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Add Optional to remaining types (#37808)

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More Optional typing

Signed-off-by: cyy <cyyever@outlook.com>
This commit is contained in:
Yuanyuan Chen 2025-04-28 21:20:45 +08:00 committed by GitHub
parent 1a9188a54e
commit da4ff2a5f5
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
206 changed files with 553 additions and 531 deletions
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3
examples/legacy/seq2seq/run_distributed_eval.py
View File

@ -19,6 +19,7 @@ import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Optional
import torch
from torch.utils.data import DataLoader
@ -54,7 +55,7 @@ def eval_data_dir(
task="summarization",
local_rank=None,
num_return_sequences=1,
dataset_kwargs: dict = None,
dataset_kwargs: Optional[dict] = None,
prefix="",
**generate_kwargs,
) -> dict:

4
examples/modular-transformers/image_processing_new_imgproc_model.py
View File

@ -74,7 +74,7 @@ class ImgprocModelImageProcessor(BaseImageProcessor):
def __init__(
self,
do_resize: bool = True,
size: dict[str, int] = None,
size: Optional[dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
@ -159,7 +159,7 @@ class ImgprocModelImageProcessor(BaseImageProcessor):
image_mean: Optional[Union[float, list[float]]] = None,
image_std: Optional[Union[float, list[float]]] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
do_convert_rgb: bool = None,
do_convert_rgb: Optional[bool] = None,
data_format: ChannelDimension = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> PIL.Image.Image:

2
src/transformers/cache_utils.py
View File

@ -359,7 +359,7 @@ class DynamicCache(Cache):
```
"""
def __init__(self, _distributed_cache_data: Iterable = None) -> None:
def __init__(self, _distributed_cache_data: Optional[Iterable] = None) -> None:
super().__init__()
self._seen_tokens = 0 # Used in `generate` to keep tally of how many tokens the cache has seen
self.key_cache: List[torch.Tensor] = []

2
src/transformers/commands/add_new_model_like.py
View File

@ -512,7 +512,7 @@ def duplicate_module(
new_model_patterns: ModelPatterns,
dest_file: Optional[str] = None,
add_copied_from: bool = True,
attrs_to_remove: List[str] = None,
attrs_to_remove: Optional[List[str]] = None,
):
"""
Create a new module from an existing one and adapting all function and classes names from old patterns to new ones.

9
src/transformers/convert_slow_tokenizer.py
View File

@ -19,6 +19,7 @@ allow to make our dependency on SentencePiece optional.
"""
import warnings
from typing import Optional
from packaging import version
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
@ -326,7 +327,9 @@ class OpenAIGPTConverter(Converter):
class GPT2Converter(Converter):
def converted(self, vocab: dict[str, int] = None, merges: list[tuple[str, str]] = None) -> Tokenizer:
def converted(
self, vocab: Optional[dict[str, int]] = None, merges: Optional[list[tuple[str, str]]] = None
) -> Tokenizer:
if not vocab:
vocab = self.original_tokenizer.encoder
if not merges:
@ -395,7 +398,9 @@ class HerbertConverter(Converter):
class Qwen2Converter(Converter):
def converted(self, vocab: dict[str, int] = None, merges: list[tuple[str, str]] = None) -> Tokenizer:
def converted(
self, vocab: Optional[dict[str, int]] = None, merges: Optional[list[tuple[str, str]]] = None
) -> Tokenizer:
if not vocab:
vocab = self.original_tokenizer.encoder
if not merges:

2
src/transformers/image_processing_utils.py
View File

@ -209,7 +209,7 @@ def convert_to_size_dict(
def get_size_dict(
size: Union[int, Iterable[int], dict[str, int]] = None,
size: Optional[Union[int, Iterable[int], dict[str, int]]] = None,
max_size: Optional[int] = None,
height_width_order: bool = True,
default_to_square: bool = True,

2
src/transformers/image_processing_utils_fast.py
View File

@ -755,7 +755,7 @@ class BaseImageProcessorFast(BaseImageProcessor):
class SemanticSegmentationMixin:
def post_process_semantic_segmentation(self, outputs, target_sizes: list[tuple] = None):
def post_process_semantic_segmentation(self, outputs, target_sizes: Optional[list[tuple]] = None):
"""
Converts the output of [`MobileNetV2ForSemanticSegmentation`] into semantic segmentation maps. Only supports PyTorch.

2
src/transformers/integrations/peft.py
View File

@ -79,7 +79,7 @@ class PeftAdapterMixin:
max_memory: Optional[str] = None,
offload_folder: Optional[str] = None,
offload_index: Optional[int] = None,
peft_config: Dict[str, Any] = None,
peft_config: Optional[Dict[str, Any]] = None,
adapter_state_dict: Optional[Dict[str, "torch.Tensor"]] = None,
low_cpu_mem_usage: bool = False,
is_trainable: bool = False,

2
src/transformers/models/albert/modeling_flax_albert.py
View File

@ -558,7 +558,7 @@ class FlaxAlbertPreTrainedModel(FlaxPreTrainedModel):
attention_mask=None,
token_type_ids=None,
position_ids=None,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,

4
src/transformers/models/aria/configuration_aria.py
View File

@ -18,7 +18,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Dict
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...modeling_rope_utils import rope_config_validation
@ -268,7 +268,7 @@ class AriaConfig(PretrainedConfig):
vision_config=None,
vision_feature_layer: int = -1,
text_config: AriaTextConfig = None,
projector_patch_to_query_dict: Dict = None,
projector_patch_to_query_dict: Optional[Dict] = None,
image_token_index: int = 9,
initializer_range: float = 0.02,
**kwargs,

4
src/transformers/models/aria/image_processing_aria.py
View File

@ -124,8 +124,8 @@ class AriaImageProcessor(BaseImageProcessor):
def __init__(
self,
image_mean: List[float] = None,
image_std: List[float] = None,
image_mean: Optional[List[float]] = None,
image_std: Optional[List[float]] = None,
max_image_size: int = 980,
min_image_size: int = 336,
split_resolutions: Optional[List[Tuple[int, int]]] = None,

6
src/transformers/models/aria/modular_aria.py
View File

@ -276,7 +276,7 @@ class AriaConfig(PretrainedConfig):
vision_config=None,
vision_feature_layer: int = -1,
text_config: AriaTextConfig = None,
projector_patch_to_query_dict: Dict = None,
projector_patch_to_query_dict: Optional[Dict] = None,
image_token_index: int = 9,
initializer_range: float = 0.02,
**kwargs,
@ -514,8 +514,8 @@ class AriaImageProcessor(BaseImageProcessor):
def __init__(
self,
image_mean: List[float] = None,
image_std: List[float] = None,
image_mean: Optional[List[float]] = None,
image_std: Optional[List[float]] = None,
max_image_size: int = 980,
min_image_size: int = 336,
split_resolutions: Optional[List[Tuple[int, int]]] = None,

10
src/transformers/models/bark/configuration_bark.py
View File

