o ?e@sdZddlZddlmZddlmZddlmZddlm Z ddl m Z ddlmZdd l mZdd l mZdd l mZdd lmZdd lmZddlmZddlmZm Z edej!ej"ej#ej$ej%ej&ej'ej(ej)ej*ej+ej, Z-edej&ej'Z.dde j/dfdej0e-dej0ej&de1de1de.dej0e.f ddZ2ede3e2Z4dej0e-dej0ej&de1de1de.dej0e.f ddZ5edej!ej"ej#ej$Z6edej&ej'Z7dndej0e7d ej0e6d!ej0e6d"ej0e6d#ej0e6de1de1dej0e6fd$d%Z8ed&e3e8Z9dej0e7d ej0e6d!ej0e6d"ej0e6d#ej0e6de1de1dej0e6fd'd(Z:ed)ej&ej'Z;ed*ej"ej#ej$Zdej0e;d+ej0e<de1de1dej0e<f d/d0Z?ed1ej"ej#Z@dod+ej0e@d2ej0e@dej0e@fd3d4ZAed5e3eAZBd+ej0e@d2ej0e@dej0e@fd6d7ZCed8ej&ej'ZDed9ej"ej#ej$ZEdndej0eDd:ej0eEde1de1dej0eEf d;d<ZFed=e3eFZGdej0eDd:ej0eEde1de1dej0eEf d>d?ZHed@ej&ej'ZIedAej"ej#ej$ej&ej'ZJedBej"ej#ej$ej&ej'ZKdde j/dfdej0eId:ej0eJde1de1dCeKdej0eKf dDdEZLedFe3eLZMdej0eId:ej0eJde1de1dCeKdej0eKf dGdHZNedIej!ejOejPejQejRej"ej#ej$ej%ej&ej'ej(ejSejTejUejVejWejXejYej)ej*ej+ej,ejZZ[dndJej0e[de1de1dej0e[fdKdLZ\edMe3e\Z]dJej0e[de1de1dej0e[fdNdOZ^edPej!ej"ej#ej$Z_edQej&ej'Z`dndej0e`dCe_de1de1dej0e_f dRdSZaedTe3eaZbdej0e`dCe_de1de1dej0e_f dUdVZcedWej!ej"ej#ej$ZdedXej&ej'Zedndej0eedCedde1de1dej0edf dYdZZfed[e3efZgdej0eedCedde1de1dej0edf d\d]Zhed^ej&ej'Zied_ej&ej'Zjdndej0ejd`ej0eidaej0eide1de1dej0eif dbdcZkedde3ekZldej0ejd`ej0eidaej0eide1de1dej0eif dedfZmedgej!ej"ej#ej$Znedhej&ej'Zodndej0eodCende1de1dej0enf didjZpedke3epZqdej0eodCende1de1dej0enf dldmZrdS)pzUPython wrappers around TensorFlow ops. This file is MACHINE GENERATED! Do not edit. N) pywrap_tfe)context)core)execute)dtypes)annotation_types)op_def_registry)ops)op_def_library)deprecated_endpoints)dispatch) tf_export)TypeVarListTV_Multinomial_TTV_Multinomial_output_dtypelogits num_samplesseedseed2 output_dtypereturnc Cs|tjpt}|j}|jrYzt|d|||d|d|d| }|WStjy8} z t | |WYd} ~ nd} ~ wtj yAYnwz t |||||||dWStj yXYnw|dur_d}t |d}|durkd}t |d}|durxtj}t |d}tjd||||||d\} } } } | dd}t rd| dd| dd | d d| df} | j}t d|| ||\}|S) aDraws samples from a multinomial distribution. Args: logits: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`. 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]` represents the unnormalized log probabilities for all classes. num_samples: A `Tensor` of type `int32`. 0-D. Number of independent samples to draw for each row slice. seed: An optional `int`. Defaults to `0`. If either seed or seed2 is set to be non-zero, the internal random number generator is seeded by the given seed. Otherwise, a random seed is used. seed2: An optional `int`. Defaults to `0`. A second seed to avoid seed collision. output_dtype: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int64`. name: A name for the operation (optional). Returns: A `Tensor` of type `output_dtype`. MultinomialrrrN)rrrnamectxr)rrrrrrT)_contextr_thread_local_datais_eagerrTFE_Py_FastPathExecute_core_NotOkStatusException_opsraise_from_not_ok_status_FallbackExceptionmultinomial_eager_fallback_SymbolicException_executemake_int_dtypesint64 