"""
Configuration management for KerdosAI.
"""

import os
import yaml
from pathlib import Path
from typing import Optional, List, Dict, Any, Union
from pydantic import BaseModel, Field, field_validator
import logging

from exceptions import ConfigurationError

logger = logging.getLogger(__name__)


class LoRAConfig(BaseModel):
    """LoRA configuration."""
    enabled: bool = True
    r: int = Field(default=8, ge=1, le=256)
    alpha: int = Field(default=32, ge=1)
    dropout: float = Field(default=0.1, ge=0.0, le=1.0)
    target_modules: Optional[List[str]] = None
    
    @field_validator('alpha')
    @classmethod
    def validate_alpha(cls, v, info):
        if 'r' in info.data and v < info.data['r']:
            logger.warning(f"LoRA alpha ({v}) is less than r ({info.data['r']}), which may reduce effectiveness")
        return v


class QuantizationConfig(BaseModel):
    """Quantization configuration."""
    enabled: bool = False
    bits: int = Field(default=4, ge=4, le=8)
    use_double_quant: bool = True
    quant_type: str = Field(default="nf4", pattern="^(nf4|fp4)$")
    compute_dtype: str = Field(default="float16", pattern="^(float16|bfloat16|float32)$")
    
    @field_validator('bits')
    @classmethod
    def validate_bits(cls, v):
        if v not in [4, 8]:
            raise ValueError("Only 4-bit and 8-bit quantization are supported")
        return v


class TrainingConfig(BaseModel):
    """Training configuration."""
    epochs: int = Field(default=3, ge=1)
    batch_size: int = Field(default=4, ge=1)
    learning_rate: float = Field(default=2e-5, gt=0.0)
    warmup_steps: int = Field(default=100, ge=0)
    gradient_accumulation_steps: int = Field(default=1, ge=1)
    max_grad_norm: float = Field(default=1.0, gt=0.0)
    weight_decay: float = Field(default=0.01, ge=0.0)
    logging_steps: int = Field(default=10, ge=1)
    save_steps: int = Field(default=100, ge=1)
    eval_steps: int = Field(default=100, ge=1)
    max_seq_length: int = Field(default=512, ge=1)
    seed: int = Field(default=42, ge=0)
    fp16: bool = False
    bf16: bool = False
    
    @field_validator('fp16', 'bf16')
    @classmethod
    def validate_precision(cls, v, info):
        if v and info.field_name == 'bf16' and info.data.get('fp16'):
            raise ValueError("Cannot use both fp16 and bf16")
        if v and info.field_name == 'fp16' and info.data.get('bf16'):
            raise ValueError("Cannot use both fp16 and bf16")
        return v


class DataConfig(BaseModel):
    """Data configuration."""
    train_file: Optional[str] = None
    validation_file: Optional[str] = None
    test_file: Optional[str] = None
    dataset_name: Optional[str] = None
    dataset_config: Optional[str] = None
    text_column: str = "text"
    max_samples: Optional[int] = None
    preprocessing_num_workers: int = Field(default=4, ge=1)
    
    @field_validator('train_file', 'validation_file', 'test_file')
    @classmethod
    def validate_file_path(cls, v):
        if v and not Path(v).exists():
            logger.warning(f"File {v} does not exist")
        return v


class DeploymentConfig(BaseModel):
    """Deployment configuration."""
    type: str = Field(default="rest", pattern="^(rest|docker|kubernetes)$")
    host: str = "0.0.0.0"
    port: int = Field(default=8000, ge=1, le=65535)
    workers: int = Field(default=1, ge=1)
    max_batch_size: int = Field(default=8, ge=1)
    timeout: int = Field(default=60, ge=1)


class MonitoringConfig(BaseModel):
    """Monitoring configuration."""
    enabled: bool = True
    wandb_project: Optional[str] = None
    wandb_entity: Optional[str] = None
    tensorboard_dir: str = "./runs"
    log_model: bool = False


class KerdosConfig(BaseModel):
    """Main KerdosAI configuration."""
    
