# 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_7/inductor_cache/ry/cryiobypkawkmdldevamc2xgthshbnrm6is6ppwpjekkiy25lx7z.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %where], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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=[512], 
    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_cat_0', '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_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 8
    x0 = xindex % 16
    x2 = (xindex // 128)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x1) + (64*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (16*((-4) + x1)) + (64*x2)), tmp6 & xmask, other=0.0)
    tmp10 = tl.load(in_ptr2 + ((-4) + x1), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = 0.0
    tmp13 = tmp11 > tmp12
    tmp14 = 0.2
    tmp15 = tmp11 * tmp14
    tmp16 = tl.where(tmp13, tmp11, tmp15)
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp6, tmp16, tmp17)
    tmp19 = tl.where(tmp4, tmp5, tmp18)
    tl.store(out_ptr0 + (x3), tmp19, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/pd/cpd4meipbe2zv2hwyhb6cjpvxof6tzs5i6jeu6dqinxcb26whgcv.py
# Topologically Sorted Source Nodes: [cat_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat_1 => cat_1
# Graph fragment:
#   %cat_1 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %where, %where_1], 1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 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_poi_fused_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_cat_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 12
    x0 = xindex % 16
    x2 = (xindex // 192)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x1) + (64*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + (16*((-4) + x1)) + (64*x2)), tmp9 & xmask, other=0.0)
    tmp11 = tl.load(in_ptr2 + ((-4) + x1), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = 0.0
    tmp14 = tmp12 > tmp13
    tmp15 = 0.2
    tmp16 = tmp12 * tmp15
    tmp17 = tl.where(tmp14, tmp12, tmp16)
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp9, tmp17, tmp18)
    tmp20 = tmp0 >= tmp7
    tmp21 = tl.full([1], 12, tl.int64)
    tmp22 = tmp0 < tmp21
    tmp23 = tl.load(in_ptr3 + (x0 + (16*((-8) + x1)) + (64*x2)), tmp20 & xmask, other=0.0)
    tmp24 = tl.load(in_ptr4 + ((-8) + x1), tmp20 & xmask, eviction_policy='evict_last', other=0.0)
    tmp25 = tmp23 + tmp24
    tmp26 = tmp25 > tmp13
    tmp27 = tmp25 * tmp15
    tmp28 = tl.where(tmp26, tmp25, tmp27)
    tmp29 = tl.full(tmp28.shape, 0.0, tmp28.dtype)
    tmp30 = tl.where(tmp20, tmp28, tmp29)
    tmp31 = tl.where(tmp9, tmp19, tmp30)
    tmp32 = tl.where(tmp4, tmp5, tmp31)
    tl.store(out_ptr0 + (x3), tmp32, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/3c/c3cglho5wyr4b4bwpkwiar5a4l3ewyyzusu3gf2a2qlomqutylik.py
# Topologically Sorted Source Nodes: [cat_2], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat_2 => cat_2
# Graph fragment:
#   %cat_2 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %where, %where_1, %where_2], 1), kwargs = {})
triton_poi_fused_cat_2 = async_compile.triton('triton_poi_fused_cat_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: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, '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_cat_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 16
    x0 = xindex % 16
    x2 = (xindex // 256)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x1) + (64*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + (16*((-4) + x1)) + (64*x2)), tmp9 & xmask, other=0.0)
    tmp11 = tl.load(in_ptr2 + ((-4) + x1), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = 0.0
    tmp14 = tmp12 > tmp13
    tmp15 = 0.2
    tmp16 = tmp12 * tmp15
    tmp17 = tl.where(tmp14, tmp12, tmp16)
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp9, tmp17, tmp18)
    tmp20 = tmp0 >= tmp7
    tmp21 = tl.full([1], 12, tl.int64)
    tmp22 = tmp0 < tmp21
    tmp23 = tmp20 & tmp22
    tmp24 = tl.load(in_ptr3 + (x0 + (16*((-8) + x1)) + (64*x2)), tmp23 & xmask, other=0.0)
    tmp25 = tl.load(in_ptr4 + ((-8) + x1), tmp23 & xmask, eviction_policy='evict_last', other=0.0)
    tmp26 = tmp24 + tmp25
    tmp27 = tmp26 > tmp13
    tmp28 = tmp26 * tmp15
    tmp29 = tl.where(tmp27, tmp26, tmp28)
    tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype)
    tmp31 = tl.where(tmp23, tmp29, tmp30)
    tmp32 = tmp0 >= tmp21
    tmp33 = tl.full([1], 16, tl.int64)
    tmp34 = tmp0 < tmp33
    tmp35 = tl.load(in_ptr5 + (x0 + (16*((-12) + x1)) + (64*x2)), tmp32 & xmask, other=0.0)
