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# Source code for torch.autograd.grad_mode

    from typing import Any, Optional

    import torch

    from torch.utils._contextlib import (
        _DecoratorContextManager,
        _NoParamDecoratorContextManager,
    )

    __all__ = [
        "no_grad",
        "enable_grad",
        "set_grad_enabled",
        "inference_mode",
        "set_multithreading_enabled",
    ]

    class no_grad(_NoParamDecoratorContextManager):
        r"""Context-manager that disables gradient calculation.

        Disabling gradient calculation is useful for inference, when you are sure
        that you will not call :meth:`Tensor.backward()`. It will reduce memory
        consumption for computations that would otherwise have `requires_grad=True`.

        In this mode, the result of every computation will have
        `requires_grad=False`, even when the inputs have `requires_grad=True`.
        There is an exception! All factory functions, or functions that create
        a new Tensor and take a requires_grad kwarg, will NOT be affected by
        this mode.

        This context manager is thread local; it will not affect computation
        in other threads.

        Also functions as a decorator.

        .. note::
            No-grad is one of several mechanisms that can enable or
            disable gradients locally see :ref:`locally-disable-grad-doc` for
            more information on how they compare.

        .. note::
            This API does not apply to :ref:`forward-mode AD <forward-mode-ad>`.
            If you want to disable forward AD for a computation, you can unpack
            your dual tensors.

        Example::
            >>> # xdoctest: +SKIP
            >>> x = torch.tensor([1.], requires_grad=True)
            >>> with torch.no_grad():
            ...     y = x * 2
            >>> y.requires_grad
            False
            >>> @torch.no_grad()
            ... def doubler(x):
            ...     return x * 2
            >>> z = doubler(x)
            >>> z.requires_grad
            False
            >>> @torch.no_grad
            ... def tripler(x):
            ...     return x * 3
            >>> z = tripler(x)
            >>> z.requires_grad
            False
            >>> # factory function exception
            >>> with torch.no_grad():
            ...     a = torch.nn.Parameter(torch.rand(10))
            >>> a.requires_grad
            True
        """

        def __init__(self) -> None:
            if not torch._jit_internal.is_scripting():
                super().__init__()
            self.prev = False

        def __enter__(self) -> None:
            self.prev = torch.is_grad_enabled()
            torch.set_grad_enabled(False)

        def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
            torch.set_grad_enabled(self.prev)

    class enable_grad(_NoParamDecoratorContextManager):
        r"""Context-manager that enables gradient calculation.

        Enables gradient calculation, if it has been disabled via :class:`~no_grad`
        or :class:`~set_grad_enabled`.

        This context manager is thread local; it will not affect computation
        in other threads.

        Also functions as a decorator.

        .. note::
            enable_grad is one of several mechanisms that can enable or
            disable gradients locally see :ref:`locally-disable-grad-doc` for
            more information on how they compare.

        .. note::
            This API does not apply to :ref:`forward-mode AD <forward-mode-ad>`.

        Example::
            >>> # xdoctest: +SKIP
            >>> x = torch.tensor([1.], requires_grad=True)
            >>> with torch.no_grad():
            ...     with torch.enable_grad():
            ...         y = x * 2
            >>> y.requires_grad
            True
            >>> y.backward()
            >>> x.grad
            tensor([2.])
            >>> @torch.enable_grad()
            ... def doubler(x):
            ...     return x * 2
            >>> with torch.no_grad():
            ...     z = doubler(x)
            >>> z.requires_grad
            True
            >>> @torch.enable_grad
            ... def tripler(x):
            ...     return x * 3
            >>> with torch.no_grad():
            ...     z = tripler(x)
            >>> z.requires_grad
            True

        """

        def __enter__(self) -> None:
            self.prev = torch.is_grad_enabled()
            torch._C._set_grad_enabled(True)

        def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
            torch._C._set_grad_enabled(self.prev)

    class set_grad_enabled(_DecoratorContextManager):
        r"""Context-manager that sets gradient calculation on or off.

        ``set_grad_enabled`` will enable or disable grads based on its argument :attr:`mode`.
        It can be used as a context-manager or as a function.

        This context manager is thread local; it will not affect computation
        in other threads.

        Args:
            mode (bool): Flag whether to enable grad (``True``), or disable
                         (``False``). This can be used to conditionally enable
                         gradients.

        .. note::
            set_grad_enabled is one of several mechanisms that can enable or
            disable gradients locally see :ref:`locally-disable-grad-doc` for
            more information on how they compare.

        .. note::
            This API does not apply to :ref:`forward-mode AD <forward-mode-ad>`.

        Example::
            >>> # xdoctest: +SKIP
            >>> x = torch.tensor([1.], requires_grad=True)
            >>> is_train = False
            >>> with torch.set_grad_enabled(is_train):
            ...     y = x * 2
            >>> y.requires_grad
            False
            >>> _ = torch.set_grad_enabled(True)
            >>> y = x * 2
            >>> y.requires_grad
            True
            >>> _ = torch.set_grad_enabled(False)
            >>> y = x * 2
            >>> y.requires_grad
            False

        """

        def __init__(self, mode: bool) -> None:
            self.prev = torch.is_grad_enabled()
            torch._C._set_grad_enabled(mode)
            self.mode = mode

        def __enter__(self) -> None:
            pass

        def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
            torch._C._set_grad_enabled(self.prev)

        def clone(self) -> "set_grad_enabled":
            return self.__class__(self.mode)

    class inference_mode(_DecoratorContextManager):
        r"""Context-manager that enables or disables inference mode

        InferenceMode is a new context manager analogous to :class:`~no_grad`
        to be used when you are certain your operations will have no interactions
        with autograd (e.g., model training). Code run under this mode gets better
        performance by disabling view tracking and version counter bumps. Note that
        unlike some other mechanisms that locally enable or disable grad,
        entering inference_mode also disables to :ref:`forward-mode AD <forward-mode-ad>`.

