##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021 #
##################################################
from __future__ import division
from __future__ import print_function

import os
import math
import numpy as np
import pandas as pd
import copy
from functools import partial
from typing import Optional
import logging

from qlib.utils import (
    unpack_archive_with_buffer,
    save_multiple_parts_file,
    create_save_path,
    drop_nan_by_y_index,
)
from qlib.log import get_module_logger, TimeInspector

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as th_data

import layers as xlayers
from utils import count_parameters

from qlib.model.base import Model
from qlib.data.dataset import DatasetH
from qlib.data.dataset.handler import DataHandlerLP


default_net_config = dict(d_feat=6, hidden_size=48, depth=5, pos_drop=0.1)

default_opt_config = dict(epochs=200, lr=0.001, batch_size=2000, early_stop=20, loss="mse", optimizer="adam")


class QuantTransformer(Model):
    """Transformer-based Quant Model"""

    def __init__(self, net_config=None, opt_config=None, metric="", GPU=0, seed=None, **kwargs):
        # Set logger.
        self.logger = get_module_logger("QuantTransformer")
        self.logger.info("QuantTransformer pytorch version...")

        # set hyper-parameters.
        self.net_config = net_config or default_net_config
        self.opt_config = opt_config or default_opt_config
        self.metric = metric
        self.device = torch.device("cuda:{:}".format(GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu")
        self.seed = seed

        self.logger.info(
            "Transformer parameters setting:"
            "\nnet_config : {:}"
            "\nopt_config : {:}"
            "\nmetric     : {:}"
            "\ndevice     : {:}"
            "\nseed       : {:}".format(
                self.net_config,
                self.opt_config,
                self.metric,
                self.device,
                self.seed,
            )
        )

        if self.seed is not None:
            np.random.seed(self.seed)
            torch.manual_seed(self.seed)

        self.model = TransformerModel(
            d_feat=self.net_config["d_feat"],
            embed_dim=self.net_config["hidden_size"],
            depth=self.net_config["depth"],
            pos_drop=self.net_config["pos_drop"],
        )
        self.logger.info("model: {:}".format(self.model))
        self.logger.info("model size: {:.3f} MB".format(count_parameters(self.model)))

        if self.opt_config["optimizer"] == "adam":
            self.train_optimizer = optim.Adam(self.model.parameters(), lr=self.opt_config["lr"])
        elif self.opt_config["optimizer"] == "adam":
            self.train_optimizer = optim.SGD(self.model.parameters(), lr=self.opt_config["lr"])
        else:
            raise NotImplementedError("optimizer {:} is not supported!".format(optimizer))

        self.fitted = False
        self.model.to(self.device)

    @property
    def use_gpu(self):
        self.device == torch.device("cpu")

    def loss_fn(self, pred, label):
        mask = ~torch.isnan(label)
        if self.opt_config["loss"] == "mse":
            return F.mse_loss(pred[mask], label[mask])
        else:
            raise ValueError("unknown loss `{:}`".format(self.loss))

    def metric_fn(self, pred, label):
        mask = torch.isfinite(label)
        if self.metric == "" or self.metric == "loss":
            return -self.loss_fn(pred[mask], label[mask])
        else:
            raise ValueError("unknown metric `{:}`".format(self.metric))

    def train_epoch(self, xloader, model, loss_fn, optimizer):
        model.train()
        scores, losses = [], []
        for ibatch, (feats, labels) in enumerate(xloader):
            feats = feats.to(self.device, non_blocking=True)
            labels = labels.to(self.device, non_blocking=True)
            # forward the network
            preds = model(feats)
            loss = loss_fn(preds, labels)
            with torch.no_grad():
                score = self.metric_fn(preds, labels)
                losses.append(loss.item())
                scores.append(loss.item())
            # optimize the network
            optimizer.zero_grad()
            loss.backward()
            torch.nn.utils.clip_grad_value_(model.parameters(), 3.0)
            optimizer.step()
        return np.mean(losses), np.mean(scores)

    def test_epoch(self, xloader, model, loss_fn, metric_fn):
        model.eval()
        scores, losses = [], []
        with torch.no_grad():
            for ibatch, (feats, labels) in enumerate(xloader):
                feats = feats.to(self.device, non_blocking=True)
                labels = labels.to(self.device, non_blocking=True)
                # forward the network
                preds = model(feats)
                loss = loss_fn(preds, labels)
                score = self.metric_fn(preds, labels)
                losses.append(loss.item())
                scores.append(loss.item())
        return np.mean(losses), np.mean(scores)

