# 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_7/inductor_cache/4u/c4urnjwbmjfo3ij7ae5ypxvyzekfoinn7jd3sustbmvje3h544wq.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
#   x => add, mul, rsqrt, sub, var_mean
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%primals_1, [0, 1, 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=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_1, %getitem_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {})
triton_per_fused_native_layer_norm_0 = async_compile.triton('triton_per_fused_native_layer_norm_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.persistent_reduction(
    size_hints=[1, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_native_layer_norm_0', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 1, '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_layer_norm_0(in_ptr0, out_ptr2, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.full([1], 256, tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp1 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = tmp0 - tmp8
    tmp15 = 256.0
    tmp16 = tmp13 / tmp15
    tmp17 = 1e-05
    tmp18 = tmp16 + tmp17
    tmp19 = libdevice.rsqrt(tmp18)
    tmp20 = tmp14 * tmp19
    tl.store(out_ptr2 + (tl.broadcast_to(r0, [RBLOCK])), tmp20, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/v7/cv7hldfsoqbu4bijoetauh7pnhw6nuyssxva2pudi4uclqd2pf2b.py
# Topologically Sorted Source Nodes: [x_1, x_2], Original ATen: [aten.leaky_relu, aten.native_layer_norm, aten.native_layer_norm_backward]
# Source node to ATen node mapping:
#   x_1 => gt, mul_1, where
#   x_2 => add_1, mul_2, rsqrt_1, sub_1, var_mean_1
# Graph fragment:
#   %gt : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_1, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, 0.01), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %view_1, %mul_1), kwargs = {})
#   %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%where, [0, 1, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_2, 1e-05), kwargs = {})
#   %rsqrt_1 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_1,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where, %getitem_3), kwargs = {})
#   %mul_2 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %rsqrt_1), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%rsqrt_1, 4096), kwargs = {})
triton_red_fused_leaky_relu_native_layer_norm_native_layer_norm_backward_1 = async_compile.triton('triton_red_fused_leaky_relu_native_layer_norm_native_layer_norm_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.reduction(
    size_hints=[1, 4096],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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': {5: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 6), equal_to_1=(5,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused_leaky_relu_native_layer_norm_native_layer_norm_backward_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 2, '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_red_fused_leaky_relu_native_layer_norm_native_layer_norm_backward_1(in_ptr0, in_ptr1, out_ptr0, out_ptr3, out_ptr4, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    tmp9_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp9_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp9_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        r0 = rindex % 64
        tmp0 = tl.load(in_ptr0 + (r2), rmask, eviction_policy='evict_last', other=0.0)
        tmp1 = tl.load(in_ptr1 + (r0), rmask, eviction_policy='evict_last', other=0.0)
        tmp2 = tmp0 + tmp1
        tmp3 = 0.0
        tmp4 = tmp2 > tmp3
        tmp5 = 0.01
        tmp6 = tmp2 * tmp5
        tmp7 = tl.where(tmp4, tmp2, tmp6)
        tmp8 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
        tmp9_mean_next, tmp9_m2_next, tmp9_weight_next = triton_helpers.welford_reduce(
            tmp8, tmp9_mean, tmp9_m2, tmp9_weight, roffset == 0
        )
        tmp9_mean = tl.where(rmask, tmp9_mean_next, tmp9_mean)
        tmp9_m2 = tl.where(rmask, tmp9_m2_next, tmp9_m2)
        tmp9_weight = tl.where(rmask, tmp9_weight_next, tmp9_weight)
        tl.store(out_ptr0 + (tl.broadcast_to(r2, [XBLOCK, RBLOCK])), tmp4, rmask)
    tmp9_tmp, tmp10_tmp, tmp11_tmp = triton_helpers.welford(
        tmp9_mean, tmp9_m2, tmp9_weight, 1
    )
    tmp9 = tmp9_tmp[:, None]
    tmp10 = tmp10_tmp[:, None]
    tmp11 = tmp11_tmp[:, None]
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        r0 = rindex % 64
        tmp12 = tl.load(out_ptr0 + (r2), rmask, eviction_policy='evict_first', other=0.0).to(tl.int1)
        tmp13 = tl.load(in_ptr0 + (r2), rmask, eviction_policy='evict_first', other=0.0)
        tmp14 = tl.load(in_ptr1 + (r0), rmask, eviction_policy='evict_last', other=0.0)
        tmp15 = tmp13 + tmp14
        tmp16 = 0.01
        tmp17 = tmp15 * tmp16
        tmp18 = tl.where(tmp12, tmp15, tmp17)
        tmp19 = tmp18 - tmp9
        tmp20 = 4096.0
        tmp21 = tmp10 / tmp20
        tmp22 = 1e-05
        tmp23 = tmp21 + tmp22
        tmp24 = libdevice.rsqrt(tmp23)
        tmp25 = tmp19 * tmp24
        tl.store(out_ptr3 + (tl.broadcast_to(r2, [XBLOCK, RBLOCK])), tmp25, rmask)
    tmp26 = 4096.0
    tmp27 = tmp10 / tmp26
    tmp28 = 1e-05
    tmp29 = tmp27 + tmp28
    tmp30 = libdevice.rsqrt(tmp29)
    tmp31 = 0.000244140625
    tmp32 = tmp30 * tmp31
    tl.store(out_ptr4 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp32, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (64, 4), (4, 1))
    assert_size_stride(primals_3, (64, ), (1, ))
    assert_size_stride(primals_4, (1, 64), (64, 1))
    assert_size_stride(primals_5, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.native_layer_norm]
        stream0 = get_raw_stream(0)
        triton_per_fused_native_layer_norm_0.run(primals_1, buf3, 1, 256, grid=grid(1), stream=stream0)
        del primals_1
        buf4 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 64), (1, 4), 0), out=buf4)
        del primals_2
        buf5 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool)
        buf9 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.float32)
        buf12 = empty_strided_cuda((1, 1, 1, 1), (1, 1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1, x_2], Original ATen: [aten.leaky_relu, aten.native_layer_norm, aten.native_layer_norm_backward]
        triton_red_fused_leaky_relu_native_layer_norm_native_layer_norm_backward_1.run(buf4, primals_3, buf5, buf9, buf12, 1, 4096, grid=grid(1), stream=stream0)
        del buf4
        del primals_3
        buf11 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf9, (64, 64), (64, 1), 0), reinterpret_tensor(primals_4, (64, 1), (1, 64), 0), alpha=1, beta=1, out=buf11)
        del primals_5
    return (reinterpret_tensor(buf11, (4, 4, 4, 1), (16, 4, 1, 1), 0), reinterpret_tensor(buf3, (64, 4), (4, 1), 0), buf5, buf9, primals_4, buf12, )


