From 62178aac5c70a5c8fa0c0d80d85b3bb1377456ce Mon Sep 17 00:00:00 2001
From: "deepsource-autofix[bot]"
 <62050782+deepsource-autofix[bot]@users.noreply.github.com>
Date: Tue, 12 Jan 2021 10:34:18 +0000
Subject: [PATCH 1/2] Refactor unnecessary `else` / `elif` when `if` block has
 a `return` statement

---
 monai/apps/datasets.py                   |  5 +-
 monai/data/dataset.py                    |  3 +-
 monai/data/decathlon_datalist.py         |  5 +-
 monai/engines/utils.py                   |  5 +-
 monai/handlers/confusion_matrix.py       |  5 +-
 monai/metrics/confusion_matrix.py        | 57 +++++++++++------------
 monai/metrics/hausdorff_distance.py      |  5 +-
 monai/metrics/rocauc.py                  | 59 ++++++++++++------------
 monai/networks/layers/factories.py       | 11 ++---
 monai/networks/nets/autoencoder.py       | 23 +++++----
 monai/networks/nets/dynunet.py           |  2 +-
 monai/networks/nets/fullyconnectednet.py |  3 +-
 monai/networks/nets/unet.py              | 21 ++++-----
 monai/transforms/croppad/array.py        | 13 ++----
 monai/transforms/intensity/array.py      |  5 +-
 monai/transforms/post/array.py           |  5 +-
 monai/transforms/utility/array.py        |  8 ++--
 monai/transforms/utils.py                |  2 +-
 monai/utils/misc.py                      |  2 +-
 monai/utils/module.py                    |  3 +-
 setup.py                                 |  4 +-
 tests/test_zipdataset.py                 |  3 +-
 22 files changed, 113 insertions(+), 136 deletions(-)

diff --git a/monai/apps/datasets.py b/monai/apps/datasets.py
index f5a9bcdcbc..b4bc40ae1f 100644
--- a/monai/apps/datasets.py
+++ b/monai/apps/datasets.py
@@ -291,10 +291,9 @@ def get_properties(self, keys: Optional[Union[Sequence[str], str]] = None):
         """
         if keys is None:
             return self._properties
-        elif self._properties is not None:
+        if self._properties is not None:
             return {key: self._properties[key] for key in ensure_tuple(keys)}
-        else:
-            return {}
+        return {}
 
     def _generate_data_list(self, dataset_dir: str) -> List[Dict]:
         section = "training" if self.section in ["training", "validation"] else "test"
diff --git a/monai/data/dataset.py b/monai/data/dataset.py
index 71aedc7413..ed0d590bf7 100644
--- a/monai/data/dataset.py
+++ b/monai/data/dataset.py
@@ -699,8 +699,7 @@ def _try_shutdown(self):
                 self._round = 0
                 self._replace_done = False
                 return True
-            else:
-                return False
+            return False
 
     def shutdown(self):
         """
diff --git a/monai/data/decathlon_datalist.py b/monai/data/decathlon_datalist.py
index 38e217f19b..6167e83e47 100644
--- a/monai/data/decathlon_datalist.py
+++ b/monai/data/decathlon_datalist.py
@@ -39,13 +39,12 @@ def _compute_path(base_dir, element):
     """
     if isinstance(element, str):
         return os.path.normpath(os.path.join(base_dir, element))
-    elif isinstance(element, list):
+    if isinstance(element, list):
         for e in element:
             if not isinstance(e, str):
                 raise TypeError(f"Every file path in element must be a str but got {type(element).__name__}.")
         return [os.path.normpath(os.path.join(base_dir, e)) for e in element]
-    else:
-        raise TypeError(f"element must be one of (str, list) but is {type(element).__name__}.")
+    raise TypeError(f"element must be one of (str, list) but is {type(element).__name__}.")
 
 
 def _append_paths(base_dir: str, is_segmentation: bool, items: List[Dict]) -> List[Dict]:
diff --git a/monai/engines/utils.py b/monai/engines/utils.py
index d3d4d62c0b..028051883f 100644
--- a/monai/engines/utils.py
+++ b/monai/engines/utils.py
@@ -95,10 +95,9 @@ def default_prepare_batch(
             batchdata[CommonKeys.IMAGE].to(device=device, non_blocking=non_blocking),
             batchdata[CommonKeys.LABEL].to(device=device, non_blocking=non_blocking),
         )
-    elif GanKeys.REALS in batchdata:
+    if GanKeys.REALS in batchdata:
         return batchdata[GanKeys.REALS].to(device=device, non_blocking=non_blocking)
-    else:
-        return batchdata[CommonKeys.IMAGE].to(device=device, non_blocking=non_blocking), None
+    return batchdata[CommonKeys.IMAGE].to(device=device, non_blocking=non_blocking), None
 
