Address comments

src/transformers/pipelines/__init__.py

@@ -224,7 +224,6 @@
         "default": {
             "model": {
                 "pt": ("impira/layoutlm-document-qa", "3a93017")
-            },  # TODO Update with custom pipeline removed, just before we land
         },
         "type": "multimodal",
     },

src/transformers/pipelines/document_question_answering.py

@@ -120,6 +120,8 @@ def __init__(self, *args, **kwargs):
 
         if self.model.config.__class__.__name__ == "VisionEncoderDecoderConfig":
             self.model_type = ModelType.VisionEncoderDecoder
+            if self.model.config.encoder.model_type != "donut-swin":
+                raise ValueError("Currently, the only supported VisionEncoderDecoder model is Donut")
         elif self.model.config.__class__.__name__ == "LayoutLMConfig":
             self.model_type = ModelType.LayoutLM
         else:
@@ -240,12 +242,7 @@ def __call__(
             inputs = image
         return super().__call__(inputs, **kwargs)
 
-    def preprocess(
-        self,
-        input,
-        lang=None,
-        tesseract_config="",
-    ):
+    def preprocess(self, input, lang=None, tesseract_config=""):
         image = None
         image_features = {}
         if input.get("image", None) is not None:
@@ -342,14 +339,14 @@ def preprocess(
             if "boxes" not in tokenizer_kwargs:
                 bbox = []
                 for batch_index in range(num_spans):
-                    for i, s, w in zip(
+                    for input_id, sequence_id, word_id in zip(
                         encoding.input_ids[batch_index],
                         encoding.sequence_ids(batch_index),
                         encoding.word_ids(batch_index),
                     ):
-                        if s == 1:
-                            bbox.append(boxes[w])
-                        elif i == self.tokenizer.sep_token_id:
+                        if sequence_id == 1:
+                            bbox.append(boxes[word_id])
+                        elif input_id == self.tokenizer.sep_token_id:
                             bbox.append([1000] * 4)
                         else:
                             bbox.append([0] * 4)
@@ -361,12 +358,7 @@ def preprocess(
 
             word_ids = [encoding.word_ids(i) for i in range(num_spans)]
 
-        return {
-            **encoding,
-            "p_mask": p_mask,
-            "word_ids": word_ids,
-            "words": words,
-        }
+        return {**encoding, "p_mask": p_mask, "word_ids": word_ids, "words": words}
 
     def _forward(self, model_inputs):
         p_mask = model_inputs.pop("p_mask", None)
@@ -396,8 +388,10 @@ def postprocess(self, model_outputs, top_k=1, **kwargs):
         return answers
 
     def postprocess_donut(self, model_outputs, **kwargs):
-        # postprocess
         sequence = self.tokenizer.batch_decode(model_outputs.sequences)[0]
+
+        # TODO: A lot of this logic is specific to Donut and should probably be handled in the tokenizer
+        # (see https://github.com/huggingface/transformers/pull/18414/files#r961747408 for more context).
         sequence = sequence.replace(self.tokenizer.eos_token, "").replace(self.tokenizer.pad_token, "")
         sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()  # remove first task start token
         ret = {
@@ -431,8 +425,8 @@ def postprocess_extractive_qa(
         )
 
         word_ids = model_outputs["word_ids"][0]
-        for s, e, score in zip(starts, ends, scores):
-            word_start, word_end = word_ids[s], word_ids[e]
+        for start, eend, score in zip(starts, ends, scores):
+            word_start, word_end = word_ids[start], word_ids[eend]
             if word_start is not None and word_end is not None:
                 answers.append(
                     {