OpfdZddlmZddlZddlmZgdZdZej gdZ d e ee DZ dd Z d Zd Zd ZddZdS)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 ci|]\}}|| Sr).0css R/var/www/html/nettyfy-visnx/env/lib/python3.11/site-packages/opt_einsum/helpers.py rs@@@daQ@@@ctg}|ddd}|D]6}fd|D}|tjj|7|S)a 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) Nz->r,c g|] }| Srr)rxdimension_dicts r zbuild_views..0s000aq!000r)_default_dim_dictsplitappendnprandomrand)stringrviewstermstermdimss ` rrrs0* E LL  q ! ' ' , ,E,,00004000 RY^T*++++ Lrc*d}|D] }|||z}|S)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 rr)indicesidx_dictretis rrr5s-. C  x{ Jrct|fdt|dD}tj|}|j}||z}||z }||||fS)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'}) c3BK|]}|VdSN)pop)rr. remainings r z#find_contraction..~s/ H H1immA H H H H H HrT)reverse)listsortedsetunionr!) positions input_sets output_setinputs idx_contract idx_remain new_result idx_removedr3s @rrrRsVZ  I H H H Hy$(G(G(G H H HF9f%L!!9-Jl*J*,K Z   y+| ;;rc`t||}td|dz }|r|dz }||zS)a 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 rr r sB>(IILAy1}%%I Q ) ##rr Fc(|tj|||zdzz}dt|D} gt fdt|Dfd} t tjt| D]\} } | |kr| | xx| z cc<tjd|} | | | vr*tjd|} | | | v*| | xx| z cc<|r\t|}tjdz|<t|D]} | | xx|z cc<|z d tjd d | }fd | D}||f}|r|fz }|S) 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)] Nr cg|]}dS)r)r_s rrz!rand_equation..s # # #Qb # # #rc3|K|]6}t|tjdzfV7dS)rN)rr"r#randint)rr.d_maxd_mins rr4z rand_equation..sEjjUVZ]]BI,=,=eUQY,O,OPjjjjjjrc3KtD]-\}}|kr||V%|V|V.dSr1) enumerater!)r.ixn_outoutput size_dicts rgenzrand_equation..genshy))  EAr5yy b!!!  rrrrMz{}->{}rcFg|]}tfd|DS)c3(K|] }|V dSr1r)rrUrXs rr4z+rand_equation...s'//bIbM//////r)tuple)roprXs rrz!rand_equation..s6 A A ABe////B///// A A Ar) r"r#seedrangerrT permutationr6rPrjoinformat)nregrVrRrQr^ global_dimreturn_size_dictnum_indsr=rYr.rUwheregdimeqshapesr-rWrXs ``` @@r rand_equationrlsWh  t3w!|e#H # #%(( # # #F FjjjjjZ_`hZiZijjjjjI       2900ccee==>>   2 q55 1IIIOIIIII%%a++Eu %% ))!Q//u %% 5MMMR MMMM(##)++E519== $q  A 1III IIII$WWRY**622 3 3F &))6 2 2BB A A A& A A AF v,C } Jrr1)rr rJNFF)__doc__ collectionsrnumpyr"parserr__all__ _valid_charsarray_sizesziprrrrr rlrrrrvs$##### S S S$ KKK L L@@cc,&?&?@@@    F:4<4<4