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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.data
from torch import 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_add_mul_pow_sub_sum_0(in_out_ptr0, in_ptr0, in_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
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp16 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp19 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp23 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp12 = tmp11 * tmp11
    tmp14 = tmp13 * tmp13
    tmp15 = tmp12 + tmp14
    tmp17 = tmp16 * tmp16
    tmp18 = tmp15 + tmp17
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp10 + tmp21
    tmp24 = 2.0
    tmp25 = tmp23 * tmp24
    tmp26 = tmp22 - 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 = 64
    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_scatter_2(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
    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 = 1.0
    tmp4 = 0.0
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tl.store(out_ptr0 + x2, tmp5, xmask)


@triton.jit
def triton_per_fused_add_mse_loss_mse_loss_backward_mul_3(in_out_ptr0,
    in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
    tmp7 = tmp1 + tmp2
    tmp8 = 0.0078125
    tmp9 = tmp2 * tmp8
    tmp10 = 256.0
    tmp11 = tmp6 / tmp10
    tmp12 = 0.25
    tmp13 = tmp11 * tmp12
    tmp14 = tmp13 + tmp11
    tl.store(out_ptr0 + tl.broadcast_to(r0, [RBLOCK]), tmp7, None)
    tl.store(out_ptr1 + tl.broadcast_to(r0, [RBLOCK]), tmp9, None)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp14, None)


@triton.jit
def triton_per_fused_mean_4(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.
    constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * r1), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_per_fused_add_exp_log_mean_mul_neg_sum_5(in_out_ptr0, in_ptr0,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = 64.0
    tmp2 = tmp0 / tmp1
    tmp3 = 1e-10
    tmp4 = tmp2 + tmp3
    tmp5 = tl_math.log(tmp4)
    tmp6 = tmp2 * tmp5
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp10 = -tmp9
    tmp11 = tl_math.exp(tmp10)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp11, None)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 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((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 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(256)](buf1, primals_1,
            primals_2, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((64,), (1,), torch.int64)
        triton_poi_fused_argmin_1[grid(64)](buf1, buf2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf3 = buf1
        del buf1
        triton_poi_fused_scatter_2[grid(256)](buf2, buf3, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf2
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf3, primals_2, out=buf4)
        del primals_2
        buf5 = empty_strided_cuda((), (), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf10 = buf5
        del buf5
        triton_per_fused_add_mse_loss_mse_loss_backward_mul_3[grid(1)](buf10,
            buf4, primals_1, buf6, buf9, 1, 256, num_warps=2, num_stages=1)
        del buf4
        del primals_1
        buf7 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_per_fused_mean_4[grid(4)](buf3, buf7, 4, 64, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf8 = empty_strided_cuda((), (), torch.float32)
        buf11 = buf8
        del buf8
        triton_per_fused_add_exp_log_mean_mul_neg_sum_5[grid(1)](buf11,
            buf7, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del buf7
    return buf6, buf10, buf11, buf9, reinterpret_tensor(buf3, (4, 64), (1, 
        4), 0)


class VectorQuantizerNew(nn.Module):
    """
    Tensorflow original: https://github.com/deepmind/sonnet/blob/v2/sonnet/src/nets/vqvae.py
    Based on: https://github.com/AntixK/PyTorch-VAE/blob/master/models/vq_vae.py
    """

    def __init__(self, num_embeddings: 'int', embedding_dim: 'int', beta:
        'float'=0.25):
        super(VectorQuantizerNew, self).__init__()
        self.K = num_embeddings
        self.D = embedding_dim
        self.beta = beta
        self.embedding = nn.Embedding(self.K, self.D)
        self.embedding.weight.data.uniform_(-1 / self.K, 1 / self.K)

    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: 0.098111