import numpy as np

np.set_printoptions(precision=4, suppress=True, linewidth=200)
import types, torch
from torch.nn import functional as F

MyModule = torch.jit.ScriptModule
MyFunction = torch.jit.script_method


class RWKV_TOKENIZER():
    table: list[list[list[bytes]]]
    good: list[set[int]]
    wlen: list[int]

    def __init__(self, file_name):
        self.idx2token = {}
        sorted = []  # must be already sorted
        lines = open(file_name, "r", encoding="utf-8").readlines()
        for l in lines:
            idx = int(l[:l.index(' ')])
            x = eval(l[l.index(' '):l.rindex(' ')])
            x = x.encode("utf-8") if isinstance(x, str) else x
            assert isinstance(x, bytes)
            assert len(x) == int(l[l.rindex(' '):])
            sorted += [x]
            self.idx2token[idx] = x

        self.token2idx = {}
        for k, v in self.idx2token.items():
            self.token2idx[v] = int(k)

        # precompute some tables for fast matching
        self.table = [[[] for j in range(256)] for i in range(256)]
        self.good = [set() for i in range(256)]
        self.wlen = [0 for i in range(256)]

        for i in reversed(range(len(sorted))):  # reverse order - match longer tokens first
            s = sorted[i]
            if len(s) >= 2:
                s0 = int(s[0])
                s1 = int(s[1])
                self.table[s0][s1] += [s]
                self.wlen[s0] = max(self.wlen[s0], len(s))
                self.good[s0].add(s1)

    def encodeBytes(self, src: bytes) -> list[int]:
        src_len: int = len(src)
        tokens: list[int] = []
        i: int = 0
        while i < src_len:
            s: bytes = src[i: i + 1]

            if i < src_len - 1:
                s1: int = int(src[i + 1])
                s0: int = int(src[i])
                if s1 in self.good[s0]:
                    sss: bytes = src[i: i + self.wlen[s0]]
                    try:
                        s = next(filter(sss.startswith, self.table[s0][s1]))
                    except:
                        pass
            tokens.append(self.token2idx[s])
            i += len(s)

        return tokens

    def decodeBytes(self, tokens):
        return b''.join(map(lambda i: self.idx2token[i], tokens))

    def encode(self, src: str):
        return self.encodeBytes(src.encode("utf-8"))

    def decode(self, tokens):
        return self.decodeBytes(tokens).decode('utf-8')

    def printTokens(self, tokens):
        for i in tokens:
            s = self.idx2token[i]
            try:
                s = s.decode('utf-8')
            except:
                pass
            print(f'{repr(s)}{i}', end=' ')
            # print(repr(s), i)
        print()


########################################################################################################

def sample_logits(out, temperature=1.0, top_p=0.8):
    probs = F.softmax(out, dim=-1).numpy()
    sorted_probs = np.sort(probs)[::-1]
    cumulative_probs = np.cumsum(sorted_probs)
    cutoff = float(sorted_probs[np.argmax(cumulative_probs > top_p)])
    probs[probs < cutoff] = 0
    if temperature != 1.0:
        probs = probs.pow(1.0 / temperature)
    probs = probs / np.sum(probs)
    out = np.random.choice(a=len(probs), p=probs)
    return out


########################################################################################################
class RWKV_RNN(MyModule):
    def __init__(self, args):
        super().__init__()
        self.args = args
        self.eval()  # set torch to inference mode

        w = torch.load(args.MODEL_NAME + '.pth', map_location='cpu')

        for k in w.keys():
            w[k] = w[k].float()  # convert to f32 type
            if '.time_' in k: w[k] = w[k].squeeze()
            if '.time_faaaa' in k: w[k] = w[k].unsqueeze(-1)

        self.n_head = w['blocks.0.att.time_faaaa'].shape[0]
        self.head_size = w['blocks.0.ln1.weight'].shape[0] // self.n_head

        self.w = types.SimpleNamespace()  # set self.w from w
        self.w.blocks = {}
        for k in w.keys():  # example: "blocks.0.att.time_first" => self.w.blocks[0].att.time_first
            parts = k.split('.')
            last = parts.pop()
            here = self.w
            for p in parts:
                if p.isdigit():
                    p = int(p)
                    if p not in here: here[p] = types.SimpleNamespace()
                    here = here[p]
                else:
                    if not hasattr(here, p): setattr(here, p, types.SimpleNamespace())
                    here = getattr(here, p)
            setattr(here, last, w[k])

    def layer_norm(self, x, w):
        return F.layer_norm(x, (self.args.n_embd,), weight=w.weight, bias=w.bias)

    @MyFunction
    def channel_mixing(self, x, state, i: int, time_maa_k, time_maa_r, kw, vw, rw):
        i0 = (2 + self.head_size) * i + 0
        sx = state[i0] - x
        xk = x + sx * time_maa_k
        xr = x + sx * time_maa_r
        state[i0] = x
        r = torch.sigmoid(rw @ xr)
        k = torch.square(torch.relu(kw @ xk))  # square relu, primer paper
        return r * (vw @ k)

