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+"""
+Exceptions and Warnings (:mod:`numpy.exceptions`)
+=================================================
+
+General exceptions used by NumPy.  Note that some exceptions may be module
+specific, such as linear algebra errors.
+
+.. versionadded:: NumPy 1.25
+
+    The exceptions module is new in NumPy 1.25.  Older exceptions remain
+    available through the main NumPy namespace for compatibility.
+
+.. currentmodule:: numpy.exceptions
+
+Warnings
+--------
+.. autosummary::
+   :toctree: generated/
+
+   ComplexWarning             Given when converting complex to real.
+   VisibleDeprecationWarning  Same as a DeprecationWarning, but more visible.
+
+Exceptions
+----------
+.. autosummary::
+   :toctree: generated/
+
+    AxisError          Given when an axis was invalid.
+    DTypePromotionError   Given when no common dtype could be found.
+    TooHardError       Error specific to `numpy.shares_memory`.
+
+"""
+
+
+__all__ = [
+    "ComplexWarning", "VisibleDeprecationWarning", "ModuleDeprecationWarning",
+    "TooHardError", "AxisError", "DTypePromotionError"]
+
+
+# Disallow reloading this module so as to preserve the identities of the
+# classes defined here.
+if '_is_loaded' in globals():
+    raise RuntimeError('Reloading numpy._globals is not allowed')
+_is_loaded = True
+
+
+class ComplexWarning(RuntimeWarning):
+    """
+    The warning raised when casting a complex dtype to a real dtype.
+
+    As implemented, casting a complex number to a real discards its imaginary
+    part, but this behavior may not be what the user actually wants.
+
+    """
+    pass
+
+
+class ModuleDeprecationWarning(DeprecationWarning):
+    """Module deprecation warning.
+
+    .. warning::
+
+        This warning should not be used, since nose testing is not relevant
+        anymore.
+
+    The nose tester turns ordinary Deprecation warnings into test failures.
+    That makes it hard to deprecate whole modules, because they get
+    imported by default. So this is a special Deprecation warning that the
+    nose tester will let pass without making tests fail.
+
+    """
+
+
+class VisibleDeprecationWarning(UserWarning):
+    """Visible deprecation warning.
+
+    By default, python will not show deprecation warnings, so this class
+    can be used when a very visible warning is helpful, for example because
+    the usage is most likely a user bug.
+
+    """
+
+
+# Exception used in shares_memory()
+class TooHardError(RuntimeError):
+    """max_work was exceeded.
+
+    This is raised whenever the maximum number of candidate solutions
+    to consider specified by the ``max_work`` parameter is exceeded.
+    Assigning a finite number to max_work may have caused the operation
+    to fail.
+
+    """
+
+    pass
+
+
+class AxisError(ValueError, IndexError):
+    """Axis supplied was invalid.
+
+    This is raised whenever an ``axis`` parameter is specified that is larger
+    than the number of array dimensions.
+    For compatibility with code written against older numpy versions, which
+    raised a mixture of `ValueError` and `IndexError` for this situation, this
+    exception subclasses both to ensure that ``except ValueError`` and
+    ``except IndexError`` statements continue to catch `AxisError`.
+
+    .. versionadded:: 1.13
+
+    Parameters
+    ----------
+    axis : int or str
+        The out of bounds axis or a custom exception message.
+        If an axis is provided, then `ndim` should be specified as well.
+    ndim : int, optional
+        The number of array dimensions.
+    msg_prefix : str, optional
+        A prefix for the exception message.
+
+    Attributes
+    ----------
+    axis : int, optional
+        The out of bounds axis or ``None`` if a custom exception
+        message was provided. This should be the axis as passed by
+        the user, before any normalization to resolve negative indices.
+
+        .. versionadded:: 1.22
+    ndim : int, optional
+        The number of array dimensions or ``None`` if a custom exception
+        message was provided.
+
+        .. versionadded:: 1.22
+
+
+    Examples
+    --------
+    >>> array_1d = np.arange(10)
+    >>> np.cumsum(array_1d, axis=1)
+    Traceback (most recent call last):
+      ...
+    numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
+
+    Negative axes are preserved:
+
+    >>> np.cumsum(array_1d, axis=-2)
+    Traceback (most recent call last):
+      ...
+    numpy.exceptions.AxisError: axis -2 is out of bounds for array of dimension 1
+
+    The class constructor generally takes the axis and arrays'
+    dimensionality as arguments:
+
+    >>> print(np.AxisError(2, 1, msg_prefix='error'))
+    error: axis 2 is out of bounds for array of dimension 1
+
+    Alternatively, a custom exception message can be passed:
+
+    >>> print(np.AxisError('Custom error message'))
+    Custom error message
+
+    """
+
+    __slots__ = ("axis", "ndim", "_msg")
+
+    def __init__(self, axis, ndim=None, msg_prefix=None):
+        if ndim is msg_prefix is None:
+            # single-argument form: directly set the error message
+            self._msg = axis
+            self.axis = None
+            self.ndim = None
+        else:
+            self._msg = msg_prefix
+            self.axis = axis
+            self.ndim = ndim
+
+    def __str__(self):
+        axis = self.axis
+        ndim = self.ndim
+
+        if axis is ndim is None:
+            return self._msg
+        else:
+            msg = f"axis {axis} is out of bounds for array of dimension {ndim}"
+            if self._msg is not None:
+                msg = f"{self._msg}: {msg}"
+            return msg
+
+
+class DTypePromotionError(TypeError):
+    """Multiple DTypes could not be converted to a common one.
+
+    This exception derives from ``TypeError`` and is raised whenever dtypes
+    cannot be converted to a single common one.  This can be because they
+    are of a different category/class or incompatible instances of the same
+    one (see Examples).
+
+    Notes
+    -----
+    Many functions will use promotion to find the correct result and
+    implementation.  For these functions the error will typically be chained
+    with a more specific error indicating that no implementation was found
+    for the input dtypes.
+
+    Typically promotion should be considered "invalid" between the dtypes of
+    two arrays when `arr1 == arr2` can safely return all ``False`` because the
+    dtypes are fundamentally different.
+
+    Examples
+    --------
+    Datetimes and complex numbers are incompatible classes and cannot be
+    promoted:
+
+    >>> np.result_type(np.dtype("M8[s]"), np.complex128)
+    DTypePromotionError: The DType <class 'numpy.dtype[datetime64]'> could not
+    be promoted by <class 'numpy.dtype[complex128]'>. This means that no common
+    DType exists for the given inputs. For example they cannot be stored in a
+    single array unless the dtype is `object`. The full list of DTypes is:
+    (<class 'numpy.dtype[datetime64]'>, <class 'numpy.dtype[complex128]'>)
+
+    For example for structured dtypes, the structure can mismatch and the
+    same ``DTypePromotionError`` is given when two structured dtypes with
+    a mismatch in their number of fields is given:
+
+    >>> dtype1 = np.dtype([("field1", np.float64), ("field2", np.int64)])
+    >>> dtype2 = np.dtype([("field1", np.float64)])
+    >>> np.promote_types(dtype1, dtype2)
+    DTypePromotionError: field names `('field1', 'field2')` and `('field1',)`
+    mismatch.
+
+    """
+    pass