From ea7c14f8ef64924f2d0ff80df3cdabf2c7299848 Mon Sep 17 00:00:00 2001
From: Titus von Koeller <titus@vonkoeller.com>
Date: Mon, 1 Aug 2022 09:32:47 -0700
Subject: reran black with linelength 80 for greater readability

---
 bitsandbytes/optim/lars.py | 20 +++++++++++++++-----
 1 file changed, 15 insertions(+), 5 deletions(-)

(limited to 'bitsandbytes/optim/lars.py')

diff --git a/bitsandbytes/optim/lars.py b/bitsandbytes/optim/lars.py
index c6cf5c6..8a89fb0 100644
--- a/bitsandbytes/optim/lars.py
+++ b/bitsandbytes/optim/lars.py
@@ -24,7 +24,9 @@ class LARS(Optimizer1State):
         max_unorm=0.02,
     ):
         if momentum == 0:
-            raise NotImplementedError(f"LARS without momentum is not supported!")
+            raise NotImplementedError(
+                f"LARS without momentum is not supported!"
+            )
         super(LARS, self).__init__(
             "lars",
             params,
@@ -56,7 +58,9 @@ class LARS8bit(Optimizer1State):
         max_unorm=0.02,
     ):
         if momentum == 0:
-            raise NotImplementedError(f"LARS without momentum is not supported!")
+            raise NotImplementedError(
+                f"LARS without momentum is not supported!"
+            )
         super(LARS8bit, self).__init__(
             "lars",
             params,
@@ -88,7 +92,9 @@ class LARS32bit(Optimizer1State):
         max_unorm=0.02,
     ):
         if momentum == 0:
-            raise NotImplementedError(f"LARS without momentum is not supported!")
+            raise NotImplementedError(
+                f"LARS without momentum is not supported!"
+            )
         super(LARS32bit, self).__init__(
             "lars",
             params,
@@ -121,7 +127,9 @@ class PytorchLARS(Optimizer):
         if momentum < 0.0:
             raise ValueError("Invalid momentum value: {}".format(momentum))
         if weight_decay < 0.0:
-            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+            raise ValueError(
+                "Invalid weight_decay value: {}".format(weight_decay)
+            )
 
         defaults = dict(
             lr=lr,
@@ -132,7 +140,9 @@ class PytorchLARS(Optimizer):
             max_unorm=max_unorm,
         )
         if nesterov and (momentum <= 0 or dampening != 0):
-            raise ValueError("Nesterov momentum requires a momentum and zero dampening")
+            raise ValueError(
+                "Nesterov momentum requires a momentum and zero dampening"
+            )
         super(PytorchLARS, self).__init__(params, defaults)
 
     def __setstate__(self, state):
-- 
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