Fix doc examples: name '...' is not defined (#14687)

* Fix doc examples: name '...' is not defined

* remove >>> and ... in some docstrings in visual_bert
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

src/transformers/models/longformer/modeling_longformer.py

@@ -1764,7 +1764,7 @@ class LongformerForMaskedLM(LongformerPreTrainedModel):
             ...                        # check ``LongformerModel.forward`` for more details how to set `attention_mask`
             >>> outputs = model(input_ids, attention_mask=attention_mask, labels=input_ids)
             >>> loss = outputs.loss
-            >>> prediction_logits = output.logits
+            >>> prediction_logits = outputs.logits
         """
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 

src/transformers/models/megatron_bert/modeling_megatron_bert.py

@@ -1189,7 +1189,7 @@ class MegatronBertForCausalLM(MegatronBertPreTrainedModel):
             >>> import torch
 
             >>> tokenizer = BertTokenizer.from_pretrained('nvidia/megatron-bert-cased-345m')
-            >>> model = MegatronBertLMHeadModel.from_pretrained('nvidia/megatron-bert-cased-345m', is_decoder=True)
+            >>> model = MegatronBertForCausalLM.from_pretrained('nvidia/megatron-bert-cased-345m', is_decoder=True)
 
             >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
             >>> outputs = model(**inputs)

src/transformers/models/visual_bert/modeling_visual_bert.py

@@ -741,27 +741,27 @@ class VisualBertModel(VisualBertPreTrainedModel):
 
         Example::
 
-            >>> # Assumption: `get_visual_embeddings(image)` gets the visual embeddings of the image.
-            >>> from transformers import BertTokenizer, VisualBertModel
-            >>> import torch
+            # Assumption: `get_visual_embeddings(image)` gets the visual embeddings of the image.
+            from transformers import BertTokenizer, VisualBertModel
+            import torch
 
-            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-            >>> model = VisualBertModel.from_pretrained('uclanlp/visualbert-vqa-coco-pre')
+            tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+            model = VisualBertModel.from_pretrained('uclanlp/visualbert-vqa-coco-pre')
 
-            >>> inputs = tokenizer("The capital of France is Paris.", return_tensors="pt")
-            >>> visual_embeds = get_visual_embeddings(image).unsqueeze(0)
-            >>> visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
-            >>> visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
+            inputs = tokenizer("The capital of France is Paris.", return_tensors="pt")
+            visual_embeds = get_visual_embeddings(image).unsqueeze(0)
+            visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
+            visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
 
-            >>> inputs.update({
-            ...     "visual_embeds": visual_embeds,
-            ...     "visual_token_type_ids": visual_token_type_ids,
-            ...     "visual_attention_mask": visual_attention_mask
-            ... })
+            inputs.update({
+                "visual_embeds": visual_embeds,
+                "visual_token_type_ids": visual_token_type_ids,
+                "visual_attention_mask": visual_attention_mask
+            })
 
-            >>> outputs = model(**inputs)
+            outputs = model(**inputs)
 
-            >>> last_hidden_states = outputs.last_hidden_state
+            last_hidden_states = outputs.last_hidden_state
         """
 
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
@@ -923,31 +923,31 @@ class VisualBertForPreTraining(VisualBertPreTrainedModel):
 
         Example::
 
-            >>> # Assumption: `get_visual_embeddings(image)` gets the visual embeddings of the image in the batch.
-            >>> from transformers import BertTokenizer, VisualBertForPreTraining
+            # Assumption: `get_visual_embeddings(image)` gets the visual embeddings of the image in the batch.
+            from transformers import BertTokenizer, VisualBertForPreTraining
 
-            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-            >>> model = VisualBertForPreTraining.from_pretrained('uclanlp/visualbert-vqa-coco-pre')
+            tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+            model = VisualBertForPreTraining.from_pretrained('uclanlp/visualbert-vqa-coco-pre')
 
-            >>> inputs = tokenizer("The capital of France is {mask}.", return_tensors="pt")
-            >>> visual_embeds = get_visual_embeddings(image).unsqueeze(0)
-            >>> visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
-            >>> visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
+            inputs = tokenizer("The capital of France is {mask}.", return_tensors="pt")
+            visual_embeds = get_visual_embeddings(image).unsqueeze(0)
+            visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
+            visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
 
