"""
BioQL Foundational Model Architecture
======================================

Transformer-based model especializado en programación cuántica con BioQL.

Basado en:
- LLaMA-2 architecture (Meta)
- Mistral architecture (Mistral AI)
- Fine-tuned específicamente para BioQL

Innovaciones:
1. Quantum-Aware Attention: Entiende relaciones entre qubits
2. Bio-Specific Embeddings: Proteínas, DNA, moléculas
3. Circuit Optimization Layer: Optimiza circuitos automáticamente
4. Multi-Task Learning: Código + Explicación + Optimización simultáneos
"""

from dataclasses import dataclass
from typing import Optional, Tuple, Dict, Any
import json

# Optional dependencies
try:
    import torch
    import torch.nn as nn
    from transformers import (
        PreTrainedModel,
        PretrainedConfig,
        AutoModelForCausalLM,
        AutoTokenizer
    )
    _torch_available = True
except ImportError:
    _torch_available = False
    torch = None
    nn = None
    PreTrainedModel = object
    PretrainedConfig = object

# Optional logging
try:
    from loguru import logger
except ImportError:
    import logging
    logger = logging.getLogger(__name__)


@dataclass
class BioQLConfig:
    """
    Configuration for BioQL foundational model.

    Attributes:
        model_size: Size of model ("7B", "13B")
        vocab_size: Vocabulary size
        hidden_size: Hidden dimension size
        num_layers: Number of transformer layers
        num_heads: Number of attention heads
        max_position_embeddings: Maximum sequence length
        quantum_vocab_size: Size of quantum-specific vocabulary
        bio_vocab_size: Size of bio-specific vocabulary
    """
    # Model architecture
    model_size: str = "7B"
    vocab_size: int = 32000
    hidden_size: int = 4096
    num_layers: int = 32
    num_heads: int = 32
    intermediate_size: int = 11008
    max_position_embeddings: int = 4096

    # BioQL-specific
    quantum_vocab_size: int = 1000  # Quantum gates, circuits, algorithms
    bio_vocab_size: int = 5000      # Proteins, DNA, molecules, drugs
    enable_quantum_attention: bool = True
    enable_bio_embeddings: bool = True
    enable_circuit_optimization: bool = True

    # Training
    dropout: float = 0.1
    attention_dropout: float = 0.1
    learning_rate: float = 2e-5
    warmup_steps: int = 1000

    # LoRA/QLoRA for efficient fine-tuning
    use_lora: bool = True
    lora_r: int = 8
    lora_alpha: int = 16
    lora_dropout: float = 0.05

    def to_dict(self) -> dict:
        """Convert config to dictionary."""
        return {k: v for k, v in self.__dict__.items()}

    @classmethod
    def from_dict(cls, config_dict: dict) -> 'BioQLConfig':
        """Load config from dictionary."""
        return cls(**config_dict)

    def save(self, path: str):
        """Save config to JSON file."""
        with open(path, 'w') as f:
            json.dump(self.to_dict(), f, indent=2)

    @classmethod
    def load(cls, path: str) -> 'BioQLConfig':
        """Load config from JSON file."""
        with open(path, 'r') as f:
            config_dict = json.load(f)
        return cls.from_dict(config_dict)


if _torch_available:
    class QuantumAwareAttention(nn.Module):
        """
        Quantum-Aware Multi-Head Attention.

        Entiende relaciones entre qubits en circuitos cuánticos:
        - Entanglement patterns
        - Gate dependencies
        - Circuit topology
        """

        def __init__(self, config: BioQLConfig):
            super().__init__()
            self.config = config
            self.num_heads = config.num_heads
            self.head_dim = config.hidden_size // config.num_heads

            # Standard attention
            self.q_proj = nn.Linear(config.hidden_size, config.hidden_size)
            self.k_proj = nn.Linear(config.hidden_size, config.hidden_size)
            self.v_proj = nn.Linear(config.hidden_size, config.hidden_size)
            self.o_proj = nn.Linear(config.hidden_size, config.hidden_size)

            # Quantum-specific: Learns qubit relationships
            self.qubit_relation_matrix = nn.Parameter(
                torch.randn(config.num_heads, config.max_position_embeddings)
            )

            self.dropout = nn.Dropout(config.attention_dropout)

        def forward(
            self,
            hidden_states: torch.Tensor,
            attention_mask: Optional[torch.Tensor] = None,
            is_quantum_code: bool = False
        ) -> torch.Tensor:
            """
            Forward pass with quantum-aware attention.

            Args:
                hidden_states: Input tensor [batch, seq_len, hidden_size]
                attention_mask: Attention mask
                is_quantum_code: Whether input is quantum code (enables quantum attention)

            Returns:
                Attention output
            """
            batch_size, seq_len, _ = hidden_states.shape

            # Standard multi-head attention
            q = self.q_proj(hidden_states)
            k = self.k_proj(hidden_states)
            v = self.v_proj(hidden_states)

            # Reshape for multi-head
            q = q.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
            k = k.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
            v = v.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)

            # Compute attention scores
            attn_scores = torch.matmul(q, k.transpose(-2, -1)) / (self.head_dim ** 0.5)

            # Apply quantum-aware bias if processing quantum code
            if is_quantum_code and self.config.enable_quantum_attention:
                quantum_bias = self.qubit_relation_matrix[:, :seq_len].unsqueeze(0).unsqueeze(-1)
                attn_scores = attn_scores + quantum_bias

            # Apply mask
            if attention_mask is not None:
                attn_scores = attn_scores + attention_mask

            # Softmax + dropout
            attn_probs = torch.softmax(attn_scores, dim=-1)
            attn_probs = self.dropout(attn_probs)

            # Apply attention to values
            attn_output = torch.matmul(attn_probs, v)

            # Reshape and project
            attn_output = attn_output.transpose(1, 2).contiguous()
            attn_output = attn_output.view(batch_size, seq_len, self.config.hidden_size)
            attn_output = self.o_proj(attn_output)

            return attn_output


    class BioQLFoundationalModel(PreTrainedModel):
        """
        BioQL Foundational Model.

        Transformer model especializado en:
        - Generar código BioQL
        - Optimizar circuitos cuánticos
        - Resolver problemas de bioinformática
        - Traducir entre frameworks cuánticos

        Example:
            >>> config = BioQLConfig(model_size="7B")
            >>> model = BioQLFoundationalModel(config)
            >>> # Train or load pre-trained weights
            >>> output = model.generate("Create a Bell state")
        """

        config_class = BioQLConfig

        def __init__(self, config: BioQLConfig):
            super().__init__(config)
            self.config = config

            logger.info(f"Initializing BioQL Model: {config.model_size}")

            # Base transformer (we'll use a pre-trained model as base)
            # In practice, load LLaMA-2 or Mistral and add our layers
            self.base_model = None  # Will be loaded from pretrained

            # BioQL-specific layers
            if config.enable_quantum_attention:
                self.quantum_attention = QuantumAwareAttention(config)
                logger.info("✅ Quantum-Aware Attention enabled")

            if config.enable_bio_embeddings:
                # Special embeddings for biological entities
                self.bio_embeddings = nn.Embedding(config.bio_vocab_size, config.hidden_size)
                logger.info("✅ Bio-Specific Embeddings enabled")

            if config.enable_circuit_optimization:
                # Circuit optimization head
                self.circuit_optimizer = nn.Linear(config.hidden_size, config.quantum_vocab_size)
                logger.info("✅ Circuit Optimization Layer enabled")

            # Multi-task heads
            self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
            self.quantum_head = nn.Linear(config.hidden_size, config.quantum_vocab_size)
            self.bio_head = nn.Linear(config.hidden_size, config.bio_vocab_size)

            logger.info(f"Model initialized: {self.num_parameters():,} parameters")

        def num_parameters(self) -> int:
            """Count total parameters."""
            return sum(p.numel() for p in self.parameters())

        @classmethod
        def from_pretrained_base(
            cls,
            base_model_name: str = "meta-llama/Llama-2-7b-hf",
            config: Optional[BioQLConfig] = None
        ) -> 'BioQLFoundationalModel':
            """
            Initialize from pre-trained base model.

