AVhȬ dZddlZddlmZddlmZddlmZddlm Z ddl m Z ddlmZdd l mZdd l mZdd l mZdd lmZdd lmZddlmZddlmZm Z m!Z!ddl"m#Z#eddddddddddddd Z$edddZ%dde jLdfde#e!e$fd e#e!ejNfd!e(d"e(d#e%d$e#e!e%ff d%Z)ed&ejTe)Z+de#e!e$fd e#e!ejNfd!e(d"e(d#e%d$e#e!e%ff d'Z,ed(ddddZ-ed)ddZ.ded*e#e!e.fd+e#e!e-fd,e#e!e-fd-e#e!e-fd.e#e!e-fd!e(d"e(d$e#e!e-ffd/Z/ed0ejTe/Z0d*e#e!e.fd+e#e!e-fd,e#e!e-fd-e#e!e-fd.e#e!e-fd!e(d"e(d$e#e!e-ffd1Z1ed2ddZ2ed3dddZ3ded*e#e!e2fd4e#e!e3fd!e(d"e(d$e#e!e3ff d5Z4ed6ejTe4Z5d*e#e!e2fd4e#e!e3fd!e(d"e(d$e#e!e3ff d7Z6ed8ddZ7dfd4e#e!e7fd9e#e!e7fd$e#e!e7ffd:Z8ed;ejTe8Z9d4e#e!e7fd9e#e!e7fd$e#e!e7ffd<Z:ed=ddZ;ed>dddZd*e#e!e;fd?e#e!erB) r!r"r#r$r%r)r*_attr_TrKrJrEs rLr5r5]s \ D   4 ($ ] E   E7 +%==L##L.A,66xwX_XgXgipivivxyFyFHOHUHUW^WcWcelerert{tDtDFMFTFTV]VbVbdkdrdrt{tBtBGEF'9F&&{GMMB++&, D'5#w &   ^Q|#)s ?' ""$ |VW6 (' .%TV_ParameterizedTruncatedNormal_dtype!TV_ParameterizedTruncatedNormal_Tshapemeansstdevsminvalsmaxvalscvtjxstj}|j} | jr# t j |d||||||d|d| } | S|d}tj|d}|d}tj|d}tj d|||||||| \} } } }|dd} tj"rjd| j%dd| j%dd| j'dd | j'd f}| j(}tj*d||| | \} | S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||| S#t j$rYUwxYw) a/Outputs random values from a normal distribution. The parameters may each be a scalar which applies to the entire output, or a vector of length shape[0] which stores the parameters for each batch. Args: shape: A `Tensor`. Must be one of the following types: `int32`, `int64`. The shape of the output tensor. Batches are indexed by the 0th dimension. means: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. The mean parameter of each batch. stdevs: A `Tensor`. Must have the same type as `means`. The standard deviation parameter of each batch. Must be greater than 0. minvals: A `Tensor`. Must have the same type as `means`. The minimum cutoff. May be -infinity. maxvals: A `Tensor`. Must have the same type as `means`. The maximum cutoff. May be +infinity, and must be more than the minval for each batch. seed: An optional `int`. Defaults to `0`. If either `seed` or `seed2` are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: An optional `int`. Defaults to `0`. A second seed to avoid seed collision. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `means`. ParameterizedTruncatedNormalr#r$Nr#r$r)r*r)rcrdrerfrgr#r$r)dtyper+)r,rr-r.rr/r0r1r2r3r4-parameterized_truncated_normal_eager_fallbackr6r7r8r<r=r>r?r@rArB)rcrdrerfrgr#r$r)rCrDrErFrGrHrIrJrKs rLparameterized_truncated_normalrmxs:    0h..0$ #\\ 11 ,dE5&&$8gn \ D   4 ($ ] E   E7 +%'88&e5/5w07d.3$ @!QX QK' ""$c''/('  )30B0B30GIF::L & fgG (' .?  & &- ##At,,  # #    : t5  # #   s0!EF E00F F  F!!F87F8z$raw_ops.ParameterizedTruncatedNormalc ^|d}tj|d}|d}tj|d}tj||||g|tjtj tj tjg\} } | \}}}}tj|g|tjtjg\} \}|||||g} d|d|d| d| f} tjdd| | ||}tjrtjd | | ||\}|S) Nrr#r$rkr+sParameterizedTruncatedNormalrOrPri) r7r8rRr9r[rYrSrTrUr:rr>rB)rcrdrerfrgr#r$r)r* _attr_dtype _inputs_dtyper_rKrJrEs rLrlrls\ \ D   4 ($ ] E   E7 +%'>>vwX_?`behohthtv}wGwGIPIXIXZaZiZihl m+}&3#5&'755ugsW]]T[TaTaDde'8E9, D'5';W M&   r?r@rArB)rcrsr#r$r)rCrDrErFrGrHrIrJrKs rL random_gammarxs2    0h..0$ #\\ 11 mT5%wOg n \ D   4 ($ ] E   E7 +%'88U%d% "!QX QK' ""$c''/(#s/A/A#/F  %'F::L |VW6 (' .9  & &- ##At,,  # #    ( TTtEE  # #   0D<<FE**FFFF/.F/zraw_ops.RandomGammac*|d}tj|d}|d}tj|d}tj|g|tjtj g\}\}tj|g|tj tjtjg\}\}||g}d|d|d|d|f} tjdd|| ||} tjrtjd || | | \} | S) Nrr#r$rvr+s RandomGammarOrPru r7r8rRr9rUr:r[rSrTrr>rB) rcrsr#r$r)r*_attr_Sr_rKrJrEs rLrwrws \ D   4 ($ ] E   E7 +%55ugsW]]T[TaTaDde'8E55ugsW\\SZSbSbdkdsdsDvw'8E, D'5#wW E&   ^Q|#)s ?' ""$ |VW6 (' .r`TV_RandomGammaGrad_Tsamplectjxstj}|j}|jr t j |d|||}|Stjd|||\}}}} | dd}tj r7d|j#df} |j$} tj&d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||S#t j$rYwxYw)aBComputes the derivative of a Gamma random sample w.r.t. `alpha`. Args: alpha: A `Tensor`. Must be one of the following types: `float32`, `float64`. sample: A `Tensor`. Must have the same type as `alpha`. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `alpha`. RandomGammaGradN)r)r*)rsr~r)r+)r,rr-r.rr/r0r1r2r3r4 random_gamma_grad_eager_fallbackr6r<r=r7r>r@rArB) rsr~r)rCrDrErFrGrHrIrJrKs rLrandom_gamma_gradr1s?    0h..0$ #\\ 11 uf6g n(88vDB!QX QK' ""$3%%c* +F::L <: (' .'  & &- ##At,,  # #    - d..  # #   s0CD"C==DDD))D?>D?zraw_ops.RandomGammaGradc*tj||g|tjtjg\}}|\}}||g}d|f}tj dd||||}tj rtjd||||\}|S)Nr+sRandomGammaGradrOrPr)r7rRr9rSrTrr>rB) rsr~r)r*r_ _inputs_TrKrJrEs rLrr[s66vgoo_f_n_nMqr'9/5&, >&   /<#)s ?' ""$ <: (' .r`TV_RandomPoisson_STV_RandomPoisson_dtyperatec dtjxstj}|j}|jr t j |d|||d|d| }|S|d}tj|d}|d}tj|d}tj d|||||\} } } } | dd}tj"rjd| j%dd| j%dd| j'dd | j'd f} | j(} tj*d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||||S#t j$rYOwxYw) aUse RandomPoissonV2 instead. Args: shape: A `Tensor`. Must be one of the following types: `int32`, `int64`. rate: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`. seed: An optional `int`. Defaults to `0`. seed2: An optional `int`. Defaults to `0`. