# AOT ID: ['0_inference']
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_4/inductor_cache/7j/c7jkqpq7g244sdbwoxdaraaqjhcray4b7wkphrkwkxlezb6or6wc.py
# Topologically Sorted Source Nodes: [gram_8], Original ATen: [aten.stack]
# Source node to ATen node mapping:
#   gram_8 => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%div, %div_1, %div_2, %div_3],), kwargs = {})
triton_poi_fused_stack_0 = async_compile.triton('triton_poi_fused_stack_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_stack_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_stack_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4)
    x0 = xindex % 4
    x2 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (4*x1)), tmp4 & xmask, other=0.0)
    tmp6 = 0.0625
    tmp7 = tmp5 * tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 8, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tmp10 & tmp12
    tmp14 = tl.load(in_ptr1 + (x0 + (4*((-4) + x1))), tmp13 & xmask, other=0.0)
    tmp15 = tmp14 * tmp6
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp13, tmp15, tmp16)
    tmp18 = tmp0 >= tmp11
    tmp19 = tl.full([1], 12, tl.int64)
    tmp20 = tmp0 < tmp19
    tmp21 = tmp18 & tmp20
    tmp22 = tl.load(in_ptr2 + (x0 + (4*((-8) + x1))), tmp21 & xmask, other=0.0)
    tmp23 = tmp22 * tmp6
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp21, tmp23, tmp24)
    tmp26 = tmp0 >= tmp19
    tmp27 = tl.full([1], 16, tl.int64)
    tmp28 = tmp0 < tmp27
    tmp29 = tl.load(in_ptr3 + (x0 + (4*((-12) + x1))), tmp26 & xmask, other=0.0)
    tmp30 = tmp29 * tmp6
    tmp31 = tl.full(tmp30.shape, 0.0, tmp30.dtype)
    tmp32 = tl.where(tmp26, tmp30, tmp31)
    tmp33 = tl.where(tmp21, tmp25, tmp32)
    tmp34 = tl.where(tmp13, tmp17, tmp33)
    tmp35 = tl.where(tmp4, tmp9, tmp34)
    tl.store(out_ptr0 + (x2), tmp35, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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, 1), torch.float32)
        # Topologically Sorted Source Nodes: [gram], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(arg0_1, (4, 16), (16, 1), 0), reinterpret_tensor(arg0_1, (16, 4), (1, 16), 0), out=buf0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [gram_2], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(arg0_1, (4, 16), (16, 1), 64), reinterpret_tensor(arg0_1, (16, 4), (1, 16), 64), out=buf1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [gram_4], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(arg0_1, (4, 16), (16, 1), 128), reinterpret_tensor(arg0_1, (16, 4), (1, 16), 128), out=buf2)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [gram_6], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(arg0_1, (4, 16), (16, 1), 192), reinterpret_tensor(arg0_1, (16, 4), (1, 16), 192), out=buf3)
        del arg0_1
        buf4 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [gram_8], Original ATen: [aten.stack]
        stream0 = get_raw_stream(0)
        triton_poi_fused_stack_0.run(buf0, buf1, buf2, buf3, buf4, 64, grid=grid(64), stream=stream0)
        del buf0
        del buf1
        del buf2
        del buf3
    return (reinterpret_tensor(buf4, (4, 4, 4), (16, 4, 1), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1])
    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 torch
import torch.nn as nn


def gram_matrix(input):
    """ gram matrix for feature assignments """
    a, b, c, d = input.size()
    allG = []
    for i in range(a):
        features = input[i].view(b, c * d)
        gram = torch.mm(features, features.t())
        gram = gram.div(c * d)
        allG.append(gram)
    return torch.stack(allG)


class StyleTrack(nn.Module):
    """ This modules tracks the content image style across many images
        for loading (partnered with next module). This is useful for
        e.g. maintaining color scheme of the content images
    """

    def forward(self, input):
        """ forward pass """
        gram = gram_matrix(input)
        self.value = gram
        return input


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


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 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_stack_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4 * x1), tmp4 & xmask, other=0.0)
    tmp6 = 0.0625
    tmp7 = tmp5 * tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 8, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tmp10 & tmp12
    tmp14 = tl.load(in_ptr1 + (x0 + 4 * (-4 + x1)), tmp13 & xmask, other=0.0)
    tmp15 = tmp14 * tmp6
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp13, tmp15, tmp16)
    tmp18 = tmp0 >= tmp11
    tmp19 = tl.full([1], 12, tl.int64)
    tmp20 = tmp0 < tmp19
    tmp21 = tmp18 & tmp20
    tmp22 = tl.load(in_ptr2 + (x0 + 4 * (-8 + x1)), tmp21 & xmask, other=0.0)
    tmp23 = tmp22 * tmp6
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp21, tmp23, tmp24)
    tmp26 = tmp0 >= tmp19
    tl.full([1], 16, tl.int64)
    tmp29 = tl.load(in_ptr3 + (x0 + 4 * (-12 + x1)), tmp26 & xmask, other=0.0)
    tmp30 = tmp29 * tmp6
    tmp31 = tl.full(tmp30.shape, 0.0, tmp30.dtype)
    tmp32 = tl.where(tmp26, tmp30, tmp31)
    tmp33 = tl.where(tmp21, tmp25, tmp32)
    tmp34 = tl.where(tmp13, tmp17, tmp33)
    tmp35 = tl.where(tmp4, tmp9, tmp34)
    tl.store(out_ptr0 + x2, tmp35, 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, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg0_1, (4, 16), (16, 1), 0),
            reinterpret_tensor(arg0_1, (16, 4), (1, 16), 0), out=buf0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg0_1, (4, 16), (16, 1), 64),
            reinterpret_tensor(arg0_1, (16, 4), (1, 16), 64), out=buf1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg0_1, (4, 16), (16, 1), 128),
            reinterpret_tensor(arg0_1, (16, 4), (1, 16), 128), out=buf2)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg0_1, (4, 16), (16, 1), 192),
            reinterpret_tensor(arg0_1, (16, 4), (1, 16), 192), out=buf3)
        del arg0_1
        buf4 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_stack_0[grid(64)](buf0, buf1, buf2, buf3, buf4, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del buf0
        del buf1
        del buf2
        del buf3
    return reinterpret_tensor(buf4, (4, 4, 4), (16, 4, 1), 0),


def gram_matrix(input):
    """ gram matrix for feature assignments """
    a, b, c, d = input.size()
    allG = []
    for i in range(a):
        features = input[i].view(b, c * d)
        gram = torch.mm(features, features.t())
        gram = gram.div(c * d)
        allG.append(gram)
    return torch.stack(allG)


class StyleTrackNew(nn.Module):
    """ This modules tracks the content image style across many images
        for loading (partnered with next module). This is useful for
        e.g. maintaining color scheme of the content images
    """

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