Add Optional typing

src/transformers/generation/utils.py

@@ -1091,7 +1091,7 @@ def _get_logits_processor(
         self,
         generation_config: GenerationConfig,
         input_ids_seq_length: Optional[int] = None,
-        encoder_input_ids: torch.LongTensor = None,
+        encoder_input_ids: Optional[torch.LongTensor] = None,
         prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], list[int]]] = None,
         logits_processor: Optional[LogitsProcessorList] = None,
         device: Optional[str] = None,

src/transformers/image_processing_utils_fast.py

@@ -243,7 +243,7 @@ def resize(
         self,
         image: "torch.Tensor",
         size: SizeDict,
-        interpolation: "F.InterpolationMode" = None,
+        interpolation: Optional["F.InterpolationMode"] = None,
         antialias: bool = True,
         **kwargs,
     ) -> "torch.Tensor":

src/transformers/integrations/flash_paged.py

@@ -1,3 +1,5 @@
+from typing import Optional
+
 import torch
 
 from ..generation.continuous_batching import PagedAttentionCache
@@ -16,7 +18,7 @@ def paged_attention_forward(
     q: torch.Tensor,
     k: torch.Tensor,
     v: torch.Tensor,
-    attention_mask: torch.Tensor = None,
+    attention_mask: Optional[torch.Tensor] = None,
     cache: PagedAttentionCache = None,
     cu_seq_lens_q=None,
     cu_seq_lens_k=None,

src/transformers/integrations/higgs.py

@@ -14,6 +14,7 @@
 "HIGGS through FLUTE (Flexible Lookup Table Engine for LUT-quantized LLMs) integration file"
 
 from math import sqrt
+from typing import Optional
 
 from ..utils import (
     is_flute_available,
@@ -496,8 +497,8 @@ def __init__(
         out_features: int,
         num_bits: int,
         bias=True,
-        dtype: torch.dtype = None,
-        device: torch.device = None,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
         group_size: int = 256,
         hadamard_size: int = 1024,
     ):

src/transformers/modeling_utils.py

@@ -1719,7 +1719,11 @@ def create_extended_attention_mask_for_decoder(input_shape, attention_mask, devi
         return extended_attention_mask
 
     def get_extended_attention_mask(
-        self, attention_mask: Tensor, input_shape: tuple[int], device: torch.device = None, dtype: torch.float = None
+        self,
+        attention_mask: Tensor,
+        input_shape: tuple[int],
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
     ) -> Tensor:
         """
         Makes broadcastable attention and causal masks so that future and masked tokens are ignored.

src/transformers/models/align/processing_align.py

@@ -16,7 +16,7 @@
 Image/Text processor class for ALIGN
 """
 
-from typing import Union
+from typing import Optional, Union
 
 from ...image_utils import ImageInput
 from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
@@ -72,7 +72,7 @@ def __init__(self, image_processor, tokenizer):
 
     def __call__(
         self,
-        images: ImageInput = None,
+        images: Optional[ImageInput] = None,
         text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
         audio=None,
         videos=None,

src/transformers/models/altclip/processing_altclip.py

@@ -16,7 +16,7 @@
 Image/Text processor class for AltCLIP
 """
 
-from typing import Union
+from typing import Optional, Union
 
 from ...image_utils import ImageInput
 from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
@@ -58,7 +58,7 @@ def __init__(self, image_processor=None, tokenizer=None):
 
     def __call__(
         self,
-        images: ImageInput = None,
+        images: Optional[ImageInput] = None,
         text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
         audio=None,
         videos=None,

src/transformers/models/aria/image_processing_aria.py

@@ -153,7 +153,7 @@ def preprocess(
         do_rescale: Optional[bool] = None,
         rescale_factor: Optional[float] = None,
         do_normalize: Optional[bool] = None,
-        resample: PILImageResampling = None,
+        resample: Optional[PILImageResampling] = None,
         return_tensors: Optional[Union[str, TensorType]] = "pt",
         data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
         input_data_format: Optional[Union[str, ChannelDimension]] = None,

