# 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_6/inductor_cache/r6/cr6zjqblm6bzp3ra5bxxxousvsozkpcmx4vt2ow2xooiqo45tdke.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten._to_copy]
# Source node to ATen node mapping:
#   x => convert_element_type
# Graph fragment:
#   %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%primals_1, torch.float64), kwargs = {})
triton_poi_fused__to_copy_0 = async_compile.triton('triton_poi_fused__to_copy_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: '*fp64', 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__to_copy_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__to_copy_0(in_ptr0, out_ptr0, 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 = tmp0.to(tl.float64)
    tl.store(out_ptr0 + (x0), tmp1, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ff/cffiuioxgfnd7gb3ukcfrnvq6qtjvejobnrgl3kgqc2qcmiswonl.py
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_3 => relu
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp64', 1: '*fp64', 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_relu_threshold_backward_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_relu_threshold_backward_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = tl.full([1], 0.0, tl.float64)
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/tx/ctxcn7g5u6cl3pjvoozpr3ibzoccygyhuddalmafgdrhgwsazwae.py
# Topologically Sorted Source Nodes: [x_5], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   x_5 => amax, exp, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_3, [1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_3, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp64', 1: '*fp64', 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__softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = libdevice.exp(tmp8)
    tl.store(out_ptr0 + (x3), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/wc/cwceujsgrc3jjty6om26j2viqcp75hv5c3fbl3mlyr7yjhoifkpm.py
# Topologically Sorted Source Nodes: [x_5], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   x_5 => div, sum_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_3 = async_compile.triton('triton_poi_fused__softmax_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp64', 1: '*fp64', 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__softmax_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x3), tmp8, xmask)
''', 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, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (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.float64)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten._to_copy]
        stream0 = get_raw_stream(0)
        triton_poi_fused__to_copy_0.run(primals_1, buf0, 256, grid=grid(256), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float64)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf1)
        del primals_2
        buf2 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf1  # reuse
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_1.run(buf2, primals_3, buf6, 256, grid=grid(256), stream=stream0)
        del primals_3
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float64)
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf2, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf3)
        del primals_5
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float64)
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_2.run(buf3, buf4, 256, grid=grid(256), stream=stream0)
        buf5 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_3.run(buf4, buf5, 256, grid=grid(256), stream=stream0)
        del buf4
    return (buf5, reinterpret_tensor(buf0, (64, 4), (4, 1), 0), reinterpret_tensor(buf2, (64, 4), (4, 1), 0), buf5, primals_4, buf6, )


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, 1), device='cuda:0', dtype=torch.float64)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float64)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float64)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float64)
    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


class NeuralNet(torch.nn.Module):

    def __init__(self, input_size, output_size, hidden_size, lr=0.0001):
        super(NeuralNet, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.HiddenLayer = torch.nn.Linear(self.input_size, self.
            hidden_size, bias=True).double()
        self.DropoutLayer = torch.nn.Dropout(p=0.0)
        self.OutputLayer = torch.nn.Linear(self.hidden_size, self.
            output_size, bias=True).double()
        self.neural_tangent_kernel = None
        self.learning_rate = lr
        self.optimizer = torch.optim.SGD(self.parameters(), lr=self.
            learning_rate)
        self.loss_arr = []
        self.__policy_loss = []

    def forward(self, x):
        x = x.double()
        x = self.HiddenLayer(x)
        x = self.DropoutLayer(x)
        x = torch.relu(x)
        x = self.OutputLayer(x)
        x = torch.softmax(x, dim=1)
        return x

    def __partial_derivatives(self, x):
        self.zero_grad()
        w1 = torch.empty(self.output_size, self.hidden_size * self.
            input_size + self.hidden_size, dtype=torch.float64)
        w2 = torch.empty(self.output_size, self.hidden_size * self.
            output_size + self.output_size, dtype=torch.float64)
        for i in range(self.output_size):
            y = self.forward(x)
            y = y[0][i]
            y.backward()
            wi1 = self.HiddenLayer.weight.grad
            wi1 = torch.reshape(wi1, [wi1.shape[0] * wi1.shape[1], 1])
            wi1 = torch.cat([wi1, self.HiddenLayer.bias.grad.unsqueeze(1)])
            wi2 = self.OutputLayer.weight.grad
            wi2 = torch.reshape(wi2, [wi2.shape[0] * wi2.shape[1], 1])
            wi2 = torch.cat([wi2, self.OutputLayer.bias.grad.unsqueeze(1)])
            wi1g = wi1.clone().detach()
            wi2g = wi2.clone().detach()
            w1[i] = wi1g.squeeze()
            w2[i] = wi2g.squeeze()
            self.zero_grad()
        return w1, w2

    def compute_neural_tangent_kernel(self, x):
        kernel = torch.zeros([x.shape[0] * self.output_size, x.shape[0] *
            self.output_size], dtype=torch.float64, requires_grad=False)
        i = 0
        for x1 in x.data:
            w1x1, w2x1 = self.__partial_derivatives(x1.unsqueeze(dim=0))
            j = 0
            for x2 in x.data:
                w1x2, w2x2 = self.__partial_derivatives(x2.unsqueeze(dim=0))
                kernel[self.output_size * i:self.output_size * i + self.
                    output_size, self.output_size * j:self.output_size * j +
                    self.output_size] = torch.matmul(w1x1, w1x2.transpose(0, 1)
                    ) + torch.matmul(w2x1, w2x2.transpose(0, 1))
                j += 1
            i += 1
        self.neural_tangent_kernel = kernel
        return kernel

