# 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_6/inductor_cache/c5/cc5y6xkdqhzhg7mu3y2nor5pfztbc2eoeyxfvy4cbcl22ncs4hmw.py
# Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten._unsafe_index, aten.constant_pad_nd]
# Source node to ATen node mapping:
#   x => _unsafe_index
#   x_1 => constant_pad_nd
# Graph fragment:
#   %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %unsqueeze, %convert_element_type_1]), kwargs = {})
#   %constant_pad_nd : [num_users=1] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%_unsafe_index, [-2, -2, -2, -2], 0.0), kwargs = {})
triton_poi_fused__unsafe_index_constant_pad_nd_0 = async_compile.triton('triton_poi_fused__unsafe_index_constant_pad_nd_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_constant_pad_nd_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 4
    x0 = xindex % 4
    x2 = (xindex // 16)
    x5 = xindex
    tmp0 = 2 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 8, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = 2 + x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tmp0.to(tl.float32)
    tmp12 = 0.5
    tmp13 = tmp11 * tmp12
    tmp14 = tmp13.to(tl.int32)
    tmp15 = tmp5.to(tl.float32)
    tmp16 = tmp15 * tmp12
    tmp17 = tmp16.to(tl.int32)
    tmp18 = tl.load(in_ptr0 + (tmp17 + (4*tmp14) + (16*x2)), tmp10 & xmask, eviction_policy='evict_last', other=0.0)
    tl.store(out_ptr0 + (x5), tmp18, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ur/curbyve7d3snnweffjbgmv7r4hdnyurumpeiwyskxsipaqv7nba4.py
# Topologically Sorted Source Nodes: [x_2, x_3, x_4], Original ATen: [aten.cat, aten.constant_pad_nd, aten.replication_pad2d]
# Source node to ATen node mapping:
#   x_2 => cat
#   x_3 => constant_pad_nd_1
#   x_4 => _unsafe_index_1, _unsafe_index_2
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%constant_pad_nd, %primals_2], 1), kwargs = {})
#   %constant_pad_nd_1 : [num_users=1] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%cat, [0, 0, 1, 0], 0.0), kwargs = {})
#   %_unsafe_index_1 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%constant_pad_nd_1, [None, None, %clamp_max, None]), kwargs = {})
#   %_unsafe_index_2 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_1, [None, None, None, %clamp_max_1]), kwargs = {})
triton_poi_fused_cat_constant_pad_nd_replication_pad2d_1 = async_compile.triton('triton_poi_fused_cat_constant_pad_nd_replication_pad2d_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: '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_cat_constant_pad_nd_replication_pad2d_1', '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_cat_constant_pad_nd_replication_pad2d_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 672
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 6) % 7
    x2 = (xindex // 42) % 4
    x0 = xindex % 6
    x3 = (xindex // 168)
    x5 = xindex
    tmp0 = (-1) + ((4) * ((4) <= (((0) * ((0) >= ((-1) + x1)) + ((-1) + x1) * (((-1) + x1) > (0))))) + (((0) * ((0) >= ((-1) + x1)) + ((-1) + x1) * (((-1) + x1) > (0)))) * ((((0) * ((0) >= ((-1) + x1)) + ((-1) + x1) * (((-1) + x1) > (0)))) < (4)))
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = x2
    tmp4 = tmp3 >= tmp1
    tmp5 = tl.full([1], 1, tl.int64)
    tmp6 = tmp3 < tmp5
    tmp7 = tmp6 & tmp2
    tmp8 = tl.load(in_ptr0 + ((-4) + (4*((4) * ((4) <= (((0) * ((0) >= ((-1) + x1)) + ((-1) + x1) * (((-1) + x1) > (0))))) + (((0) * ((0) >= ((-1) + x1)) + ((-1) + x1) * (((-1) + x1) > (0)))) * ((((0) * ((0) >= ((-1) + x1)) + ((-1) + x1) * (((-1) + x1) > (0)))) < (4)))) + (16*x3) + ((3) * ((3) <= (((0) * ((0) >= ((-1) + x0)) + ((-1) + x0) * (((-1) + x0) > (0))))) + (((0) * ((0) >= ((-1) + x0)) + ((-1) + x0) * (((-1) + x0) > (0)))) * ((((0) * ((0) >= ((-1) + x0)) + ((-1) + x0) * (((-1) + x0) > (0)))) < (3)))), tmp7 & xmask, eviction_policy='evict_last', other=0.0)