src/transformers/models/aria/modeling_aria.py

@@ -1005,9 +1005,9 @@ def get_placeholder_mask(
     @auto_docstring
     def forward(
         self,
-        input_ids: torch.LongTensor = None,
-        pixel_values: torch.FloatTensor = None,
-        pixel_mask: torch.LongTensor = None,
+        input_ids: Optional[torch.LongTensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        pixel_mask: Optional[torch.LongTensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
         past_key_values: Optional[Cache] = None,
@@ -1134,9 +1134,9 @@ def multi_modal_projector(self):
     @auto_docstring
     def forward(
         self,
-        input_ids: torch.LongTensor = None,
-        pixel_values: torch.FloatTensor = None,
-        pixel_mask: torch.LongTensor = None,
+        input_ids: Optional[torch.LongTensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        pixel_mask: Optional[torch.LongTensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
         past_key_values: Optional[Cache] = None,

src/transformers/models/aria/modular_aria.py

@@ -539,7 +539,7 @@ def preprocess(
         do_rescale: Optional[bool] = None,
         rescale_factor: Optional[float] = None,
         do_normalize: Optional[bool] = None,
-        resample: PILImageResampling = None,
+        resample: Optional[PILImageResampling] = None,
         return_tensors: Optional[Union[str, TensorType]] = "pt",
         data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
         input_data_format: Optional[Union[str, ChannelDimension]] = None,
@@ -1392,9 +1392,9 @@ def get_image_features(
 
     def forward(
         self,
-        input_ids: torch.LongTensor = None,
-        pixel_values: torch.FloatTensor = None,
-        pixel_mask: torch.LongTensor = None,
+        input_ids: Optional[torch.LongTensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        pixel_mask: Optional[torch.LongTensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
         past_key_values: Optional[Cache] = None,
@@ -1463,9 +1463,9 @@ def get_image_features(
     @auto_docstring
     def forward(
         self,
-        input_ids: torch.LongTensor = None,
-        pixel_values: torch.FloatTensor = None,
-        pixel_mask: torch.LongTensor = None,
+        input_ids: Optional[torch.LongTensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        pixel_mask: Optional[torch.LongTensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
         past_key_values: Optional[Cache] = None,

src/transformers/models/aya_vision/modeling_aya_vision.py

@@ -269,8 +269,8 @@ def get_placeholder_mask(
     @auto_docstring
     def forward(
         self,
-        input_ids: torch.LongTensor = None,
-        pixel_values: torch.FloatTensor = None,
+        input_ids: Optional[torch.LongTensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
         past_key_values: Optional[Cache] = None,

src/transformers/models/aya_vision/modular_aya_vision.py

@@ -166,8 +166,8 @@ def get_image_features(
     @auto_docstring
     def forward(
         self,
-        input_ids: torch.LongTensor = None,
-        pixel_values: torch.FloatTensor = None,
+        input_ids: Optional[torch.LongTensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
         past_key_values: Optional[Cache] = None,

src/transformers/models/bart/modeling_bart.py

@@ -88,7 +88,9 @@ def __init__(self, num_embeddings: int, embedding_dim: int):
         self.offset = 2
         super().__init__(num_embeddings + self.offset, embedding_dim)
 
-    def forward(self, input_ids: torch.Tensor, past_key_values_length: int = 0, position_ids: torch.Tensor = None):
+    def forward(
+        self, input_ids: torch.Tensor, past_key_values_length: int = 0, position_ids: Optional[torch.Tensor] = None
+    ):
         """`input_ids' shape is expected to be [bsz x seqlen]."""
 
