from __future__ import annotations

import math
from dataclasses import asdict, dataclass

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


@dataclass
class GPTConfig:
    block_size: int = 1024
    vocab_size: int = 8192
    n_layer: int = 12
    n_head: int = 12
    n_embd: int = 768
    dropout: float = 0.0
    bias: bool = False


class CausalSelfAttention(nn.Module):
    def __init__(self, config: GPTConfig) -> None:
        super().__init__()
        if config.n_embd % config.n_head:
            raise ValueError("embedding dimension must be divisible by number of heads")
        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 = nn.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, value: torch.Tensor) -> torch.Tensor:
        batch, time, channels = value.size()
        query, key, val = self.c_attn(value).split(self.n_embd, dim=2)
        head_size = channels // self.n_head
        query = query.view(batch, time, self.n_head, head_size).transpose(1, 2)
        key = key.view(batch, time, self.n_head, head_size).transpose(1, 2)
        val = val.view(batch, time, self.n_head, head_size).transpose(1, 2)
        attended = F.scaled_dot_product_attention(
            query,
            key,
            val,
            attn_mask=None,
            dropout_p=self.dropout if self.training else 0,
            is_causal=True,
        )
        attended = attended.transpose(1, 2).contiguous().view(batch, time, channels)
        return self.resid_dropout(self.c_proj(attended))


class MLP(nn.Module):
    def __init__(self, config: GPTConfig) -> None:
        super().__init__()
        self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
        self.gelu = nn.GELU()
        self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
        self.dropout = nn.Dropout(config.dropout)

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


class Block(nn.Module):
    def __init__(self, config: GPTConfig) -> None:
        super().__init__()
        self.ln_1 = nn.LayerNorm(config.n_embd, bias=config.bias)
        self.attn = CausalSelfAttention(config)
        self.ln_2 = nn.LayerNorm(config.n_embd, bias=config.bias)
        self.mlp = MLP(config)

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


class GPT(nn.Module):
    def __init__(self, config: GPTConfig) -> None:
        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": nn.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, parameter in self.named_parameters():
            if name.endswith("c_proj.weight"):
                torch.nn.init.normal_(
                    parameter, mean=0.0, std=0.02 / math.sqrt(2 * config.n_layer)
                )

    @staticmethod
    def _init_weights(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, index: torch.Tensor, targets: torch.Tensor | None = None
    ) -> tuple[torch.Tensor, torch.Tensor | None]:
        _, time = index.shape
        if time > self.config.block_size:
            raise ValueError("sequence exceeds model block size")
        positions = torch.arange(0, time, dtype=torch.long, device=index.device)
        value = self.transformer.drop(self.transformer.wte(index) + self.transformer.wpe(positions))
        for block in self.transformer.h:
            value = block(value)
        value = self.transformer.ln_f(value)
        logits = self.lm_head(value)
        loss = (
            F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
            if targets is not None
            else None
        )
        return logits, loss

    @torch.no_grad()
    def generate(
        self,
        index: torch.Tensor,
        max_new_tokens: int,
        temperature: float = 0.8,
        top_k: int | None = 200,
    ) -> torch.Tensor:
        for _ in range(max_new_tokens):
            cropped = index[:, -self.config.block_size :]
            logits, _ = self(cropped)
            logits = logits[:, -1, :] / temperature
            if top_k is not None:
                values, _ = torch.topk(logits, min(top_k, logits.size(-1)))
                logits[logits < values[:, [-1]]] = -float("Inf")
            probabilities = F.softmax(logits, dim=-1)
            index = torch.cat((index, torch.multinomial(probabilities, num_samples=1)), dim=1)
        return index

    def parameter_count(self, non_embedding: bool = False) -> int:
        count = sum(parameter.numel() for parameter in self.parameters())
        if non_embedding:
            count -= self.transformer.wpe.weight.numel()
        return count

    def config_dict(self) -> dict[str, object]:
        return asdict(self.config)