# 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/xi/cxi3ssslzv45liamqvbt6decmfms5gkzbjn7dtainfaa436qkyw3.py
# Topologically Sorted Source Nodes: [pad], Original ATen: [aten.reflection_pad2d]
# Source node to ATen node mapping:
#   pad => _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=[65536], 
    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 = 62208
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 72
    x1 = (xindex // 72) % 72
    x2 = (xindex // 5184)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4095 + ((-1)*(tl_math.abs((-63) + (tl_math.abs((-4) + x0))))) + ((-64)*(tl_math.abs((-63) + (tl_math.abs((-4) + x1))))) + (4096*x2)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/zp/czpuakvx3zciuzfmemejrltenkqbzqirfyy2fnfbmrorwkdndz6e.py
# Topologically Sorted Source Nodes: [h, h_1], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
# Source node to ATen node mapping:
#   h => convolution
#   h_1 => add, rsqrt, var_mean
# 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 = {})
#   %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 = {})
triton_red_fused__native_batch_norm_legit_convolution_1 = async_compile.triton('triton_red_fused__native_batch_norm_legit_convolution_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.reduction(
    size_hints=[128, 4096],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 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_red_fused__native_batch_norm_legit_convolution_1', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 2, '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_red_fused__native_batch_norm_legit_convolution_1(in_out_ptr0, in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 128
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x3 = xindex
    x0 = xindex % 32
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp4_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp4_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp4_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp0 = tl.load(in_out_ptr0 + (r2 + (4096*x3)), rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp2 = tmp0 + tmp1
        tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
        tmp4_mean_next, tmp4_m2_next, tmp4_weight_next = triton_helpers.welford_reduce(
            tmp3, tmp4_mean, tmp4_m2, tmp4_weight, roffset == 0
        )
        tmp4_mean = tl.where(rmask & xmask, tmp4_mean_next, tmp4_mean)
        tmp4_m2 = tl.where(rmask & xmask, tmp4_m2_next, tmp4_m2)
        tmp4_weight = tl.where(rmask & xmask, tmp4_weight_next, tmp4_weight)
        tl.store(in_out_ptr0 + (r2 + (4096*x3)), tmp2, rmask & xmask)
    tmp4_tmp, tmp5_tmp, tmp6_tmp = triton_helpers.welford(
        tmp4_mean, tmp4_m2, tmp4_weight, 1
    )
    tmp4 = tmp4_tmp[:, None]
    tmp5 = tmp5_tmp[:, None]
    tmp6 = tmp6_tmp[:, None]
    tl.store(out_ptr0 + (x3), tmp4, xmask)
    tmp7 = 4096.0
    tmp8 = tmp5 / tmp7
    tmp9 = 1e-05
    tmp10 = tmp8 + tmp9
    tmp11 = libdevice.rsqrt(tmp10)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (x3), tmp11, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ks/ckswmenayt2ao73sl6l7kozhk55mt5ivur3ckyg7uc357hmb6pde.py
# Topologically Sorted Source Nodes: [h_2, pad_1], Original ATen: [aten.relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
#   h_2 => relu
#   pad_1 => _unsafe_index_2, _unsafe_index_3
# Graph fragment:
#   %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
#   %_unsafe_index_2 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu, [None, None, %sub_6, None]), kwargs = {})
#   %_unsafe_index_3 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_2, [None, None, None, %sub_6]), kwargs = {})
triton_poi_fused_reflection_pad2d_relu_2 = async_compile.triton('triton_poi_fused_reflection_pad2d_relu_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=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 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_reflection_pad2d_relu_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_relu_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 540800
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 65
    x1 = (xindex // 65) % 65
    x2 = (xindex // 4225)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4095 + ((-1)*(tl_math.abs((-63) + (tl_math.abs((-1) + x0))))) + ((-64)*(tl_math.abs((-63) + (tl_math.abs((-1) + x1))))) + (4096*x2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp5 = tl.full([1], 0, tl.int32)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(out_ptr0 + (x3), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ve/cvelhn6cqdubkyewwgju5tagzuydam2wfkj5gfavglbncgyvea4l.py
# Topologically Sorted Source Nodes: [h_3, h_4], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
# Source node to ATen node mapping:
#   h_3 => convolution_1
#   h_4 => add_1, rsqrt_1, var_mean_1
# Graph fragment:
#   %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_3, %primals_4, %primals_5, [2, 2], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_2, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_2, 1e-05), kwargs = {})
#   %rsqrt_1 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_1,), kwargs = {})
triton_per_fused__native_batch_norm_legit_convolution_3 = async_compile.triton('triton_per_fused__native_batch_norm_legit_convolution_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.persistent_reduction(
    size_hints=[256, 1024],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 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_per_fused__native_batch_norm_legit_convolution_3', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': True, 'num_load': 2, '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_3(in_out_ptr0, in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel):
    xnumel = 256
    XBLOCK: tl.constexpr = 1
    rnumel = 1024
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + (r2 + (1024*x3)), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 1024, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp3 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = 1024.0
    tmp17 = tmp15 / tmp16
    tmp18 = 1e-05
    tmp19 = tmp17 + tmp18
    tmp20 = libdevice.rsqrt(tmp19)
    tl.store(in_out_ptr0 + (r2 + (1024*x3)), tmp2, None)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (x3), tmp20, None)
    tl.store(out_ptr0 + (x3), tmp10, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/bh/cbhw7odeljqjis3p4j6gs6v4gli6ld2npcx45ki27acdgkw74gnn.py
# Topologically Sorted Source Nodes: [h_5, pad_2], Original ATen: [aten.relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
#   h_5 => relu_1
#   pad_2 => _unsafe_index_4, _unsafe_index_5
# Graph fragment:
#   %relu_1 : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%view_3,), kwargs = {})
#   %_unsafe_index_4 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_1, [None, None, %sub_11, None]), kwargs = {})
#   %_unsafe_index_5 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_4, [None, None, None, %sub_11]), kwargs = {})
triton_poi_fused_reflection_pad2d_relu_4 = async_compile.triton('triton_poi_fused_reflection_pad2d_relu_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=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 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_reflection_pad2d_relu_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_relu_4(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 278784
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 33
    x1 = (xindex // 33) % 33
    x2 = (xindex // 1089)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (1023 + ((-1)*(tl_math.abs((-31) + (tl_math.abs((-1) + x0))))) + ((-32)*(tl_math.abs((-31) + (tl_math.abs((-1) + x1))))) + (1024*x2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp5 = tl.full([1], 0, tl.int32)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(out_ptr0 + (x3), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/5q/c5qq6iqsmnbo2fbydutydbtixcy6vnxtvcuoiir6ktetofqborvz.py
# Topologically Sorted Source Nodes: [h_6, h_7], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
# Source node to ATen node mapping:
#   h_6 => convolution_2
#   h_7 => add_2, rsqrt_2, var_mean_2
# Graph fragment:
#   %convolution_2 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_5, %primals_6, %primals_7, [2, 2], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %var_mean_2 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_4, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_4, 1e-05), kwargs = {})
#   %rsqrt_2 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_2,), kwargs = {})
triton_per_fused__native_batch_norm_legit_convolution_5 = async_compile.triton('triton_per_fused__native_batch_norm_legit_convolution_5', '''
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=[512, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 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_per_fused__native_batch_norm_legit_convolution_5', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': True, 'num_load': 2, '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_5(in_out_ptr0, in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel):
    xnumel = 512
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (r2 + (256*x3)), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 256, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp3 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = 256.0
    tmp17 = tmp15 / tmp16
    tmp18 = 1e-05
    tmp19 = tmp17 + tmp18
    tmp20 = libdevice.rsqrt(tmp19)
    tl.store(in_out_ptr0 + (r2 + (256*x3)), tmp2, None)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (x3), tmp20, None)
    tl.store(out_ptr0 + (x3), tmp10, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/uc/cuctc2mw2eg5hkoydwcrgpadafbbhusisur4gdd5cec7mhqfqire.py
# Topologically Sorted Source Nodes: [h_8, h_9], Original ATen: [aten.relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
#   h_8 => relu_2
#   h_9 => _unsafe_index_6, _unsafe_index_7
# Graph fragment:
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_5,), kwargs = {})
#   %_unsafe_index_6 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_2, [None, None, %sub_16, None]), kwargs = {})
#   %_unsafe_index_7 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_6, [None, None, None, %sub_16]), kwargs = {})
triton_poi_fused_reflection_pad2d_relu_6 = async_compile.triton('triton_poi_fused_reflection_pad2d_relu_6', '''
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=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 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_reflection_pad2d_relu_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_relu_6(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 165888
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 18
    x1 = (xindex // 18) % 18
    x2 = (xindex // 324)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (255 + ((-1)*(tl_math.abs((-15) + (tl_math.abs((-1) + x0))))) + ((-16)*(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (256*x2)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x2), None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp5 = tl.full([1], 0, tl.int32)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/bi/cbife7bkuy2d227ro5x4raqjdhl7uvlwdbqxevx3u6wldn3papve.py
# Topologically Sorted Source Nodes: [h_8, h_14, h_15, h_16], Original ATen: [aten.relu, aten.convolution, aten._native_batch_norm_legit, aten.add]
# Source node to ATen node mapping:
#   h_14 => convolution_4
#   h_15 => add_4, rsqrt_4, var_mean_4
#   h_16 => add_5
#   h_8 => relu_2
# Graph fragment:
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_5,), kwargs = {})
#   %convolution_4 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_9, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %var_mean_4 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_8, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_8, 1e-05), kwargs = {})
#   %rsqrt_4 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_4,), kwargs = {})
#   %add_5 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_9, %relu_2), kwargs = {})
triton_per_fused__native_batch_norm_legit_add_convolution_relu_7 = async_compile.triton('triton_per_fused__native_batch_norm_legit_add_convolution_relu_7', '''
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=[512, 256],
    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, 9), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_add_convolution_relu_7', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 5, '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_add_convolution_relu_7(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr2, out_ptr3, xnumel, rnumel):
    xnumel = 512
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (r2 + (256*x3)), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr1 + (r2 + (256*x3)), None)
    tmp24 = tl.load(in_ptr2 + (x3), None, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr3 + (x3), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 256, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp3 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = tmp2 - tmp10
    tmp17 = 256.0
    tmp18 = tmp15 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tmp22 = tmp16 * tmp21
    tmp25 = tmp23 - tmp24
    tmp27 = tmp25 * tmp26
    tmp28 = tl.full([1], 0, tl.int32)
    tmp29 = triton_helpers.maximum(tmp28, tmp27)
    tmp30 = tmp22 + tmp29
    tl.store(in_out_ptr0 + (r2 + (256*x3)), tmp2, None)
    tl.store(out_ptr2 + (r2 + (256*x3)), tmp30, None)
    tl.store(out_ptr3 + (x3), tmp21, None)
    tl.store(out_ptr0 + (x3), tmp10, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/st/cstfzn4z33vdn3t4r76kkdoe3fox63ob7zbuq5lr4e2aj2wo3cfw.py
# Topologically Sorted Source Nodes: [h_17], Original ATen: [aten.reflection_pad2d]
# Source node to ATen node mapping:
#   h_17 => _unsafe_index_10, _unsafe_index_11
# Graph fragment:
#   %_unsafe_index_10 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%add_5, [None, None, %sub_16, None]), kwargs = {})
#   %_unsafe_index_11 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_10, [None, None, None, %sub_16]), kwargs = {})
triton_poi_fused_reflection_pad2d_8 = async_compile.triton('triton_poi_fused_reflection_pad2d_8', '''
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=[262144], 
    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_8', '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_8(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 165888
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 18
    x1 = (xindex // 18) % 18
    x2 = (xindex // 324)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (255 + ((-1)*(tl_math.abs((-15) + (tl_math.abs((-1) + x0))))) + ((-16)*(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (256*x2)), None, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x3), tmp0, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/cl/cclv7ezsjxrzhzk6ctrgrqqtzgfpxzyophgl3ix4da7q2le5obfk.py
# Topologically Sorted Source Nodes: [h_22, h_23], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
# Source node to ATen node mapping:
#   h_22 => convolution_6
#   h_23 => add_7, rsqrt_6, var_mean_6
# Graph fragment:
#   %convolution_6 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_13, %primals_14, %primals_15, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %var_mean_6 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_12, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_7 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_12, 1e-05), kwargs = {})
#   %rsqrt_6 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_7,), kwargs = {})
triton_per_fused__native_batch_norm_legit_convolution_9 = async_compile.triton('triton_per_fused__native_batch_norm_legit_convolution_9', '''
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=[512, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_convolution_9', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, '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_9(in_out_ptr0, in_ptr0, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel):
    xnumel = 512
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (r2 + (256*x3)), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 256, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp3 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = 256.0
