# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/i3/ci3zxiw7jwdnzvz4mmk5mwwsff5vz4qx3uzzpitdind3hfy3i3vq.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%relu, %slice_2], 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=[2048], 
    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 = 2048
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = (xindex // 64) % 8
    x3 = (xindex // 512)
    x4 = xindex % 64
    x1 = (xindex // 8) % 8
    x0 = xindex % 8
    x7 = xindex
    tmp0 = x2
    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 + (x4 + (64*x2) + (256*x3)), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + (x2), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tmp13 = tl.full([1], 8, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = (-2) + x1
    tmp16 = tmp15 >= tmp1
    tmp17 = tmp15 < tmp3
    tmp18 = (-2) + x0
    tmp19 = tmp18 >= tmp1
    tmp20 = tmp18 < tmp3
    tmp21 = tmp16 & tmp17
    tmp22 = tmp21 & tmp19
    tmp23 = tmp22 & tmp20
    tmp24 = tmp23 & tmp12
    tmp25 = tl.load(in_ptr2 + ((-10) + x0 + (4*x1) + (16*((-4) + x2)) + (64*x3)), tmp24, other=0.0)
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp12, tmp25, tmp26)
    tmp28 = tl.where(tmp4, tmp11, tmp27)
    tl.store(out_ptr0 + (x7), tmp28, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/vt/cvtonbmisuslzwvf7wtu3i7uuhphfmfg3kvfexqxplfygxltvwg6.py
# Topologically Sorted Source Nodes: [conv_transpose2d, up], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv_transpose2d => convolution
#   up => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [2, 2], [0, 0], [1, 1], True, [0, 0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_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: '*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_relu_threshold_backward_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_convolution_relu_threshold_backward_1(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
    x3 = xindex
    x1 = (xindex // 64) % 4
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), 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 + (x3), tmp6, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 2, 2), (16, 4, 2, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv_transpose2d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 8, 8), (256, 64, 8, 1))
        buf1 = empty_strided_cuda((4, 8, 8, 8), (512, 64, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(buf0, primals_2, primals_4, buf1, 2048, grid=grid(2048), stream=stream0)
        del primals_4
        buf2 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv_transpose2d, up], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_1.run(buf0, primals_2, buf2, 1024, grid=grid(1024), stream=stream0)
        del buf0
        del primals_2
    return (buf1, primals_1, primals_3, buf2, )


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, 2, 2), (16, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (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((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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 UpConcat2d(nn.Module):

    def __init__(self, in_channels_conv, out_channels_conv, scale_factor=2):
        super(UpConcat2d, self).__init__()
        self.in_channels_conv = in_channels_conv
        self.out_channels_conv = out_channels_conv
        self.scale_factor = scale_factor
        self.up = nn.ConvTranspose2d(in_channels=self.in_channels_conv,
            out_channels=self.out_channels_conv, kernel_size=2, stride=2,
            padding=0)
        if scale_factor == 4:
            self.up2 = nn.ConvTranspose2d(in_channels=self.
                out_channels_conv, out_channels=self.out_channels_conv,
                kernel_size=2, stride=2, padding=0)

    def forward(self, x_down, x_enc):
        up = F.relu(self.up(x_down))
        if self.scale_factor == 4:
            up = F.relu(self.up2(up))
        if up.shape[-1] > x_enc.shape[-1]:
            p = (up.shape[-1] - x_enc.shape[-1]) // 2
            if (up.shape[-1] - x_enc.shape[-1]) % 2 != 0:
                p += 1
            x_enc = F.pad(x_enc, (p, p, p, p))
        start = [(x_enc.shape[-2] - up.shape[-2]) // 2, (x_enc.shape[-1] -
            up.shape[-1]) // 2]
        length = [up.shape[-2], up.shape[-1]]
        crop = torch.narrow(torch.narrow(x_enc, dim=2, start=start[0],
            length=length[0]), dim=3, start=start[1], length=length[1])
        cat = torch.cat(tensors=(up, crop), dim=1)
        return cat

    def initialize_weights(self):
        nn.init.normal_(self.up.weight.data, mean=0.0, std=0.02)
        nn.init.constant_(self.up.bias.data, 0.0)
        if self.scale_factor == 4:
            nn.init.normal_(self.up2.weight.data, mean=0.0, std=0.02)
            nn.init.constant_(self.up2.bias.data, 0.0)


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


def get_init_inputs():
    return [[], {'in_channels_conv': 4, 'out_channels_conv': 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
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):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex // 64 % 8
    x3 = xindex // 512
    x4 = xindex % 64
    x1 = xindex // 8 % 8
    x0 = xindex % 8
    x7 = xindex
    tmp0 = x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x4 + 64 * x2 + 256 * x3), tmp4, other=0.0)
    tmp6 = tl.load(in_ptr1 + x2, tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp15 = -2 + x1
    tmp16 = tmp15 >= tmp1
    tmp17 = tmp15 < tmp3
    tmp18 = -2 + x0
    tmp19 = tmp18 >= tmp1
    tmp20 = tmp18 < tmp3
    tmp21 = tmp16 & tmp17
    tmp22 = tmp21 & tmp19
    tmp23 = tmp22 & tmp20
    tmp24 = tmp23 & tmp12
    tmp25 = tl.load(in_ptr2 + (-10 + x0 + 4 * x1 + 16 * (-4 + x2) + 64 * x3
        ), tmp24, other=0.0)
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp12, tmp25, tmp26)
    tmp28 = tl.where(tmp4, tmp11, tmp27)
    tl.store(out_ptr0 + x7, tmp28, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_1(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
    x3 = xindex
    x1 = xindex // 64 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, 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 + x3, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 2, 2), (16, 4, 2, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2, 
            2), padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 8, 8), (256, 64, 8, 1))
        buf1 = empty_strided_cuda((4, 8, 8, 8), (512, 64, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(2048)](buf0, primals_2, primals_4, buf1,
            2048, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_4
        buf2 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_1[grid(1024)](buf0
            , primals_2, buf2, 1024, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        del primals_2
    return buf1, primals_1, primals_3, buf2


class UpConcat2dNew(nn.Module):

    def __init__(self, in_channels_conv, out_channels_conv, scale_factor=2):
        super(UpConcat2dNew, self).__init__()
        self.in_channels_conv = in_channels_conv
        self.out_channels_conv = out_channels_conv
        self.scale_factor = scale_factor
        self.up = nn.ConvTranspose2d(in_channels=self.in_channels_conv,
            out_channels=self.out_channels_conv, kernel_size=2, stride=2,
            padding=0)
        if scale_factor == 4:
            self.up2 = nn.ConvTranspose2d(in_channels=self.
                out_channels_conv, out_channels=self.out_channels_conv,
                kernel_size=2, stride=2, padding=0)

    def initialize_weights(self):
        nn.init.normal_(self.up.weight.data, mean=0.0, std=0.02)
        nn.init.constant_(self.up.bias.data, 0.0)
        if self.scale_factor == 4:
            nn.init.normal_(self.up2.weight.data, mean=0.0, std=0.02)
            nn.init.constant_(self.up2.bias.data, 0.0)

    def forward(self, input_0, input_1):
        primals_1 = self.up.weight
        primals_2 = self.up.bias
        primals_3 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
