import torch
from typing import Callable
from functools import partial
from torch import nn
import torch.distributed
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.backends


def get_activation_fn(activation: 'str'=None):
    """Returns the activation function from ``torch.nn`` by its name."""
    if activation is None or activation.lower() == 'none':

        def activation_fn(x):
            return x
    else:
        activation_fn = torch.nn.__dict__[activation]()
    return activation_fn


def wrap_metric_fn_with_activation(metric_fn: 'Callable', activation: 'str'
    =None):
    """Wraps model outputs for ``metric_fn` with specified ``activation``.

    Args:
        metric_fn: metric function to compute
        activation: activation name to use

    Returns:
        wrapped metric function with wrapped model outputs

    .. note::
        Works only with ``metric_fn`` like
        ``metric_fn(outputs, targets, *args, **kwargs)``.
    """
    activation_fn = get_activation_fn(activation)

    def wrapped_metric_fn(outputs: 'torch.Tensor', targets: 'torch.Tensor',
        *args, **kwargs):
        outputs = activation_fn(outputs)
        output = metric_fn(outputs, targets, *args, **kwargs)
        return output
    return wrapped_metric_fn


def iou(outputs: 'torch.Tensor', targets: 'torch.Tensor', class_dim: 'int'=
    1, threshold: 'float'=None, eps: 'float'=1e-07) ->torch.Tensor:
    """Computes the iou/jaccard score.

    Args:
        outputs: [N; K; ...] tensor that for each of the N examples
            indicates the probability of the example belonging to each of
            the K classes, according to the model.
        targets:  binary [N; K; ...] tensort that encodes which of the K
            classes are associated with the N-th input
        class_dim: indicates class dimention (K) for
            ``outputs`` and ``targets`` tensors (default = 1)
        threshold: threshold for outputs binarization
        eps: epsilon to avoid zero division

    Returns:
        IoU (Jaccard) score

    Examples:
        >>> size = 4
        >>> half_size = size // 2
        >>> shape = (1, 1, size, size)
        >>> empty = torch.zeros(shape)
        >>> full = torch.ones(shape)
        >>> left = torch.ones(shape)
        >>> left[:, :, :, half_size:] = 0
        >>> right = torch.ones(shape)
        >>> right[:, :, :, :half_size] = 0
        >>> top_left = torch.zeros(shape)
        >>> top_left[:, :, :half_size, :half_size] = 1
        >>> pred = torch.cat([empty, left, empty, full, left, top_left], dim=1)
        >>> targets = torch.cat([full, right, empty, full, left, left], dim=1)
        >>> iou(
        >>>     outputs=pred,
        >>>     targets=targets,
        >>>     class_dim=1,
        >>>     threshold=0.5,
        >>> )
        tensor([0.0000, 0.0000, 1.0000, 1.0000, 1.0000, 0.5])
    """
    if threshold is not None:
        outputs = (outputs > threshold).float()
    num_dims = len(outputs.shape)
    assert num_dims > 2, 'shape mismatch, please check the docs for more info'
    assert outputs.shape == targets.shape, 'shape mismatch, please check the docs for more info'
    dims = list(range(num_dims))
    if class_dim < 0:
        class_dim = num_dims + class_dim
    dims.pop(class_dim)
    sum_fn = partial(torch.sum, dim=dims)
    intersection = sum_fn(targets * outputs)
    union = sum_fn(targets) + sum_fn(outputs)
    iou_score = (intersection + eps * (union == 0).float()) / (union -
        intersection + eps)
    return iou_score


class IoULoss(nn.Module):
    """The intersection over union (Jaccard) loss.

    @TODO: Docs. Contribution is welcome.
    """

    def __init__(self, eps: 'float'=1e-07, threshold: 'float'=None,
        activation: 'str'='Sigmoid'):
        """
        Args:
            eps: epsilon to avoid zero division
            threshold: threshold for outputs binarization
            activation: An torch.nn activation applied to the outputs.
                Must be one of ``'none'``, ``'Sigmoid'``, ``'Softmax'``
        """
        super().__init__()
        metric_fn = wrap_metric_fn_with_activation(metric_fn=iou,
            activation=activation)
        self.loss_fn = partial(metric_fn, eps=eps, threshold=threshold)

    def forward(self, outputs, targets):
        """@TODO: Docs. Contribution is welcome."""
        per_class_iou = self.loss_fn(outputs, targets)
        iou = torch.mean(per_class_iou)
        return 1 - iou


class BCEIoULoss(nn.Module):
    """The Intersection over union (Jaccard) with BCE loss.

    @TODO: Docs. Contribution is welcome.
    """

    def __init__(self, eps: 'float'=1e-07, threshold: 'float'=None,
        activation: 'str'='Sigmoid', reduction: 'str'='mean'):
        """
        Args:
            eps: epsilon to avoid zero division
            threshold: threshold for outputs binarization
            activation: An torch.nn activation applied to the outputs.
                Must be one of ``'none'``, ``'Sigmoid'``, ``'Softmax'``
            reduction: Specifies the reduction to apply
                to the output of BCE
        """
        super().__init__()
        self.bce_loss = nn.BCEWithLogitsLoss(reduction=reduction)
        self.iou_loss = IoULoss(eps, threshold, activation)

    def forward(self, outputs, targets):
        """@TODO: Docs. Contribution is welcome."""
        iou = self.iou_loss.forward(outputs, targets)
        bce = self.bce_loss(outputs, targets)
        loss = iou + bce
        return loss


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {}]

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._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from typing import Callable
from functools import partial
from torch import nn
import torch.distributed
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.backends
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_mul_sigmoid_sum_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex % 16
    r2 = rindex // 16
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0 + 64 * r2), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r1 + 16 * x0 + 64 * r2), xmask, other=0.0)
    tmp2 = tl.sigmoid(tmp1)
    tmp3 = tmp0 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp10 = tl.where(xmask, tmp8, 0)
    tmp11 = tl.sum(tmp10, 1)[:, None]
    tmp12 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp14 = tl.where(xmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp7, xmask)
    tl.store(out_ptr1 + x0, tmp11, xmask)
    tl.store(out_ptr2 + x0, tmp15, xmask)


@triton.jit
def triton_per_fused__to_copy_add_div_eq_mean_mul_sub_1(in_ptr0, in_ptr1,
    in_ptr2, out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp2 = tl.load(in_ptr2 + r0, None)
    tmp3 = tmp1 + tmp2
    tmp4 = 0.0
    tmp5 = tmp3 == tmp4
    tmp6 = tmp5.to(tl.float32)
    tmp7 = 1e-07
    tmp8 = tmp6 * tmp7
    tmp9 = tmp0 + tmp8
    tmp10 = tmp3 - tmp0
    tmp11 = tmp10 + tmp7
    tmp12 = tmp9 / tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.sum(tmp13, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp15, None)


@triton.jit
def triton_per_fused__to_copy_add_binary_cross_entropy_with_logits_div_eq_mean_mul_rsub_sub_2(
    in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp3 = tl.load(in_ptr1 + r0, None)
    tmp16 = tl.load(in_out_ptr0 + 0)
    tmp17 = tl.broadcast_to(tmp16, [1])
    tmp1 = 1.0
    tmp2 = tmp1 - tmp0
    tmp4 = tmp2 * tmp3
    tmp5 = 0.0
    tmp6 = triton_helpers.minimum(tmp5, tmp3)
    tmp7 = tl_math.abs(tmp3)
    tmp8 = -tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp10 = libdevice.log1p(tmp9)
    tmp11 = tmp6 - tmp10
    tmp12 = tmp4 - tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp18 = 4.0
    tmp19 = tmp17 / tmp18
    tmp20 = tmp1 - tmp19
    tmp21 = 256.0
    tmp22 = tmp15 / tmp21
    tmp23 = tmp20 + tmp22
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp23, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4,), (1,), torch.float32)
        buf1 = empty_strided_cuda((4,), (1,), torch.float32)
        buf2 = empty_strided_cuda((4,), (1,), torch.float32)
        get_raw_stream(0)
        triton_per_fused_mul_sigmoid_sum_0[grid(4)](arg1_1, arg0_1, buf0,
            buf1, buf2, 4, 64, XBLOCK=1, num_warps=2, num_stages=1)
        buf3 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused__to_copy_add_div_eq_mean_mul_sub_1[grid(1)](buf0,
            buf1, buf2, buf3, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del buf0
        del buf1
        del buf2
        buf5 = buf3
        del buf3
        triton_per_fused__to_copy_add_binary_cross_entropy_with_logits_div_eq_mean_mul_rsub_sub_2[
            grid(1)](buf5, arg1_1, arg0_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf5,


def get_activation_fn(activation: 'str'=None):
    """Returns the activation function from ``torch.nn`` by its name."""
    if activation is None or activation.lower() == 'none':

        def activation_fn(x):
            return x
    else:
        activation_fn = torch.nn.__dict__[activation]()
    return activation_fn


def wrap_metric_fn_with_activation(metric_fn: 'Callable', activation: 'str'
    =None):
    """Wraps model outputs for ``metric_fn` with specified ``activation``.

