style: fix linting issues with black and isort

Author: Purshow

examples/nanovlm/nanovlm_train.sh

@@ -1,9 +1,5 @@
 #!/bin/bash
-export PYTHONPATH=/mnt/afs/niuyuwei/Job/lmms-engine/src:$PYTHONPATH
-export WANDB_DISABLED=true
-export WANDB_MODE=disabled
-
-DATASET_PATH="./data/llava_next.yaml"
+DATASET_PATH="/path/to/dataset.yaml"
 PROCESSOR_NAME="LMMs-Lab-Speedrun/NanoVLM_Init"
 MODEL_PATH="LMMs-Lab-Speedrun/NanoVLM_Init"
 ATTN_IMPLEMENTATION="flash_attention_2"

src/lmms_engine/datasets/processor/nanovlm_processor.py

@@ -94,9 +94,7 @@ def get_qwen_template_labels(
         image_start_from = 0
         video_start_from = 0
         if add_system_prompt and hf_messages[0]["role"] != "system":
-            input_id += self._apply_chat_template(
-                [{"role": "system", "content": system_message}], tokenize=True
-            )
+            input_id += self._apply_chat_template([{"role": "system", "content": system_message}], tokenize=True)
             target += [-100] * len(input_id)
 
         for message in hf_messages:

src/lmms_engine/models/nanovlm/configuration_nanovlm.py

@@ -42,9 +42,7 @@ def __init__(
         self.projector_hidden_size = projector_hidden_size
         self.projector_num_layers = int(projector_num_layers)
         self.projector_hidden_act = projector_hidden_act
-        self.vision_feature_dim = int(
-            vision_feature_dim or getattr(self.vision_config, "hidden_size", 1152)
-        )
+        self.vision_feature_dim = int(vision_feature_dim or getattr(self.vision_config, "hidden_size", 1152))
         self.image_token_count = int(image_token_count)
         self.vocab_size = getattr(self.text_config, "vocab_size", None)
         super().__init__(**kwargs)

src/lmms_engine/models/nanovlm/modeling_nanovlm.py

@@ -6,6 +6,7 @@
 from transformers.activations import ACT2FN
 from transformers.generation import GenerationMixin
 from transformers.modeling_utils import PreTrainedModel
+
 from .configuration_nanovlm import NanovlmConfig
 
 
@@ -101,9 +102,7 @@ def set_output_embeddings(self, new_embeddings):
         return self.language_model.set_output_embeddings(new_embeddings)
 
     def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None):
-        return self.language_model.resize_token_embeddings(
-            new_num_tokens, pad_to_multiple_of=pad_to_multiple_of
-        )
+        return self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of=pad_to_multiple_of)
 
     def forward(
         self,