import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast

try:
    from mamba_ssm import Mamba2
except ImportError:
    raise ImportError("mamba-ssm is required. pip install mamba-ssm causal-conv1d")

from .configuration_pebble import PebbleConfig

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

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

class AttentionBlock(nn.Module):
    def __init__(self, config):
        super().__init__()
        dim = config.hidden_size
        n_heads = config.num_attention_heads
        hidden = config.intermediate_size
        assert dim % n_heads == 0
        self.nh, self.hd = n_heads, dim // n_heads
        self.wqkv = nn.Linear(dim, 3 * dim, bias=False)
        self.wo   = nn.Linear(dim, dim, bias=False)
        self.fc1  = nn.Linear(dim, hidden, bias=False)
        self.fc2  = nn.Linear(hidden, dim, bias=False)
        self.ln1  = RMSNorm(dim, eps=config.rms_norm_eps)
        self.ln2  = RMSNorm(dim, eps=config.rms_norm_eps)
        self.rope_theta = config.attention.get("rope_theta", 10000.0)

    def forward(self, x):
        B, T, C = x.shape
        h = self.ln1(x)

        qkv = self.wqkv(h).view(B, T, 3, self.nh, self.hd) \
                          .permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0].float(), qkv[1].float(), qkv[2]

        half = self.hd // 2
        invf = 1.0 / (self.rope_theta ** (
            torch.arange(0, half, device=x.device, dtype=torch.float32)
            * 2.0 / self.hd))
        ang = torch.outer(
            torch.arange(T, device=x.device, dtype=torch.float32), invf)
        cos, sin = ang.cos()[None, None], ang.sin()[None, None]

        q1, q2 = q[..., :half], q[..., half:]
        k1, k2 = k[..., :half], k[..., half:]
        q = torch.cat([q1 * cos - q2 * sin,
                       q1 * sin + q2 * cos], dim=-1).to(v.dtype)
        k = torch.cat([k1 * cos - k2 * sin,
                       k1 * sin + k2 * cos], dim=-1).to(v.dtype)

        y = F.scaled_dot_product_attention(q, k, v, is_causal=True)
        y = y.transpose(1, 2).reshape(B, T, C)

        x = x + self.wo(y)
        x = x + self.fc2(F.gelu(self.fc1(self.ln2(x))))
        return x

class MambaBlock(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.ln = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        mamba_cfg = config.mamba2
        self.mixer = Mamba2(
            d_model=config.hidden_size,
            d_state=mamba_cfg.get("d_state", 128),
            d_conv=mamba_cfg.get("d_conv", 4),
            expand=mamba_cfg.get("expand", 2),
            headdim=mamba_cfg.get("headdim", 96),
            use_mem_eff_path=mamba_cfg.get("use_mem_eff_path", True),
        )

    def forward(self, x):
        return x + self.mixer(self.ln(x))

class PebbleForCausalLM(PreTrainedModel):
    config_class = PebbleConfig
    supports_gradient_checkpointing = False
    _no_split_modules = ["MambaBlock", "AttentionBlock"]

    def __init__(self, config):
        super().__init__(config)
        self.config = config
        
        self.wte = nn.Embedding(config.vocab_size, config.hidden_size)
        
        # 3:1 Mamba:Attention ratio layout
        self.blocks = nn.ModuleList([
            MambaBlock(config) if i % 4 < 3
            else AttentionBlock(config)
            for i in range(config.num_hidden_layers)
        ])
        
        self.lnf = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        
        # Tie weights
        self.tie_weights()

    def tie_weights(self):
        if self.config.tie_word_embeddings:
            self.lm_head.weight = self.wte.weight

    def forward(self, input_ids=None, attention_mask=None, labels=None, past_key_values=None, **kwargs):
        x = self.wte(input_ids)
        
        for blk in self.blocks:
            x = blk(x)
            
        logits = self.lm_head(self.lnf(x))

        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n+1
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            loss = F.cross_entropy(
                shift_logits.view(-1, shift_logits.size(-1)),
                shift_labels.view(-1)
            )

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=past_key_values,
        )

    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs):
        # Mamba handles state internally in the mixer, so we don't use past_key_values 
        # at the model level for now (standard HF generation will still work for greedy/beam).
        return {
            "input_ids": input_ids,
            "past_key_values": past_key_values,
        }