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
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_convolution_relu_threshold_backward_0(in_out_ptr0,
    in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 64 % 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,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2, 
            2), padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 8, 8), (256, 64, 8, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_threshold_backward_0[grid(1024)](buf1
            , primals_2, buf2, 1024, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
    return buf1, primals_1, primals_3, buf2


class UpConv_BlocksNew(nn.Module):

    def __init__(self, input_dim, output_dim, filter=4, padding=1,
        first_block=False, last_block=False, batch_norm=False, non_lin=
        'relu', dropout=0, skip_connection=False):
        super(UpConv_BlocksNew, self).__init__()
        self.Block = nn.Sequential()
        self.skip_connection = skip_connection
        self.first_block = first_block
        self.last_block = last_block
        if self.skip_connection and not self.first_block:
            input_dim *= 2
        else:
            pass
        self.Block.add_module('UpConv', nn.ConvTranspose2d(input_dim,
            output_dim, filter, 2, padding))
        if not last_block:
            if batch_norm:
                self.Block.add_module('BN_up', nn.BatchNorm2d(output_dim))
            if non_lin == 'tanh':
                self.Block.add_module('NonLin_up', nn.Tanh())
            elif non_lin == 'relu':
                self.Block.add_module('NonLin_up', nn.ReLU())
            elif non_lin == 'leakyrelu':
                self.Block.add_module('NonLin_up', nn.LeakyReLU())
            if dropout > 0:
                self.Block.add_module('Drop_up', nn.Dropout2d(dropout))

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


Measured latency_ms: 0.025468