@ -14,7 +14,7 @@
# limitations under the License.
"""BARK model configuration"""
from typing import Dict
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings, logging
@ -243,10 +243,10 @@ class BarkConfig(PretrainedConfig):
def __init__(
self,
semantic_config: Dict = None,
coarse_acoustics_config: Dict = None,
fine_acoustics_config: Dict = None,
codec_config: Dict = None,
semantic_config: Optional[Dict] = None,
coarse_acoustics_config: Optional[Dict] = None,
fine_acoustics_config: Optional[Dict] = None,
codec_config: Optional[Dict] = None,
initializer_range=0.02,
**kwargs,
):

8
src/transformers/models/bark/generation_configuration_bark.py
View File

@ -15,7 +15,7 @@
"""BARK model generation configuration"""
import copy
from typing import Dict
from typing import Dict, Optional
from ...generation.configuration_utils import GenerationConfig
from ...utils import logging
@ -245,9 +245,9 @@ class BarkGenerationConfig(GenerationConfig):
def __init__(
self,
semantic_config: Dict = None,
coarse_acoustics_config: Dict = None,
fine_acoustics_config: Dict = None,
semantic_config: Optional[Dict] = None,
coarse_acoustics_config: Optional[Dict] = None,
fine_acoustics_config: Optional[Dict] = None,
sample_rate=24_000,
codebook_size=1024,
**kwargs,

16
src/transformers/models/bart/modeling_flax_bart.py
View File

@ -1007,7 +1007,7 @@ class FlaxBartPreTrainedModel(FlaxPreTrainedModel):
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: PRNGKey = None,
):
r"""
@ -1068,12 +1068,12 @@ class FlaxBartPreTrainedModel(FlaxPreTrainedModel):
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
past_key_values: Optional[dict] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: PRNGKey = None,
):
r"""
@ -1186,7 +1186,7 @@ class FlaxBartPreTrainedModel(FlaxPreTrainedModel):
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: PRNGKey = None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
@ -1335,12 +1335,12 @@ class FlaxBartForConditionalGeneration(FlaxBartPreTrainedModel):
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
past_key_values: Optional[dict] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: PRNGKey = None,
):
r"""
@ -1807,8 +1807,8 @@ class FlaxBartDecoderPreTrainedModel(FlaxPreTrainedModel):
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
past_key_values: dict = None,
params: Optional[dict] = None,
past_key_values: Optional[dict] = None,
dropout_rng: PRNGKey = None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions

22
src/transformers/models/beit/image_processing_beit.py
View File

@ -106,10 +106,10 @@ class BeitImageProcessor(BaseImageProcessor):
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_center_crop: bool = True,
crop_size: Dict[str, int] = None,
crop_size: Optional[Dict[str, int]] = None,
rescale_factor: Union[int, float] = 1 / 255,
do_rescale: bool = True,
do_normalize: bool = True,
@ -194,10 +194,10 @@ class BeitImageProcessor(BaseImageProcessor):
image: ImageInput,
do_reduce_labels: Optional[bool] = None,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
do_center_crop: Optional[bool] = None,
crop_size: Dict[str, int] = None,
crop_size: Optional[Dict[str, int]] = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,
@ -226,10 +226,10 @@ class BeitImageProcessor(BaseImageProcessor):
self,
image: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
do_center_crop: Optional[bool] = None,
crop_size: Dict[str, int] = None,
crop_size: Optional[Dict[str, int]] = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,
@ -271,10 +271,10 @@ class BeitImageProcessor(BaseImageProcessor):
self,
segmentation_map: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
do_center_crop: Optional[bool] = None,
crop_size: Dict[str, int] = None,
crop_size: Optional[Dict[str, int]] = None,
do_reduce_labels: Optional[bool] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
):
@ -320,10 +320,10 @@ class BeitImageProcessor(BaseImageProcessor):
images: ImageInput,
segmentation_maps: Optional[ImageInput] = None,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
do_center_crop: Optional[bool] = None,
crop_size: Dict[str, int] = None,
crop_size: Optional[Dict[str, int]] = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,
@ -470,7 +470,7 @@ class BeitImageProcessor(BaseImageProcessor):
return BatchFeature(data=data, tensor_type=return_tensors)
def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple] = None):
def post_process_semantic_segmentation(self, outputs, target_sizes: Optional[List[Tuple]] = None):
"""
Converts the output of [`BeitForSemanticSegmentation`] into semantic segmentation maps. Only supports PyTorch.

2
src/transformers/models/beit/modeling_flax_beit.py
View File

@ -634,7 +634,7 @@ class FlaxBeitPreTrainedModel(FlaxPreTrainedModel):
self,
pixel_values,
bool_masked_pos=None,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,

4
src/transformers/models/bert/modeling_flax_bert.py
View File

@ -864,13 +864,13 @@ class FlaxBertPreTrainedModel(FlaxPreTrainedModel):
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
past_key_values: dict = None,
past_key_values: Optional[dict] = None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (

6
src/transformers/models/big_bird/modeling_flax_big_bird.py
View File

@ -1725,14 +1725,14 @@ class FlaxBigBirdPreTrainedModel(FlaxPreTrainedModel):
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: Optional[jax.random.PRNGKey] = None,
indices_rng: Optional[jax.random.PRNGKey] = None,
train: bool = False,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
past_key_values: dict = None,
past_key_values: Optional[dict] = None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
@ -2442,7 +2442,7 @@ class FlaxBigBirdForQuestionAnswering(FlaxBigBirdPreTrainedModel):
position_ids=None,
head_mask=None,
question_lengths=None,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: Optional[jax.random.PRNGKey] = None,
indices_rng: Optional[jax.random.PRNGKey] = None,
train: bool = False,

6
src/transformers/models/bit/image_processing_bit.py
View File

@ -92,10 +92,10 @@ class BitImageProcessor(BaseImageProcessor):
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_center_crop: bool = True,
crop_size: Dict[str, int] = None,
crop_size: Optional[Dict[str, int]] = None,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = True,
@ -177,7 +177,7 @@ class BitImageProcessor(BaseImageProcessor):
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
do_center_crop: Optional[bool] = None,
crop_size: Optional[int] = None,

12
src/transformers/models/blenderbot/modeling_flax_blenderbot.py
View File

@ -980,7 +980,7 @@ class FlaxBlenderbotPreTrainedModel(FlaxPreTrainedModel):
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: PRNGKey = None,
):
r"""
@ -1043,12 +1043,12 @@ class FlaxBlenderbotPreTrainedModel(FlaxPreTrainedModel):
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
past_key_values: Optional[dict] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: PRNGKey = None,
):
r"""
@ -1161,7 +1161,7 @@ class FlaxBlenderbotPreTrainedModel(FlaxPreTrainedModel):
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: PRNGKey = None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
@ -1311,12 +1311,12 @@ class FlaxBlenderbotForConditionalGeneration(FlaxBlenderbotPreTrainedModel):
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
past_key_values: Optional[dict] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: PRNGKey = None,
):
r"""

12
src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py
View File

@ -977,7 +977,7 @@ class FlaxBlenderbotSmallPreTrainedModel(FlaxPreTrainedModel):
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: PRNGKey = None,
):
r"""
@ -1040,12 +1040,12 @@ class FlaxBlenderbotSmallPreTrainedModel(FlaxPreTrainedModel):
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
past_key_values: Optional[dict] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: PRNGKey = None,
):
r"""
