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The arraypad module contains a group of functions to pad values onto the edges
of an n-dimensional array.

�N)�array_function_dispatch)�ndindex�padc�t�tj|tj��r|�|���dSdS)z�
    Rounds arr inplace if destination dtype is integer.

    Parameters
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    arr : ndarray
        Input array.
    dtype : dtype
        The dtype of the destination array.
    )�outN)�np�
issubdtype�integer�round)�arr�dtypes  �I/opt/cloudlinux/venv/lib64/python3.11/site-packages/numpy/lib/arraypad.py�_round_if_neededrs>��
�}�U�B�J�'�'���	�	�c�	��������c�6�td��f|z|fzdzS)a�
    Construct tuple of slices to slice an array in the given dimension.

    Parameters
    ----------
    sl : slice
        The slice for the given dimension.
    axis : int
        The axis to which `sl` is applied. All other dimensions are left
        "unsliced".

    Returns
    -------
    sl : tuple of slices
        A tuple with slices matching `shape` in length.

    Examples
    --------
    >>> _slice_at_axis(slice(None, 3, -1), 1)
    (slice(None, None, None), slice(None, 3, -1), (...,))
    N).��slice)�sl�axiss  r�_slice_at_axisr!s#��,
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    Get a view of the current region of interest during iterative padding.

    When padding multiple dimensions iteratively corner values are
    unnecessarily overwritten multiple times. This function reduces the
    working area for the first dimensions so that corners are excluded.

    Parameters
    ----------
    array : ndarray
        The array with the region of interest.
    original_area_slice : tuple of slices
        Denotes the area with original values of the unpadded array.
    axis : int
        The currently padded dimension assuming that `axis` is padded before
        `axis` + 1.

    Returns
    -------
    roi : ndarray
        The region of interest of the original `array`.
    �Nr)�array�original_area_slicerrs    r�	_view_roir:s8��.	�A�I�D�
��+�+��$�	�!4�T�U�U�!;�	;�B���9�rc�J�td�t|j|��D����}|jjrdnd}tj||j|���}|�|�|��td�t|j|��D����}|||<||fS)a�
    Pad array on all sides with either a single value or undefined values.

    Parameters
    ----------
    array : ndarray
        Array to grow.
    pad_width : sequence of tuple[int, int]
        Pad width on both sides for each dimension in `arr`.
    fill_value : scalar, optional
        If provided the padded area is filled with this value, otherwise
        the pad area left undefined.

    Returns
    -------
    padded : ndarray
        The padded array with the same dtype as`array`. Its order will default
        to C-style if `array` is not F-contiguous.
    original_area_slice : tuple
        A tuple of slices pointing to the area of the original array.
    c3�2K�|]\}\}}||z|zV��dS�N���.0�size�left�rights    r�	<genexpr>z_pad_simple.<locals>.<genexpr>msG�������D�-�4��	
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    Set empty-padded area in given dimension.

    Parameters
    ----------
    padded : ndarray
        Array with the pad area which is modified inplace.
    axis : int
        Dimension with the pad area to set.
    width_pair : (int, int)
        Pair of widths that mark the pad area on both sides in the given
        dimension.
    value_pair : tuple of scalars or ndarrays
        Values inserted into the pad area on each side. It must match or be
        broadcastable to the shape of `arr`.
    Nrr�rrr,)r4r�
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    Retrieve edge values from empty-padded array in given dimension.

    Parameters
    ----------
    padded : ndarray
        Empty-padded array.
    axis : int
        Dimension in which the edges are considered.
    width_pair : (int, int)
        Pair of widths that mark the pad area on both sides in the given
        dimension.

    Returns
    -------
    left_edge, right_edge : ndarray
        Edge values of the valid area in `padded` in the given dimension. Its
        shape will always match `padded` except for the dimension given by
        `axis` which will have a length of 1.
    rrr7)	r4rr8�
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    Construct linear ramps for empty-padded array in given dimension.

    Parameters
    ----------
    padded : ndarray
        Empty-padded array.
    axis : int
        Dimension in which the ramps are constructed.
    width_pair : (int, int)
        Pair of widths that mark the pad area on both sides in the given
        dimension.
    end_value_pair : (scalar, scalar)
        End values for the linear ramps which form the edge of the fully padded
        array. These values are included in the linear ramps.

    Returns
    -------
    left_ramp, right_ramp : ndarray
        Linear ramps to set on both sides of `padded`.
    c	3��K�|]:\}}}tj||����|d�j����V��;dS)F)�start�stop�num�endpointr
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    Calculate statistic for the empty-padded array in given dimension.

    Parameters
    ----------
    padded : ndarray
        Empty-padded array.
    axis : int
        Dimension in which the statistic is calculated.
    width_pair : (int, int)
        Pair of widths that mark the pad area on both sides in the given
        dimension.
    length_pair : 2-element sequence of None or int
        Gives the number of values in valid area from each side that is
        taken into account when calculating the statistic. If None the entire
        valid area in `padded` is considered.
    stat_func : function
        Function to compute statistic. The expected signature is
        ``stat_func(x: ndarray, axis: int, keepdims: bool) -> ndarray``.