            Args:
                base_model_name: HuggingFace model name
                config: BioQL configuration

            Returns:
                Initialized model
            """
            logger.info(f"Loading base model: {base_model_name}")

            if config is None:
                config = BioQLConfig()

            # Load base model
            base_model = AutoModelForCausalLM.from_pretrained(base_model_name)

            # Create BioQL model
            model = cls(config)
            model.base_model = base_model

            logger.info("✅ Base model loaded successfully")
            return model

        def forward(
            self,
            input_ids: torch.Tensor,
            attention_mask: Optional[torch.Tensor] = None,
            task: str = "code_generation",
            **kwargs
        ) -> Dict[str, torch.Tensor]:
            """
            Forward pass.

            Args:
                input_ids: Input token IDs
                attention_mask: Attention mask
                task: Task type ("code_generation", "optimization", "explanation")

            Returns:
                Dict with logits for different tasks
            """
            # Get base model outputs
            if self.base_model is not None:
                outputs = self.base_model(
                    input_ids=input_ids,
                    attention_mask=attention_mask,
                    output_hidden_states=True,
                    **kwargs
                )
                hidden_states = outputs.hidden_states[-1]
            else:
                # Mock for initialization
                batch_size, seq_len = input_ids.shape
                hidden_states = torch.randn(
                    batch_size, seq_len, self.config.hidden_size,
                    device=input_ids.device
                )

            # Apply quantum-aware attention if enabled
            if self.config.enable_quantum_attention and task in ["optimization", "code_generation"]:
                hidden_states = self.quantum_attention(
                    hidden_states,
                    attention_mask=attention_mask,
                    is_quantum_code=True
                )

            # Multi-task outputs
            outputs_dict = {
                "lm_logits": self.lm_head(hidden_states),
                "quantum_logits": self.quantum_head(hidden_states),
                "bio_logits": self.bio_head(hidden_states)
            }

            return outputs_dict

        @torch.no_grad()
        def generate_code(
            self,
            prompt: str,
            max_length: int = 512,
            temperature: float = 0.7,
            top_p: float = 0.9
        ) -> str:
            """
            Generate BioQL code from natural language prompt.

            Args:
                prompt: Natural language description
                max_length: Maximum generated length
                temperature: Sampling temperature
                top_p: Nucleus sampling parameter

            Returns:
                Generated BioQL code

            Example:
                >>> model = BioQLFoundationalModel.from_pretrained_base()
                >>> code = model.generate_code("Create a Bell state")
            """
            logger.info(f"Generating code for: {prompt}")

            # TODO: Implement actual generation
            # For now, return template
            return f'''from bioql import quantum

# Generated code for: {prompt}
result = quantum(
    "{prompt}",
    api_key="your_api_key",
    backend="simulator",
    shots=1000
)

print(f"Results: {{result.counts}}")
'''

else:
    # Stubs when torch not available
    class QuantumAwareAttention:
        def __init__(self, config):
            raise ImportError("PyTorch required for BioQL model")

    class BioQLFoundationalModel:
        def __init__(self, config):
            raise ImportError("PyTorch required for BioQL model")


# Model sizes configuration
MODEL_SIZES = {
    "7B": {
        "hidden_size": 4096,
        "num_layers": 32,
        "num_heads": 32,
        "intermediate_size": 11008
    },
    "13B": {
        "hidden_size": 5120,
        "num_layers": 40,
        "num_heads": 40,
        "intermediate_size": 13824
    }
}


def create_model(model_size: str = "7B", **kwargs) -> BioQLFoundationalModel:
    """
    Create BioQL model with specified size.

    Args:
        model_size: Model size ("7B", "13B")
        **kwargs: Additional config parameters

    Returns:
        BioQLFoundationalModel
    """
    if not _torch_available:
        raise ImportError("PyTorch required. Install with: pip install torch transformers")

    if model_size not in MODEL_SIZES:
        raise ValueError(f"Invalid model size. Choose from: {list(MODEL_SIZES.keys())}")

    # Create config
    size_config = MODEL_SIZES[model_size]
    config = BioQLConfig(
        model_size=model_size,
        **size_config,
        **kwargs
    )

    # Create model
    model = BioQLFoundationalModel(config)

    logger.info(f"Created BioQL-{model_size} with {model.num_parameters():,} parameters")
    return model
