[misc] update format (#7277)

src/llamafactory/data/collator.py

@@ -1,4 +1,4 @@
-# Copyright 2024 OpenAccess AI Collective and the LlamaFactory team.
+# Copyright 2025 OpenAccess AI Collective and the LlamaFactory team.
 #
 # This code is inspired by the OpenAccess AI Collective's axolotl library.
 # https://github.com/OpenAccess-AI-Collective/axolotl/blob/main/src/axolotl/monkeypatch/utils.py
@@ -15,7 +15,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from collections.abc import Sequence
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, Literal, Optional
 
@@ -92,7 +91,7 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
         if self.template is None:
             raise ValueError("Template is required for MultiModalDataCollator.")
 
-    def __call__(self, features: Sequence[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
+    def __call__(self, features: list[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
         batch_images, batch_videos, batch_audios = [], [], []
         batch_imglens, batch_vidlens, batch_audlens, batch_input_ids = [], [], [], []
         for feature in features:
@@ -205,7 +204,7 @@ class SFTDataCollatorWith4DAttentionMask(MultiModalDataCollatorForSeq2Seq):
     attn_implementation: Literal["eager", "sdpa", "flash_attention_2"] = "eager"
     compute_dtype: "torch.dtype" = torch.float32
 
-    def __call__(self, features: Sequence[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
+    def __call__(self, features: list[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
         features = super().__call__(features)
         if self.block_diag_attn and self.attn_implementation != "flash_attention_2":
             features["attention_mask"] = prepare_4d_attention_mask(features["attention_mask"], self.compute_dtype)
@@ -221,7 +220,7 @@ class SFTDataCollatorWith4DAttentionMask(MultiModalDataCollatorForSeq2Seq):
 class PairwiseDataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
     r"""Data collator for pairwise data."""
 
-    def __call__(self, features: Sequence[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
+    def __call__(self, features: list[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
         r"""Pad batched data to the longest sequence in the batch.
 
         We generate 2 * n examples where the first n examples represent chosen examples and
@@ -247,7 +246,7 @@ class PairwiseDataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
 class KTODataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
     r"""Data collator for KTO data."""
 
-    def __call__(self, features: Sequence[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
+    def __call__(self, features: list[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
         target_features = []
         kl_features = []
         kto_tags = []