[Nougat] Fix pipeline (#28242)

* Fix pipeline

* Remove print statements

* Address comments

* Address issue

* Remove unused imports

src/transformers/pipelines/__init__.py

@@ -12,15 +12,13 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import io
 import json
 import os
 import warnings
 from pathlib import Path
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
 
 from huggingface_hub import model_info
-from numpy import isin
 
 from ..configuration_utils import PretrainedConfig
 from ..dynamic_module_utils import get_class_from_dynamic_module
@@ -446,7 +444,8 @@
 # any tokenizer/feature_extractor might be use for a given model so we cannot
 # use the statically defined TOKENIZER_MAPPING and FEATURE_EXTRACTOR_MAPPING to
 # see if the model defines such objects or not.
-MULTI_MODEL_CONFIGS = {"SpeechEncoderDecoderConfig", "VisionEncoderDecoderConfig", "VisionTextDualEncoderConfig"}
+MULTI_MODEL_AUDIO_CONFIGS = {"SpeechEncoderDecoderConfig"}
+MULTI_MODEL_VISION_CONFIGS = {"VisionEncoderDecoderConfig", "VisionTextDualEncoderConfig"}
 for task, values in SUPPORTED_TASKS.items():
     if values["type"] == "text":
         NO_FEATURE_EXTRACTOR_TASKS.add(task)
@@ -930,7 +929,10 @@ def pipeline(
         and not load_tokenizer
         and normalized_task not in NO_TOKENIZER_TASKS
         # Using class name to avoid importing the real class.
-        and model_config.__class__.__name__ in MULTI_MODEL_CONFIGS
+        and (
+            model_config.__class__.__name__ in MULTI_MODEL_AUDIO_CONFIGS
+            or model_config.__class__.__name__ in MULTI_MODEL_VISION_CONFIGS
+        )
     ):
         # This is a special category of models, that are fusions of multiple models
         # so the model_config might not define a tokenizer, but it seems to be
@@ -941,8 +943,7 @@ def pipeline(
         and not load_image_processor
         and normalized_task not in NO_IMAGE_PROCESSOR_TASKS
         # Using class name to avoid importing the real class.
-        and model_config.__class__.__name__ in MULTI_MODEL_CONFIGS
-        and normalized_task != "automatic-speech-recognition"
+        and model_config.__class__.__name__ in MULTI_MODEL_VISION_CONFIGS
     ):
         # This is a special category of models, that are fusions of multiple models
         # so the model_config might not define a tokenizer, but it seems to be
@@ -953,7 +954,7 @@ def pipeline(
         and not load_feature_extractor
         and normalized_task not in NO_FEATURE_EXTRACTOR_TASKS
         # Using class name to avoid importing the real class.
-        and model_config.__class__.__name__ in MULTI_MODEL_CONFIGS
+        and model_config.__class__.__name__ in MULTI_MODEL_AUDIO_CONFIGS
     ):
         # This is a special category of models, that are fusions of multiple models
         # so the model_config might not define a tokenizer, but it seems to be

tests/pipelines/test_pipelines_image_to_text.py

@@ -247,14 +247,16 @@ def test_large_model_tf(self):
     @require_torch
     def test_conditional_generation_llava(self):
         pipe = pipeline("image-to-text", model="llava-hf/bakLlava-v1-hf")
-        url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
-        image = Image.open(requests.get(url, stream=True).raw)
 
         prompt = (
             "<image>\nUSER: What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud?\nASSISTANT:"
         )
 
-        outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
+        outputs = pipe(
+            "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg",
+            prompt=prompt,
+            generate_kwargs={"max_new_tokens": 200},
+        )
         self.assertEqual(
             outputs,
             [
@@ -263,3 +265,15 @@ def test_conditional_generation_llava(self):
                 }
             ],
         )
+
+    @slow
+    @require_torch
+    def test_nougat(self):
+        pipe = pipeline("image-to-text", "facebook/nougat-base")
+
+        outputs = pipe("https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/nougat_paper.png")
+
+        self.assertEqual(
+            outputs,
+            [{"generated_text": "# Nougat: Neural Optical Understanding for Academic Documents\n\n Lukas Blec"}],
+        )