`irnddlZddlZddlZdZdZdZddZdZdZdZ d Z dd Z d Z dd Z ddZdS)Ncttj|tjr||dSdS)z?Rounds arr inplace if the destination dtype is an integer. )outN)cupy issubdtypeintegerround)arrdtypes e/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/cupy/_padding/pad.py_round_if_neededr s> udl++ c cBtdf|z|fztfzS)aGConstructs a tuple of slices to slice an array in the given dimension. Args: sl(slice): The slice for the given dimension. axis(int): The axis to which `sl` is applied. All other dimensions are left "unsliced". Returns: tuple of slices: A tuple with slices matching `shape` in length. N)sliceEllipsis)slaxiss r _slice_at_axisrs% $KK>D B5 (H; 66r cX|dz }tdf|z||dz}||S)aZGets a view of the current region of interest during iterative padding. When padding multiple dimensions iteratively corner values are unnecessarily overwritten multiple times. This function reduces the working area for the first dimensions so that corners are excluded. Args: array(cupy.ndarray): The array with the region of interest. original_area_slice(tuple of slices): Denotes the area with original values of the unpadded array. axis(int): The currently padded dimension assuming that `axis` is padded before `axis` + 1. Returns: Nr)arrayoriginal_area_slicerrs r _view_roir!s8  AID ++$ !4TUU!; ;B 9r cJtdt|j|D}|jjrdnd}t j||j|}|||tdt|j|D}|||<||fS)aPads an array on all sides with either a constant or undefined values. Args: array(cupy.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. (Default value = None) c32K|]\}\}}||z|zVdSN.0sizeleftrights r z_pad_simple..BsG D-4 t er FC)r orderNc3HK|]\}\}}t|||zVdSrrrs r r#z_pad_simple..MsL   D-4 dD4K        r ) tuplezipshapeflagsfncremptyr fill)r pad_width fill_value new_shaper&paddedrs r _pad_simpler36s#&u{I#>#>I;? +CCE Z E B B BF J   #&u{I#>#>   #(F  & &&r cttd|d|}|d||<tt|j||dz d|}|d||<dS)z1Set an empty-padded area in given dimension. Nrrrrr*)r2r width_pair value_pair left_slice right_slices r _set_pad_arear:Vss dJqM : :DAAJ#AF:  fl4 :a=0$77K%Q-F;r c|d}tt||dz|}||}|j||dz }tt|dz ||}||}||fS)a6Retrieves edge values from an empty-padded array along a given axis. Args: padded(cupy.ndarray): Empty-padded array. axis(int): Dimension in which the edges are considered. width_pair((int, int)): Pair of widths that mark the pad area on both sides in the given dimension. rrr5) r2rr6 left_indexr8 left_edge right_indexr9 right_edges r _get_edgesr@bs{AJj*q. A A4HHJz"I,t$z!}4K {Q !D!DdKKK $J j  r ct|||}tj|d|d||dd|j|}tj|d|d||dd|j|}|t t ddd|}||fS)aConstructs linear ramps for an empty-padded array along a given axis. Args: padded(cupy.ndarray): Empty-padded array. axis(int): Dimension in which the ramps are constructed. width_pair((int, int)): Pair of widths that mark the pad area on both sides in the given dimension. end_value_pair((scalar, scalar)): End values for the linear ramps which form the edge of the fully padded array. These values are included in the linear ramps. rF)startstopnumendpointr rrN)r@rlinspacesqueezer rr)r2rr6end_value_pair edge_pair left_ramp right_ramps r _get_linear_rampsrMvs6444I Q q\ ! !$ ' ' qMl IQ q\ ! !