# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# 
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# 
#     http://www.apache.org/licenses/LICENSE-2.0
# 
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from .initializer import xavier_uniform_, zeros_


class ViTAttention(nn.Layer):
    def __init__(self,
                 dim,
                 num_heads=8,
                 qkv_bias=False,
                 qk_scale=None,
                 attn_drop=0.,
                 proj_drop=0.):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim**-0.5

        self.qkv = nn.Linear(dim, dim * 3, bias_attr=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            xavier_uniform_(m.weight)
            zeros_(m.bias)

    def forward(self, x):
        N, C = x.shape[1:]
        qkv = self.qkv(x).reshape((-1, N, 3, self.num_heads, C //
                                   self.num_heads)).transpose((2, 0, 3, 1, 4))
        q, k, v = qkv[0], qkv[1], qkv[2]

        attn = (q.matmul(k.transpose((0, 1, 3, 2)))) * self.scale
        attn = nn.functional.softmax(attn, axis=-1)
        attn = self.attn_drop(attn)

        x = (paddle.matmul(attn, v)).transpose((0, 2, 1, 3)).reshape((-1, N, C))
        x = self.proj(x)
        x = self.proj_drop(x)
        return x