    # Model configuration
    base_model: str
    model_revision: Optional[str] = None
    trust_remote_code: bool = False
    device: Optional[str] = None
    
    # Component configurations
    lora: LoRAConfig = Field(default_factory=LoRAConfig)
    quantization: QuantizationConfig = Field(default_factory=QuantizationConfig)
    training: TrainingConfig = Field(default_factory=TrainingConfig)
    data: DataConfig = Field(default_factory=DataConfig)
    deployment: DeploymentConfig = Field(default_factory=DeploymentConfig)
    monitoring: MonitoringConfig = Field(default_factory=MonitoringConfig)
    
    # Output configuration
    output_dir: str = "./output"
    checkpoint_dir: str = "./checkpoints"
    
    # Additional settings
    cache_dir: Optional[str] = None
    
    @classmethod
    def from_yaml(cls, config_path: Union[str, Path]) -> "KerdosConfig":
        """
        Load configuration from YAML file.
        
        Args:
            config_path: Path to YAML configuration file
            
        Returns:
            KerdosConfig instance
            
        Raises:
            ConfigurationError: If configuration is invalid
        """
        try:
            config_path = Path(config_path)
            
            if not config_path.exists():
                raise ConfigurationError(
                    f"Configuration file not found: {config_path}",
                    {"path": str(config_path)}
                )
            
            with open(config_path, 'r') as f:
                config_dict = yaml.safe_load(f)
            
            # Handle environment variable substitution
            config_dict = cls._substitute_env_vars(config_dict)
            
            return cls(**config_dict)
            
        except yaml.YAMLError as e:
            raise ConfigurationError(
                f"Error parsing YAML configuration: {str(e)}",
                {"path": str(config_path)}
            )
        except Exception as e:
            raise ConfigurationError(
                f"Error loading configuration: {str(e)}",
                {"path": str(config_path)}
            )
    
    def to_yaml(self, output_path: Union[str, Path]) -> None:
        """
        Save configuration to YAML file.
        
        Args:
            output_path: Path to save YAML configuration
        """
        try:
            output_path = Path(output_path)
            output_path.parent.mkdir(parents=True, exist_ok=True)
            
            with open(output_path, 'w') as f:
                yaml.dump(
                    self.model_dump(),
                    f,
                    default_flow_style=False,
                    sort_keys=False
                )
            
            logger.info(f"Configuration saved to {output_path}")
            
        except Exception as e:
            raise ConfigurationError(
                f"Error saving configuration: {str(e)}",
                {"path": str(output_path)}
            )
    
    @staticmethod
    def _substitute_env_vars(config: Dict[str, Any]) -> Dict[str, Any]:
        """
        Recursively substitute environment variables in configuration.
        
        Environment variables should be specified as ${VAR_NAME}.
        """
        if isinstance(config, dict):
            return {k: KerdosConfig._substitute_env_vars(v) for k, v in config.items()}
        elif isinstance(config, list):
            return [KerdosConfig._substitute_env_vars(item) for item in config]
        elif isinstance(config, str) and config.startswith("${") and config.endswith("}"):
            var_name = config[2:-1]
            return os.getenv(var_name, config)
        return config
    
    def validate_compatibility(self) -> None:
        """
        Validate configuration compatibility.
        
        Raises:
            ConfigurationError: If configuration has incompatible settings
        """
        # Check quantization and LoRA compatibility
        if self.quantization.enabled and self.lora.enabled:
            logger.info("Using LoRA with quantization - this is recommended for efficiency")
        
        # Check precision settings
        if self.training.fp16 and self.device == "cpu":
            raise ConfigurationError(
                "fp16 training is not supported on CPU",
                {"device": self.device, "fp16": True}
            )
        
        # Check data configuration
        if not self.data.train_file and not self.data.dataset_name:
            raise ConfigurationError(
                "Either train_file or dataset_name must be specified",
                {"train_file": self.data.train_file, "dataset_name": self.data.dataset_name}
            )
        
        logger.info("Configuration validation passed")


def load_config(config_path: Optional[Union[str, Path]] = None) -> KerdosConfig:
    """
    Load configuration from file or create default.
    
    Args:
        config_path: Optional path to configuration file
        
    Returns:
        KerdosConfig instance
    """
    if config_path:
        return KerdosConfig.from_yaml(config_path)
    
    # Return default configuration
    logger.info("No configuration file specified, using defaults")
    return KerdosConfig(base_model="gpt2")  # Default model for testing