make_type_op_def_library_apply_op_helpermust_record_gradient _get_attr_int_get_attr_typeinputsrecord_gradient)rrrrrr_ctxtld_resulte__op_outputs_attrs _inputs_flatr<e/home/www/facesmatcher.com/pyenv/lib/python3.10/site-packages/tensorflow/python/ops/gen_random_ops.py multinomials`      r>zraw_ops.Multinomialc Cs|durd}t|d}|durd}t|d}|durtj}t|d}t|g|tjtjtjtj tj tj tjtj tj tjtjtjg \}\}t|tj}||g}d|d|d|d|f} tjdd|| ||d} trttd || | | \} | S) Nrrrrrs Multinomialr1attrsrrr)r'r(r)r*r+args_to_matching_eagerfloat32float64int32Zuint8Zint16Zint8bfloat16Zuint16halfZuint32Zuint64r"Zconvert_to_tensorrr.r2) rrrrrrr_attr_Tr;r:r5r<r<r=r%\s0   F r%%TV_ParameterizedTruncatedNormal_dtype!TV_ParameterizedTruncatedNormal_TshapemeansstdevsminvalsmaxvalscCsltjpt}|j} | jr\zt|d||||||d|d| } | WStjy9} z t | |WYd} ~ nd} ~ wtj yBYnwzt |||||||||d WStj y[Ynw|durbd}t |d}|durnd}t |d}tjd||||||||d \} } } }|dd} t rd| dd| dd| dd | d f}| j}t d||| | \} | S) 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`. ParameterizedTruncatedNormalrrNrrrrr)rKrLrMrNrOrrrdtyper)rrrrrrr r!r"r#r$-parameterized_truncated_normal_eager_fallbackr&r'r(r,r-r.r/r0r1r2)rKrLrMrNrOrrrr3r4r5r6r7r8r9r:r;r<r<r=parameterized_truncated_normalws^       rTz$raw_ops.ParameterizedTruncatedNormalc Cs|durd}t|d}|durd}t|d}t||||g|tjtjtjtjg\} } | \}}}}t|g|tjtj g\} \}|||||g} d|d|d| d| f} tj dd| | ||d}t rjt d | | ||\}|S) NrrrrRrsParameterizedTruncatedNormalr?r@rP) r'r(rBr)rGrFrCrDrEr*rr.r2)rKrLrMrNrOrrrr _attr_dtypeZ _inputs_dtyperHr;r:r5r<r<r=rSs*  * rSTV_RandomGamma_STV_RandomGamma_Talphac CZtjpt}|j}|jrVzt|d|||d|d| }|WStjy6}z t ||WYd}~nd}~wtj y?Ynwz t ||||||dWStj yUYnw|dur\d}t |d}|durhd}t |d}tjd|||||d\} } } } | dd}t rd| dd| dd| dd | d f} | j} t d| | ||\}|S) aZOutputs random values from the Gamma distribution(s) described by alpha. This op uses the algorithm by Marsaglia et al. to acquire samples via transformation-rejection from pairs of uniform and normal random variables. See http://dl.acm.org/citation.cfm?id=358414 Args: shape: A `Tensor`. Must be one of the following types: `int32`, `int64`. 1-D integer tensor. Shape of independent samples to draw from each distribution described by the shape parameters given in alpha. alpha: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`. A tensor in which each scalar is a "shape" parameter describing the associated gamma distribution. seed: An optional `int`. Defaults to `0`. If either `seed` or `seed2` are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: An optional `int`. Defaults to `0`. A second seed to avoid seed collision. