Doc styling (#8067)

* Important files

* Styling them all

* Revert "Styling them all"

This reverts commit 7d029395fdae8513b8281cbc2a6c239f8093503e.

* Syling them for realsies

* Fix syntax error

* Fix benchmark_utils

* More fixes

* Fix modeling auto and script

* Remove new line

* Fixes

* More fixes

* Fix more files

* Style

* Add FSMT

* More fixes

* More fixes

* More fixes

* More fixes

* Fixes

* More fixes

* More fixes

* Last fixes

* Make sphinx happy

src/transformers/commands/serving.py

@@ -164,9 +164,9 @@ class ServeCommand(BaseTransformersCLICommand):
 
     def tokenize(self, text_input: str = Body(None, embed=True), return_ids: bool = Body(False, embed=True)):
         """
-        Tokenize the provided input and eventually returns corresponding tokens id:
-        - **text_input**: String to tokenize
-        - **return_ids**: Boolean flags indicating if the tokens have to be converted to their integer mapping.
+        Tokenize the provided input and eventually returns corresponding tokens id: - **text_input**: String to
+        tokenize - **return_ids**: Boolean flags indicating if the tokens have to be converted to their integer
+        mapping.
         """
         try:
             tokens_txt = self._pipeline.tokenizer.tokenize(text_input)
@@ -187,10 +187,9 @@ class ServeCommand(BaseTransformersCLICommand):
         cleanup_tokenization_spaces: bool = Body(True, embed=True),
     ):
         """
-        Detokenize the provided tokens ids to readable text:
-        - **tokens_ids**: List of tokens ids
-        - **skip_special_tokens**: Flag indicating to not try to decode special tokens
-        - **cleanup_tokenization_spaces**: Flag indicating to remove all leading/trailing spaces and intermediate ones.
+        Detokenize the provided tokens ids to readable text: - **tokens_ids**: List of tokens ids -
+        **skip_special_tokens**: Flag indicating to not try to decode special tokens - **cleanup_tokenization_spaces**:
+        Flag indicating to remove all leading/trailing spaces and intermediate ones.
         """
         try:
             decoded_str = self._pipeline.tokenizer.decode(tokens_ids, skip_special_tokens, cleanup_tokenization_spaces)