@ -1157,7 +1157,7 @@ class FlaxBlenderbotSmallPreTrainedModel(FlaxPreTrainedModel):
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: PRNGKey = None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
@ -1308,12 +1308,12 @@ class FlaxBlenderbotSmallForConditionalGeneration(FlaxBlenderbotSmallPreTrainedM
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
past_key_values: Optional[dict] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
deterministic: bool = True,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: PRNGKey = None,
):
r"""

2
src/transformers/models/blip/image_processing_blip.py
View File

@ -83,7 +83,7 @@ class BlipImageProcessor(BaseImageProcessor):
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,

2
src/transformers/models/bloom/configuration_bloom.py
View File

@ -148,7 +148,7 @@ class BloomOnnxConfig(OnnxConfigWithPast):
self,
config: PretrainedConfig,
task: str = "default",
patching_specs: List[PatchingSpec] = None,
patching_specs: Optional[List[PatchingSpec]] = None,
use_past: bool = False,
):
super().__init__(config, task=task, patching_specs=patching_specs, use_past=use_past)

4
src/transformers/models/bloom/modeling_flax_bloom.py
View File

@ -463,8 +463,8 @@ class FlaxBloomPreTrainedModel(FlaxPreTrainedModel):
self,
input_ids,
attention_mask=None,
past_key_values: dict = None,
params: dict = None,
past_key_values: Optional[dict] = None,
params: Optional[dict] = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,

6
src/transformers/models/bridgetower/image_processing_bridgetower.py
View File

@ -172,7 +172,7 @@ class BridgeTowerImageProcessor(BaseImageProcessor):
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
size_divisor: int = 32,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_rescale: bool = True,
@ -181,7 +181,7 @@ class BridgeTowerImageProcessor(BaseImageProcessor):
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
do_center_crop: bool = True,
crop_size: Dict[str, int] = None,
crop_size: Optional[Dict[str, int]] = None,
do_pad: bool = True,
**kwargs,
) -> None:
@ -385,7 +385,7 @@ class BridgeTowerImageProcessor(BaseImageProcessor):
image_std: Optional[Union[float, List[float]]] = None,
do_pad: Optional[bool] = None,
do_center_crop: Optional[bool] = None,
crop_size: Dict[str, int] = None,
crop_size: Optional[Dict[str, int]] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: ChannelDimension = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,

2
src/transformers/models/camembert/modeling_camembert.py
View File

@ -1581,7 +1581,7 @@ class CamembertForCausalLM(CamembertPreTrainedModel, GenerationMixin):
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
past_key_values: Tuple[Tuple[torch.FloatTensor]] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,

4
src/transformers/models/chameleon/configuration_chameleon.py
View File

@ -14,7 +14,7 @@
# limitations under the License.
"""chameleon model configuration"""
from typing import List
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
@ -75,7 +75,7 @@ class ChameleonVQVAEConfig(PretrainedConfig):
base_channels: int = 128,
channel_multiplier: List[int] = [1, 1, 2, 2, 4],
num_res_blocks: int = 2,
attn_resolutions: List[int] = None,
attn_resolutions: Optional[List[int]] = None,
dropout: float = 0.0,
attn_type: str = "vanilla",
initializer_range=0.02,

6
src/transformers/models/chameleon/image_processing_chameleon.py
View File

@ -88,10 +88,10 @@ class ChameleonImageProcessor(BaseImageProcessor):
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PIL.Image.LANCZOS,
do_center_crop: bool = True,
crop_size: Dict[str, int] = None,
crop_size: Optional[Dict[str, int]] = None,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 0.0078,
do_normalize: bool = True,
@ -173,7 +173,7 @@ class ChameleonImageProcessor(BaseImageProcessor):
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
do_center_crop: Optional[bool] = None,
crop_size: Optional[int] = None,

6
src/transformers/models/chinese_clip/image_processing_chinese_clip.py
View File

@ -96,10 +96,10 @@ class ChineseCLIPImageProcessor(BaseImageProcessor):
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_center_crop: bool = True,
crop_size: Dict[str, int] = None,
crop_size: Optional[Dict[str, int]] = None,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = True,
@ -170,7 +170,7 @@ class ChineseCLIPImageProcessor(BaseImageProcessor):
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
do_center_crop: Optional[bool] = None,
crop_size: Optional[int] = None,

6
src/transformers/models/clip/image_processing_clip.py
View File

@ -95,10 +95,10 @@ class CLIPImageProcessor(BaseImageProcessor):
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_center_crop: bool = True,
crop_size: Dict[str, int] = None,
crop_size: Optional[Dict[str, int]] = None,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = True,
@ -203,7 +203,7 @@ class CLIPImageProcessor(BaseImageProcessor):
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
do_center_crop: Optional[bool] = None,
crop_size: Optional[int] = None,

10
src/transformers/models/clip/modeling_flax_clip.py
View File

@ -667,7 +667,7 @@ class FlaxCLIPTextPreTrainedModel(FlaxPreTrainedModel):
input_ids,
attention_mask=None,
position_ids=None,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,
@ -745,7 +745,7 @@ class FlaxCLIPVisionPreTrainedModel(FlaxPreTrainedModel):
def __call__(
self,
pixel_values,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,
@ -823,7 +823,7 @@ class FlaxCLIPPreTrainedModel(FlaxPreTrainedModel):
pixel_values,
attention_mask=None,
position_ids=None,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,
@ -867,7 +867,7 @@ class FlaxCLIPPreTrainedModel(FlaxPreTrainedModel):
input_ids,
attention_mask=None,
position_ids=None,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: jax.random.PRNGKey = None,
train=False,
):
@ -930,7 +930,7 @@ class FlaxCLIPPreTrainedModel(FlaxPreTrainedModel):
)
def get_image_features(
self, pixel_values, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train=False
self, pixel_values, params: Optional[dict] = None, dropout_rng: jax.random.PRNGKey = None, train=False
):
r"""
Args:

2
src/transformers/models/codegen/configuration_codegen.py
View File

@ -151,7 +151,7 @@ class CodeGenOnnxConfig(OnnxConfigWithPast):
self,
config: PretrainedConfig,
task: str = "default",
patching_specs: List[PatchingSpec] = None,
patching_specs: Optional[List[PatchingSpec]] = None,
use_past: bool = False,
):
super().__init__(config, task=task, patching_specs=patching_specs, use_past=use_past)

10
src/transformers/models/conditional_detr/image_processing_conditional_detr.py
View File

@ -749,7 +749,7 @@ def compute_segments(
mask_threshold: float = 0.5,
overlap_mask_area_threshold: float = 0.8,
label_ids_to_fuse: Optional[Set[int]] = None,
target_size: Tuple[int, int] = None,
target_size: Optional[Tuple[int, int]] = None,
):
height = mask_probs.shape[1] if target_size is None else target_size[0]
width = mask_probs.shape[2] if target_size is None else target_size[1]
@ -863,13 +863,13 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
self,
format: Union[str, AnnotationFormat] = AnnotationFormat.COCO_DETECTION,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = True,
image_mean: Union[float, List[float]] = None,
image_std: Union[float, List[float]] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
do_convert_annotations: Optional[bool] = None,
do_pad: bool = True,
pad_size: Optional[Dict[str, int]] = None,
@ -1633,7 +1633,7 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
return results
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.post_process_semantic_segmentation with Detr->ConditionalDetr
def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple[int, int]] = None):
def post_process_semantic_segmentation(self, outputs, target_sizes: Optional[List[Tuple[int, int]]] = None):
"""
Converts the output of [`ConditionalDetrForSegmentation`] into semantic segmentation maps. Only supports PyTorch.

