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

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src/scvi/external/sysvi/_base_components.py

@@ -197,7 +197,9 @@ def __init__(
  Dropout(p=dropout_rate) if dropout_rate > 0 else None,
  ),
  )
- for i, (n_in, n_out) in enumerate(zip(layers_dim[:-1], layers_dim[1:]))
+ for i, (n_in, n_out) in enumerate(
+ zip(layers_dim[:-1], layers_dim[1:], strict=False)
+ )
  ]
  )
  )

src/scvi/external/sysvi/_model.py

@@ -2,7 +2,7 @@
 
 import logging
 from collections.abc import Sequence
-from typing import Literal, Tuple
+from typing import Literal
 
 import numpy as np
 import pandas as pd
@@ -118,7 +118,7 @@ def get_latent_representation(
  give_mean: bool = True,
  batch_size: int | None = None,
  return_dist: bool = False,
- ) -> np.ndarray | Tuple[np.ndarray, np.ndarray]:
+ ) -> np.ndarray | tuple[np.ndarray, np.ndarray]:
  """Return the latent representation for each cell.
 
  Parameters
@@ -300,7 +300,9 @@ def setup_anndata(
  )
 
  # Make one-hot embedding with specified order
- systems_dict = dict(zip(batch_order, ([float(i) for i in range(0, len(batch_order))])))
+ systems_dict = dict(
+ zip(batch_order, ([float(i) for i in range(0, len(batch_order))]), strict=False)
+ )
  adata.uns["batch_order"] = batch_order
  system_cat = pd.Series(
  pd.Categorical(values=adata.obs[batch_key], categories=batch_order, ordered=True),

src/scvi/external/sysvi/_module.py

@@ -1,7 +1,8 @@
 from __future__ import annotations
 
+from typing import Literal
+
 import torch
-from typing_extensions import Literal
 
 from scvi import REGISTRY_KEYS
 from scvi.module.base import BaseModuleClass, LossOutput, auto_move_data

src/scvi/external/sysvi/_utils.py

@@ -140,7 +140,7 @@ def dummies_categories(values: pd.Series, categories: list):
  cov_embed_data = []
  for cov_cat_embed_key in cov_cat_embed_keys:
  cat_order = categ_orders[cov_cat_embed_key]
- cat_map = dict(zip(cat_order, range(len(cat_order))))
+ cat_map = dict(zip(cat_order, range(len(cat_order)), strict=False))
  cov_embed_data.append(meta_data[cov_cat_embed_key].map(cat_map))
  cov_embed_data = pd.concat(cov_embed_data, axis=1)
  else: