# /// script
# dependencies = [
#     "accelerate",
#     "titans-pytorch",
#     "tqdm"
# ]
# ///

import math
import gzip
import random
import tqdm
import numpy as np

import torch
from torch.optim import Adam
from torch import nn, Tensor
from torch.nn import Module, ModuleList
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset

from einops import rearrange

from titans_pytorch.implicit_mlp_attention import ImplicitMLPAttention
from titans_pytorch.nested_attention import NestedAttention

from accelerate import Accelerator

# constants

NUM_BATCHES = int(1e5)
BATCH_SIZE = 4
GRAD_ACCUM_EVERY = 4
LEARNING_RATE = 1e-4
VALIDATE_EVERY = 100
PRIME_LENGTH = 32
GENERATE_EVERY = 250
GENERATE_LENGTH = 512
SEQ_LEN = 512

# helpers

def exists(v):
    return v is not None

def cycle(loader):
    while True:
        for data in loader:
            yield data

def decode_token(token):
    return str(chr(max(32, token)))

def decode_tokens(tokens):
    return "".join(list(map(decode_token, tokens)))

# sampling helpers

def log(t, eps = 1e-20):
    return torch.log(t.clamp(min = eps))

def gumbel_noise(t):
    noise = torch.rand_like(t)
    return -log(-log(noise))

def gumbel_sample(t, temperature = 1., dim = -1, keepdim = True):
    return ((t / max(temperature, 1e-10)) + gumbel_noise(t)).argmax(dim = dim, keepdim = keepdim)

def top_k(logits, thres = 0.9):
    k = math.ceil((1 - thres) * logits.shape[-1])
    val, ind = torch.topk(logits, k)
    probs = torch.full_like(logits, float('-inf'))
    probs.scatter_(-1, ind, val)
    return probs

class Transformer(Module):
    def __init__(
        self,
        *,
        num_tokens,
        dim,
        depth,
        heads = 8,
        implicit_mlp_attn_hiddens = (64, 96, 64),
        use_nested_attn = False,
        dim_head = 64,
        ff_expansion = 4.,
        attn_kwargs: dict = dict(),
    ):
        super().__init__()
        self.token_emb = nn.Embedding(num_tokens, dim)

        self.layers = ModuleList([])

        for _ in range(depth):

            if use_nested_attn:
                attn = NestedAttention(
                    dim = dim,
                    dim_head = dim_head,
                    heads = heads,
                    **attn_kwargs
                )
            else:
                attn = ImplicitMLPAttention(
                    dim = dim,
                    mlp_hiddens = implicit_mlp_attn_hiddens,
                    heads = heads,
                    **attn_kwargs
                )

            ff = nn.Sequential(
                nn.RMSNorm(dim),
                nn.Linear(dim, int(dim * ff_expansion)),
                nn.GELU(),
                nn.Linear(int(dim * ff_expansion), dim)
            )

            self.layers.append(ModuleList([attn, ff]))

        self.norm = nn.RMSNorm(dim)
        self.to_logits = nn.Linear(dim, num_tokens, bias = False)

    def sample(
        self,
        prompt: Tensor,
        seq_len: int,
        temperature = 1.,
        filter_thres = 0.9,
    ):
        prompt_seq_len, out = prompt.shape[-1], prompt.clone()
        sample_num_times = max(0, seq_len - prompt_seq_len)

        for _ in range(sample_num_times):
            logits = self.forward(out, return_loss = False)
            logits = logits[:, -1]

            logits = top_k(logits, thres = filter_thres)
            sample = gumbel_sample(logits, temperature = temperature, dim = -1)

            out = torch.cat((out, sample), dim = -1)

        return out[..., prompt_seq_len:]

    def forward(self, x, return_loss = False):

        if return_loss:
            x, target = x[:, :-1], x[:, 1:]

        seq_len, device = x.shape[-1], x.device

        tokens = self.token_emb(x)

        for attn, ff in self.layers:
            tokens = attn(tokens) + tokens
            tokens = ff(tokens) + tokens

        embed = self.norm(tokens)
        logits = self.to_logits(embed)

        if not return_loss:
            return logits

        return F.cross_entropy(
            rearrange(logits, 'b n l -> b l n'),
            target
        )

model = Transformer(
    num_tokens = 256,
    dim = 512,
    depth = 6,
    implicit_mlp_attn_hiddens = (64, 96, 64),
    use_nested_attn = True # test implicit mlp attn vs nested attn
)

# prepare enwik8 data

with gzip.open("./data/enwik8.gz") as file:
    data = np.frombuffer(file.read(int(95e6)), dtype=np.uint8).copy()
    np_train, np_valid = np.split(data, [int(90e6)])
    data_train, data_val = torch.from_numpy(np_train), torch.from_numpy(np_valid)

class TextSamplerDataset(Dataset):
    def __init__(self, data, seq_len):
        super().__init__()
        self.data = data
        self.seq_len = seq_len

    def __len__(self):
        return self.data.size(0) // self.seq_len

    def __getitem__(self, index):
        rand_start = torch.randint(0, self.data.size(0) - self.seq_len, (1,))
        full_seq = self.data[rand_start : rand_start + self.seq_len + 1].long()
        return full_seq

train_dataset = TextSamplerDataset(data_train, SEQ_LEN)
val_dataset = TextSamplerDataset(data_val, SEQ_LEN)
train_loader = DataLoader(train_dataset, batch_size = BATCH_SIZE)
val_loader = DataLoader(val_dataset, batch_size = BATCH_SIZE)

# optimizer

optim = Adam(model.parameters(), lr = LEARNING_RATE)

# accelerate

accelerator = Accelerator()

model, optim, train_loader, val_loader = accelerator.prepare(model, optim, train_loader, val_loader)

# cycle

train_loader = cycle(train_loader)
val_loader = cycle(val_loader)

# training

for i in tqdm.tqdm(range(NUM_BATCHES), mininterval = 10.0, desc = "training"):
    model.train()

    for _ in range(GRAD_ACCUM_EVERY):
        data = next(train_loader)

        loss = model(data, return_loss = True)

        accelerator.backward(loss / GRAD_ACCUM_EVERY)

    accelerator.print(f"training loss: {loss.item():.3f}")

    torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)

    optim.step()
    optim.zero_grad()

    if i % VALIDATE_EVERY == 0:
        model.eval()
        with torch.no_grad():
            valid_data = next(val_loader)

            loss = model(valid_data, return_loss = True)
            accelerator.print(f"validation loss: {loss.item():.3f}")

    if i % GENERATE_EVERY == 0:
        model.eval()

        inp = next(val_loader)[0, :PRIME_LENGTH]

        prime = decode_tokens(inp)
        accelerator.print(f"\n\n[prompt]: {prime}")

        prompt = inp[None, ...]

        sampled = model.sample(prompt, GENERATE_LENGTH)

        base_decode_output = decode_tokens(sampled[0])

        accelerator.print(f"\n[generated]: {base_decode_output}\n\n")