    tmp9 = tmp3 >= tmp5
    tmp10 = tl.full([1], 4, tl.int64)
    tmp11 = tmp3 < tmp10
    tmp12 = tmp9 & tmp2
    tmp13 = tl.load(in_ptr1 + ((-4) + (4*((4) * ((4) <= (((0) * ((0) >= ((-1) + x1)) + ((-1) + x1) * (((-1) + x1) > (0))))) + (((0) * ((0) >= ((-1) + x1)) + ((-1) + x1) * (((-1) + x1) > (0)))) * ((((0) * ((0) >= ((-1) + x1)) + ((-1) + x1) * (((-1) + x1) > (0)))) < (4)))) + (16*((-1) + x2)) + (48*x3) + ((3) * ((3) <= (((0) * ((0) >= ((-1) + x0)) + ((-1) + x0) * (((-1) + x0) > (0))))) + (((0) * ((0) >= ((-1) + x0)) + ((-1) + x0) * (((-1) + x0) > (0)))) * ((((0) * ((0) >= ((-1) + x0)) + ((-1) + x0) * (((-1) + x0) > (0)))) < (3)))), tmp12 & xmask, eviction_policy='evict_last', other=0.0)
    tmp14 = tl.where(tmp6, tmp8, tmp13)
    tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
    tmp16 = tl.where(tmp2, tmp14, tmp15)
    tl.store(out_ptr0 + (x5), tmp16, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/tf/ctfzvbahrv23jmpe2yopmvu5lnb4uqak6pdottlr22met7i7w2oa.py
# Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.constant_pad_nd]
# Source node to ATen node mapping:
#   x_8 => constant_pad_nd_2
# Graph fragment:
#   %constant_pad_nd_2 : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%slice_2, [0, 0, 1, 0], 0.0), kwargs = {})
triton_poi_fused_constant_pad_nd_2 = async_compile.triton('triton_poi_fused_constant_pad_nd_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=[512], 
    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_constant_pad_nd_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_constant_pad_nd_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 5
    x3 = xindex
    tmp0 = (-1) + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.load(in_ptr0 + ((-4) + x3), tmp2 & xmask, other=0.0)
    tmp4 = 0.0
    tmp5 = tmp3 > tmp4
    tmp6 = 0.1
    tmp7 = tmp3 * tmp6
    tmp8 = tl.where(tmp5, tmp3, tmp7)
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp2, tmp8, tmp9)
    tl.store(out_ptr0 + (x3), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/5l/c5lfrde5jay5wguumut2ygr2uwoj274ig772b6ur5mo33wola3fe.py
# Topologically Sorted Source Nodes: [x_9], Original ATen: [aten.replication_pad2d]
# Source node to ATen node mapping:
#   x_9 => _unsafe_index_3, _unsafe_index_4
# Graph fragment:
#   %_unsafe_index_3 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%constant_pad_nd_2, [None, None, %clamp_max, None]), kwargs = {})
#   %_unsafe_index_4 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_3, [None, None, None, %clamp_max_1]), kwargs = {})
triton_poi_fused_replication_pad2d_3 = async_compile.triton('triton_poi_fused_replication_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=[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_replication_pad2d_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_replication_pad2d_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 672
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = (xindex // 6) % 7
    x2 = (xindex // 42)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + ((4*((4) * ((4) <= (((0) * ((0) >= ((-1) + x1)) + ((-1) + x1) * (((-1) + x1) > (0))))) + (((0) * ((0) >= ((-1) + x1)) + ((-1) + x1) * (((-1) + x1) > (0)))) * ((((0) * ((0) >= ((-1) + x1)) + ((-1) + x1) * (((-1) + x1) > (0)))) < (4)))) + (20*x2) + ((3) * ((3) <= (((0) * ((0) >= ((-1) + x0)) + ((-1) + x0) * (((-1) + x0) > (0))))) + (((0) * ((0) >= ((-1) + x0)) + ((-1) + x0) * (((-1) + x0) > (0)))) * ((((0) * ((0) >= ((-1) + x0)) + ((-1) + x0) * (((-1) + x0) > (0)))) < (3)))), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ce/ccel7cpr5sdul7ce3m6tshtskeuxks6mquw3t6e3lpiaruhw6wxt.py
