ёi]>TSSKJr SSKrSSKJrJr SSKrSSKJr SSK J r SSK r SSK JrJr SSKJr SSKJrJrJr SSKJr SS KJr SS KJrJr S S KJr \(aSS K Jr SSK J!r!J"r"J#r#J$r$J%r% /r&SSjr'\SSSjj5r("SS5r)SSjr*g)) annotationsN) TYPE_CHECKINGAny)Self)Variablecore) check_type)convert_np_dtype_to_dtype_ in_pir_mode static_only) LayerHelper)DataType)get_current_insertion_pointset_insertion_point_setitem_static)Tensor) DTypeLike ShapeLikeSize1 TensorIndex TensorLikec:[U5R5S;$)N)falseoff0none)strlower)vals S/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/static/input.py evaluate_flagr#6s s8>> #@ @@c hSn[S0[5D6n[US[[4S5 [US[ [ 4S5 [ U5n[[U55HGnXcSX'[X[5(dM%XS:dM/XS:wdM9[SX35e Uc[R"5n[5(aUn[U[5(d)[R R"R%U5n['5nU"5 [R(R+XU["R,"55n [/U5 U $UR1UUU["R2R4R6S US S S 9n [8R:R=S S5n [?U 5(a`[S0[5D6n[U["R2R45(d [%U5nURAS0S U 0UUSUS .S9 U $)a This function creates a variable on the global block. The global variable can be accessed by all the following operators in the graph. The variable is a placeholder that could be fed with input, such as Executor can feed input into the variable. When `dtype` is None, the dtype will get from the global dtype by `paddle.get_default_dtype()`. Args: name (str): The name/alias of the variable, see :ref:`api_guide_Name` for more details. shape (list|tuple): List|Tuple of integers declaring the shape. You can set None or -1 at a dimension to indicate the dimension can be of any size. For example, it is useful to set changeable batch size as None or -1. dtype (np.dtype|str, optional): The type of the data. Supported dtype: bool, float16, float32, float64, int8, int16, int32, int64, uint8. Default: None. When `dtype` is not set, the dtype will get from the global dtype by `paddle.get_default_dtype()`. lod_level (int, optional): The LoD level of the DenseTensor. Usually users don't have to set this value. Default: 0. Returns: Variable: The global variable that gives access to the data. Examples: .. code-block:: python >>> # doctest: +SKIP("This has diff in xdoctest env") >>> import numpy as np >>> import paddle >>> paddle.enable_static() # Creates a variable with fixed size [3, 2, 1] # User can only feed data of the same shape to x # the dtype is not set, so it will set "float32" by # paddle.get_default_dtype(). You can use paddle.get_default_dtype() to # change the global dtype >>> x = paddle.static.data(name='x', shape=[3, 2, 1]) # Creates a variable with changeable batch size -1. # Users can feed data of any batch size into y, # but size of each data sample has to be [2, 1] >>> y = paddle.static.data(name='y', shape=[-1, 2, 1], dtype='float32') >>> z = x + y # In this example, we will feed x and y with np-ndarray "1" # and fetch z, like implementing "1 + 1 = 2" in PaddlePaddle >>> feed_data = np.ones(shape=[3, 2, 1], dtype=np.float32) >>> exe = paddle.static.Executor(paddle.framework.CPUPlace()) >>> out = exe.run(paddle.static.default_main_program(), ... feed={ ... 'x': feed_data, ... 'y': feed_data ... }, ... fetch_list=[z.name]) # np-ndarray of shape=[3, 2, 1], dtype=float32, whose elements are 2 >>> print(out) [array([[[2.], [2.]], [[2.], [2.]], [[2.], [2.]]], dtype=float32)] c$[RRR5nUR 5R n[ U5S:XagUH8nUR5S:wdM[RRU5 g g)Nrz pd_op.data) paddlepirrdefault_main_program global_blockopslennamer)r)r+ops r"_reset_data_op_insertion_point,data.._reset_data_op_insertion_pointsh%zzCCE"//155 s8q= BwwyL( ..r2r$datar-shapeNrzIOnly -1 can be used in shape to indicate unknown dimension, but received T)r-r2dtypetype stop_gradient lod_levelis_dataneed_check_feedFLAGS_enable_pir_in_executorout)r2r4placer-)r5inputsoutputsattrs)r1)!r localsr bytesrlisttupleranger, isinstanceint ValueErrorr'get_default_dtyper rr(rr r_pir_opsr1Placercreate_global_variableVarDescVarType DENSE_TENSORosenvirongetr# append_op) r-r2r4r7r/helperiir_dtypeprev_insertion_pointr; is_pir_modes r"r1r1:sX ,68 ,FtVeS\62uge}f5 KE 3u:  8 EH eh $ $A%(b.