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.triton_helpers import math as tl_math
from torch import 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_add_exp_mul_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp3 = tmp0 * tmp2
    tmp5 = tmp3 + tmp4
    tl.store(out_ptr0 + x2, tmp5, xmask)


@triton.jit
def triton_per_fused_sum_1(in_ptr0, out_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 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp3, None)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (1, 4), (4, 1))
    assert_size_stride(primals_2, (1, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_exp_mul_0[grid(256)](primals_3, primals_1,
            primals_2, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((1,), (1,), torch.float32)
        triton_per_fused_sum_1[grid(1)](primals_1, buf1, 1, 4, XBLOCK=1,
            num_warps=2, num_stages=1)
    return buf0, buf1, primals_1, primals_3


class AffineConstantFlowNew(nn.Module):
    """ 
    Scales + Shifts the flow by (learned) constants per dimension.
    In NICE paper there is a Scaling layer which is a special case of this where t is None
    """

    def __init__(self, dim, scale=True, shift=True):
        super().__init__()
        self.s = nn.Parameter(torch.randn(1, dim, requires_grad=True)
            ) if scale else None
        self.t = nn.Parameter(torch.randn(1, dim, requires_grad=True)
            ) if shift else None

    def backward(self, z):
        s = self.s if self.s is not None else z.new_zeros(z.size())
        t = self.t if self.t is not None else z.new_zeros(z.size())
        x = (z - t) * torch.exp(-s)
        log_det = torch.sum(-s, dim=1)
        return x, log_det

    def forward(self, input_0):
        primals_1 = self.s
        primals_2 = self.t
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0], output[1]


Measured latency_ms: 0.028498