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((64, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, 64), (64, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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.optim as optim
from torch import nn
from torch.nn import functional as F


class CriticNN(nn.Module):

    def __init__(self, in_channels=3):
        super(CriticNN, self).__init__()
        self.fc1 = nn.Linear(4, 64)
        self.fc2 = nn.Linear(64, 1)
        self.optimizer = optim.Adam(self.parameters(), lr=0.0001)
        None

    def forward(self, x):
        x = F.layer_norm(x, x.size())
        x = F.leaky_relu(self.fc1(x))
        x = F.layer_norm(x, x.size())
        x = self.fc2(x)
        return x

    def init_weights(self, m):
        if type(m) == nn.Linear:
            None
            m.weight.data.fill_(0)
            m.bias.data.fill_(0)


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


def get_init_inputs():
    return [[], {}]

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
import torch.optim as optim
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_per_fused_native_layer_norm_0(in_ptr0, out_ptr2, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.full([1], 256, tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp1 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = tmp0 - tmp8
    tmp15 = 256.0
    tmp16 = tmp13 / tmp15
    tmp17 = 1e-05
    tmp18 = tmp16 + tmp17
    tmp19 = libdevice.rsqrt(tmp18)
    tmp20 = tmp14 * tmp19
    tl.store(out_ptr2 + tl.broadcast_to(r0, [RBLOCK]), tmp20, None)