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `alpha`. RandomGammarrNrQr)rKrXrrrSr)rrrrrrr r!r"r#r$random_gamma_eager_fallbackr&r'r(r,r-r.r/r0r1r2)rKrXrrrr3r4r5r6r7r8r9r:r;r<r<r= random_gammasV      r]zraw_ops.RandomGammac C|durd}t|d}|durd}t|d}t|g|tjtjg\}\}t|g|tjtjtjg\}\}||g}d|d|d|d|f} tj dd|| ||d} t r]t d || | | \} | S) Nrrrr[rs RandomGammar?r@rZ r'r(rBr)rEr*rGrCrDrr.r2) rKrXrrrr_attr_SrHr;r:r5r<r<r=r\&  " r\TV_RandomGammaGrad_Tsamplec Cstjpt}|j}|jrPz t|d|||}|WStjy2}z t ||WYd}~nd}~wtj y;Ynwz t ||||dWStj yOYnwt jd|||d\}}}} | dd}tryd|df} |j} td| | ||\}|S)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)rr)rXrcrr)rrrrrrr r!r"r#r$ random_gamma_grad_eager_fallbackr&r,r-r'r.r0r1r2) rXrcrr3r4r5r6r7r8r9r:r;r<r<r=random_gamma_grad0sB   rfzraw_ops.RandomGammaGradc Csnt||g|tjtjg\}}|\}}||g}d|f}tjdd||||d}tr2td||||\}|S)NrsRandomGammaGradr?r@rd)r'rBr)rCrDrr.r2) rXrcrrrHZ _inputs_Tr;r:r5r<r<r=reZs reTV_RandomPoisson_STV_RandomPoisson_dtyperatec CrY) 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`. RandomPoissonrrNrQr)rKrirrrr[rR)rrrrrrr r!r"r#r$random_poisson_eager_fallbackr&r'r(r,r-r.r/r0r1r2)rKrirrrr3r4r5r6r7r8r9r:r;r<r<r=random_poissonksX        rlzraw_ops.RandomPoissonc Cr^) Nrrrr[rRs RandomPoissonr?r@rjr_) rKrirrrrr`rUr;r:r5r<r<r=rkrarkTV_RandomPoissonV2_STV_RandomPoissonV2_RTV_RandomPoissonV2_dtyperRc Cstjpt}|j}|jrYzt|d|||d|d|d| }|WStjy8} z t | |WYd} ~ nd} ~ wtj yAYnwz t |||||||dWStj yXYnw|dur_d}t |d}|durkd}t |d}|durxtj}t |d}tjd||||||d\} } } } | dd}t rd| dd| dd | d d | d d| df } | j}t d|| ||\}|S) aOutputs random values from the Poisson distribution(s) described by rate. This op uses two algorithms, depending on rate. If rate >= 10, then the algorithm by Hormann is used to acquire samples via transformation-rejection. See http://www.sciencedirect.com/science/article/pii/0167668793909974. Otherwise, Knuth's algorithm is used to acquire samples via multiplying uniform random variables. See Donald E. Knuth (1969). Seminumerical Algorithms. The Art of Computer Programming, Volume 2. Addison Wesley Args: shape: A `Tensor`. Must be one of the following types: `int32`, `int64`. 1-D integer tensor. Shape of independent samples to draw from each distribution described by the shape parameters given in rate. rate: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`. A tensor in which each scalar is a "rate" parameter describing the associated poisson distribution. seed: An optional `int`. Defaults to `0`. If either `seed` or `seed2` are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: An optional `int`. Defaults to `0`. A second seed to avoid seed collision. dtype: An optional `tf.DType` from: `tf.half, tf.float32, tf.float64, tf.int32, tf.int64`. Defaults to `tf.int64`. name: A name for the operation (optional). Returns: A `Tensor` of type `dtype`. RandomPoissonV2rrrRNrrrRrrr)rKrirrrRrr[R)rrrrrrr