fix: typo spelling grammar (#13212)

* fix: typo spelling grammar

* fix: make fixup

src/transformers/onnx/convert.py

@@ -158,7 +158,7 @@ def validate_model_outputs(
 
     # We flatten potential collection of outputs (i.e. past_keys) to a flat structure
     for name, value in ref_outputs.items():
-        # Overwriting the output name as "present" since it is the name used for the ONNX ouputs
+        # Overwriting the output name as "present" since it is the name used for the ONNX outputs
         # ("past_key_values" being taken for the ONNX inputs)
         if name == "past_key_values":
             name = "present"

src/transformers/onnx/features.py

@@ -114,7 +114,7 @@ class FeaturesManager:
 
         Args:
             model: The model to export
-            feature: The name of the feature to check if it is avaiable
+            feature: The name of the feature to check if it is available
 
         Returns:
             (str) The type of the model (OnnxConfig) The OnnxConfig instance holding the model export properties

src/transformers/tokenization_utils_base.py

@@ -1375,7 +1375,7 @@ INIT_TOKENIZER_DOCSTRING = r"""
           high-level keys being the ``__init__`` keyword name of each vocabulary file required by the model, the
           low-level being the :obj:`short-cut-names` of the pretrained models with, as associated values, the
           :obj:`url` to the associated pretrained vocabulary file.
-        - **max_model_input_sizes** (:obj:`Dict[str, Optinal[int]]`) -- A dictionary with, as keys, the
+        - **max_model_input_sizes** (:obj:`Dict[str, Optional[int]]`) -- A dictionary with, as keys, the
           :obj:`short-cut-names` of the pretrained models, and as associated values, the maximum length of the sequence
           inputs of this model, or :obj:`None` if the model has no maximum input size.
         - **pretrained_init_configuration** (:obj:`Dict[str, Dict[str, Any]]`) -- A dictionary with, as keys, the
@@ -1785,7 +1785,7 @@ class PreTrainedTokenizerBase(SpecialTokensMixin, PushToHubMixin):
                 config = AutoConfig.from_pretrained(pretrained_model_name_or_path)
                 config_tokenizer_class = config.tokenizer_class
             except (OSError, ValueError, KeyError):
-                # skip if an error occured.
+                # skip if an error occurred.
                 config = None
             if config_tokenizer_class is None:
                 # Third attempt. If we have not yet found the original type of the tokenizer,

src/transformers/tokenization_utils_fast.py

@@ -707,7 +707,7 @@ class PreTrainedTokenizerFast(PreTrainedTokenizerBase):
 
                 special_token_full = getattr(self, f"_{token}")
                 if isinstance(special_token_full, AddedToken):
-                    # Create an added token with the same paramters except the content
+                    # Create an added token with the same parameters except the content
                     kwargs[token] = AddedToken(
                         special_token,
                         single_word=special_token_full.single_word,