@triton.jit
def triton_red_fused_leaky_relu_native_layer_norm_native_layer_norm_backward_1(
    in_ptr0, in_ptr1, out_ptr0, out_ptr3, out_ptr4, xnumel, rnumel, XBLOCK:
    tl.constexpr, RBLOCK: tl.constexpr):
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    tmp9_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp9_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp9_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        r0 = rindex % 64
        tmp0 = tl.load(in_ptr0 + r2, rmask, eviction_policy='evict_last',
            other=0.0)
        tmp1 = tl.load(in_ptr1 + r0, rmask, eviction_policy='evict_last',
            other=0.0)
        tmp2 = tmp0 + tmp1
        tmp3 = 0.0
        tmp4 = tmp2 > tmp3
        tmp5 = 0.01
        tmp6 = tmp2 * tmp5
        tmp7 = tl.where(tmp4, tmp2, tmp6)
        tmp8 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
        tmp9_mean_next, tmp9_m2_next, tmp9_weight_next = (triton_helpers.
            welford_reduce(tmp8, tmp9_mean, tmp9_m2, tmp9_weight, roffset == 0)
            )
        tmp9_mean = tl.where(rmask, tmp9_mean_next, tmp9_mean)
        tmp9_m2 = tl.where(rmask, tmp9_m2_next, tmp9_m2)
        tmp9_weight = tl.where(rmask, tmp9_weight_next, tmp9_weight)
        tl.store(out_ptr0 + tl.broadcast_to(r2, [XBLOCK, RBLOCK]), tmp4, rmask)
    tmp9_tmp, tmp10_tmp, tmp11_tmp = triton_helpers.welford(tmp9_mean,
        tmp9_m2, tmp9_weight, 1)
    tmp9 = tmp9_tmp[:, None]
    tmp10 = tmp10_tmp[:, None]
    tmp11_tmp[:, None]
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        r0 = rindex % 64
        tmp12 = tl.load(out_ptr0 + r2, rmask, eviction_policy='evict_first',
            other=0.0).to(tl.int1)
        tmp13 = tl.load(in_ptr0 + r2, rmask, eviction_policy='evict_first',
            other=0.0)
        tmp14 = tl.load(in_ptr1 + r0, rmask, eviction_policy='evict_last',
            other=0.0)
        tmp15 = tmp13 + tmp14
        tmp16 = 0.01
        tmp17 = tmp15 * tmp16
        tmp18 = tl.where(tmp12, tmp15, tmp17)
        tmp19 = tmp18 - tmp9
        tmp20 = 4096.0
        tmp21 = tmp10 / tmp20
        tmp22 = 1e-05
        tmp23 = tmp21 + tmp22
        tmp24 = libdevice.rsqrt(tmp23)
        tmp25 = tmp19 * tmp24
        tl.store(out_ptr3 + tl.broadcast_to(r2, [XBLOCK, RBLOCK]), tmp25, rmask
            )
    tmp26 = 4096.0
    tmp27 = tmp10 / tmp26
    tmp28 = 1e-05
    tmp29 = tmp27 + tmp28
    tmp30 = libdevice.rsqrt(tmp29)
    tmp31 = 0.000244140625
    tmp32 = tmp30 * tmp31
    tl.store(out_ptr4 + tl.full([XBLOCK, 1], 0, tl.int32), tmp32, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (64, 4), (4, 1))
    assert_size_stride(primals_3, (64,), (1,))
    assert_size_stride(primals_4, (1, 64), (64, 1))
    assert_size_stride(primals_5, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_native_layer_norm_0[grid(1)](primals_1, buf3, 1, 
            256, num_warps=2, num_stages=1)
        del primals_1
        buf4 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 64), (1, 4), 0), out=buf4)
        del primals_2
        buf5 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool
            )
        buf9 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.
            float32)
        buf12 = empty_strided_cuda((1, 1, 1, 1), (1, 1, 1, 1), torch.float32)
        triton_red_fused_leaky_relu_native_layer_norm_native_layer_norm_backward_1[
            grid(1)](buf4, primals_3, buf5, buf9, buf12, 1, 4096, XBLOCK=1,
            RBLOCK=2048, num_warps=16, num_stages=1)
        del buf4
        del primals_3
        buf11 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf9, (64, 64),
            (64, 1), 0), reinterpret_tensor(primals_4, (64, 1), (1, 64), 0),
            alpha=1, beta=1, out=buf11)
        del primals_5
    return reinterpret_tensor(buf11, (4, 4, 4, 1), (16, 4, 1, 1), 0
        ), reinterpret_tensor(buf3, (64, 4), (4, 1), 0
        ), buf5, buf9, primals_4, buf12


class CriticNNNew(nn.Module):

    def __init__(self, in_channels=3):
        super(CriticNNNew, self).__init__()
        self.fc1 = nn.Linear(4, 64)
        self.fc2 = nn.Linear(64, 1)
        self.optimizer = optim.Adam(self.parameters(), lr=0.0001)
        None

    def init_weights(self, m):
        if type(m) == nn.Linear:
            None
            m.weight.data.fill_(0)
            m.bias.data.fill_(0)

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
