"""
Sheikh-2.5-Coder Model Implementation
====================================

This module implements the Sheikh-2.5-Coder model architecture, a 3B parameter
transformer model optimized for code generation and on-device deployment.
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, List
from dataclasses import dataclass
from transformers import (
    PreTrainedModel,
    PreTrainedTokenizer,
    AutoConfig,
    AutoTokenizer,
    AutoModelForCausalLM,
    BitsAndBytesConfig,
    TrainingArguments
)
import json

@dataclass
class SheikhConfig:
    """Configuration class for Sheikh-2.5-Coder model."""
    
    # Model architecture
    num_attention_heads: int = 16
    num_key_value_heads: int = 2
    hidden_size: int = 3072
    intermediate_size: int = 8192
    num_hidden_layers: int = 36
    vocab_size: int = 50257
    
    # Position embeddings
    max_position_embeddings: int = 32768
    rope_theta: float = 10000.0
    
    # Attention
    attention_dropout: float = 0.1
    hidden_dropout: float = 0.1
    
    # Normalization
    layer_norm_epsilon: float = 1e-6
    rms_norm_eps: float = 1e-6
    
    # Activation
    activation_function: str = "swiglu"
    
    # Precision
    torch_dtype: str = "bfloat16"
    
    # Cache
    use_cache: bool = True
    
    # Tie word embeddings
    tie_word_embeddings: bool = True

class SheikhRMSNorm(nn.Module):
    """Root Mean Square Layer Normalization."""
    
    def __init__(self, hidden_size: int, eps: float = 1e-6):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(hidden_size))
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        input_dtype = x.dtype
        x = x.float()
        variance = x.pow(2).mean(-1, keepdim=True)
        x = x * torch.rsqrt(variance + self.eps)
        return (self.weight * x).to(input_dtype)

class SheikhRotaryEmbedding(nn.Module):
    """Rotary Positional Embedding."""
    
    def __init__(self, dim: int, max_position_embeddings: int = 32768, base: int = 10000):
        super().__init__()
        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self._set_cos_sin_cache(
            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.float32
        )
    
    def _set_cos_sin_cache(self, seq_len: int, device: torch.device, dtype: torch.dtype):
        self.max_seq_len_cached = seq_len
        t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
        freqs = torch.outer(t, self.inv_freq)
        emb = torch.cat((freqs, freqs), dim=-1)
        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
    
    def forward(self, x: torch.Tensor, seq_len: Optional[int] = None):
        if seq_len > self.max_seq_len_cached:
            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
        return (
            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
        )

class SheikhAttention(nn.Module):
    """Multi-head attention with Grouped Query Attention."""
    
    def __init__(self, config: SheikhConfig):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.hidden_size // self.num_heads
        self.num_key_value_heads = config.num_key_value_heads
        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
        
        self.q_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=True)
        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
        self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
        
        self.rotary_emb = SheikhRotaryEmbedding(
            self.head_dim, max_position_embeddings=config.max_position_embeddings
        )
    
    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
    ):
        bsz, q_len, _ = hidden_states.size()
        
        # Query, Key, Value projections
        q = self.q_proj(hidden_states)
        k = self.k_proj(hidden_states)
        v = self.v_proj(hidden_states)
        
        # Reshape for grouped query attention
        q = q.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        k = k.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        v = v.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        
        # Apply rotary embeddings
        cos, sin = self.rotary_emb(v, seq_len=q_len)
        q, k = apply_rotary_pos_emb(q, k, cos, sin, position_ids)
        
        # Group key and value for grouped query attention
        k = repeat_kv(k, self.num_key_value_groups)
        v = repeat_kv(v, self.num_key_value_groups)
        
        # Scaled dot-product attention
        attn_output = F.scaled_dot_product_attention(
            q, k, v, attn_mask=attention_mask, dropout_p=0.0, is_causal=True
        )
        
