x-j9VVddlmZddlZddlmZddlZddladdlmZddl m Z ddl m Z ddl mZerdd lmZdd lmZdd lmZdd lmZmZmZe jjje jjje jjje jjje jjje jjjgZgd Z e jjj!e jjj"gZ#da$iddddddddddddddddddddddddddddddddddd dd d!Z%d.d*Z&Gd+d,e'Z(d-Z)dS)/) annotationsN) TYPE_CHECKING)_C_ops)size_args_decorator_patch)core)convert_np_dtype_to_dtype_)Any)NDArray)Tensor) DTypeLike PlaceLike ShapeLike) __add____radd____sub____rsub____mul____rmul____div__ __truediv____rdiv__ __rtruediv__ __matmul__Ffloat16halfbfloat16float32floatfloat64doubleint8charint16shortint32intint64longbool complex64 complex128)r,cfloatcdoubledata NDArray[Any]dtyper devicerreturnr c0tj||||SN)paddletensor)r/r1r2 requires_grads a/var/www/html/banglarbhumi/venv/lib/python3.11/site-packages/paddle/base/dygraph/math_op_patch.py_rebuild_tensorr:ks$ =     c2eZdZUded<fdZddZxZS) TensorSizez list[int]as_shapect|ttj|}||_|Sr5)super__new__r'npprodr>)clsshapeinstance __class__s r9rAzTensorSize.__new__|s777??3BGENN(;(;<<!r;NcLtj|j}||S||Sr5)r6Sizer>)selfdimrEs r9__call__zTensorSize.__call__s' DM** ;LSzr;r5)__name__ __module__ __qualname____annotations__rArL __classcell__)rGs@r9r=r=ysZ r;r=c$$%dUd%dV%fd }%fd}dWd }dXd$dY$fd }dYd}dZd}d[d}d\d}d\d}d\d} d\d} td\d} d\d} d\d} td\d}tdYd}tdYd}d d d!d!d"d]d-}td d d!d!d"d^d.}td d d!d!d"d^d/}td d d!d!d"d^d0}td_d1}|jd`d4}dadbd6}tdcd7}ddd8}d9|fd:|fd;|fd<|fd=|fd>|fd?| fd@| fdA%fdB|fdC|fdD|fdE| fdF| fdG| fd#|fdH|fdI|fdJ|fdK|fdL|fdM|fd*|fdN|fdOdP|fdQ|fg}|}||gdR}t}d5at jj}|sQ|D]&}t||d }|rt|||'|D]#} | dS}| dT}t|||$d SdSd l a tj j D]A}t||rttj |d }|rt|||Btj jD]3\}!}"ttj |"d }#|#rt||!|#4d S)ez Similar to monkey_patch_variable. The difference is, in dygraph mode, use auto-generated op functions for better performance. rJr r1r r3ct|tjjtjfst |}|j|kr|Stj||S)a$ Cast a Tensor to a specified data type if it differs from the current dtype; otherwise, return the original Tensor. Args: dtype: The target data type. Returns: Tensor: a new Tensor with target dtype Examples: .. code-block:: python >>> import paddle >>> import numpy as np >>> original_tensor = paddle.ones([2, 2]) >>> print("original tensor's dtype is: {}".format(original_tensor.dtype)) original tensor's dtype is: paddle.float32 >>> new_tensor = original_tensor.astype('float32') >>> print("new tensor's dtype is: {}".format(new_tensor.dtype)) new tensor's dtype is: paddle.float32 ) isinstancerVarDescVarTypeDataTyper r1rcast)rJr1s r9astypez(monkey_patch_math_tensor..astypesS2%$,"6 !FGG 6.u55E :  K{4'''r;c|r|d}|dS|r>|d}tjd|dS|dS)Nr"uint8z:Casting complex values to real discards the imaginary part)is_floating_point is_complexrealloggingwarning)rJr7r^rYs r9bytez&monkey_patch_math_tensor..bytes  ! ! # # )VD&))F6&'** * __   )6$))++v..D OL   6$(( (6$(( (r;cg}tD]A\}}fd}||}||_d|d|d|_|||fB|S)z? Batch create all data type conversion methods cdfd }|S)NrJr r3c|Sr5)rJrYr1s r9conversion_methodz}monkey_patch_math_tensor.._create_dtype_conversion_methods..make_conversion_method..conversion_methods!6$...r;rJr r3r re)r1rfrYs` r9make_conversion_methodzbmonkey_patch_math_tensor.._create_dtype_conversion_methods..make_conversion_methods////////)(r;z Cast a Tensor to z data type if it differs from the current dtype; otherwise, return the original Tensor. Returns: