import argparse
import math

import numpy as np
import plotille
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import tqdm
from gr.pygr import mlab
from IPython import embed
from torch.utils.data import Dataset
from torch.utils.data.dataloader import DataLoader


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--dropout', type=float, default=0.1)
    parser.add_argument('--lr', type=float, default=0.0001)
    parser.add_argument('--max_epoch', type=int, default=200)
    parser.add_argument('--batch_size', type=int, default=128)
    parser.add_argument('--data_repeat', type=int, default=1)
    parser.add_argument('--device', type=str, default='cuda')
    parser.add_argument('--block_size', type=int, default=32)
    parser.add_argument('--test_steps', type=int, default=512)
    parser.add_argument('--n_workers', type=int, default=1)
    parser.add_argument('--weight_decay', type=float, default=0.1)
    parser.add_argument('--noise_scale', type=float, default=0.1)
    parser.add_argument('--max_grad_norm', type=float, default=1.0)
    parser.add_argument('--dataset', choices=['BasicDataset', 'MotionDataset'], default='MotionDataset')
    return parser.parse_args()

args = parse_args()


class CausalSelfAttention(nn.Module):
    """
    https://github.com/karpathy/minGPT/blob/master/mingpt/model.py
    """

    def __init__(self, d_model, n_head, block_size, dropout):
        super().__init__()
        assert d_model % n_head == 0
        # key, query, value projections for all heads
        self.key = nn.Linear(d_model, d_model)
        self.query = nn.Linear(d_model, d_model)
        self.value = nn.Linear(d_model, d_model)
        # regularization
        self.attn_drop = nn.Dropout(dropout)
        self.resid_drop = nn.Dropout(dropout)
        # output projection
        self.proj = nn.Linear(d_model, d_model)
        # causal mask to ensure that attention is only applied to the left in the input sequence
        self.register_buffer(
            "mask", 
            torch.tril(torch.ones(block_size, block_size)).view(1, 1, block_size, block_size)
        )
        self.n_head = n_head

    def forward(self, x, layer_past=None):
        B, T, C = x.size()

        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
        k = self.key(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
        q = self.query(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
        v = self.value(x).view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)

        # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
        att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
        att = att.masked_fill(self.mask[:,:,:T,:T] == 0, -1e10) # todo: just use float('-inf') instead?
        att = F.softmax(att, dim=-1)
        att = self.attn_drop(att)
        y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
        y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side

        # output projection
        y = self.resid_drop(self.proj(y))
        return y

class Block(nn.Module):
    """ an unassuming Transformer block """

    def __init__(self, d_model, n_head, block_size, dropout):
        super().__init__()
        self.ln1 = nn.LayerNorm(d_model)
        self.ln2 = nn.LayerNorm(d_model)
        self.attn = CausalSelfAttention(d_model, n_head, block_size, dropout)
        self.mlp = nn.Sequential(
            nn.Linear(d_model, 4 * d_model),
            nn.GELU(),
            nn.Linear(4 * d_model, d_model),
            nn.Dropout(dropout),
        )

    def forward(self, x):
        x = x + self.attn(self.ln1(x))
        x = x + self.mlp(self.ln2(x))
        return x


class GPTModel(nn.Module):
    def __init__(self, input_dims, output_dims, block_size):
        super().__init__()
        self.n_layers = 6
        self.n_heads = 8
        self.d_model = 512
        self.block_size = block_size

        self.we = nn.Linear(input_dims, self.d_model, bias=True)
        self.wp = nn.Parameter(torch.zeros(1, self.block_size, self.d_model))
        self.blocks = nn.Sequential(*[
            Block(self.d_model, self.n_heads, self.block_size, args.dropout) 
            for _ in range(self.n_layers)
        ])
        self.norm = nn.LayerNorm(self.d_model)
        self.wd = nn.Linear(self.d_model, output_dims, bias=True)

        self.apply(self._init_weights)
        print(f'n_params: {sum(p.numel() for p in self.parameters())}')

    def _init_weights(self, module):
        if isinstance(module, (nn.Linear, nn.Embedding)):
            module.weight.data.normal_(mean=0.0, std=0.02)
            if isinstance(module, nn.Linear) and module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)

    def forward(self, src):
        B, T, C = src.size()
        src_embed = self.we(src)
        pos_embed = self.wp[:, :T, :]
        hx = src_embed + pos_embed
        hx = self.blocks(hx)
        hx = self.norm(hx)
        out = self.wd(self.norm(hx))
        src = torch.cat([src[:, 1:, :], out[:, -1:, :]], dim=1).detach()
        return out, src


class BasicDataset(Dataset):

    def __init__(self, block_size, repeat, noise_scale):
        self.block_size = block_size

        self.data = np.sin(np.arange(10240) / 10.)
        # self.data = np.sin(np.arange(10240) / 10.) * 0.5 + 2.5
        # self.data = np.abs(np.sin(np.arange(10240) / 10.))
        # data = np.sin(np.arange(10240) / 10.) * (np.sin(np.arange(10240) / 10.) > 0.0)
        self.data = self.data.astype(np.float32)
        self.data = self.data.reshape(-1, 1)
        self.data_std = self.data.std(0)
        self.repeat = repeat
        self.noise_scale = noise_scale
    
    def __len__(self):
        # return math.ceil(len(self.data) / (self.block_size + 1))
        return len(self.data) * self.repeat