    tmp17 = tmp15 / tmp16
    tmp18 = 1e-05
    tmp19 = tmp17 + tmp18
    tmp20 = libdevice.rsqrt(tmp19)
    tl.store(in_out_ptr0 + (r2 + (256*x3)), tmp2, None)
    tl.store(out_ptr2 + (x3), tmp20, None)
    tl.store(out_ptr0 + (x3), tmp10, None)
    tl.store(out_ptr1 + (x3), tmp15, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/7k/c7kujc6gfujkwqulhpklckgjx536i7vhlungcud7ktvyfr2hxyk4.py
# Topologically Sorted Source Nodes: [h_24, h_25], Original ATen: [aten.add, aten.reflection_pad2d]
# Source node to ATen node mapping:
#   h_24 => add_8
#   h_25 => _unsafe_index_14, _unsafe_index_15
# Graph fragment:
#   %add_8 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_13, %add_5), kwargs = {})
#   %_unsafe_index_14 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%add_8, [None, None, %sub_16, None]), kwargs = {})
#   %_unsafe_index_15 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_14, [None, None, None, %sub_16]), kwargs = {})
triton_poi_fused_add_reflection_pad2d_10 = async_compile.triton('triton_poi_fused_add_reflection_pad2d_10', '''
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=[262144], 
    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_add_reflection_pad2d_10', '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_add_reflection_pad2d_10(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 165888
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 18
    x1 = (xindex // 18) % 18
    x2 = (xindex // 324)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (255 + ((-1)*(tl_math.abs((-15) + (tl_math.abs((-1) + x0))))) + ((-16)*(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (256*x2)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x2), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (255 + ((-1)*(tl_math.abs((-15) + (tl_math.abs((-1) + x0))))) + ((-16)*(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (256*x2)), None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 256.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 + tmp10
    tl.store(out_ptr0 + (x3), tmp11, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/xv/cxvhgd5alq3mj4s7l5hfmhiadvliqoiybi52mlfkujjk6juw63ck.py
# Topologically Sorted Source Nodes: [h_24, h_30, h_31, h_32], Original ATen: [aten.add, aten.convolution, aten._native_batch_norm_legit]
# Source node to ATen node mapping:
#   h_24 => add_8
#   h_30 => convolution_8
#   h_31 => add_10, rsqrt_8, var_mean_8
#   h_32 => add_11
# Graph fragment:
#   %add_8 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_13, %add_5), kwargs = {})
#   %convolution_8 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_17, %primals_18, %primals_19, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %var_mean_8 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_16, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_10 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_16, 1e-05), kwargs = {})
#   %rsqrt_8 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_10,), kwargs = {})
#   %add_11 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_17, %add_8), kwargs = {})
triton_per_fused__native_batch_norm_legit_add_convolution_11 = async_compile.triton('triton_per_fused__native_batch_norm_legit_add_convolution_11', '''
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=[512, 256],
    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, 9), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_add_convolution_11', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': True, 'num_load': 6, '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_add_convolution_11(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr2, xnumel, rnumel):
    xnumel = 512
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (r2 + (256*x3)), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr1 + (r2 + (256*x3)), None)
    tmp24 = tl.load(in_ptr2 + (x3), None, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr3 + (x3), None, eviction_policy='evict_last')
    tmp31 = tl.load(in_out_ptr1 + (r2 + (256*x3)), None)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 256, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp3 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = tmp2 - tmp10
    tmp17 = 256.0
    tmp18 = tmp15 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tmp22 = tmp16 * tmp21
    tmp25 = tmp23 - tmp24
    tmp27 = tmp26 / tmp17
    tmp28 = tmp27 + tmp19
    tmp29 = libdevice.rsqrt(tmp28)
    tmp30 = tmp25 * tmp29
    tmp32 = tmp30 + tmp31
    tmp33 = tmp22 + tmp32
    tl.store(in_out_ptr0 + (r2 + (256*x3)), tmp2, None)
    tl.store(in_out_ptr1 + (r2 + (256*x3)), tmp33, None)
    tl.store(out_ptr2 + (x3), tmp21, None)
    tl.store(out_ptr0 + (x3), tmp10, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/od/cod2uorpdkpcx3rlzl4gnbqpnzu74ltjqdquzgryujjwghhe3on6.py
# Topologically Sorted Source Nodes: [h_49, h_50, h_51], Original ATen: [aten.convolution, aten._native_batch_norm_legit, aten.relu]
# Source node to ATen node mapping:
#   h_49 => convolution_13
#   h_50 => add_18, rsqrt_13, var_mean_13
#   h_51 => relu_8
# Graph fragment:
#   %convolution_13 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%add_17, %primals_28, %primals_29, [2, 2], [1, 1], [1, 1], True, [1, 1], 1), kwargs = {})
#   %var_mean_13 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_26, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_18 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_26, 1e-05), kwargs = {})
#   %rsqrt_13 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_18,), kwargs = {})
#   %relu_8 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_27,), kwargs = {})
triton_per_fused__native_batch_norm_legit_convolution_relu_12 = async_compile.triton('triton_per_fused__native_batch_norm_legit_convolution_relu_12', '''
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=[256, 1024],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_convolution_relu_12', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, '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_relu_12(in_out_ptr0, in_ptr0, out_ptr0, out_ptr2, out_ptr3, xnumel, rnumel):
    xnumel = 256
    XBLOCK: tl.constexpr = 1
    rnumel = 1024
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + (r2 + (1024*x3)), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 1024, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp3 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = tmp2 - tmp10
    tmp17 = 1024.0
    tmp18 = tmp15 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tmp22 = tmp16 * tmp21
    tmp23 = tl.full([1], 0, tl.int32)
    tmp24 = triton_helpers.maximum(tmp23, tmp22)
    tl.store(in_out_ptr0 + (r2 + (1024*x3)), tmp2, None)
    tl.store(out_ptr2 + (r2 + (1024*x3)), tmp24, None)
    tl.store(out_ptr3 + (x3), tmp21, None)
    tl.store(out_ptr0 + (x3), tmp10, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/tl/ctlger5tjzbwjm6euoarzgbgkbxawfpwur4p7f73jdctorwwdixe.py
# Topologically Sorted Source Nodes: [h_54, pad_13], Original ATen: [aten.relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
#   h_54 => relu_9
#   pad_13 => _unsafe_index_26, _unsafe_index_27
# Graph fragment:
#   %relu_9 : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%view_29,), kwargs = {})
#   %_unsafe_index_26 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_9, [None, None, %sub_1, None]), kwargs = {})
#   %_unsafe_index_27 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_26, [None, None, None, %sub_1]), kwargs = {})
triton_poi_fused_reflection_pad2d_relu_13 = async_compile.triton('triton_poi_fused_reflection_pad2d_relu_13', '''
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=[1048576], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 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_reflection_pad2d_relu_13', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_relu_13(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 663552
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 72
    x1 = (xindex // 72) % 72
    x2 = (xindex // 5184)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4095 + ((-1)*(tl_math.abs((-63) + (tl_math.abs((-4) + x0))))) + ((-64)*(tl_math.abs((-63) + (tl_math.abs((-4) + x1))))) + (4096*x2)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x2), None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp5 = tl.full([1], 0, tl.int32)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ir/cir6l5pwrenp3hy4mpch3g5fshb7l44ajgfdsuvk2owxorfrzxuf.py
# Topologically Sorted Source Nodes: [h_55, h_56, tanh, mul, h_57], Original ATen: [aten.convolution, aten._native_batch_norm_legit, aten.tanh, aten.mul, aten.add]
# Source node to ATen node mapping:
#   h_55 => convolution_15
#   h_56 => add_20, rsqrt_15, var_mean_15
#   h_57 => add_21
#   mul => mul_16
#   tanh => tanh
# Graph fragment:
#   %convolution_15 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_27, %primals_32, %primals_33, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %var_mean_15 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_30, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_20 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_30, 1e-05), kwargs = {})
#   %rsqrt_15 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_20,), kwargs = {})
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%view_31,), kwargs = {})
#   %mul_16 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%tanh, 0.5), kwargs = {})
#   %add_21 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_16, 0.5), kwargs = {})
triton_red_fused__native_batch_norm_legit_add_convolution_mul_tanh_14 = async_compile.triton('triton_red_fused__native_batch_norm_legit_add_convolution_mul_tanh_14', '''
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.reduction(
    size_hints=[16, 4096],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: '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, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused__native_batch_norm_legit_add_convolution_mul_tanh_14', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 2, '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_red_fused__native_batch_norm_legit_add_convolution_mul_tanh_14(in_out_ptr0, in_out_ptr1, in_ptr0, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 12
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x3 = xindex
    x0 = xindex % 3
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp4_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp4_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp4_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp0 = tl.load(in_out_ptr0 + (r2 + (4096*x3)), rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp2 = tmp0 + tmp1
        tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
        tmp4_mean_next, tmp4_m2_next, tmp4_weight_next = triton_helpers.welford_reduce(
            tmp3, tmp4_mean, tmp4_m2, tmp4_weight, roffset == 0
        )
        tmp4_mean = tl.where(rmask & xmask, tmp4_mean_next, tmp4_mean)
        tmp4_m2 = tl.where(rmask & xmask, tmp4_m2_next, tmp4_m2)
        tmp4_weight = tl.where(rmask & xmask, tmp4_weight_next, tmp4_weight)
        tl.store(in_out_ptr0 + (r2 + (4096*x3)), tmp2, rmask & xmask)
    tmp4_tmp, tmp5_tmp, tmp6_tmp = triton_helpers.welford(
        tmp4_mean, tmp4_m2, tmp4_weight, 1
    )
    tmp4 = tmp4_tmp[:, None]
    tmp5 = tmp5_tmp[:, None]
    tmp6 = tmp6_tmp[:, None]
    tl.store(out_ptr0 + (x3), tmp4, xmask)
    tmp7 = 4096.0
    tmp8 = tmp5 / tmp7
    tmp9 = 1e-05
    tmp10 = tmp8 + tmp9
    tmp11 = libdevice.rsqrt(tmp10)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (x3), tmp11, xmask)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp12 = tl.load(in_out_ptr0 + (r2 + (4096*x3)), rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp13 = tmp12 - tmp4
        tmp14 = tmp13 * tmp11
        tmp15 = libdevice.tanh(tmp14)
        tmp16 = 0.5
        tmp17 = tmp15 * tmp16
        tmp18 = tmp17 + tmp16
        tl.store(out_ptr1 + (r2 + (4096*x3)), tmp18, rmask & 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, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30, primals_31, primals_32, primals_33 = args
    args.clear()
    assert_size_stride(primals_1, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_2, (32, 3, 9, 9), (243, 81, 9, 1))
    assert_size_stride(primals_3, (32, ), (1, ))
    assert_size_stride(primals_4, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_5, (64, ), (1, ))
    assert_size_stride(primals_6, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (128, ), (1, ))
    assert_size_stride(primals_8, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_9, (128, ), (1, ))
    assert_size_stride(primals_10, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_11, (128, ), (1, ))
    assert_size_stride(primals_12, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_13, (128, ), (1, ))
    assert_size_stride(primals_14, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_15, (128, ), (1, ))
    assert_size_stride(primals_16, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_17, (128, ), (1, ))
    assert_size_stride(primals_18, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_19, (128, ), (1, ))
    assert_size_stride(primals_20, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_21, (128, ), (1, ))
    assert_size_stride(primals_22, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_23, (128, ), (1, ))
    assert_size_stride(primals_24, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_25, (128, ), (1, ))
    assert_size_stride(primals_26, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_27, (128, ), (1, ))
    assert_size_stride(primals_28, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_29, (64, ), (1, ))
    assert_size_stride(primals_30, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_31, (32, ), (1, ))
    assert_size_stride(primals_32, (3, 32, 9, 9), (2592, 81, 9, 1))
    assert_size_stride(primals_33, (3, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 3, 72, 72), (15552, 5184, 72, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pad], Original ATen: [aten.reflection_pad2d]
        stream0 = get_raw_stream(0)
        triton_poi_fused_reflection_pad2d_0.run(primals_1, buf0, 62208, grid=grid(62208), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [h], 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, 32, 64, 64), (131072, 4096, 64, 1))
        buf2 = buf1; del buf1  # reuse
        buf3 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 1, 1), torch.float32)
        buf4 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128), torch.float32)
        buf6 = reinterpret_tensor(buf4, (1, 128, 1, 1), (128, 1, 1, 1), 0); del buf4  # reuse
        # Topologically Sorted Source Nodes: [h, h_1], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
        triton_red_fused__native_batch_norm_legit_convolution_1.run(buf2, buf6, primals_3, buf3, 128, 4096, grid=grid(128), stream=stream0)
        del primals_3
        buf7 = empty_strided_cuda((4, 32, 65, 65), (135200, 4225, 65, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_2, pad_1], Original ATen: [aten.relu, aten.reflection_pad2d]
        triton_poi_fused_reflection_pad2d_relu_2.run(buf2, buf3, buf6, buf7, 540800, grid=grid(540800), stream=stream0)
        # Topologically Sorted Source Nodes: [h_3], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf7, primals_4, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf9 = buf8; del buf8  # reuse
        buf10 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 1, 1), torch.float32)
        buf11 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256), torch.float32)
        buf13 = reinterpret_tensor(buf11, (1, 256, 1, 1), (256, 1, 1, 1), 0); del buf11  # reuse
        # Topologically Sorted Source Nodes: [h_3, h_4], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_convolution_3.run(buf9, buf13, primals_5, buf10, 256, 1024, grid=grid(256), stream=stream0)
        del primals_5
        buf14 = empty_strided_cuda((4, 64, 33, 33), (69696, 1089, 33, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_5, pad_2], Original ATen: [aten.relu, aten.reflection_pad2d]
