[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

docs/source/adaptor.md

@@ -44,22 +44,17 @@ For example, a user can implement an `Abc` adaptor like below:
 ```python
 @adaptor_registry
 class AbcAdaptor(Adaptor):
-    def __init__(self, framework_specific_info):
-        ...
+    def __init__(self, framework_specific_info): ...
 
-    def quantize(self, tune_cfg, model, dataloader, q_func=None):
-        ...
+    def quantize(self, tune_cfg, model, dataloader, q_func=None): ...
 
     def evaluate(
         self, model, dataloader, postprocess=None, metric=None, measurer=None, iteration=-1, tensorboard=False
-    ):
-        ...
+    ): ...
 
-    def query_fw_capability(self, model):
-        ...
+    def query_fw_capability(self, model): ...
 
-    def query_fused_patterns(self, model):
-        ...
+    def query_fused_patterns(self, model): ...
 ```
 
 * `quantize` function is used to perform quantization for post-training quantization and quantization-aware training. Quantization processing includes calibration and conversion processing for post-training quantization, while for quantization-aware training, it includes training and conversion processing.

docs/source/migration.md

@@ -41,8 +41,7 @@ val_dataloader = torch.utils.data.Dataloader(
 )
 
 
-def eval_func(model):
-    ...
+def eval_func(model): ...
 
 
 # Quantization code
@@ -115,8 +114,7 @@ val_dataloader = torch.utils.data.Dataloader(
 )
 
 
-def eval_func(model):
-    ...
+def eval_func(model): ...
 
 
 # Quantization code
@@ -147,12 +145,10 @@ model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path)
 tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
 
 
-def eval_func(model):
-    ...
+def eval_func(model): ...
 
 
-def train_func(model):
-    ...
+def train_func(model): ...
 
 
 trainer = Trainer(...)
@@ -213,12 +209,10 @@ model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path)
 tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
 
 
-def eval_func(model):
-    ...
+def eval_func(model): ...
 
 
-def train_func(model):
-    ...
+def train_func(model): ...
 
 
 trainer = Trainer(...)

docs/source/pruning.md

@@ -342,6 +342,7 @@ The following section exemplifies how to use hooks in user pass-in training func
   on_after_optimizer_step() # Update weights' criteria, mask weights
   on_train_end() # End of pruner, print sparse information
   """
+
   from neural_compressor.training import prepare_compression, WeightPruningConfig
 
   config = WeightPruningConfig(configs)

docs/source/quantization.md

@@ -319,8 +319,7 @@ criterion = ...
 
 
 # Quantization code
-def train_func(model):
-    ...
+def train_func(model): ...
 
 
 from neural_compressor import QuantizationAwareTrainingConfig

docs/source/tuning_strategies.md

@@ -507,8 +507,7 @@ For example, user can implement an `Abc` strategy like below:
 ```python
 @strategy_registry
 class AbcTuneStrategy(TuneStrategy):
-    def __init__(self, model, conf, q_dataloader, q_func=None, eval_dataloader=None, eval_func=None, dicts=None):
-        ...
+    def __init__(self, model, conf, q_dataloader, q_func=None, eval_dataloader=None, eval_func=None, dicts=None): ...
 
     def next_tune_cfg(self):
         # generate the next tuning config

neural_compressor/compression/pruner/README.md

@@ -343,6 +343,7 @@ The following section exemplifies how to use hooks in user pass-in training func
   on_after_optimizer_step() # Update weights' criteria, mask weights
   on_train_end() # End of pruner, print sparse information
   """
+
   from neural_compressor.training import prepare_compression, WeightPruningConfig
 
   config = WeightPruningConfig(configs)