Tensor: a new Tensor with z dtype )_supported_dtype_conversionsitemsrM__doc__append)methods method_name target_dtyperh method_implrYs r9 _create_dtype_conversion_methodszBmonkey_patch_math_tensor.._create_dtype_conversion_methodss)E)K)K)M)M 7 7 %K ) ) ) ) ) 10>>K#.K #*##,8 ###K  NNK5 6 6 6 6r;otherc6||jSr5)rYr1)rJrrs r9type_asz)monkey_patch_math_tensor..type_ass{{5;'''r;varscalerbiascJtj|t||dS)NT)rrvr)rurvrws r9_scalar_elementwise_op_z9monkey_patch_math_tensor.._scalar_elementwise_op_s |CutT:::r;c|ddS)Nggre)rurys r9_neg_z'monkey_patch_math_tensor.._neg_s&&sD#666r;c*|Sr5)absrus r9_abs_z'monkey_patch_math_tensor.._abs_swwyyr;complexc$tj|j}|dks Jd|s Jd|s|d}t tj|S)Nz6only one element variable can be converted to complex.$variable's tensor is not initializedr+)rBrCrE_is_initializedr]rYrarrayrunumels r9 _complex_z+monkey_patch_math_tensor.._complex_s ""zzz Dzz""$$LL&LLL$~~ ***[))Crx}}%%%r;cntj|j}|dks Jd|s Jd|jt jjjks|jt j j kr| d}ttj |S)Nrz4only one element variable can be converted to float.rr)rBrCrErr1rrUrVBF16rWBFLOAT16rYrrrs r9_float_z)monkey_patch_math_tensor.._float_s ""zzz Bzz""$$LL&LLL$ I-2 2 2yDM222**Y''CRXc]]###r;r'cntj|j}|dks Jd|s Jd|jt jjjks|jt j j kr| d}ttj |S)Nrz3only one element variable can be converted to long.rrrBrCrErr1rrUrVrrWrrYr'rrs r9_long_z(monkey_patch_math_tensor.._long_s ""zzzPzzz""$$LL&LLL$ I-2 2 2yDM222**Y''C28C==!!!r;cntj|j}|dks Jd|s Jd|jt jjjks|jt j j kr| d}ttj |S)Nrz2only one element variable can be converted to int.rrrrs r9_int_z'monkey_patch_math_tensor.._int_ s ""zzzOzzz""$$LL&LLL$ I-2 2 2yDM222**Y''C28C==!!!r;c|jdks Jd|jtjjjkr3t |S|jtjjj kr3t | S|j dS)Nrzlen() of a 0-D tensor is wrong) ndimtyperrUrVVOCABlenvalueget_map_tensorSTRINGSget_string_tensorrEr~s r9_len_z'monkey_patch_math_tensor.._len_sx!|||=||| 8t|+1 1 1syy{{113344 4 X-5 5 5syy{{446677 79Q< r;cntj|j}|dks Jd|s Jd|jt jjjks|jt j j kr| d}ttj |S)Nrz;only one element variable can be converted to python index.rrrrs r9_index_z)monkey_patch_math_tensor.._index_s ""zzz Izz""$$LL&LLL$ I-2 2 2yDM222**Y''C28C==!!!r;c*t|jSr5rrEr~s r9_ndimz'monkey_patch_math_tensor.._ndim*s39~~r;c*t|jSr5rr~s r9 ndimensionz,monkey_patch_math_tensor..ndimension.39~~r;c*t|jSr5rr~s r9rKz%monkey_patch_math_tensor..dim1rr;c*t|jSr5)r=rEr~s r9_size_z(monkey_patch_math_tensor.._size_4s#)$$$r;c t|jdkr|Sttt t|j}t j||}|S)Nr)rrElistreversedranger transposerupermouts r9_T_z%monkey_patch_math_tensor.._T_8sV sy>>Q  JHU3sy>>223344sD)) r;ct|jdkrtd|jdt t t|j}|d|dc|d<|d<t j||}|S)a Return the last two dimensions of a Tensor transposed. Args: var (Tensor): The input Tensor, which must have at least 2 dimensions. Returns: Tensor: A new Tensor with its last two dimensions swapped. Examples: .. code-block:: pycon >>> import paddle >>> x = paddle.randn([2, 3, 4]) >>> x_transposed = x.mT >>> x_transposed.shape paddle.Size([2, 4, 3]) rz Tensor.ndim(z/) is required to be greater than or equal to 2.)rrE ValueErrorrrrrrrs r9_mT_z&monkey_patch_math_tensor.._mT_@s( sy>>A  XsxXXX E#ci..))