        triton_poi_fused_reflection_pad2d_relu_4.run(buf9, buf10, buf13, buf14, 278784, grid=grid(278784), stream=stream0)
        # Topologically Sorted Source Nodes: [h_6], Original ATen: [aten.convolution]
        buf15 = extern_kernels.convolution(buf14, primals_6, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf15, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf16 = buf15; del buf15  # reuse
        buf17 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.float32)
        buf18 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf20 = reinterpret_tensor(buf18, (1, 512, 1, 1), (512, 1, 1, 1), 0); del buf18  # reuse
        # Topologically Sorted Source Nodes: [h_6, h_7], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_convolution_5.run(buf16, buf20, primals_7, buf17, 512, 256, grid=grid(512), stream=stream0)
        del primals_7
        buf21 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_8, h_9], Original ATen: [aten.relu, aten.reflection_pad2d]
        triton_poi_fused_reflection_pad2d_relu_6.run(buf16, buf17, buf20, buf21, 165888, grid=grid(165888), stream=stream0)
        # Topologically Sorted Source Nodes: [h_10], Original ATen: [aten.convolution]
        buf22 = extern_kernels.convolution(buf21, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf22, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf23 = buf22; del buf22  # reuse
        buf24 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.float32)
        buf25 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf27 = reinterpret_tensor(buf25, (1, 512, 1, 1), (512, 1, 1, 1), 0); del buf25  # reuse
        # Topologically Sorted Source Nodes: [h_10, h_11], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_convolution_5.run(buf23, buf27, primals_9, buf24, 512, 256, grid=grid(512), stream=stream0)
        del primals_9
        buf28 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_12, h_13], Original ATen: [aten.relu, aten.reflection_pad2d]
        triton_poi_fused_reflection_pad2d_relu_6.run(buf23, buf24, buf27, buf28, 165888, grid=grid(165888), stream=stream0)
        # Topologically Sorted Source Nodes: [h_14], Original ATen: [aten.convolution]
        buf29 = extern_kernels.convolution(buf28, primals_10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf29, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf30 = buf29; del buf29  # reuse
        buf31 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf35 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.float32)
        buf34 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [h_8, h_14, h_15, h_16], Original ATen: [aten.relu, aten.convolution, aten._native_batch_norm_legit, aten.add]
        triton_per_fused__native_batch_norm_legit_add_convolution_relu_7.run(buf30, primals_11, buf16, buf17, buf20, buf31, buf35, buf34, 512, 256, grid=grid(512), stream=stream0)
        del primals_11
        buf36 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_17], Original ATen: [aten.reflection_pad2d]
        triton_poi_fused_reflection_pad2d_8.run(buf35, buf36, 165888, grid=grid(165888), stream=stream0)
        # Topologically Sorted Source Nodes: [h_18], Original ATen: [aten.convolution]
        buf37 = extern_kernels.convolution(buf36, primals_12, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf37, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf38 = buf37; del buf37  # reuse
        buf39 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.float32)
        buf40 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf42 = reinterpret_tensor(buf40, (1, 512, 1, 1), (512, 1, 1, 1), 0); del buf40  # reuse
        # Topologically Sorted Source Nodes: [h_18, h_19], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_convolution_5.run(buf38, buf42, primals_13, buf39, 512, 256, grid=grid(512), stream=stream0)
        del primals_13
        buf43 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_20, h_21], Original ATen: [aten.relu, aten.reflection_pad2d]
        triton_poi_fused_reflection_pad2d_relu_6.run(buf38, buf39, buf42, buf43, 165888, grid=grid(165888), stream=stream0)
        # Topologically Sorted Source Nodes: [h_22], Original ATen: [aten.convolution]
        buf44 = extern_kernels.convolution(buf43, primals_14, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf44, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf45 = buf44; del buf44  # reuse
        buf46 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf47 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf49 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [h_22, h_23], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_convolution_9.run(buf45, primals_15, buf46, buf47, buf49, 512, 256, grid=grid(512), stream=stream0)
        del primals_15
        buf50 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_24, h_25], Original ATen: [aten.add, aten.reflection_pad2d]
        triton_poi_fused_add_reflection_pad2d_10.run(buf45, buf46, buf47, buf35, buf50, 165888, grid=grid(165888), stream=stream0)
        # Topologically Sorted Source Nodes: [h_26], Original ATen: [aten.convolution]
        buf51 = extern_kernels.convolution(buf50, primals_16, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf51, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf52 = buf51; del buf51  # reuse
        buf53 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.float32)
        buf54 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf56 = reinterpret_tensor(buf54, (1, 512, 1, 1), (512, 1, 1, 1), 0); del buf54  # reuse
        # Topologically Sorted Source Nodes: [h_26, h_27], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_convolution_5.run(buf52, buf56, primals_17, buf53, 512, 256, grid=grid(512), stream=stream0)
        del primals_17
        buf57 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_28, h_29], Original ATen: [aten.relu, aten.reflection_pad2d]
        triton_poi_fused_reflection_pad2d_relu_6.run(buf52, buf53, buf56, buf57, 165888, grid=grid(165888), stream=stream0)
        # Topologically Sorted Source Nodes: [h_30], Original ATen: [aten.convolution]
        buf58 = extern_kernels.convolution(buf57, primals_18, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf58, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf59 = buf58; del buf58  # reuse
        buf60 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf64 = buf35; del buf35  # reuse
        buf63 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [h_24, h_30, h_31, h_32], Original ATen: [aten.add, aten.convolution, aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_add_convolution_11.run(buf59, buf64, primals_19, buf45, buf46, buf47, buf60, buf63, 512, 256, grid=grid(512), stream=stream0)
        del primals_19
        buf65 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_33], Original ATen: [aten.reflection_pad2d]
        triton_poi_fused_reflection_pad2d_8.run(buf64, buf65, 165888, grid=grid(165888), stream=stream0)
        # Topologically Sorted Source Nodes: [h_34], Original ATen: [aten.convolution]
        buf66 = extern_kernels.convolution(buf65, primals_20, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf66, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf67 = buf66; del buf66  # reuse
        buf68 = reinterpret_tensor(buf47, (1, 512, 1, 1), (512, 1, 1, 1), 0); del buf47  # reuse
        buf69 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf71 = reinterpret_tensor(buf69, (1, 512, 1, 1), (512, 1, 1, 1), 0); del buf69  # reuse
        # Topologically Sorted Source Nodes: [h_34, h_35], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_convolution_5.run(buf67, buf71, primals_21, buf68, 512, 256, grid=grid(512), stream=stream0)
        del primals_21
        buf72 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_36, h_37], Original ATen: [aten.relu, aten.reflection_pad2d]
        triton_poi_fused_reflection_pad2d_relu_6.run(buf67, buf68, buf71, buf72, 165888, grid=grid(165888), stream=stream0)
        # Topologically Sorted Source Nodes: [h_38], Original ATen: [aten.convolution]
        buf73 = extern_kernels.convolution(buf72, primals_22, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf73, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf74 = buf73; del buf73  # reuse
        buf75 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf76 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf78 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [h_38, h_39], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_convolution_9.run(buf74, primals_23, buf75, buf76, buf78, 512, 256, grid=grid(512), stream=stream0)
        del primals_23
        buf79 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_40, h_41], Original ATen: [aten.add, aten.reflection_pad2d]
        triton_poi_fused_add_reflection_pad2d_10.run(buf74, buf75, buf76, buf64, buf79, 165888, grid=grid(165888), stream=stream0)
        # Topologically Sorted Source Nodes: [h_42], Original ATen: [aten.convolution]
        buf80 = extern_kernels.convolution(buf79, primals_24, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf80, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf81 = buf80; del buf80  # reuse
        buf82 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.float32)
        buf83 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf85 = reinterpret_tensor(buf83, (1, 512, 1, 1), (512, 1, 1, 1), 0); del buf83  # reuse
        # Topologically Sorted Source Nodes: [h_42, h_43], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_convolution_5.run(buf81, buf85, primals_25, buf82, 512, 256, grid=grid(512), stream=stream0)
        del primals_25
        buf86 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_44, h_45], Original ATen: [aten.relu, aten.reflection_pad2d]
        triton_poi_fused_reflection_pad2d_relu_6.run(buf81, buf82, buf85, buf86, 165888, grid=grid(165888), stream=stream0)
        # Topologically Sorted Source Nodes: [h_46], Original ATen: [aten.convolution]
        buf87 = extern_kernels.convolution(buf86, primals_26, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf87, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf88 = buf87; del buf87  # reuse
        buf89 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        buf93 = buf64; del buf64  # reuse
        buf92 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512), torch.float32)
        # Topologically Sorted Source Nodes: [h_40, h_46, h_47, h_48], Original ATen: [aten.add, aten.convolution, aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_add_convolution_11.run(buf88, buf93, primals_27, buf74, buf75, buf76, buf89, buf92, 512, 256, grid=grid(512), stream=stream0)
        del buf76
        del primals_27
        # Topologically Sorted Source Nodes: [h_49], Original ATen: [aten.convolution]
        buf94 = extern_kernels.convolution(buf93, primals_28, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(1, 1), groups=1, bias=None)
        assert_size_stride(buf94, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf95 = buf94; del buf94  # reuse
        buf96 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256), torch.float32)
        buf100 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.float32)
        buf99 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256), torch.float32)
        # Topologically Sorted Source Nodes: [h_49, h_50, h_51], Original ATen: [aten.convolution, aten._native_batch_norm_legit, aten.relu]
        triton_per_fused__native_batch_norm_legit_convolution_relu_12.run(buf95, primals_29, buf96, buf100, buf99, 256, 1024, grid=grid(256), stream=stream0)
        del primals_29
        # Topologically Sorted Source Nodes: [h_52], Original ATen: [aten.convolution]
        buf101 = extern_kernels.convolution(buf100, primals_30, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(1, 1), groups=1, bias=None)
        assert_size_stride(buf101, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf102 = buf101; del buf101  # reuse
        buf103 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 1, 1), torch.float32)
        buf104 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128), torch.float32)
        buf106 = reinterpret_tensor(buf104, (1, 128, 1, 1), (128, 1, 1, 1), 0); del buf104  # reuse
        # Topologically Sorted Source Nodes: [h_52, h_53], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
        triton_red_fused__native_batch_norm_legit_convolution_1.run(buf102, buf106, primals_31, buf103, 128, 4096, grid=grid(128), stream=stream0)
        del primals_31
        buf107 = empty_strided_cuda((4, 32, 72, 72), (165888, 5184, 72, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_54, pad_13], Original ATen: [aten.relu, aten.reflection_pad2d]
        triton_poi_fused_reflection_pad2d_relu_13.run(buf102, buf103, buf106, buf107, 663552, grid=grid(663552), stream=stream0)
        # Topologically Sorted Source Nodes: [h_55], Original ATen: [aten.convolution]
        buf108 = extern_kernels.convolution(buf107, primals_32, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf108, (4, 3, 64, 64), (12288, 4096, 64, 1))
        buf109 = buf108; del buf108  # reuse
        buf110 = empty_strided_cuda((1, 12, 1, 1), (12, 1, 1, 1), torch.float32)
        buf111 = empty_strided_cuda((1, 12, 1, 1), (12, 1, 12, 12), torch.float32)
        buf113 = reinterpret_tensor(buf111, (1, 12, 1, 1), (12, 1, 1, 1), 0); del buf111  # reuse
        buf114 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_55, h_56, tanh, mul, h_57], Original ATen: [aten.convolution, aten._native_batch_norm_legit, aten.tanh, aten.mul, aten.add]
        triton_red_fused__native_batch_norm_legit_add_convolution_mul_tanh_14.run(buf109, buf113, primals_33, buf110, buf114, 12, 4096, grid=grid(12), stream=stream0)
        del primals_33
    return (buf114, primals_2, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, primals_16, primals_18, primals_20, primals_22, primals_24, primals_26, primals_28, primals_30, primals_32, buf0, buf2, buf3, buf6, buf7, buf9, buf10, buf13, buf14, buf16, buf17, buf20, buf21, buf23, buf24, buf27, buf28, buf30, reinterpret_tensor(buf34, (512, ), (1, ), 0), buf36, buf38, buf39, buf42, buf43, buf45, reinterpret_tensor(buf49, (512, ), (1, ), 0), buf50, buf52, buf53, buf56, buf57, buf59, reinterpret_tensor(buf63, (512, ), (1, ), 0), buf65, buf67, buf68, buf71, buf72, buf74, reinterpret_tensor(buf78, (512, ), (1, ), 0), buf79, buf81, buf82, buf85, buf86, buf88, reinterpret_tensor(buf92, (512, ), (1, ), 0), buf93, buf95, reinterpret_tensor(buf99, (256, ), (1, ), 0), buf100, buf102, buf103, buf106, buf107, buf109, buf110, buf113, reinterpret_tensor(buf96, (1, 256, 1, 1), (256, 1, 1, 1), 0), reinterpret_tensor(buf89, (1, 512, 1, 1), (512, 1, 1, 1), 0), reinterpret_tensor(buf75, (1, 512, 1, 1), (512, 1, 1, 1), 0), reinterpret_tensor(buf60, (1, 512, 1, 1), (512, 1, 1, 1), 0), reinterpret_tensor(buf46, (1, 512, 1, 1), (512, 1, 1, 1), 0), reinterpret_tensor(buf31, (1, 512, 1, 1), (512, 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, 3, 64, 64), (12288, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((32, 3, 9, 9), (243, 81, 9, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((64, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((128, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_23 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_24 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_25 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_26 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_27 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_28 = rand_strided((128, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_29 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_30 = rand_strided((64, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_31 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_32 = rand_strided((3, 32, 9, 9), (2592, 81, 9, 1), device='cuda:0', dtype=torch.float32)
    primals_33 = rand_strided((3, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29, primals_30, primals_31, primals_32, primals_33])
    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 import nn
from torch.nn import functional as F


