# 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_4/inductor_cache/sp/cspptmlazllfxjrnm4wtxf36nkf7a6dtwt5pgmfzbjzw3mpteini.py
# Topologically Sorted Source Nodes: [y_7], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   y_7 => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%view_2, %primals_2, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_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: '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_convolution_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_convolution_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 6) % 6
    x0 = xindex % 6
    x2 = (xindex // 36)
    x4 = xindex
    tmp0 = (-1) + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = (-1) + x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + ((-5) + x0 + (4*x1) + (16*x2)), tmp10 & xmask, other=0.0)
    tl.store(out_ptr0 + (x4), tmp11, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/bq/cbq7ippnsrke66kwwv3mtzwnftnasbovqh2ycx7sylieltdqxzwc.py
# Topologically Sorted Source Nodes: [y_9], Original ATen: [aten._unsafe_index]
# Source node to ATen node mapping:
#   y_9 => _unsafe_index
# Graph fragment:
#   %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%view_3, [None, None, %unsqueeze, %convert_element_type_1]), kwargs = {})
triton_poi_fused__unsafe_index_1 = async_compile.triton('triton_poi_fused__unsafe_index_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=[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__unsafe_index_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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__unsafe_index_1(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
    x1 = (xindex // 4) % 4
    x0 = xindex % 4
    x2 = (xindex // 16)
    x4 = xindex
    tmp0 = x1
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.75
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tmp5 = x0
    tmp6 = tmp5.to(tl.float32)
    tmp7 = tmp6 * tmp2
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.load(in_ptr0 + (tmp8 + (3*tmp4) + (9*x2)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x4), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/cb/ccboftrxjzgggc5x4t5yuvdyezgexb5fddn4klpz7y4junftadp4.py
# Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   mul => mul_4
# Graph fragment:
#   %mul_4 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_3, 0.25), 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=[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_2', '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_2(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 = 0.25
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/l3/cl3cdbofywwgmthy323oolanmjbqggupaerhdksmxevl7tpckmmj.py
# Topologically Sorted Source Nodes: [mul_1, x1], Original ATen: [aten.mul, aten.convolution]
# Source node to ATen node mapping:
#   mul_1 => mul_5
#   x1 => convolution_1
# Graph fragment:
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_4, 1), kwargs = {})
#   %convolution_1 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%view_4, %mul_4, %mul_5, [2, 2], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_mul_3 = async_compile.triton('triton_poi_fused_convolution_mul_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=[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_convolution_mul_3', '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_convolution_mul_3(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_4/inductor_cache/bn/cbn3acyzz7fdxq6e7cjupa6zfqi4a2jupiyykevg3mrh354gsock.py
# Topologically Sorted Source Nodes: [mul_1, x1, out, wrapped_sqrt, out_1], Original ATen: [aten.mul, aten.convolution, aten.leaky_relu, aten.sqrt, aten.leaky_relu_backward]
# Source node to ATen node mapping:
#   mul_1 => mul_5
#   out => gt, mul_6, where
#   out_1 => mul_7
#   wrapped_sqrt => full_default
#   x1 => convolution_1
# Graph fragment:
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_4, 1), kwargs = {})
#   %convolution_1 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%view_4, %mul_4, %mul_5, [2, 2], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_1, 0), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_1, 0.2), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution_1, %mul_6), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 1.414213562373095), kwargs = {dtype: torch.float64, layout: torch.strided, device: cpu, pin_memory: False})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where, %full_default), kwargs = {})
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%where, 0), kwargs = {})
triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_mul_sqrt_4 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_mul_sqrt_4', '''
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: '*i1', 4: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_mul_sqrt_4', '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_convolution_leaky_relu_leaky_relu_backward_mul_sqrt_4(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 4) % 4
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp8 = 1.414213562373095
    tmp9 = tmp7 * tmp8
    tmp10 = tmp7 > tmp3
    tl.store(out_ptr0 + (x3), tmp9, xmask)
    tl.store(out_ptr1 + (x3), tmp10, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 1, 6, 6), (36, 6, 6, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y_7], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(primals_1, buf0, 576, grid=grid(576), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [y_7], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (16, 1, 3, 3), (9, 9, 3, 1))
        del buf0
        del primals_2
        buf2 = empty_strided_cuda((16, 1, 4, 4), (16, 1, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [y_9], Original ATen: [aten._unsafe_index]
        triton_poi_fused__unsafe_index_1.run(buf1, buf2, 256, grid=grid(256), stream=stream0)
        del buf1
        buf3 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
        triton_poi_fused_mul_2.run(primals_3, buf3, 16, grid=grid(16), stream=stream0)
        del primals_3
        buf4 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [mul_1, x1], Original ATen: [aten.mul, aten.convolution]
        triton_poi_fused_convolution_mul_3.run(primals_4, buf4, 4, grid=grid(4), stream=stream0)
        del primals_4
        # Topologically Sorted Source Nodes: [mul_1, x1], Original ATen: [aten.mul, aten.convolution]
        buf5 = extern_kernels.convolution(reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0), buf3, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 2, 2), (16, 4, 2, 1))
        buf6 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.bool)
        # Topologically Sorted Source Nodes: [mul_1, x1, out, wrapped_sqrt, out_1], Original ATen: [aten.mul, aten.convolution, aten.leaky_relu, aten.sqrt, aten.leaky_relu_backward]
        triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_mul_sqrt_4.run(buf5, buf4, buf6, buf7, 64, grid=grid(64), stream=stream0)
        del buf4
        del buf5
    return (buf6, reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0), buf3, buf7, )


