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File: //usr/local/lib/python3.10/dist-packages/numpy/testing/_private/__pycache__/utils.cpython-310.pyc
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    Assert that works in release mode.
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    Raises an AssertionError if two objects are not equal.

    Given two objects (scalars, lists, tuples, dictionaries or numpy arrays),
    check that all elements of these objects are equal. An exception is raised
    at the first conflicting values.

    When one of `actual` and `desired` is a scalar and the other is array_like,
    the function checks that each element of the array_like object is equal to
    the scalar.

    This function handles NaN comparisons as if NaN was a "normal" number.
    That is, AssertionError is not raised if both objects have NaNs in the same
    positions.  This is in contrast to the IEEE standard on NaNs, which says
    that NaN compared to anything must return False.

    Parameters
    ----------
    actual : array_like
        The object to check.
    desired : array_like
        The expected object.
    err_msg : str, optional
        The error message to be printed in case of failure.
    verbose : bool, optional
        If True, the conflicting values are appended to the error message.

    Raises
    ------
    AssertionError
        If actual and desired are not equal.

    Examples
    --------
    >>> np.testing.assert_equal([4,5], [4,6])
    Traceback (most recent call last):
        ...
    AssertionError:
    Items are not equal:
    item=1
     ACTUAL: 5
     DESIRED: 6

    The following comparison does not raise an exception.  There are NaNs
    in the inputs, but they are in the same positions.

    >>> np.testing.assert_equal(np.array([1.0, 2.0, np.nan]), [1, 2, np.nan])

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    Test if two objects are equal, and print an error message if test fails.

    The test is performed with ``actual == desired``.

    Parameters
    ----------
    test_string : str
        The message supplied to AssertionError.
    actual : object
        The object to test for equality against `desired`.
    desired : object
        The expected result.

    Examples
    --------
    >>> np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 1])
    >>> np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 2])
    Traceback (most recent call last):
    ...
    AssertionError: Test XYZ of func xyz failed
    ACTUAL:
    [0, 1]
    DESIRED:
    [0, 2]

    TrNz failed
ACTUAL: 
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DESIRED: 
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    Raises an AssertionError if two items are not equal up to desired
    precision.

    .. note:: It is recommended to use one of `assert_allclose`,
              `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
              instead of this function for more consistent floating point
              comparisons.

    The test verifies that the elements of `actual` and `desired` satisfy.

        ``abs(desired-actual) < float64(1.5 * 10**(-decimal))``

    That is a looser test than originally documented, but agrees with what the
    actual implementation in `assert_array_almost_equal` did up to rounding
    vagaries. An exception is raised at conflicting values. For ndarrays this
    delegates to assert_array_almost_equal

    Parameters
    ----------
    actual : array_like
        The object to check.
    desired : array_like
        The expected object.
    decimal : int, optional
        Desired precision, default is 7.
    err_msg : str, optional
        The error message to be printed in case of failure.
    verbose : bool, optional
        If True, the conflicting values are appended to the error message.

    Raises
    ------
    AssertionError
      If actual and desired are not equal up to specified precision.

    See Also
    --------
    assert_allclose: Compare two array_like objects for equality with desired
                     relative and/or absolute precision.
    assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal

    Examples
    --------
    >>> from numpy.testing import assert_almost_equal
    >>> assert_almost_equal(2.3333333333333, 2.33333334)
    >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10)
    Traceback (most recent call last):
        ...
    AssertionError:
    Arrays are not almost equal to 10 decimals
     ACTUAL: 2.3333333333333
     DESIRED: 2.33333334

    >>> assert_almost_equal(np.array([1.0,2.3333333333333]),
    ...                     np.array([1.0,2.33333334]), decimal=9)
    Traceback (most recent call last):
        ...
    AssertionError:
    Arrays are not almost equal to 9 decimals
    <BLANKLINE>
    Mismatched elements: 1 / 2 (50%)
    Max absolute difference: 6.66669964e-09
    Max relative difference: 2.85715698e-09
     x: array([1.         , 2.333333333])
     y: array([1.        , 2.33333334])

    Tr)r
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    Raises an AssertionError if two items are not equal up to significant
    digits.

    .. note:: It is recommended to use one of `assert_allclose`,
              `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
              instead of this function for more consistent floating point
              comparisons.

    Given two numbers, check that they are approximately equal.
    Approximately equal is defined as the number of significant digits
    that agree.

    Parameters
    ----------
    actual : scalar
        The object to check.
    desired : scalar
        The expected object.
    significant : int, optional
        Desired precision, default is 7.
    err_msg : str, optional
        The error message to be printed in case of failure.
    verbose : bool, optional
        If True, the conflicting values are appended to the error message.

    Raises
    ------
    AssertionError
      If actual and desired are not equal up to specified precision.