    def train_network(self, log_probs, gains, gains_normed=0):
        self.__policy_loss = []
        eps = 1e-08
        batch_len = len(gains)
        gains.clone().detach().requires_grad_(True)
        if gains_normed:
            gains_norm = gains
        else:
            gains_norm = (gains - gains.mean()) / (gains.std() + eps)
        for k in range(batch_len):
            self.__policy_loss.append(-log_probs[k] * gains_norm[k])
        self.__policy_loss = torch.cat(self.__policy_loss).sum()
        self.__policy_loss.backward()
        self.optimizer.step()
        self.optimizer.zero_grad()
        self.loss_arr.append(self.__policy_loss.item())


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


def get_init_inputs():
    return [[], {'input_size': 4, 'output_size': 4, 'hidden_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._inductor.runtime.triton_helpers import libdevice
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__to_copy_0(in_ptr0, out_ptr0, 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 = tmp0.to(tl.float64)
    tl.store(out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = tl.full([1], 0.0, tl.float64)
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = libdevice.exp(tmp8)
    tl.store(out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


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, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (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.float64)
        get_raw_stream(0)
        triton_poi_fused__to_copy_0[grid(256)](primals_1, buf0, 256, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float64)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf1)
        del primals_2
        buf2 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf1
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(256)](buf2,
            primals_3, buf6, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float64)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf2, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf3)
        del primals_5
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float64)
        triton_poi_fused__softmax_2[grid(256)](buf3, buf4, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf5 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf3
        triton_poi_fused__softmax_3[grid(256)](buf4, buf5, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf4
    return buf5, reinterpret_tensor(buf0, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf2, (64, 4), (4, 1), 0), buf5, primals_4, buf6


class NeuralNetNew(torch.nn.Module):

    def __init__(self, input_size, output_size, hidden_size, lr=0.0001):
        super(NeuralNetNew, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.HiddenLayer = torch.nn.Linear(self.input_size, self.
            hidden_size, bias=True).double()
        self.DropoutLayer = torch.nn.Dropout(p=0.0)
        self.OutputLayer = torch.nn.Linear(self.hidden_size, self.
            output_size, bias=True).double()
        self.neural_tangent_kernel = None
        self.learning_rate = lr
        self.optimizer = torch.optim.SGD(self.parameters(), lr=self.
            learning_rate)
        self.loss_arr = []
        self.__policy_loss = []

    def __partial_derivatives(self, x):
        self.zero_grad()
        w1 = torch.empty(self.output_size, self.hidden_size * self.
            input_size + self.hidden_size, dtype=torch.float64)
        w2 = torch.empty(self.output_size, self.hidden_size * self.
            output_size + self.output_size, dtype=torch.float64)
        for i in range(self.output_size):
            y = self.forward(x)
            y = y[0][i]
            y.backward()
            wi1 = self.HiddenLayer.weight.grad
            wi1 = torch.reshape(wi1, [wi1.shape[0] * wi1.shape[1], 1])
            wi1 = torch.cat([wi1, self.HiddenLayer.bias.grad.unsqueeze(1)])
            wi2 = self.OutputLayer.weight.grad
            wi2 = torch.reshape(wi2, [wi2.shape[0] * wi2.shape[1], 1])
            wi2 = torch.cat([wi2, self.OutputLayer.bias.grad.unsqueeze(1)])
            wi1g = wi1.clone().detach()
            wi2g = wi2.clone().detach()
            w1[i] = wi1g.squeeze()
            w2[i] = wi2g.squeeze()
            self.zero_grad()
        return w1, w2

    def compute_neural_tangent_kernel(self, x):
        kernel = torch.zeros([x.shape[0] * self.output_size, x.shape[0] *
            self.output_size], dtype=torch.float64, requires_grad=False)
        i = 0
        for x1 in x.data:
            w1x1, w2x1 = self.__partial_derivatives(x1.unsqueeze(dim=0))
            j = 0
            for x2 in x.data:
                w1x2, w2x2 = self.__partial_derivatives(x2.unsqueeze(dim=0))
                kernel[self.output_size * i:self.output_size * i + self.
                    output_size, self.output_size * j:self.output_size * j +
                    self.output_size] = torch.matmul(w1x1, w1x2.transpose(0, 1)
                    ) + torch.matmul(w2x1, w2x2.transpose(0, 1))
                j += 1
            i += 1
        self.neural_tangent_kernel = kernel
        return kernel

    def train_network(self, log_probs, gains, gains_normed=0):
        self.__policy_loss = []
        eps = 1e-08
        batch_len = len(gains)
        gains.clone().detach().requires_grad_(True)
        if gains_normed:
            gains_norm = gains
        else:
            gains_norm = (gains - gains.mean()) / (gains.std() + eps)
        for k in range(batch_len):
            self.__policy_loss.append(-log_probs[k] * gains_norm[k])
        self.__policy_loss = torch.cat(self.__policy_loss).sum()
        self.__policy_loss.backward()
        self.optimizer.step()
        self.optimizer.zero_grad()
        self.loss_arr.append(self.__policy_loss.item())

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