o h/@sNdZddlZddlZddlZddlZddlmZmZm Z ddl Z e j e j e j e je je je je jgZe je je je jgZe je je je jgZe je je j gZ!e j"geZ#gee!Z$de j%de&de&de j%fdd Z'ddd d d dd d e e(e j)e*e(e+e(d ffde j,de e-e j.fdee&dee&de"de"de"dee j/de j%fddZ0dS)z1 This module contains tensor creation utilities. N)castOptionalUniontlowhighreturncCs<||t|jjkr||d|ddS|||S)N)torchfinfodtypemaxuniform_mul_)rrrrk/var/www/html/construction_image-detection-poc/venv/lib/python3.10/site-packages/torch/testing/_creation.py_uniform_random_$s rF)rr requires_grad noncontiguous exclude_zero memory_formatshape.r devicerrrrc sdttdttdtdtdtdtdtttfffdd } t|d kr0t|d tjjr0|d }ttt d ft|}|rK|d urKt d|d||rXt vrXt d|odt dd|d d k}|r}ttt d fg|d dd|dR}tjurttt t f| ||d dd dd\}}tj||||d} nt vrttt t f| ||tjtjtjurd nd ddd\}}tj||||d} ngtvr| ||tjtjddd\}}tj||d} ttvrt| n| ||n6tvr2| ||tjtjddd\}}tj||tjd} t| ||| } ntdd|rG| d d d df} n |d urR| j|d} |rft vr\d ntj | | d k<tvrn|| _!| 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') rrlowest_inclusivehighest_exclusive default_low default_highrc sdtdtdtdtfdd}|dur|n|}|dur|n|}tdd ||fDr2td |d |||krCtvrCtjd td dn*||krQtd|d|||ksY||krmtd|d|dd|d|d ||||}||||}tvrt |t |fS||fS)z Modifies (and raises ValueError when appropriate) low and high values given by the user (input_low, input_high) if required. alhrcSstt|||SN)minr )rrrrrrclampsz3make_tensor..modify_low_high..clampNcss$|] }t|to t|VqdSr ) isinstancefloatmathisnan).0valuerrr s"z7make_tensor..modify_low_high..z,`low` and `high` cannot be NaN, but got low=z and high=zPassing `low==high` to `torch.testing.make_tensor` for floating or complex types is deprecated since 2.1 and will be removed in 2.3. Use `torch.full(...)` instead.) stacklevelz(`low` must be less than `high`, but got z >= z5The value interval specified by `low` and `high` is [z, z), but z only supports [)) r$any ValueError_FLOATING_OR_COMPLEX_TYPESwarningswarn FutureWarning_BOOLEAN_OR_INTEGRAL_TYPESr%ceil)rrrrrrr"r rrmodify_low_highs<  z$make_tensor..modify_low_highr.NzaThe parameters `noncontiguous` and `memory_format` are mutually exclusive, but got noncontiguous=z and memory_format=zU`requires_grad=True` is not supported for boolean and integral dtypes, but got dtype=cSs||Sr r)xyrrrszmake_tensor..r )rrrr)rr i zThe requested dtype 'z' is not supported by torch.testing.make_tensor(). To request support, file an issue at: https://github.com/pytorch/pytorch/issues)r)"rr$tuplelenr# collectionsabcSequencerintr.r3 functoolsreducer boolrandintiinfor!r int64r/r emptyr_COMPLEX_TYPES view_as_real_FLOATING_8BIT_TYPESfloat32to TypeErrorclonetinyr) r rrrrrrrrr6resultrr5r make_tensor-sR 3  ,                    rT)1__doc__collections.abcr@rDr%r0typingrrrr uint8int8int16int32rIuint16uint32uint64_INTEGRAL_TYPESfloat16bfloat16rNfloat64_FLOATING_TYPES float8_e4m3fn float8_e5m2float8_e4m3fnuzfloat8_e5m2fnuzrM complex32 complex64 complex128rKrFr3r/Tensorr$rrCSizelistr>r strrrrTrrrrsj