Docs: add `kwargs` type to fix formatting (#24733)

src/transformers/configuration_utils.py

@@ -432,7 +432,7 @@ class PretrainedConfig(PushToHubMixin):
                 Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
                 repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
                 namespace).
-            kwargs:
+            kwargs (`Dict[str, Any]`, *optional*):
                 Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
         """
         if os.path.isfile(save_directory):

src/transformers/feature_extraction_utils.py

@@ -379,7 +379,7 @@ class FeatureExtractionMixin(PushToHubMixin):
                 Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
                 repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
                 namespace).
-            kwargs:
+            kwargs (`Dict[str, Any]`, *optional*):
                 Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
         """
         if os.path.isfile(save_directory):

src/transformers/generation/configuration_utils.py

@@ -353,7 +353,7 @@ class GenerationConfig(PushToHubMixin):
                 Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
                 repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
                 namespace).
-            kwargs:
+            kwargs (`Dict[str, Any]`, *optional*):
                 Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
         """
         config_file_name = config_file_name if config_file_name is not None else GENERATION_CONFIG_NAME

src/transformers/generation/flax_logits_process.py

@@ -38,7 +38,7 @@ LOGITS_PROCESSOR_INPUTS_DOCSTRING = r"""
         scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
             Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
             search or log softmax for each vocabulary token when using beam search
-        kwargs:
+        kwargs (`Dict[str, Any]`, *optional*):
             Additional logits processor specific kwargs.
 
     Return:

src/transformers/generation/flax_utils.py

@@ -296,7 +296,7 @@ class FlaxGenerationMixin:
                 Custom logits processors that complement the default logits processors built from arguments and
                 generation config. If a logit processor is passed that is already created with the arguments or a
                 generation config an error is thrown. This feature is intended for advanced users.
-            kwargs:
+            kwargs (`Dict[str, Any]`, *optional*):
                 Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be
                 forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder
                 specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*.

src/transformers/generation/logits_process.py

@@ -39,7 +39,7 @@ LOGITS_PROCESSOR_INPUTS_DOCSTRING = r"""
         scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
             Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
             search or log softmax for each vocabulary token when using beam search
-        kwargs:
+        kwargs (`Dict[str, Any]`, *optional*):
             Additional logits processor specific kwargs.
 
     Return:

src/transformers/generation/stopping_criteria.py

@@ -24,7 +24,7 @@ STOPPING_CRITERIA_INPUTS_DOCSTRING = r"""
         scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
             Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
             or scores for each vocabulary token after SoftMax.
-        kwargs:
+        kwargs (`Dict[str, Any]`, *optional*):
             Additional stopping criteria specific kwargs.
 
     Return:

src/transformers/generation/tf_logits_process.py

@@ -42,7 +42,7 @@ TF_LOGITS_PROCESSOR_INPUTS_DOCSTRING = r"""
         cur_len (`int`):
             The current length of valid input sequence tokens. In the TF implementation, the input_ids' sequence length
             is the maximum length generate can produce, and we need to know which of its tokens are valid.
-        kwargs:
+        kwargs (`Dict[str, Any]`, *optional*):
             Additional logits processor specific kwargs.
 
     Return:

src/transformers/generation/tf_utils.py

@@ -705,7 +705,7 @@ class TFGenerationMixin:
             seed (`List[int]`, *optional*):
                 Random seed to control sampling, containing two integers, used when `do_sample` is `True`. See the
                 `seed` argument from stateless functions in `tf.random`.
-            kwargs:
+            kwargs (`Dict[str, Any]`, *optional*):
                 Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be
                 forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder
                 specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*.

src/transformers/generation/utils.py

@@ -1225,7 +1225,7 @@ class GenerationMixin:
             streamer (`BaseStreamer`, *optional*):
                 Streamer object that will be used to stream the generated sequences. Generated tokens are passed
                 through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
-            kwargs:
+            kwargs (`Dict[str, Any]`, *optional*):
                 Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be
                 forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder
                 specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*.