def reflect_padding(x, f, s, half=False):
    if half:
        denom = 2
    else:
        denom = 1
    _, _, h, w = x.shape
    pad_w = w * (s / denom - 1) + f - s
    pad_h = h * (s / denom - 1) + f - s
    if pad_w % 2 == 1:
        pad_l = int(pad_w // 2) + 1
        pad_r = int(pad_w // 2)
    else:
        pad_l = pad_r = int(pad_w / 2)
    if pad_h % 2 == 1:
        pad_t = int(pad_h // 2) + 1
        pad_b = int(pad_h // 2)
    else:
        pad_t = pad_b = int(pad_h / 2)
    return F.pad(x, [pad_l, pad_r, pad_t, pad_b], mode='reflect')


class Residual(nn.Module):
    """Unlinke other blocks, this module receives unpadded inputs."""

    def __init__(self, channels, kernel_size=3):
        super(Residual, self).__init__()
        padding = int((kernel_size - 1) / 2)
        self.pad = nn.ReflectionPad2d(padding)
        self.conv1 = nn.Conv2d(channels, channels, kernel_size)
        self.bn1 = nn.InstanceNorm2d(channels)
        self.conv2 = nn.Conv2d(channels, channels, kernel_size)
        self.bn2 = nn.InstanceNorm2d(channels)

