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[deps] goodbye python 3.9 (#9677)

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Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: hiyouga <16256802+hiyouga@users.noreply.github.com>
Co-authored-by: hiyouga <hiyouga@buaa.edu.cn>
This commit is contained in:
Copilot
2025-12-27 02:50:44 +08:00
committed by GitHub
parent b44f651e09
commit eceec8ab69
48 changed files with 267 additions and 284 deletions
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30
src/llamafactory/data/mm_plugin.py
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@@ -22,7 +22,7 @@ import re
from copy import deepcopy
from dataclasses import dataclass
from io import BytesIO
from typing import TYPE_CHECKING, BinaryIO, Literal, Optional, TypedDict, Union
from typing import TYPE_CHECKING, BinaryIO, Literal, NotRequired, Optional, TypedDict, Union
import numpy as np
import torch
@@ -32,7 +32,7 @@ from transformers.models.mllama.processing_mllama import (
convert_sparse_cross_attention_mask_to_dense,
get_cross_attention_token_mask,
)
from typing_extensions import NotRequired, override
from typing_extensions import override
from ..extras.constants import AUDIO_PLACEHOLDER, IGNORE_INDEX, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
from ..extras.packages import is_pillow_available, is_pyav_available, is_transformers_version_greater_than
@@ -63,8 +63,8 @@ if TYPE_CHECKING:
from transformers.video_processing_utils import BaseVideoProcessor
class EncodedImage(TypedDict):
path: Optional[str]
bytes: Optional[bytes]
path: str | None
bytes: bytes | None
ImageInput = Union[str, bytes, EncodedImage, BinaryIO, ImageObject]
VideoInput = Union[str, BinaryIO, list[list[ImageInput]]]
@@ -144,9 +144,9 @@ def _check_video_is_nested_images(video: "VideoInput") -> bool:
@dataclass
class MMPluginMixin:
image_token: Optional[str]
video_token: Optional[str]
audio_token: Optional[str]
image_token: str | None
video_token: str | None
audio_token: str | None
expand_mm_tokens: bool = True
def _validate_input(
@@ -328,7 +328,7 @@ class MMPluginMixin:
videos: list["VideoInput"],
audios: list["AudioInput"],
processor: "MMProcessor",
imglens: Optional[list[int]] = None,
imglens: list[int] | None = None,
) -> dict[str, "torch.Tensor"]:
r"""Process visual inputs.
@@ -426,13 +426,13 @@ class BasePlugin(MMPluginMixin):
def process_token_ids(
self,
input_ids: list[int],
labels: Optional[list[int]],
labels: list[int] | None,
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
tokenizer: "PreTrainedTokenizer",
processor: Optional["MMProcessor"],
) -> tuple[list[int], Optional[list[int]]]:
) -> tuple[list[int], list[int] | None]:
r"""Pre-process token ids after tokenization for VLMs."""
self._validate_input(processor, images, videos, audios)
return input_ids, labels
@@ -1305,13 +1305,13 @@ class PaliGemmaPlugin(BasePlugin):
def process_token_ids(
self,
input_ids: list[int],
labels: Optional[list[int]],
labels: list[int] | None,
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
tokenizer: "PreTrainedTokenizer",
processor: Optional["MMProcessor"],
) -> tuple[list[int], Optional[list[int]]]:
) -> tuple[list[int], list[int] | None]:
self._validate_input(processor, images, videos, audios)
num_images = len(images)
image_seqlen = processor.image_seq_length if self.expand_mm_tokens else 0 # skip mm token
@@ -2126,9 +2126,9 @@ def register_mm_plugin(name: str, plugin_class: type["BasePlugin"]) -> None:
def get_mm_plugin(
name: str,
image_token: Optional[str] = None,
video_token: Optional[str] = None,
audio_token: Optional[str] = None,
image_token: str | None = None,
video_token: str | None = None,
audio_token: str | None = None,
**kwargs,
) -> "BasePlugin":
r"""Get plugin for multimodal inputs."""