import torch
import torch.nn as nn
import torch.cuda
import torch.distributed


class ContextGate(nn.Module):
    """
    Context gate is a decoder module that takes as input the previous word
    embedding, the current decoder state and the attention state, and
    produces a gate.
    The gate can be used to select the input from the target side context
    (decoder state), from the source context (attention state) or both.
    """

    def __init__(self, embeddings_size, decoder_size, attention_size,
        output_size):
        super(ContextGate, self).__init__()
        input_size = embeddings_size + decoder_size + attention_size
        self.gate = nn.Linear(input_size, output_size, bias=True)
        self.sig = nn.Sigmoid()
        self.source_proj = nn.Linear(attention_size, output_size)
        self.target_proj = nn.Linear(embeddings_size + decoder_size,
            output_size)

    def forward(self, prev_emb, dec_state, attn_state):
        input_tensor = torch.cat((prev_emb, dec_state, attn_state), dim=1)
        z = self.sig(self.gate(input_tensor))
        proj_source = self.source_proj(attn_state)
        proj_target = self.target_proj(torch.cat((prev_emb, dec_state), dim=1))
        return z, proj_source, proj_target


class TargetContextGate(nn.Module):
    """Apply the context gate only to the target context"""

    def __init__(self, embeddings_size, decoder_size, attention_size,
        output_size):
        super(TargetContextGate, self).__init__()
        self.context_gate = ContextGate(embeddings_size, decoder_size,
            attention_size, output_size)
        self.tanh = nn.Tanh()

    def forward(self, prev_emb, dec_state, attn_state):
        z, source, target = self.context_gate(prev_emb, dec_state, attn_state)
        return self.tanh(z * target + source)


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


def get_init_inputs():
    return [[], {'embeddings_size': 4, 'decoder_size': 4, 'attention_size':
        4, 'output_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.triton_helpers import libdevice
import torch.nn as nn
import torch.cuda
import torch.distributed
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_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 12
    x1 = xindex // 12
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tl.full([1], 12, tl.int64)
    tmp14 = tl.load(in_ptr2 + (4 * x1 + (-8 + x0)), tmp11 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp15 = tl.where(tmp9, tmp10, tmp14)
    tmp16 = tl.where(tmp4, tmp5, tmp15)
    tl.store(out_ptr0 + x2, tmp16, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_mul_sigmoid_tanh_2(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp2 = tl.load(in_ptr1 + x2, xmask)
    tmp4 = tl.load(in_out_ptr0 + x2, xmask)
    tmp5 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp6 = tmp4 + tmp5
    tmp7 = tmp3 + tmp6
    tmp8 = libdevice.tanh(tmp7)
    tl.store(in_out_ptr0 + x2, tmp8, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (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, 12), (12, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 8), (8, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 12), (12, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(48)](primals_1, primals_2, primals_3,
            buf0, 48, XBLOCK=64, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf0, reinterpret_tensor(primals_4,
            (12, 4), (1, 12), 0), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_6, (4, 4),
            (1, 4), 0), out=buf2)
        del primals_6
        buf3 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        triton_poi_fused_cat_1[grid(32)](primals_1, primals_2, buf3, 32,
            XBLOCK=32, num_warps=1, num_stages=1)
        del primals_1
        del primals_2
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_9, buf3, reinterpret_tensor(primals_8,
            (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf4)
        del primals_8
        del primals_9
        buf5 = buf2
        del buf2
        triton_poi_fused_add_mul_sigmoid_tanh_2[grid(16)](buf5, buf1, buf4,
            primals_7, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_7
    return buf5, primals_3, buf0, buf1, buf3, buf4, buf5


class ContextGate(nn.Module):
    """
    Context gate is a decoder module that takes as input the previous word
    embedding, the current decoder state and the attention state, and
    produces a gate.
    The gate can be used to select the input from the target side context
    (decoder state), from the source context (attention state) or both.
    """

    def __init__(self, embeddings_size, decoder_size, attention_size,
        output_size):
        super(ContextGate, self).__init__()
        input_size = embeddings_size + decoder_size + attention_size
        self.gate = nn.Linear(input_size, output_size, bias=True)
        self.sig = nn.Sigmoid()
        self.source_proj = nn.Linear(attention_size, output_size)
        self.target_proj = nn.Linear(embeddings_size + decoder_size,
            output_size)

    def forward(self, prev_emb, dec_state, attn_state):
        input_tensor = torch.cat((prev_emb, dec_state, attn_state), dim=1)
        z = self.sig(self.gate(input_tensor))
        proj_source = self.source_proj(attn_state)
        proj_target = self.target_proj(torch.cat((prev_emb, dec_state), dim=1))
        return z, proj_source, proj_target


class TargetContextGateNew(nn.Module):
    """Apply the context gate only to the target context"""

    def __init__(self, embeddings_size, decoder_size, attention_size,
        output_size):
        super(TargetContextGateNew, self).__init__()
        self.context_gate = ContextGate(embeddings_size, decoder_size,
            attention_size, output_size)
        self.tanh = nn.Tanh()

    def forward(self, input_0, input_1, input_2):
        primals_4 = self.context_gate.gate.weight
        primals_5 = self.context_gate.gate.bias
        primals_1 = self.context_gate.source_proj.weight
        primals_7 = self.context_gate.source_proj.bias
        primals_8 = self.context_gate.target_proj.weight
        primals_9 = self.context_gate.target_proj.bias
        primals_2 = input_0
        primals_3 = input_1
        primals_6 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]