    def forward(self, x):
        h = self.pad(x)
        h = self.conv1(h)
        h = self.bn1(h)
        h = F.relu(h)
        h = self.pad(h)
        h = self.conv2(h)
        h = self.bn2(h)
        h = h + x
        return h


class Conv(nn.Module):
    """Convolutional block. 2d-conv -> batch norm -> (optionally) relu"""

    def __init__(self, in_channels, out_channels, kernel, stride=1,
        use_relu=True):
        super(Conv, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel, stride)
        self.batch_norm = nn.InstanceNorm2d(out_channels)
        self.use_relu = use_relu

    def forward(self, x):
        h = self.conv(x)
        h = self.batch_norm(h)
        if self.use_relu:
            h = F.relu(h)
        return h


class ConvTran(nn.Module):

    def __init__(self, in_channels, out_channels):
        super(ConvTran, self).__init__()
        self.conv_t = nn.ConvTranspose2d(in_channels, out_channels, 3, 2, 1, 1)
        self.batch_norm = nn.InstanceNorm2d(out_channels)

    def forward(self, x):
        h = self.conv_t(x)
        h = self.batch_norm(h)
        return F.relu(h)


class FastStyle(nn.Module):

    def __init__(self):
        super(FastStyle, self).__init__()
        self.conv1 = Conv(3, 32, 9)
        self.conv2 = Conv(32, 64, 3, 2)
        self.conv3 = Conv(64, 128, 3, 2)
        self.res1 = Residual(128)
        self.res2 = Residual(128)
        self.res3 = Residual(128)
        self.res4 = Residual(128)
        self.res5 = Residual(128)
        self.convT1 = ConvTran(128, 64)
        self.convT2 = ConvTran(64, 32)
        self.conv_out = Conv(32, 3, 9, use_relu=False)
        self._init()

    def forward(self, x):
        h = self.conv1(reflect_padding(x, 9, 1))
        h = self.conv2(reflect_padding(h, 3, 2, True))
        h = self.conv3(reflect_padding(h, 3, 2, True))
        h = self.res1(h)
        h = self.res2(h)
        h = self.res3(h)
        h = self.res4(h)
        h = self.res5(h)
        h = self.convT1(h)
        h = self.convT2(h)
        h = self.conv_out(reflect_padding(h, 9, 1))
        h = torch.tanh(h) * 0.5 + 0.5
        return h

    def _init(self):

        def __init(m):
            if isinstance(m, nn.Conv2d):
                nn.init.xavier_uniform_(m.weight)
                nn.init.constant_(m.bias, 0)
            if isinstance(m, nn.ConvTranspose2d):
                nn.init.xavier_uniform_(m.weight)
                nn.init.constant_(m.bias, 0)
        self.apply(__init)


def get_inputs():
    return [torch.rand([4, 3, 64, 64])]


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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
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_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 62208
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 72
    x1 = xindex // 72 % 72
    x2 = xindex // 5184
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4095 + -1 * tl_math.abs(-63 + tl_math.abs(-4 +
        x0)) + -64 * tl_math.abs(-63 + tl_math.abs(-4 + x1)) + 4096 * x2),
        xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_red_fused__native_batch_norm_legit_convolution_1(in_out_ptr0,
    in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr,
    RBLOCK: tl.constexpr):
    xnumel = 128
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x3 = xindex
    x0 = xindex % 32
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp4_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp4_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp4_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp0 = tl.load(in_out_ptr0 + (r2 + 4096 * x3), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp2 = tmp0 + tmp1
        tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
        tmp4_mean_next, tmp4_m2_next, tmp4_weight_next = (triton_helpers.
            welford_reduce(tmp3, tmp4_mean, tmp4_m2, tmp4_weight, roffset == 0)
            )
        tmp4_mean = tl.where(rmask & xmask, tmp4_mean_next, tmp4_mean)
        tmp4_m2 = tl.where(rmask & xmask, tmp4_m2_next, tmp4_m2)
        tmp4_weight = tl.where(rmask & xmask, tmp4_weight_next, tmp4_weight)
        tl.store(in_out_ptr0 + (r2 + 4096 * x3), tmp2, rmask & xmask)
    tmp4_tmp, tmp5_tmp, tmp6_tmp = triton_helpers.welford(tmp4_mean,
        tmp4_m2, tmp4_weight, 1)
    tmp4 = tmp4_tmp[:, None]
    tmp5 = tmp5_tmp[:, None]
    tmp6_tmp[:, None]
    tl.store(out_ptr0 + x3, tmp4, xmask)
    tmp7 = 4096.0
    tmp8 = tmp5 / tmp7
    tmp9 = 1e-05
    tmp10 = tmp8 + tmp9
    tmp11 = libdevice.rsqrt(tmp10)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x3, tmp11, xmask)