 
 def default_make_latent(
diff --git a/monai/handlers/confusion_matrix.py b/monai/handlers/confusion_matrix.py
index 28b52f8609..fe60b964a7 100644
--- a/monai/handlers/confusion_matrix.py
+++ b/monai/handlers/confusion_matrix.py
@@ -122,6 +122,5 @@ def compute(self):
                     "ConfusionMatrix metric must have at least one example before it can be computed."
                 )
             return self._sum / self._num_examples
-        else:
-            confusion_matrix = torch.tensor([self._total_tp, self._total_fp, self._total_tn, self._total_fn])
-            return compute_confusion_matrix_metric(self.metric_name, confusion_matrix)
+        confusion_matrix = torch.tensor([self._total_tp, self._total_fp, self._total_tn, self._total_fn])
+        return compute_confusion_matrix_metric(self.metric_name, confusion_matrix)
diff --git a/monai/metrics/confusion_matrix.py b/monai/metrics/confusion_matrix.py
index b9cc92ceeb..bc06fe5855 100644
--- a/monai/metrics/confusion_matrix.py
+++ b/monai/metrics/confusion_matrix.py
@@ -103,16 +103,15 @@ def __call__(self, y_pred: torch.Tensor, y: torch.Tensor):
                 confusion_matrix = compute_confusion_matrix_metric(self.metric_name, confusion_matrix)
                 f, not_nans = do_metric_reduction(confusion_matrix, self.reduction)
                 return f, not_nans
-            else:
-                if len(self.metric_name) < 1:
-                    raise ValueError("the sequence should at least has on metric name.")
-                results = []
-                for metric_name in self.metric_name:
-                    sub_confusion_matrix = compute_confusion_matrix_metric(metric_name, confusion_matrix)
-                    f, not_nans = do_metric_reduction(sub_confusion_matrix, self.reduction)
-                    results.append(f)
-                    results.append(not_nans)
-                return results
+            if len(self.metric_name) < 1:
+                raise ValueError("the sequence should at least has on metric name.")
+            results = []
+            for metric_name in self.metric_name:
+                sub_confusion_matrix = compute_confusion_matrix_metric(metric_name, confusion_matrix)
+                f, not_nans = do_metric_reduction(sub_confusion_matrix, self.reduction)
+                results.append(f)
+                results.append(not_nans)
+            return results
         else:
             return confusion_matrix
 
@@ -264,8 +263,7 @@ def compute_confusion_matrix_metric(metric_name: str, confusion_matrix: torch.Te
 
     if isinstance(denominator, torch.Tensor):
         return torch.where(denominator != 0, numerator / denominator, nan_tensor)
-    else:
-        return numerator / denominator
+    return numerator / denominator
 
