a 8Sic## @sdZddlZddlmZmZmZmZmZddlZddl Z ej ej ej ejejejiZddeDZddddddeeejeeeedffejeeejfeeeeeeeejd d d ZdS) z1 This module contains tensor creation utilities. N)OptionalListTupleUnioncastcCsi|]\}}||qSr).0kvrrS/var/www/html/django/DPS/env/lib/python3.9/site-packages/torch/testing/_creation.py r F)lowhigh requires_grad noncontiguous exclude_zero.) shapedtypedevicerrrrrreturnc Gsrdd}t|dkr.t|dtjjr.|d}tttdft|}t j t j t j t j t jg} t jt jt jt jg} t jt jt jg} |r|| vr|| vrtd|t jurt jdd|||d} n|t j ur(t |jt |jf} ttttf|||| d| ddd |\}}t j|||||d} nr|| vrt |jt |jf} |||| d| dd d |\}}t j|||||d} n|| vrt |jt |jf}||||d|dd d |\}}t j|||d}|||d|} n|| vrt|}t |jt |jf}||||d|dd d |\}}t j|||d}t j|||d}|||d|}|||d|}t ||} nt d |d |r| !dkrt j"| ddd} | ddddf} |rZ|| vs|t jurt j#d||d}nP|| vr"t j#t |j$||d}n,t|}t j#t |j$||d}t ||}|| | dk<|| | vrn|| _%| S)aCreates 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``. 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. 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``. 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``. TypeError: If :attr:`dtype` isn't supported by this function. Examples: >>> 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) 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') cSsdd}|dur|n|}|dur$|n|}||ks8||kr@td||krPtd||||}||||}|tjtjtjtjtjfvrt|t |fS||fS)z Modifies (and raises ValueError when appropriate) low and high values given by the user (input_low, input_high) if required. cSstt|||S)N)minmax)alhrrr clampXsz4make_tensor.._modify_low_high..clampNz(make_tensor: one of low or high was NaN!z/make_tensor: low must be weakly less than high!) ValueErrortorchuint8int8int16int32int64mathfloorceil)rrZlowestZhighestZ default_lowZ default_highrrrrr _modify_low_highTs  z%make_tensor.._modify_low_highr.z;make_tensor: requires_grad must be False for integral dtype)rr i zThe requested dtype 'z' is not supported by torch.testing.make_tensor(). To request support, file an issue at: https://github.com/pytorch/pytorch/issues)dimN)&len isinstance collectionsabcSequencerrinttuplerrr r!r"r#float16bfloat16float32float64 complex32 complex64 complex128rboolrandintiinforrfinforand'complex_to_corresponding_float_type_mapcomplex TypeErrornumelrepeat_interleavetensortinyr)rrrrrrrrr'Z_integral_typesZ_floating_typesZ_complex_typesresultrangesZ ranges_floatsZrand_valZ float_dtypeZ real_rand_valZ imag_rand_valrealimag replace_withZ float_epsrrr make_tensorsbD  .         rM)__doc__rtypingrrrrrr$collections.abcr0r9r5r:r7r;r8rAitemsZ'float_to_corresponding_complex_type_mapr3Sizerstrrfloatr<TensorrMrrrr s2