import math
from dataclasses import dataclass
import torch
import torch.nn as nn
from torch.nn import functional as F

class LlamaRMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.eps = eps

    def forward(self, x):
        rms = torch.sqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
        x_norm = x / rms
        return self.weight * x_norm

class LlamaRotaryEmbedding(nn.Module):
    def __init__(self, dim, max_position_embeddings=2048, base=10000):
        super().__init__()
        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
        self.register_buffer("inv_freq", inv_freq)
        self.max_position_embeddings = max_position_embeddings
        self.dim = dim

    def forward(self, x, seq_len):
        t = torch.arange(seq_len, device=x.device).type_as(self.inv_freq)
        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
        emb = torch.cat((freqs, freqs), dim=-1)
        return emb

def rotate_half(x):
    x1, x2 = x[..., :x.shape[-1]//2], x[..., x.shape[-1]//2:]
    return torch.cat((-x2, x1), dim=-1)

def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
    cos = cos.unsqueeze(0).unsqueeze(0)
    sin = sin.unsqueeze(0).unsqueeze(0)
    cos = cos.expand(q.shape[0], q.shape[1], -1, -1)
    sin = sin.expand(k.shape[0], k.shape[1], -1, -1)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed

class LlamaSdpaAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.hidden_size = config.n_embd
        self.num_heads = config.n_head
        self.head_dim = config.n_embd // config.n_head
        self.num_key_value_heads = config.n_head // 3
        self.num_key_value_groups = self.num_heads // self.num_key_value_heads

        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
        self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
        self.rotary_emb = LlamaRotaryEmbedding(self.head_dim)

    def forward(self, x, attention_mask=None):
        B, T, C = x.size()
        q = self.q_proj(x).view(B, T, self.num_heads, self.head_dim)
        k = self.k_proj(x).view(B, T, self.num_key_value_heads, self.head_dim)
        v = self.v_proj(x).view(B, T, self.num_key_value_heads, self.head_dim)

        k = k.repeat_interleave(self.num_key_value_groups, dim=2)
        v = v.repeat_interleave(self.num_key_value_groups, dim=2)

        q = q.transpose(1, 2)
        k = k.transpose(1, 2)
        v = v.transpose(1, 2)

        rotary_emb = self.rotary_emb(x, T)
        cos, sin = rotary_emb.cos(), rotary_emb.sin()
        q, k = apply_rotary_pos_emb(q, k, cos, sin, None)

        out = F.scaled_dot_product_attention(q, k, v, is_causal=True)
        out = out.transpose(1, 2).contiguous().view(B, T, C)
        return self.o_proj(out)

class LlamaMLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.gate_proj = nn.Linear(config.n_embd, config.intermediate_size, bias=False)
        self.up_proj = nn.Linear(config.n_embd, config.intermediate_size, bias=False)
        self.down_proj = nn.Linear(config.intermediate_size, config.n_embd, bias=False)
        self.act_fn = nn.SiLU()

    def forward(self, x):
        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))

class LlamaDecoderLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.input_layernorm = LlamaRMSNorm(config.n_embd)
        self.self_attn = LlamaSdpaAttention(config)
        self.post_attention_layernorm = LlamaRMSNorm(config.n_embd)
        self.mlp = LlamaMLP(config)

    def forward(self, x):
        residual = x
        x = self.input_layernorm(x)
        x = self.self_attn(x)
        x = residual + x

        residual = x
        x = self.post_attention_layernorm(x)
        x = self.mlp(x)
        x = residual + x
        return x

@dataclass
class SmolLM2Config:
    block_size: int = 2048
    vocab_size: int = 49152
    n_layer: int = 30
    n_head: int = 9
    n_embd: int = 576
    intermediate_size: int = 1536
    num_key_value_heads: int = 3
    rms_norm_eps: float = 1e-5
    rope_theta: float = 10000.0
    initializer_range: float = 0.041666666666666664
    use_cache: bool = True

class SmolLM2(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config

        self.embed_tokens = nn.Embedding(config.vocab_size, config.n_embd)
        self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.n_layer)])
        self.norm = LlamaRMSNorm(config.n_embd, eps=config.rms_norm_eps)
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
        self.embed_tokens.weight = self.lm_head.weight
        self.apply(self._init_weights)

    def _init_weights(self, module):
        if isinstance(module, nn.Linear):
            torch.nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
            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=self.config.initializer_range)

    def forward(self, idx, targets=None):
        B, T = idx.size()
        x = self.embed_tokens(idx)
        
        for layer in self.layers:
            x = layer(x)
            
        x = self.norm(x)
        logits = self.lm_head(x)

        loss = None
        if targets is not None:
            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
            
        return logits, loss

    def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
        for _ in range(max_new_tokens):
            idx_cond = idx[:, -self.config.block_size:]
            logits, _ = self(idx_cond)
            logits = logits[:, -1, :] / temperature
            
            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)
        return idx