 
 def check_confusion_matrix_metric_name(metric_name: str):
@@ -284,37 +282,36 @@ def check_confusion_matrix_metric_name(metric_name: str):
     metric_name = metric_name.lower()
     if metric_name in ["sensitivity", "recall", "hit_rate", "true_positive_rate", "tpr"]:
         return "tpr"
-    elif metric_name in ["specificity", "selectivity", "true_negative_rate", "tnr"]:
+    if metric_name in ["specificity", "selectivity", "true_negative_rate", "tnr"]:
         return "tnr"
-    elif metric_name in ["precision", "positive_predictive_value", "ppv"]:
+    if metric_name in ["precision", "positive_predictive_value", "ppv"]:
         return "ppv"
-    elif metric_name in ["negative_predictive_value", "npv"]:
+    if metric_name in ["negative_predictive_value", "npv"]:
         return "npv"
-    elif metric_name in ["miss_rate", "false_negative_rate", "fnr"]:
+    if metric_name in ["miss_rate", "false_negative_rate", "fnr"]:
         return "fnr"
-    elif metric_name in ["fall_out", "false_positive_rate", "fpr"]:
+    if metric_name in ["fall_out", "false_positive_rate", "fpr"]:
         return "fpr"
-    elif metric_name in ["false_discovery_rate", "fdr"]:
+    if metric_name in ["false_discovery_rate", "fdr"]:
         return "fdr"
-    elif metric_name in ["false_omission_rate", "for"]:
+    if metric_name in ["false_omission_rate", "for"]:
         return "for"
-    elif metric_name in ["prevalence_threshold", "pt"]:
+    if metric_name in ["prevalence_threshold", "pt"]:
         return "pt"
-    elif metric_name in ["threat_score", "critical_success_index", "ts", "csi"]:
+    if metric_name in ["threat_score", "critical_success_index", "ts", "csi"]:
         return "ts"
-    elif metric_name in ["accuracy", "acc"]:
+    if metric_name in ["accuracy", "acc"]:
         return "acc"
-    elif metric_name in ["balanced_accuracy", "ba"]:
+    if metric_name in ["balanced_accuracy", "ba"]:
         return "ba"
-    elif metric_name in ["f1_score", "f1"]:
+    if metric_name in ["f1_score", "f1"]:
         return "f1"
-    elif metric_name in ["matthews_correlation_coefficient", "mcc"]:
+    if metric_name in ["matthews_correlation_coefficient", "mcc"]:
         return "mcc"
-    elif metric_name in ["fowlkes_mallows_index", "fm"]:
+    if metric_name in ["fowlkes_mallows_index", "fm"]:
         return "fm"
-    elif metric_name in ["informedness", "bookmaker_informedness", "bm"]:
+    if metric_name in ["informedness", "bookmaker_informedness", "bm"]:
         return "bm"
-    elif metric_name in ["markedness", "deltap", "mk"]:
+    if metric_name in ["markedness", "deltap", "mk"]:
         return "mk"
-    else:
-        raise NotImplementedError("the metric is not implemented.")
+    raise NotImplementedError("the metric is not implemented.")
diff --git a/monai/metrics/hausdorff_distance.py b/monai/metrics/hausdorff_distance.py
index b88cc77c99..8ecc19ec46 100644
--- a/monai/metrics/hausdorff_distance.py
+++ b/monai/metrics/hausdorff_distance.py
@@ -166,7 +166,6 @@ def compute_percent_hausdorff_distance(
     if not percentile:
         return surface_distance.max()
 
-    elif 0 <= percentile <= 100:
+    if 0 <= percentile <= 100:
         return np.percentile(surface_distance, percentile)
-    else:
-        raise ValueError(f"percentile should be a value between 0 and 100, get {percentile}.")
+    raise ValueError(f"percentile should be a value between 0 and 100, get {percentile}.")
diff --git a/monai/metrics/rocauc.py b/monai/metrics/rocauc.py
index e17330c35d..8d12f90867 100644
--- a/monai/metrics/rocauc.py
+++ b/monai/metrics/rocauc.py
@@ -114,33 +114,32 @@ def compute_roc_auc(
         if softmax:
             warnings.warn("y_pred has only one channel, softmax=True ignored.")
         return _calculate(y, y_pred)
-    else:
-        n_classes = y_pred.shape[1]
-        if to_onehot_y:
-            y = one_hot(y, n_classes)
-        if softmax and other_act is not None:
-            raise ValueError("Incompatible values: softmax=True and other_act is not None.")
-        if softmax:
-            y_pred = y_pred.float().softmax(dim=1)
-        if other_act is not None:
-            if not callable(other_act):
-                raise TypeError(f"other_act must be None or callable but is {type(other_act).__name__}.")
-            y_pred = other_act(y_pred)
-
-        assert y.shape == y_pred.shape, "data shapes of y_pred and y do not match."
-
-        average = Average(average)
-        if average == Average.MICRO:
-            return _calculate(y.flatten(), y_pred.flatten())
-        y, y_pred = y.transpose(0, 1), y_pred.transpose(0, 1)
-        auc_values = [_calculate(y_, y_pred_) for y_, y_pred_ in zip(y, y_pred)]
-        if average == Average.NONE:
-            return auc_values
-        if average == Average.MACRO:
-            return np.mean(auc_values)
-        if average == Average.WEIGHTED:
-            weights = [sum(y_) for y_ in y]
-            return np.average(auc_values, weights=weights)
-        raise ValueError(
-            f'Unsupported average: {average}, available options are ["macro", "weighted", "micro", "none"].'
-        )
+    n_classes = y_pred.shape[1]
+    if to_onehot_y:
+        y = one_hot(y, n_classes)
+    if softmax and other_act is not None:
+        raise ValueError("Incompatible values: softmax=True and other_act is not None.")
+    if softmax:
+        y_pred = y_pred.float().softmax(dim=1)
+    if other_act is not None:
+        if not callable(other_act):
+            raise TypeError(f"other_act must be None or callable but is {type(other_act).__name__}.")
+        y_pred = other_act(y_pred)
+
+    assert y.shape == y_pred.shape, "data shapes of y_pred and y do not match."
+
+    average = Average(average)
+    if average == Average.MICRO:
+        return _calculate(y.flatten(), y_pred.flatten())
+    y, y_pred = y.transpose(0, 1), y_pred.transpose(0, 1)
+    auc_values = [_calculate(y_, y_pred_) for y_, y_pred_ in zip(y, y_pred)]
+    if average == Average.NONE:
+        return auc_values
+    if average == Average.MACRO:
+        return np.mean(auc_values)
+    if average == Average.WEIGHTED:
+        weights = [sum(y_) for y_ in y]
+        return np.average(auc_values, weights=weights)
+    raise ValueError(
+        f'Unsupported average: {average}, available options are ["macro", "weighted", "micro", "none"].'
+    )
diff --git a/monai/networks/layers/factories.py b/monai/networks/layers/factories.py
index 64de9896a5..ec36b2ed95 100644
--- a/monai/networks/layers/factories.py
+++ b/monai/networks/layers/factories.py
@@ -178,14 +178,13 @@ def split_args(args):
 