2
src/transformers/models/conditional_detr/image_processing_conditional_detr_fast.py
View File

@ -850,7 +850,7 @@ class ConditionalDetrImageProcessorFast(BaseImageProcessorFast):
return results
def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple[int, int]] = None):
def post_process_semantic_segmentation(self, outputs, target_sizes: Optional[List[Tuple[int, int]]] = None):
"""
Converts the output of [`ConditionalDetrForSegmentation`] into semantic segmentation maps. Only supports PyTorch.

4
src/transformers/models/convnext/image_processing_convnext.py
View File

@ -91,7 +91,7 @@ class ConvNextImageProcessor(BaseImageProcessor):
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
crop_pct: Optional[float] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_rescale: bool = True,
@ -190,7 +190,7 @@ class ConvNextImageProcessor(BaseImageProcessor):
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
crop_pct: Optional[float] = None,
resample: PILImageResampling = None,
do_rescale: Optional[bool] = None,

4
src/transformers/models/cpmant/tokenization_cpmant.py
View File

@ -222,7 +222,9 @@ class CpmAntTokenizer(PreTrainedTokenizer):
index += 1
return (vocab_file,)
def build_inputs_with_special_tokens(self, token_ids_0: List[int], token_ids_1: List[int] = None) -> List[int]:
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A CPMAnt sequence has the following format:

3
src/transformers/models/dab_detr/convert_dab_detr_original_pytorch_checkpoint_to_pytorch.py
View File

@ -19,6 +19,7 @@ import gc
import json
import re
from pathlib import Path
from typing import Optional
import torch
from huggingface_hub import hf_hub_download
@ -87,7 +88,7 @@ ORIGINAL_TO_CONVERTED_KEY_MAPPING = {
# Copied from transformers.models.mllama.convert_mllama_weights_to_hf.convert_old_keys_to_new_keys
def convert_old_keys_to_new_keys(state_dict_keys: dict = None):
def convert_old_keys_to_new_keys(state_dict_keys: Optional[dict] = None):
"""
This function should be applied only once, on the concatenated keys to efficiently rename using
the key mappings.

2
src/transformers/models/dbrx/configuration_dbrx.py
View File

@ -89,7 +89,7 @@ class DbrxFFNConfig(PretrainedConfig):
def __init__(
self,
ffn_act_fn: dict = None,
ffn_act_fn: Optional[dict] = None,
ffn_hidden_size: int = 3584,
moe_num_experts: int = 4,
moe_top_k: int = 1,

8
src/transformers/models/deformable_detr/image_processing_deformable_detr.py
View File

@ -747,7 +747,7 @@ def compute_segments(
mask_threshold: float = 0.5,
overlap_mask_area_threshold: float = 0.8,
label_ids_to_fuse: Optional[Set[int]] = None,
target_size: Tuple[int, int] = None,
target_size: Optional[Tuple[int, int]] = None,
):
height = mask_probs.shape[1] if target_size is None else target_size[0]
width = mask_probs.shape[2] if target_size is None else target_size[1]
@ -861,13 +861,13 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
self,
format: Union[str, AnnotationFormat] = AnnotationFormat.COCO_DETECTION,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = True,
image_mean: Union[float, List[float]] = None,
image_std: Union[float, List[float]] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
do_convert_annotations: Optional[bool] = None,
do_pad: bool = True,
pad_size: Optional[Dict[str, int]] = None,

8
src/transformers/models/deit/image_processing_deit.py
View File

@ -84,10 +84,10 @@ class DeiTImageProcessor(BaseImageProcessor):
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PIL.Image.BICUBIC,
do_center_crop: bool = True,
crop_size: Dict[str, int] = None,
crop_size: Optional[Dict[str, int]] = None,
rescale_factor: Union[int, float] = 1 / 255,
do_rescale: bool = True,
do_normalize: bool = True,
@ -166,10 +166,10 @@ class DeiTImageProcessor(BaseImageProcessor):
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample=None,
do_center_crop: Optional[bool] = None,
crop_size: Dict[str, int] = None,
crop_size: Optional[Dict[str, int]] = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,

6
src/transformers/models/deprecated/deta/image_processing_deta.py
View File

@ -553,13 +553,13 @@ class DetaImageProcessor(BaseImageProcessor):
self,
format: Union[str, AnnotationFormat] = AnnotationFormat.COCO_DETECTION,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = True,
image_mean: Union[float, List[float]] = None,
image_std: Union[float, List[float]] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
do_convert_annotations: bool = True,
do_pad: bool = True,
pad_size: Optional[Dict[str, int]] = None,

4
src/transformers/models/deprecated/efficientformer/image_processing_efficientformer.py
View File

@ -91,7 +91,7 @@ class EfficientFormerImageProcessor(BaseImageProcessor):
do_center_crop: bool = True,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
crop_size: Dict[str, int] = None,
crop_size: Optional[Dict[str, int]] = None,
do_normalize: bool = True,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
@ -179,7 +179,7 @@ class EfficientFormerImageProcessor(BaseImageProcessor):
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
do_center_crop: Optional[bool] = None,
crop_size: Optional[int] = None,

2
src/transformers/models/deprecated/mega/modeling_mega.py
View File

@ -1684,7 +1684,7 @@ class MegaForCausalLM(MegaPreTrainedModel):
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
past_key_values: Tuple[Tuple[torch.FloatTensor]] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,

14
src/transformers/models/deprecated/tapex/tokenization_tapex.py
View File

@ -497,7 +497,7 @@ class TapexTokenizer(PreTrainedTokenizer):
self,
table: Union["pd.DataFrame", List["pd.DataFrame"]] = None,
query: Optional[Union[TextInput, List[TextInput]]] = None,
answer: Union[str, List[str]] = None,
answer: Optional[Union[str, List[str]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
@ -574,7 +574,7 @@ class TapexTokenizer(PreTrainedTokenizer):
self,
table: Union["pd.DataFrame", List["pd.DataFrame"]],
query: Optional[Union[TextInput, List[TextInput]]] = None,
answer: Union[str, List[str]] = None,
answer: Optional[Union[str, List[str]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
@ -662,10 +662,10 @@ class TapexTokenizer(PreTrainedTokenizer):
self,
table: Union["pd.DataFrame", List["pd.DataFrame"]],
query: Optional[List[TextInput]] = None,
answer: List[str] = None,
answer: Optional[List[str]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str] = None,
truncation: Optional[Union[bool, str]] = None,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
@ -884,7 +884,7 @@ class TapexTokenizer(PreTrainedTokenizer):
answer: Optional[str] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str] = None,
truncation: Optional[Union[bool, str]] = None,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
@ -1053,7 +1053,7 @@ class TapexTokenizer(PreTrainedTokenizer):
answer: List[str],
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str] = None,
truncation: Optional[Union[bool, str]] = None,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
@ -1197,7 +1197,7 @@ class TapexTokenizer(PreTrainedTokenizer):
answer: str,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str] = None,
truncation: Optional[Union[bool, str]] = None,
max_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,

14
src/transformers/models/deprecated/tvlt/image_processing_tvlt.py
View File

@ -121,12 +121,12 @@ class TvltImageProcessor(BaseImageProcessor):
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
patch_size: List[int] = [16, 16],
num_frames: int = 8,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_center_crop: bool = True,
crop_size: Dict[str, int] = None,
crop_size: Optional[Dict[str, int]] = None,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = True,
@ -221,10 +221,10 @@ class TvltImageProcessor(BaseImageProcessor):
self,
image: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
do_center_crop: Optional[bool] = None,
crop_size: Dict[str, int] = None,
crop_size: Optional[Dict[str, int]] = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,
@ -278,12 +278,12 @@ class TvltImageProcessor(BaseImageProcessor):
self,
videos: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
patch_size: List[int] = None,
size: Optional[Dict[str, int]] = None,
patch_size: Optional[List[int]] = None,
num_frames: Optional[int] = None,
resample: PILImageResampling = None,
do_center_crop: Optional[bool] = None,
crop_size: Dict[str, int] = None,
crop_size: Optional[Dict[str, int]] = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,

6
src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py
View File

@ -93,10 +93,10 @@ class ViTHybridImageProcessor(BaseImageProcessor):
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_center_crop: bool = True,
crop_size: Dict[str, int] = None,
crop_size: Optional[Dict[str, int]] = None,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = True,
@ -193,7 +193,7 @@ class ViTHybridImageProcessor(BaseImageProcessor):
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
do_center_crop: Optional[bool] = None,
crop_size: Optional[int] = None,

3
src/transformers/models/depth_pro/convert_depth_pro_weights_to_hf.py
View File

@ -15,6 +15,7 @@
import argparse
import gc
import os
from typing import Optional
import regex as re
import torch
@ -93,7 +94,7 @@ ORIGINAL_TO_CONVERTED_KEY_MAPPING = {
# fmt: on
def convert_old_keys_to_new_keys(state_dict_keys: dict = None):
def convert_old_keys_to_new_keys(state_dict_keys: Optional[dict] = None):
output_dict = {}
if state_dict_keys is not None:
old_text = "\n".join(state_dict_keys)

10
src/transformers/models/detr/image_processing_detr.py
View File

@ -732,7 +732,7 @@ def compute_segments(
mask_threshold: float = 0.5,
overlap_mask_area_threshold: float = 0.8,
label_ids_to_fuse: Optional[Set[int]] = None,
target_size: Tuple[int, int] = None,
target_size: Optional[Tuple[int, int]] = None,
):
height = mask_probs.shape[1] if target_size is None else target_size[0]
width = mask_probs.shape[2] if target_size is None else target_size[1]
@ -845,13 +845,13 @@ class DetrImageProcessor(BaseImageProcessor):
self,
format: Union[str, AnnotationFormat] = AnnotationFormat.COCO_DETECTION,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = True,
image_mean: Union[float, List[float]] = None,
image_std: Union[float, List[float]] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
do_convert_annotations: Optional[bool] = None,
do_pad: bool = True,
pad_size: Optional[Dict[str, int]] = None,
@ -1824,7 +1824,7 @@ class DetrImageProcessor(BaseImageProcessor):
return results
def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple[int, int]] = None):
def post_process_semantic_segmentation(self, outputs, target_sizes: Optional[List[Tuple[int, int]]] = None):
"""
Converts the output of [`DetrForSegmentation`] into semantic segmentation maps. Only supports PyTorch.

2
src/transformers/models/detr/image_processing_detr_fast.py
View File

@ -1088,7 +1088,7 @@ class DetrImageProcessorFast(BaseImageProcessorFast):
return results
# Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.post_process_semantic_segmentation
def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple[int, int]] = None):
def post_process_semantic_segmentation(self, outputs, target_sizes: Optional[List[Tuple[int, int]]] = None):
"""
Converts the output of [`DetrForSegmentation`] into semantic segmentation maps. Only supports PyTorch.

2
src/transformers/models/dinov2/modeling_flax_dinov2.py
View File

@ -592,7 +592,7 @@ class FlaxDinov2PreTrainedModel(FlaxPreTrainedModel):
def __call__(
self,
pixel_values,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,

2
src/transformers/models/distilbert/modeling_flax_distilbert.py
View File

@ -459,7 +459,7 @@ class FlaxDistilBertPreTrainedModel(FlaxPreTrainedModel):
input_ids,
attention_mask=None,
head_mask=None,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,

4
src/transformers/models/donut/image_processing_donut.py
View File

@ -94,7 +94,7 @@ class DonutImageProcessor(BaseImageProcessor):
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_thumbnail: bool = True,
do_align_long_axis: bool = False,
@ -313,7 +313,7 @@ class DonutImageProcessor(BaseImageProcessor):
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
do_thumbnail: Optional[bool] = None,
do_align_long_axis: Optional[bool] = None,

10
src/transformers/models/dpt/image_processing_dpt.py
View File

@ -154,7 +154,7 @@ class DPTImageProcessor(BaseImageProcessor):
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BICUBIC,
keep_aspect_ratio: bool = False,
ensure_multiple_of: int = 1,
@ -299,7 +299,7 @@ class DPTImageProcessor(BaseImageProcessor):
image: ImageInput,
do_reduce_labels: Optional[bool] = None,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
keep_aspect_ratio: Optional[bool] = None,
ensure_multiple_of: Optional[int] = None,
@ -340,7 +340,7 @@ class DPTImageProcessor(BaseImageProcessor):
self,
image: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
keep_aspect_ratio: Optional[bool] = None,
ensure_multiple_of: Optional[int] = None,
@ -391,7 +391,7 @@ class DPTImageProcessor(BaseImageProcessor):
self,
segmentation_map: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
keep_aspect_ratio: Optional[bool] = None,
ensure_multiple_of: Optional[int] = None,
@ -592,7 +592,7 @@ class DPTImageProcessor(BaseImageProcessor):
return BatchFeature(data=data, tensor_type=return_tensors)
# Copied from transformers.models.beit.image_processing_beit.BeitImageProcessor.post_process_semantic_segmentation with Beit->DPT
def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple] = None):
def post_process_semantic_segmentation(self, outputs, target_sizes: Optional[List[Tuple]] = None):
"""
Converts the output of [`DPTForSemanticSegmentation`] into semantic segmentation maps. Only supports PyTorch.

