# 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_0/inductor_cache/w7/cw7vs66kv6n6qd3sz6bqmbh334sninlwepmd5dp5tz2dc6pkvtsh.py
# Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   relu => relu
# Graph fragment:
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_2), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor,), 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=[32], 
    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_relu_0', '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_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    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)
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_0/inductor_cache/if/cifspokppsbnxoamjhgrt6bed2y2m3hucem5u4wnfilqkxalhuzx.py
# Topologically Sorted Source Nodes: [role_latent_reshaped], Original ATen: [aten.repeat]
# Source node to ATen node mapping:
#   role_latent_reshaped => repeat
# Graph fragment:
#   %repeat : [num_users=2] = call_function[target=torch.ops.aten.repeat.default](args = (%unsqueeze_1, [4, 1, 1]), kwargs = {})
triton_poi_fused_repeat_1 = async_compile.triton('triton_poi_fused_repeat_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=[64], 
    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_repeat_1', '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_repeat_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (8, 4), (4, 1))
    assert_size_stride(primals_2, (8, ), (1, ))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 8), (8, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 8), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_relu_0.run(buf1, primals_2, 32, grid=grid(32), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, buf1, reinterpret_tensor(primals_4, (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [role_latent_reshaped], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_1.run(primals_6, buf3, 64, grid=grid(64), stream=stream0)
        del primals_6
        buf4 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [role_latent_reshaped, bmm], Original ATen: [aten.repeat, aten.bmm]
        extern_kernels.bmm(buf3, reinterpret_tensor(buf2, (4, 4, 1), (4, 1, 1), 0), out=buf4)
        del buf2
    return (reinterpret_tensor(buf4, (4, 4), (4, 1), 0), primals_3, buf1, reinterpret_tensor(buf3, (4, 4, 4), (16, 1, 4), 0), primals_4, )


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((8, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
    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._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 as th
from torch.distributions import Categorical
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_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    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)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_repeat_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x2, tmp0, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (8, 4), (4, 1))
    assert_size_stride(primals_2, (8,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 8), (8, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 8),
            (1, 4), 0), out=buf0)
        del primals_1
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(32)](buf1, primals_2, 32, XBLOCK=32,
            num_warps=1, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf1, reinterpret_tensor(primals_4,
            (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_repeat_1[grid(64)](primals_6, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_6
        buf4 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(buf3, reinterpret_tensor(buf2, (4, 4, 1), (4, 1,
            1), 0), out=buf4)
        del buf2
    return reinterpret_tensor(buf4, (4, 4), (4, 1), 0
        ), primals_3, buf1, reinterpret_tensor(buf3, (4, 4, 4), (16, 1, 4), 0
        ), primals_4


class DotSelectorNew(nn.Module):

    def __init__(self, input_shape, args):
        super(DotSelectorNew, self).__init__()
        self.args = args
        self.epsilon_start = self.args.epsilon_start
        self.epsilon_finish = self.args.role_epsilon_finish
        self.epsilon_anneal_time = self.args.epsilon_anneal_time
        self.epsilon_anneal_time_exp = self.args.epsilon_anneal_time_exp
        self.delta = (self.epsilon_start - self.epsilon_finish
            ) / self.epsilon_anneal_time
        self.role_action_spaces_update_start = (self.args.
            role_action_spaces_update_start)
        self.epsilon_start_t = 0
        self.epsilon_reset = True
        self.fc1 = nn.Linear(args.rnn_hidden_dim, 2 * args.rnn_hidden_dim)
        self.fc2 = nn.Linear(2 * args.rnn_hidden_dim, args.action_latent_dim)
        self.epsilon = 0.05

    def select_role(self, role_qs, hp, test_mode=False, t_env=None):
        self.epsilon = self.epsilon_schedule(t_env)
        self.calc_roleQ_by_human(hp)
        if test_mode:
            self.epsilon = 0.0
        masked_q_values = role_qs.detach().clone()
        random_numbers = th.rand_like(role_qs[:, 0])
        pick_random = (random_numbers < self.epsilon).long()
        random_roles = Categorical(th.ones(role_qs.shape).float()).sample(
            ).long()
        picked_roles = pick_random * random_roles + (1 - pick_random
            ) * masked_q_values.max(dim=1)[1]
        return picked_roles

    def calc_roleQ_by_human(self, hp):
        human_roleQ = []
        for index1 in range(len(hp)):
            for index2 in range(len(hp[index1])):
                cur_hp = hp[index1, index2]
                if cur_hp >= 0.85:
                    human_roleQ.append(th.FloatTensor([0, 0.8, 0.1, 0.1]))
                elif cur_hp >= 0.7 and cur_hp < 0.85:
                    human_roleQ.append(th.FloatTensor([0, 0.5, 0.5, 0]))
                elif cur_hp < 0.7 and cur_hp >= 0.2:
                    human_roleQ.append(th.FloatTensor([0.5, 0.2, 0.2, 0.1]))
                else:
                    human_roleQ.append(th.FloatTensor([0.6, 0.2, 0.1, 0.1]))
        human_roleQ = th.stack(human_roleQ, dim=0)
        return human_roleQ

    def epsilon_schedule(self, t_env):
        if t_env is None:
            return 0.05
        if t_env > self.role_action_spaces_update_start and self.epsilon_reset:
            self.epsilon_reset = False
            self.epsilon_start_t = t_env
            self.epsilon_anneal_time = self.epsilon_anneal_time_exp
            self.delta = (self.epsilon_start - self.epsilon_finish
                ) / self.epsilon_anneal_time
        if t_env - self.epsilon_start_t > self.epsilon_anneal_time:
            epsilon = self.epsilon_finish
        else:
            epsilon = self.epsilon_start - (t_env - self.epsilon_start_t
                ) * self.delta
        return epsilon

    def forward(self, input_0, input_1):
        primals_1 = self.fc1.weight
        primals_2 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_3 = input_0
        primals_6 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
