"""REX: a recursive decoder-only Transformer language model."""

from __future__ import annotations

import json
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import Any

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


@dataclass
class RexConfig:
    vocab_size: int = 50_257
    max_seq_len: int = 2048
    d_model: int = 1536
    n_heads: int = 16
    n_layers: int = 8
    recurrence_steps: int = 2
    ffn_dim: int = 3968
    dropout: float = 0.0
    norm_eps: float = 1e-5
    tie_embeddings: bool = True
    use_step_embeddings: bool = True
    initializer_range: float = 0.02

    @classmethod
    def from_dict(cls, data: dict[str, Any]) -> "RexConfig":
        fields = {name for name in cls.__dataclass_fields__}
        return cls(**{k: v for k, v in data.items() if k in fields})

    def to_dict(self) -> dict[str, Any]:
        return asdict(self)


class RMSNorm(nn.Module):
    def __init__(self, dim: int, eps: float = 1e-5):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        dtype = x.dtype
        x = x.float()
        x = x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)
        return (self.weight * x).to(dtype)


class RotaryEmbedding(nn.Module):
    def __init__(self, dim: int, max_seq_len: int, base: float = 10_000.0):
        super().__init__()
        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
        positions = torch.arange(max_seq_len, dtype=torch.float)
        freqs = torch.outer(positions, inv_freq)
        self.register_buffer("cos", freqs.cos(), persistent=False)
        self.register_buffer("sin", freqs.sin(), persistent=False)

    def forward(self, seq_len: int) -> tuple[torch.Tensor, torch.Tensor]:
        return self.cos[:seq_len], self.sin[:seq_len]


def _rotate_half(x: torch.Tensor) -> torch.Tensor:
    x1 = x[..., ::2]
    x2 = x[..., 1::2]
    return torch.stack((-x2, x1), dim=-1).flatten(-2)


def apply_rotary(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
    cos = torch.repeat_interleave(cos, 2, dim=-1)[None, None, :, :]
    sin = torch.repeat_interleave(sin, 2, dim=-1)[None, None, :, :]
    return (x * cos) + (_rotate_half(x) * sin)


def _safe_torch_load(path: str | Path, map_location: str | torch.device | None) -> Any:
    try:
        return torch.load(path, map_location=map_location, weights_only=True)
    except TypeError:
        return torch.load(path, map_location=map_location)


class CausalSelfAttention(nn.Module):
    def __init__(self, cfg: RexConfig):
        super().__init__()
        if cfg.d_model % cfg.n_heads != 0:
            raise ValueError("d_model must be divisible by n_heads")
        self.n_heads = cfg.n_heads
        self.head_dim = cfg.d_model // cfg.n_heads
        if self.head_dim % 2 != 0:
            raise ValueError("attention head_dim must be even for rotary embeddings")
        self.qkv = nn.Linear(cfg.d_model, 3 * cfg.d_model, bias=False)
        self.out = nn.Linear(cfg.d_model, cfg.d_model, bias=False)
        self.dropout = cfg.dropout
        self.rotary = RotaryEmbedding(self.head_dim, cfg.max_seq_len)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        bsz, seq_len, width = x.shape
        qkv = self.qkv(x)
        q, k, v = qkv.chunk(3, dim=-1)
        q = q.view(bsz, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
        k = k.view(bsz, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
        v = v.view(bsz, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
        cos, sin = self.rotary(seq_len)
        q = apply_rotary(q, cos.to(q.device), sin.to(q.device))
        k = apply_rotary(k, cos.to(k.device), sin.to(k.device))
        y = F.scaled_dot_product_attention(
            q,
            k,
            v,
            dropout_p=self.dropout if self.training else 0.0,
            is_causal=True,
        )
        y = y.transpose(1, 2).contiguous().view(bsz, seq_len, width)
        return self.out(y)


class SwiGLU(nn.Module):
    def __init__(self, cfg: RexConfig):
        super().__init__()
        self.w1 = nn.Linear(cfg.d_model, cfg.ffn_dim, bias=False)
        self.w2 = nn.Linear(cfg.ffn_dim, cfg.d_model, bias=False)
        self.w3 = nn.Linear(cfg.d_model, cfg.ffn_dim, bias=False)
        self.dropout = nn.Dropout(cfg.dropout)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x)))


