from __future__ import annotations

import inspect
import math
from dataclasses import dataclass

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


@dataclass
class GPTConfig:
    vocab_size: int
    block_size: int = 512
    n_layer: int = 12
    n_head: int = 12
    n_embd: int = 768
    dropout: float = 0.0
    bias: bool = False
    gradient_checkpointing: bool = False


class LayerNorm(nn.Module):
    def __init__(self, ndim: int, bias: bool):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(ndim))
        self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None

    def forward(self, input: torch.Tensor) -> torch.Tensor:
        return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)


class CausalSelfAttention(nn.Module):
    def __init__(self, config: GPTConfig):
        super().__init__()
        assert config.n_embd % config.n_head == 0
        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
        self.attn_dropout = config.dropout
        self.resid_dropout = nn.Dropout(config.dropout)
        self.n_head = config.n_head
        self.n_embd = config.n_embd
        self.dropout = config.dropout

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        batch, seq_len, channels = x.size()
        q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
        head_dim = channels // self.n_head
        q = q.view(batch, seq_len, self.n_head, head_dim).transpose(1, 2)
        k = k.view(batch, seq_len, self.n_head, head_dim).transpose(1, 2)
        v = v.view(batch, seq_len, self.n_head, head_dim).transpose(1, 2)

        y = F.scaled_dot_product_attention(
            q,
            k,
            v,
            attn_mask=None,
            dropout_p=self.attn_dropout if self.training else 0.0,
            is_causal=True,
        )
        y = y.transpose(1, 2).contiguous().view(batch, seq_len, channels)
        return self.resid_dropout(self.c_proj(y))


class MLP(nn.Module):
    def __init__(self, config: GPTConfig):
        super().__init__()
        self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
        self.gelu = nn.GELU(approximate="tanh")
        self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
        self.dropout = nn.Dropout(config.dropout)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.dropout(self.c_proj(self.gelu(self.c_fc(x))))


class Block(nn.Module):
    def __init__(self, config: GPTConfig):
        super().__init__()
        self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)
        self.attn = CausalSelfAttention(config)
        self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)
        self.mlp = MLP(config)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = x + self.attn(self.ln_1(x))
        x = x + self.mlp(self.ln_2(x))
        return x


class GPT(nn.Module):
    def __init__(self, config: GPTConfig):
        super().__init__()
        self.config = config
        self.transformer = nn.ModuleDict(
            {
                "wte": nn.Embedding(config.vocab_size, config.n_embd),
                "wpe": nn.Embedding(config.block_size, config.n_embd),
                "drop": nn.Dropout(config.dropout),
                "h": nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
                "ln_f": LayerNorm(config.n_embd, bias=config.bias),
            }
        )
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
        self.transformer.wte.weight = self.lm_head.weight
        self.apply(self._init_weights)
        for name, param in self.named_parameters():
            if name.endswith("c_proj.weight"):
                torch.nn.init.normal_(param, mean=0.0, std=0.02 / math.sqrt(2 * config.n_layer))

    def _init_weights(self, module: nn.Module) -> None:
        if isinstance(module, nn.Linear):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
            if module.bias is not None:
                torch.nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)

    def forward(
        self, idx: torch.Tensor, targets: torch.Tensor | None = None
    ) -> tuple[torch.Tensor, torch.Tensor | None]:
        batch, seq_len = idx.size()
        if seq_len > self.config.block_size:
            raise ValueError(f"Sequence length {seq_len} exceeds block size {self.config.block_size}")

        pos = torch.arange(0, seq_len, dtype=torch.long, device=idx.device)
        x = self.transformer.drop(self.transformer.wte(idx) + self.transformer.wpe(pos))
        for block in self.transformer.h:
            if self.config.gradient_checkpointing and self.training:
                x = torch.utils.checkpoint.checkpoint(block, x, use_reentrant=False)
            else:
                x = block(x)
        x = self.transformer.ln_f(x)

        if targets is None:
            logits = self.lm_head(x[:, [-1], :])
            loss = None
        else:
            logits = self.lm_head(x)
            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-100)
        return logits, loss

    @torch.no_grad()
    def generate(
        self,
        idx: torch.Tensor,
        max_new_tokens: int,
        temperature: float = 0.8,
        top_k: int | None = 50,
        eos_id: int | None = None,
    ) -> torch.Tensor:
        for _ in range(max_new_tokens):
            idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size :]
            logits, _ = self(idx_cond)
            logits = logits[:, -1, :] / max(temperature, 1e-5)
            if top_k is not None:
                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
                logits[logits < v[:, [-1]]] = -float("Inf")
            probs = F.softmax(logits, dim=-1)
            idx_next = torch.multinomial(probs, num_samples=1)
            idx = torch.cat((idx, idx_next), dim=1)
            if eos_id is not None and idx_next.item() == eos_id:
                break
        return idx

    def crop_block_size(self, block_size: int) -> None:
        assert block_size <= self.config.block_size
        self.config.block_size = block_size
        self.transformer.wpe.weight = nn.Parameter(self.transformer.wpe.weight[:block_size])

    def configure_optimizers(
        self, weight_decay: float, learning_rate: float, betas: tuple[float, float], device_type: str
    ) -> torch.optim.Optimizer:
        param_dict = {pn: p for pn, p in self.named_parameters() if p.requires_grad}
        decay_params = [p for _, p in param_dict.items() if p.dim() >= 2]
        nodecay_params = [p for _, p in param_dict.items() if p.dim() < 2]
        optim_groups = [
            {"params": decay_params, "weight_decay": weight_decay},
            {"params": nodecay_params, "weight_decay": 0.0},
        ]
        fused_available = "fused" in inspect.signature(torch.optim.AdamW).parameters
        use_fused = fused_available and device_type == "cuda"
        extra_args = {"fused": True} if use_fused else {}
        return torch.optim.AdamW(optim_groups, lr=learning_rate, betas=betas, **extra_args)

    def num_parameters(self) -> int:
        return sum(p.numel() for p in self.parameters())