-            >>> inputs.update({
-            ...     "visual_embeds": visual_embeds,
-            ...     "visual_token_type_ids": visual_token_type_ids,
-            ...     "visual_attention_mask": visual_attention_mask
-            ... })
-            >>> max_length  = inputs["input_ids"].shape[-1]+visual_embeds.shape[-2]
-            >>> labels = tokenizer("The capital of France is Paris.", return_tensors="pt", padding="max_length", max_length=max_length)["input_ids"]
-            >>> sentence_image_labels = torch.tensor(1).unsqueeze(0) # Batch_size
+            inputs.update({
+                "visual_embeds": visual_embeds,
+                "visual_token_type_ids": visual_token_type_ids,
+                "visual_attention_mask": visual_attention_mask
+            })
+            max_length  = inputs["input_ids"].shape[-1]+visual_embeds.shape[-2]
+            labels = tokenizer("The capital of France is Paris.", return_tensors="pt", padding="max_length", max_length=max_length)["input_ids"]
+            sentence_image_labels = torch.tensor(1).unsqueeze(0) # Batch_size
 
 
-            >>> outputs = model(**inputs, labels=labels, sentence_image_labels=sentence_image_labels)
-            >>> loss = outputs.loss
-            >>> prediction_logits = outputs.prediction_logits
-            >>> seq_relationship_logits = outputs.seq_relationship_logits
+            outputs = model(**inputs, labels=labels, sentence_image_labels=sentence_image_labels)
+            loss = outputs.loss
+            prediction_logits = outputs.prediction_logits
+            seq_relationship_logits = outputs.seq_relationship_logits
         """
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
@@ -1057,37 +1057,38 @@ class VisualBertForMultipleChoice(VisualBertPreTrainedModel):
 
         Example::
 
-            >>> from transformers import BertTokenizer, VisualBertForMultipleChoice
-            >>> import torch
-
-            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-            >>> model = VisualBertForMultipleChoice.from_pretrained('uclanlp/visualbert-vcr')
-
-            >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
-            >>> choice0 = "It is eaten with a fork and a knife."
-            >>> choice1 = "It is eaten while held in the hand."
-
-            >>> visual_embeds = get_visual_embeddings(image)
-            >>> # (batch_size, num_choices, visual_seq_length, visual_embedding_dim)
-            >>> visual_embeds = visual_embeds.expand(1, 2, *visual_embeds.shape)
-            >>> visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
-            >>> visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
-
-            >>> labels = torch.tensor(0).unsqueeze(0)  # choice0 is correct (according to Wikipedia ;)), batch size 1
-
-            >>> encoding = tokenizer([[prompt, prompt], [choice0, choice1]], return_tensors='pt', padding=True)
-            >>> # batch size is 1
-            >>> inputs_dict = {k: v.unsqueeze(0) for k,v in encoding.items()}
-            >>> inputs_dict.update({
-            ...     "visual_embeds": visual_embeds,
-            ...     "visual_attention_mask": visual_attention_mask,
-            ...     "visual_token_type_ids": visual_token_type_ids,
-            ...     "labels": labels
-            ... })
-            >>> outputs = model(**inputs_dict)
-
-            >>> loss = outputs.loss
-            >>> logits = outputs.logits
+            # Assumption: `get_visual_embeddings(image)` gets the visual embeddings of the image in the batch.
+            from transformers import BertTokenizer, VisualBertForMultipleChoice
+            import torch
+
+            tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+            model = VisualBertForMultipleChoice.from_pretrained('uclanlp/visualbert-vcr')
+
+            prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
+            choice0 = "It is eaten with a fork and a knife."
+            choice1 = "It is eaten while held in the hand."
+
+            visual_embeds = get_visual_embeddings(image)
+            # (batch_size, num_choices, visual_seq_length, visual_embedding_dim)
+            visual_embeds = visual_embeds.expand(1, 2, *visual_embeds.shape)
+            visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
+            visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
+
+            labels = torch.tensor(0).unsqueeze(0)  # choice0 is correct (according to Wikipedia ;)), batch size 1
+
+            encoding = tokenizer([[prompt, prompt], [choice0, choice1]], return_tensors='pt', padding=True)
+            # batch size is 1
+            inputs_dict = {k: v.unsqueeze(0) for k,v in encoding.items()}
+            inputs_dict.update({
+                "visual_embeds": visual_embeds,
+                "visual_attention_mask": visual_attention_mask,
+                "visual_token_type_ids": visual_token_type_ids,
+                "labels": labels
+            })
+            outputs = model(**inputs_dict)
+
+            loss = outputs.loss
+            logits = outputs.logits
         """
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
         num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
@@ -1204,30 +1205,30 @@ class VisualBertForQuestionAnswering(VisualBertPreTrainedModel):
 