     if isinstance(args, str):
         return args, {}
-    else:
-        name_obj, name_args = args
+    name_obj, name_args = args
 
-        if not isinstance(name_obj, (str, Callable)) or not isinstance(name_args, dict):
-            msg = "Layer specifiers must be single strings or pairs of the form (name/object-types, argument dict)"
-            raise TypeError(msg)
+    if not isinstance(name_obj, (str, Callable)) or not isinstance(name_args, dict):
+        msg = "Layer specifiers must be single strings or pairs of the form (name/object-types, argument dict)"
+        raise TypeError(msg)
 
-        return name_obj, name_args
+    return name_obj, name_args
 
 
 # Define factories for these layer types
diff --git a/monai/networks/nets/autoencoder.py b/monai/networks/nets/autoencoder.py
index e97502da8f..53e96b0841 100644
--- a/monai/networks/nets/autoencoder.py
+++ b/monai/networks/nets/autoencoder.py
@@ -148,18 +148,17 @@ def _get_encode_layer(self, in_channels: int, out_channels: int, strides: int, i
                 dropout=self.dropout,
                 last_conv_only=is_last,
             )
-        else:
-            return Convolution(
-                dimensions=self.dimensions,
-                in_channels=in_channels,
-                out_channels=out_channels,
-                strides=strides,
-                kernel_size=self.kernel_size,
-                act=self.act,
-                norm=self.norm,
-                dropout=self.dropout,
-                conv_only=is_last,
-            )
+        return Convolution(
+            dimensions=self.dimensions,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            strides=strides,
+            kernel_size=self.kernel_size,
+            act=self.act,
+            norm=self.norm,
+            dropout=self.dropout,
+            conv_only=is_last,
+        )
 
     def _get_decode_layer(self, in_channels: int, out_channels: int, strides: int, is_last: bool) -> nn.Sequential:
 
diff --git a/monai/networks/nets/dynunet.py b/monai/networks/nets/dynunet.py
index e8c3b517d9..8958199d3f 100644
--- a/monai/networks/nets/dynunet.py
+++ b/monai/networks/nets/dynunet.py
@@ -136,7 +136,7 @@ def create_skips(index, downsamples, upsamples, superheads, bottleneck):
 
             if len(downsamples) == 0:  # bottom of the network, pass the bottleneck block
                 return bottleneck
-            elif index == 0:  # don't associate a supervision head with self.input_block
+            if index == 0:  # don't associate a supervision head with self.input_block
                 current_head, rest_heads = nn.Identity(), superheads
             else:
                 current_head, rest_heads = superheads[0], superheads[1:]
diff --git a/monai/networks/nets/fullyconnectednet.py b/monai/networks/nets/fullyconnectednet.py
index 91377c410c..b906bab015 100644
--- a/monai/networks/nets/fullyconnectednet.py
+++ b/monai/networks/nets/fullyconnectednet.py
@@ -25,8 +25,7 @@ def _get_adn_layer(
 ) -> ADN:
     if ordering:
         return ADN(act=act, dropout=dropout, dropout_dim=1, ordering=ordering)
-    else:
-        return ADN(act=act, dropout=dropout, dropout_dim=1)
+    return ADN(act=act, dropout=dropout, dropout_dim=1)
 
