documentation: replace "Jax" with "NumPy" and make capitalization uniform (#1206)

gymnasium/wrappers/numpy_to_torch.py

@@ -37,25 +37,25 @@ def torch_to_numpy(value: Any) -> Any:
 
 @torch_to_numpy.register(numbers.Number)
 def _number_to_numpy(value: numbers.Number) -> Any:
-    """Convert a python number (int, float, complex) to a numpy array."""
+    """Convert a python number (int, float, complex) to a NumPy array."""
     return np.array(value)
 
 
 @torch_to_numpy.register(torch.Tensor)
 def _torch_to_numpy(value: torch.Tensor) -> Any:
-    """Convert a torch.Tensor to a numpy array."""
+    """Convert a torch.Tensor to a NumPy array."""
     return value.numpy(force=True)
 
 
 @torch_to_numpy.register(abc.Mapping)
 def _mapping_torch_to_numpy(value: Mapping[str, Any]) -> Mapping[str, Any]:
-    """Converts a mapping of PyTorch Tensors into a Dictionary of Jax Array."""
+    """Converts a mapping of PyTorch Tensors into a Dictionary of NumPy Array."""
     return type(value)(**{k: torch_to_numpy(v) for k, v in value.items()})
 
 
 @torch_to_numpy.register(abc.Iterable)
 def _iterable_torch_to_numpy(value: Iterable[Any]) -> Iterable[Any]:
-    """Converts an Iterable from PyTorch Tensors to an iterable of Jax Array."""
+    """Converts an Iterable from PyTorch Tensors to an iterable of NumPy Array."""
     if hasattr(value, "_make"):
         # namedtuple - underline used to prevent potential name conflicts
         # noinspection PyProtectedMember
@@ -66,7 +66,7 @@ def _iterable_torch_to_numpy(value: Iterable[Any]) -> Iterable[Any]:
 
 @functools.singledispatch
 def numpy_to_torch(value: Any, device: Device | None = None) -> Any:
-    """Converts a Jax Array into a PyTorch Tensor."""
+    """Converts a NumPy Array into a PyTorch Tensor."""
     raise Exception(
         f"No known conversion for NumPy type ({type(value)}) to PyTorch registered. Report as issue on github."
     )
@@ -75,7 +75,7 @@ def numpy_to_torch(value: Any, device: Device | None = None) -> Any:
 @numpy_to_torch.register(numbers.Number)
 @numpy_to_torch.register(np.ndarray)
 def _numpy_to_torch(value: np.ndarray, device: Device | None = None) -> torch.Tensor:
-    """Converts a Jax Array into a PyTorch Tensor."""
+    """Converts a NumPy Array into a PyTorch Tensor."""
     assert torch is not None
     tensor = torch.tensor(value)
     if device:
@@ -87,7 +87,7 @@ def _numpy_to_torch(value: np.ndarray, device: Device | None = None) -> torch.Te
 def _numpy_mapping_to_torch(
     value: Mapping[str, Any], device: Device | None = None
 ) -> Mapping[str, Any]:
-    """Converts a mapping of Jax Array into a Dictionary of PyTorch Tensors."""
+    """Converts a mapping of NumPy Array into a Dictionary of PyTorch Tensors."""
     return type(value)(**{k: numpy_to_torch(v, device) for k, v in value.items()})
 
 
@@ -95,7 +95,7 @@ def _numpy_mapping_to_torch(
 def _numpy_iterable_to_torch(
     value: Iterable[Any], device: Device | None = None
 ) -> Iterable[Any]:
-    """Converts an Iterable from Jax Array to an iterable of PyTorch Tensors."""
+    """Converts an Iterable from NumPy Array to an iterable of PyTorch Tensors."""
     if hasattr(value, "_make"):
         # namedtuple - underline used to prevent potential name conflicts
         # noinspection PyProtectedMember
@@ -140,7 +140,7 @@ class NumpyToTorch(gym.Wrapper, gym.utils.RecordConstructorArgs):
         """Wrapper class to change inputs and outputs of environment to PyTorch tensors.
 
         Args:
-            env: The Jax-based environment to wrap
+            env: The NumPy-based environment to wrap
             device: The device the torch Tensors should be moved to
         """
         gym.utils.RecordConstructorArgs.__init__(self, device=device)