"""
XGBoost Training Step Configuration with Self-Contained Derivation Logic

This module implements the configuration class for SageMaker XGBoost Training steps
using a self-contained design where derived fields are private with read-only properties.
Fields are organized into three tiers:
1. Tier 1: Essential User Inputs - fields that users must explicitly provide
2. Tier 2: System Inputs with Defaults - fields with reasonable defaults that can be overridden
3. Tier 3: Derived Fields - fields calculated from other fields (private with properties)
"""

from pydantic import BaseModel, Field, model_validator, field_validator, PrivateAttr
from typing import List, Optional, Dict, Any, ClassVar
from pathlib import Path
import json
from datetime import datetime

from ...core.base.config_base import BasePipelineConfig


class XGBoostTrainingConfig(BasePipelineConfig):
    """
    Configuration specific to the SageMaker XGBoost Training Step.
    This version is streamlined to work with specification-driven architecture.
    Input/output paths are now provided via step specifications and dependencies.

    Fields are organized into three tiers:
    1. Tier 1: Essential User Inputs - fields that users must explicitly provide
    2. Tier 2: System Inputs with Defaults - fields with reasonable defaults that can be overridden
    3. Tier 3: Derived Fields - fields calculated from other fields (private with properties)
    """

    # ===== Essential User Inputs (Tier 1) =====
    # These are fields that users must explicitly provide

    training_entry_point: str = Field(
        description="Entry point script for XGBoost training."
    )

    # ===== System Inputs with Defaults (Tier 2) =====
    # These are fields with reasonable defaults that users can override

    # Instance configuration
    training_instance_type: str = Field(
        default="ml.m5.4xlarge", description="Instance type for XGBoost training job."
    )

    training_instance_count: int = Field(
        default=1, ge=1, description="Number of instances for XGBoost training job."
    )

    training_volume_size: int = Field(
        default=30, ge=1, description="Volume size (GB) for training instances."
    )

    # Framework versions for SageMaker XGBoost container
    framework_version: str = Field(
        default="1.7-1", description="SageMaker XGBoost framework version."
    )

    py_version: str = Field(
        default="py3", description="Python version for the SageMaker XGBoost container."
    )

    # Hyperparameters handling configuration
    skip_hyperparameters_s3_uri: bool = Field(
        default=True,
        description="Whether to skip hyperparameters_s3_uri channel during _get_inputs. "
        "If True (default), hyperparameters are loaded from script folder. "
        "If False, hyperparameters_s3_uri channel is created as TrainingInput.",
    )

    # Environment variables for preprocessing artifact control
    use_secure_pypi: bool = Field(
        default=True,
        description="Controls PyPI source for package installation. "
        "If True (default), uses secure CodeArtifact PyPI. "
        "If False, uses public PyPI.",
    )

    use_precomputed_imputation: bool = Field(
        default=False,
        description="Controls whether to use pre-computed imputation artifacts. "
        "If True, expects input data to be already imputed and loads impute_dict.pkl from model_artifacts_input, skipping inline computation. "
        "If False (default), computes imputation inline and transforms data.",
    )

    use_precomputed_risk_tables: bool = Field(
        default=False,
        description="Controls whether to use pre-computed risk table artifacts. "
        "If True, expects input data to be already risk-mapped and loads risk_table_map.pkl from model_artifacts_input, skipping inline computation. "
        "If False (default), computes risk tables inline and transforms data.",
    )

    use_precomputed_features: bool = Field(
        default=False,
        description="Controls whether to use pre-computed feature selection. "
        "If True, expects input data to be already feature-selected and loads selected_features.json from model_artifacts_input, skipping inline computation. "
        "If False (default), uses all features without selection.",
    )

    # Semi-supervised learning support
    job_type: Optional[str] = Field(
        default=None,
        description=(
            "Training job type for semi-supervised learning workflows:\n"
            "• None (default): Standard supervised learning - no step name suffix\n"
            "• 'pretrain': SSL pretraining phase - adds '-Pretrain' suffix\n"
            "• 'finetune': SSL fine-tuning phase - adds '-Finetune' suffix"
        ),
    )

    # ===== Derived Fields (Tier 3) =====
    # These are fields calculated from other fields, stored in private attributes
    # with public read-only properties for access

    _hyperparameter_file: Optional[str] = PrivateAttr(default=None)

    model_config = BasePipelineConfig.model_config

    # Public read-only properties for derived fields

    @property
    def hyperparameter_file(self) -> str:
        """Get hyperparameter file path."""
        if self._hyperparameter_file is None:
            self._hyperparameter_file = f"{self.pipeline_s3_loc}/hyperparameters/{self.region}_hyperparameters.json"
        return self._hyperparameter_file