         Example::
 
-            >>> # Assumption: `get_visual_embeddings(image)` gets the visual embeddings of the image in the batch.
-            >>> from transformers import BertTokenizer, VisualBertForQuestionAnswering
-            >>> import torch
+            # Assumption: `get_visual_embeddings(image)` gets the visual embeddings of the image in the batch.
+            from transformers import BertTokenizer, VisualBertForQuestionAnswering
+            import torch
 
-            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-            >>> model = VisualBertForQuestionAnswering.from_pretrained('uclanlp/visualbert-vqa')
+            tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+            model = VisualBertForQuestionAnswering.from_pretrained('uclanlp/visualbert-vqa')
 
-            >>> text = "Who is eating the apple?"
-            >>> inputs = tokenizer(text, return_tensors='pt')
-            >>> visual_embeds = get_visual_embeddings(image).unsqueeze(0)
-            >>> visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
-            >>> visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
+            text = "Who is eating the apple?"
+            inputs = tokenizer(text, return_tensors='pt')
+            visual_embeds = get_visual_embeddings(image).unsqueeze(0)
+            visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
+            visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
 
-            >>> inputs.update({
-            ...     "visual_embeds": visual_embeds,
-            ...     "visual_token_type_ids": visual_token_type_ids,
-            ...     "visual_attention_mask": visual_attention_mask
-            ... })
+            inputs.update({
+                "visual_embeds": visual_embeds,
+                "visual_token_type_ids": visual_token_type_ids,
+                "visual_attention_mask": visual_attention_mask
+            })
 
-            >>> labels = torch.tensor([[0.0,1.0]]).unsqueeze(0)  # Batch size 1, Num labels 2
+            labels = torch.tensor([[0.0,1.0]]).unsqueeze(0)  # Batch size 1, Num labels 2
 
-            >>> outputs = model(**inputs, labels=labels)
-            >>> loss = outputs.loss
-            >>> scores = outputs.logits
+            outputs = model(**inputs, labels=labels)
+            loss = outputs.loss
+            scores = outputs.logits
         """
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
@@ -1327,30 +1328,30 @@ class VisualBertForVisualReasoning(VisualBertPreTrainedModel):
 
         Example::
 
-            >>> # Assumption: `get_visual_embeddings(image)` gets the visual embeddings of the image in the batch.
-            >>> from transformers import BertTokenizer, VisualBertForVisualReasoning
-            >>> import torch
+            # Assumption: `get_visual_embeddings(image)` gets the visual embeddings of the image in the batch.
+            from transformers import BertTokenizer, VisualBertForVisualReasoning
+            import torch
 
-            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-            >>> model = VisualBertForVisualReasoning.from_pretrained('uclanlp/visualbert-nlvr2')
+            tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+            model = VisualBertForVisualReasoning.from_pretrained('uclanlp/visualbert-nlvr2')
 
-            >>> text = "Who is eating the apple?"
-            >>> inputs = tokenizer(text, return_tensors='pt')
-            >>> visual_embeds = get_visual_embeddings(image).unsqueeze(0)
-            >>> visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
-            >>> visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
+            text = "Who is eating the apple?"
+            inputs = tokenizer(text, return_tensors='pt')
+            visual_embeds = get_visual_embeddings(image).unsqueeze(0)
+            visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
+            visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
 