    def __getitem__(self, idx):
        # we're actually going to "cheat" and pick a spot in the dataset at random
        i = np.random.randint(0, len(self.data) - (self.block_size + 1))
        chunk = self.data[i: i+self.block_size+1]
        chunk += np.random.normal(0, args.noise_scale, chunk.shape) * self.data_std
        x = torch.tensor(chunk[:-1], dtype=torch.float32)
        y = torch.tensor(chunk[1:], dtype=torch.float32)
        return x, y

    def get_test_data(self, test_steps, device):
        i = np.random.randint(0, len(self.data) - (test_steps + 1))
        idx = np.arange(i, i+test_steps)
        data = self.data[idx].reshape(1, -1, 1)
        tgt = torch.tensor(data, device=device)
        src = tgt[:, :args.block_size]
        gen = tgt[:, :args.block_size]
        return tgt, src, gen


class MotionDataset(Dataset):
    
    def __init__(self, block_size, repeat, noise_scale):
        self.block_size = block_size

        import urllib, json
        url = "https://raw.githubusercontent.com/xbpeng/DeepMimic/master/data/motions/humanoid3d_backflip.txt"
        self.data = json.loads(urllib.request.urlopen(url).read())['Frames']
        self.data = np.array(self.data, dtype=np.float32)
        self.data = np.hstack([self.data[:, 3:4], self.data])
        self.data = np.tile(self.data, (100, 1))
        self.dims = self.data.shape[-1]
        self.data_mean = self.data.mean(0, keepdims=True)
        self.data_std = self.data.std(0, keepdims=True)
        self.data = (self.data - self.data_mean) / self.data_std

        self.data = self.data.astype(np.float32)
        self.repeat = repeat
        self.noise_scale = noise_scale
    
    def __len__(self):
        # return math.ceil(len(self.data) / (self.block_size + 1))
        return len(self.data) * self.repeat

    def __getitem__(self, idx):
        # we're actually going to "cheat" and pick a spot in the dataset at random
        i = np.random.randint(0, len(self.data) - (self.block_size + 1))
        chunk = self.data[i: i+self.block_size+1]
        chunk += np.random.normal(0, args.noise_scale, chunk.shape)
        x = torch.tensor(chunk[:-1], dtype=torch.float32)
        y = torch.tensor(chunk[1:], dtype=torch.float32)
        return x, y

    def get_test_data(self, test_steps, device):
        i = np.random.randint(0, len(self.data) - (test_steps + 1))
        idx = np.arange(i, i+test_steps)
        data = self.data[idx].reshape(1, -1, self.dims)
        tgt = torch.tensor(data, device=device)
        src = tgt[:, :args.block_size]
        gen = tgt[:, :args.block_size]
        return tgt, src, gen


if __name__ == '__main__':

    # create the dataloader
    Dataset = globals()[args.dataset]
    dataset = Dataset(args.block_size, args.data_repeat, args.noise_scale)
    loader = DataLoader(dataset, batch_size=args.batch_size, num_workers=args.n_workers)
    
    # create the model
    dim = dataset.data.shape[-1]
    model = GPTModel(dim, dim, args.block_size).to(args.device)

    # create the optimizer
    no_decay = ["bias", "LayerNorm.weight"]
    params_decay = [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)]
    params_nodecay = [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)]
    optim_groups = [
        {"params": params_decay, "weight_decay": args.weight_decay},
        {"params": params_nodecay, "weight_decay": 0.0},
    ]
    optimizer = optim.AdamW(optim_groups, lr=args.lr, betas=(0.9, 0.95))

    def warmup_cosine(optimizer, lr_max, epoch, warmup=1.0):
        s = float(epoch <= warmup)
        w = s*(epoch / warmup) + (1-s)*(0.5 * (1 + np.cos(np.pi * epoch)))
        for param_group in optimizer.param_groups:
            param_group['lr'] = w * lr_max

    step = 0
    train_loss_list = list()
    test_score_list = list()
    
    for epoch in tqdm.trange(args.max_epoch):
        # fitting
        model.train()
        for i, (src, tgt) in tqdm.tqdm(enumerate(loader), total=len(loader), leave=False):
            src, tgt = src.to(args.device), tgt.to(args.device)

            gen, _ = model(src)

            optimizer.zero_grad()
            loss = (0.5 * (tgt - gen) ** 2).mean()
            loss.backward()
            nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
            optimizer.step()
            warmup_cosine(optimizer, args.lr, epoch + i / len(loader))

            step += 1 / len(loader)
            train_loss_list.append((step, loss.item()))

        tqdm.tqdm.write(plotille.scatter(*zip(*train_loss_list[-1000:]), height=25))

        # eval
        model.eval()
        tgt, src, gen = dataset.get_test_data(args.test_steps, args.device)

        with torch.no_grad():
            for i in range(args.test_steps - args.block_size):
                gen_, src = model(src)
                gen = torch.cat([gen, gen_[:, -1:, :]], dim=1)
        
        loss = (0.5 * (tgt - gen) ** 2).mean()
        score = (-loss).exp()
        test_score_list.append((step, score.item()))

        mlab.plot(tgt.cpu().numpy()[0, :, 0])
        mlab.oplot(gen.cpu().numpy()[0, :, 0])
        tqdm.tqdm.write(plotille.scatter(*zip(*test_score_list[-1000:]), height=25))
        tqdm.tqdm.write(str(args))

    embed()