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.triton_helpers import math as tl_math
import 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_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 320
    y1 = yindex // 320
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 320 * x2 + 2880 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 25
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 192
    y1 = yindex // 192
    tmp0 = tl.load(in_ptr0 + (x2 + 25 * y3), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 192 * x2 + 4800 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_abs_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 1280
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 320
    y1 = yindex // 320
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy=
        'evict_last')
    tmp1 = tl_math.abs(tmp0)
    tl.store(out_ptr0 + (y0 + 320 * x2 + 1310720 * y1), tmp1, ymask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_3(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 192
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, None)
    tl.store(out_ptr1 + x2, tmp7, None)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_4(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 192
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, None)
    tl.store(out_ptr1 + x2, tmp7, None)


@triton.jit
def triton_poi_fused_convolution_5(in_ptr0, in_ptr1, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 768
    xnumel = 256
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 192
    y1 = yindex // 192
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 192 * x2 + 49152 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 256 * y3), tmp2, xmask & ymask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 320, 64, 64), (1310720, 4096, 64, 1))
    assert_size_stride(primals_2, (192, 320, 3, 3), (2880, 9, 3, 1))
    assert_size_stride(primals_3, (192,), (1,))
    assert_size_stride(primals_4, (192, 192, 5, 5), (4800, 25, 5, 1))
    assert_size_stride(primals_5, (192,), (1,))
    assert_size_stride(primals_6, (192, 192, 5, 5), (4800, 25, 5, 1))
    assert_size_stride(primals_7, (192,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((192, 320, 3, 3), (2880, 1, 960, 320),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(61440, 9)](primals_2, buf0, 61440, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((192, 192, 5, 5), (4800, 1, 960, 192),
            torch.float32)
        triton_poi_fused_1[grid(36864, 25)](primals_4, buf1, 36864, 25,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_4
        buf2 = empty_strided_cuda((192, 192, 5, 5), (4800, 1, 960, 192),
            torch.float32)
        triton_poi_fused_1[grid(36864, 25)](primals_6, buf2, 36864, 25,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_6
        buf3 = empty_strided_cuda((4, 320, 64, 64), (1310720, 1, 20480, 320
            ), torch.float32)
        triton_poi_fused_abs_2[grid(1280, 4096)](primals_1, buf3, 1280, 
            4096, XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_1
        buf4 = extern_kernels.convolution(buf3, buf0, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 192, 64, 64), (786432, 1, 12288, 192))
        buf5 = empty_strided_cuda((4, 192, 64, 64), (786432, 1, 12288, 192),
            torch.bool)
        buf6 = empty_strided_cuda((4, 192, 64, 64), (786432, 1, 12288, 192),
            torch.float32)
        triton_poi_fused_convolution_leaky_relu_3[grid(3145728)](buf4,
            primals_3, buf5, buf6, 3145728, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf4
        del primals_3
        buf7 = extern_kernels.convolution(buf6, buf1, stride=(2, 2),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 192, 32, 32), (196608, 1, 6144, 192))
        buf8 = empty_strided_cuda((4, 192, 32, 32), (196608, 1, 6144, 192),
            torch.bool)
        buf9 = empty_strided_cuda((4, 192, 32, 32), (196608, 1, 6144, 192),
            torch.float32)
        triton_poi_fused_convolution_leaky_relu_4[grid(786432)](buf7,
            primals_5, buf8, buf9, 786432, XBLOCK=512, num_warps=8,
            num_stages=1)
        del buf7
        del primals_5
        buf10 = extern_kernels.convolution(buf9, buf2, stride=(2, 2),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 192, 16, 16), (49152, 1, 3072, 192))
        buf11 = empty_strided_cuda((4, 192, 16, 16), (49152, 256, 16, 1),
            torch.float32)
        triton_poi_fused_convolution_5[grid(768, 256)](buf10, primals_7,
            buf11, 768, 256, XBLOCK=16, YBLOCK=256, num_warps=8, num_stages=1)
        del buf10
        del primals_7
    return buf11, buf0, buf1, buf2, buf3, buf5, buf6, buf8, buf9


class Analysis_prior_netNew(nn.Module):
    """
    Analysis prior net
    """

    def __init__(self, out_channel_N=192, out_channel_M=320):
        super(Analysis_prior_netNew, self).__init__()
        self.conv1 = nn.Conv2d(out_channel_M, out_channel_N, 3, stride=1,
            padding=1)
        torch.nn.init.xavier_normal_(self.conv1.weight.data, math.sqrt(2 *
            (out_channel_M + out_channel_N) / (out_channel_M + out_channel_M)))
        torch.nn.init.constant_(self.conv1.bias.data, 0.01)
        self.relu1 = nn.LeakyReLU()
        self.conv2 = nn.Conv2d(out_channel_N, out_channel_N, 5, stride=2,
            padding=2)
        torch.nn.init.xavier_normal_(self.conv2.weight.data, math.sqrt(2))
        torch.nn.init.constant_(self.conv2.bias.data, 0.01)
        self.relu2 = nn.LeakyReLU()
        self.conv3 = nn.Conv2d(out_channel_N, out_channel_N, 5, stride=2,
            padding=2)
        torch.nn.init.xavier_normal_(self.conv3.weight.data, math.sqrt(2))
        torch.nn.init.constant_(self.conv3.bias.data, 0.01)

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.conv3.weight
        primals_7 = self.conv3.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.947555