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.optim import *
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


@triton.jit
def triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_0(
    in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 8 % 8
    x0 = xindex % 8
    x2 = xindex // 64
    x4 = xindex
    tmp0 = x1
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.full([1], 1, tl.int64)
    tmp10 = tmp8 + tmp9
    tmp11 = tl.full([1], 3, tl.int64)
    tmp12 = triton_helpers.minimum(tmp10, tmp11)
    tmp13 = x0
    tmp14 = tmp13.to(tl.float32)
    tmp15 = tmp14 + tmp2
    tmp16 = tmp15 * tmp2
    tmp17 = tmp16 - tmp2
    tmp18 = triton_helpers.maximum(tmp17, tmp6)
    tmp19 = tmp18.to(tl.int32)
    tmp20 = tmp19 + tmp9
    tmp21 = triton_helpers.minimum(tmp20, tmp11)
    tmp22 = tl.load(in_ptr0 + (tmp21 + 4 * tmp12 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (tmp19 + 4 * tmp12 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp24 = tmp22 - tmp23
    tmp25 = tmp19.to(tl.float32)
    tmp26 = tmp18 - tmp25
    tmp27 = triton_helpers.maximum(tmp26, tmp6)
    tmp28 = 1.0
    tmp29 = triton_helpers.minimum(tmp27, tmp28)
    tmp30 = tmp24 * tmp29
    tmp31 = tmp23 + tmp30
    tmp32 = tl.load(in_ptr0 + (tmp19 + 4 * tmp8 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr0 + (tmp21 + 4 * tmp8 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp34 = tmp33 - tmp32
    tmp35 = tmp34 * tmp29
    tmp36 = tmp32 + tmp35
    tmp37 = tmp31 - tmp36
    tmp38 = tmp8.to(tl.float32)
    tmp39 = tmp7 - tmp38
    tmp40 = triton_helpers.maximum(tmp39, tmp6)
    tmp41 = triton_helpers.minimum(tmp40, tmp28)
    tmp42 = tmp37 * tmp41
    tmp43 = tmp36 + tmp42
    tl.store(in_out_ptr0 + x4, tmp43, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_1(in_out_ptr0,
    in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 25 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, 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(in_out_ptr0 + x3, tmp4, xmask)
    tl.store(out_ptr0 + x3, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32)
        buf1 = buf0
        del buf0
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_0[grid
            (1024)](buf2, primals_1, 1024, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_1
        buf3 = extern_kernels.convolution(buf2, primals_2, 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, 5, 5), (100, 25, 5, 1))
        buf4 = buf3
        del buf3
        buf5 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_1[grid(400)](buf4,
            primals_3, buf5, 400, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
    return buf4, primals_2, buf2, buf5


class UpsampleConvLayerNew(nn.Module):
    """
    Upsampling layer (bilinear interpolation + Conv2d) to increase spatial resolution (x2) in a decoder.
    Default: bias, ReLU, no downsampling, no batch norm.
    """

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, activation='relu', norm=None):
        super(UpsampleConvLayerNew, self).__init__()
        bias = False if norm == 'BN' else True
        self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size,
            stride, padding, bias=bias)
        if activation is not None:
            self.activation = getattr(torch, activation)
        else:
            self.activation = None
        self.norm = norm
        if norm == 'BN':
            self.norm_layer = nn.BatchNorm2d(out_channels)
        elif norm == 'IN':
            self.norm_layer = nn.InstanceNorm2d(out_channels,
                track_running_stats=True)

    def forward(self, input_0):
        primals_1 = self.conv2d.weight
        primals_3 = self.conv2d.bias
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.024756