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `rate`. RandomPoissonr#r$Nrjr)rcrr#r$r)rvrk)r,rr-r.rr/r0r1r2r3r4random_poisson_eager_fallbackr6r7r8r<r=r>r?r@rArB)rcrr#r$r)rCrDrErFrGrHrIrJrKs rLrandom_poissonrls    0h..0$ #\\ 11 otUD&$ gn \ D   4 ($ ] E   E7 +%'88u4d%"$!QX QK' ""$c''/(#s/A/A#/Fs))'24F::L vw8 (' .9  & &- ##At,,  # #    * DDdDD  # #   ryzraw_ops.RandomPoissonc*|d}tj|d}|d}tj|d}tj|g|tjtj g\}\}tj|g|tj tjtjg\}\}||g}d|d|d|d|f} tjdd|| ||} tjrtjd || | | \} | S) Nrr#r$rvrks RandomPoissonrOrPrr{) rcrr#r$r)r*r|rorKrJrEs rLrrs  \ D   4 ($ ] E   E7 +%55ugsW]]T[TaTaDde'8E!88$w||U\UdUdfmfufuFxy+w, D'5#w M&   -q#)s ?' ""$ vw8 (' .r`TV_RandomPoissonV2_STV_RandomPoissonV2_RTV_RandomPoissonV2_dtyperkc tjxstj}|j}|jr" t j |d|||d|d|d| }|S|d}tj|d}|d}tj|d}|tj }tj"|d}t%j&d||||||\} } } } | dd}tj(r{d| j+dd| j+dd | j-d d | j-d d| j-df } | j.}tj0d|| ||\}|S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t|||||||S#t j$rYwxYw) aOutputs random values from the Poisson distribution(s) described by rate. This op uses two algorithms, depending on rate. If rate >= 10, then the algorithm by Hormann is used to acquire samples via transformation-rejection. See http://www.sciencedirect.com/science/article/pii/0167668793909974. Otherwise, Knuth's algorithm is used to acquire samples via multiplying uniform random variables. See Donald E. Knuth (1969). Seminumerical Algorithms. The Art of Computer Programming, Volume 2. Addison Wesley Args: shape: A `Tensor`. Must be one of the following types: `int32`, `int64`. 1-D integer tensor. Shape of independent samples to draw from each distribution described by the shape parameters given in rate. rate: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`. A tensor in which each scalar is a "rate" parameter describing the associated poisson distribution. seed: An optional `int`. Defaults to `0`. If either `seed` or `seed2` are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: An optional `int`. Defaults to `0`. A second seed to avoid seed collision. dtype: An optional `tf.DType` from: `tf.half, tf.float32, tf.float64, tf.int32, tf.int64`. Defaults to `tf.int64`. name: A name for the operation (optional). Returns: A `Tensor` of type `dtype`. RandomPoissonV2r#r$rkNr#r$rkr)r*r)rcrr#r$rkr)rvR)r,rr-r.rr/r0r1r2r3r4 random_poisson_v2_eager_fallbackr6r7r8r9r:r;r<r=r>r?r@rArB)rcrr#r$rkr)rCrDrErFrGrHrIrJrKs rLrandom_poisson_v2rs@    0h..0$ #\\ 11 udFD' wgn \ D   4 ($ ] E   E7 +% ] MME   UG ,%'88TE!&T3!QX QK' ""$c''/(#s/A/A#/F  %w0B0B70KMF::L <: (' .A  & &- ##At,,  # #    - DU  # #   s0 E88F? F&&F?