Simplify unnecessary Optional typing (#40839)

Remove Optional

Signed-off-by: Yuanyuan Chen <cyyever@outlook.com>

Author: cyyever

src/transformers/audio_utils.py

@@ -370,7 +370,7 @@ def chroma_filter_bank(
     tuning: float = 0.0,
     power: Optional[float] = 2.0,
     weighting_parameters: Optional[tuple[float, float]] = (5.0, 2.0),
-    start_at_c_chroma: Optional[bool] = True,
+    start_at_c_chroma: bool = True,
 ):
     """
     Creates a chroma filter bank, i.e a linear transformation to project spectrogram bins onto chroma bins.
@@ -391,7 +391,7 @@ def chroma_filter_bank(
         weighting_parameters (`tuple[float, float]`, *optional*, defaults to `(5., 2.)`):
             If specified, apply a Gaussian weighting parameterized by the first element of the tuple being the center and
             the second element being the Gaussian half-width.
-        start_at_c_chroma (`float`, *optional*, defaults to `True`):
+        start_at_c_chroma (`bool`, *optional*, defaults to `True`):
             If True, the filter bank will start at the 'C' pitch class. Otherwise, it will start at 'A'.
     Returns:
         `np.ndarray` of shape `(num_frequency_bins, num_chroma)`
@@ -627,7 +627,7 @@ def spectrogram(
     reference: float = 1.0,
     min_value: float = 1e-10,
     db_range: Optional[float] = None,
-    remove_dc_offset: Optional[bool] = None,
+    remove_dc_offset: bool = False,
     dtype: np.dtype = np.float32,
 ) -> np.ndarray:
     """
@@ -838,7 +838,7 @@ def spectrogram_batch(
     reference: float = 1.0,
     min_value: float = 1e-10,
     db_range: Optional[float] = None,
-    remove_dc_offset: Optional[bool] = None,
+    remove_dc_offset: bool = False,
     dtype: np.dtype = np.float32,
 ) -> list[np.ndarray]:
     """

src/transformers/commands/serving.py

@@ -141,7 +141,7 @@ class TransformersTranscriptionCreateParams(TranscriptionCreateParamsBase, total
 
         file: bytes  # Overwritten -- pydantic isn't happy with `typing.IO[bytes]`, present in the original type
         generation_config: str
-        stream: Optional[bool] = False
+        stream: bool = False
 
     # Contrarily to OpenAI's output types, input types are `TypedDict`, which don't have built-in validation.
     response_validator = TypeAdapter(TransformersResponseCreateParamsStreaming)
@@ -600,7 +600,7 @@ def validate_transcription_request(self, request: dict):
 
     def build_chat_completion_chunk(
         self,
-        request_id: Optional[str] = "",
+        request_id: str = "",
         content: Optional[int] = None,
         model: Optional[str] = None,
         role: Optional[str] = None,

src/transformers/data/datasets/squad.py

@@ -122,9 +122,9 @@ def __init__(
         tokenizer: PreTrainedTokenizer,
         limit_length: Optional[int] = None,
         mode: Union[str, Split] = Split.train,
-        is_language_sensitive: Optional[bool] = False,
+        is_language_sensitive: bool = False,
         cache_dir: Optional[str] = None,
-        dataset_format: Optional[str] = "pt",
+        dataset_format: str = "pt",
     ):
         self.args = args
         self.is_language_sensitive = is_language_sensitive

src/transformers/generation/beam_search.py

@@ -165,10 +165,10 @@ def __init__(
         batch_size: int,
         num_beams: int,
         device: torch.device,
-        length_penalty: Optional[float] = 1.0,
-        do_early_stopping: Optional[Union[bool, str]] = False,
-        num_beam_hyps_to_keep: Optional[int] = 1,
-        num_beam_groups: Optional[int] = 1,
+        length_penalty: float = 1.0,
+        do_early_stopping: Union[bool, str] = False,
+        num_beam_hyps_to_keep: int = 1,
+        num_beam_groups: int = 1,
         max_length: Optional[int] = None,
     ):
         logger.warning_once(
@@ -214,7 +214,7 @@ def __init__(
 
     @property
     def is_done(self) -> bool:
-        return self._done.all()
+        return self._done.all().item()
 
     def process(
         self,
@@ -225,8 +225,8 @@ def process(
         pad_token_id: Optional[Union[int, torch.Tensor]] = None,
         eos_token_id: Optional[Union[int, list[int], torch.Tensor]] = None,
         beam_indices: Optional[torch.LongTensor] = None,
-        group_index: Optional[int] = 0,
-        decoder_prompt_len: Optional[int] = 0,
+        group_index: int = 0,
+        decoder_prompt_len: int = 0,
     ) -> dict[str, torch.Tensor]:
         # add up to the length which the next_scores is calculated on (including decoder prompt)
         cur_len = input_ids.shape[-1] + 1
@@ -460,9 +460,9 @@ def __init__(
         num_beams: int,
         constraints: list[Constraint],
         device: torch.device,
-        length_penalty: Optional[float] = 1.0,
-        do_early_stopping: Optional[Union[bool, str]] = False,
-        num_beam_hyps_to_keep: Optional[int] = 1,
+        length_penalty: float = 1.0,
+        do_early_stopping: Union[bool, str] = False,
+        num_beam_hyps_to_keep: int = 1,
         max_length: Optional[int] = None,
     ):
         logger.warning_once(
@@ -495,7 +495,7 @@ def __init__(
 