        # Reshape and project output
        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
        attn_output = self.o_proj(attn_output)
        
        if not output_attentions:
            attn_weights = None
        
        outputs = (attn_output,)
        if output_attentions:
            outputs += (attn_weights,)
        
        return outputs

def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
    """Repeat key/value states for grouped query attention."""
    batch, slen, num_key_value_heads, head_dim = hidden_states.shape
    if n_rep == 1:
        return hidden_states
    hidden_states = hidden_states[:, :, :, None, :].repeat(1, 1, 1, n_rep, 1)
    return hidden_states.reshape(batch, slen, num_key_value_heads * n_rep, head_dim)

def apply_rotary_pos_emb(q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, position_ids: torch.Tensor):
    """Apply rotary positional embeddings."""
    def rotate_half(x):
        x1 = x[..., : x.shape[-1] // 2]
        x2 = x[..., x.shape[-1] // 2 :]
        return torch.cat((-x2, x1), dim=-1)
    
    cos = cos.squeeze(1).squeeze(0)
    sin = sin.squeeze(1).squeeze(0)
    
    cos = cos[position_ids].unsqueeze(1)
    sin = sin[position_ids].unsqueeze(1)
    
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed

class SheikhMLP(nn.Module):
    """SwiGLU MLP."""
    
    def __init__(self, config: SheikhConfig):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size
        
        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))

class SheikhTransformerBlock(nn.Module):
    """Transformer block for Sheikh-2.5-Coder."""
    
    def __init__(self, config: SheikhConfig):
        super().__init__()
        self.self_attn = SheikhAttention(config)
        self.mlp = SheikhMLP(config)
        self.input_layernorm = SheikhRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = SheikhRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
    
    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
    ):
        # Self-attention
        attn_output, _ = self.self_attn(
            self.input_layernorm(hidden_states),
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
        )
        hidden_states = hidden_states + attn_output
        
        # MLP
        mlp_output = self.mlp(self.post_attention_layernorm(hidden_states))
        hidden_states = hidden_states + mlp_output
        
        return hidden_states

class SheikhModel(PreTrainedModel):
    """Sheikh-2.5-Coder base model."""
    
    def __init__(self, config: SheikhConfig):
        super().__init__(config)
        self.config = config
        
        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
        self.layers = nn.ModuleList([SheikhTransformerBlock(config) for _ in range(config.num_hidden_layers)])
        self.norm = SheikhRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        
        # Initialize weights
        self.apply(self._init_weights)
    
    def _init_weights(self, module):
        """Initialize model weights."""
        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 get_input_embeddings(self):
        return self.embed_tokens
    
    def set_input_embeddings(self, value):
        self.embed_tokens = value
    
    def forward(
        self,
        input_ids: torch.Tensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        past_key_values: Optional[List[torch.Tensor]] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ):
        # Implementation continues...
        pass

# Model loading utilities
def load_sheikh_model(
    model_name_or_path: str,
    device_map: Optional[str] = "auto",
    torch_dtype: torch.dtype = torch.bfloat16,
    load_in_8bit: bool = False,
    load_in_4bit: bool = False,
) -> AutoModelForCausalLM:
    """Load Sheikh-2.5-Coder model with optional quantization."""
    
    # Setup quantization config
    quantization_config = None
    if load_in_8bit:
        quantization_config = BitsAndBytesConfig(load_in_8bit=True)
    elif load_in_4bit:
        quantization_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_compute_dtype=torch.bfloat16,
            bnb_4bit_use_double_quant=True,
            bnb_4bit_quant_type="nf4",
        )
    
    # Load tokenizer and model
    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
    model = AutoModelForCausalLM.from_pretrained(
        model_name_or_path,
        device_map=device_map,
        torch_dtype=torch_dtype,
        quantization_config=quantization_config,
    )
    
    return model, tokenizer

# Model training utilities
def setup_training_args(output_dir: str, learning_rate: float = 1e-4) -> TrainingArguments:
    """Setup training arguments for Sheikh-2.5-Coder."""
    
    return TrainingArguments(
        output_dir=output_dir,
        learning_rate=learning_rate,
        per_device_train_batch_size=8,
        per_device_eval_batch_size=8,
        num_train_epochs=3,
        max_steps=100000,
        logging_steps=100,
        save_steps=2000,
        eval_steps=1000,
        warmup_steps=2000,
        fp16=True,
        bf16=True,
        gradient_accumulation_steps=4,
        gradient_checkpointing=True,
        remove_unused_columns=False,
        dataloader_pin_memory=True,
        report_to="wandb",
        run_name="sheikh-2.5-coder",
    )

if __name__ == "__main__":
    # Example usage
    config = SheikhConfig()
    model = SheikhModel(config)
    
    # Save configuration
    with open("config.json", "w") as f:
        json.dump(config.__dict__, f, indent=2)
    
    print("Sheikh-2.5-Coder model configuration created successfully!")
    print(f"Model parameters: {sum(p.numel() for p in model.parameters()):,}")