformer.py
 src/transformers/models/squeezebert/configuration_squeezebert.py
 src/transformers/models/swin/configuration_swin.py
 src/transformers/models/swin/modeling_swin.py
+src/transformers/models/swin2sr/modeling_swin2sr.py
 src/transformers/models/swinv2/configuration_swinv2.py
 src/transformers/models/table_transformer/modeling_table_transformer.py
 src/transformers/models/time_series_transformer/configuration_time_series_transformer.py