"""
POET Train Phase - Learning and Improvement

This module implements the Train (T) phase of the POET pipeline, responsible for:
1. Learning from function execution results
2. Collecting feedback for future improvements
3. Basic pattern recognition and optimization hints
4. Simple performance metrics tracking

Copyright © 2025 Aitomatic, Inc.
MIT License
"""

from typing import Any

from dana.common.utils.logging import DANA_LOGGER

from .core import POETConfig


class TrainPhase:
    """Train phase implementation - simple learning and feedback collection."""

    def __init__(self, config: POETConfig):
        """Initialize Train phase with configuration."""
        self.config = config
        self.logger = DANA_LOGGER.getLogger(__name__)

    def train(
        self, input_args: tuple[Any, ...], input_kwargs: dict[str, Any], output: Any, context: dict[str, Any], execution_time: float
    ) -> dict[str, Any]:
        """
        Learn from function execution and collect feedback.

        Args:
            input_args: Original function arguments
            input_kwargs: Original function keyword arguments
            output: Function output
            context: Execution context
            execution_time: Time taken to execute

        Returns:
            Training metadata and insights
        """
        training_data = {
            "domain": self.config.domain,
            "execution_time": execution_time,
            "input_count": len(input_args) + len(input_kwargs),
            "output_type": type(output).__name__,
            "success": True,
            "insights": [],
        }

        try:
            # Basic performance tracking
            self._track_performance(training_data, execution_time)

            # Domain-specific learning if enabled
            if self.config.domain:
                self._domain_learning(training_data, input_args, input_kwargs, output, context)

            # Pattern recognition (basic)
            self._basic_pattern_recognition(training_data, input_args, output)

            self.logger.info(f"Training completed for domain: {self.config.domain}")

        except Exception as e:
            self.logger.error(f"Train phase failed: {e}")
            training_data["success"] = False
            training_data["error"] = str(e)

        return training_data

    def _track_performance(self, training_data: dict[str, Any], execution_time: float) -> None:
        """Track basic performance metrics."""
        if execution_time > 1.0:
            training_data["insights"].append("Consider optimization - execution time > 1s")
        elif execution_time < 0.001:
            training_data["insights"].append("Very fast execution - good performance")

        training_data["performance_category"] = "slow" if execution_time > 1.0 else "fast" if execution_time < 0.1 else "normal"

    def _domain_learning(
        self, training_data: dict[str, Any], input_args: tuple[Any, ...], input_kwargs: dict[str, Any], output: Any, context: dict[str, Any]
    ) -> None:
        """Basic domain-specific learning patterns."""
        domain = self.config.domain

        if domain == "financial_services":
            self._financial_learning(training_data, input_args, output)
        elif domain == "healthcare":
            self._healthcare_learning(training_data, input_args, output)
        elif domain == "data_processing":
            self._data_processing_learning(training_data, input_args, output)
        else:
            training_data["insights"].append(f"Generic learning for domain: {domain}")

    def _basic_pattern_recognition(self, training_data: dict[str, Any], input_args: tuple[Any, ...], output: Any) -> None:
        """Basic pattern recognition for common issues."""
        # Check for common patterns
        if len(input_args) > 5:
            training_data["insights"].append("Many input arguments - consider using structured input")

        if isinstance(output, list | tuple) and len(output) > 100:
            training_data["insights"].append("Large output collection - consider pagination")

        if output is None:
            training_data["insights"].append("None output - verify this is expected behavior")

    def _financial_learning(self, training_data: dict[str, Any], input_args: tuple[Any, ...], output: Any) -> None:
        """Simple financial domain learning."""
        training_data["insights"].append("Financial calculation completed")

        # Basic financial patterns
        if isinstance(output, int | float) and output < 0:
            training_data["insights"].append("Negative financial result - verify calculation")

    def _healthcare_learning(self, training_data: dict[str, Any], input_args: tuple[Any, ...], output: Any) -> None:
        """Simple healthcare domain learning."""
        training_data["insights"].append("Healthcare processing completed")

        # Basic healthcare patterns
        if isinstance(output, dict) and "patient_id" in str(output):
            training_data["insights"].append("Patient data processed - ensure privacy compliance")

    def _data_processing_learning(self, training_data: dict[str, Any], input_args: tuple[Any, ...], output: Any) -> None:
        """Simple data processing domain learning."""
        training_data["insights"].append("Data processing completed")

        # Basic data processing patterns
        if isinstance(output, list | tuple):
            training_data["insights"].append(f"Processed {len(output)} items")
