Opf4dZddlZddlmZddgZd dZdZdS) z1 Determines if a contraction can use BLAS or not N)helperscan_blas tensor_blasct|dkrdS|\}}t||zD]b}||||}}|dks|dks ||zdkrdS||zdz t||vkrdSc|L|D]I}|d|||d||krdSJt|dkrdSd|D} | d|z } | d|z } t|} |d|dkrdS| d| dkrd S|| d|d| krd S|d| || dkrd S|| d|| dkrd S|d| |d| krd St| dkst| dkrd Sd S) a Checks if we can use a BLAS call. Parameters ---------- inputs : list of str Specifies the subscripts for summation. result : str Resulting summation. idx_removed : set Indices that are removed in the summation shapes : sequence of tuple[int], optional If given, check also that none of the indices are broadcast dimensions. Returns ------- type : str or bool The type of BLAS call to be used or False if none. Notes ----- We assume several operations are not efficient such as a transposed DDOT, therefore 'ijk,jki->' should prefer einsum. These return the blas type appended with "/EINSUM" to differentiate when they can still be done with tensordot if required, e.g. when a backend has no einsum. Examples -------- >>> can_blas(['ij', 'jk'], 'ik', set('j')) 'GEMM' >>> can_blas(['ijj', 'jk'], 'ik', set('j')) False >>> can_blas(['ab', 'cd'], 'abcd', set()) 'OUTER/EINSUM' >>> # looks like GEMM but actually 'j' is broadcast: >>> can_blas(['ij', 'jk'], 'ik', set('j'), shapes=[(4, 1), (5, 6)]) False FrNrz OUTER/EINSUMc,g|]}t|S)set).0xs O/var/www/html/nettyfy-visnx/env/lib/python3.11/site-packages/opt_einsum/blas.py zcan_blas..Us # # #qCFF # # #DOTz DOT/EINSUMGEMMz GEMV/EINSUMTDOT)lenr countintfind) inputsresult idx_removedshapes input_left input_rightcnlnrsets keep_left keep_rightrss rrr scV 6{{au$J k) * *  !!!$$k&7&7&:&:B FFQBGaKK55 7Q;#a6k** * *55 +   Aay++,q +:J:J1:M:M0NNNuuO  ;1~ $ #F # # #DQ+%Ia;&J [  BayF1Iu aDG  |2#$$;ss+++v CRCK- - -v RCDD [". . .v CRCK, , ,v i..A  3z??a#7#7}vrct|}t||z }t||z }it||jD] \}} | |< t||jD] \}} | |< t|} t j|} t j|} t j|} ||z}|D]} || d}||kr;tj| | }n|| d|d| kr?tj| | | | | | }ni|d| || dkrItj| | | j | | | j }n || d|| dkrCtj| | | | | | j }n|d| |d| krCtj| | | j | | | }nUd\}}|D]4} || | fz }|| | fz }5tj ||||f}tfd|D}|j|kr/t|dkr||_ntj|}||krtj|dz|z|}|S)a Computes the dot product between two tensors, attempts to use np.dot and then tensordot if that fails. Parameters ---------- view_left : array_like The left hand view input_left : str Indices of the left view view_right : array_like The right hand view input_right : str Indices of the right view index_result : str The resulting indices idx_removed : set Indices removed in the contraction Returns ------- type : array The resulting BLAS operation. Notes ----- Interior function for tensor BLAS. This function will attempt to use `np.dot` by the iterating through the four possible transpose cases. If this fails all inner and matrix-vector operations will be handed off to einsum while all matrix-matrix operations will first copy the data, perform the DGEMM, and then copy the data to the required order. Examples -------- >>> a = np.random.rand(4, 4) >>> b = np.random.rand(4, 4) >>> tmp = tensor_blas(a, 'ij', b, 'jk', 'ik', set('j')) >>> np.allclose(tmp, np.dot(a, b)) N)r r )axesc3(K|] }|V dSNr )r r dimension_dicts r ztensor_blas..s(BBq*BBBBBBrrz->)r zipshaperrcompute_size_by_dictreplacenpdotravelreshapeTr tensordottuplesqueezeeinsum) view_leftr view_rightr index_resultrr"r#isr$dim_left dim_right dim_removed tensor_resultnew_viewleft_pos right_pos tensor_shaper*s @rrr{sZk""KJ+-I[!!K/JNJ 001qK!1221q [  B+I~FFH,ZHHI.{NKKK,M 55%--a44 [  6)//++Z-=-=-?-?@@ RCDD ["- - -6)++HkBBJDVDVWbdmDnDnoo CRCK- - -6)++KBBDjFXFXYbdoFpFpFrss RCDD [". . .6)++HkBBJDVDVW`bmDnDnDpqq CRCK, , ,6)++KBBDjFXFXYdfoFpFpqq %) 1 1A ++. .H +**1--0 0II< :XyrIsp } %llll^xxxxxr