"""
Kayra Turkish GPT Model
"""

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


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):
        rms = torch.sqrt(torch.mean(x ** 2, dim=-1, keepdim=True) + self.eps)
        return x / rms * self.weight


class Attention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.n_heads = config.num_attention_heads
        self.head_dim = config.hidden_size // config.num_attention_heads
        
        self.qkv = nn.Linear(config.hidden_size, 3 * config.hidden_size, bias=False)
        self.proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
        self.dropout = nn.Dropout(config.hidden_dropout)
        
        mask = torch.triu(torch.ones(config.max_position_embeddings, config.max_position_embeddings), diagonal=1).bool()
        self.register_buffer("mask", mask)
    
    def forward(self, x):
        B, T, C = x.shape
        
        qkv = self.qkv(x).reshape(B, T, 3, self.n_heads, self.head_dim)
        q, k, v = qkv.permute(2, 0, 3, 1, 4)
        
        attn = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(self.head_dim))
        attn = attn.masked_fill(self.mask[:T, :T], float('-inf'))
        attn = F.softmax(attn, dim=-1)
        attn = self.dropout(attn)
        
        out = (attn @ v).transpose(1, 2).reshape(B, T, C)
        return self.proj(out)


class FeedForward(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.w1 = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
        self.w2 = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
        self.w3 = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
        self.dropout = nn.Dropout(config.hidden_dropout)
    
    def forward(self, x):
        return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x)))


class Block(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.norm1 = RMSNorm(config.hidden_size)
        self.attn = Attention(config)
        self.norm2 = RMSNorm(config.hidden_size)
        self.ff = FeedForward(config)
    
    def forward(self, x):
        x = x + self.attn(self.norm1(x))
        x = x + self.ff(self.norm2(x))
        return x


class KayraPreTrainedModel(PreTrainedModel):
    config_class = KayraConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    
    def _init_weights(self, module):
        if isinstance(module, nn.Linear):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
        elif isinstance(module, nn.Embedding):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)


class KayraForCausalLM(KayraPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.config = config
        
        self.tok_emb = nn.Embedding(config.vocab_size, config.hidden_size)
        self.pos_emb = nn.Embedding(config.max_position_embeddings, config.hidden_size)
        self.drop = nn.Dropout(config.hidden_dropout)
        
        self.blocks = nn.ModuleList([Block(config) for _ in range(config.num_hidden_layers)])
        self.norm = RMSNorm(config.hidden_size)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        
        if config.tie_word_embeddings:
            self.lm_head.weight = self.tok_emb.weight
        
        self.post_init()
    
    def get_input_embeddings(self):
        return self.tok_emb
    
    def set_input_embeddings(self, value):
        self.tok_emb = value
    
    def forward(self, input_ids=None, attention_mask=None, labels=None, **kwargs):
        B, T = input_ids.shape
        
        pos = torch.arange(T, device=input_ids.device)
        x = self.drop(self.tok_emb(input_ids) + self.pos_emb(pos))
        
        for block in self.blocks:
            x = block(x)
        
        x = self.norm(x)
        logits = self.lm_head(x)
        
        loss = None
        if labels is not None:
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            loss = F.cross_entropy(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1))
        
        return CausalLMOutputWithPast(loss=loss, logits=logits)
    
    def prepare_inputs_for_generation(self, input_ids, **kwargs):
        return {"input_ids": input_ids}