# 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/wi/cwiyl3lwwtancorrifw77xt3aqb4lermdintht45zvkj3bg54nbl.py
# Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   mul => mul
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_3, 0.5), 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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_0', 'mutated_arg_names': [], '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_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/2o/c2oqkq7zaubqmw7vuixxlseb2ff5jzqqbyczicxlmsahuxwdpdyp.py
# Topologically Sorted Source Nodes: [mul_1], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   mul_1 => mul_1
# Graph fragment:
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_4, 1), kwargs = {})
triton_poi_fused_mul_1 = async_compile.triton('triton_poi_fused_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=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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_mul_1', 'mutated_arg_names': [], '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_mul_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/on/conl6eemb3vyjzkllydlouehrcxphkzifo5kmslz6fgiz6ixsw5h.py
# Topologically Sorted Source Nodes: [mul_2, weight], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   mul_2 => mul_2
#   weight => mul_3
# Graph fragment:
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, 0.5), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, %view), kwargs = {})
triton_poi_fused_mul_2 = async_compile.triton('triton_poi_fused_mul_2', '''
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: '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_2', '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_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex % 12
    x0 = xindex % 4
    x2 = (xindex // 12)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 * tmp3
    tl.store(out_ptr0 + (x4), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/go/cgoav6av4bzem4wmdmkiowlmjpeiubwc67bqu6es4aivwlfpxzhh.py
# Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   out_3 => add
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_3, %primals_6), kwargs = {})
triton_poi_fused_add_3 = async_compile.triton('triton_poi_fused_add_3', '''
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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_3', 'mutated_arg_names': ['in_out_ptr0'], '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_add_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 3
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 3, 4, 1, 1), (12, 4, 1, 1, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, ), (1, ))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (1, 3, 1, 1), (3, 1, 1, 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: [mul], Original ATen: [aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_0.run(primals_3, buf0, 16, grid=grid(16), stream=stream0)
        del primals_3
        buf1 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [mul_1], Original ATen: [aten.mul]
        triton_poi_fused_mul_1.run(primals_4, buf1, 4, grid=grid(4), stream=stream0)
        del primals_4
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul_1, out], Original ATen: [aten.mul, aten.addmm]
        extern_kernels.addmm(buf1, primals_5, reinterpret_tensor(buf0, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del buf0
        del buf1
        buf3 = empty_strided_cuda((4, 3, 4, 1, 1), (12, 4, 1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul_2, weight], Original ATen: [aten.mul]
        triton_poi_fused_mul_2.run(primals_2, buf2, buf3, 48, grid=grid(48), stream=stream0)
        # Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(reinterpret_tensor(primals_1, (1, 16, 4, 4), (256, 16, 4, 1), 0), reinterpret_tensor(buf3, (12, 4, 1, 1), (4, 1, 0, 0), 0), stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf4, (1, 12, 4, 4), (192, 16, 4, 1))
        buf5 = reinterpret_tensor(buf4, (4, 3, 4, 4), (48, 16, 4, 1), 0); del buf4  # reuse
        # Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.add]
        triton_poi_fused_add_3.run(buf5, primals_6, 192, grid=grid(192), stream=stream0)
        del primals_6
    return (buf5, primals_2, primals_5, buf2, reinterpret_tensor(buf3, (12, 4, 1, 1), (4, 1, 1, 1), 0), reinterpret_tensor(primals_1, (1, 16, 4, 4), (256, 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
    primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, 3, 4, 1, 1), (12, 4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((1, 3, 1, 1), (3, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
    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
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
from torch.autograd import Function
import math
import random
from torch import nn
from torch.nn import functional as F
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, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_mul_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_mul_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex % 12
    x0 = xindex % 4
    x2 = xindex // 12
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 * tmp3
    tl.store(out_ptr0 + x4, tmp4, xmask)


