
"""
The MIT License

Copyright (c) 2020 Yeong-Dae Kwon

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.



THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""

import torch.nn as nn
import torch.nn.functional as F

import numpy as np

# Hyper Parameters
from source.MODEL_HYPER_PARAMS import *
from source.TORCH_OBJECTS import *

model_params = {
    'embedding_dim': 64,
    'sqrt_embedding_dim': 64**(1/2),
    'encoder_layer_num': 6,
    'qkv_dim': 8,
    'head_num': 8,
    'logit_clipping': 10,
    'ff_hidden_dim': 512,
    'eval_type': 'argmax',
}

########################################
# ACTOR
########################################

class ACTOR(nn.Module):

    def __init__(self):
        super().__init__()
        self.model_params = model_params

        self.encoder = TSP_Encoder()
        self.decoder = TSP_Decoder()
        self.encoded_nodes = None
        # shape: (batch, problem, EMBEDDING_DIM)
        if model_params['embedding_dim'] != 128:
            self.W_hidden = nn.Linear(64, 128, bias=False)
            self.W_embed = nn.Linear(64, 128, bias=False)
 
    def reset(self, group_state):
        self.batch_s = group_state.data.size(0)
        self.encoded_nodes = self.encoder(group_state.data)
        # shape = (batch_s, TSP_SIZE, EMBEDDING_DIM)
#         self.encoded_graph = self.encoded_nodes.mean(dim=1, keepdim=True)
        # shape = (batch, 1, EMBEDDING_DIM)

        self.decoder.reset(self.encoded_nodes)

    def soft_reset(self, group_state):
        self.decoder.reset(self.encoded_nodes, group_ninf_mask=group_state.ninf_mask)

    def get_action_probabilities(self, group_state):
        encoded_LAST_NODES = pick_nodes_for_each_group(self.encoded_nodes, group_state.current_node)
        # shape = (batch_s, group, EMBEDDING_DIM)

        probs = self.decoder(encoded_LAST_NODES, group_state.ninf_mask)
        # shape = (batch_s, group, TSP_SIZE)
        return probs


########################################
# ACTOR_SUB_NN : ENCODER
########################################

class TSP_Encoder(nn.Module):
    def __init__(self):
        super().__init__()
        self.model_params = model_params
        embedding_dim = self.model_params['embedding_dim']
        encoder_layer_num = self.model_params['encoder_layer_num']

        self.embedding = nn.Linear(2, embedding_dim)
        self.layers = nn.ModuleList([EncoderLayer() for _ in range(encoder_layer_num)])

    def forward(self, data):
        # data.shape: (batch, problem, 2)

        embedded_input = self.embedding(data)
        # shape: (batch, problem, embedding)

        out = embedded_input
        for layer in self.layers:
            out = layer(out)

        return out
    
class EncoderLayer(nn.Module):
    def __init__(self):
        super().__init__()
        self.model_params = model_params
        embedding_dim = self.model_params['embedding_dim']
        head_num = self.model_params['head_num']
        qkv_dim = self.model_params['qkv_dim']

        self.Wq = nn.Linear(embedding_dim, head_num * qkv_dim, bias=False)
        self.Wk = nn.Linear(embedding_dim, head_num * qkv_dim, bias=False)
        self.Wv = nn.Linear(embedding_dim, head_num * qkv_dim, bias=False)
        self.multi_head_combine = nn.Linear(head_num * qkv_dim, embedding_dim)

        self.addAndNormalization1 = Add_And_Normalization_Module(**model_params)
        self.feedForward = Feed_Forward_Module(**model_params)
        self.addAndNormalization2 = Add_And_Normalization_Module(**model_params)

    def forward(self, input1):
        # input.shape: (batch, problem, EMBEDDING_DIM)
        head_num = self.model_params['head_num']

        q = reshape_by_heads(self.Wq(input1), head_num=head_num)
        k = reshape_by_heads(self.Wk(input1), head_num=head_num)
        v = reshape_by_heads(self.Wv(input1), head_num=head_num)
        # q shape: (batch, HEAD_NUM, problem, KEY_DIM)

        out_concat = multi_head_attention(q, k, v)
        # shape: (batch, problem, HEAD_NUM*KEY_DIM)

        multi_head_out = self.multi_head_combine(out_concat)
        # shape: (batch, problem, EMBEDDING_DIM)

        out1 = self.addAndNormalization1(input1, multi_head_out)
        out2 = self.feedForward(out1)
        out3 = self.addAndNormalization2(out1, out2)

        return out3
        # shape: (batch, problem, EMBEDDING_DIM)
########################################
# ACTOR_SUB_NN : Next_Node_Probability_Calculator
########################################

class TSP_Decoder(nn.Module):
    def __init__(self):
        super().__init__()
        self.model_params = model_params
        embedding_dim = self.model_params['embedding_dim']
        head_num = self.model_params['head_num']
        qkv_dim = self.model_params['qkv_dim']

        self.Wq_first = nn.Linear(embedding_dim, head_num * qkv_dim, bias=False)
        self.Wq_last = nn.Linear(embedding_dim, head_num * qkv_dim, bias=False)
        self.Wk = nn.Linear(embedding_dim, head_num * qkv_dim, bias=False)
        self.Wv = nn.Linear(embedding_dim, head_num * qkv_dim, bias=False)

        self.multi_head_combine = nn.Linear(head_num * qkv_dim, embedding_dim)

        self.k = None  # saved key, for multi-head attention
        self.v = None  # saved value, for multi-head_attention
        self.single_head_key = None  # saved, for single-head attention
        self.q_first = None  # saved q1, for multi-head attention

    def set_kv(self, encoded_nodes):
        # encoded_nodes.shape: (batch, problem, embedding)
        head_num = self.model_params['head_num']

        self.k = reshape_by_heads(self.Wk(encoded_nodes), head_num=head_num)
        self.v = reshape_by_heads(self.Wv(encoded_nodes), head_num=head_num)
        # shape: (batch, head_num, pomo, qkv_dim)
        self.single_head_key = encoded_nodes.transpose(1, 2)
        # shape: (batch, embedding, problem)

    def set_q1(self, encoded_q1):
        # encoded_q.shape: (batch, n, embedding)  # n can be 1 or pomo
        head_num = self.model_params['head_num']

        self.q_first = reshape_by_heads(self.Wq_first(encoded_q1), head_num=head_num)
        # shape: (batch, head_num, n, qkv_dim)
        
    def reset(self, encoded_nodes):
        # encoded_nodes.shape = (batch_s, TSP_SIZE, EMBEDDING_DIM)

        # encoded_graph = encoded_nodes.mean(dim=1, keepdim=True)
        # shape = (batch_s, 1, EMBEDDING_DIM)
#         self.q_graph = reshape_by_heads(self.Wq_graph(encoded_graph), head_num=HEAD_NUM)
        # shape = (batch_s, HEAD_NUM, 1, KEY_DIM)
        self.q_first = None
        # shape = (batch_s, HEAD_NUM, group, KEY_DIM)
        self.k = reshape_by_heads(self.Wk(encoded_nodes), head_num=HEAD_NUM)
        self.v = reshape_by_heads(self.Wv(encoded_nodes), head_num=HEAD_NUM)
        # shape = (batch_s, HEAD_NUM, TSP_SIZE, KEY_DIM)
        self.single_head_key = encoded_nodes.transpose(1, 2)
        # shape = (batch_s, EMBEDDING_DIM, TSP_SIZE)
        # self.group_ninf_mask = group_ninf_mask
        # shape = (batch_s, group, TSP_SIZE)

    def forward(self, encoded_last_node, ninf_mask):
        # encoded_last_node.shape: (batch, pomo, embedding)
        # ninf_mask.shape: (batch, pomo, problem)
        head_num = self.model_params['head_num']

        if self.q_first is None:
            self.q_first = reshape_by_heads(self.Wq_first(encoded_last_node), head_num=head_num)
            

        #  Multi-Head Attention
        #######################################################
        q_last = reshape_by_heads(self.Wq_last(encoded_last_node), head_num=head_num)
        # shape: (batch, head_num, pomo, qkv_dim)

        q = self.q_first + q_last
        # shape: (batch, head_num, pomo, qkv_dim)

        out_concat = multi_head_attention(q, self.k, self.v, rank3_ninf_mask=ninf_mask)
        # shape: (batch, pomo, head_num*qkv_dim)

        mh_atten_out = self.multi_head_combine(out_concat)
        # shape: (batch, pomo, embedding)

        #  Single-Head Attention, for probability calculation
        #######################################################
        score = torch.matmul(mh_atten_out, self.single_head_key)
        # shape: (batch, pomo, problem)

        sqrt_embedding_dim = self.model_params['sqrt_embedding_dim']
        logit_clipping = self.model_params['logit_clipping']

        score_scaled = score / sqrt_embedding_dim
        # shape: (batch, pomo, problem)

        score_clipped = logit_clipping * torch.tanh(score_scaled)

        score_masked = score_clipped + ninf_mask

        probs = F.softmax(score_masked, dim=2)
        # shape: (batch, pomo, problem)

        return probs


########################################
# NN SUB CLASS / FUNCTIONS
########################################

def reshape_by_heads(qkv, head_num):
    # q.shape: (batch, n, head_num*key_dim)   : n can be either 1 or PROBLEM_SIZE

    batch_s = qkv.size(0)
    n = qkv.size(1)

    q_reshaped = qkv.reshape(batch_s, n, head_num, -1)
    # shape: (batch, n, head_num, key_dim)

    q_transposed = q_reshaped.transpose(1, 2)
    # shape: (batch, head_num, n, key_dim)

    return q_transposed



class Add_And_Normalization_Module(nn.Module):
    def __init__(self, **model_params):
        super().__init__()
        embedding_dim = model_params['embedding_dim']
        self.norm = nn.InstanceNorm1d(embedding_dim, affine=True, track_running_stats=False)

    def forward(self, input1, input2):
        # input.shape: (batch, problem, embedding)

        added = input1 + input2
        # shape: (batch, problem, embedding)

        transposed = added.transpose(1, 2)
        # shape: (batch, embedding, problem)

        normalized = self.norm(transposed)
        # shape: (batch, embedding, problem)

        back_trans = normalized.transpose(1, 2)
        # shape: (batch, problem, embedding)

        return back_trans


class Feed_Forward_Module(nn.Module):
    def __init__(self, **model_params):
        super().__init__()
        embedding_dim = model_params['embedding_dim']
        ff_hidden_dim = model_params['ff_hidden_dim']

        self.W1 = nn.Linear(embedding_dim, ff_hidden_dim)
        self.W2 = nn.Linear(ff_hidden_dim, embedding_dim)

    def forward(self, input1):
        # input.shape: (batch, problem, embedding)

        return self.W2(F.relu(self.W1(input1)))
    

########################################
# NN SUB CLASS / FUNCTIONS
########################################

def pick_nodes_for_each_group(encoded_nodes, node_index_to_pick):
    # encoded_nodes.shape = (batch, problem, EMBEDDING_DIM)
    # node_index_to_pick.shape = (batch, group)

    gathering_index = node_index_to_pick[:, :, None].expand(-1, -1, EMBEDDING_DIM)
    # shape = (batch, group, EMBEDDING_DIM)

    picked_nodes = encoded_nodes.gather(dim=1, index=gathering_index)
    # shape = (batch, group, EMBEDDING_DIM)

    return picked_nodes



def multi_head_attention(q, k, v, ninf_mask=None, group_ninf_mask=None):
    # q shape = (batch_s, head_num, n, key_dim)   : n can be either 1 or TSP_SIZE
    # k,v shape = (batch_s, head_num, TSP_SIZE, key_dim)
    # ninf_mask.shape = (batch_s, TSP_SIZE)
    # group_ninf_mask.shape = (batch_s, group, TSP_SIZE)

    batch_s = q.size(0)
    head_num = q.size(1)
    n = q.size(2)
    key_dim = q.size(3)
    problem_s = k.size(2)

    score = torch.matmul(q, k.transpose(2, 3))
    # shape = (batch_s, head_num, n, TSP_SIZE)

    score_scaled = score / np.sqrt(key_dim)
    if ninf_mask is not None:
        score_scaled = score_scaled + ninf_mask[:, None, None, :].expand(batch_s, head_num, n, problem_s)
    if group_ninf_mask is not None:
        score_scaled = score_scaled + group_ninf_mask[:, None, :, :].expand(batch_s, head_num, n, problem_s)

    weights = nn.Softmax(dim=3)(score_scaled)
    # shape = (batch_s, head_num, n, TSP_SIZE)

    out = torch.matmul(weights, v)
    # shape = (batch_s, head_num, n, key_dim)

    out_transposed = out.transpose(1, 2)
    # shape = (batch_s, n, head_num, key_dim)

    out_concat = out_transposed.reshape(batch_s, n, head_num * key_dim)
    # shape = (batch_s, n, head_num*key_dim)

    return out_concat
