sh"FSrSSKJr SSKJr SSKrSSKJrJr /SQr S\ 4S jr S\ 4S jr SS \ S\\R4S jjrS\ 4S jr\"S\S9"\5rS\SS4Sjr\"S\S9"\5rSS\S\R*4SjjrS\R*SS4SjrSS\SS4Sjjrg)z] This package introduces support for the current :ref:`accelerator` in python. )Optional) deprecatedN) _device_t_get_device_index) current_acceleratorcurrent_device_idxcurrent_device_indexcurrent_stream device_count is_availableset_device_idxset_device_index set_stream synchronizereturnc>[RR5$)zReturn the number of current :ref:`accelerator` available. Returns: int: the number of the current :ref:`accelerator` available. If there is no available accelerators, return 0. )torch_C_accelerator_deviceCountn/Users/tiagomarins/Projetos/claudeai/copy_bank/venv/lib/python3.13/site-packages/torch/accelerator/__init__.pyr r s 88 , , ..rcj[5nUcg[R"U5nUR5$)aCheck if the current accelerator is available at runtime: it was build, all the required drivers are available and at least one device is visible. See :ref:`accelerator` for details. Returns: bool: A boolean indicating if there is an available :ref:`accelerator`. Example:: >>> assert torch.accelerator.is_available() "No available accelerators detected." F)rrget_device_moduler )accmods rr r %s3  C {  ! !# &C    rcheck_availablec[RR5=nbU(aU(a[5(aU$g)aReturn the device of the accelerator available at compilation time. If no accelerator were available at compilation time, returns None. See :ref:`accelerator` for details. Args: check_available (bool, optional): if True, will also do a runtime check to see if the device :func:`torch.accelerator.is_available` on top of the compile-time check. Default: ``False`` Returns: torch.device: return the current accelerator as :class:`torch.device`. .. note:: The index of the returned :class:`torch.device` will be ``None``, please use :func:`torch.accelerator.current_device_index` to know the current index being used. Example:: >>> # xdoctest: >>> # If an accelerator is available, sent the model to it >>> model = torch.nn.Linear(2, 2) >>> if (current_device := current_accelerator(check_available=True)) is not None: >>> model.to(current_device) N)rr_accelerator_getAcceleratorr )rrs rrr=s/2xx3355B_J rc>[RR5$)zReturn the index of a currently selected device for the current :ref:`accelerator`. Returns: int: the index of a currently selected device. )rr_accelerator_getDeviceIndexrrrr r \s 88 / / 11rz#Use `current_device_index` instead.)categorydevicecX[U5n[RRU5 g)a Set the current device index to a given device. Args: device (:class:`torch.device`, str, int): a given device that must match the current :ref:`accelerator` device type. .. note:: This function is a no-op if this device index is negative. N)rrr_accelerator_setDeviceIndexr$ device_indexs rrrks %V,L HH((6rzUse `set_device_index` instead.cX[US5n[RRU5$)a|Return the currently selected stream for a given device. Args: device (:class:`torch.device`, str, int, optional): a given device that must match the current :ref:`accelerator` device type. If not given, use :func:`torch.accelerator.current_device_index` by default. Returns: torch.Stream: the currently selected stream for a given device. T)rrr_accelerator_getStreamr's rr r ~s%%VT2L 88 * *< 88rstreamcB[RRU5 g)a Set the current stream to a given stream. Args: stream (torch.Stream): a given stream that must match the current :ref:`accelerator` device type. .. note:: This function will set the current device index to the device index of the given stream. N)rr_accelerator_setStream)r+s rrrs HH##F+rcZ[US5n[RRU5 g)aWait for all kernels in all streams on the given device to complete. Args: device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match the current :ref:`accelerator` device type. If not given, use :func:`torch.accelerator.current_device_index` by default. .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized. Example:: >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) >>> assert torch.accelerator.is_available() "No available accelerators detected." >>> start_event = torch.Event(enable_timing=True) >>> end_event = torch.Event(enable_timing=True) >>> start_event.record() >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator()) >>> sum = torch.sum(tensor) >>> end_event.record() >>> torch.accelerator.synchronize() >>> elapsed_time_ms = start_event.elapsed_time(end_event) TN)rrr_accelerator_synchronizeDevicer's rrrs".%VT2L HH++L9r)F)N)__doc__typingrtyping_extensionsrr_utilsrr__all__intr boolr r$rr FutureWarningr rrStreamr rrrrrr9s( 0 /c/d0(5<<:P>2c2 )  7Y 7d 7%  99 95<< 9,u||,,: ::r