@triton.jit
def triton_poi_fused_add_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 3
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 3, 4, 1, 1), (12, 4, 1, 1, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (1, 3, 1, 1), (3, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(16)](primals_3, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_3
        buf1 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_mul_1[grid(4)](primals_4, buf1, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del primals_4
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(buf1, primals_5, reinterpret_tensor(buf0, (4, 
            4), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del buf0
        del buf1
        buf3 = empty_strided_cuda((4, 3, 4, 1, 1), (12, 4, 1, 1, 1), torch.
            float32)
        triton_poi_fused_mul_2[grid(48)](primals_2, buf2, buf3, 48, XBLOCK=
            64, num_warps=1, num_stages=1)
        buf4 = extern_kernels.convolution(reinterpret_tensor(primals_1, (1,
            16, 4, 4), (256, 16, 4, 1), 0), reinterpret_tensor(buf3, (12, 4,
            1, 1), (4, 1, 0, 0), 0), stride=(1, 1), padding=(0, 0),
            dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=4, bias=None)
        assert_size_stride(buf4, (1, 12, 4, 4), (192, 16, 4, 1))
        buf5 = reinterpret_tensor(buf4, (4, 3, 4, 4), (48, 16, 4, 1), 0)
        del buf4
        triton_poi_fused_add_3[grid(192)](buf5, primals_6, 192, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_6
    return buf5, primals_2, primals_5, buf2, reinterpret_tensor(buf3, (12, 
        4, 1, 1), (4, 1, 1, 1), 0), reinterpret_tensor(primals_1, (1, 16, 4,
        4), (256, 16, 4, 1), 0)


def upsample(in_tens, out_H=64):
    in_H = in_tens.shape[2]
    scale_factor = 1.0 * out_H / in_H
    return nn.Upsample(scale_factor=scale_factor, mode='bilinear',
        align_corners=False)(in_tens)


def make_kernel(k):
    k = torch.tensor(k, dtype=torch.float32)
    if k.ndim == 1:
        k = k[None, :] * k[:, None]
    k /= k.sum()
    return k


def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0,
    pad_x1, pad_y0, pad_y1):
    _, channel, in_h, in_w = input.shape
    input = input.reshape(-1, in_h, in_w, 1)
    _, in_h, in_w, minor = input.shape
    kernel_h, kernel_w = kernel.shape
    out = input.view(-1, in_h, 1, in_w, 1, minor)
    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
    out = out.view(-1, in_h * up_y, in_w * up_x, minor)
    out = F.pad(out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0),
        max(pad_y1, 0)])
    out = out[:, max(-pad_y0, 0):out.shape[1] - max(-pad_y1, 0), max(-
        pad_x0, 0):out.shape[2] - max(-pad_x1, 0), :]
    out = out.permute(0, 3, 1, 2)
    out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x +
        pad_x0 + pad_x1])
    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
    out = F.conv2d(out, w)
    out = out.reshape(-1, minor, in_h * up_y + pad_y0 + pad_y1 - kernel_h +
        1, in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1)
    out = out.permute(0, 2, 3, 1)
    out = out[:, ::down_y, ::down_x, :]
    out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
    out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
    return out.view(-1, channel, out_h, out_w)


def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
    if input.device.type == 'cpu':
        out = upfirdn2d_native(input, kernel, up, up, down, down, pad[0],
            pad[1], pad[0], pad[1])
    else:
        out = UpFirDn2d.apply(input, kernel, (up, up), (down, down), (pad[0
            ], pad[1], pad[0], pad[1]))
    return out


def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5):
    if input.device.type == 'cpu':
        if bias is not None:
            rest_dim = [1] * (input.ndim - bias.ndim - 1)
            return F.leaky_relu(input + bias.view(1, bias.shape[0], *
                rest_dim), negative_slope=0.2) * scale
        else:
            return F.leaky_relu(input, negative_slope=0.2) * scale
    else:
        return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)


class UpFirDn2dBackward(Function):

