# 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_1/inductor_cache/gi/cgi2hszrjniw6ncyter6qipkbjwmsxrmjql3fibjwudjvgclte25.py
# Topologically Sorted Source Nodes: [X, X_1, X_2], Original ATen: [aten.constant_pad_nd, aten.relu]
# Source node to ATen node mapping:
#   X => constant_pad_nd
#   X_1 => relu
#   X_2 => constant_pad_nd_1
# Graph fragment:
#   %constant_pad_nd : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%primals_3, [0, 1, 0, 1]), kwargs = {})
#   %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%primals_3,), kwargs = {})
#   %constant_pad_nd_1 : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%relu, [0, 1, 0, 1]), kwargs = {})
triton_poi_fused_constant_pad_nd_relu_0 = async_compile.triton('triton_poi_fused_constant_pad_nd_relu_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: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_constant_pad_nd_relu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_relu_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 5) % 5
    x0 = xindex % 5
    x2 = (xindex // 25)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = x0
    tmp4 = tmp3 < tmp1
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (x0 + (4*x1) + (16*x2)), tmp5 & xmask, other=0.0)
    tmp7 = tl.full([1], 0, tl.int32)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp5, tmp8, tmp9)
    tl.store(out_ptr0 + (x3), tmp6, xmask)
    tl.store(out_ptr1 + (x3), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/35/c35wrtw4jur3xaxq7ut2gk27upe5scdzvcwpdctdxqpgzvbnh735.py
# Topologically Sorted Source Nodes: [X_4, X_5], Original ATen: [aten.relu, aten.constant_pad_nd]
# Source node to ATen node mapping:
#   X_4 => relu_1
#   X_5 => constant_pad_nd_2
# Graph fragment:
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_3,), kwargs = {})
#   %constant_pad_nd_2 : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%relu_1, [0, 1, 0, 1]), kwargs = {})
triton_poi_fused_constant_pad_nd_relu_1 = async_compile.triton('triton_poi_fused_constant_pad_nd_relu_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=[2048], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_constant_pad_nd_relu_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_relu_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 9) % 9
    x0 = xindex % 9
    x4 = (xindex // 81)
    x2 = (xindex // 81) % 4
    x5 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 8, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = x0
    tmp4 = tmp3 < tmp1
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + ((4*((x1 // 2) % 4)) + (16*(x0 % 2)) + (32*(x1 % 2)) + (64*x4) + ((x0 // 2) % 4)), tmp5 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tl.load(in_ptr1 + ((2*(x1 % 2)) + (4*x2) + (x0 % 2)), tmp5 & xmask, eviction_policy='evict_last', other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp11 = tl.full(tmp10.shape, 0.0, tmp10.dtype)
    tmp12 = tl.where(tmp5, tmp10, tmp11)
    tl.store(out_ptr0 + (x5), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/i5/ci53bsz62habasu4hzydebxtd4pbocmefvo3vbus4kflpf7gq7ay.py
# Topologically Sorted Source Nodes: [X_5, X_6], Original ATen: [aten.convolution, aten.add]
# Source node to ATen node mapping:
#   X_5 => convolution_2
#   X_6 => add
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%constant_pad_nd_2, %primals_6, %primals_7, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_2, %view_1), kwargs = {})
triton_poi_fused_add_convolution_2 = async_compile.triton('triton_poi_fused_add_convolution_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=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x2 = (xindex // 64) % 4
    x0 = xindex % 8
    x1 = (xindex // 8) % 8
    x5 = (xindex // 64)
    tmp0 = tl.load(in_out_ptr0 + (x4), xmask)
    tmp1 = tl.load(in_ptr0 + (x2), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + ((4*(x1 // 2)) + (16*(x0 % 2)) + (32*(x1 % 2)) + (64*x5) + (x0 // 2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + ((2*(x1 % 2)) + (4*x2) + (x0 % 2)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tl.store(in_out_ptr0 + (x4), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/74/c74vzy264zbtl3kgxuqdurfai2hahnzoqrv7bczdbuntickhynq4.py
# Topologically Sorted Source Nodes: [X_4], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   X_4 => relu_1
# Graph fragment:
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_3,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_3 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_relu_threshold_backward_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = (xindex // 8) % 8
    x4 = (xindex // 64)
    x2 = (xindex // 64) % 4
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + ((4*(x1 // 2)) + (16*(x0 % 2)) + (32*(x1 % 2)) + (64*x4) + (x0 // 2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + ((2*(x1 % 2)) + (4*x2) + (x0 % 2)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x5), 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, primals_7 = args
    args.clear()
    assert_size_stride(primals_1, (16, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (16, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (16, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (16, ), (1, ))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.float32)
        # Topologically Sorted Source Nodes: [X, X_1, X_2], Original ATen: [aten.constant_pad_nd, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_relu_0.run(primals_3, buf0, buf2, 400, grid=grid(400), stream=stream0)
        del primals_3
        # Topologically Sorted Source Nodes: [X], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 16, 4, 4), (256, 16, 4, 1))
        # Topologically Sorted Source Nodes: [X_2], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 16, 4, 4), (256, 16, 4, 1))
        buf4 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32)
        # Topologically Sorted Source Nodes: [X_4, X_5], Original ATen: [aten.relu, aten.constant_pad_nd]
        triton_poi_fused_constant_pad_nd_relu_1.run(buf3, primals_5, buf4, 1296, grid=grid(1296), stream=stream0)
        # Topologically Sorted Source Nodes: [X_5], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 8, 8), (256, 64, 8, 1))
        buf6 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [X_5, X_6], Original ATen: [aten.convolution, aten.add]
        triton_poi_fused_add_convolution_2.run(buf6, primals_7, buf1, primals_2, 1024, grid=grid(1024), stream=stream0)
        del buf1
        del primals_2
        del primals_7
        buf7 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.bool)
        # Topologically Sorted Source Nodes: [X_4], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_3.run(buf3, primals_5, buf7, 1024, grid=grid(1024), stream=stream0)
        del buf3
        del primals_5
    return (buf6, primals_1, primals_4, primals_6, buf0, buf2, buf4, buf7, )


