+ Pj^RIHt^RIHtHtHtHtHt^RIH t ^RI H t ^RI H t ]!R4t]]]R3,]],3,t]!^R7R R l4tR R ltR RltRRltR#)) lru_cache)ListSequenceTupleTypeVarUnion) EinopsError) get_backend)ParsedExpressionTensor.)maxsizec hV^8dQhR\R\R\\\\3,/#)patternopnamereturn)strrint)formats"g/Users/ahmed/devFolder/Ultron/claude-voice/gateway/.venv/lib/python3.14/site-packages/einops/packing.py __annotate__rs,11S1#1%S# 2F1c VP4p\V4p\V4\V48wd\RV RV R24hRV9d\RV RV R24hVFApVR8wgK \P !V4wrVV'dK.\RV RV RV R24h VP R4p\V4V, ^, pWx,p WxV 3#)zDuplicates in axes names in z(..., "z")*z No *-axis in zInvalid axis name z in )splitsetlenr r check_axis_name_return_reasonindex) rraxesaxes_setaxisis_validreason n_axes_before n_axes_aftermin_axess && ranalyze_patternr( s ==?D4yH 4yCM!8yPRSTT (M& DEE 3;/MMdS H8!$6tfDPWyXZ"[\\  JJsOMt9},q0L+H  00rc V^8dQhR\\,R\R\\\\ ,3,/#)rtensorsrr)rr rrrShape)rs"rrr!s96O6O(6"6OS6OU64;;N5O6Orc \VR4wr#p\V^,4p.p.p\V4FwrVPV 4p \ V 4V8d\ RV RV RV RV R2 4h\ V 4V, p VP WV 4VP VPV .V RVOR NWRO544K VPWbR7V3#) a Packs several tensors into one. See einops tutorial for introduction into packing (and how it replaces stack and concatenation). Parameters: tensors: tensors to be packed, can be of different dimensionality pattern: pattern that is shared for all inputs and output, e.g. "i j * k" or "batch seq *" Returns: (packed_tensor, packed_shapes aka PS) Example: ```python >>> from numpy import zeros as Z >>> inputs = [Z([2, 3, 5]), Z([2, 3, 7, 5]), Z([2, 3, 7, 9, 5])] >>> packed, ps = pack(inputs, 'i j * k') >>> packed.shape, ps ((2, 3, 71, 5), [(), (7,), (7, 9)]) ``` In this example, axes were matched to: i=2, j=3, k=5 based on order (first, second, and last). All other axes were 'packed' and concatenated. PS (packed shapes) contains information about axes that were matched to '*' in every input. Resulting tensor has as many elements as all inputs in total. Packing can be reversed with unpack, which additionally needs PS (packed shapes) to reconstruct order. ```python >>> inputs_unpacked = unpack(packed, ps, 'i j * k') >>> [x.shape for x in inputs_unpacked] [(2, 3, 5), (2, 3, 7, 5), (2, 3, 7, 9, 5)] ``` Read the tutorial for introduction and application scenarios. packzpacked tensor #z' (enumeration starts with 0) has shape z, while pattern z assumes at least z axesN)r") r(r enumerateshaperr appendreshapeconcat) r*rr%r&r'backendreshaped_tensors packed_shapesitensorr0axis_after_packed_axess && rr-r-!sH->*> ? NNrc0V^8dQhR\R\/#)rxr)r+r)rs"rrrZsEcrc,^pVF pW,pK V#))r;resultr7s& rprodr@Zs F   Mrc tV^8dQhR\R\\,R\R\\,/#)rr8r6rr)r rr+r)rs"rrras6\\6\$u+\\V \rc\VRR7wr4p\V4pVPV4p\V4V^,V,8wd\ RV RV 24hTpVU u.uFp R V 9dR M \ V 4NK p p \ RV 44p V ^8d\ RV RV R24h^.\V4,Wx,.,p V ^8Xd1\V RR 4FwrW,V,W^,&K MV PR 4p\V4F!p W,W,,W^,&K# \V^,\V 44RRR 1,F$pV V^,,V V,, V V&K& VRVpWx^,Rp\RR43V,p\V4U Uu.uFHwp pVPV.VO\W,W^,,4N5,.VOVOVO54NKJ upp #uup iuupp i \d"p\ R T R T R , R T 24ThRp?ii;i) a Unpacks a single tensor into several by splitting over a selected axes. See einops tutorial for introduction into packing (and how it replaces stack and concatenation). Parameters: tensor: tensor to be unpacked packed_shapes: packed_shapes (aka PS) is a list of shapes that take place of '*' in each output. output will contain a single tensor for every provided shape pattern: pattern that is shared for input and all outputs, e.g. "i j * k" or "batch seq *", where * designates an axis to be unpacked Returns: list of tensors If framework supports views, results are views to the original tensor. Example: ```python >>> from numpy import zeros as Z >>> inputs = [Z([2, 3, 5]), Z([2, 3, 7, 5]), Z([2, 3, 7, 9, 5])] >>> packed, ps = pack(inputs, 'i j * k') >>> packed.shape, ps ((2, 3, 71, 5), [(), (7,), (7, 9)]) ``` In this example, axes were matched to: i=2, j=3, k=5 based on order (first, second, and last). All other axes were 'packed' and concatenated. PS (packed shapes) contains information about axes that were matched to '*' in every input. Resulting tensor has as many elements as all inputs in total. Packing can be reversed with unpack, which additionally needs PS (packed shapes) to reconstruct order. ```python >>> inputs_unpacked = unpack(packed, ps, 'i j * k') >>> [x.shape for x in inputs_unpacked] [(2, 3, 5), (2, 3, 7, 5), (2, 3, 7, 9, 5)] ``` Read the tutorial for introduction and application scenarios. unpack)rz unpack(..., z)) received input of wrong dim with shape c3>"TFp\VR8H4xK R#5i)r=Nr.)r).0r;s& r unpack..s!Q8P1#a2g,,8Psz) received more than one -1 in z and can't infer dimensionsNzError during unpack(..., "z!"): could not split axis of size z into requested r.) r(r r0rr r@sumr/rrangeslicer2 Exception)r8r6rr%r&r'r4 input_shape unpacked_axisp_shapelengths_of_composed_axesn_unknown_composed_axessplit_positionsr7r;unknown_composed_axisj shape_start shape_end slice_filler element_shapees&&& rrCrCasR-%C%CB%G,-A%4%7:R:U%UOE ".,q0#6N2OPQUSUQUVVA!0Q!7:RST:U!UOA Wn}-KA-/0I$%'-7L%.m$<  %= = OOYYu_-?UVQVAW'XYZ:+: : :  %=   ;+p:  ( 1RSbceSfRg}o /  s1"H8H#AHH#H## I.I  IN) functoolsrtypingrrrrreinopsr einops._backendsr einops.parsingr r rr+r(r-r@rCr>rrr^sg88(+   eCHotCy() 311&6Or\r