    @staticmethod
    def forward(ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad,
        in_size, out_size):
        up_x, up_y = up
        down_x, down_y = down
        g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad
        grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1)
        grad_input = upfirdn2d_op.upfirdn2d(grad_output, grad_kernel,
            down_x, down_y, up_x, up_y, g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1)
        grad_input = grad_input.view(in_size[0], in_size[1], in_size[2],
            in_size[3])
        ctx.save_for_backward(kernel)
        pad_x0, pad_x1, pad_y0, pad_y1 = pad
        ctx.up_x = up_x
        ctx.up_y = up_y
        ctx.down_x = down_x
        ctx.down_y = down_y
        ctx.pad_x0 = pad_x0
        ctx.pad_x1 = pad_x1
        ctx.pad_y0 = pad_y0
        ctx.pad_y1 = pad_y1
        ctx.in_size = in_size
        ctx.out_size = out_size
        return grad_input

    @staticmethod
    def backward(ctx, gradgrad_input):
        kernel, = ctx.saved_tensors
        gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.
            in_size[3], 1)
        gradgrad_out = upfirdn2d_op.upfirdn2d(gradgrad_input, kernel, ctx.
            up_x, ctx.up_y, ctx.down_x, ctx.down_y, ctx.pad_x0, ctx.pad_x1,
            ctx.pad_y0, ctx.pad_y1)
        gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.in_size[1],
            ctx.out_size[0], ctx.out_size[1])
        return gradgrad_out, None, None, None, None, None, None, None, None


class UpFirDn2d(Function):

    @staticmethod
    def forward(ctx, input, kernel, up, down, pad):
        up_x, up_y = up
        down_x, down_y = down
        pad_x0, pad_x1, pad_y0, pad_y1 = pad
        kernel_h, kernel_w = kernel.shape
        _batch, channel, in_h, in_w = input.shape
        ctx.in_size = input.shape
        input = input.reshape(-1, in_h, in_w, 1)
        ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1]))
        out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
        out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
        ctx.out_size = out_h, out_w
        ctx.up = up_x, up_y
        ctx.down = down_x, down_y
        ctx.pad = pad_x0, pad_x1, pad_y0, pad_y1
        g_pad_x0 = kernel_w - pad_x0 - 1
        g_pad_y0 = kernel_h - pad_y0 - 1
        g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1
        g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1
        ctx.g_pad = g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1
        out = upfirdn2d_op.upfirdn2d(input, kernel, up_x, up_y, down_x,
            down_y, pad_x0, pad_x1, pad_y0, pad_y1)
        out = out.view(-1, channel, out_h, out_w)
        return out

    @staticmethod
    def backward(ctx, grad_output):
        kernel, grad_kernel = ctx.saved_tensors
        grad_input = UpFirDn2dBackward.apply(grad_output, kernel,
            grad_kernel, ctx.up, ctx.down, ctx.pad, ctx.g_pad, ctx.in_size,
            ctx.out_size)
        return grad_input, None, None, None, None


class Upsample(nn.Module):

    def __init__(self, kernel, factor=2):
        super().__init__()
        self.factor = factor
        kernel = make_kernel(kernel) * factor ** 2
        self.register_buffer('kernel', kernel)
        p = kernel.shape[0] - factor
        pad0 = (p + 1) // 2 + factor - 1
        pad1 = p // 2
        self.pad = pad0, pad1

    def forward(self, input):
        out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=
            self.pad)
        return out


class Blur(nn.Module):

    def __init__(self, kernel, pad, upsample_factor=1):
        super().__init__()
        kernel = make_kernel(kernel)
        if upsample_factor > 1:
            kernel = kernel * upsample_factor ** 2
        self.register_buffer('kernel', kernel)
        self.pad = pad

    def forward(self, input):
        out = upfirdn2d(input, self.kernel, pad=self.pad)
        return out


class FusedLeakyReLUFunctionBackward(Function):