    tmp36 = tl.load(in_ptr6 + ((-12) + x1), tmp32 & xmask, eviction_policy='evict_last', other=0.0)
    tmp37 = tmp35 + tmp36
    tmp38 = tmp37 > tmp13
    tmp39 = tmp37 * tmp15
    tmp40 = tl.where(tmp38, tmp37, tmp39)
    tmp41 = tl.full(tmp40.shape, 0.0, tmp40.dtype)
    tmp42 = tl.where(tmp32, tmp40, tmp41)
    tmp43 = tl.where(tmp23, tmp31, tmp42)
    tmp44 = tl.where(tmp9, tmp19, tmp43)
    tmp45 = tl.where(tmp4, tmp5, tmp44)
    tl.store(out_ptr0 + (x3), tmp45, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/la/clagauvrhsnsmt2s2mrzhhfmw6omixx6owqe7iwdjdtls52tkc5v.py
# Topologically Sorted Source Nodes: [cat_3], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat_3 => cat_3
# Graph fragment:
#   %cat_3 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %where, %where_1, %where_2, %where_3], 1), kwargs = {})
triton_poi_fused_cat_3 = async_compile.triton('triton_poi_fused_cat_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=[2048], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: '*fp32', 10: '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, 10), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, '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_cat_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1280
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 20
    x0 = xindex % 16
    x2 = (xindex // 320)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x1) + (64*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + (16*((-4) + x1)) + (64*x2)), tmp9 & xmask, other=0.0)
    tmp11 = tl.load(in_ptr2 + ((-4) + x1), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = 0.0
    tmp14 = tmp12 > tmp13
    tmp15 = 0.2
    tmp16 = tmp12 * tmp15
    tmp17 = tl.where(tmp14, tmp12, tmp16)
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp9, tmp17, tmp18)
    tmp20 = tmp0 >= tmp7
    tmp21 = tl.full([1], 12, tl.int64)
    tmp22 = tmp0 < tmp21
    tmp23 = tmp20 & tmp22
    tmp24 = tl.load(in_ptr3 + (x0 + (16*((-8) + x1)) + (64*x2)), tmp23 & xmask, other=0.0)
    tmp25 = tl.load(in_ptr4 + ((-8) + x1), tmp23 & xmask, eviction_policy='evict_last', other=0.0)
    tmp26 = tmp24 + tmp25
    tmp27 = tmp26 > tmp13
    tmp28 = tmp26 * tmp15
    tmp29 = tl.where(tmp27, tmp26, tmp28)
    tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype)
    tmp31 = tl.where(tmp23, tmp29, tmp30)
    tmp32 = tmp0 >= tmp21
    tmp33 = tl.full([1], 16, tl.int64)
    tmp34 = tmp0 < tmp33
    tmp35 = tmp32 & tmp34
    tmp36 = tl.load(in_ptr5 + (x0 + (16*((-12) + x1)) + (64*x2)), tmp35 & xmask, other=0.0)
    tmp37 = tl.load(in_ptr6 + ((-12) + x1), tmp35 & xmask, eviction_policy='evict_last', other=0.0)
    tmp38 = tmp36 + tmp37
    tmp39 = tmp38 > tmp13
    tmp40 = tmp38 * tmp15
    tmp41 = tl.where(tmp39, tmp38, tmp40)
    tmp42 = tl.full(tmp41.shape, 0.0, tmp41.dtype)
    tmp43 = tl.where(tmp35, tmp41, tmp42)
    tmp44 = tmp0 >= tmp33
    tmp45 = tl.full([1], 20, tl.int64)
    tmp46 = tmp0 < tmp45
    tmp47 = tl.load(in_ptr7 + (x0 + (16*((-16) + x1)) + (64*x2)), tmp44 & xmask, other=0.0)
    tmp48 = tl.load(in_ptr8 + ((-16) + x1), tmp44 & xmask, eviction_policy='evict_last', other=0.0)
    tmp49 = tmp47 + tmp48
    tmp50 = tmp49 > tmp13
    tmp51 = tmp49 * tmp15
    tmp52 = tl.where(tmp50, tmp49, tmp51)
    tmp53 = tl.full(tmp52.shape, 0.0, tmp52.dtype)
    tmp54 = tl.where(tmp44, tmp52, tmp53)
    tmp55 = tl.where(tmp35, tmp43, tmp54)
    tmp56 = tl.where(tmp23, tmp31, tmp55)
    tmp57 = tl.where(tmp9, tmp19, tmp56)
    tmp58 = tl.where(tmp4, tmp5, tmp57)
    tl.store(out_ptr0 + (x3), tmp58, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/iu/ciubqy3qr45bvfhdurk7hjkhteasgy5mlezftic2idhmf7qyh6ar.py
# Topologically Sorted Source Nodes: [out5, mul, out], Original ATen: [aten.convolution, aten.mul, aten.add]
# Source node to ATen node mapping:
#   mul => mul_4
#   out => add
#   out5 => convolution_4
# Graph fragment:
#   %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_3, %primals_10, %primals_11, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_4, 0.2), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_4, %primals_1), kwargs = {})
triton_poi_fused_add_convolution_mul_4 = async_compile.triton('triton_poi_fused_add_convolution_mul_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: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_mul_4', 'mutated_arg_names': ['in_out_ptr0'], '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_add_convolution_mul_4(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x3), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.2
