import torch
import numpy as np
import torch.nn as nn
from torch.nn import functional as F


def activation_factory(activation_type):
    if activation_type == 'RELU':
        return F.relu
    elif activation_type == 'TANH':
        return torch.tanh
    elif activation_type == 'ELU':
        return nn.ELU()
    else:
        raise ValueError('Unknown activation_type: {}'.format(activation_type))


def attention(query, key, value, mask=None, dropout=None):
    """
    Compute a Scaled Dot Product Attention.

    Parameters
    ----------
    query
        size: batch, head, 1 (ego-entity), features
    key
        size: batch, head, entities, features
    value
        size: batch, head, entities, features
    mask
        size: batch,  head, 1 (absence feature), 1 (ego-entity)
    dropout

    Returns
    -------
    The attention softmax(QK^T/sqrt(dk))V
    """
    d_k = query.size(-1)
    scores = torch.matmul(query, key.transpose(-2, -1)) / np.sqrt(d_k)
    if mask is not None:
        scores = scores.masked_fill(mask, -1000000000.0)
    p_attn = F.softmax(scores, dim=-1)
    if dropout is not None:
        p_attn = dropout(p_attn)
    output = torch.matmul(p_attn, value)
    return output, p_attn


class BaseModule(torch.nn.Module):
    """
    Base torch.nn.Module implementing basic features:
        - initialization factory
        - normalization parameters
    """

    def __init__(self, activation_type='RELU', reset_type='XAVIER'):
        super().__init__()
        self.activation = activation_factory(activation_type)
        self.reset_type = reset_type

    def _init_weights(self, m):
        if hasattr(m, 'weight'):
            if self.reset_type == 'XAVIER':
                torch.nn.init.xavier_uniform_(m.weight.data)
            elif self.reset_type == 'ZEROS':
                torch.nn.init.constant_(m.weight.data, 0.0)
            else:
                raise ValueError('Unknown reset type')
        if hasattr(m, 'bias') and m.bias is not None:
            torch.nn.init.constant_(m.bias.data, 0.0)

    def reset(self):
        self.apply(self._init_weights)


class EgoAttention(BaseModule):

    def __init__(self, feature_size=64, heads=4, dropout_factor=0):
        super().__init__()
        self.feature_size = feature_size
        self.heads = heads
        self.dropout_factor = dropout_factor
        self.features_per_head = int(self.feature_size / self.heads)
        self.value_all = nn.Linear(self.feature_size, self.feature_size,
            bias=False)
        self.key_all = nn.Linear(self.feature_size, self.feature_size, bias
            =False)
        self.query_ego = nn.Linear(self.feature_size, self.feature_size,
            bias=False)
        self.attention_combine = nn.Linear(self.feature_size, self.
            feature_size, bias=False)

    @classmethod
    def default_config(cls):
        return {}

    def forward(self, ego, others, mask=None):
        batch_size = others.shape[0]
        n_entities = others.shape[1] + 1
        input_all = torch.cat((ego.view(batch_size, 1, self.feature_size),
            others), dim=1)
        key_all = self.key_all(input_all).view(batch_size, n_entities, self
            .heads, self.features_per_head)
        value_all = self.value_all(input_all).view(batch_size, n_entities,
            self.heads, self.features_per_head)
        query_ego = self.query_ego(ego).view(batch_size, 1, self.heads,
            self.features_per_head)
        key_all = key_all.permute(0, 2, 1, 3)
        value_all = value_all.permute(0, 2, 1, 3)
        query_ego = query_ego.permute(0, 2, 1, 3)
        if mask is not None:
            mask = mask.view((batch_size, 1, 1, n_entities)).repeat((1,
                self.heads, 1, 1))
        value, attention_matrix = attention(query_ego, key_all, value_all,
            mask, nn.Dropout(self.dropout_factor))
        result = (self.attention_combine(value.reshape((batch_size, self.
            feature_size))) + ego.squeeze(1)) / 2
        return result, attention_matrix


def get_inputs():
    return [torch.rand([4, 1, 64]), torch.rand([4, 1, 64])]


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 numpy as np
import torch.nn as nn
from torch.nn import functional as F
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, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 64 % 2
    x0 = xindex % 64
    x2 = xindex // 128
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 64 * x2), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 2, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 64 * x2), tmp6 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 256
    xnumel = 2
    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
    x2 = xindex
    y0 = yindex % 64
    y1 = yindex // 64
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 64 * x2 + 128 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 2 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused__softmax_sqrt_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 32
    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)
    tmp8 = tl.load(in_ptr0 + 2 * x1, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (1 + 2 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl.full([1], 4.0, tl.float64)
    tmp2 = tl.full([1], 0.0, tl.float64)
    tmp3 = tmp1 >= tmp2
    tmp4 = 1.0
    tmp5 = -1.0
    tmp6 = tl.where(tmp3, tmp4, tmp5)
    tmp7 = tmp0 * tmp6
    tmp9 = tmp8 * tmp6
    tmp11 = tmp10 * tmp6
    tmp12 = triton_helpers.maximum(tmp9, tmp11)
    tmp13 = tmp7 - tmp12
    tmp14 = tmp6.to(tl.float64)
    tmp15 = tmp14 * tmp1
    tmp16 = tmp15.to(tl.float32)
    tmp17 = tmp13 / tmp16
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp9 - tmp12
    tmp20 = tmp19 / tmp16
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp11 - tmp12
    tmp23 = tmp22 / tmp16
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp21 + tmp24
    tmp26 = tmp18 / tmp25
    tl.store(out_ptr0 + x2, tmp26, xmask)


@triton.jit
def triton_poi_fused_clone_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16 % 2
    x2 = xindex // 32 % 4
    x3 = xindex // 128
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x2 + 64 * x1 + 128 * x3), xmask)
    tl.store(out_ptr0 + x4, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_div_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
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.5
    tmp4 = tmp2 * tmp3
    tl.store(in_out_ptr0 + x0, tmp4, 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, 1, 64), (64, 64, 1))
    assert_size_stride(primals_2, (4, 1, 64), (64, 64, 1))
    assert_size_stride(primals_3, (64, 64), (64, 1))
    assert_size_stride(primals_4, (64, 64), (64, 1))
    assert_size_stride(primals_5, (64, 64), (64, 1))
    assert_size_stride(primals_6, (64, 64), (64, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2, 64), (128, 64, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(512)](primals_2, primals_1, buf0, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((8, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (8, 64), (64, 1), 0),
            reinterpret_tensor(primals_3, (64, 64), (1, 64), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((8, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (8, 64), (64, 1), 0),
            reinterpret_tensor(primals_4, (64, 64), (1, 64), 0), out=buf2)
        del primals_4
        buf3 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (4, 64), (64, 1), 0
            ), reinterpret_tensor(primals_5, (64, 64), (1, 64), 0), out=buf3)
        del primals_5
        buf4 = empty_strided_cuda((4, 4, 16, 2), (128, 32, 2, 1), torch.float32
            )
        triton_poi_fused_clone_1[grid(256, 2)](buf1, buf4, 256, 2, XBLOCK=2,
            YBLOCK=64, num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((16, 1, 2), (2, 2, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf3, (16, 1, 16), (16, 16, 1
            ), 0), reinterpret_tensor(buf4, (16, 16, 2), (32, 2, 1), 0),
            out=buf5)
        buf6 = empty_strided_cuda((4, 4, 1, 2), (8, 2, 2, 1), torch.float32)
        triton_poi_fused__softmax_sqrt_2[grid(32)](buf5, buf6, 32, XBLOCK=
            32, num_warps=1, num_stages=1)
        del buf5
        buf7 = reinterpret_tensor(buf1, (4, 4, 2, 16), (128, 32, 16, 1), 0)
        del buf1
        triton_poi_fused_clone_3[grid(512)](buf2, buf7, 512, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf2
        buf8 = empty_strided_cuda((16, 1, 16), (16, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf6, (16, 1, 2), (2, 2, 1), 
            0), reinterpret_tensor(buf7, (16, 2, 16), (32, 16, 1), 0), out=buf8
            )
        buf9 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf8, (4, 64), (64, 1), 0),
            reinterpret_tensor(primals_6, (64, 64), (1, 64), 0), out=buf9)
        buf10 = buf9
        del buf9
        triton_poi_fused_add_div_4[grid(256)](buf10, primals_2, 256, XBLOCK
            =128, num_warps=4, num_stages=1)
    return buf10, buf6, reinterpret_tensor(buf0, (8, 64), (64, 1), 0
        ), reinterpret_tensor(primals_2, (4, 64), (64, 1), 0
        ), buf6, reinterpret_tensor(buf8, (4, 64), (64, 1), 0
        ), primals_6, reinterpret_tensor(buf7, (16, 16, 2), (32, 1, 16), 0
        ), reinterpret_tensor(buf3, (16, 16, 1), (16, 1, 64), 0
        ), reinterpret_tensor(buf4, (16, 2, 16), (32, 1, 2), 0)


def activation_factory(activation_type):
    if activation_type == 'RELU':
        return F.relu
    elif activation_type == 'TANH':
        return torch.tanh
    elif activation_type == 'ELU':
        return nn.ELU()
    else:
        raise ValueError('Unknown activation_type: {}'.format(activation_type))


def attention(query, key, value, mask=None, dropout=None):
    """
    Compute a Scaled Dot Product Attention.

    Parameters
    ----------
    query
        size: batch, head, 1 (ego-entity), features
    key
        size: batch, head, entities, features
    value
        size: batch, head, entities, features
    mask
        size: batch,  head, 1 (absence feature), 1 (ego-entity)
    dropout

    Returns
    -------
    The attention softmax(QK^T/sqrt(dk))V
    """
    d_k = query.size(-1)
    scores = torch.matmul(query, key.transpose(-2, -1)) / np.sqrt(d_k)
    if mask is not None:
        scores = scores.masked_fill(mask, -1000000000.0)
    p_attn = F.softmax(scores, dim=-1)
    if dropout is not None:
        p_attn = dropout(p_attn)
    output = torch.matmul(p_attn, value)
    return output, p_attn


class BaseModule(torch.nn.Module):
    """
    Base torch.nn.Module implementing basic features:
        - initialization factory
        - normalization parameters
    """

    def __init__(self, activation_type='RELU', reset_type='XAVIER'):
        super().__init__()
        self.activation = activation_factory(activation_type)
        self.reset_type = reset_type

    def _init_weights(self, m):
        if hasattr(m, 'weight'):
            if self.reset_type == 'XAVIER':
                torch.nn.init.xavier_uniform_(m.weight.data)
            elif self.reset_type == 'ZEROS':
                torch.nn.init.constant_(m.weight.data, 0.0)
            else:
                raise ValueError('Unknown reset type')
        if hasattr(m, 'bias') and m.bias is not None:
            torch.nn.init.constant_(m.bias.data, 0.0)

    def reset(self):
        self.apply(self._init_weights)


class EgoAttentionNew(BaseModule):

    def __init__(self, feature_size=64, heads=4, dropout_factor=0):
        super().__init__()
        self.feature_size = feature_size
        self.heads = heads
        self.dropout_factor = dropout_factor
        self.features_per_head = int(self.feature_size / self.heads)
        self.value_all = nn.Linear(self.feature_size, self.feature_size,
            bias=False)
        self.key_all = nn.Linear(self.feature_size, self.feature_size, bias
            =False)
        self.query_ego = nn.Linear(self.feature_size, self.feature_size,
            bias=False)
        self.attention_combine = nn.Linear(self.feature_size, self.
            feature_size, bias=False)

    @classmethod
    def default_config(cls):
        return {}

    def forward(self, input_0, input_1):
        primals_3 = self.value_all.weight
        primals_4 = self.key_all.weight
        primals_5 = self.query_ego.weight
        primals_6 = self.attention_combine.weight
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0], output[1]
