import torch
import torch.nn as nn


class DynamicPreHead(nn.Module):

    def __init__(self, in_dim=3, embed_dim=100, kernel_size=1):
        super(DynamicPreHead, self).__init__()
        self.conv = nn.Conv2d(in_dim, embed_dim, kernel_size=kernel_size,
            stride=1, padding=int((kernel_size - 1) / 2))
        self.bn = nn.GroupNorm(int(embed_dim / 4), embed_dim)
        self.relu = nn.ReLU(True)
        nn.init.kaiming_normal_(self.conv.weight, mode='fan_out',
            nonlinearity='relu')

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x


def get_inputs():
    return [torch.rand([4, 3, 64, 64])]


def get_init_inputs():
    return [[], {}]

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 libdevice
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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 12
    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 % 3
    y1 = yindex // 3
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 3 * x2 + 12288 * y1), tmp0, ymask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_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 % 100
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, None)


@triton.jit
def triton_per_fused_native_group_norm_2(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 12800
    RBLOCK: tl.constexpr = 128
    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)
    r3 = rindex
    x0 = xindex % 50
    x1 = xindex // 50 % 64
    x2 = xindex // 3200
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 100 * ((r3 + 128 * x1) % 4096) + 
        409600 * x2 + (r3 + 128 * x1) // 4096), xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 128, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tl.store(out_ptr0 + x4, tmp10, xmask)
    tl.store(out_ptr1 + x4, tmp16, xmask)
    tl.store(out_ptr2 + x4, tmp9, xmask)


@triton.jit
def triton_per_fused_native_group_norm_3(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 200
    RBLOCK: tl.constexpr = 64
    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)
    r2 = rindex
    x0 = xindex % 50
    x1 = xindex // 50
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 50 * r2 + 3200 * x1), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (x0 + 50 * r2 + 3200 * x1), xmask, other=0.0)
    tmp2 = tl.load(in_ptr2 + (x0 + 50 * r2 + 3200 * x1), xmask, other=0.0)
    tmp3 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp5 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp7 = tl.where(xmask, tmp3, 0)
    tmp8 = tl.where(xmask, tmp4, 0)
    tmp9 = tl.where(xmask, tmp5, 0)
    tmp10, tmp11, tmp12 = triton_helpers.welford(tmp7, tmp8, tmp9, 1)
    tmp13 = tmp10[:, None]
    tmp14 = tmp11[:, None]
    tmp15 = tmp12[:, None]
    tl.store(out_ptr0 + x3, tmp13, xmask)
    tl.store(out_ptr1 + x3, tmp14, xmask)
    tl.store(out_ptr2 + x3, tmp15, xmask)


@triton.jit
def triton_per_fused_native_group_norm_4(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 100
    RBLOCK: tl.constexpr = 2
    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 + 2 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r1 + 2 * x0), xmask, other=0.0)
    tmp2 = tl.load(in_ptr2 + (r1 + 2 * x0), xmask, other=0.0)
    tmp3 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp5 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp7 = tl.where(xmask, tmp3, 0)
    tmp8 = tl.where(xmask, tmp4, 0)
    tmp9 = tl.where(xmask, tmp5, 0)
    tmp10, tmp11, tmp12 = triton_helpers.welford(tmp7, tmp8, tmp9, 1)
    tmp13 = tmp10[:, None]
    tmp14 = tmp11[:, None]
    tmp12[:, None]
    tmp16 = 16384.0
    tmp17 = tmp14 / tmp16
    tmp18 = 1e-05
    tmp19 = tmp17 + tmp18
    tmp20 = libdevice.rsqrt(tmp19)
    tl.store(out_ptr2 + x0, tmp20, xmask)
    tl.store(out_ptr0 + x0, tmp13, xmask)
    tl.store(out_ptr1 + x0, tmp14, xmask)