src/transformers/hf_argparser.py

@@ -122,8 +122,8 @@ class HfArgumentParser(ArgumentParser):
         Args:
             dataclass_types:
                 Dataclass type, or list of dataclass types for which we will "fill" instances with the parsed args.
-            kwargs:
-                (Optional) Passed to `argparse.ArgumentParser()` in the regular way.
+            kwargs (`Dict[str, Any]`, *optional*):
+                Passed to `argparse.ArgumentParser()` in the regular way.
         """
         # To make the default appear when using --help
         if "formatter_class" not in kwargs:

src/transformers/image_processing_utils.py

@@ -208,7 +208,7 @@ class ImageProcessingMixin(PushToHubMixin):
                 Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
                 repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
                 namespace).
-            kwargs:
+            kwargs (`Dict[str, Any]`, *optional*):
                 Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
         """
         if os.path.isfile(save_directory):

src/transformers/modeling_flax_utils.py

@@ -1043,7 +1043,7 @@ class FlaxPreTrainedModel(PushToHubMixin, FlaxGenerationMixin):
 
                 </Tip>
 
-            kwargs:
+            kwargs (`Dict[str, Any]`, *optional*):
                 Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
         """
         if os.path.isfile(save_directory):

src/transformers/modeling_tf_utils.py

@@ -2371,8 +2371,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, Pu
                 Whether or not to create a PR with the uploaded files or directly commit.
             safe_serialization (`bool`, *optional*, defaults to `False`):
                 Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
-
-            kwargs:
+            kwargs (`Dict[str, Any]`, *optional*):
                 Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
         """
         if os.path.isfile(save_directory):
@@ -3166,7 +3165,7 @@ class TFConv1D(tf.keras.layers.Layer):
             The number of input features.
         initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation to use to initialize the weights.
-        kwargs:
+        kwargs (`Dict[str, Any]`, *optional*):
             Additional keyword arguments passed along to the `__init__` of `tf.keras.layers.Layer`.
     """
 
@@ -3208,7 +3207,7 @@ class TFSharedEmbeddings(tf.keras.layers.Layer):
         initializer_range (`float`, *optional*):
             The standard deviation to use when initializing the weights. If no value is provided, it will default to
             \\(1/\sqrt{hidden\_size}\\).
-        kwargs:
+        kwargs (`Dict[str, Any]`, *optional*):
             Additional keyword arguments passed along to the `__init__` of `tf.keras.layers.Layer`.
     """
     # TODO (joao): flagged for delection due to embeddings refactor
@@ -3322,7 +3321,7 @@ class TFSequenceSummary(tf.keras.layers.Layer):
             - **summary_last_dropout** (`float`)-- Optional dropout probability after the projection and activation.
 
         initializer_range (`float`, defaults to 0.02): The standard deviation to use to initialize the weights.
-        kwargs:
+        kwargs (`Dict[str, Any]`, *optional*):
             Additional keyword arguments passed along to the `__init__` of `tf.keras.layers.Layer`.
     """
 

src/transformers/modeling_utils.py

@@ -1700,8 +1700,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
                 Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
             variant (`str`, *optional*):
                 If specified, weights are saved in the format pytorch_model.<variant>.bin.
-
-            kwargs:
+            kwargs (`Dict[str, Any]`, *optional*):
                 Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
         """
         # Checks if the model has been loaded in 8-bit

src/transformers/models/jukebox/tokenization_jukebox.py

@@ -202,9 +202,6 @@ class JukeboxTokenizer(PreTrainedTokenizer):
         """
         Performs any necessary transformations before tokenization.
 