# Topologically Sorted Source Nodes: [x_21], Original ATen: [aten.leaky_relu, aten.leaky_relu_backward]
# Source node to ATen node mapping:
#   x_21 => gt_3, mul_7, where_3
# Graph fragment:
#   %gt_3 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_3, 0), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_3, 0.1), kwargs = {})
#   %where_3 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_3, %convolution_3, %mul_7), kwargs = {})
#   %gt_4 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%where_3, 0), kwargs = {})
triton_poi_fused_leaky_relu_leaky_relu_backward_4 = async_compile.triton('triton_poi_fused_leaky_relu_leaky_relu_backward_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=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i1', 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_leaky_relu_leaky_relu_backward_4', '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_leaky_relu_leaky_relu_backward_4(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.1
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp6 = tmp5 > tmp1
    tl.store(out_ptr0 + (x0), tmp6, xmask)
    tl.store(out_ptr1 + (x0), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/wj/cwj6bijtxnckw467udrvu5e7mpuklkk5yv4734l2hdbkkmu5swzf.py
# Topologically Sorted Source Nodes: [x_16], Original ATen: [aten.leaky_relu, aten.leaky_relu_backward]
# Source node to ATen node mapping:
#   x_16 => gt_2, mul_6, where_2
# Graph fragment:
#   %gt_2 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_2, 0), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_2, 0.1), kwargs = {})
#   %where_2 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %convolution_2, %mul_6), kwargs = {})
#   %gt_5 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%where_2, 0), kwargs = {})
triton_poi_fused_leaky_relu_leaky_relu_backward_5 = async_compile.triton('triton_poi_fused_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=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i1', 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_leaky_relu_leaky_relu_backward_5', '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_leaky_relu_leaky_relu_backward_5(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.1
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp6 = tmp5 > tmp1
    tl.store(out_ptr0 + (x0), tmp6, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_2, (4, 3, 4, 4), (48, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_5, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_6, (4, 4, 3, 3), (36, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten._unsafe_index, aten.constant_pad_nd]
        stream0 = get_raw_stream(0)
        triton_poi_fused__unsafe_index_constant_pad_nd_0.run(primals_1, buf0, 64, grid=grid(64), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 7, 6), (168, 42, 6, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2, x_3, x_4], Original ATen: [aten.cat, aten.constant_pad_nd, aten.replication_pad2d]
        triton_poi_fused_cat_constant_pad_nd_replication_pad2d_1.run(buf0, primals_2, buf1, 672, grid=grid(672), stream=stream0)
        del buf0
        del primals_2
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_3, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 5, 4), (80, 20, 4, 1))
