# 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/v6/cv6oewqqnsshd7he7ylh2kikzu4smtrhj2dmv6nb5csosp7g6vw5.py
# Topologically Sorted Source Nodes: [h], Original ATen: [aten.reflection_pad2d]
# Source node to ATen node mapping:
#   h => _unsafe_index, _unsafe_index_1
# Graph fragment:
#   %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %sub_1, None]), kwargs = {})
#   %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_1]), kwargs = {})
triton_poi_fused_reflection_pad2d_0 = async_compile.triton('triton_poi_fused_reflection_pad2d_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = (xindex // 6) % 6
    x2 = (xindex // 36)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-1) + x0))))) + ((-4)*(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (16*x2)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/t6/ct6syu6rq3n7yx3zuog2yujcrfreefdccraqz7zj2m3c5xhvp5vl.py
# Topologically Sorted Source Nodes: [h_1, h_2], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
# Source node to ATen node mapping:
#   h_1 => convolution
#   h_2 => 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_per_fused__native_batch_norm_legit_convolution_1 = async_compile.triton('triton_per_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.persistent_reduction(
    size_hints=[16, 16],
    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_1', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, '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_1(in_out_ptr0, in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (r2 + (16*x3)), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp3 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 16.0
    tmp20 = tmp18 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(in_out_ptr0 + (r2 + (16*x3)), tmp2, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (x3), tmp23, xmask)
    tl.store(out_ptr0 + (x3), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/4g/c4gxhiwlpoal6lv5zh3kpusgrnvvqwr3ii4nio63jz2y3dy4a2zd.py
# Topologically Sorted Source Nodes: [h_3, h_4], Original ATen: [aten.leaky_relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
#   h_3 => gt, mul_1, where
#   h_4 => _unsafe_index_2, _unsafe_index_3
# Graph fragment:
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_1, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, 0.01), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %view_1, %mul_1), kwargs = {})
#   %_unsafe_index_2 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where, [None, None, %sub_1, 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_1]), kwargs = {})
triton_poi_fused_leaky_relu_reflection_pad2d_2 = async_compile.triton('triton_poi_fused_leaky_relu_reflection_pad2d_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=[1024], 
    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_leaky_relu_reflection_pad2d_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_leaky_relu_reflection_pad2d_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = (xindex // 6) % 6
    x2 = (xindex // 36)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-1) + x0))))) + ((-4)*(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (16*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 = 0.0
    tmp6 = tmp4 > tmp5
    tmp7 = 0.01
    tmp8 = tmp4 * tmp7
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tl.store(out_ptr0 + (x3), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ri/criczngteyj7wcvmicmqifwre7qk2r7n4sjhob5tk23p3nr7iook.py
# Topologically Sorted Source Nodes: [h_8], Original ATen: [aten.reflection_pad2d]
# Source node to ATen node mapping:
#   h_8 => iota_4
# Graph fragment:
#   %iota_4 : [num_users=2] = call_function[target=torch.ops.prims.iota.default](args = (4,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
triton_poi_fused_reflection_pad2d_3 = async_compile.triton('triton_poi_fused_reflection_pad2d_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '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,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_reflection_pad2d_3(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/hj/chj77o4nupyfxq6d3hw54z3aiz5ovrwexany7zkndbjuhpnaugk3.py
# Topologically Sorted Source Nodes: [h_7, h_8], Original ATen: [aten.leaky_relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
#   h_7 => gt_1, mul_3, where_1
#   h_8 => _unsafe_index_4, _unsafe_index_5
# Graph fragment:
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_3, 0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_3, 0.01), kwargs = {})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %view_3, %mul_3), kwargs = {})
#   %_unsafe_index_4 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%where_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_leaky_relu_reflection_pad2d_4 = async_compile.triton('triton_poi_fused_leaky_relu_reflection_pad2d_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=[256], 
    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_leaky_relu_reflection_pad2d_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_leaky_relu_reflection_pad2d_4(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-3) + x0))) + ((-4)*(tl_math.abs((-3) + x1))) + (16*x2)), xmask)
    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 = 0.0
    tmp6 = tmp4 > tmp5
    tmp7 = 0.01
    tmp8 = tmp4 * tmp7