    def fit(
        self,
        dataset: DatasetH,
        evals_result=dict(),
        verbose=True,
        save_path=None,
    ):
        def _prepare_dataset(df_data):
            return th_data.TensorDataset(
                torch.from_numpy(df_data["feature"].values).float(),
                torch.from_numpy(df_data["label"].values).squeeze().float(),
            )

        df_train, df_valid, df_test = dataset.prepare(
            ["train", "valid", "test"],
            col_set=["feature", "label"],
            data_key=DataHandlerLP.DK_L,
        )
        train_dataset, valid_dataset, test_dataset = (
            _prepare_dataset(df_train),
            _prepare_dataset(df_valid),
            _prepare_dataset(df_test),
        )

        train_loader = th_data.DataLoader(
            train_dataset, batch_size=self.opt_config["batch_size"], shuffle=True, drop_last=False, pin_memory=True
        )
        valid_loader = th_data.DataLoader(
            valid_dataset, batch_size=self.opt_config["batch_size"], shuffle=False, drop_last=False, pin_memory=True
        )
        test_loader = th_data.DataLoader(
            test_dataset, batch_size=self.opt_config["batch_size"], shuffle=False, drop_last=False, pin_memory=True
        )

        if save_path == None:
            save_path = create_save_path(save_path)
        stop_steps, best_score, best_epoch = 0, -np.inf, 0
        train_loss = 0
        evals_result["train"] = []
        evals_result["valid"] = []

        # train
        self.logger.info("Fit procedure for [{:}] with save path={:}".format(self.__class__.__name__, save_path))

        def _internal_test():
            train_loss, train_score = self.test_epoch(train_loader, self.model, self.loss_fn, self.metric_fn)
            valid_loss, valid_score = self.test_epoch(valid_loader, self.model, self.loss_fn, self.metric_fn)
            test_loss, test_score = self.test_epoch(test_loader, self.model, self.loss_fn, self.metric_fn)
            xstr = "train-score={:.6f}, valid-score={:.6f}, test-score={:.6f}".format(
                train_score, valid_score, test_score
            )
            return dict(train=train_score, valid=valid_score, test=test_score), xstr

        _, eval_str = _internal_test()
        self.logger.info("Before Training: {:}".format(eval_str))
        for iepoch in range(self.opt_config["epochs"]):
            self.logger.info("Epoch={:03d}/{:03d} ::==>>".format(iepoch, self.opt_config["epochs"]))

            train_loss, train_score = self.train_epoch(train_loader, self.model, self.loss_fn, self.train_optimizer)
            self.logger.info("Training :: loss={:.6f}, score={:.6f}".format(train_loss, train_score))

            eval_score_dict, eval_str = _internal_test()
            self.logger.info("Evaluating :: {:}".format(eval_str))
            evals_result["train"].append(eval_score_dict["train"])
            evals_result["valid"].append(eval_score_dict["valid"])

            if eval_score_dict["valid"] > best_score:
                stop_steps, best_epoch, best_score = 0, iepoch, eval_score_dict["valid"]
                best_param = copy.deepcopy(self.model.state_dict())
            else:
                stop_steps += 1
                if stop_steps >= self.opt_config["early_stop"]:
                    self.logger.info("early stop at {:}-th epoch, where the best is @{:}".format(iepoch, best_epoch))
                    break

        self.logger.info("The best score: {:.6f} @ {:02d}-th epoch".format(best_score, best_epoch))
        self.model.load_state_dict(best_param)
        torch.save(best_param, save_path)

        if self.use_gpu:
            torch.cuda.empty_cache()
        self.fitted = True

    def predict(self, dataset):

        if not self.fitted:
            raise ValueError("model is not fitted yet!")

        x_test = dataset.prepare("test", col_set="feature")
        index = x_test.index
        self.model.eval()
        x_values = x_test.values
        sample_num = x_values.shape[0]
        preds = []

        for begin in range(sample_num)[:: self.batch_size]:

            if sample_num - begin < self.batch_size:
                end = sample_num
            else:
                end = begin + self.batch_size

            x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)

            with torch.no_grad():
                if self.use_gpu:
                    pred = self.model(x_batch).detach().cpu().numpy()
                else:
                    pred = self.model(x_batch).detach().numpy()

            preds.append(pred)

        return pd.Series(np.concatenate(preds), index=index)


# Real Model


class Attention(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0):
        super(Attention, self).__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
        self.scale = qk_scale or math.sqrt(head_dim)

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

    def forward(self, x):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)

        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class Block(nn.Module):
    def __init__(
        self,
        dim,
        num_heads,
        mlp_ratio=4.0,
        qkv_bias=False,
        qk_scale=None,
        attn_drop=0.0,
        mlp_drop=0.0,
        drop_path=0.0,
        act_layer=nn.GELU,
        norm_layer=nn.LayerNorm,
    ):
        super(Block, self).__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=mlp_drop
        )
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = xlayers.DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = xlayers.MLP(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=mlp_drop)

    def forward(self, x):
        x = x + self.drop_path(self.attn(self.norm1(x)))
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x


class SimpleEmbed(nn.Module):
    def __init__(self, d_feat, embed_dim):
        super(SimpleEmbed, self).__init__()
        self.d_feat = d_feat
        self.embed_dim = embed_dim
        self.proj = nn.Linear(d_feat, embed_dim)

    def forward(self, x):
        x = x.reshape(len(x), self.d_feat, -1)  # [N, F*T] -> [N, F, T]
        x = x.permute(0, 2, 1)  # [N, F, T] -> [N, T, F]
        out = self.proj(x) * math.sqrt(self.embed_dim)
        return out


class TransformerModel(nn.Module):
    def __init__(
        self,
        d_feat: int,
        embed_dim: int = 64,
        depth: int = 4,
        num_heads: int = 4,
        mlp_ratio: float = 4.0,
        qkv_bias: bool = True,
        qk_scale: Optional[float] = None,
        pos_drop=0.0,
        mlp_drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=0.0,
        norm_layer=None,
    ):
        """
        Args:
          d_feat (int, tuple): input image size
          embed_dim (int): embedding dimension
          depth (int): depth of transformer
          num_heads (int): number of attention heads
          mlp_ratio (int): ratio of mlp hidden dim to embedding dim
          qkv_bias (bool): enable bias for qkv if True
          qk_scale (float): override default qk scale of head_dim ** -0.5 if set
          pos_drop (float): dropout rate for the positional embedding
          mlp_drop_rate (float): the dropout rate for MLP layers in a block
          attn_drop_rate (float): attention dropout rate
          drop_path_rate (float): stochastic depth rate
          norm_layer: (nn.Module): normalization layer
        """
        super(TransformerModel, self).__init__()
        self.embed_dim = embed_dim
        self.num_features = embed_dim
        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)

        self.input_embed = SimpleEmbed(d_feat, embed_dim=embed_dim)

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.pos_embed = xlayers.PositionalEncoder(d_model=embed_dim, max_seq_len=65, dropout=pos_drop)

        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
        self.blocks = nn.ModuleList(
            [
                Block(
                    dim=embed_dim,
                    num_heads=num_heads,
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                    qk_scale=qk_scale,
                    attn_drop=attn_drop_rate,
                    mlp_drop=mlp_drop_rate,
                    drop_path=dpr[i],
                    norm_layer=norm_layer,
                )
                for i in range(depth)
            ]
        )
        self.norm = norm_layer(embed_dim)

        # regression head
        self.head = nn.Linear(self.num_features, 1)

        xlayers.trunc_normal_(self.cls_token, std=0.02)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            xlayers.trunc_normal_(m.weight, std=0.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    def forward_features(self, x):
        batch, flatten_size = x.shape
        feats = self.input_embed(x)  # batch * 60 * 64

        cls_tokens = self.cls_token.expand(batch, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
        feats_w_ct = torch.cat((cls_tokens, feats), dim=1)
        feats_w_tp = self.pos_embed(feats_w_ct)

        xfeats = feats_w_tp
        for block in self.blocks:
            xfeats = block(xfeats)

        xfeats = self.norm(xfeats)[:, 0]
        return xfeats

    def forward(self, x):
        feats = self.forward_features(x)
        predicts = self.head(feats).squeeze(-1)
        return predicts