class RexBlock(nn.Module):
    def __init__(self, cfg: RexConfig):
        super().__init__()
        self.attn_norm = RMSNorm(cfg.d_model, cfg.norm_eps)
        self.attn = CausalSelfAttention(cfg)
        self.ffn_norm = RMSNorm(cfg.d_model, cfg.norm_eps)
        self.ffn = SwiGLU(cfg)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = x + self.attn(self.attn_norm(x))
        x = x + self.ffn(self.ffn_norm(x))
        return x


class RexForCausalLM(nn.Module):
    """Decoder-only LM with a stack of blocks reused across recursive passes."""

    def __init__(self, cfg: RexConfig):
        super().__init__()
        if cfg.recurrence_steps < 1:
            raise ValueError("recurrence_steps must be >= 1")
        self.cfg = cfg
        self.token_embedding = nn.Embedding(cfg.vocab_size, cfg.d_model)
        self.drop = nn.Dropout(cfg.dropout)
        self.blocks = nn.ModuleList([RexBlock(cfg) for _ in range(cfg.n_layers)])
        self.final_norm = RMSNorm(cfg.d_model, cfg.norm_eps)
        self.lm_head = nn.Linear(cfg.d_model, cfg.vocab_size, bias=False)
        if cfg.tie_embeddings:
            self.lm_head.weight = self.token_embedding.weight
        if cfg.use_step_embeddings:
            self.step_embedding = nn.Parameter(torch.zeros(cfg.recurrence_steps, cfg.d_model))
        else:
            self.register_parameter("step_embedding", None)
        self.apply(self._init_weights)

    def _init_weights(self, module: nn.Module) -> None:
        if isinstance(module, nn.Linear):
            nn.init.normal_(module.weight, mean=0.0, std=self.cfg.initializer_range)
            if module.bias is not None:
                nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            nn.init.normal_(module.weight, mean=0.0, std=self.cfg.initializer_range)

    def encode(self, input_ids: torch.Tensor, normalize: bool = True) -> torch.Tensor:
        """Return contextual token representations for downstream tasks."""
        if input_ids.ndim != 2:
            raise ValueError("input_ids must have shape [batch, seq]")
        if input_ids.size(1) > self.cfg.max_seq_len:
            raise ValueError(f"sequence length exceeds max_seq_len={self.cfg.max_seq_len}")

        x = self.drop(self.token_embedding(input_ids))
        for step in range(self.cfg.recurrence_steps):
            if self.step_embedding is not None:
                x = x + self.step_embedding[step].view(1, 1, -1)
            for block in self.blocks:
                x = block(x)
        if normalize:
            x = self.final_norm(x)
        return x

    def forward(
        self,
        input_ids: torch.Tensor,
        labels: torch.Tensor | None = None,
    ) -> dict[str, torch.Tensor | None]:
        hidden_states = self.encode(input_ids, normalize=True)
        logits = self.lm_head(hidden_states)
        loss = None
        if labels is not None:
            loss = F.cross_entropy(
                logits[:, :-1].contiguous().view(-1, logits.size(-1)),
                labels[:, 1:].contiguous().view(-1),
                ignore_index=-100,
            )
        return {"logits": logits, "loss": loss}

    @torch.no_grad()
    def generate(
        self,
        input_ids: torch.Tensor,
        max_new_tokens: int,
        temperature: float = 1.0,
        top_k: int | None = None,
        no_repeat_ngram_size: int = 0,
    ) -> torch.Tensor:
        self.eval()
        if no_repeat_ngram_size < 0:
            raise ValueError("no_repeat_ngram_size must be >= 0")
        for _ in range(max_new_tokens):
            context = input_ids[:, -self.cfg.max_seq_len :]
            logits = self(context)["logits"][:, -1, :]
            logits = self._apply_no_repeat_ngram(logits, input_ids, no_repeat_ngram_size)
            if temperature < 0:
                raise ValueError("temperature must be >= 0")
            if temperature == 0:
                next_token = torch.argmax(logits, dim=-1, keepdim=True)
                input_ids = torch.cat([input_ids, next_token], dim=1)
                continue
            logits = logits / temperature
            if top_k is not None:
                values, _ = torch.topk(logits, min(top_k, logits.size(-1)))
                logits = logits.masked_fill(logits < values[:, [-1]], float("-inf"))
            probs = F.softmax(logits, dim=-1)
            next_token = torch.multinomial(probs, num_samples=1)
            input_ids = torch.cat([input_ids, next_token], dim=1)
        return input_ids

