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
import torch.nn.functional as F
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):
    ynumel = 70400
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(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
    y3 = yindex
    y0 = yindex % 220
    y1 = yindex // 220
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 220 * x2 + 1980 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 880
    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 % 220
    y1 = yindex // 220
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 220 * x2 + 901120 * y1), tmp0, ymask)


@triton.jit
def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 25
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(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 + 25 * y3), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 320 * x2 + 8000 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_3(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 320
    x1 = xindex // 320 % 68
    x2 = xindex // 21760 % 68
    x3 = xindex // 1479680
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (1310400 + x0 + -20480 * tl_math.abs(-63 +
        tl_math.abs(-2 + x2)) + -320 * tl_math.abs(-63 + tl_math.abs(-2 +
        x1)) + 1310720 * x3), None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + x4, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_4(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 320
    x1 = xindex // 320 % 36
    x2 = xindex // 11520 % 36
    x3 = xindex // 414720
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (327360 + x0 + -10240 * tl_math.abs(-31 +
        tl_math.abs(-2 + x2)) + -320 * tl_math.abs(-31 + tl_math.abs(-2 +
        x1)) + 327680 * x3), None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + x4, tmp4, None)


@triton.jit
def triton_poi_fused_convolution_5(in_ptr0, in_ptr1, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 1280
    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 % 320
    y1 = yindex // 320
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 320 * x2 + 81920 * 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)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_6(in_ptr0, in_ptr1,
    out_ptr0, 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 % 320
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x2, tmp6, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_7(in_ptr0, in_ptr1,
    out_ptr0, 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 % 320
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x2, tmp6, None)


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, (320, 220, 3, 3), (1980, 9, 3, 1))
    assert_size_stride(primals_2, (320,), (1,))
    assert_size_stride(primals_3, (4, 220, 64, 64), (901120, 4096, 64, 1))
    assert_size_stride(primals_4, (320, 320, 5, 5), (8000, 25, 5, 1))
    assert_size_stride(primals_5, (320,), (1,))
    assert_size_stride(primals_6, (320, 320, 5, 5), (8000, 25, 5, 1))
    assert_size_stride(primals_7, (320,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((320, 220, 3, 3), (1980, 1, 660, 220),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(70400, 9)](primals_1, buf0, 70400, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 220, 64, 64), (901120, 1, 14080, 220),
            torch.float32)
        triton_poi_fused_1[grid(880, 4096)](primals_3, buf1, 880, 4096,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((320, 320, 5, 5), (8000, 1, 1600, 320),
            torch.float32)
        triton_poi_fused_2[grid(102400, 25)](primals_4, buf2, 102400, 25,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((320, 320, 5, 5), (8000, 1, 1600, 320),
            torch.float32)
        triton_poi_fused_2[grid(102400, 25)](primals_6, buf3, 102400, 25,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_6
        buf4 = extern_kernels.convolution(buf1, 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, 320, 64, 64), (1310720, 1, 20480, 320))
        buf5 = empty_strided_cuda((4, 320, 68, 68), (1479680, 1, 21760, 320
            ), torch.float32)
        triton_poi_fused_convolution_reflection_pad2d_relu_3[grid(5918720)](
            buf4, primals_2, buf5, 5918720, XBLOCK=512, num_warps=8,
            num_stages=1)
        buf6 = extern_kernels.convolution(buf5, buf2, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 320, 32, 32), (327680, 1, 10240, 320))
        buf7 = empty_strided_cuda((4, 320, 36, 36), (414720, 1, 11520, 320),
            torch.float32)
        triton_poi_fused_convolution_reflection_pad2d_relu_4[grid(1658880)](
            buf6, primals_5, buf7, 1658880, XBLOCK=1024, num_warps=4,
            num_stages=1)
        buf8 = extern_kernels.convolution(buf7, buf3, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 320, 16, 16), (81920, 1, 5120, 320))
        buf9 = empty_strided_cuda((4, 320, 16, 16), (81920, 256, 16, 1),
            torch.float32)
        triton_poi_fused_convolution_5[grid(1280, 256)](buf8, primals_7,
            buf9, 1280, 256, XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del buf8
        del primals_7
        buf10 = empty_strided_cuda((4, 320, 32, 32), (327680, 1, 10240, 320
            ), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_6[grid(1310720)](
            buf6, primals_5, buf10, 1310720, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf6
        del primals_5
        buf11 = empty_strided_cuda((4, 320, 64, 64), (1310720, 1, 20480, 
            320), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_7[grid(5242880)](
            buf4, primals_2, buf11, 5242880, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf4
        del primals_2
    return buf9, buf0, buf1, buf2, buf3, buf5, buf7, buf10, buf11


class HyperpriorAnalysisNew(nn.Module):
    """
    Hyperprior 'analysis model' as proposed in [1]. 

    [1] Ballé et. al., "Variational image compression with a scale hyperprior", 
        arXiv:1802.01436 (2018).

    C:  Number of input channels
    """

    def __init__(self, C=220, N=320, activation='relu'):
        super(HyperpriorAnalysisNew, self).__init__()
        cnn_kwargs = dict(kernel_size=5, stride=2, padding=2, padding_mode=
            'reflect')
        self.activation = getattr(F, activation)
        self.n_downsampling_layers = 2
        self.conv1 = nn.Conv2d(C, N, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(N, N, **cnn_kwargs)
        self.conv3 = nn.Conv2d(N, N, **cnn_kwargs)

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


Measured latency_ms: 0.015766