        buf3 = empty_strided_cuda((4, 4, 5, 4), (80, 20, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.constant_pad_nd]
        triton_poi_fused_constant_pad_nd_2.run(buf2, buf3, 320, grid=grid(320), stream=stream0)
        buf4 = empty_strided_cuda((4, 4, 7, 6), (168, 42, 6, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_9], Original ATen: [aten.replication_pad2d]
        triton_poi_fused_replication_pad2d_3.run(buf3, buf4, 672, grid=grid(672), stream=stream0)
        # Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(buf4, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 5, 4), (80, 20, 4, 1))
        buf6 = empty_strided_cuda((4, 4, 5, 4), (80, 20, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_13], Original ATen: [aten.constant_pad_nd]
        triton_poi_fused_constant_pad_nd_2.run(buf5, buf6, 320, grid=grid(320), stream=stream0)
        buf7 = empty_strided_cuda((4, 4, 7, 6), (168, 42, 6, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_14], Original ATen: [aten.replication_pad2d]
        triton_poi_fused_replication_pad2d_3.run(buf6, buf7, 672, grid=grid(672), stream=stream0)
        # Topologically Sorted Source Nodes: [x_15], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf7, primals_5, 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, 5, 4), (80, 20, 4, 1))
        buf9 = empty_strided_cuda((4, 4, 5, 4), (80, 20, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_18], Original ATen: [aten.constant_pad_nd]
        triton_poi_fused_constant_pad_nd_2.run(buf8, buf9, 320, grid=grid(320), stream=stream0)
        buf10 = empty_strided_cuda((4, 4, 7, 6), (168, 42, 6, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_19], Original ATen: [aten.replication_pad2d]
        triton_poi_fused_replication_pad2d_3.run(buf9, buf10, 672, grid=grid(672), stream=stream0)
        # Topologically Sorted Source Nodes: [x_20], Original ATen: [aten.convolution]
        buf11 = extern_kernels.convolution(buf10, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 4, 5, 4), (80, 20, 4, 1))
        buf12 = empty_strided_cuda((4, 4, 5, 4), (80, 20, 4, 1), torch.bool)
        buf16 = empty_strided_cuda((4, 4, 5, 4), (80, 20, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_21], Original ATen: [aten.leaky_relu, aten.leaky_relu_backward]
        triton_poi_fused_leaky_relu_leaky_relu_backward_4.run(buf11, buf12, buf16, 320, grid=grid(320), stream=stream0)
        del buf11
        buf13 = empty_strided_cuda((4, 4, 5, 4), (80, 20, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_16], Original ATen: [aten.leaky_relu, aten.leaky_relu_backward]
        triton_poi_fused_leaky_relu_leaky_relu_backward_5.run(buf8, buf13, 320, grid=grid(320), stream=stream0)
        del buf8
        buf14 = empty_strided_cuda((4, 4, 5, 4), (80, 20, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_11], Original ATen: [aten.leaky_relu, aten.leaky_relu_backward]
        triton_poi_fused_leaky_relu_leaky_relu_backward_5.run(buf5, buf14, 320, grid=grid(320), stream=stream0)
        del buf5
        buf15 = empty_strided_cuda((4, 4, 5, 4), (80, 20, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.leaky_relu, aten.leaky_relu_backward]
        triton_poi_fused_leaky_relu_leaky_relu_backward_5.run(buf2, buf15, 320, grid=grid(320), stream=stream0)
        del buf2
    return (reinterpret_tensor(buf16, (4, 4, 4, 4), (80, 20, 4, 1), 0), primals_3, primals_4, primals_5, primals_6, buf1, buf3, buf4, buf6, buf7, buf9, buf10, buf12, buf13, buf14, buf15, )