-        This method should pop the arguments from kwargs and return the remaining `kwargs` as well. We test the
-        `kwargs` at the end of the encoding process to be sure all the arguments have been used.
-
         Args:
             artist (`str`):
                 The artist name to prepare. This will mostly lower the string
@@ -216,8 +213,6 @@ class JukeboxTokenizer(PreTrainedTokenizer):
                 Whether or not the input is already pre-tokenized (e.g., split into words). If set to `True`, the
                 tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
                 which it will tokenize. This is useful for NER or token classification.
-            kwargs:
-                Keyword arguments to use for the tokenization.
         """
         for idx in range(len(self.version)):
             if self.version[idx] == "v3":

src/transformers/models/musicgen/modeling_musicgen.py

@@ -1228,7 +1228,7 @@ class MusicgenForCausalLM(MusicgenPreTrainedModel):
                 generation config an error is thrown. This feature is intended for advanced users.
             synced_gpus (`bool`, *optional*, defaults to `False`):
                 Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
-            kwargs:
+            kwargs (`Dict[str, Any]`, *optional*):
                 Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be
                 forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder
                 specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*.
@@ -2225,7 +2225,7 @@ class MusicgenForConditionalGeneration(PreTrainedModel):
                 generation config an error is thrown. This feature is intended for advanced users.
             synced_gpus (`bool`, *optional*, defaults to `False`):
                 Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
-            kwargs:
+            kwargs (`Dict[str, Any]`, *optional*):
                 Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be
                 forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder
                 specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*.

src/transformers/models/rag/modeling_rag.py

@@ -962,7 +962,7 @@ class RagSequenceForGeneration(RagPreTrainedModel):
                 Number of beams for beam search. 1 means no beam search.
             n_docs (`int`, *optional*, defaults to `config.n_docs`)
                 Number of documents to retrieve and/or number of documents for which to generate an answer.
-            kwargs:
+            kwargs (`Dict[str, Any]`, *optional*):
                 Additional kwargs will be passed to [`~generation.GenerationMixin.generate`].
 
         Return:
@@ -1444,7 +1444,7 @@ class RagTokenForGeneration(RagPreTrainedModel):
                 Custom stopping criteria that complement the default stopping criteria built from arguments and a
                 model's config. If a stopping criteria is passed that is already created with the arguments or a
                 model's config an error is thrown.
-            kwargs:
+            kwargs (`Dict[str, Any]`, *optional*):
                 Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be
                 forwarded to the `forward` function of the model.
 

src/transformers/models/rag/modeling_tf_rag.py

@@ -1051,7 +1051,7 @@ class TFRagTokenForGeneration(TFRagPreTrainedModel, TFCausalLanguageModelingLoss
                 Custom logits processors that complement the default logits processors built from arguments and a
                 model's config. If a logit processor is passed that is already created with the arguments or a model's
                 config an error is thrown.
-            kwargs:
+            kwargs (`Dict[str, Any]`, *optional*):
                 Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be
                 forwarded to the `forward` function of the model.
 
@@ -1629,7 +1629,7 @@ class TFRagSequenceForGeneration(TFRagPreTrainedModel, TFCausalLanguageModelingL
                 Number of beams for beam search. 1 means no beam search.
             n_docs (`int`, *optional*, defaults to `config.n_docs`)
                 Number of documents to retrieve and/or number of documents for which to generate an answer.
-            kwargs:
+            kwargs (`Dict[str, Any]`, *optional*):
                 Additional kwargs will be passed to [`~generation.GenerationMixin.generate`]
 
         Return:

src/transformers/models/whisper/modeling_tf_whisper.py

@@ -1394,7 +1394,7 @@ class TFWhisperForConditionalGeneration(TFWhisperPreTrainedModel, TFCausalLangua
                 Whether to return token-level timestamps with the text. This can be used with or without the
                 `return_timestamps` option. To get word-level timestamps, use the tokenizer to group the tokens into
                 words.
-            kwargs:
+            kwargs (`Dict[str, Any]`, *optional*):
                 Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be
                 forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder
                 specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*.