8
src/transformers/models/efficientnet/image_processing_efficientnet.py
View File

@ -87,10 +87,10 @@ class EfficientNetImageProcessor(BaseImageProcessor):
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PIL.Image.NEAREST,
do_center_crop: bool = False,
crop_size: Dict[str, int] = None,
crop_size: Optional[Dict[str, int]] = None,
rescale_factor: Union[int, float] = 1 / 255,
rescale_offset: bool = False,
do_rescale: bool = True,
@ -213,10 +213,10 @@ class EfficientNetImageProcessor(BaseImageProcessor):
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample=None,
do_center_crop: Optional[bool] = None,
crop_size: Dict[str, int] = None,
crop_size: Optional[Dict[str, int]] = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
rescale_offset: Optional[bool] = None,

4
src/transformers/models/electra/modeling_flax_electra.py
View File

@ -777,13 +777,13 @@ class FlaxElectraPreTrainedModel(FlaxPreTrainedModel):
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
past_key_values: dict = None,
past_key_values: Optional[dict] = None,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (

2
src/transformers/models/emu3/configuration_emu3.py
View File

@ -304,7 +304,7 @@ class Emu3Config(PretrainedConfig):
self,
vq_config: Union[Dict, Emu3VQVAEConfig] = None,
text_config: Union[Dict, Emu3TextConfig] = None,
vocabulary_map: Dict[int, int] = None,
vocabulary_map: Optional[Dict[int, int]] = None,
**kwargs,
):
if vq_config is None:

2
src/transformers/models/emu3/image_processing_emu3.py
View File

@ -309,7 +309,7 @@ class Emu3ImageProcessor(BaseImageProcessor):
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,

2
src/transformers/models/encoder_decoder/modeling_encoder_decoder.py
View File

@ -550,7 +550,7 @@ class EncoderDecoderModel(PreTrainedModel, GenerationMixin):
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.BoolTensor] = None,
encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None,
past_key_values: Tuple[Tuple[torch.FloatTensor]] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,

8
src/transformers/models/encoder_decoder/modeling_flax_encoder_decoder.py
View File

@ -436,7 +436,7 @@ class FlaxEncoderDecoderModel(FlaxPreTrainedModel):
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: PRNGKey = None,
):
r"""
@ -508,12 +508,12 @@ class FlaxEncoderDecoderModel(FlaxPreTrainedModel):
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
past_key_values: Optional[dict] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: PRNGKey = None,
):
r"""
@ -638,7 +638,7 @@ class FlaxEncoderDecoderModel(FlaxPreTrainedModel):
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
params: Optional[dict] = None,
dropout_rng: PRNGKey = None,
):
r"""

6
src/transformers/models/fastspeech2_conformer/configuration_fastspeech2_conformer.py
View File

@ -14,7 +14,7 @@
# limitations under the License.
"""FastSpeech2Conformer model configuration"""
from typing import Dict
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
@ -459,8 +459,8 @@ class FastSpeech2ConformerWithHifiGanConfig(PretrainedConfig):
def __init__(
self,
model_config: Dict = None,
vocoder_config: Dict = None,
model_config: Optional[Dict] = None,
vocoder_config: Optional[Dict] = None,
**kwargs,
):
if model_config is None:

10
src/transformers/models/flava/configuration_flava.py
View File

@ -14,7 +14,7 @@
# limitations under the License.
"""FLAVA model configurations"""
from typing import Any, Dict
from typing import Any, Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
@ -472,10 +472,10 @@ class FlavaConfig(PretrainedConfig):
def __init__(
self,
image_config: Dict[str, Any] = None,
text_config: Dict[str, Any] = None,
multimodal_config: Dict[str, Any] = None,
image_codebook_config: Dict[str, Any] = None,
image_config: Optional[Dict[str, Any]] = None,
text_config: Optional[Dict[str, Any]] = None,
multimodal_config: Optional[Dict[str, Any]] = None,
image_codebook_config: Optional[Dict[str, Any]] = None,
hidden_size: int = 768,
layer_norm_eps: float = 1e-12,
projection_dim: int = 768,

10
src/transformers/models/flava/image_processing_flava.py
View File

@ -228,10 +228,10 @@ class FlavaImageProcessor(BaseImageProcessor):
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_center_crop: bool = True,
crop_size: Dict[str, int] = None,
crop_size: Optional[Dict[str, int]] = None,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = True,
@ -392,10 +392,10 @@ class FlavaImageProcessor(BaseImageProcessor):
self,
image: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
do_center_crop: Optional[bool] = None,
crop_size: Dict[str, int] = None,
crop_size: Optional[Dict[str, int]] = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,
@ -457,7 +457,7 @@ class FlavaImageProcessor(BaseImageProcessor):
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
do_center_crop: Optional[bool] = None,
crop_size: Optional[Dict[str, int]] = None,

2
src/transformers/models/fuyu/image_processing_fuyu.py
View File

@ -537,7 +537,7 @@ class FuyuImageProcessor(BaseImageProcessor):
}
return FuyuBatchFeature(data=data, tensor_type=return_tensors)
def get_num_patches(self, image_height: int, image_width: int, patch_size: Dict[str, int] = None) -> int:
def get_num_patches(self, image_height: int, image_width: int, patch_size: Optional[Dict[str, int]] = None) -> int:
"""
Calculate number of patches required to encode an image.

4
src/transformers/models/gemma/modeling_flax_gemma.py
View File

@ -485,8 +485,8 @@ class FlaxGemmaPreTrainedModel(FlaxPreTrainedModel):
input_ids,
attention_mask=None,
position_ids=None,
params: dict = None,
past_key_values: dict = None,
params: Optional[dict] = None,
past_key_values: Optional[dict] = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,

4
src/transformers/models/gemma3/image_processing_gemma3.py
View File

@ -95,7 +95,7 @@ class Gemma3ImageProcessor(BaseImageProcessor):
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
@ -241,7 +241,7 @@ class Gemma3ImageProcessor(BaseImageProcessor):
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,

2
src/transformers/models/got_ocr2/convert_got_ocr2_weights_to_hf.py
View File

@ -61,7 +61,7 @@ ORIGINAL_TO_CONVERTED_KEY_MAPPING = {
CONTEXT_LENGTH = 8000
def convert_old_keys_to_new_keys(state_dict_keys: dict = None):
def convert_old_keys_to_new_keys(state_dict_keys: Optional[dict] = None):
"""
This function should be applied only once, on the concatenated keys to efficiently rename using
the key mappings.