$ ' ' qMl JN5tR+@+@$GGHJ j  r c|d}|j||dz }||z }|\}} |||kr|}| || kr|} tt|||z|} || } || |d} t| |j|| cxkr|krnn| | fStt|| z ||} || }|||d}t||j| |fS)aCalculates a statistic for an empty-padded array along a given axis. Args: padded(cupy.ndarray): Empty-padded array. axis(int): Dimension in which the statistic is calculated. width_pair((int, int)): Pair of widths that mark the pad area on both sides in the given dimension. length_pair(2-element sequence of None or int): Gives the number of values in valid area from each side that is taken into account when calculating the statistic. If None the entire valid area in `padded` is considered. stat_func(function): Function to compute statistic. The expected signature is ``stat_func(x: ndarray, axis: int, keepdims: bool) -> ndarray``. rrNT)rkeepdims)r*rrr r )r2rr6 length_pair stat_funcr<r> max_length left_length right_lengthr8 left_chunk left_statr9 right_chunk right_stats r _get_statsrYsL"AJ,t$z!}4Kz)J!,Kj;66 zL88!   j*{233TJ #J *4$???IY ---l0000j00000)##! kL(+66K%K;TDAAAJZ... j  r Fc&|\}}|j||z |z }|rd}nd}|dz}|dkrt||} ||z } | | z} tt| | d|} || } |dkr/tt||dz|}d||z| z } || z } |} tt| | |}| ||<|| z}|dkrt||} | |zdz } | | z } tt| | d|}||}|dkr1tt| dz | |}d||z|z }|j||z } | | z} tt| | |}|||<|| z}||fS)a,Pads an `axis` of `arr` using reflection. Args: padded(cupy.ndarray): Input array of arbitrary shape. axis(int): Axis along which to pad `arr`. width_pair((int, int)): Pair of widths that mark the pad area on both sides in the given dimension. method(str): Controls method of reflection; options are 'even' or 'odd'. include_edge(bool, optional): If true, edge value is included in reflection, otherwise the edge value forms the symmetric axis to the reflection. (Default value = False) rrrFodd)r*minrr)r2rr6method include_edgeleft_pad right_pad old_length edge_offset chunk_lengthrCrBr8rU edge_slicepad_arear9rWs r _set_reflect_bothrgs%Hid#i/(:J  a !||:x00 +%|##E%r$:$:DAA J' U??'h1 (E(EtLLJVJ//*K T"Y.|#!%t"4"4d;;&x\! Y r c |\}}|j||z |z }d}d}|dkrtt| t||z |dkr| nd|}||} ||kr'tt||z ||} ||z }nttd||} | || <|dkrtt||t||z|} || } ||kr)tt| | |z|} ||z }ntt| d|} | || <||fS)aPads an `axis` of `arr` with wrapped values. Args: padded(cupy.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. rN)r*rrr]) r2rr6r`raperiod new_left_pad new_right_padr9rWrfr8rUs r _set_wrap_bothrls%Hi \$ ) +h 6F LM!|| %  S222'1nn $      [) f  %eHv,=x&H&H$OOH#f,LL&eD(&;&;TBBH&x1}} $ (Hs69'='== > >  J' v  %yj9*v"566H&.MM&eYJ&=&=tDDH%x  &&r c2|d|zSt|tjr|rt|}||ff|zSt j|}|r2t jt j|tj}|jdkr|j dkr?| }|r|dkrtd|d|dff|zS|j dkr\|j d krQ| }|r'|ddks |ddkrtd|d|dff|zS|r'| dkrtd|}|df|_ |S) a}Broadcasts `x` to an array with shape (`ndim`, 2). A helper function for `pad` that prepares and validates arguments like `pad_width` for iteration in pairs. Args: x(scalar or array-like, optional): 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 `cupy.intp`) and ensure every element is positive. (Default value = False) Returns: nested iterables, shape (`ndim`, 2): The broadcasted version of `x`. N))NN)r rrz#index can't contain negative valuesr\)r\r) isinstancenumbersNumberrnumpyrasarrayintpndimr ravel ValueErrorr*r]viewtolist)xruas_indexx_views r _as_pairsr}\s" y%% Aw~ & &  aAAy4 AA< M%+a.. ; ; ;vzz 6Q;; A HAEE !FGGGqT1Q4L?T) ) 6Q;;17f,,  A HQqTAXX1 !FGGGqT1Q4L?T) )@AEEGGaKK>???VVXXF!9FL ==??r constantc 8 t|tjr||ff|jz}nJt j|}|jjdkstdt||jd}t|r|}t||d\}}t|jD]d}tj||d}t j|jdd} d | D} | D]} ||| ||||e|Sgggd gd gd gd gd gd gd gd } t#|t#| |z } n0#t$$r#t'd|wxYw| r#t'd|| |dkrc|d d} t| tjr3| dkr-|jdks |jdkrt||ddStjtjtjd}t||\}}t|j}|dkrRt| |j} t7||| D])\}}}t9|||}t;||||*n|dkrn|jdkrZt7||D]G\}}|j|dkr1t=|r"t'd|Hn}|dkrLt7||D]9\}}t9|||}t?|||}t;||||:n+|dkrz|d d}t||j}t7|||D];\}}}t9|||}tA||||}t;||||<n||vr||}|d d}t||jd}t7|||D]<\}}}t9|||}tC|||||}t;||||=n"|dvr|d d}|dkrdnd}t7||D]\}\}}|j|dkr4|dks|dkr(t?