# 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/cp/ccpm2rc3cohajsucjilmian5aydpml7ivg7frstqwp7fovfahaai.py
# Topologically Sorted Source Nodes: [fx], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   fx => relu
# Graph fragment:
#   %relu : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%primals_1,), kwargs = {})
#   %copy_ : [num_users=0] = call_function[target=torch.ops.aten.copy_.default](args = (%primals_1, %relu), kwargs = {})
triton_poi_fused_relu_0 = async_compile.triton('triton_poi_fused_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_0', 'mutated_arg_names': ['in_ptr0', 'out_ptr1'], '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_relu_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    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 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + (x0), tmp2, xmask)
    tl.store(out_ptr1 + (x0), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/al/calalowfg74cvq7dmqa6tzd2ti3a7fyfo53zwdtlwn2ugibgkear.py
# Topologically Sorted Source Nodes: [fx_1, fx_2], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
# Source node to ATen node mapping:
#   fx_1 => convolution
#   fx_2 => add, mul, rsqrt, sub, var_mean
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_2, %primals_3, [2, 2], [1, 1], [1, 1], True, [0, 0], 1), kwargs = {})
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %getitem_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {})
triton_per_fused__native_batch_norm_legit_convolution_1 = async_compile.triton('triton_per_fused__native_batch_norm_legit_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.persistent_reduction(
    size_hints=[16, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 4, '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}
)
@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_1(in_out_ptr0, in_ptr0, out_ptr0, out_ptr2, out_ptr3, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (r2 + (64*x3)), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 64, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp3 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = tmp2 - tmp12
    tmp20 = 64.0
    tmp21 = tmp18 / tmp20
    tmp22 = 1e-05
    tmp23 = tmp21 + tmp22
    tmp24 = libdevice.rsqrt(tmp23)
    tmp25 = tmp19 * tmp24
    tl.store(in_out_ptr0 + (r2 + (64*x3)), tmp2, xmask)
    tl.store(out_ptr2 + (r2 + (64*x3)), tmp25, xmask)
    tl.store(out_ptr3 + (x3), tmp24, xmask)
    tl.store(out_ptr0 + (x3), tmp12, 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), (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, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [fx], Original ATen: [aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_relu_0.run(primals_1, buf0, primals_1, 256, grid=grid(256), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [fx_1], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 8, 8), (256, 64, 8, 1))
        buf2 = buf1; del buf1  # reuse
        buf3 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        buf7 = empty_strided_cuda((1, 16, 8, 8), (1024, 64, 8, 1), torch.float32)
        buf6 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        # Topologically Sorted Source Nodes: [fx_1, fx_2], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
        triton_per_fused__native_batch_norm_legit_convolution_1.run(buf2, primals_3, buf3, buf7, buf6, 16, 64, grid=grid(16), stream=stream0)
        del primals_3
    return (reinterpret_tensor(buf7, (4, 4, 8, 8), (256, 64, 8, 1), 0), primals_2, buf0, buf2, reinterpret_tensor(buf6, (16, ), (1, ), 0), reinterpret_tensor(buf3, (1, 16, 1, 1), (16, 1, 1, 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), (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
from torch import nn


class DecoderBlock(nn.Module):
    """Decoder block"""

    def __init__(self, inplanes, outplanes, kernel_size=4, stride=2,
        padding=1, dropout=False):
        super().__init__()
        self.relu = nn.ReLU(inplace=True)
        self.deconv = nn.ConvTranspose2d(inplanes, outplanes, kernel_size,
            stride, padding)
        self.bn = nn.InstanceNorm2d(outplanes)
        self.dropout = None
        if dropout:
            self.dropout = nn.Dropout2d(p=0.5, inplace=True)

    def forward(self, x):
        fx = self.relu(x)
        fx = self.deconv(fx)
        fx = self.bn(fx)
        if self.dropout is not None:
            fx = self.dropout(fx)
        return fx


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


def get_init_inputs():
    return [[], {'inplanes': 4, 'outplanes': 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._inductor.runtime.triton_helpers import libdevice
from torch import 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_relu_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 256
    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 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr1 + x0, tmp2, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_1(in_out_ptr0,
    in_ptr0, out_ptr0, out_ptr2, out_ptr3, xnumel, rnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (r2 + 64 * x3), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 64, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp3 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = tmp2 - tmp12
    tmp20 = 64.0
    tmp21 = tmp18 / tmp20
    tmp22 = 1e-05
    tmp23 = tmp21 + tmp22
    tmp24 = libdevice.rsqrt(tmp23)
    tmp25 = tmp19 * tmp24
    tl.store(in_out_ptr0 + (r2 + 64 * x3), tmp2, xmask)
    tl.store(out_ptr2 + (r2 + 64 * x3), tmp25, xmask)
    tl.store(out_ptr3 + x3, tmp24, xmask)
    tl.store(out_ptr0 + x3, tmp12, 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), (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, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(256)](primals_1, buf0, primals_1, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 8, 8), (256, 64, 8, 1))
        buf2 = buf1
        del buf1
        buf3 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        buf7 = empty_strided_cuda((1, 16, 8, 8), (1024, 64, 8, 1), torch.
            float32)
        buf6 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        triton_per_fused__native_batch_norm_legit_convolution_1[grid(16)](buf2,
            primals_3, buf3, buf7, buf6, 16, 64, XBLOCK=8, num_warps=4,
            num_stages=1)
        del primals_3
    return reinterpret_tensor(buf7, (4, 4, 8, 8), (256, 64, 8, 1), 0
        ), primals_2, buf0, buf2, reinterpret_tensor(buf6, (16,), (1,), 0
        ), reinterpret_tensor(buf3, (1, 16, 1, 1), (16, 1, 1, 1), 0)


class DecoderBlockNew(nn.Module):
    """Decoder block"""

    def __init__(self, inplanes, outplanes, kernel_size=4, stride=2,
        padding=1, dropout=False):
        super().__init__()
        self.relu = nn.ReLU(inplace=True)
        self.deconv = nn.ConvTranspose2d(inplanes, outplanes, kernel_size,
            stride, padding)
        self.bn = nn.InstanceNorm2d(outplanes)
        self.dropout = None
        if dropout:
            self.dropout = nn.Dropout2d(p=0.5, inplace=True)

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