From bfa0e33294f2b1dc25e65a33be2397f989824298 Mon Sep 17 00:00:00 2001
From: Titus von Koeller <titus@vonkoeller.com>
Date: Mon, 1 Aug 2022 03:31:48 -0700
Subject: ran black and isort for coherent code formatting

---
 bitsandbytes/optim/adagrad.py | 114 ++++++++++++++++++++++++++++++++++--------
 1 file changed, 93 insertions(+), 21 deletions(-)

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

diff --git a/bitsandbytes/optim/adagrad.py b/bitsandbytes/optim/adagrad.py
index 4f51250..43e3973 100644
--- a/bitsandbytes/optim/adagrad.py
+++ b/bitsandbytes/optim/adagrad.py
@@ -1,12 +1,25 @@
-# Copyright (c) Facebook, Inc. and its affiliates. 
-#   
-# This source code is licensed under the MIT license found in the 
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 from bitsandbytes.optim.optimizer import Optimizer1State
 
+
 class Adagrad(Optimizer1State):
-    def __init__(self, params, lr=1e-2, lr_decay=0, weight_decay=0, initial_accumulator_value=0, eps=1e-10,
-            optim_bits=32, args=None, min_8bit_size=4096, percentile_clipping=100, block_wise=True):
+    def __init__(
+        self,
+        params,
+        lr=1e-2,
+        lr_decay=0,
+        weight_decay=0,
+        initial_accumulator_value=0,
+        eps=1e-10,
+        optim_bits=32,
+        args=None,
+        min_8bit_size=4096,
+        percentile_clipping=100,
+        block_wise=True,
+    ):
         if not 0.0 <= lr:
             raise ValueError("Invalid learning rate: {}".format(lr))
         if not 0.0 <= weight_decay:
@@ -14,15 +27,39 @@ class Adagrad(Optimizer1State):
         if not 0.0 <= eps:
             raise ValueError("Invalid epsilon value: {}".format(eps))
         if initial_accumulator_value != 0.0:
-            raise ValueError('Initial accumulator value != 0.0 not supported!')
+            raise ValueError("Initial accumulator value != 0.0 not supported!")
         if lr_decay != 0.0:
-            raise ValueError('Lr Decay != 0.0 not supported!')
-        super(Adagrad, self).__init__('adagrad', params, lr, (0.0, 0.0), eps,
-                weight_decay, optim_bits, args, min_8bit_size, percentile_clipping, block_wise)
+            raise ValueError("Lr Decay != 0.0 not supported!")
+        super(Adagrad, self).__init__(
+            "adagrad",
+            params,
+            lr,
+            (0.0, 0.0),
+            eps,
+            weight_decay,
+            optim_bits,
+            args,
+            min_8bit_size,
+            percentile_clipping,
+            block_wise,
+        )
+
 
 class Adagrad8bit(Optimizer1State):
-    def __init__(self, params, lr=1e-2, lr_decay=0, weight_decay=0, initial_accumulator_value=0, eps=1e-10,
-            optim_bits=8, args=None, min_8bit_size=4096, percentile_clipping=100, block_wise=True):
+    def __init__(
+        self,
+        params,
+        lr=1e-2,
+        lr_decay=0,
+        weight_decay=0,
+        initial_accumulator_value=0,
+        eps=1e-10,
+        optim_bits=8,
+        args=None,
+        min_8bit_size=4096,
+        percentile_clipping=100,
+        block_wise=True,
+    ):
         if not 0.0 <= lr:
             raise ValueError("Invalid learning rate: {}".format(lr))
         if not 0.0 <= weight_decay:
@@ -30,16 +67,40 @@ class Adagrad8bit(Optimizer1State):
         if not 0.0 <= eps:
             raise ValueError("Invalid epsilon value: {}".format(eps))
         if initial_accumulator_value != 0.0:
-            raise ValueError('Initial accumulator value != 0.0 not supported!')
+            raise ValueError("Initial accumulator value != 0.0 not supported!")
         if lr_decay != 0.0:
-            raise ValueError('Lr Decay != 0.0 not supported!')
+            raise ValueError("Lr Decay != 0.0 not supported!")
         assert block_wise
-        super(Adagrad8bit, self).__init__('adagrad', params, lr, (0.0, 0.0), eps,
-                weight_decay, 8, args, min_8bit_size, percentile_clipping, block_wise)
+        super(Adagrad8bit, self).__init__(
+            "adagrad",
+            params,
+            lr,
+            (0.0, 0.0),
+            eps,
+            weight_decay,
+            8,
+            args,
+            min_8bit_size,
+            percentile_clipping,
+            block_wise,
+        )
+
 
 class Adagrad32bit(Optimizer1State):
-    def __init__(self, params, lr=1e-2, lr_decay=0, weight_decay=0, initial_accumulator_value=0, eps=1e-10,
-            optim_bits=32, args=None, min_8bit_size=4096, percentile_clipping=100, block_wise=True):
+    def __init__(
+        self,
+        params,
+        lr=1e-2,
+        lr_decay=0,
+        weight_decay=0,
+        initial_accumulator_value=0,
+        eps=1e-10,
+        optim_bits=32,
+        args=None,
+        min_8bit_size=4096,
+        percentile_clipping=100,
+        block_wise=True,
+    ):
         if not 0.0 <= lr:
             raise ValueError("Invalid learning rate: {}".format(lr))
         if not 0.0 <= weight_decay:
@@ -47,8 +108,19 @@ class Adagrad32bit(Optimizer1State):
         if not 0.0 <= eps:
             raise ValueError("Invalid epsilon value: {}".format(eps))
         if initial_accumulator_value != 0.0:
-            raise ValueError('Initial accumulator value != 0.0 not supported!')
+            raise ValueError("Initial accumulator value != 0.0 not supported!")
         if lr_decay != 0.0:
-            raise ValueError('Lr Decay != 0.0 not supported!')
-        super(Adagrad32bit, self).__init__('adagrad', params, lr, (0.0, 0.0), eps,
-                weight_decay, 32, args, min_8bit_size, percentile_clipping, block_wise)
+            raise ValueError("Lr Decay != 0.0 not supported!")
+        super(Adagrad32bit, self).__init__(
+            "adagrad",
+            params,
+            lr,
+            (0.0, 0.0),
+            eps,
+            weight_decay,
+            32,
+            args,
+            min_8bit_size,
+            percentile_clipping,
+            block_wise,
+        )
-- 
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