-            >>> inputs.update({
-            ...     "visual_embeds": visual_embeds,
-            ...     "visual_token_type_ids": visual_token_type_ids,
-            ...     "visual_attention_mask": visual_attention_mask
-            ... })
+            inputs.update({
+                "visual_embeds": visual_embeds,
+                "visual_token_type_ids": visual_token_type_ids,
+                "visual_attention_mask": visual_attention_mask
+            })
 
-            >>> labels = torch.tensor(1).unsqueeze(0)  # Batch size 1, Num choices 2
+            labels = torch.tensor(1).unsqueeze(0)  # Batch size 1, Num choices 2
 
-            >>> outputs = model(**inputs, labels=labels)
-            >>> loss = outputs.loss
-            >>> scores = outputs.logits
+            outputs = model(**inputs, labels=labels)
+            loss = outputs.loss
+            scores = outputs.logits
         """
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
@@ -1488,32 +1489,32 @@ class VisualBertForRegionToPhraseAlignment(VisualBertPreTrainedModel):
 
         Example::
 
-            >>> # Assumption: `get_visual_embeddings(image)` gets the visual embeddings of the image in the batch.
-            >>> from transformers import BertTokenizer, VisualBertForRegionToPhraseAlignment
-            >>> import torch
+            # Assumption: `get_visual_embeddings(image)` gets the visual embeddings of the image in the batch.
+            from transformers import BertTokenizer, VisualBertForRegionToPhraseAlignment
+            import torch
 
-            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-            >>> model = VisualBertForRegionToPhraseAlignment.from_pretrained('uclanlp/visualbert-vqa-coco-pre')
+            tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+            model = VisualBertForRegionToPhraseAlignment.from_pretrained('uclanlp/visualbert-vqa-coco-pre')
 
-            >>> text = "Who is eating the apple?"
-            >>> inputs = tokenizer(text, return_tensors='pt')
-            >>> visual_embeds = get_visual_embeddings(image).unsqueeze(0)
-            >>> visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
-            >>> visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
-            >>> region_to_phrase_position = torch.ones((1, inputs["input_ids"].shape[-1]+visual_embeds.shape[-2]))
+            text = "Who is eating the apple?"
+            inputs = tokenizer(text, return_tensors='pt')
+            visual_embeds = get_visual_embeddings(image).unsqueeze(0)
+            visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
+            visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
+            region_to_phrase_position = torch.ones((1, inputs["input_ids"].shape[-1]+visual_embeds.shape[-2]))
 
-            >>> inputs.update({
-            ...     "region_to_phrase_position": region_to_phrase_position,
-            ...     "visual_embeds": visual_embeds,
-            ...     "visual_token_type_ids": visual_token_type_ids,
-            ...     "visual_attention_mask": visual_attention_mask
-            ... })
+            inputs.update({
+                "region_to_phrase_position": region_to_phrase_position,
+                "visual_embeds": visual_embeds,
+                "visual_token_type_ids": visual_token_type_ids,
+                "visual_attention_mask": visual_attention_mask
+            })
 
-            >>> labels = torch.ones((1, inputs["input_ids"].shape[-1]+visual_embeds.shape[-2], visual_embeds.shape[-2])) # Batch size 1
+            labels = torch.ones((1, inputs["input_ids"].shape[-1]+visual_embeds.shape[-2], visual_embeds.shape[-2])) # Batch size 1
 
-            >>> outputs = model(**inputs, labels=labels)
-            >>> loss = outputs.loss
-            >>> scores = outputs.logits
+            outputs = model(**inputs, labels=labels)
+            loss = outputs.loss
+            scores = outputs.logits
         """
         if region_to_phrase_position is None:
             raise ValueError("`region_to_phrase_position` should not be None when using Flickr Model.")

src/transformers/models/wav2vec2/modeling_wav2vec2.py

@@ -1517,7 +1517,7 @@ class Wav2Vec2ForMaskedLM(Wav2Vec2PreTrainedModel):
 
         Example::
 
-            >>> from transformers import Wav2Vec2Processor, Wav2Vec2Model
+            >>> from transformers import Wav2Vec2Processor, Wav2Vec2ForMaskedLM
             >>> from datasets import load_dataset
             >>> import soundfile as sf