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 + tmp5
    tl.store(in_out_ptr0 + (x3), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/s5/cs5o65bd5qls62jyofgsrvlsxw6ku55z7wudf4ziucds3lbj4c7j.py
# Topologically Sorted Source Nodes: [conv2d_3, out4], Original ATen: [aten.convolution, aten.leaky_relu, aten.leaky_relu_backward]
# Source node to ATen node mapping:
#   conv2d_3 => convolution_3
#   out4 => gt_3, mul_3, where_3
# Graph fragment:
#   %convolution_3 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%cat_2, %primals_8, %primals_9, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_3 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_3, 0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_3, 0.2), kwargs = {})
#   %where_3 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_3, %convolution_3, %mul_3), kwargs = {})
#   %gt_4 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%where_3, 0), kwargs = {})
triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_5 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_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.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 4
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp8 = tmp7 > tmp3
    tl.store(out_ptr0 + (x3), tmp8, 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 = 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, 8, 3, 3), (72, 9, 3, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 12, 3, 3), (108, 9, 3, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    assert_size_stride(primals_10, (4, 20, 3, 3), (180, 9, 3, 1))
    assert_size_stride(primals_11, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_1, buf0, primals_3, buf1, 512, grid=grid(512), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
        buf3 = empty_strided_cuda((4, 12, 4, 4), (192, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat_1], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(primals_1, buf0, primals_3, buf2, primals_5, buf3, 768, grid=grid(768), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 4, 4, 4), (64, 16, 4, 1))
        buf5 = empty_strided_cuda((4, 16, 4, 4), (256, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat_2], Original ATen: [aten.cat]
        triton_poi_fused_cat_2.run(primals_1, buf0, primals_3, buf2, primals_5, buf4, primals_7, buf5, 1024, grid=grid(1024), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
        buf7 = empty_strided_cuda((4, 20, 4, 4), (320, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat_3], Original ATen: [aten.cat]
        triton_poi_fused_cat_3.run(primals_1, buf0, primals_3, buf2, primals_5, buf4, primals_7, buf6, primals_9, buf7, 1280, grid=grid(1280), stream=stream0)
        # Topologically Sorted Source Nodes: [out5], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1), padding=(1, 1), 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
        # Topologically Sorted Source Nodes: [out5, mul, out], Original ATen: [aten.convolution, aten.mul, aten.add]
        triton_poi_fused_add_convolution_mul_4.run(buf9, primals_11, primals_1, 256, grid=grid(256), stream=stream0)
        del primals_11
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_3, out4], Original ATen: [aten.convolution, aten.leaky_relu, aten.leaky_relu_backward]
        triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_5.run(buf6, primals_9, buf10, 256, grid=grid(256), stream=stream0)
        del buf6
        del primals_9
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_2, out3], Original ATen: [aten.convolution, aten.leaky_relu, aten.leaky_relu_backward]
        triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_5.run(buf4, primals_7, buf11, 256, grid=grid(256), stream=stream0)
        del buf4
        del primals_7
        buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_1, out2], Original ATen: [aten.convolution, aten.leaky_relu, aten.leaky_relu_backward]
        triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_5.run(buf2, primals_5, buf12, 256, grid=grid(256), stream=stream0)
        del buf2
        del primals_5
        buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d, out1], Original ATen: [aten.convolution, aten.leaky_relu, aten.leaky_relu_backward]
        triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_5.run(buf0, primals_3, buf13, 256, grid=grid(256), stream=stream0)
        del buf0
        del primals_3
    return (buf9, primals_1, primals_2, primals_4, primals_6, primals_8, primals_10, buf1, buf3, buf5, buf7, buf10, buf11, buf12, buf13, )


