refactor: types to type

Author: kozistr

pyproject.toml

@@ -97,7 +97,7 @@ select = [
     "TID", "ARG", "ERA", "RUF", "YTT", "PL", "Q"
 ]
 ignore = [
-    "A005", "B905",
+    "B905",
     "D100", "D102", "D104", "D105", "D107", "D203", "D213", "D413",
     "PLR0912", "PLR0913", "PLR0915", "PLR2004",
     "Q003", "ARG002",

pytorch_optimizer/base/optimizer.py

@@ -6,7 +6,7 @@
 from torch.optim import Optimizer
 
 from pytorch_optimizer.base.exception import NegativeLRError, NegativeStepError
-from pytorch_optimizer.base.types import (
+from pytorch_optimizer.base.type import (
     BETAS,
     CLOSURE,
     DEFAULTS,

pytorch_optimizer/base/types.py renamed to pytorch_optimizer/base/type.py

@@ -4,7 +4,7 @@
 from torch.nn.functional import logsigmoid, one_hot
 from torch.nn.modules.loss import _Loss
 
-from pytorch_optimizer.base.types import CLASS_MODE
+from pytorch_optimizer.base.type import CLASS_MODE
 
 
 def soft_dice_score(

pytorch_optimizer/loss/dice.py

@@ -4,7 +4,7 @@
 from torch.nn.functional import logsigmoid, one_hot
 from torch.nn.modules.loss import _Loss
 
-from pytorch_optimizer.base.types import CLASS_MODE
+from pytorch_optimizer.base.type import CLASS_MODE
 
 
 def soft_jaccard_score(

pytorch_optimizer/loss/jaccard.py

@@ -14,7 +14,7 @@
     StepLR,
 )
 
-from pytorch_optimizer.base.types import SCHEDULER
+from pytorch_optimizer.base.type import SCHEDULER
 from pytorch_optimizer.lr_scheduler.chebyshev import get_chebyshev_perm_steps, get_chebyshev_schedule
 from pytorch_optimizer.lr_scheduler.cosine_anealing import CosineAnnealingWarmupRestarts
 from pytorch_optimizer.lr_scheduler.experimental.deberta_v3_lr_scheduler import deberta_v3_large_lr_scheduler

pytorch_optimizer/lr_scheduler/__init__.py

@@ -1,6 +1,6 @@
 from torch import nn
 
-from pytorch_optimizer.base.types import PARAMETERS
+from pytorch_optimizer.base.type import PARAMETERS
 
 
 def deberta_v3_large_lr_scheduler(

pytorch_optimizer/lr_scheduler/experimental/deberta_v3_lr_scheduler.py

@@ -7,7 +7,7 @@
 from torch import nn
 from torch.optim import SGD, Adam, AdamW, Optimizer
 
-from pytorch_optimizer.base.types import OPTIMIZER, PARAMETERS
+from pytorch_optimizer.base.type import OPTIMIZER, PARAMETERS
 from pytorch_optimizer.optimizer.a2grad import A2Grad
 from pytorch_optimizer.optimizer.adabelief import AdaBelief
 from pytorch_optimizer.optimizer.adabound import AdaBound

pytorch_optimizer/optimizer/__init__.py

@@ -5,7 +5,7 @@
 
 from pytorch_optimizer.base.exception import NoSparseGradientError
 from pytorch_optimizer.base.optimizer import BaseOptimizer
-from pytorch_optimizer.base.types import CLOSURE, DEFAULTS, LOSS, PARAMETERS
+from pytorch_optimizer.base.type import CLOSURE, DEFAULTS, LOSS, PARAMETERS
 
 VARIANTS = Literal['uni', 'inc', 'exp']
 

pytorch_optimizer/optimizer/a2grad.py

@@ -4,7 +4,7 @@
 
 from pytorch_optimizer.base.exception import NoSparseGradientError
 from pytorch_optimizer.base.optimizer import BaseOptimizer
-from pytorch_optimizer.base.types import BETAS, CLOSURE, DEFAULTS, LOSS, PARAMETERS
+from pytorch_optimizer.base.type import BETAS, CLOSURE, DEFAULTS, LOSS, PARAMETERS
 
 
 class AdaBelief(BaseOptimizer):