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, 1, 4, 4), (16, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 3, 4, 4), (48, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 3, 3), (36, 9, 3, 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, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn as nn
import torch.nn.functional as F


class crop(nn.Module):

    def __init__(self):
        super().__init__()

    def forward(self, x):
        N, C, H, W = x.shape
        x = x[0:N, 0:C, 0:H - 1, 0:W]
        return x


class Conv(nn.Module):

    def __init__(self, in_channels, out_channels, bias=False, blind=True):
        super().__init__()
        self.blind = blind
        if blind:
            self.shift_down = nn.ZeroPad2d((0, 0, 1, 0))
            self.crop = crop()
        self.replicate = nn.ReplicationPad2d(1)
        self.conv = nn.Conv2d(in_channels, out_channels, 3, bias=bias)
        self.relu = nn.LeakyReLU(0.1, inplace=True)

    def forward(self, x):
        if self.blind:
            x = self.shift_down(x)
        x = self.replicate(x)
        x = self.conv(x)
        x = self.relu(x)
        if self.blind:
            x = self.crop(x)
        return x


class DEC_Conv(nn.Module):

    def __init__(self, in_channels, mid_channels, out_channels, bias=False,
        blind=True):
        super().__init__()
        self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
        self.conv1 = Conv(in_channels, mid_channels, bias=bias, blind=blind)
        self.conv2 = Conv(mid_channels, mid_channels, bias=bias, blind=blind)
        self.conv3 = Conv(mid_channels, mid_channels, bias=bias, blind=blind)
        self.conv4 = Conv(mid_channels, out_channels, bias=bias, blind=blind)

    def forward(self, x, x_in):
        x = self.upsample(x)
        diffY = x_in.size()[2] - x.size()[2]
        diffX = x_in.size()[3] - x.size()[3]
        x = F.pad(x, [diffX // 2, diffX - diffX // 2, diffY // 2, diffY - 
            diffY // 2])
        x = torch.cat((x, x_in), dim=1)
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)
        return x


def get_inputs():
    return [torch.rand([4, 1, 4, 4]), torch.rand([4, 3, 4, 4])]


def get_init_inputs():
    return [[], {'in_channels': 4, 'mid_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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused__unsafe_index_constant_pad_nd_0(in_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x2 = xindex // 16
    x5 = xindex
    tmp0 = 2 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 8, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = 2 + x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tmp0.to(tl.float32)
    tmp12 = 0.5
    tmp13 = tmp11 * tmp12
    tmp14 = tmp13.to(tl.int32)
    tmp15 = tmp5.to(tl.float32)
    tmp16 = tmp15 * tmp12
    tmp17 = tmp16.to(tl.int32)
    tmp18 = tl.load(in_ptr0 + (tmp17 + 4 * tmp14 + 16 * x2), tmp10 & xmask,
        eviction_policy='evict_last', other=0.0)
    tl.store(out_ptr0 + x5, tmp18, xmask)


@triton.jit
def triton_poi_fused_cat_constant_pad_nd_replication_pad2d_1(in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 672
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 6 % 7
    x2 = xindex // 42 % 4
    x0 = xindex % 6
    x3 = xindex // 168
    x5 = xindex
    tmp0 = -1 + (4 * (4 <= 0 * (0 >= -1 + x1) + (-1 + x1) * (-1 + x1 > 0)) +
        (0 * (0 >= -1 + x1) + (-1 + x1) * (-1 + x1 > 0)) * (0 * (0 >= -1 +
        x1) + (-1 + x1) * (-1 + x1 > 0) < 4))
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = x2
    tmp5 = tl.full([1], 1, tl.int64)
    tmp6 = tmp3 < tmp5
    tmp7 = tmp6 & tmp2
    tmp8 = tl.load(in_ptr0 + (-4 + 4 * (4 * (4 <= 0 * (0 >= -1 + x1) + (-1 +
        x1) * (-1 + x1 > 0)) + (0 * (0 >= -1 + x1) + (-1 + x1) * (-1 + x1 >
        0)) * (0 * (0 >= -1 + x1) + (-1 + x1) * (-1 + x1 > 0) < 4)) + 16 *
        x3 + (3 * (3 <= 0 * (0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) + (
        0 * (0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) * (0 * (0 >= -1 +
        x0) + (-1 + x0) * (-1 + x0 > 0) < 3))), tmp7 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp9 = tmp3 >= tmp5
    tl.full([1], 4, tl.int64)
    tmp12 = tmp9 & tmp2
    tmp13 = tl.load(in_ptr1 + (-4 + 4 * (4 * (4 <= 0 * (0 >= -1 + x1) + (-1 +
        x1) * (-1 + x1 > 0)) + (0 * (0 >= -1 + x1) + (-1 + x1) * (-1 + x1 >
        0)) * (0 * (0 >= -1 + x1) + (-1 + x1) * (-1 + x1 > 0) < 4)) + 16 *
        (-1 + x2) + 48 * x3 + (3 * (3 <= 0 * (0 >= -1 + x0) + (-1 + x0) * (
        -1 + x0 > 0)) + (0 * (0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) *
        (0 * (0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0) < 3))), tmp12 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp14 = tl.where(tmp6, tmp8, tmp13)
    tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
    tmp16 = tl.where(tmp2, tmp14, tmp15)
    tl.store(out_ptr0 + x5, tmp16, xmask)


