import torch
import torch.nn as nn
import torch.nn.functional as F


class Attention_SEblock(nn.Module):

    def __init__(self, channels, reduction, temperature):
        super(Attention_SEblock, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc1 = nn.Linear(channels, channels // reduction)
        self.relu = nn.ReLU(inplace=True)
        self.fc2 = nn.Linear(channels // reduction, 2)
        self.fc2.bias.data[0] = 0.1
        self.fc2.bias.data[1] = 2
        self.temperature = temperature
        self.channels = channels

    def forward(self, x):
        x = self.avg_pool(x).view(-1, self.channels)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = F.gumbel_softmax(x, tau=1, hard=True)
        return x


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


def get_init_inputs():
    return [[], {'channels': 4, 'reduction': 4, 'temperature': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
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 math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused_mean_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    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 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 16.0
    tmp6 = tmp4 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 4
    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 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tl.store(in_out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused__softmax_add_log_neg_2(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 2 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr2 + 2 * x0, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (1 + 2 * x0), xmask, eviction_policy='evict_last'
        )
    tmp11 = tl.load(in_ptr1 + 1)
    tmp12 = tl.broadcast_to(tmp11, [XBLOCK])
    tmp14 = tl.load(in_ptr2 + (1 + 2 * x0), xmask, eviction_policy='evict_last'
        )
    tmp3 = tmp0 + tmp2
    tmp5 = tl_math.log(tmp4)
    tmp6 = -tmp5
    tmp7 = tmp3 + tmp6
    tmp8 = 1.0
    tmp9 = tmp7 * tmp8
    tmp13 = tmp10 + tmp12
    tmp15 = tl_math.log(tmp14)
    tmp16 = -tmp15
    tmp17 = tmp13 + tmp16
    tmp18 = tmp17 * tmp8
    tmp19 = triton_helpers.maximum(tmp9, tmp18)
    tmp20 = tmp9 - tmp19
    tmp21 = tmp20 * tmp8
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp18 - tmp19
    tmp24 = tmp23 * tmp8
    tmp25 = tl_math.exp(tmp24)
    tmp26 = tmp22 + tmp25
    tl.store(out_ptr0 + x0, tmp19, xmask)
    tl.store(out_ptr1 + x0, tmp26, xmask)


@triton.jit
def triton_poi_fused__softmax_add_log_neg_3(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 2
    x1 = xindex // 2
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp9 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tl_math.log(tmp3)
    tmp5 = -tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = 1.0
    tmp8 = tmp6 * tmp7
    tmp10 = tmp8 - tmp9
    tmp11 = tmp10 * tmp7
    tmp12 = tl_math.exp(tmp11)
    tmp14 = tmp12 / tmp13
    tl.store(in_out_ptr0 + x2, tmp14, xmask)


@triton.jit
def triton_poi_fused_add_max_scatter_sub_4(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 2
    x0 = xindex % 2
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 2 * x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x1), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr0 + x2, xmask)
    tmp2 = tmp0 > tmp1
    tmp3 = tmp0 == tmp1
    tmp4 = tmp0 != tmp0
    tmp5 = tmp1 != tmp1
    tmp6 = tmp4 > tmp5
    tmp7 = tmp2 | tmp6
    tmp8 = tmp4 & tmp5
    tmp9 = tmp3 | tmp8
    tmp10 = tl.full([1], 0, tl.int64)
    tmp11 = tl.full([1], 1, tl.int64)
    tmp12 = tmp10 < tmp11
    tmp13 = tmp9 & tmp12
    tmp14 = tmp7 | tmp13
    tl.where(tmp14, tmp0, tmp1)
    tmp16 = tl.where(tmp14, tmp10, tmp11)
    tmp17 = x0
    tmp18 = tmp16 == tmp17
    tmp19 = 1.0
    tmp20 = 0.0
    tmp21 = tl.where(tmp18, tmp19, tmp20)
    tmp23 = tmp21 - tmp22
    tmp24 = tmp23 + tmp22
    tl.store(out_ptr0 + x2, tmp24, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4), (4, 1))
    assert_size_stride(primals_3, (1,), (1,))
    assert_size_stride(primals_4, (2, 1), (1, 1))
    assert_size_stride(primals_5, (2,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_mean_0[grid(16)](buf1, primals_1, 16, 16, XBLOCK=8,
            num_warps=2, num_stages=1)
        del primals_1
        buf2 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (4, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 1), (1, 4), 0), out=buf2)
        del primals_2
        buf3 = buf2
        del buf2
        triton_poi_fused_relu_1[grid(4)](buf3, primals_3, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del primals_3
        buf4 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        extern_kernels.mm(buf3, reinterpret_tensor(primals_4, (1, 2), (1, 1
            ), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        buf6 = torch.ops.aten.exponential.default(buf5)
        del buf5
        buf7 = buf6
        del buf6
        buf8 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf9 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        triton_poi_fused__softmax_add_log_neg_2[grid(4)](buf4, primals_5,
            buf7, buf8, buf9, 4, XBLOCK=4, num_warps=1, num_stages=1)
        buf10 = buf4
        del buf4
        triton_poi_fused__softmax_add_log_neg_3[grid(8)](buf10, primals_5,
            buf7, buf8, buf9, 8, XBLOCK=8, num_warps=1, num_stages=1)
        del buf8
        del buf9
        del primals_5
        buf11 = buf7
        del buf7
        triton_poi_fused_add_max_scatter_sub_4[grid(8)](buf10, buf11, 8,
            XBLOCK=8, num_warps=1, num_stages=1)
    return buf11, reinterpret_tensor(buf1, (4, 4), (4, 1), 0
        ), buf3, buf10, primals_4


class Attention_SEblockNew(nn.Module):

    def __init__(self, channels, reduction, temperature):
        super(Attention_SEblockNew, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc1 = nn.Linear(channels, channels // reduction)
        self.relu = nn.ReLU(inplace=True)
        self.fc2 = nn.Linear(channels // reduction, 2)
        self.fc2.bias.data[0] = 0.1
        self.fc2.bias.data[1] = 2
        self.temperature = temperature
        self.channels = channels

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