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((16, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((16, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 4, 4), (64, 16, 4, 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


class ConvShuffle(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, padding=
        'same', upscale_factor=2, padding_mode='zeros'):
        super(ConvShuffle, self).__init__()
        self.upscale_factor = upscale_factor
        self.conv = nn.Conv2d(in_channels, out_channels * upscale_factor **
            2, kernel_size, padding=padding, padding_mode=padding_mode)

    def forward(self, X):
        X = self.conv(X)
        return nn.functional.pixel_shuffle(X, self.upscale_factor)


class Upscale_Conv_block(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, padding=
        'same', upscale_factor=2, main_upscale=ConvShuffle, shortcut=
        ConvShuffle, padding_mode='zeros', activation=nn.functional.relu,
        Ch=0, Cw=0):
        assert isinstance(upscale_factor, int)
        super(Upscale_Conv_block, self).__init__()
        self.shortcut = shortcut(in_channels, out_channels, kernel_size,
            padding=padding, padding_mode=padding_mode, upscale_factor=
            upscale_factor)
        self.activation = activation
        self.upscale = main_upscale(in_channels, out_channels, kernel_size,
            padding=padding, padding_mode=padding_mode, upscale_factor=
            upscale_factor)
        self.conv = nn.Conv2d(out_channels, out_channels, kernel_size,
            padding=padding, padding_mode=padding_mode)
        self.Ch = Ch
        self.Cw = Cw

    def forward(self, X):
        X_shortcut = self.shortcut(X)
        X = self.activation(X)
        X = self.upscale(X)
        X = self.activation(X)
        X = self.conv(X)
        H, W = X.shape[2:]
        H2, W2 = X_shortcut.shape[2:]
        if H2 > H or W2 > W:
            padding_height = (H2 - H) // 2
            padding_width = (W2 - W) // 2
            X = X + X_shortcut[:, :, padding_height:padding_height + H,
                padding_width:padding_width + W]
        else:
            X = X + X_shortcut
        return X[:, :, self.Ch:, self.Cw:]


