from torch.nn import Module
import torch
from torchvision.datasets import *
from torch.nn import Parameter
from torch.nn import Conv2d
from torch.nn import Linear
from torch.nn import Softmax
from torchvision.transforms import *


class CPAMDec(Module):
    """
    CPAM decoding module
    """

    def __init__(self, in_channels):
        super(CPAMDec, self).__init__()
        self.softmax = Softmax(dim=-1)
        self.scale = Parameter(torch.zeros(1))
        self.conv_query = Conv2d(in_channels=in_channels, out_channels=
            in_channels // 4, kernel_size=1)
        self.conv_key = Linear(in_channels, in_channels // 4)
        self.conv_value = Linear(in_channels, in_channels)

    def forward(self, x, y):
        """
            inputs :
                x : input feature(N,C,H,W) y:gathering centers(N,K,M)
            returns :
                out : compact position attention feature
                attention map: (H*W)*M
        """
        m_batchsize, C, width, height = x.size()
        m_batchsize, K, _M = y.size()
        proj_query = self.conv_query(x).view(m_batchsize, -1, width * height
            ).permute(0, 2, 1)
        proj_key = self.conv_key(y).view(m_batchsize, K, -1).permute(0, 2, 1)
        energy = torch.bmm(proj_query, proj_key)
        attention = self.softmax(energy)
        proj_value = self.conv_value(y).permute(0, 2, 1)
        out = torch.bmm(proj_value, attention.permute(0, 2, 1))
        out = out.view(m_batchsize, C, width, height)
        out = self.scale * out + x
        return out


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


def get_init_inputs():
    return [[], {'in_channels': 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
from torch.nn import Module
from torchvision.datasets import *
from torch.nn import Parameter
from torch.nn import Conv2d
from torch.nn import Linear
from torch.nn import Softmax
from torchvision.transforms import *
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_poi_fused_convolution_0(in_out_ptr0, in_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 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tl.store(in_out_ptr0 + x0, tmp3, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_mul_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = tl.load(in_ptr1 + x0, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask)
    tmp3 = tmp1 * tmp2
    tmp5 = tmp3 + tmp4
    tl.store(out_ptr0 + x0, tmp5, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (1,), (1,))
    assert_size_stride(primals_5, (1, 4), (4, 1))
    assert_size_stride(primals_6, (1,), (1,))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_3, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 1, 4, 4), (16, 16, 4, 1))
        buf2 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_6, reinterpret_tensor(primals_2, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 1), (1, 4), 0
            ), alpha=1, beta=1, out=buf2)
        del primals_5
        del primals_6
        buf3 = reinterpret_tensor(buf0, (4, 1, 4, 4), (16, 1, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(64)](buf3, primals_4, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_4
        buf4 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf3, (4, 16, 1), (16, 1, 0),
            0), reinterpret_tensor(buf2, (4, 1, 4), (4, 1, 1), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(256)](buf4, buf5, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf6 = buf4
        del buf4
        triton_poi_fused__softmax_2[grid(256)](buf5, buf6, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf7 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_8, reinterpret_tensor(primals_2, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf7)
        del primals_7
        del primals_8
        buf8 = reinterpret_tensor(buf5, (4, 4, 16), (64, 16, 1), 0)
        del buf5
        extern_kernels.bmm(reinterpret_tensor(buf7, (4, 4, 4), (16, 1, 4), 
            0), reinterpret_tensor(buf6, (4, 4, 16), (64, 1, 4), 0), out=buf8)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_3[grid(256)](primals_9, buf8, primals_1,
            buf9, 256, XBLOCK=128, num_warps=4, num_stages=1)
    return buf9, primals_1, primals_3, primals_9, reinterpret_tensor(primals_2,
        (16, 4), (4, 1), 0), buf6, buf8, reinterpret_tensor(buf7, (4, 4, 4),
        (16, 4, 1), 0), reinterpret_tensor(buf3, (4, 1, 16), (16, 16, 1), 0
        ), reinterpret_tensor(buf2, (4, 4, 1), (4, 1, 1), 0)


class CPAMDecNew(Module):
    """
    CPAM decoding module
    """

    def __init__(self, in_channels):
        super(CPAMDecNew, self).__init__()
        self.softmax = Softmax(dim=-1)
        self.scale = Parameter(torch.zeros(1))
        self.conv_query = Conv2d(in_channels=in_channels, out_channels=
            in_channels // 4, kernel_size=1)
        self.conv_key = Linear(in_channels, in_channels // 4)
        self.conv_value = Linear(in_channels, in_channels)

    def forward(self, input_0, input_1):
        primals_4 = self.scale
        primals_3 = self.conv_query.weight
        primals_6 = self.conv_query.bias
        primals_5 = self.conv_key.weight
        primals_9 = self.conv_key.bias
        primals_7 = self.conv_value.weight
        primals_8 = self.conv_value.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]
