iSrSSKJr SSKrSSKJr /SQrSr\R"/SQ5r \ "\\ 5VVs0sHupX_M snnr SS jr S rS rS rSS jrgs snnf)z: Contains helper functions for opt_einsum testing scripts ) OrderedDictN) get_symbol) build_viewscompute_size_by_dictfind_contraction flop_countabcdefghijklmopqABC)r r r rr r r rr r r r r cUc[n/nURS5SRS5nUHCnUVs/sHoQUPM nnUR[RR "U65 ME U$s snf)ad Builds random numpy arrays for testing. Parameters ---------- string : list of str List of tensor strings to build dimension_dict : dictionary Dictionary of index _sizes Returns ------- ret : list of np.ndarry's The resulting views. Examples -------- >>> view = build_views(['abbc'], {'a': 2, 'b':3, 'c':5}) >>> view[0].shape (2, 3, 3, 5) z->r,)_default_dim_dictsplitappendnprandomrand)stringdimension_dictviewstermstermxdimss R/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/opt_einsum/helpers.pyrrsx0* E LL q ! ' ' ,E+/04aq!40 RYY^^T*+ L1sA:c*SnUH nX!U-nM U$)a\ Computes the product of the elements in indices based on the dictionary idx_dict. Parameters ---------- indices : iterable Indices to base the product on. idx_dict : dictionary Dictionary of index _sizes Returns ------- ret : int The resulting product. Examples -------- >>> compute_size_by_dict('abbc', {'a': 2, 'b':3, 'c':5}) 90 r)indicesidx_dictretis r rr5s$. C  { Jc^[U5mU4Sj[USS95n[R"U6nUR"T6nXT-nXF- nTR U5 UTXt4$)a| Finds the contraction for a given set of input and output sets. Parameters ---------- positions : iterable Integer positions of terms used in the contraction. input_sets : list List of sets that represent the lhs side of the einsum subscript output_set : set Set that represents the rhs side of the overall einsum subscript Returns ------- new_result : set The indices of the resulting contraction remaining : list List of sets that have not been contracted, the new set is appended to the end of this list idx_removed : set Indices removed from the entire contraction idx_contraction : set The indices used in the current contraction Examples -------- # A simple dot product test case >>> pos = (0, 1) >>> isets = [set('ab'), set('bc')] >>> oset = set('ac') >>> find_contraction(pos, isets, oset) ({'a', 'c'}, [{'a', 'c'}], {'b'}, {'a', 'b', 'c'}) # A more complex case with additional terms in the contraction >>> pos = (0, 2) >>> isets = [set('abd'), set('ac'), set('bdc')] >>> oset = set('ac') >>> find_contraction(pos, isets, oset) ({'a', 'c'}, [{'a', 'c'}, {'a', 'c'}], {'b', 'd'}, {'a', 'b', 'c', 'd'}) c3F># UHnTRU5v M g7fN)pop).0r& remainings r #find_contraction..~s H(G1immA(Gs!T)reverse)listsortedsetunionr) positions input_sets output_setinputs idx_contract idx_remain new_result idx_removedr-s @r rrRsjVZ I Hy$(G HF99f%L!!9-J*J,K Z y+ ;;r'cV[X5n[SUS- 5nU(aUS- nXE-$)aB Computes the number of FLOPS in the contraction. Parameters ---------- idx_contraction : iterable The indices involved in the contraction inner : bool Does this contraction require an inner product? num_terms : int The number of terms in a contraction size_dictionary : dict The size of each of the indices in idx_contraction Returns ------- flop_count : int The total number of FLOPS required for the contraction. Examples -------- >>> flop_count('abc', False, 1, {'a': 2, 'b':3, 'c':5}) 90 >>> flop_count('abc', True, 2, {'a': 2, 'b':3, 'c':5}) 270 r)rmax)idx_contractioninner num_termssize_dictionary overall_size op_factors r r r s3>(ILAy1}%I Q  ##r'c$^^^^^Ub[RRU5 X-S-T-n[U5V s/sHn SPM n n /m[ UU4Sj[U555mUUU4Sjn [ [RR [U "5555HvupX:aX==U - ss'M[RRSU5nXU;a*[RRSU5nXU;aM*X==U - ss'Mx U(aT[U5n[RRTTS-5TU'[U5Hn X==U- ss'M TU- mSR[RR T55mSRSRU 5T5nU Vs/sHn[U4S jU55PM nnUU4nU(aUT4- nU$s sn fs snf) aGenerate a random contraction and shapes. Parameters ---------- n : int Number of array arguments. reg : int 'Regularity' of the contraction graph. This essentially determines how many indices each tensor shares with others on average. n_out : int, optional Number of output indices (i.e. the number of non-contracted indices). Defaults to 0, i.e., a contraction resulting in a scalar. d_min : int, optional Minimum dimension size. d_max : int, optional Maximum dimension size. seed: int, optional If not None, seed numpy's random generator with this. global_dim : bool, optional Add a global, 'broadcast', dimension to every operand. return_size_dict : bool, optional Return the mapping of indices to sizes. Returns ------- eq : str The equation string. shapes : list[tuple[int]] The array shapes. size_dict : dict[str, int] The dict of index sizes, only returned if ``return_size_dict=True``. Examples -------- >>> eq, shapes = rand_equation(n=10, reg=4, n_out=5, seed=42) >>> eq 'oyeqn,tmaq,skpo,vg,hxui,n,fwxmr,hitplcj,kudlgfv,rywjsb->cebda' >>> shapes [(9, 5, 4, 5, 4), (4, 4, 8, 5), (9, 4, 6, 9), (6, 6), (6, 9, 7, 8), (4,), (9, 3, 9, 4, 9), (6, 8, 4, 6, 8, 6, 3), (4, 7, 8, 8, 6, 9, 6), (9, 5, 3, 3, 9, 5)] r c3># UH3n[U5[RRTTS-54v M5 g7f)rN)rrrrandint)r,r&d_maxd_mins r r. rand_equation..s2jZiUVZ]BII,=,=eUQY,OPZi;>c3># [T5H*upUT:aTRU5 Uv M"Uv Uv M, g7fr*) enumerater)r&ixn_outoutput size_dicts r genrand_equation..gens;y)EA5y b!*rLrrz{}->{}rc3.># UH nTUv M g7fr*r")r,rOrRs r r.rKs/BbIbMBs) rrseedrangerrN permutationr1rHrjoinformattuple)nregrPrJrIrV global_dimreturn_size_dictnum_inds_r8rSr&rOwheregdimeqopshapesr%rQrRs ``` @@r rand_equationrgsh  tw!|e#H( #(Qb(F # FjZ_`hZijjI 29900ce=> 5 IOIII%%a+Eu % ))!Q/u % MR M?(#))++E519= $qA I I$WWRYY**62 3F &)6 2B;A A&Be/B//&F A v,C } Je$VBs HH r*)rr NFF)__doc__ collectionsrnumpyrparserr__all__ _valid_charsarray_sizesziprrrrr rg)css00r rtst$ S$ K L&),&?@&?daQT&?@ F:4