Χgr.\dZddlZddlZddlZddlmZmZmZm Z m Z ddl Z e j e j e je je je je je jgZe je je je jgZe je je je jgZe je j e j!gZ"e j#geZ$gee"Z%de j&de'de'de j&fdZ(ddd d d dd d e e)e j*ee)e e)d ffd e j+de e,e j-fdee'dee'de#de#de#dee j.de j&fdZ/dS)z1 This module contains tensor creation utilities. N)castListOptionalTupleUniontlowhighreturnc||z tj|jjkr/||dz |dz dS|||S)N)torchfinfodtypemaxuniform_mul_)rr r s S/var/www/html/ai-engine/env/lib/python3.11/site-packages/torch/testing/_creation.py_uniform_random_r#s` czU[))---zz#'4!8,,11!444zz#t$$$F)r r requires_grad noncontiguous exclude_zero memory_formatshape.rdevicerrrrcdttdttdtdtdtdtdtttfffd } t|d kr-t |d t jjr|d }tttd ft|}|r|td |d||rtvrtdtjurTttttf| ||d dd d\}}tj||||} nՉtvrttttf| ||tjjtjjtjurd nd zdd\}}tj||||} n7t(vr| ||tjjtjjdd\}}tj||} t/t0vrtj| n| ||nt4vr| ||tjjtjjdd\}}tj||tj} t/| ||| } nt;dd|r=| d kr%tj| dd} | d d d df} n|| |} |r+tvrd ntjj!| | d k<t(vr|| _"| S)asCreates a tensor with the given :attr:`shape`, :attr:`device`, and :attr:`dtype`, and filled with values uniformly drawn from ``[low, high)``. If :attr:`low` or :attr:`high` are specified and are outside the range of the :attr:`dtype`'s representable finite values then they are clamped to the lowest or highest representable finite value, respectively. If ``None``, then the following table describes the default values for :attr:`low` and :attr:`high`, which depend on :attr:`dtype`. +---------------------------+------------+----------+ | ``dtype`` | ``low`` | ``high`` | +===========================+============+==========+ | boolean type | ``0`` | ``2`` | +---------------------------+------------+----------+ | unsigned integral type | ``0`` | ``10`` | +---------------------------+------------+----------+ | signed integral types | ``-9`` | ``10`` | +---------------------------+------------+----------+ | floating types | ``-9`` | ``9`` | +---------------------------+------------+----------+ | complex types | ``-9`` | ``9`` | +---------------------------+------------+----------+ Args: shape (Tuple[int, ...]): Single integer or a sequence of integers defining the shape of the output tensor. dtype (:class:`torch.dtype`): The data type of the returned tensor. device (Union[str, torch.device]): The device of the returned tensor. low (Optional[Number]): Sets the lower limit (inclusive) of the given range. If a number is provided it is clamped to the least representable finite value of the given dtype. When ``None`` (default), this value is determined based on the :attr:`dtype` (see the table above). Default: ``None``. high (Optional[Number]): Sets the upper limit (exclusive) of the given range. If a number is provided it is clamped to the greatest representable finite value of the given dtype. When ``None`` (default) this value is determined based on the :attr:`dtype` (see the table above). Default: ``None``. .. deprecated:: 2.1 Passing ``low==high`` to :func:`~torch.testing.make_tensor` for floating or complex types is deprecated since 2.1 and will be removed in 2.3. Use :func:`torch.full` instead. requires_grad (Optional[bool]): If autograd should record operations on the returned tensor. Default: ``False``. noncontiguous (Optional[bool]): If `True`, the returned tensor will be noncontiguous. This argument is ignored if the constructed tensor has fewer than two elements. Mutually exclusive with ``memory_format``. exclude_zero (Optional[bool]): If ``True`` then zeros are replaced with the dtype's small positive value depending on the :attr:`dtype`. For bool and integer types zero is replaced with one. For floating point types it is replaced with the dtype's smallest positive normal number (the "tiny" value