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_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 63504
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3969 % 4
    x0 = xindex % 3969
    x4 = xindex // 3969
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + (x0 + 4000 * x4), tmp4, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 57600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 60
    x1 = xindex // 60 % 60
    x2 = xindex // 3600
    x3 = xindex
    x4 = xindex % 3600
    tmp0 = tl.load(in_ptr0 + (x0 + 63 * x1 + 4000 * x2), xmask)
    tmp1 = tl.load(in_ptr0 + (1 + x0 + 63 * x1 + 4000 * x2), xmask)
    tmp3 = tl.load(in_ptr0 + (2 + x0 + 63 * x1 + 4000 * x2), xmask)
    tmp5 = tl.load(in_ptr0 + (3 + x0 + 63 * x1 + 4000 * x2), xmask)
    tmp7 = tl.load(in_ptr0 + (63 + x0 + 63 * x1 + 4000 * x2), xmask)
    tmp9 = tl.load(in_ptr0 + (64 + x0 + 63 * x1 + 4000 * x2), xmask)
    tmp11 = tl.load(in_ptr0 + (65 + x0 + 63 * x1 + 4000 * x2), xmask)
    tmp13 = tl.load(in_ptr0 + (66 + x0 + 63 * x1 + 4000 * x2), xmask)
    tmp15 = tl.load(in_ptr0 + (126 + x0 + 63 * x1 + 4000 * x2), xmask)
    tmp17 = tl.load(in_ptr0 + (127 + x0 + 63 * x1 + 4000 * x2), xmask)
    tmp19 = tl.load(in_ptr0 + (128 + x0 + 63 * x1 + 4000 * x2), xmask)
    tmp21 = tl.load(in_ptr0 + (129 + x0 + 63 * x1 + 4000 * x2), xmask)
    tmp23 = tl.load(in_ptr0 + (189 + x0 + 63 * x1 + 4000 * x2), xmask)
    tmp25 = tl.load(in_ptr0 + (190 + x0 + 63 * x1 + 4000 * x2), xmask)
    tmp27 = tl.load(in_ptr0 + (191 + x0 + 63 * x1 + 4000 * x2), xmask)
    tmp29 = tl.load(in_ptr0 + (192 + x0 + 63 * x1 + 4000 * x2), xmask)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tmp14 = triton_helpers.maximum(tmp13, tmp12)
    tmp16 = triton_helpers.maximum(tmp15, tmp14)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tmp20 = triton_helpers.maximum(tmp19, tmp18)
    tmp22 = triton_helpers.maximum(tmp21, tmp20)
    tmp24 = triton_helpers.maximum(tmp23, tmp22)
    tmp26 = triton_helpers.maximum(tmp25, tmp24)
    tmp28 = triton_helpers.maximum(tmp27, tmp26)
    tmp30 = triton_helpers.maximum(tmp29, tmp28)
    tmp31 = tmp1 > tmp0
    tmp32 = tl.full([1], 1, tl.int8)
    tmp33 = tl.full([1], 0, tl.int8)
    tmp34 = tl.where(tmp31, tmp32, tmp33)
    tmp35 = tmp3 > tmp2
    tmp36 = tl.full([1], 2, tl.int8)
    tmp37 = tl.where(tmp35, tmp36, tmp34)
    tmp38 = tmp5 > tmp4
    tmp39 = tl.full([1], 3, tl.int8)
    tmp40 = tl.where(tmp38, tmp39, tmp37)
    tmp41 = tmp7 > tmp6
    tmp42 = tl.full([1], 4, tl.int8)
    tmp43 = tl.where(tmp41, tmp42, tmp40)
    tmp44 = tmp9 > tmp8
    tmp45 = tl.full([1], 5, tl.int8)
    tmp46 = tl.where(tmp44, tmp45, tmp43)
    tmp47 = tmp11 > tmp10
    tmp48 = tl.full([1], 6, tl.int8)
    tmp49 = tl.where(tmp47, tmp48, tmp46)
    tmp50 = tmp13 > tmp12
    tmp51 = tl.full([1], 7, tl.int8)
    tmp52 = tl.where(tmp50, tmp51, tmp49)
    tmp53 = tmp15 > tmp14
    tmp54 = tl.full([1], 8, tl.int8)
    tmp55 = tl.where(tmp53, tmp54, tmp52)
    tmp56 = tmp17 > tmp16
    tmp57 = tl.full([1], 9, tl.int8)
    tmp58 = tl.where(tmp56, tmp57, tmp55)
    tmp59 = tmp19 > tmp18
    tmp60 = tl.full([1], 10, tl.int8)
    tmp61 = tl.where(tmp59, tmp60, tmp58)
    tmp62 = tmp21 > tmp20
    tmp63 = tl.full([1], 11, tl.int8)
    tmp64 = tl.where(tmp62, tmp63, tmp61)
    tmp65 = tmp23 > tmp22
    tmp66 = tl.full([1], 12, tl.int8)
    tmp67 = tl.where(tmp65, tmp66, tmp64)
    tmp68 = tmp25 > tmp24
    tmp69 = tl.full([1], 13, tl.int8)
    tmp70 = tl.where(tmp68, tmp69, tmp67)
    tmp71 = tmp27 > tmp26
    tmp72 = tl.full([1], 14, tl.int8)
    tmp73 = tl.where(tmp71, tmp72, tmp70)
    tmp74 = tmp29 > tmp28
    tmp75 = tl.full([1], 15, tl.int8)
    tmp76 = tl.where(tmp74, tmp75, tmp73)
    tl.store(out_ptr0 + x3, tmp30, xmask)
    tl.store(out_ptr1 + (x4 + 3712 * x2), tmp76, 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, 64, 64), (16384, 4096, 64, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 63, 63), (15876, 3969, 63, 1))
        buf1 = empty_strided_cuda((4, 4, 63, 63), (16000, 4000, 63, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(63504)](buf0, primals_2,
            buf1, 63504, XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 60, 60), (14400, 3600, 60, 1),
            torch.float32)
        buf3 = empty_strided_cuda((4, 4, 60, 60), (14848, 3712, 60, 1),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_1[grid(57600)](buf1, buf2,
            buf3, 57600, XBLOCK=256, num_warps=4, num_stages=1)
    return buf2, primals_1, primals_3, buf1, buf3


def Conv2d(fIn, fOut, k, stride=1):
    """torch Conv2d with same padding"""
    assert k % 2 == 0
    pad = int((k - 1) / 2)
    return torch.nn.Conv2d(fIn, fOut, k, stride=stride, padding=pad)


def Pool(k, stride=1, pad=0):
    return torch.nn.MaxPool2d(k, stride=stride, padding=pad)


class ConvReluPoolNew(nn.Module):

    def __init__(self, fIn, fOut, k, stride=1, pool=2):
        super().__init__()
        self.conv = Conv2d(fIn, fOut, k, stride)
        self.pool = Pool(k)

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


Measured latency_ms: 0.03749