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

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

src/scvi/external/decipher/_components.py

@@ -1,4 +1,4 @@
-from typing import Sequence
+from collections.abc import Sequence
 
 import numpy as np
 import torch
@@ -52,7 +52,7 @@ def __init__(
 
         # The multiple outputs are computed as a single output layer, and then split
         indices = np.concatenate(([0], np.cumsum(self.output_dims)))
-        self.output_slices = [slice(s, e) for s, e in zip(indices[:-1], indices[1:])]
+        self.output_slices = [slice(s, e) for s, e in zip(indices[:-1], indices[1:], strict=False)]
 
         # Create masked layers
         deep_context_dim = self.context_dim if self.deep_context_injection else 0
@@ -63,21 +63,15 @@ def __init__(
             batch_norms.append(nn.BatchNorm1d(hidden_dims[0]))
             for i in range(1, len(hidden_dims)):
                 layers.append(
-                    torch.nn.Linear(
-                        hidden_dims[i - 1] + deep_context_dim, hidden_dims[i]
-                    )
+                    torch.nn.Linear(hidden_dims[i - 1] + deep_context_dim, hidden_dims[i])
                 )
                 batch_norms.append(nn.BatchNorm1d(hidden_dims[i]))
 
             layers.append(
-                torch.nn.Linear(
-                    hidden_dims[-1] + deep_context_dim, self.output_total_dim
-                )
+                torch.nn.Linear(hidden_dims[-1] + deep_context_dim, self.output_total_dim)
             )
         else:
-            layers.append(
-                torch.nn.Linear(input_dim + context_dim, self.output_total_dim)
-            )
+            layers.append(torch.nn.Linear(input_dim + context_dim, self.output_total_dim))
 
         self.layers = torch.nn.ModuleList(layers)
 

src/scvi/external/decipher/_model.py

@@ -62,9 +62,7 @@ def setup_anndata(
         anndata_fields = [
             LayerField(REGISTRY_KEYS.X_KEY, layer, is_count_data=True),
         ]
-        adata_manager = AnnDataManager(
-            fields=anndata_fields, setup_method_args=setup_method_args
-        )
+        adata_manager = AnnDataManager(fields=anndata_fields, setup_method_args=setup_method_args)
         adata_manager.register_fields(adata, **kwargs)
         cls.register_manager(adata_manager)
 
@@ -113,9 +111,7 @@ def get_latent_representation(
         self._check_if_trained(warn=False)
         adata = self._validate_anndata(adata)
 
-        scdl = self._make_data_loader(
-            adata=adata, indices=indices, batch_size=batch_size
-        )
+        scdl = self._make_data_loader(adata=adata, indices=indices, batch_size=batch_size)
         latent_locs = []
         for tensors in scdl:
             x = tensors[REGISTRY_KEYS.X_KEY]

src/scvi/external/decipher/_module.py

@@ -82,9 +82,7 @@ def device(self):
         return self._dummy_param.device
 
     @staticmethod
-    def _get_fn_args_from_batch(
-        tensor_dict: dict[str, torch.Tensor]
-    ) -> Iterable | dict:
+    def _get_fn_args_from_batch(tensor_dict: dict[str, torch.Tensor]) -> Iterable | dict:
         x = tensor_dict[REGISTRY_KEYS.X_KEY]
         return (x,), {}
 
@@ -125,9 +123,7 @@ def model(self, x: torch.Tensor):
                 self.theta + self._epsilon
             )
             # noinspection PyUnresolvedReferences
-            x_dist = dist.NegativeBinomial(
-                total_count=self.theta + self._epsilon, logits=logit
-            )
+            x_dist = dist.NegativeBinomial(total_count=self.theta + self._epsilon, logits=logit)
             pyro.sample("x", x_dist.to_event(1), obs=x)
 
     @auto_move_data
@@ -188,9 +184,7 @@ def predictive_log_likelihood(self, x: torch.Tensor, n_samples=5):
                 model_trace = poutine.trace(
                     poutine.replay(self.model, trace=guide_trace)
                 ).get_trace(x)
-                log_weights.append(
-                    model_trace.log_prob_sum() - guide_trace.log_prob_sum()
-                )
+                log_weights.append(model_trace.log_prob_sum() - guide_trace.log_prob_sum())
 
         finally:
             self.beta = old_beta

src/scvi/external/decipher/_trainingplan.py

@@ -41,9 +41,7 @@ def __init__(
             optim_kwargs.update({"lr": 5e-3})
         if "weight_decay" not in optim_kwargs.keys():
             optim_kwargs.update({"weight_decay": 1e-4})
-        self.optim = (
-            pyro.optim.ClippedAdam(optim_args=optim_kwargs) if optim is None else optim
-        )
+        self.optim = pyro.optim.ClippedAdam(optim_args=optim_kwargs) if optim is None else optim
         # We let SVI take care of all optimization
         self.automatic_optimization = False