BVh!A,dZddlmZddlZddlmZddlmZddl m Z ddl m Z ddl m Z dd l mZdd l mZdd l mZdd l mZdd l mZddl mZddlmZddlmZddlmZdZdZdZd%dej<fdZedg d&deej@ej<ffdZ!edg d'deej@ej<ffdZ"deej@ej<ffdZ#dej<fdZ$dZ% d(dZ&ejNejPe&d d(d Z)ejNejTe)dGd!d"Z+ejXe+Gd#d$Z-ejXe-y))zvOperations that generate constants. See the [constants guide](https://tensorflow.org/api_guides/python/constant_op). )UnionN) types_pb2) struct_pb2)context)execute)constant_tensor_conversion)dtypes)ops)tensor)tensor_conversion_registry) tensor_shape) tensor_util)trace)nested_structure_coder) tf_exportc|j}tj|g|tjtj gtj\}\}||g}d|d|f}tjdd|||\}|S)zEEager-only version of Reshape op; requires tensor is an eager Tensor.TTshapesReshapeinputsattrsctx)_datatype_enumrargs_to_matching_eagerr int32int64)r shaperattr_t attr_tshape inputs_flatrresults W/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/framework/constant_op.py_eager_reshaper$,s{  "&!88 gsV\\6<<0&,,@+x+ + .% __!Ku#?(6 -c|jj}t||tj}||g}d|dt j f}tjdd|||\}|S)zAEager-only version of Fill op; requires value is an eager Tensor.r index_typesFillrr)dtypeas_datatype_enumconvert_to_eager_tensorr rrDT_INT32r)dimsvaluerrr!rr"s r# _eager_fillr.8s` ;; ' '& sFLL 9$u +  i&8&8 9% __ qEs<(6 -r%cpd|jjf}tjdd|g||\}|S)zFEager-only version of Identity op; requires tensor is an eager Tensor.rsIdentityrr)r(r)r)r rrr"s r#_eager_identityr0Cs8  -- .% __1fXU=(6 -r%returnc t|tjr|j}t|tj r2|.|j |k7rtd|d|d|j d|S| |j}|jt j ||j|S#t$r"tj|j}Y[wxYw)aConverts the given `value` to an `EagerTensor`. Note that this function could return cached copies of created constants for performance reasons. Args: value: value to convert to EagerTensor. ctx: value of context.context(). dtype: optional desired dtype of the converted EagerTensor. Returns: EagerTensor created from value. Raises: TypeError: if `dtype` is not compatible with the type of t. zExpected tensor z with dtype z, but got dtype .) isinstancenpndarraycopyr EagerTensorr( TypeErrorr)AttributeErrorr as_dtypeensure_initialized device_name)r-rr(s r#r*r*Ks"rzz" JJLEs' U[[E1 (|E9E${{oQ0 11 L 6$$e  77 6ooe$55e6s: B77(C"!C"constant)v1c$t|||||dS)a{Creates a constant tensor. The resulting tensor is populated with values of type `dtype`, as specified by arguments `value` and (optionally) `shape` (see examples below). The argument `value` can be a constant value, or a list of values of type `dtype`. If `value` is a list, then the length of the list must be less than or equal to the number of elements implied by the `shape` argument (if specified). In the case where the list length is less than the number of elements specified by `shape`, the last element in the list will be used to fill the remaining entries. The argument `shape` is optional. If present, it specifies the dimensions of the resulting tensor. If not present, the shape of `value` is used. If the argument `dtype` is not specified, then the type is inferred from the type of `value`. For example: ```python # Constant 1-D Tensor populated with value list. tensor = tf.constant([1, 2, 3, 4, 5, 6, 7]) => [1 2 3 4 5 6 7] # Constant 2-D tensor populated with scalar value -1. tensor = tf.constant(-1.0, shape=[2, 3]) => [[-1. -1. -1.] [-1. -1. -1.]] ``` `tf.constant` differs from `tf.fill` in a few ways: * `tf.constant` supports arbitrary constants, not just uniform scalar Tensors like `tf.fill`. * `tf.constant` creates a `Const` node in the computation