2Vh\"ddlZddlmZddlmZddlmZddlm Z ddlm Z ddl m Z ddl mZdd l mZdd lmZdd lmZGd d ZdZdZeddgdZdZdZeddZeddZdZGddZy)Nbackend) keras_export)config)dtypes) global_state) current_path)get_stateless_scope)in_stateless_scope) tensorflow) auto_nameceZdZdZ dEdZdZdZdZdZe dZ e d Z e d Z d Z d Zd Ze dZe dZe dZe dZej(dZe dZe dZe dZej(dZe dZej(dZe dZej(dZdZdZdZdFdZdZd Zd!Z dFd"Z!d#Z"d$Z#d%Z$d&Z%d'Z&d(Z'd)Z(d*Z)d+Z*d,Z+d-Z,d.Z-d/Z.d0Z/d1Z0d2Z1d3Z2d4Z3d5Z4d6Z5d7Z6d8Z7d9Z8d:Z9d;Z:d<Z;d=ZZ=d?Z>d@Z?dAZ@dBZAdCZBdFdDZCy)GVariablea Represents a backend-agnostic variable in Keras. A `Variable` acts as a container for state. It holds a tensor value and can be updated. With the JAX backend, variables are used to implement "functionalization", the pattern of lifting stateful operations out of a piece of computation to turn it into a stateless function. Args: initializer: Initial value or callable for initialization. If a callable is used, it should take the arguments `shape` and `dtype`. shape: Optional. Tuple for the variable's shape. Required if `initializer` is a callable. dtype: Optional. Data type of the variable. Defaults to the global float dtype type (`"float32"` if never configured). trainable: Optional. Boolean indicating if variable is trainable. Defaults to `True`. autocast: Optional. Boolean indicating whether the variable supports autocasting. If `True`, the layer may first convert the variable to the compute data type when accessed. Defaults to `True`. aggregation: Optional string, one of `None`, `"none"`, `"mean"`, `"sum"` or `"only_first_replica"` specifying how a distributed variable will be aggregated. This serves as a semantic annotation, to be taken into account by downstream backends or users. Defaults to `"none"`. name: Optional. A unique name for the variable. Automatically generated if not set. Attributes: shape: The shape of the variable (tuple of integers). ndim: The number of dimensions of the variable (integer). dtype: The data type of the variable (string). trainable: Whether the variable is trainable (boolean). autocast: Whether the variable supports autocasting (boolean). aggregation: How a distributed variable will be aggregated (string). value: The current value of the variable (NumPy array or tensor). name: The name of the variable (string). path: The path of the variable within the Keras model or layer (string). kwargs: Additional backend-specific keyword arguments. Examples: **Initializing a `Variable` with a NumPy array:** ```python import numpy as np import keras initial_array = np.ones((3, 3)) variable_from_array = keras.Variable(initializer=initial_array) ``` **Using a Keras initializer to create a `Variable`:** ```python from keras.src.initializers import Ones variable_from_initializer = keras.Variable( initializer=Ones(), shape=(3, 3), dtype="float32" ) ``` **Updating the value of a `Variable`:** ```python new_value = np.zeros((3, 3), dtype="float32") variable_from_array.assign(new_value) ``` **Marking a `Variable` as non-trainable:** ```python non_trainable_variable = keras.Variable( initializer=np.ones((3, 3), dtype="float32"), trainable=False ) ``` Nc  ~ |xst|jj}t|trd|vrt d||dvrt d||d}|dvrt d||d}||_t} | rtdz|z|_n||_d|_ d|_ d|_ d|_ t||_t||_||_||_d|_t|trd d lm} | j+|}t-|r|2t d |d ||j/|| }| |j0}t3||_t7rFt-|r0d|_||_ |j;||_ t=|nyt dt-|r(|j;||_ |j?