,Vh0dZddlZddlmZmZmZmZmZmZm Z m Z ddl Z ddl m Z ddlmZmZmZdZgdZeeeeZdded eeeefd eed ffd Z dded eeeefd eed eefdZe d dedededededeedededd eeeeeefffdZe d!dedededededeedededd eeeffdZ d"dedededededeededed e eeeeeeffeeefffdZded eefdZy)#z Testing routines for opt_einsum.N)AnyDictListLiteralOptionalTupleUnionoverload) get_symbol) ArrayTypePathTypeTensorShapeTypeabcdefghijklmopqABC)rrrrrrrrrrrrrstringdimension_dictreturn.c|t}g}|jddjd}|D].}|Dcgc]}|| }}|jt|0t|Scc}w)aBuilds random tensor shapes for testing. Parameters: string: List of tensor strings to build dimension_dict: Dictionary of index sizes, defaults to indices size of 2-7 Returns: The resulting shapes. Examples: ```python >>> shapes = build_shapes('abbc', {'a': 2, 'b':3, 'c':5}) >>> shapes [(2, 3), (3, 3, 5), (5,)] ``` z->r,)_default_dim_dictsplitappendtuple)rrshapestermstermxdimss B/home/dcms/DCMS/lib/python3.12/site-packages/opt_einsum/testing.py build_shapesr%sz$* F LL q ! ' ' ,E#+/0aq!00 eDk"# =1s A,array_functionc|+tjd}|jj}g}t ||D]<}|r|j |||j tj>t |S)aBuilds random numpy arrays for testing. Parameters: string: List of tensor strings to build dimension_dict: Dictionary of index _sizes array_function: Function to build the arrays, defaults to np.random.rand Returns: The resulting views. Examples: ```python >>> view = build_views('abbc', {'a': 2, 'b':3, 'c':5}) >>> view[0].shape (2, 3, 3, 5) ``` numpy)r)pytest importorskiprandomrandr%rr)rrr&npviewsshapes r$ build_viewsr0-st*   ) Ef^D*  LL/ 0 LL ) * <n regularityn_outd_mind_maxseed global_dimreturn_size_dictTcyNr2r3r4r5r6r7r8r9s r$ rand_equationr>Os,/r1Fcyr;r<r=s r$r>r>]sr1c :tjd}||jj|||zdzz} t |D cgc]} d} } gt | D cic]+} t | |jj ||dz-c} fd} t|jjt| D]l\} }| |kr| | xx|z cc<|jj d|}|| |vr$|jj d|}|| |vr$| |xx|z cc<n|rOt | }|jj ||dz|<t |D]} | | xx|z cc<|z dj|jjdjdj| }| Dcgc]}tfd |D}}||f}|r|fzS|Scc} wcc} wcc}w) a9Generate a random contraction and shapes. Parameters: n: Number of array arguments. regularity: 'Regularity' of the contraction graph. This essentially determines how many indices each tensor shares with others on average. n_out: Number of output indices (i.e. the number of non-contracted indices). Defaults to 0, i.e., a contraction resulting in a scalar. d_min: Minimum dimension size. d_max: Maximum dimension size. seed: If not None, seed numpy's random generator with this. global_dim: Add a global, 'broadcast', dimension to every operand. return_size_dict: Return the mapping of indices to sizes. Returns: eq: The equation string. shapes: The array shapes. size_dict: The dict of index sizes, only returned if ``return_size_dict=True``. Examples: ```python >>> eq, shapes = rand_equation(n=10, regularity=4, n_out=5, seed=42) >>> eq 'oyeqn,tmaq,skpo,vg,hxui,n,fwxmr,hitplcj,kudlgfv,rywjsb->cebda' >>> shapes [(9, 5, 4, 5, 4), (4, 4, 8, 5), (9, 4, 6, 9), (6, 6), (6, 9, 7, 8), (4,), (9, 3, 9, 4, 9), (6, 8, 4, 6, 8, 6, 3), (4, 7, 8, 8, 6, 9, 6), (9, 5, 3, 3, 9, 5)] ``` r(rc3zKtD](\}}|krj||!||*ywr;) enumerater)iixr4output size_dicts r$genzrand_equation..gensCy) EAr5y b! s8;rz{}->{}rc3(K|] }| ywr;r<).0rFrHs r$ z rand_equation..s/bIbM/s) r)r*r+r7ranger randintrD permutationlistjoinformatr)r2r3r4r5r6r7r8r9r-num_inds_inputsrErIrFwheregdimeqoprretrGrHs ` @@r$r>r>js`   W %B  t:~"U*H( #Qb #F # FMRS[_]A 1 1% CC]I 29900ce=>  2 q5 1IOIII%%a+Eu % ))!Q/u % 5MR M  (#))++E519= $q A 1I I $WWRYY**62 3F &)6 2B;A ABe/B// AF A v,Ci\!! e$^PBs H(0H#Hpathctjd}|Dcgc]}|jj|c}Scc}w)zBuild random numpy arrays from a path. Parameters: path: The path to build arrays from. Returns: The resulting arrays. r()r)r*r+r,)r[r-r"s r$build_arrays_from_tuplesr]s7   W %B(, -1NBIINNA  -- -s =r;)NN).....)......)rr NFF)__doc__r+typingrrrrrrr r r)opt_einsum.parserr opt_einsum.typingr r r _valid_chars_sizesdictziprstrintr%r0boolr>r]r<r1r$rjs& MMM (BB$ B\623htCH~.FRWXgilXlRm<cg !)$sCx.!9RZ[^R_ 9D  /  / /  /  /  / 3- / /dm / 3$sCx. () /  / '*           3-  en  3=   "h hh h  h  h 3- hhh 5hS#X. /sH}1E EFhV .8 .S .r1