    @staticmethod
    def forward(ctx, grad_output, out, bias, negative_slope, scale):
        ctx.save_for_backward(out)
        ctx.negative_slope = negative_slope
        ctx.scale = scale
        empty = grad_output.new_empty(0)
        grad_input = fused.fused_bias_act(grad_output, empty, out, 3, 1,
            negative_slope, scale)
        dim = [0]
        if grad_input.ndim > 2:
            dim += list(range(2, grad_input.ndim))
        if bias:
            grad_bias = grad_input.sum(dim).detach()
        else:
            grad_bias = None
        return grad_input, grad_bias

    @staticmethod
    def backward(ctx, gradgrad_input, gradgrad_bias):
        out, = ctx.saved_tensors
        gradgrad_out = fused.fused_bias_act(gradgrad_input, gradgrad_bias,
            out, 3, 1, ctx.negative_slope, ctx.scale)
        return gradgrad_out, None, None, None, None


class FusedLeakyReLUFunction(Function):

    @staticmethod
    def forward(ctx, input, bias, negative_slope, scale):
        empty = input.new_empty(0)
        if bias is None:
            bias = empty
        ctx.bias = bias is not None
        out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope,
            scale)
        ctx.save_for_backward(out)
        ctx.negative_slope = negative_slope
        ctx.scale = scale
        return out

    @staticmethod
    def backward(ctx, grad_output):
        out, = ctx.saved_tensors
        grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(
            grad_output, out, ctx.bias, ctx.negative_slope, ctx.scale)
        return grad_input, grad_bias, None, None


class EqualLinear(nn.Module):

    def __init__(self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1,
        activation=None):
        super().__init__()
        self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
        if bias:
            self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
        else:
            self.bias = None
        self.activation = activation
        self.scale = 1 / math.sqrt(in_dim) * lr_mul
        self.lr_mul = lr_mul

    def forward(self, input):
        if self.activation:
            out = F.linear(input, self.weight * self.scale)
            out = fused_leaky_relu(out, self.bias * self.lr_mul)
        else:
            out = F.linear(input, self.weight * self.scale, bias=self.bias *
                self.lr_mul)
        return out

    def __repr__(self):
        return (
            f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})'
            )


class ModulatedConv2d(nn.Module):

    def __init__(self, in_channel, out_channel, kernel_size, style_dim,
        modulation_type='style', factorization_rank=5, num_kernels=1,
        use_sigmoid=False, demodulate=True, upsample=False, downsample=
        False, blur_kernel=[1, 3, 3, 1]):
        super().__init__()
        assert modulation_type in ['style', 'factorized']
        assert num_kernels > 0
        self.eps = 1e-08
        self.kernel_size = kernel_size
        self.in_channel = in_channel
        self.out_channel = out_channel
        self.upsample = upsample
        self.downsample = downsample
        self.factorization_rank = factorization_rank
        self.use_sigmoid = use_sigmoid
        self.modulation_type = modulation_type
        self.num_kernels = num_kernels
        if upsample:
            factor = 2
            p = len(blur_kernel) - factor - (kernel_size - 1)
            pad0 = (p + 1) // 2 + factor - 1
            pad1 = p // 2 + 1
            self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor
                =factor)
        if downsample:
            factor = 2
            p = len(blur_kernel) - factor + (kernel_size - 1)
            pad0 = (p + 1) // 2
            pad1 = p // 2
            self.blur = Blur(blur_kernel, pad=(pad0, pad1))
        fan_in = in_channel * kernel_size ** 2
        self.scale = 1 / math.sqrt(fan_in)
        self.padding = kernel_size // 2
        self.weight = nn.Parameter(torch.randn(num_kernels, out_channel,
            in_channel, kernel_size, kernel_size))
        if num_kernels > 1:
            self.kernel_attention = EqualLinear(style_dim, num_kernels,
                bias_init=0, lr_mul=1.0)
        if modulation_type == 'style':
            self.modulation = EqualLinear(style_dim, in_channel, bias_init=1)
        if modulation_type == 'factorized':
            if use_sigmoid:
                self.modulation = EqualLinear(style_dim, (in_channel +
                    out_channel) * self.factorization_rank, bias_init=0)
            else:
                self.modulation = EqualLinear(style_dim, (in_channel +
                    out_channel) * self.factorization_rank, bias_init=1)
        self.demodulate = demodulate