|n;|jA||j;|j8jB|_ tE|j|_#y)N/zRArgument `name` must be a string and cannot contain character `/`. Received: name=)Nnonemeansumonly_first_replicazInvalid value for argument `aggregation`. Expected one of `None`, `'none'`, `'mean'`, `'sum'`, `'only_first_replica'`. Received: aggregation=r)Nron_readon_writeautozInvalid value for argument `synchronization`. Expected one of `None`, `'none'`, `'on_read'`, `'on_write'`, `'auto'`. Received: synchronization=Fr) initializersznWhen creating a Variable from an initializer, the `shape` argument should be specified. Received: initializer=z and shape=dtypeadYou are attempting to create a variable while in a stateless scope. This is disallowed. Make sure that all variables are created before you start using your layer/model objects. In some cases, you might be seeing this error because you need to implement a `def build(self, input_shape)` method on your layer/model, which will create its variables. In some other cases, you might be seeing this error because you are instantiating a `Variable` and assigning it to a layer without going through self.add_variable()/self.add_weight(). Always prefer using these methods (with a `shape` and `initializer` argument).)$r __class____name__ isinstancestr ValueError_namer _path_shape _initializer _regularizer _constraintbool _trainable _autocast _aggregation_synchronization_overwrite_with_gradient keras.srcrgetcallable_convert_to_tensorrstandardize_dtype_dtyper _value_validate_shaperegister_uninitialized_variable_initialize_with_initializer _initializeshapelen_ndim) self initializerr8r trainableautocast aggregationsynchronizationnamekwargs parent_pathrs R/home/dcms/DCMS/lib/python3.12/site-packages/keras/src/backend/common/variables.py__init__zVariable.__init__\sn 9y!8!89$$t ""&)    )*5 7    K #  -.=,=?   "$O "n %#-4DJDJ   y/h' /).% k3 ' .&**;7K K } --8M:!!&)11+U1KK}#))'.   $" $/!"2259 /5 C$ $"2259 11+>  -"224;;3D3DE % c|jtd|jdtr td|j |j d|_y)Nz Variable z is already initialized.zYou are attempting to initialize a variable while in a stateless scope. This is disallowed. Make sure that all variables are initialized before you start using your layer/model objects.)r3r pathr r6r$r;s rD_deferred_initializezVariable._deferred_initializes\ ;; "y 3KLM M  C  ))$*;*;< rFc\t|}d|vrtd|d|jd|S)NzbShapes used to initialize variables must be fully-defined (no `None` dimensions). Received: shape=z for variable path='')standardize_shaper rH)r;r8s rDr4zVariable._validate_shapesD!%( 5=3DII;aA   rFcXt}|jr||j|S|SN)get_autocast_scoper) maybe_cast)r;valueautocast_scopes rD_maybe_autocastzVariable._maybe_autocasts,+- >>n8!,,U3 3 rFc,tj|SrO)nparrayrIs rDnumpyzVariable.numpysxx~rFc|jS)z+The strategy for aggregating this variable.)r*rIs rDr?zVariable.aggregations   rFc|jS)z-The strategy for synchronizing this variable.)r+rIs rDr@zVariable.synchronizations$$$rFc,tr.t}|j|}||j|S|j6|j|j |j |jS|j|jS)zBThe current value of the variable (numpy array or backend tensor).r)r r get_current_valuerTr3r$r#r2)r;scoperRs rDrRzVariable.values  ')E++D1E ++E22 ;;  ''!!