     @property
     def is_done(self) -> bool:
-        return self._done.all()
+        return self._done.all().item()
 
     def make_constraint_states(self, n):
         return [ConstraintListState([constraint.copy() for constraint in self.constraints]) for _ in range(n)]
@@ -515,7 +515,7 @@ def process(
         pad_token_id: Optional[Union[int, torch.Tensor]] = None,
         eos_token_id: Optional[Union[int, list[int], torch.Tensor]] = None,
         beam_indices: Optional[torch.LongTensor] = None,
-        decoder_prompt_len: Optional[int] = 0,
+        decoder_prompt_len: int = 0,
     ) -> tuple[torch.Tensor]:
         r"""
         Args:
@@ -912,7 +912,9 @@ def finalize(
 
 
 class BeamHypotheses:
-    def __init__(self, num_beams: int, length_penalty: float, early_stopping: bool, max_length: Optional[int] = None):
+    def __init__(
+        self, num_beams: int, length_penalty: float, early_stopping: Union[bool, str], max_length: Optional[int] = None
+    ):
         """
         Initialize n-best list of hypotheses.
         """

src/transformers/generation/configuration_utils.py

@@ -1282,11 +1282,11 @@ class WatermarkingConfig(BaseWatermarkingConfig):
 
     def __init__(
         self,
-        greenlist_ratio: Optional[float] = 0.25,
-        bias: Optional[float] = 2.0,
-        hashing_key: Optional[int] = 15485863,
-        seeding_scheme: Optional[str] = "lefthash",
-        context_width: Optional[int] = 1,
+        greenlist_ratio: float = 0.25,
+        bias: float = 2.0,
+        hashing_key: int = 15485863,
+        seeding_scheme: str = "lefthash",
+        context_width: int = 1,
     ):
         self.greenlist_ratio = greenlist_ratio
         self.bias = bias

src/transformers/image_processing_utils_fast.py

@@ -85,7 +85,7 @@ def validate_fast_preprocess_arguments(
     size: Optional[SizeDict] = None,
     interpolation: Optional["F.InterpolationMode"] = None,
     return_tensors: Optional[Union[str, TensorType]] = None,
-    data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
+    data_format: ChannelDimension = ChannelDimension.FIRST,
 ):
     """
     Checks validity of typically used arguments in an `ImageProcessorFast` `preprocess` method.

src/transformers/integrations/peft.py

@@ -96,7 +96,7 @@ def load_adapter(
         adapter_name: Optional[str] = None,
         revision: Optional[str] = None,
         token: Optional[str] = None,
-        device_map: Optional[str] = "auto",
+        device_map: str = "auto",
         max_memory: Optional[str] = None,
         offload_folder: Optional[str] = None,
         offload_index: Optional[int] = None,

src/transformers/model_debugging_utils.py

@@ -269,8 +269,8 @@ def clean(val):
 
 def _attach_debugger_logic(
     model,
-    debug_path: Optional[str] = ".",
-    do_prune_layers: Optional[bool] = True,
+    debug_path: str = ".",
+    do_prune_layers: bool = True,
     use_repr: bool = True,
 ):
     """
@@ -399,8 +399,8 @@ def top_wrapped_forward(*inps, **kws):
 def model_addition_debugger_context(
     model,
     debug_path: Optional[str] = None,
-    do_prune_layers: Optional[bool] = True,
-    use_repr: Optional[bool] = True,
+    do_prune_layers: bool = True,
+    use_repr: bool = True,
 ):
     """
     # Model addition debugger - context manager for model adders

src/transformers/modeling_utils.py

@@ -3556,7 +3556,7 @@ def _get_resized_lm_head(
         self,
         old_lm_head: nn.Linear,
         new_num_tokens: Optional[int] = None,
-        transposed: Optional[bool] = False,
+        transposed: bool = False,
         mean_resizing: bool = True,
     ) -> nn.Linear:
         """
@@ -3713,7 +3713,7 @@ def _init_added_lm_head_weights_with_mean(
         old_lm_head_dim,
         old_num_tokens,
         added_num_tokens,
-        transposed=False,
+        transposed: bool = False,
     ):
         if transposed:
             # Transpose to the desired shape for the function.

src/transformers/pipelines/token_classification.py

@@ -160,7 +160,7 @@ def _sanitize_parameters(
         ignore_subwords: Optional[bool] = None,
         aggregation_strategy: Optional[AggregationStrategy] = None,
         offset_mapping: Optional[list[tuple[int, int]]] = None,
-        is_split_into_words: Optional[bool] = False,
+        is_split_into_words: bool = False,
         stride: Optional[int] = None,
         delimiter: Optional[str] = None,
     ):