import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Linear
from torch.nn.init import xavier_uniform_


class MultiheadAttention(nn.Module):
    """Allows the model to jointly attend to information
    from different representation subspaces.
    See reference: Attention Is All You Need

    .. math::
        	ext{MultiHead}(Q, K, V) = 	ext{Concat}(head_1,\\dots,head_h)W^O
        	ext{where} head_i = 	ext{Attention}(QW_i^Q, KW_i^K, VW_i^V)

    Args:
        embed_dim: total dimension of the model
        num_heads: parallel attention layers, or heads

    """

    def __init__(self, embed_dim, num_heads, dropout=0.0, bias=True,
        add_bias_kv=False, add_zero_attn=False):
        super(MultiheadAttention, self).__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads
        assert self.head_dim * num_heads == self.embed_dim, 'embed_dim must be divisible by num_heads'
        self.scaling = self.head_dim ** -0.5
        self.out_proj = Linear(embed_dim, embed_dim, bias=bias)
        self._reset_parameters()
        self.conv1 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
            embed_dim, kernel_size=(1, 1))
        self.conv2 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
            embed_dim, kernel_size=(1, 1))
        self.conv3 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
            embed_dim, kernel_size=(1, 1))

    def _reset_parameters(self):
        xavier_uniform_(self.out_proj.weight)

    def forward(self, query, key, value, key_padding_mask=None,
        incremental_state=None, attn_mask=None):
        """
        Inputs of forward function
            query: [target length, batch size, embed dim]
            key: [sequence length, batch size, embed dim]
            value: [sequence length, batch size, embed dim]
            key_padding_mask: if True, mask padding based on batch size
            incremental_state: if provided, previous time steps are cashed
            need_weights: output attn_output_weights
            static_kv: key and value are static

        Outputs of forward function
            attn_output: [target length, batch size, embed dim]
            attn_output_weights: [batch size, target length, sequence length]
        """
        q_shape = query.shape
        src_shape = key.shape
        q = self._in_proj_q(query)
        k = self._in_proj_k(key)
        v = self._in_proj_v(value)
        q *= self.scaling
        q = torch.reshape(q, (q_shape[0], q_shape[1], self.num_heads, self.
            head_dim))
        q = q.permute(1, 2, 0, 3)
        k = torch.reshape(k, (src_shape[0], q_shape[1], self.num_heads,
            self.head_dim))
        k = k.permute(1, 2, 0, 3)
        v = torch.reshape(v, (src_shape[0], q_shape[1], self.num_heads,
            self.head_dim))
        v = v.permute(1, 2, 0, 3)
        if key_padding_mask is not None:
            assert key_padding_mask.shape[0] == q_shape[1]
            assert key_padding_mask.shape[1] == src_shape[0]
        attn_output_weights = torch.matmul(q, k.permute(0, 1, 3, 2))
        if attn_mask is not None:
            attn_mask = torch.unsqueeze(torch.unsqueeze(attn_mask, 0), 0)
            attn_output_weights += attn_mask
        if key_padding_mask is not None:
            attn_output_weights = torch.reshape(attn_output_weights, [
                q_shape[1], self.num_heads, q_shape[0], src_shape[0]])
            key = torch.unsqueeze(torch.unsqueeze(key_padding_mask, 1), 2)
            key = key.type(torch.float32)
            y = torch.full(size=key.shape, fill_value=float('-Inf'), dtype=
                torch.float32)
            y = torch.where(key == 0.0, key, y)
            attn_output_weights += y
        attn_output_weights = F.softmax(attn_output_weights.type(torch.
            float32), dim=-1, dtype=torch.float32 if attn_output_weights.
            dtype == torch.float16 else attn_output_weights.dtype)
        attn_output_weights = F.dropout(attn_output_weights, p=self.dropout,
            training=self.training)
        attn_output = torch.matmul(attn_output_weights, v)
        attn_output = torch.reshape(attn_output.permute(2, 0, 1, 3), [
            q_shape[0], q_shape[1], self.embed_dim])
        attn_output = self.out_proj(attn_output)
        return attn_output

    def _in_proj_q(self, query):
        query = query.permute(1, 2, 0)
        query = torch.unsqueeze(query, dim=2)
        res = self.conv1(query)
        res = torch.squeeze(res, dim=2)
        res = res.permute(2, 0, 1)
        return res

    def _in_proj_k(self, key):
        key = key.permute(1, 2, 0)
        key = torch.unsqueeze(key, dim=2)
        res = self.conv2(key)
        res = torch.squeeze(res, dim=2)
        res = res.permute(2, 0, 1)
        return res

    def _in_proj_v(self, value):
        value = value.permute(1, 2, 0)
        value = torch.unsqueeze(value, dim=2)
        res = self.conv3(value)
        res = torch.squeeze(res, dim=2)
        res = res.permute(2, 0, 1)
        return res


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


def get_init_inputs():
    return [[], {'embed_dim': 4, 'num_heads': 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 torch.nn as nn
from torch.nn import Linear
from torch.nn.init import xavier_uniform_
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_convolution_0(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 16 * x1), xmask & ymask, eviction_policy
        ='evict_last')
    tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_1(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
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_mul_2(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 4
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x3 = xindex
    y0 = yindex
    x1 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x3), xmask & ymask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tl.store(out_ptr0 + (x3 + 16 * y0), tmp4, xmask & ymask)


@triton.jit
def triton_poi_fused__softmax_3(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)
    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_4(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)
    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_clone_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 4
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (x1 + 16 * y0), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_6(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
    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)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_8, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4, 4), (4, 1))
    assert_size_stride(primals_11, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(16, 4)](primals_1, buf0, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 1, 4), (16, 4, 4, 1))
        buf2 = buf0
        del buf0
        triton_poi_fused_convolution_0[grid(16, 4)](primals_2, buf2, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf3 = extern_kernels.convolution(buf2, primals_5, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 1, 4), (16, 4, 4, 1))
        buf4 = buf2
        del buf2
        triton_poi_fused_convolution_0[grid(16, 4)](primals_7, buf4, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf5 = extern_kernels.convolution(buf4, primals_8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 1, 4), (16, 4, 4, 1))
        buf6 = buf3
        del buf3
        triton_poi_fused_convolution_1[grid(64)](buf6, primals_6, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_6
        buf7 = reinterpret_tensor(buf4, (4, 4, 4), (16, 4, 1), 0)
        del buf4
        triton_poi_fused_mul_2[grid(4, 16)](buf1, primals_4, buf7, 4, 16,
            XBLOCK=16, YBLOCK=4, num_warps=1, num_stages=1)
        del primals_4
        buf8 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf7, (16, 4, 1), (1, 16, 0),
            0), reinterpret_tensor(buf6, (16, 1, 4), (4, 0, 1), 0), out=buf8)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_3[grid(256)](buf8, buf9, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf10 = reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf8
        triton_poi_fused__softmax_4[grid(256)](buf9, buf10, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf9
        buf11 = buf5
        del buf5
        triton_poi_fused_convolution_1[grid(64)](buf11, primals_9, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_9
        buf12 = reinterpret_tensor(buf1, (16, 4, 1), (4, 1, 1), 0)
        del buf1
        extern_kernels.bmm(reinterpret_tensor(buf10, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf11, (16, 4, 1), (4, 1, 0), 0), out=buf12)
        buf13 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_5[grid(4, 16)](buf12, buf13, 4, 16, XBLOCK=
            16, YBLOCK=4, num_warps=1, num_stages=1)
        buf14 = reinterpret_tensor(buf12, (16, 4), (4, 1), 0)
        del buf12
        extern_kernels.mm(reinterpret_tensor(buf13, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), out=buf14)
        buf15 = reinterpret_tensor(buf14, (4, 4, 4), (16, 4, 1), 0)
        del buf14
        triton_poi_fused_add_6[grid(64)](buf15, primals_11, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_11
        buf16 = empty_strided_cuda((16, 1, 4), (4, 4, 1), torch.float32)
        triton_poi_fused_convolution_0[grid(16, 4)](buf7, buf16, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        del buf7
    return buf15, primals_3, primals_5, primals_8, reinterpret_tensor(primals_1
        , (4, 4, 1, 4), (4, 1, 64, 16), 0), reinterpret_tensor(primals_2, (
        4, 4, 1, 4), (4, 1, 64, 16), 0), reinterpret_tensor(primals_7, (4, 
        4, 1, 4), (4, 1, 64, 16), 0), buf10, reinterpret_tensor(buf13, (16,
        4), (4, 1), 0), primals_10, reinterpret_tensor(buf11, (16, 1, 4), (
        4, 4, 1), 0), buf16, reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 4), 0)


class MultiheadAttentionNew(nn.Module):
    """Allows the model to jointly attend to information
    from different representation subspaces.
    See reference: Attention Is All You Need

    .. math::
        	ext{MultiHead}(Q, K, V) = 	ext{Concat}(head_1,\\dots,head_h)W^O
        	ext{where} head_i = 	ext{Attention}(QW_i^Q, KW_i^K, VW_i^V)

    Args:
        embed_dim: total dimension of the model
        num_heads: parallel attention layers, or heads

    """

    def __init__(self, embed_dim, num_heads, dropout=0.0, bias=True,
        add_bias_kv=False, add_zero_attn=False):
        super(MultiheadAttentionNew, self).__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads
        assert self.head_dim * num_heads == self.embed_dim, 'embed_dim must be divisible by num_heads'
        self.scaling = self.head_dim ** -0.5
        self.out_proj = Linear(embed_dim, embed_dim, bias=bias)
        self._reset_parameters()
        self.conv1 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
            embed_dim, kernel_size=(1, 1))
        self.conv2 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
            embed_dim, kernel_size=(1, 1))
        self.conv3 = torch.nn.Conv2d(in_channels=embed_dim, out_channels=
            embed_dim, kernel_size=(1, 1))

    def _reset_parameters(self):
        xavier_uniform_(self.out_proj.weight)

    def _in_proj_q(self, query):
        query = query.permute(1, 2, 0)
        query = torch.unsqueeze(query, dim=2)
        res = self.conv1(query)
        res = torch.squeeze(res, dim=2)
        res = res.permute(2, 0, 1)
        return res

    def _in_proj_k(self, key):
        key = key.permute(1, 2, 0)
        key = torch.unsqueeze(key, dim=2)
        res = self.conv2(key)
        res = torch.squeeze(res, dim=2)
        res = res.permute(2, 0, 1)
        return res

    def _in_proj_v(self, value):
        value = value.permute(1, 2, 0)
        value = torch.unsqueeze(value, dim=2)
        res = self.conv3(value)
        res = torch.squeeze(res, dim=2)
        res = res.permute(2, 0, 1)
        return res

    def forward(self, input_0, input_1, input_2):
        primals_10 = self.out_proj.weight
        primals_4 = self.out_proj.bias
        primals_3 = self.conv1.weight
        primals_6 = self.conv1.bias
        primals_5 = self.conv2.weight
        primals_9 = self.conv2.bias
        primals_8 = self.conv3.weight
        primals_11 = self.conv3.bias
        primals_1 = input_0
        primals_2 = input_1
        primals_7 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11])
        return output[0]