||||f}t;||||f|Mt9|||}|dks|dkr$tE||||f||\}}|dk|dk$n^|dkrXt7||D]G\}\}}t9|||}|dks|dkr"tG||||f\}}|dk|dk"H|S)aPads an array with specified widths and values. Args: array(cupy.ndarray): The array to pad. pad_width(sequence, array_like or 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),) yields same before and after pad for each axis. (pad,) or int is a shortcut for before = after = pad width for all axes. You cannot specify ``cupy.ndarray``. 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. (Not Implemented) '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. 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),) 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. You cannot specify ``cupy.ndarray``. 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),) 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. You cannot specify ``cupy.ndarray``. 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),) 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. You cannot specify ``cupy.ndarray``. 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: cupy.ndarray: Padded array with shape extended by ``pad_width``. .. note:: 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 (cupy.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 = cupy.array([1, 2, 3, 4, 5]) >>> cupy.pad(a, (2, 3), 'constant', constant_values=(4, 6)) array([4, 4, 1, ..., 6, 6, 6]) >>> cupy.pad(a, (2, 3), 'edge') array([1, 1, 1, ..., 5, 5, 5]) >>> cupy.pad(a, (2, 3), 'linear_ramp', end_values=(5, -4)) array([ 5, 3, 1, 2, 3, 4, 5, 2, -1, -4]) >>> cupy.pad(a, (2,), 'maximum') array([5, 5, 1, 2, 3, 4, 5, 5, 5]) >>> cupy.pad(a, (2,), 'mean') array([3, 3, 1, 2, 3, 4, 5, 3, 3]) >>> a = cupy.array([[1, 2], [3, 4]]) >>> cupy.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 = cupy.array([1, 2, 3, 4, 5]) >>> cupy.pad(a, (2, 3), 'reflect') array([3, 2, 1, 2, 3, 4, 5, 4, 3, 2]) >>> cupy.pad(a, (2, 3), 'reflect', reflect_type='odd') array([-1, 0, 1, 2, 3, 4, 5, 6, 7, 8]) >>> cupy.pad(a, (2, 3), 'symmetric') array([2, 1, 1, 2, 3, 4, 5, 5, 4, 3]) >>> cupy.pad(a, (2, 3), 'symmetric', reflect_type='odd') array([0, 1, 1, 2, 3, 4, 5, 5, 6, 7]) >>> cupy.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 = cupy.arange(6) >>> a = a.reshape((2, 3)) >>> cupy.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]]) >>> cupy.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]]) iz%`pad_width` must be of integral type.T)r{r)r0rFNc3,K|]}|tfzVdSr)r)rinds r r#zpad..ns)66#C8+%666666r constant_values end_values stat_length reflect_type) r-edgewrapr~ linear_rampmaximummeanminimumreflect symmetriczmode '{}' is not supportedz/unsupported keyword arguments for mode '{}': {}r~rgNA)rrrr-zGcan't extend empty axis {} using modes other than 'constant' or 'empty'rr>rrevenrFr)$rorpIntegralrurrrsr kind TypeErrorr}callabler3rangermoveaxisndindexr*setKeyErrorrwformatgetrqr maxr]rr)rr:anyr@rMrYrgrl)rr/modekwargsfunctionr2_rrxindsrallowed_kwargsunsupported_kwargsvaluesstat_functionsraxesr6r7roirJr ramp_pairfunclengthrP stat_pairr^r_r<r>s r padrs`)W-.. D+- : M),, #s**CDD DidCCC ~~yQ??? &+&& C CD =r22D=CRC11D66666D C CcIdOT6BBBB C &'$~!?!?"#$%  ND [[3~d/C+D+DD DDD5<>C #tZ < < < < =  q!$D) 4 4   D*{4 A%%#j//% ,,2F4LL   #D) 4 4 < < D*F$7>>C"3j99I #tZ ; ; ; ; <   ZZ a00 z6;77 ,/i,L,L < < (D*jF$7>>C)#tZLLI #tZ ; ; ; ; <   d#M40066;>>>-0y&-I-I < < )D*kF$7>>C"3j+tLLI #tZ ; ; ; ; < ) ) )NF33#{22tt /24/C/C   +D+:{{4 A%%:>>[1__'vtj+5NOO D:{";YF$7>>Cq..K!OO+< K8&,++' K q..K!OO & /24/C/C   +D+:{F$7>>Cq..K!OO+9 K8++' K q..K!OO Ms )%E-E<r)F)r~)rprrrr rrr3r:r@rMrYrgrlr}rrr r rs  7 7 7*''''@ ( ( (!!!($!$!$!N0!0!0!fGGGGT?'?'?'D;;;;LNNNNNNr