changed the type of the parameter data_range from the classes and fun…

src/torchmetrics/functional/image/psnr.py

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from collections.abc import Sequence
 from typing import Optional, Union
 
 import torch
@@ -25,7 +26,7 @@ def _psnr_compute(
     num_obs: Tensor,
     data_range: Tensor,
     base: float = 10.0,
-    reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
+    reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
 ) -> Tensor:
     """Compute peak signal-to-noise ratio.
 
@@ -58,7 +59,7 @@ def _psnr_compute(
 def _psnr_update(
     preds: Tensor,
     target: Tensor,
-    dim: Optional[Union[int, tuple[int, ...]]] = None,
+    dim: Optional[Union[int, Sequence[int]]] = None,
 ) -> tuple[Tensor, Tensor]:
     """Update and return variables required to compute peak signal-to-noise ratio.
 
@@ -95,18 +96,18 @@ def _psnr_update(
 def peak_signal_noise_ratio(
     preds: Tensor,
     target: Tensor,
-    data_range: Union[float, tuple[float, float]],
+    data_range: Union[float, Sequence[float]],
     base: float = 10.0,
-    reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
-    dim: Optional[Union[int, tuple[int, ...]]] = None,
+    reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
+    dim: Optional[Union[int, Sequence[int]]] = None,
 ) -> Tensor:
     """Compute the peak signal-to-noise ratio.
 
     Args:
         preds: estimated signal
         target: groun truth signal
         data_range:
-            the range of the data. If a tuple is provided then the range is calculated as the difference and
+            the range of the data. If a Sequence is provided then the range is calculated as the difference and
             input is clamped between the values.
         base: a base of a logarithm to use
         reduction: a method to reduce metric score over labels.
@@ -136,7 +137,7 @@ def peak_signal_noise_ratio(
     if dim is None and reduction != "elementwise_mean":
         rank_zero_warn(f"The `reduction={reduction}` will not have any effect when `dim` is None.")
 
-    if isinstance(data_range, tuple):
+    if isinstance(data_range, Sequence):
         preds = torch.clamp(preds, min=data_range[0], max=data_range[1])
         target = torch.clamp(target, min=data_range[0], max=data_range[1])
         data_range_val = tensor(data_range[1] - data_range[0])

src/torchmetrics/functional/image/psnrb.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import math
+from collections.abc import Sequence
 from typing import Union
 
 import torch
@@ -102,7 +103,7 @@ def _psnrb_update(preds: Tensor, target: Tensor, block_size: int = 8) -> tuple[T
 def peak_signal_noise_ratio_with_blocked_effect(
     preds: Tensor,
     target: Tensor,
-    data_range: Union[float, tuple[float, float]],
+    data_range: Union[float, Sequence[float]],
     block_size: int = 8,
 ) -> Tensor:
     r"""Computes `Peak Signal to Noise Ratio With Blocked Effect` (PSNRB) metrics.
@@ -115,7 +116,7 @@ def peak_signal_noise_ratio_with_blocked_effect(
     Args:
         preds: estimated signal
         target: ground truth signal
-        data_range: the range of the data. If a tuple is provided then the range is calculated as the difference and
+        data_range: the range of the data. If a Sequence is provided then the range is calculated as the difference and
             input is clamped between the values.
         block_size: integer indication the block size
 
@@ -131,7 +132,7 @@ def peak_signal_noise_ratio_with_blocked_effect(
         tensor(7.8402)
 
     """
-    if isinstance(data_range, tuple):
+    if isinstance(data_range, Sequence):
         preds = torch.clamp(preds, min=data_range[0], max=data_range[1])
         target = torch.clamp(target, min=data_range[0], max=data_range[1])
         data_range_val = tensor(data_range[1] - data_range[0])

src/torchmetrics/image/psnr.py

@@ -48,7 +48,7 @@ class PeakSignalNoiseRatio(Metric):
 
     Args:
         data_range:
-            the range of the data. If a tuple is provided, then the range is calculated as the difference and
+            the range of the data. If a Sequence is provided, then the range is calculated as the difference and
             input is clamped between the values.
         base: a base of a logarithm to use.
         reduction: a method to reduce metric score over labels.
@@ -80,10 +80,10 @@ class PeakSignalNoiseRatio(Metric):
 
     def __init__(
         self,
-        data_range: Union[float, tuple[float, float]],
+        data_range: Union[float, Sequence[float]],
         base: float = 10.0,
-        reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
-        dim: Optional[Union[int, tuple[int, ...]]] = None,
+        reduction: Optional[Literal["elementwise_mean", "sum", "none"]] = "elementwise_mean",
+        dim: Optional[Union[int, Sequence[int]]] = None,
         **kwargs: Any,
     ) -> None:
         super().__init__(**kwargs)
@@ -99,15 +99,15 @@ def __init__(
             self.add_state("total", default=[], dist_reduce_fx="cat")
 
         self.clamping_fn = None
-        if isinstance(data_range, tuple):
+        if isinstance(data_range, Sequence):
             self.add_state("data_range", default=tensor(data_range[1] - data_range[0]), dist_reduce_fx="mean")
             self.clamping_fn = partial(torch.clamp, min=data_range[0], max=data_range[1])
         else:
             self.add_state("data_range", default=tensor(float(data_range)), dist_reduce_fx="mean")
 
         self.base = base
         self.reduction = reduction
-        self.dim = tuple(dim) if isinstance(dim, Sequence) else dim
+        self.dim = dim
 
     def update(self, preds: Tensor, target: Tensor) -> None:
         """Update state with predictions and targets."""

src/torchmetrics/image/psnrb.py

@@ -48,7 +48,7 @@ class PeakSignalNoiseRatioWithBlockedEffect(Metric):
     - ``psnrb`` (:class:`~torch.Tensor`): float scalar tensor with aggregated PSNRB value
 
     Args:
-        data_range: the range of the data. If a tuple is provided then the range is calculated as the difference and
+        data_range: the range of the data. If a Sequence is provided then the range is calculated as the difference and
             input is clamped between the values.
         block_size: integer indication the block size
         kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
@@ -74,7 +74,7 @@ class PeakSignalNoiseRatioWithBlockedEffect(Metric):
 
     def __init__(
         self,
-        data_range: Union[float, tuple[float, float]],
+        data_range: Union[float, Sequence[float]],
         block_size: int = 8,
         **kwargs: Any,
     ) -> None:
@@ -87,7 +87,7 @@ def __init__(
         self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
         self.add_state("bef", default=tensor(0.0), dist_reduce_fx="sum")
 
-        if isinstance(data_range, tuple):
+        if isinstance(data_range, Sequence):
             self.add_state("data_range", default=tensor(data_range[1] - data_range[0]), dist_reduce_fx="mean")
             self.clamping_fn = lambda x: torch.clamp(x, min=data_range[0], max=data_range[1])
         else: