# Copyright 2022 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""GATv2Conv Layer"""
import math
import mindspore as ms
from mindspore.common.initializer import initializer
from mindspore.common.initializer import XavierUniform
from mindspore_gl import Graph
from .. import GNNCell


class GATv2Conv(GNNCell):
    r"""
    Graph Attention Network v2. From the paper `How Attentive Are Graph Attention Networks?
    <https://arxiv.org/pdf/2105.14491.pdf>`_, which fixes the static attention problem of GATv2.

    .. math::
        h_i^{(l+1)} = \sum_{j\in \mathcal{N}(i)} \alpha_{i,j} W^{(l)} h_j^{(l)}

    :math:`\alpha_{i, j}` represents the attention score between node :math:`i` and node :math:`j`.

    .. math::
        \alpha_{ij}^{l} = \mathrm{softmax_i} (e_{ij}^{l}) \\
        e_{ij}^{l} = \vec{a}^T \mathrm{LeakyReLU}\left(W [h_{i} \| h_{j}]\right)

    Args:
        in_feat_size (int): Input node feature size.
        out_size (int): Output node feature size.
        num_attn_head (int): Number of attention head used in GATv2.
        input_drop_out_rate (float, optional): Dropout rate of input drop out. Default: ``0.0``.
        attn_drop_out_rate (float, optional): Dropout rate of attention drop out. Default: ``0.0``.
        leaky_relu_slope (float, optional): Slope for leaky relu. Default: ``0.2``.
        activation (Cell, optional): Activation function. Default: ``None``.
        add_norm (bool, optional): Whether the edge information needs normalization or not. Default: ``False``.

    Inputs:
        - **x** (Tensor) - The input node features. The shape is :math:`(N,D_{in})`
          where :math:`N` is the number of nodes and :math:`D_{in}` could be of any shape.
        - **g** (Graph) - The input graph.

    Outputs:
        - Tensor, the output feature of shape :math:`(N,D_{out})` where :math:`D_{out}` should be equal to
          :math:`D_{in} * num\_attn\_head`.

    Raises:
        TypeError: If `in_feat_size`, `out_size`, or `num_attn_head` is not an int.
        TypeError: If `input_drop_out_rate`, `attn_drop_out_rate`, or `leaky_relu_slope` is not a float.
        TypeError: If `activation` is not a Cell.
        ValueError: If `input_drop_out_rate` or `attn_drop_out_rate` is not in range [0.0, 1.0).

    Supported Platforms:
        ``Ascend`` ``GPU``

    Examples:
        >>> import mindspore as ms
        >>> from mindspore_gl.nn import GATv2Conv
        >>> from mindspore_gl import GraphField
        >>> n_nodes = 4
        >>> n_edges = 7
        >>> feat_size = 4
        >>> src_idx = ms.Tensor([0, 1, 1, 2, 2, 3, 3], ms.int32)
        >>> dst_idx = ms.Tensor([0, 0, 2, 1, 3, 0, 1], ms.int32)
        >>> ones = ms.ops.Ones()
        >>> feat = ones((n_nodes, feat_size), ms.float32)
        >>> graph_field = GraphField(src_idx, dst_idx, n_nodes, n_edges)
        >>> gatv2conv = GATv2Conv(in_feat_size=4, out_size=2, num_attn_head=3)
        >>> res = gatv2conv(feat, *graph_field.get_graph())
        >>> print(res.shape)
        (4, 6)
    """

    def __init__(self,
                 in_feat_size: int,
                 out_size: int,
                 num_attn_head: int,
                 input_drop_out_rate: float = 0.0,
                 attn_drop_out_rate: float = 0.0,
                 leaky_relu_slope: float = 0.2,
                 activation=None,
                 add_norm=False):
        super().__init__()
        if in_feat_size <= 0 or not isinstance(in_feat_size, int):
            raise ValueError("in_feat_size must be positive int")
        if out_size <= 0 or not isinstance(out_size, int):
            raise ValueError("out_size must be positive int")
        if num_attn_head <= 0 or not isinstance(num_attn_head, int):
            raise ValueError("num_attn_head must be positive int")
        if not isinstance(input_drop_out_rate, float):
            raise ValueError("input_drop_out_rate must be float")
        if not isinstance(attn_drop_out_rate, float):
            raise ValueError("attn_drop_out_rate must be float")
        if not isinstance(leaky_relu_slope, float):
            raise ValueError("leaky_relu_slope must be float")
        if not isinstance(add_norm, bool):
            raise ValueError("add_norm must be bool")

        self.num_attn_head = num_attn_head
        self.out_size = out_size
        if input_drop_out_rate < 0.0 or input_drop_out_rate >= 1.0:
            raise ValueError(f"For '{self.cls_name}', the 'input_drop_out_rate' should be a number in range [0.0, 1.0),"
                             f"but got {input_drop_out_rate}.")
        if attn_drop_out_rate < 0.0 or attn_drop_out_rate >= 1.0:
            raise ValueError(f"For '{self.cls_name}', the 'attn_drop_out_rate' should be a number in range [0.0, 1.0),"
                             f"but got {attn_drop_out_rate}.")
        self.reshape = ms.ops.Reshape()
        gain = math.sqrt(2)  # gain for relu
        self.fc_s = ms.nn.Dense(in_feat_size, out_size * num_attn_head, weight_init=XavierUniform(gain))
        self.fc_d = ms.nn.Dense(in_feat_size, out_size * num_attn_head, weight_init=XavierUniform(gain))
        self.attn = ms.Parameter(initializer(XavierUniform(gain), [num_attn_head, out_size], ms.float32),
                                 name="attention")
        self.bias = ms.Parameter(initializer('zero', [num_attn_head, out_size], ms.float32), name='bias')
        self.feat_drop = ms.nn.Dropout(p=input_drop_out_rate)
        self.attn_drop = ms.nn.Dropout(p=attn_drop_out_rate)
        self.leaky_relu = ms.nn.LeakyReLU(leaky_relu_slope)
        self.exp = ms.ops.Exp()
        if add_norm:
            self.norm_constant = ms.Tensor(100, ms.float32)
            self.norm_div = ms.ops.Div()
        else:
            self.norm_div = None
        self.activation = activation
        self.reduce_sum = ms.ops.ReduceSum()
        self.unsqueeze = ms.ops.ExpandDims()

    # pylint: disable=arguments-differ
    def construct(self, x, g: Graph):
        """
        Construct function for GATv2Conv.
        """
        x = self.feat_drop(x)
        feat_src = self.fc_s(x)
        feat_dst = self.fc_d(x)
        feat_src = ms.ops.Reshape()(feat_src, (-1, self.num_attn_head, self.out_size))
        feat_dst = ms.ops.Reshape()(feat_dst, (-1, self.num_attn_head, self.out_size))
        g.set_vertex_attr({'es': feat_src, 'ed': feat_dst, 'feat_src': feat_src})
        for v in g.dst_vertex:
            edge = [self.attn * self.leaky_relu(u.es + v.ed) for u in v.innbs]
            edge = self.reduce_sum(edge, -1)
            if self.norm_div is not None:
                edge = self.exp(self.norm_div(edge, self.norm_constant))
            else:
                edge = self.exp(edge)
            attn = self.attn_drop([c / g.sum(edge) for c in edge])
            attn = self.unsqueeze(attn, -1)
            feat = [u.feat_src for u in v.innbs]
            v.h = g.sum(attn * feat)
            v.h = v.h + self.bias
            if self.activation is not None:
                v.h = self.activation(v.h)
        return ms.ops.Flatten()([v.h for v in g.dst_vertex])