import torch
import torch.nn as nn
import torch.nn.functional as Func


class CrossAttN_v8(nn.Module):

    def __init__(self, in_planes, clip_dim):
        super(CrossAttN_v8, self).__init__()
        self.f = nn.Conv2d(in_planes, in_planes, 1, 1, 0)
        self.g = nn.Conv2d(in_planes, in_planes, 1, 1, 0)
        self.h = nn.Conv2d(in_planes, in_planes, 1, 1, 0)
        self.output = nn.Conv2d(in_planes, in_planes, 1, 1, 0)
        return

    def forward(self, F_c, F_s):
        b, _c = F_c.shape[0], F_c.shape[1]
        F = self.f(F_c)
        F = F.view(b, F.shape[1], -1)
        G = self.g(F_s)
        G = G.view(b, G.shape[1], -1)
        H = self.h(F_s)
        H = H.view(b, H.shape[1], -1)
        S = torch.bmm(F.permute(0, 2, 1), G)
        S = Func.softmax(S, dim=-1)
        result = torch.bmm(H, S.permute(0, 2, 1))
        result = result.view(b, result.shape[1], F_c.shape[2], F_c.shape[3])
        result = self.output(result)
        return result


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


def get_init_inputs():
    return [[], {'in_planes': 4, 'clip_dim': 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_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_per_fused__softmax_1(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    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, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp5 = tmp0 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tmp6 / tmp10
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp11, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_7, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_10, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = extern_kernels.convolution(primals_6, primals_4, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(256)](buf2, primals_5, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf3 = extern_kernels.convolution(primals_6, primals_7, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
        buf4 = buf3
        del buf3
        triton_poi_fused_convolution_0[grid(256)](buf4, primals_8, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_8
        buf5 = buf0
        del buf0
        triton_poi_fused_convolution_0[grid(256)](buf5, primals_3, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf6 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf5, (4, 16, 4), (64, 1, 16),
            0), reinterpret_tensor(buf2, (4, 4, 16), (64, 16, 1), 0), out=buf6)
        buf9 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
        triton_per_fused__softmax_1[grid(64)](buf6, buf9, 64, 16, XBLOCK=8,
            num_warps=2, num_stages=1)
        del buf6
        buf10 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf4, (4, 4, 16), (64, 16, 1),
            0), reinterpret_tensor(buf9, (4, 16, 16), (256, 1, 16), 0), out
            =buf10)
        buf11 = extern_kernels.convolution(reinterpret_tensor(buf10, (4, 4,
            4, 4), (64, 16, 4, 1), 0), primals_9, stride=(1, 1), padding=(0,
            0), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf11, (4, 4, 4, 4), (64, 16, 4, 1))
        buf12 = buf11
        del buf11
        triton_poi_fused_convolution_0[grid(256)](buf12, primals_10, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_10
    return (buf12, primals_1, primals_2, primals_4, primals_6, primals_7,
        primals_9, buf9, reinterpret_tensor(buf10, (4, 4, 4, 4), (64, 16, 4,
        1), 0), reinterpret_tensor(buf4, (4, 16, 4), (64, 1, 16), 0),
        reinterpret_tensor(buf5, (4, 4, 16), (64, 16, 1), 0),
        reinterpret_tensor(buf2, (4, 16, 4), (64, 1, 16), 0))


class CrossAttN_v8New(nn.Module):

    def __init__(self, in_planes, clip_dim):
        super(CrossAttN_v8New, self).__init__()
        self.f = nn.Conv2d(in_planes, in_planes, 1, 1, 0)
        self.g = nn.Conv2d(in_planes, in_planes, 1, 1, 0)
        self.h = nn.Conv2d(in_planes, in_planes, 1, 1, 0)
        self.output = nn.Conv2d(in_planes, in_planes, 1, 1, 0)
        return

    def forward(self, input_0, input_1):
        primals_2 = self.f.weight
        primals_3 = self.f.bias
        primals_4 = self.g.weight
        primals_5 = self.g.bias
        primals_7 = self.h.weight
        primals_8 = self.h.bias
        primals_9 = self.output.weight
        primals_10 = self.output.bias
        primals_1 = input_0
        primals_6 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9, primals_10])
        return output[0]