    # Custom model_dump method to include derived properties
    def model_dump(self, **kwargs) -> Dict[str, Any]:
        """Override model_dump to include derived properties."""
        data = super().model_dump(**kwargs)
        # Add derived properties to output
        data["hyperparameter_file"] = self.hyperparameter_file
        return data

    # Initialize derived fields at creation time to avoid potential validation loops
    @model_validator(mode="after")
    def initialize_derived_fields(self) -> "XGBoostTrainingConfig":
        """Initialize all derived fields once after validation."""
        # Call parent validator first
        super().initialize_derived_fields()

        # Initialize training-specific derived fields
        self._hyperparameter_file = (
            f"{self.pipeline_s3_loc}/hyperparameters/{self.region}_hyperparameters.json"
        )

        return self

    @field_validator("job_type")
    @classmethod
    def validate_job_type(cls, v: Optional[str]) -> Optional[str]:
        """Validate job_type is one of allowed values."""
        if v is None:
            return None  # Standard supervised learning

        allowed = {"pretrain", "finetune"}
        if v not in allowed:
            raise ValueError(
                f"job_type must be None (standard) or one of {allowed}, got '{v}'. "
                f"Use None for standard training, 'pretrain' for SSL pretraining, "
                f"'finetune' for SSL fine-tuning."
            )
        return v

    @field_validator("training_instance_type")
    @classmethod
    def _validate_sagemaker_xgboost_instance_type(cls, v: str) -> str:
        # Common CPU instances for XGBoost. XGBoost can also use GPU instances (e.g., ml.g4dn, ml.g5)
        # if tree_method='gpu_hist' is used and framework supports it.
        valid_cpu_instances = [
            "ml.m5.large",
            "ml.m5.xlarge",
            "ml.m5.2xlarge",
            "ml.m5.4xlarge",
            "ml.m5.12xlarge",
            "ml.m5.24xlarge",
            "ml.c5.large",
            "ml.c5.xlarge",
            "ml.c5.2xlarge",
            "ml.c5.4xlarge",
            "ml.c5.9xlarge",
            "ml.c5.18xlarge",
        ]
        valid_gpu_instances = [  # For GPU accelerated XGBoost
            "ml.g4dn.xlarge",
            "ml.g4dn.2xlarge",
            "ml.g4dn.4xlarge",
            "ml.g4dn.8xlarge",
            "ml.g4dn.12xlarge",
            "ml.g4dn.16xlarge",
            "ml.g5.xlarge",
            "ml.g5.2xlarge",
            "ml.g5.4xlarge",
            "ml.g5.8xlarge",
            "ml.g5.12xlarge",
            "ml.g5.16xlarge",
            "ml.p3.2xlarge",  # Older but sometimes used
        ]
        valid_instances = valid_cpu_instances + valid_gpu_instances
        if v not in valid_instances:
            raise ValueError(
                f"Invalid training instance type for XGBoost: {v}. "
                f"Must be one of: {', '.join(valid_instances)}"
            )
        return v

    def get_public_init_fields(self) -> Dict[str, Any]:
        """
        Override get_public_init_fields to include XGBoost training-specific fields.
        Gets a dictionary of public fields suitable for initializing a child config.
        Includes both base fields (from parent) and XGBoost training-specific fields.

        Returns:
            Dict[str, Any]: Dictionary of field names to values for child initialization
        """
        # Get fields from parent class (BasePipelineConfig)
        base_fields = super().get_public_init_fields()

        # Add XGBoost training-specific fields (Tier 1 and Tier 2)
        training_fields = {
            "training_entry_point": self.training_entry_point,
            "training_instance_type": self.training_instance_type,
            "training_instance_count": self.training_instance_count,
            "training_volume_size": self.training_volume_size,
            "framework_version": self.framework_version,
            "py_version": self.py_version,
            "skip_hyperparameters_s3_uri": self.skip_hyperparameters_s3_uri,
            "use_secure_pypi": self.use_secure_pypi,
            "use_precomputed_imputation": self.use_precomputed_imputation,
            "use_precomputed_risk_tables": self.use_precomputed_risk_tables,
            "use_precomputed_features": self.use_precomputed_features,
            "job_type": self.job_type,
        }

        # Combine base fields and training fields (training fields take precedence if overlap)
        init_fields = {**base_fields, **training_fields}

        return init_fields
