From 4dc98ee9d2fbba2a67e8c1e2807aaa3e4fe4a31c Mon Sep 17 00:00:00 2001
From: "deepsource-autofix[bot]"
 <62050782+deepsource-autofix[bot]@users.noreply.github.com>
Date: Fri, 27 Dec 2024 09:07:43 +0000
Subject: [PATCH] style: format code with Autopep8, isort and Ruff Formatter

This commit fixes the style issues introduced in 1ed1e17 according to the output
from Autopep8, isort and Ruff Formatter.

Details: None
---
 src/everyai/classfier/classfy.py             | 14 ++++----------
 src/everyai/classfier/multi_feature/model.py | 10 +++++++---
 2 files changed, 11 insertions(+), 13 deletions(-)

diff --git a/src/everyai/classfier/classfy.py b/src/everyai/classfier/classfy.py
index 309007c..0a92151 100644
--- a/src/everyai/classfier/classfy.py
+++ b/src/everyai/classfier/classfy.py
@@ -175,8 +175,7 @@ def __init__(
         )
         self.model_config = None
         self.model_path = (
-            MODEL_PATH
-            / f"{self.model_name}_{self.tokenizer_name}_{self.data_name}.pkl"
+            MODEL_PATH / f"{self.model_name}_{self.tokenizer_name}_{self.data_name}.pkl"
         )
         self.pipeline = pipeline if pipeline is not None else None
 
@@ -186,9 +185,7 @@ def load_data(self, texts, labels, data_name):
             raise ValueError("Length of texts and labels should be same")
         self.texts = texts
         self.labels = labels
-        logging.info(
-            f"Loading data: {data_name} to classfier {self.model_name}"
-        )
+        logging.info(f"Loading data: {data_name} to classfier {self.model_name}")
         self.data_name = data_name
         self.classfier_name = (
             f"{self.model_name}_{self.tokenizer_name}_{self.data_name}"
@@ -288,9 +285,7 @@ def __init__(self, **classfiy_config):
         else:
             logging.warning("Split size not provided or not valid")
         if self.texts is None or self.labels is None or self.data_name is None:
-            logging.warning(
-                "Data not provided, please use the load_data method"
-            )
+            logging.warning("Data not provided, please use the load_data method")
         if "device" in classfiy_config and classfiy_config["device"] == "cuda":
             logging.warning(
                 "Cuda is not supported in sklearn and setting device to cpu"
@@ -359,8 +354,7 @@ def _split_data(self, x, y):
                 x_train,
                 y_train,
                 train_indices,
-                test_size=self.valid_size
-                / (self.train_size + self.valid_size),
+                test_size=self.valid_size / (self.train_size + self.valid_size),
                 random_state=42,
             )
         )
diff --git a/src/everyai/classfier/multi_feature/model.py b/src/everyai/classfier/multi_feature/model.py
index 275eee5..2783d77 100644
--- a/src/everyai/classfier/multi_feature/model.py
+++ b/src/everyai/classfier/multi_feature/model.py
@@ -5,11 +5,15 @@
 class CrossAttentionFusion(nn.Module):
     def __init__(self, d1, d2, d3, hidden_dim, output_dim, num_heads):
         super(CrossAttentionFusion, self).__init__()
-        self.cross_attention = nn.MultiheadAttention(embed_dim=hidden_dim, num_heads=num_heads)
+        self.cross_attention = nn.MultiheadAttention(
+            embed_dim=hidden_dim, num_heads=num_heads
+        )
         self.fc1 = nn.Linear(hidden_dim, output_dim)
-    
+
     def forward(self, f1, f2, f3):
         # 将所有特征拼接后输入注意力模块
         merged_features = torch.cat((f1, f2, f3), dim=-1).unsqueeze(0)  # 添加batch维度
-        attn_output, _ = self.cross_attention(merged_features, merged_features, merged_features)
+        attn_output, _ = self.cross_attention(
+            merged_features, merged_features, merged_features
+        )
         return torch.relu(self.fc1(attn_output.squeeze(0)))