import torch
import torch.nn as nn


class Repeat_Explore_Mechanism(nn.Module):

    def __init__(self, device, hidden_size, seq_len, dropout_prob):
        super(Repeat_Explore_Mechanism, self).__init__()
        self.dropout = nn.Dropout(dropout_prob)
        self.hidden_size = hidden_size
        self.device = device
        self.seq_len = seq_len
        self.Wre = nn.Linear(hidden_size, hidden_size, bias=False)
        self.Ure = nn.Linear(hidden_size, hidden_size, bias=False)
        self.tanh = nn.Tanh()
        self.Vre = nn.Linear(hidden_size, 1, bias=False)
        self.Wcre = nn.Linear(hidden_size, 2, bias=False)

    def forward(self, all_memory, last_memory):
        """
        calculate the probability of Repeat and explore
        """
        all_memory_values = all_memory
        all_memory = self.dropout(self.Ure(all_memory))
        last_memory = self.dropout(self.Wre(last_memory))
        last_memory = last_memory.unsqueeze(1)
        last_memory = last_memory.repeat(1, self.seq_len, 1)
        output_ere = self.tanh(all_memory + last_memory)
        output_ere = self.Vre(output_ere)
        alpha_are = nn.Softmax(dim=1)(output_ere)
        alpha_are = alpha_are.repeat(1, 1, self.hidden_size)
        output_cre = alpha_are * all_memory_values
        output_cre = output_cre.sum(dim=1)
        output_cre = self.Wcre(output_cre)
        repeat_explore_mechanism = nn.Softmax(dim=-1)(output_cre)
        return repeat_explore_mechanism


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


def get_init_inputs():
    return [[], {'device': 0, 'hidden_size': 4, 'seq_len': 4,
        'dropout_prob': 0.5}]

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, math as tl_math
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_add_repeat_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x3, tmp3, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), 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 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_mul_repeat_sum_3(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0 + 16 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (4 + x0 + 16 * x1), xmask)
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (8 + x0 + 16 * x1), xmask)
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + (12 + x0 + 16 * x1), xmask)
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tl.store(out_ptr0 + x2, tmp14, xmask)


@triton.jit
def triton_poi_fused__softmax_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 2
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 2 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 2 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp4 = tmp0 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp1 - tmp3
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp2 - tmp3
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp7 + tmp9
    tmp11 = tmp5 / tmp10
    tl.store(out_ptr0 + x2, tmp11, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (1, 4), (4, 1))
    assert_size_stride(primals_6, (2, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_4, reinterpret_tensor(primals_3, (4, 4),
            (1, 4), 0), out=buf1)
        del primals_3
        buf2 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_repeat_tanh_0[grid(64)](buf2, buf1, 64, XBLOCK
            =64, num_warps=1, num_stages=1)
        buf3 = reinterpret_tensor(buf1, (16, 1), (1, 1), 0)
        del buf1
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 1), (1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused__softmax_1[grid(16)](buf3, buf4, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf5 = reinterpret_tensor(buf3, (4, 4, 1), (4, 1, 1), 0)
        del buf3
        triton_poi_fused__softmax_2[grid(16)](buf4, buf5, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf6 = reinterpret_tensor(buf4, (4, 4), (4, 1), 0)
        del buf4
        triton_poi_fused_mul_repeat_sum_3[grid(16)](buf5, primals_1, buf6, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf7 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        extern_kernels.mm(buf6, reinterpret_tensor(primals_6, (4, 2), (1, 4
            ), 0), out=buf7)
        buf8 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        triton_poi_fused__softmax_4[grid(8)](buf7, buf8, 8, XBLOCK=8,
            num_warps=1, num_stages=1)
        del buf7
    return (buf8, primals_1, primals_4, buf2, buf5, buf6, buf8, primals_6,
        primals_5)


class Repeat_Explore_MechanismNew(nn.Module):

    def __init__(self, device, hidden_size, seq_len, dropout_prob):
        super(Repeat_Explore_MechanismNew, self).__init__()
        self.dropout = nn.Dropout(dropout_prob)
        self.hidden_size = hidden_size
        self.device = device
        self.seq_len = seq_len
        self.Wre = nn.Linear(hidden_size, hidden_size, bias=False)
        self.Ure = nn.Linear(hidden_size, hidden_size, bias=False)
        self.tanh = nn.Tanh()
        self.Vre = nn.Linear(hidden_size, 1, bias=False)
        self.Wcre = nn.Linear(hidden_size, 2, bias=False)

    def forward(self, input_0, input_1):
        primals_2 = self.Wre.weight
        primals_3 = self.Ure.weight
        primals_5 = self.Vre.weight
        primals_6 = self.Wcre.weight
        primals_1 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