@triton.jit
def triton_poi_fused_reflection_pad2d_relu_2(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 540800
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 65
    x1 = xindex // 65 % 65
    x2 = xindex // 4225
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4095 + -1 * tl_math.abs(-63 + tl_math.abs(-1 +
        x0)) + -64 * tl_math.abs(-63 + tl_math.abs(-1 + x1)) + 4096 * x2),
        xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp5 = tl.full([1], 0, tl.int32)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(out_ptr0 + x3, tmp6, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_3(in_out_ptr0,
    in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + (r2 + 1024 * x3), None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 1024, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp3 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = 1024.0
    tmp17 = tmp15 / tmp16
    tmp18 = 1e-05
    tmp19 = tmp17 + tmp18
    tmp20 = libdevice.rsqrt(tmp19)
    tl.store(in_out_ptr0 + (r2 + 1024 * x3), tmp2, None)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x3, tmp20, None)
    tl.store(out_ptr0 + x3, tmp10, None)


@triton.jit
def triton_poi_fused_reflection_pad2d_relu_4(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 278784
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 33
    x1 = xindex // 33 % 33
    x2 = xindex // 1089
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (1023 + -1 * tl_math.abs(-31 + tl_math.abs(-1 +
        x0)) + -32 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 1024 * x2),
        xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp5 = tl.full([1], 0, tl.int32)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(out_ptr0 + x3, tmp6, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_5(in_out_ptr0,
    in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (r2 + 256 * x3), None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 256, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp3 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = 256.0
    tmp17 = tmp15 / tmp16
    tmp18 = 1e-05
    tmp19 = tmp17 + tmp18
    tmp20 = libdevice.rsqrt(tmp19)
    tl.store(in_out_ptr0 + (r2 + 256 * x3), tmp2, None)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x3, tmp20, None)
    tl.store(out_ptr0 + x3, tmp10, None)


@triton.jit
def triton_poi_fused_reflection_pad2d_relu_6(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 18
    x1 = xindex // 18 % 18
    x2 = xindex // 324
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (255 + -1 * tl_math.abs(-15 + tl_math.abs(-1 +
        x0)) + -16 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 256 * x2),
        None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x2, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp5 = tl.full([1], 0, tl.int32)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(out_ptr0 + x3, tmp6, None)


@triton.jit
def triton_per_fused__native_batch_norm_legit_add_convolution_relu_7(
    in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr2,
    out_ptr3, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (r2 + 256 * x3), None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr1 + (r2 + 256 * x3), None)
    tmp24 = tl.load(in_ptr2 + x3, None, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr3 + x3, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 256, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp3 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = tmp2 - tmp10
    tmp17 = 256.0
    tmp18 = tmp15 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tmp22 = tmp16 * tmp21
    tmp25 = tmp23 - tmp24
    tmp27 = tmp25 * tmp26
    tmp28 = tl.full([1], 0, tl.int32)
    tmp29 = triton_helpers.maximum(tmp28, tmp27)
    tmp30 = tmp22 + tmp29
    tl.store(in_out_ptr0 + (r2 + 256 * x3), tmp2, None)
    tl.store(out_ptr2 + (r2 + 256 * x3), tmp30, None)
    tl.store(out_ptr3 + x3, tmp21, None)
    tl.store(out_ptr0 + x3, tmp10, None)


@triton.jit
def triton_poi_fused_reflection_pad2d_8(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 18
    x1 = xindex // 18 % 18
    x2 = xindex // 324
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (255 + -1 * tl_math.abs(-15 + tl_math.abs(-1 +
        x0)) + -16 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 256 * x2),
        None, eviction_policy='evict_last')
    tl.store(out_ptr0 + x3, tmp0, None)


@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_9(in_out_ptr0,
    in_ptr0, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (r2 + 256 * x3), None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 256, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp3 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = 256.0
    tmp17 = tmp15 / tmp16
    tmp18 = 1e-05
    tmp19 = tmp17 + tmp18
    tmp20 = libdevice.rsqrt(tmp19)
    tl.store(in_out_ptr0 + (r2 + 256 * x3), tmp2, None)
    tl.store(out_ptr2 + x3, tmp20, None)
    tl.store(out_ptr0 + x3, tmp10, None)
    tl.store(out_ptr1 + x3, tmp15, None)


@triton.jit
def triton_poi_fused_add_reflection_pad2d_10(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 18
    x1 = xindex // 18 % 18
    x2 = xindex // 324
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (255 + -1 * tl_math.abs(-15 + tl_math.abs(-1 +
        x0)) + -16 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 256 * x2),
        None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x2, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (255 + -1 * tl_math.abs(-15 + tl_math.abs(-1 +
        x0)) + -16 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 256 * x2),
        None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 256.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 + tmp10
    tl.store(out_ptr0 + x3, tmp11, None)


@triton.jit
def triton_per_fused__native_batch_norm_legit_add_convolution_11(in_out_ptr0,
    in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr2,
    xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + (r2 + 256 * x3), None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr1 + (r2 + 256 * x3), None)
    tmp24 = tl.load(in_ptr2 + x3, None, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr3 + x3, None, eviction_policy='evict_last')
    tmp31 = tl.load(in_out_ptr1 + (r2 + 256 * x3), None)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 256, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp3 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = tmp2 - tmp10
    tmp17 = 256.0
    tmp18 = tmp15 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tmp22 = tmp16 * tmp21
    tmp25 = tmp23 - tmp24
    tmp27 = tmp26 / tmp17
    tmp28 = tmp27 + tmp19
    tmp29 = libdevice.rsqrt(tmp28)
    tmp30 = tmp25 * tmp29
    tmp32 = tmp30 + tmp31
    tmp33 = tmp22 + tmp32
    tl.store(in_out_ptr0 + (r2 + 256 * x3), tmp2, None)
    tl.store(in_out_ptr1 + (r2 + 256 * x3), tmp33, None)
    tl.store(out_ptr2 + x3, tmp21, None)
    tl.store(out_ptr0 + x3, tmp10, None)


@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_relu_12(in_out_ptr0,
    in_ptr0, out_ptr0, out_ptr2, out_ptr3, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + (r2 + 1024 * x3), None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 1024, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp3 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = tmp2 - tmp10
    tmp17 = 1024.0
    tmp18 = tmp15 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tmp22 = tmp16 * tmp21
    tmp23 = tl.full([1], 0, tl.int32)
    tmp24 = triton_helpers.maximum(tmp23, tmp22)
    tl.store(in_out_ptr0 + (r2 + 1024 * x3), tmp2, None)
    tl.store(out_ptr2 + (r2 + 1024 * x3), tmp24, None)
    tl.store(out_ptr3 + x3, tmp21, None)
    tl.store(out_ptr0 + x3, tmp10, None)


@triton.jit
def triton_poi_fused_reflection_pad2d_relu_13(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 72
    x1 = xindex // 72 % 72
    x2 = xindex // 5184
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4095 + -1 * tl_math.abs(-63 + tl_math.abs(-4 +
        x0)) + -64 * tl_math.abs(-63 + tl_math.abs(-4 + x1)) + 4096 * x2),
        None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x2, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp5 = tl.full([1], 0, tl.int32)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(out_ptr0 + x3, tmp6, None)


