import math
import torch
from torch import Tensor
from torch.nn import Linear
import torch.nn.functional as F
from torch.nn import Parameter
import torch.utils.data


def uniform(size, tensor):
    bound = 1.0 / math.sqrt(size)
    if tensor is not None:
        tensor.data.uniform_(-bound, bound)


def kaiming_uniform(tensor, fan, a):
    if tensor is not None:
        bound = math.sqrt(6 / ((1 + a ** 2) * fan))
        tensor.data.uniform_(-bound, bound)


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


class Linear(torch.nn.Module):

    def __init__(self, in_channels, out_channels, groups=1, bias=True):
        super(Linear, self).__init__()
        assert in_channels % groups == 0 and out_channels % groups == 0
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.groups = groups
        self.weight = Parameter(Tensor(groups, in_channels // groups, 
            out_channels // groups))
        if bias:
            self.bias = Parameter(torch.Tensor(out_channels))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        kaiming_uniform(self.weight, fan=self.weight.size(1), a=math.sqrt(5))
        uniform(self.weight.size(1), self.bias)

    def forward(self, src):
        if self.groups > 1:
            size = list(src.size())[:-1]
            src = src.view(-1, self.groups, self.in_channels // self.groups)
            src = src.transpose(0, 1).contiguous()
            out = torch.matmul(src, self.weight)
            out = out.transpose(1, 0).contiguous()
            out = out.view(*(size + [self.out_channels]))
        else:
            out = torch.matmul(src, self.weight.squeeze(0))
        if self.bias is not None:
            out += self.bias
        return out

    def __repr__(self):
        return '{}({}, {}, groups={}, bias={})'.format(self.__class__.
            __name__, self.in_channels, self.out_channels, self.groups, 
            self.bias is not None)


class Attention(torch.nn.Module):

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

    def forward(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)


class MultiHead(Attention):

    def __init__(self, in_channels, out_channels, heads=1, groups=1,
        dropout=0, bias=True):
        super(MultiHead, self).__init__(dropout)
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.heads = heads
        self.groups = groups
        self.bias = bias
        assert in_channels % heads == 0 and out_channels % heads == 0
        assert in_channels % groups == 0 and out_channels % groups == 0
        assert max(groups, self.heads) % min(groups, self.heads) == 0
        self.lin_q = Linear(in_channels, out_channels, groups, bias)
        self.lin_k = Linear(in_channels, out_channels, groups, bias)
        self.lin_v = Linear(in_channels, out_channels, groups, bias)
        self.reset_parameters()

    def reset_parameters(self):
        self.lin_q.reset_parameters()
        self.lin_k.reset_parameters()
        self.lin_v.reset_parameters()

    def forward(self, query, key, value):
        assert query.dim() == key.dim() == value.dim() >= 2
        assert query.size(-1) == key.size(-1) == value.size(-1)
        assert key.size(-2) == value.size(-2)
        query = self.lin_q(query)
        key = self.lin_k(key)
        value = self.lin_v(value)
        size = list(query.size())[:-2]
        out_channels_per_head = self.out_channels // self.heads
        query_size = size + [query.size(-2), self.heads, out_channels_per_head]
        query = query.view(*query_size).transpose(-2, -3)
        key_size = size + [key.size(-2), self.heads, out_channels_per_head]
        key = key.view(*key_size).transpose(-2, -3)
        value_size = size + [value.size(-2), self.heads, out_channels_per_head]
        value = value.view(*value_size).transpose(-2, -3)
        out = super(MultiHead, self).forward(query, key, value)
        out = out.transpose(-3, -2).contiguous()
        out = out.view(*(size + [query.size(-2), self.out_channels]))
        return out

    def __repr__(self):
        return '{}({}, {}, heads={}, groups={}, dropout={}, bias={})'.format(
            self.__class__.__name__, self.in_channels, self.out_channels,
            self.heads, self.groups, self.dropout, self.bias)


