import copy
from pathlib import Path

from tqdm import tqdm
from beartype import beartype
from beartype.typing import Tuple, Optional

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

from einops import rearrange, repeat, reduce, pack, unpack
from einops.layers.torch import Rearrange, Reduce

from src.rlhf.utils import masked_mean, gumbel_sample
from src.model import RWKV

# helper functions

def exists(val):
    return val is not None

# Reward Model - RWKV with a scalar head

@beartype
class RewardModel(nn.Module):
    def __init__(
        self,
        rwkv: RWKV,
        dropout = 0.1,
        num_binned_output = 0.
    ):
        super().__init__()

        # 用预训练模型初始化奖励模型
        self.rwkv = copy.deepcopy(rwkv)
        self.rwkv.set_dropout(dropout)  # todo(luxin)

        # 输出 token 向量的维度
        dim = rwkv.dim  # todo(luxin)

        # 打分等级，如果为5，打分等级分为 [0, 1, 2, 3, 4]，共 5 个等级
        self.binned_output = num_binned_output > 1

        # todo(luxin)：prompt_embed 和 response_embed 都是初始化为全0？不应该有区分么
        self.prompt_embed = nn.Parameter(torch.zeros(1, 1, dim))
        self.response_embed = nn.Parameter(torch.zeros(1, 1, dim))
        # self.response_embed = nn.Parameter(torch.ones(1, 1, dim))

        if self.binned_output:
            # 如果打分等级的类别数大于1，则为多分类问题
            self.to_pred = nn.Linear(dim, num_binned_output)
        else:
            # 否则，直接是一个二分类问题
            self.to_pred = nn.Sequential(
                nn.Linear(dim, 1, bias = False),
                Rearrange('... 1 -> ...')   # 降维
            )

    def load(self, path):
        path = Path(path)
        assert path.exists()
        self.load_state_dict(torch.load(str(path)))

    def finetune_parameters(self):
        return [
            *self.to_pred.parameters(),
            *self.rwkv.parameters()
        ]

    def forward(
        self,
        x,
        mask = None,
        prompt_mask = None,
        prompt_lengths = None,
        labels = None,
        sample = False,
        sample_temperature = 1.
    ):

        # prompt_mask 和 prompt_lengths 只能给1个
        assert not (exists(prompt_mask) and exists(prompt_lengths))

        # derive prompt mask from prompt lengths
        if exists(prompt_lengths):
            batch, seq_len = x.shape
            arange = torch.arange(seq_len, device = x.device)
            prompt_mask = repeat(arange, 'n -> b n', b = batch) < rearrange(prompt_lengths, 'b -> b 1')

        # reward model should have an understanding of which section is prompt, and which section is response
        # 根据 prompt_mask 中 token 的 True 和 False，从 prompt_embed 或 response_embed 中取值
        # 如果为 True，则从 prompt_embed 中选，否则从 response_embed 中选
        extra_embed = None
        if exists(prompt_mask):
            extra_embed = torch.where(
                rearrange(prompt_mask, 'b n -> b n 1'),
                self.prompt_embed,
                self.response_embed
            )

        # todo(luxin) get embeddings from rwkv
        embeds = self.rwkv(
            x,
            extra_embed = extra_embed,
            return_only_embedding = True
        )

        # 所有的 token 向量求平均，并输入到打分模块进行打分
        pooled = masked_mean(embeds, mask, dim = 1)
        pred = self.to_pred(pooled)

        if sample and self.binned_output:
            assert not exists(labels)
            pred = gumbel_sample(pred, temperature = sample_temperature, dim = -1)

        if not exists(labels):
            return pred

        # todo(luxin) 作者没有使用论文中考虑两个样本的 loss，而是单个样本的 loss
        if not self.binned_output:
            return F.mse_loss(pred, labels)

        return F.cross_entropy(pred, labels)