# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_8/inductor_cache/b4/cb4ar3ryyt4mp4d5ipmgfvm4veyllxljd2ymzyfikn6eexxqe2ep.py
# Topologically Sorted Source Nodes: [image], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   image => mul
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %primals_1), kwargs = {})
triton_poi_fused_mul_0 = async_compile.triton('triton_poi_fused_mul_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/y5/cy5rhdemzf6dqbzpnuqnokcny6y6tjsftb3h6y7lac6lexyo22ba.py
# Topologically Sorted Source Nodes: [mul_1, result], Original ATen: [aten.mul, aten.add]
# Source node to ATen node mapping:
#   mul_1 => mul_1
#   result => add
# Graph fragment:
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_2, 0.25), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, 128), kwargs = {})
triton_poi_fused_add_mul_1 = async_compile.triton('triton_poi_fused_add_mul_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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)
        # Topologically Sorted Source Nodes: [image], Original ATen: [aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_0.run(primals_2, primals_1, buf0, 1024, grid=grid(1024), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((16, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tensordot], Original ATen: [aten.mm]
        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  # reuse
        # Topologically Sorted Source Nodes: [mul_1, result], Original ATen: [aten.mul, aten.add]
        triton_poi_fused_add_mul_1.run(buf2, 1024, grid=grid(1024), stream=stream0)
    return (buf2, primals_2, reinterpret_tensor(buf0, (64, 16), (1, 64), 0), reinterpret_tensor(primals_3, (64, 64), (1, 64), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((8, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 8, 8), (256, 64, 8, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((8, 8, 8, 8), (512, 64, 8, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

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]
