[ProphetNet] Correct Doc string example (huggingface#7944)

* correct xlm prophetnet auto model and examples

* fix line-break docs

src/transformers/modeling_auto.py

@@ -335,7 +335,7 @@
         (CTRLConfig, CTRLLMHeadModel),
         (ReformerConfig, ReformerModelWithLMHead),
         (BertGenerationConfig, BertGenerationDecoder),
-        (ProphetNetConfig, XLMProphetNetForCausalLM),
+        (XLMProphetNetConfig, XLMProphetNetForCausalLM),
         (ProphetNetConfig, ProphetNetForCausalLM),
     ]
 )

src/transformers/modeling_prophetnet.py

@@ -1931,14 +1931,21 @@ def forward(
             >>> logits = outputs.logits
 
             >>> # Model can also be used with EncoderDecoder framework
-            >>> from transformers import BertTokenizer, EncoderDecoderModel
+            >>> from transformers import BertTokenizer, EncoderDecoderModel, ProphetNetTokenizer
             >>> import torch
 
-            >>> tokenizer = BertTokenizer.from_pretrained('bert-uncased-large')
-            >>> model = EncoderDecoderModel.from_encoder_decoder_pretrained("bert-uncased-large", "patrickvonplaten/prophetnet-decoder-clm-large-uncased")
-
-            >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
-            >>> outputs = model(input_ids=inputs["input_ids"], labels=inputs["input_ids"], return_dict=True)
+            >>> tokenizer_enc = BertTokenizer.from_pretrained('bert-large-uncased')
+            >>> tokenizer_dec = ProphetNetTokenizer.from_pretrained('microsoft/prophetnet-large-uncased')
+            >>> model = EncoderDecoderModel.from_encoder_decoder_pretrained("bert-large-uncased", "patrickvonplaten/prophetnet-decoder-clm-large-uncased")
+
+            >>> ARTICLE = (
+            ... "the us state department said wednesday it had received no "
+            ... "formal word from bolivia that it was expelling the us ambassador there "
+            ... "but said the charges made against him are `` baseless ."
+            ... )
+            >>> input_ids = tokenizer_enc(ARTICLE, return_tensors="pt").input_ids
+            >>> labels = tokenizer_dec("us rejects charges against its ambassador in bolivia", return_tensors="pt").input_ids
+            >>> outputs = model(input_ids=input_ids, decoder_input_ids=labels[:, :-1], labels=labels[:, 1:], return_dict=True)
 
             >>> loss = outputs.loss
         """

src/transformers/modeling_xlm_prophetnet.py

@@ -144,14 +144,21 @@ class XLMProphetNetForCausalLM(ProphetNetForCausalLM):
         >>> logits = outputs.logits
 
         >>> # Model can also be used with EncoderDecoder framework
-        >>> from transformers import BertTokenizer, EncoderDecoderModel
+        >>> from transformers import EncoderDecoderModel, XLMProphetNetTokenizer, XLMRobertaTokenizer
         >>> import torch
 
-        >>> tokenizer = BertTokenizer.from_pretrained('bert-uncased-large')
-        >>> model = EncoderDecoderModel.from_encoder_decoder_pretrained("bert-uncased-large", "patrickvonplaten/xprophetnet-decoder-clm-large-uncased")
-
-        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
-        >>> outputs = model(input_ids=inputs["input_ids"], labels=inputs["input_ids"])
+        >>> tokenizer_enc = XLMRobertaTokenizer.from_pretrained('xlm-roberta-large')
+        >>> tokenizer_dec = XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')
+        >>> model = EncoderDecoderModel.from_encoder_decoder_pretrained("xlm-roberta-large", "patrickvonplaten/xprophetnet-decoder-clm-large-uncased")
+
+        >>> ARTICLE = (
+        ... "the us state department said wednesday it had received no "
+        ... "formal word from bolivia that it was expelling the us ambassador there "
+        ... "but said the charges made against him are `` baseless ."
+        ... )
+        >>> input_ids = tokenizer_enc(ARTICLE, return_tensors="pt").input_ids
+        >>> labels = tokenizer_dec("us rejects charges against its ambassador in bolivia", return_tensors="pt").input_ids
+        >>> outputs = model(input_ids=input_ids, decoder_input_ids=labels[:, :-1], labels=labels[:, 1:], return_dict=True)
 
         >>> loss = outputs.loss
     """