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, 1, 4, 4), (16, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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 numpy as np
import torch.nn as nn
import torch.nn.functional as F


def _setup_kernel(k):
    k = np.asarray(k, dtype=np.float32)
    if k.ndim == 1:
        k = np.outer(k, k)
    k /= np.sum(k)
    assert k.ndim == 2
    assert k.shape[0] == k.shape[1]
    return k


class ConvDownsample2d(nn.Module):

    def __init__(self, kernel_size, input_channels, output_channels, k=[1, 
        3, 3, 1], factor=2, gain=1, use_wscale=True, lrmul=1, bias=True):
        """
            ConvDownsample2D method in D_stylegan2.
        :param k: FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
                  The default is `[1] * factor`, which corresponds to average pooling.
        :param factor: Integer downsampling factor (default: 2).
        :param gain:   Scaling factor for signal magnitude (default: 1.0).

            Returns: Tensor of the shape `[N, C, H // factor, W // factor]`
        """
        super().__init__()
        assert isinstance(factor, int
            ) and factor >= 1, 'factor must be larger than 1! (default: 2)'
        assert kernel_size >= 1 and kernel_size % 2 == 1
        he_std = gain / (input_channels * output_channels * kernel_size *
            kernel_size) ** 0.5
        self.kernel_size = kernel_size
        if use_wscale:
            init_std = 1.0 / lrmul
            self.w_lrmul = he_std * lrmul
        else:
            init_std = he_std / lrmul
            self.w_lrmul = lrmul
        self.weight = torch.nn.Parameter(torch.randn(output_channels,
            input_channels, kernel_size, kernel_size) * init_std)
        self.convH, self.convW = self.weight.shape[2:]
        if bias:
            self.bias = torch.nn.Parameter(torch.zeros(output_channels))
            self.b_lrmul = lrmul
        else:
            self.bias = None
        self.gain = gain
        self.factor = factor
        self.k = _setup_kernel(k) * self.gain
        self.k = torch.FloatTensor(self.k).unsqueeze(0).unsqueeze(0)
        self.k = nn.Parameter(self.k, requires_grad=False)
        self.p = self.k.shape[-1] - self.factor + (self.convW - 1)
        self.padx0, self.pady0 = (self.p + 1) // 2, (self.p + 1) // 2
        self.padx1, self.pady1 = self.p // 2, self.p // 2
        self.kernelH, self.kernelW = self.k.shape[2:]

    def forward(self, x):
        y = x.clone()
        y = y.reshape([-1, x.shape[2], x.shape[3], 1])
        inC, inH, inW = x.shape[1:]
        y = torch.reshape(y, (-1, inH, inW, 1))
        y = F.pad(y, (0, 0, max(self.pady0, 0), max(self.pady1, 0), max(
            self.padx0, 0), max(self.padx1, 0), 0, 0))
        y = y[:, max(-self.pady0, 0):y.shape[1] - max(-self.pady1, 0), max(
            -self.padx0, 0):y.shape[2] - max(-self.padx1, 0), :]
        y = y.permute(0, 3, 1, 2)
        y = y.reshape(-1, 1, inH + self.pady0 + self.pady1, inW + self.
            padx0 + self.padx1)
        y = F.conv2d(y, self.k)
        y = y.view(-1, 1, inH + self.pady0 + self.pady1 - self.kernelH + 1,
            inW + self.padx0 + self.padx1 - self.kernelW + 1)
        if inH != y.shape[1]:
            y = F.interpolate(y, size=(inH, inW))
        y = y.permute(0, 2, 3, 1)
        y = y.reshape(-1, inC, inH, inW)
        x1 = F.conv2d(y, self.weight * self.w_lrmul, self.bias * self.
            b_lrmul, stride=self.factor, padding=self.convW // 2)
        out = F.leaky_relu(x1, 0.2, inplace=True)
        out = out * np.sqrt(2)
        return out


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


def get_init_inputs():
    return [[], {'kernel_size': 1, 'input_channels': 4, 'output_channels': 4}]