[\a\d[ef   }((*}}(H--zzAA%HH:<&(oo""4$**,G01  ' '  \\ ! ! . . ( C**..!?FK[!!0vx0%!5!566.u5ECL    Jr$c\rSrSrSrSSSjjrSrSSjr\SSSjj5r \SSSjj5r SS jr SS jr S r SS jrSS jrSSjrSrg) InputSpeca InputSpec describes the signature information of the model input, such as ``shape`` , ``dtype`` , ``name`` . This interface is often used to specify input tensor information of models in high-level API. It's also used to specify the tensor information for each input parameter of the forward function decorated by `@paddle.jit.to_static`. Args: shape (tuple(integers)|list[integers]): List|Tuple of integers declaring the shape. You can set "None" or -1 at a dimension to indicate the dimension can be of any size. For example, it is useful to set changeable batch size as "None" or -1. dtype (np.dtype|str, optional): The type of the data. Supported dtype: bool, float16, float32, float64, int8, int16, int32, int64, uint8. Default: float32. name (str): The name/alias of the variable, see :ref:`api_guide_Name` for more details. stop_gradient (bool, optional): A boolean that mentions whether gradient should flow. Default is False, means don't stop calculate gradients. Examples: .. code-block:: python >>> import paddle >>> from paddle.static import InputSpec >>> input = InputSpec([None, 784], 'float32', 'x') >>> label = InputSpec([None, 1], 'int64', 'label') >>> print(input) InputSpec(shape=(-1, 784), dtype=paddle.float32, name=x, stop_gradient=False) >>> print(label) InputSpec(shape=(-1, 1), dtype=paddle.int64, name=label, stop_gradient=False) NcURU5UlUb0[U[R[ 45(a [ U5nX lX0lX@lgN) _verifyr2rEnpr4rr r-r6)selfr2r4r-r6s r"__init__InputSpec.__init__sI\\%(  %"((C11259  *r$cT[URURURS9$)Nr2r4)r1r-r2r4r_s r"_create_feed_layerInputSpec._create_feed_layersDIITZZtzzBBr$c [U5RSURSURSURSUR S3 $)Nz(shape=z, dtype=z, name=z, stop_gradient=))r5__name__r2r4r-r6rds r"__repr__InputSpec.__repr__sbt*%%&gdjj\$**WUYU^U^T__optqCqCpDDEF Fr$c8[U[[RR[ R R45(a1U"URURU=(d UR5$[S[U5RS35e)a; Generates a InputSpec based on the description of input tensor. Args: tensor(Tensor): the source tensor to generate a InputSpec instance Returns: A InputSpec instance generated from Tensor. Examples: .. code-block:: python >>> import paddle >>> from paddle.static import InputSpec >>> paddle.disable_static() >>> x = paddle.ones([2, 2], dtype="float32") >>> x_spec = InputSpec.from_tensor(x, name='x') >>> print(x_spec) InputSpec(shape=(2, 2), dtype=paddle.float32, name=x, stop_gradient=False) z0Input `tensor` should be a Tensor, but received .)rErreagerrr'r(Valuer2r4r-rGr5ri)clstensorr-s r" from_tensorInputSpec.from_tensorsq2 fx):):FJJU"URURU5$)a Generates a InputSpec based on the description of input np.ndarray. Args: tensor(Tensor): the source numpy ndarray to generate a InputSpec instance Returns: A InputSpec instance generated from Tensor. Examples: .. code-block:: python >>> import numpy as np >>> from paddle.static import InputSpec >>> x = np.ones([2, 2], np.float32) >>> x_spec = InputSpec.from_numpy(x, name='x') >>> print(x_spec) InputSpec(shape=(2, 2), dtype=paddle.float32, name=x, stop_gradient=False) rc)rpndarrayr-s r" from_numpyInputSpec.from_numpy$s27=='--66r$cZ[U[[45(a0[U5S:wa[ SUS[U5S35eUSnO7[U[ 5(d"[ S[U5RS35eU/[UR5Qn[U5Ul U$)a Inserts `batch_size` in front of the `shape`. Args: batch_size(int): the inserted integer value of batch size. Returns: The original InputSpec instance by inserting `batch_size` in front of `shape`. Examples: .. code-block:: python >>> from paddle.static import InputSpec >>> x_spec = InputSpec(shape=[64], dtype='float32', name='x') >>> x_spec.batch(4) >>> print(x_spec) InputSpec(shape=(4, 64), dtype=paddle.float32, name=x, stop_gradient=False) zLength of batch_size: z shall be 1, but received rmrz.type(batch_size) shall be `int`, but received ) rErBrCr,rGrF TypeErrorr5rir2)r_ batch_size new_shapes r"batchInputSpec.batch?s* j4- 0 0:!# ,ZL8RSVWaSbRccde$AJJ,,@jAQAZAZ@[[\]  3$tzz"23 9%  r$c[UR5S:Xa [S5eURURSS5UlU$)a Removes the first element of `shape`. Returns: The original InputSpec instance by removing the first element of `shape` . Examples: .. code-block:: python >>> from paddle.static import InputSpec >>> x_spec = InputSpec(shape=[4, 64], dtype='float32', name='x') >>> x_spec.unbatch() >>> print(x_spec) # InputSpec(shape=(64,), dtype=paddle.float32, name=x) InputSpec(shape=(64,), dtype=paddle.float32, name=x, stop_gradient=False) rz8Not support to unbatch a InputSpec when len(shape) == 0.ryN)r,r2rGr]rds r"unbatchInputSpec.unbatchdsE$ tzz?a J \\$**QR.1  r$c X[U[[45(d"[S[ U5R S35e[ U5HTup#Ub=[U[5(d([SUS[ U5R SUS35eUbUS:dMPSX'MV [U5$)z9 Verifies the input shape and modifies `None` into `-1`. zJType of `shape` in InputSpec should be one of (tuple, list), but received rmzshape[z$] should be an `int`, but received `z`:r3) rErBrCrzr5ri enumeraterFrG)r_r2rTeles r"r]InputSpec._verify~s%$//\]abg]h]q]q\rrst  &FA!#s++$ #GS HZHZG[[]^a]bbcd{cBh'U|r$cl[[UR5URUR45$r\)hashrCr2r4r6rds r"__hash__InputSpec.__hash__s)U4::& D4F4FGHHr$ct^^/SQn[T5[T5L=(a [UU4SjU55$)N)r2r4r-r6c3V># UHn[TU5[TU5:Hv M g7fr\)getattr).0attrotherr_s r" #InputSpec.__eq__..s'1 DIDGD$ 75$#7 7Es&))r5all)r_rslotss`` r"__eq__InputSpec.__eq__s6;DzT%[( S1 DI1 .  r$c X:X+$r\)r_rs r"__ne__InputSpec.__ne__s   r$)r4r-r2r6)float32NF) r2rr4rr- str | Noner6boolreturnNone)rrr\)rqrr-rrr)ruznpt.NDArray[Any]r-rrr)r{z int | Size1rr)rr)rrF)rrrrrr)ri __module__ __qualname____firstlineno____doc__r`rerj classmethodrrrvr}rr]rrr__static_attributes__rr$r"rYrYs!L%# +++ +  +  +$CF>;?7&7.87 774#J4(I" !r$rYc[XU5$)a x(Tensor): input Tensor. index(Scalar|Tuple|List|Tensor): Where should be set value. value(Scalar|Tensor): The value which is going to be set. [How to write index?] 1. ':' -> slice(), (1) a[:]=v -> setitem(a, slice(None,None,None), v) (2) a[1::2] -> setitem(a, slice(1,None,2), v) 2. if there are multiple indexes for axes, use TUPLE (Not LIST) to pack them. (1) a[1, 2]=v -> setitem(a, (1, 2), v) (2) a[[1,2],[2,3]]=v -> setitem(a, ([1,2],[2,3]), v) (3) a[1,:, 3] = v -> setitem(a, (1, slice(None,None,None),3), v) (4) a[1, ..., 2]=v -> setitem(a, (1, ..., 2), v) 3. You can always use TUPLE as index input, even there is only one index. (1) a[Tensor([10,10])]=v -> setitem(a, (Tensor([10,10]),), v) (2) a[1] = v -> setitem(a, (1,), v) r)xindexvalues r"setitemrs2 1U ++r$r)Nr) r-rr2rr4zDTypeLike | Noner7rFrz paddle.Tensor)rrrrrrrr)+ __future__rrOtypingrrnumpyr^ numpy.typingnpttyping_extensionsrr' paddle.baserrpaddle.base.data_feederr paddle.base.frameworkr r r paddle.base.layer_helperr paddle.base.libpaddlerpaddle.base.libpaddle.pirrrbase.variable_indexrrpaddle._typingrrrrr__all__r#r1rYrrr$r"rs# %" &. 1* 2 A # J J J J J  J JZb!b!J, , , , ,r$