$++T[[!A ##DKK00rFcX|j||j}t|j|js2t d|j jd|jd|t rt}|j||f|S|j||S)NrzzThe shape of the target variable and the shape of the target value in `variable.assign(value)` must match. variable.shape=z, Received: value.shape=z. Target variable: ) r0r shape_equalr8r rRr r add_update_direct_assign)r;rRr]s rDassignzVariable.assigns''TZZ'@5;; 3"#'**"2"2!34)). 6$$(6 +   ')E   dE] +     & rFc*|j||zSrOrbr;rRs rD assign_addzVariable.assign_add#{{4%<((rFc*|j||z SrOrdres rD assign_subzVariable.assign_sub&rgrFct}|jr$|"t|jr |j}n |j}t j |S)zThe data type of the variable.)rPr)is_float_dtyper2rrr1)r;rSrs rDrzVariable.dtype)sJ,- NN*t{{+"((EKKE((//rFc|jS)zThe shape of the variable.)r#rIs rDr8zVariable.shape7s{{rFc|jS)z)The number of dimensions of the variable.)r:rIs rDndimz Variable.ndim<zzrFc|jS)z"Whether the variable is trainable.)r(rIs rDr=zVariable.trainableAsrFc$t||_yrO)r'r(res rDr=zVariable.trainableFs u+rFc|jS)zThe name of the variable.)r!rIs rDrAz Variable.nameJrorFc|jS)z9The path of the variable within the Keras model or layer.)r"rIs rDrHz Variable.pathOrorFc|jS)aWhether this variable should be overwritten by the gradient. This property is designed for a special case where we want to overwrite the variable directly with its computed gradient. For example, in float8 training, new `scale` and `amax_history` are computed as gradients, and we want to overwrite them directly instead of following the typical procedure such as gradient descent with a learning rate, gradient clipping and weight decaying. )r,rIs rDoverwrite_with_gradientz Variable.overwrite_with_gradientTs,,,rFcNt|tstd|||_y)Nz7`overwrite_with_gradient` must be a boolean. Received: )rr' TypeErrorr,res rDruz Variable.overwrite_with_gradientas2%&"G% ).%rFc|jSrO)r%rIs rD regularizerzVariable.regularizerjs   rFcVddlm}|t||std|||_y)Nr) RegularizerzInvalid value for attribute `regularizer`. Expected an instance of `keras.regularizers.Regularizer`, or `None`. Received: regularizer=)keras.src.regularizersr{rr r%)r;rRr{s rDryzVariable.regularizerns:6  Z{%C)).1  "rFc|jSrO)r&rIs rD constraintzVariable.constraintzsrFcVddlm}|t||std|||_y)Nr) ConstraintzInvalid value for attribute `constraint`. Expected an instance of `keras.constraints.Constraint`, or `None`. Received: constraint=)keras.src.constraintsrrr r&)r;rRrs rDr~zVariable.constraint~s:4  Zz%B((-w0  !rFcd}t|dr5|j)tjj |j}|d|nd}d|j d|j d|j|dS)Nr3z, value=z)hasattrr3rcoreconvert_to_numpyrHr8r)r;rR value_strs rD__repr__zVariable.__repr__sx 4 "t{{'>LL11$++>E*/*;hug& dii[ =ZZL 1 . rFctrONotImplementedErrorres rDr7zVariable._initialize!!rFc|j||j|j}|j|y)Nr)r0r#r2r7)r;r<rRs rDr6z%Variable._initialize_with_initializers4''  4;; 7  rFctrOr)r;rRrs rDr0zVariable._convert_to_tensorrrFc8|jj|SrO)rR __getitem__)r;idxs rDrzVariable.__getitem__szz%%c**rFcz|jdkDrtd|jt|jSNrzBOnly scalar arrays can be converted to Python scalars. Got: shape=)rnrwr8intrRrIs rD__int__zVariable.__int__s= 99q="jj\+ 4::rFcz|jdkDrtd|jt|jSr)rnrwr8floatrRrIs rD __float__zVariable.__float__s> 99q="jj\+ TZZ  rFc^tj|jj|SrO)rVasarrayrR __array__r;rs rDrzVariable.__array__s" zz$**..u566rFctd)Nz-A Keras Variable cannot be used as a boolean.)rwrIs rD__bool__zVariable.__bool__sGHHrFc6|jjSrO)rR__neg__rIs rDrzVariable.__neg__zz!!