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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import numpy as np
import torch.cuda
import torch.fft
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_abs_acos_cos_div_max_mul_round_0(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
    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 = tl_math.abs(tmp1)
    tmp4 = tl_math.abs(tmp3)
    tmp5 = triton_helpers.maximum(tmp2, tmp4)
    tmp7 = tl_math.abs(tmp6)
    tmp8 = triton_helpers.maximum(tmp5, tmp7)
    tmp10 = tl_math.abs(tmp9)
    tmp11 = triton_helpers.maximum(tmp8, tmp10)
    tmp12 = tmp0 / tmp11
    tmp13 = libdevice.acos(tmp12)
    tmp14 = 4.7746482927568605
    tmp15 = tmp13 * tmp14
    tmp16 = libdevice.nearbyint(tmp15)
    tmp17 = 0.20943951023931953
    tmp18 = tmp16 * tmp17
    tmp19 = tl_math.cos(tmp18)
    tmp20 = tmp19 * tmp11
    tl.store(out_ptr0 + x2, tmp20, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (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_abs_acos_cos_div_max_mul_round_0[grid(256)](arg0_1,
            buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


def diagonal_quantize_function(x, bit, phase_noise_std=0, random_state=None,
    gradient_clip=False):

    class DiagonalQuantizeFunction(torch.autograd.Function):

        @staticmethod
        def forward(ctx, x):
            S_scale = x.abs().max(dim=-1, keepdim=True)[0]
            x = (x / S_scale).acos()
            ratio = np.pi / (2 ** bit - 1)
            x.div_(ratio).round_().mul_(ratio)
            if phase_noise_std > 1e-05:
                noise = gen_gaussian_noise(x, noise_mean=0, noise_std=
                    phase_noise_std, trunc_range=[-2 * phase_noise_std, 2 *
                    phase_noise_std], random_state=random_state)
                x.add_(noise)
            x.cos_().mul_(S_scale)
            return x

        @staticmethod
        def backward(ctx, grad_output):
            if gradient_clip:
                grad_input = grad_output.clamp(-1, 1)
            else:
                grad_input = grad_output.clone()
            return grad_input
    return DiagonalQuantizeFunction.apply(x)


class DiagonalQuantizerNew(torch.nn.Module):

    def __init__(self, bit, phase_noise_std=0.0, random_state=None, device=
        torch.device('cuda')):
        """2021/02/18: New phase quantizer for Sigma matrix in MZI-ONN. Gaussian phase noise is supported. All singular values are normalized by a TIA gain (S_scale), the normalized singular values will be achieved by cos(phi), phi will have [0, pi] uniform quantization.
        We do not consider real MZI implementation, thus voltage quantization and gamma noises are not supported.
        Args:
            bit (int): bitwidth for phase quantization.
            phase_noise_std (float, optional): Std dev for Gaussian phase noises. Defaults to 0.
            random_state (int, optional): random_state to control random noise injection. Defaults to None.
            device (torch.Device, optional): torch.Device. Defaults to torch.device("cuda").
        """
        super().__init__()
        self.bit = bit
        self.phase_noise_std = phase_noise_std
        self.random_state = random_state
        self.device = device

    def set_phase_noise_std(self, phase_noise_std=0, random_state=None):
        self.phase_noise_std = phase_noise_std
        self.random_state = random_state

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]


Measured latency_ms: 0.014136