4
src/transformers/models/got_ocr2/image_processing_got_ocr2.py
View File

@ -172,7 +172,7 @@ class GotOcr2ImageProcessor(BaseImageProcessor):
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
crop_to_patches: bool = False,
min_patches: int = 1,
max_patches: int = 12,
@ -419,7 +419,7 @@ class GotOcr2ImageProcessor(BaseImageProcessor):
min_patches: int,
max_patches: int,
use_thumbnail: bool = True,
patch_size: Union[Tuple, int, dict] = None,
patch_size: Optional[Union[Tuple, int, dict]] = None,
data_format: ChannelDimension = None,
):
"""

2
src/transformers/models/got_ocr2/image_processing_got_ocr2_fast.py
View File

@ -114,7 +114,7 @@ class GotOcr2ImageProcessorFast(BaseImageProcessorFast):
min_patches: int,
max_patches: int,
use_thumbnail: bool = True,
patch_size: Union[Tuple, int, dict] = None,
patch_size: Optional[Union[Tuple, int, dict]] = None,
interpolation: Optional["F.InterpolationMode"] = None,
):
"""

2
src/transformers/models/gpt2/configuration_gpt2.py
View File

@ -194,7 +194,7 @@ class GPT2OnnxConfig(OnnxConfigWithPast):
self,
config: PretrainedConfig,
task: str = "default",
patching_specs: List[PatchingSpec] = None,
patching_specs: Optional[List[PatchingSpec]] = None,
use_past: bool = False,
):
super().__init__(config, task=task, patching_specs=patching_specs, use_past=use_past)

4
src/transformers/models/gpt2/modeling_flax_gpt2.py
View File

@ -461,8 +461,8 @@ class FlaxGPT2PreTrainedModel(FlaxPreTrainedModel):
position_ids=None,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
params: dict = None,
past_key_values: dict = None,
params: Optional[dict] = None,
past_key_values: Optional[dict] = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,

4
src/transformers/models/gpt_neo/modeling_flax_gpt_neo.py
View File

@ -404,8 +404,8 @@ class FlaxGPTNeoPreTrainedModel(FlaxPreTrainedModel):
input_ids,
attention_mask=None,
position_ids=None,
params: dict = None,
past_key_values: dict = None,
params: Optional[dict] = None,
past_key_values: Optional[dict] = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,

2
src/transformers/models/gptj/configuration_gptj.py
View File

@ -140,7 +140,7 @@ class GPTJOnnxConfig(OnnxConfigWithPast):
self,
config: PretrainedConfig,
task: str = "default",
patching_specs: List[PatchingSpec] = None,
patching_specs: Optional[List[PatchingSpec]] = None,
use_past: bool = False,
):
super().__init__(config, task=task, patching_specs=patching_specs, use_past=use_past)

4
src/transformers/models/gptj/modeling_flax_gptj.py
View File

@ -438,8 +438,8 @@ class FlaxGPTJPreTrainedModel(FlaxPreTrainedModel):
input_ids,
attention_mask=None,
position_ids=None,
params: dict = None,
past_key_values: dict = None,
params: Optional[dict] = None,
past_key_values: Optional[dict] = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,

8
src/transformers/models/grounding_dino/image_processing_grounding_dino.py
View File

@ -756,7 +756,7 @@ def compute_segments(
mask_threshold: float = 0.5,
overlap_mask_area_threshold: float = 0.8,
label_ids_to_fuse: Optional[Set[int]] = None,
target_size: Tuple[int, int] = None,
target_size: Optional[Tuple[int, int]] = None,
):
height = mask_probs.shape[1] if target_size is None else target_size[0]
width = mask_probs.shape[2] if target_size is None else target_size[1]
@ -899,13 +899,13 @@ class GroundingDinoImageProcessor(BaseImageProcessor):
self,
format: Union[str, AnnotationFormat] = AnnotationFormat.COCO_DETECTION,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = True,
image_mean: Union[float, List[float]] = None,
image_std: Union[float, List[float]] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
do_convert_annotations: Optional[bool] = None,
do_pad: bool = True,
pad_size: Optional[Dict[str, int]] = None,

2
src/transformers/models/grounding_dino/modeling_grounding_dino.py
View File

@ -2554,7 +2554,7 @@ class GroundingDinoForObjectDetection(GroundingDinoPreTrainedModel):
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: List[Dict[str, Union[torch.LongTensor, torch.FloatTensor]]] = None,
labels: Optional[List[Dict[str, Union[torch.LongTensor, torch.FloatTensor]]]] = None,
):
r"""
labels (`List[Dict]` of len `(batch_size,)`, *optional*):

2
src/transformers/models/idefics/image_processing_idefics.py
View File

@ -101,7 +101,7 @@ class IdeficsImageProcessor(BaseImageProcessor):
image_size: Optional[Dict[str, int]] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
transform: Callable = None,
transform: Optional[Callable] = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,

2
src/transformers/models/idefics2/image_processing_idefics2.py
View File

@ -190,7 +190,7 @@ class Idefics2ImageProcessor(BaseImageProcessor):
self,
do_convert_rgb: bool = True,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_rescale: bool = True,
rescale_factor: float = 1 / 255,

4
src/transformers/models/idefics3/image_processing_idefics3.py
View File

@ -295,10 +295,10 @@ class Idefics3ImageProcessor(BaseImageProcessor):
self,
do_convert_rgb: bool = True,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.LANCZOS,
do_image_splitting: bool = True,
max_image_size: Dict[str, int] = None,
max_image_size: Optional[Dict[str, int]] = None,
do_rescale: bool = True,
rescale_factor: float = 1 / 255,
do_normalize: bool = True,

3
src/transformers/models/ijepa/convert_ijepa_to_hf.py
View File

@ -21,6 +21,7 @@ import argparse
import gc
import re
from pathlib import Path
from typing import Optional
import requests
import torch
@ -63,7 +64,7 @@ ORIGINAL_TO_CONVERTED_KEY_MAPPING = {
# fmt: on
def convert_old_keys_to_new_keys(state_dict_keys: dict = None):
def convert_old_keys_to_new_keys(state_dict_keys: Optional[dict] = None):
"""
Converts old keys to new keys using the mapping and dynamically removes the 'ijepa.' prefix if necessary.

