"""EvoX seed search strategy S0 — uniform-random parent + uniform-random inspirations.

This module is the GENOME of the meta-loop: the scaffold reads this file as TEXT (the meta-LLM
rewrites its source) AND executes it (the class becomes the active search strategy). Port of
SkyDiscover's ``search/evox/database/initial_search_strategy.py`` translated to galapagos Genomes.
It must import only from galapagos and the Python standard library, and everything evolvable lives
inside the EVOLVE-BLOCK markers.
"""
# EVOLVE-BLOCK-START
from galapagos.records import Genome
from galapagos.scaffolds.evox.strategy import StrategyBase


class EvolvedStrategy(StrategyBase):
    """Initial search strategy: plain insert + uniform-random parent and inspirations."""

    def add(self, genome: Genome, iteration=None, **kwargs) -> str:
        """Add a genome to the population."""
        self.genomes[genome.id] = genome
        self._update_best(genome)
        return genome.id

    def sample(self, num_context_programs=4, **kwargs):
        """Pick a random parent and a set of random context genomes (free-form label)."""
        candidates = list(self.genomes.values())
        if len(candidates) == 0:
            raise ValueError("No candidates available for sampling")
        parent = self.rng.choice(candidates)
        sample_size = min(num_context_programs + 1, len(candidates))
        examples = self.rng.sample(candidates, sample_size)
        examples = [g for g in examples if g.id != parent.id][:num_context_programs]
        return {"": parent}, {"": examples}
# EVOLVE-BLOCK-END