    See Also
    --------
    assert_allclose: Compare two array_like objects for equality with desired
                     relative and/or absolute precision.
    assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal

    Examples
    --------
    >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20)
    >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20,
    ...                                significant=8)
    >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20,
    ...                                significant=8)
    Traceback (most recent call last):
        ...
    AssertionError:
    Items are not equal to 8 significant digits:
     ACTUAL: 1.234567e-21
     DESIRED: 1.2345672e-21

    the evaluated condition that raises the exception is

    >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1)
    True

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    Raises an AssertionError if two array_like objects are not equal.

    Given two array_like objects, check that the shape is equal and all
    elements of these objects are equal (but see the Notes for the special
    handling of a scalar). An exception is raised at shape mismatch or
    conflicting values. In contrast to the standard usage in numpy, NaNs
    are compared like numbers, no assertion is raised if both objects have
    NaNs in the same positions.

    The usual caution for verifying equality with floating point numbers is
    advised.

    Parameters
    ----------
    x : array_like
        The actual object to check.
    y : array_like
        The desired, expected object.
    err_msg : str, optional
        The error message to be printed in case of failure.
    verbose : bool, optional
        If True, the conflicting values are appended to the error message.
    strict : bool, optional
        If True, raise an AssertionError when either the shape or the data
        type of the array_like objects does not match. The special
        handling for scalars mentioned in the Notes section is disabled.

        .. versionadded:: 1.24.0

    Raises
    ------
    AssertionError
        If actual and desired objects are not equal.

    See Also
    --------
    assert_allclose: Compare two array_like objects for equality with desired
                     relative and/or absolute precision.
    assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal

    Notes
    -----
    When one of `x` and `y` is a scalar and the other is array_like, the
    function checks that each element of the array_like object is equal to
    the scalar. This behaviour can be disabled with the `strict` parameter.

    Examples
    --------
    The first assert does not raise an exception:

    >>> np.testing.assert_array_equal([1.0,2.33333,np.nan],
    ...                               [np.exp(0),2.33333, np.nan])

    Assert fails with numerical imprecision with floats:

    >>> np.testing.assert_array_equal([1.0,np.pi,np.nan],
    ...                               [1, np.sqrt(np.pi)**2, np.nan])
    Traceback (most recent call last):
        ...
    AssertionError:
    Arrays are not equal
    <BLANKLINE>
    Mismatched elements: 1 / 3 (33.3%)
    Max absolute difference: 4.4408921e-16
    Max relative difference: 1.41357986e-16
     x: array([1.      , 3.141593,      nan])
     y: array([1.      , 3.141593,      nan])

    Use `assert_allclose` or one of the nulp (number of floating point values)
    functions for these cases instead:

    >>> np.testing.assert_allclose([1.0,np.pi,np.nan],
    ...                            [1, np.sqrt(np.pi)**2, np.nan],
    ...                            rtol=1e-10, atol=0)

    As mentioned in the Notes section, `assert_array_equal` has special
    handling for scalars. Here the test checks that each value in `x` is 3:

    >>> x = np.full((2, 5), fill_value=3)
    >>> np.testing.assert_array_equal(x, 3)

    Use `strict` to raise an AssertionError when comparing a scalar with an
    array:

    >>> np.testing.assert_array_equal(x, 3, strict=True)
    Traceback (most recent call last):
        ...
    AssertionError:
    Arrays are not equal
    <BLANKLINE>
    (shapes (2, 5), () mismatch)
     x: array([[3, 3, 3, 3, 3],
           [3, 3, 3, 3, 3]])
     y: array(3)

    The `strict` parameter also ensures that the array data types match:

    >>> x = np.array([2, 2, 2])
    >>> y = np.array([2., 2., 2.], dtype=np.float32)
    >>> np.testing.assert_array_equal(x, y, strict=True)
    Traceback (most recent call last):
        ...
    AssertionError:
    Arrays are not equal
    <BLANKLINE>
    (dtypes int64, float32 mismatch)
     x: array([2, 2, 2])
     y: array([2., 2., 2.], dtype=float32)
    TzArrays are not equal)r�r#r�r�N)r5�operator�__eq__)r�r�r�r#r�rVrCrCrDr(s
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�rc	sdd}ddlm�m�m�ddlm�ddlm�������fdd�}t|||||d��d	�d
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    Raises an AssertionError if two objects are not equal up to desired
    precision.

    .. note:: It is recommended to use one of `assert_allclose`,
              `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
              instead of this function for more consistent floating point
              comparisons.

    The test verifies identical shapes and that the elements of ``actual`` and
    ``desired`` satisfy.

        ``abs(desired-actual) < 1.5 * 10**(-decimal)``

    That is a looser test than originally documented, but agrees with what the
    actual implementation did up to rounding vagaries. An exception is raised
    at shape mismatch or conflicting values. In contrast to the standard usage
    in numpy, NaNs are compared like numbers, no assertion is raised if both
    objects have NaNs in the same positions.

