~\;j2dZddlZddlmZddgZd dZdZy) z1 Determines if a contraction can use BLAS or not N)helperscan_blas tensor_blasct|dk7ry|\}}t||zD]P}|j||j|}}|dkDs |dkDs||zdkDry||zdz t||vk(sPy|8|D]3}|d|j ||d|j |k7s3yt|dk(ry|D cgc] } t| } } | d|z } | d|z } t|} |d|dk(ry| d| dk(ry|| d|d| k(ry |d| || dk(ry || d|| dk(ry |d| |d| k(ry t| dk(st| dk(ry y cc} w) 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/EINSUMDOTz DOT/EINSUMGEMMz GEMV/EINSUMTDOT)lensetcountintfind)inputsresult idx_removedshapes input_left input_rightcnlnrxsets keep_left keep_rightrss XG:\00. PROJECTS\API\Inventory\templateJSON\kerjaOCR\Lib\site-packages\opt_einsum/blas.pyrr sV 6{a$J k) *!!!$k&7&7&:B FQBGaK 7Q;#a6k* *+Aay+,q +:J:J1:M0NN  ;1# #FqCFFD #Q+%Ia;&J [ BayF1I aDG 2#$;s++ CRK- - RCD [". . CRK, , i.A 3z?a#7G $sEct|}t||z }t||z }it||jD] \}} | |< t||jD] \}} | |< t|} t j |} t j |} t j |} ||z}|D]} |j | d}||k(r4tj|j|j}n|| d|d| k(r8tj|j| | |j| | }nA|d| || dk(rKtj|j| | j|j| | j}n|| d|| dk(rAtj|j| | |j| | j}n|d| |d| k(rAtj|j| | j|j| | }nPd\}}|D],} ||j| fz }||j| fz }.tj||||f}tfd|D}|j|k7r+t|dkDr||_ntj|}||k7rtj |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")axesc3(K|] }| ywNr").0rdimension_dicts r ztensor_blas..sBMq*Msrz->)r zipshaper rcompute_size_by_dictreplacenpdotravelreshapeTr tensordottuplesqueezeeinsum) view_leftr view_rightr index_resultrrrisrdim_left dim_right dim_removed tensor_resultnew_viewleft_pos right_pos tensor_shaper's @rrr{sZk"KJ+-I[!K/JNJ 01q1K!1!121q3" [ B++I~FH,,ZHI..{NKK,M %--a4  [ 66)//+Z-=-=-?@ RCD ["- -66)++HkBJDVDVWbdmDno CRK- -66)++KBDDjFXFXYbdoFpFrFrs RCD [". .66)++HkBJDVDVW`bmDnDpDpq CRK, ,66)++KBDDjFXFXYdfoFpq %)A +. .H +**1-0 0I<< :XyrGs) } %l^xrC