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
import torch.nn as nn
import torch.utils.data
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_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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (16 * x0 + 64 * (r1 // 16) + r1 % 16), xmask,
        other=0.0)
    tmp2 = tl.load(in_ptr1 + (16 * x0 + 64 * (r1 // 16) + r1 % 16), xmask,
        other=0.0)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tmp1 * tmp1
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.where(xmask, tmp9, 0)
    tmp12 = tl.sum(tmp11, 1)[:, None]
    tmp13 = tmp2 * tmp2
    tmp14 = tl.broadcast_to(tmp13, [XBLOCK, RBLOCK])
    tmp16 = tl.where(xmask, tmp14, 0)
    tmp17 = tl.sum(tmp16, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp7, xmask)
    tl.store(out_ptr1 + x0, tmp12, xmask)
    tl.store(out_ptr2 + x0, tmp17, xmask)


@triton.jit
def triton_per_fused_add_clamp_div_mul_rsub_sum_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 = 1e-07
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp0 / tmp5
    tmp7 = 2.0
    tmp8 = tmp6 * tmp7
    tmp9 = 1.0
    tmp10 = tmp9 - tmp8
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13 = tl.sum(tmp11, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp13, None)


@triton.jit
def triton_per_fused_add_binary_cross_entropy_with_logits_mul_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 = tmp17 * tmp1
    tmp19 = 256.0
    tmp20 = tmp15 / tmp19
    tmp21 = tmp20 * tmp1
    tmp22 = tmp18 + tmp21
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp22, 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_sum_0[grid(4)](arg0_1, arg1_1, buf0, buf1,
            buf2, 4, 64, XBLOCK=1, num_warps=2, num_stages=1)
        buf3 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_add_clamp_div_mul_rsub_sum_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_add_binary_cross_entropy_with_logits_mul_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 flatten_samples(input_):
    """
    Flattens a tensor or a variable such that the channel axis is first and the sample axis
    is second. The shapes are transformed as follows:
        (N, C, H, W) --> (C, N * H * W)
        (N, C, D, H, W) --> (C, N * D * H * W)
        (N, C) --> (C, N)
    The input must be atleast 2d.
    """
    num_channels = input_.size(1)
    permute_axes = list(range(input_.dim()))
    permute_axes[0], permute_axes[1] = permute_axes[1], permute_axes[0]
    permuted = input_.permute(*permute_axes).contiguous()
    flattened = permuted.view(num_channels, -1)
    return flattened


def dice_score(input_, target, invert=False, channelwise=True, eps=1e-07):
    if channelwise:
        input_ = flatten_samples(input_)
        target = flatten_samples(target)
        numerator = (input_ * target).sum(-1)
        denominator = (input_ * input_).sum(-1) + (target * target).sum(-1)
        channelwise_score = 2 * (numerator / denominator.clamp(min=eps))
        if invert:
            channelwise_score = 1.0 - channelwise_score
        score = channelwise_score.sum()
    else:
        numerator = (input_ * target).sum()
        denominator = (input_ * input_).sum() + (target * target).sum()
        score = 2.0 * (numerator / denominator.clamp(min=eps))
        if invert:
            score = 1.0 - score
    return score


class BCEDiceLossWithLogitsNew(nn.Module):

    def __init__(self, alpha=1.0, beta=1.0, channelwise=True, eps=1e-07):
        super().__init__()
        self.alpha = alpha
        self.beta = beta
        self.channelwise = channelwise
        self.eps = eps
        self.init_kwargs = {'alpha': alpha, 'beta': beta, 'channelwise':
            channelwise, 'eps': self.eps}

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


Measured latency_ms: 0.033767