         if position_ids is None:

src/transformers/models/beit/image_processing_beit.py

@@ -183,7 +183,7 @@ def _preprocess(
         do_reduce_labels: Optional[bool] = None,
         do_resize: Optional[bool] = None,
         size: Optional[dict[str, int]] = None,
-        resample: PILImageResampling = None,
+        resample: Optional[PILImageResampling] = None,
         do_center_crop: Optional[bool] = None,
         crop_size: Optional[dict[str, int]] = None,
         do_rescale: Optional[bool] = None,
@@ -215,7 +215,7 @@ def _preprocess_image(
         image: ImageInput,
         do_resize: Optional[bool] = None,
         size: Optional[dict[str, int]] = None,
-        resample: PILImageResampling = None,
+        resample: Optional[PILImageResampling] = None,
         do_center_crop: Optional[bool] = None,
         crop_size: Optional[dict[str, int]] = None,
         do_rescale: Optional[bool] = None,
@@ -260,7 +260,7 @@ def _preprocess_segmentation_map(
         segmentation_map: ImageInput,
         do_resize: Optional[bool] = None,
         size: Optional[dict[str, int]] = None,
-        resample: PILImageResampling = None,
+        resample: Optional[PILImageResampling] = None,
         do_center_crop: Optional[bool] = None,
         crop_size: Optional[dict[str, int]] = None,
         do_reduce_labels: Optional[bool] = None,
@@ -308,7 +308,7 @@ def preprocess(
         segmentation_maps: Optional[ImageInput] = None,
         do_resize: Optional[bool] = None,
         size: Optional[dict[str, int]] = None,
-        resample: PILImageResampling = None,
+        resample: Optional[PILImageResampling] = None,
         do_center_crop: Optional[bool] = None,
         crop_size: Optional[dict[str, int]] = None,
         do_rescale: Optional[bool] = None,

src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py

@@ -81,7 +81,9 @@ class BigBirdPegasusLearnedPositionalEmbedding(nn.Embedding):
     def __init__(self, num_embeddings: int, embedding_dim: int):
         super().__init__(num_embeddings, embedding_dim)
 
-    def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0, position_ids: torch.Tensor = None):
+    def forward(
+        self, input_ids_shape: torch.Size, past_key_values_length: int = 0, position_ids: Optional[torch.Tensor] = None
+    ):
         """`input_ids' shape is expected to be [bsz x seqlen]."""
 
         if position_ids is None:

src/transformers/models/bit/image_processing_bit.py

@@ -178,7 +178,7 @@ def preprocess(
         images: ImageInput,
         do_resize: Optional[bool] = None,
         size: Optional[dict[str, int]] = None,
-        resample: PILImageResampling = None,
+        resample: Optional[PILImageResampling] = None,
         do_center_crop: Optional[bool] = None,
         crop_size: Optional[int] = None,
         do_rescale: Optional[bool] = None,

src/transformers/models/blip/image_processing_blip.py

@@ -162,7 +162,7 @@ def preprocess(
         images: ImageInput,
         do_resize: Optional[bool] = None,
         size: Optional[dict[str, int]] = None,
-        resample: PILImageResampling = None,
+        resample: Optional[PILImageResampling] = None,
         do_rescale: Optional[bool] = None,
         rescale_factor: Optional[float] = None,
         do_normalize: Optional[bool] = None,

src/transformers/models/blip/processing_blip.py

@@ -65,7 +65,7 @@ def __init__(self, image_processor, tokenizer, **kwargs):
 
     def __call__(
         self,
-        images: ImageInput = None,
+        images: Optional[ImageInput] = None,
         text: Optional[Union[str, list[str], TextInput, PreTokenizedInput]] = None,
         audio=None,
         videos=None,

src/transformers/models/blip_2/processing_blip_2.py

@@ -79,7 +79,7 @@ def __init__(self, image_processor, tokenizer, num_query_tokens=None, **kwargs):
 
     def __call__(
         self,
-        images: ImageInput = None,
+        images: Optional[ImageInput] = None,
         text: Optional[Union[str, list[str], TextInput, PreTokenizedInput]] = None,
         audio=None,
         videos=None,

src/transformers/models/bridgetower/image_processing_bridgetower.py

@@ -378,7 +378,7 @@ def preprocess(
         do_resize: Optional[bool] = None,
         size: Optional[dict[str, int]] = None,
         size_divisor: Optional[int] = None,
-        resample: PILImageResampling = None,
+        resample: Optional[PILImageResampling] = None,
         do_rescale: Optional[bool] = None,
         rescale_factor: Optional[float] = None,
         do_normalize: Optional[bool] = None,

