# 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/wl/cwldpc2k6v7rbizd6tlddleva3alwxblabsherkqjtef5e45djwk.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.reflection_pad2d]
# Source node to ATen node mapping:
#   x => _unsafe_index, _unsafe_index_1
# Graph fragment:
#   %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %sub_1, None]), kwargs = {})
#   %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_1]), kwargs = {})
triton_poi_fused_reflection_pad2d_0 = async_compile.triton('triton_poi_fused_reflection_pad2d_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_reflection_pad2d_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_reflection_pad2d_0(in_ptr0, 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 % 8
    x1 = (xindex // 8) % 8
    x2 = (xindex // 64)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-2) + x0))))) + ((-4)*(tl_math.abs((-3) + (tl_math.abs((-2) + x1))))) + (16*x2)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/te/cte5wpqgaldjke263lq4fybbgqhrxtnmvvgpbhlmctt2v3edvn4l.py
# Topologically Sorted Source Nodes: [x_1, out], Original ATen: [aten.convolution, aten.repeat, aten._native_batch_norm_legit]
# Source node to ATen node mapping:
#   out => add, add_1, mul, mul_1, repeat, rsqrt, sub_4, var_mean
#   x_1 => convolution
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %repeat : [num_users=2] = call_function[target=torch.ops.aten.repeat.default](args = (%primals_4, [4]), kwargs = {})
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %getitem_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_4, %rsqrt), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %unsqueeze_1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %unsqueeze_3), kwargs = {})
triton_per_fused__native_batch_norm_legit_convolution_repeat_1 = async_compile.triton('triton_per_fused__native_batch_norm_legit_convolution_repeat_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.persistent_reduction(
    size_hints=[16, 32],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: 'i32', 9: '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, 6, 7, 8), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_convolution_repeat_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 4, '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}
)
@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_repeat_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr3, out_ptr4, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 25
    RBLOCK: tl.constexpr = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = rindex < rnumel
    x0 = xindex
    r3 = rindex
    x1 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x0 % 4), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_out_ptr0 + (r3 + (25*x0)), rmask & xmask, other=0.0)
    tmp2 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr2 + (x0 % 4), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(rmask & xmask, tmp4, 0)
    tmp7 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp9 = tl.where(rmask & xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tl.full([XBLOCK, 1], 25, tl.int32)
    tmp12 = tmp11.to(tl.float32)
    tmp13 = tmp10 / tmp12
    tmp14 = tmp4 - tmp13
    tmp15 = tmp14 * tmp14
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
    tmp18 = tl.where(rmask & xmask, tmp16, 0)
    tmp19 = tl.sum(tmp18, 1)[:, None]
    tmp20 = tmp3 - tmp13
    tmp21 = 25.0
    tmp22 = tmp19 / tmp21
    tmp23 = 1e-05
    tmp24 = tmp22 + tmp23
    tmp25 = libdevice.rsqrt(tmp24)
    tmp26 = tmp20 * tmp25
    tmp27 = tmp26 * tmp0
    tmp29 = tmp27 + tmp28
    tl.store(out_ptr0 + (x0), tmp0, xmask)
    tl.store(in_out_ptr0 + (r3 + (25*x0)), tmp3, rmask & xmask)
    tl.store(out_ptr3 + (r3 + (25*x0)), tmp29, rmask & xmask)
    tl.store(out_ptr4 + (x0), tmp25, xmask)
    tl.store(out_ptr1 + (x0), tmp13, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, ), (1, ))
    assert_size_stride(primals_5, (4, ), (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: [x], Original ATen: [aten.reflection_pad2d]
        stream0 = get_raw_stream(0)
        triton_poi_fused_reflection_pad2d_0.run(primals_1, buf0, 1024, grid=grid(1024), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [x_1], 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, (4, 4, 5, 5), (100, 25, 5, 1))
        buf3 = empty_strided_cuda((16, ), (1, ), torch.float32)
        buf2 = buf1; del buf1  # reuse
        buf4 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        buf8 = empty_strided_cuda((1, 16, 5, 5), (400, 25, 5, 1), torch.float32)
