"""
RoPE variant of the GPT language model used by this project.

This file mirrors model/transformer.py, but removes the learned absolute
position embedding (wpe) and applies rotary position embedding to q/k inside
causal self-attention. The public surface is intentionally kept compatible with
GPTConfig/GPT: transformer.h exists for hooks, forward returns (logits, loss),
and generate/configure_optimizers are reused from the base GPT implementation.
"""

import math
from dataclasses import dataclass

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

from .transformer import (
    GPT as BaseGPT,
    LayerNorm,
    MLP,
    NonLinearPrefixScan,
    DyadicFixedAttention,
)


class RotaryEmbedding(nn.Module):
    def __init__(self, dim, base=10000.0):
        super().__init__()
        assert dim % 2 == 0, "RoPE requires an even head dimension"
        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)

    def forward(self, x, pos_offset=0):
        t = x.size(-2)
        positions = torch.arange(
            pos_offset, pos_offset + t,
            device=x.device,
            dtype=self.inv_freq.dtype,
        )
        freqs = torch.outer(positions, self.inv_freq.to(x.device))
        cos = freqs.cos().to(dtype=x.dtype).view(1, 1, t, -1)
        sin = freqs.sin().to(dtype=x.dtype).view(1, 1, t, -1)
        return cos, sin


def apply_rotary_emb(x, cos, sin):
    x_pair = x.reshape(*x.shape[:-1], -1, 2)
    x_even = x_pair[..., 0]
    x_odd = x_pair[..., 1]
    x_rot = torch.stack(
        (x_even * cos - x_odd * sin, x_even * sin + x_odd * cos),
        dim=-1,
    )
    return x_rot.flatten(-2).type_as(x)


class CausalSelfAttentionRoPE(nn.Module):
    def __init__(self, config):
        super().__init__()
        assert config.n_embd % config.n_head == 0
        head_size = config.n_embd // config.n_head
        assert head_size % 2 == 0, "RoPE requires n_embd / n_head to be even"
        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
        self.rope = RotaryEmbedding(head_size, base=getattr(config, 'rope_base', 10000.0))
        self.flash = config.use_flash and hasattr(torch.nn.functional, 'scaled_dot_product_attention')
        if not self.flash:
            if not config.use_flash:
                print("INFO: Flash attention disabled via --local flag (for local GPU compatibility)")
            else:
                print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")
        self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size))
                                        .view(1, 1, config.block_size, config.block_size))

    def forward(self, x, kv_cache=None):
        bsz, t, channels = x.size()
        q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
        n_head = self.n_head
        head_size = channels // n_head
        k = k.view(bsz, t, n_head, head_size).transpose(1, 2)
        q = q.view(bsz, t, n_head, head_size).transpose(1, 2)
        v = v.view(bsz, t, n_head, head_size).transpose(1, 2)

        cur_len = kv_cache.get('len', 0) if kv_cache is not None else 0
        cos, sin = self.rope(q, pos_offset=cur_len)
        q = apply_rotary_emb(q, cos, sin)
        k = apply_rotary_emb(k, cos, sin)

        if kv_cache is None:
            if self.flash:
                y = torch.nn.functional.scaled_dot_product_attention(
                    q, k, v, attn_mask=None,
                    dropout_p=self.dropout if self.training else 0,
                    is_causal=True)
            else:
                att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
                att = att.masked_fill(self.bias[:, :, :t, :t] == 0, float('-inf'))
                att = F.softmax(att, dim=-1)
                att = self.attn_dropout(att)
                y = att @ v
        else:
            max_len = kv_cache['max_L']
            if 'k_buf' not in kv_cache:
                kv_cache['k_buf'] = torch.empty(bsz, n_head, max_len, head_size, dtype=k.dtype, device=k.device)
                kv_cache['v_buf'] = torch.empty(bsz, n_head, max_len, head_size, dtype=v.dtype, device=v.device)
            new_end = cur_len + t
            assert new_end <= max_len, f"KV cache overflow: {new_end} > {max_len}"
            kv_cache['k_buf'][:, :, cur_len:new_end].copy_(k)
            kv_cache['v_buf'][:, :, cur_len:new_end].copy_(v)
            kv_cache['len'] = new_end

            k_full = kv_cache['k_buf'][:, :, :new_end]
            v_full = kv_cache['v_buf'][:, :, :new_end]

            if t == 1:
                if self.flash:
                    y = torch.nn.functional.scaled_dot_product_attention(
                        q, k_full, v_full, attn_mask=None, dropout_p=0, is_causal=False)
                else:
                    att = (q @ k_full.transpose(-2, -1)) * (1.0 / math.sqrt(head_size))
                    att = F.softmax(att, dim=-1)
                    y = att @ v_full
            else:
                device = q.device
                q_abs = torch.arange(cur_len, new_end, device=device).unsqueeze(1)
                k_abs = torch.arange(0, new_end, device=device).unsqueeze(0)
                mask = (k_abs <= q_abs).view(1, 1, t, new_end)
                if self.flash:
                    y = torch.nn.functional.scaled_dot_product_attention(
                        q, k_full, v_full, attn_mask=mask, dropout_p=0, is_causal=False)
                else:
                    att = (q @ k_full.transpose(-2, -1)) * (1.0 / math.sqrt(head_size))
                    att = att.masked_fill(~mask, float('-inf'))
                    att = F.softmax(att, dim=-1)
                    y = att @ v_full