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 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_convolution_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 6 % 6
    x0 = xindex % 6
    x2 = xindex // 36
    x4 = xindex
    tmp0 = -1 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -1 + x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (-5 + x0 + 4 * x1 + 16 * x2), tmp10 & xmask,
        other=0.0)
    tl.store(out_ptr0 + x4, tmp11, xmask)


@triton.jit
def triton_poi_fused__unsafe_index_1(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
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x2 = xindex // 16
    x4 = xindex
    tmp0 = x1
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.75
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tmp5 = x0
    tmp6 = tmp5.to(tl.float32)
    tmp7 = tmp6 * tmp2
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.load(in_ptr0 + (tmp8 + 3 * tmp4 + 9 * x2), xmask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + x4, tmp9, xmask)


@triton.jit
def triton_poi_fused_mul_2(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 = 0.25
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_convolution_mul_3(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_convolution_leaky_relu_leaky_relu_backward_mul_sqrt_4(
    in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp8 = 1.414213562373095
    tmp9 = tmp7 * tmp8
    tmp10 = tmp7 > tmp3
    tl.store(out_ptr0 + x3, tmp9, xmask)
    tl.store(out_ptr1 + x3, tmp10, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 1, 6, 6), (36, 6, 6, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(576)](primals_1, buf0, 576,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (16, 1, 3, 3), (9, 9, 3, 1))
        del buf0
        del primals_2
        buf2 = empty_strided_cuda((16, 1, 4, 4), (16, 1, 4, 1), torch.float32)
        triton_poi_fused__unsafe_index_1[grid(256)](buf1, buf2, 256, XBLOCK
            =256, num_warps=4, num_stages=1)
        del buf1
        buf3 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        triton_poi_fused_mul_2[grid(16)](primals_3, buf3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_3
        buf4 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_convolution_mul_3[grid(4)](primals_4, buf4, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
        del primals_4
        buf5 = extern_kernels.convolution(reinterpret_tensor(buf2, (4, 4, 4,
            4), (64, 16, 4, 1), 0), buf3, stride=(2, 2), padding=(0, 0),
            dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 2, 2), (16, 4, 2, 1))
        buf6 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.bool)
        triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_mul_sqrt_4[
            grid(64)](buf5, buf4, buf6, buf7, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf4
        del buf5
    return buf6, reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), buf3, buf7


def _setup_kernel(k):
    k = np.asarray(k, dtype=np.float32)
    if k.ndim == 1:
        k = np.outer(k, k)
    k /= np.sum(k)
    assert k.ndim == 2
    assert k.shape[0] == k.shape[1]
    return k


class ConvDownsample2dNew(nn.Module):

    def __init__(self, kernel_size, input_channels, output_channels, k=[1, 
        3, 3, 1], factor=2, gain=1, use_wscale=True, lrmul=1, bias=True):
        """
            ConvDownsample2D method in D_stylegan2.
        :param k: FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
                  The default is `[1] * factor`, which corresponds to average pooling.
        :param factor: Integer downsampling factor (default: 2).
        :param gain:   Scaling factor for signal magnitude (default: 1.0).

            Returns: Tensor of the shape `[N, C, H // factor, W // factor]`
        """
        super().__init__()
        assert isinstance(factor, int
            ) and factor >= 1, 'factor must be larger than 1! (default: 2)'
        assert kernel_size >= 1 and kernel_size % 2 == 1
        he_std = gain / (input_channels * output_channels * kernel_size *
            kernel_size) ** 0.5
        self.kernel_size = kernel_size
        if use_wscale:
            init_std = 1.0 / lrmul
            self.w_lrmul = he_std * lrmul
        else:
            init_std = he_std / lrmul
            self.w_lrmul = lrmul
        self.weight = torch.nn.Parameter(torch.randn(output_channels,
            input_channels, kernel_size, kernel_size) * init_std)
        self.convH, self.convW = self.weight.shape[2:]
        if bias:
            self.bias = torch.nn.Parameter(torch.zeros(output_channels))
            self.b_lrmul = lrmul
        else:
            self.bias = None
        self.gain = gain
        self.factor = factor
        self.k = _setup_kernel(k) * self.gain
        self.k = torch.FloatTensor(self.k).unsqueeze(0).unsqueeze(0)
        self.k = nn.Parameter(self.k, requires_grad=False)
        self.p = self.k.shape[-1] - self.factor + (self.convW - 1)
        self.padx0, self.pady0 = (self.p + 1) // 2, (self.p + 1) // 2
        self.padx1, self.pady1 = self.p // 2, self.p // 2
        self.kernelH, self.kernelW = self.k.shape[2:]

    def forward(self, input_0):
        primals_3 = self.weight
        primals_4 = self.bias
        primals_2 = self.k
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