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tl.store(out_ptr0 + (x3), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/5n/c5n7uaqqcbcr4yru2xvurb7r7cjxulmu2fwssq5bczumc7gy2if7.py
# Topologically Sorted Source Nodes: [h_9, h_10, h_11], Original ATen: [aten.convolution, aten._native_batch_norm_legit, aten.leaky_relu]
# Source node to ATen node mapping:
#   h_10 => add_2, rsqrt_2, var_mean_2
#   h_11 => gt_2, mul_5, where_2
#   h_9 => convolution_2
# Graph fragment:
#   %convolution_2 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_5, %primals_6, %primals_7, [1, 1], [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 = {})
#   %gt_2 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_5, 0), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_5, 0.01), kwargs = {})
#   %where_2 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %view_5, %mul_5), kwargs = {})
triton_per_fused__native_batch_norm_legit_convolution_leaky_relu_5 = async_compile.triton('triton_per_fused__native_batch_norm_legit_convolution_leaky_relu_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=[16, 16],
    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_leaky_relu_5', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, '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_leaky_relu_5(in_out_ptr0, in_out_ptr1, in_ptr0, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (r2 + (16*x3)), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp3 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 16.0
    tmp20 = tmp18 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tmp24 = tmp2 - tmp12
    tmp25 = tmp24 * tmp23
    tmp26 = 0.0
    tmp27 = tmp25 > tmp26
    tmp28 = 0.01
    tmp29 = tmp25 * tmp28
    tmp30 = tl.where(tmp27, tmp25, tmp29)
    tl.store(in_out_ptr0 + (r2 + (16*x3)), tmp2, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (x3), tmp23, xmask)
    tl.store(out_ptr1 + (r2 + (16*x3)), tmp30, xmask)
    tl.store(out_ptr0 + (x3), tmp12, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h], Original ATen: [aten.reflection_pad2d]
        stream0 = get_raw_stream(0)
        triton_poi_fused_reflection_pad2d_0.run(primals_1, buf0, 576, grid=grid(576), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [h_1], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = buf1; del buf1  # reuse
        buf3 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        buf4 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        buf6 = reinterpret_tensor(buf4, (1, 16, 1, 1), (16, 1, 1, 1), 0); del buf4  # reuse
        # Topologically Sorted Source Nodes: [h_1, h_2], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_convolution_1.run(buf2, buf6, primals_3, buf3, 16, 16, grid=grid(16), stream=stream0)
        del primals_3
        buf7 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_3, h_4], Original ATen: [aten.leaky_relu, aten.reflection_pad2d]
        triton_poi_fused_leaky_relu_reflection_pad2d_2.run(buf2, buf3, buf6, buf7, 576, grid=grid(576), stream=stream0)
        # Topologically Sorted Source Nodes: [h_5], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf7, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 4, 4, 4), (64, 16, 4, 1))
        buf9 = buf8; del buf8  # reuse
        buf10 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        buf11 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        buf13 = reinterpret_tensor(buf11, (1, 16, 1, 1), (16, 1, 1, 1), 0); del buf11  # reuse
        # Topologically Sorted Source Nodes: [h_5, h_6], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_convolution_1.run(buf9, buf13, primals_5, buf10, 16, 16, grid=grid(16), stream=stream0)
        del primals_5
        buf14 = empty_strided_cuda((4, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [h_8], Original ATen: [aten.reflection_pad2d]
        triton_poi_fused_reflection_pad2d_3.run(buf14, 4, grid=grid(4), stream=stream0)
        buf15 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_7, h_8], Original ATen: [aten.leaky_relu, aten.reflection_pad2d]
        triton_poi_fused_leaky_relu_reflection_pad2d_4.run(buf9, buf10, buf13, buf15, 256, grid=grid(256), stream=stream0)
        # Topologically Sorted Source Nodes: [h_9], Original ATen: [aten.convolution]
        buf16 = extern_kernels.convolution(buf15, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 4, 4, 4), (64, 16, 4, 1))
        buf17 = buf16; del buf16  # reuse
        buf18 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        buf19 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        buf21 = reinterpret_tensor(buf19, (1, 16, 1, 1), (16, 1, 1, 1), 0); del buf19  # reuse
        buf22 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_9, h_10, h_11], Original ATen: [aten.convolution, aten._native_batch_norm_legit, aten.leaky_relu]
        triton_per_fused__native_batch_norm_legit_convolution_leaky_relu_5.run(buf17, buf21, primals_7, buf18, buf22, 16, 16, grid=grid(16), stream=stream0)
        del primals_7
    return (buf22, primals_2, primals_4, primals_6, buf0, buf2, buf3, buf6, buf7, buf9, buf10, buf13, buf14, buf15, buf17, buf18, buf21, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
    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 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 _TextureConvGroup(nn.Module):
    """Group of 3 convolutional blocks.