@triton.jit
def triton_poi_fused_constant_pad_nd_2(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 5
    x3 = xindex
    tmp0 = -1 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.load(in_ptr0 + (-4 + x3), tmp2 & xmask, other=0.0)
    tmp4 = 0.0
    tmp5 = tmp3 > tmp4
    tmp6 = 0.1
    tmp7 = tmp3 * tmp6
    tmp8 = tl.where(tmp5, tmp3, tmp7)
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp2, tmp8, tmp9)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_replication_pad2d_3(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 672
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = xindex // 6 % 7
    x2 = xindex // 42
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * (4 * (4 <= 0 * (0 >= -1 + x1) + (-1 + x1) *
        (-1 + x1 > 0)) + (0 * (0 >= -1 + x1) + (-1 + x1) * (-1 + x1 > 0)) *
        (0 * (0 >= -1 + x1) + (-1 + x1) * (-1 + x1 > 0) < 4)) + 20 * x2 + (
        3 * (3 <= 0 * (0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) + (0 * (0 >=
        -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) * (0 * (0 >= -1 + x0) + (-1 +
        x0) * (-1 + x0 > 0) < 3))), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_leaky_relu_backward_4(in_ptr0, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.1
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp6 = tmp5 > tmp1
    tl.store(out_ptr0 + x0, tmp6, xmask)
    tl.store(out_ptr1 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_leaky_relu_backward_5(in_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.1
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp6 = tmp5 > tmp1
    tl.store(out_ptr0 + x0, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_2, (4, 3, 4, 4), (48, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_5, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_6, (4, 4, 3, 3), (36, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__unsafe_index_constant_pad_nd_0[grid(64)](primals_1,
            buf0, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 7, 6), (168, 42, 6, 1), torch.float32)
        triton_poi_fused_cat_constant_pad_nd_replication_pad2d_1[grid(672)](
            buf0, primals_2, buf1, 672, XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_3, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 5, 4), (80, 20, 4, 1))
        buf3 = empty_strided_cuda((4, 4, 5, 4), (80, 20, 4, 1), torch.float32)
        triton_poi_fused_constant_pad_nd_2[grid(320)](buf2, buf3, 320,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((4, 4, 7, 6), (168, 42, 6, 1), torch.float32)
        triton_poi_fused_replication_pad2d_3[grid(672)](buf3, buf4, 672,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf5 = extern_kernels.convolution(buf4, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 5, 4), (80, 20, 4, 1))
        buf6 = empty_strided_cuda((4, 4, 5, 4), (80, 20, 4, 1), torch.float32)
        triton_poi_fused_constant_pad_nd_2[grid(320)](buf5, buf6, 320,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf7 = empty_strided_cuda((4, 4, 7, 6), (168, 42, 6, 1), torch.float32)
        triton_poi_fused_replication_pad2d_3[grid(672)](buf6, buf7, 672,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf8 = extern_kernels.convolution(buf7, primals_5, 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, 5, 4), (80, 20, 4, 1))
        buf9 = empty_strided_cuda((4, 4, 5, 4), (80, 20, 4, 1), torch.float32)
        triton_poi_fused_constant_pad_nd_2[grid(320)](buf8, buf9, 320,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf10 = empty_strided_cuda((4, 4, 7, 6), (168, 42, 6, 1), torch.float32
            )
        triton_poi_fused_replication_pad2d_3[grid(672)](buf9, buf10, 672,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf11 = extern_kernels.convolution(buf10, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 4, 5, 4), (80, 20, 4, 1))
        buf12 = empty_strided_cuda((4, 4, 5, 4), (80, 20, 4, 1), torch.bool)
        buf16 = empty_strided_cuda((4, 4, 5, 4), (80, 20, 4, 1), torch.float32)
        triton_poi_fused_leaky_relu_leaky_relu_backward_4[grid(320)](buf11,
            buf12, buf16, 320, XBLOCK=256, num_warps=4, num_stages=1)
        del buf11
        buf13 = empty_strided_cuda((4, 4, 5, 4), (80, 20, 4, 1), torch.bool)
        triton_poi_fused_leaky_relu_leaky_relu_backward_5[grid(320)](buf8,
            buf13, 320, XBLOCK=128, num_warps=4, num_stages=1)
        del buf8
        buf14 = empty_strided_cuda((4, 4, 5, 4), (80, 20, 4, 1), torch.bool)
        triton_poi_fused_leaky_relu_leaky_relu_backward_5[grid(320)](buf5,
            buf14, 320, XBLOCK=128, num_warps=4, num_stages=1)
        del buf5
        buf15 = empty_strided_cuda((4, 4, 5, 4), (80, 20, 4, 1), torch.bool)
        triton_poi_fused_leaky_relu_leaky_relu_backward_5[grid(320)](buf2,
            buf15, 320, XBLOCK=128, num_warps=4, num_stages=1)
        del buf2
    return (reinterpret_tensor(buf16, (4, 4, 4, 4), (80, 20, 4, 1), 0),
        primals_3, primals_4, primals_5, primals_6, buf1, buf3, buf4, buf6,
        buf7, buf9, buf10, buf12, buf13, buf14, buf15)