        buf7 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        # Topologically Sorted Source Nodes: [x_1, out], Original ATen: [aten.convolution, aten.repeat, aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_convolution_repeat_1.run(buf2, primals_4, primals_3, primals_5, buf3, buf4, buf8, buf7, 16, 25, grid=grid(16), stream=stream0)
        del primals_3
        del primals_4
        del primals_5
    return (reinterpret_tensor(buf8, (4, 4, 5, 5), (100, 25, 5, 1), 0), primals_2, buf0, buf2, buf3, reinterpret_tensor(buf7, (16, ), (1, ), 0), reinterpret_tensor(buf4, (1, 16, 1, 1), (16, 1, 1, 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((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((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, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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._inductor.runtime.triton_helpers import libdevice, math as tl_math
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_reflection_pad2d_0(in_ptr0, 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 % 8
    x1 = xindex // 8 % 8
    x2 = xindex // 64
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-2 +
        x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-2 + x1)) + 16 * x2),
        xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_repeat_1(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr3, out_ptr4,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    rnumel = 25
    RBLOCK: tl.constexpr = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    x0 = xindex
    r3 = rindex
    x1 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x0 % 4, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_out_ptr0 + (r3 + 25 * x0), rmask & xmask, other=0.0)
    tmp2 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr2 + x0 % 4, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tl.where(rmask & xmask, tmp4, 0)
    tmp7 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp9 = tl.where(rmask & xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tl.full([XBLOCK, 1], 25, tl.int32)
    tmp12 = tmp11.to(tl.float32)
    tmp13 = tmp10 / tmp12
    tmp14 = tmp4 - tmp13
    tmp15 = tmp14 * tmp14
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
    tmp18 = tl.where(rmask & xmask, tmp16, 0)
    tmp19 = tl.sum(tmp18, 1)[:, None]
    tmp20 = tmp3 - tmp13
    tmp21 = 25.0
    tmp22 = tmp19 / tmp21
    tmp23 = 1e-05
    tmp24 = tmp22 + tmp23
    tmp25 = libdevice.rsqrt(tmp24)
    tmp26 = tmp20 * tmp25
    tmp27 = tmp26 * tmp0
    tmp29 = tmp27 + tmp28
    tl.store(out_ptr0 + x0, tmp0, xmask)
    tl.store(in_out_ptr0 + (r3 + 25 * x0), tmp3, rmask & xmask)
    tl.store(out_ptr3 + (r3 + 25 * x0), tmp29, rmask & xmask)
    tl.store(out_ptr4 + x0, tmp25, xmask)
    tl.store(out_ptr1 + x0, tmp13, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4,), (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_reflection_pad2d_0[grid(1024)](primals_1, buf0, 
            1024, 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, (4, 4, 5, 5), (100, 25, 5, 1))
        buf3 = empty_strided_cuda((16,), (1,), torch.float32)
        buf2 = buf1
        del buf1
        buf4 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        buf8 = empty_strided_cuda((1, 16, 5, 5), (400, 25, 5, 1), torch.float32
            )
        buf7 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        triton_per_fused__native_batch_norm_legit_convolution_repeat_1[grid(16)
            ](buf2, primals_4, primals_3, primals_5, buf3, buf4, buf8, buf7,
            16, 25, XBLOCK=8, num_warps=2, num_stages=1)
        del primals_3
        del primals_4
        del primals_5
    return reinterpret_tensor(buf8, (4, 4, 5, 5), (100, 25, 5, 1), 0
        ), primals_2, buf0, buf2, buf3, reinterpret_tensor(buf7, (16,), (1,), 0
        ), reinterpret_tensor(buf4, (1, 16, 1, 1), (16, 1, 1, 1), 0)


class ConvLayerNew(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride, norm
        ='instance'):
        super(ConvLayerNew, self).__init__()
        padding_size = kernel_size // 2
        self.reflection_pad = nn.ReflectionPad2d(padding_size)
        self.conv_layer = nn.Conv2d(in_channels, out_channels, kernel_size,
            stride)
        self.norm_type = norm
        if norm == 'instance':
            self.norm_layer = nn.InstanceNorm2d(out_channels, affine=True)
        elif norm == 'batch':
            self.norm_layer = nn.BatchNorm2d(out_channels, affine=True)

    def forward(self, input_0):
        primals_1 = self.conv_layer.weight
        primals_3 = self.conv_layer.bias
        primals_4 = self.norm_layer.weight
        primals_5 = self.norm_layer.bias
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