    1.- reflect_padding()
    2.- Conv(in_channels, out_channels, kernel=3, use_relu=False)
    3.- LeakyReLU()
    4.- reflect_padding()
    5.- Conv(out_channels, out_channels, kernel=3)
    6.- LeakyReLU()
    7.- reflect_padding()
    8.- Conv(out_channels, out_channels, kernel=1)
    9.- LeakyReLU()
    """

    def __init__(self, in_channels, out_channels):
        super(_TextureConvGroup, self).__init__()
        self.block1 = Conv(in_channels, out_channels, 3, use_relu=False)
        self.block2 = Conv(out_channels, out_channels, 3, use_relu=False)
        self.block3 = Conv(out_channels, out_channels, 1, use_relu=False)

    def forward(self, x):
        h = reflect_padding(x, 3, 1)
        h = self.block1(h)
        h = F.leaky_relu(h)
        h = reflect_padding(h, 3, 1)
        h = self.block2(h)
        h = F.leaky_relu(h)
        h = reflect_padding(h, 1, 1)
        h = self.block3(h)
        h = F.leaky_relu(h)
        return h


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
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 = 576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = xindex // 6 % 6
    x2 = xindex // 36
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
        x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2),
        xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_1(in_out_ptr0,
    in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (r2 + 16 * x3), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp3 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 16.0
    tmp20 = tmp18 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(in_out_ptr0 + (r2 + 16 * x3), tmp2, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x3, tmp23, xmask)
    tl.store(out_ptr0 + x3, tmp12, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_reflection_pad2d_2(in_ptr0, in_ptr1,
    in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = xindex // 6 % 6
    x2 = xindex // 36
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
        x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * 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 = 0.0
    tmp6 = tmp4 > tmp5
    tmp7 = 0.01
    tmp8 = tmp4 * tmp7
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tl.store(out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused_reflection_pad2d_3(out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_reflection_pad2d_4(in_ptr0, in_ptr1,
    in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + x0) + -4 * tl_math
        .abs(-3 + x1) + 16 * x2), xmask)
    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 = 0.0
    tmp6 = tmp4 > tmp5
    tmp7 = 0.01
    tmp8 = tmp4 * tmp7
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tl.store(out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_leaky_relu_5(
    in_out_ptr0, in_out_ptr1, in_ptr0, out_ptr0, out_ptr1, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (r2 + 16 * x3), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp3 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 16.0
    tmp20 = tmp18 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tmp24 = tmp2 - tmp12
    tmp25 = tmp24 * tmp23
    tmp26 = 0.0
    tmp27 = tmp25 > tmp26
    tmp28 = 0.01
    tmp29 = tmp25 * tmp28
    tmp30 = tl.where(tmp27, tmp25, tmp29)
    tl.store(in_out_ptr0 + (r2 + 16 * x3), tmp2, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x3, tmp23, xmask)
    tl.store(out_ptr1 + (r2 + 16 * x3), tmp30, xmask)
    tl.store(out_ptr0 + x3, tmp12, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_reflection_pad2d_0[grid(576)](primals_1, buf0, 576,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = buf1
        del buf1
        buf3 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        buf4 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        buf6 = reinterpret_tensor(buf4, (1, 16, 1, 1), (16, 1, 1, 1), 0)
        del buf4
        triton_per_fused__native_batch_norm_legit_convolution_1[grid(16)](buf2,
            buf6, primals_3, buf3, 16, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del primals_3
        buf7 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
        triton_poi_fused_leaky_relu_reflection_pad2d_2[grid(576)](buf2,
            buf3, buf6, buf7, 576, XBLOCK=256, num_warps=4, num_stages=1)
        buf8 = extern_kernels.convolution(buf7, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 4, 4, 4), (64, 16, 4, 1))
        buf9 = buf8
        del buf8
        buf10 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        buf11 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
            float32)
        buf13 = reinterpret_tensor(buf11, (1, 16, 1, 1), (16, 1, 1, 1), 0)
        del buf11
        triton_per_fused__native_batch_norm_legit_convolution_1[grid(16)](buf9,
            buf13, primals_5, buf10, 16, 16, XBLOCK=1, num_warps=2,
            num_stages=1)
        del primals_5
        buf14 = empty_strided_cuda((4,), (1,), torch.int64)
        triton_poi_fused_reflection_pad2d_3[grid(4)](buf14, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        buf15 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_leaky_relu_reflection_pad2d_4[grid(256)](buf9,
            buf10, buf13, buf15, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf16 = extern_kernels.convolution(buf15, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 4, 4, 4), (64, 16, 4, 1))
        buf17 = buf16
        del buf16
        buf18 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        buf19 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
            float32)
        buf21 = reinterpret_tensor(buf19, (1, 16, 1, 1), (16, 1, 1, 1), 0)
        del buf19
        buf22 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_per_fused__native_batch_norm_legit_convolution_leaky_relu_5[grid
            (16)](buf17, buf21, primals_7, buf18, buf22, 16, 16, XBLOCK=8,
            num_warps=2, num_stages=1)
        del primals_7
    return (buf22, primals_2, primals_4, primals_6, buf0, buf2, buf3, buf6,
        buf7, buf9, buf10, buf13, buf14, buf15, buf17, buf18, buf21)


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 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 _TextureConvGroupNew(nn.Module):
    """Group of 3 convolutional blocks.

    1.- reflect_padding()
    2.- Conv(in_channels, out_channels, kernel=3, use_relu=False)
    3.- LeakyReLU()
    4.- reflect_padding()
    5.- Conv(out_channels, out_channels, kernel=3)
    6.- LeakyReLU()
    7.- reflect_padding()
    8.- Conv(out_channels, out_channels, kernel=1)
    9.- LeakyReLU()
    """

    def __init__(self, in_channels, out_channels):
        super(_TextureConvGroupNew, self).__init__()
        self.block1 = Conv(in_channels, out_channels, 3, use_relu=False)
        self.block2 = Conv(out_channels, out_channels, 3, use_relu=False)
        self.block3 = Conv(out_channels, out_channels, 1, use_relu=False)

    def forward(self, input_0):
        primals_2 = self.block1.conv.weight
        primals_3 = self.block1.conv.bias
        primals_4 = self.block2.conv.weight
        primals_5 = self.block2.conv.bias
        primals_6 = self.block3.conv.weight
        primals_7 = self.block3.conv.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
