import torch
import torch.nn
import torch.nn.functional as F
import torch.nn as nn


class TransitionUp(nn.Module):

    def __init__(self, in_channels, out_channels):
        super().__init__()

    def forward(self, x, skip, concat=True):
        out = F.interpolate(x, size=(skip.size(2), skip.size(3)), mode=
            'bilinear', align_corners=True)
        if concat:
            out = torch.cat([out, skip], 1)
        return out


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4}]

import torch
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
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__to_copy__unsafe_index_add_arange_clamp_mul_sub_0(in_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x2 = xindex // 16
    x4 = xindex // 64
    x7 = xindex % 64
    tmp0 = x1
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 1.0
    tmp3 = tmp1 * tmp2
    tmp4 = 0.0
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp5.to(tl.int32)
    tmp7 = tl.full([1], 1, tl.int64)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 3, tl.int64)
    tmp10 = triton_helpers.minimum(tmp8, tmp9)
    tmp11 = x0
    tmp12 = tmp11.to(tl.float32)
    tmp13 = tmp12 * tmp2
    tmp14 = triton_helpers.maximum(tmp13, tmp4)
    tmp15 = tmp14.to(tl.int32)
    tmp16 = tl.load(in_ptr0 + (tmp15 + 4 * tmp10 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp17 = tmp15 + tmp7
    tmp18 = triton_helpers.minimum(tmp17, tmp9)
    tmp19 = tl.load(in_ptr0 + (tmp18 + 4 * tmp10 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp20 = tmp19 - tmp16
    tmp21 = tmp15.to(tl.float32)
    tmp22 = tmp14 - tmp21
    tmp23 = triton_helpers.maximum(tmp22, tmp4)
    tmp24 = triton_helpers.minimum(tmp23, tmp2)
    tmp25 = tmp20 * tmp24
    tmp26 = tmp16 + tmp25
    tmp27 = tl.load(in_ptr0 + (tmp15 + 4 * tmp6 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr0 + (tmp18 + 4 * tmp6 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp29 = tmp28 - tmp27
    tmp30 = tmp29 * tmp24
    tmp31 = tmp27 + tmp30
    tmp32 = tmp26 - tmp31
    tmp33 = tmp6.to(tl.float32)
    tmp34 = tmp5 - tmp33
    tmp35 = triton_helpers.maximum(tmp34, tmp4)
    tmp36 = triton_helpers.minimum(tmp35, tmp2)
    tmp37 = tmp32 * tmp36
    tmp38 = tmp31 + tmp37
    tl.store(out_ptr1 + (x7 + 128 * x4), tmp38, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    x1 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tl.store(out_ptr0 + (x0 + 128 * x1), tmp0, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf3 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        buf1 = reinterpret_tensor(buf3, (4, 4, 4, 4), (128, 16, 4, 1), 0)
        get_raw_stream(0)
        triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_0[grid
            (256)](arg1_1, buf1, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg1_1
        buf2 = reinterpret_tensor(buf3, (4, 4, 4, 4), (128, 16, 4, 1), 64)
        triton_poi_fused_cat_1[grid(256)](arg0_1, buf2, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg0_1
    return buf3,


class TransitionUpNew(nn.Module):

    def __init__(self, in_channels, out_channels):
        super().__init__()

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
