Docs: formatting nits (#32247)

* doc formatting nits

* ignore non-autodocs

* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/esm/modeling_esm.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/esm/modeling_esm.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* make fixup

---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

src/transformers/models/yolos/modeling_yolos.py

@@ -647,8 +647,9 @@ class YolosModel(YolosPreTrainedModel):
         Prunes heads of the model.
 
         Args:
-            heads_to_prune (`dict` of {layer_num: list of heads to prune in this layer}):
-                See base class `PreTrainedModel`.
+            heads_to_prune (`dict`):
+                See base class `PreTrainedModel`. The input dictionary must have the following format: {layer_num:
+                list of heads to prune in this layer}
         """
         for layer, heads in heads_to_prune.items():
             self.encoder.layer[layer].attention.prune_heads(heads)

src/transformers/pipelines/base.py

@@ -218,7 +218,7 @@ def infer_framework_load_model(
     If both frameworks are installed and available for `model`, PyTorch is selected.
 
     Args:
-        model (`str`, [`PreTrainedModel`] or [`TFPreTrainedModel`]):
+        model (`str`, [`PreTrainedModel`] or [`TFPreTrainedModel]`):
             The model to infer the framework from. If `str`, a checkpoint name. The model to infer the framewrok from.
         config ([`AutoConfig`]):
             The config associated with the model to help using the correct class
@@ -322,7 +322,7 @@ def infer_framework_from_model(
     If both frameworks are installed and available for `model`, PyTorch is selected.
 
     Args:
-        model (`str`, [`PreTrainedModel`] or [`TFPreTrainedModel`]):
+        model (`str`, [`PreTrainedModel`] or [`TFPreTrainedModel]`):
             The model to infer the framework from. If `str`, a checkpoint name. The model to infer the framewrok from.
         model_classes (dictionary `str` to `type`, *optional*):
             A mapping framework to class.
@@ -349,7 +349,7 @@ def get_framework(model, revision: Optional[str] = None):
     Select framework (TensorFlow or PyTorch) to use.
 
     Args:
-        model (`str`, [`PreTrainedModel`] or [`TFPreTrainedModel`]):
+        model (`str`, [`PreTrainedModel`] or [`TFPreTrainedModel]`):
             If both frameworks are installed, picks the one corresponding to the model passed (either a model class or
             the model name). If no specific model is provided, defaults to using PyTorch.
     """
@@ -385,7 +385,7 @@ def get_default_model_and_revision(
     Select a default model to use for a given task. Defaults to pytorch if ambiguous.
 
     Args:
-        targeted_task (`Dict` ):
+        targeted_task (`Dict`):
            Dictionary representing the given task, that should contain default models
 
         framework (`str`, None)

src/transformers/pipelines/fill_mask.py

@@ -22,7 +22,7 @@ logger = logging.get_logger(__name__)
 @add_end_docstrings(
     build_pipeline_init_args(has_tokenizer=True),
     r"""
-        top_k (`int`, defaults to 5):
+        top_k (`int`, *optional*, defaults to 5):
             The number of predictions to return.
         targets (`str` or `List[str]`, *optional*):
             When passed, the model will limit the scores to the passed targets instead of looking up in the whole

src/transformers/pipelines/pt_utils.py

@@ -31,7 +31,7 @@ class PipelineIterator(IterableDataset):
         ```
 
                 Arguments:
-                    loader (`torch.utils.data.DataLoader` or any iterator):
+                    loader (`torch.utils.data.DataLoader` or `Iterable`):
                         The iterator that will be used to apply `infer` on.
                     infer (any function):
                         The function to apply of each element of `loader`.
@@ -163,7 +163,7 @@ class PipelineChunkIterator(PipelineIterator):
         ```
 
                 Arguments:
-                    loader (`torch.utils.data.DataLoader` or any iterator):
+                    loader (`torch.utils.data.DataLoader` or `Iterable`):
                         The iterator that will be used to apply `infer` on.
                     infer (any function):
                         The function to apply of each element of `loader`.
@@ -224,7 +224,7 @@ class PipelinePackIterator(PipelineIterator):
     ```
 
         Arguments:
-            loader (`torch.utils.data.DataLoader` or any iterator):
+            loader (`torch.utils.data.DataLoader` or `Iterable`):
                 The iterator that will be used to apply `infer` on.
             infer (any function):
                 The function to apply of each element of `loader`.

src/transformers/tokenization_utils_base.py

@@ -3200,7 +3200,7 @@ class PreTrainedTokenizerBase(SpecialTokensMixin, PushToHubMixin):
         </Tip>
 
         Args:
-            text (`str`, `List[str]` or `List[int]` (the latter only for not-fast tokenizers)):
+            text (`str`, `List[str]` or (for non-fast tokenizers) `List[int]`):
                 The first sequence to be encoded. This can be a string, a list of strings (tokenized string using the
                 `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids`
                 method).

src/transformers/trainer.py

@@ -745,7 +745,7 @@ class Trainer:
         Add a callback to the current list of [`~transformers.TrainerCallback`].
 
         Args:
-           callback (`type` or [`~transformers.TrainerCallback`]):
+           callback (`type` or [`~transformers.TrainerCallback]`):
                A [`~transformers.TrainerCallback`] class or an instance of a [`~transformers.TrainerCallback`]. In the
                first case, will instantiate a member of that class.
         """
@@ -758,7 +758,7 @@ class Trainer:
         If the callback is not found, returns `None` (and no error is raised).
 
         Args:
-           callback (`type` or [`~transformers.TrainerCallback`]):
+           callback (`type` or [`~transformers.TrainerCallback]`):
                A [`~transformers.TrainerCallback`] class or an instance of a [`~transformers.TrainerCallback`]. In the
                first case, will pop the first member of that class found in the list of callbacks.
 
@@ -772,7 +772,7 @@ class Trainer:
         Remove a callback from the current list of [`~transformers.TrainerCallback`].
 
         Args:
-           callback (`type` or [`~transformers.TrainerCallback`]):
+           callback (`type` or [`~transformers.TrainerCallback]`):
                A [`~transformers.TrainerCallback`] class or an instance of a [`~transformers.TrainerCallback`]. In the
                first case, will remove the first member of that class found in the list of callbacks.
         """

src/transformers/trainer_seq2seq.py

@@ -80,7 +80,7 @@ class Seq2SeqTrainer(Trainer):
         Loads a `~generation.GenerationConfig` from the `Seq2SeqTrainingArguments.generation_config` arguments.
 
         Args:
-            gen_config_arg (`str` or [`~generation.GenerationConfig`]):
+            gen_config_arg (`str` or [`~generation.GenerationConfig]`):
                 `Seq2SeqTrainingArguments.generation_config` argument.
 
         Returns:

src/transformers/utils/import_utils.py

@@ -1605,7 +1605,7 @@ def direct_transformers_import(path: str, file="__init__.py") -> ModuleType:
 
     Args:
         path (`str`): The path to the source file
-        file (`str`, optional): The file to join with the path. Defaults to "__init__.py".
+        file (`str`, *optional*): The file to join with the path. Defaults to "__init__.py".
 
     Returns:
         `ModuleType`: The resulting imported module