    @staticmethod
    def _apply_no_repeat_ngram(
        logits: torch.Tensor,
        input_ids: torch.Tensor,
        no_repeat_ngram_size: int,
    ) -> torch.Tensor:
        if no_repeat_ngram_size <= 0:
            return logits

        logits = logits.clone()
        for batch_idx in range(input_ids.size(0)):
            banned_tokens = RexForCausalLM._get_banned_ngram_tokens(
                input_ids[batch_idx].tolist(),
                no_repeat_ngram_size,
            )
            if banned_tokens:
                logits[batch_idx, banned_tokens] = float("-inf")
        return logits

    @staticmethod
    def _get_banned_ngram_tokens(tokens: list[int], ngram_size: int) -> list[int]:
        if ngram_size == 1:
            return list(set(tokens))
        if len(tokens) < ngram_size - 1:
            return []

        prefix_to_next: dict[tuple[int, ...], set[int]] = {}
        for i in range(len(tokens) - ngram_size + 1):
            ngram = tokens[i : i + ngram_size]
            prefix = tuple(ngram[:-1])
            prefix_to_next.setdefault(prefix, set()).add(ngram[-1])

        current_prefix = tuple(tokens[-(ngram_size - 1) :])
        return list(prefix_to_next.get(current_prefix, set()))

    def parameter_count(self, trainable_only: bool = False) -> int:
        params = self.parameters()
        if trainable_only:
            params = (p for p in params if p.requires_grad)
        return sum(p.numel() for p in params)

    def save_pretrained(self, save_directory: str | Path) -> None:
        """Save model weights and config in a lightweight HF-style folder."""
        save_path = Path(save_directory)
        save_path.mkdir(parents=True, exist_ok=True)
        with open(save_path / "config.json", "w", encoding="utf-8") as f:
            json.dump(self.cfg.to_dict(), f, indent=2)
            f.write("\n")
        torch.save(self.state_dict(), save_path / "pytorch_model.bin")

    @classmethod
    def from_pretrained(
        cls,
        load_directory: str | Path,
        map_location: str | torch.device | None = "cpu",
        strict: bool = True,
    ) -> "RexForCausalLM":
        """Load a model saved by ``save_pretrained``."""
        load_path = Path(load_directory)
        with open(load_path / "config.json", "r", encoding="utf-8") as f:
            cfg = RexConfig.from_dict(json.load(f))
        model = cls(cfg)
        state_dict = _safe_torch_load(load_path / "pytorch_model.bin", map_location)
        model.load_state_dict(state_dict, strict=strict)
        return model

    @classmethod
    def from_checkpoint(
        cls,
        checkpoint_path: str | Path,
        map_location: str | torch.device | None = "cpu",
        strict: bool = True,
    ) -> "RexForCausalLM":
        """Load from a training checkpoint produced by ``train.py``."""
        checkpoint = _safe_torch_load(checkpoint_path, map_location)
        cfg_data = checkpoint.get("model_config")
        if cfg_data is None:
            cfg_data = checkpoint.get("config", {}).get("model")
        if cfg_data is None:
            raise ValueError("checkpoint does not contain model_config or config.model")
        model = cls(RexConfig.from_dict(cfg_data))
        state_dict = checkpoint.get("model", checkpoint)
        model.load_state_dict(state_dict, strict=strict)
        return model


def build_model(config: dict[str, Any] | RexConfig | None = None) -> RexForCausalLM:
    if config is None:
        cfg = RexConfig()
    elif isinstance(config, RexConfig):
        cfg = config
    else:
        cfg = RexConfig.from_dict(config)
    return RexForCausalLM(cfg)