Refactor: Format Code with Ruff and Update Deprecated G2PW Link (#2255)

* ruff check --fix

* ruff format --line-length 120 --target-version py39

* Change the link for G2PW Model

* update pytorch version and colab

tools/uvr5/bs_roformer/attend.py

@@ -7,23 +7,22 @@ import torch.nn.functional as F
 def exists(val):
     return val is not None
 
+
 def default(v, d):
     return v if exists(v) else d
 
+
 class Attend(nn.Module):
-    def __init__(
-        self,
-        dropout = 0.,
-        flash = False,
-        scale = None
-    ):
+    def __init__(self, dropout=0.0, flash=False, scale=None):
         super().__init__()
         self.scale = scale
         self.dropout = dropout
         self.attn_dropout = nn.Dropout(dropout)
 
         self.flash = flash
-        assert not (flash and version.parse(torch.__version__) < version.parse('2.0.0')), 'in order to use flash attention, you must be using pytorch 2.0 or above'
+        assert not (flash and version.parse(torch.__version__) < version.parse("2.0.0")), (
+            "in order to use flash attention, you must be using pytorch 2.0 or above"
+        )
 
     def flash_attn(self, q, k, v):
         # _, heads, q_len, _, k_len, is_cuda, device = *q.shape, k.shape[-2], q.is_cuda, q.device
@@ -34,7 +33,7 @@ class Attend(nn.Module):
 
         # pytorch 2.0 flash attn: q, k, v, mask, dropout, softmax_scale
         # with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=True):
-        return F.scaled_dot_product_attention(q, k, v,dropout_p = self.dropout if self.training else 0.)
+        return F.scaled_dot_product_attention(q, k, v, dropout_p=self.dropout if self.training else 0.0)
 
     def forward(self, q, k, v):
         """
@@ -54,7 +53,7 @@ class Attend(nn.Module):
 
         # similarity
 
-        sim = einsum(f"b h i d, b h j d -> b h i j", q, k) * scale
+        sim = einsum("b h i d, b h j d -> b h i j", q, k) * scale
 
         # attention
 
@@ -63,6 +62,6 @@ class Attend(nn.Module):
 
         # aggregate values
 
-        out = einsum(f"b h i j, b h j d -> b h i d", attn, v)
+        out = einsum("b h i j, b h j d -> b h i d", attn, v)
 
         return out