**!"XtBxR$r(sD)) r;NFr1r2r8 pin_memorysizer fill_valuebool | float | paddle.TensorDTypeLike | Noner2PlaceLike | Noner8r*rcZ||j}||j}tj||||||S)aR Create a new Tensor of specified shape and fill it with a given value. Args: var (Tensor): A reference Tensor for default dtype and device. size (ShapeLike): Shape of the new Tensor. fill_value (bool | float | Tensor): Value to fill the Tensor with. dtype (DTypeLike, optional): Desired data type of the new Tensor. Defaults to `var.dtype`. device (PlaceLike, optional): Device on which to place the new Tensor. Defaults to `var.place`. requires_grad (bool, optional): Whether to track gradients. Default: False. pin_memory (bool, optional): Whether to pin memory. Default: False. Returns: Tensor: A new Tensor filled with `fill_value`. Examples: .. code-block:: python >>> import paddle >>> x = paddle.ones([2, 2]) >>> y = x.new_full([3, 3], 5.0) >>> y.numpy() array([[5., 5., 5.], [5., 5., 5.], [5., 5., 5.]], dtype=float32) Nrr1placer6full)rurrr1r2r8rs r9 _new_full_z,monkey_patch_math_tensor.._new_full_]sHJ =IE >YF{  '!     r;cX||j}||j}tj|||||S)a Create a new uninitialized Tensor of the specified shape. Args: var (Tensor): A reference Tensor for default dtype and device. size (ShapeLike): Shape of the new Tensor. dtype (DTypeLike, optional): Desired data type of the new Tensor. Defaults to `var.dtype`. device (PlaceLike, optional): Device on which to place the new Tensor. Defaults to `var.place`. requires_grad (bool, optional): Whether to track gradients. Default: False. pin_memory (bool, optional): Whether to pin memory. Default: False. Returns: Tensor: A new uninitialized Tensor with the specified shape. Examples: .. code-block:: pycon >>> import paddle >>> x = paddle.ones([2, 2]) >>> y = x.new_empty(3, 3) # type: ignore >>> y.shape paddle.Size([3, 3]) Nr2r8r)r1rr6emptyrurr1r2r8rs r9 _new_empty_z-monkey_patch_math_tensor.._new_empty_sED =IE >YF|  '!     r;cZ||j}||j}tj|d||||S)a Create a new Tensor of the specified shape filled with ones. Args: var (Tensor): A reference Tensor for default dtype and device. size (ShapeLike): Shape of the new Tensor. dtype (DTypeLike, optional): Desired data type of the new Tensor. Defaults to `var.dtype`. device (PlaceLike, optional): Device on which to place the new Tensor. Defaults to `var.place`. requires_grad (bool, optional): Whether to track gradients. Default: False. pin_memory (bool, optional): Whether to pin memory. Default: False. Returns: Tensor: A new Tensor filled with ones. Examples: .. code-block:: python >>> import paddle >>> x = paddle.zeros([2, 2]) >>> y = x.new_ones(3, 3) # type: ignore >>> y.numpy() array([[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]], dtype=float32) Nrrrrs r9 _new_ones_z,monkey_patch_math_tensor.._new_ones_HH =IE >YF{  '!     r;cZ||j}||j}tj|d||||S)a Create a new Tensor of the specified shape filled with zeros. Args: var (Tensor): A reference Tensor for default dtype and device. size (ShapeLike): Shape of the new Tensor. dtype (DTypeLike, optional): Desired data type of the new Tensor. Defaults to `var.dtype`. device (PlaceLike, optional): Device on which to place the new Tensor. Defaults to `var.place`. requires_grad (bool, optional): Whether to track gradients. Default: False. pin_memory (bool, optional): Whether to pin memory. Default: False. Returns: Tensor: A new Tensor filled with zeros. Examples: .. code-block:: python >>> import paddle >>> x = paddle.ones([2, 2]) >>> y = x.new_zeros(3, 3) # type: ignore >>> y.numpy() array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]], dtype=float32) Nrrrrs r9 _new_zeros_z-monkey_patch_math_tensor.._new_zeros_rr;c|j S)a! Whether this Tensor requires gradient computation. This is a convenience property that returns the opposite of stop_gradient. Setting requires_grad=True is equivalent to setting stop_gradient=False. Examples: .. code-block:: python >>> import paddle >>> x = paddle.randn([2, 3]) >>> print(x.requires_grad) # False by default >>> >>> x.requires_grad = False >>> print(x.stop_gradient) # True ) stop_gradientrJs r9r8z/monkey_patch_math_tensor..requires_grad#s$%%%r;rNonec~t|tstdt|| |_dS)z Set whether this Tensor requires gradient computation. Args: value (bool): True to enable gradient computation, False to disable. $requires_grad must be bool, but got NrTr* TypeErrorrr)rJrs r9r8z/monkey_patch_math_tensor..requires_grad7sK%&& DtE{{DD "'Yr;Tc~t|tstdt|| |_|S)z Set whether this Tensor requires gradient computation. Args: requires_grad (bool): True to enable gradient computation, False to disable. rr)rJr8s r9requires_grad_z0monkey_patch_math_tensor..requires_grad_EsM-.. LtM7J7JLL "/. r;c*|S)a5 Returns the number of bytes allocated on the machine for a single element of the Tensor. Examples: .. code-block:: python >>> import paddle >>> x = paddle.randn((2,3),dtype=paddle.float64) >>> x.itemsize 8 ) element_sizers r9itemsizez*monkey_patch_math_tensor..itemsizeSs  """r;c|}t|jdd}t|jdd}|j}t ||||ffS)Nr)numpystrrr1r8r:)rJproto data_numpyrr1r8s r9 _reduce_ex_z-monkey_patch_math_tensor.._reduce_ex_bsaZZ\\ DJ"%DJ#*      !   r;__neg____abs__ __complex__ __float____long____int____len__ __index__rYrar[rtrKrrTmTnew_full new_emptynew_ones new_zerosr)__array_ufunc__Nr __reduce_ex__)rrrrrrrrrr__mod____rmod__r __rmatmul____gt____ge____lt____le__ __floordiv__ __rfloordiv____pow____rpow____eq____ne__rr)rJr r1r r3r rg)rJr rrr r3r )rur rvrrwrr3r )rur r3r )rur r3r)rur r3r)rur r3r')rur rrrrr1rr2rr8r*rr*r3r )rur rrr1rr2rr8r*rr*r3r )rJr r3r*)rJr rr*r3r)T)r8r*r3r )rJr r3r')rJr )propertyrsetterextend_already_patch_eager_tensorreagerr getattrsetattr paddle.tensorr6r7tensor_method_funchasattrmagic_method_func)&rarqrtr{rrrrrrrrrrKrrrrrrrr8rrr eager_methodsdtype_conversion_methodseager_cpp_level_patchlocal_already_patch local_tensorrnrpmethod magic_method origin_methodimplryrYs& @@r9monkey_patch_math_tensorrs((((B ) ) ) ) ) )6((((;;;; 777777&&&& $ $ $ $ " " " " " " " "     " " " "X%%%X%XXB#'#'# 1 1 1 1 1 1 f #'#'# , , , , , , \ #'#'# / / / / / / b #'#'# / / / / / / b&&&X&& ' ' ' '      # # #X #      E E  " g V E E g 6  $ G  z"   c t Z k" Z k" -( >*! X +&9M> @?AA1222:6"&:$L :0 @ @K!, TBBK @ k;???# < >D : lD999 : :r;)r/r0r1r r2rr3r )* __future__rr_typingrrrBr6rpaddle.utils.decorator_utilsrr frameworkr r numpy.typingr r paddle._typingr rrrUrVUINT8INT8INT16INT32INT64BOOL_supported_int_dtype_!_supported_promote_complex_types_ COMPLEX64 COMPLEX128_complex_dtypesrrir:r'r=rrer;r9r$s#"""""  222222?$$$$$$>>>>>>>>>> LLLLLL  % % %! L"L# $  y  I    y    Y  y  i  F  F  W   W! " W# $ 7% & W' ( G) , F- ./ 05   <            A :A :A :A :A :r;