fix minor typos in code and docs

src/refiners/fluxion/layers/chain.py

@@ -215,7 +215,7 @@ class Chain(ContextModule):
     @staticmethod
     def _pretty_print_args(*args: Any) -> str:
         """
-        Flatten nested tuples and print tensors with their shape and other informations.
+        Flatten nested tuples and print tensors with their shape and other information.
         """
 
         def _flatten_tuple(t: Tensor | tuple[Any, ...], /) -> list[Any]:

src/refiners/foundationals/latent_diffusion/image_prompt.py

@@ -447,7 +447,7 @@ class IPAdapter(Generic[T], fl.Chain, Adapter[T]):
         assert isinstance(encoder_clone[-3], fl.Lambda)  # pooling (classif token)
         for _ in range(3):
             encoder_clone.pop()
-        transfomer_layers = encoder_clone[-1]
+        transformer_layers = encoder_clone[-1]
-        assert isinstance(transfomer_layers, fl.Chain) and len(transfomer_layers) == 32
+        assert isinstance(transformer_layers, fl.Chain) and len(transformer_layers) == 32
-        transfomer_layers.pop()
+        transformer_layers.pop()
         return encoder_clone

src/refiners/training_utils/config.py

@@ -178,7 +178,7 @@ class ModelConfig(BaseModel):
 
 class GyroDropoutConfig(BaseModel):
     total_subnetworks: int = 512
-    concurent_subnetworks: int = 64
+    concurrent_subnetworks: int = 64
     iters_per_epoch: int = 512
     num_features_threshold: float = 5e5
 

src/refiners/training_utils/trainer.py

@@ -213,7 +213,7 @@ class TrainingClock:
         return int(time.time() - self.start_time)
 
     @cached_property
-    def evalution_interval_steps(self) -> int:
+    def evaluation_interval_steps(self) -> int:
         return self.convert_time_unit_to_steps(
             number=self.evaluation_interval["number"], unit=self.evaluation_interval["unit"]
         )
@@ -244,7 +244,7 @@ class TrainingClock:
 
     @property
     def is_evaluation_step(self) -> bool:
-        return self.step % self.evalution_interval_steps == 0
+        return self.step % self.evaluation_interval_steps == 0
 
     @property
     def is_checkpointing_step(self) -> bool: