import math
import torch
import torch.nn.functional as F
import torch.fx
import torch.utils.data


def restricted_softmax(src, dim: 'int'=-1, margin: 'float'=0.0):
    src_max = torch.clamp(src.max(dim=dim, keepdim=True)[0], min=0.0)
    out = (src - src_max).exp()
    out = out / (out.sum(dim=dim, keepdim=True) + (margin - src_max).exp())
    return out


class Attention(torch.nn.Module):

    def __init__(self, dropout=0):
        super(Attention, self).__init__()
        self.dropout = dropout

    def forward(self, query, key, value):
        return self.compute_attention(query, key, value)

    def compute_attention(self, query, key, value):
        assert query.dim() == key.dim() == value.dim() >= 2
        assert query.size(-1) == key.size(-1)
        assert key.size(-2) == value.size(-2)
        score = torch.matmul(query, key.transpose(-2, -1))
        score = score / math.sqrt(key.size(-1))
        score = restricted_softmax(score, dim=-1)
        score = F.dropout(score, p=self.dropout, training=self.training)
        return torch.matmul(score, value)

    def __repr__(self):
        return '{}(dropout={})'.format(self.__class__.__name__, self.dropout)


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), 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 math as tl_math
import math
import torch.nn.functional as F
import torch.fx
import torch.utils.data
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_clamp_div_exp_max_sub_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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp6 = tmp5 * tmp1
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tmp8 * tmp1
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = tmp11 * tmp1
    tmp13 = triton_helpers.maximum(tmp10, tmp12)
    tmp14 = 0.0
    tmp15 = triton_helpers.maximum(tmp13, tmp14)
    tmp16 = tmp2 - tmp15
    tmp17 = tl_math.exp(tmp16)
    tl.store(out_ptr0 + x2, tmp17, xmask)


@triton.jit
def triton_poi_fused_add_clamp_div_exp_max_rsub_sum_1(in_ptr0, in_ptr1,
    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
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp13 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp16 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = 0.5
    tmp9 = tmp7 * tmp8
    tmp11 = tmp10 * tmp8
    tmp12 = triton_helpers.maximum(tmp9, tmp11)
    tmp14 = tmp13 * tmp8
    tmp15 = triton_helpers.maximum(tmp12, tmp14)
    tmp17 = tmp16 * tmp8
    tmp18 = triton_helpers.maximum(tmp15, tmp17)
    tmp19 = 0.0
    tmp20 = triton_helpers.maximum(tmp18, tmp19)
    tmp21 = tmp19 - tmp20
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp6 + tmp22
    tl.store(out_ptr0 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused_add_clamp_div_exp_max_rsub_sum_2(in_out_ptr0, in_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 / tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(arg0_1, (16, 4, 4), (16, 4, 1
            ), 0), reinterpret_tensor(arg1_1, (16, 4, 4), (16, 1, 4), 0),
            out=buf0)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clamp_div_exp_max_sub_0[grid(256)](buf0, buf1, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        triton_poi_fused_add_clamp_div_exp_max_rsub_sum_1[grid(64)](buf1,
            buf0, buf2, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf3 = buf1
        del buf1
        triton_poi_fused_add_clamp_div_exp_max_rsub_sum_2[grid(256)](buf3,
            buf2, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf2
        buf4 = buf0
        del buf0
        extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(arg2_1, (16, 4, 4), (16, 4, 1), 0), out=buf4
            )
        del arg2_1
        del buf3
    return reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0),


def restricted_softmax(src, dim: 'int'=-1, margin: 'float'=0.0):
    src_max = torch.clamp(src.max(dim=dim, keepdim=True)[0], min=0.0)
    out = (src - src_max).exp()
    out = out / (out.sum(dim=dim, keepdim=True) + (margin - src_max).exp())
    return out


class AttentionNew(torch.nn.Module):

    def __init__(self, dropout=0):
        super(AttentionNew, self).__init__()
        self.dropout = dropout

    def compute_attention(self, query, key, value):
        assert query.dim() == key.dim() == value.dim() >= 2
        assert query.size(-1) == key.size(-1)
        assert key.size(-2) == value.size(-2)
        score = torch.matmul(query, key.transpose(-2, -1))
        score = score / math.sqrt(key.size(-1))
        score = restricted_softmax(score, dim=-1)
        score = F.dropout(score, p=self.dropout, training=self.training)
        return torch.matmul(score, value)

    def __repr__(self):
        return '{}(dropout={})'.format(self.__class__.__name__, self.dropout)

    def forward(self, input_0, input_1, input_2):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]
