import ast
from typing import Optional, List

class EvolutionaryArchitect:
    """
    Meta-Agent I: The Evolutionary Architect.
    Uses AST parsing to perform safe code mutation.
    """

    def propose_refactor(self, file_path: str, target_node_type: str, optimization_goal: str) -> Optional[str]:
        """
        Parses code, identifies nodes, and proposes a mutation.
        """
        try:
            with open(file_path, 'r') as f:
                source_code = f.read()

            tree = ast.parse(source_code)

            # 1. Identify Target Nodes
            candidates = []
            for node in ast.walk(tree):
                if type(node).__name__ == target_node_type:
                    candidates.append(node)

            if not candidates:
                return f"No nodes of type {target_node_type} found in {file_path}."

            # 2. Semantic Mutation (Simulated)
            # In a real system, an LLM would generate the replacement AST.
            # Here we just demonstrate the AST manipulation capability.

            target_node = candidates[0]

            # Example: If optimizing a 'For' loop for 'latency', suggest vectorization
            mutation_proposal = ""
            if target_node_type == "For" and optimization_goal == "latency":
                mutation_proposal = "# [Evolutionary Architect Proposal]\n# Use NumPy vectorization instead of explicit loop:\n# result = np.array(data) * 2"
            else:
                mutation_proposal = f"# [Evolutionary Architect Proposal]\n# Optimize {target_node_type} for {optimization_goal}"

            return mutation_proposal

        except Exception as e:
            return f"Error parsing {file_path}: {str(e)}"

    def verify_syntax(self, code_snippet: str) -> bool:
        """
        The Gauntlet: Verifies syntactic correctness before proposal.
        """
        try:
            ast.parse(code_snippet)
            return True
        except SyntaxError:
            return False