r!r"r#r$ random_poisson_v2_eager_fallbackr&r'r(r)r*r+r,r-r.r/r0r1r2)rKrirrrRrr3r4r5r6r7r8r9r:r;r<r<r=random_poisson_v2s`        rtzraw_ops.RandomPoissonV2c Cs|durd}t|d}|durd}t|d}|durtj}t|d}t|g|tjtjg\}\}t|g|tjtjtj tjtjgtj \}\}||g} d|d|d|d|d|f } tj dd| | ||d } t rrt d | | | | \} | S) NrrrrRr[rrsRandomPoissonV2r?r@rp) r'r(r)r*r+rBrErGrCrDrr.r2) rKrirrrRrrr`Z_attr_Rr;r:r5r<r<r=rss0   . rsTV_RandomShuffle_Tvaluec CsJtjpt}|j}|jrTzt|d||d|d|}|WStjy5}z t ||WYd}~nd}~wtj y>Ynwz t |||||dWStj ySYnw|durZd}t |d}|durfd}t |d}tjd||||d\}}} } | dd}t rd| dd| dd| df} | j} t d| | ||\}|S) a$Randomly shuffles a tensor along its first dimension. The tensor is shuffled along dimension 0, such that each `value[j]` is mapped to one and only one `output[i]`. For example, a mapping that might occur for a 3x2 tensor is: ``` [[1, 2], [[5, 6], [3, 4], ==> [1, 2], [5, 6]] [3, 4]] ``` Args: value: A `Tensor`. The tensor to be shuffled. seed: An optional `int`. Defaults to `0`. If either `seed` or `seed2` are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: An optional `int`. Defaults to `0`. A second seed to avoid seed collision. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `value`. RandomShufflerrNrQr)rvrrrr)rrrrrrr r!r"r#r$random_shuffle_eager_fallbackr&r'r(r,r-r.r/r0r1r2) rvrrrr3r4r5r6r7r8r9r:r;r<r<r=random_shuffle sR      ryzraw_ops.RandomShufflec Cs|durd}t|d}|durd}t|d}t|g|g\}\}|g}d|d|d|f}tjdd||||d}trEtd||||\}|S) Nrrrrs RandomShuffler?r@rw)r'r(rBrr.r2) rvrrrrrHr;r:r5r<r<r=rx`s$   rxTV_RandomStandardNormal_dtypeTV_RandomStandardNormal_Tc Chtjpt}|j}|jrWzt|d||d|d|d| }|WStjy7}z t ||WYd}~nd}~wtj y@Ynwz t ||||||dWStj yVYnwt |d}|durcd}t |d}|durod}t |d}tjd|||||d\} } } } | dd}t rd| dd| dd| dd | d f} | j} t d| | ||\}|S) 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`. RandomStandardNormalrrrRNrqrrKrRrrrr)rrrrrrr r!r"r#r$%random_standard_normal_eager_fallbackr&r'r+r(r,r-r.r/r0r1r2rKrRrrrr3r4r5r6r7r8r9r:r;r<r<r=random_standard_normalvsZ       rzraw_ops.RandomStandardNormalc Ct|d}|dur d}t|d}|durd}t|d}t|g|tjtjg\}\}|g}d|d|d|d|f}tjdd||||d} trQt d ||| | \} | S) NrRrrrrsRandomStandardNormalr?r@r} r'r+r(rBr)rEr*rr.r2 rKrRrrrrrHr;r:r5r<r<r=r&    rTV_RandomUniform_dtypeTV_RandomUniform_Tc Cr|) aLOutputs random values from a uniform distribution. The generated values follow a uniform distribution in the range `[0, 1)`. The lower bound 0 is included in the range, while the upper bound 1 is excluded. Args: shape: A `Tensor`. Must be one of the following types: `int32`, `int64`. The shape of the output tensor. dtype: A `tf.DType` from: `tf.half, tf.bfloat16, tf.float32, tf.float64`. The type of the output. seed: An optional `int`. Defaults to `0`. If either `seed` or `seed2` are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: An optional `int`. Defaults to `0`. A second seed to avoid seed collision. name: A name for the operation (optional). Returns: A `Tensor` of type `dtype`. RandomUniformrrrRNrqrr~r)rrrrrrr r!r"r#r$random_uniform_eager_fallbackr&r'r+r(r,r-r.r/r0r1r2rr<r<r=random_uniformsZ        rzraw_ops.RandomUniformc Cr) NrRrrrrs RandomUniformr?r@rrrr<r<r=r rrTV_RandomUniformInt_ToutTV_RandomUniformInt_Tminvalmaxvalc Cs`tjpt}|j}|jrXzt|d||||d|d| }|WStjy7} z t | |WYd} ~ nd} ~ wtj y@Ynwz t |||||||dWStj yWYnw|dur^d}t |d}|durjd}t |d}tjd||||||d\} } } } | dd}t rd| dd| dd| dd | d f} | j}t d|| ||\}|S) 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`. RandomUniformIntrrNrQr)rKrrrrrToutr)rrrrrrr r!r"r#r$!random_uniform_int_eager_fallbackr&r'r(r,r-r.r/r0r1r2)rKrrrrrr3r4r5r6r7r8r9r:r;r<r<r=random_uniform_int#sX     rzraw_ops.RandomUniformIntc Cs|durd}t|d}|durd}t|d}t||g|tjtjg\}}|\}}t|g|tjtjg\} \}|||g} d|d|d|d| f} tjdd| | ||d} tr`td | | | | \} | S) NrrrrrsRandomUniformIntr?r@r) r'r(rBr)rEr*rr.r2) rKrrrrrrZ _attr_ToutZ _inputs_ToutrHr;r:r5r<r<r=ris(    rTV_TruncatedNormal_dtypeTV_TruncatedNormal_Tc Cr|) aOutputs random values from a truncated normal distribution. The generated values follow a normal distribution with mean 0 and standard deviation 1, except that values whose magnitude is more than 2 standard deviations from the mean are dropped and re-picked. Args: shape: A `Tensor`. Must be one of the following types: `int32`, `int64`. The shape of the output tensor. dtype: A `tf.DType` from: `tf.half, tf.bfloat16, tf.float32, tf.float64`. The type of the output. seed: An optional `int`. Defaults to `0`. If either `seed` or `seed2` are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: An optional `int`. Defaults to `0`. A second seed to avoid seed collision. name: A name for the operation (optional). Returns: A `Tensor` of type `dtype`. TruncatedNormalrrrRNrqrr~r)rrrrrrr r!r"r#r$truncated_normal_eager_fallbackr&r'r+r(r,r-r.r/r0r1r2rr<r<r=truncated_normalsZ        rzraw_ops.TruncatedNormalc Cr) NrRrrrrsTruncatedNormalr?r@rrrr<r<r=rrr)rrN)N)s__doc__ collectionsZtensorflow.pythonrZtensorflow.python.eagerrrrr rr'Ztensorflow.python.frameworkrr)Ztensorflow.security.fuzzing.pyrZ_atypesrZ_op_def_registryr r"r r,Z"tensorflow.python.util.deprecationr Ztensorflow.python.utilr _dispatchZ tensorflow.python.util.tf_exportr typingrrZBFloat16ZFloat32ZFloat64ZHalfZInt16ZInt32ZInt64ZInt8ZUInt16ZUInt32ZUInt64ZUInt8rrr*ZTensorFuzzingAnnotationintr>Z to_raw_oprr%rIrJrTrPrSrVrWr]rZr\rbrfrdrergrhrlrjrkrmrnrortrprsZBoolZ Complex128Z Complex64ZFloat16ZQInt16ZQInt32ZQInt8ZQUInt16ZQUInt8ResourceStringZVariantruryrwrxrzr{rr}rrrrrrrrrrrrrrrrr<r<r<r=s            8B>6PFN2>0*'(230@J4h(=&,<*,=*<C:,>.