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 libdevice, math as tl_math
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_div_linalg_vector_norm_0(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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = tmp0 / tmp12
    tl.store(out_ptr0 + x2, tmp13, xmask)


@triton.jit
def triton_per_fused_logsumexp_mean_mul_neg_sub_sum_1(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, 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 + 4 * r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr2 + 4 * r0, None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr2 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr2 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr2 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp17 = triton_helpers.maximum(tmp15, tmp16)
    tmp19 = triton_helpers.maximum(tmp17, tmp18)
    tmp21 = triton_helpers.maximum(tmp19, tmp20)
    tmp22 = tl_math.abs(tmp21)
    tmp23 = float('inf')
    tmp24 = tmp22 == tmp23
    tmp25 = 0.0
    tmp26 = tl.where(tmp24, tmp25, tmp21)
    tmp27 = tmp15 - tmp26
    tmp28 = tl_math.exp(tmp27)
    tmp29 = tmp16 - tmp26
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp28 + tmp30
    tmp32 = tmp18 - tmp26
    tmp33 = tl_math.exp(tmp32)
    tmp34 = tmp31 + tmp33
    tmp35 = tmp20 - tmp26
    tmp36 = tl_math.exp(tmp35)
    tmp37 = tmp34 + tmp36
    tmp38 = tl_math.log(tmp37)
    tmp39 = tmp38 + tmp26
    tmp40 = tmp14 - tmp39
    tmp41 = tl.broadcast_to(tmp40, [XBLOCK, RBLOCK])
    tmp43 = tl.sum(tmp41, 1)[:, None]
    tmp44 = 4.0
    tmp45 = tmp43 / tmp44
    tmp46 = -tmp45
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp46, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, primals_3, reinterpret_tensor(
            primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_linalg_vector_norm_0[grid(16)](buf0, buf1, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_div_linalg_vector_norm_0[grid(16)](primals_4, buf2,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(buf1, (4, 4), (1, 4), 0),
            out=buf3)
        buf5 = empty_strided_cuda((), (), torch.float32)
        buf6 = buf5
        del buf5
        triton_per_fused_logsumexp_mean_mul_neg_sub_sum_1[grid(1)](buf6,
            buf2, buf1, buf3, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del buf1
    return buf6, primals_3, buf0, buf2, buf3


class CPCNew(nn.Module):
    """
        Contrastive Predictive Coding: score computation. See https://arxiv.org/pdf/1807.03748.pdf.

        Args:
            x_size (int): embedding size of input modality representation x
            y_size (int): embedding size of input modality representation y
    """

    def __init__(self, x_size, y_size, n_layers=1, activation='Tanh'):
        super().__init__()
        self.x_size = x_size
        self.y_size = y_size
        self.layers = n_layers
        self.activation = getattr(nn, activation)
        if n_layers == 1:
            self.net = nn.Linear(in_features=y_size, out_features=x_size)
        else:
            net = []
            for i in range(n_layers):
                if i == 0:
                    net.append(nn.Linear(self.y_size, self.x_size))
                    net.append(self.activation())
                else:
                    net.append(nn.Linear(self.x_size, self.x_size))
            self.net = nn.Sequential(*net)

    def forward(self, input_0, input_1):
        primals_1 = self.net.weight
        primals_2 = self.net.bias
        primals_3 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.055962