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 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):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 16 % 16
    x0 = xindex % 16
    x2 = xindex // 256
    x3 = xindex
    tmp0 = x1
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 + tmp2
    tmp4 = 0.25
    tmp5 = tmp3 * tmp4
    tmp6 = tmp5 - tmp2
    tmp7 = 0.0
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tmp8.to(tl.int32)
    tmp10 = tl.full([1], 1, tl.int64)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.full([1], 3, tl.int64)
    tmp13 = triton_helpers.minimum(tmp11, tmp12)
    tmp14 = x0
    tmp15 = tmp14.to(tl.float32)
    tmp16 = tmp15 + tmp2
    tmp17 = tmp16 * tmp4
    tmp18 = tmp17 - tmp2
    tmp19 = triton_helpers.maximum(tmp18, tmp7)
    tmp20 = tmp19.to(tl.int32)
    tmp21 = tmp20 + tmp10
    tmp22 = triton_helpers.minimum(tmp21, tmp12)
    tmp23 = tl.load(in_ptr0 + (tmp22 + 4 * tmp13 + 16 * x2), None,
        eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr0 + (tmp20 + 4 * tmp13 + 16 * x2), None,
        eviction_policy='evict_last')
    tmp25 = tmp23 - tmp24
    tmp26 = tmp20.to(tl.float32)
    tmp27 = tmp19 - tmp26
    tmp28 = triton_helpers.maximum(tmp27, tmp7)
    tmp29 = 1.0
    tmp30 = triton_helpers.minimum(tmp28, tmp29)
    tmp31 = tmp25 * tmp30
    tmp32 = tl.load(in_ptr0 + (tmp20 + 4 * tmp9 + 16 * x2), None,
        eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr0 + (tmp22 + 4 * tmp9 + 16 * x2), None,
        eviction_policy='evict_last')
    tmp34 = tmp33 - tmp32
    tmp35 = tmp34 * tmp30
    tmp36 = tmp32 + tmp35
    tmp37 = tmp24 + tmp31
    tmp38 = tmp37 - tmp36
    tmp39 = tmp9.to(tl.float32)
    tmp40 = tmp8 - tmp39
    tmp41 = triton_helpers.maximum(tmp40, tmp7)
    tmp42 = triton_helpers.minimum(tmp41, tmp29)
    tmp43 = tmp38 * tmp42
    tmp44 = tmp36 + tmp43
    tl.store(in_out_ptr0 + x3, tmp44, None)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 16, 16), (1024, 256, 16, 1), torch
            .float32)
        buf2 = buf0
        del buf0
        buf3 = buf2
        del buf2
        get_raw_stream(0)
        triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_0[grid
            (4096)](buf3, arg0_1, 4096, XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
    return buf3,


class UpsampleNew(nn.Module):

    def __init__(self, factor):
        super(UpsampleNew, self).__init__()
        self.factor = factor

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


Measured latency_ms: 0.014108