class crop(nn.Module):

    def __init__(self):
        super().__init__()

    def forward(self, x):
        N, C, H, W = x.shape
        x = x[0:N, 0:C, 0:H - 1, 0:W]
        return x


class Conv(nn.Module):

    def __init__(self, in_channels, out_channels, bias=False, blind=True):
        super().__init__()
        self.blind = blind
        if blind:
            self.shift_down = nn.ZeroPad2d((0, 0, 1, 0))
            self.crop = crop()
        self.replicate = nn.ReplicationPad2d(1)
        self.conv = nn.Conv2d(in_channels, out_channels, 3, bias=bias)
        self.relu = nn.LeakyReLU(0.1, inplace=True)

    def forward(self, x):
        if self.blind:
            x = self.shift_down(x)
        x = self.replicate(x)
        x = self.conv(x)
        x = self.relu(x)
        if self.blind:
            x = self.crop(x)
        return x


class DEC_ConvNew(nn.Module):

    def __init__(self, in_channels, mid_channels, out_channels, bias=False,
        blind=True):
        super().__init__()
        self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
        self.conv1 = Conv(in_channels, mid_channels, bias=bias, blind=blind)
        self.conv2 = Conv(mid_channels, mid_channels, bias=bias, blind=blind)
        self.conv3 = Conv(mid_channels, mid_channels, bias=bias, blind=blind)
        self.conv4 = Conv(mid_channels, out_channels, bias=bias, blind=blind)

    def forward(self, input_0, input_1):
        primals_3 = self.conv1.conv.weight
        primals_4 = self.conv2.conv.weight
        primals_5 = self.conv3.conv.weight
        primals_6 = self.conv4.conv.weight
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