 
 class FullyConnectedNet(nn.Sequential):
diff --git a/monai/networks/nets/unet.py b/monai/networks/nets/unet.py
index 3626b21a64..f3742d05b5 100644
--- a/monai/networks/nets/unet.py
+++ b/monai/networks/nets/unet.py
@@ -126,17 +126,16 @@ def _get_down_layer(self, in_channels: int, out_channels: int, strides: int, is_
                 norm=self.norm,
                 dropout=self.dropout,
             )
-        else:
-            return Convolution(
-                self.dimensions,
-                in_channels,
-                out_channels,
-                strides=strides,
-                kernel_size=self.kernel_size,
-                act=self.act,
-                norm=self.norm,
-                dropout=self.dropout,
-            )
+        return Convolution(
+            self.dimensions,
+            in_channels,
+            out_channels,
+            strides=strides,
+            kernel_size=self.kernel_size,
+            act=self.act,
+            norm=self.norm,
+            dropout=self.dropout,
+        )
 
     def _get_bottom_layer(self, in_channels: int, out_channels: int) -> nn.Module:
         """
diff --git a/monai/transforms/croppad/array.py b/monai/transforms/croppad/array.py
index 9dace83c4d..073d581ee1 100644
--- a/monai/transforms/croppad/array.py
+++ b/monai/transforms/croppad/array.py
@@ -79,8 +79,7 @@ def _determine_data_pad_width(self, data_shape: Sequence[int]) -> List[Tuple[int
                 width = max(self.spatial_size[i] - data_shape[i], 0)
                 pad_width.append((width // 2, width - (width // 2)))
             return pad_width
-        else:
-            return [(0, max(self.spatial_size[i] - data_shape[i], 0)) for i in range(len(self.spatial_size))]
+        return [(0, max(self.spatial_size[i] - data_shape[i], 0)) for i in range(len(self.spatial_size))]
 
     def __call__(self, img: np.ndarray, mode: Optional[Union[NumpyPadMode, str]] = None) -> np.ndarray:
         """
@@ -97,9 +96,8 @@ def __call__(self, img: np.ndarray, mode: Optional[Union[NumpyPadMode, str]] = N
         if not np.asarray(all_pad_width).any():
             # all zeros, skip padding
             return img
-        else:
-            img = np.pad(img, all_pad_width, mode=self.mode.value if mode is None else NumpyPadMode(mode).value)
-            return img
+        img = np.pad(img, all_pad_width, mode=self.mode.value if mode is None else NumpyPadMode(mode).value)
+        return img
 
 
 class BorderPad(Transform):
@@ -317,9 +315,8 @@ def __call__(self, img: np.ndarray) -> np.ndarray:
         assert self._size is not None
         if self.random_center:
             return img[self._slices]
-        else:
-            cropper = CenterSpatialCrop(self._size)
-            return cropper(img)
+        cropper = CenterSpatialCrop(self._size)
+        return cropper(img)
 
 
 class RandSpatialCropSamples(Randomizable, Transform):
diff --git a/monai/transforms/intensity/array.py b/monai/transforms/intensity/array.py
index 84d25c663f..d1ba8ce7fa 100644
--- a/monai/transforms/intensity/array.py
+++ b/monai/transforms/intensity/array.py
@@ -163,10 +163,9 @@ def __call__(self, img: np.ndarray) -> np.ndarray:
         """
         if self.minv is not None and self.maxv is not None:
             return rescale_array(img, self.minv, self.maxv, img.dtype)
-        elif self.factor is not None:
+        if self.factor is not None:
             return (img * (1 + self.factor)).astype(img.dtype)
-        else:
-            raise ValueError("Incompatible values: minv=None or maxv=None and factor=None.")
+        raise ValueError("Incompatible values: minv=None or maxv=None and factor=None.")
 
 
 class RandScaleIntensity(Randomizable, Transform):
diff --git a/monai/transforms/post/array.py b/monai/transforms/post/array.py
index bf6a431b72..3249fbaf71 100644
--- a/monai/transforms/post/array.py
+++ b/monai/transforms/post/array.py
@@ -414,6 +414,5 @@ def __call__(self, img: Union[Sequence[torch.Tensor], torch.Tensor]) -> torch.Te
         if self.num_classes is not None:
             # if not One-Hot, use "argmax" to vote the most common class
             return torch.argmax(img_, dim=1, keepdim=has_ch_dim)
-        else:
-            # for One-Hot data, round the float number to 0 or 1
-            return torch.round(img_)
+        # for One-Hot data, round the float number to 0 or 1
+        return torch.round(img_)
diff --git a/monai/transforms/utility/array.py b/monai/transforms/utility/array.py
index bbb78fb7bb..4268df1e25 100644
--- a/monai/transforms/utility/array.py
+++ b/monai/transforms/utility/array.py
@@ -226,10 +226,9 @@ def __call__(
         """
         if isinstance(img, np.ndarray):
             return img.astype(self.dtype if dtype is None else dtype)
-        elif torch.is_tensor(img):
+        if torch.is_tensor(img):
             return torch.as_tensor(img, dtype=self.dtype if dtype is None else dtype)
-        else:
-            raise TypeError(f"img must be one of (numpy.ndarray, torch.Tensor) but is {type(img).__name__}.")
+        raise TypeError(f"img must be one of (numpy.ndarray, torch.Tensor) but is {type(img).__name__}.")
 
