import itertools
import torch
import numpy as np
import torch.nn as nn


class idct_8x8(nn.Module):
    """ Inverse discrete Cosine Transformation
    Input:
        dcp(tensor): batch x height x width
    Output:
        image(tensor): batch x height x width
    """

    def __init__(self):
        super(idct_8x8, self).__init__()
        alpha = np.array([1.0 / np.sqrt(2)] + [1] * 7)
        self.alpha = nn.Parameter(torch.from_numpy(np.outer(alpha, alpha)).
            float())
        tensor = np.zeros((8, 8, 8, 8), dtype=np.float32)
        for x, y, u, v in itertools.product(range(8), repeat=4):
            tensor[x, y, u, v] = np.cos((2 * u + 1) * x * np.pi / 16) * np.cos(
                (2 * v + 1) * y * np.pi / 16)
        self.tensor = nn.Parameter(torch.from_numpy(tensor).float())

    def forward(self, image):
        image = image * self.alpha
        result = 0.25 * torch.tensordot(image, self.tensor, dims=2) + 128
        result.view(image.shape)
        return result


def get_inputs():
    return [torch.rand([4, 4, 8, 8])]


def get_init_inputs():
    return [[], {}]

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 itertools
import numpy as np
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_mul_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_mul_1(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = 0.25
    tmp2 = tmp0 * tmp1
    tmp3 = 128.0
    tmp4 = tmp2 + tmp3
    tl.store(in_out_ptr0 + x0, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (8, 8), (8, 1))
    assert_size_stride(primals_2, (4, 4, 8, 8), (256, 64, 8, 1))
    assert_size_stride(primals_3, (8, 8, 8, 8), (512, 64, 8, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(1024)](primals_2, primals_1, buf0, 1024,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((16, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 64), (64, 1), 0),
            reinterpret_tensor(primals_3, (64, 64), (64, 1), 0), out=buf1)
        buf2 = reinterpret_tensor(buf1, (4, 4, 8, 8), (256, 64, 8, 1), 0)
        del buf1
        triton_poi_fused_add_mul_1[grid(1024)](buf2, 1024, XBLOCK=256,
            num_warps=4, num_stages=1)
    return buf2, primals_2, reinterpret_tensor(buf0, (64, 16), (1, 64), 0
        ), reinterpret_tensor(primals_3, (64, 64), (1, 64), 0)


class idct_8x8New(nn.Module):
    """ Inverse discrete Cosine Transformation
    Input:
        dcp(tensor): batch x height x width
    Output:
        image(tensor): batch x height x width
    """

    def __init__(self):
        super(idct_8x8New, self).__init__()
        alpha = np.array([1.0 / np.sqrt(2)] + [1] * 7)
        self.alpha = nn.Parameter(torch.from_numpy(np.outer(alpha, alpha)).
            float())
        tensor = np.zeros((8, 8, 8, 8), dtype=np.float32)
        for x, y, u, v in itertools.product(range(8), repeat=4):
            tensor[x, y, u, v] = np.cos((2 * u + 1) * x * np.pi / 16) * np.cos(
                (2 * v + 1) * y * np.pi / 16)
        self.tensor = nn.Parameter(torch.from_numpy(tensor).float())

    def forward(self, input_0):
        primals_1 = self.alpha
        primals_3 = self.tensor
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
