# 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/os/coslqwvho44erxzzjds34i4gp4eijmmnr2uxhw6cfvd3hxcjq7kq.py
# Topologically Sorted Source Nodes: [pad], Original ATen: [aten.constant_pad_nd]
# Source node to ATen node mapping:
#   pad => constant_pad_nd
# Graph fragment:
#   %constant_pad_nd : [num_users=1] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%primals_1, [2, 1, 2, 1, 2, 1], 0.0), kwargs = {})
triton_poi_fused_constant_pad_nd_0 = async_compile.triton('triton_poi_fused_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=[2048], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_constant_pad_nd_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1372
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 49) % 7
    x1 = (xindex // 7) % 7
    x0 = xindex % 7
    x3 = (xindex // 343)
    x7 = xindex
    tmp0 = (-2) + x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = (-2) + x1
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = (-2) + x0
    tmp9 = tmp8 >= tmp1
    tmp10 = tmp8 < tmp3
    tmp11 = tmp2 & tmp4
    tmp12 = tmp11 & tmp6
    tmp13 = tmp12 & tmp7
    tmp14 = tmp13 & tmp9
    tmp15 = tmp14 & tmp10
    tmp16 = tl.load(in_ptr0 + ((-42) + x0 + (4*x1) + (16*x2) + (64*x3)), tmp15 & xmask, other=0.0)
    tl.store(out_ptr0 + (x7), tmp16, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/k4/ck4rl3bwj3xj3vzqgyajenblv3ghtfzm34v3ftulr7inni3fptfq.py
# Topologically Sorted Source Nodes: [conv_transpose3d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv_transpose3d => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%unsqueeze, %primals_2, %primals_3, [1, 1, 1], [3, 3, 3], [1, 1, 1], True, [0, 0, 0], 1), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[256], 
    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_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], '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_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 64)
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 7, 7, 7), (343, 49, 7, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pad], Original ATen: [aten.constant_pad_nd]
        stream0 = get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0.run(primals_1, buf0, 1372, grid=grid(1372), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [conv_transpose3d], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(reinterpret_tensor(buf0, (1, 4, 7, 7, 7), (0, 343, 49, 7, 1), 0), primals_2, stride=(1, 1, 1), padding=(3, 3, 3), dilation=(1, 1, 1), transposed=True, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1))
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [conv_transpose3d], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf2, primals_3, 256, grid=grid(256), stream=stream0)
        del primals_3
    return (reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0), primals_2, reinterpret_tensor(buf0, (1, 4, 7, 7, 7), (1372, 343, 49, 7, 1), 0), )


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, 4, 4, 4), (256, 64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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 SamePadConvTranspose3d(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        bias=True):
        super().__init__()
        if isinstance(kernel_size, int):
            kernel_size = (kernel_size,) * 3
        if isinstance(stride, int):
            stride = (stride,) * 3
        total_pad = tuple([(k - s) for k, s in zip(kernel_size, stride)])
        pad_input = []
        for p in total_pad[::-1]:
            pad_input.append((p // 2 + p % 2, p // 2))
        pad_input = sum(pad_input, tuple())
        self.pad_input = pad_input
        self.convt = nn.ConvTranspose3d(in_channels, out_channels,
            kernel_size, stride=stride, bias=bias, padding=tuple([(k - 1) for
            k in kernel_size]))

    def forward(self, x):
        return self.convt(F.pad(x, self.pad_input))


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
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_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 1372
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 49 % 7
    x1 = xindex // 7 % 7
    x0 = xindex % 7
    x3 = xindex // 343
    x7 = xindex
    tmp0 = -2 + x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = -2 + x1
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = -2 + x0
    tmp9 = tmp8 >= tmp1
    tmp10 = tmp8 < tmp3
    tmp11 = tmp2 & tmp4
    tmp12 = tmp11 & tmp6
    tmp13 = tmp12 & tmp7
    tmp14 = tmp13 & tmp9
    tmp15 = tmp14 & tmp10
    tmp16 = tl.load(in_ptr0 + (-42 + x0 + 4 * x1 + 16 * x2 + 64 * x3), 
        tmp15 & xmask, other=0.0)
    tl.store(out_ptr0 + x7, tmp16, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 64
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 7, 7, 7), (343, 49, 7, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(1372)](primals_1, buf0, 
            1372, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(reinterpret_tensor(buf0, (1, 4, 7,
            7, 7), (0, 343, 49, 7, 1), 0), primals_2, stride=(1, 1, 1),
            padding=(3, 3, 3), dilation=(1, 1, 1), transposed=True,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(256)](buf2, primals_3, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
    return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), primals_2, reinterpret_tensor(buf0, (1, 4, 7, 7, 7), (1372, 343,
        49, 7, 1), 0)


class SamePadConvTranspose3dNew(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        bias=True):
        super().__init__()
        if isinstance(kernel_size, int):
            kernel_size = (kernel_size,) * 3
        if isinstance(stride, int):
            stride = (stride,) * 3
        total_pad = tuple([(k - s) for k, s in zip(kernel_size, stride)])
        pad_input = []
        for p in total_pad[::-1]:
            pad_input.append((p // 2 + p % 2, p // 2))
        pad_input = sum(pad_input, tuple())
        self.pad_input = pad_input
        self.convt = nn.ConvTranspose3d(in_channels, out_channels,
            kernel_size, stride=stride, bias=bias, padding=tuple([(k - 1) for
            k in kernel_size]))

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