 
 class ToTensor(Transform):
@@ -460,8 +459,7 @@ def __call__(self, img: Union[np.ndarray, torch.Tensor], func: Optional[Callable
             return func(img)
         if self.func is not None:
             return self.func(img)
-        else:
-            raise ValueError("Incompatible values: func=None and self.func=None.")
+        raise ValueError("Incompatible values: func=None and self.func=None.")
 
 
 class LabelToMask(Transform):
diff --git a/monai/transforms/utils.py b/monai/transforms/utils.py
index f28f95688d..6f94660ccd 100644
--- a/monai/transforms/utils.py
+++ b/monai/transforms/utils.py
@@ -490,7 +490,7 @@ def create_shear(spatial_dims: int, coefs: Union[Sequence[float], float]) -> np.
     if spatial_dims == 2:
         coefs = ensure_tuple_size(coefs, dim=2, pad_val=0.0)
         return np.array([[1, coefs[0], 0.0], [coefs[1], 1.0, 0.0], [0.0, 0.0, 1.0]])
-    elif spatial_dims == 3:
+    if spatial_dims == 3:
         coefs = ensure_tuple_size(coefs, dim=6, pad_val=0.0)
         return np.array(
             [
diff --git a/monai/utils/misc.py b/monai/utils/misc.py
index 2b4d078ad7..343e809f70 100644
--- a/monai/utils/misc.py
+++ b/monai/utils/misc.py
@@ -120,7 +120,7 @@ def ensure_tuple_rep(tup: Any, dim: int) -> Tuple[Any, ...]:
     """
     if not issequenceiterable(tup):
         return (tup,) * dim
-    elif len(tup) == dim:
+    if len(tup) == dim:
         return tuple(tup)
 
     raise ValueError(f"Sequence must have length {dim}, got {len(tup)}.")
diff --git a/monai/utils/module.py b/monai/utils/module.py
index 7e6ca83b67..1d06585e3b 100644
--- a/monai/utils/module.py
+++ b/monai/utils/module.py
@@ -85,8 +85,7 @@ def get_full_type_name(typeobj):
     module = typeobj.__module__
     if module is None or module == str.__class__.__module__:
         return typeobj.__name__  # Avoid reporting __builtin__
-    else:
-        return module + "." + typeobj.__name__
+    return module + "." + typeobj.__name__
 
 
 def min_version(the_module, min_version_str: str = "") -> bool:
diff --git a/setup.py b/setup.py
index d62a0398d2..e8a54cc9fc 100644
--- a/setup.py
+++ b/setup.py
@@ -62,9 +62,9 @@ def torch_parallel_backend():
         backend = match.group("backend")
         if backend == "OpenMP":
             return "AT_PARALLEL_OPENMP"
-        elif backend == "native thread pool":
+        if backend == "native thread pool":
             return "AT_PARALLEL_NATIVE"
-        elif backend == "native thread pool and TBB":
+        if backend == "native thread pool and TBB":
             return "AT_PARALLEL_NATIVE_TBB"
     except (NameError, AttributeError):  # no torch or no binaries
         warnings.warn("Could not determine torch parallel_info.")
diff --git a/tests/test_zipdataset.py b/tests/test_zipdataset.py
index afd6ab11ab..1bdb6458d3 100644
--- a/tests/test_zipdataset.py
+++ b/tests/test_zipdataset.py
@@ -28,8 +28,7 @@ def __len__(self):
     def __getitem__(self, index):
         if self.index_only:
             return index
-        else:
-            return 1, 2, index
+        return 1, 2, index
 
 
 TEST_CASE_1 = [[Dataset_(5), Dataset_(5), Dataset_(5)], None, (0, 0, 0), 5]

From e78237a0dc839fbfb3bd045e0a5a23ebef1864a2 Mon Sep 17 00:00:00 2001
From: monai-bot <monai.miccai2019@gmail.com>
Date: Tue, 12 Jan 2021 12:35:22 +0000
Subject: [PATCH 2/2] [MONAI] python code formatting