@triton.jit
def triton_poi_fused_native_group_norm_relu_threshold_backward_5(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, out_ptr1, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 400
    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
    y0 = yindex % 100
    y1 = yindex // 100
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 100 * x2 + 409600 * y1), ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y3 // 4, ymask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + y3 // 4, ymask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + y0, ymask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 16384.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1, 1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp16 = 0.0
    tmp17 = tmp15 <= tmp16
    tl.store(out_ptr0 + (x2 + 4096 * y3), tmp15, ymask)
    tl.store(out_ptr1 + (y0 + 100 * x2 + 409600 * y1), tmp17, ymask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (100, 3, 1, 1), (3, 1, 1, 1))
    assert_size_stride(primals_2, (100,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (100,), (1,))
    assert_size_stride(primals_5, (100,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 3, 64, 64), (12288, 1, 192, 3), torch
            .float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(12, 4096)](primals_3, buf0, 12, 4096,
            XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_3
        buf1 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 100, 64, 64), (409600, 1, 6400, 100))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(1638400)](buf2, primals_2, 
            1638400, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf3 = empty_strided_cuda((4, 25, 1, 1, 2, 64), (3200, 2, 12800, 
            12800, 1, 50), torch.float32)
        buf4 = empty_strided_cuda((4, 25, 1, 1, 2, 64), (3200, 2, 12800, 
            12800, 1, 50), torch.float32)
        buf5 = empty_strided_cuda((4, 25, 1, 1, 2, 64), (3200, 2, 12800, 
            12800, 1, 50), torch.float32)
        triton_per_fused_native_group_norm_2[grid(12800)](buf2, buf3, buf4,
            buf5, 12800, 128, XBLOCK=8, num_warps=8, num_stages=1)
        buf6 = empty_strided_cuda((4, 25, 1, 1, 2), (50, 2, 200, 200, 1),
            torch.float32)
        buf7 = empty_strided_cuda((4, 25, 1, 1, 2), (50, 2, 200, 200, 1),
            torch.float32)
        buf8 = empty_strided_cuda((4, 25, 1, 1, 2), (50, 2, 200, 200, 1),
            torch.float32)
        triton_per_fused_native_group_norm_3[grid(200)](buf3, buf4, buf5,
            buf6, buf7, buf8, 200, 64, XBLOCK=8, num_warps=4, num_stages=1)
        del buf3
        del buf4
        del buf5
        buf9 = empty_strided_cuda((4, 25, 1, 1), (25, 1, 100, 100), torch.
            float32)
        buf10 = empty_strided_cuda((4, 25, 1, 1), (25, 1, 100, 100), torch.
            float32)
        buf12 = empty_strided_cuda((4, 25, 1, 1), (25, 1, 100, 100), torch.
            float32)
        triton_per_fused_native_group_norm_4[grid(100)](buf6, buf7, buf8,
            buf9, buf10, buf12, 100, 2, XBLOCK=8, num_warps=2, num_stages=1)
        del buf6
        del buf7
        del buf8
        buf13 = empty_strided_cuda((4, 100, 64, 64), (409600, 4096, 64, 1),
            torch.float32)
        buf14 = empty_strided_cuda((4, 100, 64, 64), (409600, 1, 6400, 100),
            torch.bool)
        triton_poi_fused_native_group_norm_relu_threshold_backward_5[grid(
            400, 4096)](buf2, buf9, buf10, primals_4, primals_5, buf13,
            buf14, 400, 4096, XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del buf10
        del primals_5
    return buf13, primals_1, buf0, primals_4, buf2, reinterpret_tensor(buf9,
        (4, 25), (25, 1), 0), reinterpret_tensor(buf12, (4, 25), (25, 1), 0
        ), buf14


class DynamicPreHeadNew(nn.Module):

    def __init__(self, in_dim=3, embed_dim=100, kernel_size=1):
        super(DynamicPreHeadNew, self).__init__()
        self.conv = nn.Conv2d(in_dim, embed_dim, kernel_size=kernel_size,
            stride=1, padding=int((kernel_size - 1) / 2))
        self.bn = nn.GroupNorm(int(embed_dim / 4), embed_dim)
        self.relu = nn.ReLU(True)
        nn.init.kaiming_normal_(self.conv.weight, mode='fan_out',
            nonlinearity='relu')

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