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
import torch.nn as nn
import torch.nn.functional as F
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_add_mul_pow_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_out_ptr0 + x2, xmask)
    tmp15 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp23 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp12 = 2.0
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 - tmp13
    tmp16 = tmp15 * tmp15
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp21 = tmp20 * tmp20
    tmp22 = tmp19 + tmp21
    tmp24 = tmp23 * tmp23
    tmp25 = tmp22 + tmp24
    tmp26 = tmp14 + tmp25
    tl.store(in_out_ptr0 + x2, tmp26, xmask)


@triton.jit
def triton_poi_fused_argmin_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp32 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 < tmp1
    tmp3 = tmp0 == tmp1
    tmp4 = tmp0 != tmp0
    tmp5 = tmp1 != tmp1
    tmp6 = tmp4 > tmp5
    tmp7 = tmp2 | tmp6
    tmp8 = tmp4 & tmp5
    tmp9 = tmp3 | tmp8
    tmp10 = tl.full([1], 0, tl.int64)
    tmp11 = tl.full([1], 1, tl.int64)
    tmp12 = tmp10 < tmp11
    tmp13 = tmp9 & tmp12
    tmp14 = tmp7 | tmp13
    tmp15 = tl.where(tmp14, tmp0, tmp1)
    tmp16 = tl.where(tmp14, tmp10, tmp11)
    tmp18 = tmp15 < tmp17
    tmp19 = tmp15 == tmp17
    tmp20 = tmp15 != tmp15
    tmp21 = tmp17 != tmp17
    tmp22 = tmp20 > tmp21
    tmp23 = tmp18 | tmp22
    tmp24 = tmp20 & tmp21
    tmp25 = tmp19 | tmp24
    tmp26 = tl.full([1], 2, tl.int64)
    tmp27 = tmp16 < tmp26
    tmp28 = tmp25 & tmp27
    tmp29 = tmp23 | tmp28
    tmp30 = tl.where(tmp29, tmp15, tmp17)
    tmp31 = tl.where(tmp29, tmp16, tmp26)
    tmp33 = tmp30 < tmp32
    tmp34 = tmp30 == tmp32
    tmp35 = tmp30 != tmp30
    tmp36 = tmp32 != tmp32
    tmp37 = tmp35 > tmp36
    tmp38 = tmp33 | tmp37
    tmp39 = tmp35 & tmp36
    tmp40 = tmp34 | tmp39
    tmp41 = tl.full([1], 3, tl.int64)
    tmp42 = tmp31 < tmp41
    tmp43 = tmp40 & tmp42
    tmp44 = tmp38 | tmp43
    tl.where(tmp44, tmp30, tmp32)
    tmp46 = tl.where(tmp44, tmp31, tmp41)
    tl.store(out_ptr0 + x0, tmp46, xmask)


@triton.jit
def triton_poi_fused__to_copy_arange_eq_view_2(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = x0
    tmp2 = tmp0 == tmp1
    tmp3 = tmp2.to(tl.int64)
    tmp4 = tmp3.to(tl.float32)
    tl.store(out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_add_sub_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask)
    tmp2 = tmp1 - tmp0
    tmp3 = tmp0 + tmp2
    tl.store(out_ptr0 + x0, tmp3, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_mul_pow_sub_sum_0[grid(64)](buf1, primals_2,
            primals_1, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((16,), (1,), torch.int64)
        triton_poi_fused_argmin_1[grid(16)](buf1, buf2, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf3 = buf1
        del buf1
        triton_poi_fused__to_copy_arange_eq_view_2[grid(64)](buf2, buf3, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf3, primals_2, out=buf4)
        del primals_2
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_sub_3[grid(64)](primals_1, buf4, buf5, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_1
    return reinterpret_tensor(buf4, (4, 4, 4), (16, 4, 1), 0
        ), buf5, reinterpret_tensor(buf2, (4, 4), (4, 1), 0
        ), reinterpret_tensor(buf3, (4, 16), (1, 4), 0)


class QuantizeNew(nn.Module):

    def __init__(self, emb_dim, emb_size, decay=0.99, eps=1e-05, ema_flag=
        False, bdt_flag=False):
        super().__init__()
        self.emb_dim = emb_dim
        self.emb_size = emb_size
        self.ema_flag = ema_flag
        self.bdt_flag = bdt_flag
        self.embedding = nn.Embedding(emb_size, emb_dim)
        self.embedding.weight.data.uniform_(-1.0 / self.emb_size, 1.0 /
            self.emb_size)
        if self.ema_flag:
            self.decay = decay
            self.eps = eps
            embed = torch.randn(emb_dim, emb_size)
            self.register_buffer('ema_size', torch.zeros(emb_size))
            self.register_buffer('ema_w', embed.clone())

    def vq(self, x):
        flatten_x = x.reshape(-1, self.emb_dim)
        dist = torch.sum(torch.pow(self.embedding.weight, 2), dim=1
            ) - 2 * torch.matmul(flatten_x, self.embedding.weight.T
            ) + torch.sum(torch.pow(flatten_x, 2), dim=1, keepdim=True)
        quantized_idx = torch.argmin(dist, dim=1).view(x.size(0), x.size(1))
        quantized_onehot = F.one_hot(quantized_idx, self.emb_size)
        return quantized_idx, quantized_onehot

    def forward(self, input_0):
        primals_2 = self.embedding.weight
        primals_1 = input_0
        output = call([primals_1, primals_2])
        return output[0], output[1], output[2]


Measured latency_ms: 0.066192