Signed-off-by: monai-bot <monai.miccai2019@gmail.com>
---
 monai/csrc/resample/pushpull_cpu.cpp | 44 ++++++++++++++++++----------
 monai/csrc/resample/pushpull_cuda.cu | 44 ++++++++++++++++++----------
 monai/metrics/rocauc.py              |  4 +--
 3 files changed, 57 insertions(+), 35 deletions(-)

diff --git a/monai/csrc/resample/pushpull_cpu.cpp b/monai/csrc/resample/pushpull_cpu.cpp
index 204fa846d3..40743a6cf1 100644
--- a/monai/csrc/resample/pushpull_cpu.cpp
+++ b/monai/csrc/resample/pushpull_cpu.cpp
@@ -97,19 +97,25 @@ MONAI_NAMESPACE_DEVICE { // cpu
         bool do_sgrad)
         : dim(dim),
           bound0(bound.size() > 0 ? bound[0] : BoundType::Replicate),
-          bound1(bound.size() > 1 ? bound[1] : bound.size() > 0 ? bound[0] : BoundType::Replicate),
+          bound1(
+              bound.size() > 1       ? bound[1]
+                  : bound.size() > 0 ? bound[0]
+                                     : BoundType::Replicate),
           bound2(
-              bound.size() > 2 ? bound[2]
-                               : bound.size() > 1 ? bound[1] : bound.size() > 0 ? bound[0] : BoundType::Replicate),
+              bound.size() > 2       ? bound[2]
+                  : bound.size() > 1 ? bound[1]
+                  : bound.size() > 0 ? bound[0]
+                                     : BoundType::Replicate),
           interpolation0(interpolation.size() > 0 ? interpolation[0] : InterpolationType::Linear),
           interpolation1(
-              interpolation.size() > 1 ? interpolation[1]
-                                       : interpolation.size() > 0 ? interpolation[0] : InterpolationType::Linear),
+              interpolation.size() > 1       ? interpolation[1]
+                  : interpolation.size() > 0 ? interpolation[0]
+                                             : InterpolationType::Linear),
           interpolation2(
-              interpolation.size() > 2
-                  ? interpolation[2]
+              interpolation.size() > 2       ? interpolation[2]
                   : interpolation.size() > 1 ? interpolation[1]
-                                             : interpolation.size() > 0 ? interpolation[0] : InterpolationType::Linear),
+                  : interpolation.size() > 0 ? interpolation[0]
+                                             : InterpolationType::Linear),
           extrapolate(extrapolate),
           do_pull(do_pull),
           do_push(do_push),
@@ -136,13 +142,14 @@ MONAI_NAMESPACE_DEVICE { // cpu
           bound2(bound),
           interpolation0(interpolation.size() > 0 ? interpolation[0] : InterpolationType::Linear),
           interpolation1(
-              interpolation.size() > 1 ? interpolation[1]
-                                       : interpolation.size() > 0 ? interpolation[0] : InterpolationType::Linear),
+              interpolation.size() > 1       ? interpolation[1]
+                  : interpolation.size() > 0 ? interpolation[0]
+                                             : InterpolationType::Linear),
           interpolation2(
-              interpolation.size() > 2
-                  ? interpolation[2]
+              interpolation.size() > 2       ? interpolation[2]
                   : interpolation.size() > 1 ? interpolation[1]
-                                             : interpolation.size() > 0 ? interpolation[0] : InterpolationType::Linear),
+                  : interpolation.size() > 0 ? interpolation[0]
+                                             : InterpolationType::Linear),
           extrapolate(extrapolate),
           do_pull(do_pull),
           do_push(do_push),
@@ -165,10 +172,15 @@ MONAI_NAMESPACE_DEVICE { // cpu
         bool do_sgrad)
         : dim(dim),
           bound0(bound.size() > 0 ? bound[0] : BoundType::Replicate),
-          bound1(bound.size() > 1 ? bound[1] : bound.size() > 0 ? bound[0] : BoundType::Replicate),
+          bound1(
+              bound.size() > 1       ? bound[1]
+                  : bound.size() > 0 ? bound[0]
+                                     : BoundType::Replicate),
           bound2(
-              bound.size() > 2 ? bound[2]
-                               : bound.size() > 1 ? bound[1] : bound.size() > 0 ? bound[0] : BoundType::Replicate),
+              bound.size() > 2       ? bound[2]
+                  : bound.size() > 1 ? bound[1]
+                  : bound.size() > 0 ? bound[0]
+                                     : BoundType::Replicate),
           interpolation0(interpolation),
           interpolation1(interpolation),
           interpolation2(interpolation),
diff --git a/monai/csrc/resample/pushpull_cuda.cu b/monai/csrc/resample/pushpull_cuda.cu
index cbb0a36a80..ecfeb562ab 100644