>F?GG,+G,zraw_ops.RandomPoissonV2c |d}tj|d}|d}tj|d}|tj}tj|d}tj |g|tj tjg\}\}tj |g|tjtjtjtj tjgtj\}\}||g} d|d|d|d|d|f } tjdd| | || } tjrtjd | | | | \} | S) Nrr#r$rkrvrsRandomPoissonV2rOrPr) r7r8r9r:r;rRrUr[rSrTrr>rB) rcrr#r$rkr)r*r|_attr_RrKrJrEs rLrrsr \ D   4 ($ ] E   E7 +% ] MME   UG ,%55ugsW]]T[TaTaDde'8E44dVS7< [1, 2], [5, 6]] [3, 4]] ``` Args: value: A `Tensor`. The tensor to be shuffled. seed: An optional `int`. Defaults to `0`. If either `seed` or `seed2` are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: An optional `int`. Defaults to `0`. A second seed to avoid seed collision. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `value`. RandomShuffler#r$Nrjr)rr#r$r)r+)r,rr-r.rr/r0r1r2r3r4random_shuffle_eager_fallbackr6r7r8r<r=r>r?r@rArB) rr#r$r)rCrDrErFrGrHrIrJrKs rLrandom_shuffler!s4    0h..0$ #\\ 11 otUFD'5Jg n \ D   4 ($ ] E   E7 +%'88u4u4I!QX QK' ""$c''/(#s/A/A#/FHF::L vw8 (' .5  & &- ##At,,  # #    * d%d>>  # #   s0D))E0<EE0/E04FFFzraw_ops.RandomShufflecT|d}tj|d}|d}tj|d}tj|g|g\}\}|g}d|d|d|f}tjdd||||}tjrtj d||||\}|S) Nrr#r$r+s RandomShufflerOrPr)r7r8rRrr>rB) rr#r$r)r*r_rKrJrEs rLrras \ D   4 ($ ] E   E7 +%55ugsBG'8E, D'5#w 7&   -q#)s ?' ""$ vw8 (' .r`TV_RandomStandardNormal_dtypeTV_RandomStandardNormal_Tc tjxstj}|j}|jr! t j |d||d|d|d| }|Stj|d}|d}tj|d}|d}tj|d}t!j"d|||||\} } } } | dd}tj$rjd| j'dd| j'dd| j)dd | j)d f} | j*} tj,d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||||S#t j$rYewxYw) aOutputs random values from a normal distribution. The generated values will have mean 0 and standard deviation 1. Args: shape: A `Tensor`. Must be one of the following types: `int32`, `int64`. The shape of the output tensor. dtype: A `tf.DType` from: `tf.half, tf.bfloat16, tf.float32, tf.float64`. The type of the output. seed: An optional `int`. Defaults to `0`. If either `seed` or `seed2` are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: An optional `int`. Defaults to `0`. A second seed to avoid seed collision. name: A name for the operation (optional). Returns: A `Tensor` of type `dtype`. RandomStandardNormalr#r$rkNrrrcrkr#r$r)r+)r,rr-r.rr/r0r1r2r3r4%random_standard_normal_eager_fallbackr6r7r;r8r<r=r>r?r@rArBrcrkr#r$r)rCrDrErFrGrHrIrJrKs rLrandom_standard_normalrws*    0h..0$ #\\ 11 $dE64 wgn   UG ,% \ D   4 ($ ] E   E7 +%'88e5t&+$8!QX QK' ""$c''/('  )30B0B30GIF::L  fg? (' .;  & &- ##At,,  # #    2 d%u4TKK  # #   0EF&FFFF//GGzraw_ops.RandomStandardNormalctj|d}|d}tj|d}|d}tj|d}tj|g|tj tj g\}\}|g}d|d|d|d|f}tjdd||||} tjrtjd ||| | \} | S) Nrkrr#r$r+sRandomStandardNormalrOrPr r7r;r8rRr9rUr:rr>rB rcrkr#r$r)r*r_rKrJrEs rLrrs   UG ,% \ D   4 ($ ] E   E7 +%55ugsW]]T[TaTaDde'8E, D'5'5#w G&   4a #)s ?' ""$  fg? (' .r`TV_RandomUniform_dtypeTV_RandomUniform_Tc tjxstj}|j}|jr! t j |d||d|d|d| }|Stj|d}|d}tj|d}|d}tj|d}t!j"d|||||\} } } } | dd}tj$rjd| j'dd| j'dd| j)dd | j)d f} | j*} tj,d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||||S#t j$rYewxYw) aLOutputs random values from a uniform distribution. The generated values follow a uniform distribution in the range `[0, 1)`. The lower bound 0 is included in the range, while the upper bound 1 is excluded. Args: shape: A `Tensor`. Must be one of the following types: `int32`, `int64`. The shape of the output tensor. dtype: A `tf.DType` from: `tf.half, tf.bfloat16, tf.float32, tf.float64`. The type of the output. seed: An optional `int`. Defaults to `0`. If either `seed` or `seed2` are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: An optional `int`. Defaults to `0`. A second seed to avoid seed collision. name: A name for the operation (optional). Returns: A `Tensor` of type `dtype`. RandomUniformr#r$rkNrrrr+)r,rr-r.rr/r0r1r2r3r4random_uniform_eager_fallbackr6r7r;r8r<r=r>r?r@rArBrs rLrandom_uniformrs,    0h..0$ #\\ 11 otUFD'5gn   UG ,% \ D   4 ($ ] E   E7 +%'88uEE"$!QX QK' ""$c''/('  )30B0B30GIF::L vw8 (' .;  & &- ##At,,  # #    * d%u4TKK  # #   rzraw_ops.RandomUniformctj|d}|d}tj|d}|d}tj|d}tj|g|tj tj g\}\}|g}d|d|d|d|f}tjdd||||} tjrtjd ||| | \} | S) Nrkrr#r$r+s RandomUniformrOrPrrrs rLrr s   UG ,% \ D   4 ($ ] E   E7 +%55ugsW]]T[TaTaDde'8E, D'5'5#w G&   -q#)s ?' ""$ vw8 (' .r`TV_RandomUniformInt_ToutTV_RandomUniformInt_Tminvalmaxvalc jtjxstj}|j}|jr! t j |d||||d|d| }|S|d}tj|d}|d}tj|d}tj d||||||\} } } } | dd}tj"rjd| j%dd| j%dd| j'dd | j'd f} | j(}tj*d|| ||\}|S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t|||||||S#t j$rYQwxYw) aOutputs random integers from a uniform distribution. The generated values are uniform integers in the range `[minval, maxval)`. The lower bound `minval` is included in the range, while the upper bound `maxval` is excluded. The random integers are slightly biased unless `maxval - minval` is an exact power of two. The bias is small for values of `maxval - minval` significantly smaller than the range of the output (either `2^32` or `2^64`). Args: shape: A `Tensor`. Must be one of the following types: `int32`, `int64`. The shape of the output tensor. minval: A `Tensor`. Must be one of the following types: `int32`, `int64`. 0-D. Inclusive lower bound on the generated integers. maxval: A `Tensor`. Must have the same type as `minval`. 0-D. Exclusive upper bound on the generated integers. seed: An optional `int`. Defaults to `0`. If either `seed` or `seed2` are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: An optional `int`. Defaults to `0`. A second seed to avoid seed collision. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `minval`. RandomUniformIntr#r$Nrjr)rcrrr#r$r)Toutr+)r,rr-r.rr/r0r1r2r3r4!random_uniform_int_eager_fallbackr6r7r8r<r=r>r?r@rArB)rcrrr#r$r)rCrDrErFrGrHrIrJrKs rLrandom_uniform_intr$s:    0h..0$ #\\ 11  $vvvtgn \ D   4 ($ ] E   E7 +%'88%v!%U?!QX QK' ""$c''/(D2DV2L3%%c*,F::L L&'; (' .9  & &- ##At,,  # #    . d%dNN  # #   s0D>>FE,,FF FF21F2zraw_ops.RandomUniformIntc|d}tj|d}|d}tj|d}tj||g|tjtj g\}}|\}}tj|g|tjtj g\} \}|||g} d|d|d|d| f} tj dd| | ||} tjrtjd | | | | \} | S) Nrr#r$rr+sRandomUniformIntrOrPr) r7r8rRr9rUr:rr>rB) rcrrr#r$r)r* _attr_Tout _inputs_Toutr_rKrJrEs rLrrjs \ D   4 ($ ] E   E7 +%%<r?r@rArBrs rLtruncated_normalrs.    0h..0$ #\\ 11 ufdGUgn   UG ,% \ D   4 ($ ] E   E7 +%'88e$e $&!QX QK' ""$c''/('  )30B0B30GIF::L <: (' .;  & &- ##At,,  # #    , d%u4TKK  # #   rzraw_ops.TruncatedNormalctj|d}|d}tj|d}|d}tj|d}tj|g|tj tj g\}\}|g}d|d|d|d|f}tjdd||||} tjrtjd ||| | \} | S) Nrkrr#r$r+sTruncatedNormalrOrPrrrs rLrrs   UG ,% \ D   4 ($ ] E   E7 +%55ugsW]]T[TaTaDde'8E, D'5'5#w G&   /<#)s ?' ""$ <: (' .r`)rrN)N)^__doc__ collectionstensorflow.pythonrtensorflow.python.eagerrr,rr0rr7tensorflow.python.frameworkrr9tensorflow.security.fuzzing.pyr_atypesr _op_def_registryr r2r r<"tensorflow.python.util.deprecationr tensorflow.python.utilr _dispatch tensorflow.python.util.tf_exportrtypingrrrtyping_extensionsrrr r:Int32intrM to_raw_opr(r5rarbrmrirlrqrrrxrurwr}rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr`rLrs 67179FK3IC86%%'-/ACTVgiwzIKZ\km{}MO_aqsBC%&C_Vefop|}ipiviv}A< #'7"78<yQTV]VcVcQcGd<kn<x{<Mh<FOPSUpPpFq<|/i-.~t~~k/JK y6F1F'GV_`celerer`rVs{~HK[vFOPSUpPpFq0)00WYkm~ARTb)c%$+,OQ`bq$r!D)C9Z4Z*[DdmnqtYoYeZDdmnqsXnXdYDdmnqsXnXdYDdmnqsXnXdYD`cDmpDBKLOQvLvBwDL Qy)OPQ_QUQ_Q_`~Q A3HiCi9js|~ACh~htis|}@Bg}gshs|}@Bg}gshs|}@Bg}gshps|OXY\^CYCOD,-P-/@BSUcd< #'7"78<3P`K`Aa<hk<ux<KTUXZjUjKk<|/i-.~t~~l/KL y6F1F'GPYZ]_oZoPpx{EHXabegwbwXx(57HJ[\%Ys,@'@A%9UXZnUnKo%@IJMOcJc@d%N7)56~t~~FW7XY Ic;O6O,P Zcdgi}d}Z~ OXY\^rYrOs 1?OT !9;LN_aop1)C);$;<1IcSiNiDj1qt1B1T]^acy^yTz1f3 12>4>>.3QR 38J3J)KS\]`bx]xSyBENQajknpFkFaG(5X57HJ[]km|NO"#=?PRcesvEGVWxyFGhohuhu|@HYs,@'@AHSVXlSlImHtwHBEHOgHENORTlOlEmHT7)56~t~~FW7XYIc;O6O,PXabeg{b{X|EHQT]uENORTlOlEm0H ;)C);$;<;3;PS;enoruGpGfH;z3 12>4>>.3QR 38J3J)KSV_br{|BT}TsU&!((GI[]nqBDR!S#$?Rab:)C1J,J"K:Tq:x{:FI:[dehjGeG[H:xAy!?@PfAgh3@Y;Y1ZdAILUXhqruwTrThU(!!9;MO`bsvDE1?OT;)C);$;<;E[;be;or;ENORTjOjEk;z3 12>4>>.3QR 38J3J)KTjruBR[\_aw\wRx(##=P_` 7/ZAi-B(BCAYWZ\tWtMuA@IJMOgJg@hAorA|AQZ[^`x[xQyAF9978HZ9[\Ys