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, 8, 3, 3), (72, 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, 12, 3, 3), (108, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, 20, 3, 3), (180, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = 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, primals_8, primals_9, primals_10, primals_11])
    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 Tensor
import torch.nn as nn


class ResidualDenseBlock(nn.Module):
    """Achieves densely connected convolutional layers.
    `Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993v5.pdf>` paper.

    Args:
            channels (int): The number of channels in the input image.
            growths (int): The number of channels that increase in each layer of convolution.
    """

    def __init__(self, channels: 'int', growths: 'int') ->None:
        super(ResidualDenseBlock, self).__init__()
        self.conv1 = nn.Conv2d(channels + growths * 0, growths, (3, 3), (1,
            1), (1, 1))
        self.conv2 = nn.Conv2d(channels + growths * 1, growths, (3, 3), (1,
            1), (1, 1))
        self.conv3 = nn.Conv2d(channels + growths * 2, growths, (3, 3), (1,
            1), (1, 1))
        self.conv4 = nn.Conv2d(channels + growths * 3, growths, (3, 3), (1,
            1), (1, 1))
        self.conv5 = nn.Conv2d(channels + growths * 4, channels, (3, 3), (1,
            1), (1, 1))
        self.leaky_relu = nn.LeakyReLU(0.2, True)
        self.identity = nn.Identity()

    def forward(self, x: 'Tensor') ->Tensor:
        identity = x
        out1 = self.leaky_relu(self.conv1(x))
        out2 = self.leaky_relu(self.conv2(torch.cat([x, out1], 1)))
        out3 = self.leaky_relu(self.conv3(torch.cat([x, out1, out2], 1)))
        out4 = self.leaky_relu(self.conv4(torch.cat([x, out1, out2, out3], 1)))
        out5 = self.identity(self.conv5(torch.cat([x, out1, out2, out3,
            out4], 1)))
        out = out5 * 0.2 + identity
        return out


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


def get_init_inputs():
    return [[], {'channels': 4, 'growths': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 8
    x0 = xindex % 16
    x2 = xindex // 128
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp6 & xmask,
        other=0.0)
    tmp10 = tl.load(in_ptr2 + (-4 + x1), tmp6 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = 0.0
    tmp13 = tmp11 > tmp12
    tmp14 = 0.2
    tmp15 = tmp11 * tmp14
    tmp16 = tl.where(tmp13, tmp11, tmp15)
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp6, tmp16, tmp17)
    tmp19 = tl.where(tmp4, tmp5, tmp18)
    tl.store(out_ptr0 + x3, tmp19, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 12
    x0 = xindex % 16
    x2 = xindex // 192
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp9 & xmask,
        other=0.0)
    tmp11 = tl.load(in_ptr2 + (-4 + x1), tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = 0.0
    tmp14 = tmp12 > tmp13
    tmp15 = 0.2
    tmp16 = tmp12 * tmp15
    tmp17 = tl.where(tmp14, tmp12, tmp16)
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp9, tmp17, tmp18)
    tmp20 = tmp0 >= tmp7
    tl.full([1], 12, tl.int64)
    tmp23 = tl.load(in_ptr3 + (x0 + 16 * (-8 + x1) + 64 * x2), tmp20 &
        xmask, other=0.0)
    tmp24 = tl.load(in_ptr4 + (-8 + x1), tmp20 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp25 = tmp23 + tmp24
    tmp26 = tmp25 > tmp13
    tmp27 = tmp25 * tmp15
    tmp28 = tl.where(tmp26, tmp25, tmp27)
    tmp29 = tl.full(tmp28.shape, 0.0, tmp28.dtype)
    tmp30 = tl.where(tmp20, tmp28, tmp29)
    tmp31 = tl.where(tmp9, tmp19, tmp30)
    tmp32 = tl.where(tmp4, tmp5, tmp31)
    tl.store(out_ptr0 + x3, tmp32, xmask)


