Refactor Code samples; Test code samples (#5036)

* Refactor code samples

* Test docstrings

* Style

* Tokenization examples

* Run rust of tests

* First step to testing source docs

* Style and BART comment

* Test the remainder of the code samples

* Style

* let to const

* Formatting fixes

* Ready for merge

* Fix fixture + Style

* Fix last tests

* Update docs/source/quicktour.rst
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Addressing @sgugger's comments + Fix MobileBERT in TF
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

src/transformers/tokenization_bart.py

@@ -66,13 +66,15 @@ class MBartTokenizer(XLMRobertaTokenizer):
     The tokenization method is <tokens> <eos> <language code>. There is no BOS token.
 
     Examples::
-        from transformers import MBartTokenizer
-        tokenizer = MBartTokenizer.from_pretrained('mbart-large-en-ro')
-        example_english_phrase = " UN Chief Says There Is No Military Solution in Syria"
-        expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
-        batch: dict = tokenizer.prepare_translation_batch(
-            example_english_phrase, src_lang="en_XX", tgt_lang="ro_RO", tgt_texts=expected_translation_romanian
-        )
+
+        >>> from transformers import MBartTokenizer
+        >>> tokenizer = MBartTokenizer.from_pretrained('facebook/mbart-large-en-ro')
+        >>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria"
+        >>> expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
+        >>> batch: dict = tokenizer.prepare_translation_batch(
+        ...     example_english_phrase, src_lang="en_XX", tgt_lang="ro_RO", tgt_texts=expected_translation_romanian
+        ... )
+
     """
 
     vocab_files_names = {"vocab_file": "sentencepiece.bpe.model"}

src/transformers/tokenization_reformer.py

@@ -81,6 +81,7 @@ class ReformerTokenizer(PreTrainedTokenizer):
     vocab_files_names = VOCAB_FILES_NAMES
     pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+    model_input_names = ["attention_mask"]
 
     def __init__(
         self,