##rFc|jSrO)rRrIs rD__pos__zVariable.__pos__s zzrFc6|jjSrO)rR__abs__rIs rDrzVariable.__abs__rrFc6|jjSrO)rR __invert__rIs rDrzVariable.__invert__szz$$&&rFcVtjj|j|SrO)rrXequalrRr;others rD__eq__zVariable.__eq__}}""4::u55rFcVtjj|j|SrO)rrX not_equalrRrs rD__ne__zVariable.__ne__s}}&&tzz599rFcVtjj|j|SrO)rrXlessrRrs rD__lt__zVariable.__lt__s}}!!$**e44rFcVtjj|j|SrO)rrX less_equalrRrs rD__le__zVariable.__le__}}'' E::rFcVtjj|j|SrO)rrXgreaterrRrs rD__gt__zVariable.__gt__s}}$$TZZ77rFcVtjj|j|SrO)rrX greater_equalrRrs rD__ge__zVariable.__ge__s}}**4::u==rFcVtjj|j|SrOrrXaddrRrs rD__add__zVariable.__add__}}  U33rFcVtjj||jSrOrrs rD__radd__zVariable.__radd__}}   33rFcVtjj|j|SrOrrXsubtractrRrs rD__sub__zVariable.__sub__}}%%djj%88rFcVtjj||jSrOrrs rD__rsub__zVariable.__rsub__}}%%eTZZ88rFcVtjj|j|SrOrrXmultiplyrRrs rD__mul__zVariable.__mul__rrFcVtjj||jSrOrrs rD__rmul__zVariable.__rmul__rrFcVtjj|j|SrOrrX true_dividerRrs rD __truediv__zVariable.__truediv__}}((U;;rFcVtjj||jSrOrrs rD __rtruediv__zVariable.__rtruediv__}}(( ;;rFcVtjj|j|SrOrrX floor_dividerRrs rD __floordiv__zVariable.__floordiv__s}}))$**e<449999<<==446677<<;;<<BrFrcVtjdgd}|j|y)Nuninitialized_variablesT)set_to_default)rget_global_attributeappend)variablers rDr5r5"s**??!2d""8,rFctjd}|r|D]}|jtjdgy)Nr)rrrJset_global_attribute) collectionvs rDinitialize_all_variablesr)sB223LMJ %A " " $ %%%&?DrFzkeras.utils.standardize_dtypezkeras.backend.standardize_dtypec|tjStjj ||}t |dr |j }n\t |dr |j}nCt |dr7dt|vs dt|vrt|jdd}|tjvrtd||S) NrAr__str__torchz jax.numpy.zInvalid dtype: ) rfloatxrPYTHON_DTYPES_MAPr.rrArrsplitALLOWED_DTYPESr rs rDr1r11s }}}  $ $ ( ( 6Euf   #  "3u:E !:E   %b) F)))?5'233 LrFc dt|tst| tdt|dstd|dt j dk(r*t|t jr|j}t|}t j dk(rttd|}|D]y}|t j dk(rd tt|vr4tt|std|d |d t|d |d ksltd|d|S)Nz#Undefined shapes are not supported.__iter__zCannot convert 'z ' to a shape.r rc | t|SdSrO)r)xs rDz#standardize_shape..VsamCFrFjax_DimExprz#' to a shape. Found invalid entry 'z ' of type 'z'. rz2' to a shape. Negative dimensions are not allowed.) rtupler rrrtf TensorShapeas_listmaprtype is_int_dtype)r8es rDrMrMFs4 eU # =BC Cuj)/wmDE E >> | +%0 e  ~~7"cEuMN  9  >> u $s47|)C DG$"5'*(()s+d1gYcC  q5"5'*77  LrFcvt|t|k7ryt||D]\}}| | ||k7syy)z8Return whether a_shape == b_shape (allows None entries).FT)r9zip)a_shapeb_shapee1e2s rDr_r_ksF 7|s7|#gw'B >bnr rFzkeras.backend.is_float_dtypec`t|}|jdxs|jdS)Nrbfloatr1 startswithrs rDrkrkus- e $E   G $ B(8(8(BBrFzkeras.backend.is_int_dtypec`t|}|jdxs|jdS)Nruintr=rs rDr3r3{s- e $E   E " >e&6&6v&>>rFc,tjdSNrS)rrrrFrDrPrPs  , ,-= >>rFc(eZdZdZdZdZdZdZy) AutocastScopezContext manager that enables the autocasting of float variables. Under this context manager, float `Variables`s will be cast to `dtype` (note that `dtype` must also be float). cl|$t|}t|std|||_d|_y)Nzh`AutocastScope` can only be used with a floating-point target dtype, such as 'float16'. Received: dtype=)r1rkr roriginal_scopers rDrEzAutocastScope.__init__sG  %e,E!%( '',g/  "rFcddlm}|j3t|jr|j||jS|S)Nrrr)r-rrrkcast)r;rRrs rDrQzAutocastScope.maybe_casts6% :: !nU[[&A7<<TZZ8 8 rFcNt|_tjd|yrB)rPrFrrrIs rD __enter__zAutocastScope.__enter__s02))*:DArFcDtjd|jyrB)rrrF)r;argsrBs rD__exit__zAutocastScope.__exit__s))*:DrUs-$+1<HG9,PBPBf-E$&GH$"J,-C.C *+?,? ?QQrF