import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn import Module
from torch import Tensor
from typing import Callable
from typing import Union
from typing import Optional
from abc import ABC
from math import ceil
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_mean_neg_0(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = 1.0
    tmp2 = tmp0 / tmp1
    tmp3 = -tmp2
    tl.store(in_out_ptr0 + x0, tmp3, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = torch.ops.aten.topk.default(arg0_1, 1, 0, False)
        del arg0_1
        buf1 = buf0[0]
        del buf0
        buf3 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0)
        del buf1
        get_raw_stream(0)
        triton_poi_fused_mean_neg_0[grid(64)](buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
    return buf3,


def bisect(fn: 'Callable[[Tensor], Tensor]', target: 'Tensor', lower:
    'Union[float, Tensor]', upper: 'Union[float, Tensor]', precision:
    'float'=1e-06, max_iter: 'int'=100000) ->Tensor:
    """Perform binary search over a tensor.

    The output tensor approximately satisfies the following relation:

    .. code-block::

        fn(output) = target

    Args:
        fn (callable[[Tensor], Tensor]): A monotone function.
        target (Tensor): Target of function values.
        lower (Tensor or float): Lower bound of binary search.
        upper (Tensor or float): Upper bound of binary search.
        precision (float, default=1e-6): Precision of output.
        max_iter (int, default 100000): If the number of iterations exceeds this
            value, abort computation and raise RuntimeError.

    Returns:
        torch.Tensor

    Raises:
        RuntimeError: If the number of iteration exceeds ``max_iter``.

    Examples:

        >>> target = torch.tensor([-1.0, 0.0, 1.0])
        >>> fn = torch.log
        >>> output = bisect(fn, target, 0.01, 10.0)
        >>> output
        tensor([0.3679, 1.0000, 2.7183])
        >>> torch.allclose(fn(output), target, atol=1e-6)
        True

        Monotone decreasing function:

        >>> fn = lambda input: -torch.log(input)
        >>> output = bisect(fn, target, 0.01, 10.0)
        >>> output
        tensor([2.7183, 1.0000, 0.3679])
        >>> torch.allclose(fn(output), target, atol=1e-6)
        True
    """
    lower, upper = map(torch.as_tensor, (lower, upper))
    if not (lower < upper).all():
        raise ValueError('condition lower < upper should be satisfied.')
    if (fn(lower) > fn(upper)).all():

        def mf(input):
            return -fn(input)
        return bisect(mf, -target, lower, upper, precision=precision,
            max_iter=max_iter)
    n_iter = 0
    while torch.max(upper - lower) > precision:
        n_iter += 1
        if n_iter > max_iter:
            raise RuntimeError(
                f'Aborting since iteration exceeds max_iter={max_iter}.')
        m = (lower + upper) / 2
        output = fn(m)
        lower = lower.where(output >= target, m)
        upper = upper.where(output < target, m)
    return upper


def topp(input: 'Tensor', p: 'float', dim: 'Optional[int]'=None, largest:
    'bool'=True):
    """Returns the largest :math:`p * N` elements of the given input tensor,
    where :math:`N` stands for the total number of elements in the input tensor.

    If ``dim`` is not given, the last dimension of the ``input`` is chosen.

    If ``largest`` is ``False`` then the smallest elements are returned.

    A namedtuple of ``(values, indices)`` is returned, where the ``indices``
    are the indices of the elements in the original ``input`` tensor.

    Args:
        input (torch.Tensor): The input tensor.
        p (float): The quantile level.
        dim (int, optional): The dimension to sort along.
        largest (bool, default=True): Controls whether to return largest or smallest
            elements.

    Returns:
        torch.Tensor

    Examples:
        >>> from pfhedge.nn.functional import topp
        >>>
        >>> input = torch.arange(1.0, 6.0)
        >>> input
        tensor([1., 2., 3., 4., 5.])
        >>> topp(input, 3 / 5)
        torch.return_types.topk(
        values=tensor([5., 4., 3.]),
        indices=tensor([4, 3, 2]))
    """
    if dim is None:
        return input.topk(ceil(p * input.numel()), largest=largest)
    else:
        return input.topk(ceil(p * input.size(dim)), dim=dim, largest=largest)


def expected_shortfall(input: 'Tensor', p: 'float', dim: 'Optional[int]'=None
    ) ->Tensor:
    """Returns the expected shortfall of the given input tensor.

    Args:
        input (torch.Tensor): The input tensor.
        p (float): The quantile level.
        dim (int, optional): The dimension to sort along.

    Returns:
        torch.Tensor

    Examples:
        >>> from pfhedge.nn.functional import expected_shortfall
        >>>
        >>> input = -torch.arange(10.0)
        >>> input
        tensor([-0., -1., -2., -3., -4., -5., -6., -7., -8., -9.])
        >>> expected_shortfall(input, 0.3)
        tensor(8.)
    """
    if dim is None:
        return -topp(input, p=p, largest=False).values.mean()
    else:
        return -topp(input, p=p, largest=False, dim=dim).values.mean(dim=dim)


class HedgeLoss(Module, ABC):
    """Base class for hedging criteria."""

    def forward(self, input: 'Tensor') ->Tensor:
        """Returns the loss of the profit-loss distribution.

        This method should be overridden.

        Args:
            input (torch.Tensor): The distribution of the profit and loss.

        Shape:
            - Input: :math:`(N, *)` where
              :math:`*` means any number of additional dimensions.
            - Output: :math:`(*)`

        Returns:
            torch.Tensor
        """

    def cash(self, input: 'Tensor') ->Tensor:
        """Returns the cash amount which is as preferable as
        the given profit-loss distribution in terms of the loss.

        The output ``cash`` is expected to satisfy the following relation:

        .. code::

            loss(torch.full_like(pnl, cash)) = loss(pnl)

        By default, the output is computed by binary search.
        If analytic form is known, it is recommended to override this method
        for faster computation.

        Args:
            input (torch.Tensor): The distribution of the profit and loss.

        Shape:
            - Input: :math:`(N, *)` where
              :math:`*` means any number of additional dimensions.
            - Output: :math:`(*)`

        Returns:
            torch.Tensor
        """
        return bisect(self, self(input), input.min(), input.max())


class ExpectedShortfallNew(HedgeLoss):
    """Creates a criterion that measures the expected shortfall.

    .. seealso::
        - :func:`pfhedge.nn.functional.expected_shortfall`

    Args:
        p (float, default=0.1): Quantile level.
            This parameter should satisfy :math:`0 < p \\leq 1`.

    Shape:
        - Input: :math:`(N, *)` where
          :math:`*` means any number of additional dimensions.
        - Output: :math:`(*)`

    Examples:
        >>> from pfhedge.nn import ExpectedShortfall
        >>>
        >>> loss = ExpectedShortfall(0.5)
        >>> input = -torch.arange(4.0)
        >>> loss(input)
        tensor(2.5000)
        >>> loss.cash(input)
        tensor(-2.5000)
    """

    def __init__(self, p: 'float'=0.1):
        if not 0 < p <= 1:
            raise ValueError('The quantile level should satisfy 0 < p <= 1.')
        super().__init__()
        self.p = p

    def extra_repr(self) ->str:
        return str(self.p)

    def cash(self, input: 'Tensor') ->Tensor:
        return -self(input)

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]


Measured latency_ms: 0.020194