[DOC] fix doc examples for bart-like models (#15093)

* fix doc examples

* remove double colons

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_flax_{{cookiecutter.lowercase_modelname}}.py

@@ -2605,35 +2605,40 @@ class Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration(Flax{{coo
 FLAX_{{cookiecutter.uppercase_modelname}}_CONDITIONAL_GENERATION_DOCSTRING = """
     Returns:
 
-    Summarization example::
+    Summarization example:
 
-        >>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
+    ```python
+    >>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
 
-        >>> model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
-        >>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
+    >>> model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
+    >>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
 
-        >>> ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
-        >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='np')
+    >>> ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
+    >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='np')
 
-        >>> # Generate Summary
-        >>> summary_ids = model.generate(inputs['input_ids']).sequences
-        >>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))
+    >>> # Generate Summary
+    >>> summary_ids = model.generate(inputs['input_ids']).sequences
+    >>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))
+    ```
 
-    Mask filling example::
+    Mask filling example:
 
-        >>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
-        >>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
-        >>> TXT = "My friends are <mask> but they eat too many carbs."
+    ```python
+    >>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration
+    
+    >>> model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
+    >>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
+    
+    >>> TXT = "My friends are <mask> but they eat too many carbs."
+    >>> input_ids = tokenizer([TXT], return_tensors='np')['input_ids']
 
-        >>> model = Flax{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
-        >>> input_ids = tokenizer([TXT], return_tensors='np')['input_ids']
-        >>> logits = model(input_ids).logits
+    >>> logits = model(input_ids).logits
+    >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
+    >>> probs = jax.nn.softmax(logits[0, masked_index], axis=0)
+    >>> values, predictions = jax.lax.top_k(probs)
 
-        >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
-        >>> probs = jax.nn.softmax(logits[0, masked_index], axis=0)
-        >>> values, predictions = jax.lax.top_k(probs)
-
-        >>> tokenizer.decode(predictions).split()
+    >>> tokenizer.decode(predictions).split()
+    ```
 """
 
 overwrite_call_docstring(

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_{{cookiecutter.lowercase_modelname}}.py

@@ -2067,19 +2067,21 @@ class {{cookiecutter.camelcase_modelname}}PreTrainedModel(PreTrainedModel):
 """
 
 {{cookiecutter.uppercase_modelname}}_GENERATION_EXAMPLE = r"""
-    Summarization example::
+    Summarization example:
 
-        >>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, {{cookiecutter.camelcase_modelname}}ForConditionalGeneration, {{cookiecutter.camelcase_modelname}}Config
+    ```python
+    >>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, {{cookiecutter.camelcase_modelname}}ForConditionalGeneration
 
-        >>> model = {{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
-        >>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
+    >>> model = {{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
+    >>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained('{{cookiecutter.checkpoint_identifier}}')
 
-        >>> ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
-        >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='pt')
+    >>> ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
+    >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='pt')
 
-        >>> # Generate Summary
-        >>> summary_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=5, early_stopping=True)
-        >>> print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])
+    >>> # Generate Summary
+    >>> summary_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=5)
+    >>> print(tokenizer.decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))
+    ```
 """
 
 {{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING = r"""