4
src/transformers/models/imagegpt/image_processing_imagegpt.py
View File

@ -89,7 +89,7 @@ class ImageGPTImageProcessor(BaseImageProcessor):
# clusters is a first argument to maintain backwards compatibility with the old ImageGPTImageProcessor
clusters: Optional[Union[List[List[int]], np.ndarray]] = None,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_normalize: bool = True,
do_color_quantize: bool = True,
@ -180,7 +180,7 @@ class ImageGPTImageProcessor(BaseImageProcessor):
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
do_normalize: Optional[bool] = None,
do_color_quantize: Optional[bool] = None,

2
src/transformers/models/informer/configuration_informer.py
View File

@ -141,7 +141,7 @@ class InformerConfig(PretrainedConfig):
distribution_output: str = "student_t",
loss: str = "nll",
input_size: int = 1,
lags_sequence: List[int] = None,
lags_sequence: Optional[List[int]] = None,
scaling: Optional[Union[str, bool]] = "mean",
num_dynamic_real_features: int = 0,
num_static_real_features: int = 0,

2
src/transformers/models/instructblipvideo/image_processing_instructblipvideo.py
View File

@ -84,7 +84,7 @@ class InstructBlipVideoImageProcessor(BaseImageProcessor):
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,

2
src/transformers/models/layoutlm/configuration_layoutlm.py
View File

@ -139,7 +139,7 @@ class LayoutLMOnnxConfig(OnnxConfig):
self,
config: PretrainedConfig,
task: str = "default",
patching_specs: List[PatchingSpec] = None,
patching_specs: Optional[List[PatchingSpec]] = None,
):
super().__init__(config, task=task, patching_specs=patching_specs)
self.max_2d_positions = config.max_2d_position_embeddings - 1

4
src/transformers/models/layoutlmv2/image_processing_layoutlmv2.py
View File

@ -129,7 +129,7 @@ class LayoutLMv2ImageProcessor(BaseImageProcessor):
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
apply_ocr: bool = True,
ocr_lang: Optional[str] = None,
@ -201,7 +201,7 @@ class LayoutLMv2ImageProcessor(BaseImageProcessor):
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
apply_ocr: Optional[bool] = None,
ocr_lang: Optional[str] = None,

2
src/transformers/models/layoutlmv2/processing_layoutlmv2.py
View File

@ -71,7 +71,7 @@ class LayoutLMv2Processor(ProcessorMixin):
images,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
boxes: Union[List[List[int]], List[List[List[int]]]] = None,
boxes: Optional[Union[List[List[int]], List[List[List[int]]]]] = None,
word_labels: Optional[Union[List[int], List[List[int]]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,

2
src/transformers/models/layoutlmv2/tokenization_layoutlmv2.py
View File

@ -406,7 +406,7 @@ class LayoutLMv2Tokenizer(PreTrainedTokenizer):
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
boxes: Union[List[List[int]], List[List[List[int]]]] = None,
boxes: Optional[Union[List[List[int]], List[List[List[int]]]]] = None,
word_labels: Optional[Union[List[int], List[List[int]]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,

2
src/transformers/models/layoutlmv2/tokenization_layoutlmv2_fast.py
View File

@ -157,7 +157,7 @@ class LayoutLMv2TokenizerFast(PreTrainedTokenizerFast):
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
boxes: Union[List[List[int]], List[List[List[int]]]] = None,
boxes: Optional[Union[List[List[int]], List[List[List[int]]]]] = None,
word_labels: Optional[Union[List[int], List[List[int]]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,

12
src/transformers/models/layoutlmv3/image_processing_layoutlmv3.py
View File

@ -146,13 +146,13 @@ class LayoutLMv3ImageProcessor(BaseImageProcessor):
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_rescale: bool = True,
rescale_value: float = 1 / 255,
do_normalize: bool = True,
image_mean: Union[float, Iterable[float]] = None,
image_std: Union[float, Iterable[float]] = None,
image_mean: Optional[Union[float, Iterable[float]]] = None,
image_std: Optional[Union[float, Iterable[float]]] = None,
apply_ocr: bool = True,
ocr_lang: Optional[str] = None,
tesseract_config: Optional[str] = "",
@ -228,13 +228,13 @@ class LayoutLMv3ImageProcessor(BaseImageProcessor):
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample=None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,
image_mean: Union[float, Iterable[float]] = None,
image_std: Union[float, Iterable[float]] = None,
image_mean: Optional[Union[float, Iterable[float]]] = None,
image_std: Optional[Union[float, Iterable[float]]] = None,
apply_ocr: Optional[bool] = None,
ocr_lang: Optional[str] = None,
tesseract_config: Optional[str] = None,

2
src/transformers/models/layoutlmv3/processing_layoutlmv3.py
View File

@ -71,7 +71,7 @@ class LayoutLMv3Processor(ProcessorMixin):
images,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
boxes: Union[List[List[int]], List[List[List[int]]]] = None,
boxes: Optional[Union[List[List[int]], List[List[List[int]]]]] = None,
word_labels: Optional[Union[List[int], List[List[int]]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,

2
src/transformers/models/layoutlmv3/tokenization_layoutlmv3.py
View File

@ -535,7 +535,7 @@ class LayoutLMv3Tokenizer(PreTrainedTokenizer):
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
boxes: Union[List[List[int]], List[List[List[int]]]] = None,
boxes: Optional[Union[List[List[int]], List[List[List[int]]]]] = None,
word_labels: Optional[Union[List[int], List[List[int]]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,

2
src/transformers/models/layoutlmv3/tokenization_layoutlmv3_fast.py
View File

@ -201,7 +201,7 @@ class LayoutLMv3TokenizerFast(PreTrainedTokenizerFast):
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
boxes: Union[List[List[int]], List[List[List[int]]]] = None,
boxes: Optional[Union[List[List[int]], List[List[List[int]]]]] = None,
word_labels: Optional[Union[List[int], List[List[int]]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,

2
src/transformers/models/layoutxlm/processing_layoutxlm.py
View File

@ -70,7 +70,7 @@ class LayoutXLMProcessor(ProcessorMixin):
images,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
boxes: Union[List[List[int]], List[List[List[int]]]] = None,
boxes: Optional[Union[List[List[int]], List[List[List[int]]]]] = None,
word_labels: Optional[Union[List[int], List[List[int]]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,

2
src/transformers/models/layoutxlm/tokenization_layoutxlm.py
View File

@ -441,7 +441,7 @@ class LayoutXLMTokenizer(PreTrainedTokenizer):
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
boxes: Union[List[List[int]], List[List[List[int]]]] = None,
boxes: Optional[Union[List[List[int]], List[List[List[int]]]]] = None,
word_labels: Optional[Union[List[int], List[List[int]]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,

2
src/transformers/models/layoutxlm/tokenization_layoutxlm_fast.py
View File

@ -269,7 +269,7 @@ class LayoutXLMTokenizerFast(PreTrainedTokenizerFast):
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
boxes: Union[List[List[int]], List[List[List[int]]]] = None,
boxes: Optional[Union[List[List[int]], List[List[List[int]]]]] = None,
word_labels: Optional[Union[List[int], List[List[int]]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,

4
src/transformers/models/levit/image_processing_levit.py
View File

@ -90,10 +90,10 @@ class LevitImageProcessor(BaseImageProcessor):
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_center_crop: bool = True,
crop_size: Dict[str, int] = None,
crop_size: Optional[Dict[str, int]] = None,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = True,

4
src/transformers/models/llama/modeling_flax_llama.py
View File

@ -467,8 +467,8 @@ class FlaxLlamaPreTrainedModel(FlaxPreTrainedModel):
input_ids,
attention_mask=None,
position_ids=None,
params: dict = None,
past_key_values: dict = None,
params: Optional[dict] = None,
past_key_values: Optional[dict] = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,

2
src/transformers/models/llama4/convert_llama4_weights_to_hf.py
View File

@ -90,7 +90,7 @@ ORIGINAL_TO_CONVERTED_KEY_MAPPING = {
# fmt: on
def convert_old_keys_to_new_keys(state_dict_keys: dict = None):
def convert_old_keys_to_new_keys(state_dict_keys: Optional[dict] = None):
"""
This function should be applied only once, on the concatenated keys to efficiently rename using
the key mappings.

2
src/transformers/models/llama4/modeling_llama4.py
View File

@ -1287,7 +1287,7 @@ class Llama4VisionEncoderLayer(nn.Module):
hidden_state: torch.Tensor,
freqs_ci: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: bool = None,
output_attentions: Optional[bool] = None,
):
# Self Attention
residual = hidden_state

4
src/transformers/models/llava/image_processing_llava.py
View File

@ -99,10 +99,10 @@ class LlavaImageProcessor(BaseImageProcessor):
self,
do_pad: bool = False,
do_resize: bool = True,
size: Dict[str, int] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_center_crop: bool = True,
crop_size: Dict[str, int] = None,
crop_size: Optional[Dict[str, int]] = None,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = True,

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