@triton.jit
def triton_red_fused__native_batch_norm_legit_add_convolution_mul_tanh_14(
    in_out_ptr0, in_out_ptr1, in_ptr0, out_ptr0, out_ptr1, xnumel, rnumel,
    XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 12
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x3 = xindex
    x0 = xindex % 3
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp4_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp4_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp4_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp0 = tl.load(in_out_ptr0 + (r2 + 4096 * x3), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp2 = tmp0 + tmp1
        tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
        tmp4_mean_next, tmp4_m2_next, tmp4_weight_next = (triton_helpers.
            welford_reduce(tmp3, tmp4_mean, tmp4_m2, tmp4_weight, roffset == 0)
            )
        tmp4_mean = tl.where(rmask & xmask, tmp4_mean_next, tmp4_mean)
        tmp4_m2 = tl.where(rmask & xmask, tmp4_m2_next, tmp4_m2)
        tmp4_weight = tl.where(rmask & xmask, tmp4_weight_next, tmp4_weight)
        tl.store(in_out_ptr0 + (r2 + 4096 * x3), tmp2, rmask & xmask)
    tmp4_tmp, tmp5_tmp, tmp6_tmp = triton_helpers.welford(tmp4_mean,
        tmp4_m2, tmp4_weight, 1)
    tmp4 = tmp4_tmp[:, None]
    tmp5 = tmp5_tmp[:, None]
    tmp6_tmp[:, None]
    tl.store(out_ptr0 + x3, tmp4, xmask)
    tmp7 = 4096.0
    tmp8 = tmp5 / tmp7
    tmp9 = 1e-05
    tmp10 = tmp8 + tmp9
    tmp11 = libdevice.rsqrt(tmp10)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x3, tmp11, xmask)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp12 = tl.load(in_out_ptr0 + (r2 + 4096 * x3), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp13 = tmp12 - tmp4
        tmp14 = tmp13 * tmp11
        tmp15 = libdevice.tanh(tmp14)
        tmp16 = 0.5
        tmp17 = tmp15 * tmp16
        tmp18 = tmp17 + tmp16
        tl.store(out_ptr1 + (r2 + 4096 * x3), tmp18, rmask & xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21, primals_22,
        primals_23, primals_24, primals_25, primals_26, primals_27,
        primals_28, primals_29, primals_30, primals_31, primals_32, primals_33
        ) = args
    args.clear()
    assert_size_stride(primals_1, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_2, (32, 3, 9, 9), (243, 81, 9, 1))
    assert_size_stride(primals_3, (32,), (1,))
    assert_size_stride(primals_4, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (128,), (1,))
    assert_size_stride(primals_8, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_9, (128,), (1,))
    assert_size_stride(primals_10, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_11, (128,), (1,))
    assert_size_stride(primals_12, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_13, (128,), (1,))
    assert_size_stride(primals_14, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_15, (128,), (1,))
    assert_size_stride(primals_16, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_17, (128,), (1,))
    assert_size_stride(primals_18, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_19, (128,), (1,))
    assert_size_stride(primals_20, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_21, (128,), (1,))
    assert_size_stride(primals_22, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_23, (128,), (1,))
    assert_size_stride(primals_24, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_25, (128,), (1,))
    assert_size_stride(primals_26, (128, 128, 3, 3), (1152, 9, 3, 1))
    assert_size_stride(primals_27, (128,), (1,))
    assert_size_stride(primals_28, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_29, (64,), (1,))
    assert_size_stride(primals_30, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_31, (32,), (1,))
    assert_size_stride(primals_32, (3, 32, 9, 9), (2592, 81, 9, 1))
    assert_size_stride(primals_33, (3,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 3, 72, 72), (15552, 5184, 72, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_reflection_pad2d_0[grid(62208)](primals_1, buf0, 
            62208, 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, 32, 64, 64), (131072, 4096, 64, 1))
        buf2 = buf1
        del buf1
        buf3 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 1, 1), torch.float32
            )
        buf4 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128), torch
            .float32)
        buf6 = reinterpret_tensor(buf4, (1, 128, 1, 1), (128, 1, 1, 1), 0)
        del buf4
        triton_red_fused__native_batch_norm_legit_convolution_1[grid(128)](buf2
            , buf6, primals_3, buf3, 128, 4096, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        del primals_3
        buf7 = empty_strided_cuda((4, 32, 65, 65), (135200, 4225, 65, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_relu_2[grid(540800)](buf2, buf3,
            buf6, buf7, 540800, XBLOCK=1024, num_warps=4, num_stages=1)
        buf8 = extern_kernels.convolution(buf7, primals_4, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf9 = buf8
        del buf8
        buf10 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 1, 1), torch.
            float32)
        buf11 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256),
            torch.float32)
        buf13 = reinterpret_tensor(buf11, (1, 256, 1, 1), (256, 1, 1, 1), 0)
        del buf11
        triton_per_fused__native_batch_norm_legit_convolution_3[grid(256)](buf9
            , buf13, primals_5, buf10, 256, 1024, num_warps=8, num_stages=1)
        del primals_5
        buf14 = empty_strided_cuda((4, 64, 33, 33), (69696, 1089, 33, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_relu_4[grid(278784)](buf9, buf10,
            buf13, buf14, 278784, XBLOCK=512, num_warps=8, num_stages=1)
        buf15 = extern_kernels.convolution(buf14, primals_6, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf15, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf16 = buf15
        del buf15
        buf17 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.
            float32)
        buf18 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf20 = reinterpret_tensor(buf18, (1, 512, 1, 1), (512, 1, 1, 1), 0)
        del buf18
        triton_per_fused__native_batch_norm_legit_convolution_5[grid(512)](
            buf16, buf20, primals_7, buf17, 512, 256, num_warps=2, num_stages=1
            )
        del primals_7
        buf21 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_relu_6[grid(165888)](buf16, buf17,
            buf20, buf21, 165888, XBLOCK=1024, num_warps=4, num_stages=1)
        buf22 = extern_kernels.convolution(buf21, primals_8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf22, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf23 = buf22
        del buf22
        buf24 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.
            float32)
        buf25 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf27 = reinterpret_tensor(buf25, (1, 512, 1, 1), (512, 1, 1, 1), 0)
        del buf25
        triton_per_fused__native_batch_norm_legit_convolution_5[grid(512)](
            buf23, buf27, primals_9, buf24, 512, 256, num_warps=2, num_stages=1
            )
        del primals_9
        buf28 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_relu_6[grid(165888)](buf23, buf24,
            buf27, buf28, 165888, XBLOCK=1024, num_warps=4, num_stages=1)
        buf29 = extern_kernels.convolution(buf28, primals_10, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf29, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf30 = buf29
        del buf29
        buf31 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf35 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
            torch.float32)
        buf34 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_add_convolution_relu_7[grid
            (512)](buf30, primals_11, buf16, buf17, buf20, buf31, buf35,
            buf34, 512, 256, num_warps=2, num_stages=1)
        del primals_11
        buf36 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_8[grid(165888)](buf35, buf36, 
            165888, XBLOCK=512, num_warps=8, num_stages=1)
        buf37 = extern_kernels.convolution(buf36, primals_12, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf37, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf38 = buf37
        del buf37
        buf39 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.
            float32)
        buf40 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf42 = reinterpret_tensor(buf40, (1, 512, 1, 1), (512, 1, 1, 1), 0)
        del buf40
        triton_per_fused__native_batch_norm_legit_convolution_5[grid(512)](
            buf38, buf42, primals_13, buf39, 512, 256, num_warps=2,
            num_stages=1)
        del primals_13
        buf43 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_relu_6[grid(165888)](buf38, buf39,
            buf42, buf43, 165888, XBLOCK=1024, num_warps=4, num_stages=1)
        buf44 = extern_kernels.convolution(buf43, primals_14, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf44, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf45 = buf44
        del buf44
        buf46 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf47 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf49 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_convolution_9[grid(512)](
            buf45, primals_15, buf46, buf47, buf49, 512, 256, num_warps=2,
            num_stages=1)
        del primals_15
        buf50 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_add_reflection_pad2d_10[grid(165888)](buf45, buf46,
            buf47, buf35, buf50, 165888, XBLOCK=1024, num_warps=4, num_stages=1
            )
        buf51 = extern_kernels.convolution(buf50, primals_16, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf51, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf52 = buf51
        del buf51
        buf53 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.
            float32)
        buf54 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf56 = reinterpret_tensor(buf54, (1, 512, 1, 1), (512, 1, 1, 1), 0)
        del buf54
        triton_per_fused__native_batch_norm_legit_convolution_5[grid(512)](
            buf52, buf56, primals_17, buf53, 512, 256, num_warps=2,
            num_stages=1)
        del primals_17
        buf57 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_relu_6[grid(165888)](buf52, buf53,
            buf56, buf57, 165888, XBLOCK=1024, num_warps=4, num_stages=1)
        buf58 = extern_kernels.convolution(buf57, primals_18, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf58, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf59 = buf58
        del buf58
        buf60 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf64 = buf35
        del buf35
        buf63 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_add_convolution_11[grid(512)
            ](buf59, buf64, primals_19, buf45, buf46, buf47, buf60, buf63, 
            512, 256, num_warps=2, num_stages=1)
        del primals_19
        buf65 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_8[grid(165888)](buf64, buf65, 
            165888, XBLOCK=512, num_warps=8, num_stages=1)
        buf66 = extern_kernels.convolution(buf65, primals_20, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf66, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf67 = buf66
        del buf66
        buf68 = reinterpret_tensor(buf47, (1, 512, 1, 1), (512, 1, 1, 1), 0)
        del buf47
        buf69 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf71 = reinterpret_tensor(buf69, (1, 512, 1, 1), (512, 1, 1, 1), 0)
        del buf69
        triton_per_fused__native_batch_norm_legit_convolution_5[grid(512)](
            buf67, buf71, primals_21, buf68, 512, 256, num_warps=2,
            num_stages=1)
        del primals_21
        buf72 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_relu_6[grid(165888)](buf67, buf68,
            buf71, buf72, 165888, XBLOCK=1024, num_warps=4, num_stages=1)
        buf73 = extern_kernels.convolution(buf72, primals_22, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf73, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf74 = buf73
        del buf73
        buf75 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf76 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf78 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_convolution_9[grid(512)](
            buf74, primals_23, buf75, buf76, buf78, 512, 256, num_warps=2,
            num_stages=1)
        del primals_23
        buf79 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_add_reflection_pad2d_10[grid(165888)](buf74, buf75,
            buf76, buf64, buf79, 165888, XBLOCK=1024, num_warps=4, num_stages=1
            )
        buf80 = extern_kernels.convolution(buf79, primals_24, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf80, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf81 = buf80
        del buf80
        buf82 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 1, 1), torch.
            float32)
        buf83 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf85 = reinterpret_tensor(buf83, (1, 512, 1, 1), (512, 1, 1, 1), 0)
        del buf83
        triton_per_fused__native_batch_norm_legit_convolution_5[grid(512)](
            buf81, buf85, primals_25, buf82, 512, 256, num_warps=2,
            num_stages=1)
        del primals_25
        buf86 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_relu_6[grid(165888)](buf81, buf82,
            buf85, buf86, 165888, XBLOCK=1024, num_warps=4, num_stages=1)
        buf87 = extern_kernels.convolution(buf86, primals_26, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf87, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf88 = buf87
        del buf87
        buf89 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        buf93 = buf64
        del buf64
        buf92 = empty_strided_cuda((1, 512, 1, 1), (512, 1, 512, 512),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_add_convolution_11[grid(512)
            ](buf88, buf93, primals_27, buf74, buf75, buf76, buf89, buf92, 
            512, 256, num_warps=2, num_stages=1)
        del buf76
        del primals_27
        buf94 = extern_kernels.convolution(buf93, primals_28, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(1, 1), groups=1, bias=None)
        assert_size_stride(buf94, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf95 = buf94
        del buf94
        buf96 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256),
            torch.float32)
        buf100 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
            torch.float32)
        buf99 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256),
            torch.float32)
        triton_per_fused__native_batch_norm_legit_convolution_relu_12[grid(256)
            ](buf95, primals_29, buf96, buf100, buf99, 256, 1024, num_warps
            =8, num_stages=1)
        del primals_29
        buf101 = extern_kernels.convolution(buf100, primals_30, stride=(2, 
            2), padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(1, 1), groups=1, bias=None)
        assert_size_stride(buf101, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf102 = buf101
        del buf101
        buf103 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 1, 1), torch.
            float32)
        buf104 = empty_strided_cuda((1, 128, 1, 1), (128, 1, 128, 128),
            torch.float32)
        buf106 = reinterpret_tensor(buf104, (1, 128, 1, 1), (128, 1, 1, 1), 0)
        del buf104
        triton_red_fused__native_batch_norm_legit_convolution_1[grid(128)](
            buf102, buf106, primals_31, buf103, 128, 4096, XBLOCK=1, RBLOCK
            =2048, num_warps=16, num_stages=1)
        del primals_31
        buf107 = empty_strided_cuda((4, 32, 72, 72), (165888, 5184, 72, 1),
            torch.float32)
        triton_poi_fused_reflection_pad2d_relu_13[grid(663552)](buf102,
            buf103, buf106, buf107, 663552, XBLOCK=1024, num_warps=4,
            num_stages=1)
        buf108 = extern_kernels.convolution(buf107, primals_32, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf108, (4, 3, 64, 64), (12288, 4096, 64, 1))
        buf109 = buf108
        del buf108
        buf110 = empty_strided_cuda((1, 12, 1, 1), (12, 1, 1, 1), torch.float32
            )
        buf111 = empty_strided_cuda((1, 12, 1, 1), (12, 1, 12, 12), torch.
            float32)
        buf113 = reinterpret_tensor(buf111, (1, 12, 1, 1), (12, 1, 1, 1), 0)
        del buf111
        buf114 = empty_strided_cuda((4, 3, 64, 64), (12288, 4096, 64, 1),
            torch.float32)
        triton_red_fused__native_batch_norm_legit_add_convolution_mul_tanh_14[
            grid(12)](buf109, buf113, primals_33, buf110, buf114, 12, 4096,
            XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1)
        del primals_33
    return (buf114, primals_2, primals_4, primals_6, primals_8, primals_10,
        primals_12, primals_14, primals_16, primals_18, primals_20,
        primals_22, primals_24, primals_26, primals_28, primals_30,
        primals_32, buf0, buf2, buf3, buf6, buf7, buf9, buf10, buf13, buf14,
        buf16, buf17, buf20, buf21, buf23, buf24, buf27, buf28, buf30,
        reinterpret_tensor(buf34, (512,), (1,), 0), buf36, buf38, buf39,
        buf42, buf43, buf45, reinterpret_tensor(buf49, (512,), (1,), 0),
        buf50, buf52, buf53, buf56, buf57, buf59, reinterpret_tensor(buf63,
        (512,), (1,), 0), buf65, buf67, buf68, buf71, buf72, buf74,
        reinterpret_tensor(buf78, (512,), (1,), 0), buf79, buf81, buf82,
        buf85, buf86, buf88, reinterpret_tensor(buf92, (512,), (1,), 0),
        buf93, buf95, reinterpret_tensor(buf99, (256,), (1,), 0), buf100,
        buf102, buf103, buf106, buf107, buf109, buf110, buf113,
        reinterpret_tensor(buf96, (1, 256, 1, 1), (256, 1, 1, 1), 0),
        reinterpret_tensor(buf89, (1, 512, 1, 1), (512, 1, 1, 1), 0),
        reinterpret_tensor(buf75, (1, 512, 1, 1), (512, 1, 1, 1), 0),
        reinterpret_tensor(buf60, (1, 512, 1, 1), (512, 1, 1, 1), 0),
        reinterpret_tensor(buf46, (1, 512, 1, 1), (512, 1, 1, 1), 0),
        reinterpret_tensor(buf31, (1, 512, 1, 1), (512, 1, 1, 1), 0))