    Parameters
    ----------
    x : array_like
        The actual object to check.
    y : array_like
        The desired, expected object.
    decimal : int, optional
        Desired precision, default is 6.
    err_msg : str, optional
      The error message to be printed in case of failure.
    verbose : bool, optional
        If True, the conflicting values are appended to the error message.

    Raises
    ------
    AssertionError
        If actual and desired are not equal up to specified precision.

    See Also
    --------
    assert_allclose: Compare two array_like objects for equality with desired
                     relative and/or absolute precision.
    assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal

    Examples
    --------
    the first assert does not raise an exception

    >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan],
    ...                                      [1.0,2.333,np.nan])

    >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
    ...                                      [1.0,2.33339,np.nan], decimal=5)
    Traceback (most recent call last):
        ...
    AssertionError:
    Arrays are not almost equal to 5 decimals
    <BLANKLINE>
    Mismatched elements: 1 / 3 (33.3%)
    Max absolute difference: 6.e-05
    Max relative difference: 2.57136612e-05
     x: array([1.     , 2.33333,     nan])
     y: array([1.     , 2.33339,     nan])

    >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
    ...                                      [1.0,2.33333, 5], decimal=5)
    Traceback (most recent call last):
        ...
    AssertionError:
    Arrays are not almost equal to 5 decimals
    <BLANKLINE>
    x and y nan location mismatch:
     x: array([1.     , 2.33333,     nan])
     y: array([1.     , 2.33333, 5.     ])

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    ordered values. Shape mismatch does not raise if an object has zero
    dimension. In contrast to the standard usage in numpy, NaNs are
    compared, no assertion is raised if both objects have NaNs in the same
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    Parameters
    ----------
    x : array_like
      The smaller object to check.
    y : array_like
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    err_msg : string
      The error message to be printed in case of failure.
    verbose : bool
        If True, the conflicting values are appended to the error message.

    Raises
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    AssertionError
      If x is not strictly smaller than y, element-wise.

    See Also
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    assert_array_equal: tests objects for equality
    assert_array_almost_equal: test objects for equality up to precision

    Examples
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    >>> np.testing.assert_array_less([1.0, 1.0, np.nan], [1.1, 2.0, np.nan])
    >>> np.testing.assert_array_less([1.0, 1.0, np.nan], [1, 2.0, np.nan])
    Traceback (most recent call last):
        ...
    AssertionError:
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    Mismatched elements: 1 / 3 (33.3%)
    Max absolute difference: 1.
    Max relative difference: 0.5
     x: array([ 1.,  1., nan])
     y: array([ 1.,  2., nan])

    >>> np.testing.assert_array_less([1.0, 4.0], 3)
    Traceback (most recent call last):
        ...
    AssertionError:
    Arrays are not less-ordered
    <BLANKLINE>
    Mismatched elements: 1 / 2 (50%)
    Max absolute difference: 2.
    Max relative difference: 0.66666667
     x: array([1., 4.])
     y: array(3)

    >>> np.testing.assert_array_less([1.0, 2.0, 3.0], [4])
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        ...
    AssertionError:
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    Returns
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    elapsed : float
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    Examples
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    >>> times = 10
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    contrast to the standard usage in numpy, NaNs are compared like numbers,
    no assertion is raised if both objects have NaNs in the same positions.

    The test is equivalent to ``allclose(actual, desired, rtol, atol)`` (note
    that ``allclose`` has different default values). It compares the difference
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    .. versionadded:: 1.5.0

    Parameters
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    actual : array_like
        Array obtained.
    desired : array_like
        Array desired.
    rtol : float, optional
        Relative tolerance.
    atol : float, optional
        Absolute tolerance.
    equal_nan : bool, optional.
        If True, NaNs will compare equal.
    err_msg : str, optional
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    verbose : bool, optional
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    Raises
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    AssertionError
        If actual and desired are not equal up to specified precision.

    See Also
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    assert_array_almost_equal_nulp, assert_array_max_ulp

    Notes
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    array_like, the function checks that each element of the array_like
    object is equal to the scalar.

    Examples
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    >>> x = [1e-5, 1e-3, 1e-1]
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    Returns
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    Raises
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    See Also
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    assert_array_max_ulp : Check that all items of arrays differ in at most
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    Notes
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    Examples
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    >>> x = np.array([1., 1e-10, 1e-20])
    >>> eps = np.finfo(x.dtype).eps
    >>> np.testing.assert_array_almost_equal_nulp(x, x*eps/2 + x)

    >>> np.testing.assert_array_almost_equal_nulp(x, x*eps + x)
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    dtype : dtype, optional
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    Returns
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    ret : ndarray
        Array containing number of representable floating point numbers between
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    Raises
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    Notes
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    See Also
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    assert_array_almost_equal_nulp : Compare two arrays relatively to their
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    Examples
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