    Returns
    -------
    left_stat, right_stat : ndarray
        Calculated statistic for both sides of `padded`.
    rrNz,stat_length of 0 yields no value for paddingT)r�keepdims)	r,r�amax�amin�
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    Pad `axis` of `arr` with reflection.

    Parameters
    ----------
    padded : ndarray
        Input array of arbitrary shape.
    axis : int
        Axis along which to pad `arr`.
    width_pair : (int, int)
        Pair of widths that mark the pad area on both sides in the given
        dimension.
    method : str
        Controls method of reflection; options are 'even' or 'odd'.
    include_edge : bool
        If true, edge value is included in reflection, otherwise the edge
        value forms the symmetric axis to the reflection.

    Returns
    -------
    pad_amt : tuple of ints, length 2
        New index positions of padding to do along the `axis`. If these are
        both 0, padding is done in this dimension.
    rrrN�odd��r,�minrr)r4rr8�method�include_edge�left_pad�	right_pad�
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    Pad `axis` of `arr` with wrapped values.

    Parameters
    ----------
    padded : ndarray
        Input array of arbitrary shape.
    axis : int
        Axis along which to pad `arr`.
    width_pair : (int, int)
        Pair of widths that mark the pad area on both sides in the given
        dimension.
    original_period : int
        Original length of data on `axis` of `arr`.

    Returns
    -------
    pad_amt : tuple of ints, length 2
        New index positions of padding to do along the `axis`. If these are
        both 0, padding is done in this dimension.
    rNrf)r4rr8�original_periodrjrk�period�new_left_pad�
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    Broadcast `x` to an array with the shape (`ndim`, 2).

    A helper function for `pad` that prepares and validates arguments like
    `pad_width` for iteration in pairs.

    Parameters
    ----------
    x : {None, scalar, array-like}
        The object to broadcast to the shape (`ndim`, 2).
    ndim : int
        Number of pairs the broadcasted `x` will have.
    as_index : bool, optional
        If `x` is not None, try to round each element of `x` to an integer
        (dtype `np.intp`) and ensure every element is positive.

    Returns
    -------
    pairs : nested iterables, shape (`ndim`, 2)
        The broadcasted version of `x`.

    Raises
    ------
    ValueError
        If `as_index` is True and `x` contains negative elements.
        Or if `x` is not broadcastable to the shape (`ndim`, 2).
    N))NNF)�copy�rrz#index can't contain negative valuesre)rer)
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    Pad an array.

    Parameters
    ----------
    array : array_like of rank N
        The array to pad.
    pad_width : {sequence, array_like, int}
        Number of values padded to the edges of each axis.
        ``((before_1, after_1), ... (before_N, after_N))`` unique pad widths
        for each axis.
        ``(before, after)`` or ``((before, after),)`` yields same before
        and after pad for each axis.
        ``(pad,)`` or ``int`` is a shortcut for before = after = pad width
        for all axes.
    mode : str or function, optional
        One of the following string values or a user supplied function.

        'constant' (default)
            Pads with a constant value.
        'edge'
            Pads with the edge values of array.
        'linear_ramp'
            Pads with the linear ramp between end_value and the
            array edge value.
        'maximum'
            Pads with the maximum value of all or part of the
            vector along each axis.
        'mean'
            Pads with the mean value of all or part of the
            vector along each axis.
        'median'
            Pads with the median value of all or part of the
            vector along each axis.
        'minimum'
            Pads with the minimum value of all or part of the
            vector along each axis.
        'reflect'
            Pads with the reflection of the vector mirrored on
            the first and last values of the vector along each
            axis.
        'symmetric'
            Pads with the reflection of the vector mirrored
            along the edge of the array.
        'wrap'
            Pads with the wrap of the vector along the axis.
            The first values are used to pad the end and the
            end values are used to pad the beginning.
        'empty'
            Pads with undefined values.

            .. versionadded:: 1.17

        <function>
            Padding function, see Notes.
    stat_length : sequence or int, optional
        Used in 'maximum', 'mean', 'median', and 'minimum'.  Number of
        values at edge of each axis used to calculate the statistic value.

        ``((before_1, after_1), ... (before_N, after_N))`` unique statistic
        lengths for each axis.

        ``(before, after)`` or ``((before, after),)`` yields same before
        and after statistic lengths for each axis.

        ``(stat_length,)`` or ``int`` is a shortcut for
        ``before = after = statistic`` length for all axes.

        Default is ``None``, to use the entire axis.
    constant_values : sequence or scalar, optional
        Used in 'constant'.  The values to set the padded values for each
        axis.

        ``((before_1, after_1), ... (before_N, after_N))`` unique pad constants
        for each axis.

        ``(before, after)`` or ``((before, after),)`` yields same before
        and after constants for each axis.

        ``(constant,)`` or ``constant`` is a shortcut for
        ``before = after = constant`` for all axes.

        Default is 0.
    end_values : sequence or scalar, optional
        Used in 'linear_ramp'.  The values used for the ending value of the
        linear_ramp and that will form the edge of the padded array.

