Fix some code annotation typos.

src/transformers/configuration_utils.py

@@ -1184,7 +1184,7 @@ def recursive_diff_dict(dict_a, dict_b, config_obj=None):
     Helper function to recursively take the diff between two nested dictionaries. The resulting diff only contains the
     values from `dict_a` that are different from values in `dict_b`.
 
-    dict_b : the default config dictionnary. We want to remove values that are in this one
+    dict_b : the default config dictionary. We want to remove values that are in this one
     """
     diff = {}
     default = config_obj.__class__().to_dict() if config_obj is not None else {}

src/transformers/modeling_gguf_pytorch_utils.py

@@ -290,7 +290,7 @@ def get_gguf_hf_weights_map(
     # hack: ggufs have a different name for cohere
     if model_type == "cohere":
         model_type = "command-r"
-    if model_type == "qwen2_moe":
+    elif model_type == "qwen2_moe":
         model_type = "qwen2moe"
     arch = None
     for key, value in MODEL_ARCH_NAMES.items():
@@ -346,7 +346,7 @@ def load_gguf_checkpoint(gguf_checkpoint_path, return_tensors=False, model_to_lo
     Args:
         gguf_checkpoint_path (`str`):
             The path the to GGUF file to load
-        return_tensors (`bool`, defaults to `True`):
+        return_tensors (`bool`, defaults to `False`):
             Whether to read the tensors from the file and return them. Not doing so is faster
             and only loads the metadata in memory.
     """

src/transformers/models/bark/processing_bark.py

@@ -78,7 +78,7 @@ def from_pretrained(
                 - a path to a *directory* containing a processor saved using the [`~BarkProcessor.save_pretrained`]
                   method, e.g., `./my_model_directory/`.
             speaker_embeddings_dict_path (`str`, *optional*, defaults to `"speaker_embeddings_path.json"`):
-                The name of the `.json` file containing the speaker_embeddings dictionnary located in
+                The name of the `.json` file containing the speaker_embeddings dictionary located in
                 `pretrained_model_name_or_path`. If `None`, no speaker_embeddings is loaded.
             **kwargs
                 Additional keyword arguments passed along to both
@@ -105,7 +105,7 @@ def from_pretrained(
                 logger.warning(
                     f"""`{os.path.join(pretrained_processor_name_or_path, speaker_embeddings_dict_path)}` does not exists
                     , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
-                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`."""
+                    dictionary if wanted, otherwise set `speaker_embeddings_dict_path=None`."""
                 )
                 speaker_embeddings = None
             else:
@@ -135,7 +135,7 @@ def save_pretrained(
                 Directory where the tokenizer files and the speaker embeddings will be saved (directory will be created
                 if it does not exist).
             speaker_embeddings_dict_path (`str`, *optional*, defaults to `"speaker_embeddings_path.json"`):
-                The name of the `.json` file that will contains the speaker_embeddings nested path dictionnary, if it
+                The name of the `.json` file that will contains the speaker_embeddings nested path dictionary, if it
                 exists, and that will be located in `pretrained_model_name_or_path/speaker_embeddings_directory`.
             speaker_embeddings_directory (`str`, *optional*, defaults to `"speaker_embeddings/"`):
                 The name of the folder in which the speaker_embeddings arrays will be saved.
@@ -246,7 +246,7 @@ def __call__(
                 `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
             voice_preset (`str`, `Dict[np.ndarray]`):
                 The voice preset, i.e the speaker embeddings. It can either be a valid voice_preset name, e.g
-                `"en_speaker_1"`, or directly a dictionnary of `np.ndarray` embeddings for each submodel of `Bark`. Or
+                `"en_speaker_1"`, or directly a dictionary of `np.ndarray` embeddings for each submodel of `Bark`. Or
                 it can be a valid file name of a local `.npz` single voice preset.
             return_tensors (`str` or [`~utils.TensorType`], *optional*):
                 If set, will return tensors of a particular framework. Acceptable values are:

src/transformers/utils/hub.py

@@ -1150,7 +1150,7 @@ def create_and_tag_model_card(
             The list of tags to add in the model card
         token (`str`, *optional*):
             Authentication token, obtained with `huggingface_hub.HfApi.login` method. Will default to the stored token.
-        ignore_metadata_errors (`str`):
+        ignore_metadata_errors (`bool`, *optional*, defaults to `False`):
             If True, errors while parsing the metadata section will be ignored. Some information might be lost during
             the process. Use it at your own risk.
     """