        y = y.transpose(1, 2).contiguous().view(bsz, t, channels)
        y = self.resid_dropout(self.c_proj(y))
        return y


class BlockRoPE(nn.Module):
    def __init__(self, config, layer_idx=0, dyadic_attn_override=None):
        super().__init__()
        self.layer_idx = layer_idx
        self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)
        if dyadic_attn_override is None:
            use_dyadic = bool(getattr(config, 'dyadic_attn', False))
        else:
            use_dyadic = bool(dyadic_attn_override)
        self.is_dyadic_attn = use_dyadic
        if use_dyadic:
            self.attn = DyadicFixedAttention(config, layer_idx)
        else:
            self.attn = CausalSelfAttentionRoPE(config)
        self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)
        self.mlp = MLP(config)

        self.per_block_gru = None
        self.ln_gru = None
        if getattr(config, 'post_gru', False):
            self.ln_gru = LayerNorm(config.n_embd, bias=config.bias)
            self.per_block_gru = nn.GRU(
                config.n_embd, config.n_embd, num_layers=1, batch_first=True,
            )
            for name, param in self.per_block_gru.named_parameters():
                if 'weight_hh' in name or 'weight_ih' in name:
                    nn.init.xavier_uniform_(param, gain=0.1)
                elif 'bias' in name:
                    nn.init.zeros_(param)

        self.per_block_nls = None
        self.ln_nls = None
        if getattr(config, 'per_block_nls', False):
            self.ln_nls = LayerNorm(config.n_embd, bias=config.bias)
            self.per_block_nls = NonLinearPrefixScan(
                config.n_embd, dropout=config.dropout,
            )

    def forward(self, x, nls_cache=None, kv_cache=None):
        x = x + self.attn(self.ln_1(x), kv_cache=kv_cache)
        x = x + self.mlp(self.ln_2(x))
        if self.per_block_gru is not None:
            orig_dtype = x.dtype
            with torch.amp.autocast(device_type='cuda', enabled=False):
                g, _ = self.per_block_gru(self.ln_gru(x).float())
            x = x + g.to(orig_dtype)
        if self.per_block_nls is not None:
            x = x + self.per_block_nls(self.ln_nls(x), cache=nls_cache)
        return x


@dataclass
class GPTRoPEConfig:
    block_size: int = 1024
    vocab_size: int = 50304
    n_layer: int = 12
    n_head: int = 12
    n_embd: int = 768
    dropout: float = 0.0
    bias: bool = True
    use_flash: bool = True
    rope_base: float = 10000.0
    post_gru: bool = False
    per_block_nls: bool = False
    dyadic_attn: bool = False
    dyadic_hybrid: bool = False


class GPTRoPE(nn.Module):
    configure_optimizers = BaseGPT.configure_optimizers
    estimate_mfu = BaseGPT.estimate_mfu
    generate = BaseGPT.generate

    def __init__(self, config):
        super().__init__()
        assert config.vocab_size is not None
        assert config.block_size is not None
        self.config = config

        if getattr(config, 'dyadic_hybrid', False):
            n_levels = max(1, math.ceil(math.log2(config.block_size))) if config.block_size > 1 else 1
            blocks = []
            for _ in range(config.n_layer):
                blocks.append(BlockRoPE(config, layer_idx=0, dyadic_attn_override=False))
                for level in range(n_levels):
                    blocks.append(BlockRoPE(config, layer_idx=level, dyadic_attn_override=True))
            block_list = nn.ModuleList(blocks)
        else:
            block_list = nn.ModuleList([BlockRoPE(config, layer_idx=i) for i in range(config.n_layer)])

        self.transformer = nn.ModuleDict(dict(
            wte=nn.Embedding(config.vocab_size, config.n_embd),
            drop=nn.Dropout(config.dropout),
            h=block_list,
            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 * len(self.transformer.h)))

        print("number of parameters: %.2fM" % (self.get_num_params() / 1e6,))

    def get_num_params(self, non_embedding=True):
        return sum(param.numel() for param in self.parameters())

    def _init_weights(self, module):
        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, targets=None, nls_caches=None, kv_caches=None):
        bsz, t = idx.size()
        if kv_caches is not None and len(kv_caches) > 0 and kv_caches[0].get('len', 0) > 0:
            pos_offset = kv_caches[0]['len']
        else:
            pos_offset = 0
        assert pos_offset + t <= self.config.block_size, (
            f"Cannot forward sequence of length {pos_offset + t}, block size is only {self.config.block_size}")

        tok_emb = self.transformer.wte(idx)
        x = self.transformer.drop(tok_emb)
        for i, block in enumerate(self.transformer.h):
            blk_nls_cache = nls_caches[i] if nls_caches is not None else None
            blk_kv_cache = kv_caches[i] if kv_caches is not None else None
            x = block(x, nls_cache=blk_nls_cache, kv_cache=blk_kv_cache)
        x = self.transformer.ln_f(x)

        if targets is not None:
            logits = self.lm_head(x)
            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=0)
        else:
            logits = self.lm_head(x[:, [-1], :])
            loss = None
        return logits, loss

    def crop_block_size(self, block_size):
        assert block_size <= self.config.block_size
        self.config.block_size = block_size
        for block in self.transformer.h:
            if hasattr(block.attn, 'bias'):
                block.attn.bias = block.attn.bias[:, :, :block_size, :block_size]


GPTConfig = GPTRoPEConfig
GPT = GPTRoPE