--- a/monai/csrc/resample/pushpull_cuda.cu
+++ b/monai/csrc/resample/pushpull_cuda.cu
@@ -94,19 +94,25 @@ MONAI_NAMESPACE_DEVICE { // cuda
         bool do_sgrad)
         : dim(dim),
           bound0(bound.size() > 0 ? bound[0] : BoundType::Replicate),
-          bound1(bound.size() > 1 ? bound[1] : bound.size() > 0 ? bound[0] : BoundType::Replicate),
+          bound1(
+              bound.size() > 1       ? bound[1]
+                  : bound.size() > 0 ? bound[0]
+                                     : BoundType::Replicate),
           bound2(
-              bound.size() > 2 ? bound[2]
-                               : bound.size() > 1 ? bound[1] : bound.size() > 0 ? bound[0] : BoundType::Replicate),
+              bound.size() > 2       ? bound[2]
+                  : bound.size() > 1 ? bound[1]
+                  : bound.size() > 0 ? bound[0]
+                                     : BoundType::Replicate),
           interpolation0(interpolation.size() > 0 ? interpolation[0] : InterpolationType::Linear),
           interpolation1(
-              interpolation.size() > 1 ? interpolation[1]
-                                       : interpolation.size() > 0 ? interpolation[0] : InterpolationType::Linear),
+              interpolation.size() > 1       ? interpolation[1]
+                  : interpolation.size() > 0 ? interpolation[0]
+                                             : InterpolationType::Linear),
           interpolation2(
-              interpolation.size() > 2
-                  ? interpolation[2]
+              interpolation.size() > 2       ? interpolation[2]
                   : interpolation.size() > 1 ? interpolation[1]
-                                             : interpolation.size() > 0 ? interpolation[0] : InterpolationType::Linear),
+                  : interpolation.size() > 0 ? interpolation[0]
+                                             : InterpolationType::Linear),
           extrapolate(extrapolate),
           do_pull(do_pull),
           do_push(do_push),
@@ -133,13 +139,14 @@ MONAI_NAMESPACE_DEVICE { // cuda
           bound2(bound),
           interpolation0(interpolation.size() > 0 ? interpolation[0] : InterpolationType::Linear),
           interpolation1(
-              interpolation.size() > 1 ? interpolation[1]
-                                       : interpolation.size() > 0 ? interpolation[0] : InterpolationType::Linear),
+              interpolation.size() > 1       ? interpolation[1]
+                  : interpolation.size() > 0 ? interpolation[0]
+                                             : InterpolationType::Linear),
           interpolation2(
-              interpolation.size() > 2
-                  ? interpolation[2]
+              interpolation.size() > 2       ? interpolation[2]
                   : interpolation.size() > 1 ? interpolation[1]
-                                             : interpolation.size() > 0 ? interpolation[0] : InterpolationType::Linear),
+                  : interpolation.size() > 0 ? interpolation[0]
+                                             : InterpolationType::Linear),
           extrapolate(extrapolate),
           do_pull(do_pull),
           do_push(do_push),
@@ -162,10 +169,15 @@ MONAI_NAMESPACE_DEVICE { // cuda
         bool do_sgrad)
         : dim(dim),
           bound0(bound.size() > 0 ? bound[0] : BoundType::Replicate),
-          bound1(bound.size() > 1 ? bound[1] : bound.size() > 0 ? bound[0] : BoundType::Replicate),
+          bound1(
+              bound.size() > 1       ? bound[1]
+                  : bound.size() > 0 ? bound[0]
+                                     : BoundType::Replicate),
           bound2(
-              bound.size() > 2 ? bound[2]
-                               : bound.size() > 1 ? bound[1] : bound.size() > 0 ? bound[0] : BoundType::Replicate),
+              bound.size() > 2       ? bound[2]
+                  : bound.size() > 1 ? bound[1]
+                  : bound.size() > 0 ? bound[0]
+                                     : BoundType::Replicate),
           interpolation0(interpolation),
           interpolation1(interpolation),
           interpolation2(interpolation),
diff --git a/monai/metrics/rocauc.py b/monai/metrics/rocauc.py
index 8d12f90867..164b32ddb7 100644
--- a/monai/metrics/rocauc.py
+++ b/monai/metrics/rocauc.py
@@ -140,6 +140,4 @@ def compute_roc_auc(
     if average == Average.WEIGHTED:
         weights = [sum(y_) for y_ in y]
         return np.average(auc_values, weights=weights)
-    raise ValueError(
-        f'Unsupported average: {average}, available options are ["macro", "weighted", "micro", "none"].'
-    )
+    raise ValueError(f'Unsupported average: {average}, available options are ["macro", "weighted", "micro", "none"].')