src/transformers/models/bridgetower/image_processing_bridgetower_fast.py

@@ -137,7 +137,7 @@ def resize(
         image: "torch.Tensor",
         size: SizeDict,
         size_divisor: int = 32,
-        interpolation: "F.InterpolationMode" = None,
+        interpolation: Optional["F.InterpolationMode"] = None,
         antialias: bool = True,
         **kwargs,
     ) -> "torch.Tensor":

src/transformers/models/chameleon/image_processing_chameleon.py

@@ -170,7 +170,7 @@ def preprocess(
         images: ImageInput,
         do_resize: Optional[bool] = None,
         size: Optional[dict[str, int]] = None,
-        resample: PILImageResampling = None,
+        resample: Optional[PILImageResampling] = None,
         do_center_crop: Optional[bool] = None,
         crop_size: Optional[int] = None,
         do_rescale: Optional[bool] = None,

src/transformers/models/chameleon/image_processing_chameleon_fast.py

@@ -14,6 +14,8 @@
 # limitations under the License.
 """Fast Image processor class for Chameleon."""
 
+from typing import Optional
+
 import numpy as np
 
 from ...image_processing_utils_fast import BaseImageProcessorFast
@@ -87,7 +89,7 @@ def resize(
         self,
         image: "torch.Tensor",
         size: SizeDict,
-        interpolation: "F.InterpolationMode" = None,
+        interpolation: Optional["F.InterpolationMode"] = None,
         **kwargs,
     ) -> "torch.Tensor":
         """

src/transformers/models/chinese_clip/image_processing_chinese_clip.py

@@ -171,7 +171,7 @@ def preprocess(
         images: ImageInput,
         do_resize: Optional[bool] = None,
         size: Optional[dict[str, int]] = None,
-        resample: PILImageResampling = None,
+        resample: Optional[PILImageResampling] = None,
         do_center_crop: Optional[bool] = None,
         crop_size: Optional[int] = None,
         do_rescale: Optional[bool] = None,

src/transformers/models/chinese_clip/processing_chinese_clip.py

@@ -17,7 +17,7 @@
 """
 
 import warnings
-from typing import Union
+from typing import Optional, Union
 
 from ...image_utils import ImageInput
 from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
@@ -69,7 +69,7 @@ def __init__(self, image_processor=None, tokenizer=None, **kwargs):
     def __call__(
         self,
         text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
-        images: ImageInput = None,
+        images: Optional[ImageInput] = None,
         audio=None,
         videos=None,
         **kwargs: Unpack[ChineseClipProcessorKwargs],

src/transformers/models/clip/image_processing_clip.py

@@ -204,7 +204,7 @@ def preprocess(
         images: ImageInput,
         do_resize: Optional[bool] = None,
         size: Optional[dict[str, int]] = None,
-        resample: PILImageResampling = None,
+        resample: Optional[PILImageResampling] = None,
         do_center_crop: Optional[bool] = None,
         crop_size: Optional[int] = None,
         do_rescale: Optional[bool] = None,

src/transformers/models/cohere2_vision/modeling_cohere2_vision.py

@@ -219,8 +219,8 @@ def get_placeholder_mask(
     @auto_docstring
     def forward(
         self,
-        input_ids: torch.LongTensor = None,
-        pixel_values: torch.FloatTensor = None,
+        input_ids: Optional[torch.LongTensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
         past_key_values: Optional[Cache] = None,

src/transformers/models/cohere2_vision/modular_cohere2_vision.py

@@ -115,8 +115,8 @@ def get_image_features(self, pixel_values: torch.FloatTensor):
     @auto_docstring
     def forward(
         self,
-        input_ids: torch.LongTensor = None,
-        pixel_values: torch.FloatTensor = None,
+        input_ids: Optional[torch.LongTensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
         past_key_values: Optional[Cache] = None,