of the :attr:`dtype`'s :func:`~torch.finfo` object), and for complex types it is replaced with a complex number whose real and imaginary parts are both the smallest positive normal number representable by the complex type. Default ``False``. memory_format (Optional[torch.memory_format]): The memory format of the returned tensor. Mutually exclusive with ``noncontiguous``. Raises: ValueError: If ``requires_grad=True`` is passed for integral `dtype` ValueError: If ``low >= high``. ValueError: If either :attr:`low` or :attr:`high` is ``nan``. ValueError: If both :attr:`noncontiguous` and :attr:`memory_format` are passed. TypeError: If :attr:`dtype` isn't supported by this function. Examples: >>> # xdoctest: +SKIP >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) >>> from torch.testing import make_tensor >>> # Creates a float tensor with values in [-1, 1) >>> make_tensor((3,), device='cpu', dtype=torch.float32, low=-1, high=1) >>> # xdoctest: +SKIP tensor([ 0.1205, 0.2282, -0.6380]) >>> # Creates a bool tensor on CUDA >>> make_tensor((2, 2), device='cuda', dtype=torch.bool) tensor([[False, False], [False, True]], device='cuda:0') r r lowest_inclusivehighest_exclusive default_low default_highr c >dtdtdtdtfd}||n|}||n|}td||fDrtd|d |||kr&tvrt jd t d nF||krtd |d|||ks||krtd|d|dd|d|d ||||}||||}tvr(tj |tj |fS||fS)z Modifies (and raises ValueError when appropriate) low and high values given by the user (input_low, input_high) if required. alhr c>tt|||SN)minr)r#r$r%s rclampz3make_tensor..modify_low_high..clampss1ayy!$$ $rNc3hK|]-}t|totj|V.dSr') isinstancefloatmathisnan).0values r z7make_tensor..modify_low_high..s;WW%z%''=DJu,=,=WWWWWWrz,`low` and `high` cannot be NaN, but got low=z and high=zPassing `low==high` to `torch.testing.make_tensor` for floating or complex types is deprecated since 2.1 and will be removed in 2.3. Use `torch.full(...)` instead.) stacklevelz(`low` must be less than `high`, but got z >= z5The value interval specified by `low` and `high` is [z, z), but z only supports [)) r,any ValueError_FLOATING_OR_COMPLEX_TYPESwarningswarn FutureWarning_BOOLEAN_OR_INTEGRAL_TYPESr-ceil)r r rrr r!r)rs rmodify_low_highz$make_tensor..modify_low_high~s %U %u % %5 % % % %_cc+'tt\ WWCQU;WWW W W M3MMdMM D[[U&@@@ M1      D[[WWWQUWWXX X $ $ $/@(@(@WWWtWWWW.>WWBSWWW  eC)+<==uT+->?? . . .9S>>49T??2 2Dyrr.NzaThe parameters `noncontiguous` and `memory_format` are mutually exclusive, but got noncontiguous=z and memory_format=zU`requires_grad=True` is not supported for boolean and integral dtypes, but got dtype=r )rrr r!)rri zThe requested dtype 'z' is not supported by torch.testing.make_tensor(). To request support, file an issue at: https://github.com/pytorch/pytorch/issues)dim)r)#rr,rlenr+ collectionsabcSequencerinttupler6r;rboolrandintiinfor(rint64r7remptyr_COMPLEX_TYPES view_as_real_FLOATING_8BIT_TYPESfloat32to TypeErrornumelrepeat_interleaveclonetinyr) rrr r rrrrrr=results ` r make_tensorrY,s^d1 e_1uo1 1 ! 1  11 ue| 111111f 5zzQ:eAh 0HIIa sCx%,, / /E 2 =$ = =,9 = =    "<<< f^c f f      #s(O O!""#       TsD%eLLL , , , #s(O O!&U!3!3!7"'+e"4"4"8U[0011a #9     T"sD%eLLL , , ,#O  "[//3#k%004    TU6???*/>*A*AE v & & &vsTX     & & &#O  "[//3#k%004    TU6GGGd+++5!! _E _ _ _   ;!++(;;;SSqS!  "M:: 444AA%+e:L:L:Q v{ ***, Mr)0__doc__collections.abcrDr-r8typingrrrrrruint8int8int16int32rLuint16uint32uint64_INTEGRAL_TYPESfloat16bfloat16rQfloat64_FLOATING_TYPES float8_e4m3fn float8_e5m2float8_e4m3fnuzfloat8_e5m2fnuzrP complex32 complex64 complex128rNrIr;r7Tensorr,rrGSizerstrrrrYrrrts 55555555555555  K J K K K L L L =%.%-O      /5?E4DE#j;?;@@@% %5%%%,%%%%  37aaa #uz49eCHo= >a ;a #u|# $a % a 5/ a  aaaE/0a \aaaaaar