        ``((before_1, after_1), ... (before_N, after_N))`` unique end values
        for each axis.

        ``(before, after)`` or ``((before, after),)`` yields same before
        and after end values for each axis.

        ``(constant,)`` or ``constant`` is a shortcut for
        ``before = after = constant`` for all axes.

        Default is 0.
    reflect_type : {'even', 'odd'}, optional
        Used in 'reflect', and 'symmetric'.  The 'even' style is the
        default with an unaltered reflection around the edge value.  For
        the 'odd' style, the extended part of the array is created by
        subtracting the reflected values from two times the edge value.

    Returns
    -------
    pad : ndarray
        Padded array of rank equal to `array` with shape increased
        according to `pad_width`.

    Notes
    -----
    .. versionadded:: 1.7.0

    For an array with rank greater than 1, some of the padding of later
    axes is calculated from padding of previous axes.  This is easiest to
    think about with a rank 2 array where the corners of the padded array
    are calculated by using padded values from the first axis.

    The padding function, if used, should modify a rank 1 array in-place. It
    has the following signature::

        padding_func(vector, iaxis_pad_width, iaxis, kwargs)

    where

        vector : ndarray
            A rank 1 array already padded with zeros.  Padded values are
            vector[:iaxis_pad_width[0]] and vector[-iaxis_pad_width[1]:].
        iaxis_pad_width : tuple
            A 2-tuple of ints, iaxis_pad_width[0] represents the number of
            values padded at the beginning of vector where
            iaxis_pad_width[1] represents the number of values padded at
            the end of vector.
        iaxis : int
            The axis currently being calculated.
        kwargs : dict
            Any keyword arguments the function requires.

    Examples
    --------
    >>> a = [1, 2, 3, 4, 5]
    >>> np.pad(a, (2, 3), 'constant', constant_values=(4, 6))
    array([4, 4, 1, ..., 6, 6, 6])

    >>> np.pad(a, (2, 3), 'edge')
    array([1, 1, 1, ..., 5, 5, 5])

    >>> np.pad(a, (2, 3), 'linear_ramp', end_values=(5, -4))
    array([ 5,  3,  1,  2,  3,  4,  5,  2, -1, -4])

    >>> np.pad(a, (2,), 'maximum')
    array([5, 5, 1, 2, 3, 4, 5, 5, 5])

    >>> np.pad(a, (2,), 'mean')
    array([3, 3, 1, 2, 3, 4, 5, 3, 3])

    >>> np.pad(a, (2,), 'median')
    array([3, 3, 1, 2, 3, 4, 5, 3, 3])

    >>> a = [[1, 2], [3, 4]]
    >>> np.pad(a, ((3, 2), (2, 3)), 'minimum')
    array([[1, 1, 1, 2, 1, 1, 1],
           [1, 1, 1, 2, 1, 1, 1],
           [1, 1, 1, 2, 1, 1, 1],
           [1, 1, 1, 2, 1, 1, 1],
           [3, 3, 3, 4, 3, 3, 3],
           [1, 1, 1, 2, 1, 1, 1],
           [1, 1, 1, 2, 1, 1, 1]])

    >>> a = [1, 2, 3, 4, 5]
    >>> np.pad(a, (2, 3), 'reflect')
    array([3, 2, 1, 2, 3, 4, 5, 4, 3, 2])

    >>> np.pad(a, (2, 3), 'reflect', reflect_type='odd')
    array([-1,  0,  1,  2,  3,  4,  5,  6,  7,  8])

    >>> np.pad(a, (2, 3), 'symmetric')
    array([2, 1, 1, 2, 3, 4, 5, 5, 4, 3])

    >>> np.pad(a, (2, 3), 'symmetric', reflect_type='odd')
    array([0, 1, 1, 2, 3, 4, 5, 5, 6, 7])

    >>> np.pad(a, (2, 3), 'wrap')
    array([4, 5, 1, 2, 3, 4, 5, 1, 2, 3])

    >>> def pad_with(vector, pad_width, iaxis, kwargs):
    ...     pad_value = kwargs.get('padder', 10)
    ...     vector[:pad_width[0]] = pad_value
    ...     vector[-pad_width[1]:] = pad_value
    >>> a = np.arange(6)
    >>> a = a.reshape((2, 3))
    >>> np.pad(a, 2, pad_with)
    array([[10, 10, 10, 10, 10, 10, 10],
           [10, 10, 10, 10, 10, 10, 10],
           [10, 10,  0,  1,  2, 10, 10],
           [10, 10,  3,  4,  5, 10, 10],
           [10, 10, 10, 10, 10, 10, 10],
           [10, 10, 10, 10, 10, 10, 10]])
    >>> np.pad(a, 2, pad_with, padder=100)
    array([[100, 100, 100, 100, 100, 100, 100],
           [100, 100, 100, 100, 100, 100, 100],
           [100, 100,   0,   1,   2, 100, 100],
           [100, 100,   3,   4,   5, 100, 100],
           [100, 100, 100, 100, 100, 100, 100],
           [100, 100, 100, 100, 100, 100, 100]])
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