Mass conversion of documentation from rst to Markdown (#14866)

* Convert docstrings of all configurations and tokenizers

* Processors and fixes

* Last modeling files and fixes to models

* Pipeline modules

* Utils files

* Data submodule

* All the other files

* Style

* Missing examples

* Style again

* Fix copies

* Say bye bye to rst docstrings forever

src/transformers/modeling_flax_pytorch_utils.py

@@ -69,7 +69,7 @@ def rename_key_and_reshape_tensor(
     """Rename PT weight names to corresponding Flax weight names and reshape tensor if necessary"""
 
     def is_key_or_prefix_key_in_dict(key: Tuple[str]) -> bool:
-        """Checks if ``key`` of ``(prefix,) + key`` is in random_flax_state_dict"""
+        """Checks if `key` of `(prefix,) + key` is in random_flax_state_dict"""
         return len(set(random_flax_state_dict) & set([key, (model_prefix,) + key])) > 0
 
     # layer norm

src/transformers/models/albert/modeling_flax_albert.py

@@ -742,18 +742,20 @@ class FlaxAlbertForPreTraining(FlaxAlbertPreTrainedModel):
 FLAX_ALBERT_FOR_PRETRAINING_DOCSTRING = """
     Returns:
 
-    Example::
+    Example:
 
-        >>> from transformers import AlbertTokenizer, FlaxAlbertForPreTraining
+    ```python
+    >>> from transformers import AlbertTokenizer, FlaxAlbertForPreTraining
 
-        >>> tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
-        >>> model = FlaxAlbertForPreTraining.from_pretrained('albert-base-v2')
+    >>> tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
+    >>> model = FlaxAlbertForPreTraining.from_pretrained('albert-base-v2')
 
-        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="np")
-        >>> outputs = model(**inputs)
+    >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="np")
+    >>> outputs = model(**inputs)
 
-        >>> prediction_logits = outputs.prediction_logits
-        >>> seq_relationship_logits = outputs.sop_logits
+    >>> prediction_logits = outputs.prediction_logits
+    >>> seq_relationship_logits = outputs.sop_logits
+    ```
 """
 
 overwrite_call_docstring(