def reflect_padding(x, f, s, half=False):
    if half:
        denom = 2
    else:
        denom = 1
    _, _, h, w = x.shape
    pad_w = w * (s / denom - 1) + f - s
    pad_h = h * (s / denom - 1) + f - s
    if pad_w % 2 == 1:
        pad_l = int(pad_w // 2) + 1
        pad_r = int(pad_w // 2)
    else:
        pad_l = pad_r = int(pad_w / 2)
    if pad_h % 2 == 1:
        pad_t = int(pad_h // 2) + 1
        pad_b = int(pad_h // 2)
    else:
        pad_t = pad_b = int(pad_h / 2)
    return F.pad(x, [pad_l, pad_r, pad_t, pad_b], mode='reflect')


class Residual(nn.Module):
    """Unlinke other blocks, this module receives unpadded inputs."""

    def __init__(self, channels, kernel_size=3):
        super(Residual, self).__init__()
        padding = int((kernel_size - 1) / 2)
        self.pad = nn.ReflectionPad2d(padding)
        self.conv1 = nn.Conv2d(channels, channels, kernel_size)
        self.bn1 = nn.InstanceNorm2d(channels)
        self.conv2 = nn.Conv2d(channels, channels, kernel_size)
        self.bn2 = nn.InstanceNorm2d(channels)

    def forward(self, x):
        h = self.pad(x)
        h = self.conv1(h)
        h = self.bn1(h)
        h = F.relu(h)
        h = self.pad(h)
        h = self.conv2(h)
        h = self.bn2(h)
        h = h + x
        return h


class Conv(nn.Module):
    """Convolutional block. 2d-conv -> batch norm -> (optionally) relu"""

    def __init__(self, in_channels, out_channels, kernel, stride=1,
        use_relu=True):
        super(Conv, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel, stride)
        self.batch_norm = nn.InstanceNorm2d(out_channels)
        self.use_relu = use_relu

    def forward(self, x):
        h = self.conv(x)
        h = self.batch_norm(h)
        if self.use_relu:
            h = F.relu(h)
        return h


class ConvTran(nn.Module):

    def __init__(self, in_channels, out_channels):
        super(ConvTran, self).__init__()
        self.conv_t = nn.ConvTranspose2d(in_channels, out_channels, 3, 2, 1, 1)
        self.batch_norm = nn.InstanceNorm2d(out_channels)

    def forward(self, x):
        h = self.conv_t(x)
        h = self.batch_norm(h)
        return F.relu(h)


class FastStyleNew(nn.Module):

    def __init__(self):
        super(FastStyleNew, self).__init__()
        self.conv1 = Conv(3, 32, 9)
        self.conv2 = Conv(32, 64, 3, 2)
        self.conv3 = Conv(64, 128, 3, 2)
        self.res1 = Residual(128)
        self.res2 = Residual(128)
        self.res3 = Residual(128)
        self.res4 = Residual(128)
        self.res5 = Residual(128)
        self.convT1 = ConvTran(128, 64)
        self.convT2 = ConvTran(64, 32)
        self.conv_out = Conv(32, 3, 9, use_relu=False)
        self._init()

    def _init(self):

        def __init(m):
            if isinstance(m, nn.Conv2d):
                nn.init.xavier_uniform_(m.weight)
                nn.init.constant_(m.bias, 0)
            if isinstance(m, nn.ConvTranspose2d):
                nn.init.xavier_uniform_(m.weight)
                nn.init.constant_(m.bias, 0)
        self.apply(__init)

    def forward(self, input_0):
        primals_2 = self.conv1.conv.weight
        primals_3 = self.conv1.conv.bias
        primals_4 = self.conv2.conv.weight
        primals_5 = self.conv2.conv.bias
        primals_6 = self.conv3.conv.weight
        primals_7 = self.conv3.conv.bias
        primals_8 = self.res1.conv1.weight
        primals_9 = self.res1.conv1.bias
        primals_10 = self.res1.conv2.weight
        primals_11 = self.res1.conv2.bias
        primals_12 = self.res2.conv1.weight
        primals_13 = self.res2.conv1.bias
        primals_14 = self.res2.conv2.weight
        primals_15 = self.res2.conv2.bias
        primals_16 = self.res3.conv1.weight
        primals_17 = self.res3.conv1.bias
        primals_18 = self.res3.conv2.weight
        primals_19 = self.res3.conv2.bias
        primals_20 = self.res4.conv1.weight
        primals_21 = self.res4.conv1.bias
        primals_22 = self.res4.conv2.weight
        primals_23 = self.res4.conv2.bias
        primals_24 = self.res5.conv1.weight
        primals_25 = self.res5.conv1.bias
        primals_26 = self.res5.conv2.weight
        primals_27 = self.res5.conv2.bias
        primals_28 = self.convT1.conv_t.weight
        primals_29 = self.convT1.conv_t.bias
        primals_30 = self.convT2.conv_t.weight
        primals_31 = self.convT2.conv_t.bias
        primals_32 = self.conv_out.conv.weight
        primals_33 = self.conv_out.conv.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21, primals_22, primals_23, primals_24,
            primals_25, primals_26, primals_27, primals_28, primals_29,
            primals_30, primals_31, primals_32, primals_33])
        return output[0]