        This context manager is thread local; it will not affect computation
        in other threads.

        Also functions as a decorator.

        .. note::
            Inference mode is one of several mechanisms that can enable or
            disable gradients locally see :ref:`locally-disable-grad-doc` for
            more information on how they compare.

        Args:
            mode (bool or function): Either a boolean flag whether to enable or
                disable inference mode or a Python function to decorate with
                inference mode enabled

        Example::
            >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
            >>> import torch
            >>> x = torch.ones(1, 2, 3, requires_grad=True)
            >>> with torch.inference_mode():
            ...     y = x * x
            >>> y.requires_grad
            False
            >>> # xdoctest: +SKIP("want string isnt quite right")
            >>> y._version
            Traceback (most recent call last):
            File "<stdin>", line 1, in <module>
            RuntimeError: Inference tensors do not track version counter.
            >>> @torch.inference_mode()
            ... def func(x):
            ...     return x * x
            >>> out = func(x)
            >>> out.requires_grad
            False
            >>> @torch.inference_mode
            ... def doubler(x):
            ...     return x * 2
            >>> out = doubler(x)
            >>> out.requires_grad
            False

        """

        def __init__(self, mode: bool = True) -> None:
            if not torch._jit_internal.is_scripting():
                super().__init__()
            # Holds a context manager that can enable or disable inference mode
            self._inference_mode_raii_context: Optional[torch._C._InferenceMode] = None
            self.mode = mode

        def __new__(cls, mode=True):
            if isinstance(mode, bool):
                return super().__new__(cls)
            return cls()(mode)

        def __enter__(self) -> None:
            self._inference_mode_context = torch._C._InferenceMode(self.mode)
            self._inference_mode_context.__enter__()

        def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
            self._inference_mode_context.__exit__(exc_type, exc_value, traceback)

        def clone(self) -> "inference_mode":
            return self.__class__(self.mode)

    class set_multithreading_enabled(_DecoratorContextManager):
        r"""Context-manager that sets multithreaded backwards on or off.

        ``set_multithreading_enabled`` will enable or disable multithreaded backwards based on its argument :attr:`mode`.
        It can be used as a context-manager or as a function.

        This context manager is thread local; it will not affect computation
        in other threads.

        Args:
            mode (bool): Flag whether to enable multithreaded backwards (``True``), or disable
                         (``False``).

        .. note::
            This API does not apply to :ref:`forward-mode AD <forward-mode-ad>`.

        """

        def __init__(self, mode: bool) -> None:
            self.prev = torch._C._is_multithreading_enabled()
            torch._C._set_multithreading_enabled(mode)
            self.mode = mode

        def __enter__(self) -> None:
            pass

        def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
            torch._C._set_multithreading_enabled(self.prev)

        def clone(self) -> "set_multithreading_enabled":
            return self.__class__(self.mode)

    class _force_original_view_tracking(_DecoratorContextManager):
        r"""Context-manager that sets whether or not to always enable view-replay in autograd.

        ``set_view_replay_enabled`` will enable or disable view-replay based on its argument :attr:`mode`.
        It can be used as a context-manager or as a function.

        This context manager is thread local; it will not affect computation
        in other threads.

        When a tensor view is mutated, the autograd engine needs to decide whether or not
        to regenerate the "updated view" by either replaying the chain of views from the updated base,
        or with a single call to as_strided.

        If set_view_replay_enabled is set to True, then autograd will always use view replay.
        Otherwise, it will fall back to its existing logic.

        Args:
            mode (bool): Flag whether to enable view-replay (``True``), or disable
                         (``False``).

        """

        def __init__(self, mode: bool) -> None:
            self.prev = torch._C._is_view_replay_enabled()
            torch._C._set_view_replay_enabled(mode)
            self.mode = mode

        def __enter__(self) -> None:
            pass

        def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
            torch._C._set_view_replay_enabled(self.prev)

        def clone(self):
            return self.__class__(self.mode)

    class _unsafe_preserve_version_counter(_DecoratorContextManager):
        r"""DO NOT USE THIS UNLESS YOU KNOW EXACTLY WHAT YOU'RE DOING!

        This context manager can lead to arbitrary silent-correctness issues in any other part of your code
        (even the ones not touched directly by the context manager)!

        Ordinarily, autograd will track mutations to tensors by incrementing it's `._version` attribute.
        This is generally important for correctness, as for example, mutating a tensor that autograd has saved
        for the backwards pass can result in incorrect gradients, and autograd uses the version counter to detect
        and error out in this situation.

        However, there are rare instances where it might be useful to hide mutations from autograd. For example:
        if a tensor is very large, and you'd like to free its memory by storing it elsewhere, and re-populate
        the tensor right before it is needed by autograd.

        Args:
            tensor (torch.Tensor): the tensor in question, that you would like to preserve the version counter of.

        .. note::
            This API does not apply to :ref:`forward-mode AD <forward-mode-ad>`.

        """

        def __init__(self, tensor: torch.Tensor) -> None:
            self.tensor = tensor
            self.prev_version = tensor._version

        def __enter__(self) -> None:
            pass

        def __exit__(self, *args) -> None:
            torch._C._autograd._unsafe_set_version_counter(self.tensor, self.prev_version)

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