    @MyFunction
    def time_mixing(self, x, state, i: int, x_maa, w_maa, k_maa, v_maa, r_maa, g_maa, tm_w1, tm_w2, td_w1, td_w2,
                    time_first, time_decay, kw, vw, rw, gw, ow, ln_w, ln_b):
        H = self.n_head
        S = self.head_size

        i1 = (2 + S) * i + 1
        sx = state[i1] - x
        state[i1] = x
        xxx = x + sx * x_maa
        xxx = torch.tanh(xxx @ tm_w1).view(5, 1, -1)
        xxx = torch.bmm(xxx, tm_w2).view(5, -1)
        mw, mk, mv, mr, mg = xxx.unbind(dim=0)

        xw = x + sx * (w_maa + mw)
        xk = x + sx * (k_maa + mk)
        xv = x + sx * (v_maa + mv)
        xr = x + sx * (r_maa + mr)
        xg = x + sx * (g_maa + mg)

        w = (time_decay + (torch.tanh(xw @ td_w1) @ td_w2).float()).view(H, S, 1)
        w = torch.exp(-torch.exp(w.float()))

        r = (rw @ xr).view(H, 1, S)
        k = (kw @ xk).view(H, S, 1)
        v = (vw @ xv).view(H, 1, S)
        g = F.silu(gw @ xg)

        s = state[(2 + S) * i + 2:(2 + S) * (i + 1), :].reshape(H, S, S)

        x = torch.zeros(H, S)
        a = k @ v
        x = r @ (time_first * a + s)
        s = a + w * s

        state[(2 + S) * i + 2:(2 + S) * (i + 1), :] = s.reshape(S, -1)
        x = x.flatten()

        x = F.group_norm(x.unsqueeze(0), num_groups=H, weight=ln_w, bias=ln_b, eps=64e-5).squeeze(
            0) * g  # same as gn(x/8, eps=1e-5)
        return ow @ x

    def forward(self, token, state):
        with torch.no_grad():
            if state == None:
                state = torch.zeros(self.args.n_layer * (2 + self.head_size), self.args.n_embd)

            x = self.w.emb.weight[token]
            x = self.layer_norm(x, self.w.blocks[0].ln0)
            for i in range(self.args.n_layer):
                att = self.w.blocks[i].att
                x = x + self.time_mixing(self.layer_norm(x, self.w.blocks[i].ln1), state, i,
                                         att.time_maa_x, att.time_maa_w, att.time_maa_k, att.time_maa_v, att.time_maa_r,
                                         att.time_maa_g, att.time_maa_w1, att.time_maa_w2,
                                         att.time_decay_w1, att.time_decay_w2, att.time_faaaa, att.time_decay,
                                         att.key.weight, att.value.weight, att.receptance.weight, att.gate.weight,
                                         att.output.weight,
                                         att.ln_x.weight, att.ln_x.bias)
                ffn = self.w.blocks[i].ffn
                x = x + self.channel_mixing(self.layer_norm(x, self.w.blocks[i].ln2), state, i,
                                            ffn.time_maa_k, ffn.time_maa_r,
                                            ffn.key.weight, ffn.value.weight, ffn.receptance.weight)

            x = self.w.head.weight @ self.layer_norm(x, self.w.ln_out)
            return x.float(), state


tokenizer = RWKV_TOKENIZER("vocab_v20230424.txt")

args = types.SimpleNamespace()
args.MODEL_NAME = 'rwkv-30'
args.n_layer = 12
args.n_embd = 768
args.vocab_size = 65536

context = "Today is a beautiful"
NUM_TRIALS = 3
LENGTH_PER_TRIAL = 50
TEMPERATURE = 1.0
TOP_P = 0.7

print(f'model= {args.MODEL_NAME}\n')
model = RWKV_RNN(args)
init_state = None
for token in tokenizer.encode(context):
    init_out, init_state = model.forward(token, init_state)

for TRIAL in range(NUM_TRIALS):
    print(f'Trial {TRIAL + 1}=', context, end="")
    all_tokens = []
    n_sampled = 0
    out, state = init_out.clone(), init_state.clone()
    for i in range(LENGTH_PER_TRIAL):
        token = sample_logits(out, TEMPERATURE, TOP_P)
        all_tokens += [token]
        try:
            tmp = tokenizer.decode(all_tokens[n_sampled:])
            if '\ufffd' not in tmp:  # only print when we have a valid utf-8 string
                print(tmp, end="", flush=True)
                n_sampled = i + 1
        except:
            pass
        out, state = model.forward(token, state)
    print("\nSampled tokens=", n_sampled, "out of", LENGTH_PER_TRIAL, "tokens\n")