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 [[], {'in_channels': 4, 'out_channels': 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 math as tl_math
import math
from torch import Tensor
from torch.nn import Linear
import torch.nn.functional as F
from torch.nn import Parameter
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_add_0(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
    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
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused_bmm_transpose_1(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2 + 64 * ((x1 + 4 * (x2 %
        4)) // 16)), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)
    tl.store(out_ptr1 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_clamp_div_exp_max_sub_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
    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_3(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_4(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):
    (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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (1, 4, 4), (16, 4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (1, 4, 4), (16, 4, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (4, 1), 0), out=buf0)
        del primals_4
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (4, 1), 0), out=buf1)
        del primals_6
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 4), (4, 1), 0), out=buf2)
        del primals_8
        buf3 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_0[grid(256)](buf3, primals_5, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_5
        buf4 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf1
        triton_poi_fused_add_0[grid(256)](buf4, primals_7, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_7
        buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        buf15 = empty_strided_cuda((16, 4, 4), (16, 1, 4), torch.float32)
        triton_poi_fused_bmm_transpose_1[grid(256)](buf3, buf5, buf15, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf6 = reinterpret_tensor(buf3, (16, 4, 4), (16, 1, 4), 0)
        del buf3
        buf16 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_bmm_transpose_1[grid(256)](buf4, buf6, buf16, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf7 = reinterpret_tensor(buf4, (16, 4, 4), (16, 4, 1), 0)
        del buf4
        extern_kernels.bmm(buf5, buf6, out=buf7)
        buf8 = reinterpret_tensor(buf6, (4, 4, 1, 4, 4), (64, 16, 256, 4, 1), 0
            )
        del buf6
        triton_poi_fused_clamp_div_exp_max_sub_2[grid(256)](buf7, buf8, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf9 = empty_strided_cuda((4, 4, 1, 4, 1), (16, 4, 64, 1, 64),
            torch.float32)
        triton_poi_fused_add_clamp_div_exp_max_rsub_sum_3[grid(64)](buf8,
            buf7, buf9, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf10 = reinterpret_tensor(buf8, (4, 4, 1, 4, 4), (64, 16, 16, 4, 1), 0
            )
        del buf8
        triton_poi_fused_add_clamp_div_exp_max_rsub_sum_4[grid(256)](buf10,
            buf9, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf9
        buf11 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf2
        triton_poi_fused_add_0[grid(256)](buf11, primals_9, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_9
        buf12 = buf5
        del buf5
        buf14 = empty_strided_cuda((16, 4, 4), (16, 1, 4), torch.float32)
        triton_poi_fused_bmm_transpose_1[grid(256)](buf11, buf12, buf14, 
            256, XBLOCK=128, num_warps=4, num_stages=1)
        buf13 = reinterpret_tensor(buf11, (16, 4, 4), (16, 4, 1), 0)
        del buf11
        extern_kernels.bmm(reinterpret_tensor(buf10, (16, 4, 4), (16, 4, 1),
            0), buf12, out=buf13)
        del buf12
    return reinterpret_tensor(buf13, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), buf7, buf10, buf14, buf15, buf16, reinterpret_tensor(primals_3,
        (4, 64), (1, 4), 0), reinterpret_tensor(primals_2, (4, 64), (1, 4), 0
        ), reinterpret_tensor(primals_1, (4, 64), (1, 4), 0)


def uniform(size, tensor):
    bound = 1.0 / math.sqrt(size)
    if tensor is not None:
        tensor.data.uniform_(-bound, bound)


def kaiming_uniform(tensor, fan, a):
    if tensor is not None:
        bound = math.sqrt(6 / ((1 + a ** 2) * fan))
        tensor.data.uniform_(-bound, bound)


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


class Linear(torch.nn.Module):

    def __init__(self, in_channels, out_channels, groups=1, bias=True):
        super(Linear, self).__init__()
        assert in_channels % groups == 0 and out_channels % groups == 0
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.groups = groups
        self.weight = Parameter(Tensor(groups, in_channels // groups, 
            out_channels // groups))
        if bias:
            self.bias = Parameter(torch.Tensor(out_channels))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        kaiming_uniform(self.weight, fan=self.weight.size(1), a=math.sqrt(5))
        uniform(self.weight.size(1), self.bias)

    def forward(self, src):
        if self.groups > 1:
            size = list(src.size())[:-1]
            src = src.view(-1, self.groups, self.in_channels // self.groups)
            src = src.transpose(0, 1).contiguous()
            out = torch.matmul(src, self.weight)
            out = out.transpose(1, 0).contiguous()
            out = out.view(*(size + [self.out_channels]))
        else:
            out = torch.matmul(src, self.weight.squeeze(0))
        if self.bias is not None:
            out += self.bias
        return out

    def __repr__(self):
        return '{}({}, {}, groups={}, bias={})'.format(self.__class__.
            __name__, self.in_channels, self.out_channels, self.groups, 
            self.bias is not None)


class Attention(torch.nn.Module):

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

    def forward(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)


class MultiHeadNew(Attention):

    def __init__(self, in_channels, out_channels, heads=1, groups=1,
        dropout=0, bias=True):
        super(MultiHeadNew, self).__init__(dropout)
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.heads = heads
        self.groups = groups
        self.bias = bias
        assert in_channels % heads == 0 and out_channels % heads == 0
        assert in_channels % groups == 0 and out_channels % groups == 0
        assert max(groups, self.heads) % min(groups, self.heads) == 0
        self.lin_q = Linear(in_channels, out_channels, groups, bias)
        self.lin_k = Linear(in_channels, out_channels, groups, bias)
        self.lin_v = Linear(in_channels, out_channels, groups, bias)
        self.reset_parameters()

    def reset_parameters(self):
        self.lin_q.reset_parameters()
        self.lin_k.reset_parameters()
        self.lin_v.reset_parameters()

    def __repr__(self):
        return '{}({}, {}, heads={}, groups={}, dropout={}, bias={})'.format(
            self.__class__.__name__, self.in_channels, self.out_channels,
            self.heads, self.groups, self.dropout, self.bias)

    def forward(self, input_0, input_1, input_2):
        primals_4 = self.lin_q.weight
        primals_5 = self.lin_q.bias
        primals_6 = self.lin_k.weight
        primals_7 = self.lin_k.bias
        primals_8 = self.lin_v.weight
        primals_9 = self.lin_v.bias
        primals_1 = input_0
        primals_2 = input_1
        primals_3 = 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]