@triton.jit
def triton_poi_fused_cat_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    in_ptr5, in_ptr6, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 16
    x0 = xindex % 16
    x2 = xindex // 256
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp9 & xmask,
        other=0.0)
    tmp11 = tl.load(in_ptr2 + (-4 + x1), tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = 0.0
    tmp14 = tmp12 > tmp13
    tmp15 = 0.2
    tmp16 = tmp12 * tmp15
    tmp17 = tl.where(tmp14, tmp12, tmp16)
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp9, tmp17, tmp18)
    tmp20 = tmp0 >= tmp7
    tmp21 = tl.full([1], 12, tl.int64)
    tmp22 = tmp0 < tmp21
    tmp23 = tmp20 & tmp22
    tmp24 = tl.load(in_ptr3 + (x0 + 16 * (-8 + x1) + 64 * x2), tmp23 &
        xmask, other=0.0)
    tmp25 = tl.load(in_ptr4 + (-8 + x1), tmp23 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp26 = tmp24 + tmp25
    tmp27 = tmp26 > tmp13
    tmp28 = tmp26 * tmp15
    tmp29 = tl.where(tmp27, tmp26, tmp28)
    tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype)
    tmp31 = tl.where(tmp23, tmp29, tmp30)
    tmp32 = tmp0 >= tmp21
    tl.full([1], 16, tl.int64)
    tmp35 = tl.load(in_ptr5 + (x0 + 16 * (-12 + x1) + 64 * x2), tmp32 &
        xmask, other=0.0)
    tmp36 = tl.load(in_ptr6 + (-12 + x1), tmp32 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp37 = tmp35 + tmp36
    tmp38 = tmp37 > tmp13
    tmp39 = tmp37 * tmp15
    tmp40 = tl.where(tmp38, tmp37, tmp39)
    tmp41 = tl.full(tmp40.shape, 0.0, tmp40.dtype)
    tmp42 = tl.where(tmp32, tmp40, tmp41)
    tmp43 = tl.where(tmp23, tmp31, tmp42)
    tmp44 = tl.where(tmp9, tmp19, tmp43)
    tmp45 = tl.where(tmp4, tmp5, tmp44)
    tl.store(out_ptr0 + x3, tmp45, xmask)


@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    in_ptr5, in_ptr6, in_ptr7, in_ptr8, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 1280
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 20
    x0 = xindex % 16
    x2 = xindex // 320
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp9 & xmask,
        other=0.0)
    tmp11 = tl.load(in_ptr2 + (-4 + x1), tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = 0.0
    tmp14 = tmp12 > tmp13
    tmp15 = 0.2
    tmp16 = tmp12 * tmp15
    tmp17 = tl.where(tmp14, tmp12, tmp16)
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp9, tmp17, tmp18)
    tmp20 = tmp0 >= tmp7
    tmp21 = tl.full([1], 12, tl.int64)
    tmp22 = tmp0 < tmp21
    tmp23 = tmp20 & tmp22
    tmp24 = tl.load(in_ptr3 + (x0 + 16 * (-8 + x1) + 64 * x2), tmp23 &
        xmask, other=0.0)
    tmp25 = tl.load(in_ptr4 + (-8 + x1), tmp23 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp26 = tmp24 + tmp25
    tmp27 = tmp26 > tmp13
    tmp28 = tmp26 * tmp15
    tmp29 = tl.where(tmp27, tmp26, tmp28)
    tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype)
    tmp31 = tl.where(tmp23, tmp29, tmp30)
    tmp32 = tmp0 >= tmp21
    tmp33 = tl.full([1], 16, tl.int64)
    tmp34 = tmp0 < tmp33
    tmp35 = tmp32 & tmp34
    tmp36 = tl.load(in_ptr5 + (x0 + 16 * (-12 + x1) + 64 * x2), tmp35 &
        xmask, other=0.0)
    tmp37 = tl.load(in_ptr6 + (-12 + x1), tmp35 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp38 = tmp36 + tmp37
    tmp39 = tmp38 > tmp13
    tmp40 = tmp38 * tmp15
    tmp41 = tl.where(tmp39, tmp38, tmp40)
    tmp42 = tl.full(tmp41.shape, 0.0, tmp41.dtype)
    tmp43 = tl.where(tmp35, tmp41, tmp42)
    tmp44 = tmp0 >= tmp33
    tl.full([1], 20, tl.int64)
    tmp47 = tl.load(in_ptr7 + (x0 + 16 * (-16 + x1) + 64 * x2), tmp44 &
        xmask, other=0.0)
    tmp48 = tl.load(in_ptr8 + (-16 + x1), tmp44 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp49 = tmp47 + tmp48
    tmp50 = tmp49 > tmp13
    tmp51 = tmp49 * tmp15
    tmp52 = tl.where(tmp50, tmp49, tmp51)
    tmp53 = tl.full(tmp52.shape, 0.0, tmp52.dtype)
    tmp54 = tl.where(tmp44, tmp52, tmp53)
    tmp55 = tl.where(tmp35, tmp43, tmp54)
    tmp56 = tl.where(tmp23, tmp31, tmp55)
    tmp57 = tl.where(tmp9, tmp19, tmp56)
    tmp58 = tl.where(tmp4, tmp5, tmp57)
    tl.store(out_ptr0 + x3, tmp58, xmask)