    Args:
        metric_fn: metric function to compute
        activation: activation name to use

    Returns:
        wrapped metric function with wrapped model outputs

    .. note::
        Works only with ``metric_fn`` like
        ``metric_fn(outputs, targets, *args, **kwargs)``.
    """
    activation_fn = get_activation_fn(activation)

    def wrapped_metric_fn(outputs: 'torch.Tensor', targets: 'torch.Tensor',
        *args, **kwargs):
        outputs = activation_fn(outputs)
        output = metric_fn(outputs, targets, *args, **kwargs)
        return output
    return wrapped_metric_fn


def iou(outputs: 'torch.Tensor', targets: 'torch.Tensor', class_dim: 'int'=
    1, threshold: 'float'=None, eps: 'float'=1e-07) ->torch.Tensor:
    """Computes the iou/jaccard score.

    Args:
        outputs: [N; K; ...] tensor that for each of the N examples
            indicates the probability of the example belonging to each of
            the K classes, according to the model.
        targets:  binary [N; K; ...] tensort that encodes which of the K
            classes are associated with the N-th input
        class_dim: indicates class dimention (K) for
            ``outputs`` and ``targets`` tensors (default = 1)
        threshold: threshold for outputs binarization
        eps: epsilon to avoid zero division

    Returns:
        IoU (Jaccard) score

    Examples:
        >>> size = 4
        >>> half_size = size // 2
        >>> shape = (1, 1, size, size)
        >>> empty = torch.zeros(shape)
        >>> full = torch.ones(shape)
        >>> left = torch.ones(shape)
        >>> left[:, :, :, half_size:] = 0
        >>> right = torch.ones(shape)
        >>> right[:, :, :, :half_size] = 0
        >>> top_left = torch.zeros(shape)
        >>> top_left[:, :, :half_size, :half_size] = 1
        >>> pred = torch.cat([empty, left, empty, full, left, top_left], dim=1)
        >>> targets = torch.cat([full, right, empty, full, left, left], dim=1)
        >>> iou(
        >>>     outputs=pred,
        >>>     targets=targets,
        >>>     class_dim=1,
        >>>     threshold=0.5,
        >>> )
        tensor([0.0000, 0.0000, 1.0000, 1.0000, 1.0000, 0.5])
    """
    if threshold is not None:
        outputs = (outputs > threshold).float()
    num_dims = len(outputs.shape)
    assert num_dims > 2, 'shape mismatch, please check the docs for more info'
    assert outputs.shape == targets.shape, 'shape mismatch, please check the docs for more info'
    dims = list(range(num_dims))
    if class_dim < 0:
        class_dim = num_dims + class_dim
    dims.pop(class_dim)
    sum_fn = partial(torch.sum, dim=dims)
    intersection = sum_fn(targets * outputs)
    union = sum_fn(targets) + sum_fn(outputs)
    iou_score = (intersection + eps * (union == 0).float()) / (union -
        intersection + eps)
    return iou_score


class IoULoss(nn.Module):
    """The intersection over union (Jaccard) loss.

    @TODO: Docs. Contribution is welcome.
    """

    def __init__(self, eps: 'float'=1e-07, threshold: 'float'=None,
        activation: 'str'='Sigmoid'):
        """
        Args:
            eps: epsilon to avoid zero division
            threshold: threshold for outputs binarization
            activation: An torch.nn activation applied to the outputs.
                Must be one of ``'none'``, ``'Sigmoid'``, ``'Softmax'``
        """
        super().__init__()
        metric_fn = wrap_metric_fn_with_activation(metric_fn=iou,
            activation=activation)
        self.loss_fn = partial(metric_fn, eps=eps, threshold=threshold)

    def forward(self, outputs, targets):
        """@TODO: Docs. Contribution is welcome."""
        per_class_iou = self.loss_fn(outputs, targets)
        iou = torch.mean(per_class_iou)
        return 1 - iou


class BCEIoULossNew(nn.Module):
    """The Intersection over union (Jaccard) with BCE loss.

    @TODO: Docs. Contribution is welcome.
    """

    def __init__(self, eps: 'float'=1e-07, threshold: 'float'=None,
        activation: 'str'='Sigmoid', reduction: 'str'='mean'):
        """
        Args:
            eps: epsilon to avoid zero division
            threshold: threshold for outputs binarization
            activation: An torch.nn activation applied to the outputs.
                Must be one of ``'none'``, ``'Sigmoid'``, ``'Softmax'``
            reduction: Specifies the reduction to apply
                to the output of BCE
        """
        super().__init__()
        self.bce_loss = nn.BCEWithLogitsLoss(reduction=reduction)
        self.iou_loss = IoULoss(eps, threshold, activation)

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