graph with the exact value at graph construction time. On the other hand, `tf.fill` creates an Op in the graph that is expanded at runtime. * Because `tf.constant` only embeds constant values in the graph, it does not support dynamic shapes based on other runtime Tensors, whereas `tf.fill` does. Args: value: A constant value (or list) of output type `dtype`. dtype: The type of the elements of the resulting tensor. shape: Optional dimensions of resulting tensor. name: Optional name for the tensor. verify_shape: Boolean that enables verification of a shape of values. Returns: A Constant Tensor. Raises: TypeError: if shape is incorrectly specified or unsupported. F verify_shapeallow_broadcast_constant_impl)r-r(rnamerBs r# constant_v1rGos| ueT (- //r%c$t||||ddS)a# Creates a constant tensor from a tensor-like object. Note: All eager `tf.Tensor` values are immutable (in contrast to `tf.Variable`). There is nothing especially _constant_ about the value returned from `tf.constant`. This function is not fundamentally different from `tf.convert_to_tensor`. The name `tf.constant` comes from the `value` being embedded in a `Const` node in the `tf.Graph`. `tf.constant` is useful for asserting that the value can be embedded that way. If the argument `dtype` is not specified, then the type is inferred from the type of `value`. >>> # Constant 1-D Tensor from a python list. >>> tf.constant([1, 2, 3, 4, 5, 6]) >>> # Or a numpy array >>> a = np.array([[1, 2, 3], [4, 5, 6]]) >>> tf.constant(a) If `dtype` is specified, the resulting tensor values are cast to the requested `dtype`. >>> tf.constant([1, 2, 3, 4, 5, 6], dtype=tf.float64) If `shape` is set, the `value` is reshaped to match. Scalars are expanded to fill the `shape`: >>> tf.constant(0, shape=(2, 3)) >>> tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3]) `tf.constant` has no effect if an eager Tensor is passed as the `value`, it even transmits gradients: >>> v = tf.Variable([0.0]) >>> with tf.GradientTape() as g: ... loss = tf.constant(v + v) >>> g.gradient(loss, v).numpy() array([2.], dtype=float32) But, since `tf.constant` embeds the value in the `tf.Graph` this fails for symbolic tensors: >>> with tf.compat.v1.Graph().as_default(): ... i = tf.compat.v1.placeholder(shape=[None, None], dtype=tf.float32) ... t = tf.constant(i) Traceback (most recent call last): ... TypeError: ... `tf.constant` will create tensors on the current device. Inputs which are already tensors maintain their placements unchanged. Related Ops: * `tf.convert_to_tensor` is similar but: * It has no `shape` argument. * Symbolic tensors are allowed to pass through. >>> with tf.compat.v1.Graph().as_default(): ... i = tf.compat.v1.placeholder(shape=[None, None], dtype=tf.float32) ... t = tf.convert_to_tensor(i) * `tf.fill`: differs in a few ways: * `tf.constant` supports arbitrary constants, not just uniform scalar Tensors like `tf.fill`. * `tf.fill` creates an Op in the graph that is expanded at runtime, so it can efficiently represent large tensors. * Since `tf.fill` does not embed the value, it can produce dynamically sized outputs. Args: value: A constant value (or list) of output type `dtype`. dtype: The type of the elements of the resulting tensor. shape: Optional dimensions of resulting tensor. name: Optional name for the tensor. Returns: A Constant Tensor. Raises: TypeError: if shape is incorrectly specified or unsupported. ValueError: if called on a symbolic tensor. FTrArD)r-r(rrFs r#r>r>sF ueT(, ..r%c4tj}|jrMtjr.tjd5t |||||cdddSt |||||St j||||||}|S#1swY4xYw)zImplementation of constant.z tf.constantN)rexecuting_eagerlyrenabledTrace_constant_eager_implr _create_graph_constant)r-r(rrFrBrCr const_tensors r#rErEs # }} ;;} %L#CulKLL UE5, GG++ UE4, LLs BBc t|||}||Stj|}||jk(r|S|r4t d|d|dt |dt |jd |jj }||j k(rt||j|S|dk(r|jtjk(rOtjd5t|jt|||}dddt|St|j||St d |d|d |d d |d |j d #1swYZxYw)z)Creates a constant on the current device.NzExpected Tensor z (converted from z ) with shape z, but got shape r3rz /device:CPU:0z>Eager execution of tf.constant with unsupported shape. Tensor z) has dz elements, but got `shape` z with z elements).)r*r as_shaperr9tuple num_elementsr$as_listr(r boolr devicer.r0)rr-r(rrBtnum_txs r#rMrM)s|eS%0! ] H    &% agg H &qc):5'U|n$4U177^4DAG HH ''   % e  "" !U]]_c 22 aZww&++ ::o &G  C)@# FG Q $$ !S 113/wfU1IF//4gV'')*+7 88 GGs -&E,,E5crt|tjr |j}n|}|jdk(S)NConst)r4 tensor_libTensoroptype) tensor_or_opr_s r# is_constantrbIs/ j//0 B B G r%c|}|jstd|d|j}d}|D] }|dk\s |}n|X|tjtj fvrt d|d|d|tjk(r3|r1td|d|rtj ntj}|d }t||| S) z*Function to convert TensorShape to Tensor.z-Cannot convert a partially known TensorShape to a Tensor.rlzCannot convert TensorShape to dtype +. Allowed dtypes are tf.int32 and tf.int64.zC to dtype int32; a dimension is too large. Consider using tf.int64.shape_as_tensorr(rF)is_fully_defined ValueErrorrUr rrr9r>)sr(rFas_ref_s_list int64_valuedims r#(_tensor_shape_tensor_conversion_functionrqQs  !     7s-H JJ 99;&+  c e|k    V\\6<<00 3A3jHBB CC   4QC8LM NN(FLLV\\E \ D &D 11r%dc|}|jtd|d|4|tjtjfvr"t d|d|dtj}|d}t |j||S)z(Function to convert Dimension to Tensor.z!Cannot convert unknown Dimension rdzCannot convert Dimension rerfrgrh)r-rjr rrr9r>)rQr(rFrlrms r#%_dimension_tensor_conversion_functionrtss  !WW_ 8=I JJ  V\\6<<00 1!JugFBB CC LLE \ D !''T 22r%c(eZdZdZdZdZdZdZy)_ConstantTensorCodeczCodec for Tensor.c6t|tjSN)r4r]r^selfpyobjs r# can_encodez_ConstantTensorCodec.can_encodes eZ.. //r%c~tj}t|tjr>|j j tj|j|S|jjdk(r6|j j |jjd|Stjdt|dt|d)z;Returns an encoded `TensorProto` for the given `tf.Tensor`.r\r-zNo encoder for object z of type r3)rStructuredValuer4r r8 tensor_valueCopyFromrmake_tensor_protonumpyr_r`get_attrrNotEncodableErrorstr)rzr encode_fnencoded_tensors r# do_encodez_ConstantTensorCodec.do_encodes//1N,0!!**  ' ' (:(:(< =     (##,,\__-E-Eg-NO  %66$S%6$78\"#1 &  r%c$|jdS)NrHasFieldrzr-s r# can_decodez_ConstantTensorCodec.can_decodes >>. ))r%c\~|j}ttj|}|S)z5Returns the `tf.Tensor` encoded by the proto `value`.)rr>r MakeNdarray)rzr- decode_fn tensor_protor s r# do_decodez_ConstantTensorCodec.do_decodes*%%L k--l; >- ((r%cJ~|j}tj|}|S)z6Returns the `np.ndarray` encoded by the proto `value`.)rrr)rzr-rrrs r#rz_NumpyCodec.do_decodes%$$L  # #L 1E Lr%Nrrr%r#rrs))r%rrx)NNr\F)NNr\)NNF).rtypingrrr5tensorflow.core.frameworkrtensorflow.core.protobufrtensorflow.python.eagerrrtensorflow.python.frameworkrr r r r]r r rtensorflow.python.profilerrtensorflow.python.saved_modelr tensorflow.python.util.tf_exportrr$r.r0_EagerTensorBaser* OperationrGr>rErMrbrq#register_tensor_conversion_function TensorShapert Dimensionrvregister_codecrrr%r#rs//++ C.+<B43,@6 !8s7K7K!8H zl>C>/ 3==#.. ./>/>/B :"(/c. 3==#.. ./c.c.L 3==#.. ./"88@4826492<?>>FM 15/3163&?>>A3H  F&%%&:&<=4&%%km4r%