@triton.jit
def triton_poi_fused_add_convolution_mul_4(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.2
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 + tmp5
    tl.store(in_out_ptr0 + x3, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_5(in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp8 = tmp7 > tmp3
    tl.store(out_ptr0 + x3, tmp8, 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) = 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, 8, 3, 3), (72, 9, 3, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 12, 3, 3), (108, 9, 3, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4, 20, 3, 3), (180, 9, 3, 1))
    assert_size_stride(primals_11, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(512)](primals_1, buf0, primals_3, buf1,
            512, XBLOCK=128, num_warps=4, num_stages=1)
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
        buf3 = empty_strided_cuda((4, 12, 4, 4), (192, 16, 4, 1), torch.float32
            )
        triton_poi_fused_cat_1[grid(768)](primals_1, buf0, primals_3, buf2,
            primals_5, buf3, 768, XBLOCK=128, num_warps=4, num_stages=1)
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 4, 4, 4), (64, 16, 4, 1))
        buf5 = empty_strided_cuda((4, 16, 4, 4), (256, 16, 4, 1), torch.float32
            )
        triton_poi_fused_cat_2[grid(1024)](primals_1, buf0, primals_3, buf2,
            primals_5, buf4, primals_7, buf5, 1024, XBLOCK=256, num_warps=4,
            num_stages=1)
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
        buf7 = empty_strided_cuda((4, 20, 4, 4), (320, 16, 4, 1), torch.float32
            )
        triton_poi_fused_cat_3[grid(1280)](primals_1, buf0, primals_3, buf2,
            primals_5, buf4, primals_7, buf6, primals_9, buf7, 1280, XBLOCK
            =256, num_warps=4, num_stages=1)
        buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1),
            padding=(1, 1), 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
        triton_poi_fused_add_convolution_mul_4[grid(256)](buf9, primals_11,
            primals_1, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_11
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_5[grid(256)
            ](buf6, primals_9, buf10, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf6
        del primals_9
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_5[grid(256)
            ](buf4, primals_7, buf11, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf4
        del primals_7
        buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_5[grid(256)
            ](buf2, primals_5, buf12, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf2
        del primals_5
        buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_5[grid(256)
            ](buf0, primals_3, buf13, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf0
        del primals_3
    return (buf9, primals_1, primals_2, primals_4, primals_6, primals_8,
        primals_10, buf1, buf3, buf5, buf7, buf10, buf11, buf12, buf13)


class ResidualDenseBlockNew(nn.Module):
    """Achieves densely connected convolutional layers.
    `Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993v5.pdf>` paper.

    Args:
            channels (int): The number of channels in the input image.
            growths (int): The number of channels that increase in each layer of convolution.
    """

    def __init__(self, channels: 'int', growths: 'int') ->None:
        super(ResidualDenseBlockNew, self).__init__()
        self.conv1 = nn.Conv2d(channels + growths * 0, growths, (3, 3), (1,
            1), (1, 1))
        self.conv2 = nn.Conv2d(channels + growths * 1, growths, (3, 3), (1,
            1), (1, 1))
        self.conv3 = nn.Conv2d(channels + growths * 2, growths, (3, 3), (1,
            1), (1, 1))
        self.conv4 = nn.Conv2d(channels + growths * 3, growths, (3, 3), (1,
            1), (1, 1))
        self.conv5 = nn.Conv2d(channels + growths * 4, channels, (3, 3), (1,
            1), (1, 1))
        self.leaky_relu = nn.LeakyReLU(0.2, True)
        self.identity = nn.Identity()

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.conv3.weight
        primals_7 = self.conv3.bias
        primals_8 = self.conv4.weight
        primals_9 = self.conv4.bias
        primals_10 = self.conv5.weight
        primals_11 = self.conv5.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])
        return output[0]