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 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 import triton_helpers
from torch import 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_constant_pad_nd_relu_0(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 5 % 5
    x0 = xindex % 5
    x2 = xindex // 25
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = x0
    tmp4 = tmp3 < tmp1
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2), tmp5 & xmask, other=0.0)
    tmp7 = tl.full([1], 0, tl.int32)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp5, tmp8, tmp9)
    tl.store(out_ptr0 + x3, tmp6, xmask)
    tl.store(out_ptr1 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_constant_pad_nd_relu_1(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 9 % 9
    x0 = xindex % 9
    x4 = xindex // 81
    x2 = xindex // 81 % 4
    x5 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 8, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = x0
    tmp4 = tmp3 < tmp1
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (4 * (x1 // 2 % 4) + 16 * (x0 % 2) + 32 * (x1 %
        2) + 64 * x4 + x0 // 2 % 4), tmp5 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp7 = tl.load(in_ptr1 + (2 * (x1 % 2) + 4 * x2 + x0 % 2), tmp5 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp11 = tl.full(tmp10.shape, 0.0, tmp10.dtype)
    tmp12 = tl.where(tmp5, tmp10, tmp11)
    tl.store(out_ptr0 + x5, tmp12, xmask)


@triton.jit
def triton_poi_fused_add_convolution_2(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x2 = xindex // 64 % 4
    x0 = xindex % 8
    x1 = xindex // 8 % 8
    x5 = xindex // 64
    tmp0 = tl.load(in_out_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr0 + x2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (4 * (x1 // 2) + 16 * (x0 % 2) + 32 * (x1 % 2) +
        64 * x5 + x0 // 2), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + (2 * (x1 % 2) + 4 * x2 + x0 % 2), xmask,
        eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tl.store(in_out_ptr0 + x4, tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_3(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8 % 8
    x4 = xindex // 64
    x2 = xindex // 64 % 4
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * (x1 // 2) + 16 * (x0 % 2) + 32 * (x1 % 2) +
        64 * x4 + x0 // 2), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (2 * (x1 % 2) + 4 * x2 + x0 % 2), xmask,
        eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x5, tmp6, 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, (16, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (16,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (16, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (16,), (1,))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_relu_0[grid(400)](primals_3, buf0,
            buf2, 400, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf1 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 16, 4, 4), (256, 16, 4, 1))
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 16, 4, 4), (256, 16, 4, 1))
        buf4 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32)
        triton_poi_fused_constant_pad_nd_relu_1[grid(1296)](buf3, primals_5,
            buf4, 1296, XBLOCK=128, num_warps=4, num_stages=1)
        buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 8, 8), (256, 64, 8, 1))
        buf6 = buf5
        del buf5
        triton_poi_fused_add_convolution_2[grid(1024)](buf6, primals_7,
            buf1, primals_2, 1024, XBLOCK=128, num_warps=4, num_stages=1)
        del buf1
        del primals_2
        del primals_7
        buf7 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_3[grid(1024)](buf3,
            primals_5, buf7, 1024, XBLOCK=128, num_warps=4, num_stages=1)
        del buf3
        del primals_5
    return buf6, primals_1, primals_4, primals_6, buf0, buf2, buf4, buf7


class ConvShuffle(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, padding=
        'same', upscale_factor=2, padding_mode='zeros'):
        super(ConvShuffle, self).__init__()
        self.upscale_factor = upscale_factor
        self.conv = nn.Conv2d(in_channels, out_channels * upscale_factor **
            2, kernel_size, padding=padding, padding_mode=padding_mode)

    def forward(self, X):
        X = self.conv(X)
        return nn.functional.pixel_shuffle(X, self.upscale_factor)


class Upscale_Conv_blockNew(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, padding=
        'same', upscale_factor=2, main_upscale=ConvShuffle, shortcut=
        ConvShuffle, padding_mode='zeros', activation=nn.functional.relu,
        Ch=0, Cw=0):
        assert isinstance(upscale_factor, int)
        super(Upscale_Conv_blockNew, self).__init__()
        self.shortcut = shortcut(in_channels, out_channels, kernel_size,
            padding=padding, padding_mode=padding_mode, upscale_factor=
            upscale_factor)
        self.activation = activation
        self.upscale = main_upscale(in_channels, out_channels, kernel_size,
            padding=padding, padding_mode=padding_mode, upscale_factor=
            upscale_factor)
        self.conv = nn.Conv2d(out_channels, out_channels, kernel_size,
            padding=padding, padding_mode=padding_mode)
        self.Ch = Ch
        self.Cw = Cw

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