]i SSKJr SSKJrJrJrJrJr SSKJ r SSK J r SSK J r \"S5r\\\S4\\4r\"SS 9S \S \S \\\\44S j5rS\\S \S \\\\44SjrS\S \4SjrS\S\\S \S \\4Sjrg)) lru_cache)ListSequenceTupleTypeVarUnion) EinopsError) get_backend)ParsedExpressionTensor.)maxsizepatternopnamereturnc UR5n[U5n[U5[U5:wa[SUSUS35eSU;a[SUSUS35eUH?nUS:wdM [R "U5upVU(aM,[SUSUSUS35e UR S5n[U5U- S- nXx-n XxU 4$) NzDuplicates 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 K/mnt/rpi/tmp/demucs-venv-sys/lib/python3.13/site-packages/einops/packing.pyanalyze_patternr# s ==?D4yH 4yCM!8yPRSTT (M& DEE 3;/MMdS H8!$6tfDPWyXZ"[\\  JJsOMt9},q0L+H  00tensorsc [US5up#n[US5n/n/n[U5HupURU 5n [ U 5U:a[ SUSU SUSUS3 5e[ U 5U- n UR XU 5 UR URU /U SUQS PXSQ755 M URXbS 9U4$) 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. packrzpacked tensor #z' (enumeration starts with 0) has shape z, while pattern z assumes at least z axesN)r) r#r enumerateshaperr appendreshapeconcat) r%rrr r!backendreshaped_tensors packed_shapesitensorr*axis_after_packed_axess r"r'r'!sH->*> ? NNr$xc$SnUHnX-nM U$)Nr)r4resultr1s r"prodr8Zs F   Mr$r2r0c[USS9up4n[U5nURU5n[U5US-U-:wa[ SUSU35eUnUV s/sHn SU ;aSO [ U 5PM n n [ SU 55n U S:a[ SUSUS 35eS /[U5-Xx/-n U S :Xa$[U S S5HupXU-XS-'M OgU RS5n[U5Hn XX-XS-'M [US-[U 55S S S2HnU US-U U- U U'M US UnXxS-S n[S S 54U-n[U5V Vs/sH8un nURU/UQ[XXS-5P7/UQUQUQ75PM: snn $s sn fs snn f![an[ S US U SSU35UeS nAff=f)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)rrz unpack(..., z)) received input of wrong dim with shape r(c3># UHn[US:H5v M g7f)r(N)int).0r4s r" unpack..s!Q8P1#a2g,,8Psz) received more than one -1 in z and can't infer dimensionsrNzError during unpack(..., "z!"): could not split axis of size z into requested ) r#r r*rr r8sumr)rrangeslicer, Exception)r2r0rrr r!r. input_shape unpacked_axisp_shapelengths_of_composed_axesn_unknown_composed_axessplit_positionsr1r4unknown_composed_axisj shape_start shape_end slice_filler element_shapees r"r:r:asR-%C%CB%G,-A%4%7:R:U%UOE ".,q0#6N2OPQUSUQUVA!0Q!7:RST:U!UOA Wn}-KA-/0I$%'-7L%.m$<  %= = OOYYu_-?UVQVAW'XYZ:+: : :  %=   ;+p:  ( 1RSbceSfRg}o /  s0F3"F>0?F8/F>8F>> G%G  G%N) functoolsrtypingrrrrreinopsr einops._backendsr einops.parsingr r r<Shapestrr#r'r8r:r6r$r"rXs88(+   eCHotCy() 31S1#1%S# 2F11&6O(6"6OS6OU64;;N5O6OrEc\6\$u+\\V \r$