a d& @sdZddlZddlZddlmZmZmZmZm Z ddl Z e j e j e j e je je jiZddeDZe jeedddZddd d d dd e ee jeeeed ffe je ee jfeeeeeeeee je jd d dZdS)z1 This module contains tensor creation utilities. N)castListOptionalTupleUnioncCsi|]\}}||qSr).0kvrr`/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torch/testing/_creation.py sr tlowhighcCs@||t|jjkr0||d|ddS|||SdS)N)torchfinfodtypemaxZuniform_Zmul_r rrr _uniform_randomsrF)rr requires_grad noncontiguous exclude_zero memory_format.) shaperdevicerrrrrrreturnc Gsfdd} 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} n:|| vrt |jt |jf}| |||d|dd d |\}}t j|||||d} n|| vrt |jt |jf}| |||d|dd d |\}}t j|||d} t| ||n|| vrRt|}t |jt |jf}| |||d|dd d |\}}t j|||d} t | }t|||nt!d |d |rv|durvJ|r| "dkrt j#| ddd} | ddddf} n|dur| j$|d} |rN|| vs|t jurt j%d||d}nP|| vrt j%t |j&||d}n,t|}t j%t |j&||d}t '||}|| | dk<|| | vrb|| _(| 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``. memory_format (Optional[torch.memory_format]): The memory format of the returned tensor. Incompatible with :attr:`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``. 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') 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)minr)alhrrr clamposz4make_tensor.._modify_low_high..clampNz(make_tensor: one of low or high was NaN!z/make_tensor: low must be weakly less than high!) 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