    def __repr__(self):
        return (
            f'{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, upsample={self.upsample}, downsample={self.downsample})'
            )

    def forward(self, input, style, epsilon_greedy=0.1, softmax=None):
        batch, in_channel, height, width = input.shape
        weight = self.weight
        if self.num_kernels > 1:
            if softmax is None:
                logits = self.kernel_attention(style) * 1.0
                softmax = nn.functional.softmax(logits, dim=1)
                if random.random() < epsilon_greedy:
                    logit_noise = torch.randn(logits.shape)
                    softmax_noise = nn.functional.softmax(logit_noise, dim=1)
                    alpha = random.random() / 2.0
                    softmax = softmax * (1.0 - alpha) + softmax_noise * alpha
            assert softmax.ndim == 2
            weight = torch.unsqueeze(weight, dim=0) * softmax.view(batch,
                self.num_kernels, 1, 1, 1, 1)
            weight = weight.sum(dim=1)
        if self.modulation_type == 'style':
            style = self.modulation(style).view(batch, 1, in_channel, 1, 1)
            weight = self.scale * weight * style
        if self.modulation_type == 'factorized':
            ab = self.modulation(style)
            a, b = ab[:, :self.out_channel * self.factorization_rank], ab[:,
                self.out_channel * self.factorization_rank:]
            a, b = a.view(batch, self.out_channel, self.factorization_rank
                ), b.view(batch, self.factorization_rank, in_channel)
            m = torch.bmm(a, b).view(batch, self.out_channel, in_channel, 1, 1)
            if self.use_sigmoid:
                m = F.sigmoid(m)
            weight = self.scale * weight * m
        if self.demodulate:
            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-08)
            weight = weight * demod.view(batch, self.out_channel, 1, 1, 1)
        weight = weight.view(batch * self.out_channel, in_channel, self.
            kernel_size, self.kernel_size)
        if self.upsample:
            input = input.view(1, batch * in_channel, height, width)
            weight = weight.view(batch, self.out_channel, in_channel, self.
                kernel_size, self.kernel_size)
            weight = weight.transpose(1, 2).reshape(batch * in_channel,
                self.out_channel, self.kernel_size, self.kernel_size)
            out = F.conv_transpose2d(input, weight, padding=0, stride=2,
                groups=batch)
            _, _, height, width = out.shape
            out = out.view(batch, self.out_channel, height, width)
            out = self.blur(out)
        elif self.downsample:
            input = self.blur(input)
            _, _, height, width = input.shape
            input = input.view(1, batch * in_channel, height, width)
            out = F.conv2d(input, weight, padding=0, stride=2, groups=batch)
            _, _, height, width = out.shape
            out = out.view(batch, self.out_channel, height, width)
        else:
            input = input.view(1, batch * in_channel, height, width)
            out = F.conv2d(input, weight, padding=self.padding, groups=batch)
            _, _, height, width = out.shape
            out = out.view(batch, self.out_channel, height, width)
        return out


class ToRGBNew(nn.Module):

    def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1,
        3, 3, 1]):
        super().__init__()
        if upsample:
            self.upsample = Upsample(blur_kernel)
        else:
            self.upsample = None
        self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate
            =False)
        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))

    def forward(self, input_0, input_1):
        primals_6 = self.bias
        primals_2 = self.conv.weight
        primals_3 = self.conv.modulation.weight
        primals_4 = self.conv.modulation.bias
        primals_1 = input_0
        primals_5 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
